Advanced brain age in community-dwelling population with combined physical and cognitive impairments

Multi-trajectories in different domains of social supports and subjective motoric cognitive risk syndrome: a 16-year group-based multi-trajectory analysis

OBJECTIVE

The aim of this study was to examine the longitudinal relationships between the trajectories of distinct subtypes of various domains of social supports and risk of subjective motoric cognitive risk (MCR) syndrome.

DESIGN

Longitudinal cohort study.

SETTING AND PARTICIPANTS

2,279 participants in the Taiwan Longitudinal Study on Aging (TLSA) between 1999 and 2011.

METHOD

A group-based multi-trajectory modeling (GBMTM) was implemented to identify distinct trajectory subtypes within various social support domains, encompassing social networks, emotional support, instrumental support, as well as working and economic status. Logistic regression models were then utilized to evaluate the associations between these trajectory subtypes and the risk of subjective MCR.

RESULTS

Among 2,279 participants, GBMTM identified four distinct trajectory subtypes: "low social support" (n = 371), "medium social support " (n = 862), "high social support" (n = 292), and "high social support with employment" (n = 754). The incidence rates of subjective MCR for these groups were 9.4%, 9.0%, 4.1%, and 0.8%, respectively. After adjusting for age, sex, education level, and comorbidities, both "low social support" (adjusted odds ratio (aOR) 4.07, 95% CI [1.60-10.34]) and "medium social support" (aOR 3.10, 95% CI [1.26-7.66]) were significantly associated with an increased risk of subjective MCR compared to the "high social support with employment" group.

CONCLUSIONS AND IMPLICATIONS

The current study demonstrates that social support significantly reduces the risk of subjective MCR, with lower support levels correlating to higher risk, necessitating further intervention studies to confirm the link between social support and risk of subjective MCR.

Longitudinal trajectories of handgrip strength and their association with motoric cognitive risk syndrome in older adults

BACKGROUND

To identify heterogeneous developmental trajectories of handgrip strength (HGS) in Chinese older adults and to explore the relationship between different developmental trajectories and motoric cognitive risk syndrome (MCR).

METHODS

We used three waves of longitudinal data from the China Health and Retirement Longitudinal Study from 2011 to 2015, which involved 3773 older adults. Growth mixture modeling (GMM) was used to estimate trajectory classes for HGS, followed by binary logistic regression to explore the association between trajectory classes and MCR.

RESULTS

GMM analyses extracted four distinct trajectories of HGS: low level-declining group (16.0 %), upper middle level group (30.9 %), high level-steady group (9.5 %), and lower middle level group (43.6 %). In addition, we found that even after adjusting for important covariates, the odds of MCR prevalence were lower in the medium level-high group, high level-steady group, and medium level-low group compared with the low level-declining group.

CONCLUSION

Appreciable heterogeneity in HGS among older people in China was revealed. Only 9.5 % of older people with HGS in the high level-steady group. And poorer grip strength levels mean a higher risk of MCR. Therefore, interventions should be taken to maintain muscle mass and thus prevent MCR in older adults.

Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.