Association between peripheral platelet markers and motoric cognitive risk syndrome: a cross-sectional analysis of data from the WCHAT study

Abstract

Objectives

To investigate the association between peripheral platelet indices and motoric cognitive risk syndrome (MCR).

Design

A cross-sectional analysis using baseline data from the West China Health and Aging Trend (WCHAT) study.

Setting

Community settings across several ethnically diverse regions of western China.

Participants

From an initial pool of 7536 community-dwelling adults in the WCHAT study, 4080 participants were included in the final analysis after applying exclusion criteria.

Primary and secondary outcome measures

The primary outcome was MCR, defined by the concurrent presence of subjective cognitive complaints and slow gait speed (≥1 SD below sex- and age-stratified mean) in the absence of dementia or mobility disability. The exposures were platelet indices, including platelet count (PLT), mean platelet volume, platelet distribution width (PDW), platelet-large cell ratio (P-LCR) and platelet crit (PCT). Multivariable logistic regression was used to examine the associations between platelet indices and MCR. Restricted cubic spline analysis was performed to explore non-linear associations.

Results

Among the 4080 participants, 320 (7.84%) met the diagnostic criteria for MCR. After adjustment for potential confounders, higher PLT (OR=1.17, 95% CI 1.04 to 1.32, p=0.007) and PCT (OR=1.13, 95% CI 1.01 to 1.26, p=0.046) were associated with greater odds of MCR. Conversely, higher PDW (OR=0.85, 95% CI 0.75 to 0.97, p=0.014) and P-LCR (OR=0.87, 95% CI 0.77 to 0.98, p=0.026) were associated with lower odds of MCR. Furthermore, a nonlinear relationship was identified for PDW and MCR (p<0.05), with a threshold at 13.40%.

Conclusions

Specific platelet indices are significantly associated with MCR. Further prospective studies are warranted to confirm these associations and explore the underlying mechanisms.

Trial registration number

China Clinical Trial Center (ChiCTR1800018895).

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This study used a large, multi-ethnic, community-based sample from western China to investigate the associations between platelet indices and motoric cognitive risk syndrome (MCR).

• The associations were examined using multivariable logistic regression and restricted cubic spline analysis, with adjustment for a broad range of sociodemographic, lifestyle and clinical covariates.

• The cross-sectional design precluded determination of the temporal relationships between platelet indices and MCR and did not permit causal inference.

• Residual confounding from unmeasured factors, including medication use, inflammatory status and genetic susceptibility, could not be excluded.

• The exclusion of individuals with dementia, severe disability, acute illness or certain chronic conditions may limit the generalisability of the findings to populations with a greater disease burden.

Introduction

Motoric cognitive risk syndrome (MCR) is a pre-dementia condition characterised by subjective cognitive complaints and slow gait speed among individuals without dementia or motor disorders.¹ The global prevalence of MCR is approximately 9%, ranging from 2% to 27% in different populations.² MCR is associated with an increased risk of adverse outcomes, including dementia, falls, disability and mortality.³ Epidemiological studies have suggested that nearly 30% of individuals with MCR progress to dementia within 5–9 years, highlighting MCR as a potentially important intermediate stage between normal ageing and dementia.⁴ Identifying modifiable risk factors for MCR and intervening at an early stage may help delay or halt the onset of dementia in older adults. However, the molecular mechanisms underlying MCR remain poorly understood.

Neurodegenerative diseases are closely associated with inflammation, oxidative stress and alterations in neurotransmitter systems.⁵ Core neuropathological hallmarks include amyloid-β (Aβ) plaques, neurofibrillary tangles formed by intracellular accumulations of hyperphosphorylated tau protein, neuroinflammation, and neuronal and synaptic loss.⁶ In peripheral blood, platelets are a major source of Aβ peptides, accounting for over 90% of circulating Aβ.⁷ Multiple platelet-derived haematological parameters have attracted extensive research attention as potential biomarkers for neurodegenerative diseases.⁸ Among these indices, platelet parameters such as platelet-large cell ratio (P-LCR),⁹ platelet crit (PCT),¹⁰ mean platelet volume (MPV),¹¹ platelet distribution width (PDW)⁹ and platelet count (PLT)¹² reflect different aspects of platelet morphology and function and have been associated with cognitive impairment and neurodegenerative processes. In addition to these indices, other platelet-related mediators also play critical roles. For example, platelet factor 4 (PF4, also known as CXCL4) has been identified as a key mediator in reducing age-related cognitive decline by enhancing hippocampal neurogenesis and attenuating neuroinflammation.^{13 14} Klotho, a longevity protein, improves cognitive performance through interactions with platelet factors.¹⁵ Other platelet-related mediators, including platelet-derived growth factor¹⁶ and inflammatory cytokines released on platelet activation, such as interleukin-1 beta, tumour necrosis factor alpha and transforming growth factor beta,¹⁷ have also been implicated in cognitive impairment and neurodegenerative diseases.

