BANT

From the SACN register of interests...

If EMs and IAs going to SLSC on introducing new NPM into Food Promotion and Advertising SIs are justified by obesity reduction, they should show the full analytical chain: calorie impacts, obesity-prevalence impacts, and the respective roles of the DH Calorie Model and DH BMI Prevalence Model.🔍

Now that @DHSCgovuk has quietly moved away from CICO model of obesity, let's focus on the 2023 DH BMI Prevalence Model. That ought to have gone for public consultation surely? What are the key assumptions mentioned later?

Apologies to all and especially @hmtreasury @YvetteCooperMP BANT had not realised that @DHSC had quietly dropped CICO obesity model from its DH Calorie Model for weight loss and now looks at benefits from calorie reduction which just infers weight loss. But while the maths has changed the DH Calorie Model narrative section has not changed since 2018. That is a big omission. Needs updating to reflect that. So about the 2023 DHSC BMI Prevalence Model.....

Dear @NICEComms Re NG246 section on Prevention and various statements on Page 45 -- how do you reconcile committee consultation response that long-term weight maintenance is out of scope of NG246?

DHSC is currently considering responses to its March 2026 consultation on the 2018 Nutrient Profiling Model. To bring the updated NPM into practice will require Statutory Instruments on each of Food Placement and Food Advertising Regs to be put to HL Secondary Legislation Scrutiny Committee with accompanying Explanatory Memorandum and Impact Assessment. These will require transparent references to DH Calorie Model and consultation responses.

Placental Methylglyoxal in Preeclampsia: Vascular and Biomarker Implications | Hypertension ahajournals.org/doi/10.1161/…

Since 2018 BANT has been campaigning for digestible maltodextrins to be classed as 'sugars' in public anti-obesity policy (SDIL and Nutrient Profiling Model) without success. DH says model was designed to reflect UK dietary recommendations on free sugars and fibre - not to predict metabolic responses in individuals. NPM uses classic chemical classifications of carbohydrate but does not directly evaluate the physiological handling of industrially processed carbohydrates - SACN 2015 Carbs Report states maltodextrins are just 'fillers'. FA2017 Part 2 Clause 29(2) to include calorific maltodextrin - Petitions

Public health nutrition dietary reference intakes are designed to avoid deficiency states and its foundational principles cannot address prevention which encompasses: individuals who respond differently to the same diet; metabolic subtypes; genetic polymorphisms; differing insulin responses; differing fuel-partitioning phenotypes; metabolic inflexibility versus flexibility; people whose physiology departs substantially from the population mean. Coming soon: consultation on BANT's Undergraduate Curriculum in Nutrition for Medical Students

AI on the EU MUL Framework I. Core Structure of the Argument Pillar A — Scientific Robustness The MUL framework relies on outdated science, conservative assumptions, and a modelling approach that structurally depresses supplement limits below levels required for efficacy. EFSA ULs were never intended to be used as hard regulatory caps and many are based on decades‑old data. Pillar B — Precision & Genetics The model assumes a uniform population, ignoring SNP‑driven metabolic diversity (MTHFR, CBS, VDR, SLC23A1, etc.). These polymorphisms materially alter nutrient requirements for large sub‑groups. A one‑size‑fits‑all MUL discriminates against these populations. Pillar C — Human Rights Restrictive MULs interfere with autonomy, private life, and health management (Article 8 ECHR; Articles 7, 35, 21 EU Charter). They disproportionately harm identifiable sub‑groups, creating indirect discrimination (Article 14 ECHR). Pillar D — Proportionality & Necessity EU law requires proportionality and the use of the least restrictive means. MULs fail this test because less intrusive tools exist: labelling, practitioner‑grade categories, warnings, monitoring, and nutrient‑specific modelling. Pillar E — Epistemic Shift (AI) AI tools allow any citizen to interrogate regulatory science. Opaque expert reasoning is no longer defensible. Regulators must justify modelling choices and engage with evidence‑based submissions. Failure to do so risks procedural unfairness. II. Expanded Argument A. Scientific Robustness and Outdated ULs ULs were designed as guidance, not regulatory ceilings. Many ULs are based on old data and do not reflect modern evidence on safe higher intakes. The Commission’s MUL model applies multiple layers of conservatism, including the flawed subtraction of P95 dietary intake (see Section IV). B. Genetic Diversity and Precision Nutrition Significant proportions of the population carry SNPs that alter nutrient metabolism: MTHFR C677T/A1298C → higher methylated folate needs CBS variants → altered sulfur amino‑acid metabolism VDR polymorphisms → higher vitamin D requirements SLC23A1 variants → altered vitamin C transport A harmonised MUL calibrated to the “average genome” is discriminatory and incompatible with EU and UK commitments to personalised medicine. C. Human Rights Restrictive MULs interfere with: personal health management genetic‑specific nutritional needs This engages: Article 8 ECHR (private life, bodily autonomy) Article 14 ECHR (non‑discrimination) Articles 7, 21, 35 EU Charter Proportionality requires a fair balance between public health and individual autonomy. The MUL model fails this balance. D. Proportionality and Less Restrictive Means The MUL framework is not necessary because: toxicity for most nutrients is rare and well‑characterised targeted warnings, practitioner‑grade categories, and monitoring are available nutrient‑specific modelling is feasible A blanket low MUL is disproportionate. E. AI and the End of Expert Deference AI tools allow stakeholders to: analyse EFSA opinions identify inconsistencies generate alternative models expose outdated assumptions Regulators must expect higher scrutiny. Failure to engage with AI‑assisted submissions is a breach of the duty to consult conscientiously. III. The Single Weakest Point in the MUL Framework The MUL model double‑counts risk by subtracting high-end dietary intake (P95) from ULs that already include large safety margins and are defined as total intake from all sources. This is a methodological error, not a scientific disagreement. It produces irrational outcomes, violates EU law, and is easy for courts and policymakers to understand. This is the keystone flaw. Why this flaw is fatal 1. ULs already include dietary intake ULs are total‑intake thresholds. Subtracting P95 treats normal food consumption as a hazard exposure. No toxicologist uses this method. 2. It produces irrational outcomes Adult limits lower than child limits Limits below therapeutic or preventive levels Clinically meaningless limits for nutrients with low toxicity SNP‑affected populations unable to obtain effective doses Irrational outcomes indicate an irrational model. 3. It violates EU law EU law requires: “generally accepted scientific data” consideration of varying sensitivity proportionality Double‑counting violates all three. 4. It is easy to explain “The EU takes a safety limit, then subtracts the highest possible dietary intake, even though the safety limit already accounts for dietary intake.” This lands with judges, policymakers, and the public. 5. It cascades into all other weaknesses It amplifies: discrimination against SNP carriers human‑rights interference outdated science lack of proportionality exclusion of practitioner‑grade products Remove this flaw and the MUL structure collapses.

