Across the plaque’s row, the cells fill in one by one. The patients are people with mild problems of memory and thinking, or mild dementia, from Alzheimer’s. The approval was Accelerated. The drug was a Monoclonal antibody, an antibody made in a lab to latch onto the plaque. Five columns, read left to right, and every one of them filled. Below the last row of the table, the finger stops on white space.

The finger scrolls back to the top of the page. The table is the US Food and Drug Administration’s own public list, the Table of Surrogate Endpoints That Were the Basis of Drug Approval or Licensure. Each row holds a stand-in: a measurement used in place of the thing a patient would notice, how they feel, function or survive. A blood pressure reading, for one, stands in for strokes that never happen. Every stand-in on the list once carried a drug to approval. The page says the list is for drug developers, who read it for endpoints “that may be considered and discussed with FDA” for their own programs. The page says its content is current as of April 29, 2026. Section 507 of the US drug law has the agency update the list every six months.

Figure 1. Selected rows, re-set word for word, from the FDA’s Table of Surrogate Endpoints, Table 1 (Adult Surrogate Endpoints, Non-Cancer), content current as of April 29, 2026. Look down the last column: three rows read Mechanism agnostic, and the plaque’s row reads Monoclonal antibody. The rows are not next to each other on the page, and the Patient Population column is left out. The grey “Aging” row is this piece’s mark, not the FDA’s. A work of the US government, public domain.

Lower on the page, the finger stops on a sentence about what leaves the list. Stand-ins that were accepted once but “are no longer acceptable” are left out. In part one, the agency’s own statistician found no evidence that the plaque was a stand-in for how patients did. By the page’s own rule, a stand-in the agency had stopped trusting would be gone. The plaque still has its row.

The finger runs down the first column again, slower this time. Osteoporosis, the thinning of bones. Chronic kidney disease. Pulmonary fibrosis, a scarring of the lungs. Getting older raises the risk of many diseases on this list, Alzheimer’s among them. Ways to slow aging are being tested in people. If one drug slowed aging itself, would it not show up in several of these rows at once? So why does the thing behind so many rows have no row of its own?

This month’s refill of a healthy-aging supplement is in your cart. The front of the jar promises younger cells. On the back, in small grey print, is a line every such label must carry. It says the product is “not intended to diagnose, treat, cure, or prevent any disease” at all. Your mother forwarded the link a year ago, and the grocery money you share has paid for eleven jars since. In one trial that assigned diets by chance, two years of eating a quarter less slowed one aging clock by a few percent. An aging clock is a blood test that estimates how fast a body is aging. Would twelve jars buy a single healthy year? The label says in law what the table says by leaving a row out. Click reorder, and the twelfth jar ships on a claim the FDA never evaluated.

Every Row Names a Disease

The first column of the table has a heading: “Disease or Use.” Under it run words a doctor would write on a chart. Acromegaly. Anthrax vaccine. Hypertension. Pulmonary fibrosis. Type 2 diabetes mellitus. Every row starts with something a drug treats or a vaccine prevents.

The closest the table comes to age is two vaccine rows. A pneumococcal vaccine row names “Persons (≥ 50 years of age),” and an RSV vaccine row names “Adults aged 60 years and above.” Even there, the first column names the vaccine or the virus, never the years. No row names aging. None names healthspan, longevity or frailty.

A stand-in stands in for something. On this table, that something is always a disease or a use. A drug that slows aging has no disease to name in the first column. A trial of the diabetes drug metformin against aging, called TAME, was meant to test such a drug. In September 2026 the trial had still not started, and it was still raising money.

Someone whose friend has a doctor’s prescription for a diabetes drug “for aging,” a pill a day for years, can check one line. The leaflet stapled to the pharmacy bag says what the drug is for. That line names a disease. The friend can read it aloud and ask what the doctor means to treat. The first column says what a stand-in is for. The next columns say how far the agency trusts it.

Two Kinds of Yes

The fourth column of the table holds one of two words: Traditional or Accelerated. The field calls a stand-in a surrogate endpoint. Section 507 of the US drug law names two kinds. One “is known to predict clinical benefit and could be used to support traditional approval.” The other “is reasonably likely to predict clinical benefit” and can support accelerated approval.

The plaque’s row says Accelerated. That is the lower yes, the faster route the plaque’s approval took in part one. The upper yes has its own name, the top of three levels in the FDA and NIH glossary.

A reasonably likely stand-in, the middle level, is “expected to be correlated” with benefit, but is “without sufficient clinical data” to be called validated. A candidate, the bottom level, is an endpoint “still under evaluation for its ability to predict clinical benefit.” The table holds the top two levels. No aging marker has a row, so none stands above the bottom level.

The levels work like the checks behind a hiring test. A job applicant may be asked to sit an hour-long online quiz, a timer ticking in the corner of the screen. The applicant can ask which level the quiz score has reached. Has the score been checked against how past hires did on the job, or is it only expected to predict how they did? One score decides whether the application goes any further. The next posting that asks for a test is when to ask. The fourth column says which yes a stand-in earned. The last column says which drugs that yes covers.

The Last Column

Back on the plaque’s row, the last column reads “Monoclonal antibody.” Its heading is “Drug Mechanism of Action,” the way a drug works in the body. The plaque was accepted on the lower yes, for one kind of drug: antibodies built to clear it. LDL cholesterol reads “Lipid-lowering,” drugs that lower fats in the blood. Hemoglobin A1C, a blood sugar measure, reads “Glucose-lowering.”

Three rows in Figure 1 read differently. Blood pressure, the kidney filtration rate and the lung test FVC all say “Mechanism agnostic,” with an asterisk: each counts whatever way the drug works. A first reading stops at the first column, where aging has no row. The last column is the harder test. A drug that slows aging could work through many pathways, the chains of steps by which a drug changes the body. A stand-in tied to one pathway would stand in for that drug, not for aging. An aging stand-in would need the word on blood pressure’s row.

Blood pressure earned that word over many trials. On June 15, 2005, an FDA advisory committee on heart and kidney drugs met in public about blood pressure pills. Outcome trials had used drugs from “numerous pharmacologic classes,” among them diuretics, beta blockers and calcium channel blockers. Strokes fell in trial after trial. The agency’s 2011 guidance wrote down what that meant. Drugs with “disparate mechanisms of action” had similar effects. So “it is the decrease in blood pressure, rather than any other property of the drugs,” that was “largely responsible for these benefits.”

