Malnutrition and Its Multidimensional Determinants in Institutionalized Older Adults: A Cross-Sectional Study from Resource-Limited Nursing Homes in China

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Methods: A cross-sectional study was conducted from February 2023 to March 2024, involving 205 elderly participants aged ≥60 years. Nutritional status was evaluated using the Mini-Nutritional Assessment Short-Form (MNA-SF), while sarcopenia, sleep quality, depression, and social support were assessed using validated scales. Descriptive statistics, correlation analysis, and multiple linear regression were performed. Results: Among participants, 14.10% were malnourished, and 27.80% were at risk of malnutrition. Lower BMI (β = 0.531, P < 0.001), higher sarcopenia risk (β = -0.367, P < 0.001), and lower social support (β = -0.077, P = 0.016) were significant predictors of malnutrition. Conclusion: Malnutrition is prevalent among nursing home residents in Southwest China, with BMI, sarcopenia, and social support being key determinants. Targeted interventions should focus on improving dietary intake, muscle health, and social engagement. Malnutrition Sarcopenia Body mass index (BMI) Social support Nursing home residents Cross-sectional study Southwest China Figures Figure 1 Figure 2 Figure 3 Introduction The global population is undergoing an unprecedented aging process, with individuals aged ≥ 60 years projected to constitute 22% of the world’ s population by 2050 [ 1 , 2 ] . China, as the most populous developing nation, faces particularly severe challenges, with its elderly population expected to exceed 366 million by 2050 [ 3 , 4 ] . This demographic shift amplifies public health concerns, particularly regarding malnutrition — a condition affecting approximately 25% of older adults globally [ 5 ] . Malnutrition in the elderly is closely linked to adverse outcomes such as sarcopenia, cognitive decline, and increased mortality [ 6 ] , yet remains understudied in institutionalized populations, especially in developing regions [ 7 ] . In China, rapid urbanization and socioeconomic disparities have led to uneven healthcare resource distribution, particularly in Southwest regions like Nan Chong. Rural-to-urban migration has left many elderly in underfunded nursing homes with limited access to nutritional support [ 8 ] .Existing studies on elderly malnutrition in China predominantly focus on urban communities or hospitals [ 9 ] , neglecting the unique challenges faced by institutionalized populations in resource-limited settings. For instance, a 2022 survey in Sichuan Province revealed that 35% of nursing home residents exhibited suboptimal dietary diversity, primarily due to financial constraints and inadequate staffing [ 10 ] . Age-related physiological declines, including reduced appetite, impaired nutrient absorption, and dental deterioration, significantly elevate malnutrition risk [ 11 ] . Institutionalized elderly individuals are further vulnerable due to social isolation, comorbidities, and reliance on standardized meal plans that may lack personalization [ 12 ] . Compounding these issues, cultural stigma surrounding institutional care in China often discourages family involvement, exacerbating social and nutritional neglect [ 13 ] . Despite these challenges, no prior studies have systematically evaluated the interplay of sarcopenia, mental health, and social support in driving malnutrition within Southwest Chinese nursing homes. This cross-sectional study investigates the nutritional status and its determinants among elderly residents in nursing homes in Nan Chong, Southwest China. Utilizing validated tools (MNA-SF, SARC-F, PSQI), we aim to: (i) Quantify malnutrition prevalence and severity in a region with limited healthcare infrastructure. (ii) Identify modifiable risk factors, including sarcopenia, sleep quality, and social support, with emphasis on culturally specific barriers. (iii) Provide evidence for targeted interventions to improve health outcomes in this underserved population. By addressing these objectives, this study contributes to the limited literature on institutionalized elderly in low-resource settings and informs policy-making for aging societies. Materials & methods Study population A cross-sectional study was conducted in three nursing homes in Nan Chong, Southwest China, from February 2023 to March 2024. Participants (n = 205) aged ≥ 60 years were included, excluding those with severe cognitive impairment or bedridden status. Data Collection Face-to-face interviews and anthropometric measurements were performed. Validated tools included: MNA-SF [ 14 ] : Assessed nutritional status (malnourished: <17; at risk: 17–23.5; normal: ≥23.5). SARC-F [ 15 ] : Screened for sarcopenia (score ≥ 4). PSQI [ 16 ] : Evaluated sleep quality (higher scores indicate poorer sleep). GDS-15 [ 17 ] : Measured depression (score ≥ 5 suggests possible depression). LSNS-6 [ 18 ] : Assessed social support (≤ 12: social isolation).。 Ethics declaration The study protocol was approved by the Institutional Review Board of Nanchong central hospital (Approval No. 2024.178) with written informed consent obtained from all participants. Statistical analysis SPSS software was used to conduct all statistical analyses. Means, standard deviations, cross tabulation, and frequency tables were used as descriptive statistics. Spearman’s correlation coefficients were used to calculate correlation significance. Analysis of variance for normally distributed dates and nonparametric methods, such as the Wilcoxon two-sample test and the Kruskal–Wallis test were used to make statistical comparisons. All results are presented as means ± standard deviation. A P value of ,0.05 was considered statistically significant. Results Demographic Characteristics A total of 205 elderly residents (median age: 83 years; range: 62–96 years) were included in the study. Among them, 75 (36.59%) were male and 130 (63.41%) were female. The majority (59.51%, n = 122) were aged ≥ 80 years. Significant differences in nutritional status were observed across sex (P = 0.037), education level (P = 0.015), and dietary type (P < 0.001). Participants with lower education (primary school or below: 38.05%) and those consuming soft or semi-liquid diets (39.51%) exhibited higher rates of malnutrition (Table 1 ). Nutritional Status Based on MNA-SF scores, 14.10% (n = 29) of participants were classified as malnourished (MNA < 17), 27.80% (n = 57) were at risk of malnutrition (MNA:17-23.5), and 58.05% (n = 119) had normal nutritional status (MNA ≥ 23.5) (Fig. 1 ). Malnourished individuals had significantly lower BMI (18.57 ± 2.30 kg/m² vs. 24.25 ± 3.19 kg/m², P < 0.001), reduced calf circumference (28.00 ± 2.90cm vs. 31.93 ± 4.86 cm, P < 0.001), and weaker grip strength (12.47 ± 7.56 kg vs. 17.20 ± 6.47 kg, P < 0.001) compared to those with normal nutritional status (Fig. 2 ). Table 1 Sociodemographic and Clinical Characteristics by Nutritional Status (N = 205) Variables Malnourished At risk of malnutrition Normal 合计 Z/H P Gender Male 13 13 49 75 6.594 0.037 Female 16 44 70 130 Year 60–70 2 6 15 23 1.623 0.805 70–80 8 15 37 60 80- 19 36 67 122 Height (cm) 156.38 ± 9.23 154.09 ± 8.58 156.00 ± 7.77 155.52 ± 8.23 1.231 0.294 Weight (kg) 45.91 ± 6.59 48.78 ± 9.48 59.01 ± 8.82 54.31 ± 10.35 69.477 < 0.001 BMI (kg/m 2 ) 18.57 ± 2.30 20.53 ± 3.51 24.25 ± 3.19 22.41 ± 3.88 74.602 < 0.001 calf circumference (cm) 28.00 ± 2.90 29.29 ± 4.10 31.93 ± 4.86 30.64 ± 4.68 41.448 < 0.001 Arm circumference (cm) 22.07 ± 2.89 23.67 ± 3.57 26.02 ± 3.46 24.81 ± 3.73 39.322 < 0.001 Strength (kg) 12.47 ± 7.56 13.83 ± 6.36 17.20 ± 6.47 15.60 ± 6.85 17.819 < 0.001 Education Primary school and below 16 23 39 78 17.477 0.015 middle school 4 22 56 82 high school 6 7 17 30 College and above 3 1 3 7 Unknown 0 4 4 8 Occupation (retired) Farmer 13 23 28 64 10.636 0.173 Worker 9 20 56 85 Government employee 5 13 27 45 Freelancer 1 1 6 8 Housewife 1 0 2 3 Marital status Married 3 15 52 70 16.312 0.004 Divorced 1 2 4 7 Widowed 25 40 62 127 Unmarried 0 0 1 1 Diet Classification Normal 10 29 84 123 23.026 < 0.001 Mechanical Soft Diet 13 21 32 66 Semi-liquid Diet 5 7 3 15 liquid Diet 1 0 0 1 Dental condition normal 3 6 30 39 15.352 0.015 denture 10 28 50 88 edentulism 8 6 7 21 half-edentulism 8 17 32 57 Mastication Capacity Well 7 21 59 87 21.643 < 0.001 Middle 6 23 41 70 Poor 16 13 19 48 Sarcopenia yes 22 33 39 94 22.048 < 0.001 no 7 24 80 111 sleep quality Very good 5 17 34 56 10.592 0.085 Quite good 9 19 54 82 General 12 18 29 59 Poor 3 3 2 8 depression Not considering 1 1 8 10 6.921 0.110 Possible 23 54 104 181 Depression 5 2 7 14 Social support Low 18 33 68 119 6.645 0.156 Middle 10 11 33 54 High 1 13 18 32 Annotation: Data presented as n (%) or mean ± SD. Comparisons made using Kruskal-Wallis test for continuous variables and χ² test for categorical variables. P-values indicate statistical significance (P < 0.05). Sarcopenia prevalence was 39.02% (n = 80) as per SARC-F criteria (score ≥ 4). Malnourished individuals had significantly higher SARC-F scores (6.93 ± 2.14 vs. 3.68 ± 2.24, P < 0.001), indicating more severe muscle loss. A moderate negative correlation was observed between MNA scores and SARC-F scores (r=-0.486, P < 0.001) (Fig. 3 ). Table 2 The scores of sarcopenia, sleep quality, depression, and social support in populations with different nutritional statuses Variables Malnourished At risk of malnutrition Normal H P SCAR-F 6.93 ± 2.14 5.00 ± 5.58 3.68 ± 2.24 37.362 < 0.001 PSQI 10.59 ± 4.59 8.54 ± 4.51 7.69 ± 4.14 9.706 0.008 GSD-15 7.66 ± 1.82 6.82 ± 1.56 6.63 ± 1.79 7.017 0.030 LSNS-6 10.03 ± 5.00 10.81 ± 6.38 10.58 ± 5.84 0.309 0.857 Annotation: Data presented as mean ± SD. Kruskal-Wallis test revealed significant differences in sarcopenia (H = 37.362, P < 0.001), sleep quality (H = 9.706, P = 0.008), and depression (H = 7.017, P = 0.030). No significant association found for social support (P = 0.857). Sleep Quality and Mental Health Poor sleep quality (PSQI > 10) was reported in 32.68% (n = 67) of participants. Malnourished individuals exhibited higher PSQI scores (10.59 ± 4.59 vs. 7.69 ± 4.14, P = 0.002), reflecting poorer sleep quality. Depressive symptoms (GDS-15 ≥ 5) were prevalent in 88.29% (n = 181), with malnourished participants scoring higher on the GDS-15 (7.66 ± 1.82 vs. 6.63 ± 1.79, P = 0.008). A moderate negative correlation was observed between MNA scores and PSQI (r=-0.223, P = 0.001). However, A weak negative correlation was observed between MNA scores and GDS-15 scores (r=-0.166, P = 0.018) (Fig. 3 ). Social Support Low social support (LSNS-6 ≤ 12) was reported by 58.05% (n = 119) of participants. However, no significant association was found between social support and nutritional status in regression models (P = 0.857). There was no significant correlation between the MNA score of the study population and the LSNS-6 score (r = 0.039, P = 0.580) (Fig. 3 ). Regression Analysis Linear regression revealed that BMI (β = 0.531, 95% CI [0.415, 0.646], P < 0.001), sarcopenia severity (β=-0.367, 95% CI [-0.545, -0.188], P < 0.001), and social support (β=-0.077, 95% CI [-0.139, -0.014], P = 0.016) were significant predictors of MNA scores. The model explained 57.2% of the variance in nutritional status (adjusted R²=0.572, F = 12.324, P < 0.001) (Table 3 ). Table 3 Multivariable Linear Regression Analysis of Nutritional Status Determinants (N = 205) Variables B (95%CI) Beta P Value VIF Constant 0.894 (-2.536, 4.325) - 0.608 - BMI 0.531 (0.415, 0.646) 0.560 < 0.001 1.813 Calf circumference -0.038 (-0.141, 0.066) -0.048 0.473 2.126 Arm circumference 0.084 (-0.060, 0.229) 0.086 0.251 2.629 Strength 0.006 (-0.052, .064) 0.011 0.834 1.419 SARC rank -0.367 (-0.545, -0.188) -0.258 < 0.001 1.929 PSQI index -0.059 (-0.150, 0.033) -0.071 0.205 1.466 GSD-15 rank 0.080 (-0.144, 0.303) 0.038 0.482 1.397 LSNS rank -0.077 (-0.139, -0.014) -0.123 0.016 1.215 Employment (retired) = Worker 0.913 (-0.394, 2.220) 0.123 0.170 3.771 Employment (retired) = Institutions 1.019 (-0.511, 2.549) 0.115 0.191 3.658 Employment (retired) = Freelancing -0.694 (-2.681, 1.293) -0.035 0.491 1.189 Employment (retired) = Housewife 1.056 (-1.868, 3.980) 0.035 0.477 1.126 Medicare = residents' basic medical insurance 0.151 (-1.139, 1.440) 0.020 0.818 3.407 Medicare = none -0.859 (-2.540, 0.822) -0.065 0.315 1.961 Medicare = Business Insurance -2.129 (-7.015, 2.757) -0.041 0.391 1.058 Medicare = Others 2.063 (-2.977, 7.103) 0.039 0.420 1.126 Marital status = Married 0.492 (-0.297, 1.280) 0.063 0.220 1.265 Marital status = Divorce 0.103 (-1.818, 2.024) 0.005 0.916 1.111 Marital status = Unmarried -0.381 (-5.313, 4.551) -0.007 0.879 1.078 Diet Classification = Mechanical Soft Diet 0.323(-0.626, 1.272) 0.041 0.503 1.792 Diet Classification = Semi-liquid Diet -0.108 (-1.717, 1.501) -0.008 0.895 1.603 Diet Classification = Liquid Diet -3.330 (-8.428, 1.767) -0.063 0.199 1.152 Mastication Capacity = Middle -0.240(-1.144, 0.664) -0.031 0.601 1.663 Mastication Capacity = Poor -0.819 (-1.906, 0.268) -0.095 0.139 1.932 Annotation: Model adjusted for occupation, medical insurance, marital status, diet type, and mastication capacity. Key predictors: BMI (β = 0.531, P < 0.001), sarcopenia severity (β=-0.367, P < 0.001), and social support (β=-0.077, P = 0.016). Model explained 57.2% variance (adjusted R²=0.572). VIF < 5 indicates no multicollinearity. Reference categories: Occupation = Farmer; Medicare = Urban Resident Basic Medical Insurance; Marital Status = Widowed. Discussion This study reveals a notable prevalence of malnutrition (14.1%) and