Alcohol Misuse Link to POEMS Syndrome in a Patient
Bibliographic record
Abstract
Previously called Crow-Fukase syndrome, POEMS syndrome is characterized by poly-neuropathy, osteo-sclerotic myeloma, organomegaly, endocrinopathy, monoclonal plasma cell disorder, and skin changes. Extremely elevated levels of serum vascular endothelial growth factor (VEGF) are characteristic of the syndrome. Chronic hepatitis B (HBV) and C (HCV) infections can also be present in POEMS. The pathogenesis of the syndrome is not well understood. The link between chronic alcohol consumption and this malignant condition has not been reported until now. In addition, no previous study has evaluated the influence of cytokine and chemokines or viruses in the severity and evolution of POEMS. OBJECTIVES: (1) to describe a heavy-alcohol user, who was diagnosed with POEMS; (2) to demonstrate the utility of quantitative measurement of serum levels of VEGF in the diagnosis of POEMS and the monitoring of therapeutic interventions; (3) to demonstrate that overproduction of pro-inflammatory cytokines is a characteristic of POEMS. METHODS: We describe a case of a POEMS patient presenting HCV and who is a heavy drinker; we compare the serum levels of cytokines and chemokines between the POEMS patient with 80 patients with HCV, 12 healthy controls, and 80 individuals with alcoholic liver disease (ALD). We quantified (ELISA pg/mL) the levels of VEGF, Interferon gamma (IFN-γ), Tumor Necrosis Factor alpha (TNF-α), Regulated-upon-Activation Normal-T-cell-Expressed and presumably-Secreted (RANTES), and Nuclear Factor kappa-B (NFκB). RESULTS: In POEMS patients, VEGF levels were elevated versus control or other diseases, TNFα levels were higher versus control, but lower when compared with HCV or ALD patients. VEGF levels in POEMS patients decreased with therapeutic intervention. CONCLUSIONS: Chronic alcohol misuse can be a strong risk factor to rare malignancies such as POEMS syndrome. Extreme elevation of VEGF levels is diagnostic for POEMS syndrome, and should be followed to assess response to therapy. In addition, other comorbidities should be considered individually to ensure personalized therapeutic intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".