Journal of Personalized Medicine

BackgroundCoronary artery disease (CAD) is a leading cause of death, and depression exacerbates CAD. Antidepressants may offer therapeutic potential for CAD. MethodsWe employed Mendelian Randomization (MR), summary-based MR (SMR), colocalization, replication analysis, and single-cell RNA annotations to assess causal relationships between antidepressant targets and CAD. Safety profiles were evaluated using the Food and Drug Administrations (FDA) Adverse Event Reporting System (FAERS). ResultsFifteen proteins demonstrated significant associations with CAD. GM2A (odds ratio [OR]: 0.975, P = 4 x 10-3), PYGL, BCHE, and several others were found to reduce the risk of CAD, while PDE4A (OR: 1.183, P < 1 x 10-3) and others were associated with an increased risk. GM2A passed sensitivity analyses and exhibited strong colocalization (posterior probability of colocalization [PPH.4] > 0.8). Elevated expression of GM2A consistently showed an inverse association with CAD risk across six tissue types, with cell-type-specific patterns observed in endothelial cells and macrophages. In SMR, FOLH1 was identified as a replicable protective factor for CAD. The FAERS recorded 52,952 adverse events (AEs) related to the selected antidepressant, affecting 6,391 patients. The predominant AEs included drug withdrawal syndrome, dizziness, paresthesia, and nausea. Significant safety signals were identified for dysphoria (reporting odds ratio [ROR] 708.12) and affect lability (ROR 362.05). Additionally, unexpected events such as insomnia, anxiety, fatigue, irritability, headache, and agitation were noted. ConclusionsOur findings suggest that antidepressants may have a therapeutic role in the treatment of CAD, with GM2A identified as a promising target for therapy. While certain antidepressants can influence CAD risk, further validation is necessary to address safety concerns.

Aims/hypothesisMetabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede proper and effective clinical management of MetS. In order for precision medicine to work for MetS, we aimed to identify clinically relevant MetS sub-phenotypes. MethodsWe conducted cluster analysis on individuals from UK Biobank based on MetS criteria to reveal endophenotypes, identified the corresponding cardiometabolic traits and established the association across 21 clinical outcomes. Genome-wide association studies were conducted to identify associated genotypic traits. We further compared the genotypic traits to reveal endophenotypes-specific genotypic traits. Lastly, potential drug targets were identified for the different endophenotypes. ResultsFive MetS subgroups were identified which were Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycaemic. Some MetS clusters had higher CVD risks such as C1 (OR=6{middle dot}765) and C5 (OR=9{middle dot}486). Despite being non-descriptive across all cardiometabolic traits, C1 had higher risks for most clinical outcomes. MetS clusters also had different risks to various types of cancers. GWAS of each MetS clusters revealed different genotypic traits. LPCAT2 was associated with all clusters except C2 and expression is specific to immune cells. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues: CN1H2, TMEM151A, MT3, and C1QTNF4. The cluster-specific genotypic traits also revealed potential drug repurposing targets specific to the endophenotypes. Conclusion/interpretationMetS is highly heterogeneous with endophenotypes that are different in terms of phenotypic and genotypic traits. GWAS of subgroups revealed novel cardiometabolic genotypes which were masked by heterogeneity of MetS. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, Science Direct and Scopus from 1st January 2012 to 30th September 2022 for "unsupervised learning" or "clustering" or "endophenotype" or "subclassifications" or "sub-phenotype" and "metabolic syndrome" or "complex diseases". Google Scholar, UK Biobank published work and approved research were also searched for similar study. This search only revealed published work in other complex diseases such as T2D (which is heavily referenced in our manuscript), Alzheimers diseases, psychiatric diseases, and asthma. None of the previously published work applied the combination of unsupervised learning and GWAS for identification of clinically relevant endophenotypes in metabolic syndrome or any complex diseases. Added value of this studyMetabolic syndrome (MetS) is a known cardiovascular disease risk factor, however the constantly changing MetS criteria and high prevalence of MetS impede proper clinical management of individuals with MetS. Through clustering, we identified MetS endophenotypes with semi-distinctive cardiometabolic traits. Some of the MetS endophenotypes correspond with T2D subgroups discovered by other research groups. However, our endophenotypes are more clinically relevant, due to the differing clinical risks across 21 clinical outcomes. We also identified a non-descriptive MetS subgroup with strikingly high cardiovascular risk which likely to be overlooked in clinical settings. Through genome-wide association studies, our endophenotypes also revealed interesting insights into the genetic causes and biological pathways of MetS. We were able to identified genotypic traits that are unique to each MetS endophenotypes and shared genotypic traits which highlights the common pathophysiology underlying MetS. Lastly, we were also able to reveal potential drug targets for drug repurposing, some drug targets are unique to specific endophenotypes. Implications of all the available evidenceOur study attempted to resolve the issue of MetS heterogeneity, by revealing clinically relevant endophenotypes which might respond to different pharmacotherapy. Furthermore, our findings challenge the "one size fits all" step-wise approach in managing complex diseases, emphasizing tailored treatment for different subgroups of patients, a key step towards precision medicine in clinical practice. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/22281926v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@c55649org.highwire.dtl.DTLVardef@1a4033aorg.highwire.dtl.DTLVardef@d00d12org.highwire.dtl.DTLVardef@10a2eb3_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundMyocardial infarction (MI) is a leading cause of global mortality. Finding effective drugs to treat MI is an urgent concern for clinicians. Proteome-wide Mendelian randomization (MR) analysis provides a new way to investigate invaluable therapeutic drug targets more efficiently. MethodsUsing a proteome-wide MR approach, we assessed the genetic predictive causality between thousands of plasma proteins and MI risk. First, by adopting several principles to judge genetic variants associated with plasma proteins and MI risk, we selected a series of suitable variants utilized as instrumental variables (IVs) for the latter Mendelian Randomization (MR) analysis. Second, we performed a proteome-wide MR analysis to select candidate proteins. Third, sensitivity tests including heterogeneity test, reverse causality test, and colocalization analyses were conducted to ensure the robustness of our selected protein. Last, we assessed the drugability of the identified potential drug targets for MI using databases including DrugBank, PharmGKB, and TTD. ResultsOf the identified IVs, 3,156 associated with 1,487 plasma proteins were validated. 15 proteins exhibited significant genetically predicted causal associations(P - value < 3.362*10-5) with MI risk, including Plasmin, MSP, Apo B, TAGLN2, LRP4, C1s, Angiostatin, Apo C-III, PCSK9, ANGL4, FN1.4,Apo B, IL-6 sRa, SWAP70, FN, FN1.3. Sensitivity analyses pinpointed Plasmin and Angiostatin for heterogeneity and proteins MSP, Apo B, and Angiostatin for reverse causality effects. Colocalization analysis found several proteins sharing genetic variants with MI, notably Apo B, TAGLN2, LRP4, C1s, Apo C-III, PCSK9 and ANGL4. When the threshold was lowered to 0.7, additional variants SWAP70 could be contained. 7 potential drug targets for MI were identified: SWP70, TAGLN2, LRP4, C1s, Apo C-III, PCSK9, and ANGL4. Drugability assessment categorized these proteins into varying therapeutic potential categories, from successfully drugged targets to those only reported in the literature. ConclusionOur comprehensive study elucidated 7 promising drug targets offering profound insights into its molecular dynamics and presenting potential pathways for therapeutic interventions against MI. Clinical PerspectiveO_ST_ABS1) What Is New?C_ST_ABS[*] The analysis of thousands of proteins has identified 7 proteins that have a potential causal role in myocardial infarction risk. [*]Four of these ten proteins have drugs approved or in development that target them, and three 5 have not been previously reported to be associated with atrial fibrillation risk. 2) What Are the Clinical Implications?[*] The results of the present study demonstrate new potential drug/therapeutic targets for myocardial infarction.

