Association between cognitive dysfunction, TYG index, and depression in older adults: based on the NHANES database, 2011-2014

Association between cognitive dysfunction, TYG index, and depression in older adults: based on the NHANES database, 2011-2014 | 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 Association between cognitive dysfunction, TYG index, and depression in older adults: based on the NHANES database, 2011-2014 Qinghua Guo, Libo Guo, Yong Wang, Shaomei Shang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4454288/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The relationship between cognitive impairment, triglyceride-glucose (TyG) index, and depression in the elderly remains unclear. This study aims to explore the associations among cognitive impairment, TyG index, and the risk of depression in older adults, providing a basis for targeted prevention strategies. Methods This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). Cognitive impairment was defined as the lowest quartile of three cognitive tests: the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) test for learning and memory, the Animal Fluency test for executive function, and the Digit Symbol Substitution Test (DSST) for attention and processing speed. The TyG index was calculated as ln [triglycerides (mg/dL) × fasting glucose (mg/dL) / 2], and participants were categorized into quartiles based on their TyG index. Multivariable logistic regression models were employed to investigate the relationships between cognitive impairment, TyG index, and depression in the elderly. Results A total of 2042 elderly participants (aged ≥ 60 years) were included in the study, among whom 312 (15.3%) were diagnosed with depression. Both cognitive impairment and higher TyG index were significantly associated with increased depressive symptoms among older adults in the United States. The risk of depression was 2.64 times higher (95% CI: 1.33, 3.98) in those with cognitive impairment compared to those with normal cognitive function. Participants in the highest TyG quartile had a multivariable-adjusted odds ratio (OR) of 1.61 (95% CI: 1.10, 2.35) for depression compared to those in the lowest quartile. Similar results were observed across different gender, age groups, and baseline comorbidities. Conclusion Our findings suggest that higher TyG index and cognitive impairment (including deficits in learning and memory, executive function, and attention/processing speed) are associated with a greater likelihood of depressive symptoms in older adults. Figures Figure 1 Highlights Significant Association: Higher TyG index and cognitive dysfunction are significantly associated with increased risk of depressive symptoms in older adults. Robust Analysis: The study used a large, nationally representative sample from the NHANES database (2011-2014), controlling for multiple confounding factors. Cognitive Measures: Cognitive impairment was assessed through comprehensive tests covering memory, executive function, attention, and processing speed, revealing strong correlations with depression. Public Health Implications: Findings suggest that monitoring TyG index and cognitive function could help in early identification and prevention of depression among the elderly. 1. Introduction Depression is a common mood disorder characterized by persistent sadness, hopelessness, and feelings of worthlessness, along with a lack of interest in previously enjoyable activities. These symptoms significantly impair social and psychological functioning and reduce the quality of life (Thapar, Eyre, Patel, & Brent, 2022 ). Depression is a major contributor to the burden of mental health-related diseases and global disability ("Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019," 2020). According to the World Health Organization (WHO), approximately 280 million people worldwide were affected by depression in 2019, and this number is projected to rise to 350 million by 2025(WHO., 2023). Among individuals aged 60 and above, the prevalence of depression ranges from 10–20%, and this trend is increasing (Raviola, Eustache, Oswald, & Belkin, 2012 ) (Mitchell & Subramaniam, 2005 ; Steel et al., 2014 ; WH., 2017). Meta-analyses have identified several risk factors for depression in the elderly, including female gender, age over 60, insomnia, low educational background, smoking, physical illnesses such as diabetes, hypertension, heart disease, stroke, head injuries, poor sleep quality, and cognitive impairment(Nguyen, Nguyen, Bui, & Giang, 2024 ). Cognitive functions, including learning, attention, memory, and decision-making, are crucial for maintaining the quality of life in healthy older adults(Dumas, 2017 ). Research indicates that depression, particularly in the elderly, is often associated with cognitive impairments, especially memory decline(Morimoto, Kanellopoulos, Manning, & Alexopoulos, 2015 ). Therefore, we focus on exploring the relationship between cognitive impairment and depression in older adults. Previous studies have shown that individuals with mild cognitive impairment (MCI) are more likely to develop depression(Arve, Tilvis, Lehtonen, Valvanne, & Sairanen, 1999 ). Furthermore, those with both MCI and depression typically exhibit slower processing speeds and impairments in executive function, flexibility, and verbal fluency(Ma, 2020 ). These findings suggest a potential link between cognitive impairment and depression in the elderly. The bidirectional relationship between cognitive impairment and depression may share common biological mechanisms, including vascular diseases, alterations in glucocorticoid steroid levels, hippocampal atrophy, increased amyloid-beta plaque deposition, inflammatory changes, and deficits in neurotrophic factors(Byers & Yaffe, 2011 ). Additionally, insulin resistance, a key feature of type 2 diabetes, hypertension, and cardiovascular diseases, has been linked to depression in observational studies(Chatterjee et al., 2016 ) (Gudala, Bansal, Schifano, & Bhansali, 2013 ; Strachan, Reynolds, Frier, Mitchell, & Price, 2008 ), particularly through the measurement of the triglyceride-glucose index (TyG index), a novel marker for assessing insulin resistance. Although the TyG index has been associated with various health issues, including cardiovascular diseases and dementia (Liu, He, Lo, Huang, & Feng, 2020 ), its relationship with depression in the elderly remains unclear. Based on this, we hypothesize that cognitive impairment and TyG index are associated with depression in older adults. Higher TyG index and cognitive impairment (across overall cognition, learning and memory, executive function, attention, and processing speed) may indicate a higher risk of severe depressive symptoms. This study will analyze data from the large, nationally representative NHANES database to quantify the relationship between cognitive impairment, TyG index, and depression in the elderly, aiming to develop prevention strategies to improve the quality of life in this population. 2. Methods 2.1 Study Population The National Health and Nutrition Examination Survey (NHANES) (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx) aims to assess the health and nutritional status of adults and children in the United States. It uses a multistage sampling method, combining questionnaires, physical examinations, and laboratory tests to obtain a nationally representative sample. The NHANES study is reviewed by the Research Ethics Review Board of the National Center for Health Statistics, with approvals obtained semiannually between full proposal reviews. Informed consent was obtained from all participants. This study utilized a cross-sectional design, collecting information through standardized interviews, physical examinations, and biospecimen tests. Participants from two NHANES cycles (2011-2014) were included, excluding individuals under 60 years of age. A total of 2042 participants completed the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms and cognitive function tests, including word learning and recall modules from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the Animal Fluency test, and the Digit Symbol Substitution Test (DSST). Detailed information is provided in Figure 1. Missing information was imputed using polynomial regression and logistic regression. 2.2 Data Measurement 2.2.1 Outcome Ascertainment: Depressive Symptoms Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), a valid measure based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)-V criteria. The PHQ-9 scores nine items from "0" (not at all) to "3" (nearly every day). The total score, ranging from 0 to 27, represents the severity of depressive symptoms. A PHQ-9 score ≥10 is defined as depressive symptoms, with a sensitivity of 88% and specificity of 88%(Kroenke, Spitzer, & Williams, 2001). Participants were categorized into no depressive symptoms (PHQ-9 score <10) and depressive symptoms (PHQ-9 score ≥10). 