Association of weight-adjusted waist index with motoric cognitive risk syndrome in Chinese adults Aged ≥ 60 years

Abstract

Objective

The primary objective of this study is to assess the association between the Weight-Adjusted Waist Index (WWI) and Motoric Cognitive Risk (MCR) syndrome in the elderly Chinese population.

Methods

A cross-sectional design was employed, drawing upon data from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). The analytical sample consisted of 7,108 Chinese participants aged 60 and over, excluding individuals with a dementia diagnosis or significant mobility limitations. Multivariable logistic regression was utilized to assess the link between WWI and MCR. Additionally, restricted cubic splines (RCS) were applied to test for non-linearity, and piecewise regression was employed to identify specific cut-off values.

Results

The findings indicated a significant positive association between elevated WWI indices and the likelihood of developing MCR. Quantitatively, for every 1-unit increment in WWI (1 cm/√kg), the odds of MCR increased by 17% (adjusted OR = 1.17; 95% CI: 1.08–1.27; P < 0.001). Analysis using restricted cubic splines suggested a linear relationship (P for non-linearity = 0.2), indicating progressively higher risks of MCR above the threshold of 10.774 cm/√kg. Subgroup analyses demonstrated consistent associations across most categories, with a significant interaction noted in marital status (P for interaction = 0.034).

Conclusion

The association between WWI and MCR in older Chinese adults is both independent of other factors and largely linear in nature. Due to its simplicity and clinical accessibility, WWI is an effective tool for early risk stratification, which aids in timely interventions that promote healthy aging and reduce the burden of neurodegenerative diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-025-02837-7.

Introduction

The accelerating pace of global population aging holds profound implications for public health, creating significant and complex challenges. Among these, the rising incidence of cognitive decline and dementia [1, 2] represents significant socioeconomic and healthcare burdens worldwide [3]. Given the prolonged latent phase of dementia, effective strategies to delay or avert its onset must prioritize the early recognition of vulnerable groups and the prompt initiation of interventions [4]. Motoric Cognitive Risk (MCR) syndrome is recognized as a pre-dementia stage [5], identified in non-demented individuals who exhibit both slow gait velocity and subjective cognitive decline. Since MCR serves as a strong, independent prognostic indicator for Alzheimer’s disease, vascular dementia, and all-cause dementia [6], it is crucial to pinpoint modifiable risk factors that could attenuate its advancement. Current neurobiological evidence provides substantial mechanistic insights: visceral adipose tissue, a central component of obesity, is metabolically active and secretes pro-inflammatory cytokines (e.g., tumor necrosis factor-alpha, interleukin-6) and adipokines [7]. These factors have the potential to traverse a disrupted blood-brain barrier. Consequently, they promote pathological changes within the CNS, including oxidative stress, neuroinflammation, and insulin resistance [8, 9]. Such pathological processes deteriorate synaptic plasticity, expedite neuronal damage, and directly contribute to the cognitive and motor deficits characteristic of MCR [10].

The demographic trend of population aging is rapidly advancing in China. Statistical data from the end of 2023 indicate that individuals aged 60 years and older numbered approximately 296.97 million, accounting for 21.1% of the entire population [11]. Furthermore, findings from the China Health and Retirement Longitudinal Study (CHARLS) highlight the critical public health burden of MCR. This nationally representative survey revealed a prevalence rate of about 7.29% among older adults living in the community [12]. This finding reinforces the critical need for early risk stratification in response to the nation’s changing demographics. A substantial volume of research has scrutinized the correlation between MCR and various factors including depression, sleep disturbances, hearing loss, low grip strength, and falls [13–15], the potential associations with obesity have not been adequately investigated [16]. In geriatric populations, traditional measures of body fat—such as Waist Circumference (WC) and Body Mass Index (BMI)—have demonstrated notable shortcomings. The prevalent condition of sarcopenia with advancing age [17] may lead BMI to underestimate true adiposity, contributing to the “obesity paradox” [18] and obscuring risks associated with disproportionate fat distribution. Recent findings pinpoint visceral adiposity, not subcutaneous fat, as the key determinant of chronic inflammation and insulin resistance [19], which in turn promote cardiometabolic dysfunction [20] and neurodegenerative processes [21].