BackgroundRemnant cholesterol (RC) have been suggested as a significant mediator of atherosclerotic cardiovascular diseases. However, the relationship between RC with cause-specific mortality in long-term remained uncertain. This study aimed to investigate the association between time-weighted RC and cause-specific mortality outcomes. MethodsThis retrospective population-based study enrolled patients attending family medicine clinics in Hong Kong between 1st January 2000, to 31st December 2003 with at least three RC testing results during follow-up. The time-weighted RC was calculated by the products of the sums of two consecutive measurements and the time interval divided by the total time. The primary outcomes were all-cause mortality and cause-specific mortality outcomes. Cox regression and marginal effective plots were applied to identify associations between time-weighted RC and mortality. ResultsA cohort of 75,342 patients (39.69% males, mean age: 61.3 years old) with at least three valid RC test were included. During up to 19 years of follow-up, in the multivariate model adjusted for demographics, comorbidities, medications, and time-weighted laboratory results, time-weighted RC was associated with all-cause mortality (Hazard ratio [HR]: 1.41; 95% Confidence Interval [CI]: 1.35-1.48) but not RC (HR: 0.99; 95% CI: 0.89-1.10). Time-weighted RC was also associated with increased risks of cardiovascular-related mortality (HR: 1.40; 95% CI: 1.27-1.54), cancer-related mortality (HR: 1.59; 95% CI: 1.43-1.77), and respiratory-related mortality (HR: 1.33; 95% CI: 1.20-1.47). The exploratory analysis of the cause of death demonstrated that time-weighted RC was associated with Ischaemic heart disease, cerebrovascular-related and pneumonia. ConclusionsTime-weighted RC was independently associated with all-cause mortality and cause-specific mortality outcomes amongst the general population.

