From particle count to residence time: a kinetic framework for atherogenic risk

Two sequential advances have narrowed the gap between lipid measurement and atherogenic mechanism. Mendelian randomization established that coronary risk tracks apolipoprotein B (ApoB) particle count, not cholesterol content: when natural discordance exists between low-density lipoprotein cholesterol (LDL-C) and ApoB, risk follows ApoB reduction independently of LDL-C. The 2026 ACC/AHA dyslipidemia guideline formalized this advance, placing ApoB above LDL-C as a risk measure in discordance contexts. That particle-count model fails systematically before a set of clinical paradoxes sharing a common feature: identical ApoB concentrations generating radically different cardiovascular risk depending on metabolic and endothelial context. Hemodialysis patients carry ten- to twenty-fold excess cardiovascular risk with normal ApoB, and statins do not reduce events in dialysis despite reducing LDL-C. PCSK9 loss-of-function (LOF) variants produce an 88% coronary risk reduction at LDL-C of 100 mg/dL, a magnitude approximately three times what the logarithmic dose-response predicts. Patients with systemic lupus erythematosus and rheumatoid arthritis develop accelerated atherosclerosis with normal lipids. Male endurance athletes with favorable lipid profiles show coronary artery calcium scores equivalent to sedentary controls with traditional risk factors. I propose that atherogenic risk is a function of the plasma residence time (RT) of ApoB-containing particles, determined by two separable kinetic components: FCR_est (structural, genotype-dependent fractional catabolic rate, d-1) and FCR_cin (functional, kmod-dependent clearance efficiency, dimensionless 0−1), modulated by an endothelial permeability function proxied by the urinary albumin-to-creatinine ratio (UACR). The framework is expressed as: Atherogenic Load = [ ApoB ] × ( 1 / FCR ) × kmod × f ( UACR , glycocalyx ) . The model generates a four-group functional classification with qualitatively distinct therapeutic implications: the rate-limiting step is the LDLR receptor in Group 1a [familial hypercholesterolemia, PCSK9 gain-of-function (GOF) variants], the ApoB-100 ligand in Group 2 (metabolic syndrome, uremia, autoimmunity), and the endothelial gate in Group 3. Treating all groups with the same LDL-C reduction strategy is a category error with quantifiable consequences. The five core predictions of the framework are falsifiable with currently available stable-isotope kinetic and anion-exchange chromatography technology.