{"paper_id":"1e3bcb19-6c27-41cf-bc71-08374d891737","body_text":"Tea Consumption Reduces Mild Cognitive Impairment Risk in Community-Dwelling Older Adults via Lowering Interleukin-6: A Mediation Analysis with Propensity Score Matching | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tea Consumption Reduces Mild Cognitive Impairment Risk in Community-Dwelling Older Adults via Lowering Interleukin-6: A Mediation Analysis with Propensity Score Matching Biying Wu, Xiaotong Chen, Xiangyi Ma, Qiudan Chen, Yong Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7601893/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective To investigate the association between tea consumption and mild cognitive impairment (MCI) among adults aged ≥ 65 years in Shanghai, China, and to examine the potential mediating role of interleukin-6 (IL-6). Methods A cross-sectional study was conducted from March to September 2023, including 272 community-dwelling older adults. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and participants were classified into MCI (n = 19) and cognitively normal (n = 253) groups according to Petersen's criteria. Propensity score matching (PSM, 2:1) was applied to balance confounders (age and gender), resulting in 57 matched participants (19 MCI, 38 controls). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify key predictors of MCI, and multivariate logistic regression was employed to analyze influencing factors. The mediating effect of IL-6 on the relationship between tea consumption and MCI was tested using bootstrap resampling (5000 repetitions). Results Multivariate logistic regression revealed that regular tea consumption was significantly associated with a reduced risk of MCI ( OR = 0.137, 95% CI : 0.015–0.786, P = 0.044). Elevated IL-6 levels were associated with an increased risk of MCI ( OR = 2.069, 95% CI : 1.217–4.083, P = 0.016), while vitamin B12 (VB12) levels were inversely associated with MCI risk ( OR = 0.984, 95% CI : 0.966–0.997, P = 0.038). Mediation analysis indicated that IL-6 partially mediated the relationship between tea consumption and MCI (ACME = 0.1356, 95% CI : 0.0123–0.3098, P = 0.022), accounting for 62.51% of the total effect. Conclusion Tea drinking may indirectly reduce the risk of MCI by lowering IL-6 levels, with IL-6 serving as a partial mediator. These findings support a multi-pathway model linking lifestyle, inflammation, and cognition, suggesting that tea consumption and anti-inflammatory interventions may be effective strategies for MCI prevention in older adults. Mild cognitive impairment tea drinking interleukin-6 mediation analysis propensity score matching elderly Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Global population aging presents unprecedented public health challenges. As of 2023, China has entered a phase of deep aging, with individuals aged 65 and above accounting for 15–20% of the total population.( 1 ) MCI, a critical transitional stage between normal cognitive aging and dementia, has attracted increasing attention due to its high prevalence and elevated risk of progression to dementia.( 2 , 3 ) Epidemiological data indicate that the prevalence of MCI among adults aged ≥ 60 years in China is approximately 15.5%, affecting about 38.77 million people.( 4 ) More notably, the annual conversion rate from MCI to dementia ranges from 13.4% to 38%, imposing substantial caregiving and economic burdens on families and society.( 5 , 6 ) Older adults in community settings are a key population for MCI prevention and control, as their cognitive status is closely linked to regional lifestyle factors and accessibility to healthcare resources.( 7 , 8 ) The Baoshan District of Shanghai represents a typical urban aging community, where residents commonly experience high work-related stress, dietary transitions, and specific lifestyle habits that may collectively influence cognitive health trajectories.( 9 , 10 ) However, systematic research on MCI in this region remains limited, with most existing studies focusing on southern China or general populations, thereby hindering the development of localized intervention strategies. Thus, identifying region-specific influencing factors and underlying mechanisms of MCI in this population is essential for early community-based prevention and intervention. Recent studies suggest that lifestyle factors, such as tea drinking habits, are closely associated with the incidence of MCI.( 11 ) Tea consumption is common among older adults in China. A meta-analysis comprising 15 prospective studies with 246,726 participants demonstrated that regular tea consumption significantly reduces the risk of cognitive impairment.( 12 , 13 ) The protective mechanisms are primarily attributed to bioactive compounds in tea, such as polyphenols (particularly epigallocatechin gallate, EGCG) and L-theanine, which exhibit anti-inflammatory, antioxidant, and neuroprotective properties.( 14 , 15 ) In vitro studies indicate that EGCG inhibits Aβ fibril formation and promotes its disaggregation, while also enhancing antioxidant defense via activation of the Nrf2/ARE pathway.( 16 – 18 ) Dose-response analyses reveal that drinking 1–2 cups of tea per day is associated with the lowest risk of cognitive impairment, with more pronounced risk reduction observed at ≥ 4 cups per day.( 13 , 19 ) Subgroup analyses suggest that green tea may offer superior neuroprotective effects compared to oolong or black tea; however, evidence regarding tea-type-specific effects among older adults in eastern urban China remains insufficient. On the other hand, chronic neuroinflammation is recognized as a core pathological mechanism underlying MCI. IL-6, a key pro-inflammatory cytokine, can exacerbate cognitive impairment by disrupting blood-brain barrier integrity, promoting Aβ deposition and tau hyperphosphorylation, and inducing neuronal apoptosis through activation of JAK-STAT3 and NF-κB signaling pathways.( 20 – 22 ) A prospective community-based cohort study showed that each 1 pg/mL increase in plasma IL-6 level was associated with a 37% increase in MCI risk, with this association being more pronounced among APOE4 carriers.( 23 , 24 ) These findings align with the \"inflammation accelerates cognitive decline\" hypothesis, suggesting that IL-6 may serve as both a predictive biomarker and a potential intervention target for MCI. Nevertheless, the association between IL-6 and MCI among older adults in Shanghai has not been thoroughly investigated, particularly regarding the modulatory effects of lifestyle factors—such as dietary patterns and physical activity—on inflammatory levels. Although both tea consumption and IL-6 have been independently associated with MCI, the potential mediating mechanism between them remains unclear. Basic research suggests that tea polyphenols may downregulate IL-6 expression by inhibiting NF-κB signaling, while caffeine and theanine may synergistically enhance anti-inflammatory effects, thereby indirectly protecting cognitive function.( 24 ) This leads to a scientific hypothesis: IL-6 may mediate the relationship between tea consumption and MCI. However, this mechanism has not been validated in human studies, particularly lacking empirical analysis focusing on community-dwelling older adults in China. In summary, existing research has three main limitations: ( 1 ) insufficient regional representation, especially regarding the epidemiological characteristics and influencing factors of MCI in urban Shanghai communities; ( 2 ) mechanistic studies predominantly conducted at the animal or cellular level, lacking validation of mediating pathways in human populations; ( 3 ) most previous studies have not employed rigorous statistical control and variable selection strategies (e.g., PSM and LASSO regression), making it difficult to identify stable factors amid multicollinearity and confounding bias. Therefore, this study aims to: ( 1 ) describe the distribution of tea drinking behavior, IL-6 levels, and MCI status among adults aged ≥ 65 years in the Youyi community of Baoshan, Shanghai; ( 2 ) apply PSM (2:1 matching) to balance confounding factors and use LASSO regression to identify key predictors of MCI; ( 3 ) examine the mediating effect of IL-6 in the relationship between tea consumption and cognitive function. This study will not only contribute to refining the multi-path theoretical model of \"lifestyle–inflammation–cognition\" in MCI but also provide empirical evidence for the precise prevention of cognitive impairment in community settings. If the mediating role of IL-6 is confirmed, targeted dietary recommendations (e.g., promoting tea consumption) or anti-inflammatory interventions could be implemented to reduce the risk of MCI, offering practical value for primary prevention of dementia among community-dwelling older adults. 2. Materials and Methods 2.1 Study Design and Participant Recruitment This community-based cross-sectional study was conducted in the Youyi Community of Baoshan District, Shanghai, from March to September 2023. Participants were permanent residents aged 65 years or older. A cluster sampling method combined with voluntary participation was employed for recruitment: six residential complexes within the community were selected as sampling units, and 10% of older adults aged ≥ 65 years were randomly selected from each complex as potential candidates. Using the community health service center’s health record system, individuals with a confirmed diagnosis of dementia or severe organ failure were initially excluded. Eligible individuals who met the preliminary screening criteria were invited to participate in the on-site survey. Inclusion criteria were: ( 1 ) age ≥ 65 years and continuous residence in the community for ≥ 1 year; ( 2 ) clear consciousness, able to complete cognitive assessments and questionnaire interviews independently or with assistance; ( 3 ) informed of the study content and voluntarily provided written informed consent. Exclusion criteria were: ( 1 ) meeting the diagnostic criteria for dementia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5); ( 2 ) presence of severe heart failure (NYHA class IV), decompensated liver cirrhosis, stage 5 chronic kidney disease, or other end-stage diseases; ( 3 ) history of severe mental disorders such as schizophrenia or bipolar disorder, or history of stroke or traumatic brain injury within the past 3 months; ( 4 ) severe hearing, visual, or communication impairments that would prevent completion of the assessments. A total of 272 participants were ultimately included. Based on the Mini-Mental State Examination (MMSE) score and educational level, and with reference to Petersen's diagnostic criteria for MCI, participants were divided into two groups: ( 1 ) MCI group (n = 19): MMSE score 20–23, presence of subjective cognitive decline, and essentially normal activities of daily living; ( 2 ) cognitively normal control group (n = 253): MMSE score > 23, no subjective cognitive complaints, and no significant decline in cognitive function over the past year. The study protocol was approved by the Ethics Committee of Huashan Hospital Affiliated to Fudan University (Approval No.: 2020-004). All participants provided written informed consent. 2.2 Data Collection 2.2.1 Cognitive Function Assessment Cognitive function was assessed face-to-face using the Chinese version of the MMSE scale (revised by Peking Union Medical College Hospital; Cronbach's α = 0.82). Assessments were conducted by uniformly trained community general practitioners (inter-rater consistency after training, Kappa = 0.89) in quiet consultation rooms at the community health service center. Each assessment took approximately 10–15 minutes. The scale includes domains of orientation (10 points), memory (3 points), attention and calculation (5 points), recall (3 points), and language ability (9 points), with a total score of 30. The assessment strictly followed the scale's operational guidelines. If participants had visual impairments, the assessor read the items aloud; if writing was not feasible due to physical mobility impairments, verbal responses were accepted. MMSE Copyright Note: An unauthorized version of the Chinese MMSE was used by the study team without permission in the initial phase of this research. This has since been rectified with Psychological Assessment Resources (PAR). The MMSE is a copyrighted instrument and may not be used or reproduced in whole or in part, in any form or language, or by any means without the written permission of PAR ( www.parinc.com ). 2.2.2 Lifestyle and Basic Information Collection A self-designed structured questionnaire (revised after a pilot survey of n = 30; Cronbach's α = 0.78) was used for face-to-face interviews. The questionnaire included: Basic information: age (continuous variable), gender (male = 1, female = 0); Tea drinking behavior: core independent variable, defined as \"regular tea consumption\" (≥ 3 times per week, ≥ 150 mL each time, for ≥ 3 months), coded as a binary variable (yes = 1, no = 0); Other lifestyle indicators: leisure-time physical activity (binary: ≥1 time per week and ≥ 30 minutes each time = 1, otherwise = 0); alcohol consumption (binary: ≥1 time per week for ≥ 3 months = 1, otherwise = 0); smoking history (binary: ever or current smoker = 1, never smoked = 0). All interviews were conducted by trained investigators. Before questionnaire administration, the items were explained to ensure participants' understanding. After completion, questionnaires were checked on-site for completeness, and any missing information was immediately obtained to minimize data loss. 2.3 Laboratory Testing 2.3.1 Sample Collection and Processing Fasting venous blood samples (5 mL) were collected from all participants by nurses after 12 hours of fasting and 8 hours of water deprivation. Among these, 2 mL was placed in EDTA-K₂ anticoagulant tubes for complete blood count analysis, and 3 mL was placed in coagulation-promoting tubes for serum separation. After standing at room temperature for 30 minutes, samples in coagulation-promoting tubes were centrifuged at 3000 r/min for 15 minutes (centrifugation radius 10 cm). The separated serum was aliquoted into EP tubes and stored at -80°C, avoiding repeated freeze-thaw cycles (≤ 2 times). Anticoagulated samples were analyzed within 2 hours of collection; those not tested immediately were temporarily stored at 4°C for no more than 4 hours. 2.3.2 Detection Indicators and Methods 12 Cytokines : Levels were detected using the RaiseCyte 2L6C flow cytometer (RaiseCare Biotechnology, Qingdao, China) with a multiplex bead-based flow immunofluorescence technique, in conjunction with the 12-plex cytokine detection kit (Cat No.: BNCBA002-96T; Saihan Biotechnology, Shanghai, China). Complete Blood Count Analyzed using an automatic hematology analyzer (Mindray BC-5180 CRP; Mindray, China). Biochemical Indicators Biochemical indicators, such as total protein, albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood glucose, lipids (total cholesterol, triglycerides, high - density lipoprotein cholesterol, low - density lipoprotein cholesterol), and vitamin B12 (VB12), were measured by an Olympus AU5800 automatic biochemical analyzer (Olympus Corporation) with original matching reagents. Before testing, quality control was carried out using standard control materials from Roche Diagnostics to guarantee accuracy and reliability. All tests were conducted by certified laboratory technicians. Instruments were routinely maintained and calibrated to ensure data precision. 2.4 Statistical Analysis Statistical analyses were performed using R software (version 4.3.1). A two-sided significance level of α = 0.05 was applied. 2.4.1 Descriptive Statistics Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation (SD) and compared using t-tests; non-normally distributed data are presented as median (P25–P75) and compared using the Mann-Whitney U test. Categorical variables are described as frequency (percentage) and compared using chi-square tests or Fisher’s exact test (when expected frequency < 5). 2.4.2 Propensity Score Matching (PSM) To control for selection bias due to imbalanced sample sizes between the MCI and control groups (253:19), propensity score matching was performed with gender as the matching variable. A 1:2 nearest neighbor matching method was used with a caliper width of 0.2 standard deviations. Balance was assessed using standardized mean differences (SMD), with SMD < 0.1 indicating good balance. Subsequent analyses were based on the matched sample (n = 57, including 19 MCI cases and 38 controls). 