Precision Medicine Initiative Research Articles

Abstract PO5-08-10: Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study

Abstract Introduction: Breast cancer is the most common cancer among women in Taiwan and the incidence rate of breast cancer has been increasing steadily over the past 30 years. To address this, a nationwide screening program with biennial mammography for women aged 40-69 was implemented in 2004, which has contributed to a significant reduction in breast cancer mortality. Asian women are more likely to have dense breast tissue, and studies have shown a correlation between breast density and the risk of developing breast cancer, with higher breast density values associated with increased risk. Polygenic risk scores (PRS) are calculated based on an individual's genetic information and can estimate their risk of developing a particular disease. Although PRS can identify low-penetrance risk variants, its use in breast cancer risk estimation is typically limited to gene-only models, and integration with breast cancer screening databases is rare. Hence, the aim of this specific study was to evaluate the predictive capacity of PRS in women with dense breast tissue by exploring potential genetic markers and constructing a PRS to investigate the genetic factors associated with clinical characteristics and the risk of developing breast cancer. Methods: The PRS was developed using genetic information from the Taiwan Precision Medicine Initiative genotyping data. A total of 6335 patients were enrolled, and 101 single nucleotide polymorphisms (SNPs) were selected as candidate markers based on the PGS Catalog (PGS000001). Clinical characteristics of the study cohort were summarised using median and range, or frequency and percentage. The distribution of characteristics between breast cancer and controls was estimated using an independent two-sample t-test, chi-square, and Fisher’s exact test. The association of the PRS and related characteristics with breast cancer risk in the study cohort was estimated using univariate and multivariate binomial logistic regression. Harrel’s C-index was reported to demonstrate the predictive performance of PRS in both univariate and multivariate models. All p-values were two-sided, and a p less than .05 is considered statistically significant. All analyses were performed using R 4.1.2 (R core team, 2023) Results: The results showed that breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs 24.8% in controls). The model's predictive performance for breast cancer risk increased from 0.565 (95% CI = 0.520-0.611) to 0.699 (95% CI = 0.644-0.755) by adding related clinical characteristics compared to the PRS-only model. Patients characterized with benign breast disease (OR = 0.50, 95% CI = 0.31-0.79, P = 0.004) showed a decreased breast cancer risk, and patients with mastalgia or palpable breast lesion (OR = 6.87, 95% CI = 4.29-10.8, P < 0.001) predicted significant greater breast cancer risk. Subgroup analysis of patients without breast symptoms was conducted as they may be overlooked or underestimated for breast cancer screening. For dense breast patients without symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI = 1.43-3.50, P < 0.001) and multivariate analyses (OR = 2.22, 95% CI = 1.41-3.46, P < 0.001). Conclusion: Breast cancer screening has undergone a shift from a general approach to a personalized, risk-based approach. Besides breast cancer screening using family history as the only risk factor, our proposed model identifies both genetic and clinical risk factors using big data analyses including the EHR, cancer screening, and registry from a single institute. The study suggests that integrating PRS into personalized screening strategies could improve risk prediction for Taiwanese females with dense breasts without prominent symptoms. Table 1. Clinical characteristics of study cohort (n=6335). Table 2. Association of the PRS quartiles in breast cancer risk. Table 3. Predictive performance of PRS for breast cancer risk. Citation Format: Chih Yean Lum, Chih Chiang Hung, Chi-Cheng Huang, Tzu-Hung Hsiao, Sin-Hua Moi. Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-08-10.

Manual therapy and exercise effects on inflammatory cytokines: a narrative overview

BackgroundMatching disease and treatment mechanisms is a goal of the Precision Medicine Initiative. Pro- and anti-inflammatory cytokines (e.g., Tumor Necrosis Factor-alpha, Transforming Growth Factor-beta, and Interleukin-2, 10, and 12) have gained a significant amount of interest in their potential role in persistent pain for musculoskeletal (MSK) conditions. Manual therapy (MT) and exercise are two guideline-recommended approaches for treating MSK conditions. The objective of this narrative overview was to investigate of the effects of MT and exercise on pro- and anti-inflammatory cytokines and determine the factors that lead to variability in results.MethodsTwo reviewers evaluated the direction and variabilities of MT and exercise literature. A red, yellow, and green light scoring system was used to define consistencies.ResultsConsistencies in responses were seen with acute and chronic exercise and both pro- and anti-inflammatory cytokines. Chronic exercise is associated with a consistent shift towards a more anti-inflammatory cytokine profile (Transforming Growth Factor-beta, and Interleukin-2 and 13, whereas acute bouts of intense exercise can transiently increase pro-inflammatory cytokine levels. The influence of MT on cytokines was less commonly studied and yielded more variable results.ConclusionVariability in findings is likely related to the subject and their baseline condition or disease, when measurement occurs, and the exercise intensity, duration, and an individual's overall health and fitness.

Integrated clinical and genomic models using machine-learning methods to predict the efficacy of paclitaxel-based chemotherapy in patients with advanced gastric cancer

BackgroundPaclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy. However, no predictive biomarkers exist to identify patients with AGC who would benefit from paclitaxel-based chemotherapy.MethodsThis study included 288 patients with AGC receiving second-line paclitaxel-based chemotherapy between 2017 and 2022 as part of the K-MASTER project, a nationwide government-funded precision medicine initiative. The data included clinical (age [young-onset vs. others], sex, histology [intestinal vs. diffuse type], prior trastuzumab use, duration of first-line chemotherapy), and genomic factors (pathogenic or likely pathogenic variants). Data were randomly divided into training and validation sets (0.8:0.2). Four machine learning (ML) methods, namely random forest (RF), logistic regression (LR), artificial neural network (ANN), and ANN with genetic embedding (ANN with GE), were used to develop the prediction model and validated in the validation sets.ResultsThe median patient age was 64 years (range 25–91), and 65.6% of those were male. A total of 288 patients were divided into the training (n = 230) and validation (n = 58) sets. No significant differences existed in baseline characteristics between the training and validation sets. In the training set, the areas under the ROC curves (AUROC) for predicting better progression-free survival (PFS) with paclitaxel-based chemotherapy were 0.499, 0.679, 0.618, and 0.732 in the RF, LR, ANN, and ANN with GE models, respectively. The ANN with the GE model that achieved the highest AUROC recorded accuracy, sensitivity, specificity, and F1-score performance of 0.458, 0.912, 0.724, and 0.579, respectively. In the validation set, the ANN with GE model predicted that paclitaxel-sensitive patients had significantly longer PFS (median PFS 7.59 vs. 2.07 months, P = 0.020) and overall survival (OS) (median OS 14.70 vs. 7.50 months, P = 0.008). The LR model predicted that paclitaxel-sensitive patients showed a trend for longer PFS (median PFS 6.48 vs. 2.33 months, P = 0.078) and OS (median OS 12.20 vs. 8.61 months, P = 0.099).ConclusionsThese ML models, integrated with clinical and genomic factors, offer the possibility to help identify patients with AGC who may benefit from paclitaxel chemotherapy.

Imputation accuracy across global human populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of references from non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative improved the imputation of admixed African-ancestry and Hispanic/Latino samples, but imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we imputed the genotypes of over 43,000 individuals across 123 populations around the world and identified numerous populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for variants with minor allele frequencies between 1% and 5% in Saudi Arabians (n= 1,061), Vietnamese (n= 1,264), Thai (n= 2,435), and Papua New Guineans (n= 776) were 0.79, 0.78, 0.76, and 0.62, respectively, compared to 0.90-0.93 for comparable European populations matched in sample size and SNP array content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European-ancestry reference increased, as predicted. Using sequencing data as ground truth, we also showed that Rsq may over-estimate imputation accuracy for non-European populations more than European populations, suggesting further disparity in accuracy between populations. Using 1,496 sequenced individuals from Taiwan Biobank as a second reference panel to TOPMed, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, but this design did not improve accuracy across frequency spectra. Taken together, our analyses suggest that we must ultimately strive to increase diversity and size to promote equity within genetics research.

Bridging horizons beyond CIRCULATE-Japan: a new paradigm in molecular residual disease detection via whole genome sequencing-based circulating tumor DNA assay.

