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e19534 Background: Immunomodulatory drugs, proteasome inhibitors plus dexamethasone are the standard triplet induction regimen for TENDMM. We analyzed the efficacy of adding either anti-CD38 monoclonal antibodies (mAbs) or anti-SLAMF7 antibody to the triplet therapy. Methods: Ovid MEDLINE, EMBASE and published abstracts were systematically searched from each database’s inception through 01-31-2024 to identify II/III trials assessing quadruplet therapy versus triplet therapy in TENDMM patients. Hazard ratios (HR) for progression-free survival (PFS) and overall survival (OS), odds ratios (OR) for overall response rates (ORR), measurable residual disease (MRD) negativity rate (with sensitivity of 10 -5) and grade 3 or higher treatment-related adverse events (G≥3 TRAE) were summarized using random effects meta-analysis via inverse variance approach. Additional subgroup analyses based on the cytogenetic risk were performed. P-value of interaction (Pint ) < 0.1 was pre-specified as statistically significant for the difference between the two groups. Results: 3806 studies were screened and seven trials were included with a total of 3546 patients. Four trials (2303 patients) assessed the addition of anti-CD-38 mAbs and three (1243 patients) assessed anti-SLAMF7 mAb, elotuzumab to the triplet regimen. Quadruplet therapy significantly improved PFS as compared to triplet (HR: 0.63; 95% CI: 0.43-0.92), and the PFS benefit was also observed in standard risk group (HR 0.5; 95% CI 0.2-0.9); however, results were insignificant for high-risk group (HR 0.8; 95% CI 0.62-1.93). No significant OS benefit was observed (HR: 0.79; 95% CI 0.49–1.26); however, data for three trials (CASSIOPEIA, DSMM XVII, PERSEUS) is immature, with longer-term follow-up ongoing. Quadruplet therapy showed significant results for very good partial response or better (OR: 1.7; 95% CI 1.2-2.36), stringent complete response (OR: 1.74; 95% CI 1.27-2.07) and complete response or better (OR: 1.91; 95% CI 1.19-3.06). MRD rate shows benefit with quadruplet therapy (OR: 2.59; 95% CI 2.22-3.01). Improved MRD rate was also observed for high-risk (OR 2.13; 95% CI 1.4-3.2) and standard-risk group (OR 2.58; 95% CI 1.68-3.94). Secondary analysis for efficacy of anti-CD38 mAbs showed benefit for PFS (HR: 0.44; 95% CI: 0.35-0.56), ORR (OR: 1.77;95% CI: 1.02-3.06), and MRD (OR: 2.98; 95% CI 1.8-4.8). G ≥3 TRAE did not significantly differ between the groups (OR: 1.05; 95% CI 0.97-1.12). However, quadruplet therapy showed significant risk of infection (OR: 1.2; 95% 1.05-1.35) and thrombocytopenia (OR: 1.41; 95% CI 1.24-1.59). Conclusions: Quadruplet therapy improved PFS, ORR, and MRD rates compared to triplet regimen and has demonstrated promising benefits to be considered the new standard of care for TENDMM patients.

Introduction: We validated the “Familial Hypercholesterolemia (FH) Decision Aid” to promote shared decision making (SDM) about lipid lowering therapy, based on patient-clinician encounters. Methods: Validation of the DA included an assessment of whether the content and design of the tool facilitated SDM and patient engagement and to determine how well the tool integrated with clinical workflow. Validation was conducted during patient-clinician encounters in the Mayo preventive cardiology clinic. As inclusion criteria we considered an upcoming appointment in the clinic, an LDL-C level ≥ ~160 mg/dL, and no prior diagnosis of FH. Prior to the encounters, each clinician viewed a demonstration of the online, interactive DA. During the encounters, the DA was shared with patients using the clinic room computer screen, either in-person or virtually. Patient-clinician encounters were video recorded for further analysis by two design researchers and experts in SDM. Results: Ten patients were enrolled in the study (7 male; average age 51 years). The average patient LDL-C level was 207 mg/dL, range 157 - 248 mg/dL. Review and qualitative analysis of the encounter recordings yielded 4 key findings: i) clinicians primarily used the cardiovascular risk estimate in the DA to guide conversation with patients; ii) highly trained specialists needed to change their usual approach to care and conversation to integrate the DA with their clinical workflows; iii) as clinicians became more familiar with the DA, they moved through the tool at a more efficient pace; and iv) each clinician developed his/her own style of navigating the DA, either moving through it one screen at a time or focusing on one specific section for the duration of the conversation. The DA effectively guided conversation and facilitated active patient engagement in 6 of 10 encounters. Social distancing and use of telemedicine encounters, due to the COVID-19 pandemic, limited patient engagement in 2 of 10 encounters. Overall, the DA promoted SDM regarding FH management in 9 of 10 encounters. Conclusion: Using patient-clinician encounters, we found the FH DA to facilitate SDM regarding lipid-lowering therapy. The tool has potential to improve care of FH patients and increase patient engagement and knowledge.

