Mendelian randomization prioritizes abdominal adiposity as an independent causal factor for liver fat accumulation and cardiometabolic diseases.
Commun Med (Lond)
; 2: 130, 2022.
Article
en En
| MEDLINE
| ID: mdl-36249462
ABSTRACT
Background:
Observational studies have linked adiposity and especially abdominal adiposity to liver fat accumulation and non-alcoholic fatty liver disease. These traits are also associated with type 2 diabetes and coronary artery disease but the causal factor(s) underlying these associations remain unexplored.Methods:
We used a multivariable Mendelian randomization study design to determine whether body mass index and waist circumference were causally associated with non-alcoholic fatty liver disease using publicly available genome-wide association study summary statistics of the UK Biobank (n = 461,460) and of non-alcoholic fatty liver disease (8434 cases and 770,180 control). A multivariable Mendelian randomization study design was also used to determine the respective causal contributions of waist circumference and liver fat (n = 32,858) to type 2 diabetes and coronary artery disease.Results:
Using multivariable Mendelian randomization we show that waist circumference increase non-alcoholic fatty liver disease risk even when accounting for body mass index (odd ratio per 1-standard deviation increase = 2.35 95% CI = 1.31-4.22, p = 4.2e-03), but body mass index does not increase non-alcoholic fatty liver disease risk when accounting for waist circumference (0.86 95% CI = 0.54-1.38, p = 5.4e-01). In multivariable Mendelian randomization analyses accounting for liver fat, waist circumference remains strongly associated with both type 2 diabetes (3.27 95% CI = 2.89-3.69, p = 3.8e-80) and coronary artery disease (1.66 95% CI = 1.54-1.8, p = 3.4e-37).Conclusions:
These results identify waist circumference as a strong, independent, and causal contributor to non-alcoholic fatty liver disease, type 2 diabetes and coronary artery disease, thereby highlighting the importance of assessing body fat distribution for the prediction and prevention of cardiometabolic diseases.
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Banco de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
/
Observational_studies
/
Prognostic_studies
Idioma:
En
Año:
2022
Tipo del documento:
Article