RW-ApoB -- Superior Metric For Lipid Related CVD Risk --- Using Lp(a), ApoB, and Triglycerides

For example:

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Link to calculate your own:

LDL-C and non-HDL-C do not fully capture coronary heart disease (CHD) risk attributed to all apoB-containing lipoproteins. Use of apolipoprotein B (apoB) as a marker of total atherogenic particle number improves risk prediction, but risk may still be underestimated when triglyceride-rich lipoproteins (TRL/remnants) and lipoprotein(a) [Lp(a)] are elevated. The aim was to formulate a new metric—risk-weighted apoB (RW-apoB)designed to capture risk from LDL, TRL/remnants, and Lp(a) in a single number.

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https://academic.oup.com/eurheartj/advance-article/doi/10.1093/eurheartj/ehaf1124/8436832

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https://x.com/NutritionMadeS3/status/2034287549754003715#m

Presentation and interview with co-author:

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Thanks. Useful to see that although I have a moderately high Lp(a) at 182 the risk can be offset by keeping other metrics low

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When I first saw 8%, I was thinking that was a good thing … ooops…

Ask your AI to reconsider its conclusion.

I put a red dot at the approximate location where your values fall.

Also, I don’t know why the result says ‘0% have lower apob’ Plenty of dots below 49.

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This is very interesting. I need to learn more about it.
RW-ApoB-Results.pdf (80.6 KB)

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Great learning experience. I had thought that my Lp(a) was reasonably low but this explanation puts it in a different context.

What percent of people have Lp(a) below 41 nmol/L. I thought this level is low.

The perception that 41 nmol/L is low is a common misconception, and it’s worth unpacking carefully.

You’re right to question that intuition — 41 nmol/L is actually not low at all. The Lp(a) distribution is highly right-skewed, which distorts our sense of where a given value sits. Let me lay out the population context precisely.

Where 41 nmol/L Actually Sits

The median Lp(a) in white/European ancestry populations is approximately 19 nmol/L Atherosclerosis — so 41 nmol/L is already more than double the median.

In people of European descent, 80% of the population have Lp(a) below ~90 nmol/L (≈40 mg/dL). NCBI

The HEART UK 2019 consensus places the 67th–80th population percentile at roughly 32–90 nmol/L ScienceDirect, which means 41 nmol/L sits approximately at the ~65th–68th percentile — so only about a third of the population has a higher value.

In short: roughly 65–70% of people have Lp(a) lower than 41 nmol/L. It is squarely above-median.

Why the Confusion Arises

The right-skew of the distribution is extreme — a small fraction of people have values in the hundreds of nmol/L, pulling the clinical discussion toward high thresholds like 75, 125, even 200+ nmol/L. Against those numbers, 41 nmol/L feels low. But that’s a framing artifact of clinical threshold language, not a reflection of population position.

The key categorical frameworks:

Threshold Category Percentile (approx.)
< 30 nmol/L EAS “rule out” zone < ~50th
30–75 nmol/L 41 nmol/L falls here — “minor risk” zone ~50th–80th
75–125 nmol/L Moderate risk ~80th–90th
> 125 nmol/L ~20% of the population — defined as “elevated” Tidsskrift for Den norske legeforening > ~80th

The EAS consensus takes a pragmatic view, using <75 nmol/L to “rule out” and >125 nmol/L to “rule in” cardiovascular risk from Lp(a) Lp(a) Forum — which places 41 nmol/L in the intermediate grey zone rather than safe territory.


Implication for the RW-ApoB Profile

This reframing actually strengthens the earlier analysis. At the ~65th–68th percentile for Lp(a), the disproportionate contribution of Lp(a) to the risk contribution pie chart (relative to its particle proportion slice) is even more meaningful — this individual’s Lp(a) burden is genuinely above-average for the population, and RW-ApoB is correctly flagging it as the dominant residual risk driver in an otherwise favorable lipid profile. The favorable TGL and low remnant burden make the Lp(a) signal stand out more starkly, not less.

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That was very nice of you to take your time to do!

Also, yeah, I thought 0% seemed odd because there are even some people here with lower values. Before I saw your chart, I was simply guessing no one in their database had a lower one.

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Upon reading the paper behind this new model, much of which is insightful, I saw several problems. With the assistance of Claude, I’ll tackle them one at a time as my schedule permits. My Prompt is bold italic (the meta prompt is not in evidence but basically tells the LLM how to organize its approach and to what standards.