2.2.2 Exposure Measurement: Cognitive Function Only individuals aged 60 and above participated in the survey. Cognitive function was assessed across four dimensions. The CERAD Word Learning Test evaluated the ability to learn new verbal information, including immediate and delayed recall. The Animal Fluency Test assessed categorical verbal fluency in executive function. The DSST evaluated processing speed, sustained attention, and working memory. Cognitive impairment scores were derived from the sum of the four dimensions, with the lowest 25th percentile assigned a score of 1, and other quartiles assigned a score of 0. Higher scores indicated more severe cognitive impairment(Chen, Bhattacharya, & Pershing, 2017). Assessment of TyG Index The TyG index was calculated as TyG index = Ln [fasting TG (mg/dL) × fasting glucose (mg/dL) / 2]. Triglycerides and fasting glucose were measured using enzymatic methods on the Roche Modular P and Roche Cobas 6000 analyzers. Fasting glucose was measured using a hexokinase-mediated reaction on the Roche/Hitachi Cobas C 501 analyzer. 2.2.3 Covariate Assessment Covariates included gender, age, race, education level, body mass index (BMI), alcohol consumption, smoking, hypertension, diabetes, sleep difficulties, and cardiovascular disease. Race categories were Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other (including multiracial). Education levels were categorized as less than 9th grade, 9-11th grade, high school graduate/GED, some college or AA degree, and college graduate or above. BMI categories were 30 kg/m². Alcohol consumption was defined as drinking more than 12 times per year. Smoking was defined as having smoked at least 100 cigarettes in a lifetime. Hypertension, diabetes, and cardiovascular diseases (asthma, congestive heart failure, coronary heart disease, angina, heart attack, and stroke) were determined by self-reported doctor diagnoses. Sleep difficulties were defined as self-reported sleep problems diagnosed by a health professional. 2.3 Statistical Analysis Statistical analyses were conducted using R software (version 4.2.1; https://www.r-project.org). Given the complex sampling design of NHANES, sample weights, clustering, and stratification were included in all analyses as required((NHANES), 2006). Participants were categorized into two groups based on PHQ-9 scores: no depressive symptoms (PHQ-9 score <10) and depressive symptoms (PHQ-9 score ≥10). Continuous variables were summarized as means and standard deviations (SD), while categorical variables were expressed as frequencies and percentages. One-way ANOVA was used for continuous variables, and Pearson’s chi-square test was used for categorical variables to compare baseline characteristics between groups with and without depressive symptoms. Multivariable logistic regression models were developed to assess the relationship between cognitive function, TyG index, and depressive symptoms, including three models to control for confounders. Model 1 was unadjusted, Model 2 adjusted for age, race, gender, education, and BMI, and Model 3 further adjusted for smoking, drinking, hypertension, diabetes, sleep disorders, and cardiovascular diseases. Multiple imputation was used for missing covariate data. Stratified analyses were conducted by gender, race, education, BMI, hypertension, and diabetes. A p-value <0.05 was considered statistically significant. 2.4 Ethics Approval and Consent to Participate The NHANES protocol was approved by the Institutional Review Board of the National Center for Health Statistics, CDC. Written informed consent was obtained from each participant before participating in the study. 3. Results 3.1 Baseline Characteristics The study included 2042 participants, comprising 312 individuals with depressive symptoms and 1730 without depressive symptoms (Table 1). Compared to those without depressive symptoms, participants with depressive symptoms had higher TyG index values, a higher proportion of females, a greater percentage of individuals with BMI >30 kg/m², and higher incidences of hypertension, diabetes, sleep disorders, and cardiovascular diseases (p < 0.05). Additionally, those with depressive symptoms exhibited lower scores in overall cognitive function, CERAD test, Animal Fluency test, and DSST (p < 0.05). Table 1. Unweighted characteristics of the study population (age≧60 years) based on with and without depressive symptoms, NHANES 2011–2014, USA. t: t-test, χ²: Chi-square test Variables Total (n = 2042) Non-depressive symptoms (PHQ < 10, %) (n = 1730) Depressive symptoms (PHQ ≥ 10, %) (n = 312) Statistic P Age, Mean ± SD 69.69 ± 6.92 69.94 ± 6.92 68.31 ± 6.80 t=3.82 <.001 CERAD, Mean ± SD 24.87 ± 7.19 25.15 ± 7.12 23.29 ± 7.40 t=4.21 <.001 Animal Fluency: Score Total, Mean ± SD 16.23 ± 5.60 16.56 ± 5.60 14.42 ± 5.22 t=6.17 <.001 Digit Symbol: Score, Mean ± SD 45.29 ± 17.51 46.73 ± 17.26 36.88 ± 16.54 t=8.92 <.001 Cognitive Function Cognitive functioning scoretotal, Mean ± SD 83.59 ± 28.12 85.90 ± 27.58 70.80 ± 27.67 t=8.89 <.001 TyG, Mean ± SD 8.88 ± 0.70 8.86 ± 0.69 9.03 ± 0.76 t=-3.64 <.001 Gender, n(%) χ²=3.71 0.054 Male 867 (42.46) 750 (43.35) 117 (37.50) Female 1175 (57.54) 980 (56.65) 195 (62.50) Race, n(%) χ²=43.64 <.001 Mexican American 183 (8.96) 137 (7.92) 46 (14.74) Other Hispanic 224 (10.97) 170 (9.83) 54 (17.31) Non-Hispanic White 978 (47.89) 864 (49.94) 114 (36.54) Non-Hispanic Black 465 (22.77) 385 (22.25) 80 (25.64) Other race 192 (9.40) 174 (10.06) 18 (5.77) Education, n(%) χ²=86.02 <.001 Less than 9th grade 254 (12.44) 177 (10.23) 77 (24.68) 9-11th grade 317 (15.52) 245 (14.16) 72 (23.08) High school graduate/GED or equivalent 496 (24.29) 431 (24.91) 65 (20.83) Some college or AA degree 554 (27.13) 485 (28.03) 69 (22.12) College graduate or above 421 (20.62) 392 (22.66) 29 (9.29) BMI Category, n(%) χ²=14.63 <.001 ＜25kg/m 2 522 (25.56) 457 (26.42) 65 (20.83) 25-30kg/m 2 673 (32.96) 586 (33.87) 87 (27.88) ＞30kg/m 2 847 (41.48) 687 (39.71) 160 (51.28) Smoking status, n(%) χ²=2.30 0.129 Yes 1044 (51.18) 872 (50.46) 172 (55.13) No 996 (48.82) 856 (49.54) 140 (44.87) Drinking status, n(%) χ²=0.04 0.849 Yes 331 (48.18) 277 (48.34) 54 (47.37) No 356 (51.82) 296 (51.66) 60 (52.63) Hypertension, n(%) χ²=4.06 0.044 Yes 1365 (66.98) 1142 (66.09) 223 (71.94) No 673 (33.02) 586 (33.91) 87 (28.06) Diabetes, n(%) χ²=27.80 <.001 Yes 535 (26.23) 417 (24.12) 118 (37.94) No 1407 (68.97) 1222 (70.68) 185 (59.49) Sleeping disorders, n(%) χ²=35.74 <.001 Yes 295 (14.48) 216 (12.51) 79 (25.48) No 1742 (85.52) 1511 (87.49) 231 (74.52) Cardiovascular Diseases, n(%) χ²=14.93 <.001 Yes 155 (26.50) 100 (22.52) 55 (39.01) No 430 (73.50) 344 (77.48) 86 (60.99) 3.2 Cognitive Impairment Levels and TyG Associations with Risk of Depressive Symptoms As shown in Table 2, cognitive impairment (overall cognitive function score, CERAD test, Animal Fluency test, DSST) and TyG index were positively associated with the risk of depressive symptoms (Model 1). After adjusting for covariates (Models 2 and 3), the association between cognitive impairment and depressive symptoms became stronger. In the final model (Model 3), the odds ratio (OR) for depressive symptoms was 3.64 (1.33, 9.98) compared to participants without depressive symptoms, with other final ORs being 2.16 (1.29, 3.63), 2.30 (1.41, 3.73), and 2.12 (1.26, 3.54). When TyG index was treated as a continuous variable, the final Model 3 showed an OR of 1.68 (1.17, 2.42) for depressive symptoms compared to those without depressive symptoms. When TyG index was categorized, participants in the highest TyG quartile had a higher adjusted OR of 1.61 (1.10, 2.35) for depressive symptoms compared to those in the lowest quartile. The generalized additive model in Figure 2 further demonstrated a linear relationship between TyG index and depressive symptoms. Table 2. Logistic regression modeling of cognitive impairment levels and TYG associations with depressive symptoms. Model 1 =not adjusted； Model 2 = Model 1 + Gender + Age + Race + Education+BMI Category； Model 3 = Model 2 + Smoking + Drinking +Hypertension+Diabetes+ Sleeping disorders+Cardiovascular Diseases. a：The degree of cognitive impairment is the sum of the CERAD, Animal Fluency, and Digit Symbol scores, where the lowest 25th percentile is assigned a score of 1 and the other quartiles are assigned a score of 0. b：The lowest 25th percentile of each test is assigned a score of 1 and the other quartiles are assigned a score of 0. Model 1 Model 2 Model 3 OR (95% CI) p OR (95% CI) p OR (95% CI) p Cognitive impairment level a (reference = No) 2.44 (1.90，3.13) <0.001 2.04 (1.49,2.80) <0.001 2.64 (1.33,3.98) <0.001 CERAD b (reference = No) 1.78 (1.38，2.29) <0.001 1.65 (1.24，2.19) <0.001 2.16 (1.29，3.63) 0.004 Animal Fluency b (reference = No) 2.25 (1.75，2.89) <0.001 2.25 (1.75，2.89) <0.001 2.30 (1.41，3.73) <0.001 Digit Symbol b (reference = No) 2.41 (1.87，3.10) <0.001 2.41 (1.87，3.10) <0.001 2.12 (1.26，3.54) 0.004 TYG（Continues） 1.40 (1.18，1.67) <0.001 1.30 (1.08,1.57) 0.007 1.68 (1.17，2.42) 0.005 TYG T1 Ref Ref Ref T2 1.20 (0.83，1.74) 0.336 1.14 (0.78，1.68) 0.492 1.14 (0.78，1.69) 0.494 T3 1.06 (0.72，1.55) 0.761 0.98 (0.66，1.46) 0.909 0.92 (0.61，1.38) 0.687 T4 1.93 (1.36，2.73) <0.001 1.67 (1.15，2.44) 0.008 1.61 (1.10，2.35) 0.015 3.3 Subgroup Analysis and Sensitivity Analysis Subgroup analysis was conducted to evaluate the robustness of the association between cognitive impairment and depressive symptoms. In the adjusted Model 2, stratified analysis results indicated consistent associations between cognitive impairment, TyG, and depressive symptoms across subgroups, with no significant interactions observed (Tables 3 and 4). Sensitivity analyses also supported the robustness of our findings. Table 3 Stratified analyses of the associations between cognitive impairment level and depressive symptoms. Table4. Stratified analyses of the associations between TYG and depressive symptoms. 