In this context, more precise indicators of harmful central obesity are necessary to enhance early risk stratification in pre-dementia states. As an innovative anthropometric measure, the Weight-Adjusted Waist Index (WWI) standardizes WC relative to body weight. Consequently, this metric captures the distribution of central adiposity irrespective of total body mass [22]. Although WWI has been associated with peripheral artery disease [23], functional disability [24], and depression [25], its specific relationship with MCR syndrome has not yet been established. Previous research that relied solely on BMI or WC may have failed to capture the nuances of weight-adjusted central obesity, especially in Asian populations where “normal weight obesity” is prevalent.

Aiming to address this deficiency in current knowledge, a nationally representative dataset was leveraged to investigate the independent correlation linking WWI to MCR. This is believed to be the first investigation into the predictive capacity of WWI for MCR within an older Chinese population. This analysis pursued a dual purpose: initially, to determine whether WWI serves as a significant predictor for MCR, and subsequently, to elucidate the specific dose-dependent association linking central adiposity—quantified via WWI—to the likelihood of developing motoric-cognitive deficits.

Methods

Data source and participants

Data were extracted from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). Conducted across 28 provinces, this national survey focuses on residents aged 45 years or older, comprehensively covering lifestyle behaviors, health status, and socioeconomic positions [26]. Out of the 21,095 respondents in 2015, participants aged 60 years and older without diagnosed dementia, brain damage, or mental retardation, and without severe mobility impairment that would preclude valid assessment of MCR were included. Individuals with missing data on the WWI or any components of MCR were excluded. To enhance data quality prior to analysis, outliers were identified using the interquartile range (IQR) method and addressed through multiple imputation; missing covariate data were similarly imputed to reduce potential bias. A total of 7,108 participants were included in the final analysis, as shown in Fig. 1.

Fig. 1.

Flowchart depicting the screening and enrollment of subjects

Variable Definition

MCR Syndrome

MCR represents a pre-dementia state situated between normal aging and cognitive impairment. The syndrome is characterized by the coexistence of subjective cognitive concerns and slow gait velocity in older adults who are free from dementia and severe physical impairments [27]. Accordingly, MCR cases were identified in this study by requiring the presence of both of the following elements:

Subjective cognitive decline (SCD)

This was assessed using the CHARLS item DC004, which asks, “How do you rate your current memory? Excellent, very good, good, fair, poor, or I don’t know?” Responses indicating “fair“ or “poor” were classified as indicative of SCD. Previous analyses of CHARLS data support the reliability of self-rated memory for population research [28].

Slow gait speed

The assessment involved a 4-meter walk at a self-selected usual pace, from which gait speed in meters per second was computed. To account for potential physiological heterogeneity, the study population was stratified by sex and age (< 75 versus ≥ 75 years) [29]. Gait slowness was identified when an individual’s walking speed fell one standard deviation or more beneath the average value of their corresponding age- and gender-matched cohort [5].

Participants who met both the SCD and slow gait criteria were classified as having MCR.

Weight-adjusted waist index (WWI)

To determine WWI, waist circumference in centimeters is divided by the square root of body weight in kilograms [22]. This index adjusts WC for body mass, with the goal of offering a precise assessment of central adiposity severity and the characteristics of fat distribution. For descriptive stratification, WWI values were categorized at a threshold of 11.25 cm/√kg, based on literature pertinent to Chinese cohorts [24]. Based on the WWI, subjects were divided into a low group (< 11.25 cm/√kg) and a high group (≥ 11.25 cm/√kg).

Covariates

To control for potential confounding effects, the statistical models incorporated several covariates. These included: sociodemographic characteristics such as age, sex, area of residence, and educational attainment; behavioral factors, encompassing smoking history (categorized as ever-smokers versus never-smokers) and alcohol intake frequency (grouped into ‘never’, ‘less than monthly’, and ‘monthly or more’); and prevalent health issues like hypertension, diabetes, cardiac conditions, stroke, dyslipidemia, and arthritis. Additionally, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) was employed to assess depressive symptoms. A threshold score of ≥ 10 served as the criterion to classify depression [30]. Educational attainment was classified into four categories: high school and beyond, middle school, primary school, and illiteracy.

