Interactions of physical activity, muscular fitness, adiposity, and genetic risk for NAFLD

Integrating the environmental and genetic architectures of aging and mortality

Both environmental exposures and genetics are known to play important roles in shaping human aging. Here we aimed to quantify the relative contributions of environment (referred to as the exposome) and genetics to aging and premature mortality. To systematically identify environmental exposures associated with aging in the UK Biobank, we first conducted an exposome-wide analysis of all-cause mortality (n = 492,567) and then assessed the associations of these exposures with a proteomic age clock (n = 45,441), identifying 25 independent exposures associated with mortality and proteomic aging. These exposures were also associated with incident age-related multimorbidity, aging biomarkers and major disease risk factors. Compared with information on age and sex, polygenic risk scores for 22 major diseases explained less than 2 percentage points of additional mortality variation, whereas the exposome explained an additional 17 percentage points. Polygenic risk explained a greater proportion of variation (10.3-26.2%) compared with the exposome for incidence of dementias and breast, prostate and colorectal cancers, whereas the exposome explained a greater proportion of variation (5.5-49.4%) compared with polygenic risk for incidence of diseases of the lung, heart and liver. Our findings provide a comprehensive map of the contributions of environment and genetics to mortality and incidence of common age-related diseases, suggesting that the exposome shapes distinct patterns of disease and mortality risk, irrespective of polygenic disease risk.

Genetic Risk, BMI Status, BMI Change Patterns, and the Risk of Steatotic Liver Disease and Liver Enzyme Elevation in Chinese Adults

Background/Objectives: Whether an increased genetic risk of steatotic liver disease (SLD) can be offset by maintaining a healthy weight remains unknown. We aimed to clarify the associations among the body mass index (BMI) and its change patterns with SLD and assess whether genetic susceptibility can modify these associations in Chinese people. Methods: A total of 10,091 and 6124 participants from the Health Omics Preventive Examination (HOPE) Program were enrolled in cross-sectional and follow-up analyses, respectively. BMI change patterns were defined according to the BMI at baseline and the last follow-up visit. Genetic risk was estimated using the polygenic risk score (PRS) derived from variants in PNPLA3, TM6SF2, MBOAT7, and GCKR. Data were analyzed using logistic regression models and Cox proportional-hazards models. Results: The analyses of the BMI and genetic risk simultaneously showed a dose-response association with the risk of SLD (p-trend < 0.001). Significant interactions between BMI and PRS were found for alanine aminotransferase (ALT) elevation (p = 0.007) and aspartate aminotransferase (AST) elevation (p < 0.001). Weight loss led to a 71%, 60%, and 67% lower risk of SLD, ALT elevation, and AST elevation, compared with stable overweight/obesity. A significant interaction between the genetic risk and BMI change patterns in ALT elevation was observed (p = 0.008). The absolute risk reductions associated with weight loss were greater for participants at a high genetic risk (26.60, 12.29, and 9.31 per 100 person years for SLD, ALT elevation, and AST elevation, respectively). Conclusions: Maintaining a healthy weight reduces the liver injury risk among all individuals, and the risk reduction is greater among the subset with a high genetic risk of SLD.

The interaction of genetics and physical activity in the pathogenesis of metabolic dysfunction associated liver disease

Genetic variants associated with increased liver fat and volume have been reported, but whether physical activity (PA) can attenuate the impact of genetic susceptibility to these traits is poorly understood. We aimed to investigate whether higher PA modify genetic impact on liver-related traits in the UK Biobank cohort. PA was self-reported, while magnetic resonance images were used to estimate liver fat (n = 27,243) and liver volume (n = 24,752). Metabolic dysfunction-associated liver disease (MASLD) and chronic liver disease (CLD) were diagnosed using ICD-9 and ICD-10 codes. Ten liver fat and eleven liver volume-associated genetic variants were selected and unweighted genetic-risk scores for liver fat (GRSLF) and liver volume (GRSLV) were computed. Linear regression analyses were performed to explore interactions between GRSLF/ GRSLV and PA in relation to liver-related traits. Association between GRSLF and liver fat was not different among lower (β = 0.063, 95% CI 0.041-0.084) versus higher PA individuals (β = 0.065, 95% CI 0.054-0.077, pinteraction = 0.62). The association between the GRSLV and liver volume was not different across different PA groups (pinteraction = 0.71). Similarly, PA did not modify the effect of GRSLF and GRSLV on MASLD or CLD. Our findings show that physical activity and genetic susceptibility to liver-related phenotypes seem to act independently, benefiting all individuals regardless of genetic risk.

A functional genomic framework to elucidate novel causal non-alcoholic fatty liver disease genes

Background & Aims

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver pathology in western countries, with serious public health consequences. Efforts to identify causal genes for NAFLD have been hampered by the relative paucity of human data from gold-standard magnetic resonance quantification of hepatic fat. To overcome insufficient sample size, genome-wide association studies using NAFLD surrogate phenotypes have been used, but only a small number of loci have been identified to date. In this study, we combined GWAS of NAFLD composite surrogate phenotypes with genetic colocalization studies followed by functional in vitro screens to identify bona fide causal genes for NAFLD.

