Original article
Relationship between glycated hemoglobin and glucose concentrations in the adult Galician population: selection of optimal glycated hemoglobin cut-off points as a diagnostic tool of diabetes mellitus
Relación entre hemoglobina glucosilada y concentraciones de glucosa en la población gallega adulta: selección de los puntos de corte óptimos de la hemoglobina glucosilada como herramienta diagnóstica de la diabetes mellitus
Manuel Antonio Botana Lópeza,
, Mónica López Ratónb, María Ausencia Toméc, Alexis Fernández Mariñod, José Antonio Mato Matoe, Antonia Rego Iraetae, Román Pérez Fernándezf, Carmen Cadarso Suárezb
Corresponding author
a Endocrine Section, Hospital Lucus Augusti, Lugo, Spain
b Department of Statistics and Operations Research (Unit of Biostatistics), School of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
c Endocrine Service, University Clinical Hospital, Santiago de Compostela, Spain
d Coya Specialty Centre, Vigo, Spain
e Endocrine Service, Hospital Cristal-Piñor, Orense, Spain
f Department of Physiology, School of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
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class="elsevierStyleSup">f</span>" "identificador" => "aff0030" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Relación entre hemoglobina glucosilada y concentraciones de glucosa en la población gallega adulta: selección de los puntos de corte óptimos de la hemoglobina glucosilada como herramienta diagnóstica de la diabetes mellitus" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1536 "Ancho" => 1557 "Tamanyo" => 123071 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Receiver operating characteristic curve for identification of participants with previously undiagnosed diabetes, using glycated hemoglobin and fasting plasma glucose for diagnosis and fasting plasma glucose and glycemia at 2<span class="elsevierStyleHsp" style=""></span>h of oral glucose tolerance test as disease criteria. AUC: area under the curve; FPG: fasting plasma glucose; HbA<span class="elsevierStyleInf">1c</span>: glycated hemoglobin; ROC: receiver operating characteristic curve; Se: sensitivity; Sp: specificity.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Ascertaining the prevalence of diabetes is important because it is a disease that is becoming increasingly prevalent.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> Fasting glucose (FPG) and glycemia at 2<span class="elsevierStyleHsp" style=""></span>h after an oral glucose overload test (2hOGTT) were classical and now also glycated hemoglobin (HbA<span class="elsevierStyleInf">1c</span>) are used for diagnosis of diabetes. Although considered the “gold standard” for diagnosis, measurement of blood glucose is subject to several limitations as patient must fast at least 8<span class="elsevierStyleHsp" style=""></span>h, it has a large biological variability, samples are not stable, numerous factors alter glucose concentrations (diurnal variation, sample source, acute illness or stress), and it reflects glucose homeostasis at a single point in time.<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">HbA<span class="elsevierStyleInf">1c</span> has advantages including its familiarity to clinicians, convenience, preanalytic stability, and assay standardization. It displays none of the variability inherent in the determination of glucose, it gives a better reflection of chronic hyperglycemia, and its concentration predicts the development of microvascular complications of diabetes.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3</span></a> HbA<span class="elsevierStyleInf">1c</span> has a number of limitations: may be altered by factors other than glucose (e.g., change in erythrocyte life span, ethnicity), some conditions interfere with measurement (e.g., selected hemoglobinopathies), it may not be available in some laboratories/areas of the world and its cost is higher than glucose determination.<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">HbA<span class="elsevierStyleInf">1c</span> cut-off point has been set at ≥6.5% for diagnosis and at 5.7–6.4% for the diabetes high risk category.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> The question arises, however, as to whether HbA<span class="elsevierStyleInf">1c</span> identifies the same population as does glucose. Accordingly, the aim of this study was to analyze the relationship between glucose and HbA<span class="elsevierStyleInf">1c</span> in the adult Galician population, and evaluate the performance of HbA<span class="elsevierStyleInf">1c</span> for the screening and diagnosis of diabetes.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Methods</span><p id="par0020" class="elsevierStylePara elsevierViewall">The basic methodology of the study has been previously described.<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5,6</span></a> This study was carried out in a random sample representative of the Galician adult population (older than 18 years). Study subjects were selected by a two step cluster sampling procedure from the Galician Public Health Service (SERGAS) database, which covers more than 95% of the population. Primary health care centre dependent populations were randomly selected in each province (Galicia has four provinces; the population of each was considered as independent), and individual subjects aged over 18 years were then randomly selected within each population. Health centres were stratified by municipality type (rural or urban; coastal or interior); individuals were stratified by sex and age. Pregnant women were excluded from this study.</p><p id="par0025" class="elsevierStylePara elsevierViewall">We contacted each person by mail in order to arrange the appointment for the study. For each non-responder a substitute was randomly selected. Information was collected through a personal interview at local health centres using a structured questionnaire, followed by a physical examination to measure blood pressure and anthropometric characteristics. Blood and urine samples were collected for subsequent analysis.</p><p id="par0030" class="elsevierStylePara elsevierViewall">The study protocol was approved by the corresponding research ethics committee (Comité Ético de Investigación Clínica de Galicia). All participants signed informed consent forms.</p><p id="par0035" class="elsevierStylePara elsevierViewall">The anthropometric measurements, including weight, height, waist circumference (WC) and hip circumference (HC), were obtained by trained personnel (physicians and nurses) using standardized techniques and equipment.<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> Blood pressure was measured twice in recumbent position, with an interval of 3<span class="elsevierStyleHsp" style=""></span>min; the final value was the arithmetic mean of the two figures. Blood samples were drawn after a fasting period of 10–14<span class="elsevierStyleHsp" style=""></span>h.</p><p id="par0040" class="elsevierStylePara elsevierViewall">Blood analyses were all done in the same central laboratory to which all samples were sent within the first 24<span class="elsevierStyleHsp" style=""></span>h after immediate centrifuging and freezing.</p><p id="par0045" class="elsevierStylePara elsevierViewall">To evaluate glucose metabolism, 75<span class="elsevierStyleHsp" style=""></span>g of anhydrous glucose load was given orally in 250<span class="elsevierStyleHsp" style=""></span>ml of water to all subjects except those with known diabetes. Fasting and 2<span class="elsevierStyleHsp" style=""></span>h post-glucose load blood glucose were assessed by the glucose hexokinase method. HbA<span class="elsevierStyleInf">1c</span> was measured by HPLC. All other laboratory determinations were done using standardized procedures.</p><p id="par0050" class="elsevierStylePara elsevierViewall">We used the diagnostic criteria recommended in 2002 by the Expert Committee on the Diagnosis and Classification of Diabetes mellitus<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> to classify diabetes and lesser degrees of impaired glucose regulation, as follows (a) normal glucose, i.e., fasting plasma glucose (FPG) <100<span class="elsevierStyleHsp" style=""></span>mg/dL and 2hOGTT <140<span class="elsevierStyleHsp" style=""></span>mg/dL; (b) impaired fasting glucose (IFG): PG between 100 and 125<span class="elsevierStyleHsp" style=""></span>mg/dL; (c) impaired glucose tolerance (IGT): 2hOGTT between 140 and 199<span class="elsevierStyleHsp" style=""></span>mg/dL; and (d) diabetes: PG ≥126<span class="elsevierStyleHsp" style=""></span>mg/dL or 2hOGTT ≥200<span class="elsevierStyleHsp" style=""></span>mg/dL. For comparisons, we used also the criteria recommended in 2011.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> These included a new category of increased risk of diabetes, when HbA<span class="elsevierStyleInf">1c</span> is between 5.7% and 6.4%.</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Statistical analysis</span><p id="par0055" class="elsevierStylePara elsevierViewall">Based on their oral glucose tolerance test (OGTT) results, participants were classified into four different groups: (1) normal glucose (NGM); (2) prediabetes (subjects presenting with IFG, IGT or both); (3) unknown diabetes (UKDM); (4) known diabetes (KDM) (subjects who reported suffering from diabetes or using insulin or drugs for treatment of diabetes).</p><p id="par0060" class="elsevierStylePara elsevierViewall">The correlations between HbA<span class="elsevierStyleInf">1c</span> and FPG, and between HbA<span class="elsevierStyleInf">1c</span> and 2hOGTT were calculated using Spearman correlations, which were not based on the assumption of normality, whether in the total study population or in the respective subgroups defined.