
==== Front
Med Sci Sports Exerc
Med Sci Sports Exerc
MSSE
Medicine and Science in Sports and Exercise
0195-9131
1530-0315
Lippincott Williams & Wilkins

38768019
MSSE_240906
10.1249/MSS.0000000000003482
00010
3
Epidemiology
Genetic Liability to Cardiovascular Disease, Physical Activity, and Mortality: Findings from the Finnish Twin Cohort
JOENSUU LAURA 1 2
WALLER KATJA katja.waller@jyu.fi
1
KANKAANPÄÄ ANNA anna.k.kankaanpaa@jyu.fi
1 2
PALVIAINEN TEEMU teemu.palviainen@helsinki.fi
3
KAPRIO JAAKKO jaakko.kaprio@helsinki.fi
3
SILLANPÄÄ ELINA elina.sillanpaa@jyu.fi
1 4
1 Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
2 Gerontology Research Center, University of Jyväskylä, Jyväskylä, FINLAND
3 Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, FINLAND
4 Wellbeing Services County of Central Finland, Jyväskylä, FINLAND
Address for correspondence: Laura Joensuu, Ph.D., University of Jyväskylä, PO Box 35, FI-40014 Jyväskylä, Finland; E-mail: laura.p.joensuu@jyu.fi; @laurajoensuu.
10 2024
15 5 2024
56 10 19541963
2 2024
4 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Sports Medicine.
2024
Lippincott Williams & Wilkins
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

ABSTRACT

Purpose

We investigated whether longitudinally assessed physical activity (PA) and adherence specifically to World Health Organization PA guidelines mitigate or moderate mortality risk regardless of genetic liability to cardiovascular disease (CVD). We also estimated the causality of the PA–mortality association.

Methods

The study used the older Finnish Twin Cohort with 4897 participants aged 33 to 60 yr (54.3% women). Genetic liability to coronary heart disease and systolic and diastolic blood pressure was estimated with polygenic risk scores (PRS) derived from the Pan-UK Biobank (N ≈ 400,000; >1,000,000 genetic variants). Leisure-time PA was assessed with validated and structured questionnaires three times during 1975 to 1990. The main effects of adherence to PA guidelines and the PRS × PA interactions were evaluated with Cox proportional hazards models against all-cause and CVD mortality. A cotwin control design with 180 monozygotic twin pairs discordant for meeting the guidelines was used for causal inference.

Results

During the 17.4-yr (mean) follow-up (85,136 person-years), 1195 participants died, with 389 CVD deaths. PRS (per 1 SD increase) were associated with a 17% to 24% higher CVD mortality risk but not with all-cause mortality except for the PRS for diastolic blood pressure. Adherence to PA guidelines did not show significant independent main effects or interactions with all-cause or CVD mortality. Twins whose activity levels adhered to PA guidelines over a 15-yr period did not have statistically significantly reduced mortality risk compared with their less active identical twin sibling. The findings were similar among high, intermediate, and low genetic risk levels for CVD.

Conclusions

The genetically informed Finnish Twin Cohort data could not confirm that adherence to PA guidelines either mitigates or moderates genetic CVD risk or causally reduces mortality risk.

Key Words

EXERCISE
GENETIC EPIDEMIOLOGY
HEALTH PROMOTION
PUBLIC HEALTH
SDCT
OPEN-ACCESSTRUE
==== Body
pmcCardiovascular diseases (CVD) remain the leading cause of premature death worldwide, representing a significant public and personal health burden (1). Although modifiable lifestyle factors contribute to disease development, heritability estimates from twin studies range from 40% to 60%, indicating that the role of genetic factors is not negligible (2,3). Advances in human genetics have enabled the transition from twin designs to assessing the individual genetic disease risk directly from the genome. Novel polygenic risk scores (PRS) summarize an individual’s genome-wide information from up to 1 million single-nucleotide polymorphisms (SNP) into a single score indicating a person’s genetic liability to a disease or trait (4). Higher genetic liability to CVD as quantified by a PRS is associated with a significantly higher risk of developing a CVD (5,6), and the American Heart Association suggests that the use of PRS may enable earlier guidance of at-risk individuals for lifestyle modification and medical treatment (7).

Although an overall healthy lifestyle—including not smoking, maintaining a healthy diet, engaging in regular physical activity (PA), and having a normal body weight—has shown promise in attenuating the risks of genetic liability to CVD (8,9), less is known about the independent role of PA. Public health authorities recommend PA to prevent CVD and premature death (10), with consistent observational evidence across various demographic groups and different measures of PA supporting this recommendation (11–16). However, there is less agreement on whether PA attenuates these major adverse health outcomes irrespective of an individual’s genetic liability to CVD. There is inconsistent evidence of whether PA is independently associated with reduced health risks regardless of the genetic disease risk quantified by PRS (8,9,17). More evidence is needed, especially regarding longitudinally assessed PA and adherence to the World Health Organization’s (WHO) PA guidelines. This evidence is relevant in light of current recommendations in which PA aims to offer significant health risk mitigation for all (18).

In addition, although the observational evidence indicating PA’s association with reduced mortality risk accumulates (19), there is limited evidence regarding the causality of this association. Past randomized controlled trials (20), Mendelian randomization studies (21–23), animal models, and analyses among twins (24) have failed to prove the causal nature of this phenomenon. There are few theories as to why PA may not be causally associated with longevity: observational data may be prone to reverse causality, unmeasured confounding, and underlying genetic pleiotropy as potential sources of bias (24,25). In pleiotropy, the same genotype supports both the ability to be physically active and to resist CVD as supported by evidence from recent findings among large cohort studies (26,27). Further evidence is therefore needed (28), and more data are requested to assess PA–mortality associations among monozygotic twins who share their genome, early life environment, and many other major confounders, which are difficult to measure, but who differ in respect to their PA behavior in adulthood (29).

This study investigated whether longitudinally assessed PA and adherence specifically to current WHO PA guidelines are associated with reduced mortality risk in individuals with known genetic liability to CVD. We also evaluated the causality of the PA–mortality association, employing a cotwin control design with monozygotic twins who were discordant for their adherence to WHO PA guidelines over a 15-yr period.

