Role of L-Arginine supplementation in Long Covid-related Fatigue and Depression in Elderly Outpatients
Bibliographic record
Abstract
Introduction Chronic fatigue and psychiatric manifestations (depression, anxiety and sleep disturbances) appear to be key features of post-COVID-19 syndrome and increase significantly in prevalence over time (Lavienraj et al. J Neurol Sci 2022;434:120162). Several studies have suggested an association between altered levels of arginine metabolites and depression, anxiety and stress severity (Arisoy et al. J Psychiatr Res 2020;120:21-28). L-arginine supplementation has also been shown to improve walking performance, muscle strength, endothelial function and fatigue in adults with Long COVID (Tosato et al. Nutrients 2022;14(23):4984). Objectives To study effects of L-arginine oral supplementation on chronic fatigue and depressive symptoms reported 3 months or more after acute COVID-19 onset in elderly outpatients without severe comorbid conditions. Methods This is a parallel-group, double-blind, randomized controlled trial conducted on 96 over 65 non-hospitalized patients suffering from Long Covid-related fatigue and depression. The first group included patients that received 1,66 g L-arginine twice a day in addition to a standard antidepressant therapy based on Selective Serotonin Reuptake Inhibitors (SSRIs), whereas the second group received antidepressant only. Severity of fatigue and depressive symptoms was evaluated at baseline and after 8 weeks of treatment using Fatigue Symptom Inventory (FSI) and Hamilton Rating Scale for Depression (HAM-D), respectively. Results At baseline, 64 patients (66,7%) reported moderate fatigue (4-6) and the remaining 32 (33,3%) reported severe fatigue (7-10). In this phase the average HAM-D score was 12,85 ± 5,97; among patients, 57,3% experienced mild symptoms of depression, 32,3% experienced moderate symptoms and 6,4% experienced severe symptoms. After two months, patients treated with L-arginine supplementation exhibited a 30% greater improvement in fatigue-related symptom severity (p=0.008) and a significantly decrease in average HAM-D score (p=0.002) compared to the group treated with SSRI only. Conclusions According to our results, adding oral L-arginine to standard antidepressant therapy in elders with Long Covid-related fatigue and depression significantly decreases severity of both physical and affective symptoms. Further studies are needed to clarify the intriguing role of L-arginine in the treatment of Post Covid-19 syndrome and its potential effects in promoting geriatric patients’ health, wellbeing and quality of life. Disclosure of Interest None Declared
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".