</p><p id="par0065" class="elsevierStylePara elsevierViewall">After patients with known diabetes were excluded, HbA<span class="elsevierStyleInf">1c</span> diagnostic capacity for diagnosis of diabetes was evaluated by using the receiver operating characteristic (ROC) curve and examining HbA<span class="elsevierStyleInf">1c</span> sensitivity (Se) and specificity (Sp) measures at different cut-off values, accompanied by the corresponding area under ROC curve (AUC) and its 95% confidence intervals.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> The AUC assumes values ranging from 0 to 1, such that the nearer its value approaches 1 the higher the discriminatory capacity.</p><p id="par0070" class="elsevierStylePara elsevierViewall">Based on the ROC curve, optimal cut-off points that best discriminated between the diabetic and non-diabetic populations were then calculated on the basis of different criteria, namely: (1) judging the optimal cut-off point to be that at which the Se and Sp measures were similar or practically the same<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a>; (2) choosing the optimal cut-off point as being that at which Se and Sp were simultaneously maximized,<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> thereby assuming that the consequences of false positives and false negatives were practically identical; (3) seeking the cut-off point which ensured that the Se and Sp values were the closest possible to 1 (criterion of the point closest to the point (0, 1) on the ROC curve)<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a>; (4) taking the cut-off point that maximized Youden's index,<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> which was equivalent to maximizing the sum of the Se and Sp measures; (5) selecting the cut-off point at which Sp was higher than or equal to a designated minimum value and, subject to this pre-condition, at which Se was as high as possible (to prevent a high number of false positives, in view of the high prevalence of diabetes); and (6) deeming the optimal cut-off point to be the value that maximized the percentage of correctly classified individuals or, what amounted to the same thing, that minimized the percentage of incorrect classifications of the diagnosis.<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a></p><p id="par0075" class="elsevierStylePara elsevierViewall">To describe the effect of the different HbA<span class="elsevierStyleInf">1c</span> diagnostic thresholds obtained, we calculated the Se and Sp measures and the positive (PPV) and negative predictive values (NPV) at these cut-off values.</p><p id="par0080" class="elsevierStylePara elsevierViewall">All statistical analyses were performed using the R 2.12.0 statistical software package, with a <span class="elsevierStyleItalic">p</span>-value of ≤0.05 considered as statistical significant.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Results</span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Study population characteristics</span><p id="par0085" class="elsevierStylePara elsevierViewall">Of the 2860 patients included in the study, 2850 (99.65%) were aged between 18 and 85 years. As no HbA<span class="elsevierStyleInf">1c</span> values were available for two of the latter (0.07%), the analysis initially included a total of 2848 participants. In this sample, 125 (4.4%) subjects had known diabetes and 96 (3.4%) had unknown diabetes, if we used only the blood glucose criteria. Using also the HbA<span class="elsevierStyleInf">1c</span> criterion, the number of subjects with unknown diabetes were 119 (4.2%). There were 23 subjects (19.3% of all subjects with diabetes) with HbA1c ≥ 6.5% and normal basal and 2h OGTT blood glucose. Seventeen subjects (14.3% with diabetes) had only met the criteria of fasting glucose, 34 (28.6%) met only the criterion of glucose at 2<span class="elsevierStyleHsp" style=""></span>h, and 21 (17.6%) subjects met the three diagnostic criteria. Forty-five (37.8%) subjects met at least two criteria. Five subjects had a basal glucose higher than 126<span class="elsevierStyleHsp" style=""></span>mg/dl with an HbA<span class="elsevierStyleInf">1c</span> higher than 6.5% who did not undergo a 2hOGTT.</p><p id="par0090" class="elsevierStylePara elsevierViewall">If we consider IFG and IGT as prediabetes, there were 2033 subjects with normal glucose metabolism and 594 (20.9%) had prediabetes according to the ADA 2002 criteria. If we considered the “at risk” HbA<span class="elsevierStyleInf">1c</span> (between 5.7% and 6.4%) as another form of prediabetes, the number of subjects with prediabetes amounted 1600 (56.2%). A total of 1025 subjects had HbA<span class="elsevierStyleInf">1c</span> between 5.7% and 6.4% with normal basal and 2h OGTT glucose. These data are detailed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0095" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> shows the baseline characteristics of subjects classified according to the diagnostic criteria met. Body mass index, triglycerides and glucose concentrations (basal glucose, 2hOGTT and HbA1c) were statistically different among subgroups. BMI was lower in patients diagnosed only by OGTT than in the other subgroups (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05 vs. diagnosed only by fasting glucose and <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01 vs. those diagnosed only by HbA<span class="elsevierStyleInf">1c</span> or more than one criteria). There were no differences in the prevalence of obesity between subgroups. Triglyceride levels were significantly lower in patients diagnosed only after an OGTT or by HbA1c compared with those diagnosed by at least two criteria (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>≤<span class="elsevierStyleHsp" style=""></span>0.01). Basal glucose levels were higher in those diagnosed only by fasting glucose and in those who had at least two criteria (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001 in all cases). The highest 2hOGTT concentrations were found in the group with at least two criteria (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). This parameter was also higher in the group diagnosed only by this criterion compared with those diagnosed only by fasting glucose or by HbA1c (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). The highest HbA1c concentration was that of the group with at least two criteria (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). There were no differences between subgroups in the prevalence of hypertension, cholesterol, HDL cholesterol or microalbuminuria.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">HbA<span class="elsevierStyleInf">1c</span>, FPG and 2hOGTT correlations</span><p id="par0100" class="elsevierStylePara elsevierViewall">For the calculation of correlations and associations, patients who did not undergo a 2hOGTT (102 individuals, 3.58%) were excluded, leaving a total of 2746 patients for this analysis. Fitting a linear regression model yielded <span class="elsevierStyleItalic">R</span><span class="elsevierStyleSup">2</span> values of 0.46 for the association between FPG and HbA<span class="elsevierStyleInf">1c</span>, and 0.33 for that between 2hOGTT and HbA<span class="elsevierStyleInf">1c</span> (both significant at <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.01). <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows the Spearman correlations for HbA<span class="elsevierStyleInf">1c</span>, FPG and 2hOGTT. In the total population, the correlations between HbA<span class="elsevierStyleInf">1c</span> and FPG were higher than those between HbA<span class="elsevierStyleInf">1c</span> and 2hOGTT (0.344 and 0.270, respectively). The correlation between FPG and 2hOGTT levels was 0.445, i.e., slightly higher than those between HbA<span class="elsevierStyleInf">1c</span> and FPG.</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Diagnostic capacity of HbA<span class="elsevierStyleInf">1c</span></span><p id="par0105" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> depicts the ROC curve of HbA<span class="elsevierStyleInf">1c</span> as a tool for screening and diagnosis of diabetes. The AUC was 0.839 (95% CI 0.788–0.890). <a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> shows the diagnostic properties of different HbA<span class="elsevierStyleInf">1c</span> cut-off values. As the cut-off level rose, Sp increased and Se decreased. The cut-off point at which Se and Sp were approximately equal (0.771 and 0.717, respectively) was 5.9% (95% CI 5.9–6.0), a value close to one standard deviation above mean HbA<span class="elsevierStyleInf">1c</span> in healthy subjects. Of the individuals with HbA<span class="elsevierStyleInf">1c</span> ≥5.9%, only 9% had glucose levels the were diagnostic of diabetes. The optimal HbA<span class="elsevierStyleInf">1c</span> value which simultaneously maximized Se and Sp (0.729 and 0.824, respectively) and which corresponded to the point on the ROC curve closest to the point (0,1) was 6% (95% CI 5.9–6.0). Only 13% of patients with HbA<span class="elsevierStyleInf">1c</span> ≥6% had glucose indicative of diabetes; 33% of pre-diabetic individuals had HbA<span class="elsevierStyleInf">1c</span> ≥6%; and only 54% of subjects with UKDM had HbA<span class="elsevierStyleInf">1c</span> values ≥6%.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><p id="par0110" class="elsevierStylePara elsevierViewall">The cut-off point that maximized the sum of Se (66.7%) and Sp (90.4%) was 6.1% (95% CI 6.0–6.2). Among the individuals with HbA<span class="elsevierStyleInf">1c</span> ≥6.1%, 20% had glucose concentrations indicative of diabetes. This cut-off point would detect 69.4% of all subjects with UKDM. A total of 29% of participants at high risk of suffering diabetes (those with IFG or IGT) had HbA<span class="elsevierStyleInf">1c</span> ≥6.1%.</p><p id="par0115" class="elsevierStylePara elsevierViewall">The HbA<span class="elsevierStyleInf">1c</span> value at which Sp was greater than 95% and Se was at its highest possible level, was 6.3% (95% CI 6.2–6.3). This yielded a Se of 55.2% and a Sp of 96.8%. In this case, 39% of patients had glucose concentrations that were diagnostic of diabetes.