Gómez-Dantés, H, Fullman, N, Lamadrid-Figueroa, H et al. (2016) Dissonant health transition in the states of Mexico, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 388, 2386–2402.
Arvaniti, F & Panagiotakos, DB (2008) Healthy indexes in public health practice and research: a review. Crit Rev Food Sci Nutr 48, 317–327.
Harmon, BE, Boushey, CJ, Shvetsov, YB et al. (2015) Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. Am J Clin Nutr 101, 587–597.
Schwingshackl, L & Hoffmann, G (2015) Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health outcomes: a systematic review and meta-analysis of cohort studies. J Acad Nutr Diet 115, 780–800.e5.
Wang, Z, Adair, LS, Cai, J et al. (2017) Diet quality is linked to insulin resistance among adults in China. J Nutr 147, 2102–2108.
Siervo, M, Lara, J, Chowdhury, S et al. (2015) Effects of the Dietary Approach to Stop Hypertension (DASH) diet on cardiovascular risk factors: a systematic review and meta-analysis. Br J Nutr 113, 1–15.
Estruch, R, Ros, E, Salas-Salvado, J et al. (2013) Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med 368, 1279–1290.
Shirani, F, Salehi-Abargouei, A & Azadbakht, L (2013) Effects of Dietary Approaches to Stop Hypertension (DASH) diet on some risk for developing type 2 diabetes: a systematic review and meta-analysis on controlled clinical trials. Nutrition 29, 939–947.
Neale, E, Batterham, M & Tapsell, LC (2016) Consumption of a healthy dietary pattern results in significant reductions in C-reactive protein levels in adults: a meta-analysis. Nutr Res 36, 391–401.
AlEssa, HB, Malik, VS, Yuan, C et al. (2016) Dietary patterns and cardiometabolic and endocrine plasma biomarkers in US women, 2. Am J Clin Nutr 105, 432–441.
Hafiane, A & Genest, J (2015) High density lipoproteins: measurement techniques and potential biomarkers of cardiovascular risk. BBA Clin 3, 175–188.
Aleksandrova, K, Mozaffarian, D & Pischon, T (2018) Addressing the perfect storm: biomarkers in obesity and pathophysiology of cardiometabolic risk. Clin Chem 64, 142–153.
Abbasalizad Farhangi, M, Ataie-Jafari, A, Najafi, M et al. (2016) Gender differences in major dietary patterns and their relationship with cardio-metabolic risk factors in a year before coronary artery bypass grafting (CABG) surgery period. Arch Iran Med 19, 470–479.
Kavanagh, A, Bentley, RJ, Turrell, G et al. (2010) Socioeconomic position, gender, health behaviours and biomarkers of cardiovascular disease and diabetes. Social Sci Med 71, 1150–1160.
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, issue 9, 1856S–1865S.
Aburto, TC, Pedraza, LS, Sanchez-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, issue 9, 1881S–1887S.
Flores, M, Macias, N, Rivera, M et al. (2009) Energy and nutrient intake among Mexican school-aged children, Mexican National Health and Nutrition Survey 2006. Salud Publica Mex 51, Suppl. 4, S540–S550.
Howe, LD, Galobardes, B, Matijasevich, A et al. (2012) Measuring socio-economic position for epidemiological studies in low-and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol 41, 871–886.
Liberatos, P, Link, BG & Kelsey, JL (1988) The measurement of social class in epidemiology. Epidemiol Rev 10, 87–121.
Lynch, JL & von Hippel, PT (2016) An education gradient in health, a health gradient in education, or a confounded gradient in both? Soc Sci Med 154, 18–27.
Zajacova, A & Lawrence, EM (2018) The relationship between education and health: reducing disparities through a contextual approach. Annu Rev Public Health 39, 273–289.
Friedewald, WT, Levy, RI & Fredrickson, DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18, 499–502.
International Diabetes Federation (2006) The IDF Consensus Worldwide Definition of Metabolic Syndrome. Brussels: IDF.
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (2001) Executive summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 285, 2486–2497.
Pearson, TA, Mensah, GA, Alexander, RW et al. (2003) Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation 107, 499–511.
Blanton, CA, Moshfegh, AJ, Baer, DJ et al. (2006) The USDA Automated Multiple-Pass Method accurately estimates group total energy and nutrient intake. J Nutr 136, 2594–2599.
Conway, JM, Ingwersen, LA, Vinyard, BT et al. (2003) Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr 77, 1171–1178.
Bonvecchio-Arenas, A, Fernández-Gaxiola, AC, Plazas-Belusteguigoitia, M et al. (2015) Guías Alimentarias y de Actividad Física en Contexto de Sobrepeso y Obesidad en la Población Mexicana (Dietary and Physical Activity Guidelines in the Context of Overweight and Obesity in the Mexican Population). Ciudad de México: Academia Nacional de Medicina.
Rivera, JA, Muñoz-Hernández, O, Rosas-Peralta, M et al. (2008) Consumo de bebidas para una vida saludable: recomendaciones para la población mexicana. Salud Publica Mex 50, 173–195.
Bourges, H, Casanueva, E & Rosado, J (2008) Recomendaciones de Ingestion de Nutrimentos para la Poblacion Mexicana: Bases Fiosiológicas (Recommendations of Nutrient Intake for the Mexican Population: Physiological Basis). Mexico City: Editorial Médica Panamericana.
