Chatterjee Prasun, Jain Rashi, Attri Pooja, Chakrawarty Avinash, Rani Lata, Dey Sharmistha, Pradhan Rashmita, Kulshrestha Vidushi, Ramakrishnan Lakshmy
Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi 110029, India.
Department of Centralized Core Research Facility, All India Institute of Medical Sciences, New Delhi 110029, India.
Biol Methods Protoc. 2025 Jul 3;10(1):bpaf053. doi: 10.1093/biomethods/bpaf053. eCollection 2025.
Age-associated disease management depends significantly on chronological age and macro-level clinical data sets. However, the biological age captures bio-physiological deterioration more precisely than the chronological age. Biological ageing is the accumulation of successive damage to various cells, tissues, and individual organs over the ageing period. It is the explicit reflection of functional decline. Therefore, quantifying biological age can be highly valuable for improving clinical management of age-related changes. Various epigenetic clocks have been used to quantify biological age. However, epigenetics alone cannot fully account for the complex ageing process, which involves ageing hallmarks, signalling pathways, clinical phenotypes, physiological functions, environmental exposures, and lifestyle habits. Therefore, the primary purpose of this pilot study is the feasibility testing and trajectory mapping of the ageing biomarkers across diverse age-based subgroups. This study will help to find reliable, reproducible, robust, and integrative ageing biomarkers to quantify biological age. This community-based prospective cohort study will be conducted at the National Centre of Ageing, All India Institute of Medical Sciences, New Delhi. This study will include 250 participants from six cohorts, i.e. newborns, adolescents (10-19 years), young adults (20-39 years), middle-aged individuals (40-59 years), young olds (60-79 years), and the oldest old (above 80 years). Forty individuals from each cohort will be recruited to study blood and stool biomarkers along with a comprehensive assessment of cognitive behaviour, psychological well-being, functional capacity, gut health, nutritional behaviour, and physiological measures. Participants will also be monitored in real time through wearable devices. After five years, participants will be followed up with the same biomarkers to gain insights about the speed of ageing, predicting disease and mortality. Multi-domain data will be integrated to develop a deep learning-based multi-model algorithm for biological age estimation. This first-of-its-kind study would provide an exhaustive understanding of the ageing process throughout life, 0-100 years. Integrative biomarkers would make a precise determination of biological age. Additionally, studying change in these parameters after five years would elucidate the pace of biological ageing and predict life expectancy and disability.
与年龄相关的疾病管理在很大程度上依赖于实足年龄和宏观层面的临床数据集。然而，生物年龄比实足年龄更能精确地反映生物生理衰退情况。生物衰老指的是在衰老过程中，各种细胞、组织和单个器官持续受到的损伤积累。它是功能衰退的明确体现。因此，量化生物年龄对于改善与年龄相关变化的临床管理具有很高的价值。各种表观遗传时钟已被用于量化生物年龄。然而，仅表观遗传学无法完全解释复杂的衰老过程，这一过程涉及衰老特征、信号通路、临床表型、生理功能、环境暴露和生活习惯。因此，这项初步研究的主要目的是对不同年龄亚组的衰老生物标志物进行可行性测试和轨迹绘制。本研究将有助于找到可靠、可重复、稳健且综合的衰老生物标志物来量化生物年龄。这项基于社区的前瞻性队列研究将在新德里全印度医学科学研究所国家衰老中心进行。本研究将包括来自六个队列的250名参与者，即新生儿、青少年（10 - 19岁）、年轻成年人（20 - 39岁）、中年人（40 - 59岁）、年轻老年人（60 - 79岁）和最年长的老年人（80岁以上）。将从每个队列中招募40人，研究血液和粪便生物标志物，并对认知行为、心理健康、功能能力、肠道健康、营养行为和生理指标进行全面评估。参与者还将通过可穿戴设备进行实时监测。五年后，将对参与者进行相同生物标志物的随访，以深入了解衰老速度、预测疾病和死亡率。多领域数据将被整合，以开发一种基于深度学习的多模型算法用于生物年龄估计。这项首创的研究将提供对0至100岁整个人生衰老过程的详尽理解。综合生物标志物将精确测定生物年龄。此外，研究五年后这些参数的变化将阐明生物衰老的速度，并预测预期寿命和残疾情况。