</p><p id="par0120" class="elsevierStylePara elsevierViewall">The cut-off point that minimized the percentage of incorrect classifications was 6.7% (95% CI 6.5–7.0; Se 0.312; Sp 0.998; 83% of subjects with glucose concentrations that were diagnostic of diabetes). This cut-off value coincides exactly with that corresponding to 4 standard deviations above mean HbA<span class="elsevierStyleInf">1c</span> in normal subjects. Of all subjects having HbA<span class="elsevierStyleInf">1c</span> ≥6.5% and ≥7%, 64% and 92% had glucose concentrations diagnostic of diabetes, respectively. Of the total study population, however, only 2% had HbA<span class="elsevierStyleInf">1c</span> concentrations >6.5%, and 0.8% had HbA<span class="elsevierStyleInf">1c</span> concentrations >7%. In the present study, the cut-off value of 6.5% coincided with approximately three deviations above the mean HbA<span class="elsevierStyleInf">1c</span> of normal individuals (without diabetes and with a low risk of diabetes).</p><p id="par0125" class="elsevierStylePara elsevierViewall">To compare the diagnostic properties of HbA<span class="elsevierStyleInf">1c</span> against those of FPG, the ROC curve was also calculated for FPG (see <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>), using the criteria proposed in 2002. The AUC for FPG was 0.938 (95% CI 0.908–0.968), which was higher than that of HbA<span class="elsevierStyleInf">1c</span>.</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Discussion</span><p id="par0130" class="elsevierStylePara elsevierViewall">In this adult Galician population, we found a prevalence of diabetes of 7.8% using the ADA 2002 criteria (57% knew their condition but 43% had unknown diabetes) and a prevalence of 8.6% (51.2% KDM and 48.8% NKDM) according to the ADA 2010 criteria. This prevalence is clearly lower than that of 13.8% described recently for the whole of Spain.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> There are slight methodological differences between studies and we do not believe that they are responsible for this difference. In the present study, there were almost no resignations for the performance of the OGTT while these occurred in a significant proportion of the study of Soriguer et al. In fact, probably the study in all Spain would have had more cases of diabetes if they had performed OGTT to the entire study population and in this case the difference with our sample would have been even greater. The only reason we find for explaining differences in both prevalences is the time elapsed between the studies. Distribution of known and unknown diabetes is the same in the Galician and Spain studies which could be due to the use of the same screening methods for detecting diabetes.</p><p id="par0135" class="elsevierStylePara elsevierViewall">We had 20.9% of subjects classified as having prediabetes with the ADA 2002 criteria (12.9% only with IFG, 4% only with IGT and 4% with both IFG and IGT). Theses figures are different from those of the Di@bet.es Study (14.9% prediabetes: 3.4% IFG, 9.2% IGT, 2.2% combined IFG-IGT). It is surprising that having almost twice the prevalence of diabetes in the most recent study however the prevalence of prediabetes is lower and also has a different distribution of the types of abnormalities of glucose metabolism.</p><p id="par0140" class="elsevierStylePara elsevierViewall">On the basis of a HbA1c 5.7–6.4% criterion, 1025 individuals had prediabetes. These were a total of 1600 with this condition if we also included IFG and IGT. It is difficult to accept that more than half of the population has a metabolic disorder; thus, we believe that the HbA<span class="elsevierStyleInf">1c</span> in our population should not be used as a criterion for classifying a person as having prediabetes.</p><p id="par0145" class="elsevierStylePara elsevierViewall">The correlations between HbA<span class="elsevierStyleInf">1c</span> and glucose concentrations are low in comparison with those obtained in patients with known diabetes, and a weak linear relationship is observed between the them. These low correlations may indicate that HbA<span class="elsevierStyleInf">1c</span> and glucose reflect different metabolic conditions, essentially in the range of glucose tolerance values indicative of non-diabetic subjects. Furthermore, the degree of glycosylation is known to vary among individuals,<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> an aspect that cannot be analyzed by this study, as glycemia was not monitored throughout the day. This might also indicate that these low HbA<span class="elsevierStyleInf">1c</span>-glucose correlations obtained could be due to variability of FPG and 2hOGTT in individuals.<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> A wide range of average glucose levels for individuals with the same HbA1c levels has been described, and this range is wider at the lowest HbA1c levels amd decreases with increasing HbA1c concentrations.<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> The correlation values observed in the current study were comparable to those reported by similar previous studies.<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> The correlations between HbA<span class="elsevierStyleInf">1c</span> and glucose were higher in patients with known versus unknown diabetes. One explanation for this may lie in the degree of glycemic control, particularly among patients who are already receiving appropriate treatment with insulin or oral antidiabetics. One study<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> reported that, for any given HbA<span class="elsevierStyleInf">1c</span> level, the glycemic levels of patients in different treatment groups were not the same. The correlations observed by us among patients with known diabetes were similar to those reported by Nathan et al.,<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> who carried out glycemic monitoring in subjects with known diabetes, and Van’t Riet et al.,<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> who also obtained higher correlations among patients with known than among those with unknown diabetes.</p><p id="par0150" class="elsevierStylePara elsevierViewall">Correlations depend on differences in the ranges of the variables studied, and tend to be lower in subgroups with narrower ranges. In our study, however, the correlations between glucose and HbA<span class="elsevierStyleInf">1c</span> were higher in subjects with diabetes than in the total population, but the ranges of the HbA<span class="elsevierStyleInf">1c</span> and glucose values were nevertheless wider in the total population. It must therefore be concluded that differences in the ranges of the variables do not constitute the only explanation for differences found in the correlations between the total population and the subgroups with diabetes.</p><p id="par0155" class="elsevierStylePara elsevierViewall">In this study sample, FPG displayed a greater AUC than did HbA<span class="elsevierStyleInf">1c</span>, indicating that the diagnostic capacity of FPG is greater than that of HbA<span class="elsevierStyleInf">1c</span>. This could be related to the fact that glucose is used as the reference method to establish the criteria for diagnosing the disease. This result also agrees with those of a Dutch study<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> and other studies which were also population-based and concluded that HbA<span class="elsevierStyleInf">1c</span> had no additional diagnostic value compared with FPG used in isolation, except in groups with a high risk of suffering diabetes.<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> In these groups HbA<span class="elsevierStyleInf">1c</span> may help to optimize the use of OGTT.<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a></p><p id="par0160" class="elsevierStylePara elsevierViewall">The HbA<span class="elsevierStyleInf">1c</span> value of 6% which simultaneously maximized Se and Sp in the current sample coincides exactly with the recently proposed cut-off point for screening individuals with a high risk of suffering diabetes. This would identify 40% of patients with intermediate glucose levels. The cut-off point that maximized Se and Sp was 6.1%.</p><p id="par0165" class="elsevierStylePara elsevierViewall">An HbA<span class="elsevierStyleInf">1c</span> value ≥7% yielded an Sp of close on 100% (99.9%), and 91% of subjects had glucose indicative of diabetes. A slightly higher cut-off point would have to be set in order to be able to dispense with any additional test for diagnosis of diabetes. The principal limitation of these diagnostic criteria is their low Se in return for a high Sp, something that is in line with the results of other studies.<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">19,23</span></a> Accordingly, an OGTT would have to be performed to confirm the diagnosis in most cases.</p><p id="par0170" class="elsevierStylePara elsevierViewall">Based on HbA<span class="elsevierStyleInf">1c</span>-glucose correlation values for the total population, the Se and Sp attained, and greater AUC for glucose, the use of HbA<span class="elsevierStyleInf">1c</span> instead of glucose values in the adult Galician population would not be advisable. Indeed, the advantages of using HbA<span class="elsevierStyleInf">1c</span> instead of the OGTT for the screening and diagnosis of diabetes mellitus are limited, and in most patients measurement of glucose will still be necessary to verify the diagnosis. Even so, despite its limitations, HbA<span class="elsevierStyleInf">1c</span> may be superior to OGTT in terms of cost-effectiveness and practical utility in the clinical setting. HbA<span class="elsevierStyleInf">1c</span> is less time-consuming than OGTT, can be measured at any time of day regardless of fasting, and can be analyzed with a small amount of the sample.