Dataset Information

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

ABSTRACT:

Objective

To develop and validate a prediction nomogram based on motoric cognitive risk syndrome for cognitive impairment in healthy older adults.

Methods

Using two longitudinal cohorts of participants (aged ≥ 60 years) with 4-year follow-up, we developed (n = 1,177) and validated (n = 2,076) a prediction nomogram. LASSO (least absolute shrinkage and selection operator) regression model and multivariable Cox regression analysis were used for variable selection and for developing the prediction model, respectively. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness.

Results

The individualized prediction nomogram was assessed based on the following: motoric cognitive risk syndrome, education, gender, baseline cognition, and age. The model showed good discrimination [Harrell's concordance index (C-index) of 0.814; 95% confidence interval, 0.782-0.835] and good calibration. Comparable results were also seen in the validation cohort, which includes good discrimination (C-index, 0.772; 95% confidence interval, 0.776-0.818) and good calibration. Decision curve analysis demonstrated that the prediction nomogram was clinically useful.

Conclusion

This prediction nomogram provides a practical tool with all necessary predictors, which are accessible to practitioners. It can be used to estimate the risk of cognitive impairment in healthy older adults.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8086554 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

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

Liu Yong Y Wei Kai K Cao Xinyi X Jiang Lijuan L Gu Nannan N Feng Lei L Li Chunbo C

Frontiers in aging neuroscience 20210416

<h4>Objective</h4>To develop and validate a prediction nomogram based on motoric cognitive risk syndrome for cognitive impairment in healthy older adults.<h4>Methods</h4>Using two longitudinal cohorts of participants (aged ≥ 60 years) with 4-year follow-up, we developed (<i>n</i> = 1,177) and validated (<i>n</i> = 2,076) a prediction nomogram. LASSO (least absolute shrinkage and selection operator) regression model and multivariable Cox regression analysis were used for variable selection and fo ...[more]

PMID: 33935682

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Project description:ObjectiveAlthough some people with mild cognitive impairment may not suffer from dementia lifelong, about 5% of them will progress to dementia within 1 year in community settings. However, a general tool for predicting the risk of cognitive impairment was not adequately studied among older adults.DesignProspective cohort study.SettingCommunity-living, older adults from 22 provinces in China.ParticipantsWe included 10,066 older adults aged 65 years and above (mean age, 83.2 ± 11.1 years), with normal cognition at baseline in the 2002-2008 cohort and 9354 older adults (mean age, 83.5 ± 10.8 years) in the 2008-2014 cohort of the Chinese Longitudinal Healthy Longevity Survey.MethodsWe measured cognitive function using the Chinese version of the Mini-Mental State Examination. Demographic, medical, and lifestyle information was used to develop the nomogram via a Lasso selection procedure using a Cox proportional hazards regression model. We validated the nomogram internally with 2000 bootstrap resamples and externally in a later cohort. The predictive accuracy and discriminative ability of the nomogram were measured by area-under-the-curves and calibration curves, respectively.ResultsEight factors were identified with which to construct the nomogram: age, baseline of the Mini-Mental State Examination, activities of daily living and instrumental activities of daily living score, chewing ability, visual function, history of stroke, watching TV or listening to the radio, and growing flowers or raising pets. The area-under-the-curves for internal and external validation were 0.891 and 0.867, respectively, for predicting incident cognitive impairment. The calibration curves showed good consistency between nomogram-based predictions and observations.Conclusions and implicationsThe nomogram-based prediction yielded consistent results in 2 separate large cohorts. This feasible prognostic nomogram constructed using readily ascertained information may assist public health practitioners or physicians to provide preventive interventions of cognitive impairment.

Project description:IntroductionMotoric cognitive risk (MCR) and amnestic mild cognitive impairment (aMCI) syndromes are each reliable predictors of incident Alzheimer's disease (AD), but MCR may be a stronger predictor of vascular dementia than AD. This study contrasted cortical and hippocampal atrophy patterns in MCR and aMCI.MethodsCross-sectional data from 733 older adults without dementia or disability (M age = 73.6; 45% women) in the multicountry MCR consortium were examined. MCR was defined as presence of slow gait and cognitive concerns. Amnestic MCI was defined as poor episodic memory performance and cognitive concerns. Cortical thickness and hippocampal volumes were quantified from structural MRIs. Multivariate and univariate general linear models were used to examine associations between cortical thickness and hippocampal volume in MCR and aMCI, adjusting for age, sex, education, total intracranial volume, white matter lesions, and study site.ResultsThe prevalence of MCR and aMCI was 7.64% and 12.96%, respectively. MCR was associated with widespread cortical atrophy, including prefrontal, insular, cingulate, motor, parietal, and temporal atrophy. aMCI was associated with hippocampal atrophy.ConclusionDistinct patterns of atrophy were associated with MCR and aMCI. A distributed pattern of cortical atrophy - that is more consistent with VaD or mixed dementia- was observed in MCR. A more restricted pattern of atrophy - that is more consistent with AD - was observed in aMCI. The biological underpinnings of MCR and aMCI likely differ and may require tailored interventions.IntroductionMotoric cognitive risk (MCR) and amnestic mild cognitive impairment (aMCI) syndromes are each reliable predictors of incident Alzheimer's disease (AD), but MCR may be a stronger predictor of vascular dementia than AD. This study contrasted cortical and hippocampal atrophy patterns in MCR and aMCI.MethodsCross-sectional data from 733 older adults without dementia or disability (M age = 73.6; 45% women) in the multicountry MCR consortium were examined. MCR was defined as presence of slow gait and cognitive concerns. Amnestic MCI was defined as poor episodic memory performance and cognitive concerns. Cortical thickness and hippocampal volumes were quantified from structural MRIs. Multivariate and univariate general linear models were used to examine associations between cortical thickness and hippocampal volume in MCR and aMCI, adjusting for age, sex, education, total intracranial volume, white matter lesions, and study site.ResultsThe prevalence of MCR and aMCI was 7.64% and 12.96%, respectively. MCR was associated with widespread cortical atrophy, including prefrontal, insular, cingulate, motor, parietal, and temporal atrophy. aMCI was associated with hippocampal atrophy.ConclusionDistinct patterns of atrophy were associated with MCR and aMCI. A distributed pattern of cortical atrophy - that is more consistent with VaD or mixed dementia- was observed in MCR. A more restricted pattern of atrophy - that is more consistent with AD - was observed in aMCI. The biological underpinnings of MCR and aMCI likely differ and may require tailored interventions.

Dataset Information

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

Objective

Methods

Results

Conclusion

Publications

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

Similar Datasets

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