Mostly a compilation. Every row of the table below is copied from a claim page with its own source; that part combines nothing. The point of the page is the shape of what is held and what is missing — per the telos, the ceiling is itself a finding.
Superseded 2026-07-28. This header previously read “a compilation, not a synthesis… nothing here combines sources”, and
sources:was empty. The final section now carries a source of its own (NNR/GBD) and makes a distinction the compiled rows do not contain — population attributable burden is not individual expected benefit. That belongs on this page because it is a claim about what layer-1 ranking is, not about any one exposure. The compilation description is retained for the table; it no longer describes the whole page.
Worked against one illustrative stratum — a man of ~60 with central adiposity and metabolic syndrome, no established CVD — because the telos names “obesity with visceral adiposity and hypertension” as a dominant exposure and a hierarchy is meaningless without a stratum to hold it fixed.
The stratum is a PARAMETER of this page, not its subject. The fabric is general; only its application is personal. Titling a page like this for a specific individual’s age and condition would invert that — the telos is explicit that the wiki “is never built around one person’s situation”, and that stratification and outcome-weighting belong to layer 3, per case, after the fact. Any actual person’s values live outside the graph, never in it.
The honest answer first
The wiki cannot yet rank these interventions against one another, and the reason is specific: it holds no baseline risk for this stratum, and no magnitude at all for the two largest levers.
Layer 1 ranks by effect size x certainty, and effect size means absolute effect at a stated baseline risk. Of eight exposures below, one has an absolute effect on a hard outcome.
What is held
| Exposure | Effect | Outcome type | Certainty | Source page |
|---|---|---|---|---|
| Quit smoking (vs continue) | all-cause HR ~3.0; ~10 yr of life recovered if quit by 40 (removes ~90% of excess risk); still pays at any age | hard, mortality | High | Smoking and Mortality |
| Reduce SFA to 10%E | 15 fewer CVD events per 1000 (RR 0.83, 0.70-0.98) | hard, composite | Moderate | Saturated Fat Intake and Replacement |
| Replace SFA with PUFA | 50 fewer CVD per 1000 — at a 23.8% control event rate | hard, composite | Low | Saturated Fat Intake and Replacement |
| Low-carb for T2D remission | RD 0.32 at 6 mo, NNT ~3; attenuates to 0.10 by 12 mo | intermediate (HbA1c-defined) | Moderate | Carbohydrate Restriction and Type 2 Diabetes Remission |
| Low-carb, glycaemic control | HbA1c -0.47% at 6 mo | surrogate | High | Carbohydrate Restriction and Type 2 Diabetes Remission |
| Very-low-carb (NICE stratum) | weight -2.38 kg at 1 yr; HbA1c -0.38 | intermediate + surrogate | Very low / Low | Diets for Weight Loss - What NICE Recommends |
| Low-carb vs balanced-carb, weight | ~1 kg — judged not clinically important | intermediate | Moderate | Low-Carbohydrate vs Balanced-Carbohydrate Diets |
| Reduce sodium | -3.39 mmHg systolic; hard outcomes all very low | surrogate | High (BP only) | Sodium Intake and Blood Pressure |
| Reduce free sugars | isoenergetic swap for other carbohydrate: null (0.04 kg) | intermediate | Moderate | Free Sugars Intake |
Read the outcome-type column before the effect column. Only the first two rows are hard outcomes. Everything else is an intermediate or a surrogate, and the telos is explicit that a surrogate is not an outcome — a marker can move the right way while patients do worse.
Within-diet food-group ranking — the cross-outcome dose-response grid at Food Groups and Health Outcomes - A Dose-Response Matrix ranks 12 food groups against 5 outcome families at once, so the direction-consistency of a food lever is visible at a glance: whole grains protect across all five (two HIGH cells), processed and red meat harm across all five (the most consistent harmful levers), while SSB, dairy, fish and eggs are outcome-specific. It is the food-level input to this stratum ranking; its effects are per-serving RRs on mostly-hard outcomes, all observational-grade and from one shared evidence base (not independent).
What is held as a RECOMMENDATION but with no magnitude
- Total diet replacement, 800-1200 kcal, maximum 12 weeks — Diets for Weight Loss - What NICE Recommends carries NICE’s recommendation, its bounds and its strength, but not its effect size. The review reports one; this wiki never extracted it. The cheapest single fix on this page.
