Discordance Analysis of VLDL-C and ApoB in UK Biobank and Framingham Study: A Prospective Observational Study.

BACKGROUND: Recent observational and Mendelian randomization analyses have reported significant effects of very-low-density lipoprotein cholesterol (VLDL-C) on risk that is independent of ApoB. OBJECTIVES: To determine the independent association of VLDL-C and ApoB with the risk of new-onset cardiovascular events in the UK Biobank and the Framingham Heart Study Cohort. METHODS: We included 294 289 UK Biobank participants with a median age of 56 years, 42% men, and 2865 Framingham Heart Study participants (median age, 52 years; 47% men). The residual resulting from regressing VLDL-C on ApoB expresses the portion of VLDL-C not explained by ApoB, while the residual from regressing ApoB on VLDL-C expresses the portion of ApoB not explained by VLDL-C. Cox proportional hazards models for atherosclerotic cardiovascular disease incidence were created for residual VLDL-C and residual ApoB. Models were analyzed with and without high-density lipoprotein cholesterol (HDL-C). Furthermore, we investigated the independent effects of VLDL-C after accounting for ApoB and HDL-C and of HDL-C after accounting for ApoB and VLDL-C. RESULTS: In the UK Biobank, ApoB was highly correlated with VLDL-C (r=0.70; P<0.001) but weakly negatively correlated with HDL-C (r=-0.11; P<0.001). The ApoB residual and the VLDL-C residual were significantly associated with new-onset atherosclerotic cardiovascular disease (hazard ratio [HR], 1.08 and 1.06, respectively; P<0.001). After adjusting for HDL-C, the ApoB residual remained similar in magnitude (HR, 1.10; P<0.001), whereas the effect size of the VLDL-C residual was reduced (HR, 1.02; P=0.029). The independent effect of HDL-C (after accounting for ApoB and VLDL-C) remained robust (HR, 0.86; P<0.0001), while the independent effect of VLDL-C (after accounting for ApoB and HDL-C) was modest (HR, 1.02; P=0.029). All results were consistent in the Framingham cohort. CONCLUSIONS: When adjusted for HDL-C, the association of VLDL-C with cardiovascular risk was no longer clinically meaningful. Our residual discordance analysis suggests that adjustment for HDL-C cannot be ignored.

Discordance among apoB, non-high-density lipoprotein cholesterol, and triglycerides: implications for cardiovascular prevention.

BACKGROUND AND AIMS: Despite growing evidence that apolipoprotein B (apoB) is the most accurate marker of atherosclerotic cardiovascular disease (ASCVD) risk, its adoption in clinical practice has been low. This investigation sought to determine whether low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (HDL-C), and triglycerides are sufficient for routine cardiovascular care. METHODS: A sample of 293 876 UK Biobank adults (age: 40-73 years, 42% men), free of cardiovascular disease, with a median follow-up for new-onset ASCVD of 11 years was included. Distribution of apoB at pre-specified levels of LDL-C, non-HDL-C, and triglycerides was examined graphically, and 10-year ASCVD event rates were compared for high vs. low apoB. Residuals of apoB were constructed after regressing apoB on LDL-C, non-HDL-C, and log-transformed triglycerides and used as predictors in a proportional hazards regression model for new-onset ASCVD adjusted for standard risk factors, including HDL-C. RESULTS: ApoB was highly correlated with LDL-C and non-HDL-C (Pearson's r = .96, P < .001 for both) but less so with log triglycerides (r = .42, P < .001). However, apoB ranges necessary to capture 95% of all observations at pre-specified levels of LDL-C, non-HDL-C, or triglycerides were wide, spanning 85.8-108.8 md/dL when LDL-C 130 mg/dL, 88.3-112.4 mg/dL when non-HDL-C 160 mg/dL, and 67.8-147.4 md/dL when triglycerides 115 mg/dL. At these levels (±10 mg/dL), 10-year ASCVD rates for apoB above mean + 1 SD vs. below mean - 1 SD were 7.3 vs. 4.0 for LDL-C, 6.4 vs. 4.6 for non-HDL-C, and 7.0 vs. 4.6 for triglycerides (all P < .001). With 19 982 new-onset ASCVD events on follow-up, in the adjusted model, residual apoB remained statistically significant after accounting for LDL-C and HDL-C (hazard ratio 1.06, 95% confidence interval 1.0-1.07), after accounting for non-HDL-C and HDL-C (hazard ratio 1.04, 95% confidence interval 1.03-1.06), and after accounting for triglycerides and HDL-C (hazard ratio 1.13, 95% confidence interval 1.12-1.15). None of the residuals of LDL-C, non-HDL-C, or of log triglycerides remained significant when apoB was included in the model. CONCLUSIONS: High variability of apoB at individual levels of LDL-C, non-HDL-C, and triglycerides coupled with meaningful differences in 10-year ASCVD rates and significant residual information contained in apoB for prediction of new-onset ASCVD events demonstrate that LDL-C, non-HDL-C, and triglycerides are not adequate proxies for apoB in clinical care.

Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare.

BACKGROUND: The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of "big data" in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively. PURPOSE: We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a "real-world" AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative. METHODS: Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework. RESULTS: The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies. CONCLUSION AND RELEVANCE: As health systems learn how well-intentioned clinical algorithms can potentially perpetuate health disparities, we have an opportunity and an obligation to do better. New efforts empowering nurses to advocate for BE FAIR, involving them in AI governance, data collection methods, and the evaluation of tools intended to reduce bias, mark important steps in achieving equitable healthcare for all.

A Federated Registration System for Artificial Intelligence in Health.

This Viewpoint discusses a suggested framework of local registries to record and track all health artificial intelligence technologies used in clinical care, with the goal of providing transparency on these technologies and helping speed adoption while also protecting patient well-being.

Clinical Trials in the 21st Century - Promising Avenues for Better Studies.

Innovation in Clinical Trials in the 21st CenturyMedical evidence is rooted in randomized controlled trials but there is a pressing need for innovative designs. Pencina and Thompson introduce a new series that reviews the most promising innovations in trial design and interpretation.