Showing papers by "Martin Ridderstråle published in 2009"

Six new loci associated with body mass index highlight a neuronal influence on body weight regulation

Abstract: Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

A common variant in PNPLA3, which encodes adiponutrin, is associated with liver fat content in humans

Abstract: Aims/hypothesis It has recently been suggested that the rs738409 G allele in PNPLA3, which encodes adiponutrin, is strongly associated with increased liver fat content in three different ethnic groups. The aims of the present study were as follows: (1) to try to replicate these findings in European individuals with quantitative measures of hepatic fat content; (2) to study whether the polymorphism influences hepatic and adipose tissue insulin sensitivity; and (3) to investigate whether PNPLA3 expression is altered in the human fatty liver.

Genetic Variance in the Adiponutrin Gene Family and Childhood Obesity

Abstract: AIM: The adiponutrin gene family consists of five genes (PNPLA1-5) coding for proteins with both lipolytic and lipogenic properties. PNPLA3 has previously been associated with adult obesity. Here we investigated the possible association between genetic variants in these genes and childhood and adolescent obesity. METHODS/RESULTS: Polymorphisms in the five genes of the adiponutrin gene family were selected and genotyped using the Sequenom platform in a childhood and adolescent obesity case-control study. Six variants in PNPLA1 showed association with obesity (rs9380559, rs12212459, rs1467912, rs4713951, rs10947600, and rs12199580, p 0.05). When analyzing these SNPs in relation to phenotypes, two SNPs in the PNPLA3 gene showed association with insulin sensitivity (rs12483959: beta = -0.053, p = 0.016, and rs2072907: beta = -0.049, p = 0.024). No associations were seen for PNPLA2, PNPLA4, and PNPLA5. CONCLUSIONS: Genetic variation in the adiponutrin gene family does not seem to contribute strongly to obesity in children and adolescents. PNPLA1 exhibited a modest effect on obesity and PNPLA3 on insulin sensitivity. These data, however, require confirmation in other cohorts and ethnic groups. (Less)

Interaction between PPARG Pro12Ala and ADIPOQ G276T concerning cholesterol levels in childhood obesity.

Abstract: Aim. The aim of this study was to investigate the role of two candidate gene polymorphisms for insulin resistance and lipid levels in obese children and adolescents. Methods. Two markers of insulin...

CLINICAL—LIVER, PANCREAS, AND BILIARY TRACT Prediction of Non-Alcoholic Fatty Liver Disease and Liver Fat Using Metabolic and Genetic Factors

Abstract: BACKGROUND & AIMS: Our aims were to develop a method to accurately predict non-alcoholic fatty liver disease (NAFLD) and liver fat content based on routinely available clinical and laboratory data and to test whether knowledge of the recently discovered genetic variant in the PNPLA3 gene (rs738409) increases accuracy of the prediction. METHODS: Liver fat content was measured using proton magnetic resonance spectroscopy in 470 subjects, who were randomly divided into estimation (two thirds of the subjects, n 313) and validation (one third of the subjects, n 157) groups. Multivariate logistic and linear regression analyses were used to create an NAFLD liver fat score to diagnose NAFLD and liver fat equation to estimate liver fat percentage in each individual. RESULTS: The presence of the metabolic syndrome and type 2 diabetes, fasting serum (fS) insulin, fS-aspartate aminotransferase (AST), and the AST/alanine aminotransferase ratio were independent predictors of NAFLD. The score had an area under the receiver operating characteristic curve of 0.87 in the estimation and 0.86 in the validation group. The optimal cut-off point of 0.640 predicted increased liver fat content with sensitivity of 86% and specificity of 71%. Addition of the genetic information to the score improved the accuracy of the prediction by only 1%. Using the same variables, we developed a liver fat equation from which liver fat percentage of each individual could be estimated. CONCLUSIONS: The NAFLD liver fat score and liver fat equation provide simple and noninvasive tools to predict NAFLD and liver fat content.