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Funder: Kjell och Märta Beijers Stiftelse (Kjell and Marta Beijer Foundation); doi: https://doi.org/10.13039/501100006353, Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.

Additional file 2: Table S1a. Biventricular and left atrial absolute reference valuesfor healthy males (n=1126) and females (2080). Values reported as mean��SDwith 95% confidence intervals and normal ranges. Table S1b. Biventricular and left atrialreference values indexed to height for healthy males (n=1126)and females (2080). Values reported as mean��SD with 95% confidenceintervals and normal ranges. Table S2a. Biventricular and left atrialabsolute reference values for males 35 to 75 years, stratifiedby 10���year age categories. Values reported as mean��SDwith normal ranges. Table S2b. Biventricular and left atrial reference values indexedto height for males 35 to 75 years, stratified by 10���year agecategories. Values reported as mean��SD with normal ranges. Table S3a. Biventricular and left atrial absolute referencevalues for females 35 to 75 years, stratified by 10���yearage categories. Values reported as mean��SDwith normal ranges. Table S3b. Biventricular and left atrial reference values indexedto height for females 35 to 75 years, stratified by 10���yearage categories. Values reported as mean��SDwith normal ranges. Table S4a. Biventricular and atrial absolute reference values for healthy males (n=861) and females (1604) for white Caucasiansonly. Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S4b. Biventricular and left atrial reference values index toheight for healthy males (n=861) and females (1604) for white Caucasiansonly. Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S4c. Biventricular and atrial reference values indexed to BSAfor healthy males (n=861) and females (1604) for white Caucasiansonly. Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S5a. Biventricular and left atrial absolute reference valuesfor healthy males (n=191) and females (356) for Chinese only.Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S5b. Biventricular and left atrial reference values indexed toheight for healthy males (n=193) and females (356) for Chinese only.Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S5c Biventricular and left atrial reference values indexed toBSA for healthy males (n=193) and females (356) for Chinese only.Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S6a. Biventricular and left atrial absolute reference valuesfor healthy males (n=53) and females (70) for South Asians only.Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S6b. Biventricular and atrial reference values indexed to heightfor healthy males (n=53) and females (70) for South Asians only.Values reported as mean��SD with 95% confidence intervals and normal ranges. Table S6c. Biventricular and atrial reference values indexed to BSAfor healthy males (n=53) and females (70) for South Asians only.Values reported as mean��SD with 95% confidence intervals and normal ranges.

Additional file 1: Figure S1. Flow chartfor patient selection. RI, magnetic resonance imaging; LVEF, leftventricular ejection fraction; LV mass, left ventricular mass; CVD,cardiovascular disease; PURE, prospective urban and rural epidemiologicalstudy; CPTP, the Canadian Partnership for Tomorrow Project; BC Generations,British Columbia; OHS, Ontario Health Study; Atlantic PATH, AtlanticPartnership for Tomorrow's Health; MHI, Montreal Heart Institute. Figure S2. Age-specific trends for males and females for A) LV end-systolic volumesindexed to BSA (ml/m2); B) LV end-diastolic volumes indexed to BSA(ml/m2); and C) LVEF (%). Linear regression was applied to model the data,which are presented as mean (blue lines) and 95% confidence intervals (redlines). Figure S3. Representativeexamples of Bland Altman plots for inter-observer variability of absolute leftand right ventricular stroke volumes (ml). Figure S4. Representativeexamples of Bland Altman plots for intra-observer variability of absolute leftand right ventricular stroke volumes (ml).

Additional file1: Figure S1 Hazard ratios for doubling of serum creatinine and end-stage renal disease (ESRD) according to physical activity in patients with and without diabetes in unadjusted (right) and adjusted (left) analysis. The analyses on the right were adjusted for the variables diastolic blood pressure (DBP), baseline systolic blood pressure (SBP), heart rate (HR), age, sex, body mass index, renal function, geographical region, physical activity, formal education, alcohol consumption, tobacco use, history of hypertension, myocardial infarction, stroke, transient ischemic attack, heart rhythm, comedications, study and study medications. Figure S2 Cumulative incidence for the fourfold primary endpoint (cardiovascular death, myocardial infarction, stroke, hospitalization for heart failure worsening) (A), cardiovascular death (B), myocardial infarction (C), stroke (D) and hospitalization for heart failure worsening (E) according to physical activity level. Figure S3 Cumulative incidence for fourfold primary endpoint (cardiovascular death, myocardial infarction, stroke, hospitalization for heart failure worsening) (A), cardiovascular death (B), myocardial infarction (C), stroke (D) and hospitalization for heart failure worsening (E) according to physical activity level in patients with or without diabetes. Figure S4 Hazard ratios for the fourfold primary endpoint (A, cardiovascular death, myocardial infarction, hospitalization for heart failure worsening) and cardiovascular death (B) in patients with or without diabetes according to physical activity in unadjusted (left) and adjusted (right) analysis. The analyses on the right were adjusted for the variables diastolic blood pressure (DBP), baseline systolic blood pressure (SBP), heart rate (HR), age, sex, body mass index, renal function, geographical region, physical activity, formal education, alcohol consumption, tobacco use, history of hypertension, myocardial infarction, stroke, transient ischemic attack, heart rhythm, comedications, study and study medications.