malnutrition risk (27.8%) among elderly nursing home residents in Southwest China, aligning with previous reports of malnutrition rates in institutionalized older adults across Asia [ 19 , 20 ] . The identified predictors—lower BMI, sarcopenia risk, and reduced social support—highlight multifactorial pathways influencing nutritional status in this vulnerable population. The strong association between BMI and malnutrition (β = 0.531, P < 0.001) corroborates global evidence that BMI remains a robust anthropometric indicator of nutritional health in aging populations [ 21 ] . However, the lack of significant correlations between calf/arm circumference and MNA scores suggests limitations in using peripheral anthropometric measures alone to detect age-related muscle loss. This aligns with studies emphasizing the need for composite assessments integrating functional and biochemical markers to evaluate sarcopenic malnutrition [ 22 , 23 ] . Sarcopenia emerged as a critical determinant (β=-0.367, P < 0.001), consistent with the bidirectional relationship between muscle loss and malnutrition [ 24 ] . Progressive sarcopenia may impair mobility and self-feeding capacity, exacerbating nutritional deficits, while inadequate protein-energy intake accelerates muscle catabolism [ 25 , 26 ] . This vicious cycle underscores the necessity for dual interventions combining resistance training and targeted nutritional supplementation, as demonstrated in recent trials [ 27 , 28 ] . Social support showed a modest but significant association (β=-0.077, P = 0.016), contrasting with studies reporting stronger psychosocial influences [ 29 – 31 ] . This discrepancy may reflect institutional care settings partially mitigating social isolation, though qualitative aspects of support (e.g., mealtime companionship) likely remain under-addressed [ 32 , 33 ] . Our findings support the integration of communal dining programs and intergenerational activities to enhance social engagement [ 34 ] . Notably, sleep quality and depression showed weaker correlations than anticipated. While poor sleep and depressive symptoms are established malnutrition risk factors [ 35 , 36 ] , the homogeneity of these variables across groups (e.g., 88.3% reporting possible depression) may have attenuated statistical significance [ 37 ] . This high prevalence of psychological distress warrants systematic mental health screening in nursing homes. Limitations and Future Directions The study’s regional focus limits generalizability. Longitudinal designs and objective biomarkers (e.g., serum albumin) are needed to validate findings. Culturally tailored interventions addressing dietary diversity, social engagement, and mental health should be prioritized. Conclusion Malnutrition among nursing home residents in Southwest China is influenced by low BMI, sarcopenia, and inadequate social support. Multifaceted strategies integrating nutritional education, physical activity programs, and psychosocial support are essential to improve health outcomes in this vulnerable population. Declarations Author contribution Concept and design: Juan Han, Limei Tao. Acquisition, analysis, or interpretation of data: Juan Han, Limei Tao, Sijie Chen. Statistical analysis: Sijie Chen, Yongfeng Zhao. Drafting of the manuscript: Juan Han, Sijie Chen. Review and editing of the manuscript: Xiaoming Huang, Jiang Liu. Methodology: Juan Han, Limei Tao. Supervision: Xiaoming Huang. Funding This research was funded by the Nan Chong Philosophy and Social Science Research Planning Project. (Grant No. NC23B012) Acknowledgements We thank the staff and participants of the nursing homes in Nan Chong for their cooperation. Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics Approval and Consent to Participate: This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethical Review Committee of Nan Chong central hospital (Approval No. 2024-178). Written informed consent was obtained from all participants. (The signed informed consent forms are retained by the institutional ethics committee and available upon request.) Clinical trial number: Not applicable. Consent to Publish declaration: Not Applicable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6201610","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432580731,"identity":"63957350-0448-4796-85ad-8085c2df7df5","order_by":0,"name":"Juan Han","email":"","orcid":"","institution":"Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Han","suffix":""},{"id":432580732,"identity":"ddb1b6c4-4312-455a-82b5-2f7bea4def18","order_by":1,"name":"Limei Tao","email":"","orcid":"","institution":"The Southwest Hospital of AMU","correspondingAuthor":false,"prefix":"","firstName":"Limei","middleName":"","lastName":"Tao","suffix":""},{"id":432580733,"identity":"feb1e891-a832-4a36-bbc8-c62566d2cb21","order_by":2,"name":"Jiang Liu","email":"","orcid":"","institution":"Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Liu","suffix":""},{"id":432580734,"identity":"de1ae953-a630-4085-8631-4d2554537671","order_by":3,"name":"Yongfeng Zhao","email":"","orcid":"","institution":"Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Zhao","suffix":""},{"id":432580735,"identity":"c3a620e8-f7f4-45ac-9ba3-7ff1422c22c6","order_by":4,"name":"Xiaoming Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACAxCRYGBT38/e2PjgA/FaCtIYZ/YcbjacQbQWhg+HGTfcSG+T5iBGi7lE8jOJBwaHmQ1uPmyQZmCwk9NtIKDFckaasUGCQTqb5O3EBuMChmRjswOEHHYjwfBBgoE1Dx9QS/IMhgOJ2whrSf9wIMGAWYLh5sGGwzzEackB2eJsIHCDsbGZOC1n3hQD/ZKWINmT2Mw4w4AYvxxP3yb5449NAj/78ec/PlTYyRHUgm4CacpHwSgYBaNgFOAAAEX8R9qkmw4tAAAAAElFTkSuQmCC","orcid":"","institution":"Nanchong Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Huang","suffix":""},{"id":432580736,"identity":"b0a7605b-7333-4351-b979-ce142aec6eb5","order_by":5,"name":"Sijie Chen","email":"","orcid":"","institution":"Hospital Affiliated to Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Sijie","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-03-11 09:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6201610/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6201610/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79173626,"identity":"3d202e5d-9068-4fa4-9951-aca8f1f2df89","added_by":"auto","created_at":"2025-03-25 09:46:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38547,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of malnutrition status among participants (N=205). Proportions calculated using MNA-SF criteria: 14.10% malnourished (score\u0026lt;17), 27.80% at risk (17-23.5), and 58.05% normal (≥23.5). Pie chart with percentage labels.