4. Discussion This study, based on nationally representative cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), investigated the associations between cognitive impairment, triglyceride-glucose (TyG) index, and depressive symptoms in older adults. The main findings indicate that cognitive impairment and higher TyG index levels are significantly associated with depressive symptoms in the elderly. These results remained consistent across various stratification and sensitivity analyses, suggesting that a higher TyG index could be an effective indicator for assessing depression risk. To our knowledge, this is the first study to focus on the connection between cognitive function, TyG index, and depression in older adults. The positive correlation between cognitive impairment and depressive symptoms aligns with previous research. Numerous population-based studies have revealed the complex interplay between late-life depression and cognitive decline (Hu, Smith, Imm, Jackson, & Yang, 2019; Wei et al., 2019), with conditions like mild cognitive impairment (MCI) and dementia being closely linked to an increased risk of depression(Kopchak & Pulyk, 2017) (Mirza et al., 2016; Spira, Rebok, Stone, Kramer, & Yaffe, 2012). In fact, some researchers suggest that poor cognitive performance may indicate early dementia(Dotson, Beydoun, & Zonderman, 2010). Our study found that the risk of depression in elderly individuals with cognitive impairment was significantly higher (OR: 2.64, CI: 1.33-3.98) compared to those with normal cognitive function, consistent with prior findings that elderly individuals with depression living in rural areas are 2.58 times more likely to have cognitive impairment than those without depression living in urban areas (OR: 2.58, CI: 1.95-3.41)(Muhammad & Meher, 2021). The association between cognitive impairment and depressive symptoms encompasses complex biological, psychological, and sociological factors. These factors collectively explain why cognitive impairment is a significant risk factor for depression in the elderly(Pellegrino, Peters, Lyketsos, & Marano, 2013). First, cognitive impairment may directly affect brain structure and function, particularly in areas such as the prefrontal cortex and hippocampus, which are closely related to emotion regulation. Damage or functional decline in these areas may lead to imbalanced emotional regulation mechanisms, increasing the risk of depression. Cognitive impairment is also associated with changes in neurotransmitters, such as dopamine and serotonin imbalances, which may promote depressive symptoms. Additionally, social and psychological factors cannot be ignored. Elderly individuals with cognitive impairment may feel socially isolated or functionally impaired due to their symptoms, exacerbating the risk of depression(Muhammad & Meher, 2021). The link between depression and cognitive impairment is also influenced by various neurobiological factors, such as genetic factors like the APOE-e4 allele associated with AD and depression risk (Wang, Liu, Ruan, Wang, & Bao, 2019)and the roles of brain-derived neurotrophic factor (BDNF) and transforming growth factor-beta1 (TGF-β1) in the pathophysiological process(Borroni et al., 2009). Therefore, early cognitive function assessments in the elderly to detect potential cognitive impairments are crucial for preventing and treating depression. The TyG index reflects insulin resistance and is associated with various health issues, including cardiovascular diseases, metabolic syndrome, and depression (Bornfeldt & Tabas, 2011; Zhang et al., 2017; Zhu et al., 2020). Insulin resistance may lead to increased inflammation and oxidative stress in the brain, which are directly linked to the development of depression(Nam et al., 2020). A high TyG index may indicate not only insulin resistance but also a burden of other health conditions, which may collectively trigger depressive symptoms through physiological and psychological pathways. Therefore, early cognitive function assessments to identify potential impairments are clinically significant for preventing and treating depression in the elderly. Additionally, the preliminary evidence provided by this study on the association between the TyG index and depression suggests that healthcare professionals should consider patients' metabolic health status when assessing mental health risks. This comprehensive approach to evaluating multiple health dimensions can help us better understand and prevent depression, especially in individuals with metabolic disorders. This study has several strengths. First, it focuses on the relationship between cognitive function, TyG index, and depression in older adults. Second, it uses a complex multistage probability sampling design to select NHANES data, representing a non-institutionalized civilian population, ensuring high data quality and reliability when generalizing the results to the entire U.S. non-institutionalized population. Third, the study controlled for numerous confounding factors, including sociodemographic characteristics, hypertension, diabetes, sleep disorders, and other comorbidities, using three models to validate the consistency of the findings. Fourth, the cognitive impairment scores were derived from the sum of three cognitive tests (CERAD, Animal Fluency, DSST), objectively illustrating the relevant connections with elderly depression. These findings have additional public health implications for preventing depression in older adults. Limitations This study also has some limitations. First, being cross-sectional, it does not imply causality between cognitive function, TyG index, and depression in older adults, requiring more prospective cohort studies to validate the findings and elucidate the underlying biological mechanisms. Second, due to database limitations, the results primarily apply to the U.S. population, and these findings may not be directly generalizable to other ethnic groups and regions. Third, PHQ-9 was used to diagnose depression, a self-reported assessment without clinical confirmation. Although this method is widely used in clinical and epidemiological studies and has been validated with high sensitivity and specificity(Kroenke et al., 2001), depression encompasses mild, moderate, and severe forms, which may relate differently to cognitive impairment and TyG index. Fourth, the relationship between antidepressant use and oxidative stress was not considered due to data limitations, excluding potential confounding factors related to medication use. 5. Conclusion This study demonstrates that, after adjusting for covariates, higher TyG index levels and cognitive impairments (including learning and memory, executive function, attention, and processing speed) are positively associated with depression. These findings suggest that the TyG index and cognitive impairment may be independent secondary predictors of depression development. Declarations Acknowledgements No. Consent for publication Not applicable. Funding Not applicable. Declaration of Competing Interest All other authors declare that they have no conflicts of interest. Ethics approval and consent to participate This research analyzed de-identified information downloaded from the National Health and Nutrition Examination Survey public database. The National Center for Health Statistics Ethics Review Committee granted ethics approval. All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki). All individuals provided written informed consent before participating in the study. (https://www.cdc.gov/nchs/nhanes/irba98.htm) CRediT authorship contribution statement Study concept and design: Shaomei Shang and Qinghua Guo; Administrative support: Shaomei Shang and Qinghua Guo ; Acquisition of the data: Qinghua Guo and Yong Wang ; Analysis and interpretation of the data: Qinghua Guo and Libo Guo; Drafting of the manuscript: All authors; Critical revision of the manuscript: All authors; Final approval of manuscript: All authors Availability of data and materials The datasets generated and analyzed in the current study are available at NHANES website: https://www.cdc.gov/nchs/nhanes/index.htm. References Analytic and Reporting Guidelines Centers for Disease Control Prevention Atlanta. GA: CDC. 2006., (2006). Arve S, Tilvis RS, Lehtonen A, Valvanne J, Sairanen S. Coexistence of lowered mood and cognitive impairment of elderly people in five birth cohorts. 