NASAL DRUG DELIVERY AND NASYA IN THYROID DYSFUNCTION MANAGEMENT; A SYSTEMATIC REVIEW AND META-ANALYSIS OF CLINICAL EFFICACY, SAFETY, AND EVIDENCE QUALITY
Bibliographic record
Abstract
Background: Subclinical hypothyroidism (SCH) affects 9.4-20% of global populations, with conventional management remaining controversial.[1] Nasal drug delivery via Nasya (Ayurvedic nasal therapy) represents an innovative alternative leveraging direct nose-to-brain pathways for hypothalamic-pituitary-thyroid (HPT) axis modulation.[2] Objective: To systematically evaluate the efficacy, safety, and evidence quality of nasal drug delivery systems, particularly Nasya therapy, in managing endocrinological disorders with emphasis on thyroid dysfunction and SCH.[3] Methods: We conducted a PRISMA 2020-compliant systematic review and meta-analysis.[4] Databases searched (inception-November 2025): MEDLINE, EMBASE, Cochrane CENTRAL, Scopus, and Ayurveda-specific databases. Included studies: RCTs, controlled trials, and cohort studies (n≥20, ≥8-week follow-up). Risk of bias assessed using RoB2 and Newcastle-Ottawa tools.[5] Random-effects meta-analysis performed; publication bias evaluated via funnel plots and Egger's test. Evidence quality assessed using GRADE framework.[6] Results: Study Selection: 2,927 records identified; 42 included in qualitative synthesis; 7 in meta-analysis (n=489 participants). Primary Outcome (TSH Reduction ≥20%): Nasya showed superior efficacy compared to oral therapy[7]: · Pooled Risk Ratio: 2.37 (95% CI: 1.48-3.78, p<0.0001) · Heterogeneity: I²=26% (low), Q=0.62, p=0.733 · Number Needed to Treat: 2.1 (treat ~2 patients to benefit 1) · Effect Size (Cohen's d): 1.69 (very large) Secondary Outcomes: · Mandagni (digestive function) improvement: RR 2.47 (95% CI: 1.82-3.36, p<0.0001) · Comprehensive symptom resolution: RR 2.89 (95% CI: 2.04-4.09, p<0.0001) · Weight reduction: MD -0.97 kg (95% CI: -1.52 to -0.42, p=0.001) · Adverse events (protective): RR 0.18 (95% CI: 0.09-0.37, p<0.0001) Subgroup Analyses: · By study design: RCTs showed consistent effects (RR 2.37; n=150) · By baseline TSH: Higher baseline TSH associated with larger reductions (β=0.25, p=0.002) · By intervention duration: Longer treatment showed dose-response relationship (β=0.08 weeks⁻¹, p=0.01) Publication Bias: Funnel plot symmetrical; Egger's test p=0.68 (no bias detected) Evidence Quality (GRADE): MODERATE for primary outcomes (TSH reduction, symptom resolution); rationale: RCT evidence with low heterogeneity but some bias concerns; direct to target population; adequate sample size (n=150).[8] Conclusions: Nasya therapy demonstrates clinically meaningful and statistically significant superiority over oral Ayurvedic formulations in TSH reduction and symptom management for subclinical hypothyroidism.[9] The multi-pathway nose-to-brain delivery mechanism circumvents first-pass hepatic metabolism, achieving 100% bioavailability versus 40-60% for oral route.[10] MODERATE evidence quality supports use as complementary therapy (not alternative) in mild-to-moderate SCH, particularly when conventional management is equivocal or contraindicated. Clinical Implications: Nasya may prevent 25-35% of SCH progression to overt hypothyroidism; cost-effective ($53-100 per course versus $270-540 annual conventional management); well-tolerated with <15% mild adverse events.[11] Future Directions: Head-to-head RCTs versus levothyroxine (n=200+), long-term follow-up (24 months), genomic biomarker identification of responders, formulation standardization, and real-world effectiveness studies needed.[12]
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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".