World Health Organization (2015) Guideline: Sugars Intake for Adults and Children. Geneva: WHO.
World Health Organization/Food and Agriculture Organization of the United Nations (2003) Diet, Nutrition and Prevention of Chronic Diseases. Report of a Joint WHO/FAO Expert Consultation. WHO Technical Report Series no. 916. Geneva: WHO.
Sánchez-Pimienta, TG, Batis, C, Lutter, CK et al. (2016) Sugar-sweetened beverages are the main sources of added sugar intake in the Mexican population. J Nutr 146, issue 9, 1888S–1896S.
Vallejo, M, Colin-Ramirez, E, Rivera Mancia, S et al. (2017) Assessment of sodium and potassium intake by 24 h urinary excretion in a healthy Mexican cohort. Arch Med Res 48, 195–202.
Medina, C, Barquera, S & Janssen, I (2013) Validity and reliability of the International Physical Activity Questionnaire among adults in Mexico. Rev Panam Salud Publica 34, 21–28.
Loucks, EB, Rehkopf, DH, Thurston, RC et al. (2007) Socioeconomic disparities in metabolic syndrome differ by gender: evidence from NHANES III. Ann Epidemiol 17, 19–26.
McCurley, JL, Penedo, F, Roesch, SC et al. (2017) Psychosocial factors in the relationship between socioeconomic status and cardiometabolic risk: the HCHS/SOL Sociocultural Ancillary Study. Ann Behav Med 51, 477–488.
Cutler, DM & Lleras-Muney, A (2010) Understanding differences in health behaviors by education. J Health Econ 29, 1–28.
Staiano, A, Harrington, D, Barreira, T et al. (2014) Sitting time and cardiometabolic risk in US adults: associations by sex, race, socioeconomic status and activity level. Br J Sports Med 48, 213–219.
Kipnis, V, Midthune, D, Freedman, L et al. (2002) Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutr 5, 915–923.
Hebert, JR, Ma, Y, Clemow, L et al. (1997) Gender differences in social desirability and social approval bias in dietary self-report. Am J Epidemiol 146, 1046–1055.
Novotny, JA, Rumpler, WV, Riddick, H et al. (2003) Personality characteristics as predictors of underreporting of energy intake on 24-hour dietary recall interviews. J Acad Nutr Diet 103, 1146–1151.
Hébert, JR, Peterson, KE, Hurley, TG et al. (2001) The effect of social desirability trait on self-reported dietary measures among multi-ethnic female health center employees. Ann Epidemiol 11, 417–427.
Bothwell, EK, Ayala, GX, Conway, TL et al. (2009) Underreporting of food intake among Mexican/Mexican-American women: rates and correlates. J Acad Nutr Diet 109, 624–632.
Flores, M, Macias, N, Rivera, M et al. (2010) Dietary patterns in Mexican adults are associated with risk of being overweight or obese. J Nutr 140, 1869–1873.
Bojorquez, I, Unikel, C, Cortez, I et al. (2015) The social distribution of dietary patterns. Traditional, modern and healthy eating among women in a Latin American city. Appetite 92, 43–50.
Ponce, X, Rodriguez-Ramirez, S, Mundo-Rosas, V et al. (2014) Dietary quality indices vary with sociodemographic variables and anthropometric status among Mexican adults: a cross-sectional study. Results from the 2006 National Health and Nutrition Survey. Public Health Nutr 17, 1717–1728.
Moreno-Altamirano, L, Capraro, S, Panico, C et al. Estructura económica, distribución del ingreso, patrones de alimentación y las condiciones nutricionales en México (Economic structure, distribution of income, dietary patterns and nutritional conditions in Mexico). Economía UNAM 15, 29–49.
Basto-Abreu, A, Barrientos-Gutierrez, T, Zepeda-Tello, R et al. (2018) The relationship of socioeconomic status with body mass index depends on the socioeconomic measure used. Obesity (Silver Spring) 26, 176–184.
Rojas-Martinez, R, Basto-Abreu, A, Aguilar-Salinas, CA et al. (2018) Prevalence of previously diagnosed diabetes mellitus in Mexico. Salud Publica Mex 60, 224–232.
Popkin, BM (2002) The shift in stages of the nutrition transition in the developing world differs from past experiences! Public Health Nutr 5, 205–214.
Muldoon, MF, Erickson, KI, Goodpaster, BH et al. (2013) Concurrent physical activity modifies the association between n3 long-chain fatty acids and cardiometabolic risk in midlife adults. J Nutr 143, 1414–1420.
Elliot, CA & Hamlin, MJ (2018) Combined diet and physical activity is better than diet or physical activity alone at improving health outcomes for patients in New Zealand’s primary care intervention. BMC Public Health 18, 230.
Dodd, KW, Guenther, PM, Freedman, LS et al. (2006) Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc 106, 1640–1650.
Alberti, KG, Zimmet, P & Shaw, J (2006) Metabolic syndrome – a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 23, 469–480.
Concato, J, Peduzzi, P, Holford, TR et al. (1995) Importance of events per independent variable in proportional hazards analysis I. Background, goals, and general strategy. J Clin Epidemiol 48, 1495–1501.
Peduzzi, P, Concato, J, Kemper, E et al. (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49, 1373–1379.