Association of motoric cognitive risk syndrome and incident mild cognitive impairment in community-dwelling older adults

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Data may be preliminary. 12 August 2025 V1 Latest version Share on Association of motoric cognitive risk syndrome and incident mild cognitive impairment in community-dwelling older adults Authors : Ying Zhang , Lina Ma , Yanjun Ma , Jie Chang , Yiwei Zhao , Xue Gao , Yue Wu , Yiwen Xing , Yansu Guo , Lina Ma , Zhibin Wang , and Yi Tang 0009-0005-1211-8449 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175497125.55777011/v1 Published Frontiers in Neurology Version of record Peer review timeline 164 views 132 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Motoric cognitive risk syndrome (MCR) has been recognized as a risk factor for dementia, but its role in the transition to mild cognitive impairment (MCI) among community-dwelling older adults remains unclear. This study investigated the associations between MCR and the risk of incident MCI or cognitive decline over a one-year follow-up period. We investigated whether MCR predicted the risk of incident MCI and cognitive decline (Mini-Mental State Examination decline ≥ 4 points) in 853 individuals without dementia or disability at baseline. Logistic regression, adjusted for potential confounders, was used to assess the overall prediction value of MCR and the associations of its cognitive (subjective cognitive complaint, SCC) and motoric (gait speed) components with the risk of MCI. Participants with MCR had a higher risk of developing MCI (adjusted odds ratio [OR] = 2.22, 95% confidence interval [CI]: 1.18–4.17) and cognitive decline (adjusted OR = 2.28, 95% CI: 1.11–4.70). Among the individual components of MCR, slow gait speed was significantly associated with MCI risk (adjusted OR = 2.15, 95% CI: 1.19–3.87). Subgroup analysis in the MCR population showed that individuals under 75 years of age and women were at greater risk of incident MCI. MCR is significantly associated with an increased risk of developing MCI and cognitive decline in community-dwelling older adults. [Title page] Association of motoric cognitive risk syndrome and incident mild cognitive impairment in community-dwelling older adults Running title: Motoric cognitive risk syndrome and risk of MCI Authors Ying Zhang 1,2, # , Lixin Ma 1, # , Yanjun Ma 3 , Jie Chang 3 , Yiwei Zhao 4 , Xue Gao 5 , Yue Wu 6 , Yiwen Xing 7 , Yansu Guo 6 , Lina Ma 7 , Zhibin Wang 4* , Yi Tang 1,4,8* Affiliations: 1 Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; 2 Department of Neurology, Handan Central Hospital, Handan, Hebei, China; 3 National Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China; 4 Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; 5 Department of Neurology, Beijing Fengtai You’anmen Hospital, Beijing, China; 6 Beijing Geriatric Healthcare Center, Xuanwu Hospital, Capital Medical University, Beijing, China; 7 Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing, China; 8 Neurodegenerative Laboratory of Ministry of Education of the Peoples Republic of China, Beijing, China. # Contributed equally: Ying Zhang, Lixin Ma * Corresponding Author Yi Tang, Department of Neurology, The First Hospital of Hebei Medical University, 89 Donggang Road, Yuhua District, Shijiazhuang, Hebei 050031, China; Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Beijing 100053, China, E-mail: [email protected] Zhibin Wang, Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Beijing 100053, China, E-mail: [email protected] . Acknowledgments: We thank all study participants for their contribution. Abstract Motoric cognitive risk syndrome (MCR) has been recognized as a risk factor for dementia, but its role in the transition to mild cognitive impairment (MCI) among community-dwelling older adults remains unclear. This study investigated the associations between MCR and the risk of incident MCI or cognitive decline over a one-year follow-up period. We investigated whether MCR predicted the risk of incident MCI and cognitive decline (Mini-Mental State Examination decline ≥ 4 points) in 853 individuals without dementia or disability at baseline. Logistic regression, adjusted for potential confounders, was used to assess the overall prediction value of MCR and the associations of its cognitive (subjective cognitive complaint, SCC) and motoric (gait speed) components with the risk of MCI. Participants with MCR had a higher risk of developing MCI (adjusted odds ratio [OR] = 2.22, 95% confidence interval [CI]: 1.18–4.17) and cognitive decline (adjusted OR = 2.28, 95% CI: 1.11–4.70). Among the individual components of MCR, slow gait speed was significantly associated with MCI risk (adjusted OR = 2.15, 95% CI: 1.19–3.87). Subgroup analysis in the MCR population showed that