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Depression and associated factors among older people in Vietnam: Findings from a National Aging Survey. PLoS ONE. 2024;19(5):e0299791. 10.1371/journal.pone.0299791 . Pellegrino LD, Peters ME, Lyketsos CG, Marano CM. Depression in cognitive impairment. Curr Psychiatry Rep. 2013;15(9):384. 10.1007/s11920-013-0384-1 . Raviola G, Eustache E, Oswald C, Belkin GS. Mental health response in Haiti in the aftermath of the 2010 earthquake: a case study for building long-term solutions. Harv Rev Psychiatry. 2012;20(1):68–77. 10.3109/10673229.2012.652877 . Spira AP, Rebok GW, Stone KL, Kramer JH, Yaffe K. Depressive symptoms in oldest-old women: risk of mild cognitive impairment and dementia. Am J Geriatr Psychiatry. 2012;20(12):1006–15. 10.1097/JGP.0b013e318235b611 . Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V, Silove D. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol. 2014;43(2):476–93. 10.1093/ije/dyu038 . Strachan MW, Reynolds RM, Frier BM, Mitchell RJ, Price JF. The relationship between type 2 diabetes and dementia. Br Med Bull. 2008;88(1):131–46. 10.1093/bmb/ldn042 . Thapar A, Eyre O, Patel V, Brent D. Depression in young people. Lancet. 2022;400(10352):617–31. 10.1016/S0140-6736(22)01012-1 . Wang WW, Liu XL, Ruan Y, Wang L, Bao TH. Depression was associated with apolipoprotein E ε4 allele polymorphism: A meta-analysis. Iran J Basic Med Sci. 2019;22(2):112–7. 10.22038/ijbms.2018.30825.7436 . Wei J, Ying M, Xie L, Chandrasekar EK, Lu H, Wang T, Li C, the U.S.. (2019). Late-life depression and cognitive function among older adults in : The National Health and Nutrition Examination Survey, 2011–2014. J Psychiatr Res, 111 , 30–35. 10.1016/j.jpsychires.2019.01.012 . The mental health of older adults: fact Sheet. (2017). Depression: fact sheet. (2023). Zhang M, Wang B, Liu Y, Sun X, Luo X, Wang C, Hu D. Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study. Cardiovasc Diabetol. 2017;16(1):30. 10.1186/s12933-017-0514-x . Zhu B, Wang J, Chen K, Yan W, Wang A, Wang W, Mu Y. A high triglyceride glucose index is more closely associated with hypertension than lipid or glycemic parameters in elderly individuals: a cross-sectional survey from the Reaction Study. Cardiovasc Diabetol. 2020;19(1):112. 10.1186/s12933-020-01077-6 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4454288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309644546,"identity":"d89f9890-cfe3-49b7-aa1a-62de3431aa10","order_by":0,"name":"Qinghua Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACNvbmAwcSDP7J2R9vPkCcFj6eY4kPPlQcMGY4cyyBOC1yEjnKhjPOHEhkuJFjQKTDJHLYpHnb7iQwzsj5eOMNg52cbgMhLTxvjwG1PMtj5nm72XIOQ7Kx2QFCWtjz0oBamIvZ2HO3SfMwHEjcRlALQ44ZSEtiD0POMyK1cOQYA71/OHEGB9BTxGmBBHKasQHPMWPLOQZE+EW+HRyVNnIG7M0Pb7ypsJMjqAUFSPAQGTXIWkjVMQpGwSgYBSMCAABXM0UKUXUAmwAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University Sixth Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qinghua","middleName":"","lastName":"Guo","suffix":""},{"id":309644547,"identity":"87bbd87e-3791-4078-ac05-dfa606fe5a6d","order_by":1,"name":"Libo Guo","email":"","orcid":"","institution":"Peking University Sixth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Libo","middleName":"","lastName":"Guo","suffix":""},{"id":309644548,"identity":"679c9f56-cbcf-451f-a516-2691e3d00ac3","order_by":2,"name":"Yong Wang","email":"","orcid":"","institution":"Peking University Sixth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""},{"id":309644550,"identity":"55b04b30-55cf-4ec9-8576-fccb56eca277","order_by":3,"name":"Shaomei Shang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Shaomei","middleName":"","lastName":"Shang","suffix":""}],"badges":[],"createdAt":"2024-05-21 11:02:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4454288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4454288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58070424,"identity":"ff343066-c817-4e6a-8bd6-5ba25f96fcf9","added_by":"auto","created_at":"2024-06-10 18:32:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41828,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure101.png","url":"https://assets-eu.researchsquare.com/files/rs-4454288/v1/f5ac43421411fb934df94273.png"},{"id":83554000,"identity":"ebc8b036-7d05-474c-8898-869ef63beb92","added_by":"auto","created_at":"2025-05-28 11:09:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1164548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4454288/v1/c14adf9b-919d-4c2c-b694-7b4fb198c3e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between cognitive dysfunction, TYG index, and depression in older adults: based on the NHANES database, 2011-2014","fulltext":[{"header":"Highlights","content":"\u003cp\u003eSignificant Association: Higher TyG index and cognitive dysfunction are significantly associated with increased risk of depressive symptoms in older adults.\u003c/p\u003e\n\u003cp\u003eRobust Analysis: The study used a large, nationally representative sample from the NHANES database (2011-2014), controlling for multiple confounding factors.\u003c/p\u003e\n\u003cp\u003eCognitive Measures: Cognitive impairment was assessed through comprehensive tests covering memory, executive function, attention, and processing speed, revealing strong correlations with depression.\u003c/p\u003e\n\u003cp\u003ePublic Health Implications: Findings suggest that monitoring TyG index and cognitive function could help in early identification and prevention of depression among the elderly.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eDepression is a common mood disorder characterized by persistent sadness, hopelessness, and feelings of worthlessness, along with a lack of interest in previously enjoyable activities. These symptoms significantly impair social and psychological functioning and reduce the quality of life (Thapar, Eyre, Patel, \u0026amp; Brent, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Depression is a major contributor to the burden of mental health-related diseases and global disability (\"Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019,\" 2020). According to the World Health Organization (WHO), approximately 280\u0026nbsp;million people worldwide were affected by depression in 2019, and this number is projected to rise to 350\u0026nbsp;million by 2025(WHO., 2023). Among individuals aged 60 and above, the prevalence of depression ranges from 10\u0026ndash;20%, and this trend is increasing (Raviola, Eustache, Oswald, \u0026amp; Belkin, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (Mitchell \u0026amp; Subramaniam, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Steel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; WH., 2017). Meta-analyses have identified several risk factors for depression in the elderly, including female gender, age over 60, insomnia, low educational background, smoking, physical illnesses such as diabetes, hypertension, heart disease, stroke, head injuries, poor sleep quality, and cognitive impairment(Nguyen, Nguyen, Bui, \u0026amp; Giang, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCognitive functions, including learning, attention, memory, and decision-making, are crucial for maintaining the quality of life in healthy older adults(Dumas, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Research indicates that depression, particularly in the elderly, is often associated with cognitive impairments, especially memory decline(Morimoto, Kanellopoulos, Manning, \u0026amp; Alexopoulos, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, we focus on exploring the relationship between cognitive impairment and depression in older adults. Previous studies have shown that individuals with mild cognitive impairment (MCI) are more likely to develop depression(Arve, Tilvis, Lehtonen, Valvanne, \u0026amp; Sairanen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Furthermore, those with both MCI and depression typically exhibit slower processing speeds and impairments in executive function, flexibility, and