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

38768052
MSSE_240908
10.1249/MSS.0000000000003477
00017
3
Applied Sciences
Polygenic Risk Score, Cardiorespiratory Fitness, and Cardiometabolic Risk Factors: WASEDA’S Health Study
TANISAWA KUMPEI 1
TABATA HIROKI h.tabata.mp@juntendo.ac.jp
2 3
NAKAMURA NOBUHIRO nnakamura@aoni.waseda.jp
1
KAWAKAMI RYOKO r.kawakami@aoni.waseda.jp
3 4
USUI CHIYOKO chiyoko_usui@sophia.ac.jp
3 5
ITO TOMOKO ito-t@tokyo-kasei.ac.jp
3 6
KAWAMURA TAKUJI t.kawamura2@kurenai.waseda.jp
3 7
TORII SUGURU shunto@waseda.jp
1
ISHII KAORI ishiikaori@waseda.jp
1
MURAOKA ISAO imuraoka@waseda.jp
1
SUZUKI KATSUHIKO katsu.suzu@waseda.jp
1
SAKAMOTO SHIZUO sakamoto.shizuo@surugadai.ac.jp
1 8
HIGUCHI MITSURU mhiguchi@waseda.jp
1
OKA KOICHIRO koka@waseda.jp
1
1 Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, JAPAN
2 Sportology Center, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, JAPAN
3 Waseda Institute for Sport Sciences, Tokorozawa, Saitama, JAPAN
4 Physical Fitness Research Institute, Meiji Yasuda Life Foundation of Health and Welfare, Hachioji, Tokyo, JAPAN
5 Center for Liberal Education and Learning, Sophia University, Chiyoda-ku, Tokyo, JAPAN
6 Department of Food and Nutrition, Tokyo Kasei University, Itabashi-ku, Tokyo, JAPAN
7 Research Center for Molecular Exercise Science, Hungarian University of Sports Science, Budapest, HUNGARY
8 Faculty of Sport Science, Surugadai University, Hanno, Saitama, JAPAN
Address for correspondence: Kumpei Tanisawa, Ph.D., Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan; E-mail: tanisawa@waseda.jp.
10 2024
15 5 2024
56 10 20262038
11 2023
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

This study estimated an individual’s genetic liability to cardiometabolic risk factors by polygenic risk score (PRS) construction and examined whether high cardiorespiratory fitness (CRF) modifies the association between PRS and cardiometabolic risk factors.

Methods

This cross-sectional study enrolled 1296 Japanese adults aged ≥40 yr. The PRS for each cardiometabolic trait (blood lipids, glucose, hypertension, and obesity) was calculated using the LDpred2 and clumping and thresholding methods. Participants were divided into low-, intermediate-, and high-PRS groups according to PRS tertiles for each trait. CRF was quantified as peak oxygen uptake (V̇O2peak) per kilogram body weight. Participants were divided into low-, intermediate-, and high-CRF groups according to the tertile V̇O2peak value.

Results

Linear regression analysis revealed a significant interaction between PRS for triglyceride (PRSTG) and CRF groups on serum TG levels regardless of the PRS calculation method, and the association between PRSTG and TG levels was attenuated in the high-CRF group. Logistic regression analysis revealed a significant sub-additive interaction between LDpred2 PRSTG and CRF on the prevalence of high TG, indicating that high CRF attenuated the genetic predisposition to high TG. Furthermore, a significant sub-additive interaction between PRS for body mass index and CRF on obesity was detected regardless of the PRS calculation method. These significant interaction effects on high TG and obesity were diminished in the sensitivity analysis using V̇O2peak per kilogram fat-free mass as the CRF index. Effects of PRSs for other cardiometabolic traits were not significantly attenuated in the high-CRF group regardless of PRS calculation methods.

Conclusions

The findings of the present study suggest that individuals with high CRF overcome the genetic predisposition to high TG levels and obesity.

Key Words

GENETIC RISK
FITNESS
INTERACTION
TRIGLYCERIDE
OBESITY
SDCT
OPEN-ACCESSTRUE
==== Body
pmcCardiovascular diseases (CVDs), such as ischemic heart disease and stroke, are the leading causes of death, particularly in developed countries; an estimated 17.9 million people worldwide died from CVDs in 2019, representing 32% of global deaths (1). CVD risk increases with cardiometabolic abnormalities, including obesity (2), hypertension (3), dyslipidemia (4), and hyperglycemia (5), which are influenced by both genetic and lifestyle factors (6). Therefore, understanding the interplay of genetic and lifestyle factors in cardiometabolic risk is important for preventing CVDs.

Previous studies demonstrated that genetic factors play important roles in cardiometabolic health and the development of CVD. Twin studies have demonstrated that the contribution of genetic factors to cardiometabolic risk factors is comparable to that of environmental factors (7,8), suggesting that genetic factors cannot be ignored in the management of cardiometabolic health. Furthermore, large-scale genome-wide association studies (GWASs) have identified several loci associated with cardiometabolic traits (9–14). Although most genetic variants in loci identified by GWAS have only a small effect on complex traits, including cardiometabolic traits, the aggregated effect of these variants can partially explain the phenotypic variance in complex traits (15,16).

Considerable evidence indicates that a high level of physical activity is associated with a reduced risk of CVD incidence and mortality, as well as improved cardiometabolic risk (17). Considering the established significance of physical activity in maintaining cardiometabolic health, efforts have been made to understand the influence of the interaction between genetic factors and physical activity (exercise) on cardiometabolic health. This entails investigating whether high levels of physical activity attenuate the effect of genetic factors on cardiometabolic risk or whether the favorable effect of physical activity on cardiometabolic risk is diminished in individuals with high genetic predisposition (18,19). This understanding may contribute to optimizing physical activity promotion and tailoring exercise recommendations for individuals based on their unique genomic background.

Several studies have investigated the association between genetic factors and physical activity (mostly self-reported) with cardiometabolic risk factors (20–29). However, relatively few have focused on the interaction between genetic factors and cardiorespiratory fitness (CRF) (30–34) that is developed by regular physical activity. CRF is a stronger predictor of CVD and cardiometabolic risk factors than physical activity because it reflects not only past physical activity but also the interplay of numerous physiological systems; therefore, it is considered the best indicator of overall body health and function (35). A previous cohort study using the UK Biobank showed that high CRF was more strongly associated than high physical activity with CVD risk reduction in individuals with high genetic predisposition (36). Considering the established clinical importance of measuring CRF (35), understanding the interaction between genetic factors and CRF is necessary to determine the extent to which CRF modifies the association between genetic factors and cardiometabolic risk factors.