The same guidance admits there was “no regulatory precedent” for carrying one outcome claim across drug classes this different. It did so anyway, because there had been “consistently favorable effects on outcomes across many drug classes” in the trials. The trials gave more than one drug at a time, so the data “cannot easily be used to distinguish the contributions of individual drugs or classes.”

At a yearly checkup, the cuff tightens around the upper arm, then hisses as it lets go. That one reading is trusted whichever kind of pill lowers it. Any other number on the chart can be asked one question: has it held for more than one kind of drug? The next checkup’s blood pressure reading is the cue to ask that question. Blood pressure needed many kinds of drug to earn its word. The aging candidates would have to fill the last column the same way, trial by trial.

What the Candidates Have

A trial called CALERIE assigned 220 adults without obesity, by chance, to eat less or to eat as usual. For two years, the eat-less group aimed for 25 percent fewer calories. Later, in an analysis the trial had not planned, Waziry and colleagues ran the blood samples through several aging candidates: blood tests that estimate age from DNA.

One moved. In the group eating less, DunedinPACE, which reads how fast a body is aging, slowed by 2 to 3 percent. Two others, PhenoAge and GrimAge, did not move. The authors called the effect sizes “small.” A conclusive test, they wrote, “will require trials with long-term follow-up” on healthy-aging outcomes, such as chronic disease and death.

DunedinPACE started from 19 measures of how well the body’s organs held up, in one group of people. Each was taken four times over twenty years. Its makers turned that record into a test read from one blood draw. A reading of 1 means one year of body-wide decline per calendar year. Faster than 1 is faster aging. Faster readings go with more illness, disability and death, its makers report. So DunedinPACE has done two jobs. Elsewhere it predicts who gets sick; in CALERIE, it moved when the diet changed. The glossary keeps those jobs apart.

The first definition is written for patients who already have a disease. The people in CALERIE were healthy, so the aging markers borrow the word. A marker can do both jobs and still not stand in for aging. Standing in for aging would take the validated level and the last column’s word.

The other candidates have done the first job. A frailty index is the share of a list of health problems a person has, from 0 for none to 1 for all. In one study of older adults, “the frailty index, age and sex” were “significant predictors of mortality.” Grip strength and walking speed predict too, but modestly. Statisticians grade that sorting with the C-index, a “concordance probability”: how well a score sorts who dies first. A coin toss would score 0.5, and a perfect sort would score 1. One analysis of three studies covered 8,362 people. There, age and sex alone scored 0.65. Adding grip and walking speed “only increased” it to 0.67.

Any score also wobbles from one try to the next. That wobble has a name, measurement error. A measurement guide defines it as “the systematic and random error of a patient’s score that is not attributed to true changes” in what is measured. A change smaller than that error cannot be told apart from the wobble.

Figure 2. What each aging candidate has, in the table’s own order of proof; the last two columns read No in every row. A blank cell means no source was read for it, not that the marker failed; whether any candidate is qualified was not checked. Sources: DunedinPACE, Belsky et al. (2022) and Waziry et al. (2023); GrimAge, Waziry et al. (2023); frailty index, Searle et al. (2008); gait speed and grip strength, Westbury et al. (2024); the last two columns, the FDA table as of April 29, 2026. Drawn for this piece.

A 2026 review pooled 51 studies of ways to slow aging. The clocks “trained to predict mortality or pace of aging” responded most strongly. Responding in many studies is not the same as standing in for years lived well, whatever the drug.

Someone opening the bank’s monthly credit-score email, a needle on a red-to-green dial, meets both jobs. A score that predicts who will miss a payment is not the same as one that moves when a debt is paid down. Only the second can tell whether paying it down worked. When the next email arrives, the question is which job its number was built for. Neither job puts a marker on the list; a small mark beside two rows shows what does.

The Way Onto the List

Two osteoporosis rows on the table carry a small mark, ¤. One row is for men, the other for people whose bones thinned on steroid medicines. Both accept bone mineral density as the stand-in, with a condition in the footnote. Bone density counts only “after efficacy based on new morphometric vertebral fractures has been established in postmenopausal women.” In plain words, drugs first had to prevent new broken bones in the spine, in women past menopause. Then that number could stand in for men.

Bone density reached those rows drug program by drug program. The FDA also has a route for a marker on its own, outside any one drug.

The route has three stages: a Letter of Intent, a Qualification Plan and a Full Qualification Package. In December 2025, the FDA qualified one bone marker this way. It was the change in hip bone density over two years, for drug trials in women past menopause with osteoporosis. The key words are “within the stated context of use.” That marker is trusted for that one use, in those women.

Someone about to renew a healthy-aging supplement, whose product page cites an aging-clock study, has one question before the renewal email’s button. In whom, and for what use, has that study’s number been shown to mean better health? The FDA’s line under the claim gives part of the answer. Without the question, a year of renewals rests on a claim the FDA never evaluated. The question is worth sending to the parent who forwarded the link.

Aging has no row of its own for two reasons on the page. Every row names a disease or a use. An aging stand-in would also need “mechanism agnostic,” the word blood pressure earned across many kinds of drug, and no aging marker has that proof yet. How many trials would that proof take, and what would they have to show? From the first row on the page to the last, the finger finds no cell in the first column that says Aging.

A Closing Invitation. The missing row stood for every aging number sold before anyone showed it stands in for health. Each stand-in the regulator accepts belongs to a disease, and most to one kind of drug.

1. Turn the jar over. Now, if a supplement jar is within reach, turn it over and read the small line about the Food and Drug Administration aloud. What did the front promise: younger cells, better sleep, more years? Which of those has a number behind it, and what did the last jar cost?
2. Name the number’s job. When this month’s credit-score email or a lab result lands, ask which job its number was built for: to predict what happens, or to move when something changes. Is there a payment or a pill you chose by a number read for the wrong job?
3. Ask before the renewal. Before your next supplement renewal, or when a parent forwards a link promising younger cells, call them and ask two questions together. In whom was the product’s number shown to mean better health? For which kind of treatment? Does either answer name a disease and a drug? If not, what could next month’s jar money buy instead?

