Forskolin (Coleus forskohlii) for Testosterone
There is not enough evidence that forskolin (Coleus forskohlii) raises testosterone. The entire claim rests on a single small, industry-funded trial that has gone unreplicated for nearly two decades, and no major clinical guideline recommends it.
Why this grade7-layer evidence engine
The testosterone signal traces to just one study: Godard 2005 (PMID 16129715), a double-blind RCT in 30 overweight and obese men taking Coleus forskohlii (250 mg standardized to 10% forskolin) twice daily for 12 weeks. Free testosterone rose about 17% versus placebo, but this was a secondary endpoint in a trial designed around body composition, the study was industry-funded and single-center, and no confirmatory RCT or meta-analysis has reproduced the effect on PubMed since 2005.
Clinical authorities give it no support. The American Urological Association and Endocrine Society testosterone-deficiency guidelines never mention forskolin, instead recommending lifestyle modification and prescription therapy; Cleveland Clinic and Harvard Health are likewise silent. On the regulatory side, the US FDA has only issued warning letters over disease claims (not testosterone), and the EU EFSA has not accepted forskolin health claims, keeping them on-hold with no established causal relationship.
The proposed mechanism (cAMP upregulation) is biologically plausible but unconfirmed in humans, and the lone positive result applies only to overweight men, not healthy or low-testosterone populations. Taken together this is weak, disputed evidence: a single small trial with no replication and no clinical endorsement does not justify marketing forskolin as a testosterone booster.
Scoring transparency
All scores computed by a 7-layer evidence engine — fully auditable▸View the full decision path (audit trail)
- compute_raw_score — 加權公式: L2×0.30 + L3×0.25 + L5×0.25 + L11×0.10 + L1×0.10 = 0.413
- tier_from_score — 依分數區間映射至 tier letter
- apply_hec_rules — 無高階證據可裁決
- tier_strict_requirement_check — Tier 條件達標，未降階
- detect_disputes — 偵測到 1 個 hard + 1 個 soft dispute
- decide_status — 依 tier + dispute 結果決定 status