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Overall glycaemic index and dietary glycaemic load and all-cause and cause-specific mortality in women from the Mexican Teachers’ Cohort

Published online by Cambridge University Press:  18 September 2024

Leticia Palma
Affiliation:
Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico
Dalia Stern
Affiliation:
CONAHCYT – Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico
Salvador Zamora-Muñoz
Affiliation:
Institute for Research in Applied Mathematics and Systems, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
Adriana Monge
Affiliation:
Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico
Liliana Gómez-Flores-Ramos
Affiliation:
Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico CONAHCYT – Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico
Juan E. Hernández-Ávila
Affiliation:
Center for Research on Evaluation and Surveys, National Institute of Public Health, Cuernavaca, Mexico
Martin Lajous*
Affiliation:
Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Mexico Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
*
*Corresponding author: Dr Martin Lajous, email mlajous@insp.mx

Abstract

Previous studies have found direct associations between glycaemic index (GI) and glycaemic load (GL) with chronic diseases. However, this evidence has not been consistent in relation to mortality, and most data regarding this association come from high-income and low-carbohydrate-intake populations. The aim of this study was to evaluate the association between the overall GI and dietary GL and all-cause mortality, CVD and breast cancer mortality in Mexico. Participants from the Mexican Teachers’ Cohort (MTC) study in 2006–2008 were followed for a median of 10 years. Overall GI and dietary GL were calculated from a validated FFQ. Deaths were identified by the cross-linkage of MTC participants with two national mortality registries. Cox proportional hazard models were used to estimate the impact of GI and GL on mortality. We identified 1198 deaths. Comparing the lowest and highest quintile, dietary GI and GL appeared to be marginally associated with all-cause mortality; GI, 1·12 (95 % CI: 0·93, 1·35); GL, 1·12 (95 % CI: 0·87, 1·44). Higher GI and GL were associated with increased risk of CVD mortality, GI, 1·30 (95 % CI: 0·82, 2·08); GL, 1·64 (95 % CI: 0·87, 3·07) and with greater risk of breast cancer mortality; GI, 2·13 (95 % CI: 1·12, 4·06); GL, 2·43 (95 % CI: 0·90, 6·59). It is necessary to continue the improvement of carbohydrate quality indicators to better guide consumer choices and to lead the Mexican population to limit excessive intake of low-quality carbohydrate foods.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Nutrition Society