verbal fluency(Ma, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings suggest a potential link between cognitive impairment and depression in the elderly. The bidirectional relationship between cognitive impairment and depression may share common biological mechanisms, including vascular diseases, alterations in glucocorticoid steroid levels, hippocampal atrophy, increased amyloid-beta plaque deposition, inflammatory changes, and deficits in neurotrophic factors(Byers \u0026amp; Yaffe, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, insulin resistance, a key feature of type 2 diabetes, hypertension, and cardiovascular diseases, has been linked to depression in observational studies(Chatterjee et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (Gudala, Bansal, Schifano, \u0026amp; Bhansali, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Strachan, Reynolds, Frier, Mitchell, \u0026amp; Price, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), particularly through the measurement of the triglyceride-glucose index (TyG index), a novel marker for assessing insulin resistance. Although the TyG index has been associated with various health issues, including cardiovascular diseases and dementia (Liu, He, Lo, Huang, \u0026amp; Feng, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), its relationship with depression in the elderly remains unclear.\u003c/p\u003e \u003cp\u003eBased on this, we hypothesize that cognitive impairment and TyG index are associated with depression in older adults. Higher TyG index and cognitive impairment (across overall cognition, learning and memory, executive function, attention, and processing speed) may indicate a higher risk of severe depressive symptoms. This study will analyze data from the large, nationally representative NHANES database to quantify the relationship between cognitive impairment, TyG index, and depression in the elderly, aiming to develop prevention strategies to improve the quality of life in this population.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Study Population\u003c/p\u003e\n\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx) aims to assess the health and nutritional status of adults and children in the United States. It uses a multistage sampling method, combining questionnaires, physical examinations, and laboratory tests to obtain a nationally representative sample. The NHANES study is reviewed by the Research Ethics Review Board of the National Center for Health Statistics, with approvals obtained semiannually between full proposal reviews. Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eThis study utilized a cross-sectional design, collecting information through standardized interviews, physical examinations, and biospecimen tests. Participants from two NHANES cycles (2011-2014) were included, excluding individuals under 60 years of age. A total of 2042 participants completed the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms and cognitive function tests, including word learning and recall modules from the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD), the Animal Fluency test, and the Digit Symbol Substitution Test (DSST). Detailed information is provided in\u003cstrong\u003e\u0026nbsp;Figure 1.\u003c/strong\u003e Missing information was imputed using polynomial regression and logistic regression.\u003c/p\u003e\n\u003cp\u003e2.2 Data Measurement\u003c/p\u003e\n\u003cp\u003e2.2.1 Outcome Ascertainment: Depressive Symptoms\u003c/p\u003e\n\u003cp\u003eDepressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), a valid measure based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)-V criteria. The PHQ-9 scores nine items from \u0026quot;0\u0026quot; (not at all) to \u0026quot;3\u0026quot; (nearly every day). The total score, ranging from 0 to 27, represents the severity of depressive symptoms. A PHQ-9 score \u0026ge;10 is defined as depressive symptoms, with a sensitivity of 88% and specificity of 88%(Kroenke, Spitzer, \u0026amp; Williams, 2001). Participants were categorized into no depressive symptoms (PHQ-9 score \u0026lt;10) and depressive symptoms (PHQ-9 score \u0026ge;10).\u003c/p\u003e\n\u003cp\u003e2.2.2 Exposure Measurement: Cognitive Function\u003c/p\u003e\n\u003cp\u003eOnly individuals aged 60 and above participated in the survey. Cognitive function was assessed across four dimensions. The CERAD Word Learning Test evaluated the ability to learn new verbal information, including immediate and delayed recall. The Animal Fluency Test assessed categorical verbal fluency in executive function. The DSST evaluated processing speed, sustained attention, and working memory. Cognitive impairment scores were derived from the sum of the four dimensions, with the lowest 25th percentile assigned a score of 1, and other quartiles assigned a score of 0. Higher scores indicated more severe cognitive impairment(Chen, Bhattacharya, \u0026amp; Pershing, 2017).\u003c/p\u003e\n\u003cp\u003eAssessment of TyG Index\u003c/p\u003e\n\u003cp\u003eThe TyG index was calculated as TyG index = Ln [fasting TG (mg/dL) \u0026times; fasting glucose (mg/dL) / 2]. Triglycerides and fasting glucose were measured using enzymatic methods on the Roche Modular P and Roche Cobas 6000 analyzers. Fasting glucose was measured using a hexokinase-mediated reaction on the Roche/Hitachi Cobas C 501 analyzer.\u003c/p\u003e\n\u003cp\u003e2.2.3 Covariate Assessment\u003c/p\u003e\n\u003cp\u003eCovariates included gender, age, race, education level, body mass index (BMI), alcohol consumption, smoking, hypertension, diabetes, sleep difficulties, and cardiovascular disease. Race categories were Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other (including multiracial). Education levels were categorized as less than 9th grade, 9-11th grade, high school graduate/GED, some college or AA degree, and college graduate or above. BMI categories were \u0026lt;25, 25-30, and \u0026gt;30 kg/m\u0026sup2;. Alcohol consumption was defined as drinking more than 12 times per year. Smoking was defined as having smoked at least 100 cigarettes in a lifetime. Hypertension, diabetes, and cardiovascular diseases (asthma, congestive heart failure, coronary heart disease, angina, heart attack, and stroke) were determined by self-reported doctor diagnoses. Sleep difficulties were defined as self-reported sleep problems diagnosed by a health professional.\u003c/p\u003e\n\u003cp\u003e2.3 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using R software (version 4.2.1; https://www.r-project.org). Given the complex sampling design of NHANES, sample weights, clustering, and stratification were included in all analyses as required((NHANES), 2006). Participants were categorized into two groups based on PHQ-9 scores: no depressive symptoms (PHQ-9 score \u0026lt;10) and depressive symptoms (PHQ-9 score \u0026ge;10).\u003c/p\u003e\n\u003cp\u003eContinuous variables were summarized as means and standard deviations (SD), while categorical variables were expressed as frequencies and percentages. One-way ANOVA was used for continuous variables, and Pearson\u0026rsquo;s chi-square test was used for categorical variables to compare baseline characteristics between groups with and without depressive symptoms. Multivariable logistic regression models were developed to assess the relationship between cognitive function, TyG index, and depressive symptoms, including three models to control for confounders. Model 1 was unadjusted, Model 2 adjusted for age, race, gender, education, and BMI, and Model 3 further adjusted for smoking, drinking, hypertension, diabetes, sleep disorders, and cardiovascular diseases. Multiple imputation was used for missing covariate data. Stratified analyses were conducted by gender, race, education, BMI, hypertension, and diabetes. A p-value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e2.4 Ethics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eThe NHANES protocol was approved by the Institutional Review Board of the National Center for Health Statistics, CDC. Written informed consent was obtained from each participant before participating in the study.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Baseline Characteristics\u003c/p\u003e\n\u003cp\u003eThe study included 2042 participants, comprising 312 individuals with depressive symptoms and 1730 without depressive symptoms (Table 1). Compared to those without depressive symptoms, participants with depressive symptoms had higher TyG index values, a higher proportion of females, a greater percentage of individuals with BMI \u0026gt;30 kg/m\u0026sup2;, and higher incidences of hypertension, diabetes, sleep disorders, and cardiovascular diseases (p \u0026lt; 0.05). Additionally, those with depressive symptoms exhibited lower scores in overall cognitive function, CERAD test, Animal Fluency test, and DSST (p \u0026lt; 0.05).