BackgroundSchizophrenia (SCZ) and bipolar disorder (BD) are both associated with several autoimmune disorders including rheumatoid arthritis(RA). However, a causal association of SCZ and BD on RA is controversial and elusive. In the present study, we aimed to investigate the causal association of SCZ and BD with RA by using the Mendelian randomization (MR) approach. MethodsA two-sample MR (2SMR) study including the inverse-variance weighted(IVW), weighted median, simple mode, weighted mode and MR-Egger methods were performed. We used summary-level genome-wide association study(GWAS) data in which BD and SCZ are the exposure and RA the outcome. We used data from the Psychiatric Genomics Consortium(PGC) for BD(n= 41,917) and SCZ(n= 33,426) and RA GWAS dataset(n= 2,843) from the European ancestry for RA. ResultsWe found 48 and 52 independent single nucleotide polymorphisms (SNPs, r2 <0.001)) that were significant for respectively BD and SCZ (p <5x10-8). Subsequently, these SNPs were utilized as instrumental variables(IVs) in 2SMR analysis to explore the causality of BD and SCZ on RA. The two out of five MR methods showed a statistically significant inverse causal association between BD and RA: weighted median method(odds ratio (OR), 0.869, [95% CI, 0.764-0.989]; P= 0.034) and inverse-variance weighted(IVW) method (OR, 0.810, [95% CI, 0.689-0.953]; P= 0.011). However, we did not find any significant association of SCZ with RA (OR, 1.008, [95% CI, 0.931-1.092]; P= 0.829, using the IVW method). ConclusionsThese results provide support for an inverse causal association between BD and RA. Further investigation is needed to explain the underlying protective mechanisms in the development of RA. Key messagesO_LIMendelian randomization can offer strong insight into the cause-effect relationships in rheumatology. C_LIO_LIBipolar disorder had a protective effect on rheumatoid arthritis. C_LIO_LIThere is no inverse causal association between schizophrenia and rheumatoid arthritis contrary to the findings from observational studies. C_LI

BackgroundThe appropriateness of continuation of antiplatelet therapy in older hypertensive aspirin users with documented peptic ulcer disease (PUD) is uncertain. MethodsThis multicenter cohort study screened adults aged 65 years or older, using aspirin for primary and secondary cardiovascular disease prevention between January 2014 and December 2018. Patients with panendoscopy-proven PUD and hypertension were identified. Subsequent antiplatelet strategies were categorized as aspirin discontinuation (AD), aspirin continuation (AC), and switch to clopidogrel (SC) groups. Inverse probability of treatment weighting was applied to balance baseline characteristics. The main outcomes were incident major adverse cardiac events (MACEs) and hospitalizations for upper gastrointestinal bleeding (UGIB), followed through 31 December 2020. Results735 eligible patients were analyzed. During a median follow-up of 39.7 months, 178 MACEs occurred. Compared with AD, SC was not related to the risk of incident MACEs, but AC increased the risk of incident MACEs (adjusted HR, 1.58; 95% CI, 1.04-2.38) in secondary prevention patients. On the other hand, 102 hospitalizations for UGIB occurred during a median follow-up of 43.4 months. Compared with AD, neither AC nor SC affected the risk of hospitalization for UGIB in secondary prevention patients. However, secondary prevention patients with chronic kidney disease were at increased risk of hospitalizations for UGIB (adjusted HR, 2.41; 95% CI, 1.30-4.47). ConclusionsAC may increase the risk of incident MACEs in older hypertensive adults with PUD previously taking aspirin for secondary cardiovascular disease prevention. The appropriateness of antiplatelet therapy continuation after PUD is diagnosed in older hypertensive adults warrants rigorous considerations.

Congenital pseudarthrosis of the tibia (CPT, HP:0009736), commonly known as bowing of the tibia, is a rare congenital tibia malformation characterized by spontaneous tibial fractures and the difficulty of reunion after tibial fractures during early childhood, with a very low prevalence between 1/250,000[~]1/140,000. While 80%-84% of CPT cases present with neurofibromatosis type 1, caused by the mutations in NF1, the underlying cause of CPT is still unclear. Considering the congenital nature and the low prevalence of CPT, we hypothesized that the rare genomic mutations may contribute to CPT. In this study, we conducted whole exome sequencing on 159 patients with CPT and full-length transcriptome sequencing on an additional 3 patients with CPT. The data analysis showed there were 179 significantly up-regulated genes which were enriched in 40 biological processes among which 21 biological processes hold their loss of function (LoF) excesses between 159 cases against 208 controls from 1000 Genomes Project. From those 21 biological processes with LoF excesses, there were 259 LoF-carried genes among which 40 genes with 56 LoF variations in 63 patients were enriched in osteoclast differentiation pathway (hsa04380) with its 3 directly regulated pathways including MAPK signaling pathway (hsa04010), calcium signaling pathway (hsa04020) and PI3K-Akt signaling pathway (hsa04151), as well as fluid shear stress and atherosclerosis pathway (hsa05418) while 12 patients carried 9 LoF variations in the NF1 gene. The rare LoF variations in these pathways accounted for [~]39.6% of this CPT cohort. These findings shed light on the novel genetic mutations and molecular pathways involved in CPT, providing a new framework for understanding how the genetic variations regulate the biological processes in the pathology of CPT and indicating potential next directions to further elucidate the pathogenesis of CPT.

Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), encompasses a range of symptoms persisting for weeks or months after the acute phase of COVID-19. These symptoms, affecting multiple organ systems, significantly impact the quality of life. This study employs a machine-learning approach to identify gene targets for treating Long COVID. Using datasets GSE275334, GSE270045, and GSE157103, Recursive Ensemble Feature Selection (REFS) was applied to identify key genes associated with Long COVID. The study highlights the therapeutic potential of targeting genes such as PPP2CB, SOCS3, ARG1, IL6R, and ECHS1. Clinical trials and pharmacological interventions, including dual antiplatelet therapy and anticoagulants, are explored for their efficacy in managing COVID-19-related complications. The findings suggest that machine learning can effectively identify biomarkers and potential therapeutic targets, offering a promising avenue for personalized treatment strategies in Long COVID patients.