individuals under 75 years of age and women were at greater risk of incident MCI. MCR is significantly associated with an increased risk of developing MCI and cognitive decline in community-dwelling older adults. Keywords: Motoric cognitive risk syndrome, Mild cognitive impairment, Subjective cognitive complaint, Gait speed 1 . Introduction By mid-century, the number of people living with dementia is projected to reach 152.8 million (GBD 2019 Dementia Forecasting Collaborators, 2022), with associated costs expected to rise to $9.12 trillion worldwide (Jia et al. , 2018). Given the lack of curative treatments and the high disability burden of dementia (“2024 Alzheimer’s disease facts and figures,” 2024), early identification and risk management are critical. As the global population ages, concurrent cognitive and motor function declines have been widely observed in the elderly (Robertson et al. , 2013; Cohen et al. , 2016; Montero-Odasso et al. , 2018; Collyer et al. , 2022). This phenomenon, known as motoric cognitive risk syndrome (MCR), is characterized by the co-occurrence of subjective cognitive complaint (SCC) and slow gait speed in individuals without dementia or mobility disability (Verghese et al. , 2013). The prevalence of MCR among elderly people is approximately 10% based on worldwide population-based studies (Maggio & Lauretani, 2019). Cohort studies have established a link between MCR and an increased risk of dementia and cognitive impairment, highlighting its role in accelerating cognitive decline (Verghese et al. , 2013, 2014, 2019; Mullin et al. , 2022; Lim et al. , 2024). The concept of MCR syndrome was first introduced in 2013 through the Einstein Aging Study, which followed 997 community-dwelling individuals aged 70 and older for a median of 36.9 months. Findings revealed that participants with MCR had a higher risk of developing dementia (adjusted hazard ratio = 3.27) (Verghese et al. , 2013). Additionally, a large multinational study analyzing data from 26,802 participants across 22 cohorts in 17 countries confirmed that MCR not only predicted dementia but also served as an early risk factor for incident cognitive impairment (Verghese et al. , 2014). Despite these findings, it remains unclear whether MCR accelerates the transition to mild cognitive impairment (MCI)—a key predementia stage (Petersen, 2004; Gauthier et al. , 2006; Langa & Levine, 2014). In this study, we examined the association between MCR and the risk of developing MCI or cognitive decline. First, we assessed the combined impact of MCR as an integrated measure of cognitive and motor dysfunction on the progression to MCI and cognitive decline. Next, we analyzed the independent contributions of SCC and gait speed to this transition. Finally, we conducted subgroup analyses based on demographic characteristics, lifestyle factors, and comorbidities further to explore the association between MCR and the risk of MCI. Our findings highlight a clear relationship between MCR and longitudinal decline in cognitive function with advancing cognitive stages in MCI, suggesting its potential as a monitoring and intervention target for dementia prevention. 2. Methods 2.1 Population We conducted a prospective cohort study as part of the Beijing Disability Risk and Ageing Monitoring (BEAM) study (ClinicalTrials.gov identifier: NCT 06394817). The BEAM study is an ongoing cohort study designed to assess disability risk among older adults in the Baizhifang community in Beijing, China. Recruitment occurred from May to September 2023, and follow-up concluded in October 2024. The study included Chinese adults aged 60 years or older who had lived in the target community for at least one year before the survey date. Individuals with inadequate hearing or vision were excluded. A total of 2,018 participants were initially enrolled, with 1,622 (80.4%) retained after one year to assess disability as the primary outcome. For the current analysis, exclusion criteria were applied to focus on the relationship between MCR and MCI. Participants were excluded if they met any one of the following criteria: Barthel scale scores below 95 ( n = 139), missing gait speed data ( n = 8), MCI diagnosis ( n = 153), or cognitive impairment ( n = 27) at baseline, or loss to follow-up ( n = 69) or incomplete follow-up data ( n = 769). After applying these exclusions, 853 participants were included in the final analysis (Figure 1). All data were collected through face-to-face interviews. Before enrollment, written informed consent was obtained from all participants, and the study protocol was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (2022-234). This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies. 2.2 MCR MCR was diagnosed based on established criteria, requiring the simultaneous presence of SCC and slow gait speed in the absence of dementia or mobility disability (Verghese et al. , 2013) (Supplemental Figure S1). 