Philip S. Tsao

BACKGROUND: Epigenetic mechanisms underlying susceptibility to abdominal aortic aneurysm (AAA) remain poorly understood. Identifying causal DNA methylation markers for AAA can elucidate the regulatory processes that drive aneurysm formation and accelerate translational applications. We leveraged the Veterans Affairs (VA) Million Veteran Program to identify methylation biomarkers and delineate underlying pathways. METHODS: We first conducted an epigenome-wide association study of incident AAA (1324 cases and 42 065 noncases), performed stratified analyses by population group and smoking status, and conducted Mendelian randomization to facilitate causal inference of the cytosine-phosphate-guanine (CpG)–AAA association. Chromatin state, island context, and transcription factor binding were implicated through functional annotation of identified CpGs. To identify genes impacted by change in methylation state, we aligned associations with transcriptional data obtained in blood, aorta, and liver. We performed expression quantitative trait methylation to capture CpG–gene-expression links across the genome. Network Mendelian randomization was used to test cardiometabolic and inflammatory mediation. Finally, we developed a methylation risk score for AAA using penalized regression and evaluated its performance against a clinical model in the Million Veteran Program and 2 external studies. RESULTS: Epigenome-wide association study identified 1253 CpGs associated with incident AAA, and Mendelian randomization supported a putative causal role for 143 of these associations. Functional annotation pointed to predominantly distal, enhancer-centered regulation and enrichment of inflammatory transcription factor programs (eg, activator protein-1 and nuclear factor κB). This distal architecture was consistent with expression quantitative trait methylation results, which showed a larger number of trans associations. Among 143 CpGs associated with AAA in Mendelian randomization analyses, we identified 213 CpG–gene expression pairs with strong colocalization support, including AGER, C2 , and DDR1 . Multi-trait colocalization further prioritized 8 CpG–gene expression–AAA links with evidence of a shared genetic signal. Network Mendelian randomization identified 135 putative mediation pathways linking CpGs to AAA, including 58 via cardiometabolic traits and 77 via inflammation-related traits. Blood lipid traits mediated >40% of the associations between AAA risk and CpGs near LDLR (cg19305903) and TRIB1 (cg14128479). Among inflammation-related mediators, circulating DAG1, NEXN, MMP9, IL1RN, and MMP13 were prioritized as potential intermediates. Additionally, a CpG selection process was used to develop a methylation risk score for AAA, which strongly associated with disease in both the discovery cohort and 2 external validation cohorts, even after adjustment for a full suite of clinical covariates. CONCLUSIONS: This study identified putative causal DNA methylation markers for AAA, and multi-omics analyses implicate activator protein-1–linked inflammatory transcriptional programs, blood lipids, and multiple inflammation-related proteins as key pathways underlying methylation-associated AAA risk.

BACKGROUND: Visceral adiposity is a key driver of insulin resistance, which in turn is a strong risk factor for type 2 diabetes and cardiovascular diseases. METHODS: We investigated the overall and sex-specific associations between plasma proteomics and visceral adipose tissue volume, and their intersection with clinical and genomic data to identify critical candidate proteins for visceral adiposity possibly linked to insulin resistance. We analyzed data from UK Biobank participants, using multistep machine learning to establish linear and nonlinear associations. RESULTS: The combined model, integrating clinical, proteomic, and polygenic data, accounted for 67% of visceral adipose tissue variance. Our research revealed significant protein interactions with environmental factors and identified sex-specific proteins correlated with visceral adipose tissue variance in both males and females. CONCLUSIONS: The findings suggest that a small number of plasma proteins explain a significant portion of the variance of visceral adipose tissue beyond nonproteomic measures, offering a potential single-source, personalized measurement of visceral adiposity that is easily accessible and may facilitate improved prediction and understanding of the biology of insulin resistance.