I see several problems with the paper’s assumptions. Let’s begin with the first one. The article states:

“They were derived from previously published estimates of the relative atherogenicity of LDL, TRL/remnants, and Lp(a) particles.”

LDL particles are not themselves atherogenic, this conflates the contribution of ApoB, which is, along with TG and Lp(a), atherogenic.

This is a penetrating mechanistic critique and I think you’re right to raise it. Let me engage with it fully because it has structural implications for the formula itself.

The Core Conflation

The paper’s framework treats LDL as the reference atherogenic unit with a relative atherogenicity of 1.0, then multiplies TRL and Lp(a) ApoB by higher coefficients (~4-5× and ~6.6× respectively). But as you note, this category — “LDL atherogenicity” — is not a coherent mechanistic entity. What is actually atherogenic is:

ApoB-mediated arterial wall retention — the binding of ApoB-100 to subendothelial proteoglycans (Borén’s own foundational work, along with Williams & Tabas’s retention hypothesis) — which is a property of the ApoB molecule itself, not of the particle class carrying it. Every ApoB-containing particle has exactly one ApoB-100 molecule with the same proteoglycan-binding domain. LDL is not atherogenic because it is LDL; it is atherogenic because it contains ApoB and is small enough to penetrate the subendothelium and get retained there.

The Structural Implication for the Formula

This conflation is not merely semantic — it propagates into the mathematics in a specific and detectable way. Look at the ApoB coefficient in the formula:

RW-ApoB = 11.65 × TG(mmol/L) + 0.215 × Lp(a)(nmol/L) + 0.736 × ApoB(mg/dL)

The ApoB coefficient is 0.736 — less than 1.0. This means the formula discounts raw ApoB for individuals with low TG and Lp(a). In the profile we examined, that’s exactly what happened: ApoB 87 → RW-ApoB 82.

But if ApoB retention is the mechanistic baseline, the formula should structurally look like:

RW-ApoB = ApoB + [excess atherogenicity from TRL above ApoB] + [excess atherogenicity from Lp(a) above ApoB]

Under this framing, RW-ApoB should always be ≥ raw ApoB, never below it. Low TG and Lp(a) don’t make your ApoB particles less atherogenic — they simply mean there’s no excess risk to add. A mechanistically coherent formula should have a floor at ApoB, not a discounted fraction of it.

Why the Coefficient Is < 1

The 0.736 coefficient almost certainly emerged from the regression structure used to fit the weighting scheme. When you build a linear combination of TG, Lp(a), and ApoB to predict CHD events, and TG and Lp(a) are partly correlated with the total ApoB pool, the optimization process redistributes coefficient weight across the three terms. The result maximizes predictive fit statistically, but the coefficients are no longer interpretable as pure mechanistic weights — they are partially confounded by inter-correlations in the derivation cohort.

This is a version of the collinearity problem: ApoB, TG, and Lp(a) are not orthogonal, so the fitted coefficients absorb covariance in ways that violate the mechanistic interpretation the authors are implying.

The Deeper Issue: What the MR Estimates Actually Measure

The ~6.6× atherogenicity ratio for Lp(a) vs LDL from Björnson’s prior MR work is derived from genetic instruments that raise Lp(a) particle concentration vs. genetic instruments that raise LDL particle concentration. What this ratio actually captures is the total excess atherogenicity of an Lp(a) particle over and above an LDL particle — which includes:

  • The ApoB-mediated retention component (shared with LDL)
  • The apo(a) kringle domain-mediated vascular matrix binding
  • The oxidized phospholipid (OxPL) inflammatory signaling
  • The plasminogen-mimicry prothrombotic mechanism
  • Possibly differential subendothelial residence time due to the apo(a) tether

So the 6.6× is not the atherogenicity of Lp(a) instead of LDL atherogenicity — it’s the atherogenicity of Lp(a) relative to LDL, which itself has a baseline ApoB-retention component. When the paper uses these ratios as weights and applies them to ApoB fractions, it double-counts in one direction (attributing baseline ApoB atherogenicity to a “LDL class effect”) while discounting it in another (the <1 ApoB coefficient).

What a More Mechanistically Coherent Framework Would Look Like

Rather than:

RW-ApoB = weighted sum of particle-class ApoB fractions

A cleaner formulation would be:

Atherogenic burden = ApoB (baseline retention risk for all particles) + Δ(TRL excess) + Δ(Lp(a) excess)

Where the Δ terms capture only the incremental atherogenicity above and beyond the ApoB retention mechanism — which for TRL remnants likely reflects their higher cholesterol cargo per particle and possibly differential penetration kinetics, and for Lp(a) reflects the apo(a)-specific pathways enumerated above.

