Components of a healthy diet and different types of physical activity and risk of atherothrombotic ischemic stroke: A prospective cohort study.

Background: Diet and physical activity (PA) are modifiable risk factors thought to influence the risk of ischemic stroke (IS). However, few studies have examined their effect on different subtypes of IS. Aim: To examine components of overall diet quality and different types of PA in relation to the risk of atherothrombotic IS (aIS). Materials and methods: The study population included 23,797 participants (mean age 58 years; 63% women) from the Malmö Diet and Cancer Study cohort. Participants were enrolled between 1991 and 1996 and followed until end of 2016 (median follow-up 21.5 years). Incident aIS events were identified using national registries (total cases 1,937). Measures of PA (total, leisure-time, occupational, and domestic) were assessed using a baseline questionnaire and dietary intakes were estimated using a modified diet history method. Overall diet quality was assessed using a diet quality index. Intake of key food groups and beverages associated with overall diet quality were investigated separately. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using multivariable Cox regression models adjusting for confounders. Results: A high diet quality with high intake of fruit and vegetables, fish and shellfish and low intake of sugar-sweetened beverages and red and processed meat compared to a low diet quality was associated with lower risk of aIS (HR = 0.82, 95% CI = 0.69-0.97; p = 0.015). Leisure-time PA was associated with reduced risk of aIS (HR = 0.95 per SD increase in MET-hours/week, 95% CI = 0.91-0.99; p = 0.028) with null associations observed for total, occupational and domestic PA level. We observed no significant interaction between diet and PA on the risk of aIS. The standardized 20-year risk of aIS among subjects with low leisure-time PA and low diet quality was 8.1% compared to 6.1% among those with high leisure-time PA and high diet quality. Conclusion: Several components of a healthy diet and being physically active may reduce the risk of aIS, however, the absolute risk reduction observed was modest. A high diet quality seemed to have a risk reducing effect regardless of level of PA suggesting that individuals with a sedentary lifestyle may still gain some positive health benefits through a healthy diet.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Different domains of self-reported physical activity and risk of type 2 diabetes in a population-based Swedish cohort: the Malmö diet and Cancer study.

BACKGROUND: While a dose-response relationship between physical activity and risk of diabetes has been demonstrated, few studies have assessed the relative importance of different measures of physical activity on diabetes risk. The aim was to examine the association between different self-reported measures of physical activity and risk of type 2 diabetes in a prospective cohort study. METHODS: Out of 26,615 adults (45-74 years, 60% women) in the population-based Swedish Malmö Diet and Cancer Study cohort, 3791 type 2 diabetes cases were identified from registers during 17 years of follow-up. Leisure-time (17 activities), occupational and domestic physical activity were assessed through a questionnaire, and these and total physical activity were investigated in relation to type 2 diabetes risk. RESULTS: All physical activity measures showed weak to modest associations with type 2 diabetes risk. The strongest association was found in the lower end of leisure-time physical activity in dose-response analysis at levels approximately below 22 MET-hrs/week (300 min/week) representing around 40% of the population. Compared with the lowest quintile, the moderate leisure-time physical activity category had a 28% (95% CI: 0.71, 0.87) decreased risk of type 2 diabetes. Total physical activity showed a similar, but weaker, association with diabetes risk as to that of leisure-time physical activity. Domestic physical activity was positively and linearly related to diabetes risk, HR = 1.11 (95% CI: 0.99, 1.25) comparing highest to lowest quintile. There was no association between occupational physical activity and diabetes risk. CONCLUSION: A curvilinear association was observed between leisure-time physical activity and risk of diabetes. Beyond a threshold level of approximately 22 MET-hrs/week or 300 min/week, no additional risk reduction was observed with increase in physical activity.