METHODS

Study Oversight

The older Finnish Twin Cohort (FTC) is a prospective cohort established in 1974 with 50 yr of follow-up to date. Briefly, all same-sex twins born before 1958 and living in Finland were screened from population registers and contacted in 1975 with a baseline survey. The response rate was 84.4% (30). Following the practices of the time, returning the questionnaire was considered informed consent for study participation. Participation was voluntary and could be withdrawn at any point. The National Board of Health of Finland approved the launch of the cohort, and diverse processes of the study and data pooling have been evaluated at relevant times by the ethics committees of the University of Helsinki (113/E3/01 and 346/E0/05), Helsinki University Central Hospital (136/E3/01, 01/2011, 270/13/03/01/2008, 154/13/03/00/2011, HUS/1799/2017), and the Finnish Institute for Health and Welfare (THL/4743/6.02.04/2021). The authors assume responsibility for the accuracy and completeness of the protocol, data, and analyses, for fidelity in reporting, and for compliance with the Declaration of Helsinki and the guidelines of the Finnish Advisory Board on Research Integrity.

Study Design

This study used a genotyped subsample of the FTC (N = 8838). After excluding participants with missing data, the final complete-case sample included 4897 participants (see Supplemental Fig. 1, Supplemental Digital Content, for a flow chart, http://links.lww.com/MSS/D47). In addition, the genotyped subsample included 1348 monozygotic twin pairs. After excluding twins with missing data (412 pairs) and who were not discordant for WHO PA guidelines (756 pairs), a total of 180 twin pairs were used for analyses (see Supplemental Fig. 2, Supplemental Digital Content, for a flow chart, http://links.lww.com/MSS/D47).

All the participants answered structured, validated questionnaires in 1975, 1981, and 1990, and their vital status was assessed in 2020. The follow-up began from individual DNA-sampling dates (see Supplemental Fig. 3, Supplemental Digital Content, for details, http://links.lww.com/MSS/D47) and ended at death, migration, or end of the follow-up on December 31, 2020, whichever came first (Fig. 1).

FIGURE 1 Study design.

Outcomes

All-cause mortality and CVD mortality were assessed. Dates and causes of death by December 31, 2020, were retrieved from the Population Register Center of Finland and Statistics Finland. All-cause mortality included all events leading to death. CVD mortality was based on Statistics Finland registers and included ischemic heart diseases (International Classification of Diseases (ICD)-10 code I20 to I25); other forms of heart disease except rheumatic and alcoholism-based ones (I30 to I425, I427 to I52); cerebrovascular diseases (I60 to I69); hypertension; pulmonary heart disease; diseases of pulmonary circulation; diseases of arteries, arterioles/capillaries, veins, lymphatic vessels, and lymph nodes; and other unspecified disorders of the circulatory system (I10 to I15, I26 to I28, I70 to I99) (31,32). Mortality in the FTC does not differ from that of the general Finnish population (33).

Exposures

Polygenic risk scores

CVD are polygenic traits where many genetic variants with small individual effect sizes are associated with the trait. A PRS summarizes the genome-wide information between SNP and a certain phenotype as a single variable that quantifies a person’s genetic liability to a disease or trait (4). PRS were calculated for three CVD: coronary heart disease (CHD), systolic blood pressure (SBP), and diastolic blood pressure (DBP). The PRS were based on genome-wide association data from the Pan-UK Biobank and restricted to participants with European ancestry. The Pan-UK Biobank data included 396,663 to 419,724 participants (see Supplemental Table S1, Supplemental Digital Content, for detailed information related to the base data, http://links.lww.com/MSS/D47). We used the SBayesR pipeline to construct the PRS based on 1,005,933 to 1,006,472 genetic variants. SBayesR is a Bayesian multiple regression approach in which the summary statistics of a genome-wide association study are reweighted to consider the linkage disequilibrium between each variant and are restricted to HapMap3 SNP to ensure computational efficiency and generalizability of the findings, while representing the whole genome (34). No specific threshold is set for the variants in which weights are used in scoring. A detailed pipeline with the codes employed is available from Herranen et al. (35). For analyses, the raw PRS were standardized to z-scores.

Genotyping, quality control, and imputation

Genotyping in the UK-Biobank was performed with a customized array and imputed to the Haplotype Reference Consortium panel, yielding ~7 million SNP (imputation quality score [INFO] ≥0.1 and minor allele frequency ≥1%) (36). Genotyping in the FTC was performed with Illumina Human610-Quad v1.0 B, Human670-QuadCustom v1.0 A, Illumina HumanCoreExome (12 v1.0 A, 12 v1.1 A, 24 v1.0 A, 24 v1.1 A, 24 v1.2 A), and Affymetrix FinnGen Axiom arrays and imputed to the Haplotype Reference Consortium release 1.1 reference panel (37).

Physical activity

PA during leisure time was assessed with structured, validated questionnaires in 1975, 1981, and 1990 (30,38,39). The questions asked are shown below:

Leisure activity

Intensity: Is your PA during leisure time about as strenuous on average as follows?

(a) Walking (corresponding to 4 METs)

(b) Alternately walking and jogging (6 METs)

(c) Jogging (10 METs)

(d) Running (13 METs)

Duration: How long does the PA last at one session on average?

(a) Less than 15 min (class midpoint, 7.5 min)

(b) 15 min to less than 30 min (22.5 min)

(c) 30 min to less than 1 hour (45 min)

(d) 1 hour to less than 2 hours (90 min)

(e) Over 2 hours (120 min)

Frequency: Presently, how many times per month do you engage in PA during your leisure time?

(a) Less than once a month (class midpoint, 0.5)

(b) 1 to 2 times per month (1.5 times)

(c) 3 to 5 times per month (4 times)

(d) 6 to 10 times per month (8 times)

(e) 11 to 19 times per month (15 times)

(f) More than 20 times per month (20 times)

Commuting activity

Intensity was expected to be 4 METs.

Duration: How much of your daily journey to work is spent in walking, cycling, running, and/or cross-country skiing?

(a) Less than 15 min (class midpoint, 7 min)

(b) 15 min–less than a half an hour (22 min)

(c) Half an hour to less than an hour (45 min)

(d) An hour or more (75 min)

(e) I am presently not at work (0)

The main PA exposure was adherence to the WHO PA guidelines for aerobic activity. This recommendation for adults is 150 to 300 min of moderate-intensity or 75 to 150 min of vigorous-intensity PA, or an equivalent combination, per week (18). We note that the recommendation also includes regular muscle-strengthening activities and reduction of sedentary behavior, which were not addressed in this study. Therefore, meeting the PA guidelines was defined as a minimum of 7.5 MET·h·wk−1 (13) and was based on the accumulated mean MET-hours per week over the 15-yr observational period (1975 to 1990) in those who had answered at all three measurement points. The MET-hours per week was calculated as a product of the intensity, duration, and frequency of reported activity (38). We also utilized the continuous mean MET-hours per week during 1975 to 1990 and vigorous PA (at least intermittent jogging) in 1975, 1981, and 1990 in further sensitivity analyses.