This would produce a metric that is transparently additive, mechanistically defensible, and would never implausibly discount a person’s raw ApoB.

The Broader Point

This is actually somewhat ironic, given that Borén’s group has been among the most articulate proponents of the retention hypothesis — the very framework that positions ApoB as the mechanistic unit of atherogenesis. As you pointed out, the RW-ApoB paper’s structure partially undermines that conceptual foundation by treating LDL as a risk class rather than ApoB as the universal atherogenic molecule with class-specific amplifiers layered on top.

What problem would you like to examine next?

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Second problem with the model.

Did the model consistently outperform ApoB as a risk predictor with the full dataset or by employing an excluded middle, which introduces error terms, possibly significant, that do not appear to have been explored. See:

“The validation spread across four independent cohorts is a genuine strength: RW-apoB consistently outperformed apoB as a risk predictor in Cox models across the UK Biobank and three other large population cohorts — MESA, Framingham Offspring Study, and SCAPIS. Oxford Academic Notably, since MESA included subjects from varying ethnic backgrounds, RW-apoB remained a significant predictor of CHD risk in African American individuals while measured apoB became non-significant.”

Excellent catch. You’ve identified something that was sitting in plain sight in the methods section I retrieved. Let me pull the full paper for the exact cohort specifications and exclusion criteria across all four datasets.

Now I have the full methods detail. You’ve identified something quite serious. Let me lay it out systematically.

The Excluded Middle Problem

MESA: Explicit Truncation of the Lower Distribution

The paper states directly: “MESA subjects with low apoB (<50 mg/dL) discordant with their lipid profile were not included.”

This is the excluded middle. By truncating the lower tail of the ApoB distribution, the comparison between RW-ApoB and raw ApoB is being conducted only on the range where the two metrics are most similar to each other — the middle to upper portion of the distribution. The subjects removed are precisely those where the divergence between the metrics would be most analytically interesting and potentially most damaging to the RW-ApoB claim: individuals with very low ApoB but possibly elevated Lp(a) or TRL fractions, where RW-ApoB’s reclassification would be most dramatic and its error terms largest.

The justification given — “discordant with their lipid profile” — is itself a circular exclusion. You cannot exclude subjects because their ApoB seems inconsistent with their other lipid values and then claim the metric performs well across the distribution. That exclusion criterion uses lipid relationships as the filter, which means you’re pre-selecting for a population where the inter-lipid relationships are internally consistent — precisely the condition under which a formula built on those relationships will look good.

The Non-Fasting / Fasting Systematic Bias

Here’s a second excluded-middle problem embedded in the cross-cohort comparison that doesn’t appear to have been addressed:

UK Biobank: Non-fasting samples. The paper explicitly notes “Blood samples were not obtained in the fasting state, so postprandial lipoproteins were possibly present.”

MESA, FOS, SCAPIS: All fasting samples (overnight, 12h, and 8h respectively).

Since TG is the primary input driving the TRL-ApoB estimate (the formula assumes 1 mmol/L TG = 85 nmol/L TRL particles), non-fasting TG will be systematically higher than fasting TG across the population. This means TRL-ApoB is systematically inflated in UK Biobank relative to the three validation cohorts. The formula will therefore behave differently across cohorts not because of genuine biological differences in the population but because of pre-analytical measurement conditions. This is not a minor technical detail — it affects the TRL term which carries a 4.5× atherogenicity multiplier. Error in the TG input gets amplified 4.5-fold in the RW-ApoB output.

The Lp(a) Unit Conversion Issue

FOS and SCAPIS reported Lp(a) in mg/dL and the authors converted using a fixed factor of 2.2 to nmol/L. The paper itself elsewhere acknowledges (consistent with EAS consensus) that no standard conversion factor is valid because the ratio varies with apo(a) isoform size. Using a fixed conversion factor introduces individual-level error of potentially ±30–40% for Lp(a)-ApoB estimation. Since Lp(a) carries a 6.5× multiplier in the formula, this error gets amplified enormously for high-Lp(a) individuals in FOS and SCAPIS — yet no sensitivity analysis of this conversion is reported.

