Nutritional status mediates the relationship between depression and mild cognitive impairment among Chinese community-dwelling older adults

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Our study aimed to explore the association and mediation pathways involving depression, nutritional status, and MCI in this population. Methods: The study included 4799 community-dwelling Chinese older adults aged 60 years and older in Tianjin and Shanghai, China. We utilized the 30-item Geriatric Depression Scale (GDS-30) to assess depression presence and severity. A GDS-30 score ≥11 indicated the presence of depression. Participants were categorized by symptom severity: “None” (GDS-30<11), “Mild” (11≤GDS-30≤20), “Moderate to severe” (20<GDS-30≤30). Nutritional status was evaluated using the Mini Nutritional Assessment-Short Form (MNA-SF). Cognitive function was assessed with the Mini-Mental State Examination (MMSE), and daily living activities were measured using the Instrumental Activities of Daily Living (IADL). Logistic regression and mediation analyses were conducted with full adjustment for potential confounding factors. Results: Among 4799 older adults (2010 men, mean age 71.5±5.8 years), 11.8% exhibited depression, and 12.1% had MCI, while 18.8% were at risk of malnutrition, and 1% were malnourished. Participants at risk of malnutrition or experiencing depression were associated with MCI. Additionally, nutritional status significantly mediated the relationship between depression and cognitive function, with a slightly larger mediating effect, particularly in cases of mild depression. Conclusions: Individuals at risk of malnutrition or depressed were associated with MCI. Poor nutritional condition mediates the association between depression and MCI in older adults. Early nutritional interventions may mitigate cognitive decline in depressed older adults. Depression nutritional status mild cognitive impairment. Figures Figure 1 Introduction As the global population of older adults increases, cognitive impairment becomes a major risk factor for poor health and a significant public health burden. Recent studies estimate that over 15 million people aged 60 or older in China have dementia [1]. Mild cognitive impairment (MCI) represents an intermediate state between normal aging and dementia, with approximately 10% of individuals with MCI progressing to dementia annually, compared to a 1-3% conversion rate in the general older adult population [2, 3]. The high prevalence, progression rate to dementia, and the considerable health care expenditures make it a major public health problem. Given its high prevalence, progression to dementia, and substantial healthcare costs, MCI presents a significant public health challenge. Depression is a prevalent mental disorder among older adults and a leading cause of emotional distress in later life [4]. Unfortunately, depression often goes undiagnosed or untreated in this population [5]. Growing evidence suggests that depression independently accelerates the progression from MCI to dementia, with a two to four times increased risk [6, 7]. However, the relationship between depression and MCI is complex, as depression can lead to complaints about declining cognitive function, while cognitive deficits can result in complaints of depression [8]. Our previous study indicated that depression is an independent risk factor for MCI, necessitating further research on this relationship [9]. Thus, depression deserves increased attention in the care of individuals with MCI. Malnutrition is another critical health issue for older adults, particularly as the aging population grows. Malnutrition is associated with reduced health status, lower quality of life, poor functional status, and increased healthcare costs [10, 11]. It affects older adults psychologically through mechanisms such as brain atrophy and reduced enjoyment of food [12, 13]. Depression has been proposed as a risk factor for malnutrition in older adults[14, 15]. . Additionally, poor nutritional status plays a role in the development and progression of neurodegenerative disorders associated with cognitive decline, highlighting the importance of early nutritional assessment and intervention [16]. Although some studies have explored the relationship between depression, malnutrition, and cognition [17], their focus on total cognitive impairment, including patients with dementia, rather than MCI separately. The interrelationship of these three conditions in older adults remains controversial. Furthermore, no studies have investigated potential mediation pathways between depression, nutritional status and MCI. Therefore, this study aims to examine the complex association of MCI with depression and nutritional status in community-dwelling individuals aged 60 years or older. We explore the possible mediating effects of nutritional status on the association between depression and cognitive function. Additionally, we test whether nutritional status mediates the relationship between different severity levels of depression and cognition. Methods Participants The study population comprises residents aged 60 years or older from Tianjin and Shanghai, China, who participated in China's National Free Physical Examination program between August 2018 and October 2022. Participants were excluded as following criteria: (1) did not complete date on the depression, nutrition assessments and cognitive function; (2) had a known diagnosis of dementia; (3) unable to communicate with the study staff or provide informed consent. After excluding 306 subjects, the final sample consisted of 4799 participants (2010 males, 2789 females). Missing data on cognitive function assessment, psychological tests, and nutritional status evaluation were noted in 186, 25, and 7 participants, respectively. Another 6 participants had missing data on basic covariates, and 82 were evaluated for dementia. The study received ethical approval from the Ethics Committee of Tianjin Medical University and Shanghai Medical and Health University, adhering to the Declaration of Helsinki. Covariates Interviews were conducted using questionnaires from previous studies [18]. Socio-demographic variables included age, sex, marital status, living arrangements, and education. Behavioral characteristics such as alcohol consumption, smoking, physical activity, and falls history were recorded. Chronic diseases, including diabetes mellitus, hypertension, hyperlipidemia, coronary heart disease, stroke, kidney disease, peptic ulcer, biliary tract disease, pulmonary disease, osteoarthritis, Parkinson's disease, gout, cancer, and thyroid disease, were also documented [19]. Definition of MCI MCI was defined according to Petersen's diagnostic criteria[20]. Mini-Mental State Examination (MMSE) and Instrumental Activities of Daily Living scale (IADL) were used. Cognitive impairment thresholds for MMSE were ≤17, 20, and 24 points for illiteracy, elementary, and middle school or above, respectively[21]. IADL consists of eight items on a scale of 0 to 8, with higher scores indicating better daily living ability. IADL score ≥6 indicates normal daily living ability[9, 22]. Assessment of depression Depression was screened by the Chinese version of 30-item Geriatric Depression Scale (GDS-30), which was standardized including 30 items ranging from 0-30 points. A cutoff value of 11 points was used to define depression. [23]. Participants were also grouped according to the severity of symptoms: “None” (GDS-30<11), “Mild” (11≤GDS-30≤20), “Moderate to severe” (20<GDS-30≤30). Nutritional Status Evaluation Nutritional status was evaluated using the Mini Nutritional Assessment-Short Form (MNA-SF), a validated screening tool used in geriatric health care[24]. Compared with the MNA, the sensitivity and specificity of this version are 97.9% and 100%, respectively[25]. The MNA-SF consists of six items assessing food intake, weight loss, mobility, psychological stress, neuropsychological problems, and body mass index (BMI). The total score ranges from 0 to 14 points. Participants with scores of 12-14 were considered well-nourished, 8-11 were at risk of malnutrition, and 0-7 were malnourished. Statistical analyses Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as means ± standard deviations (SD), while categorical variables were expressed as frequencies and percentages. Group differences were analyzed using the independent t-test or chi-square test, as appropriate. Univariate logistic regression analysis was conducted to explore the association between depression, nutritional status, and MCI. Multivariate logistic regression models were then used to adjust for potential confounding factors. Mediation analyses were performed to explore the mediating role of nutritional status in the association between depression and MCI. In the mediation analysis model with full adjustment, we included GDS score and depression severe grades as independent variables (X), MMSE score as dependent variable (Y), and the MNA-SF variable as a potential mediator (M). The total effect (path c) represents the sum of the direct and indirect effects of GDS score and depression severe grades on MMSE score. The direct effect (path c’) is the effect of GDS score and depression severe grades on MMSE, and the indirect effect (path ab) is the mediating effect of the association between GDS score and depression severe grades and MMSE. The PROCESS macro for SPSS was used for mediation analysis [26]. We tested the significance of the indirect effect (mediation effect) and confirmed that the effect of independent variable on mediator, the effect of mediator on dependent variable and the total effect of independent variable on dependent variable were significant, respectively. A