Covariates

Ten principal components of ancestry were used to adjust for any genetic stratification that may occur in the Finnish population (40). Of other covariates, age, sex, educational attainment, body mass index (BMI), smoking, fruit and vegetable consumption, and alcohol consumption were assessed by questionnaires. The last known and/or most comprehensive data were used (1981 or 1990).

Educational attainment describes self-reported educational attainment by 1981 based on the Finnish education system: low (basic education degree at most), middle (basic education and additional studies), and high (at least an upper secondary degree).

Smoking was assessed with multiple questions in 1990 to derive the following categories: never (no current or prior smoking), occasional (occasional smoking but never daily), and former (prior regular smoking). Current regular smokers were classified further by smoked cigarettes per day (CPD): light (1 to 9 CPD), medium (10 to 19 CPD), and heavy (≥20 CPD).

Fruit and vegetable consumption was assessed in 1981 with the question “When you are eating a meal or a snack, how many times a day do you eat vegetables or fruits (a minimum amount equivalent of one tomato): not once, once or twice, three to five times, six times or more?”

Alcohol consumption was based on consumed grams of alcohol per day in 1990 (41): lifetime abstainer (0 g and no prior alcohol consumption), former (0 g but prior alcohol consumption), occasional (>0.1 and <1.3 g), low (≥1.3 and <25 g), medium (≥25 and <45 g), high (≥45 g and <65 g), and very high (≥65 g·d−1).

Health status was assessed by the most extensive assessment available, including data from a questionnaire in 1981 (Q1981) and data from Finnish nationwide registers in 1971 to 1983: Nationwide Hospital Discharge Register, Social Insurance Institution of Finland, and the Finnish Cancer Registry (42). Health status is a binary variable, with 1 indicating the subject to be “healthy.” Subjects with the following criteria were defined 0, “not healthy”: physician-diagnosed angina pectoris, myocardial infarction, or diabetes mellitus (Q1981); self-reported history of chest pain (Q1981); inpatient admission for diabetes (International Classification of Diseases, Eighth Revision (ICD-8), code 250); CVD other than hypertension or venous diseases (ICD-8 codes 390 to 399 and 410 to 449); and chronic obstructive pulmonary disease (ICD-8 codes 490 to 493) in 1972 to 1982 (Nationwide Hospital Discharge Register); reimbursable medication for selected chronic diseases other than hypertension before January 1, 1983 (Social Insurance Institution of Finland); and malignant cancer before 1983 (Finnish Cancer Registry).

Statistical Analysis

The data were inspected for distribution and potential outliers. The potential moderator effect of sex in the association between PA and mortality was tested, but the interaction term was found nonsignificant (P = 0.990). Therefore, the analyses were conducted for both sexes combined. For survival analysis, i.e., time to an event, we used the Cox proportional hazards model, which relies on two main assumptions: 1) the hazards are proportional over time, and 2) the relationship between log hazards and covariates is linear. We tested these model assumptions by Schoenfeld and Martingale residuals with main variables of interest and observed no violations. As the genetic liability to CVD may cause bias in who survives to take part in subsequent genotyping, the follow-up started from the date of DNA sampling. Follow-up ended at death, migration, or the end of the follow-up in 2020.

In the first phase of the analyses, we assessed the independent main effects of PRS and adherence to PA guidelines on mortality as well as the multiplicative interactions between PRS and PA (PRS × PA). Models were adjusted for covariates. We used a complete-case approach over imputation to firmly maintain the unique twin structure in the data. The most adjusted models were considered to provide the most robust evidence. We evaluated the differences between the complete cases of the most adjusted model (n = 4897) and the overall study population (N = 8838) at baseline with linear regression for continuous variables, logistic regression for binary variables, and multinominal regression for categorical variables. The complete-case sample was younger (mean, 31.4 vs 34.0 yr), had a lower BMI (23.0 vs 23.4 kg·m−2), and was more likely healthy (84.6% vs 72.1%) compared with the full sample at baseline. The complete-case sample also had higher educational attainment (at least upper secondary degree: 15.7% vs 13.8%) and more likely consumed low levels of alcohol (77.0% vs 73.0%). However, no differences were observed in PA, smoking, or diet between the complete-case sample and the full sample at baseline (see Supplemental Table S3, Supplemental Digital Content, for descriptive data, http://links.lww.com/MSS/D47).

In the second phase, the within-pair differences in mortality were assessed among monozygotic twin pairs discordant for their adherence to PA guidelines. A cotwin control design with Cox proportional hazards models was used. In a cotwin control design, one twin is exposed to a given environmental factor, whereas the other twin is not. This difference in their exposure is thereafter evaluated against their difference in survival. The cotwin control design in monozygotic twins is considered to be an elegant study design for causal inference in epidemiological studies, as many potential confounding factors (the genotype and childhood environments as well as many major covariates in adulthood, such as education and lifestyle factors) are shared among identical twins (43,44). Models are therefore controlled by design for confounders that are typically difficult to measure. The models were additionally adjusted for main lifestyle risk factors; BMI, and smoking status in analyses with the greatest amount of twin pairs. The genetic disease risk was categorized to high, intermediate, and low by sample-specific tertiles.

We also conducted sensitivity analyses. We assessed PA additionally as a continuous variable of mean MET-hours per week in 1975 to 1990. In addition, a binary variable of reporting vigorous PA in all measurement points (1975, 1981, and 1990) or not in any was formed for significant contrast. As diseases are a potential source of reverse causality, we repeated all the analyses among healthy participants. However, these analyses were not conducted for CVD mortality because of low case numbers. Similarly, pairwise analyses were not performed for vigorous PA because of the small number of cases.

Throughout the study, all analyses were conducted separately for the three distinct genetic liabilities (CHD, SBP, DBP). As the results follow a similar pattern across these genetic liabilities, results related to genetic liability to CHD are presented in the main tables and the others in the Supplemental Digital Content, http://links.lww.com/MSS/D47. Across all analyses, the twin structure of the data was acknowledged by using family identification numbers for clustering in individual-level analysis and for stratification in pairwise analysis. Statistical significance was set at P < 0.01 after Bonferroni correction for multiple testing (two-phased analyses for three different CVD), and 99% confidence intervals were reported. The analyses were performed with Stata/IC 16.0.

