Abstract
Background
Sarcopenic obesity (SO), characterized by concurrent muscle loss and excess adiposity, is an emerging public health challenge, yet prospective evidence linking SO to mild cognitive impairment (MCI) remains limited.
Methods
Baseline data were collected from the China Health and Retirement Longitudinal Study (CHARLS) in 2015. The incidences of MCI during follow-ups by 2018 and 2020 were calculated. Participants were divided into healthy, obesity only, sarcopenia only, and SO groups. Aging-associated MCI was defined as a total cognitive score at least one standard deviation below the age-specific norm for participants aged 45 and above, adjusted for age in five-year increments.
Results
In the 11,674 individuals with an average age of 59.31 ± 9.21 years, SO was detected in 160 (1.37%) individuals. The score of each dimension and total score of cognitive function were lower in the SO group than in the sarcopenia-only group, but with a difference only in the dimension of drawing (P = 0.012). By 2018, 840 MCI cases (12.7%), and by 2020, 1,107 (16.8%) MCI cases were reported. The MCI incidence was higher in the SO group than in the other groups (P < 0.001). The risk of MCI was higher in the SO group than in the other groups (3-year MCI: hazard ratio [HR] 2.11, 95% confidence interval [CI] 1.21, 3.69, P = 0.008; 5-year MCI: HR 1.88, 95% CI 1.09, 3.25, P = 0.024). Older adults and women were more vulnerable to MCI induced by SO.
Conclusion
SO is correlated with MCI and serves as a risk factor in middle-aged and older adults in China.
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-025-02073-0.
Keywords: Sarcopenic obesity, Sarcopenia, Obesity, Mild cognitive impairment, Middle-aged and older adults
Introduction
Sarcopenic obesity (SO) is a syndrome characterized by concurrent sarcopenia and obesity [1]. Sarcopenia is an aging-related condition of progressive loss of skeletal muscle mass, strength and physical performance, and obesity is defined as an excessive accumulation of body fat. The prevalence of SO ranges from 7.1% to 23% around the world [2–5]. Sarcopenia combined with obesity synergistically exacerbate functional decline and negatively impact overall health outcomes, including increased risk of cognitive impairment, coronary artery disease, dyslipidaemia, and all-cause mortality, especially in the older adults [6].
SO has a close link with cognitive function. Mild cognitive impairment (MCI) represents a transitional stage between age-associated cognitive decline and dementia. It is characterized by cognitive impairment that is severer than what is expected for an individual’s age and education level, but does not substantially interfere with basic activities of daily life, including eating, dressing and bathing [7]. Factors influencing the development of MCI remain to be identified, thus allowing the design of efforts for delaying or halting the progression to dementia.
Previous studies have explored the relationship between sarcopenia and cognitive impairment, or between obesity and MCI [8–10]. A synergic effect of SO on cognitive function remains less defined. Some evidence suggests that the interaction between a decreased muscle strength and an increased adiposity may contribute to MCI through driving chronic inflammation, insulin resistance, and metabolic dysregulation [11, 12].
In previous studies, however, inconsistent definitions and methodologies disturb the correlation between SO and MCI. In the present prospective cohort study, we investigated the correlation between SO and MCI in middle-aged and older adults in China through 3-year and 5-year follow-up. Our findings are expected to provide valuable data to develop targeted interventions for defending cognitive health and improving quality of life in the aging population.
Methods
Study design and participants
Follow-up data were available from the China Health and Retirement Longitudinal Study (CHARLS) [13]. CHARLS was approved by the Ethical Review Committee at Peking University (IRB00001052-11015). The fifth wave of CHARLS data, collected in 2020, was officially released on November 16, 2023, providing extensive high-quality microdata on Chinese households and individuals aged 45 years and older. In this study, data collected in 2015 were used as baseline. After excluding individuals involved in the CHARLS who were younger than 45 years and those unable to provide data about cognitive function, appendicular skeletal muscle mass (ASM), muscle strength and physical performance, 11,674 participants were initially included in the cross-sectional analysis. Furthermore, participants with MCI at baseline (n = 1,863) and lacked follow-up data of cognitive function during the two follow-up surveys in 2018 and 2020 (n = 3,215) were excluded. Finally, 6,596 eligible individuals were involved in the prospective cohort study. A detailed flowchart of participant recruitment is provided in Fig. 1.
