Genetic determination of body fat distribution and the attributive influence on metabolism
| Autor: | Ellen, Fehlert, Róbert, Wagner, Caroline, Ketterer, Anja, Böhm, Jürgen, Machann, Louise, Fritsche, Fausto, Machicao, Fritz, Schick, Harald, Staiger, Norbert, Stefan, Hans-Ulrich, Häring, Andreas, Fritsche, Martin, Heni |
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| Jazyk: | němčina |
| Rok vydání: | 2017 |
| Předmět: |
Male
Magnetic Resonance Spectroscopy Genotyping Techniques Waist-Hip Ratio Glucose Tolerance Test Magnetic Resonance Imaging Polymorphism Single Nucleotide White People Body Mass Index Cohort Studies Cross-Sectional Studies Liver Body Fat Distribution Humans Insulin Female Obesity Insulin Resistance Alleles Follow-Up Studies Genome-Wide Association Study |
| Zdroj: | Obesity 25, 1277-1283 (2017) |
| Popis: | ObjectiveGenome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with estimates of body fat distribution. Using predefined risk allele scores, the correlation of these scores with precisely quantified body fat distribution assessed by magnetic resonance (MR) imaging techniques and with metabolic traits was investigated. MethodsData from 4,944 MR scans from 915 subjects of European ancestry were analyzed. Body fat distribution was determined by MR imaging and liver fat content by H-1-MR spectroscopy. All subjects underwent a five-point 75-g oral glucose tolerance test. A total of 65 SNPs with reported genome-wide significant associations regarding estimates of body fat distribution were genotyped. Four genetic risk scores were created by summation of risk alleles. ResultsA higher allelic load of waist-to-hip ratio SNPs was associated with lower insulin sensitivity, higher postchallenge glucose levels, and more visceral and less subcutaneous fat mass. ConclusionsGWAS-derived polymorphisms estimating body fat distribution are associated with distinct patterns of body fat distribution exactly measured by MR. Only the risk score associated with the waist-to-hip ratio in GWAS showed an unhealthy pattern of metabolism and body fat distribution. This score might be useful for predicting diseases associated with genetically determined, unhealthy obesity. |
| Databáze: | OpenAIRE |
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