OBJECTIVE: The purpose of this study was to examine the relationship between usual sugar-sweetened beverage (SSB) consumption and prevalence of abnormal metabolic health across body mass index (BMI) categories. METHODS: The metabolic health of 6,842 non-diabetic adults was classified using cross-sectional data from the Framingham Heart Study Offspring (1998-2001) and Third Generation (2002-2005) cohorts. Adults were classified as normal weight, overweight or obese and, within these categories, metabolic health was defined based on five criteria-hypertension, elevated fasting glucose, elevated triglycerides, low HDL cholesterol, and insulin resistance. Individuals without metabolic abnormalities were considered metabolically healthy. Logistic regression was used to examine the associations between categories of SSB consumption and risk of metabolic health after stratification by BMI. RESULTS: Comparing the highest category of SSB consumers (median of 7 SSB per week) to the lowest category (non-consumers), odds ratios (95% confidence intervals) for metabolically abnormal phenotypes, compared to the metabolically normal, were 1.9 (1.1-3.4) among the obese, 2.0 (1.4-2.9) among the overweight, and 1.9 (1.4-2.6) among the normal weight individuals. CONCLUSIONS: In this cross-sectional analysis, it is observed that, irrespective of weight status, consumers of SSB were more likely to display metabolic abnormalities compared to non-consumers in a dose-dependent manner.
OBJECTIVE: The purpose of this study was to examine the relationship between usual sugar-sweetened beverage (SSB) consumption and prevalence of abnormal metabolic health across body mass index (BMI) categories. METHODS: The metabolic health of 6,842 non-diabetic adults was classified using cross-sectional data from the Framingham Heart Study Offspring (1998-2001) and Third Generation (2002-2005) cohorts. Adults were classified as normal weight, overweight or obese and, within these categories, metabolic health was defined based on five criteria-hypertension, elevated fasting glucose, elevated triglycerides, low HDL cholesterol, and insulin resistance. Individuals without metabolic abnormalities were considered metabolically healthy. Logistic regression was used to examine the associations between categories of SSB consumption and risk of metabolic health after stratification by BMI. RESULTS: Comparing the highest category of SSB consumers (median of 7 SSB per week) to the lowest category (non-consumers), odds ratios (95% confidence intervals) for metabolically abnormal phenotypes, compared to the metabolically normal, were 1.9 (1.1-3.4) among the obese, 2.0 (1.4-2.9) among the overweight, and 1.9 (1.4-2.6) among the normal weight individuals. CONCLUSIONS: In this cross-sectional analysis, it is observed that, irrespective of weight status, consumers of SSB were more likely to display metabolic abnormalities compared to non-consumers in a dose-dependent manner.
Authors: M Brochu; A Tchernof; I J Dionne; C K Sites; G H Eltabbakh; E A Sims; E T Poehlman Journal: J Clin Endocrinol Metab Date: 2001-03 Impact factor: 5.958
Authors: Ian J Neeland; Aslan T Turer; Colby R Ayers; Tiffany M Powell-Wiley; Gloria L Vega; Ramin Farzaneh-Far; Scott M Grundy; Amit Khera; Darren K McGuire; James A de Lemos Journal: JAMA Date: 2012-09-19 Impact factor: 56.272
Authors: Francisco B Ortega; Duck-Chul Lee; Peter T Katzmarzyk; Jonatan R Ruiz; Xuemei Sui; Timothy S Church; Steven N Blair Journal: Eur Heart J Date: 2012-09-04 Impact factor: 29.983
Authors: Arlene L Hankinson; Martha L Daviglus; Linda Van Horn; Queenie Chan; Ian Brown; Elaine Holmes; Paul Elliott; Jeremiah Stamler Journal: Obesity (Silver Spring) Date: 2013-03 Impact factor: 5.002
Authors: Catherine M Phillips; Christina Dillon; Janas M Harrington; Vera J C McCarthy; Patricia M Kearney; Anthony P Fitzgerald; Ivan J Perry Journal: PLoS One Date: 2013-10-17 Impact factor: 3.240
Authors: Dianne P Figlewicz; Jennifer Jay; Constance H West; Aryana Zavosh; Christiane S Hampe; Jared R Radtke; Murray A Raskind; Elaine R Peskind Journal: Am J Physiol Regul Integr Comp Physiol Date: 2017-11-01 Impact factor: 3.619
Authors: MiSung Kim; Inna I Astapova; Sarah N Flier; Sarah A Hannou; Ludivine Doridot; Ashot Sargsyan; Henry H Kou; Alan J Fowler; Guosheng Liang; Mark A Herman Journal: JCI Insight Date: 2017-12-21
Authors: Jiantao Ma; Paul F Jacques; James B Meigs; Caroline S Fox; Gail T Rogers; Caren E Smith; Adela Hruby; Edward Saltzman; Nicola M McKeown Journal: J Nutr Date: 2016-11-09 Impact factor: 4.798
Authors: Michael D T Fung; Karissa L Canning; Paul Mirdamadi; Chris I Ardern; Jennifer L Kuk Journal: Obesity (Silver Spring) Date: 2015-06 Impact factor: 5.002