Fig. 1.
Flowchart of individuals selection
Diagnosis of obesity
Measurements of standing height, weight, and waist circumference (WC) were available from CHARLS. Body mass index (BMI) was calculated by dividing weight (kg) by the square of height (m). Additionally, gender-specific body fat percentage (BF%) in the Chinese population was estimated using age (years), BMI (kg/m2), and WC (cm) as independent variables in the following formulation: BF%=−41.9277873 + 0.33718996×BMI + 0.99622038×WC-0.00403169×WC2 in men, and − 22.46354525 + 0.32551474×BMI + 0.87135268×WC + 0.00319864×age-0.00408430×WC2 in women [14]. Obesity is defined with either BMI ≥ 28 kg/m², WC > 90 cm for males and > 85 cm for females, or BF%≥ 25 for males and ≥ 35 for females [15].
Diagnosis of sarcopenia
According to the Asian Working Group for Sarcopenia (AWGS): 2019 Consensus Update on Sarcopenia Diagnosis and Treatment, sarcopenia was diagnosed based on the height-adjusted ASM, muscle strength, and physical performance [16]. ASM (kg) was calculated as follows: ASM (kg) = 0.193×weight + 0.107×height-4.157×gender-0.037×age-2.631, where gender was assigned a value of 1 for men and 2 for women [17]. Low muscle mass was defined as a height-adjusted ASM (ASM/height²) below the sex-specific lowest 20%: <7.098 kg/m² for men and < 5.485 kg/m² for women. Low muscle strength was defined as a handgrip strength of fewer than 28 kg for men and fewer than 18 kg for women. Handgrip strength was measured as an average of the maximum grip strength from both hands. Individuals with a gait speed < 1.0 m/s, a five-time chair stand test ≥ 12 s, or a Short Physical Performance Battery (SPPB) score ≤ 9 was confirmed as low physical performance. Overall, a diagnosis of sarcopenia was made by the presence of low muscle mass combined with either low muscle strength or low physical performance.
Diagnosis of SO
SO was defined as the coexistence of sarcopenia and obesity. Participants were categorized into four groups based on the status of sarcopenia and obesity: healthy (neither sarcopenia nor obesity), obesity-only, sarcopenia-only, and SO groups. SO was diagnosed by overlapping the diagnostic criteria for sarcopenia (AWGS 2019) and obesity (BMI, WC, and BF%) through a Venn diagram. In the cross-sectional analysis, 0, 160 and 66 cases of SO were identified through AWGS + BMI, AWGS + WC and AWGS + BF%, respectively (Figure S1). Similar results were found in the prospective cohort analysis. Considering the sample size, an combination of AWGS + WC was used as the diagnostic standard for SO in the present study.
Assessment of cognitive function and diagnosis of MCI
A face-to-face assessment of cognitive function was conducted in 2015 (baseline), 2018 (the first follow-up survey) and 2020 (the second follow-up survey). Following the recommendations by the Health and Retirement Study (HRS) and CHARLS [18, 19], the scores of 4 dimensions of cognitive function, including orientation, memory, computation, and drawing, as well as the total score were graded. Specifically, the dimension of orientation ranging from 0 to 5 points was assessed through the identification of the current year, month, date, week, and season. The dimension of memory was assessed by recalling words from a 10-word list either immediately or after a delay, ranging from 0 points to 20 points. The dimension of computation, graded from 0 points to 5 points, was assessed through a Serial Sevens Task, in which an individual was asked to count down from one hundred by sevens. The dimension of drawing was assessed by an intersecting pentagon copying task, ranging from 0 points to 1 point. The total score of cognitive function ranged from 0 points to 31 points, with higher scores indicating better cognitive function. MCI was described as aging-associated cognitive decline (AACD), with a minimum of 1 standard deviation (SD) of the total score of cognitive function below the age-specific standard in age groups with a 5-year interval above 45 years [8, 20].
