Predictors of incident reversible and potentially reversible cognitive frailty among Taiwanese older adults

Development and validation of a cognitive dysfunction risk prediction model for the abdominal obesity population

Objectives

This study was aimed to develop a nomogram that can accurately predict the likelihood of cognitive dysfunction in individuals with abdominal obesity by utilizing various predictor factors.

Methods

A total of 1490 cases of abdominal obesity were randomly selected from the National Health and Nutrition Examination Survey (NHANES) database for the years 2011-2014. The diagnostic criteria for abdominal obesity were as follows: waist size ≥ 102 cm for men and waist size ≥ 88 cm for women, and cognitive function was assessed by Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Word Learning subtest, Delayed Word Recall Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). The cases were divided into two sets: a training set consisting of 1043 cases (70%) and a validation set consisting of 447 cases (30%). To create the model nomogram, multifactor logistic regression models were constructed based on the selected predictors identified through LASSO regression analysis. The model's performance was assessed using several metrics, including the consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the clinical benefit of the model.

Results

The multivariate logistic regression analysis revealed that age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were significant predictors of cognitive dysfunction in individuals with abdominal obesity (p < 0.05). These predictors were incorporated into the nomogram. The C-indices for the training and validation sets were 0.814 (95% CI: 0.875-0.842) and 0.805 (95% CI: 0.758-0.851), respectively. The corresponding AUC values were 0.814 (95% CI: 0.875-0.842) and 0.795 (95% CI: 0.753-0.847). The calibration curves demonstrated a satisfactory level of agreement between the nomogram model and the observed data. The DCA indicated that early intervention for at-risk populations would provide a net benefit, as indicated by the line graph.

Conclusion

Age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were identified as predictive factors for cognitive dysfunction in individuals with abdominal obesity. In conclusion, the nomogram model developed in this study can effectively predict the clinical risk of cognitive dysfunction in individuals with abdominal obesity.

Biological and Physical Performance Markers for Early Detection of Cognitive Impairment in Older Adults

Dementia is a major cause of poor quality of life, disability, and mortality in old age. According to the geroscience paradigm, the mechanisms that drive the aging process are also involved in the pathogenesis of chronic degenerative diseases, including dementia. The dissection of such mechanisms is therefore instrumental in providing biological targets for interventions and new sources for biomarkers. Within the geroscience paradigm, several biomarkers have been discovered that can be measured in blood and that allow early identification of individuals at risk of cognitive impairment. Examples of such markers include inflammatory biomolecules, markers of neuroaxonal damage, extracellular vesicles, and DNA methylation. Furthermore, gait speed, measured at a usual and fast pace and as part of a dual task, has been shown to detect individuals at risk of future dementia. Here, we provide an overview of available biomarkers that may be used to gauge the risk of cognitive impairment in apparently healthy older adults. Further research should establish which combination of biomarkers possesses the highest predictive accuracy toward incident dementia. The implementation of currently available markers may allow the identification of a large share of at-risk individuals in whom preventive interventions should be implemented to maintain or increase cognitive reserves, thereby reducing the risk of progression to dementia.

Factors affecting cognitive frailty improvement and progression in Taiwanese older adults

BACKGROUND

Knowledge of predictors of cognitive frailty (CF) trajectories is required to develop preventive strategies to delay or reverse the progression from CF to dementia and other adverse outcomes. This 2-year prospective study aimed to investigate factors affecting the progression and improvement of CF in older Taiwanese adults.

METHODS

In total, 832 community-dwelling people aged ≥ 65 years were eligible. Fried's five frailty criteria were used to measure prefrailty and frailty, while cognitive performance was assessed by the Clinical Dementia Rating and Mini-Mental State Examination. Each component of reversible CF and potentially reversible CF was assigned a score, with a total score ranging 0 to 5 points. Two annual follow-up CF assessments were conducted. The group-based trajectory model was applied to identify latent CF trajectory groups, and a multinomial logistic regression was used to examine relationships of explanatory variables with CF trajectories.

RESULTS

According to data on 482 subjects who completed the two annual follow-ups, three CF trajectories of robust, improvement, and progression were identified. After adjusting for the baseline CF state, CF progression was significantly associated with an older age (odds ratio [OR] = 1.08; 95% confidence interval [CI], 1.02 ~ 1.14), a lower Tinetti balance score (OR = 0.72; 95% CI, 0.54 ~ 0.96), a slower gait (OR = 0.98; 95% CI, 0.97 ~ 0.99), and four or more comorbidities (OR = 2.65; 95% CI, 1.19 ~ 5.90), while CF improvement was not significantly associated with any variable except the baseline CF state. In contrast, without adjusting for the baseline CF state, CF progression was significantly associated with an older age, female sex, balance scores, gait velocity, regular exercise, the number of comorbidities, and depression, while CF improvement was significantly associated with female sex, balance scores, and the number of comorbidities.

CONCLUSIONS

The baseline CF state, an older age, poorer balance, slower gait, and a high number of comorbidities may contribute to CF progression, while the baseline CF state may account for associations of engaging in regular exercise and depression with CF development.

Development and validation of a nomogram-assisted tool to predict potentially reversible cognitive frailty in Chinese community-living older adults

BACKGROUND

Cognitive frailty (CF) is a complex and heterogeneous clinical syndrome that indicates the onset of neurodegenerative processes and poor prognosis. In order to prevent the occurrence and development of CF in real world, we intended to develop and validate a simple and timely diagnostic instrument based on comprehensive geriatric assessment that will identify patients with potentially reversible CF (PRCF).

METHODS

750 community-dwelling individuals aged over 60 years were randomly allocated to either a training or validation set at a 4:1 ratio. We used the operator regression model offering the least absolute data dimension shrinkage and feature selection among candidate predictors. PRCF was defined as the presence of physical pre-frailty, frailty, and mild cognitive impairment (MCI) occurring simultaneously. Multivariate logistic regression was conducted to build a diagnostic tool to present data as a nomogram. The performance of the tool was assessed with respect to its calibration, discrimination, and clinical usefulness.

RESULTS

PRCF was observed in 326 patients (43%). Predictors in the tool were educational background, coronary heart disease, handgrip strength, gait speed, instrumental activity of daily living (IADL) disability, subjective cognitive decline (SCD) and five-times-sit-to-stand test. The diagnostic nomogram-assisted tool exhibited good calibration and discrimination with a C-index of 0.805 and a higher C-index of 0.845 in internal validation. The calibration plots demonstrated strong agreement in both the training and validation sets, while decision curve analysis confirmed the nomogram's efficacy in clinical practice.

CONCLUSIONS

This tool can effectively identify older adults at high risk for PRCF, enabling physicians to make informed clinical decisions and implement proper patient-centered individual interventions.

Depressive and Biopsychosocial Frailty Phenotypes: Impact on Late-life Cognitive Disorders

 In older age, frailty is a detrimental transitional status of the aging process featuring an increased susceptibility to stressors defined by a clinical reduction of homoeostatic reserves. Multidimensional frailty phenotypes have been associated with all-cause dementia, mild cognitive impairment (MCI), Alzheimer's disease (AD), AD neuropathology, vascular dementia, and non-AD dementias. In the present article, we reviewed current evidence on the existing links among depressive and biopsychosocial frailty phenotypes and late-life cognitive disorders, also examining common pathways and mechanisms underlying these links. The depressive frailty phenotype suggested by the construct of late-life depression (LLD) plus physical frailty is poorly operationalized. The biopsychosocial frailty phenotype, with its coexistent biological/physical and psychosocial dimensions, defines a biological aging status and includes motivational, emotional, and socioeconomic domains. Shared biological pathways/substrates among depressive and biopsychosocial frailty phenotypes and late-life cognitive disorders are hypothesized to be inflammatory and cardiometabolic processes, together with multimorbidity, loneliness, mitochondrial dysfunction, dopaminergic neurotransmission, specific personality traits, lack of subjective/objective social support, and neuroendocrine dysregulation. The cognitive frailty phenotype, combining frailty and cognitive impairment, may be a risk factor for LLD and vice versa, and a construct of depressive frailty linking physical frailty and LLD may be a good dementia predictor. Frailty assessment may enable clinicians to better target the pharmacological and psychological treatment of LLD. Given the epidemiological links of biopsychosocial frailty with dementia and MCI, multidomain interventions might contribute to delay the onset of late-life cognitive disorders and other adverse health-related outcomes, such as institutionalization, more frequent hospitalization, disability, and mortality.