Circulating tumor DNA (ctDNA) is the fraction of cell-free DNA in patient blood that originates from a tumor. Advances in DNA sequencing technologies and our understanding of the molecular biology of tumors have increased interest in exploiting ctDNA to facilitate detection of molecular residual disease (MRD). Analysis of ctDNA as a promising MRD biomarker of solid malignancies has a central role in precision medicine initiatives exemplified by our CIRCULATE-Japan project involving patients with resectable colorectal cancer. Notably, the project underscores the prognostic significance of the ctDNA status at 4weeks post-surgery and its correlation to adjuvant therapy efficacy at interim analysis. This substantiates the hypothesis that MRD is a critical prognostic indicator of relapse in patients with colorectal cancer. Despite remarkable advancements, challenges endure, primarily attributable to the exceedingly low ctDNA concentration in peripheral blood, particularly in scenarios involving low tumor shedding and the intrinsic error rates of current sequencing technologies. These complications necessitate more sensitive and sophisticated assays to verify the clinical utility of MRD across all solid tumors. Whole genome sequencing (WGS)-based tumor-informed MRD assays have recently demonstrated the ability to detect ctDNA in the parts-per-million range. This review delineates the current landscape of MRD assays, highlighting WGS-based approaches as the forefront technique in ctDNA analysis. Additionally, it introduces our upcoming endeavor, WGS-based pan-cancer MRD detection via ctDNA, in our forthcoming project, SCRUM-Japan MONSTAR-SCREEN-3.

A regionally based precision medicine implementation initiative in North Africa:The PerMediNA consortium

Precision Medicine is being increasingly used in the developed world to improve health care. While several Precision Medicine (PM) initiatives have been launched worldwide, their implementations have proven to be more challenging particularly in low- and middle-income countries. To address this issue, the “Personalized Medicine in North Africa” initiative (PerMediNA) was launched in three North African countries namely Tunisia, Algeria and Morocco. PerMediNA is coordinated by Institut Pasteur de Tunis together with the French Ministry for Europe and Foreign Affairs, with the support of Institut Pasteur in France. The project is carried out along with Institut Pasteur d’Algérie and Institut Pasteur du Maroc in collaboration with national and international leading institutions in the field of PM including Institut Gustave Roussy in Paris. PerMediNA aims to assess the readiness level of PM implementation in North Africa, to strengthen PM infrastructure, to provide workforce training, to generate genomic data on North African populations, to implement cost effective, affordable and sustainable genetic testing for cancer patients and to inform policy makers on how to translate research knowledge into health products and services. Gender equity and involvement of young scientists in this implementation process are other key goals of the PerMediNA project.In this paper, we are describing PerMediNA as the first PM implementation initiative in North Africa. Such initiatives contribute significantly in shortening existing health disparities and inequities between developed and developing countries and accelerate access to innovative treatments for global health.

“Different names for the same thing”? Novelty, expectations, and performative nominalism in personalized and precision medicine

AbstractThis paper explores the complementary and contrasting uses of the terms ‘personalized medicine’ and ‘precision medicine’ in denotations of a biomedical approach attentive to individual specificities that harnesses genomics and other data-intensive profiling technologies. Drawing on qualitative interviews conducted with biomedical experts in the context of the Precision Medicine Initiative in the United States and the 100,000 Genomes project in the United Kingdom, we read definitional reflection and debate through the lens of the sociologies of expectations and novelty. We observed two key aspects in the shift from ‘personalized medicine’ to ‘precision medicine’ that has been especially prevalent in the United States. First, the term ‘precision medicine’ enables its proponents to rhetorically depart from the idea that this approach to medicine can be expected to deliver individually personalized treatments—an expectation that is seen as unrealistic by many. Second, it enables its proponents to assert that personalization, when understood as caring about the patient as an individual person, is not a new approach to medicine but rather something that many medical professionals have always aimed to do (eliding in the process other experiences of US healthcare as, for instance, alienating and discriminatory). We argue that the shift from ‘personalized’ to ‘precision’ medicine can be regarded as a manifestation of performative nominalism: an attribution of ‘newness’ that contributes to performing and propelling innovation, rather than solely reflecting it. In so doing, rhetorical demarcations between personalized and precision medicine emerge as performatively contributing to the production of different biomedical ontologies.

The Reasons for the Low Uptake of New Antidiabetic Drugs with Cardiovascular Effects-A Family Doctor Perspective.