ObjectiveTo fill the gaps in primary healthcare (PHC) service delivery strategies focused on adiposity-based chronic disease and chronic kidney metabolic syndrome in the non-communicable diseases (NCD) context, focus on healthcare transformation and practice redesign. Designan experimental analytics study in tertiary care hospitals with a study population of adults aged 30-65 years with ABCD (BMI [&ge;] 25 kg/m2) or stage 1 CKM syndrome (defined by coexisting overweight and/or obesity conditions). The study is designed to address locally relevant health priorities, specifically, the rising burden of cardiovascular-renal-metabolic (CKM) diseases and the need for early, affordable risk stratification tools such as ET-1 and the TG/HDL-C ratio. The research aims to generate evidence that can directly inform local clinical practice and health policy, thereby benefiting both study participants and the broader community. The collected data were then analyzed statistically; the quantification method used body mass index (BMI), then examination of ET-1 levels was carried out using the ELISA method, and lipid fractions using the enzymatic method. SettingThe study is designed to address locally relevant health priorities, specifically, the rising burden of cardiovascular-renal-metabolic (CKM) diseases and the need for early, affordable risk stratification tools such as ET-1 and the TG/HDL-C ratio. The research aims to generate evidence that can directly inform local clinical practice and health policy, thereby benefiting both study participants and the broader community. The study included adults aged 30 to 65 years, consistent with common age ranges for cardiovascular risk studies in LMICs, diagnosed with ABCD or stage I CKM syndrome, including obesity (BMI [&ge;] 25 kg/m2), and 97 participants met the inclusion criteria, with the rest of the subjects excluded. ParticipantsThe study included adults aged 30 to 65 years, consistent with common age ranges for cardiovascular risk studies in LMICs, diagnosed with ABCD or stage I CKM syndrome, including obesity (BMI [&ge;] 25 kg/m2), and 97 participants met the inclusion criteria, with the rest of the subjects excluded. ResultsThe collected data were then analyzed statistically with distribution tests, difference tests, correlation tests, and multivariate analysis. The difference test of ET-1 levels and TG/HDL-C ratios to the degree of obesity using the one-way ANOVA test found significant differences in ET-1 levels and TG/HDL-C ratios to the degree of obesity (p-value < 0.001). and (p-value < 0.001). Where based on the least significant difference in the non-obese sub-population against obesity (p-value 0.051) and significantly different from the obesity II population (p-value < 0.001), then the LSD test of the TG/HDL-C ratio against the degree of obesity was significantly different in the non-obese population against obesity I (p-value 0.002) and non-obese against obesity II (p-value < 0.001). From the multivariate analysis, there were statistically significant differences in the mean values of the ET-1 variable between the obese II sub-population and the non-obese sub-population OR: 216.29 (95% CI: 91.25 to 341.33; p-value 0.000), as well as between obesity II and obesity I OR: 119.49 (95% CI: 60.68 to 178.29; p-value 0.000). Meanwhile, the TG/HDL-C ratio variable had a statistically significant effect on the non-obese, obesity I, and obesity II populations OR: 3.16 (95% CI: 0.71 to 5.52; p-value < 0.001). From this study, all subjects were indicated to have endothelial dysfunction, where, based on the TG/HDL-C ratio, all subjects could be classified as having insulin resistance, and, based on the atherogenic index of plasma (AIP) algorithm, the study population was stratified into moderate risk for first-time incidence of atherosclerotic cardiovascular disease (n=10) and high risk for first-time incidence of atherosclerotic cardiovascular disease (n=87). ConclusionIntegrating plasma endothelin-1 (ET-1) levels and the triglyceride-to-HDL cholesterol (TG/HDL-C) ratio into cardiovascular risk assessment frameworks offers a promising strategy to enhance early detection and management of adiposity-based chronic disease (ABCD) and cardiovascular-kidney-metabolic (CKM) syndrome, particularly in low- and middle-income countries (LMICs). These biomarkers reflect key pathophysiological processes, endothelial dysfunction and atherogenic dyslipidemia, that underpin subclinical cardiovascular and renal injury. Their combined application in primary healthcare settings can bridge critical gaps in current non-communicable disease (NCD) care by enabling precision risk stratification, guiding timely interventions, and ultimately reducing morbidity and mortality. Future large-scale, longitudinal studies are warranted to validate these findings and support guideline incorporation, thereby advancing healthcare transformation aligned with national health security (NHS) and sustainable development goals (SDGs).

