A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks.

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Circulating Metabolite Biomarkers of Glycemic Control in Youth-Onset Type 2 Diabetes.

OBJECTIVE: We aimed to identify metabolites associated with loss of glycemic control in youth-onset type 2 diabetes. RESEARCH DESIGN AND METHODS: We measured 480 metabolites in fasting plasma samples from the TODAY (Treatment Options for Type 2 Diabetes in Adolescents and Youth) study. Participants (N = 393; age 10-17 years) were randomly assigned to metformin, metformin plus rosiglitazone, or metformin plus lifestyle intervention. Additional metabolomic measurements after 36 months were obtained in 304 participants. Cox models were used to assess baseline metabolites, interaction of metabolites and treatment group, and change in metabolites (0-36 months), with loss of glycemic control adjusted for age, sex, race, treatment group, and BMI. Metabolite prediction models of glycemic failure were generated using elastic net regression and compared with clinical risk factors. RESULTS: Loss of glycemic control (HbA1c ≥8% or insulin therapy) occurred in 179 of 393 participants (mean 12.4 months). Baseline levels of 33 metabolites were associated with loss of glycemic control (q < 0.05). Associations of hexose and xanthurenic acid with treatment failure differed by treatment randomization; youths with higher baseline levels of these two compounds had a lower risk of treatment failure with metformin alone. For three metabolites, changes from 0 to 36 months were associated with loss of glycemic control (q < 0.05). Changes in d-gluconic acid and 1,5-AG/1-deoxyglucose, but not baseline levels of measured metabolites, predicted treatment failure better than changes in HbA1c or measures of ß-cell function. CONCLUSIONS: Metabolomics provides insight into circulating small molecules associated with loss of glycemic control and may highlight metabolic pathways contributing to treatment failure in youth-onset diabetes.

Lac-Phe mediates the effects of metformin on food intake and body weight.

Metformin is a widely prescribed anti-diabetic medicine that also reduces body weight. There is ongoing debate about the mechanisms that mediate metformin's effects on energy balance. Here, we show that metformin is a powerful pharmacological inducer of the anorexigenic metabolite N-lactoyl-phenylalanine (Lac-Phe) in cells, in mice and two independent human cohorts. Metformin drives Lac-Phe biosynthesis through the inhibition of complex I, increased glycolytic flux and intracellular lactate mass action. Intestinal epithelial CNDP2+ cells, not macrophages, are the principal in vivo source of basal and metformin-inducible Lac-Phe. Genetic ablation of Lac-Phe biosynthesis in male mice renders animals resistant to the effects of metformin on food intake and body weight. Lastly, mediation analyses support a role for Lac-Phe as a downstream effector of metformin's effects on body mass index in participants of a large population-based observational cohort, the Multi-Ethnic Study of Atherosclerosis. Together, these data establish Lac-Phe as a critical mediator of the body weight-lowering effects of metformin.