{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T09:49:21Z","timestamp":1791712161983,"version":"4.3.4"},"reference-count":40,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:00:00Z","timestamp":1778976000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:00:00Z","timestamp":1778976000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2022YFC2009600"],"award-info":[{"award-number":["2022YFC2009600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aspenjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Parenter Enteral Nutr"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Malnutrition, frailty, and sarcopenia are common and interrelated in patients with cancer, yet their associations with nutrition impact symptom clusters remain unclear. This study examined the overlap of these conditions, and their relationships with symptom clusters in adults with cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Malnutrition was assessed using the Patient\u2010Generated Subjective Global Assessment and Global Leadership Initiative on Malnutrition criteria, frailty using the FRAIL scale, and sarcopenia using the 2019 Asian Working Group for Sarcopenia consensus. Latent class analysis identified nutrition impact symptom clusters. Logistic regression models evaluated associations between symptom clusters and the three conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This cross\u2010sectional latent class analysis included 28,377 hospitalized adults with cancer (median age 58 years; 55.3% male). Gastrointestinal (41.0%) and lung (23.1%) cancers were the most common diagnoses. The prevalence of malnutrition, frailty and sarcopenia was 56.4%, 37.7%, and 16.9%, respectively. Weight loss and low muscle mass represent key shared features of their coexistence. Five nutrition impact symptom clusters were identified and ranked by severity. Patients with severe multi\u2010symptom cluster had 3.87\u2010fold higher odds of malnutrition and 6.03\u2010fold higher odds of frailty. Patients with the gastrointestinal\u2010dominant symptom cluster were most likely to experience malnutrition, whereas those with the sensory\u2010alteration symptom cluster were most likely to exhibit frailty.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Maintaining stable body weight, particularly muscle mass, is crucial for cancer patients to reduce the risk of comorbidity among these conditions. Identifying nutrition impact symptom clusters and providing targeted interventions may help reduce the burden of these conditions and improve the efficiency of nutritional care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jpen.70103","type":"journal-article","created":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T09:24:08Z","timestamp":1779009848000},"page":"832-844","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Nutrition impact symptoms and the risk of malnutrition, frailty, and sarcopenia in adults with cancer: A cross\u2010sectional latent class analysis"],"prefix":"10.1002","volume":"50","author":[{"given":"Xite","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Health Statistics, School of Public Health, Beijing Key Laboratory of Environment and Aging Capital Medical University Beijing 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