Ambulatory blood pressure in patients with rheumatoid arthritis: association with immune activation.

OBJECTIVE: The prevalence of hypertension, a major cardiovascular risk factor, is increased in patients with rheumatoid arthritis (RA) and may be driven by immune activation. The purpose of this study was to determine if ambulatory 24-hour blood pressure (BP) is elevated in RA versus control participants and associated with immune activation. METHODS: We conducted a cross-sectional study of 46 patients with RA and 23 control participants. Participants wore an ambulatory BP monitor which obtained diurnal BP every 15-30 minutes and nocturnal BP every 30 minutes. Inflammatory mediators in plasma were measured using an inflammation proteomics panel. Differences in BP measurements were assessed by Mann-Whitney U, and association with inflammatory mediators was assessed by Spearman correlation. RESULTS: Patients with RA and control participants had similar office BP, but ambulatory systolic BP measurements: 24-hour (median RA =121 mmHg versus control =116 mmHg, P=0.007), diurnal (RA =128 mmHg versus control =120 mmHg, P=0.003), and nocturnal (RA =112 mmHg versus control =103 mmHg, P=0.002) were higher in patients with RA. Patients with RA also had higher nocturnal diastolic BP (RA =63 mmHg versus control =57 mmHg, P=0.02), but other diastolic BP measurements were similar. Nocturnal BP dipping was less in RA (12%) compared to control participants (16%, P=0.02). In RA, higher 24-hour and nocturnal systolic BPs and less nocturnal dipping were strongly correlated with a wide range of inflammatory mediators. CONCLUSION: Despite similar office measurements, 24-hour and nocturnal systolic BP measurements were higher in RA than control participants and were strongly associated with inflammation.

Large language models facilitate the generation of electronic health record phenotyping algorithms.

OBJECTIVES: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. MATERIALS AND METHODS: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). CONCLUSION: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

Clinical associations with a polygenic predisposition to benign lower white blood cell counts.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Serious Bleeding in Patients With Atrial Fibrillation Using Diltiazem With Apixaban or Rivaroxaban.

Importance: Diltiazem, a commonly prescribed ventricular rate-control medication for patients with atrial fibrillation, inhibits apixaban and rivaroxaban elimination, possibly causing overanticoagulation. Objective: To compare serious bleeding risk for new users of apixaban or rivaroxaban with atrial fibrillation treated with diltiazem or metoprolol. Design, Setting, and Participants: This retrospective cohort study included Medicare beneficiaries aged 65 years or older with atrial fibrillation who initiated apixaban or rivaroxaban use and also began treatment with diltiazem or metoprolol between January 1, 2012, and November 29, 2020. Patients were followed up to 365 days through November 30, 2020. Data were analyzed from August 2023 to February 2024. Exposures: Diltiazem and metoprolol. Main Outcomes and Measures: The primary outcome was a composite of bleeding-related hospitalization and death with recent evidence of bleeding. Secondary outcomes were ischemic stroke or systemic embolism, major ischemic or hemorrhagic events (ischemic stroke, systemic embolism, intracranial or fatal extracranial bleeding, or death with recent evidence of bleeding), and death without recent evidence of bleeding. Hazard ratios (HRs) and rate differences (RDs) were adjusted for covariate differences with overlap weighting. Results: The study included 204 155 US Medicare beneficiaries, of whom 53 275 received diltiazem and 150 880 received metoprolol. Study patients (mean [SD] age, 76.9 [7.0] years; 52.7% female) had 90 927 person-years (PY) of follow-up (median, 120 [IQR, 59-281] days). Patients receiving diltiazem treatment had increased risk for the primary outcome (RD, 10.6 [95% CI, 7.0-14.2] per 1000 PY; HR, 1.21 [95% CI, 1.13-1.29]) and its components of bleeding-related hospitalization (RD, 8.2 [95% CI, 5.1-11.4] per 1000 PY; HR, 1.22 [95% CI, 1.13-1.31]) and death with recent evidence of bleeding (RD, 2.4 [95% CI, 0.6-4.2] per 1000 PY; HR, 1.19 [95% CI, 1.05-1.34]) compared with patients receiving metoprolol. Risk for the primary outcome with initial diltiazem doses exceeding 120 mg/d (RD, 15.1 [95% CI, 10.2-20.1] per 1000 PY; HR, 1.29 [95% CI, 1.19-1.39]) was greater than that for lower doses (RD, 6.7 [95% CI, 2.0-11.4] per 1000 PY; HR, 1.13 [95% CI, 1.04-1.24]). For doses exceeding 120 mg/d, the risk of major ischemic or hemorrhagic events was increased (HR, 1.14 [95% CI, 1.02-1.27]). Neither dose group had significant changes in the risk for ischemic stroke or systemic embolism or death without recent evidence of bleeding. When patients receiving high- and low-dose diltiazem treatment were directly compared, the HR for the primary outcome was 1.14 (95% CI, 1.02-1.26). Conclusions and Relevance: In Medicare patients with atrial fibrillation receiving apixaban or rivaroxaban, diltiazem was associated with greater risk of serious bleeding than metoprolol, particularly for diltiazem doses exceeding 120 mg/d.

Characterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry.

