Impact of nutritional status according to GLIM criteria on the risk of incident frailty and mortality in community-dwelling older adults
Bibliographic record
Abstract
BACKGROUND & AIMS: Poor nutritional status leads to multiple adverse outcomes, but few studies have assessed its role as a risk factor for incident frailty and death in community-dwelling older adults. Hence, the aim of this paper is to assess the role of nutritional status using the Global Leadership Initiative on Malnutrition (GLIM) criteria in the risk of frailty and mortality in Spanish community-dwelling older adults. METHODS: We used data from two waves (waves 2 (2011-2013) and 3 (2015-2017)) from the Toledo Study of Healthy Ageing, which is an observational, prospective cohort (average follow-up = 3.18 years) of 1660 older (≥65 years) adults living in the community. Nutritional status categories were defined according to the GLIM criteria, which uses a two-step approach. First, screening for malnutrition risk. Once positive, individuals were classified as malnourished according to some phenotypic (body mass index, grip strength and unintentional weight loss) and etiologic (disease burden/inflammation and reduced food intake or assimilation) criteria. Frailty was assessed using both the Frailty Index (FI) and Frailty Trait Scale (FTS). Mortality data was obtained through the National Death Index. RESULTS: From the 1660 older adults, 248 participants (14.04%) were classified as 'at malnutrition risk' (AMR) and 209 (12.59%) as malnourished (MN). AMR and MN subjects were older and with worse functional status (frailer). Adjusted cross-sectional analysis showed an association between nutritional status and frailty by both FI and FTS. Adjusted longitudinal analyses showed that AMR was associated with higher risk of frailty, using both the FTS (OR: 1.262; 95% CI: 1.078-1.815) and the FI (OR: 1.116; 95% CI: 1.098-1.686), while being malnourished was associated with higher mortality risk (OR: 1.748; 95% CI: 1.073-2.849), but not with incident frailty at follow-up period. CONCLUSIONS: Nutritional status, assessed through GLIM, predicts in a dose-dependent manner the risk of frailty and death. Being at malnutrition risk predicts the risk of becoming frail at follow-up period, whereas being malnourished predicts mortality. These findings highlight the importance of assessing the nutritional status of community-dwelling older adults to identify the ones at risk of developing frailty or death and inform targeted nutrition-focused interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".