Murakami K, Sasaki S, Takahashi Y, Okubo H, Hosoi Y, Horiguchi H, et al. Dietary glycemic index and load in relation to metabolic risk factors in Japanese female farmers with traditional dietary habits. Du H, van Bakel M, Slimani N, Forouhi N, Wareham N, Halkjaer J, et al.

Dietary glycaemic index, glycaemic load and subsequent changes of weight and waist circumference in European men and women. Int J Obes. Xu X, Shivappa N. Dietary glycemic index, glycemic load and risk of bladder cancer: a prospective study.

Eur J Nutr. Ma X-y, Liu J-p, Song Z-y. Glycemic load, glycemic index and risk of cardiovascular diseases: meta-analyses of prospective studies. Mirrahimi A, de Souza RJ, Chiavaroli L, Sievenpiper JL, Beyene J, Hanley AJ, et al. Associations of glycemic index and load with coronary heart disease events: a systematic review and meta-analysis of prospective cohorts.

J Am Heart Association. Atanasovska B, Kumar V, Fu J, Wijmenga C, Hofker MH. GWAS as a driver of gene discovery in cardiometabolic diseases. Trends Endocrinol Metab. Drąg J, Goździalska A, Knapik-Czajka M, Gawędzka A, Gawlik K, Jaśkiewicz J.

Gol S, Pena RN, Rothschild MF, Tor M, Estany J. A polymorphism in the fatty acid desaturase-2 gene is associated with the arachidonic acid metabolism in pigs. Sci Rep. He Z, Zhang R, Zhang FJ, Zhao H, Xu A. B, et al. FADS1-FADS2 genetic polymorphisms are associated with fatty acid metabolism through changes in DNA methylation and gene expression.

Clin epigenetics. Bláhová Z, Harvey TN, Pšenička M, Mráz J. Assessment of fatty acid desaturase Fads2 structure-function properties in fish in the context of environmental adaptations and as a target for genetic engineering. Naghibi D, Mohammadzadeh S, Azami-Aghdash S.

Barriers to Evidence-Based Practice in Health System: A Systematic Review. Mazoochian L, Sadeghi HMM, Pourfarzam M. The effect of FADS2 gene rs polymorphism on desaturase activities, fatty acid profile, insulin resistance, biochemical indices, and incidence of type 2 diabetes.

J Res Med Sciences: Official J Isfahan Univ Med Sci. Muzsik A, Bajerska J, Jeleń HH, Gaca A, Chmurzynska A. Associations between fatty acid intake and status, desaturase activities, and FADS gene polymorphism in centrally obese postmenopausal Polish women.

Article PubMed Central Google Scholar. Abbasalizad Farhangi M, Vajdi M, Nikniaz L, Nikniaz Z. The interaction between dietary inflammatory index and 6 P21 rs gene variants in metabolic syndrome. Eating and Weight Disorders-Studies on Anorexia.

Bulimia and Obesity. Khodarahmi M, Asghari-Jafarabadi M, Abbasalizad Farhangi M. A structural equation modeling approach for the association of a healthy eating index with metabolic syndrome and cardio-metabolic risk factors among obese individuals.

PLoS ONE. Article CAS PubMed PubMed Central Google Scholar. Farhangi MA, Vajdi M, Nikniaz L, Nikniaz Z.

Interaction between vascular endothelial growth factor-A rs gene polymorphisms and dietary diversity score on cardiovascular risk factors in patients with metabolic syndrome. Lifestyle Genomics. Washburn RA. Assessment of physical activity in older adults. Res Q Exerc Sport.

Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem.

Rerksuppaphol L, Rerksuppaphol S. Comparison of equations for the calculation of low-density lipoprotein cholesterol in thai population. Sowndarya K, Joseph JA, Shenoy A, Hegde A.

Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.

J Clin Endocrinol Metabolism. Matthews DR, Hosker J, Rudenski A, Naylor B, Treacher D, Turner R. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man.

Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study.

Public Health Nutr. Esfahani FH, Asghari G, Mirmiran P, Azizi F. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran Lipid and Glucose Study. J Epidemiol. Ghaffarpour M, Houshiar-Rad A, Kianfar H. The manual for household measures, cooking yields factors and edible portion of foods.

Tehran: Nashre Olume Keshavarzy. Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: Wolever TM, Yang M, Zeng XY, Atkinson F, Brand-Miller JC.

Food glycemic index, as given in glycemic index tables, is a significant determinant of glycemic responses elicited by composite breakfast meals. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.

Struct Equ Modeling. Ma Y, Olendzki B, Chiriboga D, Hebert JR, Li Y, Li W, et al. Association between dietary carbohydrates and body weight. Am J Epidemiol. Salari-Moghaddam A, Keshteli AH, Haghighatdoost F, Esmaillzadeh A, Adibi P. Dietary glycemic index and glycemic load in relation to general obesity and central adiposity among adults.

Clinical nutrition Edinburgh. Roberts SB. High—glycemic index foods, hunger, and obesity: is there a connection? Nutr Rev. Pawfak D, Denyer G, Brand-Miller J, editors. Low postprandial fat oxidation after a high glycemic index meal leads to increased body fat in chronically fed rats.

Obesity Research; Schwingshackl L, Hoffmann G. Nutr Metabolism Cardiovasc Dis. Ulaganathan V, Kandiah M, Shariff ZM. A case—control study on the association of abdominal obesity and hypercholesterolemia with the risk of colorectal cancer.

Journal of Carcinogenesis. Kaartinen NE, Knekt P, Kanerva N, Valsta LM, Eriksson JG, Rissanen H, et al. Dietary carbohydrate quantity and quality in relation to obesity: a pooled analysis of three Finnish population-based studies. Scand J Public Health. Amano Y, Kawakubo K, Lee J, Tang A, Sugiyama M, Mori K.

Correlation between dietary glycemic index and cardiovascular disease risk factors among Japanese women. Ford ES, Liu S. Glycemic index and serum high-density lipoprotein cholesterol concentration among US adults. Arch Intern Med. Buyken AE, Toeller M, Heitkamp G, Karamanos B, Rottiers R, Muggeo M, et al.

Glycemic index in the diet of European outpatients with type 1 diabetes: relations to glycated hemoglobin and serum lipids. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. Pal S, Lim S, Egger G.

The effect of a low glycaemic index breakfast on blood glucose, insulin, lipid profiles, blood pressure, body weight, body composition and satiety in obese and overweight individuals: a pilot study.

J Am Coll Nutr. Mazoochian L, Sadeghi H, Pourfarzam M. J Res Med sciences: official J Isfahan Univ Med Sci. Park S, Kim DS. Carrying minor allele of FADS1 and haplotype of FADS1 and FADS2 increased the risk of metabolic syndrome and moderate but not low fat diets lowered the risk in two Korean cohorts.

Sone Y, Kido T, AINUkI T, Sonoda M, Ichi I, Kodama S, et al. Genetic variants of the fatty acid desaturase gene cluster are associated with plasma LDL cholesterol levels in Japanese males. J Nutr Sci Vitaminol. Mahmoudinezhad M, Farhangi MA, Kahroba H, Dehghan P. Personalized diet study of dietary advanced glycation end products AGEs and fatty acid desaturase 2 FADS2 genotypes in obesity.

Download references. We thank all of the study participants. We thank from research undersecretary of Tabriz University of Medical Sciences for financial support Gant number: Present study has been financially supported by a grant from Tabriz University of Medical Sciences.