2.4.3 Univariate Subgroup GLM Analysis Based on the PSM-matched sample (n = 57), univariate generalized linear models (GLM) were constructed with MCI status as the dependent variable and key variables such as IL-6 and VB12 as independent variables to assess their association with MCI risk. Subgroup analyses were conducted by gender (male/female), leisure-time physical activity (yes/no), alcohol consumption (yes/no), and tea drinking behavior (yes/no). Interaction terms were used to evaluate whether subgroup differences were statistically significant. 2.4.4 LASSO Regression for Variable Selection Fifty potential influencing factors (inflammatory factors, biochemical indicators, and lifestyle variables) were included in the LASSO regression model, with MCI status (yes = 1, no = 0) as the dependent variable. Variable selection was performed using the \"glmnet\" package, with the optimal λ value determined by 10-fold cross-validation. Variables with non-zero coefficients were retained as key influencing factors for MCI. 2.4.5 Logistic Regression Analysis Multivariate logistic regression models were constructed using variables selected by LASSO regression as independent variables and MCI as the dependent variable. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. Multicollinearity was assessed using variance inflation factors (VIF), with VIF < 10 considered acceptable. 2.4.6 Mediation Effect Analysis Based on the Baron & Kenny framework, the bootstrap method was used to examine the mediating role of IL-6 in the relationship between tea drinking behavior and MCI, adjusting for age and gender. The variables were defined as follows: Independent variable (X): tea drinking behavior (regular = 1, no = 0); Mediator variable (M): IL-6 (continuous); Dependent variable (Y): MCI (yes = 1, no = 0); Covariates (C): age, gender. Using the \"mediation\" package, 5000 bootstrap samples were drawn to calculate the average causal mediation effect (ACME), average direct effect (ADE), total effect, and mediation proportion. A significant mediation effect was concluded if the 95% CI for ACME did not include zero. 3. Results 3.1 Baseline Characteristics of the Study Participants A total of 272 community-dwelling adults aged ≥ 65 years were included in this study, comprising 253 cognitively normal controls and 19 individuals with MCI. Prior to matching, no statistically significant differences were observed between the two groups in terms of age, gender, most inflammatory cytokines, biochemical indicators, or lifestyle variables (all P > 0.05), as detailed in Table 1 . Notably, VB12 levels differed significantly between the groups ( P = 0.032), with lower levels observed in the MCI group. To control for baseline imbalances and potential confounding factors, PSM was performed using age and gender as covariates in a 2:1 ratio. This resulted in 57 successfully matched participants (19 MCI and 38 controls). After matching, the distributions of age, gender, and other variables were well-balanced between the groups, with all SMD < 0.1. Furthermore, differences between groups became more pronounced for alanine aminotransferase (ALT, P = 0.020), VB12 ( P = 0.007), and tea drinking behavior ( P = 0.058) after matching (Table 2 ). Table 1 Baseline characteristics of the study population before propensity score matching (PSM) Variable Control group (n = 253) MCI group (n = 19) P value Age 71.00 (67.00–75.00) 68.00 (66.00–74.00) 0.103 Interleukin-5 (IL-5) 3.96 (3.09–5.32) 3.58 (2.79–5.62) 0.63 Interferon-α (IFN-α) 1.83 (1.58–2.34) 1.84 (1.64–2.21) 0.746 Interleukin-2 (IL-2) 1.72 (1.49–1.94) 1.78 (1.49–2.02) 0.424 Interleukin-6 (IL-6) 2.80 (2.27–3.67) 3.16 (2.17–4.75) 0.58 Interleukin-1β (IL-1β) 3.61 (1.26–8.04) 3.98 (2.12–5.75) 0.917 Interleukin-10 (IL-10) 1.70 (1.43–2.06) 1.73 (1.53–1.98) 0.888 Interferon-γ (IFN-γ) 5.84 (4.66–7.43) 6.21 (5.06–8.09) 0.294 Interleukin-8 (IL-8) 0.00 (0.00–0.00) 0.00 (0.00–0.00) 0.944 Interleukin-17 (IL-17) 0.00 (0.00–2.91) 0.00 (0.00–4.80) 0.519 Interleukin-4 (IL-4) 0.87 (0.63–1.16) 1.14 (0.52–1.27) 0.372 Interleukin-12p70 (IL-12p70) 0.86 (0.42–1.23) 1.00 (0.14–1.47) 0.954 Tumor necrosis factor-α (TNF-α) 0.59 (0.22–1.01) 0.72 (0.22–1.22) 0.439 Serum total protein (g/L) 74.00 (71.60–76.90) 75.20 (72.00–78.40) 0.578 Aspartate transaminase (AST) (U/L) 25.00 (21.00–30.00) 22.00 (20.00–27.00) 0.248 Albumin/globulin ratio (A/G) 1.40 (1.30–1.60) 1.40 (1.30–1.70) 0.845 γ-Glutamyl transferase (GGT) (U/L) 23.00 (17.00–31.00) 19.00 (15.00–27.00) 0.241 Serum uric acid (µmol/L) 343.00 (293.00–396.00) 320.00 (304.00–397.00) 0.796 Total cholesterol (TC) (mmol/L) 5.67 (4.84–6.46) 5.97 (5.17–6.56) 0.418 Triglycerides (TG) (mmol/L) 1.36 (1.05–1.85) 1.44 (1.16–1.67) 0.925 Serum globulin (g/L) 30.40 (27.90–32.50) 30.20 (25.90–33.70) 0.994 Blood urea nitrogen (BUN) (mmol/L) 5.60 (4.80–6.50) 5.70 (4.70–7.20) 0.96 Serum creatinine (Scr) (µmol/L) 72.00 (59.00–83.00) 65.00 (58.00–77.00) 0.29 High-density lipoprotein cholesterol (HDL-C) (mmol/L) 1.29 (1.11–1.44) 1.28 (1.07–1.55) 0.67 Low-density lipoprotein cholesterol (LDL-C) (mmol/L) 3.31 (2.68–3.88) 3.24 (2.79–4.05) 0.671 Total bilirubin (TBIL) (µmol/L) 13.90 (11.20–16.60) 14.70 (11.20–20.70) 0.405 Albumin (ALB) (g/L) 43.90 (42.50–45.20) 44.30 (43.30–45.40) 0.273 Alanine transaminase (ALT) (U/L) 19.00 (15.00–24.00) 17.00 (14.00–21.00) 0.186 Fasting blood glucose (Glu) (mmol/L) 5.90 (5.50–6.30) 6.00 (5.50–7.40) 0.423 Calcium (Ca) (mmol/L) 2.42 (2.38–2.48) 2.47 (2.41–2.51) 0.13 Chloride (Cl) (mmol/L) 105.00 (103.00–106.00) 105.00 (104.00–107.00) 0.475 Sodium (Na) (mmol/L) 143.00 (141.00–144.00) 143.00 (142.00–144.00) 0.682 Potassium (K) (mmol/L) 4.29 (4.09–4.51) 4.27 (4.18–4.51) 0.781 Estimated glomerular filtration rate (eGFR_MDRD) (mL/min/1.73m²) 94.00 (81.00–108.00) 99.00 (90.00–110.00) 0.298 White blood cell count (WBC) (×10⁹/L) 5.16 (4.49–6.06) 4.68 (3.74–7.50) 0.557 Neutrophil ratio (NEUTratio) (%) 55.70 (50.80–60.70) 55.60 (49.10–60.20) 0.721 Lymphocyte ratio (LYMratio) (%) 34.40 (30.10–39.20) 35.80 (31.30–39.90) 0.7 Monocyte ratio (MONOratio) (%) 6.10 (5.20–7.10) 6.60 (5.40–7.60) 0.313 Eosinophil ratio (EOSratio) (%) 2.00 (1.30–2.90) 1.80 (1.50–2.90) 0.953 Basophil ratio (BASOratio) (%) 0.30 (0.20–0.40) 0.30 (0.10–0.60) 0.65 Neutrophil count (NEUT) (×10⁹/L) 2.89 (2.41–3.45) 2.60 (1.93–4.36) 0.658 Lymphocyte count (LYM) (×10⁹/L) 1.78 (1.47–2.16) 1.62 (1.50–1.93) 0.703 Monocyte count (MONO) (×10⁹/L) 0.32 (0.27–0.38) 0.31 (0.28–0.47) 0.715 Eosinophil count (EOS) (×10⁹/L) 0.10 (0.07–0.16) 0.10 (0.06–0.20) 0.87 Basophil count (BASO) (×10⁹/L) 0.02 (0.01–0.02) 0.01 (0.01–0.03) 0.77 Glycated hemoglobin (HbA1c) (%) 5.79 (5.55–6.09) 5.82 (5.60–6.44) 0.638 Vitamin B12 (VB12) (pg/mL) 232.77 (204.19–269.74) 214.30 (186.29–232.88) 0.032 Serum folate (ng/mL) 11.31 (8.91–13.50) 11.89 (9.64–13.81) 0.461 Homocysteine (Hcy) (µmol/L) 14.20 (11.27–19.60) 15.23 (10.74–20.07) 0.909 Household physical activity (MET·h/week) 2.00 (1.00–3.00) 2.00 (0.00–3.00) 0.202 Leisure time physical activity n (%) 0: Non-participation 62 (24.51) 5 (26.32) 0.86 1: Participation 191 (75.49) 14 (73.68) Alcohol consumption n (%) 0: Non-consumption 223 (88.14) 18 (94.74) 0.383 1: Consumption 30 (11.86) 1 (5.26) Tea consumption n (%) 0: Non-consumption 138 (54.55) 14 (73.68) 0.105 1: Regular consumption 115 (45.45) 5 (26.32) Sex n (%) 0: Male 141.00 (55.73%) 13.00 (68.42%) 0.282 1: Female 112.00 (44.27%) 6.00 (31.58%) MCI = Mild Cognitive Impairment; MMSE = Mini-Mental State Examination; Continuous variables are presented as median (P25–P75), and intergroup comparisons were conducted using the Mann-Whitney U test; Categorical variables are presented as n (%), and intergroup comparisons were conducted using the χ² test or Fisher’s exact test (when the expected frequency < 5). Table 2 Baseline characteristics of the study population after propensity score matching (PSM, 2:1 matching by age and sex) Variable Control group (n = 38) MCI group (n = 19) p -value Age (years) 68.00 (66.00–74.00) 68.00 (66.00–74.00) 0.993 IL-5 (pg/mL) 4.22 (3.11–6.09) 3.58 (2.79–5.62) 0.302 IFN-α (pg/mL) 1.77 (1.60–2.58) 1.84 (1.64–2.21) 0.756 IL-2 (pg/mL) 1.76 (1.56–1.97) 1.78 (1.49–2.02) > 0.999 IL-6 (pg/mL) 2.63 (2.36–3.09) 3.16 (2.17–4.75) 0.421 IL-1β (pg/mL) 4.37 (1.03–8.04) 3.98 (2.12–5.75) 0.98 IL-10 (pg/mL) 1.73 (1.45–2.14) 1.73 (1.53–1.98) 0.966 IFN-γ (pg/mL) 6.72 (5.60–8.36) 6.21 (5.06–8.09) 0.441 IL-8 (pg/mL) 0.00 (0.00–0.00) 0.00 (0.00–0.00) 0.79 IL-17 (pg/mL) 0.00 (0.00–3.51) 0.00 (0.00–4.80) 0.76 IL-4 (pg/mL) 0.92 (0.58–1.25) 1.14 (0.52–1.27) 0.786 IL-12p70 (pg/mL) 0.96 (0.63–1.21) 1.00 (0.14–1.47) 0.748 TNF-α (pg/mL) 0.61 (0.25–1.01) 0.72 (0.22–1.22) 0.436 Serum total protein (g/L) 73.95 (72.00–76.70) 75.20 (72.00–78.40) 0.641 AST (U/L) 25.50 (22.00–31.00) 22.00 (20.00–27.00) 0.102 A/G 1.40 (1.30–1.60) 1.40 (1.30–1.70) 0.979 GGT (U/L) 24.50 (17.00–39.00) 19.00 (15.00–27.00) 0.213 Serum uric acid (µmol/L) 343.50 (296.00–410.00) 320.00 (304.00–397.00) 0.806 TC (mmol/L) 5.78 (5.09–6.16) 5.97 (5.17–6.56) 0.412 TG (mmol/L) 1.43 (1.08–1.83) 1.44 (1.16–1.67) 0.866 Serum globulin (g/L) 30.55 (27.60–32.50) 30.20 (25.90–33.70) 0.78 BUN (mmol/L) 5.55 (4.80–6.30) 5.70 (4.70–7.20) 0.859 Scr (µmol/L) 66.00 (56.00–81.00) 65.00 (58.00–77.00) 0.98 HDL-C (mmol/L) 1.30 (1.12–1.41) 1.28 (1.07–1.55) 0.565 LDL-C (mmol/L) 3.40 (2.79–3.85) 3.24 (2.79–4.05) 0.741 TBIL (µmol/L) 13.30 (10.40–17.50) 14.70 (11.20–20.70) 0.343 ALB (g/L) 43.90 (42.60–45.80) 44.30 (43.30–45.40) 0.588 ALT (U/L) 20.50 (18.00–32.00) 17.00 (14.00–21.00) 0.02 Glu (mmol/L) 6.00 (5.40–6.80) 6.00 (5.50–7.40) 0.709 Ca (mmol/L) 2.43 (2.38–2.47) 2.47 (2.41–2.51) 0.143 Cl (mmol/L) 104.00 (103.00–106.00) 105.00 (104.00–107.00) 0.221 Na (mmol/L) 142.00 (141.00–143.00) 143.00 (142.00–144.00) 0.242 K (mmol/L) 4.29 (4.17–4.71) 4.27 (4.18–4.51) 0.553 eGFR_MDRD (mL/min/1.73m²) 97.50 (87.00–117.00) 99.00 (90.00–110.00) 0.973 WBC (×10⁹/L) 4.75 (4.33–5.70) 4.68 (3.74–7.50) 0.939 NEUTratio (%) 55.10 (51.50–59.60) 55.60 (49.10–60.20) 0.886 LYMratio (%) 34.40 (30.70–38.30) 35.80 (31.30–39.90) 0.826 MONOratio (%) 6.10 (4.90–7.40) 6.60 (5.40–7.60) 0.407 EOSratio (%) 2.20 (1.70–2.90) 1.80 (1.50–2.90) 0.482 BASOratio (%) 0.30 (0.20–0.50) 0.30 (0.10–0.60) 0.546 NEUT (×10⁹/L) 2.64 (2.44–3.33) 2.60 (1.93–4.36) 0.806 LYM (×10⁹/L) 1.74 (1.38–2.07) 1.62 (1.50–1.93) 0.986 MONO (×10⁹/L) 0.31 (0.24–0.38) 0.31 (0.28–0.47) 0.471 EOS (×10⁹/L) 0.11 (0.08–0.17) 0.10 (0.06–0.20) 0.564 BASO (×10⁹/L) 0.02 (0.01–0.03) 0.01 (0.01–0.03) 0.758 HbA1c (%) 5.96 (5.52–6.22) 5.82 (5.60–6.44) 0.939 VB12 (pg/mL) 241.01 (219.94–272.92) 214.30 (186.29–232.88) 0.007 Serum folate (ng/mL) 11.69 (9.04–13.41) 11.89 (9.64–13.81) 0.681 Hcy (µmol/L) 14.16 (10.48–18.53) 15.23 (10.74–20.07) 0.913 Household physical activity 2.00 (1.00–2.00) 2.00 (0.00–3.00) 0.454 Sex, n (%) 0: Female 24 (63.16) 13 (68.42) 0.695 1: Male 14 (36.84) 6 (31.58) Leisure time physical activity, n (%) 0: Non-participation 10 (26.32) 5 (26.32) > 0.999 1: Participation 28 (73.68) 14 (73.68) Alcohol consumption, n (%) 0: Non-consumption 34 (89.47) 18 (94.74) 0.508 1: Consumption 4 (10.53) 1 (5.26) Tea consumption, n (%) 0: Non-consumption 19 (50.00) 14 (73.68) 0.058 1: Regular consumption 19 (50.00) 5 (26.32) A two-tailed p < 0.05 was considered statistically significant. 3.2 Subgroup Analysis Based on Univariate Generalized Linear Model with Forest Plot To further investigate the association between key variables and MCI risk across different population subgroups and to assess potential heterogeneity, univariate GLM were constructed using the PSM-matched sample (n = 57). Results were visualized using forest plots (Fig. 1). The results indicated a trend toward a positive association between elevated IL-6 levels and increased risk of MCI (overall OR = 1.47, 95% CI : 0.98–2.21, P = 0.065). Although this association did not reach statistical significance, it suggests that IL-6 may be a risk factor for MCI. Subgroup analyses revealed no significant heterogeneity in the association between IL-6 and MCI across subgroups defined by gender ( P -interaction = 0.687), leisure-time physical activity ( P -interaction = 0.481), alcohol consumption ( P -interaction = 0.401), or tea drinking behavior ( P -interaction = 0.613). VB12 levels were significantly inversely associated with MCI risk (overall OR = 0.98, 95% CI : 0.96–1.00, P = 0.012). Subgroup analysis suggested that the protective effect of VB12 was more pronounced in men ( OR = 0.96, 95% CI : 0.93–0.99, P = 0.018) than in women ( OR = 0.99, 95% CI : 0.97–1.02, P = 0.482); however, the interaction between gender and VB12 did not reach statistical significance ( P -interaction = 0.084). Additionally, the association between VB12 and MCI remained consistent across subgroups of leisure-time physical activity ( P -interaction = 0.603) and alcohol consumption ( P -interaction = 0.942). In summary, the associations of IL-6 and VB12 with MCI were generally consistent across subgroups with different characteristics, and no significant interactions were observed, indicating that their effects are broadly generalizable. 3.3 Screening of Influencing Factors for MCI and Multivariate Logistic Regression Analysis LASSO regression was used to screen 50 potential predictor variables. Through 10-fold cross-validation, the optimal lambda (λ) value was identified, resulting in the retention of two variables with non-zero coefficients: IL-6 and VB12 (Fig. 2 ). Incorporating these with ALT and tea drinking behavior—which showed intergroup differences in univariate analysis—a multivariate logistic regression model was constructed. The multivariate analysis results (Table 3 ) showed that tea drinking was significantly associated with a reduced risk of MCI ( OR = 0.137, 95% CI : 0.015–0.786, P = 0.044). Elevated IL-6 levels were significantly associated with an increased risk of MCI ( OR = 2.069, 95% CI : 1.217–4.083, P = 0.016). VB12 levels were inversely associated with MCI risk ( OR = 0.984, 95% CI : 0.966–0.997, P = 0.038). ALT was not statistically significant in the final model ( P = 0.341). The performance of the model was comprehensively evaluated using multiple graphical tools, including a forest plot of predictor variables (Fig. 3 A), receiver operating characteristic (ROC) curve ( AUC = 0.841, Fig. 3 B), and a nomogram (Fig. 3 C), as well as calibration curves, decision curve analysis, residual scatter plots, and coefficient plots (Supplementary Fig. 1). The results indicated that the model had good discriminative ability and calibration. Table 3 Multivariable logistic regression analysis of factors associated with MCI Variable Estimate Std Error Z value P value 95% CI (Lower, Upper) OR (95% CI) Intercept 2.1267 1.974 1.0774 0.2813 -1.2879, 6.4940 8.3872(0.2759, 661.1931) IL-6 0.7272 0.3014 2.4127 0.0158 0.1965, 1.4069 2.0693(1.2172, 4.0834) ALT -0.0332 0.0349 -0.9518 0.3412 -0.1167, 0.0257 0.9673(0.8899, 1.0260) VB12 -0.0167 0.008 -2.0735 0.0381 -0.0349, -0.0032 0.9835(0.9657, 0.9968) Tea consumption -1.9876 0.9884 -2.0109 0.0443 -4.2290, -0.2414 0.137(0.0146, 0.7855) 3.4 Mediation Analysis of IL-6 in the Association Between Tea Drinking and MCI Mediation analysis using the bootstrap method (5000 repetitions) was performed to evaluate the mediating role of IL-6 in the relationship between tea drinking and MCI, adjusting for age and gender (Table 4 ). The results revealed a significant mediating effect of IL-6: the ACME was 0.1356 (95% CI : 0.0123–0.3098, P = 0.022), indicating that tea drinking indirectly reduces the risk of MCI by lowering IL-6 levels. The average direct effect (ADE) was − 0.3526 (95% CI : -0.5989 to -0.1132, P = 0.004), suggesting that tea drinking also has a direct protective effect on MCI independent of IL-6. The total effect was − 0.2170 (95% CI : -0.4756–0.0168, P = 0.066), and the mediation proportion was − 0.6251 (95% CI : -5.1381–1.9092, P = 0.088). A mediation effect interval plot further visually represents these results (Fig. 4 ). Table 4 Mediation effect analysis of IL-6 in the association between tea consumption and MCI Effect Type Estimate 95% CI (Lower,Upper) P value ACME (Indirect effect) 0.1356 0.0123,0.3098 0.022 ADE (Direct effect) -0.3526 -0.5989,-0.1132 0.004 Total Effect -0.217 -0.4756,0.0168 0.066 Proportion Mediated -0.6251 -5.1381,1.9092 0.088 Mediation model: Independent variable (X) = tea consumption; Mediator (M) = IL-6; Dependent variable (Y) = MCI. Adjusted for age and sex. ACME = Average Causal Mediation Effect (indirect effect of X on Y via M); ADE = Average Direct Effect (direct effect of X on Y independent of M); Total Effect = sum of ACME and ADE. Proportion Mediated = (ACME / Total Effect) × 100% (negative value indicates the indirect effect direction is opposite to the total effect). A two-tailed P < 0.05 and 95% CI not containing zero indicate a statistically significant effect. 