IntroductionStatin-associated muscle symptoms (SAMS) contribute to the nonadherence to statin therapy. In a previous study, we successfully developed a pharmacological SAMS (PSAMS) phenotyping algorithm that distinguishes objective versus nocebo SAMS using structured and unstructured electronic health records (EHRs) data. Our aim in this paper was to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using these same EHR data. MethodUsing our PSAMS phenotyping algorithm, SAMS cases and controls were identified using University of Minnesota (UMN) Fairview EHR data. The statin user cohort was temporally divided into derivation (1/1/2010 to 12/31/2018) and validation (1/1/2019 to 12/31/2020) cohorts. First, from a feature set of 38 variables, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model was fitted to identify important features for PSAMS cases and their coefficients. A PSAMS-RS score was calculated by multiplying these coefficients by 100 and then adding together for individual integer scores. The clinical utility of PSAMS-RS in stratifying PSAMS risk was assessed by comparing the hazard ratio (HR) between 4th vs 1st score quartile. ResultsPSAMS cases were identified in 1.9% (310/16128) of the derivation and 1.5% (64/4182) of the validation cohort. After fitting LASSO regression, 16 out of 38 clinical features were determined to be significant predictors for PSAMS risk. These factors are male gender, chronic pulmonary disease, neurological disease, tobacco use, renal disease, alcohol use, ACE inhibitors, polypharmacy, cerebrovascular disease, hypothyroidism, lymphoma, peripheral vascular disease, coronary artery disease and concurrent uses of fibrates, beta blockers or ezetimibe. After adjusting for statin intensity, patients in the PSAMS score 4th quartile had an over seven-fold (derivation) (HR, 7.1; 95% CI, 4.03-12.45) and six-fold (validation) (HR, 6.1; 95% CI, 2.15-17.45) higher hazard of developing PSAMS versus those in 1st score quartile. ConclusionThe PSAMS-RS score can be a simple tool to stratify patients risk of developing PSAMS after statin initiation which can facilitate clinician-guided preemptive measures that may prevent potential PSAMS-related statin non-adherence.

BACKGROUNDPulmonary arterial hypertension (PAH) is a rare disease affecting the heart and lungs. Median survival was 2.8 years historically, but prognosis improved with the advances of PAH therapy. It is currently not standard of care for clinical pharmacists to be a part of the interdisciplinary care team in outpatient PH clinics to help manage patients drug regimens, and there is limited literature that has explored the impact of clinical pharmacists on patient-related processes and outcomes. The goal of the CliPR-PH study is to characterize the activities of the clinical pharmacist as a member of the interdisciplinary team. METHODSCliPR-PH was a retrospective, descriptive, single-center cohort study of patients [&ge;] 18 years, diagnosed with pulmonary hypertension, and managed by a clinical pharmacist practicing under a collaborative practice agreement between January 1, 2018 and July 31, 2020. Patients were excluded from the study if they were not on any PAH medications at the time of pharmacist encounter. RESULTSSixty patients were included in the analysis and 331 clinical pharmacist interventions were documented over the study period. Interventions were regarding selexipag [88 (26.6%)], sildenafil [74 (22.4%)], tadalafil [46 (13.9%)], prior authorization (PA) completions [127 (38.4%), PA troubleshooting [43 (13.0%)], and Other [49 (14.8%)]. CONCLUSIONClinical pharmacists can play an important role in closely monitoring patients during the medication titration phase and ensure prior authorizations are approved in a timely manner as part of the interdisciplinary team in the outpatient PH clinic setting using a collaborative practice model approach. Clinical PerspectiveWhat is new? There are currently no studies in the United States that asses a clinical pharmacists role on the interdisciplinary team as part of a collaborative practice agreement in the outpatient pulmonary hypertension (PH) clinic. What are the clinical implications? Integrating a clinical pharmacist on the medical team in an outpatient PH clinic setting allows them to closely monitor patients who are titrating selexipag, manage adverse events (AEs) that occur during the titration phase, and ensure medications requiring prior authorizations are approved in a timely manner.

BackgroundThe value of pharmaceutical services for Chinese patients with cardiovascular chronic conditions was not recognized. AimTo investigate the comprehensive value of pharmaceutical services in China and find factors influencing patient benefits. Design and settingThis was a systematic review with multilevel meta-analysis of 183 randomized control trials focusing on the benefits of pharmaceutical services for patients with cardiovascular chronic conditions in China. MethodsEnglish databases (PubMed, EMBASE, the Cochrane Library) and Chinese databases (China National Knowledge Infrastructure, WanFang database) were searched from database inception to March 27, 2023 for studies focusing on the comparation of benefits between pharmaceutical services and usual care. ResultsOur analysis of 187 studies involving 23,895 patients demonstrated significant benefits of pharmaceutical services, particularly in reducing readmission (OR: 0.32; 95%CI: 0.2 to 0.52; I2=50.12%), mitigating ADR (OR: 0.28; 95%CI: 0.24 to 0.33; I2=18.07%), and improving patient adherence. However, no benefit was observed in terms of mortality rate and the cost of hospitalization and medication and the risk of bias was generally existed among the included studies. ConclusionsThis study highlights the significant benefits of pharmaceutical services for clinical outcomes and adherence among Chinese patients with cardiovascular chronic conditions. However, the benefits in terms of economic outcomes remain unclear. The influence of population-specific factors, such as disease and age, underscores the need for context-specific and disease- tailored studies to provide precise evidence regarding the advantages of pharmaceutical services. And our findings provide some new ideas for the subsequent research and design, standard formulation and policy implementation. How this fits inPrevious assessments showed clinical benefits of pharmaceutical services but were unclear about other benefits and didnt consider patient characteristics or contexts. There is no standardized system for pharmaceutical services in China. Our meta-analysis found clear clinical benefits for patients with cardiovascular chronic conditions and showed that age negatively impacts adherence, and medication costs vary by disease type. This study is the first to analyze comprehensive benefits for Chinese patients, highlighting the importance of considering patient characteristics in pharmaceutical services.

