Translating our knowledge of the biological aging from animal models to humans may give rise to novel approaches of targeting multiple aging-related diseases simultaneously and increasing health span. Here, for the first time, we use transcriptomic signatures of monocytes to identify biological aging pathways underlying multiple aging-related diseases in humans. The ordinal logistic regression was used to cross-sectionally investigate transcriptomics of the comorbidity index in 1264 community-based Multi-Ethnic Study of Atherosclerosis (MESA) adults, 47% Caucasian, 32% Hispanic, 21% African American, and 51% female, aged 55–94 years. The comorbidity index was defined as the number of prevalent aging-related diseases including cardiovascular disease, type-2 diabetes, hypertension, cancer, dementia, chronic kidney disease, chronic obstructive pulmonary disease, and hip fracture. We identified 708 gene transcripts associated with the comorbidity index (FDR < 0.05) after adjusting for age, sex, ethnicity, and study site. In a weighted gene co-expression network analysis, as postulated, aging-related declines in apoptosis/autophagy (OR = 1.21 per SD increment, p = 0.0006) and ribosome/mitochondrion (OR = 0.90 per SD increment, p = 0.05) were positively associated with the comorbidity index. After adjusting for multiple comparisons, we identified 10 comorbidity-associated modules (FDR < 0.05), including the module of apoptosis/autophagy. There were three inter-correlated modules of these 10 involved in the complement subcomponent C1q, Fc gamma receptor I, and Fc gamma receptor III of the immune system, respectively. Aging-related upregulation of these three modules was positively associated with the comorbidity index. The odds of comorbidity increased with more of these modules acting together in a dose–response fashion. In conclusion, transcriptomic analysis of human immune cells may identify biomarker panels indicative of comprehensive biological mechanisms, especially immune signaling pathways, contributing to health aging.

将我们对生物衰老的知识从动物模型转化为人类可能会产生同时针对多种衰老相关疾病并延长健康寿命的新方法。在这里，我们首次使用单核细胞的转录组特征来识别人类多种衰老相关疾病的生物衰老途径。使用序数逻辑回归对 1264 名基于社区的动脉粥样硬化多种族研究 (MESA) 成年人的共病指数转录组学进行横断面研究，其中 47% 为白种人，32% 为西班牙裔，21% 为非裔美国人，51% 为女性。年龄55-94岁。合并症指数定义为流行的与衰老相关的疾病的数量，包括心血管疾病、2型糖尿病、高血压、癌症、痴呆、慢性肾病、慢性阻塞性肺病和髋部骨折。在调整年龄、性别、种族和研究地点后，我们确定了 708 个与合并症指数 (FDR < 0.05) 相关的基因转录本。在加权基因共表达网络分析中，正如假设的那样，与衰老相关的细胞凋亡/自噬（ OR = 1.21 每 SD 增量， p = 0.0006）和核糖体/线粒体（ OR = 0.90 每 SD 增量， p = 0.05）下降与合并症指数呈正相关。经过多重比较调整后，我们确定了 10 个与合并症相关的模块 (FDR < 0.05)，包括细胞凋亡/自噬模块。这 10 个模块中，有 3 个相互关联的模块分别涉及免疫系统的补体亚成分 C1q、Fc γ 受体 I 和 Fc γ 受体 III。这三个模块与衰老相关的上调与合并症指数呈正相关。 随着更多这些模块以剂量反应方式共同作用，合并症的几率会增加。总之，人类免疫细胞的转录组分析可以识别指示综合生物机制的生物标志物组，特别是免疫信号通路，有助于健康衰老。