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References

Ludwig, DS, Hu, FB, Tappy, L & Brand-Miller, J (2018) Dietary carbohydrates: role of quality and quantity in chronic disease. BMJ 361, k2340.Google ScholarPubMed
Reynolds, A, Mann, J, Cummings, J, et al. (2019) Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. Lancet 393, 434445.Google Scholar
Ludwig, DS (2002) The Glycemic index. Physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. J Am Med Assoc 287, 24142423.Google ScholarPubMed
Dall’Asta, M, Del Rio, D, Tappy, L, et al. (2020) Critical and emerging topics in dietary carbohydrates and health. Int J Food Sci Nutr 71, 286295.Google ScholarPubMed
Barclay, AW, Augustin, LSA, Brighenti, F, et al. (2021) Dietary glycaemic index labelling: a global perspective. Nutrients 13, 122.Google ScholarPubMed
Ludwig, DS, Aronne, LJ, Astrup, A, et al. (2021) The carbohydrate-insulin model : a physiological perspective on the obesity pandemic. Am J Clin Nutr 114, 18731885.Google Scholar
Nagata, C, Wada, K, Tsuji, M, et al. (2014) Dietary glycaemic index and glycaemic load in relation to all-cause and cause-specific mortality in a Japanese community: the Takayama study. Br J Nutr 112, 20102017.Google Scholar
Yu, D, Zhang, X, Shu, XO, et al. (2016) Dietary glycemic index, glycemic load, and refined carbohydrates are associated with risk of stroke: a prospective cohort study in urban Chinese women. Am J Clin Nutr 104, 13451351.Google ScholarPubMed
Debras, C, Chazelas, E, Srour, B, et al. (2022) Glycaemic index, glycaemic load and cancer risk: results from the prospective NutriNet-Santé cohort. Int J Epidemiol 51, 250264.Google ScholarPubMed
Teo, K, Chow, CK, Vaz, M, et al. (2009) The prospective urban rural epidemiology (PURE) study: examining the impact of societal influences on chronic noncommunicable diseases in low-, middle-, and high-income countries. Am Heart J 158, 17.e1.Google Scholar
Jenkins, DJA, Dehghan, M, Mente, A, et al. (2021) Glycemic index, glycemic load, and cardiovascular disease and mortality. N Engl J Med 384, 13121322.Google ScholarPubMed
Amadou, A, Degoul, J, Hainaut, P, et al. (2015) Dietary carbohydrate, glycemic index, glycemic load, and breast cancer risk among mexican women. Epidemiol 26, 917924.Google ScholarPubMed
Christian, H, Guerrero Brenda Gamboa-Loira, A, Mérida-Ortega, N, et al. (2019) Dietary glycemic index and glycemic load and risk of breast cancer by molecular subtype in Mexican women. Nutr Cancer 71, 12831289.Google Scholar
Lajous, M, Willett, W, Lazcano-Ponce, E, et al. (2005) Glycemic load, glycemic index, and the risk of breast cancer among Mexican women. Cancer Causes Control 16, 11651169.Google ScholarPubMed
López-Olmedo, N, Carriquiry, AL, Rodríguez-Ramírez, S, et al. (2016) Usual intake of added sugars and saturated fats is high while dietary fiber is low in the Mexican population. J Nutr 146, 1856S1865S.Google ScholarPubMed
Aburto, TC, Pedraza, LS, Sánchez-Pimienta, TG, et al. (2016) Discretionary foods have a high contribution and fruit, vegetables, and legumes have a low contribution to the total energy intake of the Mexican population. J Nutr 146, 1881S1887S.Google Scholar
Singh, GM, Micha, R, Khatibzadeh, S, et al. (2015) Global, regional, and national consumption of sugar-sweetened beverages, fruit juices, and milk: a systematic assessment of beverage intake in 187 countries. PLOS ONE 10, 120.Google ScholarPubMed
Alegre-Díaz, J, Herrington, W, López-Cervantes, M, et al. (2016) Diabetes and cause-specific mortality in Mexico city Europe PMC funders group. N Engl J Med 375, 19611971.Google Scholar
Lajous, M, Santoyo-Vistrain, R, García-Anaya, A, et al. (2017) Cohort profile: the Mexican teachers ’ cohort (MTC). Int J Epidemiol e10, 110.Google Scholar
Hernández-Avila, M, Romieu, I & Parra, S (1998) Validity and reproducibility of a food frequency questionnaire to assess dietary intake of women living in Mexico city. Salud Publica Mex 39, S129S136.Google Scholar
Flood, A, Subar, AF, Hull, SG, et al. (2006) Methodology for adding glycemic load values to the national cancer institute diet history questionnaire database. J Am Diet Assoc 106, 393402.Google Scholar
Atkinson, FS, Foster-Powell, K & Brand-Miller, JC (2008) International tables of glycemic index and glycemic load values: 2008. Diabetes Care 31, 22812283.Google ScholarPubMed
Atkinson, FS, Brand-Miller, JC, Foster-Powell, K, et al. (2021) International tables of glycemic index and glycemic load values 2021: a systematic review. Am J Clin Nutr 114, 16251632.Google ScholarPubMed
