ArticleNutrition & diabetes2018
Phenotype and genotype predictors of BMI variability among European adults.
Article in Nutrition & diabetes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Genetic polymorphisms associated with obesity in the Arab world: a systematic review.International journal of obesity (2005) · 2021Pooled it
- The impact of slope-adjusted visit-to-visit body mass index variability on early dementia risk prediction.International journal of obesity (2005) · 2026Article
- Review
- Stability in BMI over time is associated with a better cognitive trajectory in older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- BMI variability and incident diabetes mellitus, Tehran Lipid and Glucose Study (TLGS).Scientific reports · 2022Article
- Predictive Utility of Alternate Measures of Physical Activity and Diet for Overweight and Obesity in Low-Income Minority Women.American journal of health promotion : AJHP · 2022Article
- Repercussions of absolute and time-rated BMI "yo-yo" fluctuations on cardiovascular stress-related morbidities within the vascular-metabolic CUN cohort.Frontiers in endocrinology · 2022Article
- Association between peroxisome proliferator-activated receptor-alpha, -delta and -gamma gene (Biology of sport · 2021Article
- PPARGC1A gene polymorphism is associated with exercise-induced fat loss.Molecular biology reports · 2020Article
- Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectiveObesity is a complex and multifactorial disease resulting from the interactions among genetics, metabolic, behavioral, sociocultural and environmental factors. In this sense, the aim of the present study was to identify phenotype and genotype variables that could be relevant determinants of body mass index (BMI) variability. SUBJECTS/
methodsIn the present study, a total of 1050 subjects (798 females; 76%) were included. Least angle regression (LARS) analysis was used as regression model selection technique, where the dependent variable was BMI and the independent variables were age, sex, energy intake, physical activity level, and 16 polymorphisms previously related to obesity and lipid metabolism.
resultsThe LARS analysis obtained the following formula for BMI explanation: (64.7 + 0.10 × age [years] + 0.42 × gender [0, men; 1, women] + -40.6 × physical activity [physical activity level] + 0.004 × energy intake [kcal] + 0.74 × rs9939609 [0 or 1-2 risk alleles] + -0.72 × rs1800206 [0 or 1-2 risk alleles] + -0.86 × rs1801282 [0 or 1-2 risk alleles] + 0.87 × rs429358 [0 or 1-2 risk alleles]. The multivariable regression model accounted for 21% of the phenotypic variance in BMI. The regression model was internally validated by the bootstrap method (r
conclusionIn conclusion, age, physical activity, energy intake and polymorphisms in FTO, APOE, PPARG and PPARA genes are significant predictors of the BMI trait.
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