Chronic diseases, such as type 2 diabetes (T2D), are difficult to manage because they demand continuous therapeutic review and monitoring. Beyond achieving the target HbA1c, new guidelines for the therapy of T2D have been introduced with the new groups of antidiabetics, glucagon-like peptide-1 receptor agonists (GLP-1ra) and sodium-glucose cotransporter-2 inhibitors (SGLT2-in). Despite new guidelines, clinical inertia, which can be caused by physicians, patients or the healthcare system, results in T2D not being effectively managed. This opinion paper explores the shift in T2D treatment, challenging assumptions and evidence-based recommendations, particularly for family physicians, considering the patient's overall situation in decision-making. We looked for the possible reasons for clinical inertia and the poor application of guidelines in the management of T2D. Guidelines for antidiabetic drugs should be more precise, providing case studies and clinical examples to define clinical contexts and contraindications. Knowledge communication can improve confidence and should include clear statements on areas of decision-making not supported by evidence. Precision medicine initiatives in diabetes aim to identify subcategories of T2D patients (including frail patients) using clustering techniques from data science applications, focusing on CV and poor treatment outcomes. Clear, unconditional recommendations for personalized T2D management may encourage drug prescription, especially for family physicians dealing with diverse patient contexts and clinical settings.

Investigating the Association of Polygenic Risk Scores With Thyroid Cancer Susceptibility in a Han Chinese Population.

Thyroid cancer, the leading endocrine tumor with a rising global incidence, especially in women, is influenced by both genetic and environmental factors. This study examines the relationship between polygenic risk scores (PRS) and thyroid cancer susceptibility in the Han Chinese population, as well as the impact of genetic variants on clinical outcomes. Analyzing data from 57 257 participants in the Taiwan Precision Medicine Initiative, the study employed the Affymetrix Genome-Wide TWB 2.0 SNP Array for genotyping. PRS were calculated using single nucleotide polymorphisms (SNPs) from prior genome-wide association studies, specifically PGS000087 and PGS000797, and correlated with clinical parameters like age, sex, comorbidities, and treatment methods. Among 4063 participants with thyroid tumors (839 malignant, 3224 benign), higher PRS quartiles correlated significantly with increased thyroid cancer incidence. The highest quartile showed a 1.15-fold (PGS000797) and 1.14-fold (PGS000087) greater risk than the lowest quartile. Key findings included an association between higher PRS quartiles and younger onset age, along with a notable link to chronic kidney disease and thyroid hormone levels in specific SNPs. The study demonstrates PRS's utility in predicting thyroid cancer risk in the Han Chinese population, with higher PRS associated with increased risk and distinct clinical features. While this study focuses on the Han Chinese population, we recognize the importance of comparing PRS performance across different ancestries to fully understand ethnic genetic diversity in cancer risk assessment. Future studies should aim to include such comparative analysis.

Host-Pathogen Interactions in K. pneumoniae Urinary Tract Infections: Investigating Genetic Risk Factors in the Taiwanese Population.

Klebsiella pneumoniae (K. pneumoniae) urinary tract infections pose a significant challenge in Taiwan. The significance of this issue arises because of the growing concerns about the antibiotic resistance of K. pneumoniae. Therefore, this study aimed to uncover potential genomic risk factors in Taiwanese patients with K. pneumoniae urinary tract infections through genome-wide association studies (GWAS). Genotyping data are obtained from participants with a history of urinary tract infections enrolled at the Tri-Service General Hospital as part of the Taiwan Precision Medicine Initiative (TPMI). A case-control study employing GWAS is designed to detect potential susceptibility single-nucleotide polymorphisms (SNPs) in patients with K. pneumoniae-related urinary tract infections. The associated genes are determined using a genome browser, and their expression profiles are validated via the GTEx database. The GO, Reactome, DisGeNET, and MalaCards databases are also consulted to determine further connections between biological functions, molecular pathways, and associated diseases between these genes. The results identified 11 genetic variants with higher odds ratios compared to controls. These variants are implicated in processes such as adhesion, protein depolymerization, Ca2+-activated potassium channels, SUMOylation, and protein ubiquitination, which could potentially influence the host immune response. This study implies that certain risk variants may be linked to K. pneumoniae infections by affecting diverse molecular functions that can potentially impact host immunity. Additional research and follow-up studies are necessary to elucidate the influence of these risk variants on infectious diseases and develop targeted interventions for mitigating the spread of K. pneumoniae urinary tract infections.

Pharmacometabolomics in Drug Disposition, Toxicity and Precision Medicine.