Under the diabetes row, no line says aging. The empty row waits for a number that moves the same way whatever the drug; until one earns it, the jar’s small print is aging’s only row.

Where This Came From

On October 9, 2026, I read the FDA’s table row by row, as its page stood that day, and each candidate’s paper for its own cell in Figure 2.

Intellectual Honesty Note. This piece’s own reading is the step from the last column to what an aging stand-in needs; the column and its footnote are the FDA’s. The table says which stand-ins were accepted, not how strong the proof behind each row is. TAME’s status rests on a news report until the sponsor’s own statement is read. The finger, the jar, the cart, the mother, the leaflet, the hiring test, the cuff and the credit-score email are invented.

References

21 C.F.R. § 101.93(c)(1). Certain types of statements for dietary supplements. https://www.ecfr.gov/current/title-21/chapter-I/subchapter-B/part-101/subpart-F/section-101.93

Belsky, D. W., et al. (2022). DunedinPACE, a DNA methylation biomarker of the pace of aging. eLife, 11, e73420.

COSMIN. Definitions of domains, measurement properties, and aspects of measurement properties. After Mokkink, L. B., et al. (2010). Journal of Clinical Epidemiology, 63(7), 737-745. https://www.cosmin.nl

FDA-NIH Biomarker Working Group. (2016-, last revised 2025, January 16). BEST (Biomarkers, EndpointS, and other Tools) Resource. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/books/NBK338448/

Federal Food, Drug, and Cosmetic Act § 507, 21 U.S.C. § 357.

Harrell, F. E. Regression modeling strategies (online ed.), chapter 5. https://hbiostat.org/rmsc

Sakay, Y. N. (2026, September 14). The TAME trial: Can a common diabetes drug extend human life span? Medical News Today. https://www.medicalnewstoday.com/articles/tame-trial-common-diabetes-drug-extend-human-life-span-aging

Searle, S. D., Mitnitski, A., Gahbauer, E. A., Gill, T. M., & Rockwood, K. (2008). A standard procedure for creating a frailty index. BMC Geriatrics, 8, 24.

Sehgal, R., et al. (2026). Responsiveness of epigenetic aging biomarkers to longevity interventions in humans. Nature Medicine, 32, 3477-3490. https://doi.org/10.1038/s41591-026-04562-9

U.S. Food and Drug Administration. (2011, March). Hypertension indication: Drug labeling for cardiovascular outcome claims [Guidance for industry]. https://www.fda.gov/media/134777/download

U.S. Food and Drug Administration. (2021, July 7). About biomarkers and qualification. https://www.fda.gov/drugs/biomarker-qualification-program/about-biomarkers-and-qualification

U.S. Food and Drug Administration. (2025, December 19). FDA qualifies total hip bone mineral density (BMD) as surrogate endpoint for osteoporosis drug development. https://www.fda.gov/drugs/drug-safety-and-availability/fda-qualifies-total-hip-bone-mineral-density-bmd-surrogate-endpoint-osteoporosis-drug-development

U.S. Food and Drug Administration, CDER and CBER. (2026, April 29). Table of surrogate endpoints that were the basis of drug approval or licensure. https://www.fda.gov/drugs/development-resources/table-surrogate-endpoints-were-basis-drug-approval-or-licensure

Waziry, R., et al. (2023). Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. Nature Aging, 3, 248-257.

Westbury, L. D., et al. (2024). Predictive value of sarcopenia components for all-cause mortality: Findings from population-based cohorts. Aging Clinical and Experimental Research, 36, 126. https://doi.org/10.1007/s40520-024-02783-x

The number is a pace of aging, read by a blood test called DunedinPACE. On its scale, 1 is the calendar’s own pace: a year of body-wide decline for each year lived. Her 1.1 reads as aging 10 percent faster than that, and a 0.9 would read 10 percent slower. By the scale’s own arithmetic, ten calendar years at 1.1 would hold eleven years of decline. She sticks the card to the fridge with a magnet. The card does not say what either number means for the years ahead. It does not say whether pulling 1.1 down to 0.9 would buy her one more healthy year. What would it take for her 1.1 to mean something, to her or to the agency that approves drugs?

Part one’s brain plaque cleared from a scan while the patient got worse, and part two found no row for aging on the FDA’s list of accepted stand-ins. A stand-in is a number a drug may move in place of the patient’s own health, and statisticians call it a surrogate endpoint. A scan, a table and now a mailbox: in each, a number waits for proof that it stands for the years.

A drug company holding a pill that might slow aging faces the problem the woman with the card faces. No number it could move would count toward approval. Say its pill pulled a trial’s pace from 1.1 to 1.05 in two years. The FDA’s list has no row for that number. To prove the pill works, the company has to wait for diseases and deaths to pile up, while the clock on its patent keeps running. At the FDA, a reviewer handed that 1.05 would have nothing to check it against. The woman’s 1.1, the company’s 1.05 and the reviewer’s blank page are one gap, seen by three people.

The card is one corner of a larger system, with a regulator, drug makers, insurers, trials and statisticians, each pushing on the others. Figure 1 draws that system as a map of what pushes what. The mailbox is the dashed box at its right edge.

Figure 1. The map: four loops that keep drugs for aging from approval, and the arrow nobody has drawn. Each box is something that can rise or fall, and each arrow says what pushes it: a plus means the two move together, a minus means one pulls the other down. R1 and R2 feed themselves; B1 and B2 hold things back. The boxes carry the field’s terms; a validated surrogate endpoint is a proven stand-in. The red box, proof gathered across many trials, is the only box on three loops. The dashed note, tests sold direct to consumers by mail, is a pressure, not a loop. Drawn for this piece; the loop structure is this piece’s own reading of the sources named in the text.

Every loop on the map is stalled, and one box on it is empty. Why that box, and what would fill it?

You finished a month of a new routine last night: smaller dinners, a walk after work, a capsule swallowed with breakfast coffee. Your first card said 1.1. Your partner took the test too, at that kitchen table, and your doctor, shown both cards, had nothing to read them against. The order page for a second kit glows on your phone, $499 again. Two $499 readings can show the number fell, but can they show the fall bought a single healthy year? No trial yet says. Tap order, and $998 will have measured a month of dinners on a scale with no years printed on it.