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a> Furthermore, HbA<span class="elsevierStyleInf">1c</span> is a more complete measure of total glycemic exposure, inasmuch as it is indicative of glucose, not only in a fasting and but also in a postprandial state. The high correlation between HbA<span class="elsevierStyleInf">1c</span> and the presence of microvascular diabetic complications,<a class="elsevierStyleCrossRefs" href="#bib0125"><span class="elsevierStyleSup">25–28</span></a> and its association with cardiovascular diseases, even in the nondiabetic glucose-tolerance range of values<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">29</span></a> might indicate its usefulness as a diagnostic method among patients with a longer-term risk than that predicted by glucose.</p><p id="par0175" class="elsevierStylePara elsevierViewall">The establishment of optimal cut-off points for HbA<span class="elsevierStyleInf">1c</span> in clinical practice calls for more in-depth research. There may even be different characteristics/covariates that influence the HbA<span class="elsevierStyleInf">1c</span> discriminatory capacity as a diagnostic marker of diabetes mellitus, and different optimal cut-off points would therefore have to be defined in accordance with the values of such covariates.</p><p id="par0180" class="elsevierStylePara elsevierViewall">Our study could be completed after 5–10 years with a new cross-section on the same population and detect the patients that not having diabetes, but with levels of HbA<span class="elsevierStyleInf">1c</span> between 5 and 6.5%, develop diabetes after these years. As shown in the study published by Cheng et al.,<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">30</span></a> HbA<span class="elsevierStyleInf">1c</span> levels ≥5 increase the risk of diabetes after 4 years monitoring. Also another study<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a> proved that HbA<span class="elsevierStyleInf">1c</span> is a strong predictor of diabetes when it is within the limits 5 and 6.5%,and HbA1c has also been shown as a good predictor of future diabetes in Spain.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a></p><p id="par0185" class="elsevierStylePara elsevierViewall">In conclusion, the use of glycated hemoglobin as a criterion for diagnosing diabetes mellitus does not identify exactly the same subjects than the glucose criteria and we must have this in mind when we make this diagnosis. We believe that the maximum utility could be its use for initial screening but always confirming the existence of diabetes using plasma glucose concentration. More studies are needed to establish in the long term the most useful criteria for identifying individuals with increased risk of morbidity and mortality.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Funding</span><p id="par0190" class="elsevierStylePara elsevierViewall">M. López-Ratón and C. Cadarso-Suárez gratefully acknowledge the financial support of the <span class="elsevierStyleGrantSponsor" id="gs0005">Spanish Ministry of Science & Innovation</span> (grants <span class="elsevierStyleGrantNumber" refid="gs0005">MTM2008-0163</span>, <span class="elsevierStyleGrantNumber" refid="gs0005">MTM2010-09213-E</span> and <span class="elsevierStyleGrantNumber" refid="gs0005">MTM2011-28285-C02-00</span>).</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Conflicts of interest</span><p id="par0195" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:13 [ 0 => array:2 [ "identificador" => "xres148480" "titulo" => array:5 [ 0 => "Abstract" 1 => "Aims/hypothesis" 2 => "Methods" 3 => "Results" 4 => "Conclusions/interpretation" ] ] 1 => array:2 [ "identificador" => "xpalclavsec136371" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xpalclavsec136372" "titulo" => "Abbreviations" ] 3 => array:2 [ "identificador" => "xres148479" "titulo" => array:5 [ 0 => "Resumen" 1 => "Objetivos/hipótesis" 2 => "Métodos" 3 => "Resultados" 4 => "Conclusiones/interpretación" ] ] 4 => array:2 [ "identificador" => "xpalclavsec136373" "titulo" => "Palabras clave" ] 5 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 6 => array:2 [ "identificador" => "sec0010" "titulo" => "Methods" ] 7 => array:2 [ "identificador" => "sec0015" "titulo" => "Statistical analysis" ] 8 => array:3 [ "identificador" => "sec0020" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0025" "titulo" => "Study population characteristics" ] 1 => array:2 [ "identificador" => "sec0030" "titulo" => "HbA, FPG and 2hOGTT correlations" ] 2 => array:2 [ "identificador" => "sec0035" "titulo" => "Diagnostic capacity of HbA" ] ] ] 9 => array:2 [ "identificador" => "sec0040" "titulo" => "Discussion" ] 10 => array:2 [ "identificador" => "sec0045" "titulo" => "Funding" ] 11 => array:2 [ "identificador" => "sec0050" "titulo" => "Conflicts of interest" ] 12 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2012-01-13" "fechaAceptado" => "2012-06-04" "PalabrasClave" => array:2 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec136371" "palabras" => array:3 [ 0 => "HbA<span class="elsevierStyleInf">1c</span>" 1 => "Glucose" 2 => "Optimal HbA<span class="elsevierStyleInf">1c</span> cut-off points" ] ] 1 => array:4 [ "clase" => "abr" "titulo" => "Abbreviations" "identificador" => "xpalclavsec136372" "palabras" => array:14 [ 0 => "AUC" 1 => "FPG" 2 => "2hOGTT" 3 => "HbA1c" 4 => "IFG" 5 => "IGT" 6 => "KDM" 7 => "NPV" 8 => "NGM" 9 => "PPV" 10 => "ROC" 11 => "Se" 12 => "Sp" 13 => "UKDM" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec136373" "palabras" => array:3 [ 0 => "HbA<span class="elsevierStyleInf">1c</span>" 1 => "glucosa" 2 => "puntos de corte óptimos de la HbA<span class="elsevierStyleInf">1c</span>" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span class="elsevierStyleSectionTitle">Aims/hypothesis</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">To analyze the relationship between glucose and glycated hemoglobin (HbA<span class="elsevierStyleInf">1c</span>) in the adult Galician population, evaluate the use of HbA<span class="elsevierStyleInf">1c</span> for the screening and diagnosis of diabetes, and calculate the diagnostic threshold required for this purpose.</p> <span class="elsevierStyleSectionTitle">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">We analyzed data on 2848 subjects (aged 18–85 years) drawn from a study undertaken in 2004 to assess the prevalence of diabetes in Galicia. For study purposes, diabetes was defined using the criteria recommended in 2002. Participants were classified into four glucose-based groups. The relationship between glucose and HbA<span class="elsevierStyleInf">1c</span> was described using linear regression models, generalized additive models and Spearman's correlation. Diagnostic capacity was assessed, and optimal HbA<span class="elsevierStyleInf">1c</span> cut-off points were calculated as a diabetes marker using the receiver operating characteristic curve.</p> <span class="elsevierStyleSectionTitle">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Prevalence of pre-diabetes, unknown diabetes and known diabetes was 20.86, 3.37 and 4.39%, respectively. The correlations between HbA<span class="elsevierStyleInf">1c</span> and fasting glucose were higher than those obtained for HbA<span class="elsevierStyleInf">1c</span> and glycemia at 2<span class="elsevierStyleHsp" style=""></span>h of the oral glucose overload (0.344 and 0.270, respectively). Taking glucose levels as the gold standard, a greater discriminatory capacity was obtained for HbA<span class="elsevierStyleInf">1c</span> (area under de cruve: 0.839, 95% confidence intervals: 0.788–0.890). Based on the study criteria, the optimal minimum and maximum HbA<span class="elsevierStyleInf">1c</span> values were 5.9% and 6.7%, respectively.</p> <span class="elsevierStyleSectionTitle">Conclusions/interpretation</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">HbA<span class="elsevierStyleInf">1c</span> did not prove superior to glycemia for diagnosis of diabetes in the adult Galician population, and cannot therefore be used to replace the oral glucose tolerance test for screening and diagnosis purposes. Indeed, determination of glucose is essential to verify the diagnosis in the majority of cases.</p>" ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<span class="elsevierStyleSectionTitle">Objetivos/hipótesis</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Analizar la relación entre la glucosa y la hemoglobina glucosilada (HbA<span class="elsevierStyleInf">1c</span>) en la población gallega adulta, evaluar el uso de la HbA<span class="elsevierStyleInf">1c</span> para cribado y diagnóstico de la diabetes y calcular el umbral diagnóstico necesario para este fin.</p> <span class="elsevierStyleSectionTitle">Métodos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Se analizaron datos de 2.848 sujetos (de 18–85 años de edad) procedentes de un estudio emprendido en 2004 para valorar la prevalencia de diabetes en Galicia. A efectos del estudio, se definió la diabetes de acuerdo con los criterios recomendados en 2002. Se clasificó a los participantes en cuatro grupos en función de los valores de glucosa. Se describió la relación entre glucosa y HbA<span class="elsevierStyleInf">1c</span> mediante modelos de regresión lineal, modelos aditivos generalizados y la correlación de Spearman. Se valoró la capacidad diagnóstica y se calcularon los puntos de corte óptimos de la HbA<span class="elsevierStyleInf">1c</span> como marcador de la diabetes empleando la curva de características operativas del receptor.