Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Aims: Previous observational studies have reported potential associations among attention-deficit/hyperactivity disorder (ADHD), obesity, and diabetes (including type 1 and type 2 diabetes mellitus [T1DM/T2DM]). However, whether the association between ADHD and diabetes is mediated by obesity is unknown.
Methods: With two-sample Mendelian randomization, we analysed the causal effect of ADHD on T1DM and T2DM and six obesity-related traits [including body mass index, waist circumference (WC), hip circumference, waist-to-hip ratio (WHR), body fat percentage and basal metabolic rate] and the causal effect of these obesity-related traits on T1DM/T2DM. Finally, with multivariable Mendelian randomization, we explored and quantified the possible mediation effects of obesity-related traits on the causal effect of ADHD on T1DM/T2DM.
Results: Our results showed that ADHD increased the risk of T2DM by 14% [odds ratio (OR) = 1.140, 95% confidence interval (CI) = 1.005-1.293] but with no evidence of an effect on T1DM (OR = 0.916, 95% CI = 0.735-1.141, = 0.433.). In addition, ADHD had a 6.1% increased causal effect on high WC (OR = 1.061, 95% CI = 1.024-1.099, = 0.001) and an 8.2% increased causal effect on high WHR (OR = 1.082, 95% CI = 1.035-1.131, = 0.001). In addition, a causal effect of genetically predicted high WC (OR = 1.870, 95% CI = 1.594-2.192, < 0.001) on a higher risk of T2DM was found. In further analysis, WC mediated approximately 26.75% (95% CI = 24.20%-29.30%) of the causal association between ADHD and T2DM.
Conclusions: WC mediates a substantial proportion of the causal effect of ADHD on the risk of T2DM, which indicated that the risk of T2DM induced by ADHD could be indirectly reduced by controlling WC as a main risk factor.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227470 | PMC |
| http://dx.doi.org/10.1017/S2045796023000173 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Mendelian randomisation analysis to discover plasma metabolites mediating the effect of obesity on cancer risk.
Br J Cancer
September 2025
Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, UK.
Background: Obesity is a risk factor for several cancers, but the mechanistic basis is poorly understood. We sought to identify circulating metabolites mediating the effect of obesity on the risk of eight common cancers.
Methods: Using European ancestry data, we applied two-sample Mendelian randomisation (2S-MR) to screen 856 plasma metabolites for associations with body mass index (BMI) and waist-hip ratio (WHR).
Investigating the shared genetic architecture between adiposity measures and obesity-related cancers.
Brief Bioinform
July 2025
Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Qiaokou District, Wuhan, Hubei 430030, China.
Fat distribution patterns are increasingly linked to obesity-related cancers; however, their shared genetic determinants remain unclear. To identify shared genetic architecture between adiposity measures and obesity-related cancers. Utilizing large-scale summary statistics from genome-wide association study, we conducted genome-wide cross trait analyses of nine adiposity measures [body mass index (BMI), waist-to-hip (WTH) ratio, waist-to-hip ratio adjusted for BMI, arm fat ratio, trunk fat ratio, leg fat ratio, abdominal subcutaneous adipose tissue, gluteofemoral adipose tissue, and visceral adipose tissue] in five obesity-related cancers (colorectal cancer, esophageal adenocarcinoma, breast cancer, endometrial cancer, and ovarian cancer) to characterize their shared genetic architecture, biological pathways, and causal relationships.
View Article and Find Full Text PDFRegional variation in overweight and associations with regional profiles using a Japanese national open-source database.
PLoS One
August 2025
Department of Healthcare Economics and Quality Management, School of Public Health, Kyoto University Graduate School of Medicine, Kyoto, Japan.
The objectives of this study were to describe regional variation in overweight and to investigate factors associated with overweight at the secondary medical area (SMA) level, accounting for regional economic sector profiles. We utilized data from the specific health checkup, which targets individuals aged 40-74 years. Following descriptive analyses, we employed partial least squares regression analyses using an open-access version of specific health checkup data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan.
View Article and Find Full Text PDFExploiting pleiotropy to enhance variant discovery with functional false discovery rates.
Nat Comput Sci
August 2025
Department of Medicine, University of Cambridge, Cambridge, UK.
The cost of recruiting participants for genome-wide association studies (GWASs) can limit sample sizes and hinder the discovery of genetic variants. Here we introduce the surrogate functional false discovery rate (sfFDR) framework that integrates summary statistics of related traits to increase power. The sfFDR framework provides estimates of FDR quantities such as the functional local FDR and q value, and uses these estimates to derive a functional P value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses.
View Article and Find Full Text PDFAnalyzing complex traits and diseases using GxE PRS: genotype-environment interaction in polygenic risk score models.
J Hum Genet
August 2025
Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
A deeper understanding of how environmental factors influence genetic risks is crucial for exploring their combined effects on health outcomes. This can be effectively achieved by incorporating genotype-environment (GxE) interactions in polygenic risk score (PRS) models. We applied our recently developed GxEprs model to a wide range of obesity-related complex traits and diseases, leveraging data from the UK Biobank, to capture significant GxE signals.
View Article and Find Full Text PDF