Although peripheral platelet markers have been investigated in Alzheimer’s disease (AD) and other neurodegenerative diseases, their associations with MCR remain insufficiently understood. Using data from the West China Health and Aging Trend (WCHAT) study, we aimed to examine the cross-sectional associations between peripheral platelet markers and MCR. By characterising these associations, our study may provide epidemiological evidence to inform future longitudinal research on platelet indices and MCR. A graphical abstract illustrating the overall study design and main findings is provided in online supplemental figure S1.

Methods

Study population

Data used in this research were derived from the baseline assessment of the WCHAT study.¹⁸ Briefly, a total of 7536 community-dwelling individuals aged 50 years and older were recruited during the baseline survey conducted in July 2018. It was registered with the Chinese Clinical Trial Registry under the registration number ChiCTR1800018895. All participants provided written informed consent before participating.

In this study, 5979 participants from Sichuan province were initially included based on the availability of comprehensive platelet marker testing. We excluded two individuals with a self-reported history of dementia and 15 participants identified as having severe cognitive impairment using the Chinese version of the Short Portable Mental Status Questionnaire.¹⁹ Additionally, 1036 participants were excluded because of missing Barthel Index data or a Barthel Index score below 100, indicating physical disability. Another 481 individuals were excluded because of incomplete data on platelet markers, cognitive complaints or gait speed. We also excluded 305 participants with acute illnesses or psychological trauma within the past 3 months or a self-reported history of liver disease, kidney disease or malignant tumour, as these conditions are known to affect routine haematological readouts. 60 participants were excluded because of missing data on covariates. Ultimately, 4080 participants were included in the final analysis. The detailed participant selection process is illustrated in figure 1.

Figure 1. Study flow. MCR, motoric cognitive risk syndrome.

MCR assessment

Individuals with both subjective cognitive complaints and slow gait, but without dementia or mobility disability, were classified as having MCR.²⁰ Cognitive complaints were assessed by asking ‘Do you feel that you have more problems with memory than most people?’ or ‘Do you have memory problems that affected your daily activities?’. Participants who answered ‘Yes’ to either question were categorised as having subjective cognitive complaints. Gait speed was assessed using a standardised 4-metre walking test. Slow gait was defined as a gait speed at least 1 SD below the age- and sex-specific mean,²¹ stratified by sex and age (<75 or ≥75 years). Mobility disability was defined as requiring assistance with or experiencing difficulty in performing at least one of the 10 activities included in the Barthel Index.

Peripheral blood platelet markers

Overnight-fasting venous blood samples were collected from the antecubital vein for complete blood count analysis. Peripheral blood platelet markers included PLT, MPV, PCT, PDW and P-LCR. PCT represents the total volumetric fraction of platelets in whole blood, calculated as the product of PLT and MPV. PDW reflects the variation in platelet volume. The larger the PDW, the greater the variation in platelet volume. P-LCR is the proportion of large platelets among all platelets.

Covariates

Covariates of this study included age, sex, marital status (unmarried/married/divorced/widowed), education (illiterate/primary school/secondary school and above), smoking history, drinking history, ethnicity (Han/others), body mass index (BMI) and histories of cardiovascular disease (CVD), stroke, diabetes and hypertension. BMI was calculated as body weight (kg) divided by the square of height (m²).

Statistical analysis

The distributions of continuous variables were assessed using the Shapiro-Francia test and visual inspection of histograms and quantile–quantile plots. Normally distributed variables are presented as mean±SD, whereas non-normally distributed variables are presented as median (IQR). Categorical variables are described as number (percentage). Baseline characteristics of peripheral blood platelet markers and covariates were grouped by MCR and compared using t-tests for normally distributed variables, Wilcoxon rank-sum test for non-normally distributed variables and χ² tests for categorical variables. Multivariable logistic regression models were used to examine the associations between platelet markers and MCR. Model 1 was the unadjusted model. Model 2 was adjusted for age and sex. Model 3 was adjusted for age, sex, marital status, education, smoking history, drinking history, ethnicity, BMI, CVD, stroke, diabetes and hypertension. Each platelet index was analysed in a separate multivariable logistic regression model to avoid multicollinearity. The results were reported as ORs and 95% CIs. Additionally, restricted cubic spline (RCS) regression was applied to evaluate potential nonlinear associations between platelet indices and MCR.

