Relationship Between Physical Exercise and Cognitive Impairment Among Older Adults with Type 2 Diabetes: Chain Mediating Roles of Sleep Quality and Depression

Developing an interpretable machine learning model for screening depression in older adults with functional disability

This study utilized data from the 2020 wave of the China Health and Retirement Longitudinal Study database, selecting 4322 participants aged 60 and above as the study sample. Important predictors of depression in older adults with functional disabilities were identified using LASSO regression, univariate logistic regression, and multivariate logistic regression. Five different machine learning algorithms-Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Naive Bayes-were employed to construct risk prediction models for depression in older adults with functional disabilities. The results indicated that Sleep duration, Age, Cognitive score, Gender, Residential area, Self-rated health, Arthritis, Gastrointestinal disease, Retirement status, Life satisfaction, Composite pain status, and Physical activity level were significant predictors of depression in this population. Both the Gradient Boosting model (accuracy: 0.69, precision: 0.70, recall: 0.79, F1-score: 0.74, AUC: 0.76) and the Logistic Regression model (accuracy: 0.68, precision: 0.68, recall: 0.79, F1-score: 0.73, AUC: 0.75) demonstrated good performance and strong generalizability. Additionally, SHAP interpretation was applied to the Gradient Boosting model to enhance the explainability of its predictions, while a nomogram was created for the Logistic Regression model to visually represent the predictive process, allowing for more intuitive understanding of the model's output. This study successfully developed a risk prediction model for depression in older adults with functional disabilities through machine learning. It provides a reference tool for community screening and clinical decision-making, helping to identify and manage depression risk within the older adults with functional disabilities.

Association between sleep quality and mild cognitive impairment in Chinese patients with type 2 diabetes mellitus: a cross-sectional study

BACKGROUND

Globally, the number of individuals with type 2 diabetes mellitus (T2DM) is increasing, and they are at a higher risk of developing mild cognitive impairment (MCI) than the general population. Sleep quality is thought to be a modifiable factor that may contribute to MCI, as previous studies have linked it to cognitive function in older adults. However, evidence concerning the association between sleep quality and MCI among patients with T2DM in China is limited. Therefore, this study aims to identify the association between sleep quality and MCI among patients with T2DM in China.

METHODS

This cross-sectional study was conducted among patients with T2DM who were referred to the Endocrinology Department of Xiangya Hospital, Central South University. Data regarding sociodemographic characteristics, lifestyle factors, T2DM-related information, and biochemical indicators were collected. Sleep quality and MCI were evaluated using the Pittsburgh Sleep Quality Index (PSQI) and the Mini-Mental State Examination (MMSE) scale, respectively. The association between sleep quality and MCI was analyzed using univariate and multivariate analyses.

RESULTS

This study included 1,001 patients with T2DM, with a mean age of 60.2 (standard deviation: 10.1) years. Pearson's correlation analysis showed that the total PSQI score was negatively associated with the MMSE score (r=-0.27, P < 0.05). Multivariate analyses based on four models consistently showed that those with higher total PSQI score (aOR = 1.09-1.11, P < 0.05), as well as higher scores on the subjective sleep quality (aOR = 1.32-1.46, P < 0.05), sleep latency (aOR = 1.25-1.32, P < 0.05), sleep duration (aOR = 1.30-1.32, P < 0.05), sleep efficiency (aOR = 1.36-1.41, P < 0.05), sleep disturbance (aOR = 1.66-1.86, P < 0.05), and daily dysfunction (aOR = 1.38-1.48, P < 0.05) were associated with higher rates of MCI.

CONCLUSIONS

Among Chinese patients with T2DM, poor overall sleep quality, subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, and daily dysfunction were associated with higher rates of MCI. Future studies are needed to examine whether sleep intervention could improve cognitive function in patients with T2DM. It is also suggested for clinicians working with T2DM patients to raise the awareness of cognitive impairment and sleep problems.

Physical activity and cognitive function among community-dwelling older adults: a mediating role of functional fitness

BACKGROUND

The positive correlation between physical activity and cognitive function has been increasingly documented, while the underlying mechanisms remain unclear.

METHODS

This study aimed to investigate the association among physical activity (PA), functional fitness, and cognitive function in community-dwelling older adults, and to examine whether functional fitness mediates this association. This study utilized a cross-sectional design, encompassing 224 participants aged 65 or above, with 41.07% male and 58.93% female. The data were collected in 2023 using the Mini-Mental State Examination, Physical Activity Scale for the Elderly, and Senior Fitness Test. Pearson correlation analysis was conducted to assess associations among the variables, and mediation analysis was performed using the PROCESS macro for SPSS.

RESULTS

The results indicated that PA had a moderate positive correlation with cognitive function (r = 0.437, p < 0.01) and with most functional fitness domains. In addition, significant positive correlations were found among most functional fitness domains and cognitive function. The association between PA and cognitive function was partially mediated by cardiorespiratory endurance (Effect = 0.0519, 95% CI= [0.0205, 0.0889]).