\u003c/p\u003e\nTable 1. Unweighted characteristics of the study population (age≧60 years) based on with and without depressive symptoms, NHANES 2011–2014, USA.\n t: t-test, χ²: Chi-square test\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"84%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.944812362030905%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.375275938189846%\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal (n = 2042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.97130242825607%\" rowspan=\"2\"\u003e\n \u003cp\u003eNon-depressive symptoms (PHQ\u0026nbsp;\u0026lt;\u0026nbsp;10, %) (n = 1730)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.08830022075055%\" rowspan=\"2\"\u003e\n \u003cp\u003eDepressive symptoms (PHQ\u0026nbsp;\u0026ge;\u0026nbsp;10, %)\u003c/p\u003e\n \u003cp\u003e(n = 312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141280353200884%\" rowspan=\"2\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.479028697571744%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.69 \u0026plusmn; 6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.94 \u0026plusmn; 6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.31 \u0026plusmn; 6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCERAD, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.87 \u0026plusmn; 7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.15 \u0026plusmn; 7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.29 \u0026plusmn; 7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnimal Fluency: Score Total, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.23 \u0026plusmn; 5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.56 \u0026plusmn; 5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.42 \u0026plusmn; 5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDigit Symbol: Score, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.29 \u0026plusmn; 17.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.73 \u0026plusmn; 17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.88 \u0026plusmn; 16.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive Function Cognitive functioning scoretotal, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.59 \u0026plusmn; 28.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.90 \u0026plusmn; 27.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70.80 \u0026plusmn; 27.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTyG, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.88 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.86 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.03 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=-3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e867 (42.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e750 (43.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117 (37.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1175 (57.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e980 (56.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e195 (62.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRace, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=43.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Mexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183 (8.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137 (7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 (14.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e224 (10.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e170 (9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54 (17.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e978 (47.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e864 (49.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e114 (36.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e465 (22.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e385 (22.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80 (25.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e192 (9.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174 (10.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=86.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Less than 9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e254 (12.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e177 (10.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77 (24.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;9-11th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e317 (15.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e245 (14.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72 (23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;High school graduate/GED or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e496 (24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e431 (24.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65 (20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Some college or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e554 (27.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e485 (28.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (22.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;College graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e421 (20.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e392 (22.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29 (9.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI Category, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; ＜25kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e522 (25.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e457 (26.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65 (20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;25-30kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e673 (32.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e586 (33.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87 (27.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; ＞30kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e847 (41.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e687 (39.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160 (51.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoking status, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1044 (51.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e872 (50.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172 (55.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e996 (48.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e856 (49.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140 (44.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDrinking status, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e331 (48.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e277 (48.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54 (47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e356 (51.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e296 (51.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60 (52.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1365 (66.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1142 (66.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e223 (71.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e673 (33.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e586 (33.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87 (28.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=27.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e535 (26.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e417 (24.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118 (37.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1407 (68.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1222 (70.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e185 (59.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSleeping disorders, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=35.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e295 (14.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216 (12.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79 (25.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1742 (85.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1511 (87.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e231 (74.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCardiovascular Diseases, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=14.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e155 (26.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100 (22.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (39.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e430 (73.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e344 (77.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86 (60.