bootstrapping method with 5000 resamples was applied to calculate bias-corrected 95% confidence intervals for direct and indirect effects. Results Participant Characteristics Among 4799 participants (2010 men, 2789 women) who were available to be analyzed, 583 (12.1%) had MCI, in which 9.4% for men and 14.2% for women respectively. Table 1 presents the socio-economic and health-related characteristics of individuals stratified by cognitive state. We found that participants with MCI tended to be older, female, widowed, living alone, having low education levels (P<0.05, Table 1). In regard to the health-related variables, falling, depression, poor nutrition, hyperlipidemia, coronary heart disease stroke, peptic ulcer and osteoarthritis were significantly related to MCI (P<0.05, Table 1). Regarding nutritional status, 18.8% (902 participants) were at risk of malnutrition, and 1% (50 participants) were malnourished. Association Between Depression, Nutritional Status, and MCI Logistic regression analysis revealed significant associations between depression, nutritional status, and MCI (Table 2). After adjustments for potential confounders (age, sex, BMI, widowed, living alone, education, fall history, hypertension, hyperlipidemia, coronary heart disease, stroke, peptic ulcer, osteoarthritis and gout), we observed that total MNA-SF score (OR = 0.87, 95% CI = 0.82–0.92) was significantly associated with MCI. We also found that older adults at risk of malnutrition (OR = 1.44, 95% CI = 1.14–1.81) were significantly associated with MCI compared to those with well nutritional status stratified by nutritional status. Furthermore, depression (OR = 1.56, 95% CI = 1.22–1.99) was significantly related to MCI. Interestingly, graded by depression severity, the association with MCI increased as the severity of depression increased (Mild depression: OR = 1.48, 95% CI = 1.14–1.93; Moderate to severe depression: OR = 2.02, 95% CI = 1.14–1.93). Mediation Analysis Mediation analyses were conducted to explore the potential mediating role of nutritional status in the association between depression and MCI. Table 3 and Fig 1 showed that MNA-SF score had a significant mediating role in the association between depression and MCI. Poor nutritional status plays a mediate role in the association between mild depression (indirect effect ab = -0.090; 95%CI= -0.149 to -0.046), and moderate to severe depression (indirect effect ab = -0.122; 95%CI= -0.270 to -0.004) with cognitive function, and the mediating effect was enhanced slightly larger in mild depression (18.9%) than moderate to severe depression (10.8%). Discussion The present study used mediation models to explore the mediation role of nutritional status between two important clinical conditions: depression and cognitive impairment. Our results suggest that individuals at risk of malnutrition and depression were both significantly associated with MCI in older community-dwelling Chinese adults. Notably, moderate to severe depression exhibits a stronger link with MCI compared to mild depression. Furthermore, our study reveals that poor nutritional status significantly mediates the association between depression and cognitive function, with the mediating effect being particularly pronounced in cases of mild depression. In our investigation, the prevalence of MCI was 12.1%, consistent with recent Chinese national study involving 46011 adults aged 60 years or older (15.5%)[1]. A systematic review of 22 Chinese studies in older adults also yielded similar results (12.7%)[27]. The prevalence of depression using the GDS-30 was 11.8%, aligning with our prior studies [9, 19]. Regarding nutritional status, 1% of the older adults were malnourished, 18.8% were at risk of malnutrition, and 80.2% were well-nourished. Our results are line with seven previous studies involving 2798 community- dwelling elderly persons, reporting that 1% were malnourished, 29% were at risk of malnutrition, and 70% were well-nourished[28]. However, variations exist; for instance, Mantzorou et al.[17] reported 11.3% malnourished participants, while another study indicated 20% malnutrition and 49% at risk of malnutrition [28-30]. The reason for the difference may be that the nutritional status of older people living in nursing homes, long-term care facilities and hospital settings is poorer than that of older adults who can live independently in the community. In addition, with the welfare policies of the Shanghai government and advanced medical resources, the physical condition of the older adults in the region is generally better than the rest of the country. Additionally, our results align with previous evidence on the association between nutritional status and cognitive function [17, 31]. Accumulating evidence suggest that nutrition is important for optimizing cognition and reducing the risk of dementia[32, 33]. While our findings slightly differ, showing that individuals at malnutrition risk (OR=1.44, 95%CI=1.14,1.81) are associated with MCI, whereas this relationship is absent in malnourished (OR=1.74, 95%CI=0.78,3.88) older adults, the small prevalence of malnutrition (1%) and limited sample size may explain this lack of association. We anticipate that with a larger sample, the correlation would become statistically significant. Overall, the association between depression and MCI consisted with the results of our previous studies[9]. Steffens et al. reported that depression increase the risk for progression from MCI to Alzheimer’s disease dementia[34]. Although previous study demonstrated that depression is relation to clinical symptoms onset of MCI, the casual relationship between depression and MCI remains to be explored[35]. To our knowledge, no other studies have highlighted malnutrition as a mediator of the relationship between depression and cognitive function in older adults. Our results suggest that depression may lead to malnutrition, subsequently causing MCI among older adults, independent of the direct effects of depression on MCI. This implies that improving nutritional status may mitigate the negative impact of depression on cognitive function. Given the limited effective treatments for depression and cognitive deterioration in older adults, modifiable risk factors, such as nutritional status, offer intervention opportunities. In mediation analysis, the relationship between depression and cognitive function comprises a relative direct effect and a relative indirect effect. When deeper categorization of depression into mild and moderate to severe, both the direct effect (81.1%) and indirect effect (18.9%) of nutritional status remain significant in mild depression, while the direct effect (89.2%) and indirect effect (10.8%) remain significant in moderate to severe depression, albeit to a lesser extent as a mediating effect. This suggests that nutritional status mediates the association between depression and cognitive function, especially concerning mild depression. The total effects of depression on cognitive function increase with depression severity, while the proportion of relative indirect effects (mediated by nutritional status) decreases with depression severity (Table 3). This may be attributed to a higher comorbidity burden among severely depressed older adults, introducing additional mediators beyond nutrition into the complex relationship. Hence, early nutritional intervention may be more effective in preventing cognitive deterioration in depressed older adults. However, it's important to note that the mediating role of nutritional status is not entirely robust, warranting further studies to explore common underlying determinants of depression and cognitive impairment. Several limitations characterize our study. First, the cross-sectional design limits our ability to establish causality, and only longitudinal studies can unravel the complex and bidirectional association between depression and MCI [34, 35]. Future research should extend follow-up times, increase sample sizes, and explore these associations longitudinally. Second, all participants in the present study were relatively healthy, as those unable to participate in the free annual national physical examination (e.g. those bedridden or with serious disease) were excluded. Consequently, our results might underestimate the true prevalence of depression, malnutrition, or MCI. Conclusion In conclusion, our study demonstrated that the association between depression and MCI may be in part mediated by nutritional status. Improving the nutritional status of depressed older adults may counteract the cognitive decline linked to depression. Early nutritional intervention is recommended for improved prevention. Abbreviations MCI: Mild cognitive impairment; MNA-SF: Mini Nutritional Assessment-Short Form. Declarations Acknowledgements The authors thank Qiongying Tao from the Jiading Subdistrict Community Health Center, Xiaofang Ren from Hangu welfare house for providing place and organization and Xiaoyue Gu from the Chongming public health center for providing place and organization. Author Contributions X.C. and P.H. conceived the concept and design of the study; X.C., J.G., K.D.,W.L., B.J. and H.Y. contributed to data collection, entry, and data cleaning. X.C., P. H., Z.L., and J.Z., contributed to the data analysis and interpretation of study results. L.C., Q.T., and Q.G. contributed to acquisition of funding, study design and provided administrative support. All authors wrote the manuscript and approved the final version. Funding This work was supported by the funding of Capacity Building project of Local Colleges of Shanghai Science and Technology Commission (23010502800); Shanghai Sailing Program (22YF1417900). Ethics approval and consent to participate The study was approved by the Ethics Committee of Shanghai University of Medicine and Health Sciences and written informed consent was obtained from all study participants. Competing interests The authors declare that they have no conflict of interest. Availability of data and materials All data generated or analyzed during this study are included in this published article. Any additional data are available from the corresponding author on reasonable request. Consent for publication Not applicable. References Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, Li Y, Li Y, Zhu M, Jiao H et al . Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020; 5(12):e661-e671. Doblhammer G, Fink A, Zylla S, Willekens F. Compression or expansion of dementia in Germany? An observational study of short-term trends in incidence and death rates of dementia between 2006/07 and 2009/10 based on German health insurance data. Alzheimers Res Ther. 2015; 7(1):66. Satizabal CL, Beiser AS, Chouraki V, Chene G, Dufouil C, Seshadri S. Incidence of Dementia over Three Decades in the Framingham Heart Study. N Engl J Med. 2016; 374(6):523-532. Belvederi Murri M, Cattelani L, Chesani F, Palumbo P, Triolo F, Alexopoulos GS. Risk Prediction Models for Depression in Community-Dwelling Older Adults. Am J Geriatr Psychiatry. 2022; 30(9):949-960. Song D, Yu DSF, Li PWC, Sun Q. Identifying the Factors Related to Depressive Symptoms Amongst Community-Dwelling Older Adults with Mild Cognitive Impairment. Int J Environ Res Public Health. 2019; 16(18). Richard E, Reitz C, Honig LH, Schupf N, Tang MX, Manly JJ, Mayeux R, Devanand D, Luchsinger JA. Late-life depression, mild cognitive impairment, and dementia. JAMA Neurol. 2013; 70(3):374-382. Van der Mussele S, Fransen E, Struyfs H, Luyckx J, Marien P, Saerens J, Somers N, Goeman J, De Deyn PP, Engelborghs S. Depression in mild cognitive impairment is associated with progression to Alzheimer's disease: a longitudinal study. J Alzheimers Dis. 2014; 42(4):1239-1250. Muhammad T, Meher T. Association of late-life depression with cognitive impairment: evidence from a cross-sectional study among older adults in India. BMC Geriatr. 2021; 21(1):364. Chen X, Han P, Yu X, Zhang Y, Song P, Liu Y, Jiang Z, Tao Z, Shen S, Wu Y et al . Relationships between sarcopenia, depressive symptoms, and mild cognitive impairment in Chinese community-dwelling older adults. J Affect Disord. 2021; 286:71-77. Dent E, Wright ORL, Woo J, Hoogendijk EO. Malnutrition in older adults. Lancet. 2023; 401(10380):951-966. Norman K, Hass U, Pirlich M. Malnutrition in Older Adults-Recent Advances and Remaining Challenges. Nutrients. 2021; 13(8). Volkert D, Chourdakis M, Faxen-Irving G, Fruhwald T, Landi F, Suominen MH, Vandewoude M, Wirth R, Schneider SM. ESPEN guidelines on nutrition in dementia. Clin Nutr. 2015; 34(6):1052-1073. Bailly N, Maitre I, Van Wymelbeke V. Relationships between nutritional status, depression and pleasure of eating in aging men and women. Arch Gerontol Geriatr. 2015; 61(3):330-336. Velazquez-Alva MC, Irigoyen-Camacho ME, Cabrer-Rosales MF, Lazarevich I, Arrieta-Cruz I, Gutierrez-Juarez R, Zepeda-Zepeda MA. Prevalence of Malnutrition and Depression in Older Adults Living in Nursing Homes in Mexico City. Nutrients. 2020; 12(8). Islam MZ, Disu TR, Farjana S, Rahman MM. Malnutrition and other risk factors of geriatric depression: a community-based comparative cross-sectional study in older adults in rural Bangladesh. BMC Geriatr. 2021; 21(1):572. Tsagalioti E, Trifonos C, Morari A, Vadikolias K, Giaginis C. Clinical value of nutritional status in neurodegenerative diseases: What is its impact and how it affects disease progression and management? Nutr Neurosci. 2018; 21(3):162-175. Mantzorou M, Vadikolias K, Pavlidou E, Serdari A, Vasios G, Tryfonos C, Giaginis C. Nutritional status is associated with the degree of cognitive impairment and depressive symptoms in a Greek elderly population. Nutr Neurosci. 2020; 23(3):201-209. Han P, Kang L, Guo Q, Wang J, Zhang W, Shen S, Wang X, Dong R, Ma Y, Shi Y et al . Prevalence and Factors Associated With Sarcopenia in Suburb-dwelling Older Chinese Using the Asian Working Group for Sarcopenia Definition. J Gerontol A Biol Sci Med Sci. 2016; 71(4):529-535. Chen X, Guo J, Han P, Fu L, Jia L, Yu H, Yu X, Hou L, Wang L, Zhang W et al . Twelve-Month Incidence of Depressive Symptoms in Suburb-Dwelling Chinese Older Adults: Role of Sarcopenia. J Am Med Dir Assoc. 2019; 20(1):64-69. Sun Z, Wang Z, Xu L, Lv X, Li Q, Wang H, Yu X. Characteristics of Cognitive Deficit in Amnestic Mild Cognitive Impairment With Subthreshold Depression. J Geriatr Psychiatry Neurol. 2019; 32(6):344-353. Katzman R, Zhang MY, Ouang Ya Q, Wang ZY, Liu WT, Yu E, Wong SC, Salmon DP, Grant I. A Chinese version of the Mini-Mental State Examination; impact of illiteracy in a Shanghai dementia survey. J Clin Epidemiol. 1988; 41(10):971-978. Nagamatsu LS, Chan A, Davis JC, Beattie BL, Graf P, Voss MW, Sharma D, Liu-Ambrose T. Physical activity improves verbal and spatial memory in older adults with probable mild cognitive impairment: a 6-month randomized controlled trial. J Aging Res. 2013; 2013:861893. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, Leirer VO. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982; 17(1):37-49. Kaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Cederholm T, Thomas DR, Anthony P, Charlton KE, Maggio M et al . Validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging. 2009; 13(9):782-788. Rubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci. 2001; 56(6):M366-372. Hayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. Br J Math Stat Psychol. 2014; 67(3):451-470. Nie H, Xu Y, Liu B, Zhang Y, Lei T, Hui X, Zhang L, Wu Y. The prevalence of mild cognitive impairment about elderly population in China: a meta-analysis. Int J Geriatr Psychiatry. 2011; 26(6):558-563. Guigoz Y, Lauque S, Vellas BJ. Identifying the elderly at risk for malnutrition. The Mini Nutritional Assessment. Clin Geriatr Med. 2002; 18(4):737-757. Van Nes MC, Herrmann FR, Gold G, Michel JP, Rizzoli R. Does the mini nutritional assessment predict hospitalization outcomes in older people? Age Ageing. 2001; 30(3):221-226. Gazzotti C, Albert A, Pepinster A, Petermans J. Clinical usefulness of the mini nutritional assessment (MNA) scale in geriatric medicine. J Nutr Health Aging. 2000; 4(3):176-181. Hu F, Liu H, Liu X, Jia S, Zhao W, Zhou L, Zhao Y, Hou L, Xia X, Dong B. Nutritional status mediates the relationship between sarcopenia and cognitive impairment: findings from the WCHAT study. Aging Clin Exp Res. 2021; 33(12):3215-3222. Power R, Prado-Cabrero A, Mulcahy R, Howard A, Nolan JM. The Role of Nutrition for the Aging Population: Implications for Cognition and Alzheimer's Disease. Annu Rev Food Sci Technol. 2019; 10:619-639. Abbatecola AM, Russo M, Barbieri M. Dietary patterns and cognition in older persons. Curr Opin Clin Nutr Metab Care. 2018; 21(1):10-13. Steffens DC. Depressive symptoms and mild cognitive impairment in the elderly: an ominous combination. Biol Psychiatry. 2012; 71(9):762-764. Chan CK, Soldan A, Pettigrew C, Wang MC, Wang J, Albert MS, Rosenberg PB, Team BR. Depressive symptoms in relation to clinical symptom onset of mild cognitive impairment. Int Psychogeriatr. 2019; 31(4):561-569. Tables Table1.Characteristics of study participants according to the status of cognition Characteristic Normal MCI P Value (n=4216) (n=583) Age(y) 71.27±5.56 73.27±6.91 <0.001 Sex <0.001 Male (%) 1822(43.2) 188(32.2) Female (%) 2394(56.8) 395(67.8) BMI (kg/m²) 23.96±3.31 24.08±3.51 0.394 Widowed (%) 618(14.7) 149(25.6) <0.001 Living alone (%) 516(12.2) 125(21.4) <0.001 Education level (%) <0.001 Less than high school 1972(46.8) 370(63.5) High school or higher education 2244(53.2) 213(36.5) Drinking (%) 401(9.5) 59(10.1) 0.062 Smoking (%) 625(14.8) 91(15.6) 0.065 IPAQ (Met-min/wk) 3759(1386,6720) 3360(1386,6426) 0.062 Fall history (%) 313(7.4) 71(12.2) <0.001 GDS score 5.29±4.47 6.86±5.69 <0.001 Depression (%) 459(10.9) 109(18.7) <0.001 Severity of depression (%) <0.001 None 3757(89.1) 474(81.3) Mild 400(9.5) 87(14.9) Moderate to severe 59(1.4) 22(3.8) Antidepressant medication use (%) 19(0.5) 5(0.9) 0.158 MNA-SF 12.69±1.61 12.37±1.69 <0.001 Nutritional status (%) 0.003 Well nourished 3410(80.9) 437(75.0) At risk of malnutrition 764(18.1) 138(23.7) Malnourished 42(1.0) 8(1.4) Chronic conditions (%) Diabetes mellitus 828(19.6) 115(19.7) 0.961 Hypertension 2881(68.3) 423(72.6) 0.039 Hyperlipidemia 2363(56.0) 290(49.7) 0.004 Coronary heart disease 1002(23.8) 168(28.8) 0.008 Stroke 401(9.5) 77(13.2) 0.005 Kidney disease 269(6.4) 32(5.5) 0.405 Biliary tract disease 579(13.7) 68(11.7) 0.170 Peptic ulcer 406(9.6) 39(6.7) 0.022 Pulmonary disease 310(7.4) 37(6.3) 0.379 Osteoarthritis 471(11.2) 85(14.6) 0.016 Parkinson disease 29(0.7) 6(1.0) 0.364 Gout 203(4.8) 15(2.6) 0.015 Cancer 140(3.3) 15(2.6) 0.338 Thyroid disease 229(5.4) 25(4.3) 0.248 Notes: BMI, body mass index; MNA-SF, Mini Nutritional Assessment-Short Form; IPAQ, international physical activity questionnaire; Met-min/wk, metabolic equivalent task minutes per week; GDS, Geriatric Depression Scale. Table 2. Associations of MCI with nutritional status and depression in a logistic regression analysis. Variables OR (95%CI) Crude P Adjusted model P MNA-SF score 0.89(0.85,0.94) <0.001 0.87(0.82,0.92) <0.001 Nutritional status Well nourished Ref Ref At risk of malnutrition 1.41(1.15,1.73) 0.001 1.44(1.14,1.81) 0.002 Malnourished 1.49(0.69,3.19) 0.308 1.74(0.78,3.88) 0.173 Depression 1.88(1.50,2.37) <0.001 1.56(1.22,1.99) <0.001 Depression severe grades None Ref Ref Mild 1.72(1.34,2.22) <0.001 1.48(1.14,1.93) 0.003 Moderate to severe 2.96(1.80,4.87) <0.001 2.02(1.18,3.44) 0.010 Adjusted model: Adjusted for age, sex, BMI, widowed, living alone, education, fall history, hypertension, hyperlipidemia, coronary heart disease, stroke, peptic ulcer, osteoarthritis, gout, MNA-SF and Depression. Table 3. Summary of mediation analysis for depression, MNA-SF and MMSE score Independent variable Mediating variable Dependent variable Coefficient (bias-corrected bootstrap 95% CI) Relative Proportion Depression Nutritional status cognition function Indirect effect (ab) Total effect (c) Direct effect (c') ab/c c’/c GDS MNA-SF MMSE score -0.010(-0.014, -0.006) -0.072(-0.094, -0.050) -0.062(-0.084, -0.040) 13.9% 86.1% Depression severe grades None Ref Ref Ref Mild -0.090(-0.149, -0.046) -0.477(-0.812, -0.142) -0.387(-0.721, -0.054) 18.9% 81.1% Moderate to severe -0.122(-0.270, -0.004) -1.126(-1.957.