Prevalence of NAFLD in Guatemala following exposure to a protein-energy nutrition intervention in early life

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"documento" => "article" "crossmark" => 1 "licencia" => "http://creativecommons.org/licenses/by-nc-nd/4.0/" "subdocumento" => "fla" "cita" => "Ann Hepatol. 2020;19:380-7" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:1 [ "total" => 0 ] "en" => array:12 [ "idiomaDefecto" => true "cabecera" => "Original article" "titulo" => "Is there an association between commonly employed biomarkers of liver fibrosis and liver stiffness in the general population?" 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Values whose 95%CI do not cross the 0 line have an associated p-value<0.05." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Francesco Giuseppe Foschi, Marco Domenicali, Pierluigi Giacomoni, Anna Chiara Dall’Aglio, Fabio Conti, Alberto Borghi, Vittoria Bevilacqua, Lucia Napoli, Federica Mirici, Alessandro Cucchetti, Giorgio Ercolani, Andrea Casadei Gardini, Stefano Bellentani, Amalia Gastaldelli, Mauro Giuffrè, Claudio Tiribelli, Giorgio Bedogni" "autores" => array:18 [ 0 => array:2 [ "nombre" => "Francesco Giuseppe" "apellidos" => "Foschi" ] 1 => array:2 [ "nombre" => "Marco" "apellidos" => "Domenicali" ] 2 => array:2 [ "nombre" => "Pierluigi" "apellidos" => "Giacomoni" ] 3 => array:2 [ "nombre" => "Anna Chiara" "apellidos" => "Dall’Aglio" ] 4 => array:2 [ "nombre" => "Fabio" "apellidos" => "Conti" ] 5 => array:2 [ "nombre" => "Alberto" "apellidos" => "Borghi" ] 6 => array:2 [ "nombre" => "Vittoria" "apellidos" => "Bevilacqua" ] 7 => array:2 [ "nombre" => "Lucia" "apellidos" => "Napoli" ] 8 => array:2 [ "nombre" => "Federica" "apellidos" => "Mirici" ] 9 => array:2 [ "nombre" => "Alessandro" "apellidos" => "Cucchetti" ] 10 => array:2 [ "nombre" => "Giorgio" "apellidos" => "Ercolani" ] 11 => array:2 [ "nombre" => "Andrea Casadei" "apellidos" => "Gardini" ] 12 => array:2 [ "nombre" => "Stefano" "apellidos" => "Bellentani" ] 13 => array:2 [ "nombre" => "Amalia" "apellidos" => "Gastaldelli" ] 14 => array:2 [ "nombre" => "Mauro" "apellidos" => "Giuffrè" ] 15 => array:2 [ "nombre" => "Claudio" "apellidos" => "Tiribelli" ] 16 => array:2 [ "nombre" => "Giorgio" "apellidos" => "Bedogni" ] 17 => array:1 [ "colaborador" => "Bagnacavallo Study Group" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1665268120300405?idApp=UINPBA00004N" "url" => "/16652681/0000001900000004/v2_202007040825/S1665268120300405/v2_202007040825/en/main.assets" ] "itemAnterior" => array:19 [ "pii" => "S1665268120300399" "issn" => "16652681" "doi" => "10.1016/j.aohep.2020.04.002" "estado" => "S300" "fechaPublicacion" => "2020-07-01" "aid" => "198" "copyright" => "Fundación Clínica Médica Sur, A.C." "documento" => "article" "crossmark" => 1 "licencia" => "http://creativecommons.org/licenses/by-nc-nd/4.0/" "subdocumento" => "fla" "cita" => "Ann Hepatol. 2020;19:367-72" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:1 [ "total" => 0 ] "en" => array:12 [ "idiomaDefecto" => true "cabecera" => "Original article" "titulo" => "Serum Mac-2-binding protein glycosylation isomer predicts esophagogastric varices in cirrhotic patients with chronic hepatitis C virus infection treated with IFN-free direct-acting antiviral agent: M2BPGi levels predict varices in SVR patients" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => "en" "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "367" "paginaFinal" => "372" ] ] "contieneResumen" => array:1 [ "en" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1348 "Ancho" => 2169 "Tamanyo" => 93086 ] ] "descripcion" => array:1 [ "en" => "Serum M2BPGi values associated with EGV before and after treatment. The Box and whisker charts of Serum Mac-2-binding protein glycosylation isomer (M2BPGi) values in the patients before (A) and after (B) treatment. Box areas indicate interquartile range, and horizontal bars in the interquartile range indicate median value. Whiskers represent the minimum and maximum values. M2BPGi levels were higher in the EGV-positive group than in the EGV-negative group before and after treatment (p=0.034, p=0.003). *p<0.05." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Kanako Kikukawa, Sawako Uchida-Kobayashi, Akihiro Tamori, Kanako Yoshida, Kohei Kotani, Hiroyuki Motoyama, Ritsuzo Kozuka, Atsushi Hagihara, Hideki Fujii, Hiroyasu Morikawa, Masaru Enomoto, Yoshiki Murakami, Norifumi Kawada" "autores" => array:13 [ 0 => array:2 [ "nombre" => "Kanako" "apellidos" => "Kikukawa" ] 1 => array:2 [ "nombre" => "Sawako" "apellidos" => "Uchida-Kobayashi" ] 2 => array:2 [ "nombre" => "Akihiro" "apellidos" => "Tamori" ] 3 => array:2 [ "nombre" => "Kanako" "apellidos" => "Yoshida" ] 4 => array:2 [ "nombre" => "Kohei" "apellidos" => "Kotani" ] 5 => array:2 [ "nombre" => "Hiroyuki" "apellidos" => "Motoyama" ] 6 => array:2 [ "nombre" => "Ritsuzo" "apellidos" => "Kozuka" ] 7 => array:2 [ "nombre" => "Atsushi" "apellidos" => "Hagihara" ] 8 => array:2 [ "nombre" => "Hideki" "apellidos" => "Fujii" ] 9 => array:2 [ "nombre" => "Hiroyasu" "apellidos" => "Morikawa" ] 10 => array:2 [ "nombre" => "Masaru" "apellidos" => "Enomoto" ] 11 => array:2 [ "nombre" => "Yoshiki" "apellidos" => "Murakami" ] 12 => array:2 [ "nombre" => "Norifumi" "apellidos" => "Kawada" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1665268120300399?idApp=UINPBA00004N" "url" => "/16652681/0000001900000004/v2_202007040825/S1665268120300399/v2_202007040825/en/main.assets" ] "en" => array:19 [ "idiomaDefecto" => true "cabecera" => "Original article" "titulo" => "Prevalence of NAFLD in Guatemala following exposure to a protein-energy nutrition intervention in early life" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "373" "paginaFinal" => "379" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Ahlia Sekkarie, Siran He, Jean A. Welsh, Usha Ramakrishnan, Aryeh D. Stein, Miriam B. Vos" "autores" => array:6 [ 0 => array:4 [ "nombre" => "Ahlia" "apellidos" => "Sekkarie" "email" => array:1 [ 0 => "asekkar@emory.edu" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Siran" "apellidos" => "He" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 2 => array:3 [ "nombre" => "Jean A." "apellidos" => "Welsh" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] 3 => array:3 [ "nombre" => "Usha" "apellidos" => "Ramakrishnan" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] 4 => array:3 [ "nombre" => "Aryeh D." "apellidos" => "Stein" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] 5 => array:3 [ "nombre" => "Miriam B." "apellidos" => "Vos" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Nutrition and Health Sciences Program, Laney Graduate School, Emory University, Atlanta, GA, United States" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Department of Pediatrics, Emory School of Medicine, Atlanta, GA, United States" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author at: 1518 Clifton Rd NE, Atlanta, GA 30322, United States." ] ] ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2351 "Ancho" => 2083 "Tamanyo" => 277738 ] ] "descripcion" => array:1 [ "en" => "Flow diagram. Flow diagram of original 1969–1977 INCAP Guatemala study to 2015–2017 data collection and analysis. Of the original 2398 participants 369 had died, 249 had migrated outside Guatemala, and 113 were untraceable resulting in 1661 who were eligible for enrollment in 2015–2017. Of these, 1118 provided informed consent and provided at least one sample during the clinical exam. During analysis we excluded an additional 6 for pregnancy/lactation status, 6 for high alcohol intake, and 13 for not having data on the non-alcoholic fatty liver disease (NAFLD) outcome for a final analytic sample size of 1093." ] ] ] "textoCompleto" => "1IntroductionNon-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome. The long-term consequences of NAFLD include increased risk of end-stage liver disease, liver cancer, and cardiovascular disease <a class="elsevierStyleCrossRef" href="#bib0210">[1]</a>. The global prevalence of NAFLD has risen to 25%, with some of the highest estimates reported in South and Central America <a class="elsevierStyleCrossRefs" href="#bib0215">[2,3]</a>. In Guatemala, one study reported a prevalence of 56.5% <a class="elsevierStyleCrossRef" href="#bib0225">[4]</a>. Known risk factors for NAFLD include genetics, age, nutrition, and obesity. Individuals with adipogenic genes such as Patatin-like phospholipase domain-containing protein 3 (PNPLA3), which is common in Hispanics, have a higher risk for NAFLD <a class="elsevierStyleCrossRef" href="#bib0230">[5]</a>.The clinical manifestation of NAFLD in children and the presence of steatosis at birth in some newborns suggest that its origins may lie in utero <a class="elsevierStyleCrossRefs" href="#bib0235">[6–9]</a>. Maternal restriction of calories and protein have been associated with increased hepatic fat in offspring in animal models <a class="elsevierStyleCrossRef" href="#bib0255">[10]</a>. In rats, maternal restriction of protein during pregnancy and lactation is associated with hepatic steatosis in offspring <a class="elsevierStyleCrossRefs" href="#bib0260">[11–15]</a>. Furthermore, a high fat diet intensified the effects of perinatal protein restriction on liver outcomes <a class="elsevierStyleCrossRefs" href="#bib0265">[12,14]</a>. The combination of early life undernutrition and later exposure to an obesogenic environment, characteristic of many countries undergoing the nutrition transition, may therefore contribute to the risk of developing NAFLD <a class="elsevierStyleCrossRef" href="#bib0285">[16]</a>.The impact of undernutrition in early life on later NAFLD has been studied in two cohorts. Exposure to the Great Chinese Famine in early life was associated with an increased risk of NAFLD in adulthood <a class="elsevierStyleCrossRefs" href="#bib0290">[17,18]</a>. In a study using the Helsinki Birth Cohort (n=1587), individuals who were in the smallest body mass index (BMI) tertile in early childhood and obese as adults had the greatest risk of NAFLD <a class="elsevierStyleCrossRef" href="#bib0300">[19]</a>. Other studies have found associations between NAFLD and proxies of in utero malnutrition (such as being small for gestational age and of low-birth weight), as well as accelerated growth in the first three months in early life <a class="elsevierStyleCrossRefs" href="#bib0305">[20–22]</a>. The effect of protein-energy intake in early life on later NAFLD has not been described in humans <a class="elsevierStyleCrossRef" href="#bib0320">[23]</a>.In the 1960s and 70s, the Institute of Nutrition of Central America and Panama (INCAP) conducted a nutrition supplementation trial in four villages in southeastern Guatemala to assess the effect of improved nutrition on child growth and development. Children in the improved nutrition group had greater total intake of protein (9g/day) and energy (100kcal/day) compared to the control group <a class="elsevierStyleCrossRefs" href="#bib0325">[24,25]</a>. The population in these villages was undernourished; about 45% of children under three years had severe stunting and 86% had any stunting at 24 months <a class="elsevierStyleCrossRefs" href="#bib0330">[25,26]</a>. At follow-up in 2015–2017, there was a high prevalence of cardiometabolic diseases in the cohort, including 32% with obesity and 67% with metabolic syndrome <a class="elsevierStyleCrossRef" href="#bib0340">[27]</a>. We assessed the prevalence of NAFLD and related markers of NAFLD, including alanine aminotransferase (ALT) and aspartate aminotransferase (AST), in the cohort in mid-adulthood. Additionally, we investigated the impact of exposure to a protein-energy supplement from conception to age two years on NAFLD. This period of early life, also known as the first 1000 days, is a time of rapid development and a crucial window for influencing later health <a class="elsevierStyleCrossRef" href="#bib0345">[28]</a>. We hypothesized that exposure to increased protein and energy nutrition during early life would result in decreased risk of NAFLD later in life.2Material and methods2.1Study populationBetween January 1, 1969 and February 28, 1977, two pairs of size-matched villages were randomized to receive either Atole (a protein and energy-containing supplement) or Fresco (a no-protein low-energy supplement). Atole was a nutritional supplement (6.4g protein, 0.4g fat, and 90kcal per 100mL) made from dry skimmed milk, sugar, and Incaparina, a vegetable protein mixture developed by INCAP. Fresco contained sugar water and flavoring (33kcal per 100mL) and had no protein. Atole and Fresco were fortified to have matching micronutrient content. The supplements were available to everyone in each village at a central location twice every day. Study staff in the original trial recorded supplement intake for pregnant and lactating women and children under 7 years. The full study design has been previously published <a class="elsevierStyleCrossRef" href="#bib0350">[29]</a>.A follow-up was conducted in 2015–2017 to examine the cardiometabolic health of the cohort. Of the original 2398 participants enrolled in the nutrition-supplementation trial, 1161 were not lost to follow-up and provided informed consent. Of these participants, 1118 provided at least one plasma sample (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). Characteristics of those lost to follow-up in comparison to those who participated have previously been published <a class="elsevierStyleCrossRef" href="#bib0340">[27]</a>.<elsevierMultimedia ident="fig0005"></elsevierMultimedia>Of the 1118 individuals who provided plasma samples, for the present study we excluded six who were pregnant or lactating. Since alcohol consumption also impacts liver function and can lead to steatosis, we additionally excluded six individuals who reported alcohol consumption>21 drinks/week in men and >14 drinks/week in women <a class="elsevierStyleCrossRef" href="#bib0210">[1]</a>. Finally, we excluded 13 individuals for missing the NAFLD outcome.This study and the informed consent process were approved by the Institutional Review Boards of Emory University (Atlanta, GA) and INCAP (Guatemala City, Guatemala).2.2Data collection and lab assaysIn addition to the birth village and date of birth, the original study collected data on maternal characteristics such as age, height, and schooling years. In the 2015–2017 wave of data collection, we obtained data on participant characteristics such as years of schooling, socioeconomic (SES) status, residence in Guatemala City, and self-reported alcohol intake. Trained field workers and phlebotomists collected anthropometric measurements and fasting blood samples, as previously described <a class="elsevierStyleCrossRef" href="#bib0340">[27]</a>.Plasma samples were frozen for storage at −80°C until being shipped on dry ice to a biomarker core laboratory in the United States. The samples were thawed at 4°C in batches, each containing approximately 40 plasma samples. Plasma ALT and AST values were assessed using the AU480 analyzer (Beckman Coulter Diagnostics, Fullerton CA, US) using enzymatic methods (Sekisui Diagnostics P.E.I. Inc., Canada). Insulin was assayed using immunoturbidimetric methods (Kamiya Biomedical Company, WA, US).2.3Variable specification2.3.1ExposureWe determined exposure to Atole or Fresco by birth village. We used the child's birth date and the trial start and end dates to determine the age of exposure as previously described <a class="elsevierStyleCrossRef" href="#bib0340">[27]</a>. Early exposure was defined as 1000 days from conception (assumed to be 266 days before the birth date to approximate the average length of pregnancy) to age two years. In our main analysis, we considered individuals who were exposed for the entire period from conception to age two years to be fully exposed and those who were exposed for only part of that time to not be exposed. For sensitivity analysis, we also created a three-category exposure variable: those who