The Relationship Between Motoric Cognitive Risk Syndrome and All-Cause Mortality in Older People: findings from CHARLS 2011–2023

doi:10.21203/rs.3.rs-7053324/v1

Background: This study aims to investigate the relationship between motoric cognitive risk syndrome (MCR) and all-cause mortality in older people in China, providing a reference for early intervention in MCR among elderly populations in developing countries.

Methods: Data were drawn from the China Health and Retirement Longitudinal Study (CHARLS) conducted from 2011 to 2023, involving 6,304 participants aged 60 and above. A Cox proportional hazards model was used to evaluate the impact of MCR on all-cause mortality.

Results: The study found an association between MCR and all-cause mortality in older people. Among the participants, 15.5% (934/6304) died during the follow-up period, with 20.9% (256/1227) from the MCR group and 13.4% (678/5077) from the non-MCR group. The Cox model indicated that MCR was associated with a higher risk of all-cause mortality (HR=1.54, 95% CI: 1.34-1.78). This association remained significant after adjusting for all confounders (HR=1.46, 95% CI: 1.27-1.70).

Conclusion: This study confirms that MCR is associated with an increased risk of all-cause mortality in older people.

Motoric cognitive risk syndrome

gait speed

subjective cognitive impairment

older people

all-cause mortality

This study provides large-scale, population-based evidence on the link between Motoric Cognitive Risk (MCR) syndrome and all-cause mortality in an older Chinese cohort, addressing a knowledge gap in Asian populations.
The findings confirm that MCR is a robust and independent predictor of death, with affected individuals having a 46% higher risk of all-cause mortality, even after adjusting for a comprehensive set of demographic, health, and lifestyle confounders.
By demonstrating the prognostic value of MCR in a developing country, the study supports its use as a simple, low-cost screening tool for identifying at-risk older adults in routine health checks globally

Motoric cognitive risk (MCR) syndrome is a clinical construct characterized by the co-occurrence of subjective cognitive complaints (SCC) and reduced gait speed, absent a diagnosis of dementia [1, 2]. The prevalence of MCR varies between 6.3% and 18.0%, depending on the study population[3–5]. The association between gait impairment and cognitive impairment in MCR exceeds what could be expected from normal aging, suggesting a potential causal link[6, 7]. Previous studies indicate that cognitive impairments might disrupt higher-level gait control, leading to gait disturbances[8, 9].

What distinguishes MCR is its simplicity and feasibility: its identification does not require imaging or neuropsychological testing. Studies have linked MCR to various diseases and risk factors. For example, in the Einstein Aging Study, MCR tripled the risk of dementia and increased the risk of vascular dementia twelvefold over a median follow-up of 36.9 months[1, 10]. Gait speed and executive function scores have both been predictors of dementia[11]. MCR is also associated with high BMI, frailty, depression, falls, fractures, and increased disability[3, 12, 13]. Even mild cognitive impairment (unrelated to dementia) has been predictive of mortality[14, 15], with the severity of cognitive impairment-related mortality risk comparable to other major causes of death, such as heart failure and diabetes[16]. Understanding the mortality risk associated with MCR is crucial for public health and could provide insights into its mechanisms.

Indeed, previous studies have reported that MCR significantly predicted mortality risk in the EPIDOS cohort of older French women[17]. However, there remains a lack of evidence from large-scale population-based studies in Asian settings.

Using CHARLS data, this study aims to quantify the association between MCR and all-cause mortality among Chinese older adults, contributing to evidence for the prognostic relevance of this syndrome in diverse populations.

Study Population

Data were drawn from the China Health and Retirement Longitudinal Study (CHARLS) (https://charls.pku.edu.cn/), a nationally representative survey of middle-aged and older adults. The baseline survey in 2011 included 17,705 respondents, followed up approximately every two years. Detailed data collection procedures are described elsewhere[18].

Our analytic sample included respondents aged 60 or above from waves 1–3 (2011, 2013, and 2015). Participants with dementia or hip fractures were excluded(screening process shown in Fig. 1), resulting in a final sample of 6,304 participants after following up through Wave 4 (2018) and Wave 5 (2023). Written informed consent was obtained from all participants, and the study was approved by the Institutional Review Board of Peking University (IRB number: IRB00001052-11015).