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2 Cognitive Impairment Levels and TyG Associations with Risk of Depressive Symptoms\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, cognitive impairment (overall cognitive function score, CERAD test, Animal Fluency test, DSST) and TyG index were positively associated with the risk of depressive symptoms (Model 1). After adjusting for covariates (Models 2 and 3), the association between cognitive impairment and depressive symptoms became stronger. In the final model (Model 3), the odds ratio (OR) for depressive symptoms was 3.64 (1.33, 9.98) compared to participants without depressive symptoms, with other final ORs being 2.16 (1.29, 3.63), 2.30 (1.41, 3.73), and 2.12 (1.26, 3.54). When TyG index was treated as a continuous variable, the final Model 3 showed an OR of 1.68 (1.17, 2.42) for depressive symptoms compared to those without depressive symptoms. When TyG index was categorized, participants in the highest TyG quartile had a higher adjusted OR of 1.61 (1.10, 2.35) for depressive symptoms compared to those in the lowest quartile. The generalized additive model in Figure 2 further demonstrated a linear relationship between TyG index and depressive symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003eLogistic regression modeling of cognitive impairment levels and TYG associations with depressive symptoms.\u003c/p\u003e\n\u003cp\u003eModel 1 =not adjusted；\u003c/p\u003e\n\u003cp\u003eModel 2 = Model 1 + Gender + Age + Race + Education+BMI Category；\u003c/p\u003e\n\u003cp\u003eModel 3 = Model 2 + Smoking + Drinking +Hypertension+Diabetes+ Sleeping disorders+Cardiovascular Diseases.\u003c/p\u003e\n\u003cp\u003ea：The degree of cognitive impairment is the sum of the CERAD, Animal Fluency, and Digit Symbol scores, where the lowest 25th percentile is assigned a score of 1 and the other quartiles are assigned a score of 0.\u003c/p\u003e\n\u003cp\u003eb：The lowest 25th percentile of each test is assigned a score of 1 and the other quartiles are assigned a score of 0.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eCognitive impairment level\u003csup\u003ea\u003c/sup\u003e(reference = No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e2.44 (1.90，3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e2.04 (1.49,2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e2.64 (1.33,3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eCERAD\u003csup\u003eb\u003c/sup\u003e(reference = No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.78 (1.38，2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e1.65 (1.24，2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e2.16 (1.29，3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eAnimal Fluency\u003csup\u003eb\u003c/sup\u003e(reference = No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e2.25 (1.75，2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e2.25 (1.75，2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e2.30 (1.41，3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eDigit Symbol\u003csup\u003eb\u003c/sup\u003e(reference = No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e2.41 (1.87，3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e2.41 (1.87，3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e2.12 (1.26，3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eTYG（Continues）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.40 (1.18，1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e1.30 (1.08,1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e1.68 (1.17，2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eTYG T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.20 (0.83，1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e1.14 (0.78，1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e1.14 (0.78，1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.06 (0.72，1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e0.98 (0.66，1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e0.92 (0.61，1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.93 (1.36，2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.953947368421053%\"\u003e\n \u003cp\u003e1.67 (1.15，2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.776315789473685%\"\u003e\n \u003cp\u003e1.61 (1.10，2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.881578947368421%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3 Subgroup Analysis and Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003eSubgroup analysis was conducted to evaluate the robustness of the association between cognitive impairment and depressive symptoms. In the adjusted Model 2, stratified analysis results indicated consistent associations between cognitive impairment, TyG, and depressive symptoms across subgroups, with no significant interactions observed (Tables 3 and 4). Sensitivity analyses also supported the robustness of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Stratified analyses of the associations between cognitive impairment level and depressive symptoms.\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"626\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1717596239.png\" alt=\"image\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable4.\u0026nbsp;\u003c/strong\u003eStratified analyses of the associations between TYG and depressive symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"555\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1717596240.png\" alt=\"image\"\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study, based on nationally representative cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), investigated the associations between cognitive impairment, triglyceride-glucose (TyG) index, and depressive symptoms in older adults. The main findings indicate that cognitive impairment and higher TyG index levels are significantly associated with depressive symptoms in the elderly. These results remained consistent across various stratification and sensitivity analyses, suggesting that a higher TyG index could be an effective indicator for assessing depression risk. To our knowledge, this is the first study to focus on the connection between cognitive function, TyG index, and depression in older adults.\u003c/p\u003e\n\u003cp\u003eThe positive correlation between cognitive impairment and depressive symptoms aligns with previous research. Numerous population-based studies have revealed the complex interplay between late-life depression and cognitive decline (Hu, Smith, Imm, Jackson, \u0026amp; Yang, 2019; Wei et al., 2019), with conditions like mild cognitive impairment (MCI) and dementia being closely linked to an increased risk of depression(Kopchak \u0026amp; Pulyk, 2017) (Mirza et al., 2016; Spira, Rebok, Stone, Kramer, \u0026amp; Yaffe, 2012). In fact, some researchers suggest that poor cognitive performance may indicate early dementia(Dotson, Beydoun, \u0026amp; Zonderman, 2010). Our study found that the risk of depression in elderly individuals with cognitive impairment was significantly higher (OR: 2.64, CI: 1.33-3.98) compared to those with normal cognitive function, consistent with prior findings that elderly individuals with depression living in rural areas are 2.58 times more likely to have cognitive impairment than those without depression living in urban areas (OR: 2.58, CI: 1.95-3.41)(Muhammad \u0026amp; Meher, 2021).\u003c/p\u003e\n\u003cp\u003eThe association between cognitive impairment and depressive symptoms encompasses complex biological, psychological, and sociological factors. These factors collectively explain why cognitive impairment is a significant risk factor for depression in the elderly(Pellegrino, Peters, Lyketsos, \u0026amp; Marano, 2013). First, cognitive impairment may directly affect brain structure and function, particularly in areas such as the prefrontal cortex and hippocampus, which are closely related to emotion regulation. Damage or functional decline in these areas may lead to imbalanced emotional regulation mechanisms, increasing the risk of depression. Cognitive impairment is also associated with changes in neurotransmitters, such as dopamine and serotonin imbalances, which may promote depressive symptoms. Additionally, social and psychological factors cannot be ignored. Elderly individuals with cognitive impairment may feel socially isolated or functionally impaired due to their symptoms, exacerbating the risk of depression(Muhammad \u0026amp; Meher, 2021). The link between depression and cognitive impairment is also influenced by various neurobiological factors, such as genetic factors like the APOE-e4 allele associated with AD and depression risk (Wang, Liu, Ruan, Wang, \u0026amp; Bao, 2019)and the roles of brain-derived neurotrophic factor (BDNF) and transforming growth factor-beta1 (TGF-\u0026beta;1) in the pathophysiological process(Borroni et al., 2009). Therefore, early cognitive function assessments in the elderly to detect potential cognitive impairments are crucial for preventing and treating depression.