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help understand factors mediating drug disposition, efficacy and toxicity. While important advancements in this area have been made, their remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. Significance Statement Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview pharmacometabolomics including highlights of important examples.

Intelligenza Artificiale per la cura del diabete mellito: opportunità e prospettive future

Diabetes mellitus is globally prevalent, with the International Diabetes Federation predicting 783 million cases by 2045. Type 2 diabetes (T2DM) is the seventh leading cause of death and poses a significant economic burden on global healthcare, demanding innovative approaches amid resource constraints. Moreover, the increasing volume of data requires advanced computational tools for analysis and therapeutic decision support. Digital technologies, particularly artificial intelligence (AI), offer promising solutions. AI, capable of complex tasks like reasoning and language recognition, leverages digitalization in diabetes care, handling electronic health records, imaging diagnostics, and laboratory data. Machine learning, an advanced form of AI, outperforms traditional data analysis by automatically identifying patterns and generating predictions without specific programming. Emerging technologies utilizing natural language processing and machine learning accelerate the digital transformation, enhancing cognitive capabilities. AI applications in diabetes span screening, classification, and therapy management. Predictive models utilizing machine learning algorithms achieve high accuracy in early diabetes risk identification and aids in understanding T2DM heterogeneity, crucial for precision medicine initiatives. Diagnostic AI tools, particularly for retinopathy, neuropathy, and diabetic foot, enhance accessibility and clinical decision-making. AI’s role extends to predicting and preventing complications, such as ulcers and hypoglycemic events. Challenges include data privacy concerns, algorithmic bias, and the need for transparent decision-making processes. Rigorous optimization of AI data inputs is crucial to avoid misleading outputs. While AI offers tremendous benefits, ethical considerations, regulatory frameworks, and human oversight remain critical for responsible and effective implementation in diabetes care.

Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records

BackgroundAlthough the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records.MethodsParticipants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640.ResultsA total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered.ConclusionsML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity.

Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.

The impact of home-based call on sleep patterns and wellness in genetics and metabolism physicians compared with subspecialists

PurposeWith increases in precision medicine initiatives and genetically defined rare diseases, the genetics and metabolism workforce is necessary to provide around-the-clock care for patients. Here, we describe the impact that home-based call has on the geneticist and metabolist workforce. MethodsPhysicians from 3 populations were self-identified (pediatric subspecialist, geneticist, metabolist) and completed a survey regarding the impact of home-based call service on their sleep and wellness. ResultsEstimated sleep while serving on home-based call was reduced from 7.5 to 5.4 hours per night. Safety concerns were noted by geneticists and metabolists for themselves (55%) and their families (28%), similar to other subspecialists. Geneticists and metabolists were more likely than other pediatric subspecialists to be worried about their patient’s safety while on home-based call (48% vs 9%). Themes from open-ended questions regarding the impact of home-call included positive responses, decreased access to wellness activities, sleep exhaustion, impact on life responsibility, and impact on mood. Reported coping mechanisms included work-based initiatives, off-loading personal responsibility, and creating personal accommodations. ConclusionInstitutional-based supports for home-based call were endorsed by only 29% of respondents; thus, interventions at the institutional level would be expected to have a large effect on overall provider wellness.

Genetic Susceptibility of HLA Alleles to Non-Steroidal Anti-Inflammatory Drug Hypersensitivity in the Taiwanese Population.

Human leukocyte antigen (HLA) genes are important in many immune processes and contribute to many adverse drug reactions. Whether genetic variations in the HLA region are associated with non-steroid anti-inflammatory drug (NSAID) hypersensitivity remains uncertain. Therefore, the aim of our study was to identify HLA genetic variations in patients with NSAID hypersensitivity in the Taiwanese population. This hospital-based, retrospective case-control study enrolled 37,156 participants with NSAID exposure from the Taiwan Precision Medicine Initiative (TPMI), who were all genotyped and imputed to fine map HLA typing. Our study assigned 1217 cases to the NSAID allergy group and 12,170 controls to a matched group. Logistic regression analyses were utilized to explore associations between HLA alleles and NSAID hypersensitivity. Overall, 13,387 patients were genotyped for eight major HLA alleles. Allele frequencies were different between the two groups. In the NSAID allergy group, the genotype frequencies of HLA-A*02:01, HLA-A*34:01, and HLA-DQA1*06:01 were found to be markedly elevated compared to the control group, a significance that persisted even after applying the Bonferroni correction. Furthermore, the risk of NSAID allergy demonstrated a significant association with HLA-A*02:01 (OR = 1.29, p < 0.001) and HLA-A*34:01 (OR = 9.90, p = 0.001), in comparison to their respective counterparts. Notably, the genotype frequency of HLA-B*46:01 exhibited a significant increase in the severe allergy group when compared with the mild allergy group. We identified HLA genotypes linked to the onset and severity of NSAID hypersensitivity. Our findings establish a basis for precision prescription in future clinical applications.

