The relationship between arthritis and mild cognitive impairment in different obese metabolic heterogeneity populations-the mediating role of depression

Objective: The purpose of this study is to investigate whether depression may mediate the association between arthritis and mild cognitive impairment (MCI) in diverse BMI-based obese populations with multi-parameter metabolic signatures including insulin resistance, inflammatory markers, and lipid metabolism indices.

Methods: The relationships between arthritis, MCI, and depression were investigated in a large-scale study that used logistic regression, subgroup analyses, and mediation analyses. The participants in the study came from obese individuals classified into distinct metabolic obesity phenotypes based on insulin resistance, inflammatory markers, and lipid metabolism.

Results: Arthritis is positively correlated with MCI (OR = 1.86, 95% CI = 1.60–2.17, p < 0.001), especially in the metabolically unhealthy overweight/obesity population (OR = 2.27, 95% CI = 1.73–2.97, p < 0.001), and depression plays a mediating role in the relationship between arthritis and MCI in this population (proportion of mediation: 17.5%).

Conclusion: The results of this study highlight the potential clinical value of an integrated management strategy combining metabolic regulation, anti-inflammatory treatment, and routine depression screening in obese patients with arthritis to reduce the risk of cognitive decline.

1 Introduction

Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two main types of arthritis, a degenerative disease that is closely associated with age. Progression of articular cartilage degradation and loss of subchondral bone structures are shared pathological hallmarks of these disorders. These conditions may impact one or more joints (1). Epidemiological data shows that 31.4% of China’s middle-aged and senior individuals, or around 154.4 million people, suffer from arthritis. Approximately 55% of the world’s arthritis sufferers are 55 and older (2). Population aging has led to a rising prevalence of arthritis, posing a public health challenge. The pain caused by arthritis not only affects patients’ quality of life but also results in reduced work capacity and a substantial increase in both family and societal economic burdens (3, 4). Therefore, identifying factors associated with arthritis and formulating effective prevention and intervention strategies hold significant public health importance.

According to the World Alzheimer’s Report, the incidence of dementia is steadily increasing over the world. In the next 30 years, the number of cases is projected to climb from 46.8 million to 131.5 million (5). Dementia is most often caused by Alzheimer’s disease (AD). Early preventative methods have been shown to be able to avert certain occurrences of AD (6). Mild cognitive impairment (MCI) is classified as a subtype of mild neurocognitive disorder (7), it is characterized by a subjective experience of cognitive decline, accompanied by objective evidence of deterioration in one or more cognitive domains compared to previous levels. However, individuals with MCI retain independence in instrumental activities of daily living and do not meet the diagnostic criteria for major psychiatric or neurological disorders (8, 9). Studies have shown that individuals with MCI are at a significantly increased risk of progressing to dementia, with approximately 20% of adults aged 65 years and older expected to develop dementia in the future (10). Therefore, as the most populous country with one of the fastest-aging populations, the early identification and effective intervention of MCI and its associated factors have become critical challenges for aging societies worldwide, particularly in China.

The influence of metabolic factors on both cognitive function and arthritis has been well-documented in numerous studies, with particular attention given to the metabolic heterogeneity of obesity (11, 12). Obesity is not a uniform metabolic condition; instead, distinct obesity phenotypes exhibit markedly different metabolic risks. For instance, individuals with metabolically healthy obesity (MHO) have excess body fat but maintain normal insulin sensitivity and favorable lipid profiles (13). In contrast, individuals with metabolically unhealthy obesity (MUO) exhibit multiple metabolic disturbances, including insulin resistance, hypertension, hyperglycemia, and dyslipidemia. While differences between these two obesity subtypes have been extensively studied in relation to certain diseases, the specific association between arthritis and MCI across different metabolic obesity phenotypes remains unclear.

In addition, mental health issues like depression may worsen cognitive decline via neuroendocrine dysregulation and chronic inflammatory processes. Depression may also mediate the link between metabolic dysfunction, arthritis, and mild cognitive impairment (MCI). However, evidence remains limited on the link between arthritis and MCI in metabolically diverse obese people, and the function of depression as a mediator in this setting is unclear. Hence, this research investigates the link between metabolic obesity phenotypes and arthritis and moderate cognitive impairment using baseline data from the 2011 CHARLS cohort. This study aims to provide scientific evidence for targeted therapies targeting obesity, arthritis, and cognitive impairment by elucidating the possible mediating role of depression in this connection.