2.21 Subjective Cognitive Complaint (SCC) SCC was assessed using standardized questionnaires adapted from the Geriatric Depression Scale (GDS) (Yesavage et al. , 1982) and Clinical pHysical rEsilience assEssment Scale (CHEES) (Li et al. , 2024). Participants were categorized as having SCC if they responded “yes,” “quite agree,” or “agree” to either of the following questions: “Do you feel you have more problems with memory than most people?” “Do you increasingly find it difficult to remember where you put things?” 2.22 Slow Gait Speed Participants completed two 4-meter walks at their usual pace from a standing start, with at least 1 meter beyond the course to prevent deceleration. The mean of both trials was used for analysis. Slow gait speed was defined as a walking speed at least one standard deviation below the age- and sex-specific means for our cohort (Marquez et al. , 2022) (Supplemental Figure S2). 2.23 Functional Independence and Dementia Criteria Functional independence was classified as a Barthel scale score ≥ 95 (Liu et al. , 2015). Dementia was diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (2013) or a Mini-Mental State Examination (MMSE) score ≤ 13 (Folstein et al. , 1975). 2.3 Cognitive Outcomes The primary outcome, MCI, was diagnosed using the modified Mayo Clinic criteria (Petersen et al. , 1999; Petersen, 2004). MCI was defined as objective cognitive impairment, relatively preserved general cognitive function, functional independence, and the absence of dementia (Supplemental Figure S1). Objective cognitive impairment was defined as an MMSE score at least one standard deviation below the mean for education-matched individuals in our cohort (Albert et al. , 2011) (Appendix 3). Based on established MMSE cut-offs for the Chinese population (Li et al. , 2016), general cognitive function was considered relatively preserved: 17 for illiterate individuals, 20 for those with elementary school education, and 24 for those with middle school education or higher. Cognitive decline was defined as a reduction of ≥ 4 points in MMSE scores during follow-up (Hensel et al. , 2007; Verghese et al. , 2014), which is a threshold recognized as a reliable indicator of minimal clinically important difference (MCID) (Borland et al. , 2022). 2.4 Covariates The covariates in this study included gender, age, educational level, alcohol consumption, smoking status, body mass index (BMI), hypertension, diabetes mellitus, dyslipidemia, coronary heart disease, and stroke. Education was divided four levels: elementary school or below, middle school, high school, and college or above. BMI was categorized into four levels (Yuan et al. , 2021): underweight (< 18.5 kg/m²), normal weight (18.5 ≤ BMI < 24 kg/m²), overweight (24 ≤ BMI < 28 kg/m²), and obesity (≥ 28 kg/m²). Participants were classified as having medical comorbidities if they had a physician-diagnosed condition or were undergoing pharmaceutical treatment for the disease (Supplemental Figure S3). 2.5 Statistical Analysis The baseline characteristics of participants with and without MCR were compared using independent t -tests for continuous variables and chi-square tests for categorical variables. Logistic regression models were used to examine the association between MCR and the risk of MCI or cognitive decline, adjusting for age, gender, educational level, current smoking and drinking status, BMI, hypertension, diabetes mellitus, dyslipidemia, coronary heart disease, and stroke. Subgroup analyses were conducted to explore potential variations using the same adjustments. Results were reported as odds ratios (OR) with 95% confidence intervals (CI). All statistical tests were 2-sided, with p < 0.05 considered statistically significant. All analyses were performed using SPSS (Version 27, IBM Corp.). 3. Results 3.1 Participants Characteristics The overall prevalence of MCR among individuals aged 60 years and older was 15.0% (95% CI: 12.7%–17.6%). Detailed prevalence rates of MCR stratified by age, sex, and education are provided in Supplemental Table S4. At baseline, 128 participants met the MCR diagnostic criteria, with ages ranging from 60 to 85 years (mean: 69.1 years). Among them, 47 (36.7%) were men and 81 (63.3%) were women. Table 1 compares the baseline characteristics of MCR and non-MCR participants. There were no significant differences in age, sex, educational level, MMSE scores, smoking status, alcohol consumption, or BMI. The prevalence of hypertension, diabetes mellitus, dyslipidemia, and stroke were also comparable between the two groups. However, the MCR group had a lower prevalence of coronary heart disease than the non-MCR group (14.8% vs . 23.9%, p = 0.024). 