Efficacy of iron supplementation on fatigue and physical capacity in non-anaemic iron-deficient adults: a systematic review of randomised controlled trials
Bibliographic record
Abstract
Objective Iron supplementation in iron-deficiency anaemia is standard practice, but the benefits of iron supplementation in iron-deficient non-anaemic (IDNA) individuals remains controversial. Our objective is to identify the effects of iron therapy on fatigue and physical capacity in IDNA adults. Design Systematic review and meta-analysis of randomised controlled trials (RCTs). Setting Primary care. Participants Adults (≥18 years) who were iron deficient but non-anaemic. Interventions Oral, intramuscular or intravenous iron supplementation; all therapy doses, frequencies and durations were included. Comparators Placebo or active therapy. Results We identified RCTs in Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index of Nursing and Allied Health, SportDiscus and CAB Abstracts from inception to 31 October 2016. We searched the WHO’s International Clinical Trials Registry Platform for relevant ongoing trials and performed forward searches of included trials and relevant reviews in Web of Science. We assessed internal validity of included trials using the Cochrane Risk of Bias tool and the external validity using the Grading of Recommendations Assessment, Development and Evaluation methodology. From 11 580 citations, we included 18 unique trials and 2 companion papers enrolling 1170 patients. Using a Mantel-Haenszel random-effects model, iron supplementation was associated with reduced self-reported fatigue (standardised mean difference (SMD) −0.38; 95% CI −0.52 to −0.23; I 2 0%; 4 trials; 714 participants) but was not associated with differences in objective measures of physical capacity, including maximal oxygen consumption (SMD 0.11; 95% CI −0.15 to 0.37; I 2 0%; 9 trials; 235 participants) and timed methods of exercise testing. Iron supplementation significantly increased serum haemoglobin concentration (MD 4.01 g/L; 95% CI 1.22 to 6.81; I 2 48%; 12 trials; 298 participants) and serum ferritin (MD 9.23 µmol/L; 95% CI 6.48 to 11.97; I 2 58%; 14 trials; 616 participants). Conclusion In IDNA adults, iron supplementation is associated with reduced subjective measures of fatigue but not with objective improvements in physical capacity. Given the global prevalence of both iron deficiency and fatigue, patients and practitioners could consider consumption of iron-rich foods or iron supplementation to improve symptoms of fatigue in the absence of documented anaemia. PROSPERO registration number CRD42014007085 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".