Open the website of any major direct-to-consumer men's health brand and search for outcomes data. You will find satisfaction scores. You will find Trustpilot stars. You will find before-and-after photos and testimonial videos. You will not find ApoB shifts, free testosterone trajectories, fat mass changes, side-effect rates, or any number that a clinician would recognize as an outcome.
That gap is the subject of this piece.
What outcomes data actually means
In a clinical context, an outcome is a measurable change in physiology or function attributable to an intervention. The categories that matter for men's health:
- Cardiovascular. ApoB, LDL, Lp(a) where modifiable, hsCRP, blood pressure.
- Metabolic. Fasting insulin, HbA1c, HOMA-IR, visceral fat, total body fat.
- Hormonal. Total testosterone, free testosterone, SHBG, estradiol, hematocrit.
- Body composition. Lean mass, fat mass, visceral adipose tissue, ideally by DEXA.
- Adverse events. Side-effect rates, dropout rates, discontinuation reasons.
- Patient-reported. Symptom scales validated in clinical trials (ADAM, IIEF, PHQ-9), not Net Promoter Score.
None of these are satisfaction. All of these are answerable in pre-and-post format if a company is tracking them. Most are not.
Why most companies do not publish outcomes
There are three honest reasons.
The data is mediocre. Publishing the numbers exposes the gap between the marketing claim ("optimize your testosterone") and the clinical reality (a fraction of patients move into optimal range, a fraction discontinue, a fraction have side effects). Glossy marketing requires the gap to stay opaque.
The company is not collecting the data. A meaningful share of direct-to-consumer companies in this space do not require follow-up labs. The patient pays, gets the prescription, and is never re-panelled. There is no outcome to publish because no one measured it.
The protocols are not stratified. When every patient is on the same template, the outcomes regress to the mean of the template. A well-built protocol moves a specific patient. A template moves an aggregate. The aggregate looks unimpressive in print.
What we publish
The Vane clinical team commits to publishing the following on a rolling 12-month basis, anonymized and reported by program:
- Median and IQR change in ApoB for patients on cardiovascular protocols.
- Median and IQR change in fasting insulin and HbA1c for patients on metabolic protocols, including GLP-1 protocols.
- Median and IQR change in free testosterone and SHBG for patients on hormone protocols, with the distinction between TRT and metabolic intervention without TRT preserved.
- DEXA-measured lean mass and visceral fat changes for patients who opt into body composition tracking.
- Adverse event rates by category (gastrointestinal on GLP-1s, polycythemia on TRT, mood changes on finasteride, etc.).
- Dropout rates with reasons, segmented by program.
- Time-to-target for patients on cardiovascular protocols, defined as weeks to reach the protocol's ApoB goal.
The data is anchored to the rerun panels, which are part of the protocol regardless of whether the patient consents to inclusion in the published cohort. Inclusion in the public reporting is opt-in. The internal tracking is universal.
What this reveals when you do it
When a clinic publishes real outcomes, three things become visible.
First, the floor is higher. A patient looking at outcome data can pick programs where the median patient meaningfully moves the numbers, rather than picking programs where the marketing is the most aggressive.
Second, the protocols differentiate. The Vane case for doing protocols by panel rather than template is an empirical claim. The outcome data is how that claim gets tested. If our outcomes look like the templates' outcomes, we are not adding value. If they do not, the data shows it.
Third, side effects get a fair hearing. Most men's health marketing treats side effects as small print. Side effects are not small. The men we work with deserve to see the rates, in plain language, before they sign up. The publication of adverse event rates is the part of this commitment that most peers will not match.
What we will not call an outcome
We will not publish Net Promoter Score, Trustpilot rating, satisfaction percentage, or any other proxy for patient mood. These numbers are not bad, but they are not outcomes. The men we work with can decide for themselves whether they liked the service. The clinical question is whether the protocol moved their physiology.
We will not cherry-pick. The published cohorts will be everyone who completed the rerun panel, not the responders only.
We will not benchmark against best-case trial data. Published cohorts will be our actual patients, not idealized obesity-trial subjects on maximum tolerated doses.
The path forward
The first outcome report is scheduled for the end of the calendar year. The cohort will be the patients who completed at least one rerun panel by then. Every subsequent report will expand the cohort and refine the segmentation.
We will publish methodology alongside the numbers. Sample size, inclusion criteria, statistical approach, and known limitations. If a number looks worse than the prior report, we will discuss why before the next report.
This is what we believe a serious men's health clinic should be doing. It is also what we believe most are not doing.
Where this connects to the rest
The commitment to outcomes is the same idea as the commitment to reading the panel correctly. The panel measures where you are. The outcome data measures whether the protocol changed it. Without the second number, the first is just trivia.
If your current provider cannot tell you what their median patient's ApoB shift is on their cardiovascular protocol, ask why. The answer will tell you something.
The bottom line
Outcomes data is the cleanest way to keep a clinic honest. The companies that publish it will be the companies that improve. The companies that hide behind satisfaction scores will continue to sell whatever the funnel rewards.
We chose the harder version. We will report the numbers, including the ones that do not flatter us, and we will let the data make the case the marketing should not have to.
