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Prevalence of malnutrition and analysis of related factors in elderly patients with COVID-19 in Wuhan, China
Open accessAbstract
Background/objectives To evaluate the prevalence of malnutrition and its related factors in elderly patients with COVID-19 in Wuhan, China. Subjects/methods In a cross-sectional study, we evaluated the nutritional status of elderly inpatients with COVID-19 using the Mini Nutritional Assessment (MNA). Based on MNA scores, patients were divided into non-malnutrition group (MNA ≥ 24), the group with risk of malnutrition (MNA 17–23.5) and malnutrition group (MNA score < 17). Regression analysis was conducted to screen for risk factors for malnutrition. Results A total of 182 patients were included in the study, of which 27.5% were in the group with malnutrition risk and 52.7% were in the malnutrition group. There were statistical differences in the incidence of comorbid diabetes mellitus, body mass index (BMI), calf circumference,albumin, hemoglobin, and lymphocyte counts among the three groups. Further regression analysis suggested that combined diabetes, low calf circumference, and low albumin were independent risk factors for malnutrition. Conclusions The prevalence of malnutrition in elderly patients with COVID-19 was high, and nutritional support should be strengthened during treatment, especially for those with diabetes mellitus, low calf circumference, or low albumin.
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Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationClinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Self-citationPhysical Status: The Use and Interpretation of Anthropometry
Frisancho, A. R. (1996) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/64.5.830 · Cited by 6,579
Hamming, I., Timens, W., Bulthuis, M., Lely, A., Navis, G., & van Goor, H. (2004) · The Journal of Pathology · doi:10.1002/path.1570 · Cited by 5,676
Vellas, B., Guigoz, Y., Garry, P. J., Nourhashemi, F., Bennahum, D., Lauque, S., & Albarede, J.-L. (1999) · Nutrition · doi:10.1016/s0899-9007(98)00171-3 · Cited by 2,161
Xu, X.-W., Wu, X.-X., Jiang, X.-G., Xu, K.-J., Ying, L.-J., Ma, C.-L., Li, S.-B., Wang, H.-Y., Zhang, S., Gao, H.-N., et al. (2020) · BMJ · doi:10.1136/bmj.m606 · Cited by 2,046
Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia
Song, F., Shi, N., Shan, F., Zhang, Z., Shen, J., Lu, H., Ling, Y., Jiang, Y., & Shi, Y. (2020) · Radiology · doi:10.1148/radiol.2020200274 · Cited by 1,190
Long-term psychiatric morbidities among SARS survivors
Mak, I. W. C., Chu, C. M., Pan, P. C., Yiu, M. G. C., & Chan, V. L. (2009) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2009.03.001 · Cited by 1,018 · cited 1×
“Therefore, Chinese were very sensitive to COVID-19, and its epidemic could easily cause anxiety among patients.”
BackgroundKaiser, M. J., Bauer, J. M., Rämsch, C., Uter, W., Guigoz, Y., Cederholm, T., Thomas, D. R., Anthony, P. S., Charlton, K. E., Maggio, M., et al. (2010) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2010.03016.x · Cited by 1,007
Identifying the elderly at risk for malnutrition
Guigoz, Y., Lauque, S., & Vellas, B. J. (2002) · Clinics in Geriatric Medicine · doi:10.1016/s0749-0690(02)00059-9 · Cited by 969
Cited by
Popkin, B. M., Du, S., Green, W. D., Beck, M. A., Algaith, T., Herbst, C. H., Alsukait, R. F., Alluhidan, M., Alazemi, N., & Shekar, M. (2020) · Obesity Reviews · doi:10.1111/obr.13128 · Cited by 1,150
The COVID-19 pandemic and physical activity
Woods, J. A., Hutchinson, N. T., Powers, S. K., Roberts, W. O., Gomez-Cabrera, M. C., Radak, Z., Berkes, I., Boros, A., Boldogh, I., Leeuwenburgh, C., et al. (2020) · Sports Medicine and Health Science · doi:10.1016/j.smhs.2020.05.006 · Cited by 632
Advances in muscle health and nutrition: A toolkit for healthcare professionals
Prado, C. M., Landi, F., Chew, S. T. H., Atherton, P. J., Molinger, J., Ruck, T., & Gonzalez, M. C. (2022) · Clinical Nutrition · doi:10.1016/j.clnu.2022.07.041 · Cited by 237
Residual clinical damage after COVID-19: a retrospective and prospective observational cohort study
De Lorenzo, R., Conte, C., Lanzani, C., Benedetti, F., Roveri, L., Mazza, M. G., Brioni, E., Giacalone, G., Canti, V., Sofia, V., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239570 · Cited by 179
“Malnutrition has been reported in hospitalised COVID-19 patients [31], and is likely due to systemic inflammation-related hypercatabolism [32].”
BackgroundThe Role of Nutrition in COVID-19 Susceptibility and Severity of Disease: A Systematic Review
James, P. T., Ali, Z., Armitage, A. E., Bonell, A., Cerami, C., Drakesmith, H., Jobe, M., Jones, K. S., Liew, Z., Moore, S. E., et al. (2021) · The Journal of Nutrition · doi:10.1093/jn/nxab059 · Cited by 147
“(47) conducted a crosssectional study and recruited 182 elderly hospitalized COVID19 patients ≥65 y in 1 center in Wuhan, China.”
Background
Metadata
- Authors
- Li, T., Zhang, Y., Gong, C., Wang, J., Liu, B., Shi, L., & Duan, J.
- Venue
- European Journal of Clinical Nutrition
- Published
- 2020-04-22
- Volume
- 74
- Issue
- 6
- Pages
- 871-875
- ISSN
- 0954-3007 (ISSN-L)
- 1476-5640
- OpenAlex
- W3018306069
- Cited by
- 348
- References
- 25
- FWCI
- 38.97
- Type
- journal article
- Language
- en
- Open access
- bronze
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