Introduction: Somatic mutations accrue in hematopoietic stem cells with age and can lead to selective clonal expansion, a process termed Clonal Hematopoiesis of Indeterminate Potential (CHIP). CHIP has been associated with increased risk of coronary heart disease (CHD) and myocardial infarction (MI). Whether CHIP is a risk factor for incident heart failure is unclear. Methods: The presence of CHIP was assessed in exome sequence data from 200,628 UKB participants using the Genome Analysis Toolkit pipeline and filtering somatic mutations from a pre-specified list within DNMT3A , TET2 , and ASXL1 . CHIP was defined as somatic mutations in these 3 genes with variant allele fraction (VAF) >10%. We ascertained incident heart failure (overall, ischemic, and non-ischemic), death, cardiovascular death, and coronary heart disease (CHD defined as of MI and/or coronary revascularization) as determined by UKB algorithms, self-report, and diagnosis/procedure codes. Cox proportional hazard models adjusted for age, sex, self-reported race/ethnicity, diabetes, hypertension, smoking, and BMI were used to assess whether CHIP was associated with incident adverse events. Results: We identified 3,885 participants with CHIP in DNMT3A , TET2 , and ASXL1 at a VAF of >10%. Over a median follow-up of 9 years, 2,055 participants developed incident heart failure. CHIP was associated with both ischemic and non-ischemic heart failure. We replicated previously reported associations of CHIP with death and incident CHD ( Table ). Conclusions: CHIP was associated with increased risk of both ischemic and non-ischemic heart failure. The association with ischemic subtype is likely mediated by the increased risk of CHD. The basis for the increased hazard of non-ischemic heart failure is unclear but may be due to an inflammatory milieu in CHIP carriers.

Introduction: We investigated the predictive power of a machine learning (ML) algorithm for 5-year mortality in patients with atherosclerotic cardiovascular disease (ASCVD). Candidate features included clinical variables from the electronic health record (EHR) as well as self-reported health and lifestyle data. Methods: The study cohort included 4,689 patients (66±11 years, 62% male) with ASCVD enrolled in the Vascular Disease Biorepository, which recruits patients referred to noninvasive vascular evaluation at Mayo Clinic, Rochester, MN. Clinical variables including comorbidities, lab measurements, and medication use were obtained from the EHR using validated electronic phenotyping algorithms. Information regarding social habits, physical activity levels, and personal health was obtained from a survey filled by the participants. Mortality was ascertained from the national death index. Clinical variables (n=52) and self-reported variables related to health and lifestyle habits (n=48) were added as individual features to reinforcement learning trees (RLT), a sparse decision tree algorithm, to construct the prediction model. We assessed the impact of each feature on prediction accuracy by applying feature selection and computing variable importance (VI). Results: Features with trivial VI that made no contribution to prediction were excluded. A model with 15 features had an area under the ROC curve (AUC) of 0.82 [95% CI 0.79 - 0.83] in the test set. Among the top 10 predictors of 5-year mortality, 6 were self-reported health variables (Table) , including outlook on personal health, limitations on routine daily activities, and physical activity levels. Conclusions: A ML approach had robust discrimination (AUC 0.82) in predicting 5-year mortality in patients with ASCVD. Self-reported health variables were among the top predictive features in the final model.