-0.296) -1.004(-1.830, -0.178) 10.8% 89.2% Notes: MNA-SF, Mini Nutritional Assessment-Short Form; GDS, Geriatric Depression Scale; MMSE, Mini-Mental Status Examination. Additional Declarations No competing interests reported. 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of Medicine and Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Han","suffix":""},{"id":273653387,"identity":"ed3d0b80-48d0-4ffb-9dd6-bc323eaddd39","order_by":2,"name":"Zhenwen Liang","email":"","orcid":"","institution":"Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenwen","middleName":"","lastName":"Liang","suffix":""},{"id":273653388,"identity":"2724da99-9da5-48eb-a7b5-28bb1cbdc055","order_by":3,"name":"Liou Cao","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liou","middleName":"","lastName":"Cao","suffix":""},{"id":273653389,"identity":"bc509eb6-cf0e-4ba1-867c-a78ed5da1a1e","order_by":4,"name":"Jing Gao","email":"","orcid":"","institution":"General Practice 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Ding","suffix":""},{"id":273653393,"identity":"85e31d54-27f0-4c48-8462-5aaf21cc92e9","order_by":8,"name":"Weijia Li","email":"","orcid":"","institution":"Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weijia","middleName":"","lastName":"Li","suffix":""},{"id":273653394,"identity":"1ee97e5e-76e7-41c9-bf71-bfae40a7897e","order_by":9,"name":"Biying Jing","email":"","orcid":"","institution":"Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Biying","middleName":"","lastName":"Jing","suffix":""},{"id":273653395,"identity":"f2d3ae81-0f26-4460-b849-db849e095f9f","order_by":10,"name":"Hongjuan Yu","email":"","orcid":"","institution":"Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongjuan","middleName":"","lastName":"Yu","suffix":""},{"id":273653396,"identity":"8be524b2-5ec7-499b-8b8d-13cec4ebda07","order_by":11,"name":"Qi Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYLCCBB4GORB94AEpWozBWhJIsSixAayXGKXyEckHbzyQuZM+P+zwQ6AtdnK6DQS0GN5IS7ZI4HmWu/F2mgFQS7Kx2QFCWmbkmEkk8BzO3Tg7AaTlQOI2YrWkG85O/0CcFnkJiJYEeekcIm0x4HkG9ovhBumcggMJBkT4Rb49+eDNnz135OVnp2/+8KHCTo6gFgOgAgnGngNgBpBLQDnYlgagFoYfB8CMUTAKRsEoGAVYAQDtl0jPdbk3xAAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qi","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-02-09 01:59:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3941624/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3941624/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51445656,"identity":"1e7ecb7c-7520-44fb-a6ca-c5ce266ce568","added_by":"auto","created_at":"2024-02-21 18:11:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74540,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-categorical mediation model of depression, nutritional status, and cognitive function in suburb-dwelling Chinese older adults. Paths a1 and a2 represent the effect of mild depression and moderate to severe depression on nutritional status; path b represents the effect of nutritional status on cognitive function; paths c1 and c2 represent the relative direct effect of mild depression and moderate to severe depression on cognitive function. Unstandardized regression coefficients are reported.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3941624/v1/06ffe70b4926901753ff16a7.png"},{"id":63337129,"identity":"a87dc55b-cadb-4eee-8a07-49cd7b3d1a2e","added_by":"auto","created_at":"2024-08-27 05:51:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":592862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3941624/v1/f5ce3a9a-7b66-4638-b368-8f95d362b541.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nutritional status mediates the relationship between depression and mild cognitive impairment among Chinese community-dwelling older adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the global population of older adults increases, cognitive impairment becomes a major risk factor for poor health and a significant public health burden. Recent studies estimate that over 15 million people aged 60 or older in China have dementia\u0026nbsp;[1].\u0026nbsp;Mild cognitive impairment (MCI) represents an intermediate state between normal aging and dementia, with approximately 10% of individuals with MCI progressing to dementia annually, compared to a 1-3% conversion rate in the general older adult population\u0026nbsp;[2, 3]. The high prevalence, progression rate to dementia, and the considerable health care expenditures make it a major public health problem.\u0026nbsp;Given its high prevalence, progression to dementia, and substantial healthcare costs, MCI presents a significant public health challenge.\u003c/p\u003e\n\u003cp\u003eDepression is a prevalent mental disorder among older adults and a leading cause of emotional distress in later life\u0026nbsp;[4]. Unfortunately, depression often goes undiagnosed or untreated in this population\u0026nbsp;[5].\u0026nbsp;Growing evidence suggests that depression independently accelerates the progression from MCI to dementia, with a two to four times increased risk\u0026nbsp;[6, 7]. However, the relationship between depression and MCI is complex, as depression can lead to complaints about declining cognitive function, while cognitive deficits can result in complaints of depression\u0026nbsp;[8]. Our previous study indicated that depression is an independent risk factor for MCI, necessitating further research on this relationship\u0026nbsp;[9]. Thus, depression deserves increased attention in the care of individuals with MCI.\u003c/p\u003e\n\u003cp\u003eMalnutrition is another critical health issue for older adults, particularly as the aging population grows. Malnutrition is associated with reduced health status, lower quality of life, poor functional status, and increased healthcare costs\u0026nbsp;[10, 11]. It affects older adults psychologically through mechanisms such as brain atrophy and reduced enjoyment of food\u0026nbsp;[12, 13]. Depression has been proposed as a risk factor for malnutrition in older adults[14, 15]. . Additionally, poor nutritional status plays a role in the development and progression of neurodegenerative disorders associated with cognitive decline, highlighting the importance of early nutritional assessment and intervention\u0026nbsp;[16].\u0026nbsp;Although some studies have explored the relationship between depression, malnutrition, and cognition\u0026nbsp;[17],\u0026nbsp;their focus on total cognitive impairment, including patients with dementia, rather than MCI separately.\u0026nbsp;The interrelationship of these three conditions in older adults remains controversial.\u0026nbsp;Furthermore,\u0026nbsp;no studies have investigated potential mediation pathways between depression, nutritional status and MCI.\u003c/p\u003e\n\u003cp\u003eTherefore, this study aims to examine the complex association of MCI with depression and nutritional status in community-dwelling individuals aged 60 years or older. We explore the possible mediating effects of nutritional status on the association between depression and cognitive function. Additionally, we test whether nutritional status mediates the relationship between different severity levels of depression and cognition.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population comprises residents aged 60 years or older from Tianjin and Shanghai, China, who participated in China\u0026apos;s National Free Physical Examination program between August 2018 and October 2022.\u0026nbsp;Participants were excluded as following criteria: (1)\u0026nbsp;did not complete date on the depression, nutrition assessments and cognitive function; (2)\u0026nbsp;had a known diagnosis of dementia; (3)\u0026nbsp;unable to communicate with the study staff or provide informed consent.\u0026nbsp;After excluding 306 subjects, the final sample consisted of 4799 participants (2010 males, 2789 females).\u0026nbsp;Missing data on cognitive function assessment, psychological tests, and nutritional status evaluation were noted in 186, 25, and 7 participants, respectively. Another 6 participants had missing data on basic covariates, and 82 were evaluated for dementia. The study received ethical approval from the Ethics Committee of Tianjin Medical University and Shanghai Medical and Health University, adhering to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCovariates\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterviews were conducted using questionnaires from previous studies\u0026nbsp;[18].\u0026nbsp;Socio-demographic variables included age, sex, marital status, living arrangements, and education. Behavioral characteristics such as alcohol consumption, smoking, physical activity, and falls history were recorded. Chronic diseases, including diabetes mellitus, hypertension, hyperlipidemia, coronary heart disease, stroke, kidney disease, peptic ulcer, biliary tract disease, pulmonary disease, osteoarthritis, Parkinson\u0026apos;s disease, gout, cancer, and thyroid disease, were also documented\u0026nbsp;[19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDefinition of MCI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMCI was defined according to Petersen\u0026apos;s diagnostic criteria[20].