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6201610/v1/382292cacb0910dc599b9ec9.jpg"},{"id":79173627,"identity":"4d6cfd55-f24d-4733-b6f7-816cfac99b5c","added_by":"auto","created_at":"2025-03-25 09:46:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100051,"visible":true,"origin":"","legend":"\u003cp\u003eAnthropometric and Functional Comparisons Across Nutritional Categories. (Error bars represent standard deviations. ​ns:P\u0026gt;0.05, *:P\u0026lt;0.05, **:P\u0026lt;0.01, ***P\u0026lt;0.001 by ANOVA with post-hoc Tukey test. BMI: body mass index.)\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6201610/v1/1718a9f48a276c7ba97801c2.jpg"},{"id":79173629,"identity":"1e036775-3a7a-4035-a8d0-8d5f6ae101ce","added_by":"auto","created_at":"2025-03-25 09:46:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85480,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation of Physical data, SCAR-F scores, PSQI, GDS-15 scores and LSNS scores.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6201610/v1/b7d051b5eb3245a54c7267e8.jpg"},{"id":80204294,"identity":"c95a7ddb-1ca4-451b-a0fb-38b3de370aeb","added_by":"auto","created_at":"2025-04-09 07:24:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6201610/v1/d2088019-db0d-4cae-b5e0-5756d0948574.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Malnutrition and Its Multidimensional Determinants in Institutionalized Older Adults: A Cross-Sectional Study from Resource-Limited Nursing Homes in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global population is undergoing an unprecedented aging process, with individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years projected to constitute 22% of the world\u0026rsquo; s population by 2050\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. China, as the most populous developing nation, faces particularly severe challenges, with its elderly population expected to exceed 366\u0026nbsp;million by 2050\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. This demographic shift amplifies public health concerns, particularly regarding malnutrition \u0026mdash; a condition affecting approximately 25% of older adults globally\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Malnutrition in the elderly is closely linked to adverse outcomes such as sarcopenia, cognitive decline, and increased mortality\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, yet remains understudied in institutionalized populations, especially in developing regions\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn China, rapid urbanization and socioeconomic disparities have led to uneven healthcare resource distribution, particularly in Southwest regions like Nan Chong. Rural-to-urban migration has left many elderly in underfunded nursing homes with limited access to nutritional support\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.Existing studies on elderly malnutrition in China predominantly focus on urban communities or hospitals\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, neglecting the unique challenges faced by institutionalized populations in resource-limited settings. For instance, a 2022 survey in Sichuan Province revealed that 35% of nursing home residents exhibited suboptimal dietary diversity, primarily due to financial constraints and inadequate staffing\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAge-related physiological declines, including reduced appetite, impaired nutrient absorption, and dental deterioration, significantly elevate malnutrition risk\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Institutionalized elderly individuals are further vulnerable due to social isolation, comorbidities, and reliance on standardized meal plans that may lack personalization\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Compounding these issues, cultural stigma surrounding institutional care in China often discourages family involvement, exacerbating social and nutritional neglect\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Despite these challenges, no prior studies have systematically evaluated the interplay of sarcopenia, mental health, and social support in driving malnutrition within Southwest Chinese nursing homes.\u003c/p\u003e \u003cp\u003eThis cross-sectional study investigates the nutritional status and its determinants among elderly residents in nursing homes in Nan Chong, Southwest China. Utilizing validated tools (MNA-SF, SARC-F, PSQI), we aim to: (i) Quantify malnutrition prevalence and severity in a region with limited healthcare infrastructure. (ii) Identify modifiable risk factors, including sarcopenia, sleep quality, and social support, with emphasis on culturally specific barriers. (iii) Provide evidence for targeted interventions to improve health outcomes in this underserved population. By addressing these objectives, this study contributes to the limited literature on institutionalized elderly in low-resource settings and informs policy-making for aging societies.\u003c/p\u003e"},{"header":"Materials \u0026 methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted in three nursing homes in Nan Chong, Southwest China, from February 2023 to March 2024. Participants (n\u0026thinsp;=\u0026thinsp;205) aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years were included, excluding those with severe cognitive impairment or bedridden status.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eFace-to-face interviews and anthropometric measurements were performed. Validated tools included:\u003c/p\u003e \u003cp\u003eMNA-SF\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e: Assessed nutritional status (malnourished: \u0026lt;17; at risk: 17\u0026ndash;23.5; normal: \u0026ge;23.5).\u003c/p\u003e \u003cp\u003eSARC-F\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e: Screened for sarcopenia (score\u0026thinsp;\u0026ge;\u0026thinsp;4).\u003c/p\u003e \u003cp\u003ePSQI\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e: Evaluated sleep quality (higher scores indicate poorer sleep).\u003c/p\u003e \u003cp\u003eGDS-15\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e: Measured depression (score\u0026thinsp;\u0026ge;\u0026thinsp;5 suggests possible depression).\u003c/p\u003e \u003cp\u003eLSNS-6\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e: Assessed social support (\u0026le;\u0026thinsp;12: social isolation).。\u003c/p\u003e\n\u003ch3\u003eEthics declaration\u003c/h3\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Nanchong central hospital (Approval No. 2024.178) with written informed consent obtained from all participants.