Statistical analysis

Participant characteristics were summarized for the total population and stratified by groups (Low WWI vs. High WWI). Continuous variables were expressed as means ± standard deviations (SD) or medians (interquartile ranges, IQR). Differences among groups were assessed using one-way ANOVA for normally distributed data, whereas the Kruskal-Wallis H test was applied to non-normally distributed variables. Categorical characteristics were presented as frequencies (percentages) and compared via the chi-square test. To manage potential confounders, both single-variable and multiple-variable regression models were constructed. Restricted cubic splines (RCS) were utilized to visualize the possible non-linear dose-response relationship linking WWI to MCR. Regarding the final multivariate models, covariates were chosen according to significant confounders identified in initial univariate assessments. Furthermore, stratified analyses were performed to explore potential effect modification by various participant characteristics. The diagnostic utility of WWI, WC, and BMI in identifying MCR was assessed using receiver operating characteristic (ROC) curves. The area under the curve (AUC) was computed for each index to compare their respective discriminatory power. Statistical evaluations were executed using R software (Version 4.2.2; The R Foundation for Statistical Computing, http://www.R-project.org) in conjunction with the Free Statistics Analysis Platform (Version 2.1.1; Beijing, China, http://www.clinicalscientists.cn/freestatistics). Statistical significance was defined as a two-sided P < 0.05.

Results

Baseline characteristics of the study participants

The study analyzed a total of 7,108 participants, categorized into Low and High WWI groups (Table 1). Substantial disparities were evident between the groups. Specifically, the prevalence of MCR was markedly higher among participants with elevated WWI levels compared to the low WWI group (16.2% vs. 11.4%, P < 0.001). Demographically, the high WWI subset was characterized by advanced age, a lower educational background, and a predominance of females (all P < 0.001). In terms of health status, these subjects exhibited elevated rates of depressive symptoms, arthritis, heart disease, diabetes, dyslipidemia, and hypertension (all P < 0.001).

Table 1.

Demographic characteristics by WWI group

Variables	Total (n = 7108)	Low WWI (n = 3189)	High WWI (n = 3919)	P value
Age, Mean ± SD	68.1 ± 6.5	67.1 ± 6.0	68.8 ± 6.7	< 0.001
Gender, n (%)				< 0.001
Female	3635 (51.1)	948 (29.7)	2687 (68.6)
Male	3473 (48.9)	2241 (70.3)	1232 (31.4)
Marital status, n (%)				< 0.001
Married	5471 (77.0)	2591 (81.2)	2880 (73.5)
Others	1637 (23.0)	598 (18.8)	1039 (26.5)
Education, n (%)				< 0.001
Illiteracy	3887 (54.7)	1486 (46.6)	2401 (61.3)
Elementary school	1781 (25.1)	887 (27.8)	894 (22.8)
Middle school	974 (13.7)	553 (17.3)	421 (10.7)
High school and above	466 ( 6.6)	263 (8.2)	203 (5.2)
Residence, n (%)				0.001
Rural	5356 (75.4)	2462 (77.2)	2894 (73.8)
Urban	1752 (24.6)	727 (22.8)	1025 (26.2)
Smoke, n (%)				< 0.001
No	3965 (55.8)	1293 (40.5)	2672 (68.2)
Yes	3143 (44.2)	1896 (59.5)	1247 (31.8)
Drinking status, n (%)				< 0.001
No	4764 (67.0)	1860 (58.3)	2904 (74.1)
less than once a month	532 ( 7.5)	283 (8.9)	249 (6.4)
Once or more	1812 (25.5)	1046 (32.8)	766 (19.5)
Comorbidities, n (%)
Hypertension	1930 (27.2)	701 (22)	1229 (31.4)	< 0.001
Dyslipidemia	745 (10.5)	271 (8.5)	474 (12.1)	< 0.001
Diabetes	470 ( 6.6)	164 (5.1)	306 (7.8)	< 0.001
Cancer	72 ( 1.0)	30 (0.9)	42 (1.1)	0.583
Heart problem	993 (14.0)	378 (11.9)	615 (15.7)	< 0.001
Stroke	154 ( 2.2)	63 (2)	91 (2.3)	0.318
Arthritis, n (%)	2821 (39.7)	1152 (36.1)	1669 (42.6)	< 0.001
CESD-10, Mean ± SD	9.1 ± 5.5	8.6 ± 5.3	9.5 ± 5.7	< 0.001
MCR, n (%)	996 (14.0)	363 (11.4)	633 (16.2)	< 0.001