</p> <span class="elsevierStyleSectionTitle">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Las tasas de prevalencia de prediabetes, diabetes desconocida y diabetes conocidas eran del 10,86, 3,37 y 4,39%, respectivamente. Las correlaciones entre la HbA<span class="elsevierStyleInf">1c</span> y la glucemia en ayunas eran mayores que las obtenidas entre la HbA<span class="elsevierStyleInf">1c</span> y la glucemia en ayunas dos horas después de la sobrecarga oral de glucosa (0,344 y 0,270, respectivamente). Tomando los valores de glucosa como referencia, se obtuvo una mayor capacidad discriminatoria para la HbA<span class="elsevierStyleInf">1c</span> (área bajo la curva: 0,839, intervalos de confianza del 95%: 0,788–0,890). Basándose en los criterios del estudio, los valores óptimos mínimos de la HbA<span class="elsevierStyleInf">1c</span> eran del 5,9 y el 6,7%, respectivamente.</p> <span class="elsevierStyleSectionTitle">Conclusiones/interpretación</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">La HbA1c no fue superior a la glucemia para el diagnóstico de la diabetes en la población gallega adulta, por lo que no puede utilizarse en lugar de la prueba de tolerancia oral a la glucosa con fines de cribado y diagnóstico. De hecho, la determinación de la glucosa es esencial para confirmar el diagnóstico en la mayoría de los casos.</p>" ] ] "multimedia" => array:5 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1536 "Ancho" => 1557 "Tamanyo" => 123071 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Receiver operating characteristic curve for identification of participants with previously undiagnosed diabetes, using glycated hemoglobin and fasting plasma glucose for diagnosis and fasting plasma glucose and glycemia at 2<span class="elsevierStyleHsp" style=""></span>h of oral glucose tolerance test as disease criteria. AUC: area under the curve; FPG: fasting plasma glucose; HbA<span class="elsevierStyleInf">1c</span>: glycated hemoglobin; ROC: receiver operating characteristic curve; Se: sensitivity; Sp: specificity.</p>" ] ] 1 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">HbA<span class="elsevierStyleInf">1c</span>: glycated hemoglobin; NGM: normal glucose metabolism; IFG: impaired fasting glucose; IGT: impaired glucose tolerance; FPG: fasting plasma glucose; 2hOGTT: plasma glucose value at 2<span class="elsevierStyleHsp" style=""></span>h in oral glucose tolerance test; KDM: previously known diabetics; NKDM: unknown diabetics.</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"><td 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"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">2010 criteria</td><td 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"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">2002 criteria</td></tr><tr title="table-row"><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">% \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">% \t\t\t\t\t\t\n \t\t\t\t</td></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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">NGM</span> \t\t\t\t\t\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">1004 \t\t\t\t\t\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">35.3% \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"><span class="elsevierStyleItalic">NGM</span> \t\t\t\t\t\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">2033 \t\t\t\t\t\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">71.4% \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="6" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Prediabetics</span> \t\t\t\t\t\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">1600 \t\t\t\t\t\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">56.2% \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"><span class="elsevierStyleItalic">Prediabetics</span> \t\t\t\t\t\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">594 \t\t\t\t\t\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">20.9% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Only IFG \t\t\t\t\t\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">112 \t\t\t\t\t\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">3.9% \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"><span class="elsevierStyleHsp" style=""></span>IFG \t\t\t\t\t\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">367 \t\t\t\t\t\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">12.9% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Only IGT \t\t\t\t\t\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">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">1.7% \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"><span class="elsevierStyleHsp" style=""></span>IGT \t\t\t\t\t\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">113 \t\t\t\t\t\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% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Only HbA<span class="elsevierStyleInf">1c</span> ≥5.7 and ≤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">1025 \t\t\t\t\t\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">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"><span class="elsevierStyleHsp" style=""></span>IFG and IGT \t\t\t\t\t\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">114 \t\t\t\t\t\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% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>IFG, IGT and HbA<span class="elsevierStyleInf">1c</span> \t\t\t\t\t\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">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">2.9% \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="" 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="" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>IFG and 2hOGTT (normal HbA<span class="elsevierStyleInf">1c</span>) \t\t\t\t\t\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 \t\t\t\t\t\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="" 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="" 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="" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>IFG and HbA<span class="elsevierStyleInf">1c</span> (normal 2hOGTT) \t\t\t\t\t\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">249 \t\t\t\t\t\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><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="" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>IGT and HbA<span class="elsevierStyleInf">1c</span> (normal FPG) \t\t\t\t\t\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">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">2.2% \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><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="" 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="6" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">NKDM</span> \t\t\t\t\t\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">119 \t\t\t\t\t\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">4.2% \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"><span class="elsevierStyleItalic">NKDM</span> \t\t\t\t\t\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">96 \t\t\t\t\t\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">3.4% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Only elevated FPG \t\t\t\t\t\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">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">0.6% \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">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">0.7% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Only elevated 2hOGTT \t\t\t\t\t\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">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">1.2% \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><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">46 \t\t\t\t\t\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">1.6% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Only elevated HbA<span class="elsevierStyleInf">1c</span> \t\t\t\t\t\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">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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.8% \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="" 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="" 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="" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Elevated FPG and 2hOGTT but not HbA<span class="elsevierStyleInf">1c</span> \t\t\t\t\t\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 \t\t\t\t\t\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">0.1% \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"><span class="elsevierStyleHsp" style=""></span>Elevated FPG and 2hOGTT \t\t\t\t\t\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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.9% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Elevated FPG and HbA<span class="elsevierStyleInf">1c</span> but not 2hOGTT \t\t\t\t\t\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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.1% \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="" 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="" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Elevated 2hOGTT and HbA<span class="elsevierStyleInf">1c</span> but not FPG \t\t\t\t\t\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 \t\t\t\t\t\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">0.4% \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="" 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="" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Elevated FPG, 2hOGTT and HbA<span class="elsevierStyleInf">1c</span> \t\t\t\t\t\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="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="" 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="" 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="" 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="6" 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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">KDM</span> \t\t\t\t\t\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">125 \t\t\t\t\t\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">4.4% \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">KDM \t\t\t\t\t\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">125 \t\t\t\t\t\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">4.4% \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">Total</span> \t\t\t\t\t\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">2848 \t\t\t\t\t\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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Total \t\t\t\t\t\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">2848 \t\t\t\t\t\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 => "xTab242528.