Background A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases. Hence, the combined impact of multiple biomarkers in relation to ageing free of major chronic diseases, such as cancer, cardiovascular disease and type 2 diabetes, has not been sufficiently explored. Methods We measured concentrations of 13 biomarkers in a random subcohort of 2,500 participants in the European Prospective Investigation into Cancer and Nutrition Potsdam study. Chronic disease-free ageing was defined as reaching the age of 70 years within study follow-up without major chronic diseases, including cardiovascular disease, type 2 diabetes or cancer. Using a novel machine-learning technique, we aimed to identify biomarker clusters and explore their association with chronic disease-free ageing in multivariable-adjusted logistic regression analysis taking socio-demographic, lifestyle and anthropometric factors into account. Results Of the participants who reached the age of 70 years, 321 met our criteria for chronic-disease free ageing. Machine learning analysis identified three distinct biomarker clusters, among which a signature characterised by high concentrations of high-density lipoprotein cholesterol, adiponectin and insulin-like growth factor-binding protein 2 and low concentrations of triglycerides was associated with highest odds for ageing free of major chronic diseases. After multivariable adjustment, the association was attenuated by socio-demographic, lifestyle and adiposity indicators, pointing to the relative importance of these factors as determinants of healthy ageing. Conclusion These data underline the importance of exploring combinations of biomarkers rather than single molecules in understanding complex biological pathways underpinning healthy ageing.

中文翻译：

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与无主要慢性疾病的衰老相关的生物标志物特征：来自 EPIC-Potsdam 队列的基于人群的样本结果


背景 许多表示各种病理生理学途径的生物标志物与年龄相关疾病的病因和风险有关。因此，多种生物标志物与无主要慢性疾病（如癌症、心血管疾病和2型糖尿病）的衰老相关的综合影响尚未得到充分探索。方法 我们测量了欧洲癌症和营养波茨坦研究前瞻性调查中 2,500 名参与者的随机分组中 13 种生物标志物的浓度。无慢性病衰老的定义是在研究随访期间年满70岁且没有重大慢性疾病，包括心血管疾病、2型糖尿病或癌症。使用一种新颖的机器学习技术，我们的目的是识别生物标志物簇，并在考虑社会人口、生活方式和人体测量因素的多变量调整逻辑回归分析中探索它们与慢性无病衰老的关系。结果 在年满 70 岁的参与者中，有 321 人符合我们的无慢性病老龄化标准。机器学习分析确定了三个不同的生物标志物簇，其中以高浓度的高密度脂蛋白胆固醇、脂联素和胰岛素样生长因子结合蛋白 2 以及低浓度的甘油三酯为特征的特征与无重大衰老的最高几率相关。慢性疾病。经过多变量调整后，社会人口、生活方式和肥胖指标削弱了这种关联，表明这些因素作为健康老龄化决定因素的相对重要性。 结论 这些数据强调了探索生物标志物组合而不是单个分子在理解支撑健康衰老的复杂生物途径方面的重要性。