ArticleInternational journal of genomics2018
Prediction of Blood Lipid Phenotypes Using Obesity-Related Genetic Polymorphisms and Lifestyle Data in Subjects with Excessive Body Weight.
Article in International journal of genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Comparative diagnostic accuracy of the triglyceride/HDL-c ratio and lipid accumulation product index for the early detection of metabolic syndrome among adults with obesity in Indonesia: findings from the 2023 Health Survey.Archives of endocrinology and metabolism · 2026Article
- Exploring the potential relationship between serum biomarkers in obese individuals and cancer using FTIR, metabolomics, and lipidomics.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- A Nutrigenetic Strategy for Reducing Blood Lipids and Low-Grade Inflammation in Adults with Obesity and Overweight.Nutrients · 2023Article
- Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity.Nutrients · 2022Article
- The hypertriglyceridemic-waist phenotype as a valuable and integrative mirror of metabolic syndrome traits.Scientific reports · 2021Article
- Three Different Genetic Risk Scores Based on Fatty Liver Index, Magnetic Resonance Imaging and Lipidomic for a Nutrigenetic Personalized Management of NAFLD: The Fatty Liver in Obesity Study.Diagnostics (Basel, Switzerland) · 2021Article
- Comprehensive Analysis Reveals Novel Interactions between Circulating MicroRNAs and Gut Microbiota Composition in Human Obesity.International journal of molecular sciences · 2020Article
- Association of theNutrients · 2020Article
- Male Infertility: Shining a Light on Lipids and Lipid-Modulating Enzymes in the Male Germline.Journal of clinical medicine · 2020Review
- Article
- Interplay of an Obesity-Based Genetic Risk Score with Dietary and Endocrine Factors on Insulin Resistance.Nutrients · 2019Article
- Interactions betweenNutrients · 2019Article
- Models Integrating Genetic and Lifestyle Interactions on Two Adiposity Phenotypes for Personalized Prescription of Energy-Restricted Diets With Different Macronutrient Distribution.Frontiers in genetics · 2019Article
- Genetic and nongenetic factors explaining metabolically healthy and unhealthy phenotypes in participants with excessive adiposity: relevance for personalized nutrition.Therapeutic advances in endocrinology and metabolism · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimIndividual lipid phenotypes including circulating total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and triglycerides (TG) determinations are influenced by gene-environment interactions. The aim of this study was to predict blood lipid level (TC, LDL-c, HDL-c, and TG) variability using genetic and lifestyle data in subjects with excessive body weight-for-height.
methodsThis cross-sectional study enrolled 304 unrelated overweight/obese adults of self-reported European ancestry. A total of 95 single nucleotide polymorphisms (SNPs) related to obesity and weight loss were analyzed by a targeted next-generation sequencing system. Relevant genotypes of each SNP were coded as 0 (nonrisk) and 1 (risk). Four genetic risk scores (GRS) for each lipid phenotype were calculated by adding the risk genotypes. Information concerning lifestyle (diet, physical activity, alcohol drinking, and smoking) was obtained using validated questionnaires. Total body fat (TFAT) and visceral fat (VFAT) were determined by dual-energy X-ray absorptiometry.
resultsOverall, 45 obesity-related genetic variants were associated with some of the studied blood lipids. In addition to conventional factors (age, sex, dietary intakes, and alcohol consumption), the calculated GRS significantly contributed to explain their corresponding plasma lipid trait. Thus, HDL-c, TG, TC, and LDL-c serum concentrations were predicted by approximately 28% (optimism-corrected adj.
conclusionsBesides known lifestyle influences, some obesity-related genetic variants could help to predict blood lipid phenotypes.
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