\u003c/p\u003e\n\u003cp\u003eThe TyG index reflects insulin resistance and is associated with various health issues, including cardiovascular diseases, metabolic syndrome, and depression (Bornfeldt \u0026amp; Tabas, 2011; Zhang et al., 2017; Zhu et al., 2020). Insulin resistance may lead to increased inflammation and oxidative stress in the brain, which are directly linked to the development of depression(Nam et al., 2020). A high TyG index may indicate not only insulin resistance but also a burden of other health conditions, which may collectively trigger depressive symptoms through physiological and psychological pathways.\u003c/p\u003e\n\u003cp\u003eTherefore, early cognitive function assessments to identify potential impairments are clinically significant for preventing and treating depression in the elderly. Additionally, the preliminary evidence provided by this study on the association between the TyG index and depression suggests that healthcare professionals should consider patients\u0026apos; metabolic health status when assessing mental health risks. This comprehensive approach to evaluating multiple health dimensions can help us better understand and prevent depression, especially in individuals with metabolic disorders.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. First, it focuses on the relationship between cognitive function, TyG index, and depression in older adults. Second, it uses a complex multistage probability sampling design to select NHANES data, representing a non-institutionalized civilian population, ensuring high data quality and reliability when generalizing the results to the entire U.S. non-institutionalized population. Third, the study controlled for numerous confounding factors, including sociodemographic characteristics, hypertension, diabetes, sleep disorders, and other comorbidities, using three models to validate the consistency of the findings. Fourth, the cognitive impairment scores were derived from the sum of three cognitive tests (CERAD, Animal Fluency, DSST), objectively illustrating the relevant connections with elderly depression. These findings have additional public health implications for preventing depression in older adults.\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThis study also has some limitations. First, being cross-sectional, it does not imply causality between cognitive function, TyG index, and depression in older adults, requiring more prospective cohort studies to validate the findings and elucidate the underlying biological mechanisms. Second, due to database limitations, the results primarily apply to the U.S. population, and these findings may not be directly generalizable to other ethnic groups and regions. Third, PHQ-9 was used to diagnose depression, a self-reported assessment without clinical confirmation. Although this method is widely used in clinical and epidemiological studies and has been validated with high sensitivity and specificity(Kroenke et al., 2001), depression encompasses mild, moderate, and severe forms, which may relate differently to cognitive impairment and TyG index. Fourth, the relationship between antidepressant use and oxidative stress was not considered due to data limitations, excluding potential confounding factors related to medication use.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that, after adjusting for covariates, higher TyG index levels and cognitive impairments (including learning and memory, executive function, attention, and processing speed) are positively associated with depression. These findings suggest that the TyG index and cognitive impairment may be independent secondary predictors of depression development.\n\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll other authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research analyzed de-identified information downloaded from the National Health and Nutrition Examination Survey public database. The National Center for Health Statistics Ethics Review Committee granted ethics approval. All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki). All individuals provided written informed consent before participating in the study. (https://www.cdc.gov/nchs/nhanes/irba98.htm)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: \u0026nbsp;Shaomei Shang and Qinghua Guo;\u003c/p\u003e\n\u003cp\u003eAdministrative support: Shaomei Shang and Qinghua Guo ;\u003c/p\u003e\n\u003cp\u003eAcquisition of the data: Qinghua Guo and Yong Wang ;\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of the data: \u0026nbsp;Qinghua Guo and Libo Guo;\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: All authors;\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript: All authors;\u003c/p\u003e\n\u003cp\u003eFinal approval of manuscript: All authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed in the current study are available at NHANES website: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnalytic and Reporting Guidelines Centers for Disease Control Prevention Atlanta. 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Cardiovasc Diabetol. 2017;16(1):30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-017-0514-x\u003c/span\u003e\u003cspan address=\"10.1186/s12933-017-0514-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu B, Wang J, Chen K, Yan W, Wang A, Wang W, Mu Y. A high triglyceride glucose index is more closely associated with hypertension than lipid or glycemic parameters in elderly individuals: a cross-sectional survey from the Reaction Study. Cardiovasc Diabetol. 2020;19(1):112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-020-01077-6\u003c/span\u003e\u003cspan address=\"10.1186/s12933-020-01077-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4454288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4454288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between cognitive impairment, triglyceride-glucose (TyG) index, and depression in the elderly remains unclear. This study aims to explore the associations among cognitive impairment, TyG index, and the risk of depression in older adults, providing a basis for targeted prevention strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). Cognitive impairment was defined as the lowest quartile of three cognitive tests: the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD) test for learning and memory, the Animal Fluency test for executive function, and the Digit Symbol Substitution Test (DSST) for attention and processing speed. The TyG index was calculated as ln [triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL) / 2], and participants were categorized into quartiles based on their TyG index. Multivariable logistic regression models were employed to investigate the relationships between cognitive impairment, TyG index, and depression in the elderly.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2042 elderly participants (aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years) were included in the study, among whom 312 (15.3%) were diagnosed with depression. Both cognitive impairment and higher TyG index were significantly associated with increased depressive symptoms among older adults in the United States. The risk of depression was 2.64 times higher (95% CI: 1.33, 3.98) in those with cognitive impairment compared to those with normal cognitive function. Participants in the highest TyG quartile had a multivariable-adjusted odds ratio (OR) of 1.61 (95% CI: 1.10, 2.35) for depression compared to those in the lowest quartile. Similar results were observed across different gender, age groups, and baseline comorbidities.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings suggest that higher TyG index and cognitive impairment (including deficits in learning and memory, executive function, and attention/processing speed) are associated with a greater likelihood of depressive symptoms in older adults.\u003c/p\u003e","manuscriptTitle":"Association between cognitive dysfunction, TYG index, and depression in older adults: based on the NHANES database, 2011-2014","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-10 18:32:19","doi":"10.21203/rs.3.rs-4454288/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"329bb8c4-8e03-41f2-a87d-8cc00b434ab4","owner":[],"postedDate":"June 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-28T11:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-10 18:32:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4454288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4454288","identity":"rs-4454288","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}