RESULTS

Characteristics of the study sample

The descriptive characteristics of the 4897 participants are presented in Table 1. The participants were on average 46.4 (7.6) yr old and predominantly healthy (84.6%), with 79.2% adhering to the PA guidelines. During the 17.4-yr (mean) follow-up (85,136 person-years), 1195 participants died, with 389 CVD deaths. PRS indicating genetic liability to CVD indicated valid mortality risk in the FTC. In crude models, 1 SD increment in PRS was associated with an 8% to 11% and 19% to 26% increased risk in all-cause and CVD mortality, respectively. The only exception was the PRS for SBP, which was not associated with all-cause mortality in crude models (see Supplemental Table S2, Supplemental Digital Content, for results, http://links.lww.com/MSS/D47). These significant associations persisted after adjusting for PA and covariates for all PRS in CVD mortality and for the PRS for diastolic blood pressure in all-cause mortality (Table 2 for CHD; Supplemental Tables S5 to S6 for others, http://links.lww.com/MSS/D47).

TABLE 1 Subject characteristics.

Characteristics	All	Men	Women	
N	Mean (SD) or %	N	Mean (SD) or %	N	Mean (SD) or %	
 Age (yr)	4897	46.4 (7.6)	2239	46.7 (7.6)	2658	46.2 (7.7)	
 Height (cm)	4897	168.6 (8.8)	2239	175.7 (6.2)	2658	162.6 (5.6)	
 Weight (kg)	4897	71.2 (13.3)	2239	79.0 (11.2)	2658	64.6 (11.3)	
 BMI (kg·m−2)	4897	25.0 (3.8)	2239	25.6 (3.2)	2658	24.4 (4.1)	
PAa							
 Meets PA guidelines (%)	3879	79.2	1731	77.3	2145	80.7	
 Mean MET-hours per week	4897	18.6 (15.5)	2239	19.9 (18.1)	2658	17.4 (12.8)	
 Vigorous PA (%)	942	19.2	548	11.2	394	8.0	
Educational levelb							
 Low (%)	1800	36.8	837	37.4	963	36.2	
 Middle (%)	2329	47.6	1082	48.3	1247	46.9	
 High (%)	768	15.7	320	14.3	448	16.9	
Health statusb							
 Healthy (%)	4145	84.6	1941	86.7	2204	82.9	
Smoking							
 Never (%)	2305	47.1	730	32.6	1575	59.3	
 Occasional (%)	149	3.0	76	3.4	73	2.8	
 Former (%)	1241	25.3	775	34.6	466	17.5	
 Light (%)	266	5.4	94	4.2	172	6.5	
 Medium (%)	497	10.2	247	11.0	250	9.4	
 Heavy (%)	439	9.0	317	14.2	122	4.6	
Fruit and vegetable consumption per dayb							
 Not once (%)	357	7.3	249	11.1	108	4.1	
 Once or twice (%)	4064	83.0	1839	82.1	2225	83.7	
 Three times or more (%)	476	9.7	151	6.7	325	12.2	
Alcohol consumption							
 Lifetime abstainer (%)	381	7.8	93	4.2	288	10.8	
 Former (%)	56	1.1	29	1.3	27	1.0	
 Occasional (%)	298	6.1	52	2.3	246	9.3	
 Low (%)	3692	75.4	1677	74.9	2015	75.8	
 Medium (%)	314	6.4	253	11.3	61	2.3	
 High (%)	108	2.2	96	4.3	12	0.5	
Very high (%)	48	1.0	39	1.7	9	0.3	
Values are means and SD for continuous variables and proportions of all participants for others. Data from 1990 if not else specified; a, data from 1975 to 1990; b, data from 1981; meeting PA guidelines, the average of MET-hours per week during 1975 to 1990 ≥7.5; vigorous PA, at least intermittent jogging in 1975, 1981, and 1990; educational level is based on the Finnish education system: low, basic education degree at most; middle, basic education and additional studies; high, at least upper secondary degree; healthy, no physician-diagnosed angina pectoris, myocardial infarction, or diabetes mellitus, self-reported history of chest pain, inpatient admission for diabetes, CVD other than hypertension or venous diseases, chronic obstructive pulmonary disease, reimbursable medication for selected chronic diseases other than hypertension, or malignant cancer; smoking: never, no current or prior smoking; occasional, occasional smoking but never daily; former, prior regular smoking; light, 1 to 9 CPD; medium, 10 to 19 CPD; heavy, ≥20 CPD; fruit and vegetable consumption: the amount of vegetables of fruits per day with a minimum amount equivalent of one tomato; alcohol consumption: lifetime abstainer, 0 g and no prior alcohol consumption; former, 0 g but with prior alcohol consumption; occasional, >0.1 and <1.3 g; low, ≥1.3 and <25 g; medium, ≥25 and <45 g; high, ≥45 and <65 g; and very high, ≥65 g·d−1.

TABLE 2 Associations between PRS for CHD, PA, and risk of mortality.

Risk of mortality	Model 1	Model 2	Model 3	
All-cause mortality				
 Deaths/persons	1195/4897	1195/4897	1195/4897	
 PRS CHD (z-score)	1.07 (0.99 to 1.16)	1.07 (0.99 to 1.16)	1.07 (0.99 to 1.15)	
 Meeting PA guidelines		0.86 (0.72 to 1.02)	0.93 (0.78 to 1.11)	
Cardiovascular mortality				
 Deaths/persons	389/4897	389/4897	389/4897	
 PRS CHD (z-score)	1.19 (1.04 to 1.35)	1.19 (1.04 to 1.35)	1.17 (1.03 to 1.33)	
 Meeting PA guidelines		0.80 (0.59 to 1.09)	0.92 (0.67 to 1.25)	
Values are HR with 99% confidence intervals; z-score, standardized score; meeting PA guidelines, the average of self-reported PA from 1975 to 1990 ≥7.5 MET·h·wk−1; values indicate a change in mortality risk with 1 SD increase in the PRS, and change in mortality risk with adherence to the PA guidelines; model 1, PRS CHD; model 2, additionally meeting PA guidelines; model 3, additionally, BMI, smoking, healthy diet, alcohol consumption; all models are additionally adjusted for 10 principal components, age, sex, and educational attainment, health status and clustered based on the twin structure of the data. Statistically significant associations (P < 0.01) highlighted in bold.