Data analysis was performed using R V.4.6.1 and Stata V.15.1 (Stata Corp, College Station, Texas, USA). A two-tailed p value<0.05 was considered statistically significant.

Results

Baseline characteristics of study population

The baseline characteristics of the participants are summarised in table 1. Among the 4080 participants, 320 (7.84%) met the criteria for MCR. Compared with the non-MCR group, the MCR group was significantly older (63.4±8.0 years vs 61.7±8.2 years, p<0.001) and had a higher proportion of illiteracy (37.5% vs 28.4%, p<0.001). Stroke (3.7% vs 1.1%, p<0.001) and diabetes (10.0% vs 6.6%, p=0.020) were also more prevalent in the MCR group.

Table 1. Baseline characteristics of study participants (N=4080).

Variables	Non-MCR (n=3760)	MCR (n=320)	P value
Age, mean (SD)	61.7 (8.2)	63.4 (8.0)	<0.001
BMI, median (IQR)	25.0 (22.7, 27.5)	24.9 (22.5, 27.6)	0.980
Sex, n (%)
Male	1375 (36.6)	118 (36.9)	0.913
Female	2385 (63.4)	202 (63.1)
Marital status, n (%)
Unmarried	27 (0.7)	4 (1.2)	0.410
Married	3206 (85.3)	263 (82.2)
Divorced	57 (1.5)	5 (1.6)
Widowed	470 (12.5)	48 (15.0)
Education, n (%)
Illiterate	1066 (28.4)	120 (37.5)	<0.001
Primary school	1280 (34.0)	116 (36.3)
Secondary school and above	1414 (37.6)	84 (26.2)
Smoking history, n (%)
No	3073 (81.7)	255 (79.7)	0.366
Yes	687 (18.3)	65 (20.3)
Drinking history, n (%)
No	2801 (74.5)	252 (78.7)	0.092
Yes	959 (25.5)	68 (21.3)
Ethnicity, n (%)
Han	1766 (47.0)	156 (48.8)	0.540
Others	1994 (53.0)	164 (51.2)
CVD, n (%)
No	3582 (95.3)	299 (93.4)	0.145
Yes	178 (4.7)	21 (6.6)
Stroke, n (%)
No	3717 (98.9)	308 (96.3)	<0.001
Yes	43 (1.1)	12 (3.7)
Diabetes, n (%)
No	3 513 (93.4)	288 (90.0)	0.020
Yes	247 (6.6)	32 (10.0)
Hypertension, n (%)
No	2912 (77.5)	239 (74.7)	0.258
Yes	848 (22.5)	81 (25.3)
PLT, median (IQR)	165.5 (129.0, 206.0)	178.0 (132.5, 215.5)	0.022
MPV, median (IQR)	11.0 (9.9, 12.2)	10.7 (9.6, 11.9)	0.027
PCT, median (IQR)	0.2 (0.1, 0.2)	0.2 (0.1, 0.2)	0.120
PDW, median (IQR)	13.4 (12.1, 15.3)	13.0 (11.7, 14.7)	0.004
P-LCR, median (IQR)	34.2 (26.6, 41.8)	31.9 (24.4, 40.0)	0.010

BMI, body mass index; CVD, cardiovascular disease; MCR, motoric cognitive risk syndrome; MPV, mean platelet volume; PCT, platelet crit; PDW, platelet distribution width; P-LCR, platelet-large cell ratio; PLT, platelet count.

Significant differences in platelet indices were observed between the MCR and non-MCR groups. The MCR group had a higher PLT (median 178.0 (IQR 132.5, 215.5) vs 165.5 (IQR 129.0, 206.0), p=0.022), whereas MPV (median 10.7 (IQR 9.6, 11.9) vs 11.0 (IQR 9.9, 12.2), p=0.027), PDW (median 13.0 (IQR 11.7, 14.7) vs 13.4 (IQR 12.1, 15.3), p=0.004) and P-LCR (median 31.9 (IQR 24.4, 40.0) vs 34.2 (IQR 26.6, 41.8), p=0.010) were lower in the MCR group. No significant between-group differences were observed in BMI, sex, marital status, smoking history, drinking history, ethnicity, CVD, hypertension or PCT.