3.2 Associations Between MCR and Risk of MCI or Cognitive Decline During the one-year follow-up, 16 of 128 participants (12.5%) in the MCR group developed incident MCI, compared to 45 of 725 subjects (6.2%) in the non-MCR group (risk ratio = 2.02, 95% CI: 1.10–3.67). Logistic regression analysis confirmed this association across different models (Table 2). Model 1, which presented unadjusted associations, showed that individuals with MCR had a significantly higher risk of MCI (OR = 2.16, 95% CI: 1.18–3.95), indicating a 116% increased likelihood compared to those without MCR. This association remained significant in Model 2, adjusted for covariates (adjusted OR = 2.22, 95% CI: 1.18–4.17). A separate logistic regression analysis also examined whether MCR was associated with an increased risk of cognitive decline, defined as an MMSE score reduction of ≥ 4 points. As shown in Table 2, participants with MCR had a significantly higher risk of cognitive decline, with an adjusted OR of 2.28 (95% CI: 1.11–4.70). 3.3 Associations Between SCC, Gait Speed, and the Risk of MCI or Cognitive Decline To evaluate the independent contributions of SCC and gait speed, the two components of MCR, we conducted logistic regression analyses to assess their association with the risk of incident MCI or cognitive decline. Gait speed was significantly associated with an increased risk of MCI (adjusted OR = 2.15, 95% CI: 1.19–3.87). SCC, however, was not significantly associated with MCI (adjusted OR = 1.68, 95% CI: 0.82–3.43). When assessing cognitive decline (MMSE decline ≥ 4 points), neither SCC (adjusted OR = 1.95, 95% CI: 0.65–5.58) nor gait speed (adjusted OR = 1.66, 95% CI: 0.68–4.04) showed a significant association (Table 2). These findings suggest that among the MCR components, slow gait speed independently increases the risk of incident MCI, whereas SCC alone does not. 3.4 Subgroup Analysis A subgroup analysis was conducted to explore variations in the risk of MCI among MCR participants based on age, sex, educational level, BMI, smoking status, alcohol consumption, and comorbidities (Figure 2). While interactions between MCR and subgroup variables were not statistically significant, several trends emerged. Women with MCR had a higher risk of developing MCI than those without MCR (adjusted OR = 2.31, 95% CI: 1.04–5.16). Similarly, participants younger than 75 years with MCR had more than twice the likelihood of transitioning to MCI (adjusted OR = 3.40, 95% CI: 1.67–6.91). Those with a high school education or higher were more likely to progress to MCI (adjusted OR = 2.42, 95% CI: 1.11–5.28). Participants with both MCR and dyslipidemia had a significantly increased risk of MCI (adjusted OR = 2.62, 95% CI: 1.13–6.06). These findings suggest that certain subgroups—particularly younger individuals, women, those with higher education levels, and individuals with dyslipidemia—may be at greater risk of MCI when MCR is present. 4. Discussion This study investigated the association between MCR and the risk of developing MCI or cognitive decline over a one-year follow-up. Our findings indicated that individuals with MCR face a significantly higher risk of transitioning to MCI or cognitive decline, with motor dysfunction emerging as the primary risk factor for this progression. These results underscore the clinical significance of MCR as an early screening tool for identifying individuals at risk of MCI and highlight potential early intervention and prevention opportunities. MCR has been increasingly recognized as a significant risk factor for cognitive impairment. A multi-country study reported that individuals with MCR exhibited a 1.90-fold increased risk of developing dementia (Verghese et al. , 2014). At the same time, the Mexican Health and Aging Study found a 2.52-fold higher risk of cognitive impairment among older adults with MCR (Aguilar-Navarro et al. , 2019). Consistently, our cohort revealed that MCR was associated with a 2.22-fold increased risk of developing MCI. These findings reinforced the role of MCR as a transitional stage between normal aging and dementia, providing a window for early identification and intervention (Langa & Levine, 2014). However, given the observational nature of our study, causality cannot be established. Future large-scale, multicenter, and longitudinal studies are necessary to determine whether MCR directly contributes to the progression of MCI. Although prior research has demonstrated that MCR predicts cognitive decline, the relative contributions of cognitive or motor components remain unclear. Some evidence suggests that cognitive impairment may be the primary driver, as one study found that the severity of cognitive deficits, rather than motoric impairments, were stronger predictors of dementia conversion (Verghese et al. , 2019). Additionally, up