2 Methods

2.1 Study design and participants

With the use of CHARLS data, this research used a cross-sectional design. The CHARLS research used a multistage probability sampling technique to recruit volunteers who were representative of the country. At the baseline, 17,705 people from 450 urban and rural communities in 28 Chinese provinces were polled (14). The Institutional Review Board of Peking University gave ethical clearance for the CHARLS investigation [IRB00001052-11015].

This study screened and analyzed the 2011 cross-sectional dataset from CHARLS. To ensure the robustness and credibility of the findings, a set of predefined exclusion criteria was implemented as follows: (1) respondents <45 years of age (N = 775); (2) respondents without MCI data (N = 5,288); (3) respondents without available self-rated memory records (N = 3); (4) respondents without available arthritis records (N = 18); (5) respondents without CESD-10 scores (N = 1); (6) respondents without MHO data (N = 5,045). The current study contained a final sample of 6,575 people (Figure 1).

Figure 1

Figure 1. Flowchart of participant selection.

2.2 Arthritis assessment

In the CHARLS 2011 questionnaire, respondents were considered to have arthritis if they had ever been diagnosed by a physician (15).

2.3 MCI assessment

The Word Recall Test (WRT) and the Mental Status Test (MST) are the primary measures of cognitive function included in the CHARLS 2011 questionnaire (16). The maximum score for both immediate recall and delayed recall on the WRT was 20. Eleven points were awarded for the MST, which comprised the Telephone Interview for Cognitive Status (TICS-10) and a figure-drawing task. The total score for cognitive function was obtained by summing the results of the MST and the WRT. When a person’s combined WRT and MST scores were at least one standard deviation below the age group average, the AACD criteria classified them as having mild cognitive impairment (MCI) (17, 18). The cognitive rating criteria referred to previous studies (19, 20). To further improve the accuracy of MCI diagnosis, the item of subjective memory complaint was added. The subjective memory complaints of the participants were collected through questionnaires (those who answered “average” or “poor” were considered to have subjective memory complaints), and this was used as an auxiliary diagnostic criterion.

2.4 MHO assessment

In the CHARLS 2011 questionnaire, participants were categorized into three groups as normal weight (18.5 ≤ BMI < 24.0 kg/m2), overweight (24.0 ≤ BMI < 28.0 kg/m2), and obesity (BMI ≥ 28.0 kg/m2) (21). Metabolic status was assessed based on four metabolic components. Participants who met two or more of the following four criteria were classified as metabolically unhealthy (22, 23): (1) elevated blood pressure, systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg or use of antihypertensive drugs; (2) impaired glycemic control, fasting blood glucose (FBG) ≥ 5.6 mmol/L or glycated hemoglobin A1C (HbA1c) ≥ 6.0% or use of antidiabetic drugs; (3) elevated triglyceride (TG), TG ≥ 1.7 mmol/L or use of lipid-lowering drugs; (4) reduced high-density lipoprotein cholesterol (HDL-C), HDL-C < 1.03 mmol/L for men or <1.29 mmol/L for women or use of lipid-lowering drugs. Combined with obesity and metabolic status, participants were divided into four BMI-metabolic phenotypes as MHNW, MUNW, MHOO, and MUOO to evaluate the metabolic heterogeneity of obesity. MHNW: metabolically healthy normal weight, MUNW: metabolically unhealthy normal weight, MHOO: metabolically healthy overweight/obesity, MUOO: metabolically unhealthy overweight/obesity.

2.5 Mediating variable

This study investigated depression as a mediating variable CES-D-10 was utilized in the CHARLS questionnaire to assess depressive symptom severity among participants (24). A detailed overview of the CES-D-10 scoring system is provided in Supplementary Figure S1 (25). Depressive status was defined as a CES-D-10 score ≥ 12, a validated cutoff with satisfactory sensitivity and specificity for clinically significant symptoms (24).