Definition of MCR

MCR diagnosis was based on the criteria by Verghese et al[19], defined as SCC and slow gait without dementia or physical disability. SCC was assessed through a self-reported memory loss question: "How would you rate your memory at present?" Memory complaints were self-reported using a five-point scale; responses of "fair" or "poor" qualified as positive. Gait speed was assessed using the average time to walk a 2.5-meter distance twice. Slow gait was defined as 1 standard deviation below age- and sex-adjusted cohort means.

Outcome

Mortality data were obtained from death registries and verified by relatives or community administrators in 2013, 2015, 2018, and at the end of follow-up in August 2023. Death was coded as "1" and survival as "0," with survival time calculated based on the follow-up survey year.

Covariates

Covariates included demographic information (age, sex, marital status, education, residence), lifestyle factors (smoking, alcohol use, leisure activities), and health indicators (BMI, activities of daily living (ADL), depression, history of falls, pension status). Clinical diagnoses of diabetes, hypertension, dyslipidemia, and chronic kidney disease (CKD) were included based on biomarker thresholds and self-reports, Further details on each variable can be found on the CHARLS website (https://charls.pku.edu.cn/).

A sub-cohort of 3538 CHARLS participants underwent metabolic assessments, including fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, high-sensitivity C-reactive protein, and serum creatinine. The estimated glomerular filtration rate was calculated using the Chronic Kidney Disease Epidemiology Collaboration's 2009 creatinine equation[20].

Statistical Methods

Continuous variables were described using medians and quartiles, and categorical variables using frequencies and percentages. The baseline characteristics, classified according to MCR syndrome, were analyzed using the χ² test, analysis of variance (ANOVA), or the Mann-Whitney U test, as appropriate. Assuming data were missing at random, multiple imputation was used to handle incomplete observations. Five imputed datasets were generated and aggregated using R version 4.0.2. To examine the association between MCR and all-cause mortality, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. The risk of all-cause mortality was assessed specifically in relation to MCR. Three models were estimated: Model 1 adjusted for age and gender; Model 2, in addition to the adjustments in Model 1, further controlled for residence, marital status, education level, smoking, drinking, pension status, leisure activities, and ADL; Model 3 built upon Model 2 by adding adjustments for diabetes, hypertension, dyslipidemia, CKD, depression, and BMI. To further investigate the relationship between MCR and all-cause mortality, restricted cubic spline plots were employed to explore the non-linear association between MCR and the risk of all-cause mortality. Subgroup analyses assessed potential moderators like demographics and health characteristics.

Sensitivity analyses included adjusting for metabolic biomarkers and using complete cases without imputation. All statistical analyses were performed using R and Stata, with p-values < 0.05 indicating significance.

Participant Characteristics

At baseline, 6,304 eligible older people participated, with 49.8% males and 50.2% females. The MCR group included 1,227 (19.3%) participants, while the non-MCR group had 5,077 (80.5%). During follow-up, 15.5% (934/6304) died, with higher mortality in the MCR group (20.9%) compared to the non-MCR group (13.4%). Significant differences between the MCR and non-MCR groups were found in age, marital status, education, hypertension, CKD, depression, falls, pension, ADL, and BMI (as shown in Table 1).

Table 1

Baseline characteristics of 6304 participants according to CHARLS.
	Participants, No, (%)
Characteristics	Total participants(n = 6304)	Participants without MCR(n = 5077)	Participants with MCR(n = 1227)	P-value^b
Died^a	934(14.8)	678(13.4)	256(20.9)	< 0.001
Age, median (IQR), y	63(61–69)	62 (61, 69)	64 (61, 70)	< 0.001
Men	3136 (49.8)	2535(49.19)	601(49.0)	0.57
Married	5214(82.7)	4227 (83.3)	987 (80.4)	0.021
Educational level				< 0.001
No formal education	3557(56.4)	2765 (54.5)	792 (64.5)
Primary school	1542(24.5)	1267 (25.0)	275 (22.4)
Middle or high school	818(13.0)	699 (13.8)	119 (9.7)
College or above	387(6.0)	346 (6.8)	41 (3.3)
Smoke	2722(43.2)	2194 (43.2)	528 (43.0)	0.92
Drink	2051(32.5)	1672 (32.9)	379 (30.9)	0.17
Diabetes	1028(16.3)	834 (16.4)	194 (15.8)	0.64
Hypertension	3247(51.5)	2572 (50.7)	675 (55.0)	0.006
Dyslipidemia	2645(43.8)	2117 (41.7)	528 (43.0)	0.40
CKD	663(10.5)	529 (10.4)	134 (10.9)	0.86
Depression	2052(32.6)	1543 (30.4)	509 (41.5)	< 0.001
Pension	4175(66.2)	3396 (66.9)	779 (63.5)	0.024
Fall	1167(18.5)	905(17.8)	262(21.4)	0.004
ADL				< 0.001
Self-care	5035(79.8)	4158 (81.9)	877 (71.5)
Dependency	1269(20.1)	919 (18.1)	350 (28.5)
BMI, median (IQR)^c	23.04(20.62–25.66)	23.08 (20.73, 25.67)	22.62 (20.23, 25.60)	0.004
Metabolic biomarkers^d
Total cholesterol, median (IQR), mg/dL	189.43 (166.41, 215.72)	189.43 (166.63, 214.56)	190.08 (165.27, 219.20)	0.74
Triglycerides, median (IQR), mg/dL	106.64 (76.11, 153.99)	107.09 (77.00, 155.76)	105.76 (74.34, 147.80)	0.14
Cholesterol (IQR), mg/dL
High-density lipoprotein, mg/dL	49.87 (41.70, 59.92)	49.87 (41.70, 59.92)	50.07 (41.37, 59.92)	0.99
Low-density lipoprotein, mg/dL	112.50 (91.24, 135.14)	112.11 (91.51, 134.15)	113.6604 (91.18, 138.22)	0.27
Glucose, median (IQR), mg/dL	101.88 (93.69, 114.3)	101.79 (93.69, 114.3)	102.6 (93.96, 113.31)	0.57
eGFR, median (IQR), mg/dL	89.10 (77.71, 95.52)	89.14 (77.59, 95.57)	88.85 (78.14, 95.30)	0.75
Hs-CRP, median (IQR), mg/dL	1.23 (0.65, 2.53)	1.23 (0.66, 2.5)	1.26 (0.65, 2.68)	0.78
^aDied refers to the number of deaths among the population included in the baseline at the follow-up deadline (August 2023)
^bP value was based on χ2 or analysis of variance or Mann-Whitney U test where appropriate
^cCalculated as weight in kilograms divided by height in meters squared
^dMeasured in subpopulation of 3538 participants

Multivariable Cox Model for MCR and All-Cause Mortality

The Cox model showed that MCR was associated with higher all-cause mortality (HR = 1.54, 95% CI: 1.34–1.78), remaining significant after adjusting for all confounders (HR = 1.46, 95% CI: 1.27–1.70). Additionally, a negative correlation was found between gait speed and mortality risk, indicating slower gait as an independent predictor of mortality risk, which remained significant after adjusting for demographics and health characteristics (as shown in Table 2/3 and Fig. 2).

Table 2

Association of MCR With All-Cause Mortality by Competing Risk Analysis
Model	Model1		Model2		Model3
	HR (95%CI)	P value	HR (95%CI)	P value	HR (95%CI)	P value
All-cause death^a	1.54(1.34–1.78)	<0.001	1.47(1.28–1.71)	<0.001	1.46(1.27–1.70)	<0.001
All-cause death^b	1.46(1.21–1.75)	<0.001	1.39(1.16–1.67)	<0.001	1.35(1.13–1.63)	0.001
^aAnalyze using an incomplete dataset of 6304 participants(with multiple imputations)
^bAnalyze using a complete dataset of 3631 participants(without multiple imputations)

Model 1 was adjusted for age and gender.

Model 2 was adjusted for age, gender, residence, marital status, educational level, smoking

status, leisure, and drinking status, pension, ADL.

