The association between visceral fat obesity and prefrailty in Chinese older adults: a cross-sectional study.

BACKGROUND: The prevalence of obesity is escalating. Previous research has concentrated on the link between frailty and obesity; however, the association between prefrailty and obesity has been less studied. Prefrailty screening and intervention may prevent or postpone frailty in older persons. OBJECTIVE: The study was to investigate into the relationship between prefrailty and several obesity indicators in Chinese community-dwelling older individuals. METHODS: This research employed the Frailty Screening Index to investigate the frailty phenotype of people living in Shanghai. Bioelectrical impedance analysis was used for evaluating body composition. RESULTS: There were 510 participants (39.0%) with high visceral adipose areas. Participants with a high visceral adipose area showed a higher risk of prefrailty (adjusted OR, 1.53; 95% CI, 1.19-1.96), according to multivariate models. When body mass index (BMI) and visceral fat area (VFA) were combined, it was discovered that having an overweight BMI with normal VFA was a protective factor for prefrailty (corrected OR, 0.62; 95% CI, 0.43-0.90), but having a normal weight but excess VFA increased the risk of prefrailty (corrected OR, 1.87; 95% CI, 1.15-3.03). CONCLUSION: Visceral fat obesity is an independent risk factor for prefrailty in Chinese older adults. Implementing targeted interventions, such as dietary modifications, increased physical activity, and other lifestyle changes, could play a crucial role in reducing the risk of prefrailty and improving overall health outcomes in this population.

The association between time-restricted eating and arterial stiffness status in community-dwelling elderly Chinese individuals.

BACKGROUND AND AIMS: Emerging studies indicate that time-restricted eating (TRE) may protect against cardiovascular disease (CVD); however, studies performed in elderly adults are limited. This study aimed to analyze the association of TRE with arterial stiffness (AS) in community-dwelling elderly Chinese individuals. METHODS AND RESULTS: This cross-sectional study recruited 3487 participants aged ≥60 y from Shanghai, China. TRE was determined by calculating the end time of the last meal minus the start time of the first meal of the average day. Participants were then categorized into those with a time-restricted window lasting ≤11 h (TRE) and >11 h (non-TRE). The mean age of the sample was 71.78 ± 5.75 y, and 41.2 % were men. Having a TRE pattern was 72.2 %. In the logistic analysis, TRE was associated with borderline arterial stiffness (OR = 1.419; 95 % CI = 1.077-1.869) and elevated arterial stiffness (OR = 1.699; 95 % CI = 1.276-2.263). In a subgroup analysis, the significance remained in the group at risk of malnutrition (with borderline arterial stiffness: OR = 2.270; 95 % CI = 1.229-4.190; with elevated arterial stiffness: OR = 2.459; 95 % CI = 1.287-4.700), while in well-nourished participants, the association only remained with elevated arterial stiffness (OR = 1.530; 95 % CI = 1.107-2.115) and not with borderline arterial stiffness. CONCLUSIONS: TRE is a risk factor for both borderline and elevated arterial stiffness in community-dwelling Chinese individuals and varies by nutritional status. (Protocol code 2019-WJWXM-04-310108196508064467.).

Abnormal sleep patterns are associated with depressive symptoms in Chinese community-dwelling older adults.

OBJECTIVE: Depression is an important public health issue among older adults, often associated with their sleep-related problems. We aimed to investigate the association between sleep-related problems and depressive symptoms among Chinese community-dwelling older adults. METHODS: This cross-sectional study utilized self-reported data from 2896 participants (aged ≥60 years) from Shanghai, China. Nocturnal sleep duration and difficulty initiating sleep (DIS) symptoms were obtained through face-to-face questionnaires. Nocturnal sleep duration was categorized as 'short' (<7 h), 'normal' (7-8 h), and 'long' (>8 h). Subsequently, the 3 groups were further divided into 6 groups based on the presence of DIS, and the combined sleep behaviors were termed 'sleep patterns'. Logistic regression was conducted to assess the association of sleep variables and sleep patterns with the risk of depressive symptoms. RESULTS: Compared to the reference group, 'short sleep duration' and DIS symptoms were associated with depressive symptoms (with odds ratios (OR) of 1.50 and 1.79, respectively, with 95% confidence intervals (CI) of 1.14-1.97 and 1.39-2.31). When compared to 'normal sleep duration without DIS', both 'short sleep duration with DIS' (OR = 2.60, 95% CI: 1.81-3.72) and 'normal sleep duration with DIS' (OR = 1.60, 95% CI: 1.03-2.49) were statistically associated with depressive symptoms in adjusted regression models. CONCLUSION: Short sleep duration and DIS symptoms were found to be associated with depressive symptoms. Combining DIS symptoms with sleep duration, DIS was identified as a risk factor for elevated depressive symptoms in individuals with short and normal sleep durations. In managing depressive symptoms, it is imperative to thoroughly evaluate insomnia and nighttime sleep, which can provide valuable insights for nursing and medical policy.

Improving rehabilitation motivation and motor learning ability of stroke patients using different reward strategies: study protocol for a single-center, randomized controlled trial.

Background: Stroke survivors often face challenges in motor learning and motivation during rehabilitation, which can impede their recovery progress. Traditional rehabilitation methods vary in effectiveness, prompting the exploration of novel approaches such as reward strategies. Previous research indicates that rewards can enhance rehabilitation motivation and facilitate motor learning. However, most reward paradigms have utilized fixed reward amounts, which also have limitations. Exploring alternative, more effective reward strategies, such as probabilistic rewards, is warranted to optimize stroke patient rehabilitation. Methods: A total of 81 stroke patients will be recruited and randomly assigned to control, fixed reward, or probabilistic reward groups at a ratio of 1:1:1 using a randomized number table method. Participants will undergo 10 days of daily hand motor function rehabilitation training, with sessions lasting 20 min each. The training will involve pegboard tests and box and block tests. Control group participants will receive standard training, while fixed reward group members will receive monetary incentives for completing tests, and probabilistic reward group members will have the chance to win monetary rewards through a lottery box. Rehabilitation motivation and motor performance and functional near-infrared spectroscopy brain imaging will be conducted at designated time points. The primary outcome measure is the stroke rehabilitation motivation scale, and the second outcome measures include motor performance, simple test for evaluating hand function, motivation and pleasure scale self-report, and Pittsburgh rehabilitation participation scale. Discussion: Reward-based training enhance rehabilitation participation and adherence, it also improve motor learning speed and memory retention of stroke patients. The fixed reward applied in the past studies could diminish the sensitivity of stroke patients to rewards, while probabilistic reward may provide unpredictable or variable incentives or reinforcements for motor rehabilitation. This study will compare the efficacy of different reward strategies in enhancing motor learning ability and rehabilitation motivation among stroke patients. By conducting a randomized controlled trial, the study seeks to provide valuable insights into optimizing stroke rehabilitation protocols and improving patient outcomes.Clinical Trial Registration:https://www.chictr.org.cn/, ChiCTR2400082419.

Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research.

Objective: Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics. Methods: Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction. Results: 34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better. Conclusion: Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS.

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Discovery of potential biomarkers for osteoporosis using LC/GC-MS metabolomic methods.

Purpose: For early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC-MS in a Chinese elderly population to find possible diagnostic biomarkers. Methods: A total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC-MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways. Results: We screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women. Conclusion: These findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals.