Development and Validation of a Nomogram Based on Motoric Cognitive Risk Syndrome for Cognitive Impairment

Motoric Cognitive Risk Syndrome as a Predictor of Adverse Health Outcomes: A Systematic Review and Meta-Analysis

INTRODUCTION

Motoric cognitive risk syndrome (MCR) is a newly proposed pre-dementia syndrome characterized by subjective cognitive complaints (SCCs) and slow gait (SG). Increasing evidence links MCR to several adverse health outcomes, but the specific relationship between MCR and the risk of frailty, Alzheimer's disease (AD), and vascular dementia (VaD) remains unclear. Additionally, literature lacks analysis of MCR's components and associated health outcomes, complicating risk identification. This systematic review and meta-analysis aimed to provide a comprehensive overview of MCR's predictive value for adverse health outcomes.

METHODS

Relevant cross-sectional, cohort, and longitudinal studies examining the association between MCR and adverse health outcomes were extracted from ten electronic databases. The Newcastle-Ottawa Scale (NOS) and modified NOS were used to assess the risk of bias in studies included in the analysis. Relative ratios (RRs) and 95% confidence intervals (CIs) were pooled for outcomes associated with MCR.

RESULTS

Twenty-eight longitudinal or cohort studies and four cross-sectional studies with 1,224,569 participants were included in the final analysis. The risk of bias in all included studies was rated as low or moderate. Pooled analysis of RR indicated that MCR had a greater probability of increased the risk of dementia (adjusted RR = 2.02; 95% CI = 1.94-2.11), cognitive impairment (adjusted RR = 1.72; 95% CI = 1.49-1.99), falls (adjusted RR = 1.32; 95% CI = 1.17-1.50), mortality (adjusted RR = 1.66; 95% CI = 1.32-2.10), and hospitalization (adjusted RR = 1.46; 95% CI = 1.16-1.84); MCR had more prominent predictive efficacy for AD (adjusted RR = 2.23; 95% CI = 1.81-2.76) compared to VaD (adjusted RR = 3.78; 95% CI = 0.49-28.95), while excluding analyses from the study that utilized the timed-up-and-go test and one-leg-standing to evaluate gait speed. One study examined the association between MCR and disability (hazard ratios [HR] = 1.69; 95% CI = 1.08-2.02) and frailty (OR = 5.53; 95% CI = 1.46-20.89). SG was a stronger predictor of the risk for dementia and falls than SCC (adjusted RR = 1.22; 95% CI = 1.11-1.34 vs. adjusted RR = 1.19; 95% CI = 1.03-1.38).

CONCLUSION

MCR increases the risk of developing any discussed adverse health outcomes, and the predictive value for AD is superior to VaD. Additionally, SG is a stronger predictor of dementia and falls than SCC. Therefore, MCR should be routinely assessed among adults to prevent poor prognosis and provide evidence to support future targeted interventions.

Gender-specific moderating role of abdominal obesity in the relationship between handgrip strength and cognitive impairment

BACKGROUND & AIMS

Both low handgrip strength (HGS) and abdominal obesity (AO) are associated with cognitive impairment. However, it remains unclear whether low HGS and AO interact to affect cognition, and whether the synergistic effect varies by gender. This study aimed to examine whether the association between low HGS and incident cognitive impairment was moderated by AO among Chinese older men and women.

METHODS

We used the data of participants (≥60 years) from four waves (2011-2018) of the China Health and Retirement Longitudinal Study. We defined low HGS as the maximal HGS of <28 kg in men and <18 kg in women, and AO as waist circumference of ≥90 cm for men and ≥80 cm for women. Cognitive impairment was defined as a global cognitive score in the lowest 10th percentile. For each gender, we used subdistribution hazards model to estimate subdistribution hazard ratios (SHRs) for the association of low HGS and AO with incident cognitive impairment, treating mortality as the competing event and controlling for other covariates. Multiplicative interaction was assessed through a cross-product interaction term of low HGS and AO in the model. Additive interaction between low HGS and AO was evaluated by calculating the relative excess risk due to interaction (RERI) and attributable proportion due to interaction (AP).

RESULTS

We included 3704 participants (Mean age: 66.9 ± 5.81; 54.9% male). During the 7-year follow-up, 1133 events of interest occurred (731 cognitive impairments and 402 deaths). Incidence rates of cognitive impairment and mortality were 4.1 (95% CI: 3.8 to 4.4) and 2.2 (95% CI: 2.0 to 2.5) per 100 person-years. There were positive multiplicative (SHR for the product term = 1.974, 95% CI: 1.114 to 3.500) and additive interactions (RERI = 1.056, 95% CI: 0.027 to 2.086, AP = 0.454, 95% CI: 0.158 to 0.750) of low HGS and AO on the risk of cognitive impairment among older men. Male participants with both low HGS and AO showed an increased risk of cognitive impairment (SHR = 2.325, 95% CI: 1.498 to 3.609) compared with those without either. There was no evidence of interaction among older women (SHR for the product term = 1.151, 95% CI: 0.725 to 1.825; RERI = 0.044, 95% CI: -0.524 to 0.613; AP = 0.039, 95% CI: -0.458 to 0.536).

CONCLUSIONS

Low HGS and AO may interact to synergistically increase the risk of cognitive impairment among Chinese older men. Screening the highest-risk subpopulation, who may benefit most from neurocognitive prevention strategies, may maximize potential public health gains.

Prevalence of motoric cognitive risk syndrome among older adults: a systematic review and meta-analysis

OBJECTIVE

Motoric cognitive risk syndrome (MCR) is a newly proposed pre-dementia syndrome. Several studies on the prevalence of MCR have been published; however, the data vary across studies with different epidemiological characteristics. Thus, this study aimed to quantitatively analyse the overall prevalence and associated epidemiological characteristics of MCR among older adults aged ≥ 60 years.

METHODS

The Cochrane Library, PubMed, Web of Science, CINAHL, Embase, Scopus, PsycInfo, China National Knowledge Infrastructure, Weipu Database, China Biology Medicine disc and Wanfang Database were searched from their inception to January 2022. A modified Newcastle-Ottawa Scale evaluated the risk of bias. Statistical heterogeneity among the included studies was analysed using Cochran's Q and I² tests. A random effect model calculated pooled prevalence owing to study heterogeneity. Begg's and Egger's tests were used to assess the publication bias. Additionally, subgroup analysis and meta-regression were performed based on different epidemiological characteristics to determine heterogeneity sources.

RESULTS

Sixty-two studies comprising 187,558 samples were obtained. The pooled MCR prevalence was 9.0% (95% confidence interval: 8.3-9.8). A higher MCR prevalence was observed in females, older adults with a low educational level, depression and cardiovascular risk factors, South American populations, and studies with small sample sizes and cross-section designs. Furthermore, subjective cognitive complaint using scale score and gait speed using instrument gait showed higher MCR prevalence.

CONCLUSION

MCR is common in older adults, and various epidemiological characteristics influence its prevalence. Thus, preventive measures are required for older adults with higher MCR prevalence.

Mechanisms of motoric cognitive risk-Hypotheses based on a systematic review and meta-analysis of longitudinal cohort studies of older adults

We aimed to refine the hypothesis that motoric cognitive risk (MCR), a syndrome combining measured slow gait speed and self-reported cognitive complaints, is prognostic of incident dementia and other major causes of morbidity in older age. We propose mechanisms on the relationship between motor and cognitive function and describe a roadmap to validate these hypotheses. We systematically searched major electronic databases from inception to August 2021 for original longitudinal cohort studies of adults aged ≥60 years that compared an MCR group to a non-MCR group with any health outcome. Fifteen cohorts were combined by meta-analysis. Participants with MCR were at an increased risk of cognitive impairment (adjusted hazard ratio [aHR] 1.76, 95% CI 1.49-2.08; I²  = 24.9%), dementia (aHR 2.12, 1.85-2.42; 33.1%), falls (adjusted Relative Risk 1.38, 1.15-1.66; 62.1%), and mortality (aHR 1.49, 1.16-1.91; 79.2%). The prognostic value of MCR is considerable and mechanisms underlying the syndrome are proposed.

Stable Sparse Classifiers predict cognitive impairment from gait patterns

Background

Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose.

Purposes

To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC.

Methods

A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified.

Results

The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers.

Conclusions

The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.

Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population. Method: A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model. Result: Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer-Lemeshow test yielded a P-value of 0.33, suggesting a good fit of the prediction nomogram in the validation set. Conclusion: Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients.