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Abstract Introduction High disease burden has been associated with reduced efficacy and resistance to BCMA CAR-T therapy in relapsed/refractory multiple myeloma (van de Donk, Blood Cancer Discov, 2021; Martin, J Clin Oncol, 2023). While CAR-T cells can still induce remissions in these patients, outcomes are often worse compared to those with a lower tumor load. In addition to tumor debulking, novel strategies are needed to improve responses and durability of CAR-T cells in these patients. We previously demonstrated that immunomodulatory drugs (IMiDs) improve in vivo responses to T cell engagers (TCE) in high tumor burden setting by boosting T cell activation (Meermeier, Blood Cancer Discov, 2021; Meermeier, Blood, in press). IMiDs have also been shown to promote CAR-T cell antitumor activity in moderate to low tumor burden conditions (E:T ratios ≥1:1) (Yan, J Transl Med, 2024; Wang, Clin Cancer Res, 2018). We hypothesized that combining CAR-T with an IMiD may improve CAR-T function in high tumor burden setting. Methods We generated murine anti-BCMA.CD28 CAR-T cells by transducing Con-A stimulated T lymphocytes extracted from hCRBN+ mice, expanded with IL-7 and IL-15 to express a memory phenotype. We then tested CAR-T, or untransduced T cells, ability to kill in vitro IMiD resistant, hCRBN-, Vk*MYC myeloma cells in low (E:T 1:1) and high (E:T 1:≥5) tumor burden, with and without pomalidomide (POM) and IL-2. Tumor killing and T cell phenotype were assessed by flow cytometry at 24-72 hours. This controlled system allowed us to focus on putative CAR-T cell intrinsic impact of IMiDs. Results In low tumor burden setting, CAR-T effectively killed tumor at all timepoints, with or without POM; addition of POM increased T cell number, confirming that IMiDs support CAR-T expansion. Unexpectedly, in high tumor burden (E:T 1:≥5), adding POM diminished CAR-T mediated killing over time and hindered CAR-T expansion. Moreover, combination with POM decreased granzyme B, while increased expression of exhaustion marker TOX, ultimately leading to CAR-T apoptosis. This phenotype was further intensified by increasing concentrations of IL-2 in the co-culture, resulting in increased expression of CAR-T expressing LAG-3, TIM-3, and TIGIT, suggesting that CAR-T cells may be overactivated by POM and IL-2. Our manufacturing protocol preferentially expands memory CAR-T cells, favored for their durability, proliferative capacity, and considered to rapidly expand upon encounter with antigen. We reasoned that CAR-T are already sufficiently activated by CAR/CD3z stimulation, especially with a CD28 co-stimulatory domain (versus 4-1BB), known promote rapid, effector memory expansion upon activation (Kawalekar, Immunity, 2016). Activation is intensified by high tumor burden where antigen is highly abundant. We hypothesize under intense, sustained antigen exposure in high tumor burden, the addition of POM paradoxically triggers activation induced cell death (AICD). Conclusion Although it is well accepted that POM can boost T cell function and proliferation, we demonstrate that in high tumor burden, POM drives memory CAR-T to increase exhaustion markers and apoptotic cell death by overactivation, further exacerbated by exogenous IL-2. Interestingly, we have shown opposite effects of preclinically combining POM with TCE therapy in high tumor burden, based on dosing schedule. Concomitant administration transiently boosted T cell activation improving response rates but rapidly led to T cell exhaustion (Meermeier, Blood Cancer Discov, 2021). In contrast, IMiD pretreatment in the context of step-up dosing TCE administration improved T cell fitness and longevity (Meermeier, Blood, in press).The effect of IMiDs on T cells transcriptional program is likely to differ depending on differentiation stage and mode of T cell activation. Memory cells have more sensitive TCR signaling (Kumar, Immunity, 2011) and can be hyperactivated, leading to AICD. Thus, timing of IMiD administration, when tumor burden is low, may be a critical consideration as different T cell subsets require different levels of activation. Conversely, TCE provide only initial activation via TCR signaling, therefore POM may help in providing necessary co-stimulation in this setting. Future studies will evaluate different co-stimulatory domains and test in vivo effects of POM on CAR-T expansion and persistence, particularly as CAR-T cells diminish over time as tumor burden increases.

Abstract Overview: The emergence of effective immune therapies for multiple myeloma has highlighted the importance of understanding the roles of less abundant components within the tumor microenvironment. Single-cell analyses provide snapshots of the functional relationships of individual cells at precise moments in time, as well as insight into the overall cellular composition. By integrating single-cell datasets from multiple studies, we improve our ability to identify trends within rare cell populations, such as macrophages, and explore their clinical implications. We investigate human data here because our prior research of murine models indicated that macrophages expressing C1q, particularly those co-expressing Fcgr4, tend to correlate positively with increased tumor burden. Methods: We combined 56 human bone marrow samples – primarily collected at baseline, post-induction, or post-ASCT – with three publicly available data sets: GSE210079 (post CAR-T bone marrow microenvironment); GSE223060 (single-cell analysis of the MMRF cohort); and GSE271107 (disease progression from healthy donors and MGUS to SMM and NDMM). These data sets were integrated using Seurat to generate robust cellular clusters from over 300,000 cells across 117 samples. We validated our single-cell findings with bulk Affymetrix, whole bone marrow gene expression data (collected at baseline and post-baseline) from the Total Therapy clinical trials (GSE136324). Results: A distinct cluster of C1QA/B/C-expressing macrophages was consistently identified in all single-cell cohorts. These cells co-express FCER1G, CSF1R, and CD68 (markers also expressed in murine C1Q macrophages) and typically cluster adjacent to FCGR3A-expressing monocytes. Within the disease progression cohort, C1Q macrophages were found in all phases of progression: healthy donors, MGUS, SMM, and NDMM. However, C1Q macrophages in progressive disease (SMM and NDMM) have distinct expression features including lower levels of MRC1 and higher levels of APOBEC3A and ISG15 – genes linked to inflammatory responses. Among 11 post-induction samples submitted to lenalidomide maintenance, five with shorter responses displayed elevated expression of APOBEC3A and ISG15 in C1Q-expressing macrophages compared to six samples with longer responses. In a cohort of 22 samples 100 days post-ASCT, seven poor responders showed both an increased frequency of C1Q macrophages and elevated APOBEC3A expression compared to good responders. The MMRF cohort indicated that higher baseline frequencies of C1Q macrophages often preceded rapid disease progression (e.g. MMRF_1720, MMRF_1641). Anecdotal evidence from the CAR-T therapy cohort suggests that expansion of C1Q macrophages could serve as an early warning for tumor recurrence; sample 16 showed retention and expansion of C1Q macrophages 28 days post-CAR-T, that was subsequently followed by increased tumor cell counts at 3 months post-therapy. Analysis of bulk, whole bone marrow gene expression revealed that elevated levels of C1Q genes correlated with poorer outcomes, particularly in post-baseline samples. Multivariate Cox survival analysis revealed that high C1Q expression independently predicted inferior overall survival in post-baseline samples after adjusting for age, ISS stage, high levels of tumor-associated markers (SDC1, JCHAIN, DERL3), and high-risk UAMS7 subtypes (MF, MS, PR). Moreover, post-baseline GEP70 risk scores (from matched bone marrow aspirates) positively correlated with elevated C1Q expression in whole bone marrow samples. Conclusion: Both single-cell and bulk analyses indicate that the persistence of immunosuppressive C1Q macrophages following therapy is associated with inferior clinical outcomes. Although the precise mechanisms are yet to be fully understood, the close proximity of similar macrophages to tumor cells–as observed in other cancers–likely also occurs in myeloma and reflects the crucial relationship between tumor cells and macrophages within the bone marrow. While C1Q macrophages are typically rare in individual samples, analysis of combined cohorts is building evidence of their crucial role in immune cross-talk and therapeutic resistance mechanisms.