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Distribution of sample subjects according to the number of diagnostic criteria given.</p>" ] ] 2 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:3 [ "leyenda" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Values are mean<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD, or percentage or medians (25th–75th percentils). BMI: body mass index; HDL: high density lipoprotein; FPG: fasting plasma glucose; KDM: previously known diabetes; UKDM: unknown diabetes; BG: diabetes diagnosed only by basal glucose criterion; 2h OGTT: diabetes diagnosed only by 2h OGTT criterion; HbA1c: diabetes diagnosed only by HbA<span class="elsevierStyleInf">1c</span> (glycated hemoglobin) criterion. Percentage of UKDM and KDM is from total sample. Other percentages are expressed from UKDM.</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"><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">Total population \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">Total UKDM \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">BG \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">2h OGTT \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">HbA<span class="elsevierStyleInf">1c</span> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">More than 1 criteria \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">KDM \t\t\t\t\t\t\n \t\t\t\t</td></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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\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">2848 \t\t\t\t\t\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">119 (4.18) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17 (14.29) \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">34 (28.57) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (19.33) \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">45 (37.82) \t\t\t\t\t\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">125 (4.39) \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Male sex (%) \t\t\t\t\t\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">1321 (46.38) \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">73 (61.34) \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">14 (82.35) \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">23 (67.65) \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">11 (47.83) \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">25 (55.56) \t\t\t\t\t\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">65 (52) \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (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">41.43<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15.01 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.68<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>14.99 \t\t\t\t\t\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">49.74<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11.91 \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">55.45<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>16.67 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.90<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11.20 \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">57.57<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15.65 \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">56.52<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15.45 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hypertension (%) \t\t\t\t\t\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">420 (14.78) \t\t\t\t\t\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">46 (38.66) \t\t\t\t\t\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 (23.53) \t\t\t\t\t\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">10 (29.41) \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">13 (56.52) \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">19 (42.22) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65 (52) \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BMI (kg/m<span class="elsevierStyleSup">2</span>) \t\t\t\t\t\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">26.78<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.93 \t\t\t\t\t\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">29.72<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>5.43 \t\t\t\t\t\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">30.02<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.83<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">†</span></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">27.62<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.96<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,‡,§</span></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">30.73<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.87<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">†</span></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">30.67<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>5.94<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">†</span></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">29.58<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>5.11<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">†</span></a> \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Obesity (%) \t\t\t\t\t\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">486 (17.06) \t\t\t\t\t\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">41 (34.45) \t\t\t\t\t\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">7 (41.18) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 (20.59) \t\t\t\t\t\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 (52.17) \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">15 (33.33) \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">53 (42.40) \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Total cholesterol (mg/dl) \t\t\t\t\t\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">195.68<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>39.55 \t\t\t\t\t\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">204.76<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>40.27 \t\t\t\t\t\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">205.76<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>38.49 \t\t\t\t\t\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">196.65<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>45.22 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">201.22<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>33.85 \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">212.31<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>39.84 \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">199.22<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>39.01 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HDL cholesterol (mg/dl) \t\t\t\t\t\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">61.20<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>16.78 \t\t\t\t\t\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">54.53<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>16.41 \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">54.71<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>25.75 \t\t\t\t\t\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">58.18<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>16.88 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55.70<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>13.77 \t\t\t\t\t\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">51.11<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>12.22 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.17<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>15.74 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Triglycerides (mg/dl) \t\t\t\t\t\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">80 (56–118) \t\t\t\t\t\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">113 (78.50–172.50) \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">111 (76.00–212.00) \t\t\t\t\t\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">96 (69.25–127.25)<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">§</span></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">93 (75.50–171.50)<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">§</span></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">159 (96.00–227.00)<a class="elsevierStyleCrossRefs" href="#tblfn0010"><span class="elsevierStyleSup">†,‡</span></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">100 (66–157)<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">§</span></a> \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Microalbuminuria \t\t\t\t\t\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">2.5 (1.5–4.3) \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">3.35 (1.88–8.93) \t\t\t\t\t\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">2.8 (2.00–3.70) \t\t\t\t\t\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">3.2 (1.90–6.90) \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">3.6 (1.80–8.50) \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">4.4 (1.88–18.23) \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.20 (2.30–10.03) \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">FPG (mg/dl) \t\t\t\t\t\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">94.51<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>21.54 \t\t\t\t\t\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">131.19<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>45.26 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">132.65<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>5.69<a class="elsevierStyleCrossRefs" href="#tblfn0010"><span class="elsevierStyleSup">†,‡</span></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">105.71<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11.30<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,§</span></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">106.70<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11.90<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,§</span></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">162.42<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>59.17<a class="elsevierStyleCrossRefs" href="#tblfn0010"><span class="elsevierStyleSup">†,‡</span></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">143.85<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>50.00<a class="elsevierStyleCrossRefs" href="#tblfn0010"><span class="elsevierStyleSup">†,‡</span></a> \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2-h postload glucose (mg/dl) \t\t\t\t\t\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">101.40<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>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">221.11<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>90.48 \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">152.00<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>28.10<a class="elsevierStyleCrossRefs" href="#tblfn0010"><span class="elsevierStyleSup">†,§</span></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">228.32<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>26.40<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,‡,§</span></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">144.57<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>29.39<a class="elsevierStyleCrossRefs" href="#tblfn0010"><span class="elsevierStyleSup">†,§</span></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">288.35<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>110.01<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,†,‡</span></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">112.00<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>51.37<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,†,‡,§</span></a> \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HBA<span class="elsevierStyleInf">1c</span> (%) \t\t\t\t\t\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">5.79<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.72<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.38 \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">5.99<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.28<a class="elsevierStyleCrossRefs" href="#tblfn0015"><span class="elsevierStyleSup">‡,§</span></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">5.88<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.32<a class="elsevierStyleCrossRefs" href="#tblfn0015"><span class="elsevierStyleSup">‡,§</span></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">6.64<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.20<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,†,§</span></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">7.67<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.82<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,†,‡</span></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">7.10<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.41<a class="elsevierStyleCrossRefs" href="#tblfn0005"><span class="elsevierStyleSup">*,†,§</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab242525.png" ] ] ] "notaPie" => array:4 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara">Significantly different from diabetic subjects detected only by basal glucose</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "†" "nota" => "<p class="elsevierStyleNotepara">Significantly different from diabetic subjects detected only by 2hOGTT.</p>" ] 2 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "‡" "nota" => "<p class="elsevierStyleNotepara">Significantly different from diabetic subjects detected only by HbA1c.</p>" ] 3 => array:3 [ "identificador" => "tblfn0020" "etiqueta" => "§" "nota" => "<p class="elsevierStyleNotepara">Significantly different from diabetic subjects detected by at least two criteria.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Baseline characteristics of subjects according to the way to diagnose diabetes.</p>" ] ] 3 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:3 [ "leyenda" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">HbA1c: glycated hemoglobin; FPG: fasting plasma glucose; 2hOGTT: glycemia at 2<span class="elsevierStyleHsp" style=""></span>h of oral glucose tolerance test; NGM: normal glycemia; IFG: impaired fasting glucose; IGT: impaired glucose tolerance; UKDM: unknown diabetes; KDM: known diabetes.</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"><td 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"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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">Total population \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="4" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Pre-diabetic</td></tr><tr title="table-row"><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">NGM \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">IFG<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>IGT \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">UKDM \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black">KDM \t\t\t\t\t\t\n \t\t\t\t</td></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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HbA<span class="elsevierStyleInf">1c</span> vs. FPG \t\t\t\t\t\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<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.23<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.19<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.59<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.65<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></a> \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HbA<span class="elsevierStyleInf">1c</span> vs. 2hOGTT \t\t\t\t\t\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">0.27<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.14<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.15<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.45<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.60<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></a> \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">FPG vs. 2hOGTT \t\t\t\t\t\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<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.28<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">−0.21<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.17<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></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">0.84<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab242526.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0025" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara">Statistically significant values with a 5% significance level (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05).</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Spearman correlations between glycated hemoglobin, fasting plasma glucose and glycemia at 2<span class="elsevierStyleHsp" style=""></span>h of oral glucose tolerance test in the total population and in each subgroup.</p>" ] ] 4 => array:7 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Se: sensitivity; Sp: specificity; PPV: positive predictive value; NPV: negative predictive value; HbA<span class="elsevierStyleInf">1c</span>: glycated hemoglobin; ROC: receiver operating characteristic curve; KDM: previously known diabetics.</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"><td 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">Criterion \t\t\t\t\t\t\n \t\t\t\t</td><td 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">HbA<span class="elsevierStyleInf">1c</span> \t\t\t\t\t\t\n \t\t\t\t</td><td 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">% Total population (without KDM) \t\t\t\t\t\t\n \t\t\t\t</td><td 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">% Total population \t\t\t\t\t\t\n \t\t\t\t</td><td 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">% High risk \t\t\t\t\t\t\n \t\t\t\t</td><td 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">Se \t\t\t\t\t\t\n \t\t\t\t</td><td 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">Sp \t\t\t\t\t\t\n \t\t\t\t</td><td 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">PPV \t\t\t\t\t\t\n \t\t\t\t</td><td 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">NPV \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-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2723 \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2848 \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>594 \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td 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" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td></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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Se<span class="elsevierStyleHsp" style=""></span>≈<span class="elsevierStyleHsp" style=""></span>Sp \t\t\t\t\t\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">5.9 (5.9–6.0) \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">28.72 \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">32.37 