What is EMPTY — and this is the finding
| Lever | Status | Why it is empty |
|---|---|---|
| Physical activity | ratios only, no absolutes | The WHO guideline, its 535-page evidence annex and the 779-page PAGAC report all report hazard and risk ratios and no absolute effects for adult mortality/CVD/cancer/T2D. Staged, not ingested |
| FILLED 2026-07-28 — as a NULL/ceiling | Does Weight Loss Reduce Cardiovascular Events (Look AHEAD): lifestyle weight loss did NOT reduce hard CV events in T2D (HR 0.95). The lever is real for many outcomes; its hard-CV-event benefit is unproven via the lifestyle route | |
| Baseline risk | FILLED as machinery (2026-07-26) | SCORE2 Baseline Risk and the ESC Treatment Thresholds holds the ESC thresholds and a read-off grid. A stratum defined by age and condition alone cannot use it — the chart also needs smoking status, a BP band, a cholesterol band and a region, and the ESC category moves between high and very high across plausible values. Those four are case inputs, supplied at layer 3, not fabric |
| FILLED 2026-07-28 | Semaglutide for Cardiovascular Risk in Obesity (SELECT+STEP-1) + Statins for Primary Prevention and the Power of Zero CAC (USPSTF+Nasir). Both the realistic drug comparators the telos names. Key finding is a ceiling, not a lever: see below | |
| ALL HELD 2026-07-29 | Alcohol (Alcohol and Mortality and Vascular Disease) + sleep (Sleep Duration and Mortality) + smoking now ingested (Smoking and Mortality, Jha 2013) — the #1 dominant exposure, all-cause HR ~3.0 and a decade of life, now quantified and at the TOP of the ranked table above |
What this page establishes
- The one thing rankable is also the smallest lever on the list. SFA reduction has the only absolute hard-outcome effect the wiki holds — 15 fewer cardiovascular events per 1000 — and the telos’s own attention-is-an-anti-signal rule flags dietary fat composition as exactly the contested, heavily-published, small-effect question that literature volume inflates.
- The two largest levers for this stratum are unquantified, not unfavourable. Adiposity and inactivity are empty rows, and the telos names both as dominant.
- The rows are NOT commensurable and must not be read as a ranking. Different outcomes (CVD events vs kg vs mmHg vs HbA1c), different baseline risks (the 50-fewer-per-1000 PUFA row comes from a 23.8% event-rate population, roughly 3x the 8.5% in the row above it), different populations, different follow-up. A table that looks like a ranking and is not one is a hazard; it is laid out this way to show the gaps, not to order the interventions.
- The binding acquisition is a baseline-risk instrument, not another exposure. Every ratio already held becomes an absolute effect the moment a stratum-specific baseline risk exists. That is one ingest — the ESC threshold table plus SCORE2 — and it converts the whole corpus.
- For hard CV events in a low-risk person, the ceiling is now an established finding, not a gap. The pharmacotherapy and weight-loss rows filled in 2026-07-28 all land the same way — Cardiometabolic Interventions and Hard CV Outcomes in Low-Risk People: no cardiometabolic intervention the wiki holds (semaglutide, statin, or lifestyle weight loss) has a large, proven reduction in hard CV events for a low-risk primary-prevention person. So item 2 sharpens: the weight lever is no longer unquantified on hard CV outcomes — it is quantified as unproven/null via the lifestyle route, which is the ceiling itself.
What would change the answer
In priority order, all from already-staged material:
Ingest the ESC/SCORE2 baseline-risk layer.DONE 2026-07-26 — and it revealed the next blocker: this stratum is under-specified. To read a baseline at all it needs smoking status, a BP band, a cholesterol band and a region. Until those are stated the conversion is the author’s assumptions, not the person’s data.- Extract NICE’s total-diet-replacement magnitudes onto its claim page — the recommendation is held without its effect size.
- Ingest WHO Physical Activity + PAGAC and record, as a G-gap, that the absolute layer is structurally absent from the activity literature as it reaches guidance.
Limits
- One illustrative stratum, chosen to make the gaps concrete, not induced from the corpus. The telos warns the hierarchy is stratum-dependent and that a universal ordering is not the answer. Nothing here transfers to a lean 30-year-old — and re-running it for another stratum is the intended use, not a limitation.
- This page grades coherence, never validity (method-risks R1). It says what the wiki holds and what it lacks — not what is true, and not that acting on it improves any outcome.
- No independent re-checking was done for this page. Each row is carried from its claim page and inherits that page’s audit status — nothing more.
Activity enters the hierarchy with a magnitude [2026-07-27]
Layer 1 requires magnitudes comparable enough to make the ordering visible — “an effect stated only as ‘associated with’ cannot be ranked against anything.” Physical activity now has one, and it is large: HR 0.34 (0.27-0.43) at the maximum for device-measured total activity, HIGH certainty -> Physical Activity Dose and Mortality.
Two things this changes about the ranking, both stratum-dependent.
- The big-rock framing holds for the inactive. Near-total inactivity is already named as a dominant exposure; the magnitude now supports that placement rather than merely asserting it.
- For someone already active, the ceiling arrives early — and that is itself the finding. Maximal risk reduction is reached at ~24 min/day of MVPA. Above that, the curve flattens, so additional activity is a small lever competing with other small levers. Per the ceiling rule, “your remaining levers here are small” is a result that licenses someone to stop optimizing, not a failure to find something.
The caution that keeps this honest: the ranking is by expected effect x certainty, and this estimate is observational and between-quartile, so reverse causation is not excluded by design. A large HR from cohorts does not outrank a smaller one from trials automatically.