Abstract Introduction: Cilta-cel is approved in the US and the EU for the treatment of patients (pts) with lenalidomide-refractory multiple myeloma (MM) after ≥1 prior line of therapy (pLOT), including a proteasome inhibitor and an immunomodulatory agent based on the CARTITUDE-1 (CART-1, NCT03548207) and CARTITUDE-4 (CART-4, NCT04181827) studies. Cilta-cel led to deep and durable responses and significant survival benefit in pts with relapsed/refractory MM (RRMM) after ≥3 pLOT in CART-1. In CART-4, cilta-cel led to a significant overall survival benefit with a higher proportion of pts achieving deep, sustained minimal residual disease negativity vs standard of care in pts with lenalidomide-refractory MM after 1–3 pLOT. We investigated the mechanism of action of cilta-cel by correlating biomarkers from peripheral blood and the bone marrow TME with progression-free survival (PFS) and number of pLOT in pts with RRMM from CART-1 and CART-4. Methods: Biomarker analyses were performed using peripheral blood and bone marrow aspirates (BMAs) collected in both CART-1 and CART-4 studies. Immunophenotyping by flow cytometry was performed using peripheral blood at baseline/time of apheresis in samples from both studies. Immune fitness at baseline was assessed in pts by pLOT and in association with PFS. Gene set enrichment scores were derived from RNA sequencing data of the TME using BMAs. Mixed-effects models were used to identify signatures/pathways modulated over time (day [D] 28 and at 6 months [mo] post infusion vs screening) and in association with PFS and pLOT. Results: In total, 176 CART-4 and 97 CART-1 pts received cilta-cel as study treatment. Immunophenotyping data were available from 248 peripheral blood samples (1 pLOT, n=56; 2 pLOT, n=61; ≥3 pLOT, n=131). At apheresis, CD4+ naïve T cells (%) were higher in pts with 1 or 2 pLOT, compared with those with ≥3 pLOT, while no difference was found in pts with ≥3 pLOT with further breakdown (i.e., 3–4 vs 5–6 vs ≥7 pLOT). Further, higher baseline levels of CD4+ naïve T cells were associated with longer PFS. These data suggest that the negative impact on peripheral immune fitness with the addition of LOT may plateau when pts have received ≥3 pLOT. TME gene expression data were available from 148 BMA samples (screening, n=50; D28, n=50; 6 mo, n=48) from CART-4. Analyses of gene expression signatures suggest depletion of B cells and antibodies at D28 post cilta-cel infusion, with partial recovery at 6 mo. This was corroborated in parallel by flow cytometry and immunoglobulin quantification. Further, elevated expression of genes associated with myeloid cells, including tumor-associated macrophages (TAM, likely M1), and of genes associated with cytotoxic T cells were also observed at D28. Elevated expression of genes associated with B-cell receptors, T-cell differentiation and activation, and cytokine signaling pathways were found at both D28 and 6 mo. Pts with longer PFS (>18 mo) had higher levels of M1 TAM at D28, while those with shorter PFS had higher levels of regulatory T cells and more suppressed interferon pathway genes at 6 mo, suggesting that relapse may be associated with a more suppressive immune response. In pts with fewer pLOT, more profound B-cell depletion on D28, better recovery at 6 mo, and elevated B-cell receptor signaling were inferred from gene expression data. Pts with 1 pLOT also demonstrated higher elevation of M1 TAM expression on D28 from screening as compared with pts with 3 pLOT, similar to what was observed in pts with longer PFS. Conclusions: Correlative biomarker data suggest that longer PFS is associated with better immune fitness at baseline and stronger immune responses post cilta-cel infusion, as observed in peripheral blood and within the TME of pts with RRMM in CART-1 and CART-4. The peripheral immune fitness was more pronounced in pts with 1 and 2 pLOT vs 3 pLOT and beyond, where deterioration plateaued. This suggests the impact of T-cell immune fitness on PFS may be limited beyond the third LOT. Importantly, other covariates, including factors within the immune TME and tumor burden, may play a significant role in balancing this compromised intrinsic T-cell immune fitness at apheresis, thereby contributing to PFS durability after 3 pLOT. Overall, these results support increased benefits in treating pts with MM with cilta-cel in earlier LOTs.

