ArticleNutrients2021
Predicting Malnutrition Risk with Data from Routinely Measured Clinical Biochemical Diagnostic Tests in Free-Living Older Populations.
Article in Nutrients, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Risk prediction model for malnutrition in older adults: a systematic review.BMC geriatrics · 2026Pooled it
- Pooled it
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- Article
- Cross-Sectional Associations between Clinical Biochemistry and Nutritional Biomarkers and Sarcopenic Indices of Skeletal Muscle in the Baltimore Longitudinal Study of Aging.The Journal of nutrition · 2025Article
- Predicting the unseen: nutritional interventions as a key to combat frailty.Frontiers in nutrition · 2025Article
- Preparing for and Not Waiting for Surgery.Current oncology (Toronto, Ont.) · 2024Review
- Development and Validation of a Risk Prediction Model for Sarcopenia in Chinese Older Patients with Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Metabolic Syndrome Screening and Nutritional Status of Patients with Psoriasis: A Scoping Review.Nutrients · 2023Article
- Prediabetes is associated with loss of appendicular skeletal muscle mass and sarcopenia.Frontiers in nutrition · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malnutrition (undernutrition) in older adults is often not diagnosed before its adverse consequences have occurred, despite the existence of established screening tools. As a potential method of early detection, we examined whether readily available and routinely measured clinical biochemical diagnostic test data could predict poor nutritional status. We combined 2008-2017 data of 1518 free-living individuals ≥50 years from the United Kingdom National Diet and Nutrition Survey (NDNS) and used logistic regression to determine associations between routine biochemical diagnostic test data, micronutrient deficiency biomarkers, and established malnutrition indicators (components of screening tools) in a three-step validation process. A prediction model was created to determine how effectively routine biochemical diagnostic tests and established malnutrition indicators predicted poor nutritional status (defined by ≥1 micronutrient deficiency in blood of vitamins B
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