The COVID-19 outbreak caused by the SARS-CoV-2 virus has developed into a global health emergency. In addition to causing respiratory symptoms following SARS-CoV-2 infection, COVID-19-associated coagulopathy (CAC) is the main cause of death in patients with severe COVID-19. In this study, we performed single-cell sequencing analysis of the right ventricular free wall tissue from healthy donors, patients who died in the hypercoagulable phase of CAC, and patients in the fibrinolytic phase of CAC. Among these, we collected 61,187 cells, which were enriched in 24 immune cell subsets and 13 cardiac-resident cell subsets. We found that in response to SARS-CoV-2 infection, CD9highCCR2highmonocyte-derived mo promoted hyperactivation of the immune system and initiated the extrinsic coagulation pathway by activating CXCR-GNB/G-PI3K-AKT. This sequence of events is the main process contributing the development of coagulation disorders subsequent to SARS-CoV-2 infection. In the characteristic coagulation disorder caused by SARS-CoV-2, excessive immune activation is accompanied by an increase in cellular iron content, which in turn promotes oxidative stress and intensifies intercellular competition. This induces cells to alter their metabolic environment, resulting in an increase in sugar uptake, such as that via the glycosaminoglycan synthesis pathway, in CAC coagulation disorders. In addition, high levels of reactive oxygen species generated in response elevated iron levels promote the activation of unsaturated fatty acid metabolic pathways in endothelial cell subgroups, including vascular endothelial cells. This in turn promotes the excessive production of the toxic peroxidation by-product malondialdehyde, which exacerbates both the damage caused to endothelial cells and coagulation disorders.

MotivationSystemic lupus erythematosus (SLE) is an autoimmune disease and a long-term condition affecting many body parts. Autoimmune diseases are affecting more people for reasons unknown, and the causes of these diseases remain a mystery. MethodA newly introduced robust competing (risk) max-logistic regression classifier that can simultaneously perform subtype clustering and classification for disease diagnoses and predictions becomes a new hope to solve the mystery. We use this method in the study to discover critical DNA methylation CpG sites and genome genes, which lead to the highest accuracy and interpretability. ResultsThe DNA methylation CpG site cg05883128 (DDX60) and gene NR3C2 are essentially responsible for SLE development. They can lead to 100% prediction accuracy together with a miniature set of other CpG sites and genes, respectively. cg05883128 (DDX60) reveals the LSE mechanism affecting many body parts. NR3C2 in CD4 T cells and B cells behaves reversely, leading to the cause of LSE and explaining the mechanism of the autoimmune disease. ConclusionsThis work represents a pioneering effort and intellectual discovery in applying the max-logistic competing risk factor model to identify critical genes for LSE, and the interpretability and reproducibility of the results across diverse populations suggest that the CpGs and DEGs identified can provide a comprehensive description of the transcriptomic features of SLE. The practical implications of this research include the potential for personalized risk assessment, precision diagnosis, and tailored treatment plans for patients.

PurposeThe I AM Frontier cohort was set up to support proof-of-concepts aimed at precision health and more specifically personalized prevention and health promotion. The study was designed to identify patterns, markers and processes, that play a role in the spectrum between health and early onset of disease and may provide actionable information in a clinical setting, taking into account all ethical, legal and logistical aspects. ParticipantsThe first phase of the I AM Frontier study ran for 12 months as a longitudinal small-scale cohort study (n=30) in the Antwerp region of Flanders, Belgium. Participants were employees of the company hosting the study, they did not have a clinical diagnosis and were between 45-60 years old. Findings to dateEven though no severe health problems are recorded at baseline, participants did report several physical complaints. There is a clear difference in longitudinal variation between clinical and research grade omics types, which might affect their respective ability to detect intermediate molecular changes that can be linked to phenotype changes. Future plansThis cohort is being used to further support the design and implementation of a larger population health cohort with selected modalities for investigating feasibility of personalized prevention in real life setting. Future research will build on this longitudinal dataset to derive healthy yearly fluctuations (or normal ranges) at individual level for predicting early on-set deviations RegistrationThe study was approved by the ethical committee of the Antwerp University Hospital (RegN{degrees}:B300201837314). Strengths and limitations summaryO_LIThe I AM Frontier proof-of-concept (POC) cohort study is unique in that it collected an extensive range of samples, with high longitudinal frequency, of healthy individuals for 12 months. The implemented sampling technologies (for clinical parameters, whole genome sequencing (WGS), methylation, quantitative proteomics, metabolomics, microbiome, retina scans, wearables, and standardized questionnaires on e.g. food intake and medical status in combination with genome sequencing at the start of the study) were selected to maximize overlap with large cross-sectional studies and biobanks such as e.g. UK biobank to allow comparison of phenotypical profiles present across different studies. C_LIO_LIThe highly granular (i.e. collected with high longitudinal frequency) data within this study allows us to construct dense participant profiles. Frequent longitudinal data collection of multi-omics data is emerging with new technical advancements for the in-depth analyses of molecules in small blood volumes. To allow the routine usage of such measurements in clinical practice, the temporal changes observed in this cohort can serve to evaluate the frequency and added value of such highly granular measurements. C_LIO_LIPerceptions of precision health, such as communication of clinical follow-up data, personal risks from genomics, behavioral aspects, and the ethical dilemmas that go together with all of this, are included in the scope of the cohort. C_LI