2.6 Sensitivity analysis

A sensitivity analysis was carried out to ascertain the mediation model’s robustness. The initial variable that measured depression, which was either present or absent, was replaced with a continuous variable, the C-ESD-10 scores. The same set of factors was then used to conduct the mediation study.

2.7 Covariates

A thorough evaluation of the current literature was used to establish the variables in this investigation (26, 27). Regularized surveys were used to gather sociodemographic information, such as sex, age, residence, and degree of education. In addition, self-report questionnaires were used to gather lifestyle-related characteristics, including individuals’ histories of smoking and alcohol intake. In the covariates related to diseases, diabetes and the presence or absence of cancer (28, 29) were included. In addition, to further assess the potential impact of unmeasured confounding factors on the study results, E-value analysis was introduced. E-value analysis is a powerful tool for evaluating the extent of unmeasured confounding factors that would need to be present for the conclusions of the study results to change (30).

2.8 Analytical methods

R (version 4.3.2) and EmpowerStats (version 4.2) were used to conduct statistical analyses in this research. Mean ± standard deviation (SD) was used to represent continuous data, and frequencies and percentages were used to represent categorical variables. Utilization of t-tests for continuous variables and chi-square tests for categorical data to compare groups. Then, to assess the correlation between arthritis pain and MCI, multivariate logistic regression models were used. Multivariate logistic regression was used to assess the correlation between arthritis and MCI in various MHO populations, while controlling for factors such as gender, age, residence, smoking, alcohol use, and educational attainment. We computed odds ratios (ORs) and 95% CIs for every grouping. To determine if depression mediated the relationship between arthritis and MCI, we used the “mediation” package in R 4.3.2 to do a mediation study. The mediation relationship was analyzed using the Bootstrap method (31). When all three of these criteria were satisfied—(1) a statistically significant indirect impact; (2) a statistically significant overall effect; and (3) a positive percentage of the effect attributed to the mediator—then it was determined that a mediating effect was present.

3 Results

3.1 Participants characteristics

This study included 6,575 middle-aged and older individuals aged ≥ 45 years. Table 1 presents the distribution and comparison of sociodemographic characteristics, lifestyle factors, obesity heterogeneity categories, depressive symptoms, and arthritis between individuals with and without MCI. The average age of the study population was 58.18 years, with males accounting for 50.75% of the sample. A total of 2, 207 participants reported a diagnosis of arthritis.

Table 1

Table 1. Characteristics of the study population with MCI.

Notably, age, depressive symptoms, residency, education level, arthritis, were significantly associated with MCI.

3.2 Associations of arthritis with MCI

According to Table 2, all three models demonstrated a significant positive relationship between arthritis and MCI, and this relationship was statistically significant. In Model 1, the OR was 2.00 (95% CI = 1.72–2.32, p < 0.001). In Model 2, the OR was 1.92 (95% CI = 1.65–2.23, p < 0.001). And after adjusting for all confounding factors, Model 3 showed an OR of 1.86 (95% CI = 1.60–2.17, p < 0.001), indicating a positive correlation.

Table 2

Table 2. Associations of arthritis with MCI.

3.3 Stratified analysis by BMI–metabolic phenotypes

The association between arthritis and MCI across different MHO populations is shown in Table 3. Interestingly, all subgroups showed a positive correlation, but only the MUOO group (OR = 1.51, 95% CI = 1.18–1.92) demonstrated a statistically significant and the strongest positive correlation. No significant differences were observed in the MHNW group (OR = 1.08, 95% CI = 0.85–1.38), MHOO group (OR = 1.23, 95% CI = 0.81–1.87), or MUNW group (OR = 1.16, 95% CI = 0.90–1.49).

Table 3

Table 3. Stratified analysis of associations between arthritis and MCI in MHO people.

3.4 Mediating role of depressive symptoms in metabolically unhealthy overweight/obesity people

Mediating effect analysis revealed that depression partially mediated the association between arthritis and MCI in metabolically unhealthy overweight/obesity people, as shown in Figure 2. Depression accounted for 17.5% of the association between arthritis and MCI. The indirect effect, direct effect, and total effect of arthritis on MCI were all statistically significant.

Figure 2

Figure 2. Mediating role of depressive symptoms in metabolically unhealthy overweight/obesity people.

3.5 Findings from the sensitivity analysis

Results indicated that the mediation effect remained significant when depression was treated as a continuous variable (Figure 3). Depression accounted for 33.9% of the association between arthritis and MCI. The indirect effect (0.041, 95% CI = 0.031–0.053, p < 0.001), direct effect (0.080, p < 0.001), and total effect (0.122, p < 0.001).

Figure 3

Figure 3. Sensitivity analysis.

Further E-value analysis showed that even if there were unmeasured confounding factors, their impact on the results would have to be very high to change our conclusions, with a specific E-value of 3.12. The E value reported in this study corresponds to the effect estimate.

4 Discussion

This study demonstrated a positive association between arthritis and MCI, which remained statistically significant even after comprehensive adjustment for covariates. Furthermore, stratified analyses based on metabolic obesity phenotypes revealed a consistent positive relationship between arthritis and MCI across all subgroups; however, this association reached statistical significance solely in the MUOO subgroup. Further analysis demonstrated that depression partially mediated the link between arthritis and mild cognitive impairment in the MUOO population, with an estimated mediation proportion of 17.5%. These findings highlight the complicated interrelationship between arthritis and MCI within metabolically heterogeneous obese populations.

Multiple studies have reported the complex relationship between inflammation and cognitive impairment (32, 33). According to a cross-sectional study in the United States, arthritis was negatively associated with cognitive improvement, and this relationship remained significant after adjusting for potential confounders (OR:0.796, 95% CI:0.649–0.975; OR:0.769, 95% CI:0.611–0.968). This finding aligns with the results of the current study. The precise links between arthritis and cognitive impairment have yet to be fully elucidated (34). Some researchers have proposed several potential links, including chronic pain, depression and anxiety, inflammatory processes, and genetic factors (35, 36). Chronic pain and reduced physical activity caused by arthritis may directly influence the secretion of brain-derived neurotrophic factor (BDNF). A study involving 334 individuals with cognitive decline reported that those with limited mobility had a 47.6% increased risk of developing MCI (37). Moreover, a widely accepted hypothesis suggests that arthritis, as a chronic inflammatory disease, may affect cognitive function through multiple links (38). Alexandre et al. suggested that some pro-inflammatory cytokines—interleukin-6 (IL-6) in particular—may traverse the blood–brain barrier and exert regulatory effects on neuronal activity (39).

The association between arthritis and MCI was found to be partially explained by the mediating effect of depression. This may be explained by the inflammation–depression–cognition axis: chronic pain and functional impairment in patients with arthritis can predispose them to depression, such as feelings of worthlessness or hopelessness and reduced physical activity. Depression, in turn, may upregulate pro-inflammatory cytokines (e.g., TNF-α), forming a positive feedback loop that adversely affects cognitive function (35). To our knowledge, few studies have examined the association between arthritis and MCI specifically in the metabolically unhealthy overweight/obese population, and our findings suggest that depression may mediate this relationship. The findings suggest that high inflammatory activity in patients with arthritis—such as elevated Disease Activity Score-28 (DAS28) and C-reactive protein (CRP) levels—is significantly associated with impairments in visuospatial ability, memory, and executive function (40), Individuals with metabolically unhealthy overweight/obesity (MUOO) are characterized by a state of low-grade systemic inflammation, which, when compounded by the inflammatory burden of arthritis, may be associated with neurodegenerative processes and a higher likelihood of MCI (41).

The main strength of this study lies in its use of an observational cross-sectional design to explore the associations and mediation analyses among variables within the large and nationally representative CHARLS dataset. This design was chosen because it allows for the simultaneous examination of the relationships between arthritis, MCI, and potential modulators such as depression. This supports the applicability of our findings and may inform the design of future longitudinal studies, thereby laying the groundwork for further research. Given the observed association between arthritis and MCI among individuals with metabolically unhealthy overweight/obesity, screening for depression and MHO status may hold significant clinical and public health relevance for individuals affected by arthritis in both clinical and community settings. Healthcare providers and practitioners should remain highly vigilant for potential MCI in the aging population with arthritis and metabolically unhealthy overweight/obesity, particularly when depressive symptoms are present.

This research has certain limitations. The data originated from a Chinese population sample, perhaps limiting the generalizability of the results. Future research using samples from more nations is necessary to confirm the validity of these results. Certain characteristics included in this research were obtained from self-reports. Supplied by the participants, potentially resulting in recollection bias. Despite the involvement of expert clinical professionals, many middle-aged and older adults with inadequate knowledge and health literacy may still struggle to appropriately comprehend their own health situation. In this study, the diagnosis of arthritis primarily relied on the self-reports of the participants.

This study employed a cross-sectional design, which to some extent limits our inference regarding causality and the temporal sequence of events. The exclusion of samples due to missing data in this study may introduce selection bias. To further validate these hypotheses, future research needs to adopt a longitudinal design, tracking changes in individuals over a longer period of time to clarify causality and the temporal order of events.

5 Conclusion

Our findings reveal a positive link between arthritis and mild cognitive impairment among individuals with metabolically unhealthy overweight/obesity, and underscore the significant mediating effect of depression in this relationship. For adults in midlife and later life with arthritis and metabolically unhealthy overweight/obesity, we recommend that clinicians and community health workers adopt an integrated strategy combining metabolic regulation (e.g., improving insulin sensitivity), anti-inflammatory treatment (e.g., TNF inhibitors that may delay the progression of MCI), and psychological intervention (e.g., depression screening), to break the vicious cycle of inflammation, depression, and cognitive decline.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: http://charls.pku.edu.cn/en.

Ethics statement

The studies involving humans were approved by the Institutional Review Board of Peking University gave ethical clearance for the CHARLS investigation [IRB00001052-11015]. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

CL: Writing – original draft, Writing – review & editing. YC: Writing – review & editing, Writing – original draft. JC: Methodology, Writing – original draft. ZW: Writing – review & editing, Investigation. LZ: Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2026.1653086/full#supplementary-material

SUPPLEMENTARY FIGURE S1 | Depression assessment.

References

1. Li, E, Tan, J, Xu, K, Pan, Y, and Xu, P. Global burden and socioeconomic impact of knee osteoarthritis: a comprehensive analysis. Front Med (Lausanne). (2024) 11:1323091. doi: 10.3389/fmed.2024.1323091,

PubMed Abstract | Crossref Full Text | Google Scholar

2. Liu, R, Xin, Y, Shao, Y, Wu, B, and Liu, Y. Association of improvement and worsening of depressive symptoms with arthritis. BMC Geriatr. (2024) 24:909. doi: 10.1186/s12877-024-05498-w,

PubMed Abstract | Crossref Full Text | Google Scholar

3. Qiu, F, Li, J, Gan, L, and Legerlotz, K. Metabolic diseases and lifestyle factors affect arthritis incidence in old Europeans - a cross analysis from the SHARE project. Prev Med Rep. (2025) 54:103089. doi: 10.1016/j.pmedr.2025.103089,

PubMed Abstract | Crossref Full Text | Google Scholar

4. Xie, X, Zhang, K, Li, Y, Li, Y, Li, X, Lin, Y, et al. Global, regional, and national burden of osteoarthritis from 1990 to 2021 and projections to 2035: A cross-sectional study for the global burden of disease study 2021. PLoS One. (2025) 20:e0324296. doi: 10.1371/journal.pone.0324296

Crossref Full Text | Google Scholar

5. Zhang, L, Chen, W, Miao, H, Zou, T, Xiang, X, Wu, R, et al. Association between physical activity levels and mild cognitive impairment in Chinese older adults: a cross-sectional study from the China health and retirement longitudinal study. Front Public Health. (2025) 13:1564544. doi: 10.3389/fpubh.2025.1564544,

PubMed Abstract | Crossref Full Text | Google Scholar

6. Li, JQ, Tan, L, Wang, HF, Tan, MS, Tan, L, Xu, W, et al. Risk factors for predicting progression from mild cognitive impairment to Alzheimer's disease: a systematic review and meta-analysis of cohort studies. J Neurol Neurosurg Psychiatry. (2016) 87:476–84. doi: 10.1136/jnnp-2014-310095

