Severe Acute Respiratory Syndrome Coronavirus 2 Infection History and Antibody Response to 3 Coronavirus Disease 2019 Messenger RNA Vaccine Doses.

BACKGROUND: Data on antibody kinetics are limited among individuals previously infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). From a cohort of healthcare personnel and other frontline workers in 6 US states, we assessed antibody waning after messenger RNA (mRNA) dose 2 and response to dose 3 according to SARS-CoV-2 infection history. METHODS: Participants submitted sera every 3 months, after SARS-CoV-2 infection, and after each mRNA vaccine dose. Sera were tested for antibodies and reported as area under the serial dilution curve (AUC). Changes in AUC values over time were compared using a linear mixed model. RESULTS: Analysis included 388 participants who received dose 3 by November 2021. There were 3 comparison groups: vaccine only with no known prior SARS-CoV-2 infection (n = 224); infection prior to dose 1 (n = 123); and infection after dose 2 and before dose 3 (n = 41). The interval from dose 2 and dose 3 was approximately 8 months. After dose 3, antibody levels rose 2.5-fold (95% confidence interval [CI] = 2.2-3.0) in group 2 and 2.9-fold (95% CI = 2.6-3.3) in group 1. Those infected within 90 days before dose 3 (and median 233 days [interquartile range, 213-246] after dose 2) did not increase significantly after dose 3. CONCLUSIONS: A third dose of mRNA vaccine typically elicited a robust humoral immune response among those with primary vaccination regardless of SARS-CoV-2 infection >3 months prior to boosting. Those with infection <3 months prior to boosting did not have a significant increase in antibody concentrations in response to a booster.

Mendelian Randomization of Blood Metabolites Suggests Circulating Glutamine Protects Against Late-Onset Alzheimer's Disease.

Background: Late-onset Alzheimer's disease (LOAD) represents a growing health burden. Previous studies suggest that blood metabolite levels influence risk of LOAD. Objective: We used a genetics-based study design which may overcome limitations of other epidemiological studies to assess the influence of metabolite levels on LOAD risk. Methods: We applied Mendelian randomization (MR) to evaluate bi-directional causal effects using summary statistics from the largest genome-wide association studies (GWAS) of 249 blood metabolites (n = 115,082) and GWAS of LOAD (ncase = 21,982, ncontrol = 41,944). Results: MR analysis of metabolites as exposures revealed a negative association of genetically-predicted glutamine levels with LOAD (Odds Ratio (OR) = 0.83, 95% CI = 0.73, 0.92) that was consistent in multiple sensitivity analyses. We also identified a positive association of genetically-predicted free cholesterol levels in small LDL (OR = 1.79, 95% CI = 1.36, 2.22) on LOAD. Using genetically-predicted LOAD as the exposure, we identified associations with phospholipids to total lipids ratio in large LDL (OR = 0.96, 95% CI = 0.94, 0.98), but not with glutamine, suggesting that the relationship between glutamine and LOAD is unidirectional. Conclusions: Our findings support previous evidence that higher circulating levels of glutamine may be a target for protection against LOAD.

Genome-wide Association Study of Liking for Several Types of Physical Activity in the UK Biobank and Two Replication Cohorts.

INTRODUCTION: A lack of physical activity (PA) is one of the most pressing health issues today. Our individual propensity for PA is influenced by genetic factors. Stated liking of different PA types may help capture additional and informative dimensions of PA behavior genetics. METHODS: In over 157,000 individuals from the UK Biobank, we performed genome-wide association studies of five items assessing the liking of different PA types, plus an additional derived trait of overall PA-liking. We attempted to replicate significant associations in the Netherlands Twin Register (NTR) and TwinsUK. Additionally, polygenic scores (PGS) were trained in the UK Biobank for each PA-liking item and for self-reported PA behavior, and tested for association with PA in the NTR. RESULTS: We identified a total of 19 unique significant loci across all five PA-liking items and the overall PA-liking trait, and these showed strong directional consistency in the replication cohorts. Four of these loci were previously identified for PA behavior, including CADM2 , which was associated with three PA-liking items. The PA-liking items were genetically correlated with self-reported ( rg = 0.38-0.80) and accelerometer ( rg = 0.26-0.49) PA measures, and with a wide range of health-related traits. Each PA-liking PGS significantly predicted the same PA-liking item in NTR. The PGS of liking for going to the gym predicted PA behavior in the NTR ( r2 = 0.40%) nearly as well as a PGS based on self-reported PA behavior ( r2 = 0.42%). Combining the two PGS into a single model increased the r2 to 0.59%, suggesting that PA-liking captures distinct and relevant dimensions of PA behavior. CONCLUSIONS: We have identified the first loci associated with PA-liking and extended our understanding of the genetic basis of PA behavior.

Diet and PPARG2 Pro12Ala Polymorphism Interactions in Relation to Cancer Risk: A Systematic Review.

Peroxisome proliferator-activated receptor-γ2 gene Pro12Ala allele polymorphism (PPARG2 Pro12Ala; rs1801282) has been linked to both cancer risk and dietary factors. We conducted the first systematic literature review of studies published before December 2020 using the PubMed database to summarize the current evidence on whether dietary factors for cancer may differ by individuals carrying C (common) and/or G (minor) alleles of the PPARG2 Pro12Ala allele polymorphism. The inclusion criteria were observational studies that investigated the association between food or nutrient consumption and risk of incident cancer stratified by PPARG2 Pro12Ala allele polymorphism. From 3815 identified abstracts, nine articles (18,268 participants and 4780 cancer cases) covering three cancer sites (i.e., colon/rectum, prostate, and breast) were included. CG/GG allele carriers were more impacted by dietary factors than CC allele carriers. High levels of protective factors (e.g., carotenoids and prudent dietary patterns) were associated with a lower cancer risk, and high levels of risk factors (e.g., alcohol and refined grains) were associated with a higher cancer risk. In contrast, both CG/GG and CC allele carriers were similarly impacted by dietary fats, well-known PPAR-γ agonists. These findings highlight the complex relation between PPARG2 Pro12Ala allele polymorphism, dietary factors, and cancer risk, which warrant further investigation.