Interaction of genetic and environmental factors for body fat mass control: observational study for lifestyle modification and genotyping

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Abstract

Previous studies suggested that genetic, environmental factors and their interactions could affect body fat mass (BFM). However, studies describing these effects were performed at a single time point in a population. In this study, we investigated the interaction between genetic and environmental factors in affecting BFM and implicate the healthcare utilization of lifestyle modifications from a personalized and genomic perspective. We examined how nutritional intake or physical activity changes in the individuals affect BFM concerning the genetic composition. We conducted an observational study including 259 adult participants with single nucleotide polymorphism (SNP) genotyping and longitudinal lifestyle monitoring, including food consumption and physical activities, by following lifestyle modification guidance. The participants’ lifelog data on exercise and diet were collected through a wearable device for 3 months. Moreover, we measured anthropometric and serologic markers to monitor their potential changes through lifestyle modification. We examined the influence of genetic composition on body fat reduction induced by lifestyle changes using genetic risk scores (GRSs) of three phenotypes: GRS-carbohydrate (GRS-C), GRS-fat (GRS-F), and GRS-exercise (GRS-E). Our results showed that lifestyle modifications affected BFM more significantly in the high GRS class compared to the low GRS class, indicating the role of genetic factors affecting the efficiency of the lifestyle modification-induced BFM changes. Interestingly, the influence of exercise modification in the low GRS class with active lifestyle change was lower than that in the high GRS class with inactive lifestyle change ( P = 0.022), suggesting the implication of genetic factors for efficient body fat control.

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Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants

Emily Sonestedt, Kim Overvad (2016)

Summary Background Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m2 [underweight], 18·5 kg/m2 to <20 kg/m2, 20 kg/m2 to <25 kg/m2, 25 kg/m2 to <30 kg/m2, 30 kg/m2 to <35 kg/m2, 35 kg/m2 to <40 kg/m2, ≥40 kg/m2 [morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. Findings We used 1698 population-based data sources, with more than 19·2 million adult participants (9·9 million men and 9·3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21·7 kg/m2 (95% credible interval 21·3–22·1) in 1975 to 24·2 kg/m2 (24·0–24·4) in 2014 in men, and from 22·1 kg/m2 (21·7–22·5) in 1975 to 24·4 kg/m2 (24·2–24·6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21·4 kg/m2 in central Africa and south Asia to 29·2 kg/m2 (28·6–29·8) in Polynesia and Micronesia; for women the range was from 21·8 kg/m2 (21·4–22·3) in south Asia to 32·2 kg/m2 (31·5–32·8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13·8% (10·5–17·4) to 8·8% (7·4–10·3) in men and from 14·6% (11·6–17·9) to 9·7% (8·3–11·1) in women. South Asia had the highest prevalence of underweight in 2014, 23·4% (17·8–29·2) in men and 24·0% (18·9–29·3) in women. Age-standardised prevalence of obesity increased from 3·2% (2·4–4·1) in 1975 to 10·8% (9·7–12·0) in 2014 in men, and from 6·4% (5·1–7·8) to 14·9% (13·6–16·1) in women. 2·3% (2·0–2·7) of the world’s men and 5·0% (4·4–5·6) of women were severely obese (ie, have BMI ≥35 kg/m2). Globally, prevalence of morbid obesity was 0·64% (0·46–0·86) in men and 1·6% (1·3–1·9) in women. Interpretation If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world’s poorest regions, especially in south Asia. Funding Wellcome Trust, Grand Challenges Canada.

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Genetic studies of body mass index yield new insights for obesity biology.

Adam Locke, Bratati Kahali, Sonja Berndt … (2016)

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

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Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Amit Khera, Mark J Chaffin, Krishna Aragam … (2018)

A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation. 1 Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature, 2–5 it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0%, 6.1%, 3.5%, 3.2% and 1.5% of the population at greater than three-fold increased risk for coronary artery disease (CAD), atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For CAD, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk. 6 We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care and discuss relevant issues.

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Author and article information

Contributors

Yoon Jung Yang: yjyang@dongduk.ac.kr

Jinho Kim: jinho.jk.kim@samsung.com

Woong-Yang Park: woongyang.park@samsung.com

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 23 June 2021

Publication date PMC-release: 23 June 2021

Publication date Collection: 2021

Volume: 11

Electronic Location Identifier: 13180

Affiliations

[1 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Department of Molecular Cell Biology, , Sungkyunkwan University School of Medicine, ; 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419 South Korea

[2 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Samsung Genome Institute, , Samsung Medical Center, Sungkyunkwan University, ; Ilwon-ro 81, Gangnam-gu, Seoul, 06351 South Korea

[3 ]GRID grid.412059.b, ISNI 0000 0004 0532 5816, Department of Foods and Nutrition, College of Natural Sciences, , Dongduk Women’s University, ; 60, Hwarang-ro 13-gil, Seongbuk-gu, Seoul, 02748 Korea

[4 ]AI&SW Center, SAIT, SEC, 130, Samsung-ro, Yeongtong-gu, Suwon, Gyeonggi 16678 South Korea

[5 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Samsung Advanced Institute for Health Sciences and Technology, , Sungkyunkwan University of Medicine, ; Seoul, 06351 South Korea

[6 ]GRID grid.414964.a, ISNI 0000 0001 0640 5613, Samsung Medical Center, ; Gangnam-gu, Seoul, 06351 South Korea

[7 ]GRID grid.412059.b, ISNI 0000 0004 0532 5816, Department of Clinical Nutrition, School of Public Health, , Dongduk Women’s University, ; Seoul, 02748 Korea

[8 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Department of Clinical Research Design and Evaluation, SAIHST, , Sungkyunkwan University, ; Seoul, South Korea

Article

Publisher ID: 92229

DOI: 10.1038/s41598-021-92229-5

PMC ID: 8222320

SO-VID: dad3489f-fd93-4758-9095-737118eebcc0

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 28 October 2020

Date accepted : 1 June 2021

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ScienceOpen disciplines: Uncategorized

Keywords: computational models,microarrays,biomarkers,health care,risk factors

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ScienceOpen disciplines: Uncategorized

Keywords: computational models, microarrays, biomarkers, health care, risk factors

Interaction of genetic and environmental factors for body fat mass control: observational study for lifestyle modification and genotyping

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Most cited references 40

Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants

Genetic studies of body mass index yield new insights for obesity biology.

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

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