Main Effects and Interactions of Adherence to PA Guidelines with Mortality

Meeting the PA guidelines was not associated with a reduced risk of all-cause or CVD mortality, and the estimate was further attenuated when accounting for BMI, smoking, healthy diet, and alcohol consumption (Table 2 for CHD; Supplemental Tables S5 and S6 in Supplemental Digital Content for others, http://links.lww.com/MSS/D47). No interactions were observed between PRS and adherence to PA guidelines regarding mortality (Supplemental Table S4 in Supplemental Digital Content, http://links.lww.com/MSS/D47).

The findings from sensitivity analyses with a continuous PA metric (mean MET-hours per week, 1975 to 1990) show that the favorable associations of PA were small (hazard ratio (HR): 0.99; 99% confidence intervals: 0.99–0.99) and attenuated to a nonsignificant level after adjustments with other healthy lifestyles (HR: 1.00; 0.99–1.00) (see Supplemental Tables S7 to S9 in Supplemental Digital Content, http://links.lww.com/MSS/D47). For vigorous PA, HR for all-cause mortality was 0.75 (0.59–0.97), but attenuated strongly in the most adjusted model (HR: 0.94; 0.72–1.23). The estimates for CVD mortality were not statistically significant but showed a similar pattern of attenuation (Supplemental Tables S10 to S12, http://links.lww.com/MSS/D47). No interactions were observed for additional PA metrics (Supplemental Table S4, http://links.lww.com/MSS/D47). No significant associations of adherence to PA guidelines, mean MET-hours per week, or vigorous PA with mortality were observed among healthy participants (Supplemental Tables S13 to S15 in Supplemental Digital Content, http://links.lww.com/MSS/D47).

The Causal Relationship between PA and Mortality

The twin pairs in the cotwin control design differed from one another by their PA levels (mean, 4.9 vs 15.8 MET·h·wk−1, P < 0.001) but not by other characteristics (Fig. 2). The twin meeting the PA guidelines did not have a statistically significantly decreased risk of all-cause (HR: 0.74; 0.37–1.48) or CVD mortality (HR: 0.39; 0.10–1.46) in the within-pair comparison between more and less active twins (Table 3). The findings were similar across high, intermediate, and low genetic disease risk categories (Table 3). The findings were also similar with genetic liabilities for SBP and DBP (see Supplemental Table S16 for all-cause mortality and Table S17 for CVD mortality, Supplemental Digital Content, http://links.lww.com/MSS/D47) and in sensitivity analyses among healthy twin pairs (see Supplemental Table S18 for results, Supplemental Digital Content, http://links.lww.com/MSS/D47).

FIGURE 2 Descriptive characteristics of the monozygotic twin pairs in which one twin meets the PA guidelines and the other does not. Values are means and SD or proportions (%). Meeting PA guidelines is defined as an average of self-reported PA from 1975 to 1990 ≥7.5 MET·h·wk−1. Data from 1990 if not otherwise specified; a, data from 1981; P, statistical difference between twins meeting the PA guidelines and those not; NA, not applicable due low number of twins. Statistically significant associations (P < 0.01) are highlighted in bold.

TABLE 3 Within-pair risk of mortality in more active twins compared with their less active twin for 360 twins from 180 discordant pairs.

		Twin Meeting PA guidelines	
Risk of mortality	Deaths/Twins	HR (99% CI)	
All-cause mortality			
Among all twin pairs#	87/360	0.74 (0.37 to 1.48)	
By genetic liability for CHD			
 High	36/120	0.69 (0.23 to 2.11)	
 Intermediate	22/120	0.55 (0.15 to 2.01)	
 Low	29/120	1.00 (0.30 to 3.37)	
Cardiovascular mortality			
Among all twin pairs#	36/360	0.39 (0.10 to 1.46)	
By genetic liability for CHD			
 High	16/120	0.43 (0.07 to 2.54)	
 Intermediate	10/120	0.50 (0.05 to 4.65)	
 Low	10/120	0.25 (0.01 to 4.45)	
In a cotwin control design with monozygotic twins, twin pairs are controlled for their genetic factors, and early life environmental factors. #, models are additionally adjusted for BMI and smoking status. Meeting PA guidelines, the average of self-reported PA from 1975 to 1990 ≥7.5 MET·h·wk−1.

DISCUSSION

This study evaluated the role of PA in mitigating the mortality risk of genetic liability to CVD as well as the causality of the PA–mortality association. We had the rare opportunity to use an exceptional cohort of genotyped twins in our analyses. We did not find evidence that adherence to PA guidelines was independently associated with reduced mortality in individuals with known genetic liability to CVD after adjusting for other healthy lifestyles, nor did we observe that PA moderates the genetic CVD risk or is causally associated with mortality. These findings are summed up in the words of a pioneer in this study line, Emeritus Professor Urho Kujala: “Physical activity might not bring more years to life but more life to years” (25), which provide a framework for following critical discussion.

We did not observe favorable independent main effects of longitudinally assessed PA on mortality in this study. The findings were either nonsignificant or attenuated to a nonsignificant level after adjusting for other lifestyle factors. It is recommended to acknowledge that, in quantitative estimates, the accumulated evidence of traditional observational studies indicates that regular PA is associated with extended life spans at a modest level, with estimates ranging between +0.7 and +2.7 yr on average (45,46). The long (on average, 17.4 yr) follow-up design in the present study potentially reduced the effects of PA to nonsignificant levels. We have previously observed in simulation analyses that the association of PA attenuates in a longer follow-up design (>10 yr) compared with a shorter design (<10 yr) (47).

We observed that the favorable independent main effects of longitudinally assessed PA were attenuated when adjusting for other healthy lifestyles (BMI, smoking, alcohol consumption, and diet). Clustering of healthy lifestyles is a phenomenon observed already in adolescents, among whom individuals who engage in regular leisure-time PA are often also nonsmokers, drink less, and have normal weight (48). Therefore, it is recommended to consider that PA may act as a proxy of an individual’s healthier overall lifestyle profile.