A published ranking arrives — on the wrong axis [2026-07-28]
NNR is the first source the wiki holds that publishes an explicit ordering of dietary risk factors for a defined population, via its collaboration with the Global Burden of Disease project:
«As described in the collaboration between the Global Burden of Disease project and the NNR2023 project, a diet low in whole grains is the highest- ranked dietary risk factor in the Nordic and Baltic countries. Across all countries, low whole grains diets are responsible for one fifth of the total burden of disease attributed to dietary factors and it is the greatest overall contributor to ischemic heart disease and colon and rectum cancer (Knudsen et al, 2025).» (Nordic Council of Ministers, 2023)
«Despite their conservative methodology, the collaboration between GBD and the NNR2023 project observed that a diet high in red meat is the fourth-highest dietary risk factor for Disability Adjusted Life Years (DALYs) in the Nordic and Baltic countries. It is ranked second highest in Denmark and Iceland» (Nordic Council of Ministers, 2023)
This looks like exactly what this page says it lacks. It is not, and the difference is the point.
| This page’s table | The GBD/NNR ranking | |
|---|---|---|
| Quantity ranked | absolute effect on one person in a stratum, per unit exposure change | attributable burden across a population (DALYs) |
| What drives the number | effect size x baseline risk | effect size x baseline risk x prevalence of the exposure |
| Answers | what do I gain by changing this? | what is costing this population the most? |
| Same quantity? | NO | NO |
Population attributable burden is not individual expected benefit, and the wedge between them is exposure prevalence. A risk factor can top a population ranking because it is common while offering a given individual a small gain — and a rare exposure with a large individual effect can sit far down the same list while being the dominant lever for the person who has it. The ranking answers a public-health resource-allocation question; layer 1 asks a personal one. Reading the first as the second is a unit-of-analysis error, and it is a seductive one because both are honestly called “ranking dietary risk factors”. (inferred from Nordic Council of Ministers, 2023)
What it does license, which is not nothing:
- A prevalence-weighted sanity check on where the wiki has spent its attention. Low whole-grain intake ranks first and red meat fourth in this population, while the wiki’s only rankable absolute effect is saturated-fat reduction. That is a coverage signal about the corpus, not a claim about any person.
- Stratum-dependence, demonstrated rather than asserted. The red-meat rank moves from fourth overall to second in Denmark and Iceland — the same exposure, the same evidence, a different ordering because the population differs. The telos’s “the hierarchy is stratum-dependent, not a fixed list” now has a worked instance instead of a statement.
- A named instrument to acquire.
Knudsen et al. 2025is the GBD/NNR collaboration paper and is not held; NNR reports its conclusions, not its method or its attributable fractions.AWAITSa source carrying population attributable fractions with their exposure-prevalence inputs — that, not another guideline, is what would let a population ranking be converted toward an individual one.
And a caution NNR supplies against its own ranking. The red-meat sentence opens «Despite their conservative methodology», and the preceding sentence records that the GBD review «concluded that there is a weak association between unprocessed red meat consumption and colorectal cancer, breast cancer, ischemic heart disease and type 2 diabetes». So a fourth-place burden rank coexists with a weak underlying association — which is the prevalence wedge visible inside NNR’s own text, and the cleanest available demonstration that a high rank is not a large individual effect. (Nordic Council of Ministers, 2023)
Self-critique of the NNR/GBD addition [run 2026-07-28, before commit]
- The seductive claim was NOT written. NNR hands this page a ranked list of dietary risk factors, and this page opens by saying it cannot rank anything. Presenting the GBD ranking as the fix would have closed the page’s central gap with the wrong quantity — and it would have read as the single most valuable find of the revisit. The parameter comparison was built first and returned NO.
- Unit-of-analysis check: this is the failure mode the table catches. Population attributable burden and individual absolute benefit are both honestly describable as “how much this exposure matters”, differ by a prevalence factor, and are reported in the same document.
- Prior-page reconciliation: done in the open. The header’s nothing here combines sources and
the empty
sources:were both falsified by this addition; the supersession is noted in place rather than silently rewritten. - Attribution split. The two rankings and the «Despite their conservative methodology» caveat are NNR’s, quoted. The prevalence-wedge argument is the wiki’s and is tagged.
- Absence claim, scoped. “the first source the wiki holds that publishes an explicit ordering of dietary risk factors” is a claim about the wiki’s holdings, verifiable from the registry, not a claim about the literature.
- Residual: the underlying GBD paper (
Knudsen et al. 2025) is not held. Everything here rests on NNR’s two-sentence report of it — no attributable fractions, no method, no confidence intervals. Recorded as anAWAITSrather than treated as a held magnitude.
The foundational source behind the ranking arrives — global, and it sharpens the wedge [2026-08-04, Afshin GBD 2017]
The NNR/GBD section above rested entirely on NNR’s two-sentence second-hand report of a Nordic GBD
collaboration (Knudsen et al. 2025, still not held), and closed with an AWAITS for “a source
carrying population attributable fractions with their exposure-prevalence inputs.” Afshin GBD 2017
(the global dietary-risks analysis) is that source at the global level — it supplies the primary
ranking, the attributable-burden numbers, and the method, first-hand rather than via NNR’s paraphrase.
It partly cashes the AWAITS: the global attributable fractions are now held; the Nordic-specific
ones (Knudsen) stay open.