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS software was used to conduct all statistical analyses. Means, standard deviations, cross tabulation, and frequency tables were used as descriptive statistics. Spearman\u0026rsquo;s correlation coefficients were used to calculate correlation significance. Analysis of variance for normally distributed dates and nonparametric methods, such as the Wilcoxon two-sample test and the Kruskal\u0026ndash;Wallis test were used to make statistical comparisons. All results are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. A P value of ,0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristics\u003c/h2\u003e \u003cp\u003eA total of 205 elderly residents (median age: 83 years; range: 62\u0026ndash;96 years) were included in the study. Among them, 75 (36.59%) were male and 130 (63.41%) were female. The majority (59.51%, n\u0026thinsp;=\u0026thinsp;122) were aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years. Significant differences in nutritional status were observed across sex (P\u0026thinsp;=\u0026thinsp;0.037), education level (P\u0026thinsp;=\u0026thinsp;0.015), and dietary type (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants with lower education (primary school or below: 38.05%) and those consuming soft or semi-liquid diets (39.51%) exhibited higher rates of malnutrition (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNutritional Status\u003c/h3\u003e\n\u003cp\u003eBased on MNA-SF scores, 14.10% (n\u0026thinsp;=\u0026thinsp;29) of participants were classified as malnourished (MNA\u0026thinsp;\u0026lt;\u0026thinsp;17), 27.80% (n\u0026thinsp;=\u0026thinsp;57) were at risk of malnutrition (MNA:17-23.5), and 58.05% (n\u0026thinsp;=\u0026thinsp;119) had normal nutritional status (MNA\u0026thinsp;\u0026ge;\u0026thinsp;23.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Malnourished individuals had significantly lower BMI (18.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30 kg/m\u0026sup2; vs. 24.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19 kg/m\u0026sup2;, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reduced calf circumference (28.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90cm vs. 31.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86 cm, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and weaker grip strength (12.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56 kg vs. 17.20\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47 kg, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those with normal nutritional status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and Clinical Characteristics by Nutritional Status (N\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalnourished\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt risk of malnutrition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e合计\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ/H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.38\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;9.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154.09\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156.00\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155.52\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.91\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.78\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;9.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.01\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;8.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.31\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.57\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.53\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.25\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.41\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecalf circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.29\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.93\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.64\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArm circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.07\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.67\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.02\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.81\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.47\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.83\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.20\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.60\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation (retired)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreelancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet Classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical Soft Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-liquid Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliquid Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edenture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eedentulism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehalf-edentulism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMastication Capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuite good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot considering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePossible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnnotation: Data presented as n (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Comparisons made using Kruskal-Wallis test for continuous variables and χ\u0026sup2; test for categorical variables. P-values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSarcopenia prevalence was 39.02% (n\u0026thinsp;=\u0026thinsp;80) as per SARC-F criteria (score\u0026thinsp;\u0026ge;\u0026thinsp;4). Malnourished individuals had significantly higher SARC-F scores (6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14 vs. 3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating more severe muscle loss. A moderate negative correlation was observed between MNA scores and SARC-F scores (r=-0.486, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe scores of sarcopenia, sleep quality, depression, and social support in populations with different nutritional statuses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalnourished\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt risk of malnutrition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCAR-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSD-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSNS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.03\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnnotation: Data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Kruskal-Wallis test revealed significant differences in sarcopenia (H\u0026thinsp;=\u0026thinsp;37.362, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sleep quality (H\u0026thinsp;=\u0026thinsp;9.706, P\u0026thinsp;=\u0026thinsp;0.008), and depression (H\u0026thinsp;=\u0026thinsp;7.017, P\u0026thinsp;=\u0026thinsp;0.030). No significant association found for social support (P\u0026thinsp;=\u0026thinsp;0.857).