Most previous studies focusing on the interaction between genetic factors and CRF involved the construction of a genetic risk score (GRS) to assess the cumulative effects of genetic variants by aggregating the effects of several to several dozen single nucleotide polymorphisms (SNPs) in the loci identified by GWASs (37). However, the predictive power and clinical utility of GRSs are generally limited, making it difficult to accurately assess the interaction between genetic factors and CRF. One reason for this is that a GRS, despite the highly polygenic nature of complex traits, does not account for many genetic variants with small effect sizes that do not reach statistical significance in GWAS (15,16). Therefore, a more accurate method for predicting genetic risk is required. Recently, the polygenic risk score (PRS), an extension of the genetic risk score, has been developed to estimate an individual’s genetic liability to complex traits, including cardiometabolic traits (38). PRS is calculated by summing the effects of several hundred to millions of SNPs weighted by their effect sizes from a GWAS, which enables us to estimate an individual’s genetic liability to complex traits more accurately. Therefore, assessing genetic predisposition by PRS construction provides more accurate evidence for the association of the interaction between genetic factors and CRF with cardiometabolic risk factors. Thus, the present study estimated an individual’s genetic liability to cardiometabolic risk factors by PRS construction and examined the influence of the interaction between PRS and CRF on cardiometabolic risk factors to clarify whether high CRF modifies the association between PRS and cardiometabolic risk factors.

METHODS

Participants

This cross-sectional study used baseline survey data from the Waseda Alumni’s Sports, Exercise, Daily Activity, Sedentariness, and Health Study (WASEDA’S Health Study), a prospective cohort study conducted on the alumni of Waseda University and their spouses aged ≥40 yr (39–44). The WASEDA’S Health Study consisted of four cohorts (A–D) with different measurement items, and participants selected one of the four cohorts when registering for the study. The present study included 1387 individuals (men: n = 916, women: n = 471) who participated in the baseline survey of cohort D between March 2015 and March 2020. We excluded participants who met the following criteria: those 1) lacking DNA samples or genotype data (n = 9), 2) who did not pass quality control of genotype data (n = 9), 3) who could not undergo exercise testing to assess CRF (n = 10), 4) who did not fill web-based questionnaires (n = 50), 5) who had breakfast before blood sampling (n = 12), or 6) who did not complete the dietary questionnaire (n = 1). Based on the aforementioned criteria, 1296 Japanese adults (men, n = 860; mean age, 56.9 yr; women, n = 436; mean age, 52.0 yr) were included in the analysis. All participants provided written informed consent before participating in the study. The study was approved by the Ethical Review Committee of Waseda University (reference numbers: 2014-G002 and 2018-G001) and was conducted in accordance with the principles of the Declaration of Helsinki.

Measurement and definition of outcomes

Both quantitative and binary cardiometabolic traits were used as outcomes in this study. The quantitative outcomes included fasting serum triglyceride (TG), serum low-density lipoprotein cholesterol (LDL-C), serum high-density lipoprotein cholesterol (HDL-C), plasma glucose, blood glycated hemoglobin (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI). Briefly, blood biochemical parameters (TG, LDL-C, HDL-C, glucose, and HbA1c) were determined using standard laboratory methods by BML, Inc. (Tokyo, Japan). SBP and DBP were measured using the oscillometric method (HEM-7122; OMRON, Inc., Kyoto, Japan) with the participants at rest in a seated position, and BMI was calculated as body weight (kg) divided by the body height in meters squared (m2). The detailed methods for measuring these variables have been described previously (39–44). The binary outcomes included the components of dyslipidemia (high TG, high LDL-C, and low HDL-C), diabetes mellitus (diabetes), hypertension, and obesity. High TG and high LDL-C were defined according to the diagnostic criteria of dyslipidemia in Japan (45) as follows: high TG, fasting TG level ≥150 mg·dL−1, or use of TG-lowering medication; and high LDL-C, fasting LDL-C level ≥140 mg·dL−1, or use of cholesterol-lowering medication. Low HDL-C was defined as fasting HDL-C level <40 mg·dL−1 for men and <50 mg·dL−1 for women according to the definition of metabolic syndrome by the International Diabetes Federation (46). Diabetes was defined as a fasting glucose level ≥126 mg·dL−1, HbA1c ≥6.5%, or the use of antidiabetic medication according to the diagnostic criteria for diabetes mellitus in Japan (47). Hypertension was defined as SBP ≥140 mm Hg, DBP ≥90 mm Hg, or the use of antihypertensive medication according to the diagnostic criteria for hypertension in Japan (48). Obesity was defined as BMI ≥25 kg·m−2 for men and women according to the diagnostic criteria for obesity in Japan (49).

Measurement of CRF

CRF was assessed using a maximal graded exercise test using a cycle ergometer (Ergomedic 828E; Monark, Varberg, Sweden) on the same day after blood sampling and anthropometric and blood pressure measurements, which was quantified as peak oxygen uptake per kilogram body weight (V̇O2peak/BW) as previously described (41,43,44). The detailed procedure of the exercise test is described in Supplemental Methods (Supplemental Digital Content 1, http://links.lww.com/MSS/D39). Participants were subsequently divided into the low-, intermediate-, and high-CRF groups according to the tertile V̇O2peak/BW value of each age group (10-yr intervals) in men and women, respectively. The age- and sex-stratified mean and range values of V̇O2peak/BW in the low-, intermediate-, and high-CRF groups are shown in Supplemental Table 1 (Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

Although V̇O2peak/BW is widely used as a reliable indicator of CRF, its association with cardiometabolic traits can be confounded by body composition (fat and muscle mass). Therefore, we also calculated V̇O2peak per kilogram fat-free mass (V̇O2peak/FFM) measured by bioelectrical impedance analysis (MC-980A; Tanita Corp., Tokyo, Japan), which was used for calculating the CRF index free from body composition (CRFFFM). For the sensitivity analysis using CRFFFM, participants were divided into the low-, intermediate-, and high-CRFFFM groups as described previously. Furthermore, based on recently estimated standard values of V̇O2peak/BW (for cycling exercise) in a Japanese population (CRFSV) (50), participants were categorized into under-CRFSV and over-CRFSV groups to assess whether the cardiometabolic risk conferred by high PRS was attenuated in those who met the standard values of V̇O2peak/BW as a sensitivity analysis.

Questionnaire survey

Smoking status (current, former, or never smoked), history of chronic diseases, medication status, and menopause status (only women) were assessed using a web-based questionnaire. Alcohol consumption was assessed using a validated brief self-administered diet history questionnaire (51).

DNA extraction and SNP genotyping

Total DNA was extracted from peripheral blood using the QIAamp DNA Midi kit (QIAGEN, Hilden, Germany), and SNP genotyping was performed using an Infinium CoreExome-24 BeadChip version 1.1–1.4 (Illumina, Inc., San Diego, CA) according to the manufacturer’s protocol. Approximately 550,000 (depending on the version of the BeadChip) variants were genotyped and subsequently subjected to quality control. The detailed procedure of DNA extraction and SNP genotyping is shown in Supplemental Methods (Supplemental Digital Content 1, http://links.lww.com/MSS/D39).

