Sex Differences in the Metabolome of Alzheimer's Disease Progression.

Alzheimer's disease (AD) is the leading cause of dementia; however, men and women face differential AD prevalence, presentation, and progression risks. Characterizing metabolomic profiles during AD progression is fundamental to understand the metabolic disruptions and the biological pathways involved. However, outstanding questions remain of whether peripheral metabolic changes occur equally in men and women with AD. Here, we evaluated differential effects of metabolomic and brain volume associations between sexes. We used three cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), evaluated 1,368 participants, two metabolomic platforms with 380 metabolites in total, and six brain segment volumes. Using dimension reduction techniques, we took advantage of the correlation structure of the brain volume phenotypes and the metabolite concentration values to reduce the number of tests while aggregating relevant biological structures. Using WGCNA, we aggregated modules of highly co-expressed metabolites. On the other hand, we used partial least squares regression-discriminant analysis (PLS-DA) to extract components of brain volumes that maximally co-vary with AD diagnosis as phenotypes. We tested for differences in effect sizes between sexes in the association between single metabolite and metabolite modules with the brain volume components. We found five metabolite modules and 125 single metabolites with significant differences between sexes. These results highlight a differential lipid disruption in AD progression between sexes. Men showed a greater negative association of phosphatidylcholines and sphingomyelins and a positive association of VLDL and large LDL with AD progression. In contrast, women showed a positive association of triglycerides in VLDL and small and medium LDL with AD progression. Explicitly identifying sex differences in metabolomics during AD progression can highlight particular metabolic disruptions in each sex. Our research study and strategy can lead to better-tailored studies and better-suited treatments that take sex differences into account.

Hypothalamic-pituitary-adrenal axis attenuation and obesity risk in sexually abused females.

BACKGROUND: Childhood sexual abuse (CSA) confers elevated risks for obesity in females. Mechanisms that explain this link remain unclear. This study tracked serum basal cortisol levels with body mass index (BMI) from childhood into adulthood to test whether hypothalamic-pituitary-adrenal (HPA) axis attenuation accounts for elevated obesity risks for sexually abused females. METHODS: Data drew from six timepoints of a longitudinal study of the impact of CSA on development. Participants were females aged 6-16 years at time of study enrollment with substantiated CSA and demographically matched non-abused peers. Analyses included only participants who did not have obesity at study enrollment. Main outcomes were BMI growth trajectories across ages 6-27 (n = 150; 66 abused, 84 comparisons) and early adulthood obesity status (ages 20-27; n = 133; 62 abused, 71 comparison). HPA axis functioning indicators were intercept and linear slope parameters extracted from multilevel growth trajectories of serum basal cortisol levels across development. Racial-ethnic minority status, parity, steroid medication use, depression history and disordered eating history were covaried. RESULTS: While controlling for covariates, multilevel modeling indicated that high initial serum basal cortisol levels in childhood and attenuated cortisol growth rate over time (i.e., HPA axis attenuation) were associated with accelerated BMI accumulation (p < .01). Attenuated cortisol growth rate mediated the effect of CSA on accelerated BMI accumulation and on elevated adulthood obesity rates (p < .05). CONCLUSION: This work establishes a mechanistic association between HPA axis attenuation and obesity, suggesting that trauma treatments for abuse survivors should include interventions that reduce health consequences associated with dysregulated stress physiology.

Robust genome-wide ancestry inference for heterogeneous datasets: illustrated using the 1,000 genome project with 3D facial images.

Estimates of individual-level genomic ancestry are routinely used in human genetics, and related fields. The analysis of population structure and genomic ancestry can yield insights in terms of modern and ancient populations, allowing us to address questions regarding admixture, and the numbers and identities of the parental source populations. Unrecognized population structure is also an important confounder to correct for in genome-wide association studies. However, it remains challenging to work with heterogeneous datasets from multiple studies collected by different laboratories with diverse genotyping and imputation protocols. This work presents a new approach and an accompanying open-source toolbox that facilitates a robust integrative analysis for population structure and genomic ancestry estimates for heterogeneous datasets. We show robustness against individual outliers and different protocols for the projection of new samples into a reference ancestry space, and the ability to reveal and adjust for population structure in a simulated case-control admixed population. Given that visually evident and easily recognizable patterns of human facial characteristics co-vary with genomic ancestry, and based on the integration of three different sources of genome data, we generate average 3D faces to illustrate genomic ancestry variations within the 1,000 Genome project and for eight ancient-DNA profiles, respectively.