The global ranking, in GBD’s own units (population-attributable deaths / DALYs, 2017):
«In 2017, more than half of diet-related deaths and two-thirds of diet-related DALYs were attributable to high intake of sodium (3 million [95% UI 1—5] deaths and 70 million [34—118] DALYs), low intake of whole grains (3 million [2—4] deaths and 82 million [59—109] DALYs), and low intake of fruits (2 million [1—4] deaths and 65 million [41—92] DALYs)…» (Afshin et al., 2019)
Globally dietary risks account for 11 million deaths (22% of adult deaths) and 255 million DALYs; low whole grains is the leading risk by DALYs in 16-17 of 21 regions. (Afshin et al., 2019)
This does NOT change the page’s central holding — it underwrites the distinction the NNR section made second-hand. GBD reports attributable burden, not individual expected benefit, and the wedge is still exposure prevalence. Whole grains tops the DALY ranking because near-universal low intake (global mean is 23% of optimal) multiplies a borrowed observational RR across billions — not because the per-person gain is large. The parameter table from the NNR section (population-attributable-DALYs vs individual-absolute-effect, Same quantity? NO) governs GBD’s numbers unchanged.
The sharpest instance of the wedge: diet outranks tobacco at the population level, while smoking is the corpus’s largest INDIVIDUAL effect. GBD states:
«Our findings show that suboptimal diet is responsible for more deaths than any other risks globally, including tobacco smoking…» (Afshin et al., 2019)
Set that against the top row of this page’s table: quitting smoking carries all-cause HR ~3.0 and ~10 years of life for a smoker (Smoking and Mortality) — the largest individual effect the wiki holds. Both are true, and reconciling them IS the wedge: diet’s attributable burden exceeds tobacco’s because sub-optimal diet is near-universal while smoking is a minority exposure with a huge per-person hazard. A population ranking and an individual ranking put diet and tobacco in opposite orders, from the same evidence. Reading GBD’s “diet kills more than tobacco” as “improve your diet before you quit smoking” is the exact unit-of-analysis error this page exists to name. (inferred from Afshin et al., 2019; Jha et al., 2013)
GBD independently corroborates attention-is-an-anti-signal — the discussed exposures rank low.
«High consumption of red meat, processed meat, trans fat, and sugar-sweetened beverages were towards the bottom in ranking of dietary risks for deaths and DALYs for most high-population countries.» (Afshin et al., 2019)
The consortium draws the telos’s own conclusion: promoting under-consumed healthy foods «might have a greater effect than policies only targeting sugar and fat.» So the heavily-litigated levers (meat, sugar, fat) sit at the bottom of the attributable-burden ranking, and the boring under-eaten staples (whole grains, fruit, nuts) sit at the top — a population instance of the anti-signal rule, from a source with no stake in the wiki’s framing. (Afshin et al., 2019)
Four caveats that keep the ranking honest — all GBD’s own, and they matter because a burden ranking is seductive:
- The RRs are borrowed observational estimates, from meta-analyses of prospective cohorts; residual confounding is not excluded, and GBD says the diet evidence is «generally weaker than» that for tobacco or blood pressure. So the diet>tobacco burden claim rests on weaker warrant than the comparison suggests.
- Individual effects may be over-stated because healthy factors co-occur: «the effect size of the individual dietary factors might be overestimated» — a confound GBD cannot remove. (Afshin et al., 2019)
- The exposure is share-of-diet, not absolute (energy-adjusted), so every risk is implicitly a substitution whose partner is unspecified -> Measurement Error in Dietary Assessment. A ranking of substitutions with unnamed comparators cannot be read as “eat more X, gain Y.”
- The 1-ranked risk has the thinnest exposure data — sodium’s data representativeness index is 26.2% (vs 94.9% for most foods), so the sodium estimate is modelled from ~a quarter of countries. (Afshin et al., 2019)
What it does license: the same prevalence-weighted coverage check the NNR section named, now global and first-hand — and a worked reconciliation of the diet-vs-tobacco paradox that makes the population/individual distinction concrete rather than asserted. What it still does not give: any absolute per-person effect this page could rank a stratum’s levers by. GBD is a population instrument; the page’s binding gap (stratum baseline risk) is untouched.
The largest relative effects here are the ones a person cannot change [2026-07-28, ESC chunk 03]
This page’s table holds dietary and activity exposures. ESC’s psychosocial chapter supplies effect sizes that dwarf most of them — for exposures that are not individually modifiable.
«The strongest association has been found between low income and CVD mortality, with a RR of 1.76 [95% confidence interval (CI) 1.45-2.14].» (European Society of Cardiology, 2021)
And on psychosocial stress: it «is associated, in a dose-response pattern, with the development and progression of ASCVD, independently of conven- tional risk factors and sex». (European Society of Cardiology, 2021)
RR 1.76 is larger than any dietary relative effect this wiki holds. It is also a social position, not a behaviour — and that is the finding, not an aside.
What this does to the hierarchy, stated carefully.
- It is a ceiling statement, and the telos says ceilings are results. If the largest available relative effects attach to income and social position, then the achievable gain from optimising the dietary levers on this page is bounded well below the total variation in outcomes — which licenses someone to stop optimising, rather than implying they should try harder.
- It does NOT belong in the ranked table, and adding it would break the table’s own rule. Layer 1 ranks remediable gaps; an exposure a person cannot change has no intervention to rank, however large its association. Recording it here, outside the table, is the honest placement.