Abbreviations: WWI Weight-adjusted waist index, CESD-10 the 10-item Center for Epidemiologic Studies Depression Scale, MCR Motoric Cognitive Risk syndrome

A comparative analysis of baseline demographics and clinical features was also conducted between the group included for final analysis (n = 7,108) and the excluded group (those with missing data on MCR or other key variables). This procedure aimed to evaluate the potential for selection bias and to confirm the generalizability of the study’s findings. As outlined in Supplementary Table 1, significant differences were noted in several demographic and health-related variables (all P < 0.05). Participants included in the analysis were generally older, less educated, with a higher representation from rural locations, and exhibited higher prevalences of hypertension, dyslipidemia, diabetes, heart disease, and arthritis than those excluded.

Association between WWI and MCR

Table 2 outlines the findings from the multivariable logistic regression analyses linking WWI to MCR. In the fully adjusted Model 5 (accounting for arthritis, stroke, diabetes, CESD-10, drinking habits, education, marital status, residence, and age), WWI emerged as a significant predictor. Treating WWI as a continuous metric, each standard deviation increase was linked to a 17% elevation in the odds of MCR (OR = 1.17; 95% CI: 1.08–1.27; P < 0.001). Additionally, analysis by quartiles indicated that participants in the highest group (Q4) faced significantly greater odds relative to the lowest group (Q1) (OR = 1.31, 95% CI: 1.07–1.59, P = 0.009).

Table 2.

Multivariable logistic regression assessing the relationship linking WWI to MCR

	Model 1		Model 2		Model 3		Model 4		Model 5
	OR (95%CI)	P value	OR (95%CI)	P value	OR (95%CI)	P value	OR (95%CI)	P value	OR (95%CI)	P value
WWI	1.31 (1.22 ~ 1.41)	< 0.001	1.3 (1.2 ~ 1.42)	< 0.001	1.29 (1.19 ~ 1.41)	< 0.001	1.28 (1.18 ~ 1.4)	< 0.001	1.17 (1.08 ~ 1.27)	< 0.001
WWI quartile
Q1	1(Ref)		1(Ref)		1(Ref)		1(Ref)		1(Ref)
Q2	0.94 (0.77 ~ 1.16)	0.565	0.95 (0.77 ~ 1.17)	0.646	0.97 (0.78 ~ 1.19)	0.742	0.96 (0.77 ~ 1.18)	0.667	0.9 (0.73 ~ 1.11)	0.319
Q3	1.22 (1 ~ 1.48)	0.047	1.27 (1.04 ~ 1.56)	0.021	1.28 (1.04 ~ 1.57)	0.019	1.27 (1.03 ~ 1.56)	0.023	1.12 (0.92 ~ 1.37)	0.264
Q4	1.69 (1.4 ~ 2.04)	< 0.001	1.67 (1.35 ~ 2.07)	< 0.001	1.65 (1.33 ~ 2.05)	< 0.001	1.61 (1.3 ~ 2.01)	< 0.001	1.31 (1.07 ~ 1.59)	0.009
Trend.test	1.21 (1.14 ~ 1.29)	< 0.001	1.21 (1.12 ~ 1.29)	< 0.001	1.2 (1.12 ~ 1.29)	< 0.001	1.19 (1.11 ~ 1.28)	< 0.001	1.11 (1.04 ~ 1.19)	0.001