Background: Smoking and family history are major risk factors for abdominal aortic aneurysm (AAA). Smoking and nicotine are modulators of DNA methylation, capable of causing heritable epigenetic alterations. Nicotine infusion and e-cigarette (e-cig) vaping augment murine AAA. We investigated the effects of murine maternal e-cig vaping on offspring experimental AAA, and impacts on transgenerational DNA methylation and accessibility. Methods: We performed perinatal e-cig nicotine vaping in two strains of adult female mice (ApoE-/- and C57BL/6J). Mice were mated to control males, and treated through gestation. A parallel group were instead sacrificed after treatment, for DNA analysis. Offspring were aged to adulthood, and AAAs were then induced by elastase (C57BL/6J) or angiotensin-II infusion (ApoE-/-), and ultrasound-tracked for 4 weeks. In a second cohort, parental and offspring aortic tissue and blood DNA underwent RRBS and ATAC-seq analysis respectively. Offspring abdominal aortic anatomy was examined, and stiffness was assessed with ex vivo arterial myography. Results: Maternal e-cig exposure increased offspring AAA growth in both models and genders. AAA incidence was increased in ApoE-/- males, and observed effects were overall more pronounced in males. Maternal vape exposure altered aortic DNA methylation and blood DNA accessibility in mothers and offspring, with differential sites in numerous genes identified, including AAA-related genes and pathways. Pathways were enriched for transcription factors, inflammation/immune-related genes, and angiogenesis genes, with similar changes identified in exposed mothers. Male offspring displayed more differential genes. Significant changes were located in genes known to harbor AAA risk loci in humans, and overlapped with published epigenetic alterations in human smokers. Differences also occurred in offspring aortic structure and stiffness. Conclusions: Maternal e-cig nicotine exposure augments AAA growth in multiple models across generations. These effects are accompanied by broad epigenetic DNA reprogramming of key AAA modulator genes and pathways.

Type 2 diabetes mellitus (T2D) increases the risk of atherosclerotic diseases, including coronary artery disease (CAD), but decreases that for abdominal aortic aneurysm (AAA), forming an intriguing diabetes-atherosclerosis paradox. We investigate how genetic underpinnings and drug effects shape these complex relationships. A robust positive correlation between T2D and CAD is found throughout the genome, whereas the weak genetic correlation between T2D and AAA is counter-balanced by two-thirds positive and one-third negative correlations. Through single nucleotide variant colocalization, gene annotation, pathway enrichment, and cell type associations, we discover that these positive correlations entail immune responses, whereas the negative correlation is characterized by beta-cell dysfunction and lipid metabolism. Furthermore, the effects of 98 canonical cardiovascular and metabolic drugs are elucidated by pathway pairing and drug-target Mendelian Randomization, revealing that the widely prescribed metformin and glitazones are also protective against atherosclerosis, whereas statins raise the T2D risk. Through screening the plasma proteome against 660 anti-inflammatory drugs, we identify 14 targets and 8 drugs for anti-inflammatory treatments of T2D and atherosclerosis. Our study discovers that both disease-disease and drug-disease interplay contribute to the complex relationships between diabetes and atherosclerosis. Importantly, immune responses synergize with diabetes and atherosclerotic diseases, suggesting anti-inflammatory therapies as a unified treatment strategy.

Rare coding alleles have crucial roles in the molecular diagnosis of genetic diseases. However, the systematic identification of these alleles has been challenging due to their scarcity in the general population. Here we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease (CAD), among 1,158,017 multi-ancestral individuals. Testing 2,997,401 rare coding variants, we identified 800 exome-wide significant associations (176 predicted loss of function (pLoF) and 624 missense variants). Associated alleles are enriched in functional variant classes, show significant additive and recessive associations, exhibit similar effects across populations and resolve pathogenicity for variants of unknown significance. Furthermore, we identified five lipid-associated genes associated with CAD. Among them, silencing RORC represents a potential therapeutic target for lowering low-density lipoprotein cholesterol. This study provides resources and insights for understanding causal mechanisms, quantifying the expressivity of rare coding alleles and identifying new drug targets for dyslipidemia across diverse populations.