BackgroundRheumatoid arthritis and psoriasis are inflammatory diseases which require frequent monitoring to optimize therapy. Specialty pharmacists are in the unique position to assist in the screening and monitoring of patients with complex, chronic diseases. ObjectivesThe study objective is to describe the impact of pharmacist screening services in two patient populations. In patients with rheumatoid arthritis, the goal is to describe outcome monitoring through disease severity, therapeutic switches, and adherence rates. In patients with psoriasis, the aim is to describe the utilization of a screening for psoriatic arthritis and the resulting number of potential referrals to medical providers. MethodsThe retrospective study patient population consisted of rheumatoid arthritis (RA) patients who filled one or more prescriptions at a specialty pharmacy between 8/22/2017 and 9/26/2018, and psoriasis patients who filled between 6/1/2021 to 9/1/2021. A Routine Assessment of Patient Index Data 3 (RAPID3) score was collected during a refill coordination call every three months throughout the 13-month period for RA patients, and a Psoriasis Epidemiology Screening Tool (PEST) scores reported throughout the stated timeline.1,2 Data was imported from the pharmacys electronic medical record into an Excel spreadsheet with each row representing a unique patient. Following data validation, descriptive statistics including means, standard deviations, and percentages were calculated to characterize the sample. Statistical significance was determined at an alpha of 0.05. ResultsOf the patients who had 4 assessments for RAPID3, the disease severity category significantly improved from assessment 1 to assessment 4 (p=0.021) when analyzed using a chi-square test. The RAPID3 assessment of patients with RA by pharmacists in a specialty setting identified responders (n=21, 25.6%) and stable patients (n=51, 63%), which reinforces current therapy, and non-responders (n=10, 12.2%), who may benefit from referral to their provider for reevaluation of their therapeutic plan. The PEST screening of patients with psoriasis by pharmacists in a specialty setting identified 11 of 32 patients (34%) who scored a 3 or higher and who may benefit from a referral to a rheumatologist for further assessment of psoriatic arthritis activity. ConclusionSpecialty pharmacists are an essential part of ongoing assessment and management of patients with chronic inflammatory conditions such as rheumatoid arthritis and psoriasis. Screening and monitoring of patients by pharmacists can identify the need for referral to a medical provider. Summary BulletsWhat is already known about this subject? Current guidelines for the treatment of rheumatoid arthritis recommend frequent monitoring and reassessment every three months until low disease activity or remission is achieved. Similar screening efforts in patients with psoriasis can help identify the nearly 30% of patients who have undiagnosed psoriatic arthritis. Clinical pharmacists in the specialty setting can assist with these screening initiatives to reduce disease severity and appropriately refer patients for further examination. What this study adds. This study demonstrates the ability of clinical specialty pharmacists to administer validated screening tools used in chronic inflammatory disease states to improve patient outcomes. DisclosuresThe authors of this study have no possible financial or personal relationships with commercial entities to disclose that may have a direct or indirect interest in the matter of this study. Funding sourceNone.

BackgroundFinasteride is commonly utilized in clinical practice for treating androgenetic alopecia, but real-world data regarding the long-term safety of its adverse events remains incomplete, necessitating ongoing supplementation. This study aims to evaluate the adverse events (AEs) associated with finasteride use, based on data from the US Food and Drug Administration Adverse Event Reporting System (FAERS), to contribute to its safety assessment. MethodsWe reviewed adverse event reports associated with finasteride from the FAERS database, covering the period from the first quarter of 2004 to the first quarter of 2024. We assessed the safety of finasteride medication and AEs using four proportional disproportionality analyses: reported odds ratio, proportionate reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPN), and Multi-Item Gamma Poisson Shrinkage (MGPS). These methods were used to evaluate the of finasteride medication and AEs. whether there is a significant association between finasteride drug use and AEs. To investigate potential safety issues related to drug use, we further analyzed the similarities and differences in the onset time and AEs by gender, as well as the similarities and differences in AEs by age. ResultsAmong the 11,557 adverse event reports where finasteride was the primary suspected drug, most patients affected were male (86.04%), with a significant proportion being the young adult aged 18-45 years (27.22%). We categorized 73 adverse events (AEs) into 7 different system organ categories (SOCs), which included common AEs like erectile dysfunction and sexual dysfunction. Notably, Peyronies disease and post 5 reductase inhibitor syndrome were AEs not listed in the drug insert. We identified 102 AEs for men and 7 for women. Depression and anxiety were notable AEs for both male and female. Additionally, we examined 17 adverse events (AEs) in patients under 18 years old, 157 in patients aged 18 to 65 years, and 133 in patients aged 65 years and older. Each age group exhibited unique AEs, although erectile dysfunction, decreased libido, depression, suicidal ideation, psychotic disorder, and attention disturbance were common AEs observed across different age brackets. Ultimately, the median onset time for all instances was 61 days. The onset was mainly within one month after initiation of finasteride and it is noteworthy that the second highest number of cases involved adverse drug reactions persisted beyond one year of treatment. ConclusionThe results of our study uncovered both known and novel AEs associated with finasteride medication. Some of these AEs were identical to the specification, and some of them signaled AEs that were not demonstrated in the specification. In addition, some AEs showed variations based on gender and age in our study. Consequently, our findings offer valuable insights for future research on the safety of finasteride medication and are anticipated to enhance its safe use in clinical practice.