\u0026nbsp;Mini-Mental State Examination (MMSE) and Instrumental Activities of Daily Living scale (IADL) were used. Cognitive impairment thresholds for MMSE were \u0026le;17, 20, and 24 points for illiteracy, elementary, and middle school or above, respectively[21]. IADL consists of eight items on a scale of 0 to 8, with higher scores indicating better daily living ability. IADL score \u0026ge;6 indicates normal daily living ability[9, 22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssessment of depression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepression was screened by the Chinese version of 30-item Geriatric Depression Scale (GDS-30), which was standardized including 30 items ranging from 0-30 points.\u0026nbsp;A cutoff value of 11 points was used to define depression.\u0026nbsp;[23]. Participants were also grouped according to the severity of symptoms: \u0026ldquo;None\u0026rdquo; (GDS-30\u0026lt;11), \u0026ldquo;Mild\u0026rdquo; (11\u0026le;GDS-30\u0026le;20), \u0026ldquo;Moderate to severe\u0026rdquo; (20\u0026lt;GDS-30\u0026le;30).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNutritional Status Evaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNutritional status was evaluated using the Mini Nutritional Assessment-Short Form (MNA-SF),\u0026nbsp;a validated screening tool used in geriatric health care[24].\u0026nbsp;Compared with the MNA, the sensitivity and specificity of this version are 97.9% and 100%, respectively[25].\u0026nbsp;The MNA-SF consists of six items assessing food intake, weight loss, mobility, psychological stress, neuropsychological problems, and body mass index (BMI). The total score ranges from 0 to 14 points. Participants with scores of 12-14 were considered well-nourished, 8-11 were at risk of malnutrition, and 0-7 were\u0026nbsp;malnourished.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as means \u0026plusmn; standard deviations (SD), while categorical variables were expressed as frequencies and percentages. Group differences were analyzed using the independent t-test or chi-square test, as appropriate. Univariate logistic regression analysis was conducted to explore the association between depression, nutritional status, and MCI. Multivariate logistic regression models were then used to adjust for potential confounding factors.\u003c/p\u003e\n\u003cp\u003eMediation analyses were performed to explore the mediating role of nutritional status in the association between depression and MCI.\u0026nbsp;In the mediation analysis model with full adjustment, we included GDS score and depression severe grades as independent variables (X), MMSE score as dependent variable (Y), and the MNA-SF variable as a potential mediator (M). The total effect (path c) represents the sum of the direct and indirect effects of GDS score and depression severe grades on MMSE score. The direct effect (path c\u0026rsquo;) is the effect of GDS score and depression severe grades on MMSE, and the indirect effect (path ab) is the mediating effect of the association between GDS score and depression severe grades and MMSE.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PROCESS macro for SPSS was used for mediation analysis [26]. We tested the significance of the indirect effect (mediation effect) and confirmed that the effect of independent variable on mediator, the effect of mediator on dependent variable and the total effect of independent variable on dependent variable were significant, respectively. A bootstrapping method with 5000 resamples was applied to calculate bias-corrected 95% confidence intervals for direct and indirect effects.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipant Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 4799 participants (2010 men, 2789 women) who were available to be analyzed, 583 (12.1%) had MCI, in which 9.4% for men and 14.2% for women respectively. Table 1 presents the socio-economic and health-related characteristics of individuals stratified by cognitive state. We found that participants with MCI tended to be older, female, widowed, living alone, having low education levels (P\u0026lt;0.05, Table 1). In regard to the health-related variables, falling, depression, poor nutrition,\u0026nbsp;hyperlipidemia,\u0026nbsp;coronary heart disease\u0026nbsp;stroke, peptic ulcer and osteoarthritis were significantly related to MCI (P\u0026lt;0.05, Table 1). Regarding nutritional status, 18.8% (902 participants) were at risk of malnutrition, and 1% (50 participants) were malnourished.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation Between Depression, Nutritional Status, and MCI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis revealed significant associations between depression, nutritional status, and MCI (Table 2). After adjustments for potential confounders (age, sex, BMI, widowed, living alone, education, fall history, hypertension, hyperlipidemia, coronary heart disease, stroke, peptic ulcer, osteoarthritis and gout), we observed that total MNA-SF score (OR = 0.87, 95% CI = 0.82\u0026ndash;0.92) was significantly associated with MCI. We also found that older adults at risk of malnutrition (OR = 1.44, 95% CI = 1.14\u0026ndash;1.81) were significantly associated with MCI compared to those with\u0026nbsp;well nutritional\u0026nbsp;status stratified by nutritional status. Furthermore, depression (OR = 1.56, 95% CI = 1.22\u0026ndash;1.99) was significantly related to MCI. Interestingly, graded by depression severity, the association with MCI increased as the severity of depression increased (Mild depression:\u0026nbsp;OR = 1.48, 95% CI = 1.14\u0026ndash;1.93;\u0026nbsp;Moderate to severe depression:\u0026nbsp;OR = 2.02, 95% CI = 1.14\u0026ndash;1.93).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMediation Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMediation analyses were conducted to explore the potential mediating role of nutritional status in the association between depression and MCI. Table 3 and Fig 1 showed that MNA-SF score had a significant mediating role in the association between depression and MCI. Poor nutritional status plays a mediate role in the association between mild depression (indirect effect ab = -0.090; 95%CI= -0.149 to -0.046), and moderate to severe depression (indirect effect ab = -0.122; 95%CI= -0.270 to -0.004) with cognitive function, and the mediating effect was enhanced slightly larger in mild depression (18.9%) than moderate to severe depression (10.8%).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study used mediation models to explore the mediation role of nutritional status between two important clinical conditions: depression and cognitive impairment. Our results suggest that individuals at risk of malnutrition and depression were both significantly associated with MCI in older community-dwelling Chinese adults. Notably, moderate to severe depression exhibits a stronger link with MCI compared to mild depression.\u0026nbsp;Furthermore, our study reveals that poor nutritional status significantly mediates the association between depression and cognitive function, with the mediating effect being particularly pronounced in cases of mild depression.\u003c/p\u003e\n\u003cp\u003eIn our\u0026nbsp;investigation, the prevalence of MCI was 12.1%, consistent with recent Chinese national study involving 46011 adults aged 60 years or older (15.5%)[1].\u0026nbsp;A systematic review of 22 Chinese studies in older adults also yielded similar results\u0026nbsp;(12.7%)[27]. The prevalence of depression using the GDS-30 was 11.8%,\u0026nbsp;aligning with our prior studies\u0026nbsp;[9, 19]. Regarding nutritional status, 1% of the older adults were malnourished, 18.8% were at risk of malnutrition, and 80.2% were well-nourished. Our results are line with seven previous studies\u0026nbsp;involving\u0026nbsp;2798 community- dwelling elderly persons, reporting that 1% were malnourished, 29% were at risk of malnutrition, and 70% were well-nourished[28].\u0026nbsp;However, variations exist; for instance,\u0026nbsp;Mantzorou et al.[17]\u0026nbsp;reported 11.3%\u0026nbsp;malnourished participants, while another study indicated 20% malnutrition and 49% at risk of malnutrition\u0026nbsp;[28-30]. The reason for the difference may be that the nutritional status of older people living in nursing homes, long-term care facilities and hospital settings is poorer than that of older adults who can live independently in the community. In addition, with the welfare policies of the Shanghai government and advanced medical resources, the physical condition of the older adults in the region is generally better than the rest of the country.