- The confounding structure is stated by ESC itself, and it names the culprits — psychosocial stress «has direct biological effects, but is also highly correlated with socioeconomic and behavioural risk factors (e.g. smoking, poor adherence)». Smoking and adherence are exactly the behavioural exposures the telos names as dominant, so part of what the 1.76 measures is already accounted for elsewhere in any ranking, and treating it as additive would double-count. (inferred from European Society of Cardiology, 2021)
What would make this actionable rather than merely sobering: a source on interventions that change
the stressor rather than the social position — job control, social connection, stress-management
programmes — with effects on hard outcomes. The corpus holds none. ESC recommends screening for
depression, anxiety and insomnia, which is a detection step, not an intervention with a measured
outcome effect. AWAITS such a source; this is a G gap with a named shape, not a vague one.
The Nth intervention does not deliver its trial effect [2026-07-28, ESC chunk 06]
This page ranks interventions as if each carried its measured effect independently. ESC states the constraint that breaks that assumption:
«The incremental benefit of medication when added to an already complex regimen is often uncertain. Moreover, care for multimorbid CVD patients is often fragmented and given by multiple providers, complicating decision- making and adherence to recommended treatment.» (European Society of Cardiology, 2021)
Why this belongs on a ranking page rather than a clinical one. A ranked list invites the reading do the top item, then the next, then the next. If each addition erodes adherence to the ones already in place, the list is not additive and its tail is worth less than its stated effects imply — possibly less than nothing, if a marginal addition displaces a larger established one.
This is a telos provision with a source attached. Adherence is part of the effect; cognitive cost, decision fatigue and opportunity cost are real constraints, not excuses. ESC supplies a guidance body saying so about its own recommendations, which is stronger than the wiki asserting it.
Three bounds, because the claim is easy to over-extend.
- ESC’s sentence is about medication in multimorbid patients. Whether it transfers to stacking dietary and activity changes in a healthier person is not established here — plausible by the same mechanism, unevidenced in this source.
- «often uncertain» is not «often absent». ESC claims the incremental benefit is not reliably known, not that it is zero. Reading it as the latter would be the stronger, unsupported claim.
- It does not reorder anything on this page. It bears on how far down the list is worth going, which is a different question from which item is largest — and it reinforces the ceiling finding above rather than competing with it. (inferred from European Society of Cardiology, 2021)
The absolute layer arrives for one exposure — and it shrinks the lever [2026-07-28, WHO SFA Annex 6]
This page’s binding complaint is that it holds almost no absolute effects. WHO’s Annex 6 has now been read, and it supplies the full per-outcome absolute profile for saturated-fat reduction. The result does not enlarge the top row; it bounds it.
| Outcome | Absolute per 1000 | Certainty |
|---|---|---|
| Cardiovascular events | 15 fewer (25 fewer to 2 fewer) | Moderate |
| All-cause mortality | 2 fewer (6 fewer to 2 more) | Moderate |
| CVD mortality | 1 fewer (4 fewer to 2 more) | Low |
| CHD mortality | 1 fewer (3 fewer to 3 more) | Low |
| Stroke | 2 fewer (7 fewer to 6 more) | Very low |
| Type 2 diabetes | 1 fewer (4 fewer to 3 more) | Low |
Full profile with relative effects and study counts: Saturated Fat Intake and Replacement.
Three consequences for the hierarchy.
- The one rankable exposure is rankable on ONE outcome. Cardiovascular events clears the null; every other row crosses it, including all-cause mortality at Moderate certainty. So the table’s top row is not “SFA reduction prevents 15 events per 1000” as a general benefit — it is that, on composite cardiovascular events, and approximately nothing measurable elsewhere.
- It sharpens the existing finding rather than overturning it. This page already said the one rankable thing is also the smallest lever on the list, and invoked the telos’s attention-is-an-anti-signal rule. The annex confirms it from the inside: the most-studied dietary exposure in the corpus yields one significant absolute effect on one composite outcome.
- The ceiling statement gets firmer. Combined with the ESC finding above — that the largest relative effects attach to social position — the picture is a small, well-measured dietary lever sitting under much larger unmodifiable ones. That is a result, and the telos says to report it as one.
What is still empty is unchanged. Physical activity still has ratios without absolutes; pharmacotherapy, weight loss as an exposure, alcohol, sleep and smoking remain empty rows. Annex 6 filled one cell of one row — the SFA line — and demonstrated how much work an absolute layer takes per exposure.
A heavily-discussed lever that ranks near the bottom — meal timing [2026-07-29, TREAT + eTRF]
Time-Restricted Eating enters the hierarchy as a worked instance of attention-is-an-anti-signal: enormous public discussion, small measured effect. The two RCTs the wiki holds decompose it — a self-selected eating window produced no weight advantage (and lost muscle); shifting eating early improved insulin/BP surrogates with no weight change (n=8, no hard outcome). So meal timing offers, at most, a small weight-independent surrogate signal from eating earlier, plus a negative lean-mass signal from a careless late window. It ranks below every hard-outcome row in the table above and belongs with sodium/free-sugars as a small/surrogate lever — its prominence is a fact about the literature, not about its effect size. It is not added as a table row: there is no absolute hard-outcome effect to rank, which is itself the placement.
