*For medical professionals only
Everyone should have this feeling: the rate of human aging is not uniform.
There are years when we feel that our parents, and even ourselves, seem to have suddenly aged a lot, and before that, time seems to stand still.
This may not be our delusion.
Not long ago, a research team led by Liu Guanghui of the Institute of Zoology, Chinese Academy of Sciences, Zhang Weiqi and Yang Yungui of the Beijing Institute of Genomics, Chinese Academy of Sciences, and Zhang Feng of Quzhou People's Hospital published important research results in Med, a subsidiary of Cell [1].
Based on a cohort of 113 healthy Chinese women, they established a multi-dimensional aging marker landscape map for Chinese women for the first time, and built a set of "aging clocks" for Chinese women on this basis. Notably, they found that Chinese women showed increased aging around the ages of 30 and 50.
Li Jiaming, Xiong Muzhao and Fan Yanling of the Beijing Institute of Genomics, Chinese Academy of Sciences, Fu Xianghong, Quzhou People's Hospital, and Dong Chen of Beijing University of Posts and Telecommunications are co-first authors of the paper.
Screenshot of the first page of the paper
The researchers first recruited 113 healthy Chinese women aged 20-66 in Quzhou, Zhejiang Province, with a similar number of different age groups.
After all the participants were enrolled, the researchers collected their physical examination results, blood and stool samples (the same food the day before the physical examination) for multiomics data analysis. On the same day of the checkup, they also took images of the subjects' faces to obtain their facial age.
Specifically, the physical test items include 175 items such as BMI, waist circumference, hip circumference, blood pressure, cardiopulmonary function and bone density, as well as five motor ability tests. Tests around the blood include indicators such as lipids, hormones, and liver function, as well as data such as telomere length, plasma proteomics, and metabolomic characteristics. Stool samples primarily measure the composition of the gut microbiota.
Research flowchart
Preliminary analysis showed that the older the age, the lower the score on the five motor ability tests. In addition, lipid metabolism (cholesterol, etc.), blood hormone levels (follicle-stimulating hormone and luteinizing hormone), and tissue function (liver, lungs, bones, and muscles) are closely related to aging.
From the transcriptome data of peripheral blood mononuclear cells (PBMCs), 356 genes were positively correlated with age and 1372 genes were negatively correlated with age, showing a decreasing number of naïve T cells and the accumulation of cytotoxic immune cells.
PBMC varies with age
Analysis data of plasma proteins showed that 119 proteins increased with age and 148 decreased with age, one-third of which were accumulated proteins associated with aging came from the liver and one-fifth of which were secreted by muscles.
Proteome changes with age
In addition, 99 and 46 metabolites were positively and negatively correlated with age, respectively. Surprisingly, the composition of the gut microbiota appears to correlate less with age than other types of markers.
Next, based on the above data, the researchers constructed a set of multidimensional aging "clocks", which are phenotypic age, transcription age, protein age, metabolic age and compound age, as well as hormonal age, immune age, and lipid age. Among them, compound age and facial age performed best in predicting calendar age; Transcription age, protein age, and metabolic age were similar in predicting the temporal age of participants.
Notably, compound age is highly correlated with all other clocks, indicating that it is able to reflect heterogeneous aging processes; However, the correlation between metabolic age and protein age is relatively low, suggesting that even in a person, the proteome and metabolome may have different patterns of aging. Unsurprisingly, the immune age of the immune system is highly correlated with the age of the protein.
Correlation between different clocks
After analyzing the effects of genetics, environment, and lifestyle on the rate of aging, the researchers found that the rate of aging was positively correlated with hot flashes and reproductive aging symptoms, while negatively correlated with healthy eating habits. In addition, immune age was negatively correlated with the number of tea drinks per week, while protein age for nerves and muscles was positively correlated with physical pain.
Interestingly, the researchers also explored the intensity of aging over different time periods. They found that around the age of 30 and 50, there were two peaks of aging; Coincidentally, these two peaks were also the time points in the subject's fertility history and menopausal status.
Specifically, the aging changes around the age of 30 are mainly reflected in the metabolic level, which is manifested as an increase in lipids and a decrease in steroids and derivatives; Women around the age of 50 have more obvious changes, such as decreased bone density and lung function, sharp decrease in muscle tissue protein, and increased proportion of myeloid cells.
Kinetic curves of aging
At the end of the study, the researchers found that hormone replacement therapy slowed down multiple aging clocks, such as hormonal age, protein age, immune age, and metabolic age, although phenotypic age and facial age did not slow significantly.
The effect of hormone replacement therapy
In general, this study by the team of Liu Guanghui/Zhang Weiqi/Zhang Feng/Yang Yungui systematically studied the characteristics of Chinese women's aging for the first time, allowing us to have a deeper understanding of women's aging.
More importantly, this study provides the possibility of evaluating the effects of aging interventions in multiple ways, and suggests that hormone replacement therapy may be a potential strategy for aging interventions.
Bibliography:
[1]. Li J, Xiong M, Fu XH, et al. Determining a multimodal aging clock in a cohort of Chinese women. Med. 2023; S2666-6340(23)00197-6. doi:10.1016/j.medj.2023.06.010
The author of this article 丨BioTalker