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Avelar A, Araujo MYC, da Silva CB, de Lima MCS, Codogno JS, Turi-Lynch BC, Fernandes RA, Mantovani AM. The impact of early sports participation on body fatness in adulthood is not mediated by current physical activity. Am J Hum Biol 2024; 36:e23981. [PMID: 37610138 DOI: 10.1002/ajhb.23981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/27/2023] [Accepted: 08/09/2023] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVE The aim was to analyze the relationship between early sports participation (ESP) and body fatness (BF) in adults, as well as to identify whether this possible relationship is directly influenced by the current physical activity (PA) level. METHODS This cross-sectional study combined baseline data of two cohort. The BF estimated by DXA. The ESP, the subjects reported the engagement in sports during childhood (7-10 years) and adolescence (11-17 years) through two yes/no questions and current PA (described as steps) was device-measured using pedometers. Were identified as potential covariates and therefore adjusted the multivariate models: age, ethnicity, alcohol consumption, smoking, and sleep quality. Statistical analysis consisted of the chi-square test, analysis of variance/covariance, and structural equation modeling (software BioEstat version 5.0; p-value < .05). RESULTS Adults engaged in ESP had lower BF; among women, the variance in BF explained by ESP was 25.5%; among men, it was 9.2%. Sports participation in early life (r = -.436 [95% CI: -0.527 to -0.346]) and current PA (r = -.431 [95% CI: -0.522 to -0.340]) were inversely related to BF, as well as positively related to each other (r = .328 [95% CI: 0.226 to 0.430]). In the mediation model, current PA partially mediated (18.5%) the impact of ESP on BF, while current PA and ESP remained relevant determinants of BF. CONCLUSION Early sports participation and current PA have a significant impact on BF in adulthood, which is of similar magnitude and independent of each other.
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Affiliation(s)
- Ademar Avelar
- Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Sao Paulo State University (UNESP), Presidente Prudente, Brazil
- Department of Physical Education, State University of Maringá, Maringá, Brazil
| | - Monique Yndawe Castanho Araujo
- Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Sao Paulo State University (UNESP), Presidente Prudente, Brazil
| | - Camila Buonani da Silva
- Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Sao Paulo State University (UNESP), Presidente Prudente, Brazil
| | - Manoel Carlos Spiguel de Lima
- Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Sao Paulo State University (UNESP), Presidente Prudente, Brazil
| | - Jamile Sanches Codogno
- Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Sao Paulo State University (UNESP), Presidente Prudente, Brazil
| | - Bruna Camilo Turi-Lynch
- Department of Physical Education & Exercise Science, Lander University, Greenwood, South Carolina, USA
| | - Rômulo Araújo Fernandes
- Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Sao Paulo State University (UNESP), Presidente Prudente, Brazil
| | - Alessandra Madia Mantovani
- Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Sao Paulo State University (UNESP), Presidente Prudente, Brazil
- Toledo Prudente University Center, Presidente Prudente, Sao Paulo, Brazil
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Nakamura S, Fang X, Saito Y, Narimatsu H, Ota A, Ikezaki H, Shimanoe C, Tanaka K, Kubo Y, Tsukamoto M, Tamura T, Hishida A, Oze I, Koyanagi YN, Nakamura Y, Kusakabe M, Takezaki T, Nishimoto D, Suzuki S, Otani T, Kuriyama N, Matsui D, Kuriki K, Kadota A, Nakamura Y, Arisawa K, Katsuura-Kamano S, Nakatochi M, Momozawa Y, Kubo M, Takeuchi K, Wakai K. Effects of gene-lifestyle interactions on obesity based on a multi-locus risk score: A cross-sectional analysis. PLoS One 2023; 18:e0279169. [PMID: 36753494 PMCID: PMC9907830 DOI: 10.1371/journal.pone.0279169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 12/01/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND The relationship between lifestyle and obesity is a major focus of research. Personalized nutrition, which utilizes evidence from nutrigenomics, such as gene-environment