Glycemic Index, Glycemic Load, And Metabolic Syndrome In .

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Castro-Quezada et al. BMC Nutrition (2017) 3:44DOI 10.1186/s40795-017-0162-2RESEARCH ARTICLEOpen AccessGlycemic index, glycemic load, andmetabolic syndrome in Mexicanadolescents: a cross-sectional study fromthe NHNS-2012Itandehui Castro-Quezada1,2, Salomón Angulo-Estrada3, Almudena Sánchez-Villegas1,4, María Dolores Ruiz-López2,5,Reyes Artacho2, Lluís Serra-Majem1,4 and Teresa Shamah-Levy3*AbstractBackground: The role of dietary glycemic index (GI) and dietary glycemic load (GL) on metabolic syndrome (MetS)in youth populations remains unclear. The aim of the present study was to evaluate the association among dietaryGI, dietary GL, and MetS and its components in Mexican adolescents.Methods: This study was conducted within the framework of the National Health and Nutrition Survey 2012, across-sectional, probabilistic, population-based survey with a multistage stratified cluster sampling design. Weanalyzed a sample of 1346 subjects aged 12–19 years, representing 13,164,077 adolescents. Dietary habits wereassessed through a validated semiquantitative food-frequency questionnaire. We assigned GI values using theInternational Tables of GI values. We defined MetS according to the International Diabetes Federation criteriadeveloped for adolescents. Multiple logistic regression models were used to estimate odds ratios (ORs) and their95% confidence intervals (CIs) to evaluate the association between categories of dietary GI and GL and theprevalence of MetS and its components.Results: We observed no associations between dietary GI or GL and MetS prevalence. Female adolescents in thehighest category of dietary GI had higher odds of abnormal blood pressure (OR 3.66; 95% CI, 1.46–9.22; P fortrend 0.012). A high dietary GL was also associated with higher odds of abnormal blood pressure in femaleadolescents (OR 5.67; 95% CI, 1.84–17.46; P for trend 0.003).Conclusions: We found higher odds of abnormal blood pressure for female adolescents with a high dietary GI anddietary GL.Keywords: Glycemic index, Glycemic load, Metabolic syndrome, Adolescent, MexicoBackgroundThe prevalence of metabolic syndrome (MetS) is highamong children and adolescents with obesity [1, 2]. InMexico, almost 35% of adolescents are either overweightor obese [3] and the prevalence of MetS oscillates between6.5% [4] and 19.2% [5]. Therefore, special attention shouldbe given to modifiable risk factors, such as lifestyle and* Correspondence: tshamah@insp.mx3Center for Nutrition and Health Research, National Institute of Public Healthof Mexico, Universidad No. 655, Colonia Santa María Ahuacatitlán, 62100Cuernavaca, Morelos, MexicoFull list of author information is available at the end of the articledietary habits: they play an important role in the development and progression of MetS. Among dietary factors,carbohydrates are the main energy source in the diets ofmost populations and have a special function in energymetabolism and homoeostasis [6]. However, evidenceindicates that some carbohydrate sources can be beneficial; others are not, depending on their quality and fibercontent [7]. The quality of carbohydrates can be measuredusing the glycemic index (GI); this is defined as the incremental area under the curve of blood glucose responseafter eating 50 g of available carbohydrates from a certainfood and expressed as a percentage of the glycemic The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (, which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication o/1.0/) applies to the data made available in this article, unless otherwise stated.