Amyloid β‐specific T cell response is enhanced in individuals with mild cognitive impairment

AbstractBackgroundNeuroinflammation is a key process in initiating and propagating Alzheimer’s disease (AD). Even though it is widely known that the deposit of amyloid plaques and CSF levels of amyloid distinguishes patients with AD or mild cognitive impairment (MCI) from cognitively normal (CN) individuals, little is known about the role of amyloid‐specific immune response in cognitive decline.MethodUsing a polyfunctionality assay typically used for detecting virus‐specific T cell responses, we tested participants from the Epidemiology of Mild Cognitive Impairment in Taiwan study (EMCIT) and the Taiwan Precision Medicine Initiative of Cognitive impairment and dementia (TPMIC) study to compare the amyloid‐specific T cell responses between CN and MCI individuals. The abilities of T cell response parameters and plasma p‐Tau181 to distinguish MCI from CN were tested.ResultResults from both cohorts showed an enhanced amyloid‐specific T‐cell response in individuals with MCI. In the EMCIT cohort, the individual’s amyloid‐specific CD4+ response frequency of total CD4+ cells was significantly larger in MCI (n = 69, 0.93%) than in CN (n = 69, 0.51%, p &lt; 0.001). CD4+ T cell response discriminated MCI versus CN (area under curve [AUC], 0.72 [0.64‐0.81]) with significantly higher accuracy than p‐Tau181 (AUC: 0.59 [0.5‐0.69], p &lt; 0.01). In the TPMIC cohort, both CD4+ and CD8+ response frequencies were higher in MCI individuals (n = 21, CD4: 1.2%, CD8: 2.02%) than in CN (n = 30, CD4: 0.14%, CD8:0.27%; both p &lt; 0.001). CD4+ T cell response frequency and CD8+ response frequency also outperform p‐Tau181 in their discriminative accuracy of MCI versus NC (CD4+ AUC, 0.97, [0.94‐1.01]; CD8+ AUC, 0.96, [0.92‐1.01]; p‐Tau181 AUC, 0.83, [0.69‐0.96]; both p &lt; 0.05).ConclusionOur study validates the amyloid hypothesis by showing that amyloid‐associated neuroinflammation is involved in the process of neurodegeneration and demonstrated the accuracy of using amyloid‐specific T cell response to discriminate MCI from CN individuals. The TPMIC cohort is an ongoing longitudinal study that includes amyloid PET results and thus we will investigate the prognostic value of amyloid‐T cell response in the future.

Utilize polygenic risk score to enhance fracture risk estimation and improve the performance of FRAX in patients with osteoporosis.

Osteoporosis and fragility fractures are multifactorial, with contributions from both clinical and genetic determinants. However, whether using polygenic risk scores (PGS) may enhance the risk estimation of osteoporotic fracture in addition to Fracture Risk Assessment Tool (FRAX) remains unknown. This study investigated the collective association of PGS and FRAX with fragility fracture. We conducted a cohort study from the Taiwan Precision Medicine Initiative (TPMI) at Taichung Veterans General Hospital, Taiwan. Genotyping was performed to compute PGS associated with bone mineral density (BMD). Phenome-wide association studies were executed to pinpoint phenotypes correlated with the PGS. Logistic regression analysis was conducted to ascertain factors associated with osteoporotic fractures. Among all 57,257 TPMI participants, 3744 (904 men and 2840 women, with a mean age of 66.7) individuals had BMD testing, with 540 (14.42%) presenting with fractures. The 3744 individuals who underwent BMD testing were categorized into four quartiles (Q1-Q4) based on PGS; 540 (14.42%) presented with fractures. Individuals with PGS-Q1 exhibited lower BMD, a higher prevalence of major fractures, and elevated FRAX-major and FRAX-hip than those with PGS-Q4. PGS was associated with major fractures after adjusting age, sex, and FRAX scores. Notably, the risk of major fractures (PGS-Q1 vs. Q4) was significantly higher in the subgroups of FRAX-major scores < 10% and 10-20%, but not in participants with a FRAX-major score ≧ 20%. Our study highlights the potential of PGS to augment fracture risk estimation in conjunction with FRAX, particularly in individuals with middle to low risks. Incorporating genetic testing could empower physicians to tailor personalized preventive strategies for osteoporosis.