4. Discussion The present study investigated the association between tea consumption and MCI among community-dwelling older adults in Shanghai, China, and explored the potential mediating role of IL-6. Our findings indicate that regular tea consumption is associated with a significantly reduced risk of MCI. This protective effect may be attributed to the bioactive compounds present in tea, such as polyphenols (e.g., epigallocatechin gallate, EGCG) and L-theanine, which possess anti-inflammatory, antioxidant, and neuroprotective properties. Moreover, our mediation analysis demonstrated that a portion of the protective effect of tea consumption is mediated through the reduction of IL-6 levels, providing population-level evidence for the \"tea consumption–anti-inflammation–cognitive protection\" pathway. Secondly, elevated IL-6 levels were significantly correlated with an increased risk of MCI, supporting the \"inflammatory hypothesis\" of cognitive decline. Neuroinflammation may compromise cognitive function through mechanisms such as disrupting blood-brain barrier integrity, promoting Aβ deposition, and inducing tau hyperphosphorylation. The mediation analysis indicated that IL-6 partially mediates the relationship between tea consumption and MCI, suggesting that tea consumption may downregulate IL-6 expression by inhibiting inflammatory pathways such as NF-κB, thereby indirectly preserving cognitive function. This finding establishes a connection between lifestyle factors and inflammatory mechanisms and provides novel targets for early MCI intervention. Thirdly, vitamin B12 levels were inversely related to the risk of MCI, which may be associated with the role of VB12 in homocysteine metabolism and myelination. VB12 deficiency may lead to hyperhomocysteinemia, consequently inducing vascular endothelial damage and neurodegeneration. Our study also suggested that the protective effect of VB12 was more prominent in men, potentially due to gender-based differences in metabolism and lifestyle; however, the interaction effect was not statistically significant, necessitating further validation in larger samples. Methodologically, this study utilized PSM, LASSO regression, and bootstrap mediation analysis, effectively controlling for confounding bias and multicollinearity, thus enhancing the robustness of variable selection and the interpretability of the results. After PSM, the between-group differences in ALT, VB12, and tea consumption became more distinct, indicating improved comparability between groups. LASSO regression further identified IL-6 and VB12 as key factors from 50 variables, avoiding overfitting. Nonetheless, several limitations of this study should be recognized. Firstly, the cross-sectional design precludes causal inference; future prospective cohort studies or intervention trials are required to validate the mediating mechanism of IL-6. Secondly, the sample size was relatively small (particularly with only 19 cases in the MCI group). Although PSM improved statistical efficiency, it may still impact the power of subgroup analyses and interaction tests. Thirdly, other potential mediating variables (e.g., oxidative stress markers, neurotrophic factors) were not measured; future studies could incorporate multi-omics indicators to construct a more comprehensive mediation model. Finally, although tea consumption was defined based on frequency and duration, tea types (e.g., green tea, black tea) and brewing concentration were not differentiated, which may obscure tea-specific effects. Despite these limitations, the findings of this study have significant practical implications. If tea consumption indeed indirectly safeguards cognitive function by reducing IL-6 levels, community-based MCI prevention and control could adopt a two-pronged strategy: firstly, promoting anti-inflammatory dietary patterns such as tea consumption; secondly, implementing anti-inflammatory interventions (e.g., exercise, nutritional supplementation) targeting populations with high IL-6 levels. Additionally, the protective role of VB12 underscores the importance of monitoring the nutritional status of the elderly; regular screening and VB12 supplementation may contribute to cognitive maintenance. 5. Conclusion This study confirms that tea consumption, IL-6, and VB12 are independent influencing factors for MCI among older adults in the Baoshan community of Shanghai, and for the first time demonstrates the partial mediating role of IL-6 in the relationship between tea consumption and MCI. The results support the existence of a \"lifestyle–inflammation–cognition\" pathway, suggesting that modulating inflammatory levels through tea consumption may be an effective strategy for community-based MCI prevention and control. Future larger-scale prospective studies are warranted to further validate the mediating mechanisms and explore tea-type-specific effects, thereby providing a basis for developing precise and regionally tailored MCI interventions. Abbreviations Abbreviation Full Term MCI Mild Cognitive Impairment IL-6 Interleukin-6 MMSE Mini-Mental State Examination PSM Propensity Score Matching LASSO Least Absolute Shrinkage and Selection Operator OR Odds Ratio CI Confidence Interval VB12 Vitamin B12 ACME Average Causal Mediation Effect ADE Average Direct Effect SD Standard Deviation SMD Standardized Mean Difference GLM Generalized Linear Model ROC Receiver Operating Characteristic AUC Area Under the Curve ALT Alanine Aminotransferase AST Aspartate Aminotransferase DSM-5 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition NYHA New York Heart Association EGCG Epigallocatechin Gallate Declarations Ethics Approval and Consent to Participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Huashan Hospital Affiliated to Fudan University (Approval No.: 2020-004). Written informed consent was obtained from all participants prior to their inclusion in the study. Consent for Publication All participants provided consent for the publication of anonymized data collected in this study. Availability of Data and Materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This work is supported by An In-Hospital Scientific Research Project of Shanghai Fifth People's Hospital (2022WYZD03) and Medical Specialty Construction Project of Minhang District, Shanghai (2025MWTZB01). Authors' Contributions Wu: Conceptualization, Methodology, Formal analysis Investigation, Writing - Original Draft, Visualization. Xiaotong Chen: Validation, Formal analysis, Data Curation. Ma: Resources, Investigation, Project administration. Qiudan Chen: Software, Validation, Formal analysis, Data Curation, Writing - Review & Editing. Lin: Supervision, Writing - Review & Editing, Funding acquisition. All authors have read and approved the final manuscript. Acknowledgements The authors would like to thank all the participants and their families for their time and cooperation. We also extend our gratitude to the staff of the Youyi Community Health Service Center for their assistance in participant recruitment and data collection. References Global, regional, and national trends in routine childhood vaccination coverage from 1980 to 2023 with forecasts to 2030: a systematic analysis for the Global Burden of Disease Study 2023. LANCET. [Journal Article]. 2025 2025/7/19;406(10500):235-60. Petersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D, et al. Practice guideline update summary: Mild cognitive impairment [RETIRED]: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. NEUROLOGY. [Journal Article; Practice Guideline; Research Support, Non-U.S. Gov't; Systematic Review]. 2018 2018/1/16;90(3):126-35. Xie Y, Zhao T, Zhang W, Chen Q, Qiu A, Li Y, et al. Neural deterioration and compensation in visual short-term memory among individuals with amnestic mild cognitive impairment. ALZHEIMERS DEMENT. [Journal Article]. 2025 2025/2/1;21(2):e14475. Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. LANCET PUBLIC HEALTH. [Journal Article; Research Support, Non-U.S. Gov't]. 2020 2020/12/1;5(12):e661-71. Jones A, Ali MU, Kenny M, Mayhew A, Mokashi V, He H, et al. Potentially Modifiable Risk Factors for Dementia and Mild Cognitive Impairment: An Umbrella Review and Meta-Analysis. DEMENT GERIATR COGN. [Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov't; Systematic Review]. 2024 2024/1/20;53(2):91-106. Wang Q, Zhou S, Zhang J, Wang Q, Hou F, Han X, et al. Risk assessment and stratification of mild cognitive impairment among the Chinese elderly: attention to modifiable risk factors. J EPIDEMIOL COMMUN H. [Journal Article; Research Support, Non-U.S. Gov't]. 2023 2023/8/1;77(8):521-6. Mian M, Tahiri J, Eldin R, Altabaa M, Sehar U, Reddy PH. Overlooked cases of mild cognitive impairment: Implications to early Alzheimer's disease. AGEING RES REV. [Journal Article; Research Support, N.I.H., Extramural; Systematic Review]. 2024 2024/7/1;98:102335. Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA-J AM MED ASSOC. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review]. 2014 2014/12/17;312(23):2551-61. Wang T, Xiao S, Chen K, Yang C, Dong S, Cheng Y, et al. Prevalence, Incidence, Risk and Protective Factors of Amnestic Mild Cognitive Impairment in the Elderly in Shanghai. CURR ALZHEIMER RES. [Journal Article; Research Support, Non-U.S. Gov't]. 2017 2017/1/20;14(4):460-6. Zhou B, Zhao Q, Kojima S, Ding D, Higashide S, Fukushima M, et al. Early Detection of Dementia using Risk Classification in MCI: Outcomes of Shanghai Mild Cognitive Impairment Cohort Study. CURR ALZHEIMER RES. [Journal Article; Research Support, Non-U.S. Gov't]. 2023 2023/1/20;20(6):431-9. Su N, Li W, Li X, Wang T, Zhu M, Liu Y, et al. The Relationship between the Lifestyle of the Elderly in Shanghai Communities and Mild Cognitive Impairment. Shanghai Arch Psychiatry. [Journal Article]. 2017 2017/12/25;29(6):352-7. Ran LS, Liu WH, Fang YY, Xu SB, Li J, Luo X, et al. Alcohol, coffee and tea intake and the risk of cognitive deficits: a dose-response meta-analysis. EPIDEMIOL PSYCH SCI. [Journal Article; Meta-Analysis]. 2021 2021/2/11;30:e13. Liu X, Du X, Han G, Gao W. Association between tea consumption and risk of cognitive disorders: A dose-response meta-analysis of observational studies. Oncotarget. [Journal Article; Meta-Analysis]. 2017 2017/6/27;8(26):43306-21. Deb S, Dutta A, Phukan BC, Manivasagam T, Justin Thenmozhi A, Bhattacharya P, et al. Neuroprotective attributes of L-theanine, a bioactive amino acid of tea, and its potential role in Parkinson's disease therapeutics. NEUROCHEM INT. [Journal Article; Research Support, Non-U.S. Gov't; Review]. 2019 2019/10/1;129:104478. Xie X, Wan J, Zheng X, Pan W, Yuan J, Hu B, et al. Synergistic effects of epigallocatechin gallate and l-theanine in nerve repair and regeneration by anti-amyloid damage, promoting metabolism, and nourishing nerve cells. FRONT NUTR. [Journal Article]. 2022 2022/1/20;9:951415. Nanri H, Yoshida T, Watanabe Y, Fujita H, Kimura M, Yamada Y. The Association between Habitual Green Tea Consumption and Comprehensive Frailty as Assessed by Kihon Checklist Indexes among an Older Japanese Population. NUTRIENTS. 2021 2021/1/1;13(11):10. Shen K, Zhang B, Feng Q. Association between tea consumption and depressive symptom among Chinese older adults. BMC GERIATR. 2019;19(1):8. Li W, Yue L, Xiao S. Prospective Associations of Tea Consumption With Risk of Cognitive Decline in the Elderly: A 1-Year Follow-Up Study in China. FRONT NUTR. [Journal Article]. 2022 2022/1/20;9:752833. Liu X, Du X, Han G, Gao W. Association between tea consumption and risk of cognitive disorders: A dose-response meta-analysis of observational studies. Oncotarget. [Journal Article; Meta-Analysis]. 2017 2017/6/27;8(26):43306-21. Kerkis I, Da Silva AP, Araldi RP. The impact of interleukin-6 (IL-6) and mesenchymal stem cell-derived IL-6 on neurological conditions. FRONT IMMUNOL. [Journal Article; Review]. 2024 2024/1/20;15:1400533. Teoh NSN, Gyanwali B, Lai MKP, Chai YL, Chong JR, Chong EJY, et al. Association of Interleukin-6 and Interleukin-8 with Cognitive Decline in an Asian Memory Clinic Population. J ALZHEIMERS DIS. [Journal Article; Research Support, Non-U.S. Gov't]. 2023 2023/1/20;92(2):445-55. Ng A, Tam WW, Zhang MW, Ho CS, Husain SF, McIntyre RS, et al. IL-1beta, IL-6, TNF- alpha and CRP in Elderly Patients with Depression or Alzheimer's disease: Systematic Review and Meta-Analysis. SCI REP-UK. [Journal Article; Meta-Analysis; Systematic Review]. 2018 2018/8/13;8(1):12050. Chen G, Xu T, Yan Y, Zhou Y, Jiang Y, Melcher K, et al. Amyloid beta: structure, biology and structure-based therapeutic development. ACTA PHARMACOL SIN. [Journal Article; Review]. 2017 2017/9/1;38(9):1205-35. Deb S, Borah A. l-theanine, the unique constituent of tea, improves neuronal survivability by curtailing inflammatory responses in MPTP model of Parkinson's disease. NEUROCHEM INT. [Journal Article; Research Support, Non-U.S. Gov't]. 2024 2024/10/1;179:105830. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 28 Nov, 2025 Editor invited by journal 04 Nov, 2025 Editor assigned by journal 15 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 14 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Cross-validation error versus lambda values;\\u003c/p\\u003e\\n\\u003cp\\u003eB. Variable coefficient paths.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7601893/v1/905ace49e3ab6d11eef677ba.jpg\"},{\"id\":97268212,\"identity\":\"8db625b2-a7d3-4d99-828d-2d1cf15c5346\",\"added_by\":\"auto\",\"created_at\":\"2025-12-02 14:42:46\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":55174,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePerformance Evaluation and Predictive Visualization of the Multivariate Logistic Regression Model\\u003c/p\\u003e\\n\\u003cp\\u003eA. Forest plot; B. ROC curve; C. 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Background\",\"content\":\"\\u003cp\\u003eGlobal population aging presents unprecedented public health challenges. As of 2023, China has entered a phase of deep aging, with individuals aged 65 and above accounting for 15\\u0026ndash;20% of the total population.(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) MCI, a critical transitional stage between normal cognitive aging and dementia, has attracted increasing attention due to its high prevalence and elevated risk of progression to dementia.(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) Epidemiological data indicate that the prevalence of MCI among adults aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 years in China is approximately 15.5%, affecting about 38.77\\u0026nbsp;million people.(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e) More notably, the annual conversion rate from MCI to dementia ranges from 13.4% to 38%, imposing substantial caregiving and economic burdens on families and society.(\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e) Older adults in community settings are a key population for MCI prevention and control, as their cognitive status is closely linked to regional lifestyle factors and accessibility to healthcare resources.(\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e) The Baoshan District of Shanghai represents a typical urban aging community, where residents commonly experience high work-related stress, dietary transitions, and specific lifestyle habits that may collectively influence cognitive health trajectories.(\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e) However, systematic research on MCI in this region remains limited, with most existing studies focusing on southern China or general populations, thereby hindering the development of localized intervention strategies. Thus, identifying region-specific influencing factors and underlying mechanisms of MCI in this population is essential for early community-based prevention and intervention.\\u003c/p\\u003e\\u003cp\\u003eRecent studies suggest that lifestyle factors, such as tea drinking habits, are closely associated with the incidence of MCI.(\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) Tea consumption is common among older adults in China. A meta-analysis comprising 15 prospective studies with 246,726 participants demonstrated that regular tea consumption significantly reduces the risk of cognitive impairment.(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e) The protective mechanisms are primarily attributed to bioactive compounds in tea, such as polyphenols (particularly epigallocatechin gallate, EGCG) and L-theanine, which exhibit anti-inflammatory, antioxidant, and neuroprotective properties.(\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e) In vitro studies indicate that EGCG inhibits Aβ fibril formation and promotes its disaggregation, while also enhancing antioxidant defense via activation of the Nrf2/ARE pathway.(\\u003cspan additionalcitationids=\\\"CR17\\\" citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e) Dose-response analyses reveal that drinking 1\\u0026ndash;2 cups of tea per day is associated with the lowest risk of cognitive impairment, with more pronounced risk reduction observed at \\u0026ge;\\u0026thinsp;4 cups per day.