were fully exposed, those who were partially exposed, and those without any exposure from conception to age two years.2.3.2NAFLD and related outcomesWe used the NAFLD liver fat score equations developed by Kotronen, et al. to determine the presence of hepatic steatosis (liver fat score) and its quantity (liver fat percent) <a class="elsevierStyleCrossRef" href="#bib0355">[30]</a>. The equations include metabolic syndrome, type 2 diabetes, fasting insulin, fasting AST, and the AST/ALT ratio. A higher score indicates greater steatosis. We used the optimal cut off point of −0.640 for the NALFD liver fat score to define the presence of suspected NAFLD. This cut off was determined by Kotronen, et al. using the Youden index <a class="elsevierStyleCrossRef" href="#bib0355">[30]</a>.We defined metabolic syndrome by the presence of at least three of the following criteria: (1) central obesity (waist circumference>88cm in women, >102cm in men), (2) elevated fasting glucose (≥100mg/dL) or use of diabetes medication, (3) elevated triglycerides (≥150mg/dL) or statin use, (4) low high density lipoprotein (HDL cholesterol (<40mg/dL in men, <50mg/dL in women), and (5) hypertension (≥85mm Hg diastolic) or hypertension medication use <a class="elsevierStyleCrossRef" href="#bib0360">[31]</a>. We defined type 2 diabetes as a fasting plasma glucose ≥126mg/dL, a post challenge glucose of ≥200mg/dL or the use of diabetes medication <a class="elsevierStyleCrossRef" href="#bib0365">[32]</a>. We calculated the AST/ALT ratio by dividing AST by ALT.2.3.3CovariatesWe calculated body mass index (BMI) by weight in kilograms divided by height in meters squared <a class="elsevierStyleCrossRef" href="#bib0370">[33]</a>. We calculated the waist-to-height ratio by dividing the waist circumference in centimeters by height in centimeters. We categorized childhood and adulthood socioeconomic status into tertiles. We had previously derived socioeconomic status from a principal component analysis of household characteristics and consumer durable goods <a class="elsevierStyleCrossRef" href="#bib0340">[27]</a>. We characterized current residence dichotomously as Guatemala City or other to capture rural vs urban location. We included age at follow-up and completed grades of schooling for mothers and their children as continuous variables.2.4Statistical analysisWe calculated median and interquartile range (IQR) values for continuous variables and percent for categorical variables. We used Wilcoxon Rank Sum tests to compare differences in medians between men and women for continuous clinical covariates and NAFLD related outcomes. We used a Pearson chi-square test to compare NAFLD, type 2 diabetes, and metabolic syndrome percent in men and women.We used multiple imputation to deal with missing data for maternal height (20.5% missing), maternal years of schooling (3.5% missing), and maternal age (1.6% missing). We used the fully conditional specification method for five imputations. To impute missing values, we used predictive mean matching for continuous variables and the logistic regression method for ordinal variables. We included all predictor and outcome variables that were not linear effects of each other as predictors in the imputation model <a class="elsevierStyleCrossRef" href="#bib0375">[34]</a>.We used generalized regression models to estimate the difference-in-difference intent-to-treat effect of Atole vs Fresco from conception to age two years, net of the differences attributable to village or birth year effects. We estimated the difference-in-difference effect by an interaction term between the type of exposure (Atole vs. Fresco) and the timing of exposure (during the full first 1000 days versus other). The base model included birth village in the form of three dummy variables (this also serves to account for treatment assignment of Atole vs Fresco), a dichotomous variable to account for duration of exposure (full vs other), birth year, maternal height (as an indicator of maternal nutrition status), maternal years of schooling, sex, and childhood SES. We additionally controlled for several adult sociodemographic and clinical factors including SES status, years of schooling, residence, BMI, waist-to-height ratio, and height. We considered sex as a potential effect modifier. We tested for stratum heterogeneity by examining the significance of the third-order interaction between sex, exposure type and duration.We conducted all statistical analysis in SAS version 9.4. We used PROC GENMOD, for modeling and PROC MI and PROC MIANALYZE for multiple imputation. Statistical significance was determined by p<0.05 and p-values were two-sided.3ResultsThe final sample size was 1093 individuals (61.1% women) (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). Selected characteristics of the study population are in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. The median age at follow-up was 44 years (IQR 41–47). Twenty-two percent of participants were exposed to Atole during the full period from conception to age two years. An additional 17.8% were exposed to Atole for part of the period from conception to age 2 years.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>The median NAFLD liver fat score was 0.2 (IQR: −1.2, 1.6) in women and −1.2 (IQR: −2.2, 0.5) in men (p<0.0001). The median liver fat percent was 5.4% (IQR: 3.1, 8.0) in women and 3.0% (IQR: 2.0, 6.) in men (p<0.0001) (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>; <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). Median ALT was 19.7U/L (IQR: 14.0, 27.4), median AST was 26.0U/L (IQR: 21.3, 32.8), and the median AST/ALT ratio was 1.3 (IQR 1.0, 1.8). Men had significantly higher ALT and AST values (p<0.0001). The prevalence of NAFLD was 56.5% and was higher in women (67.4%) than men (39.5%; p<0.0001).<elsevierMultimedia ident="fig0010"></elsevierMultimedia>The assocations between exposure to Atole in early life and NAFLD-related parameters are provided in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. The estimates for ALT, AST, AST/ALT ratio, liver fat score, and liver fat percent were not statistically significant in the base model or with additional adjustment for adult factors. Similarly, the odds ratio for NAFLD was not significant in the base model (OR: 1.26, 95% CI: 0.75–2.11) or after accounting for adult factors (OR: 0.90, 95% CI: 0.50–1.63) (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>). There was no significant stratum heterogeneity by sex. In sensitivity analysis, we found no significant differences in the models for those with partial vs no exposure and full vs no exposure.<elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia>4DiscussionWe describe the prevalence of NAFLD among a cohort that participated in an improved protein-energy supplementation trial during early life in Guatemala. Their prevalence of NAFLD in mid-adulthood was 56.5% and was significantly higher in women compared to men. Exposure to the protein-energy supplement from conception to two years was not associated with NAFLD in mid-adulthood.The NAFLD prevalence we report is consistent with the only other study on NAFLD prevalence in Guatemala, despite the use of different NAFLD criteria <a class="elsevierStyleCrossRef" href="#bib0225">[4]</a>. Rivera-Andrade used the Fatty Liver Index and found a NAFLD prevalence of 66.5% and 50.6% in women and men over 40 years old, respectively <a class="elsevierStyleCrossRef" href="#bib0225">[4]</a>. NAFLD is usually more common in men, although this difference is less pronounced with age <a class="elsevierStyleCrossRef" href="#bib0380">[35]</a>. Women in our study had a higher prevalence of metabolic syndrome and type 2 diabetes, which could partially explain their higher NAFLD prevalence, especially since we used a NAFLD score that includes type 2 diabetes and metabolic syndrome in its calculation.In a previous publication using the same population, the Atole supplement had mixed effects on cardiometabolic outcomes. It was associated with increased odds of obesity (OR: 1.99, 95% CI: 1.16–3.41), reduced odds of diabetes (OR: 0.47, 95% CI: 0.22–0.99), and was not significantly associated with metabolic syndrome <a class="elsevierStyleCrossRef" href="#bib0340">[27]</a>. The diverging associations between early-life nutrition and later cardiometabolic conditions may be due to various interlinked mechanisms, including the role of protein in directing the development of metabolically active tissues <a class="elsevierStyleCrossRef" href="#bib0385">[36]</a>.Additionally, we may have failed to observe a significant association between early-life protein-energy supplementation and adulthood NAFLD because the lifetime cumulative effect of other factors such as BMI trajectory or diet that dominate over the effect of early life exposure to Atole. Excluding those with obesity, which might overpower any effects from early life, did not meaningfully change our estimates.Animal models have linked early life protein and energy restriction to later NAFLD and described potential pathways through which this occurs. In rats, maternal protein restriction results in upregulation of the transcription factors sterol regulatory element binding protein (SREBP-1c), carbohydrate-responsive element-binding protein (ChREBP) and peroxisome proliferator-activated receptor-γ (PPAR-γ). This effects downstream genes important to lipid metabolism and insulin signaling <a class="elsevierStyleCrossRefs" href="#bib0260">[11,14,15]</a>. At the organ level, growth restricted offspring exhibit fewer but larger hepatic lobules and enzymatic alterations, which, along with a decreased pancreatic b-cell mass, lead to insulin resistance <a class="elsevierStyleCrossRefs" href="#bib0390">[37,38]</a>. In the liver, insulin resistance inhibits b-oxidation and leads to decreased output of lipids. This, along with the increase of free fatty acid influx into the liver due to insulin resistance, results in steatosis of the liver.There are several strengths in our study. One strength is the sample size and use of multiple imputation to account for missing data to maximize model size. Although attrition is a concern as about 50% of the original cohort were in the 2015–2017 wave of data collection, there is no evidence that attrition has affected the validity of this study. Ford, et al. found that attrition was not differential with respect to Atole exposure from conception to age two years. Additionally, individuals lost to follow-up were similar to those who participated in the 2015–2017 study, except for sex <a class="elsevierStyleCrossRef" href="#bib0340">[27]</a>.Another strength of this study is the use of the composite NAFLD liver fat score. ALT levels alone have poor sensitivity and specificity in adults <a class="elsevierStyleCrossRefs" href="#bib0400">[39,40]</a>. Therefore, scores like the NAFLD liver fat score are useful as non-invasive predictors of the disease <a class="elsevierStyleCrossRef" href="#bib0355">[30]</a>. The original NAFLD liver fat score was developed in a large Finnish cohort in 2009 so it is possible it is not transferable to other populations. However, when compared to three other NAFLD prediction scores, the NAFLD liver fat score had the best non-invasive prediction score for NAFLD and associated mortality including in Hispanics <a class="elsevierStyleCrossRef" href="#bib0410">[41]</a>.There are a few limitations in our study. The ideal exposure period may only be a subset of the first 1000 days such as during liver organogenesis in utero, which begins at four weeks and continues throughout gestation. In our study, it is not possible to examine exposure during pregnancy and early childhood separately due to the limited sample size; 3.7% (n=40) of the original cohort were exposed to Atole only in pregnancy. Additionally, sufficient data were not available in our cohort on variables that have been previously associated with NAFLD including gestational age and birthweight of which 63% and 57% were missing, respectively. Among those with data available about 10% were born pre-term (<37 weeks gestation) or with low birthweight (<2.5kg).5ConclusionsUnderstanding the developmental origins of NAFLD and other metabolic conditions is particularly important because of the growing prevalence of these diseases <a class="elsevierStyleCrossRef" href="#bib0255">[10]</a>. The high prevalence of NAFLD in Guatemala highlights the need for population-based studies that will help identify the underlying mechanisms of the onset and development of NAFLD throughout the life course, including and beyond early-life nutritional exposure.AbbreviationsNAFLDnon-alcoholic fatty liver diseaseALTalanine aminotransferaseASTaspartate aminotransferaseINCAPInstitute of Nutrition of Central America and PanamaIQRinterquartile rangeORodds ratioBMIbody mass indexSESsocioeconomic statusFundingThis work was supported by the National Institutes of Health [HD075784].Conflict of interestMBV reports personal fees from Intercept, grants from Immuron, grants and personal fees from Target Pharmasolutions, personal fees from Boehringer Ingelheim, grants and personal fees from Bristol Myers Squibb, personal fees from Prosciento, and personal fees from Novo Nordisk during the conduct of the study." "textoCompletoSecciones" => array:1 [ "secciones" => array:11 [ 0 => array:3 [ "identificador" => "xres1358379" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1249325" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 3 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Data collection and lab assays" ] 2 => array:3 [ "identificador" => "sec0025" "titulo" => "Variable specification" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0030" "titulo" => "Exposure" ] 1 => array:2 [ "identificador" => "sec0035" "titulo" => "NAFLD and related outcomes" ] 2 => array:2 [ "identificador" => "sec0040" "titulo" => "Covariates" ] ] ] 3 => array:2 [ "identificador" => "sec0045" "titulo" => "Statistical analysis" ] ] ] 4 => array:2 [ "identificador" => "sec0050" "titulo" => "Results" ] 5 => array:2 [ "identificador" => "sec0055" "titulo" => "Discussion" ] 6 => array:2 [ "identificador" => "sec0060" "titulo" => "Conclusions" ] 7 => array:2 [ "identificador" => "sec0065" "titulo" => "Funding" ] 8 => array:2 [ "identificador" => "sec0070" "titulo" => "Conflict of interest" ] 9 => array:2 [ "identificador" => "xack471348" "titulo" => "Acknowledgments" ] 10 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2020-03-04" "fechaAceptado" => "2020-04-16" "PalabrasClave" => array:1 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1249325" "palabras" => array:6 [ 0 => "Central America" 1 => "Cardiovascular diseases/epidemiology" 2 => "Follow-up studies" 3 => "Guatemala/epidemiology" 4 => "Middle aged" 5 => "Child" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "Introduction and objectivesThe global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 25%, with Hispanic populations at greatest risk. We describe the prevalence of NAFLD in a cohort of Guatemalan adults and examine whether exposure to a protein-energy supplement from conception to two years is associated with lower prevalence of NAFLD. Materials and methodsFrom 1969 to 1977, four villages in Guatemala were cluster-randomized to receive a protein-energy supplement (Atole) or a no-protein, low-energy beverage (Fresco). We conducted a follow-up of participants from 2015 to 2017. We assessed blood samples (n=1093; 61.1% women; aged 37–53 years) for alanine aminotransferase (ALT) and aspartate aminotransferase (AST) and estimated NAFLD prevalence using the liver fat score. We used generalized linear and logistic models to estimate the difference-in-difference effect of Atole from conception to two years on NAFLD. ResultsMedian ALT and AST were 19.7U/L (interquartile range, IQR: 14.1, 27.4) and 26.0U/L (IQR: 21.4, 32.8), respectively. The median NAFLD liver fat score was 0.2 (IQR: −1.2, 1.6) in women and −1.2 (IQR: −2.2, 0.5) in men (p<0.0001). The prevalence of NAFLD was 67.4% among women and 39.5% among men (p<0.0001). The association between Atole exposure from conception to two years and NAFLD was not significant (OR: 0.90, 95% CI: 0.50–1.63). ConclusionsNAFLD prevalence among Guatemalan adults exceeds the global average. Protein-energy supplementation in early life was not associated with later NAFLD. There is a need for further studies on the causes and onset of NAFLD throughout the life course." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] ] "multimedia" => array:5 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2351 "Ancho" => 2083 "Tamanyo" => 277738 ] ] "descripcion" => array:1 [ "en" => "Flow diagram. Flow diagram of original 1969–1977 INCAP Guatemala study to 2015–2017 data collection and analysis. Of the original 2398 participants 369 had died, 249 had migrated outside Guatemala, and 113 were untraceable resulting in 1661 who were eligible for enrollment in 2015–2017. Of these, 1118 provided informed consent and provided at least one sample during the clinical exam. During analysis we excluded an additional 6 for pregnancy/lactation status, 6 for high alcohol intake, and 13 for not having data on the non-alcoholic fatty liver disease (NAFLD) outcome for a final analytic sample size of 1093." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1614 "Ancho" => 2167 "Tamanyo" => 110797 ] ] "descripcion" => array:1 [ "en" => "Liver fat percent boxplot by sex. Histogram of liver fat percent by sex (blue(0)=women, red(+)=men) in the Guatemala INCAP study cohort 2015–2017 (n=668 women, 425 men). The liver fat percent is calculated using presence of metabolic syndrome, type 2 diabetes, fasting insulin, fasting aspartate aminotransferase(AST), and fasting AST/ALT ratio.<a class="elsevierStyleCrossRef" href="#bib0355">[30]</a> The dotted black line indicates the 5% cut off for NAFLD." ] ] 2 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "Abbreviations: ALT=alanine aminotransferase; AST=aspartate aminotransferase; AST/ALT=ratio between AST and ALT; NAFLD=non-alcoholic fatty liver disease; IQR=interquartile range." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Pooled (n=1093)</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Women (n=668)</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Men (n=425)</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Median (IQR) or %</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Median (IQR) or %</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Median (IQR) or %</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="7" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maternal characteristics</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maternal age, years<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(21, 32) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(21, 32) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(21, 32) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maternal height, cm<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">148.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(145.4, 152.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">148.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(145.3, 152.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">148.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(145.7, 152.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maternal schooling, years<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0, 2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0, 2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0, 3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="7" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="7" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Participant characteristics</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age at follow-up, years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(41, 47) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(41, 47) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(40, 47) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Schooling, years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2, 6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2, 6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2, 6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Residing in Guatemala City (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Atole from conception to 2 yrs (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="7" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="7" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Clinical<a class="elsevierStyleCrossRef" href="#tblfn0015">c</a></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Height, cm \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">155.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(150.1, 162.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">151.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(148, 155.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">163.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(159.6, 167.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Waist circumference, cm \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">97.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(90.8, 105.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">100.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(93.3, 108.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(87.5, 100.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Body mass index, kg/m2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(24.8, 31.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(25.7, 32.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(23.8, 29.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Waist-to-height ratio \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.6, 0.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.6, 0.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.57 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.5, 0.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Insulin, mU/L \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(7.7, 20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(9.1, 22.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(6.3, 16.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Type 2 diabetes (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Metabolic syndrome (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">69.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ALT, U/L \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(14.1, 27.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(13.4, 26.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(15.3, 29.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AST, U/L \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(21.4, 32.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(20.7, 31.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(22.6, 33.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AST/ALT \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1, 1.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1, 1.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(1, 1.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Liver fat score<a class="elsevierStyleCrossRef" href="#tblfn0010">b</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−1.7, 1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−1.2, 1.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−1.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−2.2, 0.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Liver fat percent<a class="elsevierStyleCrossRef" href="#tblfn0010">b</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.4, 7.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(3.1, 8.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(2.0, 6.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NAFLD (%)<a class="elsevierStyleCrossRef" href="#tblfn0010">b</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">67.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2333758.png" ] ] ] "notaPie" => array:3 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "Maternal age (n=1075), maternal height (n=869), maternal schooling (n=1054) and post-challenge glucose (n=1011) were missing values." ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "b" "nota" => "Calculated using fasting insulin, type 2 diabetes, metabolic syndrome, fasting AST, and fasting AST/ALT ratio <a class="elsevierStyleCrossRef" href="#bib0355">[30]</a>." ] 2 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "c" "nota" => "p<0.0001 for all clinical covariates and NAFLD outcomes comparing men and women (except for AST/ALT p=0.04)." ] ] ] "descripcion" => array:1 [ "en" => "Sociodemographic and clinical characteristics (n=1093)." ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "Abbreviations: ALT=alanine aminotransferase; AST=aspartate aminotransferase; AST/ALT=ratio between AST and ALT; CI=confidence interval; SES=socioeconomic status." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Pooled</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Women</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Men</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">β \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">β \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">β \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ALT, U/L</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.60 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−3.30, 4.51) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.76 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−5.92, 4.35) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.76 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−3.05, 8.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.34 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.71 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−4.54, 3.11) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.71 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−1.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−6.26, 3.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.65 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−5.91, 5.41) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.93 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AST, U/L</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.31 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−2.31, 4.92) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.65 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−3.11, 6.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.88 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−4.64, 6.41) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.75 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.37 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−3.23, 3.97) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.84 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−3.59, 5.85) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.67 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−6.2, 4.87) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.81 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AST/ALT</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.16, 0.14) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.09, 0.28) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.43, 0.09) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.20 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.14, 0.17) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.86 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.09, 0.28) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.31 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.35, 0.17) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.51 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Liver fat score</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.32 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.27, 0.91) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.29 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.36 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.44, 1.16) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.38 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.28 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.55, 1.12) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.51 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.52, 0.51) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.97 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.63, 0.83) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.78, 0.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.66 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Liver fat percent</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.34 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.68, 1.36) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−1.17, 1.52) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.60 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−0.94, 2.15) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.44 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.23 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−1.15, 0.69) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.63 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.22 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−1.47, 1.02) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(−1.45, 1.11) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.79 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2333759.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0020" "etiqueta" => "a" "nota" => "Model 1 (base model) adjusted for birth village, duration of exposure, birth year, maternal height, maternal years of schooling, sex (in pooled models), and childhood SES. Model 2 additionally adjusted for adult SES, years of schooling, residence, body mass index, waist-to-height ratio, and height." ] ] ] "descripcion" => array:1 [ "en" => "Adjusted<a class="elsevierStyleCrossRef" href="#tblfn0020">a</a> linear difference-in-difference estimates for exposure to Atole from conception to age 2 years vs other, by non-alcoholic fatty liver disease related outcomes (n=1093)." ] ] 4 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "Abbreviations: OR=odds ratio; CI=confidence interval; NAFLD=non-alcoholic fatty liver disease; SES=socioeconomic status." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Pooled</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Women</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Men</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="10" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NAFLD</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.75, 2.11) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.71, 2.74) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.34 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.44, 2.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.98 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Model 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.50, 1.63) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.73 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.52, 2.33) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.57 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(0.18, 1.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.34 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2333757.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0025" "etiqueta" => "a" "nota" => "Model 1 (base model) adjusted for birth village, duration of exposure, birth year, maternal height, maternal years of schooling, sex (in pooled models), and childhood SES. 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Original article