A Map of What Pushes What

On July 6, 2023, the FDA fully approved the Alzheimer’s drug lecanemab. Medicare answered the same day. Its statement opened: “Broader Medicare coverage is now available.” The coverage came with a condition: a place for each patient in a registry that Medicare helps run. The head of the agency that runs Medicare, Chiquita Brooks-LaSure, put the trade in one line. The agency would “cover this medication broadly while continuing to gather data that will help us understand how the drug works.” Approval turned into coverage.

Coverage turns into sales, and sales pay for the maker’s next trial, which can bring the next approval. Those four steps close a circle, and each turn of it makes the next turn easier. On Figure 1, that circle is the loop marked R2. Donella Meadows, a scientist who studied how systems behave, defined loops like it in one line.

Maps like Figure 1 call the first kind balancing, marked B, and the second reinforcing, marked R. A reinforcing loop “exhibits amplified or spiralling behaviour,” in the words of one guide for health researchers. The whole drawing is a causal loop diagram, a picture of “what actions or mechanisms drive behaviour in a system.”

R1, on the map’s left, is the loop drugs for aging need first, and today it turns backwards. Nothing is proven, so there is no route to approval. Without a route, little money goes in. Without money, no trial runs long enough to prove anything.

A map like this is drawn to find the box worth pushing. People who study systems, Meadows wrote, “have a great belief in ’leverage points.'”

On Figure 1, which box is that place?

Someone comparing two health plans’ lists of covered medicines at open enrollment is looking at the end of R2. Look for a medicine on one list and not the other, and the year it was approved. Coverage follows approval, so a medicine with no route to approval is on no list at all. Miss the difference, and a needed medicine stays uncovered for a whole plan year. This fall’s open-enrollment packet, thick in the mailbox, is the cue. For pills against aging, both reinforcing loops are stuck. What holds them still?

What Holds It Still

TAME, part two’s trial of the diabetes pill metformin, has waited years for money and is still raising it. Part of what holds TAME back is a clock. Money for a pill against aging means a trial long enough to count diseases and deaths. The longer the trial has to run, the more of the patent’s life it eats, and the less the pill is worth making.

Follow the link out of sponsor money the other way on Figure 1, and it runs into that clock. That path is B1, a balancing loop that works like a brake: the harder investment pushes, the harder the clock holds it back.

The second brake is memory. When a drug is approved on a stand-in and the stand-in later fails, regulators and insurers grow wary of the next one. Part one’s plaque, accepted although its own reviewer found no evidence that it stood in for patients, is that kind of case. Doubt raises the bar for any stand-in, and a higher bar means fewer approvals. That loop is B2, and it makes the obvious fix a trap. Approve on a weak stand-in to speed things up, and B2 tightens. Demand long outcome trials instead, and B1 holds the pill back.

Someone on a team whose last shortcut failed knows both brakes. Every proposal since has needed twice the proof, and the proof takes months. At the next planning meeting, coffee going cold on the table, the question is which brake holds the plan, the clock or the memory of last time. What evidence, gathered once, would loosen both brakes?

The Arrow

One red box on the map loosens both brakes, and it is the only box on three loops. It holds proof, gathered across many trials, that a drug’s effect on a marker, a number such as DunedinPACE, predicts its effect on years lived well. Drawn out, the box is an arrow from the marker to the years.

Fill it, and R1 starts turning forward: a proven stand-in opens a route, and money follows. B1 loosens, because a proven stand-in shortens the trial. B2 stays slack, because proof up front gives doubt nothing to feed on. The box is a leverage point of the kind Meadows ranked sixth on her list, “the structure of information flows.” “Missing feedback is one of the most common causes of system malfunction,” she wrote. “Adding or restoring information can be a powerful intervention, usually much easier and cheaper than rebuilding physical infrastructure.” The box holds evidence, not money or law. A statistician can draw it.

The usual first check on a marker is whether people with a slower pace of aging live longer, and DunedinPACE passes it. The statistician’s turn is to change what a dot is: put the trials on the axes, not the patients. In Figure 2, each dot is one randomized trial. Across is the trial’s effect on the marker. Up is that trial’s effect on the outcome. If the dots line up, a new trial’s marker effect predicts its outcome effect.

Figure 2. The arrow, drawn: one dot per trial. Across: how much each trial slowed DunedinPACE at 1 to 2 years. Up: its effect on years without a second chronic disease or death. The blue line is fitted to the dots, and the shaded band shows where a new trial’s dot is predicted to fall. The red mark is the smallest marker effect that predicts any benefit. Hollow dots are one mechanism, left out to test the line. A schematic drawn for this piece, with no data: no trial yet has both axes, and every dot is invented.

Its measure is R²trial, which says how tightly the dots hug the line: 1 means a trial’s marker effect predicts its outcome effect perfectly. A second measure asks a sharper question. How big a drop in the marker must a new trial show before it predicts any benefit at all?

On Figure 2, it is the red mark, where the band’s lower edge crosses no benefit. One more test asks whether the line works for any kind of treatment. Take away every trial of one mechanism, the hollow dots, and ask whether the line still predicts them. A marker that passes earns the label part two’s table gives blood pressure, mechanism agnostic: it works whatever way a drug acts.

For blood pressure, this arrow was drawn once across drug classes. The FDA’s 2011 guidance says so, in the sentence after the one part two quoted. “Numerous meta-analyses and a few large trials have found no consistent differences by class in effects on survival, myocardial infarction, or stroke,” it says. That held “for regimens achieving the same blood pressure goals, but some differences may exist.” A meta-analysis pools many trials into one analysis, and here each drug class is one mechanism’s group of dots. “No consistent differences by class” is the line still predicting a class when that class is set aside.

For aging, the dots are not there. Part two’s trial, CALERIE, slowed DunedinPACE 2 to 3 percent. Its authors wrote that this small effect “may be substantive” and cited another study. In their words, “in an independent study of older adults, 3% slower DunedinPACE is associated with a 15% lower risk of death.” That is an association among people, a dot on a chart of patients. It is not a dot on Figure 2. The 51 treatment studies in part two’s 2026 review give the horizontal axis only: aging tests, with no years lived well to put on the vertical one.