Model 3 was adjusted as model 2 plus history of diabetes, hypertension, dyslipidemia, and chronic kidney disease; depression, BMI;

Table 3

Association of MCR With All-Cause Mortality in Subpopulations of 3538 Participants With Metabolic Biomarkers Measurements
Model	All-Cause Mortality
	HR (95%CI)	P value
Model3	1.46(1.27–1.70)	<0.001
Model adjusted as model 3 plus
Total Cholesterol	1.44(1.23–1.68)	<0.001
Triglycerides	1.43(1.23–1.68)	<0.001
HDL cholesterol	1.44(1.23–1.68)	<0.001
LDL cholesterol	1.44(1.23–1.68)	<0.001
Glucose	1.45(1.24–1.69)	<0.001
eGFR	1.44(1.23–1.68)	<0.001
Hs-CRP	1.45(1.24–1.70)	<0.001
All biomarkers	1.47(1.25–1.72)	<0.001

Stratified Analysis

Increased mortality risk in the MCR group was consistent across most subgroups, suggesting MCR as a universal predictor of mortality risk. However, depression showed a significant interaction, indicating it might moderate the MCR-mortality relationship (as shown in Fig. 3).

This investigation establishes MCR syndrome as a robust and independent predictor of all-cause mortality in older adults. Our analysis reveals that individuals with MCR exhibited a markedly higher mortality risk compared to their non-MCR counterparts. This association remained strong even after adjusting for a range of potential confounders, including age, sex, educational attainment, and residence. The robustness of this finding was further validated through sensitivity analyses, which confirmed that the core relationship was not driven by other extraneous factors. Concurrently, our study reinforces the well-documented link between slower walking speed and an increased risk of all-cause mortality.

Previous studies have extensively reported the association between slow gait and poor cognitive function[21–23]. Verghese et al[19]. identified executive function abnormalities in individuals with MCR through studies on regular walking speed. Since executive function is linked to vascular dementia, MCR serves as a strong predictor for this type of dementia. Additionally, research has found that brain atrophy and increased white matter hyperintensities are more common in MCR patients[24]. Declines in cognitive abilities among non-demented individuals have been shown to predict mortality rates, likely due to various pathophysiological processes[25]. A non-linear relationship exists between gait speed and fall risk, with slower walkers facing a higher risk of indoor falls[26]. Our study also observed a higher fall rate among MCR patients, which poses significant harm to the elderly and increases their mortality risk[27]. Thus, both slow walking speed and cognitive impairment can predict mortality. Furthermore, previous research suggests that MCR may lead to declines in psychological and social functions, such as social isolation and depression[28], which further deteriorate physical health and elevate mortality risk. Loneliness and depression have been widely studied and are known to be associated with higher mortality rates[29, 30]. Our findings also indicate that MCR patients have higher rates of depression and dependency on others for activities of ADL.

The study's results align with findings from other cohorts like the Health and Retirement Study, National Health and Aging Trends Study, and the Survey of Health, Ageing and Retirement in Europe[28], indicating MCR's strong predictive value for mortality in developing countries. The relationship between gait speed and mortality further highlights the importance of gait assessment in predicting mortality risk among older people.

This community-based prospective study has national representativeness and adjusts for potential confounders, strengthening existing evidence. However, limitations include the uneven grouping of MCR, the simplicity of gait speed and subjective cognitive impairment measures, and the potential underreporting of deaths unrelated to health.

MCR is associated with increased all-cause mortality risk in older people, independent of demographic characteristics, health behaviors, and physical conditions. MCR assessment should be included in routine health checks, with appropriate interventions implemented when slow gait and subjective cognitive impairment are detected.

Ethics approval and consent to participate

Written informed consent was obtained from all participants. The study involving human participants was reviewed and approved by the Institutional Review Board of Peking University (IRB number: IRB00001052-11015) in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Funding

This work was supported by the Ningbo Science and Technology Project (No.2023H017), Medical Science and Technology Project of Zhejiang Province(No.2025KY1332), Ningbo Top Medical and Health Research Program (contract No.2022020304).

Author Contribution

Conceptualization: Li X, Wei C. Data curation: Li X, Hu k. Formal analysis: Li X, Wei C, Gao X. Project administration: Sun J, Yang H. Visualization: Li X. Writing-original draft: Li X, Wei C. Writing-review & editing: Wei C, Yang H.

Data Availability

The data is stored on the CHARLS website, further details on each variable can be found on the CHARLS website（ https://charls.pku.edu.cn/).

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No competing interests reported.

The Relationship Between Motoric Cognitive Risk Syndrome and All-Cause Mortality in Older People: findings from CHARLS 2011–2023

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Abstract

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