(\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e) Subgroup analyses suggest that green tea may offer superior neuroprotective effects compared to oolong or black tea; however, evidence regarding tea-type-specific effects among older adults in eastern urban China remains insufficient.\\u003c/p\\u003e\\u003cp\\u003eOn the other hand, chronic neuroinflammation is recognized as a core pathological mechanism underlying MCI. IL-6, a key pro-inflammatory cytokine, can exacerbate cognitive impairment by disrupting blood-brain barrier integrity, promoting Aβ deposition and tau hyperphosphorylation, and inducing neuronal apoptosis through activation of JAK-STAT3 and NF-κB signaling pathways.(\\u003cspan additionalcitationids=\\\"CR21\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e) A prospective community-based cohort study showed that each 1 pg/mL increase in plasma IL-6 level was associated with a 37% increase in MCI risk, with this association being more pronounced among APOE4 carriers.(\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e) These findings align with the \\\"inflammation accelerates cognitive decline\\\" hypothesis, suggesting that IL-6 may serve as both a predictive biomarker and a potential intervention target for MCI. Nevertheless, the association between IL-6 and MCI among older adults in Shanghai has not been thoroughly investigated, particularly regarding the modulatory effects of lifestyle factors\\u0026mdash;such as dietary patterns and physical activity\\u0026mdash;on inflammatory levels.\\u003c/p\\u003e\\u003cp\\u003eAlthough both tea consumption and IL-6 have been independently associated with MCI, the potential mediating mechanism between them remains unclear. Basic research suggests that tea polyphenols may downregulate IL-6 expression by inhibiting NF-κB signaling, while caffeine and theanine may synergistically enhance anti-inflammatory effects, thereby indirectly protecting cognitive function.(\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e) This leads to a scientific hypothesis: IL-6 may mediate the relationship between tea consumption and MCI. However, this mechanism has not been validated in human studies, particularly lacking empirical analysis focusing on community-dwelling older adults in China.\\u003c/p\\u003e\\u003cp\\u003eIn summary, existing research has three main limitations: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) insufficient regional representation, especially regarding the epidemiological characteristics and influencing factors of MCI in urban Shanghai communities; (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) mechanistic studies predominantly conducted at the animal or cellular level, lacking validation of mediating pathways in human populations; (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) most previous studies have not employed rigorous statistical control and variable selection strategies (e.g., PSM and LASSO regression), making it difficult to identify stable factors amid multicollinearity and confounding bias. Therefore, this study aims to: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) describe the distribution of tea drinking behavior, IL-6 levels, and MCI status among adults aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years in the Youyi community of Baoshan, Shanghai; (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) apply PSM (2:1 matching) to balance confounding factors and use LASSO regression to identify key predictors of MCI; (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) examine the mediating effect of IL-6 in the relationship between tea consumption and cognitive function.\\u003c/p\\u003e\\u003cp\\u003eThis study will not only contribute to refining the multi-path theoretical model of \\\"lifestyle\\u0026ndash;inflammation\\u0026ndash;cognition\\\" in MCI but also provide empirical evidence for the precise prevention of cognitive impairment in community settings. If the mediating role of IL-6 is confirmed, targeted dietary recommendations (e.g., promoting tea consumption) or anti-inflammatory interventions could be implemented to reduce the risk of MCI, offering practical value for primary prevention of dementia among community-dwelling older adults.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1 Study Design and Participant Recruitment\\u003c/h2\\u003e\\n \\u003cp\\u003eThis community-based cross-sectional study was conducted in the Youyi Community of Baoshan District, Shanghai, from March to September 2023. Participants were permanent residents aged 65 years or older. A cluster sampling method combined with voluntary participation was employed for recruitment: six residential complexes within the community were selected as sampling units, and 10% of older adults aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years were randomly selected from each complex as potential candidates. Using the community health service center\\u0026rsquo;s health record system, individuals with a confirmed diagnosis of dementia or severe organ failure were initially excluded. Eligible individuals who met the preliminary screening criteria were invited to participate in the on-site survey.\\u003c/p\\u003e\\n \\u003cp\\u003eInclusion criteria were: (\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) age\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years and continuous residence in the community for \\u0026ge;\\u0026thinsp;1 year; (\\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) clear consciousness, able to complete cognitive assessments and questionnaire interviews independently or with assistance; (\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) informed of the study content and voluntarily provided written informed consent. Exclusion criteria were: (\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) meeting the diagnostic criteria for dementia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5); (\\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) presence of severe heart failure (NYHA class IV), decompensated liver cirrhosis, stage 5 chronic kidney disease, or other end-stage diseases; (\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) history of severe mental disorders such as schizophrenia or bipolar disorder, or history of stroke or traumatic brain injury within the past 3 months; (\\u003cspan class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e) severe hearing, visual, or communication impairments that would prevent completion of the assessments.\\u003c/p\\u003e\\n \\u003cp\\u003eA total of 272 participants were ultimately included. Based on the Mini-Mental State Examination (MMSE) score and educational level, and with reference to Petersen\\u0026apos;s diagnostic criteria for MCI, participants were divided into two groups: (\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) MCI group (n\\u0026thinsp;=\\u0026thinsp;19): MMSE score 20\\u0026ndash;23, presence of subjective cognitive decline, and essentially normal activities of daily living; (\\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) cognitively normal control group (n\\u0026thinsp;=\\u0026thinsp;253): MMSE score\\u0026thinsp;\\u0026gt;\\u0026thinsp;23, no subjective cognitive complaints, and no significant decline in cognitive function over the past year. The study protocol was approved by the Ethics Committee of Huashan Hospital Affiliated to Fudan University (Approval No.: 2020-004). All participants provided written informed consent.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2 Data Collection\\u003c/h2\\u003e\\n \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.2.1 Cognitive Function Assessment\\u003c/h2\\u003e\\n \\u003cp\\u003eCognitive function was assessed face-to-face using the Chinese version of the MMSE scale (revised by Peking Union Medical College Hospital; Cronbach\\u0026apos;s \\u0026alpha;\\u0026thinsp;=\\u0026thinsp;0.82). Assessments were conducted by uniformly trained community general practitioners (inter-rater consistency after training, Kappa\\u0026thinsp;=\\u0026thinsp;0.89) in quiet consultation rooms at the community health service center. Each assessment took approximately 10\\u0026ndash;15 minutes. The scale includes domains of orientation (10 points), memory (3 points), attention and calculation (5 points), recall (3 points), and language ability (9 points), with a total score of 30. The assessment strictly followed the scale\\u0026apos;s operational guidelines. If participants had visual impairments, the assessor read the items aloud; if writing was not feasible due to physical mobility impairments, verbal responses were accepted.\\u003c/p\\u003e\\n \\u003cp\\u003eMMSE Copyright Note: An unauthorized version of the Chinese MMSE was used by the study team without permission in the initial phase of this research. This has since been rectified with Psychological Assessment Resources (PAR). The MMSE is a copyrighted instrument and may not be used or reproduced in whole or in part, in any form or language, or by any means without the written permission of PAR (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ewww.parinc.com\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.2.2 Lifestyle and Basic Information Collection\\u003c/h2\\u003e\\n \\u003cp\\u003eA self-designed structured questionnaire (revised after a pilot survey of n\\u0026thinsp;=\\u0026thinsp;30; Cronbach\\u0026apos;s \\u0026alpha;\\u0026thinsp;=\\u0026thinsp;0.78) was used for face-to-face interviews. The questionnaire included:\\u003c/p\\u003e\\n \\u003cp\\u003eBasic information: age (continuous variable), gender (male\\u0026thinsp;=\\u0026thinsp;1, female\\u0026thinsp;=\\u0026thinsp;0);\\u003c/p\\u003e\\n \\u003cp\\u003eTea drinking behavior: core independent variable, defined as \\u0026quot;regular tea consumption\\u0026quot; (\\u0026ge;\\u0026thinsp;3 times per week, \\u0026ge;\\u0026thinsp;150 mL each time, for \\u0026ge;\\u0026thinsp;3 months), coded as a binary variable (yes\\u0026thinsp;=\\u0026thinsp;1, no\\u0026thinsp;=\\u0026thinsp;0);\\u003c/p\\u003e\\n \\u003cp\\u003eOther lifestyle indicators: leisure-time physical activity (binary: \\u0026ge;1 time per week and \\u0026ge;\\u0026thinsp;30 minutes each time\\u0026thinsp;=\\u0026thinsp;1, otherwise\\u0026thinsp;=\\u0026thinsp;0); alcohol consumption (binary: \\u0026ge;1 time per week for \\u0026ge;\\u0026thinsp;3 months\\u0026thinsp;=\\u0026thinsp;1, otherwise\\u0026thinsp;=\\u0026thinsp;0); smoking history (binary: ever or current smoker\\u0026thinsp;=\\u0026thinsp;1, never smoked\\u0026thinsp;=\\u0026thinsp;0).\\u003c/p\\u003e\\n \\u003cp\\u003eAll interviews were conducted by trained investigators. Before questionnaire administration, the items were explained to ensure participants\\u0026apos; understanding. After completion, questionnaires were checked on-site for completeness, and any missing information was immediately obtained to minimize data loss.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.3 Laboratory Testing\\u003c/h2\\u003e\\n \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.3.1 Sample Collection and Processing\\u003c/h2\\u003e\\n \\u003cp\\u003eFasting venous blood samples (5 mL) were collected from all participants by nurses after 12 hours of fasting and 8 hours of water deprivation. Among these, 2 mL was placed in EDTA-K₂ anticoagulant tubes for complete blood count analysis, and 3 mL was placed in coagulation-promoting tubes for serum separation. After standing at room temperature for 30 minutes, samples in coagulation-promoting tubes were centrifuged at 3000 r/min for 15 minutes (centrifugation radius 10 cm). The separated serum was aliquoted into EP tubes and stored at -80\\u0026deg;C, avoiding repeated freeze-thaw cycles (\\u0026le;\\u0026thinsp;2 times). Anticoagulated samples were analyzed within 2 hours of collection; those not tested immediately were temporarily stored at 4\\u0026deg;C for no more than 4 hours.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.3.2 Detection Indicators and Methods\\u003c/h2\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e12 Cytokines\\u003c/strong\\u003e: Levels were detected using the RaiseCyte 2L6C flow cytometer (RaiseCare Biotechnology, Qingdao, China) with a multiplex bead-based flow immunofluorescence technique, in conjunction with the 12-plex cytokine detection kit (Cat No.: BNCBA002-96T; Saihan Biotechnology, Shanghai, China).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eComplete Blood Count\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eAnalyzed using an automatic hematology analyzer (Mindray BC-5180 CRP; Mindray, China).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBiochemical Indicators\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eBiochemical indicators, such as total protein, albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood glucose, lipids (total cholesterol, triglycerides, high - density lipoprotein cholesterol, low - density lipoprotein cholesterol), and vitamin B12 (VB12), were measured by an Olympus AU5800 automatic biochemical analyzer (Olympus Corporation) with original matching reagents. Before testing, quality control was carried out using standard control materials from Roche Diagnostics to guarantee accuracy and reliability.\\u003c/p\\u003e\\n \\u003cp\\u003eAll tests were conducted by certified laboratory technicians. Instruments were routinely maintained and calibrated to ensure data precision.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.4 Statistical Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eStatistical analyses were performed using R software (version 4.3.1). A two-sided significance level of \\u003cem\\u003e\\u0026alpha;\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.05 was applied.\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.4.1 Descriptive Statistics\\u003c/h2\\u003e\\n \\u003cp\\u003eContinuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD) and compared using t-tests; non-normally distributed data are presented as median (P25\\u0026ndash;P75) and compared using the Mann-Whitney U test. Categorical variables are described as frequency (percentage) and compared using chi-square tests or Fisher\\u0026rsquo;s exact test (when expected frequency\\u0026thinsp;\\u0026lt;\\u0026thinsp;5).\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.4.2 Propensity Score Matching (PSM)\\u003c/h2\\u003e\\n \\u003cp\\u003eTo control for selection bias due to imbalanced sample sizes between the MCI and control groups (253:19), propensity score matching was performed with gender as the matching variable. A 1:2 nearest neighbor matching method was used with a caliper width of 0.2 standard deviations. Balance was assessed using standardized mean differences (SMD), with SMD\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1 indicating good balance. Subsequent analyses were based on the matched sample (n\\u0026thinsp;=\\u0026thinsp;57, including 19 MCI cases and 38 controls).\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.4.3 Univariate Subgroup GLM Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eBased on the PSM-matched sample (n\\u0026thinsp;=\\u0026thinsp;57), univariate generalized linear models (GLM) were constructed with MCI status as the dependent variable and key variables such as IL-6 and VB12 as independent variables to assess their association with MCI risk. Subgroup analyses were conducted by gender (male/female), leisure-time physical activity (yes/no), alcohol consumption (yes/no), and tea drinking behavior (yes/no). Interaction terms were used to evaluate whether subgroup differences were statistically significant.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.4.4 LASSO Regression for Variable Selection\\u003c/h2\\u003e\\n \\u003cp\\u003eFifty potential influencing factors (inflammatory factors, biochemical indicators, and lifestyle variables) were included in the LASSO regression model, with MCI status (yes\\u0026thinsp;=\\u0026thinsp;1, no\\u0026thinsp;=\\u0026thinsp;0) as the dependent variable. Variable selection was performed using the \\u0026quot;glmnet\\u0026quot; package, with the optimal \\u0026lambda; value determined by 10-fold cross-validation. Variables with non-zero coefficients were retained as key influencing factors for MCI.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.4.5 Logistic Regression Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eMultivariate logistic regression models were constructed using variables selected by LASSO regression as independent variables and MCI as the dependent variable. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. Multicollinearity was assessed using variance inflation factors (VIF), with VIF\\u0026thinsp;\\u0026lt;\\u0026thinsp;10 considered acceptable.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.4.6 Mediation Effect Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eBased on the Baron \\u0026amp; Kenny framework, the bootstrap method was used to examine the mediating role of IL-6 in the relationship between tea drinking behavior and MCI, adjusting for age and gender. The variables were defined as follows: Independent variable (X): tea drinking behavior (regular\\u0026thinsp;=\\u0026thinsp;1, no\\u0026thinsp;=\\u0026thinsp;0); Mediator variable (M): IL-6 (continuous); Dependent variable (Y): MCI (yes\\u0026thinsp;=\\u0026thinsp;1, no\\u0026thinsp;=\\u0026thinsp;0); Covariates (C): age, gender. Using the \\u0026quot;mediation\\u0026quot; package, 5000 bootstrap samples were drawn to calculate the average causal mediation effect (ACME), average direct effect (ADE), total effect, and mediation proportion. A significant mediation effect was concluded if the 95% CI for ACME did not include zero.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.1 Baseline Characteristics of the Study Participants\\u003c/h2\\u003e\\n \\u003cp\\u003eA total of 272 community-dwelling adults aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years were included in this study, comprising 253 cognitively normal controls and 19 individuals with MCI. Prior to matching, no statistically significant differences were observed between the two groups in terms of age, gender, most inflammatory cytokines, biochemical indicators, or lifestyle variables (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), as detailed in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Notably, VB12 levels differed significantly between the groups (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.032), with lower levels observed in the MCI group.\\u003c/p\\u003e\\n \\u003cp\\u003eTo control for baseline imbalances and potential confounding factors, PSM was performed using age and gender as covariates in a 2:1 ratio. This resulted in 57 successfully matched participants (19 MCI and 38 controls). After matching, the distributions of age, gender, and other variables were well-balanced between the groups, with all SMD\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1. Furthermore, differences between groups became more pronounced for alanine aminotransferase (ALT, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.020), VB12 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.007), and tea drinking behavior (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.058) after matching (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eBaseline characteristics of the study population before propensity score matching (PSM)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eControl group (n\\u0026thinsp;=\\u0026thinsp;253)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMCI group (n\\u0026thinsp;=\\u0026thinsp;19)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e71.00 (67.00\\u0026ndash;75.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e68.00 (66.00\\u0026ndash;74.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.103\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-5 (IL-5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.96 (3.09\\u0026ndash;5.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.58 (2.79\\u0026ndash;5.62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterferon-\\u0026alpha; (IFN-\\u0026alpha;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.83 (1.58\\u0026ndash;2.34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.84 (1.64\\u0026ndash;2.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.746\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-2 (IL-2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.72 (1.49\\u0026ndash;1.94)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.78 (1.49\\u0026ndash;2.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.424\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-6 (IL-6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.80 (2.27\\u0026ndash;3.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.16 (2.17\\u0026ndash;4.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-1\\u0026beta; (IL-1\\u0026beta;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.61 (1.26\\u0026ndash;8.04)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.98 (2.12\\u0026ndash;5.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.917\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-10 (IL-10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.70 (1.43\\u0026ndash;2.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.73 (1.53\\u0026ndash;1.98)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.888\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterferon-\\u0026gamma; (IFN-\\u0026gamma;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.84 (4.66\\u0026ndash;7.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.21 (5.06\\u0026ndash;8.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.294\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-8 (IL-8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;0.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;0.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.944\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-17 (IL-17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;2.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;4.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.519\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-4 (IL-4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87 (0.63\\u0026ndash;1.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.14 (0.52\\u0026ndash;1.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.372\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eInterleukin-12p70 (IL-12p70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.86 (0.42\\u0026ndash;1.23)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 (0.14\\u0026ndash;1.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.954\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTumor necrosis factor-\\u0026alpha; (TNF-\\u0026alpha;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59 (0.22\\u0026ndash;1.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72 (0.22\\u0026ndash;1.22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.439\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum total protein (g/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e74.00 (71.60\\u0026ndash;76.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e75.20 (72.00\\u0026ndash;78.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.578\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAspartate transaminase (AST) (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25.00 (21.00\\u0026ndash;30.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22.00 (20.00\\u0026ndash;27.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.248\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAlbumin/globulin ratio (A/G)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.40 (1.30\\u0026ndash;1.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.40 (1.30\\u0026ndash;1.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.845\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u0026gamma;-Glutamyl transferase (GGT) (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e23.00 (17.00\\u0026ndash;31.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19.00 (15.00\\u0026ndash;27.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.241\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum uric acid (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e343.00 (293.00\\u0026ndash;396.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e320.00 (304.00\\u0026ndash;397.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.796\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTotal cholesterol (TC) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.67 (4.84\\u0026ndash;6.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.97 (5.17\\u0026ndash;6.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.418\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTriglycerides (TG) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.36 (1.05\\u0026ndash;1.85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.44 (1.16\\u0026ndash;1.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.925\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum globulin (g/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30.40 (27.90\\u0026ndash;32.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30.20 (25.90\\u0026ndash;33.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.994\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBlood urea nitrogen (BUN) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.60 (4.80\\u0026ndash;6.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.70 (4.70\\u0026ndash;7.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum creatinine (Scr) (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e72.00 (59.00\\u0026ndash;83.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e65.00 (58.00\\u0026ndash;77.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHigh-density lipoprotein cholesterol (HDL-C) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.29 (1.11\\u0026ndash;1.44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.28 (1.07\\u0026ndash;1.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLow-density lipoprotein cholesterol (LDL-C) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.31 (2.68\\u0026ndash;3.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.24 (2.79\\u0026ndash;4.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.671\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTotal bilirubin (TBIL) (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.90 (11.20\\u0026ndash;16.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.70 (11.20\\u0026ndash;20.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.405\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAlbumin (ALB) (g/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e43.90 (42.50\\u0026ndash;45.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44.30 (43.30\\u0026ndash;45.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.273\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAlanine transaminase (ALT) (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19.00 (15.00\\u0026ndash;24.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e17.00 (14.00\\u0026ndash;21.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.186\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eFasting blood glucose (Glu) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.90 (5.50\\u0026ndash;6.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.00 (5.50\\u0026ndash;7.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.423\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCalcium (Ca) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.42 (2.38\\u0026ndash;2.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.47 (2.41\\u0026ndash;2.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eChloride (Cl) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e105.00 (103.00\\u0026ndash;106.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e105.00 (104.00\\u0026ndash;107.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.475\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSodium (Na) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e143.00 (141.00\\u0026ndash;144.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e143.00 (142.00\\u0026ndash;144.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.682\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ePotassium (K) (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.29 (4.09\\u0026ndash;4.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.27 (4.18\\u0026ndash;4.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.781\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eEstimated glomerular filtration rate (eGFR_MDRD) (mL/min/1.73m\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e94.00 (81.00\\u0026ndash;108.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e99.00 (90.00\\u0026ndash;110.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.298\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eWhite blood cell count (WBC) (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.16 (4.49\\u0026ndash;6.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.68 (3.74\\u0026ndash;7.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.557\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eNeutrophil ratio (NEUTratio) (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55.70 (50.80\\u0026ndash;60.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55.60 (49.10\\u0026ndash;60.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.721\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLymphocyte ratio (LYMratio) (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e34.40 (30.10\\u0026ndash;39.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e35.80 (31.30\\u0026ndash;39.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eMonocyte ratio (MONOratio) (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.10 (5.20\\u0026ndash;7.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.60 (5.40\\u0026ndash;7.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.313\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eEosinophil ratio (EOSratio) (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.00 (1.30\\u0026ndash;2.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.80 (1.50\\u0026ndash;2.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.953\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBasophil ratio (BASOratio) (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.30 (0.20\\u0026ndash;0.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.30 (0.10\\u0026ndash;0.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eNeutrophil count (NEUT) (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.89 (2.41\\u0026ndash;3.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.60 (1.93\\u0026ndash;4.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.658\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLymphocyte count (LYM) (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.78 (1.47\\u0026ndash;2.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.62 (1.50\\u0026ndash;1.93)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.703\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eMonocyte count (MONO) (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.32 (0.27\\u0026ndash;0.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.31 (0.28\\u0026ndash;0.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.715\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eEosinophil count (EOS) (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.10 (0.07\\u0026ndash;0.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.10 (0.06\\u0026ndash;0.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBasophil count (BASO) (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.02 (0.01\\u0026ndash;0.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01 (0.01\\u0026ndash;0.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eGlycated hemoglobin (HbA1c) (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.79 (5.55\\u0026ndash;6.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.82 (5.60\\u0026ndash;6.44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.638\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eVitamin B12 (VB12) (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e232.77 (204.19\\u0026ndash;269.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e214.30 (186.29\\u0026ndash;232.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.032\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum folate (ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11.31 (8.91\\u0026ndash;13.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11.89 (9.64\\u0026ndash;13.81)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.461\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHomocysteine (Hcy) (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.20 (11.27\\u0026ndash;19.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15.23 (10.74\\u0026ndash;20.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.909\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHousehold physical activity (MET\\u0026middot;h/week)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.00 (1.00\\u0026ndash;3.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.00 (0.00\\u0026ndash;3.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.202\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLeisure time physical activity n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Non-participation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e62 (24.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (26.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.86\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Participation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e191 (75.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (73.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlcohol consumption n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Non-consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e223 (88.14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (94.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.383\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 (11.86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (5.26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTea consumption n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Non-consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e138 (54.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (73.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.105\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Regular consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e115 (45.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (26.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSex n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Male\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e141.00 (55.73%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.00 (68.42%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.282\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e112.00 (44.27%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.00 (31.58%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003eMCI\\u0026thinsp;=\\u0026thinsp;Mild Cognitive Impairment; MMSE\\u0026thinsp;=\\u0026thinsp;Mini-Mental State Examination; Continuous variables are presented as median (P25\\u0026ndash;P75), and intergroup comparisons were conducted using the Mann-Whitney U test; Categorical variables are presented as n (%), and intergroup comparisons were conducted using the \\u0026chi;\\u0026sup2; test or Fisher\\u0026rsquo;s exact test (when the expected frequency\\u0026thinsp;\\u0026lt;\\u0026thinsp;5).\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eBaseline characteristics of the study population after propensity score matching (PSM, 2:1 matching by age and sex)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eControl group (n\\u0026thinsp;=\\u0026thinsp;38)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMCI group (n\\u0026thinsp;=\\u0026thinsp;19)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAge (years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e68.00 (66.00\\u0026ndash;74.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e68.00 (66.00\\u0026ndash;74.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.993\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-5 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.22 (3.11\\u0026ndash;6.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.58 (2.79\\u0026ndash;5.62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.302\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIFN-\\u0026alpha; (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.77 (1.60\\u0026ndash;2.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.84 (1.64\\u0026ndash;2.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.756\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-2 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.76 (1.56\\u0026ndash;1.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.78 (1.49\\u0026ndash;2.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;0.999\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-6 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.63 (2.36\\u0026ndash;3.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.16 (2.17\\u0026ndash;4.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.421\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-1\\u0026beta; (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.37 (1.03\\u0026ndash;8.04)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.98 (2.12\\u0026ndash;5.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-10 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.73 (1.45\\u0026ndash;2.14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.73 (1.53\\u0026ndash;1.98)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.966\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIFN-\\u0026gamma; (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.72 (5.60\\u0026ndash;8.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.21 (5.06\\u0026ndash;8.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.441\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-8 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;0.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;0.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-17 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;3.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00 (0.00\\u0026ndash;4.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-4 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.92 (0.58\\u0026ndash;1.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.14 (0.52\\u0026ndash;1.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.786\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eIL-12p70 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.96 (0.63\\u0026ndash;1.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00 (0.14\\u0026ndash;1.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTNF-\\u0026alpha; (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.61 (0.25\\u0026ndash;1.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72 (0.22\\u0026ndash;1.22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.436\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum total protein (g/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e73.95 (72.00\\u0026ndash;76.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e75.20 (72.00\\u0026ndash;78.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAST (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25.50 (22.00\\u0026ndash;31.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22.00 (20.00\\u0026ndash;27.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eA/G\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.40 (1.30\\u0026ndash;1.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.40 (1.30\\u0026ndash;1.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.979\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eGGT (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24.50 (17.00\\u0026ndash;39.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19.00 (15.00\\u0026ndash;27.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.213\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum uric acid (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e343.50 (296.00\\u0026ndash;410.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e320.00 (304.00\\u0026ndash;397.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.806\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTC (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.78 (5.09\\u0026ndash;6.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.97 (5.17\\u0026ndash;6.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.412\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTG (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.43 (1.08\\u0026ndash;1.83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.44 (1.16\\u0026ndash;1.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.866\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum globulin (g/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30.55 (27.60\\u0026ndash;32.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30.20 (25.90\\u0026ndash;33.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBUN (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.55 (4.80\\u0026ndash;6.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.70 (4.70\\u0026ndash;7.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eScr (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e66.00 (56.00\\u0026ndash;81.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e65.00 (58.00\\u0026ndash;77.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHDL-C (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.30 (1.12\\u0026ndash;1.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.28 (1.07\\u0026ndash;1.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.565\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLDL-C (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.40 (2.79\\u0026ndash;3.85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.24 (2.79\\u0026ndash;4.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.741\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTBIL (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.30 (10.40\\u0026ndash;17.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.70 (11.20\\u0026ndash;20.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.343\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eALB (g/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e43.90 (42.60\\u0026ndash;45.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44.30 (43.30\\u0026ndash;45.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.588\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eALT (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20.50 (18.00\\u0026ndash;32.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e17.00 (14.00\\u0026ndash;21.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eGlu (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.00 (5.40\\u0026ndash;6.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.00 (5.50\\u0026ndash;7.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.709\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCa (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.43 (2.38\\u0026ndash;2.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.47 (2.41\\u0026ndash;2.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.143\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCl (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e104.00 (103.00\\u0026ndash;106.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e105.00 (104.00\\u0026ndash;107.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.221\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eNa (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e142.00 (141.00\\u0026ndash;143.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e143.00 (142.00\\u0026ndash;144.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.242\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eK (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.29 (4.17\\u0026ndash;4.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.27 (4.18\\u0026ndash;4.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.553\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eeGFR_MDRD (mL/min/1.73m\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e97.50 (87.00\\u0026ndash;117.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e99.00 (90.00\\u0026ndash;110.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.973\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eWBC (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.75 (4.33\\u0026ndash;5.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.68 (3.74\\u0026ndash;7.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.939\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eNEUTratio (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55.10 (51.50\\u0026ndash;59.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e55.60 (49.10\\u0026ndash;60.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.886\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLYMratio (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e34.40 (30.70\\u0026ndash;38.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e35.80 (31.30\\u0026ndash;39.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.826\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eMONOratio (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.10 (4.90\\u0026ndash;7.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.60 (5.40\\u0026ndash;7.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.407\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eEOSratio (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.20 (1.70\\u0026ndash;2.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.80 (1.50\\u0026ndash;2.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.482\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBASOratio (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.30 (0.20\\u0026ndash;0.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.30 (0.10\\u0026ndash;0.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.546\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eNEUT (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.64 (2.44\\u0026ndash;3.33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.60 (1.93\\u0026ndash;4.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.806\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLYM (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.74 (1.38\\u0026ndash;2.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.62 (1.50\\u0026ndash;1.93)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.986\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eMONO (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.31 (0.24\\u0026ndash;0.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.31 (0.28\\u0026ndash;0.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.471\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eEOS (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.11 (0.08\\u0026ndash;0.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.10 (0.06\\u0026ndash;0.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.564\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBASO (\\u0026times;10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.02 (0.01\\u0026ndash;0.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01 (0.01\\u0026ndash;0.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.758\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHbA1c (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.96 (5.52\\u0026ndash;6.22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.82 (5.60\\u0026ndash;6.44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.939\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eVB12 (pg/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e241.01 (219.94\\u0026ndash;272.92)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e214.30 (186.29\\u0026ndash;232.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSerum folate (ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11.69 (9.04\\u0026ndash;13.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11.89 (9.64\\u0026ndash;13.81)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.681\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHcy (\\u0026micro;mol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.16 (10.48\\u0026ndash;18.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15.23 (10.74\\u0026ndash;20.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.913\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHousehold physical activity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.00 (1.00\\u0026ndash;2.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.00 (0.00\\u0026ndash;3.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.454\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSex, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24 (63.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13 (68.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.695\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Male\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (36.84)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (31.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLeisure time physical activity, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Non-participation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 (26.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (26.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;0.999\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Participation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e28 (73.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (73.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlcohol consumption, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Non-consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e34 (89.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (94.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.508\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (10.