Crossref Full Text | Google Scholar

7. Sachs-Ericsson, N, and Blazer, DG. The new DSM-5 diagnosis of mild neurocognitive disorder and its relation to research in mild cognitive impairment. Aging Ment Health. (2015) 19:2–12. doi: 10.1080/13607863.2014.920303

Crossref Full Text | Google Scholar

8. Masuda, T, Segawa, E, Kimura, N, Sato, T, Anabuki, K, Nakamura, Y, et al. Association between daily mode of transportation and cognitive function among older adults with mild cognitive impairment: a cross-sectional observational study. Cureus. (2025) 17:e83021. doi: 10.7759/cureus.83021

Crossref Full Text | Google Scholar

9. Au, B, Dale-McGrath, S, and Tierney, MC. Sex differences in the prevalence and incidence of mild cognitive impairment: a meta-analysis. Ageing Res Rev. (2017) 35:176–99. doi: 10.1016/j.arr.2016.09.005,

PubMed Abstract | Crossref Full Text | Google Scholar

10. Foster, NL, Bondi, MW, Das, R, Foss, M, Hershey, LA, Koh, S, et al. Quality improvement in neurology: mild cognitive impairment quality measurement set. Neurology. (2019) 93:705–13. doi: 10.1212/WNL.0000000000008259

Crossref Full Text | Google Scholar

11. Dye, L, Boyle, NB, Champ, C, and Lawton, C. The relationship between obesity and cognitive health and decline. Proc Nutr Soc. (2017) 76:443–54. doi: 10.1017/s0029665117002014,

PubMed Abstract | Crossref Full Text | Google Scholar

12. Batushansky, A, Zhu, S, Komaravolu, RK, South, S, Mehta-D'souza, P, and Griffin, TM. Fundamentals of OA. An initiative of osteoarthritis and cartilage. Obesity and metabolic factors in OA. Osteoarthr Cartil. (2022) 30:501–15. doi: 10.1016/j.joca.2021.06.013,

PubMed Abstract | Crossref Full Text | Google Scholar

13. Iacobini, C, Pugliese, G, Blasetti Fantauzzi, C, Federici, M, and Menini, S. Metabolically healthy versus metabolically unhealthy obesity. Metabolism. (2019) 92:51–60. doi: 10.1016/j.metabol.2018.11.009,

PubMed Abstract | Crossref Full Text | Google Scholar

14. Zhao, Y, Hu, Y, Smith, JP, Strauss, J, and Yang, G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. (2014) 43:61–8. doi: 10.1093/ije/dys203

Crossref Full Text | Google Scholar

15. Zhong, T, Li, T, Hu, J, Hu, J, Jin, L, Xie, Y, et al. Application of elastic networks and Bayesian networks to explore influencing factors associated with arthritis in middle-aged and older adults in the Chinese community. Front Public Health. (2025) 13:1437213.doi: 10.3389/fpubh.2025.1437213

Crossref Full Text | Google Scholar

16. Cao, L, Zhao, Z, Ji, C, and Xia, Y. Association between solid fuel use and cognitive impairment: a cross-sectional and follow-up study in a middle-aged and older Chinese population. Environ Int. (2021) 146:106251. doi: 10.1016/j.envint.2020.106251,

PubMed Abstract | Crossref Full Text | Google Scholar

17. Cui, Y, Xu, Z, Cui, Z, Guo, Y, Wu, P, and Zhou, X. Comparative study of insulin resistance surrogate indices to predict mild cognitive impairment among Chinese non-diabetic adults. Lipids Health Dis. (2024) 23:357. doi: 10.1186/s12944-024-02353-0,

PubMed Abstract | Crossref Full Text | Google Scholar

18. Xu, L, Xu, W, and Qin, L. Association of cystatin C kidney function measures with motoric cognitive risk syndrome: evidence from two cohort studies. J Nutr Health Aging. (2025) 29:100484. doi: 10.1016/j.jnha.2025.100484,