Castro-Quezada et al. BMC Nutrition (2017) 3:44response elicited by 50 g of glucose or white bread [8].Moreover, the glycemic load (GL) considers both the quality and quantity of carbohydrate intake [9, 10].In adults, evidence from different meta-analysis of randomized controlled trials (RCTs) demonstrated that lowGI or GL diets resulted in lower fasting blood glucoseand glycated hemoglobin levels [11] and a greater decrease in total cholesterol and low density lipoproteincholesterol (LDL-c) compared to control diets [12, 13].Nevertheless, the latter findings have not been observedin overweight/obese subjects who followed low GI/GLdiets [14]. Furthermore, results from RCTs have demonstrated a favorable effect of a low-GI diet on triglyceridelevels [15] or concentration of high-density lipoproteincholesterol (HDL-c) [16]. However, such findings are inconsistent and have not been confirmed by a recentmeta-analysis [13].In children and adolescents, a meta-analysis has demonstrated that low-GI diets might reduce serum triglycerides and homeostasis model assessment index inoverweight or obese children and adolescents [17].The association among GI, GL, and MetS has beenmostly studied in prospective studies in adult populations [18, 19] and produced varying results. The evidence for such an association in young people is scarce.Two cross-sectional studies conducted in Australia haveidentified higher odds of developing MetS for each unitincrease in breakfast GL [20] and per 20 unit dietary GLincrease [21].To our knowledge, no evidence is available on the relationship between the quality of carbohydrates and MetSin a Mexican youth population. Therefore, the main objective of this study was to evaluate the association amongdietary GI, dietary GL, and MetS and its components in anationally representative sample of Mexican adolescents.MethodsStudy populationThis study was conducted within the framework of the National Health and Nutrition Survey 2012 (NHNS-2012), across-sectional, probabilistic, population-based survey witha multistage stratified cluster sampling design conducted inMexico. The design and methods of the NHNS-2012 havebeen described elsewhere [22]. The main objective of theNHNS-2012 was to quantify the frequency, distribution,and trends in health and nutrition conditions and their determinants in the Mexican population [22]. Data were collected by computer-assisted interviews at participants’homes. Child interviewees under the age of 14 years wereassisted in their responses by a relative.In the NHNS-2012 an original probabilistic sample of17,000 adolescents was drawn. For the present study, weused the NHNS-2012 subsample of 2203 adolescentsaged 12–19 years evaluated by means of a validatedPage 2 of 12semiquantitative food-frequency questionnaire (SFFQ)to assess dietary habits [23]. We excluded subjects withmissing values for biochemical measurements (19.7%) orother covariates used in the statistical analyses (12.4%).Furthermore, we excluded subjects with energy valuesoutside predefined limits (6.8%). The methodology forcleaning dietary data has been broadly described elsewhere [24]. First, the weight in grams of food consumedby each study subject was evaluated according to agegroup. We excluded from the analysis subjects who consumed above three standard deviations (SDs) of one ormore food items. The biological plausibility of food intake and the percentage contribution of each food tototal dietary intake was used to verify data identified ashigh values. Second, we estimated very high values ofenergy intake by the ratio of energy intake/estimated energy requirement. The equations of the Institute ofMedicine were used as reference [25]. The physical activity level of each subject was considered according toprevious studies regarding data of the NHNS-1999 [26].We excluded very low values of energy intake: under 0.5of energy intake/basal metabolic rate (BMR). We estimated BMR for adults ( 19 years of age) using theMifflin-St Jeor equations [27]. For subjects under19 years of age, we used the age- and sex-specific equations of the Food and Agriculture Organization [28]. Accordingly, we included a final sample of 1346 subjects inour analyses, representing a total of 13,164,077 Mexicanadolescents (Fig. 1).Exposure assessmentDietary assessmentTrained personnel applied a validated SFFQ to evaluatedietary habits during the 7 days before the interview date[23, 24]. For each food item, the questionnaire measuredthe frequency of intake according to set categories: therange was “never” to “six times a day.” Participants alsodesignated the food portion sizes, using defined categories and number of servings consumed during that week.We first converted the data to number of times a day,and we then estimated the daily portion size. To calculate the consumption of energy (kcal/day) and daily nutrient intakes, we multiplied the daily frequency ofconsumption (portions/day) of each food by the amountof energy and nutrients in a standard serving or portionsize of that food. For that purpose, we used the food composition tables compiled by the National Institute of PublicHealth of Mexico (INSP: Databases of the nutritional valueof food. Compilation of the National Institute of PublicHealth, unpublished). We totaled the contributions of allfoods using Microsoft Visual FoxPro 7.0 (Microsoft Corporation, Seattle, WA, USA). The average Pearson correlation coefficient, between SFFQ and two 24-h dietaryrecalls, for absolute nutrient intake was 0.374 for

Castro-Quezada et al. BMC Nutrition (2017) 3:44Page 3 of 12Outcome assessmentAnthropometric assessmentWeight and height were measured using electronic scalesand wall stadiometers, respectively. We calculated theBMI as weight (kg) divided by height squared (m2). Weused the BMI z-score (number of SDs by which a childdiffers from the mean BMI of children of the same ageand sex) to classify subjects according to weight status asunderweight, normal, overweight, or obese according tothe World Health Organization (WHO) growth referencevalues for adolescents [34]. We measured waist circumference (WC) midway between the lowest rib and the iliaccrest using an anthropometric tape parallel to the floor.Blood pressure was measured twice by a trained nurse inthe dominant arm by means of a mercury sphygmomanometer [35]. The first reading was conducted after at least5 min of seated rest. The second reading was taken 5 minafter the first. The first Korotkoff sound was used as ameasure for systolic blood pressure and the fifth sound fordiastolic blood pressure.Biochemical measurementsFig. 1 Flow chart showing study participant selectionadolescents. The unadjusted, adjusted and deattenuatedPearson correlation coefficients for carbohydrate intake inadolescent population were 0.51, 0.25 and 0.36 respectively[23]. The intake of carbohydrate, protein, fat, and dietaryfiber was sex-specific adjusted for total energy intake usingthe residual method proposed by Willett et al. [29].Dietary GI and dietary GL assessmentWe used the protocol of Louie et al. [30] to assign a GIvalue to each food item in the SFFQ. We obtained theGI values from available studies conducted in normalsubjects, using glucose as reference food [31, 32]. Wecalculated the dietary GI of each subject by summingthe products of the available carbohydrate content perserving for each food multiplied by the average numberof daily servings of that food multiplied by its GI; wethen divided this by the total amount of daily carbohydrate intake [10, 33]. In a similar manner but withoutdividing by the total amount of carbohydrate, we estimated dietary GL [10]. Dietary GL was energy-adjustedusing sex-specific residuals [29] owing to a high correlation with energy intake (r 0.880, P 0.001). Finally,we categorized dietary GI and energy-adjusted dietaryGL into sex-specific tertiles.Fasting blood samples were collected by trainedpersonnel of the NHNS-2012. The day before blood collection, subjects were instructed to avoid eating any solidor liquid food prior to collection. Blood was drawn froman antecubital vein and collected in tubes without anticoagulant. The blood was centrifuged in situ at 3000 g.For subjects who reported a previous diagnosis of type 2diabetes mellitus (T2D), a second sample was collectedin heparinized tubes. Serum aliquots were stored incryovials and frozen in liquid nitrogen. Samples weretransported to the Mexican National Institute of PublicHealth and stored at 70 C for posterior analyses in thebiochemistry laboratory.We measured serum glucose concentrations using theglucose oxidase method through chemiluminescencewith an automated analyzer (Architect ci8200, AbbottDiagnostics, Wiesbaden, Germany). To verify the accuracy and precision of the procedure, the 965 material ofthe National Institute of Standards and Technology wasmeasured simultaneously. We determined serum triglyceride levels after lipase hydrolysis in an automaticanalyzer (Architect ci8200, Abbott Diagnostics, Wiesbaden, Germany). HDL-c was measured using an enzymatic colorimetric direct method after eliminatingchylomicrons, very-low-density lipoproteins (VLDL),and low-density lipoproteins by enzymatic digestion. Toassure the precision and accuracy of these measurements, the concentrations of HDL-c and triglycerideswere measured simultaneously at a second laboratory(Lipids Laboratory, National Institute of Medical Scienceand Nutrition Salvador Zubiran of Mexico).