interactions, has been attracting attention in recent years. However, evidence for gene-environment interactions that can inform treatment strategies is lacking, despite some reported interactions involving dietary intake or physical activity. Utilizing gene-lifestyle interactions in practice could aid in optimizing interventions according to genetic risk. METHODS This study aimed to elucidate the effects of gene-lifestyle interactions on body mass index (BMI). Cross-sectional data from the Japan Multi-Institutional Collaborative Cohort Study were used. Interactions between a multi-locus genetic risk score (GRS), calculated from 76 ancestry-specific single nucleotide polymorphisms, and nutritional intake or physical activity were assessed using a linear mixed-effect model. RESULTS The mean (standard deviation) BMI and GRS for all participants (n = 12,918) were 22.9 (3.0) kg/m2 and -0.07 (0.16), respectively. The correlation between GRS and BMI was r(12,916) = 0.13 (95% confidence interval [CI] 0.11-0.15, P < 0.001). An interaction between GRS and saturated fatty acid intake was observed (β = -0.11, 95% CI -0.21 to -0.02). An interaction between GRS and n-3 polyunsaturated fatty acids was also observed in the females with normal-weight subgroup (β = -0.12, 95% CI -0.22 to -0.03). CONCLUSION Our results provide evidence of an interaction effect between GRS and nutritional intake and physical activity. This gene-lifestyle interaction provides a basis for developing prevention or treatment interventions for obesity according to individual genetic predisposition.
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Affiliation(s)
- Sho Nakamura
- Graduate School of Health Innovation, Kanagawa University of Human Services, Kawasaki, Kanagawa, Japan
- Cancer Prevention and Control Division, Kanagawa Cancer Center Research Institute, Yokohama, Kanagawa, Japan
- * E-mail:
| | - Xuemin Fang
- Graduate School of Health Innovation, Kanagawa University of Human Services, Kawasaki, Kanagawa, Japan
| | - Yoshinobu Saito
- Cancer Prevention and Control Division, Kanagawa Cancer Center Research Institute, Yokohama, Kanagawa, Japan
- Center for Innovation Policy, Kanagawa University of Human Services, Kawasaki, Kanagawa, Japan
- Faculty of Sport Management, Nippon Sport Science University, Yokohama, Kanagawa, Japan
| | - Hiroto Narimatsu
- Graduate School of Health Innovation, Kanagawa University of Human Services, Kawasaki, Kanagawa, Japan
- Cancer Prevention and Control Division, Kanagawa Cancer Center Research Institute, Yokohama, Kanagawa, Japan
- Center for Innovation Policy, Kanagawa University of Human Services, Kawasaki, Kanagawa, Japan
- Department of Genetic Medicine, Kanagawa Cancer Center, Yokohama, Kanagawa, Japan
| | - Azusa Ota
- Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
| | - Hiroaki Ikezaki
- Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
- Department of Comprehensive General Internal Medicine, Faculty of Medical Sciences, Fukuoka, Fukuoka, Japan
| | - Chisato Shimanoe
- Department of Pharmacy, Saga University Hospital, Nabeshima, Saga, Japan
| | - Keitaro Tanaka
- Department of Preventive Medicine, Faculty of Medicine, Saga University, Nabeshima, Saga, Japan
| | - Yoko Kubo
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Mineko Tsukamoto
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Isao Oze
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Yuriko N. Koyanagi
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Yohko Nakamura
- Cancer Prevention Center, Chiba Cancer Center Research Institute, Chiba, Chiba, Japan
| | - Miho Kusakabe
- Cancer Prevention Center, Chiba Cancer Center Research Institute, Chiba, Chiba, Japan
| | - Toshiro Takezaki
- Department of International Island and Community Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Sakuragaoka, Kagoshima, Japan
| | - Daisaku Nishimoto
- School of Health Sciences, Faculty of Medicine, Kagoshima University, Kagoshima, Kagoshima, Japan
| | - Sadao Suzuki
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan
| | - Takahiro Otani
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan
| | - Nagato Kuriyama
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Nagoya, Aichi, Japan
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka, Japan