A parent reading the school district’s report card can treat each school as one dot. One school raised its test scores the most. The better question covers every school: did the schools that raised scores most also raise how many students finish? If they did, a jump in scores predicts a jump in finishing. This fall’s parent-teacher conference, before next year’s school choice, is the place to ask. For aging, which trials would make the dots, and which years would they count?

The Question

The 1.1 on the card from the mailbox is one woman’s number, not a trial’s. In my question, each trial gets a number like it, on Figure 2’s horizontal axis. My question: across randomized trials of treatments that work in different ways, does a trial’s effect on DunedinPACE predict its effect on what I would call multimorbidity-free survival?

That outcome counts the years before a second chronic disease, or death. Multimorbidity means two or more chronic conditions in the same person.

Why this question is mine comes down to one line. Proving that a surrogate works across multiple trials matters because a drug can successfully change the stand-in without actually saving or improving human lives. A question built on the arrow is built to catch a stand-in like that.

The trials are finished randomized trials of adults, with blood stored at the start and later, and long follow-up for disease and death. The interventions come from at least three mechanisms, such as caloric restriction, metformin, a structured lifestyle program and exercise. The marker is the change in DunedinPACE at 1 to 2 years. The outcome needs an estimand: a statement of what the trial sets out to estimate, with a rule for what happens after randomization. Events after randomization, such as quitting the routine, are what the guideline calls intercurrent events. For most of them, I would use this strategy.

Put the woman with the card into such a trial. If she drops her routine in the second year, or her doctor starts her on a statin, she still counts in the group chance put her in. Death is different. The guideline says the treatment policy strategy “cannot be implemented for intercurrent events that are terminal events,” since nothing is measured after them. So death goes into the outcome itself, the way a composite outcome counts it: a second chronic disease or death, whichever comes first.

Someone reading a fitness program’s ad, which reports how much the members who finished lost, can ask the question that strategy asks. How did everyone who started do, including the ones who quit or got sick? If half the starters quit, the ad describes half the people who paid. The next sign-up offer in the inbox, its before-and-after photos side by side, is the cue. Where is the blood for such trials, and what would the analysis do with it?

The Plan, Item by Item

In a federal repository, freezers hold blood from a diabetes prevention trial. The agency that runs it wrote on its blog in 2017 that the repository “houses vetted data, genetic samples, and an array of biologic specimens” from many studies. One is the Diabetes Prevention Program (DPP) and its Outcomes Study (DPPOS), which tested a lifestyle program and metformin, two mechanisms the question needs. A literature search turned up no DunedinPACE analysis of those samples. They are a candidate for dots, not a result.

The plan for those dots is part one’s checklist, turned around: the steps that would draw the arrow. Part one ran these tests on the plaque after the fact. Here they are set before any data.

1. Within each trial. Does a person’s change in DunedinPACE go with that person’s outcome? This is the patient-level test part one’s reviewer ran on the plaque, the Prentice criterion. It is needed, and not enough.
2. Across trials. A meta-analytic model across trials fits the line on Figure 2, and reports R²trial and the surrogate threshold effect.
3. Across mechanisms. The hollow-dot test from Figure 2 has a name, leave-one-mechanism-out prediction. It is a grouped form of leave-one-out cross-validation. In that method, one observation is held back, the model is fitted to the rest, and the held-back one is predicted. Here the held-back group is every trial of one mechanism. Part one’s reviewer split the drug’s effect by gene, carrier or not; here the split is by mechanism.
4. A negative control. In part one, a drug cleared the plaque on the scan, and the patient did not do better. Epidemiologists build that case in on purpose and call it a negative control. Its purpose is “to reproduce a condition that cannot involve the hypothesized causal mechanism,” in the words of Lipsitch and colleagues. Yet it should be “very likely to involve the same sources of bias that may have been present in the original association.” Here it is a trial that moves DunedinPACE with no plausible route to health. If the line, fed that trial’s drop in DunedinPACE, predicts a benefit the trial never showed, the marker can move without the patient, as the plaque did.
5. Rules set before the data. The plan is registered and locked before any DunedinPACE reading is linked to any outcome, as part one’s rules required. It names in advance the tipping-point analyses: how badly the people who left would have to fare before the answer flips.

The plan’s main risk is power, the chance of finding a link that is really there. A line fitted to a handful of trials has wide bands, so a real link can hide. A Bayesian hierarchical model is the plan’s answer. It treats each trial’s effect as drawn from one shared spread of effects, so a few small trials borrow strength from each other. Even a careful “not yet” from the hierarchical model would say how large the next trials must be.

Someone handed a consent form for a research blood sample, at a clinic visit or in a study invitation, holds part of one future dot on Figure 2. The step is to read it, and to ask whether the sample will be linked to health records for years. The question is worth forwarding to anyone who runs a study that keeps blood. Who links the blood to the years? That link is the arrow, and until someone draws it, each frozen tube is half a dot.

A Closing Invitation. The card stood for every number sold or approved before anyone showed that moving it moves the years. What aging lacks is one arrow of evidence, drawn across many trials, that makes a marker a stand-in for the years.

1. Name the year. Now, if a health app is open on your phone, look at one number it tells you to change, such as steps or resting heart rate, and at what you give up each week to move it. Which year of your life is that number meant to stand for? Does anything on the glowing screen say?
2. Ask how many trials. This month, about the last kit, supplement or plan you paid for to move a number, ask the seller or your doctor one question, and hear how long the pause is. In how many trials did changing this number change how people did? What did it cost you?
3. Give a tube its years. The next time a consent form for research blood reaches you, at a clinic or by mail, ask whether the sample will be linked to your health records for years. A linked tube helps the next patient; one without costs a needle and answers nothing. What would your tube need to become a dot?

The plaque left the scan while the patient got worse. Until a linked tube becomes a dot, and enough dots draw the arrow, the card on the fridge cannot show that its 1.1 is not another plaque.