to 45% of older adults with SCC progress to MCI within four years, and their dementia risk is twice that of cognitively normal individuals (Reisberg et al. , 2008; van Harten et al. , 2018). However, our findings support motor dysfunction as a key predictor, as participants with slow gait speed were 2.15 times more likely to develop MCI. This aligns with previous studies demonstrating that slow gait speed is an early marker of cognitive decline, often deteriorating up to 12 years before MCI onset (Buracchio et al. , 2010; Abellan van Kan et al. , 2012; Cohen et al. , 2016; Kikkert et al. , 2016; Semba et al. , 2020). Therefore, the motoric component of MCR plays a crucial role in identifying individuals at high risk of MCI and cognitive decline. The interplay between cognitive and motoric function may stem from shared neural and physiological mechanisms. Both cognition and gait speed rely on overlapping cortical regions and executive function, and they share common risk factors such as cardiovascular disease and diabetes (Montero-Odasso & Hachinski, 2014; Chhetri et al. , 2017; Zhang et al. , 2023). Additionally, age-related declines in muscle strength may contribute to both slower gait and cognitive impairment (Szulc et al. , 2004; Kikkert et al. , 2016; Wilkinson et al. , 2018), further reinforcing their link. This relationship suggests that MCR—by integrating cognitive and motoric phenotypes—represents a key transitional stage in the aging process (Sekhon et al. , 2017). Given its potential role in accelerating progression toward MCI and dementia, MCR serves as a critical framework for identifying individuals at high risk of cognitive decline. This study has several limitations. First, as an observational study, it cannot establish a causal relationship between MCR and incident MCI nor determine whether cognitive or motor impairments play a dominant role in this transition. Future multicenter cohort studies with larger sample sizes are needed to evaluate intervention strategies targeting MCR and their effectiveness in slowing dementia progression. Second, recall bias is an inherent limitation, as SCC was assessed solely through self-reported questionnaires at baseline. To mitigate this, the questionnaire wording was optimized during the development phase. Finally, as a single-center cohort study, our findings may have limited generalizability. Validation in diverse populations and multicenter cohort studies is necessary to minimize selection bias and confirm the broader applicability. This study highlights MCR as a significant risk factor for MCI among community-dwelling older adults. By establishing MCR as a predictor of MCI, our findings extend dementia risk prediction to earlier stages of cognitive decline, reinforcing the importance of early screening and intervention. Acknowledgment: We thank all study participants for their contribution. Conflicts of Interest: None. Funding: This work was supported by the National Key Research and Development Program of China (2022YFC3602600), the National Natural Science Foundation of China (82220108009, 82401664), Beijing Outstanding Young Scientist Program (JWZQ20240101023), STI2030-Major Projects (2021ZD0201801) and Beijing Hospitals Authority Youth Programme (QML20230802). References 2024 Alzheimer’s disease facts and figures (2024) . Alzheimer’s & Dementia , 20 , 3708–3821. 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Keywords gait speed mild cognitive impairment motoric cognitive risk syndrome subjective cognitive complaint Authors Affiliations Ying Zhang The First Hospital of Hebei Medical University View all articles by this author Lina Ma The First Hospital of Hebei Medical University View all articles by this author Yanjun Ma Xuanwu Hospital Capital Medical University View all articles by this author Jie Chang Xuanwu Hospital Capital Medical University View all articles by this author Yiwei Zhao Xuanwu Hospital Capital Medical University View all articles by this author Xue Gao Beijing Fengtai You'anmen Hospital View all articles by this author Yue Wu Xuanwu Hospital Capital Medical University View all articles by this author Yiwen Xing Xuanwu Hospital Capital Medical University View all articles by this author Yansu Guo Xuanwu Hospital Capital Medical University View all articles by this author Lina Ma Xuanwu Hospital Capital Medical University View all articles by this author Zhibin Wang Xuanwu Hospital Capital Medical University View all articles by this author Yi Tang 0009-0005-1211-8449 [email protected] The First Hospital of Hebei Medical University View all articles by this author Metrics & Citations Metrics Article Usage 164 views 132 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ying Zhang, Lina Ma, Yanjun Ma, et al. 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