Ahlia Sekkariea,

Corresponding author

asekkar@emory.edu

Corresponding author at: 1518 Clifton Rd NE, Atlanta, GA 30322, United States.

, Siran Hea, Jean A. Welshb, Usha Ramakrishnana,c, Aryeh D. Steinc, Miriam B. Vosb

a Nutrition and Health Sciences Program, Laney Graduate School, Emory University, Atlanta, GA, United States

b Department of Pediatrics, Emory School of Medicine, Atlanta, GA, United States

c Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States

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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="sect0035">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Non-alcoholic fatty liver disease &#40;NAFLD&#41; is the hepatic manifestation of metabolic syndrome&#46; The long-term consequences of NAFLD include increased risk of end-stage liver disease&#44; liver cancer&#44; and cardiovascular disease <a class="elsevierStyleCrossRef" href="#bib0210">&#91;1&#93;</a>&#46; The global prevalence of NAFLD has risen to 25&#37;&#44; with some of the highest estimates reported in South and Central America <a class="elsevierStyleCrossRefs" href="#bib0215">&#91;2&#44;3&#93;</a>&#46; In Guatemala&#44; one study reported a prevalence of 56&#46;5&#37; <a class="elsevierStyleCrossRef" href="#bib0225">&#91;4&#93;</a>&#46; Known risk factors for NAFLD include genetics&#44; age&#44; nutrition&#44; and obesity&#46; Individuals with adipogenic genes such as Patatin-like phospholipase domain-containing protein 3 &#40;PNPLA3&#41;&#44; which is common in Hispanics&#44; have a higher risk for NAFLD <a class="elsevierStyleCrossRef" href="#bib0230">&#91;5&#93;</a>&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">The clinical manifestation of NAFLD in children and the presence of steatosis at birth in some newborns suggest that its origins may lie in utero <a class="elsevierStyleCrossRefs" href="#bib0235">&#91;6&#8211;9&#93;</a>&#46; Maternal restriction of calories and protein have been associated with increased hepatic fat in offspring in animal models <a class="elsevierStyleCrossRef" href="#bib0255">&#91;10&#93;</a>&#46; In rats&#44; maternal restriction of protein during pregnancy and lactation is associated with hepatic steatosis in offspring <a class="elsevierStyleCrossRefs" href="#bib0260">&#91;11&#8211;15&#93;</a>&#46; Furthermore&#44; a high fat diet intensified the effects of perinatal protein restriction on liver outcomes <a class="elsevierStyleCrossRefs" href="#bib0265">&#91;12&#44;14&#93;</a>&#46; The combination of early life undernutrition and later exposure to an obesogenic environment&#44; characteristic of many countries undergoing the nutrition transition&#44; may therefore contribute to the risk of developing NAFLD <a class="elsevierStyleCrossRef" href="#bib0285">&#91;16&#93;</a>&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">The impact of undernutrition in early life on later NAFLD has been studied in two cohorts&#46; Exposure to the Great Chinese Famine in early life was associated with an increased risk of NAFLD in adulthood <a class="elsevierStyleCrossRefs" href="#bib0290">&#91;17&#44;18&#93;</a>&#46; In a study using the Helsinki Birth Cohort &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1587&#41;&#44; individuals who were in the smallest body mass index &#40;BMI&#41; tertile in early childhood and obese as adults had the greatest risk of NAFLD <a class="elsevierStyleCrossRef" href="#bib0300">&#91;19&#93;</a>&#46; Other studies have found associations between NAFLD and proxies of in utero malnutrition &#40;such as being small for gestational age and of low-birth weight&#41;&#44; as well as accelerated growth in the first three months in early life <a class="elsevierStyleCrossRefs" href="#bib0305">&#91;20&#8211;22&#93;</a>&#46; The effect of protein-energy intake in early life on later NAFLD has not been described in humans <a class="elsevierStyleCrossRef" href="#bib0320">&#91;23&#93;</a>&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">In the 1960s and 70s&#44; the Institute of Nutrition of Central America and Panama &#40;INCAP&#41; conducted a nutrition supplementation trial in four villages in southeastern Guatemala to assess the effect of improved nutrition on child growth and development&#46; Children in the improved nutrition group had greater total intake of protein &#40;9<span class="elsevierStyleHsp" style=""></span>g&#47;day&#41; and energy &#40;100<span class="elsevierStyleHsp" style=""></span>kcal&#47;day&#41; compared to the control group <a class="elsevierStyleCrossRefs" href="#bib0325">&#91;24&#44;25&#93;</a>&#46; The population in these villages was undernourished&#59; about 45&#37; of children under three years had severe stunting and 86&#37; had any stunting at 24 months <a class="elsevierStyleCrossRefs" href="#bib0330">&#91;25&#44;26&#93;</a>&#46; At follow-up in 2015&#8211;2017&#44; there was a high prevalence of cardiometabolic diseases in the cohort&#44; including 32&#37; with obesity and 67&#37; with metabolic syndrome <a class="elsevierStyleCrossRef" href="#bib0340">&#91;27&#93;</a>&#46; We assessed the prevalence of NAFLD and related markers of NAFLD&#44; including alanine aminotransferase &#40;ALT&#41; and aspartate aminotransferase &#40;AST&#41;&#44; in the cohort in mid-adulthood&#46; Additionally&#44; we investigated the impact of exposure to a protein-energy supplement from conception to age two years on NAFLD&#46; This period of early life&#44; also known as the first 1000 days&#44; is a time of rapid development and a crucial window for influencing later health <a class="elsevierStyleCrossRef" href="#bib0345">&#91;28&#93;</a>&#46; We hypothesized that exposure to increased protein and energy nutrition during early life would result in decreased risk of NAFLD later in life&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="sect0040">Material and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0045">Study population</span><p id="par0025" class="elsevierStylePara elsevierViewall">Between January 1&#44; 1969 and February 28&#44; 1977&#44; two pairs of size-matched villages were randomized to receive either <span class="elsevierStyleItalic">Atole</span> &#40;a protein and energy-containing supplement&#41; or <span class="elsevierStyleItalic">Fresco</span> &#40;a no-protein low-energy supplement&#41;&#46; <span class="elsevierStyleItalic">Atole</span> was a nutritional supplement &#40;6&#46;4<span class="elsevierStyleHsp" style=""></span>g protein&#44; 0&#46;4<span class="elsevierStyleHsp" style=""></span>g fat&#44; and 90<span class="elsevierStyleHsp" style=""></span>kcal per 100<span class="elsevierStyleHsp" style=""></span>mL&#41; made from dry skimmed milk&#44; sugar&#44; and <span class="elsevierStyleItalic">Incaparina</span>&#44; a vegetable protein mixture developed by INCAP&#46; <span class="elsevierStyleItalic">Fresco</span> contained sugar water and flavoring &#40;33<span class="elsevierStyleHsp" style=""></span>kcal per 100<span class="elsevierStyleHsp" style=""></span>mL&#41; and had no protein&#46; <span class="elsevierStyleItalic">Atole</span> and <span class="elsevierStyleItalic">Fresco</span> were fortified to have matching micronutrient content&#46; The supplements were available to everyone in each village at a central location twice every day&#46; Study staff in the original trial recorded supplement intake for pregnant and lactating women and children under 7 years&#46; The full study design has been previously published <a class="elsevierStyleCrossRef" href="#bib0350">&#91;29&#93;</a>&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">A follow-up was conducted in 2015&#8211;2017 to examine the cardiometabolic health of the cohort&#46; Of the original 2398 participants enrolled in the nutrition-supplementation trial&#44; 1161 were not lost to follow-up and provided informed consent&#46; Of these participants&#44; 1118 provided at least one plasma sample &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#46; Characteristics of those lost to follow-up in comparison to those who participated have previously been published <a class="elsevierStyleCrossRef" href="#bib0340">&#91;27&#93;</a>&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">Of the 1118 individuals who provided plasma samples&#44; for the present study we excluded six who were pregnant or lactating&#46; Since alcohol consumption also impacts liver function and can lead to steatosis&#44; we additionally excluded six individuals who reported alcohol consumption<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>21 drinks&#47;week in men and &#62;14 drinks&#47;week in women <a class="elsevierStyleCrossRef" href="#bib0210">&#91;1&#93;</a>&#46; Finally&#44; we excluded 13 individuals for missing the NAFLD outcome&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">This study and the informed consent process were approved by the Institutional Review Boards of Emory University &#40;Atlanta&#44; GA&#41; and INCAP &#40;Guatemala City&#44; Guatemala&#41;&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0050">Data collection and lab assays</span><p id="par0045" class="elsevierStylePara elsevierViewall">In addition to the birth village and date of birth&#44; the original study collected data on maternal characteristics such as age&#44; height&#44; and schooling years&#46; In the 2015&#8211;2017 wave of data collection&#44; we obtained data on participant characteristics such as years of schooling&#44; socioeconomic &#40;SES&#41; status&#44; residence in Guatemala City&#44; and self-reported alcohol intake&#46; Trained field workers and phlebotomists collected anthropometric measurements and fasting blood samples&#44; as previously described <a class="elsevierStyleCrossRef" href="#bib0340">&#91;27&#93;</a>&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">Plasma samples were frozen for storage at &#8722;80<span class="elsevierStyleHsp" style=""></span>&#176;C until being shipped on dry ice to a biomarker core laboratory in the United States&#46; The samples were thawed at 4<span class="elsevierStyleHsp" style=""></span>&#176;C in batches&#44; each containing approximately 40 plasma samples&#46; Plasma ALT and AST values were assessed using the AU480 analyzer &#40;Beckman Coulter Diagnostics&#44; Fullerton CA&#44; US&#41; using enzymatic methods &#40;Sekisui Diagnostics P&#46;E&#46;I&#46; Inc&#46;&#44; Canada&#41;&#46; Insulin was assayed using immunoturbidimetric methods &#40;Kamiya Biomedical Company&#44; WA&#44; US&#41;&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3</span><span class="elsevierStyleSectionTitle" id="sect0055">Variable specification</span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0060">Exposure</span><p id="par0055" class="elsevierStylePara elsevierViewall">We determined exposure to <span class="elsevierStyleItalic">Atole</span> or <span class="elsevierStyleItalic">Fresco</span> by birth village&#46; We used the child&#39;s birth date and the trial start and end dates to determine the age of exposure as previously described <a class="elsevierStyleCrossRef" href="#bib0340">&#91;27&#93;</a>&#46; Early exposure was defined as 1000 days from conception &#40;assumed to be 266 days before the birth date to approximate the average length of pregnancy&#41; to age two years&#46; In our main analysis&#44; we considered individuals who were exposed for the entire period from conception to age two years to be fully exposed and those who were exposed for only part of that time to not be exposed&#46; For sensitivity analysis&#44; we also created a three-category exposure variable&#58; those who were fully exposed&#44; those who were partially exposed&#44; and those without any exposure from conception to age two years&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0065">NAFLD and related outcomes</span><p id="par0060" class="elsevierStylePara elsevierViewall">We used the NAFLD liver fat score equations developed by Kotronen&#44; et al&#46; to determine the presence of hepatic steatosis &#40;liver fat score&#41; and its quantity &#40;liver fat percent&#41; <a class="elsevierStyleCrossRef" href="#bib0355">&#91;30&#93;</a>&#46; The equations include metabolic syndrome&#44; type 2 diabetes&#44; fasting insulin&#44; fasting AST&#44; and the AST&#47;ALT ratio&#46; A higher score indicates greater steatosis&#46; We used the optimal cut off point of &#8722;0&#46;640 for the NALFD liver fat score to define the presence of suspected NAFLD&#46; This cut off was determined by Kotronen&#44; et al&#46; using the Youden index <a class="elsevierStyleCrossRef" href="#bib0355">&#91;30&#93;</a>&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">We defined metabolic syndrome by the presence of at least three of the following criteria&#58; &#40;1&#41; central obesity &#40;waist circumference<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>88<span class="elsevierStyleHsp" style=""></span>cm in women&#44; &#62;102<span class="elsevierStyleHsp" style=""></span>cm in men&#41;&#44; &#40;2&#41; elevated fasting glucose &#40;&#8805;100<span class="elsevierStyleHsp" style=""></span>mg&#47;dL&#41; or use of diabetes medication&#44; &#40;3&#41; elevated triglycerides &#40;&#8805;150<span class="elsevierStyleHsp" style=""></span>mg&#47;dL&#41; or statin use&#44; &#40;4&#41; low high density lipoprotein &#40;HDL cholesterol &#40;&#60;40<span class="elsevierStyleHsp" style=""></span>mg&#47;dL in men&#44; &#60;50<span class="elsevierStyleHsp" style=""></span>mg&#47;dL in women&#41;&#44; and &#40;5&#41; hypertension &#40;&#8805;85<span class="elsevierStyleHsp" style=""></span>mm Hg diastolic&#41; or hypertension medication use <a class="elsevierStyleCrossRef" href="#bib0360">&#91;31&#93;</a>&#46; We defined type 2 diabetes as a fasting plasma glucose &#8805;126<span class="elsevierStyleHsp" style=""></span>mg&#47;dL&#44; a post challenge glucose of &#8805;200<span class="elsevierStyleHsp" style=""></span>mg&#47;dL or the use of diabetes medication <a class="elsevierStyleCrossRef" href="#bib0365">&#91;32&#93;</a>&#46; We calculated the AST&#47;ALT ratio by dividing AST by ALT&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3&#46;3</span><span class="elsevierStyleSectionTitle" id="sect0070">Covariates</span><p id="par0070" class="elsevierStylePara elsevierViewall">We calculated body mass index &#40;BMI&#41; by weight in kilograms divided by height in meters squared <a class="elsevierStyleCrossRef" href="#bib0370">&#91;33&#93;</a>&#46; We calculated the waist-to-height ratio by dividing the waist circumference in centimeters by height in centimeters&#46; We categorized childhood and adulthood socioeconomic status into tertiles&#46; We had previously derived socioeconomic status from a principal component analysis of household characteristics and consumer durable goods <a class="elsevierStyleCrossRef" href="#bib0340">&#91;27&#93;</a>&#46; We characterized current residence dichotomously as Guatemala City or other to capture rural vs urban location&#46; We included age at follow-up and completed grades of schooling for mothers and their children as continuous variables&#46;</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;4</span><span class="elsevierStyleSectionTitle" id="sect0075">Statistical analysis</span><p id="par0075" class="elsevierStylePara elsevierViewall">We calculated median and interquartile range &#40;IQR&#41; values for continuous variables and percent for categorical variables&#46; We used Wilcoxon Rank Sum tests to compare differences in medians between men and women for continuous clinical covariates and NAFLD related outcomes&#46; We used a Pearson chi-square test to compare NAFLD&#44; type 2 diabetes&#44; and metabolic syndrome percent in men and women&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">We used multiple imputation to deal with missing data for maternal height &#40;20&#46;5&#37; missing&#41;&#44; maternal years of schooling &#40;3&#46;5&#37; missing&#41;&#44; and maternal age &#40;1&#46;6&#37; missing&#41;&#46; We used the fully conditional specification method for five imputations&#46; To impute missing values&#44; we used predictive mean matching for continuous variables and the logistic regression method for ordinal variables&#46; We included all predictor and outcome variables that were not linear effects of each other as predictors in the imputation model <a class="elsevierStyleCrossRef" href="#bib0375">&#91;34&#93;</a>&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">We used generalized regression models to estimate the difference-in-difference intent-to-treat effect of <span class="elsevierStyleItalic">Atole</span> vs <span class="elsevierStyleItalic">Fresco</span> from conception to age two years&#44; net of the differences attributable to village or birth year effects&#46; We estimated the difference-in-difference effect by an interaction term between the type of exposure &#40;<span class="elsevierStyleItalic">Atole</span> vs&#46; <span class="elsevierStyleItalic">Fresco</span>&#41; and the timing of exposure &#40;during the full first 1000 days versus other&#41;&#46; The base model included birth village in the form of three dummy variables &#40;this also serves to account for treatment assignment of <span class="elsevierStyleItalic">Atole</span> vs <span class="elsevierStyleItalic">Fresco</span>&#41;&#44; a dichotomous variable to account for duration of exposure &#40;full vs other&#41;&#44; birth year&#44; maternal height &#40;as an indicator of maternal nutrition status&#41;&#44; maternal years of schooling&#44; sex&#44; and childhood SES&#46; We additionally controlled for several adult sociodemographic and clinical factors including SES status&#44; years of schooling&#44; residence&#44; BMI&#44; waist-to-height ratio&#44; and height&#46; We considered sex as a potential effect modifier&#46; We tested for stratum heterogeneity by examining the significance of the third-order interaction between sex&#44; exposure type and duration&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">We conducted all statistical analysis in SAS version 9&#46;4&#46; We used PROC GENMOD&#44; for modeling and PROC MI and PROC MIANALYZE for multiple imputation&#46; Statistical significance was determined by <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05 and <span class="elsevierStyleItalic">p</span>-values were two-sided&#46;</p></span></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="sect0080">Results</span><p id="par0095" class="elsevierStylePara elsevierViewall">The final sample size was 1093 individuals &#40;61&#46;1&#37; women&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46; Selected characteristics of the study population are in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46; The median age at follow-up was 44 years &#40;IQR 41&#8211;47&#41;&#46; Twenty-two percent of participants were exposed to <span class="elsevierStyleItalic">Atole</span> during the full period from conception to age two years&#46; An additional 17&#46;8&#37; were exposed to <span class="elsevierStyleItalic">Atole</span> for part of the period from conception to age 2 years&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0100" class="elsevierStylePara elsevierViewall">The median NAFLD liver fat score was 0&#46;2 &#40;IQR&#58; &#8722;1&#46;2&#44; 1&#46;6&#41; in women and &#8722;1&#46;2 &#40;IQR&#58; &#8722;2&#46;2&#44; 0&#46;5&#41; in men &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46; The median liver fat percent was 5&#46;4&#37; &#40;IQR&#58; 3&#46;1&#44; 8&#46;0&#41; in women and 3&#46;0&#37; &#40;IQR&#58; 2&#46;0&#44; 6&#46;&#41; in men &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#59; <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#46; Median ALT was 19&#46;7<span class="elsevierStyleHsp" style=""></span>U&#47;L &#40;IQR&#58; 14&#46;0&#44; 27&#46;4&#41;&#44; median AST was 26&#46;0<span class="elsevierStyleHsp" style=""></span>U&#47;L &#40;IQR&#58; 21&#46;3&#44; 32&#46;8&#41;&#44; and the median AST&#47;ALT ratio was 1&#46;3 &#40;IQR 1&#46;0&#44; 1&#46;8&#41;&#46; Men had significantly higher ALT and AST values &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46; The prevalence of NAFLD was 56&#46;5&#37; and was higher in women &#40;67&#46;4&#37;&#41; than men &#40;39&#46;5&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0105" class="elsevierStylePara elsevierViewall">The assocations between exposure to <span class="elsevierStyleItalic">Atole</span> in early life and NAFLD-related parameters are provided in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#46; The estimates for ALT&#44; AST&#44; AST&#47;ALT ratio&#44; liver fat score&#44; and liver fat percent were not statistically significant in the base model or with additional adjustment for adult factors&#46; Similarly&#44; the odds ratio for NAFLD was not significant in the base model &#40;OR&#58; 1&#46;26&#44; 95&#37; CI&#58; 0&#46;75&#8211;2&#46;11&#41; or after accounting for adult factors &#40;OR&#58; 0&#46;90&#44; 95&#37; CI&#58; 0&#46;50&#8211;1&#46;63&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41;&#46; There was no significant stratum heterogeneity by sex&#46; In sensitivity analysis&#44; we found no significant differences in the models for those with partial vs no exposure and full vs no exposure&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="sect0085">Discussion</span><p id="par0110" class="elsevierStylePara elsevierViewall">We describe the prevalence of NAFLD among a cohort that participated in an improved protein-energy supplementation trial during early life in Guatemala&#46; Their prevalence of NAFLD in mid-adulthood was 56&#46;5&#37; and was significantly higher in women compared to men&#46; Exposure to the protein-energy supplement from conception to two years was not associated with NAFLD in mid-adulthood&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">The NAFLD prevalence we report is consistent with the only other study on NAFLD prevalence in Guatemala&#44; despite the use of different NAFLD criteria <a class="elsevierStyleCrossRef" href="#bib0225">&#91;4&#93;</a>&#46; Rivera-Andrade used the Fatty Liver Index and found a NAFLD prevalence of 66&#46;5&#37; and 50&#46;6&#37; in women and men over 40 years old&#44; respectively <a class="elsevierStyleCrossRef" href="#bib0225">&#91;4&#93;</a>&#46; NAFLD is usually more common in men&#44; although this difference is less pronounced with age <a class="elsevierStyleCrossRef" href="#bib0380">&#91;35&#93;</a>&#46; Women in our study had a higher prevalence of metabolic syndrome and type 2 diabetes&#44; which could partially explain their higher NAFLD prevalence&#44; especially since we used a NAFLD score that includes type 2 diabetes and metabolic syndrome in its calculation&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">In a previous publication using the same population&#44; the <span class="elsevierStyleItalic">Atole</span> supplement had mixed effects on cardiometabolic outcomes&#46; It was associated with increased odds of obesity &#40;OR&#58; 1&#46;99&#44; 95&#37; CI&#58; 1&#46;16&#8211;3&#46;41&#41;&#44; reduced odds of diabetes &#40;OR&#58; 0&#46;47&#44; 95&#37; CI&#58; 0&#46;22&#8211;0&#46;99&#41;&#44; and was not significantly associated with metabolic syndrome <a class="elsevierStyleCrossRef" href="#bib0340">&#91;27&#93;</a>&#46; The diverging associations between early-life nutrition and later cardiometabolic conditions may be due to various interlinked mechanisms&#44; including the role of protein in directing the development of metabolically active tissues <a class="elsevierStyleCrossRef" href="#bib0385">&#91;36&#93;</a>&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">Additionally&#44; we may have failed to observe a significant association between early-life protein-energy supplementation and adulthood NAFLD because the lifetime cumulative effect of other factors such as BMI trajectory or diet that dominate over the effect of early life exposure to <span class="elsevierStyleItalic">Atole</span>&#46; Excluding those with obesity&#44; which might overpower any effects from early life&#44; did not meaningfully change our estimates&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">Animal models have linked early life protein and energy restriction to later NAFLD and described potential pathways through which this occurs&#46; In rats&#44; maternal protein restriction results in upregulation of the transcription factors sterol regulatory element binding protein &#40;SREBP-1c&#41;&#44; carbohydrate-responsive element-binding protein &#40;ChREBP&#41; and peroxisome proliferator-activated receptor-&#947; &#40;PPAR-&#947;&#41;&#46; This effects downstream genes important to lipid metabolism and insulin signaling <a class="elsevierStyleCrossRefs" href="#bib0260">&#91;11&#44;14&#44;15&#93;</a>&#46; At the organ level&#44; growth restricted offspring exhibit fewer but larger hepatic lobules and enzymatic alterations&#44; which&#44; along with a decreased pancreatic b-cell mass&#44; lead to insulin resistance <a class="elsevierStyleCrossRefs" href="#bib0390">&#91;37&#44;38&#93;</a>&#46; In the liver&#44; insulin resistance inhibits b-oxidation and leads to decreased output of lipids&#46; This&#44; along with the increase of free fatty acid influx into the liver due to insulin resistance&#44; results in steatosis of the liver&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">There are several strengths in our study&#46; One strength is the sample size and use of multiple imputation to account for missing data to maximize model size&#46; Although attrition is a concern as about 50&#37; of the original cohort were in the 2015&#8211;2017 wave of data collection&#44; there is no evidence that attrition has affected the validity of this study&#46; Ford&#44; et al&#46; found that attrition was not differential with respect to <span class="elsevierStyleItalic">Atole</span> exposure from conception to age two years&#46; Additionally&#44; individuals lost to follow-up were similar to those who participated in the 2015&#8211;2017 study&#44; except for sex <a class="elsevierStyleCrossRef" href="#bib0340">&#91;27&#93;</a>&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">Another strength of this study is the use of the composite NAFLD liver fat score&#46; ALT levels alone have poor sensitivity and specificity in adults <a class="elsevierStyleCrossRefs" href="#bib0400">&#91;39&#44;40&#93;</a>&#46; Therefore&#44; scores like the NAFLD liver fat score are useful as non-invasive predictors of the disease <a class="elsevierStyleCrossRef" href="#bib0355">&#91;30&#93;</a>&#46; The original NAFLD liver fat score was developed in a large Finnish cohort in 2009 so it is possible it is not transferable to other populations&#46; However&#44; when compared to three other NAFLD prediction scores&#44; the NAFLD liver fat score had the best non-invasive prediction score for NAFLD and associated mortality including in Hispanics <a class="elsevierStyleCrossRef" href="#bib0410">&#91;41&#93;</a>&#46;</p><p id="par0145" class="elsevierStylePara elsevierViewall">There are a few limitations in our study&#46; The ideal exposure period may only be a subset of the first 1000 days such as during liver organogenesis in utero&#44; which begins at four weeks and continues throughout gestation&#46; In our study&#44; it is not possible to examine exposure during pregnancy and early childhood separately due to the limited sample size&#59; 3&#46;7&#37; &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>40&#41; of the original cohort were exposed to <span class="elsevierStyleItalic">Atole</span> only in pregnancy&#46; Additionally&#44; sufficient data were not available in our cohort on variables that have been previously associated with NAFLD including gestational age and birthweight of which 63&#37; and 57&#37; were missing&#44; respectively&#46; Among those with data available about 10&#37; were born pre-term &#40;&#60;37 weeks gestation&#41; or with low birthweight &#40;&#60;2&#46;5<span class="elsevierStyleHsp" style=""></span>kg&#41;&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5</span><span class="elsevierStyleSectionTitle" id="sect0090">Conclusions</span><p id="par0150" class="elsevierStylePara elsevierViewall">Understanding the developmental origins of NAFLD and other metabolic conditions is particularly important because of the growing prevalence of these diseases <a class="elsevierStyleCrossRef" href="#bib0255">&#91;10&#93;</a>&#46; The high prevalence of NAFLD in Guatemala highlights the need for population-based studies that will help identify the underlying mechanisms of the onset and development of NAFLD throughout the life course&#44; including and beyond early-life nutritional exposure&#46;<span class="elsevierStyleDefList"><span class="elsevierStyleSectionTitle" id="sect0095">Abbreviations</span><span class="elsevierStyleDefTerm">NAFLD</span><span class="elsevierStyleDefDescription"><p id="par0155" class="elsevierStylePara elsevierViewall">non-alcoholic fatty liver disease</p></span><span class="elsevierStyleDefTerm">ALT</span><span class="elsevierStyleDefDescription"><p id="par0160" class="elsevierStylePara elsevierViewall">alanine aminotransferase</p></span><span class="elsevierStyleDefTerm">AST</span><span class="elsevierStyleDefDescription"><p id="par0165" class="elsevierStylePara elsevierViewall">aspartate aminotransferase</p></span><span class="elsevierStyleDefTerm">INCAP</span><span class="elsevierStyleDefDescription"><p id="par0170" class="elsevierStylePara elsevierViewall">Institute of Nutrition of Central America and Panama</p></span><span class="elsevierStyleDefTerm">IQR</span><span class="elsevierStyleDefDescription"><p id="par0175" class="elsevierStylePara elsevierViewall">interquartile range</p></span><span class="elsevierStyleDefTerm">OR</span><span class="elsevierStyleDefDescription"><p id="par0180" class="elsevierStylePara elsevierViewall">odds ratio</p></span><span class="elsevierStyleDefTerm">BMI</span><span class="elsevierStyleDefDescription"><p id="par0185" class="elsevierStylePara elsevierViewall">body mass index</p></span><span class="elsevierStyleDefTerm">SES</span><span class="elsevierStyleDefDescription"><p id="par0190" class="elsevierStylePara elsevierViewall">socioeconomic status</p></span></span></p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Funding</span><p id="par0195" class="elsevierStylePara elsevierViewall">This work was supported by the <span class="elsevierStyleGrantSponsor" id="gs1">National Institutes of Health</span> &#91;<span class="elsevierStyleGrantNumber" refid="gs1">HD075784</span>&#93;&#46;</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Conflict of interest</span><p id="par0200" class="elsevierStylePara elsevierViewall">MBV reports personal fees from Intercept&#44; grants from Immuron&#44; grants and personal fees from Target Pharmasolutions&#44; personal fees from Boehringer Ingelheim&#44; grants and personal fees from Bristol Myers Squibb&#44; personal fees from Prosciento&#44; and personal fees from Novo Nordisk during the conduct of the study&#46;</p></span></span>"
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    "fechaRecibido" => "2020-03-04"
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            0 => "Central America"
            1 => "Cardiovascular diseases&#47;epidemiology"
            2 => "Follow-up studies"
            3 => "Guatemala&#47;epidemiology"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction and objectives</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The global prevalence of non-alcoholic fatty liver disease &#40;NAFLD&#41; is approximately 25&#37;&#44; with Hispanic populations at greatest risk&#46; We describe the prevalence of NAFLD in a cohort of Guatemalan adults and examine whether exposure to a protein-energy supplement from conception to two years is associated with lower prevalence of NAFLD&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">From 1969 to 1977&#44; four villages in Guatemala were cluster-randomized to receive a protein-energy supplement &#40;<span class="elsevierStyleItalic">Atole</span>&#41; or a no-protein&#44; low-energy beverage &#40;<span class="elsevierStyleItalic">Fresco</span>&#41;&#46; We conducted a follow-up of participants from 2015 to 2017&#46; We assessed blood samples &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1093&#59; 61&#46;1&#37; women&#59; aged 37&#8211;53 years&#41; for alanine aminotransferase &#40;ALT&#41; and aspartate aminotransferase &#40;AST&#41; and estimated NAFLD prevalence using the liver fat score&#46; We used generalized linear and logistic models to estimate the difference-in-difference effect of <span class="elsevierStyleItalic">Atole</span> from conception to two years on NAFLD&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Median ALT and AST were 19&#46;7<span class="elsevierStyleHsp" style=""></span>U&#47;L &#40;interquartile range&#44; IQR&#58; 14&#46;1&#44; 27&#46;4&#41; and 26&#46;0<span class="elsevierStyleHsp" style=""></span>U&#47;L &#40;IQR&#58; 21&#46;4&#44; 32&#46;8&#41;&#44; respectively&#46; The median NAFLD liver fat score was 0&#46;2 &#40;IQR&#58; &#8722;1&#46;2&#44; 1&#46;6&#41; in women and &#8722;1&#46;2 &#40;IQR&#58; &#8722;2&#46;2&#44; 0&#46;5&#41; in men &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46; The prevalence of NAFLD was 67&#46;4&#37; among women and 39&#46;5&#37; among men &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46; The association between <span class="elsevierStyleItalic">Atole</span> exposure from conception to two years and NAFLD was not significant &#40;OR&#58; 0&#46;90&#44; 95&#37; CI&#58; 0&#46;50&#8211;1&#46;63&#41;&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">NAFLD prevalence among Guatemalan adults exceeds the global average&#46; Protein-energy supplementation in early life was not associated with later NAFLD&#46; There is a need for further studies on the causes and onset of NAFLD throughout the life course&#46;</p></span>"
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          "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Flow diagram&#46; Flow diagram of original 1969&#8211;1977 INCAP Guatemala study to 2015&#8211;2017 data collection and analysis&#46; Of the original 2398 participants 369 had died&#44; 249 had migrated outside Guatemala&#44; and 113 were untraceable resulting in 1661 who were eligible for enrollment in 2015&#8211;2017&#46; Of these&#44; 1118 provided informed consent and provided at least one sample during the clinical exam&#46; During analysis we excluded an additional 6 for pregnancy&#47;lactation status&#44; 6 for high alcohol intake&#44; and 13 for not having data on the non-alcoholic fatty liver disease &#40;NAFLD&#41; outcome for a final analytic sample size of 1093&#46;</p>"
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          "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Liver fat percent boxplot by sex&#46; Histogram of liver fat percent by sex &#40;blue&#40;0&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>women&#44; red&#40;&#43;&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>men&#41; in the Guatemala INCAP study cohort 2015&#8211;2017 &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>668 women&#44; 425 men&#41;&#46; The liver fat percent is calculated using presence of metabolic syndrome&#44; type 2 diabetes&#44; fasting insulin&#44; fasting aspartate aminotransferase&#40;AST&#41;&#44; and fasting AST&#47;ALT ratio&#46;<a class="elsevierStyleCrossRef" href="#bib0355">&#91;30&#93;</a> The dotted black line indicates the 5&#37; cut off for NAFLD&#46;</p>"
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                  \t\t\t\t">148&#46;8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#40;145&#46;7&#44; 152&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Maternal schooling&#44; years<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#44; 2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#44; 2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#44; 3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Participant characteristics</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Age at follow-up&#44; years&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;41&#44; 47&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;41&#44; 47&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;40&#44; 47&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Schooling&#44; years&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;2&#44; 6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;2&#44; 6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;2&#44; 6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Residing in Guatemala City &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">18&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">18&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">19&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">Atole</span> from conception to 2 yrs &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">22&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">22&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Clinical</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">c</span></a></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Height&#44; cm&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">155&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;150&#46;1&#44; 162&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">151&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;148&#44; 155&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">163&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;159&#46;6&#44; 167&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Waist circumference&#44; cm&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">97&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;90&#46;8&#44; 105&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">100&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;93&#46;3&#44; 108&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">94&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;87&#46;5&#44; 100&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Body mass index&#44; kg&#47;m<span class="elsevierStyleSup">2</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;24&#46;8&#44; 31&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">28&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;25&#46;7&#44; 32&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">26&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;23&#46;8&#44; 29&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Waist-to-height ratio&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;6&#44; 0&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;6&#44; 0&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;57&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;5&#44; 0&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Insulin&#44; mU&#47;L&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;7&#46;7&#44; 20&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#40;9&#46;1&#44; 22&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#40;6&#46;3&#44; 16&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Type 2 diabetes &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">13&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">16&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Metabolic syndrome &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">56&#46;1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">69&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">35&#46;1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>ALT&#44; U&#47;L&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">19&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;14&#46;1&#44; 27&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">18&#46;8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#40;13&#46;4&#44; 26&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>AST&#44; U&#47;L&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#40;21&#46;4&#44; 32&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#40;20&#46;7&#44; 31&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#40;22&#46;6&#44; 33&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>AST&#47;ALT&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">&#40;1&#44; 1&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Liver fat score<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">b</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#40;&#8722;2&#46;2&#44; 0&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Liver fat percent<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">b</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;2&#46;4&#44; 7&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;3&#46;1&#44; 8&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;0&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;2&#46;0&#44; 6&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>NAFLD &#40;&#37;&#41;<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">b</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">56&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">39&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2333758.png"
              ]
            ]
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          "notaPie" => array:3 [
            0 => array:3 [
              "identificador" => "tblfn0005"
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              "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Maternal age &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1075&#41;&#44; maternal height &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>869&#41;&#44; maternal schooling &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1054&#41; and post-challenge glucose &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1011&#41; were missing values&#46;</p>"
            ]
            1 => array:3 [
              "identificador" => "tblfn0010"
              "etiqueta" => "b"
              "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Calculated using fasting insulin&#44; type 2 diabetes&#44; metabolic syndrome&#44; fasting AST&#44; and fasting AST&#47;ALT ratio <a class="elsevierStyleCrossRef" href="#bib0355">&#91;30&#93;</a>&#46;</p>"
            ]
            2 => array:3 [
              "identificador" => "tblfn0015"
              "etiqueta" => "c"
              "nota" => "<p class="elsevierStyleNotepara" id="npar0015"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001 for all clinical covariates and NAFLD outcomes comparing men and women &#40;except for AST&#47;ALT <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;04&#41;&#46;</p>"
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          "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Sociodemographic and clinical characteristics &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1093&#41;&#46;</p>"
        ]
      ]
      3 => array:8 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at2"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:3 [