Where This Came From

I drew the map from the homework that made parts one and two: the review read section by section, then the table read column by column. The trial-level test I lean on grew up in cancer trials; Buyse and colleagues’ 2016 review works through its examples there.

Intellectual Honesty Note. Figure 1’s loops are this piece’s own reading of the sources it names: a causal loop diagram, not a model with estimated links. Figure 2’s dots, line and band are invented, and its vertical axis is simplified. A real analysis would plot each trial’s hazard ratio on a log scale; here the axis is glossed as the effect on years without a second chronic disease or death. Leaving out one mechanism at a time is this piece’s own reading; the surrogacy papers validate across trials, not across mechanisms by name.

Using a negative control on a trial-level model is this piece’s own reading; Lipsitch and colleagues write for observational studies. Multimorbidity-free survival is my own name. It is built from the WHO’s definition of multimorbidity and ICH’s composite strategy, and no source I searched defines it as a trial outcome. Reading the 2011 guidance’s class meta-analyses as the arrow drawn once, informally, is this piece’s own reading; the guidance does not call them a surrogacy analysis. The 15 percent is an association in another study, as CALERIE’s authors report it, not a trial result.

The seller’s page never says that 1 is the calendar rate, so the scale’s meaning is taken from the literature. The people in the scenes are invented: the woman and her household, the company and its reviewer, and the reader in each example.

References

Burzykowski, T., & Buyse, M. (2006). Surrogate threshold effect: An alternative measure for meta-analytic surrogate endpoint validation. Pharmaceutical Statistics, 5(3), 173-186.

Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., & Geys, H. (2000). The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics, 1(1), 49-67.

Buyse, M., Molenberghs, G., Paoletti, X., Oba, K., Alonso, A., Van der Elst, W., & Burzykowski, T. (2016). Statistical evaluation of surrogate endpoints with examples from cancer clinical trials. Biometrical Journal, 58(1), 104-132.

Cassidy, R., Borghi, J., Semwanga, A. R., Binyaruka, P., Singh, N. S., & Blanchet, K. (2022). How to do (or not to do)… using causal loop diagrams for health system research in low and middle-income settings. Health Policy and Planning, 37(10), 1328-1336. https://doi.org/10.1093/heapol/czac064

Centers for Medicare & Medicaid Services. (2023, July 6). Statement: Broader Medicare coverage of Leqembi available following FDA traditional approval. https://www.cms.gov/newsroom/press-releases/statement-broader-medicare-coverage-leqembi-available-following-fda-traditional-approval

Diabetes Prevention Program Research Group. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine, 346(6), 393-403. https://doi.org/10.1056/NEJMoa012512

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press.

International Council for Harmonisation. (2019). E9(R1) addendum on estimands and sensitivity analysis in clinical trials. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning (2nd ed.). Springer.

Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010). Negative controls: A tool for detecting confounding and bias in observational studies. Epidemiology, 21(3), 383-388.

Meadows, D. (1999). Leverage points: Places to intervene in a system. The Sustainability Institute. (A shorter version appeared in Whole Earth, winter 1997.) https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/

National Institute of Diabetes and Digestive and Kidney Diseases. (2017). Dig data without dollars. Diabetes Discoveries & Practice [Blog].

Sehgal, R., et al. (2026). Responsiveness of epigenetic aging biomarkers to longevity interventions in humans. Nature Medicine, 32, 3477-3490. https://doi.org/10.1038/s41591-026-04562-9

TruDiagnostic. (n.d.). TruAge complete epigenetic collection. Retrieved October 9, 2026, from https://shop.trudiagnostic.com/products/truage-complete-epigenetic-collection

U.S. Food and Drug Administration. (2011, March). Hypertension indication: Drug labeling for cardiovascular outcome claims [Guidance for industry]. https://www.fda.gov/media/134777/download

Waziry, R., et al. (2023). Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. Nature Aging, 3, 248-257.

World Health Organization. (2016). Multimorbidity: Technical series on safer primary care.

The drug was aducanumab, and the FDA, the US agency that approves medicines, approved it early. Plaque is clumps of a sticky protein that build up between nerve cells in the brains of people with Alzheimer’s disease. The clumps show up on a PET scan, a brain picture taken after an injection of a tracer that sticks to them. Clearing the plaque was meant to stand in for a slower loss of memory, years before anyone could see the memory itself. Two large trials had tested the drug, and both were built the same way, to follow each patient for 78 weeks. Both were stopped halfway, in March 2019, because neither looked likely to win. Then the rest of the data came in. One trial said yes, and the other said no.

The FDA’s statistician on the case, Dr. Massie, wrote the agency’s statistical review. It runs past a hundred pages and takes the trials one problem at a time. Its summary of the results opens with a single line: “Inconsistency on many levels summarizes the final clinical efficacy data from these trials.” Because both trials were stopped, the application “doesn’t contain a single phase 3 study that was fully completed according to the plan.” Halfway through the review is a plot of every high-dose patient whose plaque was measured (Figure 1). Each dot is one patient, and each color one trial. Across is how much plaque the patient lost by Week 78, and further left is more plaque gone. Up is how much worse the patient’s dementia score got.

Figure 1. Plaque change against dementia-score change at Week 78, for every high-dose patient in the two trials’ plaque-scan groups: trial 301 in blue, trial 302 in red. The red dot at the far left is the patient who lost the most plaque. FDA statistical review of BLA 761178 (Massie, 2021), Figure 18, p. 55. A work of the US government, public domain.

That patient was in trial 302, the trial that said yes. In both trials, the drug cleared about the same amount of plaque. In one trial, patients on the drug did a little better than patients on a dummy drug. In the other, they did a little worse. The scan told one story twice, and the patients told two. How did a yes and a no, from two trials built the same, become a yes?

Your father’s memory-clinic visit is three weeks away, and the referral letter is folded in your coat pocket. He still hums the songs from his wedding, but he asks the date twice an hour. The doctor may offer a drug that clears plaque, with a scan to show it working. You would be the one driving him to the infusions. In the trial that won, over 78 weeks, scores on the dummy drug worsened 1.74 points and on the high dose 1.35. Eighteen months of drives bought 0.39 points; would his clean scan mean he was doing better? Say yes at the visit, and the drives start before anyone can answer.