PubMed Abstract | Crossref Full Text | Google Scholar

19. Pestana, PC, Cardoso, S, Guerreiro, M, Maroco, J, Jessen, F, do Couto, FS, et al. Frequency, sociodemographic, and neuropsychological features of patients with subjective cognitive decline diagnosed using different neuropsychological criteria. Alzheimer's Res Ther. (2024) 16:261. doi: 10.1186/s13195-024-01634-1,

PubMed Abstract | Crossref Full Text | Google Scholar

20. Vance, DE, Del Bene, VA, Frank, JS, Billings, R, Triebel, K, Buchholz, A, et al. Cognitive intra-individual variability in HIV: an integrative review. Neuropsychol Rev. (2022) 32:855–76. doi: 10.1007/s11065-021-09528-x,

PubMed Abstract | Crossref Full Text | Google Scholar

21. He, D, Qiu, Y, Yan, M, Zhou, T, Cheng, Z, Li, J, et al. Associations of metabolic heterogeneity of obesity with frailty progression: results from two prospective cohorts. J Cachexia Sarcopenia Muscle. (2023) 14:632–41. doi: 10.1002/jcsm.13169

Crossref Full Text | Google Scholar

22. Zhou, Y, Zhang, X, Zhang, L, Li, Z, Wu, Q, Jin, Z, et al. Increased stroke risk in metabolically abnormal normal weight: a 10-year follow-up of 102, 037 participants in China. Transl Stroke Res. (2021) 12:725–34. doi: 10.1007/s12975-020-00866-1

Crossref Full Text | Google Scholar

23. Lee, JY, Han, K, Han, E, Kim, G, Cho, H, Kim, KJ, et al. Risk of incident dementia according to metabolic health and obesity status in late life: a population-based cohort study. J Clin Endocrinol Metab. (2019) 104:2942–52. doi: 10.1210/jc.2018-01491

Crossref Full Text | Google Scholar

24. Du, X, Liao, J, Ye, Q, and Wu, H. Multidimensional internet use, social participation, and depression among middle-aged and elderly Chinese individuals: nationwide cross-sectional study. J Med Internet Res. (2023) 25:e44514. doi: 10.2196/44514,

PubMed Abstract | Crossref Full Text | Google Scholar

25. Cheng, ST, Chan, AC, and Fung, HH. Factorial structure of a short version of the Center for Epidemiologic Studies Depression Scale. Int J Geriatr Psychiatry. (2006) 21:333–6. doi: 10.1002/gps.1467,

PubMed Abstract | Crossref Full Text | Google Scholar

26. Concha-Cisternas, Y, Castro-Piñero, J, Petermann-Rocha, F, Troncoso-Pantoja, C, Díaz, X, Cigarroa, I, et al. Asociación entre nivel educacional y sospecha de deterioro cognitivo en personas mayores chilenas: resultados de la Encuesta Nacional de Salud 2016-2017 [association between educational level and suspicion of cognitive imparirment in Chilean older people]. Rev Med Chile. (2022) 150:1575–84. doi: 10.4067/s0034-98872022001201575

Crossref Full Text | Google Scholar

27. Anieto, AC, Owolabi, AO, Owolabi, MO, Nwajei, AI, and Onwuka, MO. Risk factors for mild cognitive impairment among older adults in a hospital in southern Nigeria. Afr J Prim Health Care Fam Med. (2023) 15:e1–9. doi: 10.4102/phcfm.v15i1.3942

Crossref Full Text | Google Scholar

28. McEligot, AJ, Xiao, C, and Do, TQ. Associations between comorbid conditions with mild cognitive impairment and Alzheimer's disease by race-ethnicity and sex based on NACC data. Alzheimers Dement. (2025) 21:e70650. doi: 10.1002/alz.70650,

PubMed Abstract | Crossref Full Text | Google Scholar

29. Crowder, SL, Gudenkauf, LM, Hoogland, AI, Han, HS, Small, BJ, Carson, TL, et al. Cancer-related cognitive impairment and the potential of dietary interventions for the prevention and mitigation of neurodegeneration. Cancer Res. (2025) 85:203–17. doi: 10.1158/0008-5472.CAN-24-3041,