CONCLUSION

This study demonstrated a positive correlation between PA and cognitive function in older adults, with cardiorespiratory endurance mediating this association. Our study provides valuable preliminary evidence that cardiorespiratory endurance exerts a mediating role between PA and cognitive function. Future longitudinal research is warranted to elucidate the causal relationship and underlying mechanisms between PA and cognitive function.

Unraveling the Complex Interplay Between Neuroinflammation and Depression: A Comprehensive Review

The relationship between neuroinflammation and depression is a complex area of research that has garnered significant attention in recent years. Neuroinflammation, characterized by the activation of glial cells and the release of pro-inflammatory cytokines, has been implicated in the pathophysiology of depression. The relationship between neuroinflammation and depression is bidirectional; not only can inflammation contribute to the onset of depressive symptoms, but depression itself can also exacerbate inflammatory responses, creating a vicious cycle that complicates treatment and recovery. The present comprehensive review aimed to explore the current findings on the interplay between neuroinflammation and depression, as well as the mechanisms, risk factors, and therapeutic implications. The mechanisms by which neuroinflammation induces depressive-like behaviors are diverse. Neuroinflammation can increase pro-inflammatory cytokines, activate the hypothalamus-pituitary-adrenal (HPA) axis, and impair serotonin synthesis, all of which contribute to depressive symptoms. Furthermore, the activation of microglia has been linked to the release of inflammatory mediators that can disrupt neuronal function and contribute to mood disorders. Stress-induced neuroinflammatory responses can lead to the release of pro-inflammatory cytokines that not only affect brain function but also influence behavior and mood. Understanding these mechanisms is crucial for developing targeted therapies that can mitigate the effects of neuroinflammation on mood disorders.

Association between sleep quality and cognitive impairment in older adults hypertensive patients in China: a case-control study

Background

Previous studies have found that poor sleep quality promotes the occurrence of cognitive impairment (CI), but this relationship has been rarely reported in older adults hypertensive patients. The purpose of this study was to investigate the relationship between sleep quality and CI in older adults hypertensive patients and the mediating effect of sleep quality between physical activity (PA) and CI.

Methods

A total of 2072 older adults hypertensive patients were included in this case-control study. Five hundred and eighteen older adults hypertensive patients with CI were matched 1:3 by age and sex to 1,554 older adults hypertensive patients with normal cognitive function. The International Physical Activity Questionnaire-Long Form, Pittsburgh Sleep Quality Index, and Mini-Mental State Examination were used to evaluate PA intensity, sleep quality, and cognitive function in older adults hypertensive patients. Multivariate logistic regression and the mediation package in R Language were used to analyze the relationship between sleep quality and CI and the mediating effect of sleep quality between PA intensity and CI in older adults hypertensive patients.

Results

After adjusting for all confounding factors, sleep quality was positively correlated with CI in older adults hypertensive patients (OR = 2.565, 95%CI: 1.958-3.360, p < 0.001), and this relationship also existed in the older adults hypertensive patients with education levels of primary school and below and junior high school and above (OR = 2.468, 95%CI: 1.754-3.473, p < 0.001; OR = 2.385, 95%CI: 1.367-4.161, p = 0.002). In addition, sleep quality mediated part of the mediating effect between PA intensity and CI in older adults hypertensive patients (Za*Zb: - 17.19339; 95%CI: -0.37312, -0.04194).

Conclusion

Poor sleep quality was associated with the occurrence of CI in older adults hypertensive patients, and this relationship also existed in older adults hypertensive patients with education levels of primary school and below and junior high school and above.

Temporal Relationship between HbA1c and Depressive Symptom Trajectories in a Longitudinal Cohort Study: The Mediating Role of Healthy Lifestyles

This study analyzed China Health and Retirement Longitudinal Study data to explore the HbA1c-depression link, including depressive trajectories, while considering the mitigating impact of healthy lifestyles. Cross-lagged panel models and group-based trajectory modeling were performed to investigate the temporal relationship between HbA1c levels and depressive symptoms, as well as the depressive trajectories. Structural equation models were used to assess the mediating effects of healthy lifestyles. The mean age of the participants was 57.66 ± 9.04 years, with 53.68% being female. Analyzing 8826 participants across three waves, we observed a significant prediction of subsequent depressive symptoms by the preceding HbA1c levels (β = 0.296; p < 0.001). Four distinct trajectories of depressive symptoms were distinguished: stable low, stable moderate, increasing, and stable high. Elevated HbA1c levels were associated with a higher risk of developing stable high (OR 1.12 and 95% CI 1.02-1.23), increasing (OR 1.21 and 95% CI 1.11-1.32), and stable moderate depressive symptoms (OR 1.07 and 95% CI 1.01-1.13). Engaging in two healthy life behaviors reduced stable high and increasing depressive pattern risks by 32% and 30%, respectively. Adherence to a healthy lifestyle lessened 7.2% of the impact of high HbA1c levels on the subsequent depressive symptoms. These findings highlight the potential benefits of incorporating adequate sleep and light physical activities, which might reduce the adverse impact of elevated HbA1c levels on depressive symptoms.