Castro-Quezada et al. BMC Nutrition (2017) 3:44Page 4 of 12Metabolic syndromeStatistical analysesThe presence of MetS was identified according to theInternational Diabetes Federation (IDF) definition of MetSfor children and adolescents [36, 37]. For adolescents aged12–16 years, MetS was defined according to the followingcriteria: (1) presence of abdominal obesity (WC 90th percentile for age and sex or adult cutoff if lower); and (2) thepresence of two or more other conditions among triglycerides 150 mg/dL, HDL-c 40 mg/dL, systolic bloodpressure 130 or diastolic blood pressure 85 mmHg,fasting plasma glucose 100 mg/dL, and known T2D.Adult IDF criteria were used for subjects aged 16 years orolder: central obesity (defined as WC 90 cm for maleand 80 cm for female adolescents); and at least two ofthe following factors: triglycerides 150 mg/dL or specifictreatment for high triglycerides; HDL-c 40 mg/dL inmales and 50 mg/dL in females or specific treatment forthese lipid abnormalities; systolic blood pressure 130,diastolic blood pressure 85 mmHg, or treatment of previously diagnosed hypertension; fasting plasma glucose 100 mg/dL; or previously diagnosed T2D.The sample design characteristics (sample weights, cluster,and strata variables) were considered for all the analyses.We estimated the baseline characteristics of the populationand dietary intake according to sex-specific tertiles of dietary GI and energy-adjusted dietary GL. To explore differences across categories of dietary GI and energy-adjusteddietary GL, we used linear regression models and designbased Wald statistics for quantitative variables; weemployed the design-based F statistic (corrected, weightedPearson chi-square statistic) for categorical data.We used multiple logistic regression models to estimateodds ratios (ORs) and their 95% confidence intervals (CIs)to evaluate the association between categories of dietaryGI and GL and the prevalence of MetS. The first modelwas adjusted for age (years). The second multivariatemodel further included the following: SES (low, middle,high); geographic regions of Mexico (north, central, south,metropolitan area) and dietary fiber intake (continuous,energy-adjusted). To examine the associations betweencategories of dietary GI and GL and the prevalence ofMetS components (elevated WC, abnormal blood pressure, elevated fasting serum triglycerides, low HDL-c, elevated fasting serum glucose concentrations), we fittedlogistic regression models with the same covariates asthose used for the main analyses. We selected covariatesusing a hypothesis-based analysis. The addition of potential confounders, such as physical activity levels or screentime as covariates in the multivariate models, did notchange the magnitude or effect of our results; thus, we didnot use those factors in the final models. We took the lowest categories of dietary GI and GL as references in all themodels. The tests of the linear trend across increasing categories of dietary GI and GL were conducted by assigningthe sex-specific median value within each category. Wetreated those variables as continuous in the logistic regression models.To examine a possible interaction between dietary GIand GL and age (under and over 16 years), and weightstatus (underweight/normal, overweight/obese), we introduced the product terms in the different multivariablemodels; we considered P 0.05 in the likelihood ratio testas statistically significant. All statistical analyses wereperformed using Stata 12.0 (StataCorp, College Station,TX, USA), and the significance level was set at P 0.05.CovariatesWe used specific questionnaires to assess sociodemographic characteristics, medical history, and lifestylehabits. Socioeconomic status (SES) information was basedon well-being. Using these data, we calculated an index(well-being index) by principal-components analysis,which included home conditions and presence in thehome of household appliances, goods, and services. Thecontinuous variable was categorized into tertiles and usedas a proxy for low, medium, and high SES levels.To collect information on physical activity and sedentary lifestyle in the 12- to 14-year age-group, we used aquestionnaire of eight items [38]. The questions includedhours of sleep, screen time, means of transportation toschool, and formal physical activity (e.g., skating, dancing, and soccer) over the previous year. We also identified the means of transportation and length of timespent on the home-to-school route and vice versa. Furthermore, we categorized formal or competitive physicalactivities performed in the previous year according tothe following criteria: (1) inactive; (2) one or two activities; and (3) three or more activities.We assessed physical activity in adolescents aged 15–19 years using the short version of the International Physical Activity Questionnaire [39]. In addition, participantswere asked about their usual hours of sleep, inactive transport time, and usual screen time [40, 41]. The evaluationcomprised 14 questions and allowed us to differentiate theactivity during the week and on weekends. Finally, inagreement with WHO criteria, we classified physical activity into three categories: active, moderately active, andinactive [42].ResultsIn this study, the mean (SD) dietary GI and GL of adolescents in the NHNS-2012 was 51.8 (5.3) and 150.0(27.3), respectively. The MetS prevalence in the overallsample was 8.8%, with a higher proportion among female (12.0%) than male adolescents (6.4%; P 0.019).Tables 1 and 2 present the main characteristics of thesample according to sex-specific tertiles of dietary GI

Castro-Quezada et al. BMC Nutrition (2017) 3:44Page 5 of 12Table 1 General characteristics of the sample according to sex-specific categories of dietary glycemic indexaCharacteristicsDietary glycemic indexbFemale adolescentsMale adolescentsLowModerateHighMean SDMean SDMean SDP valueLowModerateHighMean SDMean SDMean SDP valueDietary GI (units)46.80.3 51.00.1 56.60.4 0.00147.60.3 51.90.1 57.00.3 0.001Age (years)15.50.2 16.20.3 16.00.2 0.12315.40.2 16.00.2 15.60.2 0.18530.025.630.927.732.631.2Socioeconomic status ographic region etropolitan 7.031.4Weight status .013.3Obese13.39.712.713.94.916.3Screen time(computer, TV, and video) (%) 2 h/day0.13129.643.828.90.01133.739.829.42–4 h/day35.029.145.729.339.537.1 4 h/day34.125.324.536.817.833.5No data available131. activity(%, age 12–14 –2 activities18.920.036.444.950.541.1 3 activities2. data available2. activity(%, age 15–19 oderately 62.366.6No data available0. intakeTotal energy 215690.118Carbohydrate intake(g/d)c246.03.3 271.54.2 269.73.3 0.001298.03.6 315.84.3 311.54.1 0.002Carbohydrate intake(% energy)54.90.7 61.51.2 60.80.8 0.00155.80.7 59.50.8 59.10.7 0.001Protein intake (g/d)c58.51.0 52.71.2 51.90.8 0.00167.91.1 63.81.0 60.51.2 0.001Protein intake (% energy)13.20.2 11.80.3 11.50.2 0.00113.00.2 12.10.2 11.40.2 0.001

Castro-Quezada et al. BMC Nutrition (2017) 3:44Page 6 of 12Table 1 General characteristics of the sample according to sex-specific categories of dietary glycemic indexa (Continued)Fat intake (g/d)c67.21.1 58.81.3 59.11.3 0.00177.51.3 70.51.6 69.51.5 0.001Fat intake (% energy)33.90.5 28.80.9 29.40.7 0.00133.20.5 29.80.7 29.60.5 0.001MUFA (g/d)c22.60.5 19.70.5 20.50.6 0.00125.90.6 23.60.5 24.70.6 0.020PUFA (g/d)14.30.4 14.10.4 14.50.4 0.75017.40.4 17.50.7 16.80.4 0.424SFA (g/d)c25.80.5 22.10.6 22.70.7 0.00129.40.7 26.20.7 26.50.7 0.002Trans fatty acids(g/d)c0.50.0 0.50.0 0.50.0 0.0540.50.0 0.50.0 0.60.0 0.692Dietary fiber intake(g/d)c21.60.5 22.71.3 21.30.7 0.68325.80.7 27.01.0 22.80.8 0.003Dietary sugar intake(g/d)94.14.9 109.97.2 112.34.4 0.021108.44.1 116.26.4 134.64.4 0.001WC (cm)76.81.0 76.81.2 78.31.4 0.64077.01.5 77.51.0 78.71.2 0.660cTriglycerides (mg/dL)116.97.5 135.39.7 113.55.6 