Approach & Results

We used the UK Biobank to explore the associations of our novel NAFLD score, and genetic colocalization to prioritize putative causal genes for in vitro validation. We created a functional genomic framework to study NAFLD genes in vitro using CRISPRi. Our data identify VKORC1, TNKS, LYPLAL1 and GPAM as regulators of lipid accumulation in hepatocytes and suggest the involvement of VKORC1 in the lipid storage related to the development of NAFLD.

Conclusions

Complementary genetic and genomic approaches are useful for the identification of NAFLD genes. Our data supports VKORC1 as a bona fide NAFLD gene. We have established a functional genomic framework to study at scale putative novel NAFLD genes from human genetic association studies.

Development and Validation of a Risk Prediction Model for NAFLD: A Study Based on a Physical Examination Population

Purpose

To construct and validate a precise and personalized predictive model for non-alcoholic fatty liver disease (NAFLD) to enhance NAFLD screening and healthcare administration.

Patients and Methods

A total of 730 participants' clinical information and outcome measurements were gathered and randomly divided into training and validation sets in a ratio of 3:7. Using the least absolute shrinkage and selection operator (LASSO) regression and multiple logistic regression, a nomogram was established to select risk predictor variables. The NAFLD prediction model was validated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).

Results

After random grouping, the cohort comprised 517 in the training set and 213 in the validation set. The prediction model employed nine of the 20 selected variables, namely gender, hypertension, waist circumference, body mass index, blood platelet, triglycerides, high-density lipoprotein cholesterol, plasma glucose, and alanine aminotransferase. ROC curve analysis yielded an area under the curve values of 0.877 (95% Confidence Interval [CI]: 0.848-0.907) for the training set and 0.871 (95% CI: 0.825-0.917) for the validation set. Optimal critical values were determined as 0.472 (0.786, 0.825) in the training set and 0.457 (0.743, 0.839) in the validation set. Calibration curves for both sets showed proximity to the ideal diagonal, with P-values of 0.972 and 0.370 for the training and validation sets, respectively (P > 0.05). DCA indicated favorable clinical applicability of the model.

Conclusion

We constructed a nomogram model that could complement traditional NAFLD detection methods, aiding in individualized risk assessment for NAFLD.

Physical Activity and Nonalcoholic Fatty Liver Disease: A Roundtable Statement from the American College of Sports Medicine

ABSTRACT

Although physical activity (PA) is crucial in the prevention and clinical management of nonalcoholic fatty liver disease, most individuals with this chronic disease are inactive and do not achieve recommended amounts of PA. There is a robust and consistent body of evidence highlighting the benefit of participating in regular PA, including a reduction in liver fat and improvement in body composition, cardiorespiratory fitness, vascular biology, and health-related quality of life. Importantly, the benefits of regular PA can be seen without clinically significant weight loss. At least 150 min of moderate or 75 min of vigorous intensity PA are recommended weekly for all patients with nonalcoholic fatty liver disease, including those with compensated cirrhosis. If a formal exercise training program is prescribed, aerobic exercise with the addition of resistance training is preferred. In this roundtable document, the benefits of PA are discussed, along with recommendations for 1) PA assessment and screening; 2) how best to advise, counsel, and prescribe regular PA; and 3) when to refer to an exercise specialist.

Behavioural activity pattern, genetic factors, and the risk of nonalcoholic fatty liver disease: A prospective study in the UK Biobank

BACKGROUND & AIMS

Physical activity, sedentary behaviour, and genetic variants have been associated with the nonalcoholic fatty liver disease (NAFLD). However, whether and how the degree of healthy activity patterns may modify the impact of genetic susceptibility on NAFLD remains unknown.

METHODS

Behaviour activity factors were determined according to total physical activity (TPA) and sedentary time. The polygenic risk score (PRS) was calculated by variants in PNPLA3, TM6SF2, MBOAT7, and GCKR. Cox regression was used to analyse the associations of genetic and behaviour activity factors with incident NAFLD in the UK Biobank (N = 338 087).

RESULTS

During a median follow-up of 12.4 years, 3201 incident NAFLD cases were ascertained. Analyses of TPA and sedentary time simultaneously showed a dose-response association with the risk of NAFLD (ptrend  < .001). The association of behaviour activity patterns with NAFLD varied by genetic variants. Of the subjects with high genetic risk, we observed a null protective effect of moderate or high TPA on NAFLD risk, while sitting less than three hours a day significantly decreased the risk of NAFLD (p = 3.50 × 10-4 ). The high genetic risk of NAFLD can also be offset by the combination of moderate physical activity and shorter sedentary time. Moreover, the high genetic risk group has the greatest reduction of 10-year absolute risk (6.95 per 1000 person-years) if reaching both healthy activities.

CONCLUSIONS

Moderate-to-high physical activity and favourable sedentary behaviour may be lifestyle modifications in preventing NAFLD, which could offset the harmful effect of predisposing genetic factors.