Assessment of covariates
Covariates in this study were divided into three categories: demographic characteristics, behavioral risk factors, and comorbidities. Demographic characteristics included age, gender, education level, residence, marital status, and self-reported health status. Behavioral risk factors encompassed drinking, smoking, social activity, depressive symptoms assessed by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), exercise, and sleep duration. Comorbidities were those of self-reported diabetes, dyslipidemia, hypertension, heart disease, stroke, kidney disease, and liver disease. Except for age, all covariates were binary (Table 1).
Table 1.
Baseline characteristics of study population based on sarcopenia and obesity
| Characteristic | Overall | Healthy | Only obesity | Only sarcopenia | Sarcopenico obesity | p-value2 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 11,6741 | N = 4,7411 | N = 5,2881 | N = 1,4851 | N = 1601 | ||||||||
| Gender (female) | 5,564 (47.66%) | 1,742 (36.74%) | 3,044 (57.56%) | 635 (42.76%) | 143 (89.38%) | < 0.001 | ||||||
| Age (years) | 59.31 ± 9.21 | 57.08 ± 8.33 | 58.48 ± 8.73 | 68.06 ± 7.55 | 71.82 ± 6.96 | < 0.001 | ||||||
| Older adults (Age > = 60) | 5,608 (48.04) | 1,750 (36.91) | 2,352 (44.48) | 1,351 (90.98) | 155 (96.88) | < 0.001 | ||||||
| Illiterate or primary education | 10,142 (86.88%) | 4,039 (85.19%) | 4,541 (85.87%) | 1,406 (94.68%) | 156 (97.50%) | < 0.001 | ||||||
| Urban residence | 4,800 (41.12%) | 1,761 (37.14%) | 2,552 (48.26%) | 432 (29.09%) | 55 (34.38%) | < 0.001 | ||||||
| Married | 9,862 (84.48%) | 4,098 (86.44%) | 4,524 (85.55%) | 1,150 (77.44%) | 90 (56.25%) | < 0.001 | ||||||
| Self-reported good health status | 2,980 (25.53%) | 1,270 (26.79%) | 1,396 (26.40%) | 290 (19.53%) | 24 (15.00%) | < 0.001 | ||||||
| Current smoking | 5,448 (46.67%) | 2,562 (54.04%) | 2,023 (38.26%) | 833 (56.09%) | 30 (18.75%) | < 0.001 | ||||||
| Current drinking | 5,725 (49.04%) | 2,589 (54.61%) | 2,367 (44.76%) | 722 (48.62%) | 47 (29.38%) | < 0.001 | ||||||
| Participate in social activities | 6,177 (52.91%) | 2,410 (50.83%) | 3,047 (57.62%) | 656 (44.18%) | 64 (40.00%) | < 0.001 | ||||||
| Depressive symptom | 3,512 (30.08%) | 1,388 (29.28%) | 1,479 (27.97%) | 576 (38.79%) | 69 (43.13%) | < 0.001 | ||||||
| Physical activity or exercise | 4,032 (34.54%) | 1,763 (37.19%) | 1,797 (33.98%) | 435 (29.29%) | 37 (23.13%) | < 0.001 | ||||||
| Sleep duration (hours) | < 0.001 | |||||||||||
| <=6 | 3,896 (33.37%) | 1,554 (32.78%) | 1,676 (31.69%) | 599 (40.34%) | 67 (41.88%) | |||||||
| 6–8 | 4,710 (40.35%) | 1,964 (41.43%) | 2,174 (41.11%) | 517 (34.81%) | 55 (34.38%) | |||||||
| >8 | 3,068 (26.28%) | 1,223 (25.80%) | 1,438 (27.19%) | 369 (24.85%) | 38 (23.75%) | |||||||
| Diabetes | 1,941 (16.63%) | 507 (10.69%) | 1,220 (23.07%) | 182 (12.26%) | 32 (20.00%) | < 0.001 | ||||||
| Dyslipidemia | 2,055 (17.60%) | 554 (11.69%) | 1,304 (24.66%) | 167 (11.25%) | 30 (18.75%) | < 0.001 | ||||||