Code: IR. The funders had no role in hypothesis generation, recruiting and designing the study. Their role was only financial supporting. Department of Internal Medicine, Amir Alam Hospital, Tehran University of Medical Sciences, Tehran, Iran. Department of Community Nutrition, Faculty of Nutrition, Tabriz University of Medical Sciences, Tabriz, Iran.

Department of Internal Medicine and Rheumatology, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran. Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. You can also search for this author in PubMed Google Scholar.

All authors approved the final version of the article. MAF and AMA designed the study and served as supervisors for this research. MAF also contributed in statistical analysis, and manuscript writing.

MM was involved in hypothesis generation and statistical approach. MM and AMA were also involved in writing the paper, revision and also English editing. GS was involved in idea generation and revision of manuscript. Correspondence to Mahdieh Abbasalizad Farhangi or Abnoos Mokhtari Ardekani.

All subjects provided a written informed consent before participation in the study. The study protocol was approved and registered by the ethics committee of Tabriz University of Medical Sciences Registration number: IIR. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Siri, G. The association between dietary glycemic index and cardio-metabolic risk factors in obese individuals.

BMC Nutr 8 , Download citation. Received : 09 April Revised : 27 September Accepted : 29 September Published : 17 October Anyone you share the following link with will be able to read this content:.

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Search all BMC articles Search. Download PDF. Abstract Background: The dietary glycemic index GI has been introduced as a novel index to elucidate the potential of foods to increase postprandial glucose.

Method and material: Three hundred forty-seven obese adults were recruited in the present cross-sectional study. Conclusion: Calculated dietary GI was associated with several cardio-metabolic risk factors in obese individuals.

Background Non-communicable diseases NCDs , as a global emergency, impose a large economic burden on society and contribute to a greater rate of comorbidities worldwide [ 1 , 2 ].

Methods and materials Participants In the present cross-sectional study, healthy obese individuals were enrolled. Demographic, anthropometric, and blood pressure assessments A trained interviewer completed the questionnaire on socioeconomic status SES and demographic information.

Biochemical assessments Biochemical parameters were evaluated in 10 ml blood samples that were taken from each participant. Dietary assessments All individuals completed a validated and interviewer-administered item food frequency questionnaire FFQ with the mean energy-adjusted reliability coefficients alpha Cronbach of 0.

Genotyping Blood samples were collected from all individuals and the chloroform technique was used to extract genomic DNA. Sample size calculation The sample size of the current study was calculated based on Hu LT et al.

Statistical analysis Statistical analysis was conducted using SPSS SPSS, Inc. Results The socio-demographic and anthropometric characteristics of the study participants are summarized in Table 1. Table 1 Comparison of demographic and anthropometric characteristics between different dietary GI tertiles Full size table.

Table 2 Comparison of dietary macronutrient intakes and food groups between different dietary GI tertiles Full size table. Table 3 Comparison of demographic and anthropometric characteristics between different dietary GI tertiles Full size table. Table 4 Comparison of dietary GI between different FADS2 rs gene variants Full size table.

Discussion To the best of our knowledge, this is the first study that evaluated the association between dietary GI and cardio-metabolic risk factors in the north-west population of Iran.

Full size image. Conclusion Calculated dietary GI was associated with some cardio-metabolic risk factors in northwest population of Iran. Data Availability All of the data are available with reasonable request from the corresponding author.

References Clark H. Article Google Scholar Nugent R, Fottrell E. Article PubMed Google Scholar Rao GH. Article Google Scholar Rao G.

Article Google Scholar Paul R, Mukkadan J. CAS Google Scholar Guerra ZC, Moore JR, Londoño T, Castro Y. Article PubMed Google Scholar Schroeder K, Kubik MY, Sirard JR, Lee J, Fulkerson JA.

Article PubMed PubMed Central Google Scholar PAVLOS S. Article Google Scholar Rezaei M, Sanagoo A, Jouybari L, Behnampoo N, Kavosi A. Google Scholar Aghajani R, Nemati N, Hojjati Zidashti Z, Bagherpour T.

Google Scholar Xavier J, Farias CP, Soares MSP, Silveira GdO, Spanevello RM, Yonamine M, et al. Google Scholar Farvid MS, Tamimi RM, Poole EM, Chen WY, Rosner BA, Willett WC, et al.

Article PubMed Google Scholar Gokalp G, Berksoy E, Bardak S, Demir G, Demir S, Anil M. Google Scholar Baker K, Figueroa R. Article PubMed Google Scholar Evans CE, Greenwood DC, Threapleton DE, Gale CP, Cleghorn CL, Burley VJ. Article CAS PubMed Google Scholar Mirrahimi A, Chiavaroli L, Srichaikul K, Augustin LS, Sievenpiper JL, Kendall CW, et al.

Article PubMed Google Scholar Hyder KM, Mohan J, Varma V, Sivasankaran P, Raja D. Article CAS Google Scholar Esmaillzadeh A, Mirmiran P, Azizi F. Article CAS PubMed Google Scholar Murakami K, Sasaki S, Takahashi Y, Okubo H, Hosoi Y, Horiguchi H, et al.

Article CAS PubMed Google Scholar Du H, van Bakel M, Slimani N, Forouhi N, Wareham N, Halkjaer J, et al. Article CAS Google Scholar Xu X, Shivappa N. Article CAS PubMed Google Scholar Ma X-y, Liu J-p, Song Z-y.

Article CAS PubMed Google Scholar Mirrahimi A, de Souza RJ, Chiavaroli L, Sievenpiper JL, Beyene J, Hanley AJ, et al.

Article Google Scholar Atanasovska B, Kumar V, Fu J, Wijmenga C, Hofker MH. Article CAS PubMed Google Scholar Drąg J, Goździalska A, Knapik-Czajka M, Gawędzka A, Gawlik K, Jaśkiewicz J. Article Google Scholar Gol S, Pena RN, Rothschild MF, Tor M, Estany J.

Article PubMed PubMed Central Google Scholar He Z, Zhang R, Zhang FJ, Zhao H, Xu A. Article PubMed PubMed Central Google Scholar Bláhová Z, Harvey TN, Pšenička M, Mráz J.

Article Google Scholar Naghibi D, Mohammadzadeh S, Azami-Aghdash S. Google Scholar Mazoochian L, Sadeghi HMM, Pourfarzam M. Article Google Scholar Muzsik A, Bajerska J, Jeleń HH, Gaca A, Chmurzynska A. Article PubMed Central Google Scholar Abbasalizad Farhangi M, Vajdi M, Nikniaz L, Nikniaz Z.

Google Scholar Khodarahmi M, Asghari-Jafarabadi M, Abbasalizad Farhangi M. Article CAS PubMed PubMed Central Google Scholar Farhangi MA, Vajdi M, Nikniaz L, Nikniaz Z. Article Google Scholar Washburn RA. Article PubMed Google Scholar Friedewald WT, Levy RI, Fredrickson DS. Article CAS PubMed Google Scholar Rerksuppaphol L, Rerksuppaphol S.

Article CAS Google Scholar Sowndarya K, Joseph JA, Shenoy A, Hegde A. Article CAS Google Scholar Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Article CAS Google Scholar Matthews DR, Hosker J, Rudenski A, Naylor B, Treacher D, Turner R.

Article CAS PubMed Google Scholar Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Article PubMed Google Scholar Esfahani FH, Asghari G, Mirmiran P, Azizi F.

