In this study we use aggregated weighted scores of environmental effects to study environmental influences on well-being and happiness. To this end, we split a sample of Netherlands Twin Register (NTR) participants into a training (N = 4857) and test (N = 2077) sample. In the training sample, we use elastic net regression to estimate effect sizes for associations between life satisfaction and two sets of environmental variables: one based on self-report socioenvironmental data, and one based on objective physical environmental data. Based on these effect sizes, we create two poly-environmental scores (PES-S and PES-O, for self-reports and objective data respectively). In the test sample, we perform association analyses between different measures of well-being and the two PESs. We find that the PES-S explains ∼36% of the variance in well-being, while the PES-O does not significantly contribute to the model. Variance in other well-being measures (i.e., different life satisfaction domains, subjective happiness, quality of life, flourishing, psychological well-being, self-rated health, depressive problems, and loneliness) are explained to varying extents by the PESs, ranging from 6.36% (self-rated health) to 36.66% (loneliness). These predictive values did not change during the COVID-19 pandemic (N = 3214). Validating the PES-S in the UK biobank (N = 40,614), we find that the UK biobank PES-S explains about ∼12% of the variance in happiness. Lastly, we examine if there is any indication for gene-environment correlation (rGE), the phenomenon where one's genetic predisposition influences exposure to the environment, by associating the PESs with polygenic scores (PGS) in a sample of Netherlands Twin Register (NTR) and UK Biobank participants. While the PES and PGS were not correlated in the NTR sample, they were correlated in the larger UK biobank sample, indicating the potential presence of rGE. We discuss several limitations pertaining to our dataset, such as a potential influence of common method bias, and reflect on how PESs might be used in future research.

在这项研究中，我们使用环境影响的汇总加权分数来研究环境对福祉和幸福的影响。为此，我们将荷兰双胞胎注册 (NTR) 参与者的样本分为训练样本 (N = 4857) 和测试样本 (N = 2077)。在训练样本中，我们使用弹性网络回归来估计生活满意度与两组环境变量之间关联的效应大小：一组基于自我报告的社会环境数据，一组基于客观的物理环境数据。根据这些效应大小，我们创建了两个多环境评分（PES-S 和 PES-O，分别用于自我报告和客观数据）。在测试样本中，我们对不同的幸福感指标与两种 PES 之间进行关联分析。我们发现 PES-S 解释了大约 36% 的幸福感方差，而 PES-O 对模型没有显着贡献。 PES 在不同程度上解释了其他幸福感指标（即不同的生活满意度领域、主观幸福感、生活质量、繁荣、心理健康、自评健康、抑郁问题和孤独感）的差异，范围从 6.36%（自我评价健康）到 36.66%（孤独）。这些预测值在 COVID-19 大流行期间没有变化 (N = 3214)。在英国生物银行 (N = 40,614) 中验证 PES-S，我们发现英国生物银行 PES-S 解释了约 12% 的幸福差异。最后，我们通过将荷兰双胞胎登记册 (NTR) 样本中的 PES 与多基因评分 (PGS) 相关联，检查是否存在基因与环境相关性 (rGE) 的迹象，即一个人的遗传倾向影响环境暴露的现象。和英国生物银行参与者。 虽然 PES 和 PGS 在 NTR 样本中不相关，但它们在较大的英国生物样本库样本中相关，表明 rGE 的潜在存在。我们讨论了与我们的数据集相关的一些限制，例如共同方法偏差的潜在影响，并反思了 PES 在未来研究中的使用方式。