This unobserved confounding is one potential source of bias in observational studies, but other sources of bias may also occur. In healthy exerciser bias, individuals with good health and the resources to maintain healthy lifestyles exhibit both higher levels of habitual PA and lower risk of death in prospective studies (25). In addition to these environmental exposures, the underlying genetics are associated with an individual’s health behavior and may cause bias (27,37). Individuals with high genetic liability for PA (whose PRS for PA is >90th percentile of the genetic spectrum) gain on average 1200 steps and 10 min more moderate-to-vigorous PA every day compared with individuals with a low PRS for PA (<10th percentile) (P < 0.001) (37). This same genotype is observably associated with a lower risk of CVD (hypertensive diseases, cerebrovascular disease, and stroke) and related risk factors (lower risk of obesity, waist circumference, type 2 diabetes, hyperlipidemia, SBP, and higher high-density lipoprotein) in recent large-scale cohort studies (26,27). Animal studies support this hypothesis of genetic pleiotropy as manifested in bred rat strains as phenotypes with both higher self-selected PA levels and longer lifespans (24). These environmental and genetic sources of bias are difficult to quantify in traditional study designs, but it is important to consider them in future studies when critically evaluating the benefits of PA.

Our study found no evidence of interactions between PA and genetic risk in relation to mortality. Although the existing literature is limited to date, our findings are in line with previous findings that PA may not moderate the association between genetic CVD risk and mortality (17,49,50). These findings imply that the associations of PA with mortality will remain similar across the genetic disease risk strata, i.e., in individuals with high, intermediate, or low genetic risk for CVD. Further evidence is needed, but this interpretation is supported by current evidence from similar studies assessing the role of an overall healthy lifestyle, which also observed no interactions (8,9,49).

Finally, assessing the causality between PA and mortality is challenging, predominantly for methodological reasons (28,51). Previous findings from randomized controlled trials, animal studies, and Mendelian randomization studies have not confirmed the causal relationship between PA and mortality (20–24). Cotwin control designs are one potential strategy to provide additional evidence. In the cotwin control designs, we compare identical twins to each other, and the models are by design adjusted for genetics and environmental factors during the critical early developmental phases. Many identical twins also express similar phenotypes in adulthood as shown in our study by similar educational and health status, BMI, and various lifestyles. Our data show that the twin sibling whose PA volume on average exceeded the PA guidelines during the 15-yr observational period was not statistically significantly at a lower mortality risk than the less active cotwin, and hence, not supporting a causal association. The findings remain similar with different genetic CVD risks, at different disease risk levels, and in sensitivity analyses with continuous and vigorous PA metrics and among healthy participants. The main critique of this study design is the relatively small sample sizes. Notably, monozygotic twins are rare, and twin pairs who differ considerably in both their PA behavior and the outcome represent a further minority (43,52). However, to put into perspective, previous studies in the FTC have shown that 109 monozygotic twin pairs discordant for smoking were sufficient to detect differences in cancer rates (53). Correspondingly, discordances in PA in the FTC have been associated with observable differences in bodily structures (fat and weight accumulation; structure of heart, arteries, bones, and brain) (54–56), glucose homeostasis (56), biological aging (57), and physical fitness (58) but not with lifespan (42).

A critical interpretation of this study’s findings is that observational studies may be affected by various sources of bias that show PA to be associated with a reduced risk of mortality, whereas the association may not be causal (20,25–27). Such findings do not, however, dispute the plethora of literature describing the benefits of exercise based on randomized controlled trials. For example, in relation to cardiometabolic risk factors, the findings of 160 randomized controlled trials show that exercise significantly improves the status of lipid and lipoprotein metabolism, glucose intolerance and insulin resistance, and systemic inflammation (59). We highlight that although the evidence of PA in relation to longevity remains unconfirmed, people may influence their biological milieu and quality of life through exercise (60).

Strengths and Limitations

The major strengths of this study include the use of polygenic scores to quantify genetic liability to CVD. In addition, the use of cotwin control designs in exercise science is rare, and the FTC includes unique data of longitudinally assessed PA among monozygotic twins (30). We also acknowledge major limitations. We note that the longitudinal assessment covered a maximum of 15 yr of an individual’s life, during which three measurements were taken. The majority of the variables are based on self-reported metrics, which are prone to various sources of bias (61). However, it is notable that device-based metrics for PA became more accessible only after the last data collection, in 1990. The division of discordant twins by adherence to PA guidelines may not provide sufficient difference in exposure to PA. Data lost during the long observational period reduced the number of subjects in complete-case analysis, potentially introducing bias. Larger sample sizes or PA discordances might have strengthened some of the observed associations. We recognize that there are several decades of follow-up during which individuals’ activity levels, other lifestyles, health, and attitudes toward health-promoting behaviors may have changed, causing potential bias. We also acknowledge that in our study the adherence to WHO PA guidelines was assessed only through leisure-time PA, which does not represent the individual’s 24-h PA behavior. This may introduce bias by not recognizing the complex nature of PA from different domains, such as occupational PA, and the accumulation of potentially detrimental sedentary time (62,63).

CONCLUSIONS

We assessed whether longitudinally assessed PA and adherence specifically to current WHO PA guidelines mitigates or moderates the risk of mortality, regardless of genetic liability to CVD. We also estimated the causality of the PA–mortality association. We could not confirm that adherence to PA guidelines mitigates the genetic CVD risk or is causally associated with mortality. With the findings of this study, we wish to contribute to the demand for more evidence assessing gene–environment interactions of PA and the causality between PA and mortality.

The study was funded by the Academy of Finland (grant nos. 341750 and 346509 to E.S. and grant nos. 265240 and 263278 to J.K.), by the Juho Vainio Foundation (to E.S.), by the Päivikki and Sakari Sohlberg Foundation (to E.S.), and by the Sigrid Juselius Foundation (to J.K.). The phenotype and genotype data collection were funded by the Wellcome Trust Sanger Institute, the Broad Institute, the European Network for Genetic and Genomic Epidemiology (FP7-HEALTH-F4-2007, grant agreement no. 201413), and the Academy of Finland (grant nos. 100499, 205585, 118555, 141054, 264146, 308248, 312073, 336823, and 352792 to J.K.). The authors thank the numerous researchers and staff members who have facilitated this research over the years. They also thank the twin participants for their time and commitment to the study.

