Association of Genetic Risk of Obesity with Postoperative Complications Using Mendelian Randomization

The causal association between body fat distribution and risk of abdominal wall hernia: a two-sample Mendelian randomization study

PURPOSE

Obesity and a high body mass index (BMI) are considered as risk factors for abdominal wall hernia (AWH). However, anthropometric measures of body fat distribution (BFD) seem to be better indicators in the hernia field. This Mendelian randomization analysis aimed to generate more robust evidence for the impact of waist circumstance (WC), body, trunk, arm, and leg fat percentages (BFP, TFP, AFP, LFP) on AWH.

METHODS

A univariable MR design was employed and the summary statistics allowing for assessment were obtained from the genome-wide association studies (GWASs). An inverse variance weighted (IVW) method was applied as the primary analysis, and the odds ratio value was used to evaluate the causal relationship between BFD and AWH.

RESULTS

None of the MR-Egger regression intercepts deviated from null, indicating no evidence of horizontal pleiotropy (p > 0.05). The Cochran Q test showed heterogeneity between the genetic IVs for WC (p = 0.005; p = 0.005), TFP (p < 0.001; p < 0.001), AFP-L (p = 0.016; p = 0.015), LFP-R (p = 0.012; p = 0.009), and LFP-L (p < 0.001; p < 0.001). Taking the IVW random-effects model as gold standard, each standard deviation increment in genetically determined WC, BFP, TFP, AFP-R, AFP-L, LFP-R, and LFP-L raised the risk of AWH by 70.9%, 70.7%, 56.5%, 69.7%, 78.3%, 87.7%, and 72.5%, respectively.

CONCLUSIONS

This study proves the causal relationship between AWH and BFD, attracting more attention from BMI to BFD. It provides evidence-based medical evidence that healthy figure management can prevent AWH.

Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics

OBJECTIVE

High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach.

METHODS

This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank.

RESULTS

Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux.

CONCLUSIONS

This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS

Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.