| | - Daisuke Matsui
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Nagoya, Aichi, Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, Shizuoka, Shizuoka, Japan
| | - Aya Kadota
- NCD Epidemiology Research Center, Shiga University of Medical Science, Otsu, Shiga, Japan
| | - Yasuyuki Nakamura
- NCD Epidemiology Research Center, Shiga University of Medical Science, Otsu, Shiga, Japan
- Takeda Hospital Medical Examination Center, Kyoto, Kyoto, Japan
| | - Kokichi Arisawa
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Tokushima, Japan
| | - Sakurako Katsuura-Kamano
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Tokushima, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Michiaki Kubo
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Kenji Takeuchi
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Miyagi, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Menniti G, Paquet C, Han HY, Dube L, Nielsen DE. Multiscale Risk Factors of Cardiovascular Disease: CLSA Analysis of Genetic and Psychosocial Factors. Front Cardiovasc Med 2021; 8:599671. [PMID: 33796568 PMCID: PMC8007777 DOI: 10.3389/fcvm.2021.599671] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Cardiovascular disease (CVD) is a complex disease resulting from multiscale risk factors including genetics, age, and psychosocial factors (PSFs) such as depression and social isolation. However, previous research has lacked in operationalizing multiscale risk factors to determine individual and interactive associations over the life course. Therefore, this study aimed to evaluate individual and interactive associations of multiscale risk factors for CVD outcomes including genetics and PSFs at middle and older-aged stages of the life course. Methods: Baseline data from the Canadian Longitudinal Study on Aging (CLSA; n = 9,892 with genome-wide genotyping data) was used for this investigation. A 39 single nucleotide polymorphism polygenic risk score (PRS) for CVD was constructed. PSFs consisted of: (1) Depressive symptoms categorized into: "none" (Group 1, reference), "current" (Group 2), "clinical depression with no current symptoms" (Group 3), and "potential, recurrent depression" (Group 4); and (2) Social isolation index as a binary variable comprised of marital status, living arrangements, retirement status, contacts, and social participation. Heart-related disorders (HRD: myocardial infarction, angina and heart disease) was the primary outcome of interest and peripheral/vascular-related disorders (PVRD: stroke, peripheral vascular disease and hypertension) was the secondary outcome. Multivariable logistic regression models adjusted for socio-demographic factors were conducted stratified by age group (middle-aged: 45-69 years, older-aged: ≥70 years). Results: PRS was associated with HRD among middle- and older-aged participants [OR (95% confidence interval)] [1.06 (1.03-1.08), 1.06 (1.03-1.08), respectively]. Most depressive symptoms groups compared to the reference associated with HRD and PVRD, but only Group 4 associated with PVRD among older-aged [1.69 (1.08-2.64)]. Social isolation was associated with only PVRD among middle-aged [1.84 (1.04-3.26)]; however, socially isolated CLSA participants were underrepresented in the genotyped cohort (1.2%). No significant PRS*PSFs interactions were observed. Conclusions: Genetics and PSFs are independently associated with CVD. Varying observations across age groups underscores the need to advance research on multiscale risk factors operating both at a given point in time and over the life course. Future cohort studies may benefit from use of mobile assessment units to enable better reach to socially isolated participants for collection of biospecimens.
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Affiliation(s)
| | - Catherine Paquet
- Faculté des Sciences Administratives, Université Laval, Québec, QC, Canada.,Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia
| | - Hannah Yang Han
- School of Human Nutrition, McGill University, Montreal, QC, Canada
| | - Laurette Dube
- McGill Center for the Convergence of Health and Economics, Desautels Faculty of Management, Montreal, QC, Canada
| | - Daiva E Nielsen
- School of Human Nutrition, McGill University, Montreal, QC, Canada
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