Quality control and genotype imputation

Quality control of the genotype data was performed using PLINK version 1.9 (52) to exclude SNPs and samples that may cause bias. After quality control, 256,409 SNPs and 1369 samples were available for genotype imputation. Haplotype phasing and genotype imputation were performed using Beagle 5.4 (53,54) with the default parameter settings, with 1000 Genomes Project Phase 3 (n = 2504) (55) as the reference panel. We excluded SNPs with low imputation quality scores (R2 < 0.9) and retained 5,316,861 SNPs for PRS calculation. The detailed quality control and genotype imputation procedures are shown in Supplemental Methods (Supplemental Digital Content 1, http://links.lww.com/MSS/D39).

Calculation of PRS

The PRS for each trait was calculated by summing the number of cardiometabolic risk alleles harbored by an individual weighted by their effect sizes. We used the LDpred2 algorithm (56) for PRS calculation. LDpred2 adopts a Bayesian approach that infers the posterior mean effect sizes of each SNP from GWAS summary statistics while accounting for linkage disequilibrium (LD) between SNPs. We used an in-sample LD matrix calculated directly from the target samples and GWAS summary statistics corresponding to each outcome from the BioBank Japan Project as inputs for LDpred2 (10–12). Consequently, PRS for TG (PRSTG), LDL-C (PRSLDL-C), HDL-C (PRSHDL-C), glucose (PRSGlucose), HbA1c (PRSHbA1c), SBP (PRSSBP), DBP (PRSDBP), BMI (PRSBMI), diabetes (PRSDM), and hypertension (PRSHT) were constructed. The calculated PRSs were converted into Z-scores for standardization. For the interaction analysis between PRS and CRF, the participants were divided into low-, intermediate-, and high-PRS groups according to the tertile of PRS for each trait. We also calculated PRSs using the classical clumping and thresholding (C + T) method using the PRSice-2 software (57) to validate the results obtained from LDpred2 PRS. The detailed procedure of PRS calculation is shown in Supplemental Methods (Supplemental Digital Content 1, http://links.lww.com/MSS/D39)

Statistical analysis

The prediction performance of PRS was assessed using linear (quantitative outcome) or logistic (binary outcome) regression analyses. In each regression model, the phenotypic variance explained by the PRS (R2 for quantitative traits and Nagelkerke’s pseudo-R2 for binary traits) was calculated and used to measure the prediction performance. We also calculated means and 95% confidence intervals (95% CIs) of quantitative outcomes, as well as prevalence and 95% CIs of binary outcomes by the decile of corresponding PRSs. The detailed procedure of the analysis of the prediction performance of PRS is shown in Supplemental Methods (Supplemental Digital Content 1, http://links.lww.com/MSS/D39).

The differences in the characteristics of participants between the CRF groups were assessed using linear regression analysis (for continuous variables) and the Mantel–Haenszel χ2 test (for categorical variables). To assess whether CRF modifies the association between PRSs and quantitative outcomes, we performed a linear regression analysis, considering PRS (continuous)–CRF group (categorical) as an interaction term, and calculated the multivariate-adjusted regression coefficients (beta) and 95% CIs per SD increment in PRS. We used a categorical CRF group classified by age group and sex for the linear regression analysis because the association between V̇O2peak/BW and outcomes could be confounded by age and sex. Rank-based inverse normal transformation was applied to the dependent variables to achieve the normality of the residuals for the regression model. Individuals taking TG-lowering, cholesterol-lowering, antihypertensive, or antidiabetic medications were excluded from the analysis of quantitative outcomes as they may be affected by the medication (e.g., individuals taking TG-lowering medications were excluded from TG analysis). The models were adjusted for covariates including sex, age, BMI (except for BMI trait), smoking status, alcohol drinking status, menopause status (only women), history of CVD, history of cancer, TG-lowering medication use, cholesterol-lowering medication use, antidiabetic medication use, antihypertensive medication use, and the top 10 principal components (PCs) by the PC analysis of the genotype data. Similarly, a sensitivity analysis of the interaction between the PRS and CRFFFM groups was performed. We also showed the means and 95% CIs of quantitative outcomes in the PRS and CRF groups, and the interaction between these groups (categorical) for these outcomes was assessed using two-way ANCOVA adjusted for the covariates that were used in the linear regression analysis. All quantitative trait analyses were performed for men and women separately and combined.

To assess whether high CRF influenced the increased prevalence of cardiometabolic risk factors in the high-PRS group, the interaction between the PRS and CRF groups on binary outcomes was assessed using logistic regression analysis. As recommended by Knol and VanderWeele (58), we evaluated both the additive and multiplicative interactions using categorical PRS (low/intermediate vs high) and CRF (low/intermediate vs high). The relative excess risk due to interaction (RERI) was calculated as a measure of additive interactions, whereas multiplicative interactions were assessed using the Wald test. We further examined the joint association between PRS, CRF, and binary outcomes. The participants were categorized into the following four groups based on the tertiles of PRS and CRF: 1) low/intermediate PRS with low/intermediate CRF, 2) low/intermediate PRS with high CRF, 3) high PRS with low/intermediate CRF, and 4) high PRS with high CRF. We calculated the multivariate-adjusted ORs and 95% CIs for each group using low/intermediate PRS with low/intermediate CRF as the reference category. In both interaction and joint association analyses, covariates used for the linear regression models were also included in logistic regression models. Because the number of cases for some binary traits was small, including many covariates in the models for these traits may cause overfitting. Therefore, we additionally performed the logistic regression analysis with fewer covariates including age, sex, and top 10 PCs of the genotype data. We also performed a sensitivity analysis of the interaction between the PRS and CRFFFM or CRFSV groups (50). These analyses were performed for men and women combined but not separately because of the limited number of case samples for several outcomes in women.

P < 0.05 was considered significant. All statistical analyses were performed using SPSS Statistics (version 29.0; SPSS Inc., Chicago, IL), Stata SE (version 18.0; StataCorp LP, College Station, TX), and R software (version 4.1.2; R Core Team, 2021).

RESULTS

Characteristics of participants in the CRF groups

The characteristics of the participants in the three CRF groups are shown in Table 1. High CRF was significantly associated with low body weight, BMI, low levels of TG, HDL-C, fasting glucose, HbA1c, SBP, and DBP, and a reduced prevalence of high TG, high LDL-C, low HDL-C, hypertension, obesity, history of CVD, TG-lowering medication use, cholesterol-lowering medication use, and antihypertensive medication use. In contrast, it was significantly associated with high HDL-C levels (all P < 0.05). Similar results were obtained when the data were analyzed separately for men and women (Supplemental Tables 2 and 3, Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

TABLE 1 Characteristics of participants in the three CRF groups.