Development and Validation of a Community-Based Prediction Model for Depression in Elderly Patients with Diabetes: A Cross-Sectional Study

Background

In elderly diabetic patients, depression is often overlooked because professional evaluation requires psychiatrists, but such specialists are lacking in the community. Therefore, we aimed to create a simple depression screening model that allows earlier detection of depressive disorders in elderly diabetic patients by community health workers.

Methods

The prediction model was developed in a primary cohort that consisted of 210 patients with diabetes, and data were gathered from December 2022 to February 2023. The independent validation cohort included 99 consecutive patients from February 2023 to March 2023. Multivariable logistic regression analysis was used to develop the predictive model. We incorporated common demographic characteristics, diabetes-specific factors, family structure characteristics, the self-perceived burden scale (SPBS) score, and the family APGAR (adaptation, partnership, growth, affection, resolution) score. The performance of the nomogram was assessed with respect to its calibration (calibration curve, the Hosmer-Lemeshow test), discrimination (the area under the curve (AUC)), and clinical usefulness (Decision curve analysis (DCA)).

Results

The prediction nomogram incorporated 5 crucial factors such as glucose monitoring status, exercise status, monthly income, sleep disorder status, and the SPBS score. The model demonstrated strong discrimination in the primary cohort, with an AUC of 0.839 (95% CI, 0.781-0.897). This discriminative ability was further validated in the validation cohort, with an AUC of 0.857 (95% CI, 0.779-0.935). Moreover, the nomogram exhibited satisfactory calibration. DCA suggested that the prediction of depression in elderly patients with diabetes mellitus was of great clinical value.

Conclusion

The prediction model provides precise and user-friendly guidance for community health workers in preliminary screenings for depression among elderly patients with diabetes.

Leisure-Time Physical Activity May Attenuate the Impact of Diabetes on Cognitive Decline in Middle-Aged and Older Adults: Findings From the ELSA-Brasil Study

OBJECTIVE

To assess leisure-time physical activity (LTPA) as a modifier of the diabetes/cognitive decline association in middle-aged and older participants in the Estudo Longitudinal de Saude do Adulto (ELSA-Brasil) study.

RESEARCH DESIGN AND METHODS

ELSA-Brasil is a cohort of 15,105 participants (age 35-74 years) enrolled between 2008 and 2010. We evaluated global cognitive function, summing the scores of six standardized tests evaluating memory and verbal fluency, including the Trail-Making Test, at baseline and follow-up. Incident cognitive impairment was defined as a global cognitive function score at follow-up lower than -1 SD from baseline mean. Participants reporting ≥150 min/week of moderate to vigorous LTPA at baseline were classified as physically active. We assessed the association of LTPA with global cognition change in those with diabetes in the context of our overall sample through multivariable regression models.

RESULTS

Participants' (N = 12,214) mean age at baseline was 51.4 (SD 8.8) years, and 55.5% were women. During a mean follow-up of 8.1 (SD 0.6) years, 9,345 (76.5%) inactive participants and 1,731 (14.1%) participants with diabetes at baseline experienced faster declines in global cognition than those who were active (β = -0.003, -0.004, and -0.002) and those without diabetes (β = -0.004, -0.005, and -0.003), respectively. Diabetes increased the risk of cognitive impairment (hazard ratio [HR] 1.71; 95% Cl 1.22, 2.39) in inactive but not in active adults (HR 1.18; 95% CI 0.73, 1.90). Among participants with diabetes, those who were active showed a delay of 2.73 (95% CI 0.94, 4.51) years in the onset of cognitive impairment.

CONCLUSIONS

In adults living with diabetes, LTPA attenuated the deleterious association between diabetes and cognitive function.

Association between depression and quality of life in older adults with type 2 diabetes: A moderated mediation of cognitive impairment and sleep quality

BACKGROUND

The acceleration of aging and the increase in life expectancy have resulted in an increasing number of older adults developing physical and mental comorbidities. We examined the association between depression and quality of life (QoL) using cognitive impairment (COI) as a mediator and sleep quality (SQ) as a moderator among older adults with type 2 diabetes.

METHODS

A total of 2646 participants from Weifang, Shandong, China completed the survey. Their depression, cognitive function, SQ, and QoL were assessed. PROCESS was used to investigate mediating and moderating effects.

RESULTS

COI mediated the association between depression and QoL (indirect effect = -0.1058, bootstrapped 95 % CI [-0.1417, -0.0725]). Moderated mediation analyses indicated that SQ moderated the first half of the pathway of depression's impact on QoL through COI (moderating effect = -0.1128, bootstrapped 95 % CI [-0.1981, -0.0348]). Depression negatively impacted cognitive function in participants with poor (vs. better) SQ.

LIMITATIONS

First, multiple assessment tools should be considered to increase objective assessment. Second, the cross-sectional design limited our ability to make causal inferences. Third, additional diabetes-related variables should be included to explore this relationship. Finally, the pathways of influence and mechanisms of action of COI in older adults should be explored further.

CONCLUSION

Depression could impair the QoL of older adults by aggravating their COI. Fortunately, improving patients' SQ may undermine this negative effect. These findings may play an integral role in promoting the psychiatric health of older adults with type 2 diabetes.