Article PubMed Google Scholar Ghaffarpour M, Houshiar-Rad A, Kianfar H. Google Scholar Foster-Powell K, Holt SH, Brand-Miller JC. Article CAS PubMed Google Scholar Wolever TM, Yang M, Zeng XY, Atkinson F, Brand-Miller JC.

Article CAS PubMed Google Scholar Hu LT, Bentler PM. Article Google Scholar Ma Y, Olendzki B, Chiriboga D, Hebert JR, Li Y, Li W, et al.

Article PubMed Google Scholar Salari-Moghaddam A, Keshteli AH, Haghighatdoost F, Esmaillzadeh A, Adibi P. CAS Google Scholar Roberts SB. Rev Esp Cardiol 65 3 — Barbosa PJB, Lessa I, de Almeida Filho N, Magalhães LBNC, Araújo J Criteria for Central Obesity in a Brazilian Population: Impact on the Metabolic Syndrome.

Arq Bras Cardiol — Vianna CA, da Silva Linhares R, Bielemann RM, Machado EC, González-Chica DA, Matijasevich AM et al Accuracy and adequacy of waist circumference cut-off points currently recommended in Brazilian adults.

Public Health Nutr 17 4 — JAMA — CAS Google Scholar. Song S, Paik HY, Song WO, Park M, Song Y Three distinct clustering patterns in metabolic syndrome abnormalities are differentially associated with dietary factors in Korean adults.

Nutr Res 34 5 — Nutrition 26 6 — Recio-Rodriguez JI, Gomez-Marcos MA, Patino-Alonso MC, Rodrigo-De Pablo E, Cabrejas-Sánchez A, Arietaleanizbeaskoa MS et al Glycemic index, glycemic load, and pulse wave reflection in adults.

Nutr Metab Cardiovasc Dis 25 1 — Song M, Fung TT, Hu FB, Willett WC, Longo VD, Chan AT et al Association of Animal and Plant Protein Intake With All-Cause and Cause-Specific Mortality JAMA. Intern Med 10 — Ludwig DS The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease.

JAMA 18 — Pawlak DB, Kushner JA, Ludwig DS Effects of dietary glycaemic index on adiposity, glucose homoeostasis, and plasma lipids in animals. Lancet — Blaak EE, Antoine J-M, Benton D, Bjork I, Bozzetto L, Brouns F et al Impact of postprandial glycaemia on health and prevention of disease—Etiology and Pathophysiology.

Obes Rev 13 10 — Atkinson FS, Foster-Powell K, Brand-Miller JC International tables of glycemic index and glycemic load values: Diabetes Care 31 12 — Fabricatore AN, Ebbeling CB, Wadden TA, Ludwig DS Continuous glucose monitoring to assess the ecologic validity of dietary glycemic index and glycemic load.

Am J Clin Nutr 94 6 — Dodd H, Williams S, Brown R, Venn B Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index. Am J Clin Nutr 94 4 — Brand-Miller JC Glycaemic index and glycaemic load: crunch time?

Nutr Diet 66, — Salmeron J, Ascherio A, Rimm E, Colditz GA, Spiegelman D, Jenkins DJ et al Dietary fiber, glycemic load, and risk of NIDDM in men. Diabetes Care — Salmeron J, Manson J, Stampfer M, Colditz G, Wing A, Willett W Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women.

Ministério do Planejamento, Orçamento e Gestão Indicadores Sociais Municipais — Uma análise dos resultados do universo do Censo Demográfico , Rio de Janeiro.

Download references. We are grateful to Prof. Chester Luiz Galvão Cesar, Prof. Moisés Goldbaum, Maria Cecilia Goi Porto Alves, and all Health Survey of São Paulo staff for conception and design of the study and to the Food Consumption Research Group GAC for their support.

MMF was involved in the analysis and interpretation of the data and drafting the article. CHS and AAFC were involved in the analysis and interpretation of the data, drafting the article, and revising it critically for important intellectual content.

DMM was involved in the conception and design of the study and revising it critically for important intellectual content. RMF was involved in the conception and design of the study, interpretation of the data, and revising the manuscript critically for important intellectual content. All authors approved the final version to be submitted.

Department of Nutrition, School of Public Health, University of São Paulo, Avenida Dr. Arnaldo, , Cerqueira Cesar, , Sao Paulo, SP, Brazil.

You can also search for this author in PubMed Google Scholar. Correspondence to Regina Mara Fisberg. Reprints and permissions. de Mello Fontanelli, M. et al. The relationship between carbohydrate quality and the prevalence of metabolic syndrome: challenges of glycemic index and glycemic load.

Eur J Nutr 57 , — Download citation. Received : 12 November Accepted : 10 February Published : 01 March Issue Date : April Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Abstract Purpose To estimate the prevalence of metabolic syndrome MetS and its components in adults and older adults residents of São Paulo, the association of MetS with the glycemic index GI and glycemic load GL and the foods that contribute to dietary GI and GL in this population.

Methods Data from adults and older adults participants in the Health Survey of São Paulo were used. Results The prevalence of MetS in the adult and older adults residents of São Paulo was Conclusions Considering the high prevalence of low HDL-c in the population of São Paulo, GI and GL may contribute to the nutritional therapy of this dyslipidemia.

Access this article Log in via an institution. References International Diabetes Federation The IDF consensus worldwide definition of the Metabolic Syndrome , Brussels Vidigal FC, Bressan J, Babio N, Salas-Salvadó J Prevalence of metabolic syndrome in Brazilian adults: a systematic review.

BMC Public Health Article Google Scholar Aguilar M, Bhuket T, Torres S, Liu B, Wong RJ Prevalence of the Metabolic Syndrome in the United States, — JAMA 19 — Article CAS Google Scholar World Health Organization Global status report on noncommunicable diseases , Geneva Brasil.

Ministério da Saúde VIGITEL Brasil —Vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico, Brasília Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society and International Association for the Study of Obesity.

Circulation 16 — Article CAS Google Scholar Institute of Medicine, Food and Nutrition Board Dietary reference intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. National Academy Press, Washington DC Google Scholar Jenkins DJ, Wolever TM, Taylor RH, Barker HM, Fielden H, Baldwin JM et al Glycemic index of foods: a physiological basis for carbohydrate exchange.

Am J Clin Nutr 34 3 — Article CAS Google Scholar Salmerón J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. JAMA 6 — Article Google Scholar Augustin LS, Kendall CW, Jenkins DJ, Willett WC, Astrup A, Barclay AW et al Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium ICQC.

Nutr Metab Cardiovasc Dis 25 9 — Article CAS Google Scholar Finley CE, Barlow CE, Halton TL, Haskell WL Glycemic index, glycemic load, and prevalence of the metabolic syndrome in the Cooper Center Longitudinal Study. J Am Diet Assoc 12 — Article CAS Google Scholar Kim K, Yun HS, Choi BY, Kim MK Cross-sectional relationship between dietary carbohydrate, glycaemic index, glycaemic load and risk of the metabolic syndrome in a Korean population.

Br J Nutr — Article CAS Google Scholar Klemsdal TO, Holme I, Nerland H, Pedersen TR, Tonstad S Effects of a low glycemic load diet versus a low-fat diet in subjects with and without the metabolic syndrome.

Nutr Metab Cardiovasc Dis — Article CAS Google Scholar Juanola-Falgarona M, Salas-Salvadó J, Buil-Cosiales P, Corella D, Estruch R, Ros E et al Dietary glycemic index and glycemic load are positively associated with risk of developing metabolic syndrome in middle-aged and elderly adults.