Variable	Total	Low CRF	Intermediate CRF	High CRF	P	
n	1296	439	434	423		
Sex (% women)	33.6	33.9	32.7	34.3	0.921	
Age (yr)	55.3 (10.0)	55.7 (10.3)	55.4 (9.7)	54.6 (9.9)	0.086	
Height (cm)	166.5 (7.8)	166.6 (8.2)	166.6 (7.5)	166.3 (7.7)	0.573	
Body weight (kg)	63.8 (11.4)	66.8 (12.9)	64.0 (11.0)	60.5 (9.1)	<0.001	
BMI (kg·m−2)	22.9 (3.1)	24.0 (3.6)	22.9 (2.9)	21.8 (2.3)	<0.001	
Body fat (%)	23.1 (6.8)	25.9 (6.6)	23.1 (6.5)	20.2 (6.1)	<0.001	
FFM (kg·m−2)	48.9 (8.8)	49.3 (9.4)	49.1 (8.7)	48.3 (8.2)	0.081	
V̇O2peak (mL·min−1)	1820 (510)	1554 (418)	1812 (434)	2105 (517)	<0.001	
V̇O2peak/BW (mL·kg−1·min−1)	28.5 (6.3)	23.1 (3.6)	28.1 (3.3)	34.6 (5.5)	<0.001	
V̇O2peak/FFM (mL·kg−1·min−1)	37.0 (6.9)	31.3 (4.3)	36.7 (4.0)	43.3 (5.9)	<0.001	
TG (mg·dL−1)	99.2 (74.0)	111.5 (73.6)	101.4 (67.6)	84.1 (78.1)	<0.001	
LDL-C (mg·dL−1)	122.7 (30.6)	124 (32.3)	123.9 (30.5)	120.1 (28.8)	0.065	
HDL-C (mg·dL−1)	66.5 (17.4)	63.1 (17.3)	64.3 (16)	72.1 (17.4)	<0.001	
Fasting glucose (mg·dL−1)	96.1 (13.7)	97.4 (14.4)	96.2 (12.9)	94.6 (13.5)	0.003	
HbA1c (%)	5.45 (0.44)	5.48 (0.49)	5.46 (0.39)	5.41 (0.43)	0.013	
SBP (mm Hg)	127.3 (19.7)	130.9 (21.0)	125.9 (19.5)	125.1 (18.1)	<0.001	
DBP (mm Hg)	80.5 (12.0)	82.8 (12.9)	80.2 (11.6)	78.6 (10.9)	<0.001	
Smoking status					0.061	
 Current (%)	6.6	8.0	6.9	4.7	
 Former (%)	37.2	36.2	40.6	34.8	
 Never (%)	56.3	55.8	52.5	60.5	
Drinking status (frequency per week)					0.054	
 0 (%)	33.5	38.0	30.6	31.7	
 1–2 (%)	23.1	23.2	20.7	25.3	
 3–4 (%)	21.6	19.4	26.0	19.4	
 5 or more (%)	21.8	19.4	22.6	23.6	
High TG (%)	16.2	21.9	17.7	8.7	<0.001	
High LDL-C (%)	35.1	38.8	35.7	30.5	0.011	
Low HDL-C (%)	4.6	7.5	3.7	2.4	<0.001	
Diabetes (%)	5.0	6.6	4.4	4.0	0.081	
Hypertension (%)	37.5	47.6	37.1	27.4	<0.001	
Obesity (%)	21.1	33.9	21.7	7.3	<0.001	
History of cancer (%)	6.9	8.4	5.5	6.9	0.356	
History of CVD (%)	8.8	10.9	8.8	6.6	0.025	
TG-lowering medication use (%)	3.8	6.4	3.0	1.9	<0.001	
Cholesterol-lowering medication use (%)	9.8	12.8	8.8	7.8	0.014	
Antidiabetic medication use (%)	2.6	3.6	2.1	2.1	0.161	
Antihypertensive medication use (%)	16.0	23.2	16.4	8.3	<0.001	
Data are presented as mean (SD) for continuous variables and percentage for categorical variables. P values were obtained from a linear regression analysis for continuous variables and the Mantel–Haenszel χ2 test for categorical variables.

Prediction performance of PRS

The prediction performance of LDpred2 PRS is summarized in Table 2. Most PRSs showed modest-to-good predictive performance for most cardiometabolic traits, with R2 > 0.05. The prediction performance of PRSSBP, PRSDBP, and PRSHT, compared with other cardiometabolic traits, was relatively poor (R2 < 0.03). The performance of C + T PRS is shown in Supplemental Table 4 (Supplemental Digital Content 3, http://links.lww.com/MSS/D41). C + T PRS for each cardiometabolic trait showed comparable performance to LDpred2 PRS. As shown in Supplemental Figure 1 (Supplemental Digital Content 2, http://links.lww.com/MSS/D40), the mean values of all quantitative cardiometabolic traits constantly increased (decreased for HDL-C) with an increase in the deciles of the corresponding LDpred2 and C + T PRSs. The prevalence of all cardiometabolic risk factors also increased with an increase in the deciles of the corresponding LDpred2 and C + T PRSs (Supplemental Fig. 2, Supplemental Digital Content 2, http://links.lww.com/MSS/D40).

TABLE 2 Prediction performance of LDpred2 PRS.

Trait	PRS	Number of Participants	Number of SNPs Used for Calculating PRS	Beta/OR Per SD Increment in PRS (95% CI)	Proportion of Variance Explained (R2) by PRSa	Pb	GWAS Summary statisticsc	
Quantitative								
 TG	PRSTG	1246d	813,267	0.30 (0.25 to 0.35)	0.087	4.9 × 10−27	Result of GWAS for TG in the Biobank Japan (n = 105,597)	
 LDL-C	PRSLDL-C	1168e	813,267	0.31 (0.26 to 0.37)	0.094	2.3 × 10−26	Result of GWAS for LDL-C in the Biobank Japan (n = 72,866)	
 HDL-C	PRSHDL-C	1168e	813,267	−0.38 (−0.44 to −0.33)	0.147	1.5 × 10−41	Result of GWAS for HDL-C in the Biobank Japan (n = 70,657)	
 Glucose	PRSGlucose	1262f	813,267	0.26 (0.21 to 0.32)	0.068	5.1 × 10−21	Result of GWAS for blood sugar in the Biobank Japan (n = 93,146)	
 HbA1c	PRSHbA1c	1262f	813,267	0.26 (0.20 to 0.31)	0.065	6.5 × 10−20	Result of GWAS for HbA1c in the Biobank Japan (n = 42,790)	
 SBP	PRSSBP	1088g	813,267	0.17 (0.11 to 0.23)	0.030	1.6 × 10−08	Result of GWAS for SBP in the Biobank Japan (n = 136,597)	
 DBP	PRSDBP	1088g	813,267	0.15 (0.09 to 0.21)	0.020	2.9 × 10−06	Result of GWAS for DBP in the Biobank Japan (n = 136,615)	
 BMI	PRSBMI	1296	813,267	0.28 (0.22 to 0.33)	0.076	1.3 × 10−23	Result of GWAS for BMI in the Biobank Japan (n = 158,284)	
Binary								
 High TG	PRSTG	1295h	813,267	2.13 (1.77 to 2.56)	0.080	5.7 × 10−16	Result of GWAS for TG in the Biobank Japan (n = 105,597)	
 High LDL-C	PRSLDL-C	1295h	813,267	1.74 (1.52 to 1.99)	0.069	5.7 × 10−16	Result of GWAS for LDL-C in the Biobank Japan (n = 72,866)	
 Low HDL-C	PRSHDL-C	1295h	813,267	2.54 (1.86 to 3.48)	0.090	4.9 × 10−09	Result of GWAS for HDL-C in the Biobank Japan (n = 70,657)	
 Diabetes	PRSDM	1296	813,871	2.42 (1.79, 3.28)	0.077	1.2 × 10−08	Results of GWAS for type 2 diabetes in the Biobank Japan (n = 191764)	
 Hypertension	PRSHT	1296	813,267	1.56 (1.35 to 1.80)	0.027	2.4 × 10−09	Result of GWAS for mean arterial pressure in the Biobank Japan (n = 136,482)	
 Obesity	PRSBMI	1296	813,267	1.75 (1.50 to 2.04)	0.057	1.8 × 10−12	Result of GWAS for BMI in the Biobank Japan (n = 158,284)	
a Nagelkerke’s pseudo-R2 was calculated for binary traits.

