Evidence Density Map 2026 methods

This methods page documents how Healthy Aging Atlas generated the Supplement Evidence Density Map 2026, including source data, cleanup rules, included fields, and limitations.

Written by Healthy Aging Atlas Research Team·Status note: Methods documentation for an original-data report; no personalized supplement or treatment recommendation is made.·Updated August 1, 2026

This content is for educational purposes only and is not medical advice. These statements have not been evaluated by the FDA. This product is not intended to diagnose, treat, cure, or prevent any disease.

Source data

The report uses HAA's DEC-068 v2 supplement-goal evidence matrix: Full 282-supplement matrix re-score. Supersedes supplement-matrix-scored.json (184-supplement, 1629 cells).

The source file reports 2,221 valid scored cells before publication cleanup, 2,442 raw pairings, and formula weights of 35% demand, 25% evidence, 20% commercial intent, 15% corpus-gap lift, and -5% YMYL penalty.

Cleanup rules before publication

  • Removed rows marked as editorial rejects, including L-DOPA rows because levodopa is a prescription medication class rather than a dietary supplement.
  • Removed rows where `conditionSlug` was `probiotic`, because that is a category/product-type artifact rather than a health outcome.
  • Collapsed rows with the same normalized supplement name and goal. Legacy aliases and formulation slugs were treated as one ingredient-goal identity; the row with the stronger mapped evidence record was retained.
  • Canonicalized known duplicate supplement slugs and recalculated `rank` and `tier` so the public cleaned dataset is contiguous from rank 1.
  • Kept broad marketed claim categories such as antioxidant because HAA has an antioxidant-support claim family; these are disclosed as broad claims, not disease outcomes.
  • Did not remove rows only because evidence was thin. The purpose of the report is to show thin and missing evidence, not hide it.
Step Count
Source matrix cells2221
Editorial and category rows removed34
Duplicate supplement-goal identities removed31
Total rows removed during publication cleanup65
Cleaned report denominator2156

Field definitions

Field Meaning
supplementNameNormalized supplement or ingredient label.
conditionSlugMapped marketed goal or claim target.
rctCountNumber of PubMed-indexed randomized controlled trials this pipeline matched to the pairing in a capped per-supplement sample. It is not a PubMed census; a zero can be a matching gap.
metaAnalysisCountNumber of mapped systematic review or meta-analysis signals in the source matrix.
monthlyDemandSearch-demand proxy from DataForSEO or evidence proxy where fresh keyword data was unavailable.
evidenceStrengthMatrix-level evidence-strength label used for prioritization, not a clinical recommendation.
ymylHazardLevelEditorial risk label used to prioritize medical-review caution.

Headline stat calculation

The lead stat is calculated as no-match rows divided by all cleaned rows: 475 / 2,156 = 22%. It reproduces this pipeline's matching result and is an upper bound on the true evidence gap, not a count of pairings without published trials.

The 3+ RCT stat is 820 / 2,156 = 38%. The 10+ RCT stat is 79 / 2,156 = 3.7%. The at-least-one-meta-analysis stat is 1,128 / 2,156 = 52.3%.

Cluster summary

Cluster names come from the source matrix and are used to show evidence-density asymmetry across the supplement landscape.

Cluster Pairings Zero R C T3+ R C T10+ R C T Meta-analysis
General Wellness46329.6%30%2.2%55.1%
Cardiovascular25416.5%46.9%9.4%55.5%
Metabolic Health24020%34.2%1.7%59.2%
Immune Support23012.6%55.2%5.7%43%
Cognitive Health17131%33.3%1.8%60.2%
Stress & Mood15023.3%38.7%4%54%
Liver & Detox9318.3%36.6%2.2%55.9%
Inflammation8411.9%32.1%1.2%44%
Athletic Performance8017.5%37.5%1.3%40%
Gut Health6319%42.9%4.8%42.9%
Energy & Fatigue6011.7%45%1.7%28.3%
Beauty & Hair517.8%54.9%5.9%35.3%
Joint Health4714.9%36.2%6.4%59.6%
Hormonal Health4425%29.5%4.5%61.4%
Longevity2846.4%10.7%0%71.4%

Limitations

  • The dataset is a structured evidence map, not a clinical guideline.
  • RCT count is not trial quality. It does not adjust for sample size, risk of bias, funding, dose, duration, comparator, or outcome relevance.
  • Monthly demand is a search-demand proxy, not a direct measurement of market claims or consumer purchasing.
  • The matrix is generated from HAA evidence-mining and demand-validation workflows; it is not a complete regulatory audit of all supplements sold online.
  • Alias normalization is incomplete. Confirmed examples such as CoQ10 / Coenzyme Q10, Citrulline / L-Citrulline, and Carnitine / L-Carnitine survive as separate identities and can carry different mapped counts for the same goal.
  • The report should be cited as a snapshot generated on the publication date, not as a live PubMed census.
  • `rctCount` was derived by keyword-matching a capped per-supplement PubMed sample rather than by querying every supplement-goal pairing. The no-match bucket therefore mixes genuine evidence gaps with an unquantified share of sampling, keyword, and alias failures.

Reproducibility assets

The cleaned JSON summary, generated CSV, and QA report are emitted by the generator script so future HAA agents can regenerate the report and compare denominators.

Frequently Asked Questions

Why remove probiotic as a goal?

In the source matrix, probiotic appeared as a condition keyword for several probiotic strains. That describes a product category, not a health outcome, so those rows were removed from the published denominator.

Why keep antioxidant?

Antioxidant is broad, but HAA has an antioxidant-support claim family. The report keeps it as a marketed claim category and discloses that broad claim categories are not the same as disease outcomes.

Why are editorial rejects removed?

Rows marked as editorial rejects were excluded so the public denominator reflects supplement-goal pairings HAA is willing to discuss as consumer-facing supplement topics.

Does RCT count measure evidence quality?

No. RCT count is a density signal only. It does not score risk of bias, sample size, intervention dose, duration, comparator, outcome relevance, or funding source.

Can another researcher reproduce the headline stat?

Yes. The generator emits the cleaned CSV, JSON summary, and QA report. The displayed percentage is no-match rows divided by all cleaned rows. It reproduces this pipeline result, not the true prevalence of pairings without published trials.

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Citations & Research

  1. [1]Supplement Evidence Density Map 2026Source
  2. [2]Supplement Evidence Density Map 2026 CSVSource
  3. [3]Supplement Evidence Density Map 2026Source

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Draft research page pending medical/legal review · Editorial policy · Affiliate disclosure