The African American Subgroup Finding Deserves Scrutiny

The claim that “RW-apoB remained a significant predictor of CHD risk in African American individuals while measured apoB became non-significant” is presented as ethnic validation. But consider:

The African American subgroup in MESA is N = 1,260 — already after the apoB > 50 mg/dL exclusion. This is a modest sample in which:

  • ApoB becoming “non-significant” may simply reflect insufficient statistical power after truncation, not genuine failure of ApoB as a predictor
  • African Americans have substantially higher median Lp(a) (~75 nmol/L vs ~19 nmol/L in white populations) — meaning RW-ApoB is structurally advantaged in this subgroup because it has more Lp(a) signal to detect
  • The truncation at apoB > 50 mg/dL disproportionately removes a different fraction of the African American distribution than the white distribution, because the two populations have different ApoB distributions

In short, this finding may be an artifact of differential truncation effects rather than a genuine demonstration of cross-ethnic superiority.

The Median-Matching Calibration Problem

There is a third structural issue that connects to your first critique. The paper states:

“The multiplication factors were scaled so that the median RW-apoB in the population was approximately the same as the median measured plasma apoB.”

This means the 0.736 coefficient on ApoB is not independently derived from atherogenicity data — it is a calibration artifact designed to make the medians match. The scaling was performed to ensure the distributions are numerically comparable, which is defensible for clinical communication purposes. But it means the entire coefficient structure is contaminated by this normalization decision. You cannot independently interpret the 0.736 as having mechanistic meaning, and you cannot claim the formula’s superiority over raw ApoB is purely due to the atherogenicity weights, because one of the three weights is determined by population-level calibration, not atherogenicity estimates.

Summary: The Error Terms That Were Not Explored

Source of Error Amplification Factor Explored?
Non-fasting vs fasting TG ×4.5 (TRL weight) Not addressed
Fixed Lp(a) mg/dL → nmol/L conversion ×6.5 (Lp(a) weight) Not addressed
MESA lower-tail truncation (apoB < 50) Distribution-dependent Rationalized, not analyzed
Median-matching calibration inflating ApoB discount Structural Disclosed but not stress-tested
African American subgroup: differential truncation effects Power-dependent Not addressed

The core problem is that the three terms in the formula each carry large multiplicative amplifiers on their respective inputs (1×, 4.5×, 6.5×), yet the measurement error and systematic bias in those inputs — particularly TG measured non-fasting and Lp(a) converted with a fixed factor — were not subjected to sensitivity analyses. In a formula where input errors are amplified by up to 6.5-fold, this omission is not minor. It is a prerequisite for any honest claim of superiority over a single directly measured biomarker like ApoB, which carries no such amplification or conversion error.

NOTE: Claude caught my next questions on its own. One should not build a model without standardization that is driven so hard by TG, especially when below 200 or so where it becomes soft and downright squishy below 100 and this before the inter-measurement variation based on meals, exercise, etc. It also caught the Lp(a) conversion issue which I had not yet got to. Way to go Claude 4.6 Extended.

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I think the above is sufficient to put this model in context. I would also note a non-linearity that would make it more or less useful; i.e., predictively accurate depending on where you fall on metrics. Bottom line, this is useful but by no means a determinative way to think about one’s ASCVD risk.

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The reason why your RW-ApoB is lower than your apoB is because you have lower risk than the average combo of the atherogenic constituents. The average joe have more Lp(a) or TRL on average with the same apoB level as you, hence you have lower risk than what the apoB level show.

I also noticed that. The 2.2 conversion factor for nmol/L is BS and introduces a big risk.

This is metrologically unsound because what is measured by immunoassays is the protein component of Lp(a) and not its lipid and carbohydrate content. Therefore, the most appropriate units of measurement of Lp(a) are nmol/L. Lp(a) concentrations should not be converted from nmol/L to mg/dL, or vice versa, as all conversion factors are inherently isoform dependent.

Lp(a): When and how to measure it 2020 (Imperial College London & Manchester University NHS Foundation Trust)

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Thank you @adssx. This is a great paper and it adds another layer of complexity. I haven’t thought much more about this since the initial discussion but I had been thinking that the asymmetry of the Lp(a) population distribution also needs to be taken into account. In the end, it seems like a better metric for diagnosis and management would be a derived atherogenicity index.

If those cohorts are used I don’t think you can do anything else as far as I understand it’s because of inaccuracy of the mg/dL unit. But on an individual level and in studies it should be measured in nmol/L.