Assessing causal associations of hyperparathyroidism with blood counts and biochemical indicators: a Mendelian randomization study

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BackgroundPrevious research on the association between risk factors and gestational diabetes mellitus (GDM) primarily comprises observational studies with inconclusive results. The objective of this study is to investigate the causal relationship between 108 traits and GDM by employing a two-sample Mendelian randomization (MR) analysis to identify potential risk factors of GDM.MethodsWe conducted MR analyses to explore the relationships between traits and GDM. The genome-wide association studies (GWAS) for traits were primarily based on data from the UK Biobank (UKBB), while the GWAS for GDM utilized data from FinnGen. We employed a false discovery rate (FDR) of 5% to account for multiple comparisons.ResultsThe inverse-variance weighted (IVW) method indicated that the genetically predicted 24 risk factors were significantly associated with GDM, such as “Forced expiratory volume in 1-second (FEV1)” (OR=0.76; 95% CI: 0.63, 0.92), “Forced vital capacity (FVC)” (OR=0.74; 95% CI: 0.64, 0.87), “Usual walking pace” (OR=0.19; 95% CI: 0.09, 0.39), “Sex hormone-binding globulin (SHBG)” (OR=0.86; 95% CI: 0.78, 0.94). The sensitivity analyses with MR-Egger and weighted median methods indicated consistent results for most of the trats.ConclusionOur study has uncovered a significant causal relationship between 24 risk factors and GDM. These results offer a new theoretical foundation for preventing or mitigating the risks associated with GDM.

Section: Methodsmentioning

confidence: 99%

Assessment of potential risk factors associated with gestational diabetes mellitus: evidence from a Mendelian randomization study

Fu,

Chen,

et al. 2024

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“…To adhere to the fundamental principles of a two-sample MR design, data on exposure and outcome were collected from distinct European populations as described previously (11)(12)(13). The genome-wide association study (GWAS) datasets for 53 different exposures were extracted from sources such as the UK Biobank (UKBB) and the Genetic Investigation of ANthropometric Traits (GIANT), and can be accessed on the IEU OpenGWAS project website (https://gwas.mrcieu.ac.uk/).…”

Section: Data Sourcesmentioning

confidence: 99%

Endocrine and metabolic factors and the risk of idiopathic pulmonary fibrosis: a Mendelian randomization study

Jiang,

Chen,

et al. 2024

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BackgroundPrevious observational studies have investigated the association between endocrine and metabolic factors and idiopathic pulmonary fibrosis (IPF), yet have produced inconsistent results. Therefore, it is imperative to employ the Mendelian randomization (MR) analysis method to conduct a more comprehensive investigation into the impact of endocrine and metabolic factors on IPF.MethodsThe instrumental variables (IVs) for 53 endocrine and metabolic factors were sourced from publicly accessible genome-wide association study (GWAS) databases, with GWAS summary statistics pertaining to IPF employed as the dependent variables. Causal inference analysis encompassed the utilization of three methods: inverse-variance weighted (IVW), weighted median (WM), and MR-Egger. Sensitivity analysis incorporated the implementation of MR-PRESSO and leave-one-out techniques to identify potential pleiotropy and outliers. The presence of horizontal pleiotropy and heterogeneity was evaluated through the MR-Egger intercept and Cochran’s Q statistic, respectively.ResultsThe IVW method results reveal correlations between 11 traits and IPF. After correcting for multiple comparisons, seven traits remain statistically significant. These factors include: “Weight” (OR= 1.44; 95% CI: 1.16, 1.78; P=8.71×10-4), “Body mass index (BMI)” (OR= 1.35; 95% CI: 1.13, 1.62; P=1×10-3), “Whole body fat mass” (OR= 1.40; 95% CI: 1.14, 1.74; P=1.72×10-3), “Waist circumference (WC)” (OR= 1.54; 95% CI: 1.16, 2.05; P=3.08×10-3), “Trunk fat mass (TFM)” (OR=1.35; 95% CI: 1.10,1.65; P=3.45×10-3), “Body fat percentage (BFP)” (OR= 1.55; 95% CI: 1.15,2.08; P=3.86×10-3), “Apoliprotein B (ApoB)” (OR= 0.78; 95% CI: 0.65,0.93; P=5.47×10-3). Additionally, the sensitivity analysis results confirmed the reliability of the MR results.ConclusionThe present study identified causal relationships between seven traits and IPF. Specifically, ApoB exhibited a negative impact on IPF, while the remaining six factors demonstrated a positive impact. These findings offer novel insights into the underlying etiopathological mechanisms associated with IPF.

“…We used Mendelian randomization (MR) to evaluate the causal relationship between numerous metabolic factors and OC. MR methods successfully reduce reverse causation and residual confounding by utilizing instrumental variables (IVs) that are closely related to the exposure ( 31 ). Our goal is to improve understanding of OC risk factors and develop new prevention strategies.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the causal link between metabolic factors and ovarian cancer risk using Mendelian randomization analysis

Han,

Xu,

Zhou

et al. 2024

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BackgroundMetabolic abnormalities are closely tied to the development of ovarian cancer (OC), yet the relationship between anthropometric indicators as risk indicators for metabolic abnormalities and OC lacks consistency.MethodThe Mendelian randomization (MR) approach is a widely used methodology for determining causal relationships. Our study employed summary statistics from the genome-wide association studies (GWAS), and we used inverse variance weighting (IVW) together with MR-Egger and weighted median (WM) supplementary analyses to assess causal relationships between exposure and outcome. Furthermore, additional sensitivity studies, such as leave-one-out analyses and MR-PRESSO were used to assess the stability of the associations.ResultThe IVW findings demonstrated a causal associations between 10 metabolic factors and an increased risk of OC. Including “Basal metabolic rate” (OR= 1.24, P= 6.86×10-4); “Body fat percentage” (OR= 1.22, P= 8.20×10-3); “Hip circumference” (OR= 1.20, P= 5.92×10-4); “Trunk fat mass” (OR= 1.15, P= 1.03×10-2); “Trunk fat percentage” (OR= 1.25, P= 8.55×10-4); “Waist circumference” (OR= 1.23, P= 3.28×10-3); “Weight” (OR= 1.21, P= 9.82×10-4); “Whole body fat mass” (OR= 1.21, P= 4.90×10-4); “Whole body fat-free mass” (OR= 1.19, P= 4.11×10-3) and “Whole body water mass” (OR= 1.21, P= 1.85×10-3).ConclusionSeveral metabolic markers linked to altered fat accumulation and distribution are significantly associated with an increased risk of OC.