Introduction: Severe hypertriglyceridemia (SHTG), defined as plasma triglyceride (TG) level ≥ 500 mg/dl, is a risk factor for cardiovascular disease. In the NHANES dataset (2001 - 2006), the prevalence of SHTG was 1.7%. However, the prevalence of primary SHTG in the community is unknown. We conducted a community-based study to estimate the prevalence of primary SHTG, in Olmsted County, Minnesota. Methods: We used the Rochester Epidemiology Project (REP) records linkage system to identify individuals with TG ≥ 500 mg/dl between 1998-2015. Index date was defined as the first documentation of TG ≥500 mg/dl during the study period. We excluded individuals with mixed hyperlipidemia (TG ≥500 mg/dl and non-high density lipoprotein (HDL) cholesterol ≥190 mg/dl). HTG was defined as secondary in the presence of hypothyroidism, biliary obstruction, nephrotic syndrome, uncontrolled diabetes, pregnancy, liver disease, renal failure, BMI ≥35 and medication use (isotretinoin, tetracyclines, anti-retrovirals). Primary HTG was defined as absence of an identifiable secondary etiology. Characteristics of the primary SHTG individuals were ascertained from the electronic health record. Results: SHTG was identified in 3802 individuals of whom 2511 had mixed hyperlipidemia, 743 had a secondary cause for SHTG and the remaining 548 had primary SHTG. Using the 2015 population census of 151,436 for Olmsted County, the prevalence of SHTG was 2.5%, of secondary SHTG was 0.49% and of primary SHTG was 0.36%. Only half of the primary SHTG group had a diagnosis of dyslipidemia in the electronic health record and 44.3% were on lipid-lowering therapy on index date. Characteristics of individuals with primary SHTG are summarized in Table 1. Conclusions: These data highlight that primary SHTG is relatively common (≈1 in 300, similar to the prevalence of familial hypercholesterolemia), underdiagnosed and undertreated.

Introduction: Prior reports of the genetic etiology of hypercholesterolemia are affected by referral bias, inclusion of individuals with secondary hypercholesterolemia and variability in identifying pathogenic variants. We sought to assess the prevalence of monogenic and polygenic etiologies in a community based cohort of adults with primary hypercholesterolemia (LDL-C ≥155 mg/dL after excluding secondary causes). Methods: We identified 1719 individuals aged 18-70 years with primary hypercholesterolemia from SE Minnesota. The Dutch Lipid Clinic Network (DLCN) score was ascertained by an electronic health record (EHR) based algorithm followed by manual review. Familial hypercholesterolemia (FH) was considered present with a DLCN score ≥6 whereas a DLCN score of 3-5 was considered possible FH. Participants underwent sequencing of FH genes ( LDLR, APOB, PCSK9 ) and genotyping of 12 SNPs known to be associated with LDL-C. Rare (frequency <0.1%) and putatively functional variants were identified and pathogenicity was assigned based on the ACMG guidelines using relevant database, literature and EHR review. A polygenic score (PGS) for LDL-C (derived from 12 SNPs) >90 th percentile was considered an indicator of polygenic etiology. Results: A pathogenic/likely pathogenic variant (16 in LDLR , 1in APOB and 2 in PCSK9 ) was present in 24 (1.3%) individuals, a PGS >90 th percentile was present in 265 (15.4%) and 4 (0.2%) had both a monogenic and polygenic etiology. The overall prevalence of an identifiable genetic etiology was 17%. Based on clinical criteria the overall prevalence of FH (DLCN ≥6) was 7.0% (6.1% probable and 0.9% definite) and that of possible FH was 30.0%. In the possible, probable and definite FH categories, a monogenic etiology was present in 2.4%, 9.5% and 26.7% and a polygenic etiology in 19.1%, 21.0% and 13.3% respectively. 52.0% of those with a monogenic etiology and 9.7% of those with a polygenic etiology met the DLCN criteria for FH. Conclusions: This study provides for the first time estimates of monogenic and polygenic etiology of hypercholesterolemia in adults from the community, excluding cases of secondary hypercholesterolemia and using standardized methods of variant annotation.

Introduction: Familial Hypercholesterolemia (FH) is a common inherited disorder of lipid metabolism. FH patients are at an increased risk of atherosclerotic cardiovascular disease (ASCVD) due to elevated low-density lipoprotein cholesterol (LDL-C) levels. Patients with FH may not reach target LDL-C levels despite maximally tolerated statin (±ezetimibe) therapy (MTT). The AHA scientific statement on FH highlighted the need for developing decision aids (DAs) to facilitate shared decision making (SDM) regarding additional therapy including PCSK9 inhibitors. Methods: A user-centered design process was initiated to understand FH treatment decisions and to develop prototype interventions. Patients with FH had their diagnosis confirmed by genetic testing. Encounters between FH patients and health care providers were video recorded and analyzed to assess how decisions are made regarding lipid lowering therapy. Nine encounters were recorded: 3 with a genetic counselor and 6 with an FH specialist. Informed by clinical encounters and literature review, we developed content for a DA related to available therapeutic options for FH patients on MTT. Results: A prototype online DA was created to support in-encounter discussion of a patient’s risk of adverse cardiovascular events and which therapies may be best suited to the patient (Figure). 5 and 10-year ASCVD risk is estimated using the SAFEHEART Risk Score (SRS). Risk is communicated in numbers using a 100-person icon array and the language “Out of 100 persons like you...”. Therapeutic options include watchful waiting, bile acid sequestrants, and PCSK9 inhibitors. For each option, the degree of risk reduction, costs, and side effects are presented. The tool is currently being refined by analyzing additional encounters with FH patients. Conclusion: We developed an interactive prototype DA to facilitate SDM for FH patients on MTT. After further iterative testing the tool will be deployed in the electronic health record.

Background: Atherosclerotic cardiovascular disease (ASCVD) often affects > 1 arterial location (polyvascular disease, PVD). The risk of adverse outcomes increases with the number of arterial territories involved. The genetic susceptibility to PVD is unknown. Hypothesis: We hypothesized that a genetic risk score representing cumulative effects of susceptibility variants to ASCVD is associated with presence of PVD. Methods: We searched genome-wide association study (GWAS) catalog to identify genetic variants associated with coronary heart disease (CHD), ischemic stroke, peripheral arterial disease (PAD) or abdominal aortic aneurysm (AAA). No variants were reported to be associated with PVD in ≥ 3 arterial territories at GWAS significance. 10 loci were associated with PVD in 2 arterial territories. We calculated a multi-locus GRS using 10 lead single nuclear polymorphisms (SNP) from these distinct loci (1 per each loci) in 7486 participants in Mayo Vascular Disease Biorepository (age 68±10 years, 61% men, 31% with CHD; 27% carotid artery stenosis; 39% PAD; 14% AAA; 38% without any of these vascular diseases). Results: Of all participants 34% had PVD (2%, 10% and 22% with exact 4, 3 and 2 arterial territories involved respectively). A higher GRS was associated with greater number of arterial territories involved (P for trend of ANOVA <0.001). Patients with PVD in ≥ 3 arterial locations had higher GRS compared to those with 2 arterial locations involved after adjustment for age, sex and cardiovascular risk factors (P =0.008). One unit increase in GRS was associated with 9% higher likelihood for each additional arterial territory involved after adjustment for all covariates (odds ratio from ordinal regression model= 1.09, P <0.001). The result remained significant after further adjustment for combinations of AAA, CHD, carotid arterial stenosis and PAD (all P ≤ 0.01). 5 Of the 10 SNPs were each associated with presence of PVD after adjustment for all covariates (all P <0.01). None of them remained significant after further adjustment for combinations of PVD in different arterial territories (all P ≥ 0.4). Conclusions: A GRS derived from 10 SNPs of ASCVD is associated with PVD,suggesting genetic contribution to the dimension of PVD that may lead to adverse outcome.