\u003c/p\u003e\n\u003ch3\u003eSleep Quality and Mental Health\u003c/h3\u003e\n\u003cp\u003ePoor sleep quality (PSQI\u0026thinsp;\u0026gt;\u0026thinsp;10) was reported in 32.68% (n\u0026thinsp;=\u0026thinsp;67) of participants. Malnourished individuals exhibited higher PSQI scores (10.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59 vs. 7.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14, P\u0026thinsp;=\u0026thinsp;0.002), reflecting poorer sleep quality. Depressive symptoms (GDS-15\u0026thinsp;\u0026ge;\u0026thinsp;5) were prevalent in 88.29% (n\u0026thinsp;=\u0026thinsp;181), with malnourished participants scoring higher on the GDS-15 (7.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82 vs. 6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79, P\u0026thinsp;=\u0026thinsp;0.008). A moderate negative correlation was observed between MNA scores and PSQI (r=-0.223, P\u0026thinsp;=\u0026thinsp;0.001). However, A weak negative correlation was observed between MNA scores and GDS-15 scores (r=-0.166, P\u0026thinsp;=\u0026thinsp;0.018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSocial Support\u003c/h2\u003e \u003cp\u003eLow social support (LSNS-6\u0026thinsp;\u0026le;\u0026thinsp;12) was reported by 58.05% (n\u0026thinsp;=\u0026thinsp;119) of participants. However, no significant association was found between social support and nutritional status in regression models (P\u0026thinsp;=\u0026thinsp;0.857). There was no significant correlation between the MNA score of the study population and the LSNS-6 score (r\u0026thinsp;=\u0026thinsp;0.039, P\u0026thinsp;=\u0026thinsp;0.580) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRegression Analysis\u003c/h2\u003e \u003cp\u003eLinear regression revealed that BMI (β\u0026thinsp;=\u0026thinsp;0.531, 95% CI [0.415, 0.646], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sarcopenia severity (β=-0.367, 95% CI [-0.545, -0.188], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social support (β=-0.077, 95% CI [-0.139, -0.014], P\u0026thinsp;=\u0026thinsp;0.016) were significant predictors of MNA scores. The model explained 57.2% of the variance in nutritional status (adjusted R\u0026sup2;=0.572, F\u0026thinsp;=\u0026thinsp;12.324, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Linear Regression Analysis of Nutritional Status Determinants (N\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.894 (-2.536, 4.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.531 (0.415, 0.646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalf circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.038 (-0.141, 0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArm circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.084 (-0.060, 0.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006 (-0.052, .064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARC rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.367 (-0.545, -0.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.059 (-0.150, 0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSD-15 rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.080 (-0.144, 0.303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSNS rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.077 (-0.139, -0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment (retired)\u0026thinsp;=\u0026thinsp;Worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.913 (-0.394, 2.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment (retired)\u0026thinsp;=\u0026thinsp;Institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.019 (-0.511, 2.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment (retired)\u0026thinsp;=\u0026thinsp;Freelancing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.694 (-2.681, 1.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment (retired)\u0026thinsp;=\u0026thinsp;Housewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.056 (-1.868, 3.980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u0026thinsp;=\u0026thinsp;residents' basic medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151 (-1.139, 1.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u0026thinsp;=\u0026thinsp;none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.859 (-2.540, 0.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u0026thinsp;=\u0026thinsp;Business Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.129 (-7.015, 2.757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u0026thinsp;=\u0026thinsp;Others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.063 (-2.977, 7.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u0026thinsp;=\u0026thinsp;Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.492 (-0.297, 1.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u0026thinsp;=\u0026thinsp;Divorce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.103 (-1.818, 2.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u0026thinsp;=\u0026thinsp;Unmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.381 (-5.313, 4.551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet Classification\u0026thinsp;=\u0026thinsp;Mechanical Soft Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.323(-0.626, 1.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet Classification\u0026thinsp;=\u0026thinsp;Semi-liquid Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.108 (-1.717, 1.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet Classification\u0026thinsp;=\u0026thinsp;Liquid Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.330 (-8.428, 1.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMastication Capacity\u0026thinsp;=\u0026thinsp;Middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.240(-1.144, 0.664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMastication Capacity\u0026thinsp;=\u0026thinsp;Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.819 (-1.906, 0.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnnotation: Model adjusted for occupation, medical insurance, marital status, diet type, and mastication capacity. Key predictors: BMI (β\u0026thinsp;=\u0026thinsp;0.531, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sarcopenia severity (β=-0.367, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social support (β=-0.077, P\u0026thinsp;=\u0026thinsp;0.016). Model explained 57.2% variance (adjusted R\u0026sup2;=0.572). VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 indicates no multicollinearity. Reference categories: Occupation\u0026thinsp;=\u0026thinsp;Farmer; Medicare\u0026thinsp;=\u0026thinsp;Urban Resident Basic Medical Insurance; Marital Status\u0026thinsp;=\u0026thinsp;Widowed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reveals a notable prevalence of malnutrition (14.1%) and malnutrition risk (27.8%) among elderly nursing home residents in Southwest China, aligning with previous reports of malnutrition rates in institutionalized older adults across Asia\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The identified predictors\u0026mdash;lower BMI, sarcopenia risk, and reduced social support\u0026mdash;highlight multifactorial pathways influencing nutritional status in this vulnerable population.