Few high-certainty, high-impact dietary levers — a challenge the assembled fabric confirms, and sharpens [2026-08-17, Challenge]
A maintainer doubt, registered as a Challenge: the evidence for fibre is not convincing or big; the big levers are movement/muscle and loss of visceral/ectopic fat and dyslipidaemia; and other than fibre there are very few high-certainty, high-impact dietary levers. Adjudicated against the held fabric — never by authority — the doubt is substantially correct, and the fabric already holds it, scattered across the pages this section now assembles. Assembling it makes one distinction the individual pages do not, and adds a certainty-type asterisk to each lever the doubt names.
The distinction the pieces hide: a “dietary lever” is two different objects. Split it and the apparent emptiness resolves.
- Composition — which nutrient (more PUFA, less SFA, less free sugar, less sodium, more fibre). Every composition lever the wiki holds is small, conditional, observational, or mostly spent (the enumeration below). This is the set the doubt calls nearly empty, and the fabric agrees.
- Quantity — how much total energy. This is the one large dietary lever, and it acts by changing body fat, so it appears on the ranking not as “diet” but as fat loss. The fabric holds that the carbohydrate-insulin “metabolic advantage” is refuted in direction and magnitude — “a calorie is a calorie”, “cutting carbs is not a distinct fat-loss lever at equal calories… the decision moves to energy intake” -> What Drives Fat Gain - Energy Balance vs the Carbohydrate-Insulin Model; the adiposity lever “is negative energy balance, whatever delivers it” -> Ectopic Fat and Depot-Specific Risk.
So the doubt’s own framing — big levers = movement + fat loss, dietary levers = small — is the fabric’s position once “fat loss” is recognized as the one large dietary lever wearing a body-composition label.
The composition set, enumerated — each cross-linked, none re-derived here:
- Fibre — the doubt’s granted exception, and the fabric is more skeptical than the doubt. The strong signal (CHD RR 0.76) is observational; the trialled causal mechanism is transmission-bounded to a <4% CHD reduction (challenge r40), “an order of magnitude smaller than the association the cohorts report” — “a real but modest lever, and the strongest evidence sits on the smallest effect” -> Dietary Fibre and Health. The one dietary lever the doubt keeps is itself shakier than granted.
- Saturated fat -> PUFA — the only rankable composition lever, and the smallest thing on the table: 15 fewer CV events/1000 on one composite outcome (RR 0.83, Moderate), mortality graded null, the SFA-harm premise itself contested -> Saturated Fat Intake and Replacement.
- Free sugar — isoenergetic swap null (0.04 kg); the WHO 10% limit is a dental recommendation, not a cardiometabolic one -> Free Sugars Intake.
- Sodium — HIGH certainty on the surrogate (SBP -3.39 mmHg), VERY LOW on direct hard outcomes; a route-(a) conditional lever that pays with baseline BP risk, not a universal one -> Sodium Intake and Blood Pressure.
The two honest exceptions the claim must survive (symmetric standards — test the counter-evidence, not just the confirming):
- Trans fat is the one firm composition finding — “the firmest fat finding held” -> Dietary Fat. But it is largely policy-eliminated, so little individual room remains, and WHO issued only a conditional recommendation below 1%E because LDL “is not a physical manifestation… of disease.” A high-certainty lever that is mostly already spent.
- The Mediterranean pattern carries the best hard-outcome dietary signal held (PREDIMED primary composite HR 0.70, 0.55-0.89) — but in a high-baseline-risk population (~49% diabetic, ~82% hypertensive), carried by stroke, with all-cause mortality null (0.98), on a provenance-repaired (2018) trial whose authors say generalization to lower-risk people “requires further research”, and pooled RCTs are null except diabetes -> Mediterranean Diet and Cardiovascular Events. It is a multi-component pattern in high-risk people, not a high-certainty lever for the low-risk individual — so it bounds the claim without overturning it.
The sharpening the fabric adds to the doubt’s big levers — each is real, and each carries an evidence-type asterisk the doubt’s framing omits:
- Movement is the largest association the vault holds (HR ~0.34 self-report, ~0.27 device) but it is observational — structurally unprovable by RCT for hard CV events; a predictor of large effect, not a proven-by-trial one -> Physical Activity Dose and Mortality, Cardiometabolic Interventions and Hard CV Outcomes in Low-Risk People.
- Muscle / grip strength is a predictor, not a proven treatment target: grip is “a number to MEASURE (it places you in a stratum), not a number to STEER toward”; that training it lowers mortality is not established -> Grip Strength and Mortality, Low Muscle Mass and Mortality.
- Visceral / ectopic fat loss is outcome-specific: proven for T2D remission (DiRECT 46%), MASLD regression, and all-cause mortality (Ma 54-RCT RR 0.82, “not routed through the heart”), but the hard-CV-event benefit by the lifestyle route is unproven (Look AHEAD HR 0.95; Ma CV events RR 0.93 null). “Pursue fat loss for the outcomes it demonstrably moves… do not count on a lifestyle-route reduction in heart attacks” -> Body Fat, Does Weight Loss Reduce Cardiovascular Events.