b P values for PRS–trait association by a linear or logistic regression analysis.

c All GWAS summary statistics are available via the following link: http://jenger.riken.jp/result.

d Forty-nine participants who used TG-lowering medication and one participant with missing value for TG were removed.

e One hundred twenty-seven participants who used cholesterol-lowering medication and one participant with missing value for LDL-C or HDL-C were removed.

f Thirty-four participants who used antidiabetic medication were removed.

g Two hundred eight participants who used antihypertensive medication were removed.

h One participant with missing values for TG, LDL-C, or HDL-C was removed.

Interaction between PRS and CRF and quantitative cardiometabolic traits

The association between LDpred2 PRS and the corresponding quantitative traits in the CRF groups is shown in Figure 1. LDpred2 PRS for each quantitative trait was significantly associated with all the corresponding traits, except DBP, regardless of the CRF group. A significant PRSTG–CRF group interaction effect on serum TG levels was observed (Pinteraction = 0.001); the association between PRSTG and TG was attenuated in the high-CRF group. Sex-stratified analysis showed the significance of PRSTG–CRF group interaction on TG in both men and women (Pinteraction = 0.016 in men; Pinteraction = 0.016 in women; Supplemental Table 5, Supplemental Digital Content 3, http://links.lww.com/MSS/D41). We also observed the significance of PRSDBP–CRF interaction on DBP (Pinteraction = 0.027). The direction of the PRSDBP–CRF interaction effect on DBP was in contrast to that on TG, and the association between PRSDBP and DBP was attenuated in the low-CRF group. A borderline significant PRSBMI–CRF interaction effect on BMI was observed (Pinteraction = 0.095); the association between PRSBMI and BMI was attenuated in the high- and intermediate-CRF groups. Sex-stratified analysis showed a significant PRSBMI–CRF interaction effect on BMI in only men (Pinteraction = 0.017 in men; Pinteraction = 0.238 in women; Supplemental Table 5, Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

FIGURE 1 Association between LDpred2 PRS and quantitative cardiometabolic traits in the three CRF groups. Multivariate-adjusted regression coefficients with 95% CIs of the rank-based inverse normal transformed residuals from linear regression analysis for each trait are shown.

The association between C + T PRS and the corresponding quantitative traits in the three CRF groups is shown in Supplemental Figure 3 (Supplemental Digital Content 2, http://links.lww.com/MSS/D40). The interaction analysis using C + T PRS revealed a significant PRSTG–CRF interaction effect on TG levels (Pinteraction = 0.023), as observed in the interaction analysis using LDpred2 PRSTG. However, the PRSDBP–CRF interaction effect was not significant on DBP levels (Pinteraction = 0.228). The PRSBMI–CRF interaction effect on BMI was borderline significant (Pinteraction = 0.071) and was significant in only men (Pinteraction = 0.013 in men; Pinteraction = 0.566 in women; Supplemental Table 5, Supplemental Digital Content 3, http://links.lww.com/MSS/D41), as observed in the interaction analysis using LDpred2 PRSBMI.

The association between PRS and the corresponding quantitative traits in the three CRFFFM groups is shown in Supplemental Table 6 (Supplemental Digital Content 3, http://links.lww.com/MSS/D41). The PRSTG–CRFFFM interaction effect on TG levels was borderline significant when using LDpred2 (Pinteraction = 0.086), but not using C + T (Pinteraction = 0.970) for PRS calculation. The PRSBMI–CRFFFM interaction on BMI was not significant regardless of the method used for PRS calculation (LDpred2: Pinteraction = 0.831; C + T: Pinteraction = 0.388).

The means and 95% CIs of the quantitative cardiometabolic traits in the LDpred2 PRS and CRF groups are shown in Supplemental Table 7 (Supplemental Digital Content 3, http://links.lww.com/MSS/D41). Except for SBP and DBP, the highest values for each cardiometabolic trait (the lowest value for HDL-C) were observed in the high-PRS with low-CRF group (individuals having a high PRS and high CRF), whereas the lowest values (the highest value for HDL-C) were observed in the low-PRS with high-CRF group, indicating that PRS and CRF additively influenced these traits. The same trends were observed in the analysis of men and women separately (Supplemental Table 7, Supplemental Digital Content 3, http://links.lww.com/MSS/D41), as well as in the analysis of participants categorized by C + T PRS (Supplemental Table 8, Supplemental Digital Content 3, http://links.lww.com/MSS/D41). We visualized the mean and 95% CIs of serum TG levels in the LDpred2 PRSTG and CRF groups to show the extent to which high CRF attenuates the association between PRSTG and TG levels (Fig. 2). Two-way ANCOVA adjusted for covariates detected a significant interaction between the PRSTG and CRF groups on TG (Pinteraction = 0.026). The TG level was 50.1 mg·dL−1 higher in the high-PRSTG with low-CRF group than in the low-PRSTG with low-CRF group, whereas the difference in TG levels between the high-PRSTG with high-CRF group and the low-PRSTG with high-CRF group was only 18.8 mg·dL−1.

FIGURE 2 Mean values of serum TG levels in the LDpred2 PRSTG and CRF groups. Data are presented as mean with 95% CIs. P values were obtained from two-way ANCOVA adjusted for covariates using the rank-based inverse normal transformed residuals from linear regression analysis as the dependent variable.

Interaction between PRS and CRF groups on binary cardiometabolic traits

The effect of the additive and multiplicative interactions between LDpred2 PRS (low/intermediate vs high) and CRF (low/intermediate vs high) on corresponding binary traits is shown in Table 3. The sub-additive interaction between PRSTG and CRF on the prevalence of high TG was significant (Pinteraction = 0.027). The sub-additive interaction between PRSBMI and CRF on obesity was also significant (Pinteraction = 0.005). These results indicated that high CRF attenuated the odds of high TG and obesity conferred by high PRSTG and PRSBMI, respectively. The interaction analysis with fewer covariates yielded almost the same results as those achieved in the fully adjusted model (Supplemental Table 9, Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

TABLE 3 Additive and multiplicative interactions between LDpred2 PRS and CRF groups on binary cardiometabolic traits.