\u003c/p\u003e\n\u003cp\u003eAdditionally, our results align with previous evidence on the association between nutritional status and cognitive function\u0026nbsp;[17, 31]. Accumulating evidence suggest that nutrition is important for optimizing cognition and reducing the risk of dementia[32, 33]. While our findings slightly differ, showing that individuals at malnutrition risk (OR=1.44, 95%CI=1.14,1.81) are associated with MCI, whereas this relationship is absent in malnourished (OR=1.74, 95%CI=0.78,3.88) older adults, the small prevalence of malnutrition (1%) and limited sample size may explain this lack of association. We anticipate that with a larger sample, the correlation would become statistically significant. Overall, the association between depression and MCI consisted with the results of our previous studies[9]. Steffens et al. reported that depression increase the risk for progression from MCI to Alzheimer\u0026rsquo;s disease dementia[34]. Although previous study demonstrated that depression is relation to clinical symptoms onset of MCI, the casual relationship between depression and MCI remains to be explored[35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo our knowledge, no other studies have highlighted malnutrition as a mediator of the relationship between depression and cognitive function in older adults. Our results suggest that depression may lead to malnutrition, subsequently causing MCI among older adults, independent of the direct effects of depression on MCI. This implies that improving nutritional status may mitigate the negative impact of depression on cognitive function. Given the limited effective treatments for depression and cognitive deterioration in older adults, modifiable risk factors, such as nutritional status, offer intervention opportunities. In mediation analysis, the relationship between depression and cognitive function comprises a relative direct effect and a relative indirect effect. When deeper categorization of depression into mild and moderate to severe, both the direct effect (81.1%) and indirect effect (18.9%) of nutritional status remain significant in mild depression, while the direct effect (89.2%) and indirect effect (10.8%) remain significant in moderate to severe depression, albeit to a lesser extent as a mediating effect. This suggests that nutritional status mediates the association between depression and cognitive function, especially concerning mild depression. The total effects of depression on cognitive function increase with depression severity, while the proportion of relative indirect effects (mediated by nutritional status) decreases with depression severity (Table 3). This may be attributed to a higher comorbidity burden among severely depressed older adults, introducing additional mediators beyond nutrition into the complex relationship. Hence, early nutritional intervention may be more effective in preventing cognitive deterioration in depressed older adults. However, it\u0026apos;s important to note that the mediating role of nutritional status is not entirely robust, warranting further studies to explore common underlying determinants of depression and cognitive impairment.\u003c/p\u003e\n\u003cp\u003eSeveral limitations characterize our study. First, the cross-sectional design limits our ability to establish causality, and only longitudinal studies can unravel the complex and bidirectional association between depression and MCI [34, 35]. Future research should extend follow-up times, increase sample sizes, and explore these associations longitudinally. Second, all participants in the present study were relatively healthy, as those unable to participate in the free annual national physical examination (e.g. those bedridden or with serious disease) were excluded. Consequently, our results might underestimate the true prevalence of depression, malnutrition, or MCI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrated that the association between depression and MCI\u0026nbsp;may be in part mediated by nutritional status. Improving the nutritional status of depressed older adults may counteract the cognitive decline linked to depression. Early nutritional intervention is recommended for improved prevention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMCI: Mild cognitive impairment; MNA-SF: Mini Nutritional Assessment-Short Form.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Qiongying Tao from the\u0026nbsp;Jiading Subdistrict Community Health Center,\u0026nbsp;Xiaofang Ren from Hangu welfare house for providing place and organization and Xiaoyue Gu from the Chongming public health center for providing place and organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.C. and P.H. conceived the concept and design of the study; X.C., J.G., K.D.,W.L., B.J. and H.Y. contributed to data collection, entry, and data cleaning. X.C., P. H., Z.L., and J.Z., contributed to the data analysis and interpretation of study results. L.C., Q.T., and Q.G. contributed to acquisition of funding, study design and provided administrative support. All authors wrote the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the funding of Capacity Building project of Local Colleges of Shanghai Science and Technology Commission (23010502800);\u0026nbsp;Shanghai Sailing Program (22YF1417900).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the\u0026nbsp;Ethics Committee of Shanghai University of Medicine and Health Sciences\u0026nbsp;and written informed consent was obtained from all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. Any additional data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, Li Y, Li Y, Zhu M, Jiao H\u003cem\u003e et al\u003c/em\u003e. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020; 5(12):e661-e671.\u003c/li\u003e\n\u003cli\u003eDoblhammer G, Fink A, Zylla S, Willekens F. Compression or expansion of dementia in Germany? An observational study of short-term trends in incidence and death rates of dementia between 2006/07 and 2009/10 based on German health insurance data. Alzheimers Res Ther. 2015; 7(1):66.\u003c/li\u003e\n\u003cli\u003eSatizabal CL, Beiser AS, Chouraki V, Chene G, Dufouil C, Seshadri S. Incidence of Dementia over Three Decades in the Framingham Heart Study. N Engl J Med. 2016; 374(6):523-532.\u003c/li\u003e\n\u003cli\u003eBelvederi Murri M, Cattelani L, Chesani F, Palumbo P, Triolo F, Alexopoulos GS. Risk Prediction Models for Depression in Community-Dwelling Older Adults. Am J Geriatr Psychiatry. 2022; 30(9):949-960.\u003c/li\u003e\n\u003cli\u003eSong D, Yu DSF, Li PWC, Sun Q. Identifying the Factors Related to Depressive Symptoms Amongst Community-Dwelling Older Adults with Mild Cognitive Impairment. Int J Environ Res Public Health. 2019; 16(18).\u003c/li\u003e\n\u003cli\u003eRichard E, Reitz C, Honig LH, Schupf N, Tang MX, Manly JJ, Mayeux R, Devanand D, Luchsinger JA. Late-life depression, mild cognitive impairment, and dementia. JAMA Neurol. 2013; 70(3):374-382.\u003c/li\u003e\n\u003cli\u003eVan der Mussele S, Fransen E, Struyfs H, Luyckx J, Marien P, Saerens J, Somers N, Goeman J, De Deyn PP, Engelborghs S. Depression in mild cognitive impairment is associated with progression to Alzheimer's disease: a longitudinal study. J Alzheimers Dis. 2014; 42(4):1239-1250.\u003c/li\u003e\n\u003cli\u003eMuhammad T, Meher T. Association of late-life depression with cognitive impairment: evidence from a cross-sectional study among older adults in India. BMC Geriatr. 2021; 21(1):364.\u003c/li\u003e\n\u003cli\u003eChen X, Han P, Yu X, Zhang Y, Song P, Liu Y, Jiang Z, Tao Z, Shen S, Wu Y\u003cem\u003e et al\u003c/em\u003e. Relationships between sarcopenia, depressive symptoms, and mild cognitive impairment in Chinese community-dwelling older adults. J Affect Disord. 2021; 286:71-77.\u003c/li\u003e\n\u003cli\u003eDent E, Wright ORL, Woo J, Hoogendijk EO. Malnutrition in older adults. Lancet. 2023; 401(10380):951-966.\u003c/li\u003e\n\u003cli\u003eNorman K, Hass U, Pirlich M. Malnutrition in Older Adults-Recent Advances and Remaining Challenges. Nutrients. 2021; 13(8).\u003c/li\u003e\n\u003cli\u003eVolkert D, Chourdakis M, Faxen-Irving G, Fruhwald T, Landi F, Suominen MH, Vandewoude M, Wirth R, Schneider SM. ESPEN guidelines on nutrition in dementia. Clin Nutr. 2015; 34(6):1052-1073.\u003c/li\u003e\n\u003cli\u003eBailly N, Maitre I, Van Wymelbeke V. Relationships between nutritional status, depression and pleasure of eating in aging men and women. Arch Gerontol Geriatr. 2015; 61(3):330-336.\u003c/li\u003e\n\u003cli\u003eVelazquez-Alva MC, Irigoyen-Camacho ME, Cabrer-Rosales MF, Lazarevich I, Arrieta-Cruz I, Gutierrez-Juarez R, Zepeda-Zepeda MA. Prevalence of Malnutrition and Depression in Older Adults Living in Nursing Homes in Mexico City. Nutrients. 2020; 12(8).\u003c/li\u003e\n\u003cli\u003eIslam MZ, Disu TR, Farjana S, Rahman MM. Malnutrition and other risk factors of geriatric depression: a community-based comparative cross-sectional study in older adults in rural Bangladesh. BMC Geriatr. 2021; 21(1):572.\u003c/li\u003e\n\u003cli\u003eTsagalioti E, Trifonos C, Morari A, Vadikolias K, Giaginis C. Clinical value of nutritional status in neurodegenerative diseases: What is its impact and how it affects disease progression and management? Nutr Neurosci. 2018; 21(3):162-175.\u003c/li\u003e\n\u003cli\u003eMantzorou M, Vadikolias K, Pavlidou E, Serdari A, Vasios G, Tryfonos C, Giaginis C. Nutritional status is associated with the degree of cognitive impairment and depressive symptoms in a Greek elderly population. Nutr Neurosci. 2020; 23(3):201-209.\u003c/li\u003e\n\u003cli\u003eHan P, Kang L, Guo Q, Wang J, Zhang W, Shen S, Wang X, Dong R, Ma Y, Shi Y\u003cem\u003e et al\u003c/em\u003e. Prevalence and Factors Associated With Sarcopenia in Suburb-dwelling Older Chinese Using the Asian Working Group for Sarcopenia Definition. J Gerontol A Biol Sci Med Sci. 2016; 71(4):529-535.\u003c/li\u003e\n\u003cli\u003eChen X, Guo J, Han P, Fu L, Jia L, Yu H, Yu X, Hou L, Wang L, Zhang W\u003cem\u003e et al\u003c/em\u003e. Twelve-Month Incidence of Depressive Symptoms in Suburb-Dwelling Chinese Older Adults: Role of Sarcopenia. J Am Med Dir Assoc. 2019; 20(1):64-69.\u003c/li\u003e\n\u003cli\u003eSun Z, Wang Z, Xu L, Lv X, Li Q, Wang H, Yu X. Characteristics of Cognitive Deficit in Amnestic Mild Cognitive Impairment With Subthreshold Depression. J Geriatr Psychiatry Neurol. 2019; 32(6):344-353.\u003c/li\u003e\n\u003cli\u003eKatzman R, Zhang MY, Ouang Ya Q, Wang ZY, Liu WT, Yu E, Wong SC, Salmon DP, Grant I. A Chinese version of the Mini-Mental State Examination; impact of illiteracy in a Shanghai dementia survey. J Clin Epidemiol. 1988; 41(10):971-978.