Andre Franke and colleagues perform a genome-wide association study for the gut microbiome, examining the influence of host genetics on overall microbial variation and individual taxa. They find significant associations at the VDR (vitamin D receptor) locus and observe correlations between microbiota and metabolites of VDR, including bile acids. Human gut microbiota is an important determinant for health and disease, and recent studies emphasize the numerous factors shaping its diversity. Here we performed a genome-wide association study (GWAS) of the gut microbiota using two cohorts from northern Germany totaling 1,812 individuals. Comprehensively controlling for diet and non-genetic parameters, we identify genome-wide significant associations for overall microbial variation and individual taxa at multiple genetic loci, including the VDR gene (encoding vitamin D receptor). We observe significant shifts in the microbiota of Vdr−/− mice relative to control mice and correlations between the microbiota and serum measurements of selected bile and fatty acids in humans, including known ligands and downstream metabolites of VDR. Genome-wide significant (P < 5 × 10−8) associations at multiple additional loci identify other important points of host–microbe intersection, notably several disease susceptibility genes and sterol metabolism pathway components. Non-genetic and genetic factors each account for approximately 10% of the variation in gut microbiota, whereby individual effects are relatively small.

Aging is regarded as a major risk factor for neurodegenerative diseases. Thus, a better understanding of the similarities between the aging process and neurodegenerative diseases at the cellular and molecular level may reveal better understanding of this detrimental relationship. In the present study, we mined publicly available gene expression datasets from healthy individuals and patients affected by neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease) across a broad age spectrum and compared those with mouse aging and mouse cell-type specific gene expression profiles. We performed weighted gene co-expression network analysis (WGCNA) and found a gene network strongly related with both aging and neurodegenerative diseases. This network was significantly enriched with a microglial signature as imputed from cell type-specific sequencing data. Since mouse models are extensively used for the study of human diseases, we further compared these human gene regulatory networks with age-specific mouse brain transcriptomes. We discovered significantly preserved networks with both human aging and human disease and identified 17 shared genes in the top-ranked immune/microglia module, among which we found five human hub genes TYROBP, FCER1G, ITGB2, MYO1F, PTPRC, and two mouse hub genes Trem2 and C1qa. Taken together, these results support the hypothesis that microglia are key players involved in human aging and neurodegenerative diseases, and suggest that mouse models should be appropriate for studying these microglial changes in human.

Evidence regarding the relation between SARS-CoV-2 mortality and the underlying medical condition is scarce. We conducted an observational, retrospective study based on Romanian official data about location, age, gender and comorbidities for COVID-19 fatalities. Our findings indicate that males, hypertension, diabetes, obesity and chronic kidney disease were most frequent in the COVID-19 fatalities, that the burden of disease was low, and that the prognosis for 1-year survival probability was high in the sample. Evidence shows that age-dependent pairs of comorbidities could be a negative prognosis factor for the severity of disease for the SARS-CoV 2 infection.

We consider the inverse problem to identify coefficient functions in boundary value problems from noisy measurements of the solutions. Our estimators are defined as minimizers of a Tikhonov functional, which is the sum of a nonlinear data misfit term and a quadratic penalty term involving a Hilbert scale norm. In this abstract framework we derive estimates of the expected squared error under certain assumptions on the forward operator. These assumptions are shown to be satisfied for two classes of inverse elliptic boundary value problems. The theoretical results are confirmed by Monte Carlo simulations.

Next to the information on frequency of food consumption, information on consumption-day amounts is important to estimate usual dietary intake in epidemiological studies. Our objective was to identify determinants of consumption-day amounts to derive person-specific standard consumption-day amounts applicable for the estimation of usual dietary intake using separate sources to assesss information on consumption probability and amount consumed. 24-h Dietary recall data from the German National Nutrition Survey II (n = 8522; aged 20–80 years) conducted between 2005 and 2007 were analysed for determinants of consumption-day amounts of thirty-eight food and beverage groups using LASSO variable selection for linear mixed-effects models. Determinants included sex, age, BMI, smoking status, years of education, household net income, living status and employment status. Most often, sex, age and smoking status were selected as predictors for consumption-day amounts across thirty-eight food groups. In contrast, living with a partner, employment status and household net income were less frequently chosen. Overall, different determinants were of relevance for different food groups. The number of selected determinants ranged from eight for coffee and juice to zero for cabbage, tea, root vegetables, leafy vegetables, fruit vegetables, legumes, offal, vegetable oils, and other fats. For the estimation of usual dietary intake in a combined approach with a 24-h food list, person-specific standard consumption-day amounts could be used. Sex, age and smoking status were shown to be the most relevant predictors in our analysis. Their impact on the estimation of usual dietary intake needs to be evaluated in future studies.

We consider an inverse problem of determining a spacewise-dependent heat source in the time-dependent heat equation using the usual conditions of the direct problem and information from temperature measurements at a given single instant of time. This spacewise-dependent temperature measurement ensures that the inverse problem has a unique solution, but the solution is unstable and hence the problem is ill-posed. Furthermore, it is assumed that information about this temperature measurement is available only at a finite number of points, and moreover, these measurements are blurred by a stochastic noise. The continuous problem is discretized using a finite element method and for the discrete model an estimate of the heat source is obtained by a Tikhonov regularization. It is proved that increasing the number of measurements and decreasing the mesh size produces a sequence of solutions which under appropriate conditions converge to the continuous heat source.