\u003c/p\u003e \u003cp\u003eThe strong association between BMI and malnutrition (β\u0026thinsp;=\u0026thinsp;0.531, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) corroborates global evidence that BMI remains a robust anthropometric indicator of nutritional health in aging populations\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, the lack of significant correlations between calf/arm circumference and MNA scores suggests limitations in using peripheral anthropometric measures alone to detect age-related muscle loss. This aligns with studies emphasizing the need for composite assessments integrating functional and biochemical markers to evaluate sarcopenic malnutrition\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSarcopenia emerged as a critical determinant (β=-0.367, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with the bidirectional relationship between muscle loss and malnutrition\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Progressive sarcopenia may impair mobility and self-feeding capacity, exacerbating nutritional deficits, while inadequate protein-energy intake accelerates muscle catabolism\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This vicious cycle underscores the necessity for dual interventions combining resistance training and targeted nutritional supplementation, as demonstrated in recent trials\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSocial support showed a modest but significant association (β=-0.077, P\u0026thinsp;=\u0026thinsp;0.016), contrasting with studies reporting stronger psychosocial influences\u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. This discrepancy may reflect institutional care settings partially mitigating social isolation, though qualitative aspects of support (e.g., mealtime companionship) likely remain under-addressed\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Our findings support the integration of communal dining programs and intergenerational activities to enhance social engagement\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, sleep quality and depression showed weaker correlations than anticipated. While poor sleep and depressive symptoms are established malnutrition risk factors\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, the homogeneity of these variables across groups (e.g., 88.3% reporting possible depression) may have attenuated statistical significance\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. This high prevalence of psychological distress warrants systematic mental health screening in nursing homes.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s regional focus limits generalizability. Longitudinal designs and objective biomarkers (e.g., serum albumin) are needed to validate findings. Culturally tailored interventions addressing dietary diversity, social engagement, and mental health should be prioritized.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMalnutrition among nursing home residents in Southwest China is influenced by low BMI, sarcopenia, and inadequate social support. Multifaceted strategies integrating nutritional education, physical activity programs, and psychosocial support are essential to improve health outcomes in this vulnerable population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Juan Han, Limei Tao.\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data: Juan Han, Limei Tao, Sijie Chen.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Sijie Chen, Yongfeng Zhao.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Juan Han, Sijie Chen.\u003c/p\u003e\n\u003cp\u003eReview and editing of the manuscript: Xiaoming Huang, Jiang Liu.\u003c/p\u003e\n\u003cp\u003eMethodology: Juan Han, Limei Tao.\u003c/p\u003e\n\u003cp\u003eSupervision: Xiaoming Huang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Nan Chong Philosophy and Social Science Research Planning Project. (Grant No. NC23B012)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff and participants of the nursing homes in Nan Chong for their cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethical Review Committee of Nan Chong central hospital (Approval No. 2024-178). Written informed consent was obtained from all participants. (The signed informed consent forms are retained by the institutional ethics committee and available upon request.)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study may be available from the corresponding author (Xiaoming Huang, [email protected]) upon reasonable request and with permission of the Ethical Review Committee of Nan Chong central hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld population ageing. 2019:highlights [Z]. New York; UN; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOGURA S, JAKOVLJEVIC MM, Editorial. Global Population Aging - Health Care, Social and Economic Consequences [J]. 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Public Health Nutr. 2018;21:1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiaochun Qiao. How Many and Who Are Living in Institution for the Aged in China [J]. Social Policy Res, 2022, (4): 40\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCORISH C A, BARDON LA. Malnutrition in older adults: screening and determinants [J]. Proceedings of the Nutrition Society, 2019, 78(3): 372-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWESTERGREN A, HAGELL P, SJ\u0026ouml;DAHL HAMMARLUNDC. Malnutrition and risk of falling among elderly without home-help service\u0026ndash;a cross sectional study [J]. J Nutr Health Aging. 2014;18(10):905\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHAN HJ, LIU G, GUAN X, et al. Recent developments in institutional elder care in China: changing concepts and attitudes [J]. J Aging Soc Policy. 2006;18(2):85\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKAISER M J, BAUER J M, RAMSCH C, et al. Validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status [J]. J Nutr Health Aging. 2009;13(9):782\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMALMSTROM T, MORLEY J. SARC-F:. A Simple Questionnaire to Rapidly Diagnose Sarcopenia [J]. J Am Med Dir Assoc, 2013, 14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBUYSSE D J, REYNOLDS C F TH 3RD, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research [J]. Psychiatry Res. 1989;28(2):193\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYESAVAGE JA, BRINK T L, ROSE T L, et al. Development and validation of a geriatric depression screening scale: A preliminary report [J]. J Psychiatr Res. 1982;17(1):37\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGRAY J, KIM J. Rasch Analysis of the Lubben Social Network Scale-6 (LSNS-6) [J]. J Appl Gerontol. 