Cross-sectional associations between platelet indices and MCR

Table 2 presents the associations between platelet indices and MCR at baseline in the WCHAT study. After adjustment for all covariates, a higher PLT was associated with higher odds of MCR (OR=1.17, 95% CI 1.04 to 1.32, p=0.007). For each 1-SD increase in PLT, the odds of MCR were 17% higher. Higher PCT was also associated with increased odds of MCR (OR=1.13, 95% CI 1.01 to 1.26, p=0.046). In contrast, higher PDW (OR=0.85, 95% CI 0.75 to 0.97, p=0.014) and P-LCR (OR=0.87, 95% CI 0.77 to 0.98, p=0.026) were associated with lower odds of MCR. After full adjustment for confounders, a nonlinear association was observed between PDW and MCR, with a threshold at 13.40% (p for non-linearity <0.05). No evidence of nonlinearity was found for the other platelet markers. As shown in figure 2, higher levels of PLT were associated with higher odds of MCR, whereas lower levels of MPV and P-LCR were associated with higher odds of MCR.

Table 2. Associations between platelet indices and motoric cognitive risk syndrome.

Variables (+SD)	Model 1			Model 2			Model 3
	OR	95% CI	P value	OR	95% CI	P value	OR	95% CI	P value
PLT	1.15	(1.03 to 1.28)	0.015	1.19	(1.06 to 1.33)	0.003	1.17	(1.04 to 1.32)	0.007
PCT	1.10	(0.98 to 1.23)	0.099	1.14	(1.02 to 1.28)	0.025	1.13	(1.01 to 1.26)	0.046
MPV	0.89	(0.80 to 1.00)	0.056	0.88	(0.79 to 1.00)	0.041	0.89	(0.79 to 1.00)	0.059
PDW	0.86	(0.77 to 0.97)	0.016	0.86	(0.76 to 0.97)	0.012	0.85	(0.75 to 0.97)	0.014
P-LCR	0.88	(0.78 to 0.99)	0.029	0.87	(0.77 to 0.98)	0.020	0.87	(0.77 to 0.98)	0.026

Model 1 was unadjusted. Model 2 adjusted for age and sex. Model 3 further adjusted for marital status, education, smoking history, drinking history, ethnicity, body mass index, cardiovascular disease, stroke, diabetes and hypertension.

MPV, mean platelet volume; PCT, platelet crit; PDW, platelet distribution width; P-LCR, platelet-large cell ratio; PLT, platelet count.

Figure 2. Association of peripheral platelet markers and motoric cognitive risk syndrome by restricted cubic spline analysis. The model was adjusted for age, sex, marital status, education, smoking history, drinking history, ethnicity, body mass index, cardiovascular disease, stroke, diabetes and hypertension. MPV, mean platelet volume; PCT, platelet crit; PDW, platelet distribution width; P-LCR, platelet large cell ratio; PLT, platelet count.

Discussion

In this large, community-based cross-sectional study conducted in western China, we observed significant associations between platelet indices and MCR among middle-aged and older adults. To our knowledge, this is the first study to examine these associations in this population. Although the relationships between platelet indices and cognitive impairment have long attracted research interest, few studies have specifically investigated their associations with MCR. Our results showed that elevated PLT and PCT and reduced PDW and P-LCR were associated with higher odds of MCR. The observed associations between MCR and alterations in platelet size and homogeneity might indicate platelet exhaustion or impaired megakaryopoiesis, potentially driven by chronic low-grade inflammation or endothelial dysfunction.²² However, these potential mechanisms require confirmation in longitudinal and mechanistic studies.

The prevalence of MCR in our study population (7.84%) is consistent with a large-scale national study conducted in China (7.29%).²³ We observed that higher PLT was associated with higher odds of MCR. This finding is consistent with previous evidence linking PLT to neurodegenerative conditions and systemic inflammation. For instance, in AD, elevated PLT has been correlated with increased Aβ production, as platelets are a major source of circulating Aβ peptides.²⁴ Similarly, studies of malignancies and CVD have also suggested that elevated PLT may reflect a pro-thrombotic and pro-inflammatory state, which may contribute to microvascular dysfunction and neuroinflammation.²⁵ The association between PLT and MCR may be driven by similar mechanisms: chronic low-grade inflammation could stimulate thrombopoiesis, leading to higher PLT and simultaneously promoting endothelial dysfunction and impaired cerebral blood flow, ultimately contributing to cognitive and motor decline.^{26 27}

We also found that higher PCT was significantly associated with higher odds of MCR. PCT, which reflects the proportion of blood volume occupied by platelets, has been less studied in neurodegenerative contexts but is increasingly recognised as a marker of platelet activation and hypercoagulability.²⁸ In cardiovascular research, elevated PCT was independently associated with adverse outcomes in ST-segment elevation myocardial infarction patients, suggesting a role in thrombosis and vascular inflammation.²⁹ Similarly, a Mendelian randomisation study using UK Biobank data revealed a significant positive causal relationship between PCT and the onset of stroke, along with a positive correlation between PCT and MPV.³⁰ In the context of MCR, elevated PCT may be related to a state of heightened platelet turnover and activation, potentially driven by systemic inflammation or endothelial injury.¹² This could exacerbate cerebrovascular damage through microthrombosis, hypoperfusion or amplification of neuroinflammatory cascades.³¹ However, these explanations remain speculative and require confirmation in further longitudinal and mechanistic studies.