- Dyslipidaemia is where the fabric most corrects the framing: the high-certainty, mortality-moving, high-impact lever is pharmacological, not dietary. Statin LDL-lowering per 1.0 mmol/L: major vascular events RR 0.78, all-cause mortality RR 0.90 — but the fabric firewalls it, “the magnitude does not transfer to a dietary LDL-C change” -> LDL Lowering and Cardiovascular Events, LDL ApoB and Cumulative Exposure. Naming dyslipidaemia among lifestyle levers conflates a real, large, high-certainty target with a route (diet) that reaches it only weakly.
The decision-change. This licenses someone to stop hunting for a magic dietary-composition lever — there is no large, high-certainty, hard-outcome one for a low-risk individual, and the fabric says so across a dozen pages. Redirect instead: the large dietary lever is energy quantity -> adiposity, cashed on metabolic and all-cause outcomes rather than heart-attacks-avoided; movement is the largest behavioural lever (evidence-type-limited); not-smoking dominates wherever present; and if hard-ASCVD prevention at elevated baseline risk is the goal, the high-certainty high-impact lever is apoB-lowering, whose proven instantiation is a drug — a layer-3/prescriber act the wiki appraises but does not prescribe.
Honest posture — this is agreement, not divergence. Under the telos’s symmetric-standards rule a confirmed convention is as reportable as a refuted one, and manufacturing an overturning here would be the bias the rule exists to catch. The value added is configurative (the composition/quantity split; the per-lever certainty-type asterisk), not a new magnitude — the doubt was right, the fabric held it, and this section states it once instead of leaving it distributed.
Tiering a lever: the association is not the tier [2026-08-17, Challenge]
The public bands (big rock / medium stone / small pebble) are this page’s effect x certainty ranking made legible, and a lever’s band is not read off its headline relative association — it is absolute effect at the person’s baseline risk, discounted by certainty. Two levers a maintainer questioned show the two ways that distinction bites, and they resolve in opposite directions.
- Plant foods is a MEDIUM lever, not a big rock — and no stratum rescues it. Its large-looking numbers are observational associations (fruit/veg all-cause mortality RR ~0.90 per 200 g/day -> Fruit and Vegetable Intake and Health; fibre CHD RR 0.76 -> Dietary Fibre and Health) whose causal core is small — the trialled fibre mechanism is transmission-bounded to a <4% CHD reduction (challenge r40). The evidence is observational everywhere (no whole-food RCT to a hard outcome), so there is no baseline stratum at which it becomes large-and-certain. Every owning page already tiers it there — “a real but modest supporting lever, not a big rock” -> Fibre; “for most people this is a small lever” -> Plant Foods — and Big Rocks (Median) omits it from its big-rock buckets entirely.
- Blood pressure is a BIG rock CONDITIONALLY — Big for an elevated-BP/high-risk person, Medium-to-Pebble for a normotensive. Unlike plant foods it carries a proven hard-outcome transmission that reaches primary prevention: 5 mmHg systolic -> ~10% fewer major CV events (BPLTTC, HIGH), so route-(a) baseline scaling makes the same millimetres a large absolute gain at high risk and a small one at low. The owning deliverable already holds this as a two-column tier table keyed to baseline risk, governed by “rank on absolute risk, not the BP number” -> Blood Pressure. Its dietary levers (sodium, DASH, potassium) are Medium; the big-rock instantiation is the drug route plus weight loss — which is why Sodium Intake and Blood Pressure tiers below the integrator it feeds.
The discriminator, stated once. A large association with a small or unproven causal transmission tiers Medium and cannot be promoted by baseline risk (plant foods); a lever with a proven transmission tiers by the person’s baseline risk and so is stratum-conditional (blood pressure). Reading a headline RR as a tier is the same unit-of-analysis error as reading a population-attributable rank as an individual benefit (the prevalence-wedge section above) — one level down, at the individual lever.
The cross-domain superset arrives — and diet is no longer the #1 population risk [2026-08-26, GBD-87]
Every GBD section above is the diet-only slice (Afshin 2019, Knudsen et al. 2025 via NNR). GBD-87
is the whole-telos superset: the same instrument run across 87 environmental, occupational,
behavioural, and metabolic risk factors — the full cross-domain big-rocks ordering that no diet-only
source can produce. In 2019 those 87 risks jointly account for 47.8% (95% UI 45.3-50.1) of global
DALYs (GBD 2019 Risk Factors Collaborators, 2020).