Additional file 1: Fig. S1. [consort chart for MetS case selection]

Additional file 4: Table S2. [Unadjusted and intermediate adjusted models]

Additional file 2. Supplemental Methods [IDEFICS MetS definitions and MSI-CE-MS data processing]

Additional file 6: Fig. S2. [Clustering of MetS cases]

Additional file 6: Fig. S2. [Clustering of MetS cases]

Additional file 2. Supplemental Methods [IDEFICS MetS definitions and MSI-CE-MS data processing]

Additional file 5: Table S3. [Pathway analysis]

Additional file 3: Table S1. [Serum metabolites characteristics]

Additional file 3: Table S1. [Serum metabolites characteristics]

Additional file 5: Table S3. [Pathway analysis]

Research statement: To investigate the age specific blood pressure change in South Asian children from childhood to adolescence over a six year period and to determine whether this change of blood pressure varies by baseline characteristics. Rationale: Children exhibit age related incremental changes in blood pressure from birth onwards which reach adult levels during late adolescence. The best way to measure age-related changes in blood pressure from childhood to adolescence is through prospective longitudinal studies. Objectives: to investigate the changes in blood pressure among South Asian children aged 6 to 11 years who were followed for a period of six years. Methods: Blood pressure and anthropometric data were collected from 703 children who were 5 to 11 years of age in 2005 and were re-examined after six years (age range of 11 to 17 years in 2011). Average difference between baseline and follow-up blood pressure measurements was evaluated using the paired t-test. A linear regression model with follow up blood pressure and blood pressure change as outcomes and sex, weight, and height as regressors were fitted to assess whether these predictor variables were associated with the dependent variables. Summary of results: Systolic and diastolic blood pressure increases from childhood to adolescence. Baseline blood pressure appears to be the strongest predictor of follow up blood pressure. Change in body mass index influences blood pressure more than baseline body mass index. Boys showed higher levels of change in systolic blood pressure compared to girls. Age showed an interaction with sex for change in systolic blood pressure. Boys exhibited higher change in systolic blood pressure than girls in older age groups compared to younger age groups. Potential implications: The study findings will increase awareness about high blood pressure in children and lead to preventive strategies to contain the burden of hypertension in future. Master of Science (MSc)

Smoking/tobacco use is the leading preventable cause of death worldwide. Despite understanding the health consequences of smoking, less is known as to how and why the effects of smoking emerge differently between countries and within populations both over time and across socioeconomic groups and geographic locales. In this thesis, we examined socioeconomic status (SES) and geography as two potential causes of variability in current rates of tobacco use and cessation in Canada and India, countries at diverse levels of economic development and epidemiological transition. The major findings were: (i) low SES, defined by education, income, and occupation, was associated with increased risk of tobacco consumption in both Canada and India, although there was variability in the strength of this association by form of tobacco use in India; (ii) in a 60-year longitudinal study, rates of smoking have fallen over time in Canada but socioeconomic gaps have widened; (iii) smoking quit rates were higher in Canada than India; although in both countries there was a positive association between SES and quitting; (iv) geographic variation in tobacco use and quit rates remained after accounting for individual socioeconomic and demographic characteristics, suggesting the importance of place in shaping patterns of tobacco use in Canada and India. Taken together, these findings indicate that tobacco use in populations is strongly patterned along socioeconomic and geographic dimensions. Future prevention and cessation programs will need explicit consideration of socioeconomic and geographic aspects of the tobacco use distribution to effectively improve the situation across all areas and groups. Doctor of Philosophy (PhD)