Our study found that lower PDW was significantly associated with higher odds of MCR. Similar findings have been reported in studies of other cognitive disorders. A cross-sectional study conducted in China found that reduced PDW is associated with a higher risk of dementia, possibly due to its role in reflecting impaired platelet function and reduced responsiveness to inflammatory stimuli.⁹ Similarly, a UK-based prospective cohort study with a 9-year follow-up found that lower PDW levels were associated with an increased risk of incident dementia.¹⁰ However, conflicting results have also been documented. For instance, patients with AD exhibited higher PDW levels than healthy controls, although PDW showed no significant correlation with AD severity.³² Additionally, a genetic correlation study reported a positive association between PDW and the risk of depression, a major prodromal non-motor symptom of Parkinson’s disease, both at the epidemiological and genetic levels.³³ In our study, RCS analysis suggested a nonlinear association between PDW and MCR. The estimated odds of MCR appeared to be higher at both lower and higher PDW levels, suggesting a possible U-shaped pattern. Further longitudinal studies are needed to confirm this pattern and determine whether there is a clinically relevant PDW range associated with the lowest risk of MCR.

P-LCR reflects the proportion of larger, metabolically active platelets and serves as an indicator of platelet function. A lower P-LCR may indicate diminished platelet functionality.³⁴ The Chinese Alzheimer’s Biomarker and LifestylE cohort reported a significant correlation between elevated P-LCR and an increased risk of AD, as well as associations with cerebrospinal fluid biomarkers.⁹ Another 1 year study conducted at West China Hospital of Sichuan University further demonstrated that higher P-LCR was correlated with Aβ deposition, neuroinflammation and APOE ε4-related pathological changes in AD patients, with P-LCR levels increasing alongside disease progression.³² Elevated P-LCR is thought to reflect abnormal platelet activation, enhanced aggregation and adhesion during the pathophysiological process of AD, which may contribute to neurodegeneration through vascular damage or neuroinflammatory cascades.⁶ However, our study found that lower P-LCR was associated with higher odds of MCR. This association may be related to impaired platelet regeneration and dysregulated megakaryopoiesis, potentially resulting from chronic inflammation or nutritional deficiencies.¹⁴ Suboptimal platelet turnover could affect cerebrovascular microcirculation and neuronal repair capacity, thereby increasing susceptibility to MCR.³⁵ Although RCS analysis did not reveal a significant nonlinear relationship between P-LCR and MCR in our study, large-scale longitudinal studies are crucial to elucidate this association further.

Our study used a large, community-based sample from western China, providing a comprehensive estimate of the associations between platelet indices and MCR. However, certain limitations should be acknowledged. First, due to the cross-sectional design, the temporal sequence between platelet indices and MCR could not be determined, and causal inferences cannot be drawn. Second, despite adjustment for multiple potential confounders, residual confounding from unmeasured factors remains possible. Particular medications (eg, antiplatelet agents, anticoagulants and statins) may influence platelet indices. However, detailed medication history was not available in the WCHAT baseline survey; therefore, we were unable to adjust for medications affecting platelet function, which may confound the observed associations between platelet indices and MCR. Third, the exclusion of individuals with severe comorbidities and the community-dwelling nature of the cohort may have resulted in the selection of a comparatively healthier population, potentially limiting the generalisability of our findings to populations with a greater burden of comorbidity. Future longitudinal studies incorporating repeated measurements of platelet indices, detailed medication information and relevant molecular biomarkers are needed to clarify the temporal relationships, underlying mechanisms and potential predictive value of platelet indices for the development and progression of MCR. Studies involving more clinically diverse and vulnerable populations are also warranted to assess the external validity of these findings.

Conclusions

In this cross-sectional study, our findings suggest that platelet indices were associated with MCR among middle-aged and older adults. Prospective studies are needed to examine whether these associations are replicated and to clarify their underlying mechanisms.

The funder had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication. The funder did not influence the results/outcomes of the study despite author affiliations with the funder.

Data availability statement

Data are available upon reasonable request.