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (5.26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTea consumption, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0: Non-consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19 (50.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (73.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.058\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1: Regular consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19 (50.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 (26.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003eA two-tailed \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.2 Subgroup Analysis Based on Univariate Generalized Linear Model with Forest Plot\\u003c/h2\\u003e\\n \\u003cp\\u003eTo further investigate the association between key variables and MCI risk across different population subgroups and to assess potential heterogeneity, univariate GLM were constructed using the PSM-matched sample (n\\u0026thinsp;=\\u0026thinsp;57). Results were visualized using forest plots (Fig.\\u0026nbsp;1).\\u003c/p\\u003e\\n \\u003cp\\u003eThe results indicated a trend toward a positive association between elevated IL-6 levels and increased risk of MCI (overall \\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;1.47, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.98\\u0026ndash;2.21, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.065). Although this association did not reach statistical significance, it suggests that IL-6 may be a risk factor for MCI. Subgroup analyses revealed no significant heterogeneity in the association between IL-6 and MCI across subgroups defined by gender (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;=\\u0026thinsp;0.687), leisure-time physical activity (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;=\\u0026thinsp;0.481), alcohol consumption (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;=\\u0026thinsp;0.401), or tea drinking behavior (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;=\\u0026thinsp;0.613).\\u003c/p\\u003e\\n \\u003cp\\u003eVB12 levels were significantly inversely associated with MCI risk (overall \\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.98, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.96\\u0026ndash;1.00, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.012). Subgroup analysis suggested that the protective effect of VB12 was more pronounced in men (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.96, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.93\\u0026ndash;0.99, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.018) than in women (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.99, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.97\\u0026ndash;1.02, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.482); however, the interaction between gender and VB12 did not reach statistical significance (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;=\\u0026thinsp;0.084). Additionally, the association between VB12 and MCI remained consistent across subgroups of leisure-time physical activity (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;=\\u0026thinsp;0.603) and alcohol consumption (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;=\\u0026thinsp;0.942).\\u003c/p\\u003e\\n \\u003cp\\u003eIn summary, the associations of IL-6 and VB12 with MCI were generally consistent across subgroups with different characteristics, and no significant interactions were observed, indicating that their effects are broadly generalizable.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.3 Screening of Influencing Factors for MCI and Multivariate Logistic Regression Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eLASSO regression was used to screen 50 potential predictor variables. Through 10-fold cross-validation, the optimal lambda (\\u0026lambda;) value was identified, resulting in the retention of two variables with non-zero coefficients: IL-6 and VB12 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Incorporating these with ALT and tea drinking behavior\\u0026mdash;which showed intergroup differences in univariate analysis\\u0026mdash;a multivariate logistic regression model was constructed.\\u003c/p\\u003e\\n \\u003cp\\u003eThe multivariate analysis results (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) showed that tea drinking was significantly associated with a reduced risk of MCI (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.137, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.015\\u0026ndash;0.786, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.044). Elevated IL-6 levels were significantly associated with an increased risk of MCI (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;2.069, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 1.217\\u0026ndash;4.083, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.016). VB12 levels were inversely associated with MCI risk (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.984, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.966\\u0026ndash;0.997, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.038). ALT was not statistically significant in the final model (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.341).\\u003c/p\\u003e\\n \\u003cp\\u003eThe performance of the model was comprehensively evaluated using multiple graphical tools, including a forest plot of predictor variables (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), receiver operating characteristic (ROC) curve (\\u003cem\\u003eAUC\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.841, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB), and a nomogram (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC), as well as calibration curves, decision curve analysis, residual scatter plots, and coefficient plots (Supplementary Fig. 1). The results indicated that the model had good discriminative ability and calibration.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eMultivariable logistic regression analysis of factors associated with MCI\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEstimate\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStd Error\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eZ value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95% \\u003cem\\u003eCI\\u003c/em\\u003e (Lower, Upper)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eOR\\u003c/em\\u003e (95% CI)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIntercept\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.1267\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.974\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.0774\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.2813\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.2879, 6.4940\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.3872(0.2759, 661.1931)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIL-6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.7272\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3014\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.4127\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0158\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.1965, 1.4069\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.0693(1.2172, 4.0834)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eALT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.0332\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0349\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.9518\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3412\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.1167, 0.0257\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9673(0.8899, 1.0260)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVB12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.0167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.0735\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0381\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.0349, -0.0032\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9835(0.9657, 0.9968)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTea consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.9876\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9884\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.0109\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0443\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-4.2290, -0.2414\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.137(0.0146, 0.7855)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.4 Mediation Analysis of IL-6 in the Association Between Tea Drinking and MCI\\u003c/h2\\u003e\\n \\u003cp\\u003eMediation analysis using the bootstrap method (5000 repetitions) was performed to evaluate the mediating role of IL-6 in the relationship between tea drinking and MCI, adjusting for age and gender (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The results revealed a significant mediating effect of IL-6: the ACME was 0.1356 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.0123\\u0026ndash;0.3098, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.022), indicating that tea drinking indirectly reduces the risk of MCI by lowering IL-6 levels. The average direct effect (ADE) was \\u0026minus;\\u0026thinsp;0.3526 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: -0.5989 to -0.1132, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), suggesting that tea drinking also has a direct protective effect on MCI independent of IL-6. The total effect was \\u0026minus;\\u0026thinsp;0.2170 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: -0.4756\\u0026ndash;0.0168, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.066), and the mediation proportion was \\u0026minus;\\u0026thinsp;0.6251 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: -5.1381\\u0026ndash;1.9092, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.088). A mediation effect interval plot further visually represents these results (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eMediation effect analysis of IL-6 in the association between tea consumption and MCI\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEffect Type\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEstimate\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95% CI (Lower,Upper)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eACME (Indirect effect)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.1356\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0123,0.3098\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.022\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eADE (Direct effect)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.3526\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.5989,-0.1132\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal Effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.217\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.4756,0.0168\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.066\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProportion Mediated\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.6251\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.1381,1.9092\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.088\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eMediation model: Independent variable (X)\\u0026thinsp;=\\u0026thinsp;tea consumption; Mediator (M)\\u0026thinsp;=\\u0026thinsp;IL-6; Dependent variable (Y)\\u0026thinsp;=\\u0026thinsp;MCI. Adjusted for age and sex. ACME\\u0026thinsp;=\\u0026thinsp;Average Causal Mediation Effect (indirect effect of X on Y via M); ADE\\u0026thinsp;=\\u0026thinsp;Average Direct Effect (direct effect of X on Y independent of M); Total Effect\\u0026thinsp;=\\u0026thinsp;sum of ACME and ADE. Proportion Mediated = (ACME / Total Effect) \\u0026times; 100% (negative value indicates the indirect effect direction is opposite to the total effect). A two-tailed P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 and 95% CI not containing zero indicate a statistically significant effect.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThe present study investigated the association between tea consumption and MCI among community-dwelling older adults in Shanghai, China, and explored the potential mediating role of IL-6. Our findings indicate that regular tea consumption is associated with a significantly reduced risk of MCI. This protective effect may be attributed to the bioactive compounds present in tea, such as polyphenols (e.g., epigallocatechin gallate, EGCG) and L-theanine, which possess anti-inflammatory, antioxidant, and neuroprotective properties. Moreover, our mediation analysis demonstrated that a portion of the protective effect of tea consumption is mediated through the reduction of IL-6 levels, providing population-level evidence for the \\\"tea consumption\\u0026ndash;anti-inflammation\\u0026ndash;cognitive protection\\\" pathway.\\u003c/p\\u003e\\u003cp\\u003eSecondly, elevated IL-6 levels were significantly correlated with an increased risk of MCI, supporting the \\\"inflammatory hypothesis\\\" of cognitive decline. Neuroinflammation may compromise cognitive function through mechanisms such as disrupting blood-brain barrier integrity, promoting Aβ deposition, and inducing tau hyperphosphorylation. The mediation analysis indicated that IL-6 partially mediates the relationship between tea consumption and MCI, suggesting that tea consumption may downregulate IL-6 expression by inhibiting inflammatory pathways such as NF-κB, thereby indirectly preserving cognitive function. This finding establishes a connection between lifestyle factors and inflammatory mechanisms and provides novel targets for early MCI intervention.\\u003c/p\\u003e\\u003cp\\u003eThirdly, vitamin B12 levels were inversely related to the risk of MCI, which may be associated with the role of VB12 in homocysteine metabolism and myelination. VB12 deficiency may lead to hyperhomocysteinemia, consequently inducing vascular endothelial damage and neurodegeneration. Our study also suggested that the protective effect of VB12 was more prominent in men, potentially due to gender-based differences in metabolism and lifestyle; however, the interaction effect was not statistically significant, necessitating further validation in larger samples.\\u003c/p\\u003e\\u003cp\\u003eMethodologically, this study utilized PSM, LASSO regression, and bootstrap mediation analysis, effectively controlling for confounding bias and multicollinearity, thus enhancing the robustness of variable selection and the interpretability of the results. After PSM, the between-group differences in ALT, VB12, and tea consumption became more distinct, indicating improved comparability between groups. LASSO regression further identified IL-6 and VB12 as key factors from 50 variables, avoiding overfitting.\\u003c/p\\u003e\\u003cp\\u003eNonetheless, several limitations of this study should be recognized. Firstly, the cross-sectional design precludes causal inference; future prospective cohort studies or intervention trials are required to validate the mediating mechanism of IL-6. Secondly, the sample size was relatively small (particularly with only 19 cases in the MCI group). Although PSM improved statistical efficiency, it may still impact the power of subgroup analyses and interaction tests. Thirdly, other potential mediating variables (e.g., oxidative stress markers, neurotrophic factors) were not measured; future studies could incorporate multi-omics indicators to construct a more comprehensive mediation model. Finally, although tea consumption was defined based on frequency and duration, tea types (e.g., green tea, black tea) and brewing concentration were not differentiated, which may obscure tea-specific effects.\\u003c/p\\u003e\\u003cp\\u003eDespite these limitations, the findings of this study have significant practical implications. If tea consumption indeed indirectly safeguards cognitive function by reducing IL-6 levels, community-based MCI prevention and control could adopt a two-pronged strategy: firstly, promoting anti-inflammatory dietary patterns such as tea consumption; secondly, implementing anti-inflammatory interventions (e.g., exercise, nutritional supplementation) targeting populations with high IL-6 levels. Additionally, the protective role of VB12 underscores the importance of monitoring the nutritional status of the elderly; regular screening and VB12 supplementation may contribute to cognitive maintenance.