2016;35(5):508\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVAN REGEMORTER V, DOLLASE J, COULIE R, et al. Olfactory Dysfunction Predicts Frailty and Poor Postoperative Outcome in Older Patients Scheduled for Elective Non-Cardiac Surgery [J]. J Nutr health aging. 2022;26(11):981\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCOBRE A F, SUREK M, VILHENA R O, et al. Influence of foods and nutrients on COVID-19 recovery: A multivariate analysis of data from 170 countries using a generalized linear model [J]. Clin Nutr. 2022;41(12):3077\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG Z, PEREIRA S L, LUO M et al. Evaluation of Blood Biomarkers Associated with Risk of Malnutrition in Older Adults: A Systematic Review and Meta-Analysis [J]. Nutrients, 2017, 9(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCRUZ-JENTOFT A J, BAHAT G, BAUER J, et al. Sarcopenia: revised European consensus on definition and diagnosis [J]. Age Ageing. 2019;48(1):16\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN L-K, WOO J, ASSANTACHAI P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment [J]. J Am Med Dir Assoc. 2020;21(3):300\u0026ndash;e72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSIEBER C C. Malnutrition and sarcopenia [J]. Aging Clin Exp Res. 2019;31(6):793\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXU W, CHEN T, CAI Y, et al. Sarcopenia in Community-Dwelling Oldest Old Is Associated with Disability and Poor Physical Function [J]. J Nutr Health Aging. 2020;24(23):339\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKHALIL R. Ubiquitin-Proteasome Pathway and Muscle Atrophy [J]. Adv Exp Med Biol, 2018, 1088: 235\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBEAUDART C, SANCHEZ-RODRIGUEZ D, LOCQUET M et al. Malnutrition as a Strong Predictor of the Onset of Sarcopenia [J]. Nutrients, 2019, 11(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWRIGHT J. Oral nutritional support with or without exercise in the management of malnutrition in nutritionally vulnerable older people: A systematic review and meta-analysis [J]. Clin Nutr. 2018;37(6):1879\u0026ndash;91. Part A).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHARIFI M H, AFSHARI M, VARDANJANI HM, et al. Correlates of malnutrition in patients with heart failure: the role of social support [J]. ESC Heart Fail. 2024;11(2):719\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSWEE SING, L, VANOH D. MALNUTRITION AMONG CANCER PATIENTS:. ROLE OF SOCIAL SUPPORT [J]. Malaysian J Public Health Med. 2022;22(1):182\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLUGER E, DORNER T E HAIDERS et al. Effects of a Home-Based and Volunteer-Administered Physical Training, Nutritional, and Social Support Program on Malnutrition and Frailty in Older Persons: A Randomized Controlled Trial [J]. Journal of the American Medical Directors Association, 2016, 17(7): 671.e9-.e16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBOAMAH SA, WELDRICK R, LEE T-S J et al. Social Isolation Among Older Adults in Long-Term Care: A Scoping Review [J]. Journal of aging and health, 2021, 33(7\u0026ndash;8): 618\u0026thinsp;\u0026ndash;\u0026thinsp;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJOE L, ANNETTE B et al. FELICIANO V,. Older People in Long-Term Care Institutions: A Case of Multidimensional Social Exclusion [Z]. Springer International Publishing. 2021.10.1007/978-3-030-51406-8_23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSAKURAI R, YASUNAGA M, MURAYAMA Y, et al. Long-term effects of an intergenerational program on functional capacity in older adults: Results from a seven-year follow-up of the REPRINTS study [J]. Arch Gerontol Geriatr. 2016;64:13\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHAO W-Y, ZHANG Y, JIA S-L, et al. The association of sleep quality and sleep duration with nutritional status in older adults: Findings from the WCHAT study [J]. Maturitas. 2021;145:1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJIANG H, YE L, ZHANG S, et al. The association between nutritional status and sleep quality of Chinese community-dwelling older adults [J]. Aging Clin Exp Res. 2023;35(9):1945\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSARıARSLAN HA, GULHAN Y B UNALAND, et al. The relationship of sleep problems to life quality and depression [J]. Neurosciences J. 2015;20(3):236.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Malnutrition, Sarcopenia, Body mass index (BMI), Social support, Nursing home residents, Cross-sectional study, Southwest China","lastPublishedDoi":"10.21203/rs.3.rs-6201610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6201610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo assess the nutritional status and its influencing factors among elderly residents in nursing homes in Nan Chong, Southwest China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA cross-sectional study was conducted from February 2023 to March 2024, involving 205 elderly participants aged ≥60 years. Nutritional status was evaluated using the Mini-Nutritional Assessment Short-Form (MNA-SF), while sarcopenia, sleep quality, depression, and social support were assessed using validated scales. Descriptive statistics, correlation analysis, and multiple linear regression were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among participants, 14.10% were malnourished, and 27.80% were at risk of malnutrition. Lower BMI (β = 0.531, P \u0026lt; 0.001), higher sarcopenia risk (β = -0.367, P \u0026lt; 0.001), and lower social support (β = -0.077, P = 0.016) were significant predictors of malnutrition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Malnutrition is prevalent among nursing home residents in Southwest China, with BMI, sarcopenia, and social support being key determinants. Targeted interventions should focus on improving dietary intake, muscle health, and social engagement.\u003c/p\u003e","manuscriptTitle":"Malnutrition and Its Multidimensional Determinants in Institutionalized Older Adults: A Cross-Sectional Study from Resource-Limited Nursing Homes in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 09:46:43","doi":"10.21203/rs.3.rs-6201610/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":"25690aa6-63a9-4dc8-a8ac-c6a58ff7827b","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-15T14:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 09:46:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6201610","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6201610","identity":"rs-6201610","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}