J Am Geriatr Soc — Article Google Scholar Cullberson A, Kafai MR, Ganji V Glycemic load is associated with HDL cholesterol but not with the other components and prevalence of metabolic syndrome in the third National Health and Nutrition Examination Survey, — Int Arch Med 2 1 :3 Article Google Scholar Vrolix R, Mensink RP Effects of glycemic load on metabolic risk markers in subjects at increased risk of developing metabolic syndrome.

Am J Clin Nutr 92 2 — Article CAS Google Scholar Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE et al International physical activity questionnaire: country reliability and validity.

Med Sci Sports Exerc — Article Google Scholar World Health Organization Consultation on Obesity: Preventing and managing the global epidemic, Report, Geneva Lebrão ML, Duarte YAO SABE—Saúde, Bem-estar e Envelhecimento—O Projeto Sabe no município de São Paulo: uma abordagem inicial, 1rst ed.

Brasília: Organização Pan-Americana da Saúde Sociedade Brasileira de Cardiologia V Diretrizes Brasileiras de Hipertensão Arterial. Arq Bras Cardiol 89 3 —79 Google Scholar Guenther PM, DeMaio TJ, Ingwersen LA, Berlin M The multiple-pass approach for the hour recall in the continuing survey of food intakes by individuals CSFII International Conference on Dietary Assessment Methods, Boston Flood A, Subar AF, Hull SG, Zimmerman TP, Jenkins DJ, Schatzkin A Methodology for adding glycemic load values to the National Cancer Institute Diet History Questionnaire database.

J Am Diet Assoc 3 — Article Google Scholar Tabela Brasileira de Composição de Alimentos-USP. Accessed 5 Nov Haubrock J, NÓ§thlings U, Volatier J, Dekkers A, Ocke M, Harttig U Estimating usual food intake distributions by using the Multiple Source Method in the EPIC-Potsdam Calibration Study.

J Nutr — Article CAS Google Scholar Willett WC, Howe GR, Kushi LW Adjustment for total energy intake in epidemiologic studies.

Am J Clin Nutr S—S Article Google Scholar Block G, Dresser CM, Hartman AM, Carroll MD Nutrient sources in the American diet: quantitative data from the NHANESII survey.

Am J Epidemiol —40 Article CAS Google Scholar Alkerwi A, Donneau A, Sauvageot N, Lair ML, Scheen A, Albert A et al Prevalence of the metabolic syndrome in Luxembourg according to the Joint Interim Statement definition estimated from the ORISCAV-LUX study.

BMC Public Health 11 1 :4 Article Google Scholar Fernández-Bergés D, Cabrera de León A, Sanz H, Elosua R, Guembe MJ, Alzamora M et al Metabolic syndrome in Spain: prevalence and coronary risk associated with harmonized definition and WHO proposal.

Rev Esp Cardiol 65 3 — Article Google Scholar Barbosa PJB, Lessa I, de Almeida Filho N, Magalhães LBNC, Araújo J Criteria for Central Obesity in a Brazilian Population: Impact on the Metabolic Syndrome. Arq Bras Cardiol — Article Google Scholar Vianna CA, da Silva Linhares R, Bielemann RM, Machado EC, González-Chica DA, Matijasevich AM et al Accuracy and adequacy of waist circumference cut-off points currently recommended in Brazilian adults.

Public Health Nutr 17 4 — Article Google Scholar Vrolix R, Mensink RP Effects of glycemic load on metabolic risk markers in subjects at increased risk of developing metabolic syndrome.

JAMA — CAS Google Scholar Song S, Paik HY, Song WO, Park M, Song Y Three distinct clustering patterns in metabolic syndrome abnormalities are differentially associated with dietary factors in Korean adults. Nutrition 26 6 — Article CAS Google Scholar Recio-Rodriguez JI, Gomez-Marcos MA, Patino-Alonso MC, Rodrigo-De Pablo E, Cabrejas-Sánchez A, Arietaleanizbeaskoa MS et al Glycemic index, glycemic load, and pulse wave reflection in adults.

In the past, carbohydrates were classified as simple or complex based on the number of simple sugars in the molecule. Carbohydrates composed of one or two simple sugars like fructose or sucrose table sugar; a disaccharide composed of one molecule of glucose and one molecule of fructose were labeled simple, while starchy foods were labeled complex because starch is composed of long chains of the simple sugar, glucose.

Advice to eat less simple and more complex carbohydrates i. This assumption turned out to be too simplistic since the blood glucose glycemic response to complex carbohydrates has been found to vary considerably.

The concept of glycemic index GI has thus been developed in order to rank dietary carbohydrates based on their overall effect on postprandial blood glucose concentration relative to a referent carbohydrate, generally pure glucose 2.

The GI is meant to represent the relative quality of a carbohydrate-containing food. Intermediate-GI foods have a GI between 56 and 69 3. The GI of selected carbohydrate-containing foods can be found in Table 1.

To determine the glycemic index GI of a food, healthy volunteers are typically given a test food that provides 50 grams g of carbohydrate and a control food white, wheat bread or pure glucose that provides the same amount of carbohydrate, on different days 4.

Blood samples for the determination of glucose concentrations are taken prior to eating, and at regular intervals for a few hours after eating. The changes in blood glucose concentration over time are plotted as a curve. The GI is calculated as the incremental area under the glucose curve iAUC after the test food is eaten, divided by the corresponding iAUC after the control food pure glucose is eaten.

The value is multiplied by to represent a percentage of the control food 5 :. In contrast, cooked brown rice has an average GI of 50 relative to glucose and 69 relative to white bread. In the traditional system of classifying carbohydrates, both brown rice and potato would be classified as complex carbohydrates despite the difference in their effects on blood glucose concentrations.

While the GI should preferably be expressed relative to glucose, other reference foods e. Additional recommendations have been suggested to improve the reliability of GI values for research, public health, and commercial application purposes 2 , 6. By definition, the consumption of high-GI foods results in higher and more rapid increases in blood glucose concentrations than the consumption of low-GI foods.

Rapid increases in blood glucose resulting in hyperglycemia are potent signals to the β-cells of the pancreas to increase insulin secretion 7.

Over the next few hours, the increase in blood insulin concentration hyperinsulinemia induced by the consumption of high-GI foods may cause a sharp decrease in the concentration of glucose in blood resulting in hypoglycemia. In contrast, the consumption of low-GI foods results in lower but more sustained increases in blood glucose and lower insulin demands on pancreatic β-cells 8.

Many observational studies have examined the association between GI and risk of chronic disease , relying on published GI values of individual foods and using the following formula to calculate meal or diet GI 9 :.

Yet, the use of published GI values of individual foods to estimate the average GI value of a meal or diet may be inappropriate because factors such as food variety, ripeness, processing, and cooking are known to modify GI values.

In a study by Dodd et al. Besides the GI of individual foods, various food factors are known to influence the postprandial glucose and insulin responses to a carbohydrate-containing mixed diet.

A recent cross-over , randomized trial in 14 subjects with type 2 diabetes mellitus examined the acute effects of four types of breakfasts with high- or low-GI and high- or low- fiber content on postprandial glucose concentrations. Plasma glucose was found to be significantly higher following consumption of a high-GI and low-fiber breakfast than following a low-GI and high-fiber breakfast.

However, there was no significant difference in postprandial glycemic responses between high-GI and low-GI breakfasts of similar fiber content In this study, meal GI values derived from published data failed to correctly predict postprandial glucose response, which appeared to be essentially influenced by the fiber content of meals.