Model 1: Unadjusted analysis. Model 2: Controlled for residence, education, marital status, sex, and age. Model 3: Further adjusted for variables in Model 2 plus depression (CESD-10), alcohol consumption, and smoking habits. Model 4: Accounted for Model 3 factors and comorbidities: arthritis, stroke, heart disease, cancer, diabetes, dyslipidemia, and hypertension. Model 5: Controlled for arthritis, stroke, diabetes, CESD-10, alcohol use, education, marital status, residence, and age. Abbreviations: CI Confidence interval, OR Odds ratio, Ref Reference category

Receiver operating characteristic (ROC) curve analysis

As illustrated in Figure 2, ROC curve analysis indicated that WWI outperformed traditional metrics in identifying MCR. Specifically, WWI achieved a higher AUC (0.567, 95% CI: 0.547–0.587) than both WC (AUC: 0.499, 95% CI: 0.476–0.518) and BMI (AUC: 0.524, 95% CI: 0.504–0.545).

Fig. 2.

ROC curves for WWI, WC, and BMI predicting MCR

Smooth curve fitting and threshold analysis

Restricted cubic splines (RCS) were employed to investigate potential non-linearity in the association connecting WWI with MCR. The models included covariates that showed significance (P < 0.1) in preliminary univariate assessments (Supplementary Table 2). As shown in Figure 3, a significant overall association was observed (P-overall < 0.001), although the formal test for non-linearity was non-significant (P = 0.2). Due to the visual J-shaped appearance of the trajectory, segmented regression was utilized to identify thresholds. This analysis pinpointed a critical turning point at a WWI value of 10.774 (Table 3). Below this cutoff, no significant correlation was detected (OR = 1.061; 95% CI: 0.751–1.499; P = 0.738). Conversely, above the inflection point, the likelihood of MCR increased by 23.3% per unit increment in WWI (OR = 1.233; 95% CI: 1.096–1.387; P < 0.001).

Fig. 3.

Association between WWI and MCR modeled using restricted cubic splines

Table 3.

Analysis of inflection points in the relationship between WWI and MCR

Item	OR (95%CI)	P value
Inflection point	10.774
< 10.774	1.061 (0.751 ~ 1.499)	0.7381
> 10.774	1.233 (1.096 ~ 1.387)	< 0.001
Likelihood Ratio test		0.363

Sensitivity analysis

Across prespecified subgroups, elevated WWI levels consistently correlated with an increased risk of developing MCR; most stratum-specific estimates exceeded 1.0, and several reached statistical significance (Table 4). A significant interaction effect of marital status was identified on the WWI-MCR association (P-interaction = 0.034). Specifically, the correlation was less pronounced among married participants cohabiting with spouses (OR = 1.1; 95% CI: 1.0–1.21). In contrast, a stronger risk was observed in the "others" group—comprising those single, divorced, widowed, or living alone (OR = 1.36; 95% CI: 1.18–1.57)—indicating that marital situation modifies this relationship. Regarding other covariates, such as age, gender, education, residency, lifestyle factors (smoking, drinking), depression, and comorbidities, no significant interactions were detected (all P > 0.05), which implies the findings are robust across these strata.

Table 4.

