👤 Hans Hauner

White adipose tissue dysfunction has emerged as a critical factor in cardiometabolic disease development, yet the cellular microstructure and genetic architecture of adipocyte morphology remain poorly explored. We introduce Adipocyte U-Net 2.0, an advanced deep learning method for the semantic segmentation of adipose tissue histology, enabling analysis of over 27 million adipocytes from 2,667 individuals. Our approach revealed that adipocyte hypertrophy associates with metabolic dysfunction, including increased fasting glucose, glycated hemoglobin, leptin, and triglycerides, with decreased adiponectin and HDL cholesterol levels. Through the largest genome-wide association study of adipocyte size to date (N Our findings demonstrate the utility of deep learning for adipocyte phenotyping at scale and provide novel insights into the genetic basis of adipocyte morphology and its relationship to metabolic disease. Show less

Changes in DNA methylation can increase or suppress the expression of health-relevant genes. We investigated for the first time the relationship between habitual food consumption and changes in DNA methylation. The German KORA FF4 and KORA Fit studies were used to study the change in methylation over a median follow-up of 4 years. Only subjects participating in both surveys and with available dietary and methylation data were included in the analysis (n = 465). DNA methylation was measured using the Infinium MethylationEPIC BeadChip (Illumina), resulting in 735,527 shared CpGs across both studies. Generalized estimating equation models with an interaction term of exposure and time point were used to analyze the association of 34 food groups, folic acid, and two dietary patterns with changes in DNA methylation over time. The results were corrected for genomic inflation. Significant interaction terms indicate different effects between both time points. We observed only a few significant associations between food intake and change in DNA methylation, except for cream and spirit consumption. The annotated genes include CLN3, PROM1, DLEU7, TLL2, and UGT1A10. We identified weak associations between food consumption and DNA methylation change. The differential results for cream and spirits, both consumed in low quantities, require replication in independent studies. Show less

Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated ( Show less

A better understanding of the genetic underpinning of total energy, carbohydrate, and fat intake is a prerequisite to develop personalized dietary recommendations. For this purpose, we systematically reviewed associations between single nucleotide polymorphisms (SNPs) and total energy, carbohydrate, and fat intakes. Four databases were searched for studies that assessed an association between SNPs and total energy, carbohydrate, and fat intakes. Screening of articles and data extraction was performed independently by 2 reviewers. Articles in English or German language, published between 1994 and September 2017, on human studies in adults and without specific populations were considered for the review. In total, 39 articles, including 86 independent loci, met the inclusion criteria. The fat mass and obesity-associated (FTO) gene as well as the melanocortin 4 receptor (MC4R) locus were most frequently studied. Limited significant evidence of an association between the FTO SNP rs9939609 and lower total energy intake and between the MC4R SNP rs17782313 and higher total energy intake was reported. Most of the other identified loci showed inconsistent results. In conclusion, there is no consistent evidence that the investigated SNPs are associated with and predictive for total energy, carbohydrate, and fat intakes. Show less

Adverse levels of lipoproteins are highly heritable and constitute risk factors for cardiovascular outcomes. Hitherto, genome-wide association studies revealed 95 lipid-associated loci. However, due to the small effect sizes of these associations large sample numbers (>100 000 samples) were needed. Here we show that analyzing more refined lipid phenotypes, namely lipoprotein subfractions, can increase the number of significantly associated loci compared with bulk high-density lipoprotein and low-density lipoprotein analysis in a study with identical sample numbers. Moreover, lipoprotein subfractions provide novel insight into the human lipid metabolism. We measured 15 lipoprotein subfractions (L1-L15) in 1791 samples using (1)H-NMR (nuclear magnetic resonance) spectroscopy. Using cluster analyses, we quantified inter-relationships among lipoprotein subfractions. Additionally, we analyzed associations with subfractions at known lipid loci. We identified five distinct groups of subfractions: one (L1) was only marginally captured by serum lipids and therefore extends our knowledge of lipoprotein biochemistry. During a lipid-tolerance test, L1 lost its special position. In the association analysis, we found that eight loci (LIPC, CETP, PLTP, FADS1-2-3, SORT1, GCKR, APOB, APOA1) were associated with the subfractions, whereas only four loci (CETP, SORT1, GCKR, APOA1) were associated with serum lipids. For LIPC, we observed a 10-fold increase in the variance explained by our regression models. In conclusion, NMR-based fine mapping of lipoprotein subfractions provides novel information on their biological nature and strengthens the associations with genetic loci. Future clinical studies are now needed to investigate their biomedical relevance. Show less