Prevalence and risk factors of cognitive frailty in community-dwelling older adults with diabetes: A systematic review and meta-analysis

Association between frailty index and cognitive dysfunction in older adults: insights from the 2011-2014 NHANES data

Background

As the population ages, the occurrence of cognitive decline and dementia is continuously increasing. Frailty is a prevalent problem among older adults. Epidemiologic studies have shown a comorbidity between frailty and cognitive impairment. However, their relationship remains unclear. The frailty index is an important indicator for measuring frailty. This study aims to investigate the relationship between frailty index and cognitive dysfunction in older adults aged 60 years and older in the United States from the 2011-2014 National Health and Nutrition Examination Survey (NHANES).

Methods

Community-dwelling older adults aged 60 years or older from 2011 to 2014 were extracted from the NHANES database. The frailty index was calculated using the formula: frailty index = total number of deficits present/total number of deficits measured. The Animal Fluency (AF), the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's disease Delayed Recall (CERAD-DR), and Word Learning (CERAD-WL) were used to evaluate cognitive dysfunction. Firstly, weighted logistic regression analysis was used to explore the relationship between frailty index and cognitive dysfunction. Secondly, the influence of covariates on the frailty index was evaluated by subgroup analysis and interaction. Finally, the non-linear relationship is discussed by using the restricted cubic spline regression model.

Results

Our study included a total of 2,574 patients, weighted logistic regression analysis, after adjusting for all covariates, showed that the frailty index was associated with every test score. The interaction showed that covariates had no significant effect on this association in AF. The association between the frailty index and AF in the restricted cubic spline regression model is non-linear. As the frailty index increased, the risk of AF reduction increased, suggesting a higher risk of cognitive dysfunction.

Conclusion

In general, a high frailty index appears to be associated with an increased risk of cognitive dysfunction in the elderly. Consequently, protecting against cognitive decline necessitates making geriatric frailty prevention and treatment top priorities.

Development and Validation of Prediction Models for Incident Reversible Cognitive Frailty Based on Social-Ecological Predictors Using Generalized Linear Mixed Model and Machine Learning Algorithms: A Prospective Cohort Study

This study aimed to develop and validate prediction models for incident reversible cognitive frailty (RCF) based on social-ecological predictors. Older adults aged ≥60 years from China Health and Retirement Longitudinal Study (CHARLS) 2011-2013 survey were included as training set (n = 1230). The generalized linear mixed model (GLMM), eXtreme Gradient Boosting, support vector machine, random forest, and Binary Mixed Model forest were used to develop prediction models. All models were evaluated internally with 5-fold cross-validation and evaluated externally via CHARLS 2013-2015 survey (n = 1631). Only GLMM showed good discrimination (AUC = 0.765, 95% CI = 0.736, 0.795) in training set, and all models showed fair discrimination (AUC = 0.578-0.667, 95% CI = 0.545, 0.725) in internal and external validation. All models showed acceptable calibration, overall prediction performance, and clinical usefulness in training and validation sets. Older adults were divided into three groups using risk score based on GLMM, which could assist healthcare providers to predict incident RCF, facilitating early identification of high-risk population.

Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among community-dwelling older adults: A prospective cohort study

AIM

Reversible cognitive frailty (RCF) is an ideal target to prevent asymptomatic cognitive impairment and dependency. This study aimed to develop and validate prediction models for incident RCF.

METHODS

A total of 1230 older adults aged ≥60 years from China Health and Retirement Longitudinal Study 2011-2013 survey were included as the training set. The modified Poisson regression and three machine learning algorithms including eXtreme Gradient Boosting, support vector machine and random forest were used to develop prediction models. All models were evaluated internally with fivefold cross-validation, and evaluated externally using a temporal validation method through the China Health and Retirement Longitudinal Study 2013-2015 survey.

RESULTS

The incidence of RCF was 27.4% in the training set and 27.5% in the external validation set. A total of 13 important predictors were selected to develop the model, including age, education, contact with their children, medical insurance, vision impairment, heart diseases, medication types, self-rated health, pain locations, loneliness, self-medication, night-time sleep and having running water. All models showed acceptable or approximately acceptable discrimination (AUC 0.683-0.809) for the training set, but fair discrimination (AUC 0.568-0.666) for the internal and external validation. For calibration, only modified Poisson regression and eXtreme Gradient Boosting were acceptable in the training set. All models had acceptable overall prediction performance and clinical usefulness. Older adults were divided into three groups by the risk scoring tool constructed based on modified Poisson regression: low risk (≤24), median risk (24-29) and high risk (>29).

CONCLUSIONS

This risk tool could assist healthcare providers to predict incident RCF among older adults in the next 2 years, facilitating early identification of a high-risk population of RCF. Geriatr Gerontol Int 2024; ••: ••-••.

Nocturia and frailty in older adults: a scoping review

BACKGROUND

More than one in two older people wake up several times a night to urinate. Far from being a minor inconvenience, nocturia is associated with poor health outcomes. Given the importance of sleep as a foundation for resilience and healthy ageing, nocturia may promote the development of frailty, which is inextricably linked to physical decline, disability, and mortality. The aim of this scoping review was to collate published evidence on the relationship between nocturia and frailty, using the methodological framework of Arksey and O'Malley, together with the Joanna Briggs Institute methodology as guidance (OSF registration: osf.io/d5ct7).