Since the amounts and types of carbohydrate, fat, protein , and other dietary factors in a mixed meal modify the glycemic impact of carbohydrate GI values, the GI of a mixed meal calculated using the above-mentioned formula is unlikely to accurately predict the postprandial glucose response to this meal 3.

Using direct measures of meal GIs in future trials — rather than estimates derived from GI tables — would increase the accuracy and predictive value of the GI method 2 , 6. In addition, in a recent meta-analysis of 28 studies examining the effect of low- versus high-GI diets on serum lipids , Goff et al.

indicated that the mean GI of low-GI diets varied from 21 to 57 across studies, while the mean GI of high-GI diets ranged from 51 to 75 Therefore, a stricter use of GI cutoff values may also be warranted to provide more reliable information about carbohydrate-containing foods.

The glycemic index GI compares the potential of foods containing the same amount of carbohydrate to raise blood glucose. However, the amount of carbohydrate contained in a food serving also affects blood glucose concentrations and insulin responses.

For example, the mean GI of watermelon is 76, which is as high as the GI of a doughnut see Table 1. Yet, one serving of watermelon provides 11 g of available carbohydrate, while a medium doughnut provides 23 g of available carbohydrate.

The concept of glycemic load GL was developed by scientists to simultaneously describe the quality GI and quantity of carbohydrate in a food serving, meal, or diet. The GL of a single food is calculated by multiplying the GI by the amount of carbohydrate in grams g provided by a food serving and then dividing the total by 4 :.

Using the above-mentioned example, despite similar GIs, one serving of watermelon has a GL of 8, while a medium-sized doughnut has a GL of Dietary GL is the sum of the GLs for all foods consumed in the diet. It should be noted that while healthy food choices generally include low-GI foods, this is not always the case.

For example, intermediate-to-high-GI foods like parsnip, watermelon, banana, and pineapple, have low-to-intermediate GLs see Table 1. The consumption of high-GI and -GL diets for several years might result in higher postprandial blood glucose concentration and excessive insulin secretion.

This might contribute to the loss of the insulin-secreting function of pancreatic β-cells and lead to irreversible type 2 diabetes mellitus A US ecologic study of national data from to found that the increased consumption of refined carbohydrates in the form of corn syrup, coupled with the declining intake of dietary fiber , has paralleled the increased prevalence of type 2 diabetes In addition, high-GI and -GL diets have been associated with an increased risk of type 2 diabetes in several large prospective cohort studies.

Moreover, obese participants who consumed foods with high-GI or -GL values had a risk of developing type 2 diabetes that was more than fold greater than lean subjects consuming low-GI or -GL diets However, a number of prospective cohort studies have reported a lack of association between GI or GL and type 2 diabetes The use of GI food classification tables based predominantly on Australian and American food products might be a source of GI value misassignment and partly explain null associations reported in many prospective studies of European and Asian cohorts.

Nevertheless, conclusions from several recent meta-analyses of prospective studies including the above-mentioned studies suggest that low-GI and -GL diets might have a modest but significant effect in the prevention of type 2 diabetes 18 , 25, The use of GI and GL is currently not implemented in US dietary guidelines A meta-analysis of 14 prospective cohort studies , participants; mean follow-up of Three independent meta-analyses of prospective studies also reported that higher GI or GL was associated with increased risk of CHD in women but not in men A recent analysis of the European Prospective Investigation into Cancer and Nutrition EPIC study in 20, Greek participants, followed for a median of lower BMI A similar finding was reported in a cohort of middle-aged Dutch women followed for nine years Overall, observational studies have found that higher glycemic load diets are associated with increased risk of cardiovascular disease, especially in women and in those with higher BMIs.

A meta-analysis of 27 randomized controlled trials published between and examining the effect of low-GI diets on serum lipid profile reported a significant reduction in total and LDL - cholesterol independent of weight loss Yet, further analysis suggested significant reductions in serum lipids only with the consumption of low-GI diets with high fiber content.

In a three-month, randomized controlled study, an increase in the values of flow-mediated dilation FMD of the brachial artery, a surrogate marker of vascular health, was observed following the consumption of a low- versus high-GI hypocaloric diet in obese subjects High dietary GLs have been associated with increased concentrations of markers of systemic inflammation , such as C-reactive protein CRP , interleukin-6, and tumor necrosis factor-α TNF-α 40, In a small week dietary intervention study, the consumption of a Mediterranean-style, low-GL diet without caloric restriction significantly reduced waist circumference, insulin resistance , systolic blood pressure , as well as plasma fasting insulin , triglycerides , LDL-cholesterol, and TNF-α in women with metabolic syndrome.

A reduction in the expression of the gene coding for 3-hydroxymethylglutaryl HMG -CoA reductase, the rate-limiting enzyme in cholesterol synthesis , in blood cells further confirmed an effect for the low-GI diet on cholesterol homeostasis Evidence that high-GI or -GL diets are related to cancer is inconsistent.

A recent meta-analysis of 32 case-control studies and 20 prospective cohort studies found modest and nonsignificant increased risks of hormone -related cancers breast, prostate , ovarian, and endometrial cancers and digestive tract cancers esophageal , gastric , pancreas , and liver cancers with high versus low dietary GI and GL A significant positive association was found only between a high dietary GI and colorectal cancer Yet, earlier meta-analyses of prospective cohort studies failed to find a link between high-GI or -GL diets and colorectal cancer Another recent meta-analysis of prospective studies suggested a borderline increase in breast cancer risk with high dietary GI and GL.

Adjustment for confounding factors across studies found no modification of menopausal status or BMI on the association Further investigations are needed to verify whether GI and GL are associated with various cancers. Whether low-GI foods could improve overall blood glucose control in people with type 1 or type 2 diabetes mellitus has been investigated in a number of intervention studies.

A meta-analysis of 19 randomized controlled trials that included diabetic patients with type 1 diabetes and with type 2 diabetes found that consumption of low-GI foods improved short-term and long-term control of blood glucose concentrations, reflected by significant decreases in fructosamine and glycated hemoglobin HbA1c levels However, these results need to be cautiously interpreted because of significant heterogeneity among the included studies.

The American Diabetes Association has rated poorly the current evidence supporting the substitution of low-GL foods for high-GL foods to improve glycemic control in adults with type 1 or type 2 diabetes 51, A randomized controlled study in 92 pregnant women weeks diagnosed with gestational diabetes found no significant effects of a low-GI diet on maternal metabolic profile e.

The low-GI diet consumed during the pregnancy also failed to improve maternal glucose tolerance , insulin sensitivity , and other cardiovascular risk factors, or maternal and infant anthropometric data in a three-month postpartum follow-up study of 55 of the mother-infant pairs At present, there is no evidence that a low-GI diet provides benefits beyond those of a healthy, moderate-GI diet in women at high risk or affected by gestational diabetes.

Obesity is often associated with metabolic disorders, such as hyperglycemia , insulin resistance , dyslipidemia , and hypertension , which place individuals at increased risk for type 2 diabetes mellitus , cardiovascular disease , and early death 56, Lowering the GI of conventional energy-restricted, low-fat diets was proven to be more effective to reduce postpartum body weight and waist and hip circumferences and prevent type 2 diabetes mellitus in women with prior gestational diabetes mellitus Yet, the consumption of a low-GL diet increased HDL - cholesterol and decreased triglyceride concentrations significantly more than the low-fat diet, but LDL -cholesterol concentration was significantly more reduced with the low-fat than low-GI diet Weight loss with each diet was equivalent ~4 kg.