\u003c/li\u003e\n\u003cli\u003eNagamatsu LS, Chan A, Davis JC, Beattie BL, Graf P, Voss MW, Sharma D, Liu-Ambrose T. Physical activity improves verbal and spatial memory in older adults with probable mild cognitive impairment: a 6-month randomized controlled trial. J Aging Res. 2013; 2013:861893.\u003c/li\u003e\n\u003cli\u003eYesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, Leirer VO. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982; 17(1):37-49.\u003c/li\u003e\n\u003cli\u003eKaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Cederholm T, Thomas DR, Anthony P, Charlton KE, Maggio M\u003cem\u003e et al\u003c/em\u003e. Validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging. 2009; 13(9):782-788.\u003c/li\u003e\n\u003cli\u003eRubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci. 2001; 56(6):M366-372.\u003c/li\u003e\n\u003cli\u003eHayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. Br J Math Stat Psychol. 2014; 67(3):451-470.\u003c/li\u003e\n\u003cli\u003eNie H, Xu Y, Liu B, Zhang Y, Lei T, Hui X, Zhang L, Wu Y. The prevalence of mild cognitive impairment about elderly population in China: a meta-analysis. Int J Geriatr Psychiatry. 2011; 26(6):558-563.\u003c/li\u003e\n\u003cli\u003eGuigoz Y, Lauque S, Vellas BJ. Identifying the elderly at risk for malnutrition. The Mini Nutritional Assessment. Clin Geriatr Med. 2002; 18(4):737-757.\u003c/li\u003e\n\u003cli\u003eVan Nes MC, Herrmann FR, Gold G, Michel JP, Rizzoli R. Does the mini nutritional assessment predict hospitalization outcomes in older people? Age Ageing. 2001; 30(3):221-226.\u003c/li\u003e\n\u003cli\u003eGazzotti C, Albert A, Pepinster A, Petermans J. Clinical usefulness of the mini nutritional assessment (MNA) scale in geriatric medicine. J Nutr Health Aging. 2000; 4(3):176-181.\u003c/li\u003e\n\u003cli\u003eHu F, Liu H, Liu X, Jia S, Zhao W, Zhou L, Zhao Y, Hou L, Xia X, Dong B. Nutritional status mediates the relationship between sarcopenia and cognitive impairment: findings from the WCHAT study. Aging Clin Exp Res. 2021; 33(12):3215-3222.\u003c/li\u003e\n\u003cli\u003ePower R, Prado-Cabrero A, Mulcahy R, Howard A, Nolan JM. The Role of Nutrition for the Aging Population: Implications for Cognition and Alzheimer's Disease. Annu Rev Food Sci Technol. 2019; 10:619-639.\u003c/li\u003e\n\u003cli\u003eAbbatecola AM, Russo M, Barbieri M. Dietary patterns and cognition in older persons. Curr Opin Clin Nutr Metab Care. 2018; 21(1):10-13.\u003c/li\u003e\n\u003cli\u003eSteffens DC. Depressive symptoms and mild cognitive impairment in the elderly: an ominous combination. Biol Psychiatry. 2012; 71(9):762-764.\u003c/li\u003e\n\u003cli\u003eChan CK, Soldan A, Pettigrew C, Wang MC, Wang J, Albert MS, Rosenberg PB, Team BR. Depressive symptoms in relation to clinical symptom onset of mild cognitive impairment. Int Psychogeriatr. 2019; 31(4):561-569.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable1.Characteristics of study participants according to the status of cognition\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"538\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003eP\u0026nbsp;Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e(n=4216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e(n=583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eAge(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e71.27\u0026plusmn;5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e73.27\u0026plusmn;6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Male (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e1822(43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e188(32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Female (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e2394(56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e395(67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e23.96\u0026plusmn;3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e24.08\u0026plusmn;3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.394\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eWidowed (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e618(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e149(25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eLiving alone (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e516(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e125(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp;Less than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e1972(46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e370(63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp;High school or higher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e2244(53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e213(36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eDrinking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e401(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e59(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.062\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e625(14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e91(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.065\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eIPAQ (Met-min/wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e3759(1386,6720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e3360(1386,6426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.062\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eFall history (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e313(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e71(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eGDS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e5.29\u0026plusmn;4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e6.86\u0026plusmn;5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eDepression (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e459(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e109(18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eSeverity of depression (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e3757(89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e474(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e400(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e87(14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eModerate to severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e59(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e22(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eAntidepressant medication use (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e19(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e5(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eMNA-SF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e12.69\u0026plusmn;1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e12.37\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eNutritional status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Well nourished\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e3410(80.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e437(75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; At risk of malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e764(18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e138(23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Malnourished\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e42(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e8(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003eChronic conditions (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e828(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e115(19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.961\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e2881(68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e423(72.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.039\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e2363(56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e290(49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Coronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e1002(23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e168(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e401(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e77(13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e269(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e32(5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.405\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Biliary tract disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e579(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e68(11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.170\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Peptic ulcer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e406(9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e39(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.022\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e310(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e37(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.379\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Osteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e471(11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e85(14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Parkinson disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e29(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e6(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Gout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e203(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e15(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e140(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e15(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.338\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.78438661710037%\" style=\"width: 33.996%;\"\u003e\n \u003cp\u003e\u0026nbsp; Thyroid disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.42007434944238%\" style=\"width: 25.0497%;\"\u003e\n \u003cp\u003e229(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.53531598513011%\" style=\"width: 26.2425%;\"\u003e\n \u003cp\u003e25(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.754646840148698%\" style=\"width: 14.7117%;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: BMI, body mass index;\u0026nbsp;MNA-SF, Mini Nutritional Assessment-Short Form;\u0026nbsp;IPAQ, international physical activity questionnaire; Met-min/wk, metabolic equivalent task minutes per week; GDS, Geriatric Depression Scale.