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Effects of food items and related nutrients on metabolic syndrome using Bayesian multilevel modelling using the Tehran Lipid and Glucose Study (TLGS): a cohort study.

ABSTRACT: OBJECTIVES:Diet and nutrition might play an important role in the aetiology of metabolic syndrome (MetS). Most studies that examine the effects of food intake on MetS have used conventional statistical analyses which usually investigate only a limited number of food items and are subject to sparse data bias. This study was undertaken with the goal of investigating the concurrent effect of numerous food items and related nutrients on the incidence of MetS using Bayesian multilevel modelling which can control for sparse data bias. DESIGN:Prospective cohort study. SETTING:This prospective study was a subcohort of the Tehran Lipid and Glucose Study. We analysed dietary intake as well as pertinent covariates for cohort members in the fourth (2008-2011) and fifth (2011-2014) follow-up examinations. We fitted Bayesian multilevel model and compared the results with two logistic regression models: (1) full model which included all variables and (2) reduced model through backward selection of dietary variables. PARTICIPANTS:3616 healthy Iranian adults, aged ?20 years. PRIMARY AND SECONDARY OUTCOME MEASURES:Incident cases of MetS. RESULTS:Bayesian multilevel approach produced results that were more precise and biologically plausible compared with conventional logistic regression models. The OR and 95% confidence limits for the effects of the four foods comparing the Bayesian multilevel with the full conventional model were as follows: (1) noodle soup (1.20 (0.67 to 2.14) vs 1.91 (0.65 to 5.64)), (2) beans (0.96 (0.5 to 1.85) vs 0.55 (0.03 to 11.41)), (3) turnip (1.23 (0.68 to 2.23) vs 2.48 (0.82 to 7.52)) and (4) eggplant (1.01 (0.51 to 2.00) vs 1?09?396 (0.152×10-6 to 768×1012)). For most food items, the Bayesian multilevel analysis gave narrower confidence limits than both logistic regression models, and hence provided the highest precision. CONCLUSIONS:This study demonstrates that conventional regression methods do not perform well and might even be biased when assessing highly correlated exposures such as food items in dietary epidemiological studies. Despite the complexity of the Bayesian multilevel models and their inherent assumptions, this approach performs superior to conventional statistical models in studies that examine multiple nutritional exposures that are highly correlated.

SUBMITTER: Cheraghi Z

PROVIDER: S-EPMC6303646 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Effects of food items and related nutrients on metabolic syndrome using Bayesian multilevel modelling using the Tehran Lipid and Glucose Study (TLGS): a cohort study.

Cheraghi Zahra Z Nedjat Saharnaz S Mirmiran Parvin P Moslehi Nazanin N Mansournia Nasrin N Etminan Mahyar M Mansournia Mohammad Ali MA McCandless Lawrence C LC

BMJ open 20181218 12

<h4>Objectives</h4>Diet and nutrition might play an important role in the aetiology of metabolic syndrome (MetS). Most studies that examine the effects of food intake on MetS have used conventional statistical analyses which usually investigate only a limited number of food items and are subject to sparse data bias. This study was undertaken with the goal of investigating the concurrent effect of numerous food items and related nutrients on the incidence of MetS using Bayesian multilevel modelli ...[more]

PMID: 30567817

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Project description:BackgroundWe aimed to investigate the familial resemblance of dietary intakes, including energy and nutrients, and the family-based heritability of dietary intake in different age-sex dyads of the Tehran cardiometabolic genetic study.MethodsThis cross-sectional study was conducted on 9,798 participants, aged ≥ 18 years, with complete data in each of the third, fourth, fifth, and sixth surveys of the Tehran Cardiometabolic Genetic study, who were eligible to enter the current study based on inclusion and exclusion criteria. Nutrient intake was determined using a valid and reliable food frequency questionnaire (FFQ). FCOR command of the S.A.G.E. software was used to estimate the intra-class correlation coefficients of all relative pairs to verify the family resemblance of dietary nutrient intakes. Classical likelihood-based is used to assess the family-based heritability of dietary nutrient traits.ResultsThere were 4338 families with a mean family size of 3.20 ± 2.89, including 1 to 32 members (2567 constituent pedigrees and 1572 singletons) and 3627 sibships. The mean ± SD age of participants was 42.0 ± 15.2 years, and 44.5% were males. The heritability of nutrient intake ranged from 3 to 21%. The resemblance degree of energy intake and most nutrients between spouses or between parents and children is weak to moderate; however, a high resemblance of intake was observed for some food components, especially among spouses, including trans fatty acids (TFAs) (r:0.70), chromium (r:0.44), fiber(r:0.35), pantothenic acid (r:0.31), and vitamin C(r:0.31). Based on our findings, the resemblance of nutrient intake in spouses was greater than in parent-offspring. The similarity in parent-offspring nutrient intake was different, and the correlation in mother-girls nutrient intakes was greater than other parent-child correlations. Also, the lowest resemblance in nutrient intake was observed among siblings.ConclusionsOur findings suggested a weak-to-moderate similarity between the nutrient intakes of parents and offspring. The resemblance degree in nutrient intake varied between different family pairs; the strongest correlation of nutrients was observed between spouses, which includes TFAs, chromium, fiber, pantothenic acid, and vitamin C. The lowest correlation of nutrients was between siblings, such as carbohydrates, thiamine, niacin, and vitamin K. An individual's nutrient intake can somewhat be influenced by genetics, family relationships, and the effects of parents, although the significant influence of environmental factors should not be ignored.

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Effects of food items and related nutrients on metabolic syndrome using Bayesian multilevel modelling using the Tehran Lipid and Glucose Study (TLGS): a cohort study.

Publications

Effects of food items and related nutrients on metabolic syndrome using Bayesian multilevel modelling using the Tehran Lipid and Glucose Study (TLGS): a cohort study.

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