Both interventions similarly reduced triglycerides, C-reactive protein CRP , and fasting insulin , and increased HDL-cholesterol. Yet, the reduction in waist and hip circumferences was greater with the low-fat diet, while blood pressure was significantly more reduced with the low-GL diet Additionally, the low-GI diet improved fasting insulin concentration, β-cell function, and insulin resistance better than the low-fat diet.

None of the diets modulated hunger or satiety or affected biomarkers of endothelial function or inflammation. Finally, no significant differences were observed in low- compared to high-GL diets regarding weight loss and insulin metabolism It has been suggested that the consumption of low-GI foods delayed the return of hunger, decreased subsequent food intake, and increased satiety when compared to high-GI foods The effect of isocaloric low- and high-GI test meals on the activity of brain regions controlling appetite and eating behavior was evaluated in a small randomized , blinded, cross-over study in 12 overweight or obese men During the postprandial period, blood glucose and insulin rose higher after the high-GI meal than after the low-GI meal.

In addition, in response to the excess insulin secretion, blood glucose dropped below fasting concentrations three to five hours after high-GI meal consumption. Cerebral blood flow was significantly higher four hours after ingestion of the high-GI meal compared to a low-GI meal in a specific region of the striatum right nucleus accumbens associated with food intake reward and craving.

If the data suggested that consuming low- rather than high-GI foods may help restrain overeating and protect against weight gain, this has not yet been confirmed in long-term randomized controlled trials.

However, the dietary interventions only achieved a modest difference in GI ~5 units between high- and low-GI diets such that the effect of GI in weight maintenance remained unknown. Table 1 includes GI and GL values of selected foods relative to pure glucose Originally written in by: Jane Higdon, Ph.

Linus Pauling Institute Oregon State University. Updated in December by: Jane Higdon, Ph. Updated in February by: Victoria J.

Drake, Ph. Updated in March by: Barbara Delage, Ph. Reviewed in March by: Simin Liu, M. Professor of Epidemiology, Professor of Medicine Brown University. Liu S, Willett WC. Dietary glycemic load and atherothrombotic risk.

Curr Atheroscler Rep. Brouns F, Bjorck I, Frayn KN, et al. Glycaemic index methodology. Nutr Res Rev. Augustin LS, Kendall CW, Jenkins DJ, et al. Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium ICQC.

Nutr Metab Cardiovasc Dis. Monro JA, Shaw M. Glycemic impact, glycemic glucose equivalents, glycemic index, and glycemic load: definitions, distinctions, and implications. Am J Clin Nutr. The University of Sydney. About Glycemic Index. The International Organization for Standardization.

Food products - Determination of the glycaemic index GI and recommendation for food classification. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. Willett WC. Eat, Drink, and be Healthy: The Harvard Medical School Guide to Healthy Eating.

Dodd H, Williams S, Brown R, Venn B. Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index.

Silva FM, Kramer CK, Crispim D, Azevedo MJ. A high-glycemic index, low-fiber breakfast affects the postprandial plasma glucose, insulin, and ghrelin responses of patients with type 2 diabetes in a randomized clinical trial.

J Nutr. Ranawana V, Leow MK, Henry CJ. Mastication effects on the glycaemic index: impact on variability and practical implications. Eur J Clin Nutr. Sun L, Ranawana DV, Tan WJ, Quek YC, Henry CJ. The impact of eating methods on eating rate and glycemic response in healthy adults. Physiol Behav.

Venn BS, Williams SM, Mann JI. Comparison of postprandial glycaemia in Asians and Caucasians. Diabet Med. Wolever TM, Jenkins AL, Vuksan V, Campbell J. The glycaemic index values of foods containing fructose are affected by metabolic differences between subjects.

Goff LM, Cowland DE, Hooper L, Frost GS. Low glycaemic index diets and blood lipids: a systematic review and meta-analysis of randomised controlled trials. Willett W, Manson J, Liu S. Glycemic index, glycemic load, and risk of type 2 diabetes. Gross LS, Li L, Ford ES, Liu S.

Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment. Bhupathiraju SN, Tobias DK, Malik VS, et al. Glycemic index, glycemic load, and prevalence of the metabolic syndrome in the cooper center longitudinal study.

J Am Diet Assoc. Juanola-Falgarona M, Salas-Salvadó J, Buil-Cosiales P, Corella D, Estruch R, Ros E, et al. Dietary Glycemic Index and Glycemic Load Are Positively Associated with Risk of Developing Metabolic Syndrome in Middle-Aged and Elderly Adults.

J Am Geriatr Soc. Nicholl A, du Heaume M, Mori TA, Beilin LJ, Oddy WH, Bremner AP, et al. Higher breakfast glycaemic load is associated with increased metabolic syndrome risk, including lower HDL-cholesterol concentrations and increased TAG concentrations, in adolescent girls.

Br J Nutr. O'Sullivan TA, Lyons-Wall P, Bremner AP, Ambrosini GL, Huang RC, Beilin LJ, et al. Dietary glycaemic carbohydrate in relation to the metabolic syndrome in adolescents: comparison of different metabolic syndrome definitions. Diabet Med. Romero-Martinez M, Shamah-Levy T, Franco-Nunez A, Villalpando S, Cuevas-Nasu L, Gutierrez JP, et al.

National Health and Nutrition Survey design and coverage. Salud Publica Mex. PubMed Google Scholar. Denova-Gutiérrez E, Ramírez-Silva I, Rodríguez-Ramírez S, Jiménez-Aguilar A, Shamah-Levy T, Rivera-Dommarco JA. Validity of a food frequency questionnaire to assess food intake in Mexican adolescent and adult population.

Ramírez-Silva I, Jiménez-Aguilar A, Valenzuela-Bravo D, Martinez-Tapia B, Rodríguez-Ramírez S, Gaona-Pineda E, et al. Methodology for estimating dietary data from the semi-quantitative food frequency questionnaire of the Mexican National Health and Nutrition Survey Institute of Medicine.

Energy, Dietary reference intakes for energy, carbohydrates, fiber, fat, protein and amino acids macronutrients. Washington, DC: Institute of Medicine, National Academies Press; Hernández B, de Haene J, Barquera S, Monterrubio E, Rivera J, Shamah T, et al.

Factores asociados con la actividad física en mujeres mexicanas en edad reproductiva. Pan Am J Public Health. Article Google Scholar. Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review.

Food and Agriculture Organization of the United Nations, World Health Organization, United Nations University. Rome: FAO; Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies.

discussion 9SS. Louie JC, Flood V, Turner N, Everingham C, Gwynn J. Methodology for adding glycemic index values to hour recalls. Atkinson FS, Foster-Powell K, Brand-Miller JC. International tables of glycemic index and glycemic load values: The University of Sydney.

Sydney University Glycemic Index Research Service Accessed 17 Nov Wolever TM, Nguyen PM, Chiasson JL, Hunt JA, Josse RG, Palmason C, et al. Determinants of diet glycemic index calculated retrospectively from diet records of individuals with non-insulin-dependent diabetes mellitus.

de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ.

Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. Alberti G, Zimmet P, Kaufman F, Tajima N, Silink M, Arslanian S, et al.

The IDF consensus definition of the metabolic syndrome in children and adolescents. International Diabetes Federation: Brussels; Zimmet P, Alberti KG, Kaufman F, Tajima N, Silink M, Arslanian S, et al. The metabolic syndrome in children and adolescents - an IDF consensus report.

