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Philos Trans R Soc Lond B Biol Sci. 2023 Sep 11;378(1885):20220208. doi: 10.1098/rstb.2022.0208. Epub 2023 Jul 24.
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Height, social position and coronary heart disease incidence: the contribution of genetic and environmental factors.身高、社会地位与冠心病发病:遗传和环境因素的作用。
J Epidemiol Community Health. 2023 Jun;77(6):384-390. doi: 10.1136/jech-2022-219907. Epub 2023 Mar 24.
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Polygenic and socioeconomic risk for high body mass index: 69 years of follow-up across life.多基因和社会经济因素与高体重指数的关系:跨越一生的 69 年随访研究。
PLoS Genet. 2022 Jul 14;18(7):e1010233. doi: 10.1371/journal.pgen.1010233. eCollection 2022 Jul.
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Body mass trajectories and multimorbidity in old age: 12-year results from a population-based study.老年人体重轨迹与多种疾病:一项基于人群的 12 年研究结果。
Clin Nutr. 2021 Dec;40(12):5764-5770. doi: 10.1016/j.clnu.2021.10.012. Epub 2021 Oct 22.
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Polygenic risk for obesity and its interaction with lifestyle and sociodemographic factors in European children and adolescents.肥胖的多基因风险及其与生活方式和社会人口因素在欧洲儿童和青少年中的相互作用。
Int J Obes (Lond). 2021 Jun;45(6):1321-1330. doi: 10.1038/s41366-021-00795-5. Epub 2021 Mar 22.
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Obesity and eating behavior from the perspective of twin and genetic research.从双胞胎和遗传研究的角度看肥胖和饮食行为。
Neurosci Biobehav Rev. 2020 Feb;109:150-165. doi: 10.1016/j.neubiorev.2019.12.012.
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Improved polygenic prediction by Bayesian multiple regression on summary statistics.基于汇总统计数据的贝叶斯多元回归提高多基因预测能力。
Nat Commun. 2019 Nov 8;10(1):5086. doi: 10.1038/s41467-019-12653-0.
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Protocol of a research project 'Projections of the burden of disease and disability in Finland - health policy prospects' using cross-sectional health surveys and register-based follow-up.研究项目“芬兰疾病和残疾负担预测——卫生政策展望”的方案,使用横断面健康调查和基于登记的随访。
BMJ Open. 2019 Jun 20;9(6):e029338. doi: 10.1136/bmjopen-2019-029338.
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Parental Education and Genetics of BMI from Infancy to Old Age: A Pooled Analysis of 29 Twin Cohorts.从婴儿期到老年期的 BMI 的父母教育和遗传学:29 项双胞胎队列的汇总分析。
Obesity (Silver Spring). 2019 May;27(5):855-865. doi: 10.1002/oby.22451. Epub 2019 Apr 5.
10
The effect of weight on labor market outcomes: An application of genetic instrumental variables.体重对劳动力市场结果的影响:遗传工具变量的应用。
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社会经济地位对体重指数的影响:遗传因素的贡献。

Socio-economic differences in body mass index: the contribution of genetic factors.

机构信息

University of Helsinki, Faculty of Social Sciences, Population Research Unit, Helsinki, Finland.

Max Planck - University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland.

出版信息

Int J Obes (Lond). 2024 May;48(5):741-745. doi: 10.1038/s41366-024-01459-w. Epub 2024 Jan 10.

DOI:10.1038/s41366-024-01459-w
PMID:38200145
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11058309/
Abstract

BACKGROUND

Higher mean body mass index (BMI) among lower socioeconomic position (SEP) groups is well established in Western societies, but the influence of genetic factors on these differences is not well characterized.

METHODS

We analyzed these associations using Finnish health surveys conducted between 1992 and 2017 (N = 33 523; 53% women) with information on measured weight and height, polygenic risk scores of BMI (PGS-BMI) and linked data from administrative registers to measure educational attainment, occupation-based social class and personal income.

RESULTS

In linear regressions, largest adjusted BMI differences were found between basic and tertiary educated men (1.4 kg/m, 95% confidence interval [CI] 1.2; 1.6) and women (2.5 kg/m, 95% CI 2.3; 2.8), and inverse BMI gradients were also found for social class and income. These SEP differences arose partly because mean PGS-BMI was higher and partly because PGS-BMI predicted BMI more strongly in lower SEP groups. The inverse SEP gradients of BMI were steeper in women than in men, but sex differences were not found in the genetic contributions to these differences.

CONCLUSIONS

Better understanding of the interplay between genes and environment provides insight into the mechanisms explaining SEP differences in BMI.

摘要

背景

在西方社会,社会经济地位较低的人群平均体质量指数(BMI)较高,这一现象已得到充分证实,但遗传因素对这些差异的影响尚未得到充分描述。

方法

我们使用芬兰在 1992 年至 2017 年期间进行的健康调查(N=33523;53%为女性)中的信息,分析了这些关联,这些信息包括测量得到的体重和身高、BMI 的多基因风险评分(PGS-BMI)以及从行政登记处链接的数据,以衡量受教育程度、职业社会阶层和个人收入。

结果

在线性回归中,我们发现基本受教育程度和高等受教育程度的男性(1.4kg/m,95%置信区间 [CI] 1.2; 1.6)和女性(2.5kg/m,95% CI 2.3; 2.8)之间的 BMI 差异最大,社会阶层和收入也呈现出 BMI 的逆梯度分布。这些社会经济地位差异的出现部分是因为平均 PGS-BMI 较高,部分是因为 PGS-BMI 在社会经济地位较低的人群中对 BMI 的预测更为强烈。BMI 的逆社会经济地位梯度在女性中比在男性中更为陡峭,但在遗传因素对这些差异的贡献方面,并未发现性别差异。

结论

更好地理解基因与环境之间的相互作用,可以深入了解解释 BMI 中社会经济地位差异的机制。