The authors declare that they have no competing interests. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

Authors’ contributions: L.J., K.W., A.K., J.K., and E.S. conceptualized the research question, study design, and statistical analysis. L.J. constructed the PRS under the supervision of T.P. L.J. drafted the first version of the manuscript, and A.K., K.W., J.K., and E.S. contributed significantly to the writing. All the authors contributed to critically interpreting the findings and revising the manuscript. J.K. and K.W. participated in the FTC data collection. E.S. and J.K. acquired funding for the study. The corresponding author attests that all the listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Data availability statement” The FTC subsample data were taken from the Biobank of the National Institute for Health and Welfare. The data are available to qualified researchers through a standardized application procedure (for details, visit https://thl.fi/en/web/thl-biobank/for-researchers). The full cohort data are available through the Institute for Molecular Medicine Finland (FIMM) Data Access Committee (DAC) to authorized researchers who have IRB/ethics approval and an institutionally approved study plan. For more details, please contact the FIMM DAC (fimm-dac@ helsinki.fi). The data cannot be made publicly available because of consent restrictions and the high degree of identifiability of twin siblings in Finland.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.acsm-msse.org).
==== Refs
REFERENCES

1 Kyu HH Abate D Abate KH , . Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392 (10159 ):1859–922.30415748
2 Marenberg ME Risch N Berkman LF Floderus B de Faire U . Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med. 1994;330 (15 ):1041–6.8127331
3 Zdravkovic S Wienke A Pedersen NL Marenberg ME Yashin AI De Faire U . Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J Intern Med. 2002;252 (3 ):247–54.12270005
4 Choi SW Mak TSH O’Reilly PF . Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15 (9 ):2759–72.32709988
5 King A Wu L Deng HW Shen H Wu C . Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease. BMC Med. 2022;20 (1 ):385.36336692
6 Mars N Koskela JT Ripatti P , . Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med. 2020;26 (4 ):549–57.32273609
7 O’Sullivan JW Raghavan S Marquez-Luna C , . Polygenic risk scores for cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2022;146 (8 ):e98–118.
8 Khera AV Emdin CA Drake I , . Genetic risk, adherence to a healthy lifestyle, and coronary disease. N Engl J Med. 2016;375 (24 ):2349–58.27959714
9 Rutten-Jacobs LC Larsson SC Malik R , . Genetic risk, incident stroke, and the benefits of adhering to a healthy lifestyle: cohort study of 306 473 UK Biobank participants. BMJ. 2018;363 :k4168.30355576
10 OECD, World Health Organization. Step Up! Tackling the Burden of Insufficient Physical Activity in Europe. OECD; 2023. [cited 17 Feb 2023]. Available from: https://www.oecd-ilibrary.org/social-issues-migration-health/step-up-tackling-the-burden-of-insufficient-physical-activity-in-europe_500a9601-en
11 Garcia L Pearce M Abbas A , . Non-occupational physical activity and risk of cardiovascular disease, cancer and mortality outcomes: a dose–response meta-analysis of large prospective studies. Br J Sports Med. 2023;57 (15 ):979–89.36854652
12 Jayedi A Gohari A Shab-Bidar S . Daily step count and all-cause mortality: a dose–response meta-analysis of prospective cohort studies. Sports Med. 2022;52 (1 ):89–99.34417979
13 Watts EL Matthews CE Freeman JR , . Association of leisure time physical activity types and risks of all-cause, cardiovascular, and cancer mortality among older adults. JAMA Netw Open. 2022;5 (8 ):e2228510.36001316
14 Tarp J Fagerland MW Dalene KE , . Device-measured physical activity, adiposity and mortality: a harmonised meta-analysis of eight prospective cohort studies. Br J Sports Med. 2022;56 (13 ):725–32.34876405
15 Paluch AE Bajpai S Bassett DR , . Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health. 2022;7 (3 ):e219–28.35247352
16 Shaked O Cohen G Goshen A Shimony T Shohat T Gerber Y . Physical activity and long-term mortality risk in older adults with and without cardiovascular disease: a nationwide cohort study. Gerontology. 2022;68 (5 ):529–37.34515134
17 Tikkanen E Gustafsson S Ingelsson E . Associations of fitness, physical activity, strength, and genetic risk with cardiovascular disease: longitudinal analyses in the UK biobank study. Circulation. 2018;137 (24 ):2583–91.29632216
18 Bull FC Al-Ansari SS Biddle S , . World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54 (24 ):1451–62.33239350
19 Stens NA Bakker EA Mañas A , . Relationship of daily step counts to all-cause mortality and cardiovascular events. J Am Coll Cardiol. 2023;82 (15 ):1483–94.37676198
20 Ballin M Nordström P . Does exercise prevent major non-communicable diseases and premature mortality? A critical review based on results from randomized controlled trials. J Intern Med. 2021;290 (6 ):1112–29.34242442
21 Wang Z Emmerich A Pillon NJ , . Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention. Nat Genet. 2022;54 (9 ):1332–44.36071172
22 van Oort S Beulens JWJ van Ballegooijen AJ Burgess S Larsson SC . Cardiovascular risk factors and lifestyle behaviours in relation to longevity: a Mendelian randomization study. J Intern Med. 2021;289 (2 ):232–43.33107078
23 Ni X Su H Lv Y , . Modifiable pathways for longevity: a Mendelian randomization analysis. Clin Nutr. 2023;42 (6 ):1041–7.37172463
24 Karvinen S Waller K Silvennoinen M , . Physical activity in adulthood: genes and mortality. Sci Rep. 2015;5 :18259.26666586
25 Kujala UM . Is physical activity a cause of longevity? It is not as straightforward as some would believe. A critical analysis. Br J Sports Med. 2018;52 (14 ):914–8.29545237
26 Sillanpää E Palviainen T Ripatti S Kujala UM Kaprio J . Polygenic score for physical activity is associated with multiple common diseases. Med Sci Sports Exercise. 2022;54 (2 ):280–7.
27 Tynkkynen NP Törmäkangas T Palviainen T , . Associations of polygenic inheritance of physical activity with aerobic fitness, cardiometabolic risk factors and diseases: the HUNT Study. Eur J Epidemiol. 2023;38 (9 ):995–1008.37603226
28 Ekelund U Sanchez-Lastra MA Dalene KE Tarp J . Dose–response associations, physical activity intensity and mortality risk: a narrative review. J Sport Health Sci. 2024;13 (1 ):24–9.37734548
