An Seokyung, Ahn Choonghyun, Moon Sungji, Sim Eun Ji, Park Sue-Kyung
Department of Biomedical Science, Graduate School, Seoul National University, Seoul 03080, Korea.
Department of Preventive Medicine, College of Medicine, Seoul National University, Seoul 03080, Korea.
J Pers Med. 2022 Mar 21;12(3):505. doi: 10.3390/jpm12030505.
Chronological age (CA) predicts health status but its impact on health varies with anthropometry, socioeconomic status (SES), and lifestyle behaviors. Biological age (BA) is, therefore, considered a more precise predictor of health status. We aimed to develop a BA prediction model from self-assessed risk factors and validate it as an indicator for predicting the risk of chronic disease. A total of 101,980 healthy participants from the Korean Genome and Epidemiology Study were included in this study. BA was computed based on body measurements, SES, lifestyle behaviors, and presence of comorbidities using elastic net regression analysis. The effects of BA on diabetes mellitus (DM), hypertension (HT), combination of DM and HT, and chronic kidney disease were analyzed using Cox proportional hazards regression. A younger BA was associated with a lower risk of DM (HR = 0.63, 95% CI: 0.55-0.72), hypertension (HR = 0.74, 95% CI: 0.68-0.81), and combination of DM and HT (HR = 0.65, 95% CI: 0.47-0.91). The largest risk of disease was seen in those with a BA higher than their CA. A consistent association was also observed within the 5-year follow-up. BA, therefore, is an effective tool for detecting high-risk groups and preventing further risk of chronic diseases through individual and population-level interventions.
实际年龄（CA）可预测健康状况，但其对健康的影响会因人体测量学、社会经济地位（SES）和生活方式行为而有所不同。因此，生物年龄（BA）被认为是健康状况更精确的预测指标。我们旨在通过自我评估的风险因素开发一个生物年龄预测模型，并将其验证为预测慢性病风险的指标。本研究纳入了韩国基因组与流行病学研究中的101980名健康参与者。使用弹性网络回归分析，根据身体测量、社会经济地位、生活方式行为和合并症的存在情况计算生物年龄。使用Cox比例风险回归分析生物年龄对糖尿病（DM）、高血压（HT）、糖尿病和高血压合并症以及慢性肾脏病的影响。较低的生物年龄与糖尿病（风险比[HR]=0.63，95%置信区间[CI]：0.55-0.72）、高血压（HR=0.74，95%CI：0.68-0.81）以及糖尿病和高血压合并症（HR=0.65，95%CI：0.47-0.91）的较低风险相关。疾病风险最高的是那些生物年龄高于实际年龄的人。在5年随访期间也观察到了一致的关联。因此，生物年龄是通过个体和人群层面的干预来检测高危人群和预防慢性病进一步风险的有效工具。