| Hypertension | 3,322 (28.46%) | 914 (19.28%) | 2,003 (37.88%) | 350 (23.57%) | 55 (34.38%) | < 0.001 | ||||||
| Heart disease | 1,818 (15.57%) | 526 (11.09%) | 1,022 (19.33%) | 245 (16.50%) | 25 (15.63%) | < 0.001 | ||||||
| Stroke | 310 (2.66%) | 103 (2.17%) | 161 (3.04%) | 43 (2.90%) | 3 (1.88%) | 0.043 | ||||||
| Kidney disease | 1,048 (8.98%) | 407 (8.58%) | 471 (8.91%) | 159 (10.71%) | 11 (6.88%) | 0.065 | ||||||
| Liver disease | 675 (5.78%) | 266 (5.61%) | 308 (5.82%) | 95 (6.40%) | 6 (3.75%) | 0.472 | ||||||
1 n (%); Mean ± SD
2 Pearson’s Chi-squared test
One-way ANOVA
Fisher’s exact test
Statistical analysis
For the cross-sectional and cohort analyses, we first described baseline characteristics using mean ± SD for age and frequency (%) for categorical variables. ANOVA and Chi-square tests were used for between-group comparisons. In the cross-sectional analysis, differences in the score of each dimension of cognitive function and total scores among the four groups were analyzed by ANOVA, followed by pairwise tests. Subsequently, logistic regression analysis (for the dimension of drawing) and linear regression analysis (for other dimensions and total score of cognitive function) were used to examine the correlation between SO and baseline cognitive function, and analysis of covariance (ANCOVA) adjusted for age and gender was performed as a complementary analysis. The crude and adjusted regression coefficients with 95% confidence intervals (CI) were estimated (Models 1–4). In the cohort study, the incidences of MCI among the four groups were compared by the log-rank test. Cox proportional hazards regression models were used to evaluate the correlation between SO and the risk of MCI during the 3-year and 5-year periods, and crude and adjusted hazard ratios (HRs) with 95% CIs were estimated (Models 1–4). In detail, Model 1 was non-adjusted, and Model 2–4 was adjusted for demographic characteristics, behavioral risk factors and comorbidities, respectively. Subgroup analyses were performed by age (< 60 years and ≥ 60 years) and gender to further evaluate the effect of SO on the incidence of MCI.
Multiple sensitivity analyses were conducted to examine the robustness of our results. First, to address potential attrition bias due to loss to follow-up, we used inverse probability weighting (IPW) based on the predicted probability of follow-up from a logistic regression model with baseline covariates as predictors. Second, to assess the impact of possible outcome misclassification, we adopted several alternative definitions for MCI: (1) MCI defined as a cognitive score at least 1.2 standard deviations below the age-specific normative average, and (2) MCI defined as a cognitive score at least 1.5 standard deviations below the age-specific normative average. Finally, we re-estimated the association between SO and MCI risk in the subgroup of participants aged ≥ 65 years.
A two-tailed p value of less than 0.05 was considered as statistically significant. R software version 4.4.1 was used for data processing and statistical analysis.