Salmerón, J, Ascherio, A, Rimm, EB, et al. (1997) Dietary fiber, glycemic load, and risk of NIDDM in men. Diabetes Care 20, 545550.Google ScholarPubMed
Liu, S, Manson, JE, Stampfer, MJ, et al. (2001) Dietary glycemic load assessed by food-frequency questionnaire in relation to plasma high-density-lipoprotein cholesterol and fasting plasma triacylglycerols in postmenopausal women. Am J Clin Nutr 73, 560566.Google ScholarPubMed
Willett, W, Manson, J & Liu, S (2002) Glycemic index, glycemic load, and risk of type 2 diabetes. Am J Clin Nutr 76, 274280.Google ScholarPubMed
Xavier Pi-Sunyer, F (2002) Glycemic index and disease. Am J Clin Nutr 76, 290S298S.Google Scholar
Ebbeling, CB, Feldman, HA, Klein, GL, et al. (2018) Effects of a low carbohydrate diet on energy expenditure during weight loss maintenance: randomized trial. BMJ 363, k4583.Google ScholarPubMed
Vega-López, S, Venn, BJ & Slavin, JL (2018) Relevance of the glycemic index and glycemic load for body weight, diabetes, and cardiovascular disease. Nutrients 10, 127.Google ScholarPubMed
Instituto Nacional de Estadística y Geografía (2020) Mortalidad. Subsistema de Información Demográfica y Social. https://www.inegi.org.mx/programas/mortalidad/ (accessed November 2023).Google Scholar
Dirección General de Información en Salud (2014) Subsistema Epidemiológico y Estadístico de Defunciones (SEED). México: Secretaría de Salud.Google Scholar
Quezada-Sánchez, AD, Espín-Arellano, I, Morales-Carmona, E, et al. (2022) Implementation and validation of a probabilistic linkage method for population databases without identification variables. Heliyon 8, e12311.Google ScholarPubMed
Lozano-Esparza, S, Zazueta, OE, Hernández-Ávila, JE, et al. (2022) Comparing the usefulness of two mortality registries for data-linkage for prospective cohorts in Mexico. Salud Publica Mex 64, 9699.Google Scholar
Organización Mundial de la Salud (2021) International Statistical Classification of Diseases and Related Health Problems (ICD). Geneva: WHP.Google Scholar
Medina, C, Monge, A, Denova-Gutiérrez, E, et al. (2022) Validity and reliability of the international physical activity questionnaire (IPAQ) long-form in a subsample of female Mexican teachers. Salud Publica Mex 64, 5765.Google Scholar
Kleinbaum, DGD & Klein, M (2011) Survival Analysis: A Self-Learning Text, 3rd ed. New York: Springer.Google Scholar
Pan, W, Han, Y, Hu, H, et al. (2022) Association between hemoglobin and chronic kidney disease progression: a secondary analysis of a prospective cohort study in Japanese patients. BMC Nephrol 23, 119.Google ScholarPubMed
SAS Instute Inc (2011) User’s Guide: Effect Statement. Cary, NC: SAS Institute Inc.Google Scholar
Lajous, M, Banack, HR, Kaufman, JS, et al. (2015) Should patients with chronic disease be told to gain weight? The obesity paradox and selection bias. Am J Med 128, 334336.Google ScholarPubMed
Hernán, MA, Hsu, J & Healy, B (2019) A second chance to get causal inference right: a classification of data science tasks. Chance 32, 4249.Google Scholar
Shahdadian, F, Saneei, P, Milajerdi, A, et al. (2019) Dietary glycemic index, glycemic load, and risk of mortality from all causes and cardiovascular diseases: a systematic review and dose-response meta-analysis of prospective cohort studies. Am J Clin Nutr 110, 921937.Google ScholarPubMed
Zhao, LG, Li, HL, Liu, DK, et al. (2022) Dietary glycemic index, glycemic load, and cause-specific mortality: two population-based prospective cohort studies. Eur J Clin Nutr 76, 11421149.Google ScholarPubMed
Murphy, N, Knuppel, A, Papadimitriou, N, et al. (2020) Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, and breast cancer risk: observational and Mendelian randomization analyses with ∼430 000 women. Ann Oncol 31, 641649.Google ScholarPubMed
Lajous, M, Boutron-Ruault, M-C, Fabre, A, et al. (2008) Carbohydrate intake, glycemic index, glycemic load, and risk of postmenopausal breast cancer in a prospective study of French women1. Am J Clin Nutr 87, 13841391.Google Scholar
Turati, F, Dilis, V, Rossi, M, et al. (2015) Glycemic load and coronary heart disease in a Mediterranean population: the EPIC Greek cohort study. Nutr Metab Cardiovasc Dis 25, 336342.Google Scholar
Allison, PD (2010) Survival Analysis Using SAS: A Practical Guide. Second Edition. Cary, NC: SAS Institute Inc.Google Scholar
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