0.142113.26.6 113.36.6 132.19.4 0.212HDL-c (mg/dL)45.10.8 48.72.0 43.01.4 0.07543.30.9 41.30.9 43.00.8 0.231Systolic blood pressure(mmHg)107.10.9 108.91.1 110.01.2 0.169111.21.5 110.91.0 113.31.0 0.219Diastolic blood pressure(mmHg)70.00.8 72.01.1 73.21.0 0.05070.31.1 71.10.9 73.30.8 0.051Fasting serum glucose (mg/dL)80.41.0 79.01.2 77.61.0 0.17281.30.8 80.21.3 81.51.4 0.733MetS prevalence (%)d9.516.90.2349. GI Glycemic index, GL glycemic load, kcal/d kilocalories per day, grams per day (g/d), MUFA monounsaturated fatty acids, PUFA polyunsaturated fattyacids, SFA saturated fatty acids, WC waist circumference, HDL-c high-density lipoprotein cholesterol, MetS metabolic syndromeaValues are expressed as means and standard deviations (SD) for continuous variables, and data from categorical variables are shown as percentagesbCategories based on sex-specific tertiles of dietary GI. cValues were adjusted for energy intake using sex-specific residuals. dThe age-specific InternationalDiabetes Foundation definition of the metabolic syndrome was used [36, 37]and energy-adjusted dietary GL. Participants in the highest category of dietary GI had higher carbohydrate andsugar intake and lower values of protein and total fat,than subjects in the lowest category of dietary GI. Similar characteristics were found across categories of dietaryGL, in addition, we observed a higher dietary fiber intake in the top tertile of dietary GL compared with thosein the lowest tertile. We found no differences in theprevalence of MetS or the mean of its componentsacross dietary GL categories.Table 3 shows the ORs and 95% CI for MetS and its components according to sex-specific categories of dietary GI.We observed no association of MetS with either dietary GIor dietary GL. However, when MetS components were analyzed separately, a direct association between the highestdietary GI and abnormal blood pressure was evident in female adolescents (Model 1: OR 3.66; 95% CI, 1.59–8.39;P for trend 0.009). This association remained statisticallysignificant after multivariate adjustment. Table 4 shows theORs and 95% CI for MetS and its components according tosex-specific categories of energy-adjusted dietary GL. Ourresults from the multivariate model also indicated that female adolescents with the highest dietary GL had higherodds of abnormal blood pressure (OR 5.67; 95% CI,1.84–17.46); there was a significant trend across categoriesof dietary GL (P for trend 0.003). Among males, nostatistically significant associations were found betweendietary GI or dietary GL and abnormal BP. We found nostatistically significant associations for the remaining MetScriteria with dietary GI or GL.None of the interactions assessed was statistically significant in the association between dietary GI and GLand MetS (P for interaction 0.05)DiscussionIn this cross-sectional study, we found no associations between dietary GI or GL and MetS. However, in an analysisof MetS components, high dietary GI and GL were associated with higher odds of abnormal blood pressure in femaleadolescents.We found no associations between dietary GI orGL and MetS. Similar results were observed in a clinical trial performed in European children and adolescents (5–18 years) did not reveal an association between alow-GI diet and MetS [43]. A cross-sectional study conducted in 516 Australian adolescents found no associationbetween overall dietary GI or dietary GL and MetS [20].In that study, however, breakfast GL was found to be predictive of MetS in female, but not male, adolescents. Inthe present study, we used SFFQ to assess dietary intake,and we were unable to estimate dietary GI or GL at