Trait	Additive Interaction	Multiplicative Interaction	
RERIa (95% CI)	P interaction	OR (95% CI)	P interaction	
High TG	−1.75 (−3.29 to −0.20)	0.027	0.59 (0.26–1.35)	0.211	
High LDL-C	−0.01 (−0.88 to 0.87)	0.989	1.09 (0.63–1.88)	0.750	
Low HDL-C	−1.84 (−5.48 to 1.79)	0.319	0.69 (0.16–3.00)	0.625	
Diabetes	0.19 (−3.10 to 3.47)	0.911	1.34 (0.36–4.94)	0.664	
Hypertension	−0.12 (−1.06 to 0.82)	0.805	1.20 (0.63–2.29)	0.572	
Obesity	−1.16 (−1.97 to −0.36)	0.005	0.75 (0.33–1.73)	0.504	
a RERI = OR [high PRS with high CRF] − OR [high PRS with low/intermediate CRF] − OR [low/intermediate PRS with high CRF] + 1.

The additive and multiplicative interactions between C + T PRS (low/intermediate vs high) and CRF (low/intermediate vs high) on corresponding binary traits are shown in Supplemental Table 10 (Supplemental Digital Content 3, http://links.lww.com/MSS/D41). The interaction analysis using C + T PRS revealed a significant sub-additive interaction between PRSBMI and CRF on the prevalence of obesity (Pinteraction = 0.006), as observed in the interaction analysis using LDpred2 PRSTG. The additive interaction between C + T PRSTG and CRF on the prevalence of high TG was not statistically significant (Pinteraction = 0.098), although the RERI for high TG was comparable to that observed in the LDpred2 PRSTG–CRF interaction. The interaction analysis with fewer covariates yielded almost the same results as those achieved in the fully adjusted model, although the significant sub-additive interaction between PRSTG and CRF on high TG (Pinteraction = 0.042), as well as that between PRSHT and CRF interaction on hypertension (Pinteraction = 0.049), was detected only in the age-, sex, and PC-adjusted model (Supplemental Table 10, Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

The additive and multiplicative interactions between the PRS and CRFFFM groups are shown in Supplemental Table 11 (Supplemental Digital Content 3, http://links.lww.com/MSS/D41). The PRSTG–CRFFFM sub-additive interaction on high TG was borderline significant when using LDpred2 (Pinteraction = 0.083), but not using C + T (Pinteraction = 0.867) for PRS calculation. The additive interaction between PRSBMI and CRFFFM on obesity was not significant regardless of the method used for PRS calculation (LDpred2: Pinteraction = 0.736; C + T: Pinteraction = 0.470). The sensitivity analysis using CRFSV as the CRF category revealed a borderline significant sub-additive interaction between PRSTG and CRFSV groups on the prevalence of high TG when using LDpred2 (Pinteraction = 0.052), but not using C + T (Pinteraction = 0.360) for PRS calculation (Supplemental Table 12, Supplemental Digital Content 3, http://links.lww.com/MSS/D41). The sub-additive interaction between PRSBMI and CRFSV groups on obesity was borderline significant when using LDpred2 (Pinteraction = 0.058) and significant when using C + T (Pinteraction = 0.035) for PRS calculation.

Joint association of PRS and CRF with binary cardiometabolic traits

The joint associations of the LDpred2 PRS and CRF groups with the corresponding binary traits are shown in Figure 3. The high-PRS with low-/intermediate-CRF group for each binary trait was significantly associated with an increased prevalence of all the corresponding traits. The high-PRS with high-CRF group for high LDL-C, low HDL-C, and diabetes was significantly associated with an increased prevalence of the corresponding traits (high LDL-C: P = 6.85 × 10−6; low HDL-C: P = 2.23 × 10−6; diabetes: P = 7.07 × 10−5). There was no significant increase in the prevalence of high TG in the high-PRSTG with a high-CRF group (P = 0.155), which is in line with the result of the additive interaction analysis for high TG. Furthermore, there was no significant increase in the prevalence of hypertension in the high-PRSHT with high-CRF group (P = 0.079), although the RERI for hypertension was not significant. Furthermore, the high-PRSBMI with high-CRF group was associated with a decreased prevalence of obesity (P = 0.004). The joint association analysis with fewer covariates showed almost the same results as those from the fully adjusted model (Supplemental Table 13, Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

FIGURE 3 Joint association of LDpred2 PRS and CRF groups with binary cardiometabolic traits. Multivariate-adjusted ORs with 95% CIs for each group using the low/intermediate PRS with low/intermediate CRF as the reference category are shown.

The joint associations of the C + T PRS and CRF groups with the corresponding binary traits are shown in Supplemental Figure 4 (Supplemental Digital Content 2, http://links.lww.com/MSS/D40). The results obtained using C + T PRS were almost the same as those obtained using LDpred2 PRS; however, there was a significant increase in the prevalence of high TG in the high-PRSTG with high-CRF group (P = 0.010). The joint association analysis with fewer covariates yielded almost the same results as those achieved in the fully adjusted model (Supplemental Table 13, Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

The joint associations of the PRS and CRFFFM groups with the corresponding binary traits are shown in Supplemental Table 14 (Supplemental Digital Content 3, http://links.lww.com/MSS/D41). The joint associations of the PRS and CRFFFM groups yielded results different from that in the main analysis, and ORs for all traits in the high-PRS with high-CRFFFM group were comparable to those in the high-PRS with low-CRFFFM group. The sensitivity analysis using CRFSV as the CRF category yielded almost the same results as those from the main analysis (Supplemental Table 15, Supplemental Digital Content 3, http://links.lww.com/MSS/D41).

DISCUSSION

In this study, we examined the interaction between a constructed PRS and CRF (V̇O2peak/BW) in relation to cardiometabolic risk factors. The main finding of the present study was that 1) high CRF attenuated the association of PRSTG with serum TG levels and the prevalence of high TG, and 2) high CRF attenuated the association of PRSBMI with the prevalence of obesity. These results suggest that individuals with high CRF overcome the genetic predisposition to high TG levels and obesity.

We found that the interaction between PRSTG and CRF was significant for TG levels in the analysis of both quantitative and binary traits. Several studies have examined whether physical activity or CRF modifies the association between TG-associated genetic variants and TG levels, most of which have focused on the interaction between a single genetic variant and physical activity or CRF (20,25–27,30). Our previous study involved the construction of a GRS to investigate the interaction between genetic predisposition and CRF on TG levels (30) and demonstrated that high CRF attenuated the association between GRS and TG levels. However, the GRS was constructed using only seven TG-associated SNPs, and the participants were limited to 170 men. Therefore, the significance of the interaction between genetic factors and CRF on TG needed to be examined using a more accurate method of genetic risk prediction in a study with a larger sample size. In this study, the prediction performance (R2) of the LDpred2 PRSTG (R2 = 0.087) was higher than that of GRS for TG in our previous study (R2 = 0.063, calculated from our previous data), and the sample size of the present study was larger than that of our previous study, providing more accurate evidence of the significance of the interaction between genetic factors and CRF on TG. Furthermore, although our previous study included only male participants, the present study revealed that high CRF attenuated TG levels in both men and women. This result suggests that individuals with high CRF overcome the genetic predisposition to increased TG levels, regardless of sex.