One Yes and One No

On March 21, 2019, a press announcement stopped both trials. Each had enrolled about 1,640 people with early Alzheimer’s disease, split among a dummy drug, a low dose and a high dose. A check of the data so far gave each trial less than a 20 percent “chance of success” if it ran to the end. Stopping a trial early for that reason is called stopping for futility.

When the scores from the closing visits came in, the picture flipped. In the review’s words, the final analysis “on face showed a statistically significant effect for the high dose in one of the two trials (p=0.01) but not the other (p=0.83).”

Peter Stein, director of the FDA’s Office of New Drugs, named the score: “the Clinical Dementia Rating-Sum of Boxes (CDR-SB), the standard clinical trial endpoint in AD.” In trial 302, the high dose slowed the worsening by 0.39 points against the dummy drug. The review gives that gap a 95 percent range. The range runs from 0.09 to 0.69 points, so the true slowing could be small. A p-value of 0.01 says a gap that big would turn up about once in a hundred tries if the drug did nothing. In trial 301, patients on the high dose did 0.03 points worse than the dummy drug, a result chance explains easily. Figure 2 sets the two trials side by side, the stand-in above and the scores below.

Figure 2. Same drop, opposite scores. Top: the fall in plaque at Week 78, high dose against the dummy drug, in trial 301 (0.24) and trial 302 (0.28). Bottom: the difference in CDR-SB at Week 78, 0.03 worse in 301 (p = 0.83) and 0.39 better in 302 (p = 0.012). Drawn for this piece from the FDA statistical review.

US drug law asks for a particular kind of proof before a drug can be sold.

The law allows one strong trial plus other proof, so one yes can be enough. Dr. Massie counted “only one positive study at best”. The review’s verdict was plain: “substantial evidence has not been met in this application.”

A headline that pings into the family chat says a new drug “worked.” It names one trial. The reader’s question is how many trials there were, and what the other one found. Which of two results counts depends on rules written before the data came in.

Rules Set Before the Data

Biogen, the drug’s maker, wrote back to the FDA with a new reading of its own rules. The trials’ written plan tested two doses, a low one and a high one, on the main score first and then on the scores after it. The plan said what had to win before the next test could run. The maker argued “that if the high dose was significant on the primary then it could be tested on the secondary regardless of the primary result for the low dose.”

Dr. Massie did the arithmetic. Read that way, the chance of at least one false win “could be as high as .0975.” The plan had been built to hold that chance to 0.05, or one in twenty. The new reading nearly doubled it. “For this reason strong control is needed,” the review answered. The FDA has a name for that rise.

A Type I error is a false win: calling a drug better when it is not. Writing the order down first works like calling a pool shot: a ball that drops into a pocket nobody called does not count. A test the plan did not name first is that uncalled ball, and it may have dropped by luck.

A second rule covers when the data stop changing. In trial 302, staff were still correcting patient records after the blind was broken, after the team learned who had the drug. A second memory test moved from p = 0.0620 to p = 0.0493 while they did. The line it crossed was 0.05.

A team that checks a dashboard against six goals each quarter meets the multiplicity problem too. A goal that has not truly changed still has a one-in-twenty chance of looking as if it moved. By this piece’s own arithmetic, six such goals give about a one-in-four chance of a lucky win somewhere. If only the goal that moved gets reported, the team can spend the next quarter chasing luck. The question for the next quarterly review deck is which goal was named first, before the numbers came in. Rules decide which test counts. They cannot say which patients the win belongs to.

Who the Drug Helped

While trial 302 was running, a change to its plan raised the dose for one group of patients. Protocol amendment 4 “increased the dose from 6 mg/kg to 10 mg/kg for APOE carriers.” APOE is a gene, and ε4 is its risky form, or allele. The US National Institute on Aging puts it this way: “APOE ε4 increases risk for Alzheimer’s and is associated with an earlier age of disease onset in certain populations. About 15% to 25% of people have this allele.”

Dr. Massie saw a problem in the timing. Patients on the dummy drug who joined after the change seemed to decline faster. A worse dummy group makes any drug look better. “The study 302 success could be explained by a higher placebo progression after the implementation of protocol amendment 4.” Peter Stein read the data and disagreed. In his memo, the effects before and after the amendment “are very similar, not supporting the concern raised by Dr. Massie on this point.”

An effect that depends on something else about the patient has a name in trials.

An interaction works like a fertilizer that feeds the tomatoes and wilts the basil. One average for the whole garden hides two answers. The garden differs in one way: in trial 302, the gene also changed a carrier’s dose.

The review found three such splits. On a second memory test, high-dose patients without the gene did worse than the dummy drug, and the split between carriers and non-carriers gave p = 0.0096. That is a change of direction, the qualitative kind. The effect also changed by country (p = 0.0095). Leaving out Japan moved that to 0.040. Leaving out Spain alone took it to 0.0599, past the 0.05 line. The third split was the amendment itself.

An adult child whose parent has just had a genetic test at the memory clinic can use the gene split. The thing to look for is whether the drug’s effect was shown for people with the parent’s result: a carrier, like about one person in five, or not. The follow-up visit, when the result is read out, is the moment to ask. Every one of these splits counted only patients who reached Week 78.

The Patients Who Left

For almost half the patients, Week 78 never came. About 45 percent of the patients enrolled “did not have the opportunity to complete Week 78 due to the futility stopping of the trials.” The patients did not leave. The trial left them, on March 21, 2019. The stop that flipped into a yes is also the hole in the trials’ data.

What to do about a missing score depends on what the trial set out to estimate, and what counts when something interrupts a patient’s course. Someone has to guess how the missing patients would have scored, then check whether the answer survives a different guess. An international trial guideline, ICH E9(R1), calls that check a sensitivity analysis.

The review ran one kind, called a tipping point. It asks how much worse the missing patients would have to be before the win disappears. The answer was small. If high-dose patients with no Week 78 score did a little more than 0.3 points worse than those who finished, the result was no longer significant. The whole effect was 0.39 points. A result that tips over at 0.3 is standing on one foot.