Results
Baseline characteristics of participants
Baseline characteristics of 11,674 participants in the healthy (neither sarcopenia nor obesity), obesity-only, sarcopenia-only, and SO groups are listed in Table 1. The average age of all participants was 59.31 ± 9.21 years, and 48.04% of them were older adults (≥ 60 years) and 47.66% were female. Compared to other groups, the SO group had significantly higher proportions of females, older adults, education level of illiteracy and primary school, rural residency, unmarried status, self-reported poor health status, non-smokers, non-drinkers, non-engagement in social activities, depressive symptoms, less engagement in physical activities or exercise, shorter sleep duration, and comorbidities of diabetes, dyslipidemia, hypertension, heart disease, and stroke (P < 0.05). Baseline characteristics of 6,596 individuals in the cohort analysis were consistently distributed (Table S1).
Correlation between SO and baseline cognitive function
The average total score of cognitive function among the 11,674 individuals was 15.80 ± 4.83 points. Compared to the healthy group and the obesity-only group, both the sarcopenia-only and SO groups showed significantly a lower score in each dimension and in total (P < 0.05, Table 2). Although the score of each dimension and the total score of cognitive function were lower in the SO group than in the sarcopenia-only group, a significant difference was only observed in the dimension of drawing, even after adjusting for covariates (P = 0.012, Table S2, Table S3).
Table 2.
Association between sarcopenic obesity and different cognitive function score in baseline
| Cognitive dimensions | Overall, N = 11,6741 | Healthy, N = 4,7411 | Only obesity, N = 5,2881 | Only sarcopenia, N = 1,4851 | Sarcopenic obesity, N = 1601 | p-value 2 |
|---|---|---|---|---|---|---|
| Orientation score | 4.08 ± 1.13 | 4.12 ± 1.08cd | 4.15 ± 1.09cd | 3.70 ± 1.29ab | 3.69 ± 1.39ab | <0.001 |
| Computation score | 3.78 ± 1.44 | 3.86 ± 1.38cd | 3.82 ± 1.41cd | 3.44 ± 1.59ab | 3.06 ± 1.72ab | <0.001 |
| Memory score | 7.23 ± 3.48 | 7.44 ± 3.43cd | 7.58 ± 3.38cd | 5.55 ± 3.46ab | 5.41 ± 3.46ab | <0.001 |
| Drawing score | 0.71 ± 0.45 | 0.75 ± 0.43bcd | 0.73 ± 0.45acd | 0.57 ± 0.50abd | 0.38 ± 0.49abc | <0.001 |
| Total cognitive score | 15.80 ± 4.83 | 16.17 ± 4.64cd | 16.27 ± 4.67cd | 13.25 ± 5.01ab | 12.53 ± 5.31ab | <0.001 |
1 Mean ± SD; 2 One-way ANOVA
a Compared with healthy group (P < 0.05)
b Compared with only obesity group (P < 0.05)
c Compared with only sarcopenia group (P < 0.05)
d Compared with sarcopenic obesity group (P < 0.05)
Correlation between SO and the risk of MCI during the 3-year and 5-year follow-up
In the prospective cohort study, 6,596 individuals were followed up for an average of 3.669 years. In total, 840 (12.7%) cases of MCI were identified during the 3-year follow-up, and 1,107 (16.8%) were reported in the 5-year follow-up. Compared to other groups, the incidence of MCI was significantly higher in the SO group (log-rank test, P < 0.001; Figure S2). After adjusting for relevant covariates, regression analysis indicated a significant correlation of SO with both 3-year and 5-year incidences of MCI. Following adjustments for demographic characteristics, behavioral risk factors, and comorbidities, a significantly higher risk of MCI remained in the SO group, compared to the healthy group (3-year MCI: HR 2.11, 95% CI 1.21, 3.69, P = 0.008; 5-year MCI: HR 1.88, 95% CI 1.09, 3.25, P = 0.024; Table 3, Model 4).
Table 3.