There are several possible mechanisms underlying the PRSTG–CRF interaction on TG levels. One of the primary mechanisms may be increased activity and expression of lipoprotein lipase (LPL) due to regular physical activity, as discussed in our previous study (30). Both acute and chronic exercises decrease TG levels, which primarily depend on the increased activity and expression of LPL, the major enzyme responsible for the hydrolysis of TG (59). The risk alleles of several TG-associated SNPs have been reported to reduce LPL activity (60,61). Considering that some of the TG-associated SNPs identified by the GWASs are involved in the regulation of LPL (13,62), the aggregation of risk alleles of these SNPs may account for, at least in part, the association between PRSTG and TG levels in the present study. Therefore, increased LPL activity in individuals with high CRF may mitigate the genetic predisposition to high TG levels conferred by SNPs associated with decreased LPL activity. Improved body composition (less fat mass and more muscle mass) with high CRF can also explain the PRSTG–CRF interaction on TG levels. Our sensitivity analysis revealed that PRSTG–CRFFFM (V̇O2peak/FFM) interaction on TG levels did not reach statistical significance, suggesting that the interaction between PRS and CRF (V̇O2peak/BW) on TG levels was confounded by body composition (mainly fat mass), and the confounding was not eliminated by factoring BMI in the model. Several studies showed that obesity status modified the association between TG-associated SNPs and TG levels (63,64). Therefore, the attenuation of the PRSTG effects on TG by high CRF may be partly explained by reduced fat mass. Another possible mechanism underlying the PRSTG–CRF interaction on TG levels is “quantile-dependent expressivity,” a novel concept that the effect size of genetic variants depends on whether the phenotype is high or low relative to its distribution in the population (65,66). For example, a previous study demonstrated that the effect of GRS on TG levels was 3.3-fold greater at the 90th percentile of TG distribution than at the 10th percentile (65). Consistent with a previous study, a stronger association between the PRSTG and TG levels was observed in the high percentile of TG distribution in the present study (data not shown), suggesting that the low TG level itself may attenuate the effect of the PRSTG in the high-CRF group. However, a significant PRSTG–CRF interaction on TG levels was detected in women, whose TG distribution was significantly lower than that in men. Therefore, the effect of the PRSTG–CRF interaction on TG levels cannot be explained by quantile-dependent expressivity alone, and CRF itself likely acts as a PRS modifier.

We also demonstrated that high CRF attenuated the odds of obesity conferred by a high PRSBMI and that the interaction between PRSBMI and CRF on BMI was observed only in men. Several studies have examined the effects of gene–physical activity interactions on BMI or the prevalence of obesity to clarify whether a high level of physical activity attenuates the genetic predisposition to obesity (21–23,28,29). Although a limited number of studies have focused on the effect of CRF on the genetic predisposition to obesity, Celis-Morales et al. (22) performed a large-scale gene–fitness interaction study consisting of 67,702 participants from the UK Biobank. They constructed a genetic profile risk score for obesity (GPRS–obesity) using 93 obesity-associated SNPs and showed a stronger interaction between GPRS–obesity and CRF on BMI than between GRS and physical activity. In the present study, the magnitude of the interaction between LDpred2 PRSBMI and CRF on BMI in men was comparable to that reported by Celis-Morales et al. (Celis-Morales et al.: 3.0 kg·m−2 difference in BMI between low and high CRF for a low GPRS–obesity vs 4.0 kg·m−2 difference for a high GPRS–obesity; present study: 1.6 kg·m−2 difference in BMI between low and high CRF for a low PRSBMI vs 2.8 kg·m−2 difference for a high PRSBMI). In contrast, the present study did not detect a significant interaction between PRSBMI and CRF on BMI in women regardless of the PRS calculation method. Although the exact reason for this remains unknown, the lower BMI distribution of the female participants in the present study may have affected the interaction between PRSBMI and CRF on BMI. The mean BMI value and the prevalence of obesity in the female participants were 21.4 kg·m−2 and 8.7%, which are remarkably lower than those in the representative Japanese population (22.5 kg·m−2 and 22.3%) reported in the National Health and Nutrition Survey in 2019 (67). Interestingly, we found that the PRSBMI–CRF interaction on BMI was not significant even in men when the analysis was restricted to those with BMI <25.0 kg·m−2 (n = 624, beta = 0.04, Pinteraction = 0.313), suggesting that high CRF does not modify the association between PRSBMI and BMI in the population with lower BMI distribution regardless of sex. Further studies including female participants with the standard BMI distribution of the Japanese general population are needed to clarify this phenomenon.

The most plausible mechanism underlying the effect of the PRSBMI–CRF interaction on the prevalence of obesity in the high-CRF group is increased daily energy expenditure due to regular physical activity. A high amount of moderate-to-vigorous-intensity physical activity is needed to maintain a high CRF, which consequently leads to a negative energy balance and possibly prevents an increase in body weight, even in individuals with a high genetic risk for obesity. Another possible mechanism is that the effects of obesity-associated SNPs on food intake and energy balance are mitigated by high CRF. A previous GWAS of obesity revealed that the majority of SNPs associated with obesity play roles in the central nervous system, which controls food intake and energy balance (9,68), suggesting that obesity risk is increased by the disruption of energy homeostasis due to obesity-associated SNPs. It is well known that the balance between food intake and energy expenditure is better in individuals performing moderate to high levels of physical activity than those performing low levels of physical activity (69). Therefore, better control of energy homeostasis through regular physical activity in the high-CRF group may overcome the risk of energy homeostasis disruption due to obesity-associated SNPs, thereby mitigating genetic predisposition to obesity. It should also be noted that the mitigation of the genetic risk of obesity in the high-CRF groups was possibly caused by reverse causality. We used V̇O2peak/BW as the CRF index in the main analysis, which is closely related to BMI; individuals with lower BMI likely have higher CRF. Therefore, the low prevalence of obesity in the high-PRSBMI with high-CRF group was possibly due to the reverse causality between CRF and the prevalence of obesity, which is in part supported by the sensitivity analysis showing no significant PRSBMI–CRFFFM interaction on BMI and obesity. Nevertheless, a high CRF overcomes the high genetic predisposition to obesity. Individuals with a high genetic predisposition to obesity can prevent obesity through regular physical activity or exercise training to maintain a high CRF.

