Gut microbiome and frailty: insight from genetic correlation and mendelian randomization

Abstract

Observational studies have shown that the gut microbiome is associated with frailty. However, whether these associations underlie causal effects remains unknown. Thus, this study aimed to assess the genetic correlation and causal relationships between the genetically predicted gut microbiome and frailty using linkage disequilibrium score regression (LDSC) and Mendelian Randomization (MR). Summary statistics for the gut microbiome were obtained from a genome-wide association study (GWAS) meta-analysis of the MiBioGen consortium ( N = 18,340). Summary statistics for frailty were obtained from a GWAS meta-analysis, including the UK Biobank and TwinGene ( N = 175,226). We used LDSC and MR analyses to estimate the genetic correlation and causality between the genetically predicted gut microbiome and frailty. Our findings indicate a suggestive genetic correlation between Christensenellaceae R-7 and frailty. Moreover, we found evidence for suggestive causal effects of twelve genus-level gut microbes on frailty using at least two MR methods. There was no evidence of horizontal pleiotropy or heterogeneity in the MR analysis. This study provides suggestive evidence for a potential genetic correlation and causal association between several genetically predicted gut microbes and frailty. More population-based observational studies and animal experiments are required to clarify this association and the underlying mechanisms.

Anorexia nervosa and bulimia nervosa: a Mendelian randomization study of gut microbiota

“…However, there might be heterogeneity in IVs from different analysis platforms, experiments, and populations, which can affect the results of MR analysis ( Cui et al, 2023 ). To assess potential heterogeneity, Cochran’s Q statistic was computed, with all p -values exceeding 0.05, indicating the absence of heterogeneity in the study.…”

Roles of gut microbiota in androgenetic alopecia: insights from Mendelian randomization analysis

“…This extensive, multi-ethnic GWAS involved the coordination of 16S ribosomal RNA gene sequencing profiles and genotyping data from 18,340 participants. A total of 211 taxa, comprising 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla, were encompassed in the analysis ( Kurilshikov et al, 2021 ; Cui et al, 2023 ). The dataset for AGA included 201,214 European participants sourced from the freely accessible website FinnGen biobank.…”

“…We implemented a set of criteria to carefully select eligible genetic IVs:(1) Significance Threshold: Due to the limited number of IVs meeting the genome-wide significance threshold ( p < 5 × 10 –8 ) ( Kurilshikov et al, 2021 ; Herbert et al, 2022 ), we opted for a relatively less stringent threshold ( p < 1 × 10 –5 ) based on previous research ( Cui et al, 2023 ; Dai et al, 2023 ; Liu et al, 2023 ; Xiao et al, 2023 ). This less stringent threshold was selected to identify potential sets of variants that are likely to be enriched for association, allowing for a more comprehensive assessment and exploration of results.…”

Causal association of gut microbiota with type 2 diabetes, type 1 diabetes and glycemic traits: a two-sample Mendelian randomization study

“…We initially selected the SNPs that reached the genomewide signi cant level (P < 5×10 − 8 ). Since the number of eligible SNPs (P < 5×10 − 8 ) was too small, we ultimately used a more comprehensive threshold (P < 1×10 − 5 ) to obtain appropriate SNPs [22,23].Then, we excluded the SNPs with linkage disequilibrium (window size = 500kb, r 2 < 0.01) [24]. Palindromic SNPs were deleted to avoid the distortion of strand orientation or allele coding.…”