          "leyenda" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Abbreviations&#58; ALT<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>alanine aminotransferase&#59; AST<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>aspartate aminotransferase&#59; AST&#47;ALT<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>ratio between AST and ALT&#59; CI<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>confidence interval&#59; SES<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>socioeconomic status&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="3" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Pooled</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="3" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Women</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="3" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Men</th></tr><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">&#946;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">&#946;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">&#946;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">ALT&#44; U&#47;L</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;60&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;3&#46;30&#44; 4&#46;51&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;76&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;79&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;5&#46;92&#44; 4&#46;35&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;76&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;3&#46;05&#44; 8&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;34&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;71&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;4&#46;54&#44; 3&#46;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;71&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;1&#46;18&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;6&#46;26&#44; 3&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;65&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;5&#46;91&#44; 5&#46;41&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;93&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">AST&#44; U&#47;L</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;31&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;2&#46;31&#44; 4&#46;92&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;48&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;65&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;3&#46;11&#44; 6&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;50&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;88&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;4&#46;64&#44; 6&#46;41&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;37&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;3&#46;23&#44; 3&#46;97&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;84&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;3&#46;59&#44; 5&#46;85&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;64&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;67&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;6&#46;2&#44; 4&#46;87&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;81&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">AST&#47;ALT</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;01&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;16&#44; 0&#46;14&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;09&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;09&#44; 0&#46;28&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;33&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;43&#44; 0&#46;09&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;01&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;14&#44; 0&#46;17&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;86&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;09&#44; 0&#46;28&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;31&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;09&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;35&#44; 0&#46;17&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;51&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Liver fat score</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;27&#44; 0&#46;91&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;29&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;36&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;44&#44; 1&#46;16&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;38&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;28&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;55&#44; 1&#46;12&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;51&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;01&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;52&#44; 0&#46;51&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;97&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;63&#44; 0&#46;83&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;80&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;14&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;78&#44; 0&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;66&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Liver fat percent</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;34&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;68&#44; 1&#46;36&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;51&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;1&#46;17&#44; 1&#46;52&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;80&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;60&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;0&#46;94&#44; 2&#46;15&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;23&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;1&#46;15&#44; 0&#46;69&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;22&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;1&#46;47&#44; 1&#46;02&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;72&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#8722;0&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;&#8722;1&#46;45&#44; 1&#46;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;79&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2333759.png"
              ]
            ]
          ]
          "notaPie" => array:1 [
            0 => array:3 [
              "identificador" => "tblfn0020"
              "etiqueta" => "a"
              "nota" => "<p class="elsevierStyleNotepara" id="npar0020">Model 1 &#40;base model&#41; adjusted for birth village&#44; duration of exposure&#44; birth year&#44; maternal height&#44; maternal years of schooling&#44; sex &#40;in pooled models&#41;&#44; and childhood SES&#46; Model 2 additionally adjusted for adult SES&#44; years of schooling&#44; residence&#44; body mass index&#44; waist-to-height ratio&#44; and height&#46;</p>"
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Adjusted<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">a</span></a> linear difference-in-difference estimates for exposure to <span class="elsevierStyleItalic">Atole</span> from conception to age 2 years vs other&#44; by non-alcoholic fatty liver disease related outcomes &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1093&#41;&#46;</p>"
        ]
      ]
      4 => array:8 [
        "identificador" => "tbl0015"
        "etiqueta" => "Table 3"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at3"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:3 [
          "leyenda" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Abbreviations&#58; OR<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>odds ratio&#59; CI<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>confidence interval&#59; NAFLD<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>non-alcoholic fatty liver disease&#59; SES<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>socioeconomic status&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="3" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Pooled</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="3" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Women</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="3" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Men</th></tr><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95&#37; CI&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " colspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">NAFLD</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Model 1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;75&#44; 2&#46;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;39&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;39&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;71&#44; 2&#46;74&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;34&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;01&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;44&#44; 2&#46;30&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;98&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;50&#44; 1&#46;63&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;73&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1&#46;10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;52&#44; 2&#46;33&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;80&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;57&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#40;0&#46;18&#44; 1&#46;80&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;34&nbsp;\t\t\t\t\t\t\n
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          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Adjusted binomial logistic difference-in-difference OR for exposure to <span class="elsevierStyleItalic">Atole</span> for full period from conception to age 2 y vs&#46; other&#44; for NAFLD &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1093&#41;&#46;<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">a</span></a></p>"
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                  "contribucion" => array:1 [
                    0 => array:2 [
                      "titulo" => "The diagnosis and management of non-alcoholic fatty liver disease&#58; practice guideline by the American Gastroenterological Association American Association for the Study of Liver Diseases&#44; and American College of Gastroenterology"
                      "autores" => array:1 [
                        0 => array:2 [
                          "etal" => true
                          "autores" => array:6 [
                            0 => "N&#46; Chalasani"
                            1 => "Z&#46; Younossi"
                            2 => "J&#46;E&#46; Lavine"
                            3 => "A&#46;M&#46; Diehl"
                            4 => "E&#46;M&#46; Brunt"
                            5 => "K&#46; Cusi"
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                        "tituloSerie" => "Gastroenterology"
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                0 => array:2 [
                  "contribucion" => array:1 [
                    0 => array:2 [
                      "titulo" => "Global epidemiology of nonalcoholic fatty liver disease&#8212;meta-analytic assessment of prevalence&#44; incidence&#44; and outcomes"
                      "autores" => array:1 [
                        0 => array:2 [
                          "etal" => false
                          "autores" => array:6 [
                            0 => "Z&#46;M&#46; Younossi"
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                            2 => "D&#46; Abdelatif"
                            3 => "Y&#46; Fazel"
                            4 => "L&#46; Henry"
                            5 => "M&#46; Wymer"
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                        ]
                      ]
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                    0 => array:2 [
                      "doi" => "10.1002/hep.28431"
                      "Revista" => array:7 [
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                        "fecha" => "2016"
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                        "paginaInicial" => "73"
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                        "link" => array:1 [
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                  "contribucion" => array:1 [
                    0 => array:2 [
                      "titulo" => "The prevalence of nonalcoholic fatty liver disease in the Americas"
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                          "etal" => true
                          "autores" => array:6 [
                            0 => "J&#46;A&#46; L&#243;pez-Vel&#225;zquez"
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                            2 => "G&#46; Ponciano-Rodr&#237;guez"
                            3 => "N&#46;C&#46; Ch&#225;vez-Tapia"
                            4 => "M&#46; Arrese"
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                    0 => array:1 [
                      "Revista" => array:7 [
                        "tituloSerie" => "Ann Hepatol"
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                        "volumen" => "13"
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                    0 => array:2 [
                      "titulo" => "High prevalence of non-alcoholic fatty liver disease and metabolic risk factors in Guatemala&#58; a population-based study"
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                        0 => array:2 [
                          "etal" => true
                          "autores" => array:6 [
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                            2 => "M&#46; Lazo"
                            3 => "N&#46;D&#46; Freedman"
                            4 => "J&#46;W&#46; Smith"
                            5 => "O&#46; Torres"
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                    0 => array:2 [
                      "doi" => "10.1016/j.numecd.2018.10.008"
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                    0 => array:2 [
                      "titulo" => "Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease"
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                        0 => array:2 [
                          "etal" => true
                          "autores" => array:6 [
                            0 => "S&#46; Romeo"
                            1 => "J&#46; Kozlitina"
                            2 => "C&#46; Xing"
                            3 => "A&#46; Pertsemlidis"
                            4 => "D&#46; Cox"
                            5 => "L&#46;A&#46; Pennacchio"
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                    ]
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                    0 => array:2 [
                      "doi" => "10.1038/ng.257"
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                          "etal" => false
                          "autores" => array:3 [
                            0 => "K&#46;R&#46; Patel"
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                        "paginaFinal" => "158"
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                            2 => "R&#46; Ruager-Martin"
                            3 => "E&#46;L&#46; Thomas"
                            4 => "M&#46;J&#46; Hyde"
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Article information

