Development of a Prediction Model for Healthy Life Years Without Activity Limitation: National Cross-sectional Study.

BACKGROUND: In some countries, including Japan-the leading country in terms of longevity, life expectancy has been increasing; meanwhile, healthy life years have not kept pace, necessitating an effective health policy to narrow the gap. OBJECTIVE: The aim of this study is to develop a prediction model for healthy life years without activity limitations and deploy the model in a health policy to prolong healthy life years. METHODS: The Comprehensive Survey of Living Conditions, a cross-sectional national survey of Japan, was conducted by the Japanese Ministry of Health, Labour and Welfare in 2013, 2016, and 2019. The data from 1,537,773 responders were used for modelling using machine learning. All participants were randomly split into training (n=1,383,995, 90%,) and test (n=153,778, 10%) subsets. Extreme gradient boosting classifier was implemented. Activity limitations were set as the target. Age, sex, and 40 types of diseases or injuries were included as features. Healthy life years without activity limitations were calculated by incorporating the predicted prevalence rate of activity limitations in a life table. For the wide utility of the model in individuals, we developed an application tool for the model. RESULTS: In the groups without (n=1,329,901) and with (n=207,872) activity limitations, the median age was 47 (IQR 30-64) and 69 (IQR 54-80) years, respectively (P<.001); female sex comprised 51.3% (n=681,794) in the group without activity limitations and 56.9% (n=118,339) in the group with activity limitations (P<.001). A total of 42 features were included in the feature set. Age had the highest impact on model accuracy, followed by depression or other mental diseases; back pain; bone fracture; other neurological disorders, pain, or paralysis; stroke, cerebral hemorrhage, or infarction; arthritis; Parkinson disease; dementia; and other injuries or burns. The model exhibited high performance with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.842-0.849) with exact calibration for the average probability and fraction of positives. The prediction results were consistent with the observed values of healthy life years for both sexes in each year (range of difference between predictive and observed values: -0.89 to 0.16 in male and 0.61 to 1.23 in female respondents). We applied the prediction model to a regional health policy to prolong healthy life years by adjusting the representative predictors to a target prevalence rate. Additionally, we presented the health condition without activity limitations index, followed by the application development for individual health promotion. CONCLUSIONS: The prediction model will enable national or regional governments to establish an effective health promotion policy for risk prevention at the population and individual levels to prolong healthy life years. Further investigation is needed to validate the model's adaptability to various ethnicities and, in particular, to countries where the population exhibits a short life span.

Association Between Cerebral Microbleeds and Circulating Levels of Mid-Regional Pro-Adrenomedullin.

BACKGROUND: Mid-regional pro-adrenomedullin (MR-proADM) is a novel biomarker for cognitive decline based on its association with cerebral small vessel disease (SVD). Cerebral microbleeds (MBs) are characteristic of SVD; however, a direct association between MR-proADM and MBs has not been explored. OBJECTIVE: We aimed to examine whether circulating levels of MR-proADM are associated with the identification of MBs by brain magnetic resonance imaging (MRI) and whether this association could be linked with cognitive impairment. METHODS: In total, 214 participants (mean age: 75.9 years) without history of cerebral infarction or dementia were prospectively enrolled. All participants underwent brain MRI, higher cognitive function testing, blood biochemistry evaluation, lifestyle examination, and blood MR-proADM measurement using a time-resolved amplified cryptate emission technology assay. For between-group comparisons, the participants were divided into two groups according to whether their levels of MR-proADM were normal (< 0.65 nmol/L) or high (≥0.65 nmol/L). RESULTS: The mean MR-proADM level was 0.515±0.127 nmol/L. There were significant between-group differences in age, hypertension, and HbA1c levels (p < 0.05). In the high MR-proADM group, the MR-proADM level was associated with the identification of MBs on brain MR images and indications of mild cognitive impairment (MCI). In participants with ≥3 MBs and MCI, high MR-proADM levels remained a risk factor after multivariate adjustment (OR: 2.94; p < 0.05). CONCLUSION: High levels of MR-proADM may be a surrogate marker for the early detection of cognitive decline associated with the formation of cerebral MBs. This marker would be valuable during routine clinical examinations of geriatric patients.

Evaluation of myostatin as a possible regulator and marker of skeletal muscle-cortical bone interaction in adults.

INTRODUCTION: Bone mass was recently reported to be related to skeletal muscle mass in humans, and a decrease in cortical bone is a risk factor for osteoporosis. Because circulating myostatin is a factor that primarily controls muscle metabolism, this study examined the role of myostatin in bone mass-skeletal muscle mass interactions. METHODS: The subjects were 375 middle-aged community residents with no history of osteoporosis or sarcopenia who participated in a health check-up. Cortical bone thickness and cancellous bone density were measured by ultrasonic bone densitometry in a health check-up survey. The subjects were divided into those with low cortical bone thickness (LCT) or low cancellous bone density (LBD) and those with normal values (NCT/NBD). Bone metabolism markers (TRACP-5b, etc.), skeletal muscle mass, serum myostatin levels, and lifestyle were then compared between the groups. RESULTS: The percentage of diabetic participants, TRACP-5b, and myostatin levels were significantly higher, and the frequency of physical activity, skeletal muscle mass, grip strength, and leg strength were significantly lower in the LCT group than in the NCT group. The odds ratio (OR) of high myostatin levels in the LCT group compared with the NCT group was significant (OR 2.17) even after adjusting for related factors. Between the low cancellous bone density (LBD) and normal cancellous bone density (NBD) groups, significant differences were observed in the same items as between the LCT and NCT groups, but no significant differences were observed in skeletal muscle mass and blood myostatin levels. The myostatin level was significantly negatively correlated with cortical bone thickness and skeletal muscle mass. CONCLUSIONS: A decrease in cortical bone thickness was associated with a decrease in skeletal muscle mass accompanied by an increase in the blood myostatin level. Blood myostatin may regulate the bone-skeletal muscle relationship and serve as a surrogate marker of bone metabolism, potentially linking muscle mass to bone structure.