Abstract 14786: Disparities in Composite Cardiovascular Risk Factor Control in US Adults With Diabetes in the NIH Precision Medicine Initiative All of Us Research Program

Background/Objective: There are limited contemporary data in real-world populations on composite cardiovascular risk factor (RF) control in adults with diabetes mellitus (DM). We studied the proportions of patients with DM at target for individual and multiple cardiovascular RFs including HbA1c, LDL-C, BMI, blood pressure, and smoking status. Methods: We included adults ≥18 years of age with DM from the NIH Precision Medicine Initiative All of Us Research Program 2018-2022. RFs and target values were defined as 1) HbA1c (&lt;7% w/ atherosclerotic cardiovascular disease (ASCVD) or &lt;8% w/o ASCVD), 2) LDL-C (&lt;55mg/dL w/ ASCVD or &lt;70mg/dL w/o ASCVD), 3) blood pressure (&lt;130/80 mmHg), 4) BMI (&lt;25 kg/m 2 ), and 5) smoking status (never or former smoker). We investigated the proportion (%) of patients at target for individual RFs, ≥3 RFs, ≥4 RFs, and mean number at target by sex, race/ethnicity, and ASCVD status. Results: Individual and composite cardiovascular RF control was found to be low among the 83,093 participants with DM. Control of &gt; 3 RFs and ≥ 4 RFs was only 25% and 5% overall, respectively, with fewer than 1% at target for all 5 RFs; female sex, Asian race/ethnicity, and those with prior ASCVD were more likely to have multiple RFs at target. Non-Hispanic black participants had the lowest proportion of RFs at target. In a multiple logistic regression, higher income, higher education level, employment, female sex, and Non-Hispanic White &amp; Asian race/ethnicity positively predicted control of ≥ 4 RFs with p-value &lt; 0.001. Conclusions: Individual and composite RF control remains suboptimal in many patients with DM, with disparities across certain demographic subsets. Further efforts are required to address social determinants that may be responsible for continuing gaps in adequate RF control and to educate clinicians and patients on the importance of controlling key RFs that impact ASCVD and other outcomes in those with DM.

Effect of Obesity on the Pharmacokinetics and Pharmacodynamics of Anticancer Agents.

An objective of the Precision Medicine Initiative, launched in 2015 by the US Food and Drug Administration and National Institutes of Health, is to optimize and individualize dosing of drugs, especially anticancer agents, with high pharmacokinetic and pharmacodynamic variability. The American Society of Clinical Oncology recently reported that 40% of obese patients receive insufficient chemotherapy doses and exposures, which may lead to reduced efficacy, and recommended pharmacokinetic studies to guide appropriate dosing in these patients. These issues will only increase in importance as the incidence of obesity in the population increases. This publication reviews the effects of obesity on (1) tumor biology, development of cancer, and antitumor response; (2) pharmacokinetics and pharmacodynamics of small-molecule anticancer drugs; and (3) pharmacokinetics and pharmacodynamics of complex anticancer drugs, such as carrier-mediated agents and biologics. These topics are not only important from a scientific research perspective but also from a drug development and regulator perspective. Thus, it is important to evaluate the effects of obesity on the pharmacokinetics and pharmacodynamics of anticancer agents in all categories of body habitus and especially in patients who are obese and morbidly obese. As the effects of obesity on the pharmacokinetics and pharmacodynamics of anticancer agents may be highly variable across drug types, the optimal dosing metric and algorithm for difference classes of drugs may be widely different. Thus, studies are needed to evaluate current and novel metrics and methods for measuring body habitus as related to optimizing the dose and reducing pharmacokinetic and pharmacodynamic variability of anticancer agents in patients who are obese and morbidly obese.