Stratified analysis regarding the association between WWI and MCR

Subgroup	OR (95%CI)	P value	P for interaction
Age			0.453
๤75	1.16 (1.06 ~ 1.27)	0.001
≧ 75	1.21 (1.02 ~ 1.42)	0.028
Gender			0.576
Female	1.23 (1.11 ~ 1.38)	< 0.001
Male	1.34 (1.17 ~ 1.54)	< 0.001
Residence			0.74
Rural	1.17 (1.07 ~ 1.27)	< 0.001
Urban	1.23 (1.02 ~ 1.48)	0.032
Marital status			0.034
Married	1.1 (1 ~ 1.21)	0.041
Others	1.36 (1.18 ~ 1.57)	< 0.001
Education			0.688
Illiteracy	1.19 (1.08 ~ 1.31)	< 0.001
Elementary school	1.15 (0.97 ~ 1.36)	0.107
Middle school	1.05 (0.81 ~ 1.36)	0.705
High school and above	1.56 (0.95 ~ 2.56)	0.076
Smoke			0.758
No	1.21 (1.09 ~ 1.35)	< 0.001
Yes	1.22 (1.07 ~ 1.39)	0.002
Drinking status			0.099
No	1.14 (1.04 ~ 1.25)	0.004
less than once a month	1.85 (1.29 ~ 2.65)	0.001
Once or more	1.14 (0.96 ~ 1.37)	0.144
CESD-10			0.251
๤10	1.15 (1.03 ~ 1.28)	0.017
≧ 10	1.2 (1.08 ~ 1.34)	0.001
Hypertension			0.447
No	1.16 (1.06 ~ 1.27)	0.001
Yes	1.2 (1.03 ~ 1.4)	0.017
Dyslipidemia			0.088
No	1.14 (1.05 ~ 1.24)	0.001
Yes	1.62 (1.21 ~ 2.17)	0.001
Diabetes			0.515
No	1.18 (1.09 ~ 1.28)	< 0.001
Yes	1.04 (0.75 ~ 1.43)	0.818
Cancer			0.815
No	1.17 (1.08 ~ 1.27)	< 0.001
Yes	1.08 (0.53 ~ 2.21)	0.824
Heart problem			0.976
No	1.17 (1.08 ~ 1.28)	< 0.001
Yes	1.12 (0.91 ~ 1.39)	0.281
Stroke			0.7
No	1.17 (1.08 ~ 1.26)	< 0.001
Yes	1.38 (0.91 ~ 2.1)	0.125
Arthritis			0.681
No	1.14 (1.02 ~ 1.27)	0.017
Yes	1.21 (1.08 ~ 1.36)	0.001

Models were adjusted for age, marital status, education level, residence, and alcohol consumption, as well as CESD-10 scores, stroke, diabetes, and arthritis. Abbreviations: OR Odds ratio, CI Confidence interval

Discussion

Utilizing data from the 2015 CHARLS wave, elevated WWI was identified as a significant prognostic factor for MCR among elderly Chinese subjects. Specifically, a single-unit rise in WWI (cm/√kg) was associated with a 17% elevation in the odds of MCR (OR: 1.17; 95% CI: 1.08–1.27; P < 0.001). This correlation remained stable in fully adjusted models. Moreover, spline regression analysis indicated a linear dose-response pattern (P-non-linearity = 0.2), characterized by a steady escalation in risk, particularly when WWI levels surpassed 10.774. Subgroup analyses revealed consistent effects across most demographic strata; however, marital status significantly modified the effect (P for interaction = 0.034), with a weaker association observed among married participants residing with their spouses and a stronger association among those who were unmarried, divorced, widowed, or living alone. These results collectively suggest a stable and generalizable relationship between higher WWI and elevated risk of MCR. The observed linkage between WWI and MCR likely originates from the detrimental impacts of central obesity, particularly the accumulation of visceral fat, on neurobiological pathways. Visceral fat is acknowledged as a principal contributor to chronic low-grade inflammation [31]. Secreted by visceral fat depots, pro-inflammatory adipokines possess the ability to traverse the blood-brain barrier. Upon entry, they elicit oxidative stress and neuroinflammation within the central nervous system [32]. Such processes compromise cerebral microvascular function and neuronal health [33], which in turn precipitate declines in cognitive and motor functions [34]. Moreover, obesity may induce mitochondrial dysfunction within the brain [35], impairing neuronal energy supply and exacerbating neuronal damage [36]. By adjusting for body weight, WWI helps to mitigate the confounding effects of prevalent sarcopenia on the assessment of BMI in older adults, thus allowing WWI to more accurately reflect the impact of detrimental fat distribution on these pathological processes [37]. The present results substantiate the theory suggesting that the topographic distribution of adipose tissue, particularly abdominal fat, may exert a more significant influence on neurodegenerative processes than overall body fat mass. The results align closely with extant literature on metabolic dysfunction and neurodegenerative diseases [38, 39]. The present study provides novel insights by highlighting the link connecting WWI to MCR syndrome. Timely recognition of MCR is critical, considering the substantial evidence positioning this syndrome as a strong precursor to dementia [6]. At the same time, numerous studies indicate that abdominal obesity impairs cognitive performance and raises the likelihood of dementia in the elderly [40, 41]. These insights emphasize the importance of early identification of MCR to enable timely and personalized interventions [42].