The association between sarcopenic obesity and incidence of MCI during the follow-up
| Cox regression models | 3 years follow-up | 5 years follow-up | ||||
|---|---|---|---|---|---|---|
| HR1 | 95% CI1 | p-value | HR1 | 95% CI1 | p-value | |
| Model 1 | ||||||
| Healthy | – | – | – | – | ||
| Only Obesity | 0.97 | 0.84, 1.13 | 0.707 | 0.99 | 0.87, 1.13 | 0.877 |
| Only Sarcopenia | 1.64 | 1.33, 2.02 | < 0.001 | 1.74 | 1.45, 2.09 | < 0.001 |
| Sarcopenic Obesity | 2.53 | 1.48, 4.32 | < 0.001 | 2.22 | 1.30, 3.78 | 0.003 |
| Model 2 | ||||||
| Healthy | – | – | – | |||
| Only Obesity | 1.00 | 0.86, 1.16 | 0.992 | 1.02 | 0.89, 1.16 | 0.812 |
| Only Sarcopenia | 1.38 | 1.10, 1.73 | 0.006 | 1.46 | 1.19, 1.78 | < 0.001 |
| Sarcopenic Obesity | 2.10 | 1.21, 3.65 | 0.009 | 1.90 | 1.10, 3.29 | 0.021 |
| Model 3 | ||||||
| Healthy | – | – | – | |||
| Only Obesity | 1.02 | 0.87, 1.19 | 0.818 | 1.04 | 0.91, 1.19 | 0.564 |
| Only Sarcopenia | 1.33 | 1.06, 1.67 | 0.014 | 1.40 | 1.15, 1.71 | < 0.001 |
| Sarcopenic Obesity | 2.12 | 1.22, 3.70 | 0.008 | 1.93 | 1.12, 3.34 | 0.019 |
| Model 4 | ||||||
| Healthy | – | – | – | – | ||
| Only Obesity | 1.05 | 0.90, 1.23 | 0.553 | 1.06 | 0.93, 1.22 | 0.367 |
| Only Sarcopenia | 1.31 | 1.04, 1.65 | 0.02 | 1.39 | 1.14, 1.70 | 0.001 |
| Sarcopenic Obesity | 2.11 | 1.21, 3.69 | 0.008 | 1.88 | 1.09, 3.25 | 0.024 |
1 HR = Hazard Ratio for MCI, CI = Confidence Interval
Cox proportional hazards models were used to estimate HR and their 95% CIs for the relationship between sarcopenic obesity and the incidence of MCI (Model 1–4)
Model 1 analysis was non-adjusted
Model 2 analysis was adjusted for age, gender, education level, residence, marital status, and self-reported health status to account for potential demographic confounders.
Model 3 analysis was additionally adjusted for drinking, smoking, social activity, depressive symptom, exercise, and sleep duration to account for potential behaviors confounders.
Model 4 analysis further adjusted for comorbidities like diabetes, dyslipidemia, hypertension, heart disease, stroke, kidney disease, and liver disease
Subgroup analyses showed that the correlation between SO and risk of MCI was pronounced in the older adults and females (Fig. 2).
Fig. 2.
The impact of sarcopenic obesity on MCI Incidence at 3 and 5 years across different subgroups. Sections a1-e1 illustrate the risk of MCI at 3 years within each subgroup, while sections a2-e2 show the risk at 5 years. The models were adjusted for demographic characteristics, behavioral risk factors, and comorbidities. The numbers in the boxes represent the hazard ratios (HR) and their 95% confidence intervals (CI). Darker colors indicate a higher HR. Among all individuals, the older adults (age ≥ 60), and females, those in the sarcopenic obesity group exhibited significantly higher HRs compared to the other three groups
Sensitivity analyses
Table S4 presents the results of the sensitivity analyses, which were generally consistent with the primary analyses. In the IPW-adjusted analyses, compared with the control group, participants with SO had a HR for 3-year MCI risk of 2.26 (95% CI: 1.29, 3.96) and a 5-year MCI risk of 2.02 (95% CI: 1.14, 3.58), after adjusting for potential confounders and selection bias due to attrition. Using different definitions of MCI or restricting the analysis to participants aged 65 years and above yielded similar results: although limited sample size prevented some estimates from reaching statistical significance, the association between SO and MCI risk remained directionally consistent with the primary analyses, with the SO group showing the highest HR for MCI risk compared to the other groups.