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">46.80 \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">0.771 \t\t\t\t\t\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">0.717 \t\t\t\t\t\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">0.090 \t\t\t\t\t\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">0.988 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maximizing Se and Sp simultaneously \t\t\t\t\t\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">6.0 (5.9–6.0) \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">18.68 \t\t\t\t\t\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">22.19 \t\t\t\t\t\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">33.33 \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">0.729 \t\t\t\t\t\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">0.824 \t\t\t\t\t\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">0.132 \t\t\t\t\t\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">0.988 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Point under ROC closest to point (0,1) \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">6.0 (6.0–6.1) \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">18.68 \t\t\t\t\t\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">22.19 \t\t\t\t\t\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">33.33 \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">0.729 \t\t\t\t\t\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">0.824 \t\t\t\t\t\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">0.132 \t\t\t\t\t\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">0.988 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Youden Index (maximizing Se<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>Sp) \t\t\t\t\t\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">6.1 (6.0–6.2) \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">11.10 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14.40 \t\t\t\t\t\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">22.56 \t\t\t\t\t\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">0.667 \t\t\t\t\t\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">0.904 \t\t\t\t\t\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">0.203 \t\t\t\t\t\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">0.987 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sp<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>0.95 and maximizing Se \t\t\t\t\t\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">6.3 (6.2–6.3) \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">4.81 \t\t\t\t\t\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">7.79 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.93 \t\t\t\t\t\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">0.552 \t\t\t\t\t\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">0.968 \t\t\t\t\t\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">0.387 \t\t\t\t\t\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">0.983 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Recommended for diagnosis of 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">6.5 \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">2.25 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.81 \t\t\t\t\t\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">3.20 \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">0.427 \t\t\t\t\t\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">0.991 \t\t\t\t\t\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">0.641 \t\t\t\t\t\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">0.979 \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 " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Maximizing % correct classifications \t\t\t\t\t\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">6.7 (6.5–7.0) \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">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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.48 \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">1.01 \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="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.312 \t\t\t\t\t\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">0.998 \t\t\t\t\t\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">0.833 \t\t\t\t\t\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">0.975 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab242527.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Sensitivity, specificity, positive predictive value, and negative predictive value for diabetes mellitus using different glycated hemoglobin cut-off points. 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Díaz-Cadórniga" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.diabet.2010.07.002" "Revista" => array:6 [ "tituloSerie" => "Diabetes Metab" "fecha" => "2011" "volumen" => "37" "paginaInicial" => "27" "paginaFinal" => "32" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/20934897" "web" => "Medline" ] ] ] ] ] ] ] ] ] ] ] ] ] "idiomaDefecto" => "en" "url" => "/15750922/0000005900000008/v1_201305082316/S1575092212001994/v1_201305082316/en/main.assets" "Apartado" => array:4 [ "identificador" => "8580" "tipo" => "SECCION" "es" => array:2 [ "titulo" => "Diabetes y Obesidad" "idiomaDefecto" => true ] "idiomaDefecto" => "es" ] "PDF" => "https://static.elsevier.es/multimedia/15750922/0000005900000008/v1_201305082316/S1575092212001994/v1_201305082316/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1575092212001994?idApp=UINPBA00004N" ]
| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 3 | 1 | 4 |
| 2024 October | 19 | 4 | 23 |
| 2024 September | 29 | 3 | 32 |
| 2024 August | 15 | 1 | 16 |
| 2024 July | 12 | 5 | 17 |
| 2024 June | 17 | 2 | 19 |
| 2024 May | 14 | 7 | 21 |
| 2024 April | 19 | 7 | 26 |
| 2024 March | 16 | 4 | 20 |
| 2024 February | 16 | 3 | 19 |
| 2024 January | 22 | 5 | 27 |
| 2023 December | 9 | 3 | 12 |
| 2023 November | 20 | 12 | 32 |
| 2023 October | 20 | 7 | 27 |
| 2023 September | 19 | 5 | 24 |
| 2023 August | 10 | 3 | 13 |
| 2023 July | 14 | 10 | 24 |
| 2023 June | 24 | 4 | 28 |
| 2023 May | 30 | 5 | 35 |
| 2023 April | 23 | 2 | 25 |
| 2023 March | 60 | 4 | 64 |
| 2023 February | 47 | 3 | 50 |
| 2023 January | 50 | 6 | 56 |
| 2022 December | 37 | 13 | 50 |
| 2022 November | 34 | 11 | 45 |
| 2022 October | 33 | 11 | 44 |
| 2022 September | 45 | 11 | 56 |
| 2022 August | 57 | 12 | 69 |
| 2022 July | 34 | 12 | 46 |
| 2022 June | 43 | 7 | 50 |
| 2022 May | 26 | 9 | 35 |
| 2022 April | 27 | 9 | 36 |
| 2022 March | 48 | 18 | 66 |
| 2022 February | 43 | 6 | 49 |
| 2022 January | 64 | 9 | 73 |
| 2021 December | 70 | 14 | 84 |
| 2021 November | 40 | 16 | 56 |
| 2021 October | 68 | 19 | 87 |
| 2021 September | 41 | 21 | 62 |
| 2021 August | 55 | 3 | 58 |
| 2021 July | 57 | 21 | 78 |
| 2021 June | 70 | 8 | 78 |
| 2021 May | 124 | 17 | 141 |
| 2021 April | 598 | 25 | 623 |
| 2021 March | 216 | 51 | 267 |
| 2021 February | 175 | 19 | 194 |
| 2021 January | 134 | 14 | 148 |
| 2020 December | 111 | 19 | 130 |
| 2020 November | 111 | 12 | 123 |
| 2020 October | 62 | 9 | 71 |
| 2020 September | 66 | 36 | 102 |
| 2020 August | 69 | 19 | 88 |
| 2020 July | 64 | 15 | 79 |
| 2020 June | 51 | 8 | 59 |
| 2020 May | 53 | 13 | 66 |
| 2020 April | 55 | 11 | 66 |
| 2020 March | 86 | 15 | 101 |
| 2020 February | 129 | 16 | 145 |
| 2020 January | 62 | 17 | 79 |
| 2019 December | 64 | 16 | 80 |
| 2019 November | 68 | 6 | 74 |
| 2019 October | 37 | 7 | 44 |
| 2019 September | 40 | 8 | 48 |
| 2019 August | 34 | 7 | 41 |
| 2019 July | 29 | 23 | 52 |
| 2019 June | 80 | 17 | 97 |
| 2019 May | 245 | 36 | 281 |
| 2019 April | 87 | 20 | 107 |
| 2019 March | 27 | 4 | 31 |
| 2019 February | 38 | 10 | 48 |
| 2019 January | 29 | 8 | 37 |
| 2018 December | 24 | 13 | 37 |
| 2018 November | 33 | 2 | 35 |
| 2018 October | 31 | 16 | 47 |
| 2018 September | 21 | 6 | 27 |
| 2018 August | 14 | 12 | 26 |
| 2018 July | 27 | 3 | 30 |
| 2018 June | 11 | 1 | 12 |
| 2018 May | 33 | 6 | 39 |
| 2018 April | 14 | 4 | 18 |
| 2018 March | 13 | 1 | 14 |
| 2018 February | 14 | 3 | 17 |
| 2018 January | 9 | 3 | 12 |
| 2017 December | 12 | 2 | 14 |
| 2017 November | 18 | 4 | 22 |
| 2017 October | 22 | 4 | 26 |
| 2017 September | 13 | 2 | 15 |
| 2017 August | 19 | 6 | 25 |
| 2017 July | 26 | 6 | 32 |
| 2017 June | 33 | 4 | 37 |
| 2017 May | 29 | 6 | 35 |
| 2017 April | 25 | 1 | 26 |
| 2017 March | 37 | 30 | 67 |
| 2017 February | 30 | 0 | 30 |
| 2017 January | 20 | 4 | 24 |
| 2016 December | 33 | 5 | 38 |
| 2016 November | 41 | 5 | 46 |
| 2016 October | 66 | 3 | 69 |
| 2016 September | 49 | 5 | 54 |
| 2016 August | 45 | 2 | 47 |
| 2016 July | 34 | 1 | 35 |
| 2016 June | 35 | 7 | 42 |
| 2016 May | 38 | 12 | 50 |
| 2016 April | 26 | 9 | 35 |
| 2016 March | 25 | 24 | 49 |
| 2016 February | 34 | 13 | 47 |
| 2016 January | 25 | 14 | 39 |
| 2015 December | 26 | 14 | 40 |
| 2015 November | 35 | 12 | 47 |
| 2015 October | 55 | 22 | 77 |
| 2015 September | 25 | 7 | 32 |
| 2015 August | 30 | 6 | 36 |
| 2015 July | 54 | 7 | 61 |
| 2015 June | 32 | 3 | 35 |
| 2015 May | 41 | 5 | 46 |
| 2015 April | 18 | 11 | 29 |
| 2015 March | 44 | 3 | 47 |
| 2015 February | 41 | 4 | 45 |
| 2015 January | 54 | 7 | 61 |
| 2014 December | 50 | 11 | 61 |
| 2014 November | 45 | 4 | 49 |
| 2014 October | 62 | 7 | 69 |
| 2014 March | 25 | 5 | 30 |
| 2014 February | 33 | 5 | 38 |
| 2014 January | 23 | 2 | 25 |
| 2013 December | 28 | 8 | 36 |
| 2013 November | 24 | 2 | 26 |
| 2013 October | 23 | 9 | 32 |
| 2013 September | 25 | 7 | 32 |
| 2013 August | 17 | 4 | 21 |
| 2013 July | 26 | 7 | 33 |
| 2013 June | 25 | 2 | 27 |
| 2013 May | 20 | 10 | 30 |
| 2013 April | 21 | 7 | 28 |
| 2013 March | 10 | 6 | 16 |
| 2013 February | 11 | 10 | 21 |
| 2013 January | 7 | 2 | 9 |
| 2012 December | 1 | 2 | 3 |
| 2012 November | 3 | 2 | 5 |
| 2012 October | 3 | 1 | 4 |
| 2012 September | 1315 | 0 | 1315 |
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