Castro-Quezada et al. BMC Nutrition (2017) 3:44Page 7 of 12Table 2 General characteristics of the sample according to sex-specific categories of energy-adjusted dietary glycemic loadaCharacteristicsEnergy-adjusted dietary glycemic loadbcFemale adolescentsMale adolescentsLowModerateHighMean SDMean SDMean SDP valueLowModerateHighMean SDMean SDMean SDP valueDietary GLc (units)110.01.3 135.00.6 162.91.4 0.001132.11.5 159.90.7 192.51.4 0.001Age (years)15.90.3 15.90.2 15.90.3 0.99815.80.3 15.90.2 15.30.2 0.11221.330.634.716.929.944.8Socioeconomic status (%)Low0.013 951.837.018.526.819.311.323.222.712.9Geographic region etropolitan 5.440.7Weight status .315.3Obese14.612. time(computer, TV, and video) (%) 2 h/day0.86835.832.833.7 0.00135.927.639.62–4 h/day32.737.140.123.547.934.6 4 h/day31.228.124.640.324.323.2No data available0. activity(%, age 12–14 –2 activities16.624.335.642.346.646.5 3 activities3. data available1. activity(%, age 15–19 oderately 956.665.7No data available0. intakeTotal energy intake(kcal/d)1844Carbohydrate intake(g/d)c228.22.1 263.01.8 296.52.3 0.001266.12.8 308.62.0 350.92.9 0.001Carbohydrate intake(% energy)50.80.5 59.50.4 67.00.7 0.00150.20.5 58.20.4 66.10.5 0.001Protein intake (g/d)c61.20.8 53.40.7 48.51.0 0.00171.41.2 64.10.8 56.80.9 0.00113.80.2 12.00.2 10.80.2 0.00113.60.2 12.20.2 10.70.2 0.001Protein intake(% energy)581697591828620.1902169532019652179600.141

Castro-Quezada et al. BMC Nutrition (2017) 3:44Page 8 of 12Table 2 General characteristics of the sample according to sex-specific categories of energy-adjusted dietary glycemic loada(Continued)Fat intake (g/d)c73.00.9 61.90.8 50.10.9 0.00185.61.2 72.80.7 59.11.1 0.001Fat intake (% energy)36.90.4 30.60.4 24.60.6 0.00136.50.5 30.90.4 25.10.4 0.001MUFA (g/d)c25.00.5 21.00.4 16.80.4 0.00128.80.6 25.10.3 20.20.5 0.001PUFA (g/d)14.90.4 14.30.4 13.80.4 0.13019.00.7 16.60.3 16.10.4 0.002SFA (g/d)c28.60.6 23.70.4 18.30.4 0.00132.80.6 28.00.4 21.30.6 0.001ccTrans fatty acids (g/d)0.60.0 0.50.0 0.40.0 0.0010.70.0 0.50.0 0.40.0 0.001Dietary fiber intake (g/d)c18.70.7 21.00.5 25.91.1 0.00120.90.5 24.60.7 30.20.9 0.001Dietary sugar intake (g/d)WC (cm)94.46.0 102.55.5 119.66.2 0.014111.73.9 117.35.6 129.95.2 0.01577.51.2 78.11.1 76.31.4 0.59278.11.5 79.11.3 76.01.1 0.133Triglycerides (mg/dL)117.56.6 115.08.2 133.39.4 0.300114.07.7 121.56.9 122.88.4 0.711HDL-c (mg/dL)47.82.0 44.11.0 44.91.5 0.23343.00.9 41.60.8 43.10.9 0.302Systolic blood pressure(mmHg)108.20.9 108.61.0 109.31.3 0.794111.51.6 112.00.9 111.80.9 0.955Diastolic blood pressure(mmHg)71.30.9 71.31.0 72.51.1 0.65370.51.1 71.50.9 72.70.8 0.231Fasting serum glucose(mg/dL)80.41.2 79.91.2 76.70.9 0.01881.41.0 81.51.4 80.11.2 0.620MetS prevalence (%)d9. GI Glycemic index, GL glycemic load, kcal/d kilocalories per day, g/d grams per day, MUFA monounsaturated fatty acids, PUFA polyunsaturated fattyacids, SFA saturated fatty acids, WC waist circumference, HDL-c high-density lipoprotein cholesterol, MetS metabolic syndromeaValues are expressed as means and standard deviations (SD) for continuous variables, and data from categorical variables are shown as percentagesbCategories based on sex-specific tertiles of dietary GL. cValues were adjusted for energy intake using sex-specific residuals. dThe age-specific InternationalDiabetes Foundation definition of the metabolic syndrome was used [36, 37]different mealtimes. Thus, it was not possible for us toconfirm the results of that Australian study.Our results also contrast with those of a cross-sectionalstudy, in which dietary GL was associated with a higherprevalence of MetS in 769 adolescents (13–15 years) [21].The variance with our results may be explained by the different methods used for dietary assessment. The 3-dayfood record used in that study may in fact have as

Glycemic index, glycemic load, and metabolic syndrome in Mexican adolescents: a cross-sectional study from the NHNS-2012 . International Tables of GI values. We defined MetS according to the International Diabetes Federation criteria developed for adolescents. Multiple logistic regression

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