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Associations of MCI with nutritional status and depression in a logistic regression analysis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003eAdjusted model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003eMNA-SF score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e0.89(0.85,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e0.87(0.82,0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003eNutritional status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003e\u0026nbsp; Well nourished\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003e\u0026nbsp; At risk of malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e1.41(1.15,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e1.44(1.14,1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Malnourished\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e1.49(0.69,3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e1.74(0.78,3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e1.88(1.50,2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e1.56(1.22,1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003eDepression severe grades\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003e\u0026nbsp; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003e\u0026nbsp; Mild\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e1.72(1.34,2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e1.48(1.14,1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.97826086956522%\"\u003e\n \u003cp\u003e\u0026nbsp; Moderate to severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\"\u003e\n \u003cp\u003e2.96(1.80,4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.195652173913043%\"\u003e\n \u003cp\u003e2.02(1.18,3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eAdjusted model: Adjusted for age, sex, BMI, widowed, living alone, education,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003efall history, hypertension, hyperlipidemia, coronary heart disease, stroke, peptic ulcer, osteoarthritis, gout, MNA-SF and Depression.\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\u003eTable 3. Summary of mediation analysis for depression, MNA-SF and MMSE score\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"918\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.527233115468409%\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.239651416122005%\"\u003e\n \u003cp\u003eMediating variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.25925925925926%\"\u003e\n \u003cp\u003eDependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.50544662309368%\" colspan=\"3\"\u003e\n \u003cp\u003eCoefficient (bias-corrected bootstrap 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.468409586056644%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eRelative Proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.513601741022851%\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.228509249183896%\"\u003e\n \u003cp\u003eNutritional status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\"\u003e\n \u003cp\u003ecognition function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003eIndirect effect (ab)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003eTotal effect (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003eDirect effect (c\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.2023939064200215%\" valign=\"top\"\u003e\n \u003cp\u003eab/c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ec\u0026rsquo;/c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.513601741022851%\"\u003e\n \u003cp\u003eGDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.228509249183896%\"\u003e\n \u003cp\u003eMNA-SF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\"\u003e\n \u003cp\u003eMMSE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-0.010(-0.014, -0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-0.072(-0.094, -0.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-0.062(-0.084, -0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.2023939064200215%\" valign=\"top\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e86.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.513601741022851%\"\u003e\n \u003cp\u003eDepression severe grades\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.228509249183896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.2023939064200215%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.513601741022851%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.228509249183896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.2023939064200215%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.513601741022851%\"\u003e\n \u003cp\u003eMild\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.228509249183896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-0.090(-0.149, -0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-0.477(-0.812, -0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-0.387(-0.721, -0.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.2023939064200215%\" valign=\"top\"\u003e\n \u003cp\u003e18.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e81.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.513601741022851%\"\u003e\n \u003cp\u003eModerate to severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.228509249183896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-0.122(-0.270, -0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-1.126(-1.957.-0.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.51904243743199%\"\u003e\n \u003cp\u003e-1.004(-1.830, -0.178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.2023939064200215%\" valign=\"top\"\u003e\n \u003cp\u003e10.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.249183895538629%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e89.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: MNA-SF, Mini Nutritional Assessment-Short Form; GDS, Geriatric Depression Scale; MMSE, Mini-Mental Status Examination.\u003c/p\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":"Depression, nutritional status, mild cognitive impairment.","lastPublishedDoi":"10.21203/rs.3.rs-3941624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3941624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMild cognitive impairment (MCI) and depression are prominent public health concerns among older adults, closely linked to their nutritional status. Our study aimed to explore the association and mediation pathways involving depression, nutritional status, and MCI in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study included 4799 community-dwelling Chinese older adults aged 60 years and older in Tianjin and Shanghai, China. We utilized the 30-item Geriatric Depression Scale (GDS-30) to assess depression presence and severity. A GDS-30 score ≥11 indicated the presence of depression. Participants were categorized by symptom severity: “None” (GDS-30\u0026lt;11), “Mild” (11≤GDS-30≤20), “Moderate to severe” (20\u0026lt;GDS-30≤30). Nutritional status was evaluated using the Mini Nutritional Assessment-Short Form (MNA-SF). Cognitive function was assessed with the Mini-Mental State Examination (MMSE), and daily living activities were measured using the Instrumental Activities of Daily Living (IADL). Logistic regression and mediation analyses were conducted with full adjustment for potential confounding factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 4799 older adults (2010 men, mean age 71.5±5.8 years), 11.8% exhibited depression, and 12.1% had MCI, while 18.8% were at risk of malnutrition, and 1% were malnourished. Participants at risk of malnutrition or experiencing depression were associated with MCI. Additionally, nutritional status significantly mediated the relationship between depression and cognitive function, with a slightly larger mediating effect, particularly in cases of mild depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Individuals at risk of malnutrition or depressed were associated with MCI. Poor nutritional condition mediates the association between depression and MCI in older adults. Early nutritional interventions may mitigate cognitive decline in depressed older adults.\u003c/p\u003e","manuscriptTitle":"Nutritional status mediates the relationship between depression and mild cognitive impairment among Chinese community-dwelling older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 18:11:02","doi":"10.21203/rs.3.rs-3941624/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":"39fb8980-d130-4e82-9435-f79a1141d9f8","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-27T05:51:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 18:11:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3941624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3941624","identity":"rs-3941624","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}