The Level-2 attributable-DEATH ranking, both sexes, 2019:
«For both sexes combined, the leading Level 2 risk factor for deaths was high SBP, accounting for 10·8 million (9·51—12·1) deaths in 2019 (19·2% [16·9—21·3] of all deaths that year), followed by tobacco, with 8·71 million (8·12—9·31) deaths (15·4% [14·6—16·2] of all deaths that year).» (GBD 2019 Risk Factors Collaborators, 2020)
Dietary risks rank third — female diet deaths 3.48 million (rank 2 in females), male 4.47 million (rank 3 in males, explicitly below both tobacco and high SBP), summing to ~7.9 million both-sexes, under tobacco’s 8.71 million. (GBD 2019 Risk Factors Collaborators, 2020) (inferred from GBD 2019 Risk Factors Collaborators, 2020)
This SUPERSEDES the Afshin headline this page reconciled above — «suboptimal diet is responsible for more deaths than any other risks globally, including tobacco smoking». That claim used GBD 2017 data (11 million diet deaths, diet ranked #1); GBD-87 uses GBD 2019 data and ranks diet third, behind high SBP and tobacco. This is a genuine same-quantity refinement, not the population/individual wedge — the parameter table returns YES:
| Afshin (GBD 2017 data) | GBD-87 (GBD 2019 data) | Same quantity? | |
|---|---|---|---|
| Metric | GBD Level-2 attributable deaths, global, both sexes | GBD Level-2 attributable deaths, global, both sexes | YES |
| Diet deaths | 11 million — ranked #1, above tobacco | ~7.9 million — ranked 3rd, below tobacco (8.71M) and high SBP (10.8M) | YES |
The reordering is mostly METHOD, not real-world trend — which is the finding. GBD-87’s own discussion states the diet burden was revised downward between its two cycles on methodology alone:
«Compared with GBD 2017, our GBD 2019 estimates of the burden (as measured by percentage of total DALYs) attributable to diet quality in 2017 were 29·7% lower.» (GBD 2019 Risk Factors Collaborators, 2020)
«These reductions stem from three major sources: changes in the crosswalks between alternative and reference methods for estimating diet intake, new systematic reviews and meta-regressions, and more empirical standardised methods for selecting the TMREL for protective factors.» (GBD 2019 Risk Factors Collaborators, 2020)
So the same consortium cut its own diet-burden estimate by ~30% on modelling choices (intake crosswalks, new meta-regressions, a re-derived optimum for protective factors), and that revision — not a change in how people eat — is most of why diet fell from first to third. This is the concrete demonstration of the caution the earlier GBD sections asserted: a population attributable-burden ranking is model-version-sensitive, and its headline order can flip on the estimating body’s own methodology between cycles. It is a type-F refinement of the same instrument, NOT independent-E (one consortium updating itself; no independent route corroborates it).
What the superset CONFIRMS — big rocks first, at population scale. The cross-domain death ranking is dominated by a handful of exposures — high SBP, tobacco, dietary risks, air pollution, high FPG, high BMI — and the rising ones are metabolic:
«Many of the increasing risks are metabolic risk factors; in fact, taken together, the exposure to metabolic risks increased 1·37% per year (95% UI 1·17—1·56) from 1990 to 2019 and 1·46% per year (1·26—1·69) from 2010 to 2019.» (GBD 2019 Risk Factors Collaborators, 2020)
The metabolic cluster (SBP, FPG, BMI, LDL) plus tobacco is exactly the big-rock set the telos names, and GBD-87 places it at the top of the population burden from a source with no stake in the wiki’s framing. It also demonstrates stratum(age)-dependence rather than asserting it — the leading risk moves by age band: «Iron deficiency was the leading risk factor for those aged 10—24 years, alcohol use for those aged 25—49 years, and high systolic blood pressure for those aged 50—74 years and 75 years and older.» (GBD 2019 Risk Factors Collaborators, 2020) — the same “the hierarchy is stratum-dependent, not a fixed list” point the red-meat-rank-by-country instance made, now on the age axis.
What is UNCHANGED — the population/individual wedge still governs, and the binding gap is untouched. GBD-87 reports attributable burden, not individual expected benefit; the prevalence-wedge parameter table from the NNR/Afshin sections above applies to every number here unaltered. And the magnitude caveat is if anything sharper for the superset: GBD’s relative risks are borrowed, modelled estimates, not primary effect sizes —
«(2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression.» (GBD 2019 Risk Factors Collaborators, 2020)
— so an attributable-burden rank is never citable for a per-person effect; the underlying SR is. The joint burden further rests on a multiplicative-RR assumption with only partial mediation correction (non-mediated RRs multiplied; super-multiplicative synergy not captured), and GBD names its own binding constraint as the availability and quality of the primary data. None of this supplies the absolute per-person effect at a stratum baseline risk that this page’s ranked table still lacks — GBD-87 broadens the population instrument across all domains without moving the individual-ranking gap an inch.
One cross-link worth flagging: GBD-87 relaxed the log-linear RR assumption for diet, kidney dysfunction and air pollution and found «the relative risk functions tend to flatten out at higher exposure levels; the previous practice of imposing a log-linear functional form on the risk equation… might have led to overestimation» (GBD 2019 Risk Factors Collaborators, 2020) — a directional corroboration, from an independent modelling exercise, of the fabric’s plateau/knee thread (that protective curves flatten and a hidden plateau means over-shooting merely fails to help). GBD then set the protective-factor TMREL to the 85th percentile of studied intake (GBD 2019 Risk Factors Collaborators, 2020) — i.e. the apparent optimum tracks the sampling edge, the exact caution of The Underivable Optimum (and the measurement-error driver behind it -> Measurement Error in Dietary Assessment). (inferred from GBD 2019 Risk Factors Collaborators, 2020)