INTRODUCTION: Alzheimers disease (AD) incidence is thought to be higher among Black than White individuals. METHODS: We studied the US Medicare population from 2000 to 2018. Cox regression was used to determine the roles of race and co-morbidities for AD incidence. RESULTS: We studied 11,880,906 Medicare beneficiaries, with 774,548 AD cases. Hazard ratios (HRs) by increasing numbers of co-morbidities (1-7) were 1.51, 2.00, 2.55, 3.16, 2.89, 4.77, and 5.65. Among those with no co-morbidities, Black individuals had a lower rate than those who are White (HR = 0.69), while among those with one more co-morbidities, Black individuals had a higher rate (HR = 1.19). The presence of hypertension increased AD rates by 14% for White individuals, but 69% for those who are Black. DISCUSSION: More co-morbidities was strongly associated with higher AD rates. The higher rates for Black versus White individuals was apparent only for those with co-morbidities and appears driven both by more co-morbidities, and the greater effect of hypertension. HIGHLIGHTS: Black individuals have been shown to have higher Alzheimers disease (AD) rates than those who are White. Some co-morbidities are known to increase AD risk. Among those In Medicare data with no co-morbidities, Black individuals have less risk than those who are White. Among those with co-morbidities, Black individuals have higher rates than those who are White. Hypertension results in a much stronger increase in AD risk for Black versus White individuals.

Existing air pollution metabolomics studies showed inconsistent results, often limited by small sample size and individual air pollutants effects. We conducted a metabolome-wide association study among 1096 women (68.2 ± 5.7 years) who provided blood samples (1998-2001) within the Cancer Prevention Study-II Nutrition Cohort. Annual average individual exposures to particulate matter, nitrogen dioxide, ozone, sulfur dioxide, and carbon monoxide in the year of blood draw were used. Metabolomics profiling was conducted on serum samples by Metabolon. We evaluated the individual air pollutants effects using multiple linear regression and the mixture effect using quantile g-computation, adjusting for confounders and false discovery rate (FDR). Ninety-five metabolites were significantly associated with at least one air pollutant or mixture (FDR < 0.05). These metabolites were enriched in pathways related to oxidative stress, systemic inflammation, energy metabolism, signals transduction, nucleic acid damage and repair, and xenobiotics. Sixty metabolites were confirmed with level 1 or 2 evidence, among which 21 have been previously linked to air pollution exposure, including taurine, creatinine, and sebacate. Overall, our results replicate prior findings in a large sample and provide novel insights into biological responses to long-term air pollution exposure using mixture analysis.

Introduction

Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.

Objective

The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.

Methods

We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.

Results

Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coef_meta = 0.40 [95% CI 0.10,0.70], P_meta = 9.7 × 10^-3), guanidinoacetate (Coef_meta = - 0.28 [- 0.54, - 0.02], P_meta = 0.033), 3-ureidopropionate (Coef_meta = 0.22 [0.017,0.41], P_meta = 0.033), 1-methylhistidine (Coef_meta = - 0.18 [- 0.33, - 0.04], P_meta = 0.011), serine (Coef_meta = - 0.18 [- 0.36, - 0.01], P_meta = 0.034), and lysine (Coef_meta = - 0.16 [- 0.32, - 0.01], P_meta = 0.044). No associations were robust to multiple testing correction.

Conclusions

Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.