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study confirms that tea consumption, IL-6, and VB12 are independent influencing factors for MCI among older adults in the Baoshan community of Shanghai, and for the first time demonstrates the partial mediating role of IL-6 in the relationship between tea consumption and MCI. The results support the existence of a \\\"lifestyle\\u0026ndash;inflammation\\u0026ndash;cognition\\\" pathway, suggesting that modulating inflammatory levels through tea consumption may be an effective strategy for community-based MCI prevention and control. Future larger-scale prospective studies are warranted to further validate the mediating mechanisms and explore tea-type-specific effects, thereby providing a basis for developing precise and regionally tailored MCI interventions.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"144\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 72px;\\\"\\u003e\\n \\u003cp\\u003eAbbreviation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 72px;\\\"\\u003e\\n \\u003cp\\u003eFull Term\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMCI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMild Cognitive Impairment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eIL-6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eInterleukin-6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMMSE\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMini-Mental State Examination\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePSM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePropensity Score Matching\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLASSO\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLeast Absolute Shrinkage and Selection Operator\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eOR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eOdds Ratio\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eConfidence Interval\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eVB12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eVitamin B12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eACME\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAverage Causal Mediation Effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eADE\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAverage Direct Effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eStandard Deviation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSMD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eStandardized Mean Difference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGLM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGeneralized Linear Model\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eROC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eReceiver Operating Characteristic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAUC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eArea Under the Curve\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eALT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAlanine Aminotransferase\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAST\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAspartate Aminotransferase\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDSM-5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNYHA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNew York Heart Association\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eEGCG\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eEpigallocatechin Gallate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval and Consent to Participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Huashan Hospital Affiliated to Fudan University (Approval No.: 2020-004). Written informed consent was obtained from all participants prior to their inclusion in the study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for Publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll participants provided consent for the publication of anonymized data collected in this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of Data and Materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work is supported by An In-Hospital Scientific Research Project of Shanghai Fifth People\\u0026apos;s Hospital (2022WYZD03) and Medical Specialty Construction Project of Minhang District, Shanghai (2025MWTZB01).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWu: Conceptualization, Methodology, Formal analysis Investigation, Writing - Original Draft, Visualization.\\u003c/p\\u003e\\n\\u003cp\\u003eXiaotong Chen: Validation, Formal analysis, Data Curation.\\u003c/p\\u003e\\n\\u003cp\\u003eMa: Resources, Investigation, Project administration.\\u003c/p\\u003e\\n\\u003cp\\u003eQiudan Chen: Software, Validation, Formal analysis, Data Curation, Writing - Review \\u0026amp; Editing.\\u003c/p\\u003e\\n\\u003cp\\u003eLin: Supervision, Writing - Review \\u0026amp; Editing, Funding acquisition.\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to thank all the participants and their families for their time and cooperation. We also extend our gratitude to the staff of the Youyi Community Health Service Center for their assistance in participant recruitment and data collection.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eGlobal, regional, and national trends in routine childhood vaccination coverage from 1980 to 2023 with forecasts to 2030: a systematic analysis for the Global Burden of Disease Study 2023. LANCET. [Journal Article]. 2025 2025/7/19;406(10500):235-60.\\u003c/li\\u003e\\n\\u003cli\\u003ePetersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D, et al. Practice guideline update summary: Mild cognitive impairment [RETIRED]: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. NEUROLOGY. [Journal Article; Practice Guideline; Research Support, Non-U.S. Gov\\u0026apos;t; Systematic Review]. 2018 2018/1/16;90(3):126-35.\\u003c/li\\u003e\\n\\u003cli\\u003eXie Y, Zhao T, Zhang W, Chen Q, Qiu A, Li Y, et al. Neural deterioration and compensation in visual short-term memory among individuals with amnestic mild cognitive impairment. ALZHEIMERS DEMENT. [Journal Article]. 2025 2025/2/1;21(2):e14475.\\u003c/li\\u003e\\n\\u003cli\\u003eJia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. LANCET PUBLIC HEALTH. [Journal Article; Research Support, Non-U.S. Gov\\u0026apos;t]. 2020 2020/12/1;5(12):e661-71.\\u003c/li\\u003e\\n\\u003cli\\u003eJones A, Ali MU, Kenny M, Mayhew A, Mokashi V, He H, et al. Potentially Modifiable Risk Factors for Dementia and Mild Cognitive Impairment: An Umbrella Review and Meta-Analysis. DEMENT GERIATR COGN. [Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov\\u0026apos;t; Systematic Review]. 2024 2024/1/20;53(2):91-106.\\u003c/li\\u003e\\n\\u003cli\\u003eWang Q, Zhou S, Zhang J, Wang Q, Hou F, Han X, et al. Risk assessment and stratification of mild cognitive impairment among the Chinese elderly: attention to modifiable risk factors. J EPIDEMIOL COMMUN H. [Journal Article; Research Support, Non-U.S. Gov\\u0026apos;t]. 2023 2023/8/1;77(8):521-6.\\u003c/li\\u003e\\n\\u003cli\\u003eMian M, Tahiri J, Eldin R, Altabaa M, Sehar U, Reddy PH. Overlooked cases of mild cognitive impairment: Implications to early Alzheimer\\u0026apos;s disease. AGEING RES REV. [Journal Article; Research Support, N.I.H., Extramural; Systematic Review]. 2024 2024/7/1;98:102335.\\u003c/li\\u003e\\n\\u003cli\\u003eLanga KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA-J AM MED ASSOC. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov\\u0026apos;t; Review]. 2014 2014/12/17;312(23):2551-61.\\u003c/li\\u003e\\n\\u003cli\\u003eWang T, Xiao S, Chen K, Yang C, Dong S, Cheng Y, et al. Prevalence, Incidence, Risk and Protective Factors of Amnestic Mild Cognitive Impairment in the Elderly in Shanghai. CURR ALZHEIMER RES. [Journal Article; Research Support, Non-U.S. Gov\\u0026apos;t]. 2017 2017/1/20;14(4):460-6.\\u003c/li\\u003e\\n\\u003cli\\u003eZhou B, Zhao Q, Kojima S, Ding D, Higashide S, Fukushima M, et al. Early Detection of Dementia using Risk Classification in MCI: Outcomes of Shanghai Mild Cognitive Impairment Cohort Study. CURR ALZHEIMER RES. [Journal Article; Research Support, Non-U.S. Gov\\u0026apos;t]. 2023 2023/1/20;20(6):431-9.\\u003c/li\\u003e\\n\\u003cli\\u003eSu N, Li W, Li X, Wang T, Zhu M, Liu Y, et al. The Relationship between the Lifestyle of the Elderly in Shanghai Communities and Mild Cognitive Impairment. Shanghai Arch Psychiatry. [Journal Article]. 2017 2017/12/25;29(6):352-7.\\u003c/li\\u003e\\n\\u003cli\\u003eRan LS, Liu WH, Fang YY, Xu SB, Li J, Luo X, et al. Alcohol, coffee and tea intake and the risk of cognitive deficits: a dose-response meta-analysis. EPIDEMIOL PSYCH SCI. [Journal Article; Meta-Analysis]. 2021 2021/2/11;30:e13.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu X, Du X, Han G, Gao W. Association between tea consumption and risk of cognitive disorders: A dose-response meta-analysis of observational studies. Oncotarget. [Journal Article; Meta-Analysis]. 2017 2017/6/27;8(26):43306-21.\\u003c/li\\u003e\\n\\u003cli\\u003eDeb S, Dutta A, Phukan BC, Manivasagam T, Justin Thenmozhi A, Bhattacharya P, et al. Neuroprotective attributes of L-theanine, a bioactive amino acid of tea, and its potential role in Parkinson\\u0026apos;s disease therapeutics. NEUROCHEM INT. [Journal Article; Research Support, Non-U.S. Gov\\u0026apos;t; Review]. 2019 2019/10/1;129:104478.\\u003c/li\\u003e\\n\\u003cli\\u003eXie X, Wan J, Zheng X, Pan W, Yuan J, Hu B, et al. Synergistic effects of epigallocatechin gallate and l-theanine in nerve repair and regeneration by anti-amyloid damage, promoting metabolism, and nourishing nerve cells. FRONT NUTR. [Journal Article]. 2022 2022/1/20;9:951415.\\u003c/li\\u003e\\n\\u003cli\\u003eNanri H, Yoshida T, Watanabe Y, Fujita H, Kimura M, Yamada Y. The Association between Habitual Green Tea Consumption and Comprehensive Frailty as Assessed by Kihon Checklist Indexes among an Older Japanese Population. NUTRIENTS. 2021 2021/1/1;13(11):10.\\u003c/li\\u003e\\n\\u003cli\\u003eShen K, Zhang B, Feng Q. Association between tea consumption and depressive symptom among Chinese older adults. BMC GERIATR. 2019;19(1):8.\\u003c/li\\u003e\\n\\u003cli\\u003eLi W, Yue L, Xiao S. Prospective Associations of Tea Consumption With Risk of Cognitive Decline in the Elderly: A 1-Year Follow-Up Study in China. FRONT NUTR. [Journal Article]. 2022 2022/1/20;9:752833.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu X, Du X, Han G, Gao W. Association between tea consumption and risk of cognitive disorders: A dose-response meta-analysis of observational studies. Oncotarget. [Journal Article; Meta-Analysis]. 2017 2017/6/27;8(26):43306-21.\\u003c/li\\u003e\\n\\u003cli\\u003eKerkis I, Da Silva AP, Araldi RP. The impact of interleukin-6 (IL-6) and mesenchymal stem cell-derived IL-6 on neurological conditions. FRONT IMMUNOL. [Journal Article; Review]. 2024 2024/1/20;15:1400533.\\u003c/li\\u003e\\n\\u003cli\\u003eTeoh NSN, Gyanwali B, Lai MKP, Chai YL, Chong JR, Chong EJY, et al. Association of Interleukin-6 and Interleukin-8 with Cognitive Decline in an Asian Memory Clinic Population. J ALZHEIMERS DIS. [Journal Article; Research Support, Non-U.S. Gov\\u0026apos;t]. 2023 2023/1/20;92(2):445-55.\\u003c/li\\u003e\\n\\u003cli\\u003eNg A, Tam WW, Zhang MW, Ho CS, Husain SF, McIntyre RS, et al. IL-1beta, IL-6, TNF- alpha and CRP in Elderly Patients with Depression or Alzheimer\\u0026apos;s disease: Systematic Review and Meta-Analysis. SCI REP-UK. [Journal Article; Meta-Analysis; Systematic Review]. 2018 2018/8/13;8(1):12050.\\u003c/li\\u003e\\n\\u003cli\\u003eChen G, Xu T, Yan Y, Zhou Y, Jiang Y, Melcher K, et al. Amyloid beta: structure, biology and structure-based therapeutic development. ACTA PHARMACOL SIN. [Journal Article; Review]. 2017 2017/9/1;38(9):1205-35.\\u003c/li\\u003e\\n\\u003cli\\u003eDeb S, Borah A. l-theanine, the unique constituent of tea, improves neuronal survivability by curtailing inflammatory responses in MPTP model of Parkinson\\u0026apos;s disease. NEUROCHEM INT. [Journal Article; Research Support, Non-U.S. Gov\\u0026apos;t]. 2024 2024/10/1;179:105830.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-geriatrics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bgtc\",\"sideBox\":\"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bgtc/default.aspx\",\"title\":\"BMC Geriatrics\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Mild cognitive impairment, tea drinking, interleukin-6, mediation analysis, propensity score matching, elderly\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7601893/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7601893/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eObjective\\u003c/h2\\u003e\\u003cp\\u003eTo investigate the association between tea consumption and mild cognitive impairment (MCI) among adults aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years in Shanghai, China, and to examine the potential mediating role of interleukin-6 (IL-6).\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eA cross-sectional study was conducted from March to September 2023, including 272 community-dwelling older adults. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and participants were classified into MCI (n\\u0026thinsp;=\\u0026thinsp;19) and cognitively normal (n\\u0026thinsp;=\\u0026thinsp;253) groups according to Petersen's criteria. Propensity score matching (PSM, 2:1) was applied to balance confounders (age and gender), resulting in 57 matched participants (19 MCI, 38 controls). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify key predictors of MCI, and multivariate logistic regression was employed to analyze influencing factors. The mediating effect of IL-6 on the relationship between tea consumption and MCI was tested using bootstrap resampling (5000 repetitions).\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eMultivariate logistic regression revealed that regular tea consumption was significantly associated with a reduced risk of MCI (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.137, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.015\\u0026ndash;0.786, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.044). Elevated IL-6 levels were associated with an increased risk of MCI (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;2.069, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 1.217\\u0026ndash;4.083, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.016), while vitamin B12 (VB12) levels were inversely associated with MCI risk (\\u003cem\\u003eOR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.984, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.966\\u0026ndash;0.997, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.038). Mediation analysis indicated that IL-6 partially mediated the relationship between tea consumption and MCI (ACME\\u0026thinsp;=\\u0026thinsp;0.1356, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.0123\\u0026ndash;0.3098, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.022), accounting for 62.51% of the total effect.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eTea drinking may indirectly reduce the risk of MCI by lowering IL-6 levels, with IL-6 serving as a partial mediator. These findings support a multi-pathway model linking lifestyle, inflammation, and cognition, suggesting that tea consumption and anti-inflammatory interventions may be effective strategies for MCI prevention in older adults.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Tea Consumption Reduces Mild Cognitive Impairment Risk in Community-Dwelling Older Adults via Lowering Interleukin-6: A Mediation Analysis with Propensity Score Matching\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-02 14:42:41\",\"doi\":\"10.21203/rs.3.rs-7601893/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-10T04:51:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"223890235324937108923490572456654964437\",\"date\":\"2025-12-01T08:14:30+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-28T12:51:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-11-04T10:15:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-15T10:57:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-14T12:19:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Geriatrics\",\"date\":\"2025-10-14T12:15:26+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-geriatrics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bgtc\",\"sideBox\":\"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bgtc/default.aspx\",\"title\":\"BMC Geriatrics\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"f1a42ae8-4948-41b6-ace5-05e7138a3f00\",\"owner\":[],\"postedDate\":\"December 2nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-02T14:42:41+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-02 14:42:41\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7601893\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7601893\",\"identity\":\"rs-7601893\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}