A manager opening a staff survey with nearly half the team’s rows blank is missing answers too. The thing to look for is how the result would change if the silent half felt a little worse than those who wrote back. The share who did not answer sizes the risk, and the next survey results email is the cue. The plaque scans, the trials’ stand-in for memory, had a test of their own to pass.

What the Scan Stood In For

The June 2021 approval rested on the plaque, through a faster route. The rule, as it read when the drug was approved:

In the rule’s words, the plaque was the surrogate endpoint: a stand-in for how a patient feels, functions or survives. Peter Stein’s memo gives the rule’s plain version. The pathway “is intended to provide earlier access to drugs for serious diseases with unmet medical needs.” It is used “where there is some uncertainty at the time of approval regarding the drug’s ultimate clinical benefit.” That is how one yes and one no became a yes: the plaque fell in both trials, and the rule let the plaque stand in.

A drug built to clear plaque should move the plaque. That is its job. The test of a stand-in runs the other way. Once the plaque change is known, is there anything left of the drug’s effect to explain? If the plaque carries the benefit, nothing should be left. Statisticians named that test after Ross Prentice, who wrote it down in 1989.

Dr. Massie ran the test. A PET scan turns the plaque into one number, which the review calls the SUVR. Added to the main analysis, each patient’s plaque change added nothing (p = 0.884). The maker’s own analysis said the plaque explained 33 percent of the high dose’s effect and 36 percent of the low dose’s. Its ranges ran down to zero. How much of the effect the plaque explains, if any, is not known. Among high-dose patients, the plaque change and the score change were “essentially uncorrelated.” The review concludes: “there is no evidence that the SUVR change is a surrogate for clinical change.”

Back on Figure 1, the cloud of dots lies flat. If the plaque stood in for the patients, the dots would slope down toward the left: more plaque gone, less decline. The red dot at the far left is no longer a surprise. It is the test’s answer, drawn by one patient.

Someone opening a patient portal to a lab result marked “improved” on a new medicine can ask the test’s question. Has this number been shown to move when patients feel, function or live better, or only when the drug is taken? One number on a report can decide a year of refills. The next lab result is the cue, and the question is worth passing to the sibling who drives a parent to the memory clinic. “Reasonably likely” was enough for the approval. Which stand-ins has the agency accepted, and on what proof? On the far left of Figure 1, one red dot stays three points up, beside a scan that came back clean.

A Closing Invitation. The plaque that was gone stood for every number a drug moves before anyone has shown it moves with the patient. A stand-in earns its place only when it explains what the drug did to people.

1. Read one number. Now, with the phone in your hand, open the last lab result in your patient portal, a cholesterol reading or a blood sugar. What does that number stand in for: a heart attack, a lost toe, a tired afternoon? Is there one you were never told?
2. Ask about the other trial. The next time a drug headline lands in the family chat this month, type back one line, or ask it out loud over dinner. How many trials were there, and what did the other one find? Does a pill a relative already takes rest on one yes?
3. Write the question on the letter. Before a parent’s next memory-clinic visit, or your own next checkup, write one line in pen on the appointment letter: has this number been shown to move when patients do better? At the visit, before any infusion or refill is booked, listen: does the answer name a patient or a scan?

The plaque was gone from the scan, and the score still climbed. A line in pen asks the clean picture what Dr. Massie asked it: did it move with the person?

Where This Came From

I read the FDA statistical review of aducanumab, BLA 761178, section by section, with Peter Stein’s concurrence memo beside it. Different specialists review an FDA application, each in a distinct technical domain, and reading the review in order shows how the safety, manufacturing and clinical data build upon one another. I read some FDA reviews, not all the time, because I took classes on bioequivalence, and some examples in those classes require referring back to the FDA review. The aducanumab review’s last test is older than the drug. Three years after Prentice, in 1992, Freedman, Graubard and Schatzkin turned it into the share of an effect a marker explains, the number the maker reported for the plaque.

Intellectual Honesty Note. The test for the plaque is Freedman, Graubard and Schatzkin’s criterion as Dr. Massie applied it, checked against their abstract. The opening patient is one patient, an illustration of the flat cloud, not evidence alone; the faded bright patches are how an amyloid PET scan shows less plaque, not this patient’s own image. The plaque figures come from the trials’ scan groups, a subset of patients. The statute quoted is the drug law; aducanumab is a biologic, and the review applies the phrase to this application. The father, the letter, the dashboard, the survey, the pool shot and the garden are invented. The one-in-four is this piece’s arithmetic, assuming six independent goals. The p-value readings are simplified.

References

21 C.F.R. § 601.41. Legal Information Institute. https://www.law.cornell.edu/cfr/text/21/601.41

Federal Food, Drug, and Cosmetic Act § 505(d), 21 U.S.C. § 355(d). Legal Information Institute. https://www.law.cornell.edu/uscode/text/21/355

Freedman, L. S., Graubard, B. I., & Schatzkin, A. (1992). Statistical validation of intermediate endpoints for chronic diseases. Statistics in Medicine, 11(2), 167-178. https://doi.org/10.1002/sim.4780110204

International Council for Harmonisation. (1998). E9: Statistical principles for clinical trials. https://database.ich.org/sites/default/files/E9_Guideline.pdf

International Council for Harmonisation. (2019). E9(R1) addendum on estimands and sensitivity analysis in clinical trials. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf

Massie, T. (2021). Statistical review and evaluation [BLA 761178, aducanumab] (with concurrence by K. Jin, S.-J. Wang and J. Hung). U.S. Food and Drug Administration, Center for Drug Evaluation and Research. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2021/761178Orig1s000StatR_Redacted.pdf

National Institute on Aging. (2023). Alzheimer’s disease genetics fact sheet. https://www.nia.nih.gov/health/genetics-and-family-history/alzheimers-disease-genetics-fact-sheet

Prentice, R. L. (1989). Surrogate endpoints in clinical trials: Definition and operational criteria. Statistics in Medicine, 8(4), 431-440.

Stein, P. (2021, June 7). [Concurrence memorandum], BLA 761178. U.S. Food and Drug Administration, Office of New Drugs. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2021/Aducanumab_BLA761178_Stein_2021_06_07.pdf

U.S. Food and Drug Administration. (2022). Multiple endpoints in clinical trials: Guidance for industry. https://www.fda.gov/media/162416/download