Aspartame exposure promotes endometriosis progression through PTGS2-mediated oxidative stress and mitochondrial dysfunction - ScienceDirect @NICEComms NG246 should have adopted WHO position on sweeteners. Cannot import NG246 into PMOS safely

What is wrong with current DRVs? 🐭 The Trojan Mouse Analogy A “Trojan mouse” nutrient is one that appears benign at the population level, yet quietly carries mechanistic liabilities that only reveal themselves when the organism is stressed. The phenotype emerges not because the nutrient is unimportant, but because the requirement architecture is blind to: inter‑individual variation genetic polymorphisms metabolic inflexibility life‑course transitions multi‑system carbon allocation pressures The mouse looks harmless until the right stressor opens it — and then the hidden vulnerability spills out. This is the perfect metaphor for how UK DRVs/RNIs fail. 🎯 Which nutrient makes the strongest Trojan mouse? Choline is the best candidate. It is structurally perfect for this analogy because: the UK has no RNI deficiency phenotypes emerge only under stress polymorphisms are common (PEMT, MTHFD1, CHDH) endogenous synthesis is variable and hormonally regulated metabolic inflexibility amplifies requirement pregnancy, NAFLD, insulin resistance, and folate insufficiency unmask the phenotype Choline deficiency is not obvious in a “healthy population” — but the moment you introduce a stressor (low folate, high metabolic load, pregnancy, hepatic carbon pressure), the Trojan mouse opens and the phenotype appears. 🧬 How the deficiency phenotype emerges (mechanistic narrative) Stage 1 — The mouse enters unnoticed Population‑level DRVs assume endogenous choline synthesis is adequate. SACN does not set a requirement. Policy assumes “no problem”. Stage 2 — The stressor arrives Pick any of the following: PEMT or MTHFD1 polymorphism pregnancy (oestrogen‑driven PEMT upregulation required) folate insufficiency NAFLD or insulin resistance high dietary fructose or UPF load increased methylation demand metabolic inflexibility These stressors increase choline demand or reduce synthesis. Stage 3 — The Trojan mouse opens The hidden liabilities spill out: hepatic fat accumulation (impaired VLDL export) elevated homocysteine (methylation bottleneck) reduced phosphatidylcholine synthesis impaired membrane turnover cognitive and neuromuscular symptoms pregnancy complications (NTD risk, placental dysfunction) None of these look like “classic deficiency” in the vitamin‑C or iron sense. They are systems‑level failure modes. Stage 4 — Policy failure becomes visible Because the UK has no RNI and no stratified requirement model, the deficiency phenotype is misclassified as: NAFLD metabolic syndrome cognitive decline pregnancy complications elevated homocysteine “poor diet quality” The Trojan mouse has done its work: the system is breached, but the breach is misattributed. 🧠 Why this analogy is strategically powerful for policy reform 1. It exposes the flaw in “healthy population” DRVs A Trojan mouse only works because the defenders assume the object is harmless. UK DRVs assume the population is homogeneous and healthy. Your analogy shows why this assumption is structurally unsafe. 2. It demonstrates why single‑point RNIs cannot work A Trojan mouse opens only under specific stressors. A single RNI cannot capture stressor‑dependent requirements. 3. It links directly to metabolic inflexibility and carbon allocation Choline deficiency phenotypes are carbon‑flux failures. 4. It is politically elegant You are not accusing SACN of negligence — you are showing that their architecture cannot detect Trojan mice by design. 🧪 If you want a second Trojan mouse nutrient Vitamin B6 is ideal: high prevalence polymorphisms depletion by UPFs requirement increases with metabolic load deficiency phenotype emerges under stress (inflammation, insulin resistance, high glycaemic load) homocysteine and neurotransmitter pathways unmask insufficiency Pairing choline (structural omission) with B6 (structural mis‑specification) gives you a devastating methodological critique. 🔧

This is horrifying for obesity policy gov.uk/government/publicatio…