Pediatr Diabetes. Pereira MA, FitzerGerald SJ, Gregg EW, Joswiak ML, Ryan WJ, Suminski RR, et al. A collection of Physical Activity Questionnaires for health-related research. Med Sci Sports Exerc. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al.

International physical activity questionnaire: country reliability and validity. Hernandez B, Gortmaker SL, Colditz GA, Peterson KE, Laird NM, Parra-Cabrera S.

Association of obesity with physical activity, television programs and other forms of video viewing among children in Mexico city. Int J Obes Relat Metab Disord. Pols MA, Peeters PH, Bueno-De-Mesquita HB, Ocké MC, Wentink CA, Kemper HC, et al.

Validity and repeatability of a modified Baecke questionnaire on physical activity. Int J Epidemiol. Global Recommendations on Physical Activity for Health.

Geneva, Switzerland: World Health Organization; Damsgaard CT, Papadaki A, Jensen SM, Ritz C, Dalskov SM, Hlavaty P, et al. Higher protein diets consumed ad libitum improve cardiovascular risk markers in children of overweight parents from eight European countries.

J Nutr. Rouhani MH, Kelishadi R, Hashemipour M, Esmaillzadeh A, Azadbakht L. The effect of low glycemic index diet on body weight status and blood pressure in overweight adolescent girls: a randomized clinical trial.

Nutr Res Pract. Article CAS PubMed PubMed Central Google Scholar. Gopinath B, Flood VM, Rochtchina E, Baur LA, Smith W, Mitchell P. Influence of high glycemic index and glycemic load diets on blood pressure during adolescence. Kong AP, Choi KC, Chan RS, Lok K, Ozaki R, Li AM, et al.

A randomized controlled trial to investigate the impact of a low glycemic index GI diet on body mass index in obese adolescents. BMC Public Health. Mirza NM, Palmer MG, Sinclair KB, McCarter R, He J, Ebbeling CB, et al.

Effects of a low glycemic load or a low-fat dietary intervention on body weight in obese Hispanic American children and adolescents: a randomized controlled trial.

Zakrzewski JK, Stevenson EJ, Tolfrey K. Effect of breakfast glycemic index on metabolic responses during rest and exercise in overweight and non-overweight adolescent girls. Rovner AJ, Nansel TR, Gellar L.

The effect of a low-glycemic diet vs a standard diet on blood glucose levels and macronutrient intake in children with type 1 diabetes. Fajcsak Z, Gabor A, Kovacs V, Martos E. J Am Coll Nutr. Jones M, Barclay AW, Brand-Miller JC, Louie JC.

Dietary glycaemic index and glycaemic load among Australian children and adolescents: results from the Australian Health Survey. Forbes LE, Storey KE, Fraser SN, Spence JC, Plotnikoff RC, Raine KD, et al.

Dietary patterns associated with glycemic index and glycemic load among Alberta adolescents. Appl Physiol Nutr Metab. Murakami K, McCaffrey TA, Livingstone MB.

Dietary glycaemic index and glycaemic load in relation to food and nutrient intake and indices of body fatness in British children and adolescents. Murakami K, Miyake Y, Sasaki S, Tanaka K, Arakawa M. Dietary glycemic index and glycemic load in relation to risk of overweight in Japanese children and adolescents: the Ryukyus Child Health Study.

Int J Obes. Article CAS Google Scholar. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.

Wolever T. Physiological mechanisms and observed health impacts related to the glycaemic index: some observations. Goletzke J, Herder C, Joslowski G, Bolzenius K, Remer T, Wudy SA, et al. Habitually higher dietary glycemic index during puberty is prospectively related to increased risk markers of type 2 diabetes in younger adulthood.

Landsberg L. Insulin-mediated sympathetic stimulation: role in the pathogenesis of obesity-related hypertension or, how insulin affects blood pressure, and why. J Hypertens. Goran MI, Gower BA. Longitudinal study on pubertal insulin resistance. Oliveira RG, Guedes DP. Physical Activity, Sedentary Behavior, Cardiorespiratory Fitness and Metabolic Syndrome in Adolescents: Systematic Review and Meta-Analysis of Observational Evidence.

PLoS One. Serra-Majem L, Ribas L, Pérez-Rodrigo C, García-Closas R, Peña-Quintana L, Aranceta J. Determinants of nutrient intake among children and adolescents: results from the enKid Study.

Ann Nutr Metab. Hare-Bruun H, Nielsen BM, Grau K, Oxlund AL, Heitmann BL. Should glycemic index and glycemic load be considered in dietary recommendations? Nutr Rev.

Wolever TM. Is glycaemic index GI a valid measure of carbohydrate quality? Download references. IC-Q received grants from Consejo Nacional de Ciencia y Tecnología de México CONACYT , Secretaria de Educación Pública SEP , the Mexican Government, and the PhD International Mobility Programme, University of Granada and CEI-BioTicGranada.

In order to analyze data from the NHNS survey, permission was obtained from the Ethics Review Board of the National Public Health Institute of Mexico. The datasets of the current study are available from the corresponding author on reasonable request.

IC-Q and SA-E contributed to the study design, data analyses, and interpretation of findings and wrote the manuscript with important input and feedback from all coauthors; AS-V, MDR-L, RA, and LS-M contributed to the study design and to the critical revision of the manuscript; TS-L contributed to the study design, interpretation of findings, and critical revision of the manuscript.

All the authors read and approved the final version of the manuscript. This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Ethics Review Board of the National Public Health Institute of Mexico.

Written informed consent was obtained from all subjects or their legal guardians prior to the study. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Department of Nutrition and Food Science, School of Pharmacy, University of Granada, Campus Universitario de la Cartuja, , Granada, Spain. Center for Nutrition and Health Research, National Institute of Public Health of Mexico, Universidad No.

Institute of Nutrition and Food Technologies, University of Granada, Avda. del Conocimiento, Armilla, , Granada, Spain. You can also search for this author in PubMed Google Scholar. Correspondence to Teresa Shamah-Levy.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4. Reprints and permissions. Castro-Quezada, I. et al. Glycemic index, glycemic load, and metabolic syndrome in Mexican adolescents: a cross-sectional study from the NHNS BMC Nutr 3 , 44 Download citation.

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Download PDF. Abstract Background The role of dietary glycemic index GI and dietary glycemic load GL on metabolic syndrome MetS in youth populations remains unclear. Methods This study was conducted within the framework of the National Health and Nutrition Survey , a cross-sectional, probabilistic, population-based survey with a multistage stratified cluster sampling design.

Results We observed no associations between dietary GI or GL and MetS prevalence. Conclusions We found higher odds of abnormal blood pressure for female adolescents with a high dietary GI and dietary GL.

Background The prevalence of metabolic syndrome MetS is high among children and adolescents with obesity [ 1 , 2 ]. Methods Study population This study was conducted within the framework of the National Health and Nutrition Survey NHNS , a cross-sectional, probabilistic, population-based survey with a multistage stratified cluster sampling design conducted in Mexico.

Flow chart showing study participant selection. Full size image. Results In this study, the mean SD dietary GI and GL of adolescents in the NHNS was Table 1 General characteristics of the sample according to sex-specific categories of dietary glycemic index a Full size table.

Table 2 General characteristics of the sample according to sex-specific categories of energy-adjusted dietary glycemic load a Full size table. Table 3 Association between metabolic syndrome and sex-specific categories of dietary glycemic index Full size table. Table 4 Association between metabolic syndrome and sex-specific categories of energy-adjusted dietary glycemic load Full size table.