Abstract Background Mosaic chromosomal alterations (mCAs) are large-scale somatic gains, losses, or copy-neutral loss of heterozygosity (CNLOH) detectable by genotyping of peripheral blood DNA. They represent a form of clonal hematopoiesis and are observed in 0.5-5% of cancer-free individuals. The presence of any autosomal mCA has been associated with >3-fold risk of hematological malignancies. In particular, mCAs occurring in regions related to lymphoid malignancies have an 11-fold increased risk of lymphoid malignancies and a 68.6-fold risk for chronic lymphocytic leukemia (CLL). Monoclonal B-cell lymphocytosis (MBL), the precursor state to CLL, is defined by <5×10⁹/L clonal B cells in otherwise healthy individuals and is classified as low-count (<0.5x109/L) or high-count (0.5-5x10⁹/L) based on clonal B-cell count. MBL is also associated with increased risk of lymphoid malignancy (beyond CLL), with 4.3-fold and 74-fold risk in individuals with low- and high-count MBL, respectively. We previously reported a significant enrichment of autosomal mCAs in MBL, demonstrating a 1.7-fold association in those with low-count MBL and a 50.5-fold association in those with high-count MBL. Whether mCAs independently predict lymphoid malignancy risk after accounting for MBL status is unknown. Here, we evaluated the impact of mCAs on risk of incident lymphoid malignancies in individuals with MBL and in individuals screened negative for MBL. Methods CLL-type MBL (CD5 positive) was identified using flow cytometry assay on peripheral blood. Participants were age ≥40, had no prior hematologic malignancy, and were enrolled through the Mayo Clinic Biobank or the Division of Hematology at Mayo Clinic, Rochester. Participants were followed for incident lymphoid malignancy, including CLL, with diagnoses confirmed by medical record review. mCAs with cell fraction >1% were detected from array-based genotyping of whole blood DNA. Cox proportional hazards models, adjusted for age and sex, estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of developing an incident lymphoid malignancy in individuals with autosomal mCA versus those without an autosomal mCA. Results In 4,331 participants with available MBL and mCA status, the median age was 67 years, and 45% were male. Flow cytometry identified 3,231 individuals who screened negative for MBL, 754 individuals with low-count MBL, and 346 individuals with high-count MBL. Autosomal mCAs were present in 6.5% of individuals without MBL, 7.6% with low-count MBL, and 71.1% with high-count MBL. Over a median follow-up of 6.9 years, 237 individuals developed an incident lymphoid malignancy. The most common types were CLL, multiple myeloma, and diffuse large B-cell lymphoma. Ignoring MBL status, the presence of an autosomal mCA was associated with increased risk of lymphoid malignancy (HR = 2.65; 95% CI: 1.45–4.86; P = 0.002), replicating previous results. However, after excluding individuals with MBL, this association attenuated; those with mCA alone did not have a significantly elevated risk (HR = 1.60; 95% CI: 0.57-4.52; P = 0.38) compared to individuals without MBL or mCA. We next examined the effect of mCAs among individuals with MBL. In those with low-count MBL, the most frequent mCA events were 13q loss (2.1%), 12 gain (1.9%), and 4q CNLOH (1.2%), and individuals with an mCA had a borderline significant risk of developing a lymphoid malignancy (HR = 2.78; 95% CI: 1.01–7.67; P = 0.049) compared to those without an mCA. Similarly, in those with high-count MBL, the most frequent mCA events were 13q loss/CNLOH (31.2%), 12 gain (13.0%), and 11q loss (4.9%), and the presence of an mCA conferred an increased risk compared to those without mCA (HR = 1.51; 95% CI: 1.09-2.08; P = 0.012). Conclusions Autosomal mCAs were more frequent in individuals with high-count MBL compared to those with low-count or no MBL. mCAs showed limited predictive value for lymphoid malignancy among individuals without MBL. In contrast, in individuals with low-count MBL, autosomal mCAs emerged as a potential biomarker for identifying those at risk of developing a lymphoid malignancy. In high-count MBL, mCAs were also a biomarker, but were driven by known chromosomal abnormalities identified through clinical testing. These findings suggest that the utility of mCAs as a predictive marker for lymphoid malignancy is contingent on the presence of MBL and may inform risk stratification and surveillance strategies in this population.

Context.— Clinical laboratories are increasingly implementing pharmacogenomic (PGx) testing. Although PGx is similar to genetic testing for other indications, there are unique aspects that laboratories should consider. Objective.— To aid clinical laboratories that are implementing clinical PGx testing by describing characteristics of PGx test design and validation, as well as approaches to reporting. Resources that are useful for clinical laboratories performing PGx testing will be highlighted. Design.— The College of American Pathologists formed a workgroup composed of laboratorians with expertise in clinical PGx testing. The workgroup included representatives from the Association for Molecular Pathology and the American College of Medical Genetics and Genomics. The workgroup reviewed pertinent literature, as well as experience from proficiency testing and from members’ laboratories. Results.— The workgroup recommends that laboratories implementing PGx consider the following concepts: testing platform, test design (ie, selection of pharmacogenes and variants/alleles), use of reference materials during test development and as controls during clinical runs, star allele and standard nomenclature systems, translations from genotype to predicted phenotype, and considerations for result reporting including making medication recommendations. The workgroup provides considerations when using report vendors, emphasizing the clinical laboratory’s role and responsibility when implementing such reporting tools from vendors. Conclusions.— Clinical laboratories should be familiar with the fundamentals of PGx, ensure that PGx testing meets the applicable regulatory requirements for all aspects of the clinical laboratory testing process, and follow recommendations for standardization of nomenclature and reporting.

Abstract Introduction/Objective Plasmablastic lymphoma (PBL) is a rare type of B-cell lymphoma with poor prognosis. They can arise de novo in immune deficiency/dysregulation, associated with transplant or transformation of low grade lymphoma. PBL has been reported with anti-CD19 chimeric antigen receptor (CAR) T-cell therapy. Methods/Case Report 69-year-old male with CLL/SLL, treated with multiple chemotherapy regimens and was in remission. He developed generalized weaknesses. Radiology showed lymphadenopathy. Pathology was positive for DLBCL without MYC and BCL2 rearrangement. Flow cytometry revealed 1% CD5 and CD23 positive. TP53 is negative and mutated IGHV. The PD-1 was negative, favored de novo DLBCL. PET-CT and biopsy confirmed residual disease. For refractory DLBCL, CAR-T cell therapy was initiated. Patient developed PBL diffusely positive for CD138, MUM1, c-MYC and kappa; negative for CD19, CD20, PAX5, CD10, BCL6 and CD79a. MIB-1 was 90%. EBV and HHV8 were negative. BCMA was >95 %, Teclistamab was initiated. The renal function deteriorated and proceeded with palliative care. Results NA Conclusion The nature of Richter transformation or de novo DLBCL into PBL in the setting of immunomodulation with CAR-T is rare occurrence. Molecular and clonality studies are needed to evaluate the pathways of losing B-cell markers to evade lineage specific therapy.

Importance Treatment for cognitive dysfunction due to postacute sequelae of long COVID (ie, symptoms of fatigue, malaise, weakness, confusion that persist beyond 12 weeks after an initial COVID infection) remains a significant unmet need. Objective To test evidence-based rehabilitation strategies for improving cognitive symptoms in persons with long COVID. Design, Setting, and Participants This was a 5-arm, multicenter, randomized clinical trial of 3 remotely delivered interventions conducted between August 17, 2023, and June 10, 2024. The study took place at 22 trial sites and included the screening of individuals with cognitive long COVID. Interventions Participants were randomized to 1 of 5 arms: adaptive computerized cognitive training (BrainHQ [Posit Science]), cognitive-behavioral rehabilitation involving both group and individual counseling sessions (PASC-Cognitive Recovery [PASC-CoRE]) paired with BrainHQ, and transcranial direct current stimulation (tDCS) paired with BrainHQ. Two comparator arms were included as follows: unstructured computer puzzles and games (active comparator) and sham tDCS paired with BrainHQ. The interventions occurred 5 times per week over 10 weeks. Main Outcomes and Measures Cognitive and behavioral in-person assessments were performed at baseline, midintervention, at the end of intervention, and 3 months after the end of the intervention. The primary outcome measure was the modified Everyday Cognition Scale 2 (ECog2) completed at the end of the intervention compared to the baseline visit based on participant self-report looking back over the prior 7 days. Results A total of 378 individuals were screened, from which there were 328 participants (median [IQR] age, 48.0 [37.0-58.0] years; 241 female [73.5%]; race: 15 Asian [4.6%], 47 Black [14.3%], and 235 White [71.6%]; ethnicity: 52 Hispanic [15.9%]). None of the 3 active interventions demonstrated benefits on the modified ECog2 in the intention-to-treat population by the end of the intervention period. The adjusted differences in mean change were 0.0 (95% CI, −0.2 to 0.2) for BrainHQ vs active comparator, 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs active comparator, 0.0 (95% CI, −0.2 to 0.2) for tDCS-active + BrainHQ vs tDCS-sham + BrainHQ, and 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs BrainHQ alone. Secondary participant-reported outcomes and neuropsychological tests showed no differential benefits for any treatment arm. All 5 arms demonstrated some improvements over time on the modified ECog2 and on secondary outcomes. There were no serious adverse events attributable to the interventions. Conclusions and Relevance This phase 2 randomized clinical trial failed to demonstrate differential benefits for online cognitive training, a structured cognitive rehabilitation program, and tDCS for cognitive long COVID. Trial Registration ClinicalTrials.gov Identifier: NCT05965739 .

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.