29 Wade KH Richmond RC Davey Smith G . Physical activity and longevity: how to move closer to causal inference. Br J Sports Med. 2018;52 (14 ):890–1.29545236
30 Kaprio J Bollepalli S Buchwald J , . The older Finnish Twin Cohort—45 years of follow-up. Twin Res Hum Genet. 2019;22 (4 ):240–54.31462340
31 World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision. [cited 30 Oct 2022]. Available from: https://icd.who.int/browse10/2019/en#/
32 Statistics Finland. Data resources catalogue. Causes of death. [cited 30 Oct 2022]. Available from: https://aineistokatalogi.fi/catalog/studies/778c33bf-aceb-423f-89d9-e5abb5a0585c/datasets/fcc7f03d-3f83-49df-973d-f2d94b3742a6
33 Kaprio J . The Finnish Twin Cohort study: an update. Twin Res Hum Genet. 2013;16 (1 ):157–62.23298696
34 Lloyd-Jones LR Zeng J Sidorenko J , . Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat Commun. 2019;10 (1 ):5086.31704910
35 Herranen P Palviainen T Rantanen T , . A polygenic risk score for hand grip strength predicts muscle strength and proximal and distal functional outcomes among older women. Med Sci Sports Exercise. 2022;54 (11 ):1889–96.
36 McCarthy S Das S Kretzschmar W , . A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48 (10 ):1279–83.27548312
37 Kujala UM Palviainen T Pesonen P , . Polygenic risk scores and physical activity. Med Sci Sports Exercise. 2020;52 (7 ):1518–24.
38 Kujala UM Kaprio J Sarna S Koskenvuo M . Relationship of leisure-time physical activity and mortality: the Finnish Twin Cohort. JAMA. 1998;279 (6 ):440–4.9466636
39 Waller K Kujala UM Kaprio J Koskenvuo M Rantanen T . Effect of physical activity on health in twins: a 30-yr longitudinal study. Med Sci Sports Exercise. 2010;42 (4 ):658–64.
40 Price AL Patterson NJ Plenge RM Weinblatt ME Shadick NA Reich D . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38 (8 ):904–9.16862161
41 Virtanen S Kaprio J Viken R Rose RJ Latvala A . Birth cohort effects on the quantity and heritability of alcohol consumption in adulthood: a Finnish longitudinal twin study. Addiction. 2019;114 (5 ):836–46.30569536
42 Kujala UM Kaprio J Koskenvuo M . Modifiable risk factors as predictors of all-cause mortality: the roles of genetics and childhood environment. Am J Epidemiol. 2002;156 (11 ):985–93.12446254
43 Kujala UM Leskinen T Rottensteiner M , . Physical activity and health: findings from Finnish monozygotic twin pairs discordant for physical activity. Scand J Med Sci Sports. 2022;32 (9 ):1316–23.35770444
44 Goldberg J Fischer M . Co-twin control methods. In: Everitt BS Howell DC , editors. Encyclopedia of Statistics in Behavioral Science. Chichester, UK: John Wiley & Sons, Ltd; 2005.
45 Reimers CD Knapp G Reimers AK . Does physical activity increase life expectancy? A review of the literature. J Aging Res. 2012;2012 :243958.22811911
46 Lee I-M Shiroma EJ Lobelo F Puska P Blair SN Katzmarzyk PT , Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380 (9838 ):219–29.22818936
47 Kankaanpää A Tolvanen A Joensuu L , . The associations of long-term physical activity in adulthood with later biological ageing and all-cause mortality—a prospective twin study. medRxiv [Preprint]. 2023;2023.06.02.23290916. [cited 6 May 2023]. Available from: http://medrxiv.org/lookup/doi/10.1101/2023.06.02.23290916.
48 Kankaanpää A Tolvanen A Heikkinen A Kaprio J Ollikainen M Sillanpää E . The role of adolescent lifestyle habits in biological aging: a prospective twin study. Elife. 2022;11 :e80729.36345722
49 Yun JS Jung SH Shivakumar M , . Associations between polygenic risk of coronary artery disease and type 2 diabetes, lifestyle, and cardiovascular mortality: a prospective UK Biobank study. Front Cardiovasc Med. 2022;9 :919374.36061534
50 Sotos-Prieto M Baylin A Campos H Qi L Mattei J . Lifestyle cardiovascular risk score, genetic risk score, and myocardial infarction in hispanic/latino adults living in Costa Rica. J Am Heart Assoc. 2016;5 (12 ):e004067.27998913
51 Bahls M Baurecht H Hanssen H Van Craenenbroeck EM . How to establish causality between physical inactivity and mortality? Eur J Prev Cardiol. 2022;29 (8 ):e266–7.35104849
52 Iso-Markku P Waller K Hautasaari P Kaprio J Kujala UM Tarkka IM . Twin studies on the association of physical activity with cognitive and cerebral outcomes. Neurosci Biobehav Rev. 2020;114 :1–11.32325068
53 Korhonen T Hjelmborg J Harris JR , . Cancer in twin pairs discordant for smoking: the Nordic Twin Study of Cancer. Int J Cancer. 2022;151 (1 ):33–43.35143046
54 Leskinen T Kujala UM . Health-related findings among twin pairs discordant for leisure-time physical activity for 32 years: the TWINACTIVE study synopsis. Twin Res Hum Genet. 2015;18 (3 ):266–72.25906784
55 Waller K Kaprio J Kujala UM . Associations between long-term physical activity, waist circumference and weight gain: a 30-year longitudinal twin study. Int J Obes (Lond). 2008;32 (2 ):353–61.17653065
56 Rottensteiner M Leskinen T Niskanen E , . Physical activity, fitness, glucose homeostasis, and brain morphology in twins. Med Sci Sports Exercise. 2015;47 (3 ):509–18.
57 Kankaanpää A Tolvanen A Bollepalli S , . Leisure-time and occupational physical activity associates differently with epigenetic aging. Med Sci Sports Exercise. 2021;53 (3 ):487–95.
58 Leskinen T Waller K Mutikainen S , . Effects of 32-year leisure time physical activity discordance in Twin Pairs on Health (TWINACTIVE Study): aims, design and results for physical fitness. Twin Res Hum Genet. 2009;12 (1 ):108–17.19210186
59 Lin X Zhang X Guo J , . Effects of exercise training on cardiorespiratory fitness and biomarkers of cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials. J Am Heart Assoc. 2015;4 (7 ):e002014.26116691
60 Marquez DX Aguiñaga S Vásquez PM , . A systematic review of physical activity and quality of life and well-being. Transl Behav Med. 2020;10 (5 ):1098–109.33044541
61 Choi BCK Pak AWP . A catalog of biases in questionnaires. Prev Chronic Dis. 2005;2 (1 ):A13.
62 Coenen P Huysmans MA Holtermann A , . Do highly physically active workers die early? A systematic review with meta-analysis of data from 193 696 participants. Br J Sports Med. 2018;52 (20 ):1320–6.29760168
63 Biswas A Oh PI Faulkner GE , . Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann Intern Med. 2015;162 (2 ):123–32.25599350