Discussion In this cross-sectional study, we found no associations between dietary GI or GL and MetS. Conclusions We observed no association between dietary GI or dietary GL and MetS in a nationally representative sample of Mexican adolescents. Abbreviations BMI: Body mass index CI: Confidence interval GI: Glycemic index GL: Glycemic load HDL-c: High-density lipoprotein cholesterol IDF: International Diabetes Federation MetS: Metabolic syndrome MUFA: Monounsaturated fatty acids NHNS National Health and Nutrition Survey OR: Odds ratio PUFA: Polyunsaturated fatty acids RCTs: Randomized controlled trials SD: Standard deviation SES: Socioeconomic status SFA: Saturated fatty acids SFFQ: Semiquantitative food-frequency questionnaire T2D: Type 2 diabetes mellitus WC: Waist circumference WHO: World Health Organization.

References Evia-Viscarra ML, Rodea-Montero ER, Apolinar-Jiménez E, Quintana-Vargas S. Article PubMed PubMed Central Google Scholar Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Article CAS PubMed Google Scholar Gutierrez JP, Rivera-Domarco J, Shamah-Levy T, Villalpando-Hernández S, Franco A, Cuevas-Nasu L, et al.

Google Scholar Rodriguez-Moran M, Salazar-Vazquez B, Violante R, Guerrero-Romero F. Article PubMed Google Scholar Mann J, Cummings JH, Englyst HN, Key T, Liu S, Riccardi G, et al. Article CAS PubMed Google Scholar Augustin LS, Kendall CW, Jenkins DJ, Willett WC, Astrup A, Barclay AW, et al.

CAS PubMed Google Scholar Jenkins DJ, Kendall CW, Augustin LS, Franceschi S, Hamidi M, Marchie A, et al. CAS PubMed Google Scholar Salmerón J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC.

Article PubMed Google Scholar Livesey G, Taylor R, Hulshof T, Howlett J. CAS PubMed Google Scholar Thomas DE, Elliott EJ, Baur L. Google Scholar Goff LM, Cowland DE, Hooper L, Frost GS. Article CAS PubMed Google Scholar Schwingshackl L, Hoffmann G.

Article CAS PubMed Google Scholar Sacks FM, Carey VJ, Anderson CA, Miller ER, Copeland T, Charleston J, et al. Article PubMed PubMed Central Google Scholar Jenkins DJ, Kendall CW, McKeown-Eyssen G, Josse RG, Silverberg J, Booth GL, et al.

Article CAS PubMed Google Scholar Schwingshackl L, Hobl LP, Hoffmann G. Article PubMed PubMed Central Google Scholar Finley CE, Barlow CE, Halton TL, Haskell WL. Article CAS PubMed Google Scholar Juanola-Falgarona M, Salas-Salvadó J, Buil-Cosiales P, Corella D, Estruch R, Ros E, et al.

Article PubMed Google Scholar Nicholl A, du Heaume M, Mori TA, Beilin LJ, Oddy WH, Bremner AP, et al. Article CAS PubMed Google Scholar O'Sullivan TA, Lyons-Wall P, Bremner AP, Ambrosini GL, Huang RC, Beilin LJ, et al.

Article PubMed Google Scholar Romero-Martinez M, Shamah-Levy T, Franco-Nunez A, Villalpando S, Cuevas-Nasu L, Gutierrez JP, et al. PubMed Google Scholar Denova-Gutiérrez E, Ramírez-Silva I, Rodríguez-Ramírez S, Jiménez-Aguilar A, Shamah-Levy T, Rivera-Dommarco JA.

Article PubMed Google Scholar Ramírez-Silva I, Jiménez-Aguilar A, Valenzuela-Bravo D, Martinez-Tapia B, Rodríguez-Ramírez S, Gaona-Pineda E, et al. Article PubMed Google Scholar Institute of Medicine. Google Scholar Hernández B, de Haene J, Barquera S, Monterrubio E, Rivera J, Shamah T, et al.

Article Google Scholar Frankenfield D, Roth-Yousey L, Compher C. Article PubMed Google Scholar Food and Agriculture Organization of the United Nations, World Health Organization, United Nations University.

Google Scholar Willett WC, Howe GR, Kushi LH. discussion 9SS CAS PubMed Google Scholar Louie JC, Flood V, Turner N, Everingham C, Gwynn J.

Article PubMed Google Scholar Atkinson FS, Foster-Powell K, Brand-Miller JC. Article PubMed PubMed Central Google Scholar The University of Sydney. CAS PubMed Google Scholar de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J.

Article PubMed PubMed Central Google Scholar Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, et al. Article CAS PubMed Google Scholar Alberti G, Zimmet P, Kaufman F, Tajima N, Silink M, Arslanian S, et al. Google Scholar Zimmet P, Alberti KG, Kaufman F, Tajima N, Silink M, Arslanian S, et al.

Article PubMed Google Scholar Pereira MA, FitzerGerald SJ, Gregg EW, Joswiak ML, Ryan WJ, Suminski RR, et al. CAS PubMed Google Scholar Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al.

Article PubMed Google Scholar Hernandez B, Gortmaker SL, Colditz GA, Peterson KE, Laird NM, Parra-Cabrera S. Article CAS PubMed Google Scholar Pols MA, Peeters PH, Bueno-De-Mesquita HB, Ocké MC, Wentink CA, Kemper HC, et al.

Article CAS PubMed Google Scholar WHO. Article CAS PubMed Google Scholar Rouhani MH, Kelishadi R, Hashemipour M, Esmaillzadeh A, Azadbakht L. Article CAS PubMed PubMed Central Google Scholar Gopinath B, Flood VM, Rochtchina E, Baur LA, Smith W, Mitchell P.

Article CAS PubMed Google Scholar Kong AP, Choi KC, Chan RS, Lok K, Ozaki R, Li AM, et al. Article PubMed PubMed Central Google Scholar Mirza NM, Palmer MG, Sinclair KB, McCarter R, He J, Ebbeling CB, et al.

Article CAS PubMed Google Scholar Zakrzewski JK, Stevenson EJ, Tolfrey K. Article CAS PubMed Google Scholar Rovner AJ, Nansel TR, Gellar L. Article CAS PubMed PubMed Central Google Scholar Fajcsak Z, Gabor A, Kovacs V, Martos E. Article CAS PubMed Google Scholar Jones M, Barclay AW, Brand-Miller JC, Louie JC.

Article CAS PubMed Google Scholar Forbes LE, Storey KE, Fraser SN, Spence JC, Plotnikoff RC, Raine KD, et al. Article PubMed Google Scholar Murakami K, McCaffrey TA, Livingstone MB.

Article CAS PubMed Google Scholar Murakami K, Miyake Y, Sasaki S, Tanaka K, Arakawa M. Article CAS Google Scholar Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al.

Article Google Scholar Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Article CAS PubMed Google Scholar Wolever T. Article Google Scholar Goletzke J, Herder C, Joslowski G, Bolzenius K, Remer T, Wudy SA, et al. Article CAS PubMed PubMed Central Google Scholar Landsberg L.

Article CAS PubMed Google Scholar Goran MI, Gower BA. Article CAS PubMed Google Scholar Oliveira RG, Guedes DP. Article PubMed PubMed Central Google Scholar Serra-Majem L, Ribas L, Pérez-Rodrigo C, García-Closas R, Peña-Quintana L, Aranceta J.

Article CAS PubMed Google Scholar Hare-Bruun H, Nielsen BM, Grau K, Oxlund AL, Heitmann BL. Article PubMed Google Scholar Wolever TM.