BACKGROUND: Metabolic dysfunction–associated steatotic liver disease (MASLD) has a substantial inherited component. Rare variants in apolipoprotein B gene ( APOB ) have been implicated in susceptibility to liver steatosis, but their role in disease progression and outcomes is unclear. METHODS: We investigated APOB rare variants in a case-control cohort of people with advanced MASLD versus healthy controls ( n = 510 and 261, respectively), a family-based study ( n = 43 and literature meta-analysis), the Million Veteran Program (MVP) cohort ( n = 94,885), and the UK Biobank (UKBB) ( n = 417,657). RESULTS: In the clinical cohort, APOB variants were enriched in people with advanced MASLD (OR 13 . 8, 95% CI: 2 . 7–70 . 7, P = 0 . 002) and associated with lower circulating lipids, but higher MASLD activity and fibrosis ( P < 0 . 05). In the family study, APOB variants segregated with hepatic steatosis and fibrosis ( P < 0 . 05). Cross-ancestry meta-analysis of the study cohorts yielded pooled ORs for cirrhosis and hepatocellular carcinoma (HCC) of 1 . 82, 95% CI: 1 . 33–2 . 49 and 3 . 53, 95% CI: 2 . 09–5 . 98, respectively. Variants affecting specifically ApoB100 had a 3-fold greater effect on hepatic lipid metabolism compared with those impairing also ApoB48 and were specifically protective against coronary artery disease ( P < 0 . 05). The variants affected cirrhosis risk similarly, but ApoB48/100 had a larger effect on HCC ( P < 0 . 05). CONCLUSIONS: Rare APOB variants predispose individuals to advanced MASLD and HCC, with distinct contributions from disrupted VLDL and chylomicrons secretion. These findings highlight the interplay between hepatic and intestinal lipid handling, suggesting that APOB genotyping may enhance MASLD risk stratification and patient identification. FUNDING: European Union, Italian Ministry of Health, Swedish Research Council, Veterans Health Administration, NIH.

Background The Department of Veterans Affairs (VA) Million Veteran Program (MVP) began in 2011 with the goal of recruiting at least one million Veterans to participate in a large population genetic and health research program. The Million Milestone Campaign (MMC) was implemented between 2022 and 2023 to reach pre-pandemic recruitment rates and the millionth enrollee by Veterans Day of 2023. The objectives are to describe and evaluate the effectiveness of MMC recruitment strategies and to characterize the cumulative MVP cohort. Methods During the MMC, multiple recruitment strategies, including paper invitation mailings, recruitment calls, mass emails, enroll by mail invitations, and digital marketing, were implemented using a “surround sound” model. Strategy effectiveness was assessed via enrollment rates for each enrollment modality from September 2022 through July 2024. Email and digital media reach were measured by click-through rates and ads viewed. Standard mean differences were used to compare the demographics of enrollees during the MMC with those of enrollees prior, and the cumulative MVP cohort with those of non-MVP VA users. Results More than 6.5 million Veterans were contacted and 109,912 enrolled during the MMC. Enrollment rates from recruitment calls (13.7%) were highest, followed by invitations to enroll by mail (7.8%), email (0.8%), and paper invitations (0.6%). Emails yielded the most enrollments (n = 46,999, 43% of total enrollments), followed by recruitment calls (n = 22,069, 20%), paper invitations (n = 16,062, 14.6%), and invitations to enroll by mail (n = 7,762,7.1%). There were small differences in demographics between those enrolled during the MMC and prior enrollees (n = 906,672); likewise, the cumulative cohort (N = 1,016,584) of largely male participants resembles the non-MVP VA user population. Conclusions In the year leading to the culmination of one million MVP participants, the multi-strategy MMC was effective in recruiting nearly 110,000 Veterans and informing ongoing recruitment. Combined strategies used in a surround sound model of recruitment yielded enrollments that aligned with pre-pandemic rates and accumulated one-tenth the largest cohort of Veterans in VA research history. MVP’s integration within the VHA as a learning health care system, along with linkages to health records and other data sources, is a resource for investigators to improve Veteran health care with precision medicine.