Discussion
This large-scale, population-based cohort study investigated the association between SO and MCI risk among middle-aged and older Chinese adults. The findings revealed that individuals with SO exhibited significantly lower baseline cognitive function (mean total cognitive score 12.53 ± 5.31 vs. 16.17 ± 4.64 in the healthy group, representing a 22.5% deficit) and a markedly elevated risk of incident MCI over five years of follow-up (fully adjusted HR 1.88, 95% CI 1.09–3.25). These results demonstrate that sarcopenia and obesity exert a synergistic detrimental effect on cognitive health in the aging population, highlighting SO as a clinically significant risk factor warranting early identification and intervention.
A diagnostic paradigm of SO has never reached, due to various measures of obesity [1, 21]. The prevalence of SO in this cohort varied by diagnostic indicators of obesity, which was lower when assessed by BMI compared to that by WC and BF%. The marked variation in SO prevalence across definitions is a well-established phenomenon in obesity research, reflecting inherent limitations of different adiposity measures. Although widely used, BMI may not adequately reflect adiposity in older adults, due to such age-related changes as loss of height and redistribution of fat and muscle mass [22]. More importantly, BMI can neither describe fat distribution, nor differentiate fat mass from lean muscle mass [23]. WC, BF% and visceral fat area, by contrast, are superior to BMI in precisely and definitively assessing obesity-associated health risks [24, 25]. The Japanese Working Group on Sarcopenic Obesity proposes that obesity can be determined by visceral fat area or BF% [26]. Yu et al. [27] identified that the muscle-to-fat ratio, evaluated by either appendicular muscle mass or total body mass divided by total body fat mass, is a promising biomarker of SO.
In the present study, we observed an overall cognitive impairment in all the dimensions in the SO group. The cognitive function in drawing that reflects visuospatial and executive functions was remarkably impaired in the SO group. The correlation between SO and declined cognitive function persisted after adjusting confounders, suggesting the independent effect of SO in cognitive impairment. The prospective cohort analysis further confirmed the cross-sectional findings. Over an average follow-up of 3.669 years, individuals with SO had a significantly higher incidence of MCI compared to other groups even after adjusting for demographic characteristics, lifestyle factors, and comorbidities. Subgroup analyses showed a pronounced correlation between SO and risk of MCI in females and older adults, which is consistent with a previous finding of sex-specific impact of SO on cognitive function [28, 29]. Age-specific effects of SO on cognitive function require a further exploration.
Both sarcopenia and obesity can contribute to MCI [8–10, 30]. However, the synergistic effect of SO on cognitive decline appears more detrimental than a single condition. Someya et al. [28] have found that SO is independently associated with MCI and dementia among older Japanese adults, even after adjusting for confounding factors. Fu et al. [15] have discovered a positive correlation of SO, diagnosed based on various measures of obesity (WC, visceral fat area, and body fat percentage), with MCI. In a longitudinal study involving community-dwelling older adults aged 65 years and above in the United States, SO is associated with an elevated long-term risk of cognitive impairment [31]. SO is linked with a poorer cognitive performance in patients with type 2 diabetes mellitus, particularly in the domains of memory and language [32].