PubMed Abstract | Crossref Full Text | Google Scholar

30. Chung, WT, and Chung, KC. The use of the E-value for sensitivity analysis. J Clin Epidemiol. (2023) 163:92–4. doi: 10.1016/j.jclinepi.2023.09.014,

PubMed Abstract | Crossref Full Text | Google Scholar

31. Imai, K, Keele, L, and Tingley, D. A general approach to causal mediation analysis. Psychol Methods. (2010) 15:309–34. doi: 10.1037/a0020761

Crossref Full Text | Google Scholar

32. Chen, BA, Lee, WJ, Chung, CP, Peng, LN, and Chen, LK. Age- and sex-different associations between cognitive performance and inflammatory biomarkers in community dwelling older adults: towards precision preventive strategies. J Prev Alzheimers Dis. (2023) 10:104–11. doi: 10.14283/jpad.2022.83,

PubMed Abstract | Crossref Full Text | Google Scholar

33. Luo, J, le Cessie, S, Blauw, GJ, Franceschi, C, Noordam, R, and van Heemst, D. Systemic inflammatory markers in relation to cognitive function and measures of brain atrophy: a Mendelian randomization study. Geroscience. (2022) 44:2259–70. doi: 10.1007/s11357-022-00602-7,

PubMed Abstract | Crossref Full Text | Google Scholar

34. Meade, T, Manolios, N, Cumming, SR, Conaghan, PG, and Katz, P. Cognitive impairment in rheumatoid arthritis: a systematic review. Arthritis Care Res (Hoboken). (2018) 70:39–52. doi: 10.1002/acr.23243

Crossref Full Text | Google Scholar

35. He, YY, Ding, KR, Tan, WY, Ke, YF, Hou, CL, Jia, FJ, et al. The role of depression and anxiety in the relationship between arthritis and cognitive impairment in Chinese older adults. Am J Geriatr Psychiatry. (2024) 32:856–66. doi: 10.1016/j.jagp.2024.01.228,

PubMed Abstract | Crossref Full Text | Google Scholar

36. Duan, L, Xiao, R, Liu, S, Shi, Y, and Feng, Y. Causality between cognitive performance and cardiovascular disease: a bidirectional Mendelian randomization study. Gene. (2024) 891:147822:15. doi: 10.1016/j.gene.2023.147822

Crossref Full Text | Google Scholar

37. Kim, JI, and Kang, B. A comparative retrospective longitudinal study of arthritis risk and cognitive decline in older adults. Sci Rep. (2024) 14:24739. doi: 10.1038/s41598-024-75774-7,

PubMed Abstract | Crossref Full Text | Google Scholar

38. Bradburn, S, Murgatroyd, C, and Ray, N. Neuroinflammation in mild cognitive impairment and Alzheimer's disease: a meta-analysis. Ageing Res Rev. (2019) 50:1–8. doi: 10.1016/j.arr.2019.01.002,

PubMed Abstract | Crossref Full Text | Google Scholar

39. Schmidt-Morgenroth, I, Michaud, P, Gasparini, F, and Avrameas, A. Central and peripheral inflammation in mild cognitive impairment in the context of Alzheimer's disease. Int J Mol Sci. (2023) 24:10523:23. doi: 10.3390/ijms241310523

Crossref Full Text | Google Scholar

40. Mena-Vázquez, N, Ortiz-Márquez, F, Ramírez-García, T, Cabezudo-García, P, García-Studer, A, Mucientes-Ruiz, A, et al. Impact of inflammation on cognitive function in patients with highly inflammatory rheumatoid arthritis. RMD Open. (2024) 10:e004422. doi: 10.1136/rmdopen-2024-004422,

PubMed Abstract | Crossref Full Text | Google Scholar

41. Braga, GC, Simões, JLB, Teixeira Dos Santos, YJ, Filho, JCM, and Bagatini, MD. The impacts of obesity in rheumatoid arthritis and insights into therapeutic purinergic modulation. Int Immunopharmacol. (2024) 136:112357. doi: 10.1016/j.intimp.2024.112357

Crossref Full Text | Google Scholar