Introduction: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide. The genetic contribution to mortality in patients with ASCVD is unknown. Hypothesis: Abdominal aortic aneurysm (AAA) often co-exists with ASCVD and two with shared genetic susceptibility underlying disease development. We hypothesiz that a genetic risk score (GRS) for susceptibility variants to AAA and ASCVD increases the risk for all-cause mortality in patients with ASCVD. Methods: We analyzed high-density genotyping data in 3053 participants (age 71 ±10 years, 72% men) with ASCVD in Mayo Vascular Disease Biorepository. We used 4 single nucleotide polymorphisms (SNPs) (rs2383207, rs7025486, rs599839, rs1466535) previously shown to be associated with AAA and ASCVD at GWAS significance (P ≤10^-8) in the NHGRI GWAS catalog to build the GRS. We defined ASCVD as a history of myocardial infarction / coronary revascularization / ischemic stroke / peripheral arterial disease with revascularization. Results: Patients with GRS > median level had similar age (70±10 vs. 71±10), prevalence of men (72% vs. 73%), hypertension (83% vs. 82%), diabetes (32% vs. 31%) and smokers (74% vs. 72%) compared to those with GRS ≤ median (all P>0.2), except for dyslipidemia (91% vs. 88%, P =0.02). After a median follow-up of 4.56 years (interquartile: 3.07-6.26), 620 patients died. Patients with GRS > median had poorer survival compared to those with GRS ≤ median (Figure: Kaplan-Meier curve); and with 23% higher risk for death after adjustment for age, sex and cardiovascular risk factors (hazard ratio HR, 95% CI: 1.23, 1.05-1.45). The association of GRS with all-cause mortality remained significant after excluding patients with known AAA (n=672) (HR, 95%CI: 1.22, 1.01-1.48). Conclusions: A GRS for susceptibility variants of AAA and ASCVD is a prognostic marker in patients with ASCVD.

Objectives: We investigated the association of 47 candidate proteomic markers in pathways of inflammation, thrombosis, lipoprotein metabolism, vascular remodeling, and vasoregulation, with the ankle-brachial index (ABI) and peripheral arterial disease (PAD) in a bi-ethnic cohort of African Americans (AA) and non-Hispanic Whites (NHW). Methods: Multiple assay platforms were used to measure 47 candidate proteomic markers in 1324 AA and 1237 NHW participants belonging to hypertensive sibships. ABI was measured using a standard protocol, with values >1.4 excluded and PAD defined as ABI <0.9. Multivariable regression analyses (with generalized estimating equations) were employed to identify markers independently associated with ABI and PAD after adjusting for age, BMI, smoking, hypertension, diabetes, history of heart attack and stroke, medication use, total and HDL cholesterol, and estimated GFR as well as physical activity, alcohol consumption and education level. Results: Higher circulating levels of CRP, myeloperoxidase, NT-proBNP, osteoprotegerin, D-Dimer and fibrinogen were each associated with lower ABI in both AA and NHW. Additional markers significantly associated with lower ABI were; in AA - serum amyloid A, ICAM, IL-6, TNFR-I, TNFR-II, MCP-1, Lp(a), pro-atrial natriuretic peptide, pro-arginine vasopressin, pro-adrenomedullin, pro-endothelin, osteonectin, Factor VIII; and in NHW- Hsp27, ox-LDL and resistin. Markers associated with presence of PAD were: in AA - CRP, NT-proBNP, Lp(a), pro-arginine vasopressin and osteoprotegerin; in NHW -CRP, NT-proBNP, Hsp27, myeloperoxidase, oxidized-LDL, Factor VIII, and D-Dimer. When the significant markers were included in a final model, the R 2 for ABI increased from 15.9 to 19.4 for AA and from 18.1 to 22.1 for NHW; the c-statistic for PAD increased from 0.791 to 0.833 inAA and from 0.841 to 0.866 in NHW. Conclusions: Novel protein markers significantly add to prediction of ABI and PAD in both AA and NHW and may have clinical utility in diagnosis and prognosis of PAD as well as in identifying new therapies for atherosclerotic vascular disease.