Moreover, a significant interaction was observed concerning marital status, indicating that the correlation between WWI and MCR varies depending on cohabitation status. The association appeared weaker among married participants who resided with their spouses, yet it was more pronounced among those who were unmarried, divorced, widowed, or living alone. This pattern suggests that social support and cohabitation may mitigate the negative cognitive impacts associated with higher adiposity [43, 44]. In contrast, individuals lacking such support may be more vulnerable to MCR.

In addition to its biological implications, the correlation between WWI and MCR highlights its practical significance in clinical and community-based screenings. The WWI, which can be calculated easily using WC and body weight without the need for specialized equipment, is minimally affected by age-related sarcopenia, thus making it particularly appropriate for elderly populations [45]. In this cohort, both RCS and piecewise analyses revealed a monotonic increase in the risk of MCR, with a practical threshold of 10.774 identifying individuals who might require more intensive clinical monitoring. Incorporating WWI into regular health screenings for older adults may help identify those at risk at an early stage. This would allow for prompt interventions—targeting lifestyle or metabolism—to slow down or prevent cognitive deterioration [46].

Strengths and limitations

This research has several key advantages. First, by using RCS and piecewise regression, it was possible to detect non-linear patterns and precise cut-off points in the data. A practical threshold of 10.774 cm/√kg was determined, providing a potential cut-off point for identifying individuals in need of enhanced clinical scrutiny. Secondly, the derivation of WWI from simple measurements of WC and body weight, without the necessity for specialized equipment, effectively reduces the confounding influence of sarcopenia prevalent in older adults, thereby rendering it a viable tool for community health evaluations. Third, the present analysis leveraged a vast, nationally representative sample from CHARLS, thereby enhancing the generalizability of the results to the broader Chinese geriatric population. Despite these strengths, several constraints must be acknowledged. Primarily, the cross-sectional design inherently precludes the determination of a causal link between WWI and MCR; longitudinal studies are necessary to ascertain temporal relationships. Secondly, the exclusion of participants with missing data, despite adjustments in multivariable models for key demographic and health covariates, does not entirely eliminate the possibility of selection bias. Third, since the participants were all older Chinese adults, these results may not apply to other ethnicities or younger populations. Therefore, care is needed when generalizing these conclusions.

Conclusion

In summary, this research establishes a strong, linear relationship between elevated WWI and MCR in older Chinese adults. Clinically, these findings suggest that WWI is a superior, cost-effective screening tool compared to BMI, particularly for detecting central obesity in older adults who may also have sarcopenia and could be overlooked by other measures. The establishment of a specific risk threshold at 10.774 cm/√kg offers a practical reference for healthcare providers, with individuals exceeding this threshold meriting prioritized screening for cognitive and motor deficits. Looking forward, the inclusion of WWI in routine geriatric health assessments could enable early identification of individuals at high risk, facilitating timely interventions aimed at targeting abdominal obesity to potentially delay the onset of MCR and subsequent dementia. Integrating such cost-effective strategies into primary care is in line with global public health objectives focused on promoting healthy longevity and addressing the increasing prevalence of non-communicable diseases.

Authors' contributions

GQ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.BL: Methodology, Project administration, Resources.ZL: Data curation, Investigation, Methodology.WY: Data curation, Software, Supervision.YZ: Critical review and writing.XT: Conceptualization, Data curation, Formal analysis, Methodology, Project administration.

Funding

No funding was received for this research.

Data availability

This study was conducted using publicly accessible datasets. The specific data employed in this analysis are available for retrieval at: https://charls.charlsdata.com.

Declarations

Ethics approval and consent to participate

The Biomedical Ethics Review Committee at Peking University reviewed and approved the CHARLS protocol (approval number: IRB00001052-11015). Prior to inclusion in the study, every participant provided signed informed consent.

Competing interests

The authors declare no competing interests.