Dietary Docosahexaenoic Acid-Rich Supplementation Decreases Neurotoxic Lipid Mediators in Participants with Type 2 Diabetes and Neuropathic Pain
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: There is increasing evidence linking circulating neurotoxic lipids to the progression of chronic neuroinflammatory diseases in the peripheral and central nervous systems. Strategies to modify lipid profiles, such as docosahexaenoic acid (DHA)-rich supplementation, may aid in managing conditions like painful diabetic neuropathy (pDN). In a previous study, we demonstrated that three months of DHA supplementation significantly altered the metabolomic profile of patients with painful diabetic neuropathy (pDN), resulting in symptom improvement. This study investigates whether DHA-rich supplementation reduces neurotoxic lipid mediators associated with pDN in individuals with type 2 diabetes mellitus (T2DM). METHODS: Forty individuals with type 2 diabetes participated in the "En Balance-PLUS" study, attending weekly lifestyle and nutrition education sessions while receiving daily supplementation of 1000 mg DHA and 200 mg EPA. Pain levels were assessed using the Short-Form McGill Pain Questionnaire (SF-MPQ) at baseline and after three months. Blood serum samples collected at these time points underwent untargeted lipidomic analyses, with ELISA used to evaluate biomarkers of necrosis (MLKL), autophagy (ATG5), and lipid chaperone protein (FABP5). RESULTS: Untargeted lipidomic analysis revealed that several neurotoxic-associated lipids significantly decreased after DHA-rich supplementation. Also, circulating levels of MLKL were reduced, while protein levels of ATG5 and FABP5 significantly increased. CONCLUSIONS: The reduction of circulating neurotoxic lipids and increase in neuroprotective lipids following DHA-rich supplementation are consistent with the reported roles of omega-3 polyunsaturated fatty acids (PUFAs) in reducing adverse symptoms associated with neuroinflammatory diseases and painful neuropathy.
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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 | 0.000 | 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) | 0.003 | 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".