ISSN: 16652681
Original language: English

DOI:10.1016/j.aohep.2020.04.004

The statistics are updated each day

Year/Month	Html	Pdf	Total

2024 November	7	0	7
2024 October	32	13	45
2024 September	21	17	38
2024 August	13	24	37
2024 July	55	9	64
2024 June	11	2	13
2024 May	10	5	15
2024 April	20	4	24
2024 March	16	10	26
2024 February	20	2	22
2024 January	10	4	14
2023 December	16	7	23
2023 November	10	4	14
2023 October	24	10	34
2023 September	10	1	11
2023 August	15	5	20
2023 July	8	4	12
2023 June	23	5	28
2023 May	37	0	37
2023 April	38	1	39
2023 March	39	4	43
2023 February	18	11	29
2023 January	9	1	10
2022 December	13	7	20
2022 November	10	15	25
2022 October	18	9	27
2022 September	18	11	29
2022 August	38	5	43
2022 July	19	7	26
2022 June	37	5	42
2022 May	25	8	33
2022 April	14	5	19
2022 March	28	6	34
2022 February	20	5	25
2022 January	52	5	57
2021 December	41	8	49
2021 November	33	15	48
2021 October	52	14	66
2021 September	24	6	30
2021 August	15	9	24
2021 July	9	7	16
2021 June	12	9	21
2021 May	13	8	21
2021 April	67	19	86
2021 March	19	5	24
2021 February	11	7	18
2021 January	18	12	30
2020 December	11	7	18
2020 November	12	9	21
2020 October	18	3	21
2020 September	28	11	39
2020 August	73	14	87
2020 July	47	17	64
2020 June	3	9	12
2020 May	0	3	3

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