Multiple factors are involved in the association between SO and MCI, including chronic systemic inflammation, insulin resistance and vascular changes. Abundant pro-inflammatory cytokines, like interleukin-6 and tumor necrosis factor-alpha, are secreted under the stimuli of excessive adipose tissue that contribute to neuroinflammation and neuronal damage [33]. Concurrently, a less release of anti-inflammatory cytokines by skeletal muscle in sarcopenia patients further exacerbate the inflammatory response [34]. An imbalance tilting towards the pro-inflammatory state accelerates neurodegenerative processes and cognitive decline. In addition, both sarcopenia and obesity are linked to disruption on glucose metabolism and insulin signaling [35, 36]. Insulin plays a crucial role in brain function, including synaptic plasticity and neurotransmitter regulation. Insulin resistance is a risk factor of cognitive impairment and neurodegenerative disease through reducing insulin availability in the brain [37]. Obesity-related vascular changes, such as atherosclerosis and hypertension, are also linked with cerebrovascular damage and cognitive decline [38].
Our findings underscores the necessary interventions targeting both muscle loss and adiposity. Rational resistance training and nutritional support to enhance muscle strength and reduce excessive accumulation of adipose tissue may prevent against SO [39]. Besides, active measures targeting modifiable risk factors, such as physical inactivity, poor diet, and metabolic disorders, contribute to mitigating the progression of SO and its negative impact on cognitive function.
To our knowledge, this is the first study to evaluate the correlation between SO and MCI in Chinese middle-aged and older adults based on cross-sectional and longitudinal analysis while adjusting for multiple confounders. Additionally, this large-scale population-based investigation with a long follow-up period was a nationally representative longitudinal survey in China, yielding convincing data for further research. Despite these strengths, several limitations should be acknowledged. First, muscle mass and body composition were calculated by formulas, and the accuracy may be inferior to dual-energy X-ray absorptiometry (DEXA). Second, other confounders like dietary habits, genetic predispositions, and detailed socioeconomic variables were not considered in the present study. Finally, the study population was limited to Chinese adults, and the generalizability of the findings to other ethnic groups or regions needs a future exploration.
Conclusion
SO is a distinct contributor to cognitive decline in middle-aged and older adults in China. Individualized exercise programs, dietary modifications, and public health initiatives are recommended to maintain a healthy weight, protect cognitive function, and improve the quality of life among the aging population. These results extend previous research by demonstrating the synergistic cognitive impact of combined sarcopenia and obesity beyond their independent effects. Future longitudinal studies should establish temporal relationships and elucidate underlying mechanisms to guide evidence-based preventive strategies.
Supplementary Information
Acknowledgements
The data used in this research were obtained from the China Health and Retirement Longitudinal Study (CHARLS). We would like to thank the workers, researchers, and participants involved in CHARLS.
Author contributions
Jielong Wu: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Miaona Cai: Data curation, Formal analysis, Investigation, Writing - review & editing. Zhihai Zhang: Writing - review & editing. Hongfei Ke: Writing - review & editing. Xiaoqing Huang: Writing - review & editing. Jieyi Zhang: Writing - review & editing. Mengyuan Wang: Writing - review & editing. Jingjing Gao: Writing - review & editing. Yingxin Chen: Writing - review & editing. Bing Yan: Supervision, Writing - review & editing. Nengjiang Zhao: Supervision, Writing - review & editing. Bo Li: Conceptualization, Formal analysis, Project Administration, Supervision, Writing - review & editing. Xin Hu: Conceptualization, Formal analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing - original draft, Writing - review & editing. All authors have approved the fnal version of the paper.
Funding
This work was supported by funds from the Natural Science Foundation of China (No. 82205050).
Data availability
The data used in this research were obtained from the China Heath and Retirement Longitudinal Study (CHARLS, https://charls.pku.edu.cn/).
Declarations
Ethics approval and consent to participate
This study was approved by the Ethical Review Committee at Peking University (IRB00001052-11015).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jielong Wu and Miaona Cai contributed equally to this work.
Contributor Information
Bo Li, Email: libo@xmu.edu.cn.
Xin Hu, Email: huxin1987xm@163.com.
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Supplementary Materials
Data Availability Statement
The data used in this research were obtained from the China Heath and Retirement Longitudinal Study (CHARLS, https://charls.pku.edu.cn/).