We observed the interaction between LDpred2 PRSDBP and CRF to be significant on DBP; high CRF strengthened the association between PRSDBP and DBP. The reason behind this remains unclear but may be partly due to the poor prediction performance of PRSDBP. Furthermore, we observed no significance of the interaction between PRS and CRF on other cardiometabolic risk factors except for TG, DBP, high TG, and obesity. These results suggest that high CRF does not attenuate the genetic predisposition to several cardiometabolic risk factors. Nevertheless, CRF was inversely associated with most cardiometabolic risk factors (Table 1), and within the same PRS group, individuals in the high-CRF group showed a better cardiometabolic profile than those in the low-CRF group (Supplemental Tables 7 and 8, Supplemental Digital Content 2, http://links.lww.com/MSS/D40), suggesting the importance of maintaining CRF for cardiometabolic health regardless of genetic predisposition.

In the interaction analysis between PRS and CRF for binary outcomes, we detected significant sub-additive interactions for high TG levels and obesity, whereas no significant multiplicative interaction was detected for these traits. This discrepancy can be explained by the differences in the concepts of additive and multiplicative interactions. Interaction on an additive scale indicates that the combined effect of two factors is larger (or smaller) than the sum of their individual effects (70). For example, the RERI, a measure of additive interaction used in the present study, was calculated using the absolute difference in the ORs of risk factors (PRS and CRF) and their combination. In contrast, interaction on a multiplicative scale indicates that the combined effect is larger (or smaller) than the product of the individual effects (70), which was calculated by the ratio of hazard ratios or ORs of risk factors. In the present study, the prevalence of high TG and obesity in the low-/intermediate PRS with high-CRF group was low, and the ORs for the increase in PRS were comparable between the low-/intermediate-CRF and high-CRF groups (data not shown), resulting in no significant multiplicative interaction between PRS and CRF. Nevertheless, because the additive interaction for absolute risk is considered a more relevant public health measure than multiplicative interaction (58), the significant sub-additive interactions detected in the present study are meaningful.

Implications in public health

Our findings regarding the PRS–CRF interaction have implications for public health. Recently, Akiyama et al. (50) presented the estimated standard values of aerobic capacity (V̇O2peak/BW and anaerobic threshold) according to sex and age in a Japanese population, which may be useful in updating the reference values of CRF for health promotion established in the physical activity guidelines of Japan. The mean V̇O2peak/BW according to sex and age group (10-yr intervals) in the present study was almost equivalent to the estimated standard values of V̇O2peak/BW (for cycling exercises) in the Japanese population (Supplemental Table 1, Supplemental Digital Content 2, http://links.lww.com/MSS/D40). The lower limit of V̇O2peak/BW in the high-CRF group in the present study was slightly higher than the standard values of V̇O2peak/BW in all sex and age groups (Supplemental Table 1, Supplemental Digital Content 2, http://links.lww.com/MSS/D40), and the effects of PRSTG on the prevalence of high TG levels and that of PRSBMI on obesity were mitigated in the high-CRF group. Furthermore, sensitivity analysis using the estimated standard values of V̇O2peak/BW in the Japanese population as the cutoff for the CRF group showed a borderline significance of the interaction between PRSTG and CRF on the prevalence of high TG as well as that between PRSTG and CRF on obesity. These results suggest that the estimated standard values of V̇O2peak/BW in the Japanese population are reasonable for the reference values of CRF in Japan for mitigating the genetic predisposition to high TG and obesity.

Strength and limitations

This study is valuable in that it used directly measured CRF (V̇O2peak) from a maximal graded exercise test, which provided more accurate evidence for the interaction between genetic factors and CRF on cardiometabolic risk factors than estimated CRF. Another strength of the present study is constructing PRSs based on a large number of SNPs across the whole genome, which enables us to estimate an individual’s genetic liability to cardiometabolic risk factors more accurately than GRS based on a relatively small number of SNPs. To the best of our knowledge, this is the first study that investigated the interaction between directly measured CRF and PRS on cardiometabolic risk factors. Although the clinical importance of measuring CRF has been proposed, the extent to which CRF attenuates the high genetic predisposition to cardiometabolic risk factors has not been determined due to lack of evidence. Our findings obtained by more accurate measures of CRF as well as genetic liability provide more robust evidence for gene-fitness interaction than previous studies, which may contribute to establishing the reference value of CRF for cardiometabolic disease prevention considering an individual’s genetic liability.

This study has several limitations. First, its cross-sectional design led to reverse causality between CRF and outcomes, as previously discussed. Prospective cohort or intervention studies require the elucidation of the effect of the interaction between PRS and CRF on cardiometabolic risk factors. Second, the sample size in the present study was relatively small, which might have led to a type II error in the interaction analysis. Furthermore, we did not analyze the interaction between PRS and CRF on binary traits in men and women separately because of the limited sample size of cases in women. Third, we did not perform replication studies in other populations to verify the results obtained from the present study. Although only a few cohorts are collecting both directly measured CRF and comprehensive genotype data, the results of the present study should be replicated in other cohorts to provide robust evidence for the interaction between PRS and CRF on cardiometabolic risk factors. Fourth, although CRF itself is not only acquired by regular exercise but also determined by genetic factors (71), we did not distinguish genetic and acquired CRF in the present study. Given that CRF-associated SNPs are associated with the incidence of cardiometabolic diseases (72), individuals with high CRF in the present study may have been genetically resistant to high TG and obesity regardless of genetic predisposition to these traits. Fifth, our study participants were alumni of the same university and their spouses in Japan, which might have introduced a selection bias. However, the mean V̇O2peak/BW according to sex and age group in the present study was almost equivalent to the estimated standard values of V̇O2peak/BW in the Japanese population (50), suggesting that our study participants represented the general Japanese population in terms of CRF. Nevertheless, further investigations of representative populations are necessary to generalize our findings to the entire Japanese population and other populations.

CONCLUSIONS

In conclusion, this study revealed that high CRF (V̇O2peak/BW) attenuated the association between PRSTG and high TG levels, as well as that between PRSBMI and obesity. Although high CRF did not attenuate the association between PRS and other cardiometabolic risk factors, it was associated with a better cardiometabolic profile regardless of PRS. Our findings highlight the importance of maintaining high CRF to mitigate the cardiometabolic risk conferred by genetic factors.

This study was supported in part by grants from the Japan Society for Promotion of Science (JSPS): Grant-in-Aid for Scientific Research (B) (M. H., grant number 18H03198; I. M., grant number 19H04008; K. T., grant number 19H04065), Grant-in-Aid for Young Scientists (B) (K. T., grant number 17 K13241), Grant-in-Aid for Challenging Exploratory Research (S. S., grant number 16 K13056), Grant-in-Aid for Challenging Research (Exploratory) (S. S., grant number 18 K19759), Grant-in-Aid for JSPS Fellows (K. T., grant number 16 J09593), and MEXT-Supported Program for the Strategic Research Foundation at Private Universities, 2015–2019 from the Ministry of Education, Culture, Sports, Science and Technology (grant number S1511017). The authors would like to thank all participants and staff of the WASEDA’S Health Study.

There are no conflicts of interest. The results of the study are presented honestly and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

Data Availability Statement: Data presented in this study are available upon request from the corresponding author. The data are not publicly available as this is an ongoing cohort study.

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).
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