METHODS

Relevant publications were retrieved via PubMed, Embase, the Cochrane Library and Google Scholar. The Rayyan tool was used to facilitate the screening process. Data were extracted by two independent reviewers. 250 publications were initially identified, of which 87 met the eligibility criteria.

RESULTS

Most of the evidence came from cross-sectional studies, most of which had been published within the last 5 years. The researchers were diverse, with 27% having a geriatric background. Only few publications established a clear association between nocturia and frailty. Other topics included: the association between nocturia and poor sleep quality and duration; the association between sleep and frailty; the association between frailty, multimorbidity, and age-related changes in the lower urinary tract.

CONCLUSION

The findings emphasize the increasing interest and interdisciplinary nature of research into the relationship between frailty, nocturia, lower urinary tract symptoms, and sleep disturbances. Further research is required to enhance understanding, establish causality, and identify potential therapeutic approaches.

Association between cognitive dysfunction and diabetes in patients over 65 years old: a cross-sectional study using propensity score matching

OBJECTIVES

To investigate the association between diabetes and cognitive dysfunction in the elderly population, and examine the impact of cognitive dysfunction on level of activities of daily living (ADL) in patients with diabetes.

METHODS

Data analysis was conducted on 2,951 individuals aged over 65 years from the Chinese Longitudinal Healthy Longevity Survey cohort. Propensity score matching was utilized to mitigate selection bias. Multivariate binary logistic regression was performed to analyse the association between diabetes and cognitive dysfunction in the study subjects. In addition, the relationship between ADL and cognitive function in patients with diabetes was analysed using the Wilcoxon rank-sum test.

RESULTS

A significant association (p = 0.017) was found between diabetes and the occurrence of cognitive dysfunction in older adults. Subgroup analyses revealed that diabetes patients with cognitive dysfunction exhibited a worse ADL dependence compared with those without cognitive dysfunction (p < 0.001).

CONCLUSION

These findings indicate that diabetes is associated with cognitive dysfunction in older adults. Meanwhile, there is an association between cognitive impairment and ADL level in subjects with diabetes. As such, healthcare professionals should pay close attention to the occurrence of cognitive dysfunction and ADL decline during diagnosis and treatment, and proactive prevention and intervention strategies should be implemented.

Diabetes, antidiabetic medications and risk of dementia: A systematic umbrella review and meta-analysis

AIMS

The objective of this umbrella review and meta-analysis was to evaluate the effect of diabetes on risk of dementia, as well as the mitigating effect of antidiabetic treatments.

MATERIALS AND METHODS

We conducted a systematic umbrella review on diabetes and its treatment, and a meta-analysis focusing on treatment. We searched MEDLINE/PubMed, Embase, PsycINFO, CINAHL and the Cochrane Library for systematic reviews and meta-analyses assessing the risk of cognitive decline/dementia in individuals with diabetes until 2 July 2023. We conducted random-effects meta-analyses to obtain risk ratios and 95% confidence intervals estimating the association of metformin, thiazolidinediones, pioglitazone, dipeptidyl peptidase-4 inhibitors, α-glucosidase inhibitors, meglitinides, insulin, sulphonylureas, glucagon-like peptide-1 receptor agonists (GLP1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2is) with risk of dementia from cohort/case-control studies. The subgroups analysed included country and world region. Risk of bias was assessed with the AMSTAR tool and Newcastle-Ottawa Scale.

RESULTS

We included 100 reviews and 27 cohort/case-control studies (N = 3 046 661). Metformin, thiazolidinediones, pioglitazone, GLP1RAs and SGLT2is were associated with significant reduction in risk of dementia. When studies examining metformin were divided by country, the only significant effect was for the United States. Moreover, the effect of metformin was significant in Western but not Eastern populations. No significant effect was observed for dipeptidyl peptidase-4 inhibitors, α-glucosidase inhibitors, or insulin, while meglitinides and sulphonylureas were associated with increased risk.

CONCLUSIONS

Metformin, thiazolidinediones, pioglitazone, GLP1RAs and SGLT2is were associated with reduced risk of dementia. More longitudinal studies aimed at determining their relative benefit in different populations should be conducted.

Cognitive frailty in older adults with diabetes: prevalence and risk factors

In older adults living with diabetes, there is a higher prevalence of frailty and a greater risk of cognitive impairment. Cognitive frailty is defined by the presence of both and is associated with an increased risk of mortality. A systematic review was undertaken to estimate the prevalence of cognitive frailty in community-dwelling older adults living with diabetes and associated risk factors. This commentary critically appraises the review and explores the implications of the findings for community practice.

Sleep and Depression in Older Adults: A Narrative Review

PURPOSE OF REVIEW

The sleep-depression association has been recognized for decades. Efforts to clarify this association continue at an increasing pace. This review summarizes recent research on the sleep-depression association in older adults.

RECENT FINDINGS

Research over the past 4 years has utilized cross-sectional, longitudinal, cohort, and intervention designs to examine these associations. Short (< 7 h) and long (> 8-9 h) sleep durations and insomnia symptoms are risk factors for depression in older adults. Similarly, short sleep, long sleep, insomnia symptoms, and depression are all risk factors for poorer health in late life, including increased risk of cognitive decline, falls, and poorer quality-of-life. Intervention studies have produced mixed findings, with some studies suggesting that sleep interventions may be potentially effective in improving both insomnia and mood symptoms. Intervention studies incorporating both behavioral and physiological measures of sleep, and larger and diverse samples may enhance the field's understanding of the complex interplay between sleep and mood in older adults.