BACKGROUNDAlthough higher prevalence, disease activity, damage accumulation and mortality of systemic lupus erythematosus (SLE) are observed among Latin American, North American admixed population, African descendants and Native Americans, the information about SLE in Latin American countries, such as Mexico, is scarce. OBJECTIVESTo present Lupus RGMX, a multidisciplinary effort to generate a national digital patient registry to enrich the understanding of Mexican people with SLE. METHODSMexican patients with SLE registered between May 2021 and January 2023 in Lupus RGMX were included. Sociodemographic, socioeconomic, and clinical characteristics, along with quality-of-life perception (QoL) were assessed using self-reported data. We compared the QoL obtained from patients with SLE with two groups of non-SLE Mexican subjects. Descriptive statistics, comparisons analyses and a multivariate nonparametric regression model were performed. RESULTSA total of 1172 of lupus patients were included; of which 93.9% were women. The mean age{+/-}SD was 36.6{+/-}10.7 years, with 37.1% of the individuals between 41 and 50 years. The 24.9% reported a calculated monthly income of 430 USD (8,612 MXN). Lower QoL scores were observed in the SLE group, especially in subjects with lower socioeconomic level. Health perception, QoL perception and socioeconomic status were the variables with greater importance to predict total WHOQoL scores. CONCLUSIONThe design and implementation of Lupus RGMX imply a pioneering approach to unraveling SLE in Mexicans. Further studies from Lupus RGMX will be focused on enriching the representation of the Mexican population and include other aspects that may allow us to improve our understanding of the disease in our population.

Since December 2019, a newly identified coronavirus (2019 novel coronavirus, 2019-nCov) is causing outbreak of pneumonia in one of largest cities, Wuhan, in Hubei province of China and has draw significant public health attention. The same as severe acute respiratory syndrome coronavirus (SARS-CoV), 2019-nCov enters into host cells via cell receptor angiotensin converting enzyme II (ACE2). In order to dissect the ACE2-expressing cell composition and proportion and explore a potential route of the 2019-nCov infection in digestive system infection, 4 datasets with single-cell transcriptomes of lung, esophagus, gastric, ileum and colon were analyzed. The data showed that ACE2 was not only highly expressed in the lung AT2 cells, esophagus upper and stratified epithelial cells but also in absorptive enterocytes from ileum and colon. These results indicated along with respiratory systems, digestive system is a potential routes for 2019-nCov infection. In conclusion, this study has provided the bioinformatics evidence of the potential route for infection of 2019-nCov in digestive system along with respiratory tract and may have significant impact for our healthy policy setting regards to prevention of 2019-nCoV infection.

Genome-wide association studies (GWAS) are surging again owing to newer high-quality T2T-CHM13 and human pangenome references. Conventional GWAS methods have several limitations, including high false negatives. Non-conventional machine learning-based methods are warranted for analyzing newly sequenced, albeit complex, genomic regions. We present a robust machine learning-based framework for feature selection and association analysis, incorporating functional enrichment analysis to avoid false negatives. We benchmarked four popular single nucleotide polymorphism (SNP) feature selection methods: least absolute shrinkage and selection operator, ridge regression, elastic-net, and mutual information. Furthermore, we evaluated four association methods: linear regression, random forest, support vector regression (SVR), and XGBoost. We assessed proposed framework on diverse datasets, including subsets of publicly available PennCATH datasets as well as imputed, rare-variants, and simulated datasets. Low-density lipoprotein (LDL) cholesterol level was used as a phenotype for illustration. Our analysis revealed elastic-net combined with SVR consistently outperformed other methods across various datasets. Functional annotation of top 100 SNPs from PennCATH-real dataset revealed their expression in LDL cholesterol-related tissues. Our analysis validated three previously known genes (APOB, TRAPPC9, and EEPD1) implicated in cholesterol-regulated pathways. Also, rare-variant dataset analysis confirmed 37 known genes associated with LDL cholesterol. We identified several important genes, including APOB (familial-hypercholesterolemia), PTK2B (Alzheimers disease), and PTPN12 (myocardial ischemia/reperfusion injuries) as potential drug targets for cholesterol-related diseases. Our comprehensive analyses highlight elastic-net combined with SVR for association analysis could overcome limitations of conventional GWAS approaches. Our framework effectively detects common and rare variants associated with complex traits, enhancing the understanding of complex diseases.