Hypertriglyceridemia (HTG) is a common cardiovascular risk factor characterized by elevated circulating triglyceride (TG) levels. Researchers have assessed the genetic factors that influence HTG in studies focused predominantly on individuals of European ancestry (EA). However, relatively little is known about the contribution of genetic variation to HTG in people of AA, potentially constraining research and treatment opportunities; the lipid profile for African ancestry (AA) populations differs from that of EA populations-which may be partially attributable to genetics. Our objective was to characterize genetic profiles among individuals of AA with mild-to-moderate HTG and severe HTG versus those with normal TGs by leveraging whole genome sequencing (WGS) data and longitudinal electronic health records (EHRs) available in the All of Us (AoU) program. We compared the enrichment of functional variants within five canonical TG metabolism genes, an AA-specific polygenic risk score for TGs, and frequencies of 145 known potentially causal TG variants between patients with HTG and normal TG among a cohort of AA patients (N=15,373). Those with mild-to-moderate HTG (N=342) and severe HTG (N≤20) were more likely to carry APOA5 p.S19W (OR=1.94, 95% CI [1.48-2.54], p=1.63×10 -6 and OR=3.65, 95% CI [1.22-10.93], p=0.02, respectively) than those with normal TG. They were also more likely to have an elevated (top 10%) PRS, elevated carriage of potentially causal variant alleles, and carry any genetic risk factor. Alternative definitions of HTG yielded comparable results. In conclusion, individuals of AA with HTG were enriched for genetic risk factors compared to individuals with normal TGs.

Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation.

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: (1) Vanderbilt University Medical Center and (2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms.

Objectives: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (i.e., type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

Antipsychotic Medications and Mortality in Children and Young Adults.

Importance: Dose-related effects of antipsychotic medications may increase mortality in children and young adults. Objective: To compare mortality for patients aged 5 to 24 years beginning treatment with antipsychotic vs control psychiatric medications. Design, Setting, and Participants: This was a US national retrospective cohort study of Medicaid patients with no severe somatic illness or schizophrenia or related psychoses who initiated study medication treatment. Study data were analyzed from November 2022 to September 2023. Exposures: Current use of second-generation antipsychotic agents in daily doses of less than or equal to 100-mg chlorpromazine equivalents or greater than 100-mg chlorpromazine equivalents vs that for control medications (α agonists, atomoxetine, antidepressants, and mood stabilizers). Main Outcome and Measures: Total mortality, classified by underlying cause of death. Rate differences (RDs) and hazard ratios (HRs) adjusted for potential confounders with propensity score-based overlap weights. Results: The 2 067 507 patients (mean [SD] age, 13.1 [5.3] years; 1 060 194 male [51.3%]) beginning study medication treatment filled 21 749 825 prescriptions during follow-up with 5 415 054 for antipsychotic doses of 100 mg or less, 2 813 796 for doses greater than 100 mg, and 13 520 975 for control medications. Mortality was not associated with antipsychotic doses of 100 mg or less (RD, 3.3; 95% CI, -5.1 to 11.7 per 100 000 person-years; HR, 1.08; 95% CI, 0.89-1.32) but was associated with doses greater than 100 mg (RD, 22.4; 95% CI, 6.6-38.2; HR, 1.37; 95% CI, 1.11-1.70). For higher doses, antipsychotic treatment was significantly associated with overdose deaths (RD, 8.3; 95% CI, 0-16.6; HR, 1.57; 95% CI, 1.02-2.42) and other unintentional injury deaths (RD, 12.3; 95% CI, 2.4-22.2; HR, 1.57; 95% CI, 1.12-2.22) but was not associated with nonoverdose suicide deaths or cardiovascular/metabolic deaths. Mortality for children aged 5 to 17 years was not significantly associated with either antipsychotic dose, whereas young adults aged 18 to 24 years had increased risk for doses greater than 100 mg (RD, 127.5; 95% CI, 44.8-210.2; HR, 1.68; 95% CI, 1.23-2.29). Conclusions and Relevance: In this cohort study of more than 2 million children and young adults without severe somatic disease or diagnosed psychosis, antipsychotic treatment in doses of 100 mg or less of chlorpromazine equivalents or in children aged 5 to 17 years was not associated with increased risk of death. For doses greater than 100 mg, young adults aged 18 to 24 years had significantly increased risk of death, with 127.5 additional deaths per 100 000 person-years.

Association between APOL1 risk variants and the occurrence of sepsis in Black patients hospitalized with infections: a retrospective cohort study.

Background: Two risk variants in the apolipoprotein L1 gene (APOL1) have been associated with increased susceptibility to sepsis in Black patients. However, it remains unclear whether APOL1 high-risk genotypes are associated with occurrence of either sepsis or sepsis-related phenotypes in patients hospitalized with infections, independent of their association with pre-existing severe renal disease. Methods: A retrospective cohort study of 2242 Black patients hospitalized with infections. We assessed whether carriage of APOL1 high-risk genotypes was associated with the risk of sepsis and sepsis-related phenotypes in patients hospitalized with infections. The primary outcome was sepsis; secondary outcomes were short-term mortality, and organ failure related to sepsis. Results: Of 2242 Black patients hospitalized with infections, 565 developed sepsis. Patients with high-risk APOL1 genotypes had a significantly increased risk of sepsis (odds ratio [OR]=1.29 [95% CI, 1.00-1.67; p=0.047]); however, this association was not significant after adjustment for pre-existing severe renal disease (OR = 1.14 [95% CI, 0.88-1.48; p=0.33]), nor after exclusion of those patients with pre-existing severe renal disease (OR = 0.99 [95% CI, 0.70-1.39; p=0.95]). APOL1 high-risk genotypes were significantly associated with the renal dysfunction component of the Sepsis-3 criteria (OR = 1.64 [95% CI, 1.21-2.22; p=0.001]), but not with other sepsis-related organ dysfunction or short-term mortality. The association between high-risk APOL1 genotypes and sepsis-related renal dysfunction was markedly attenuated by adjusting for pre-existing severe renal disease (OR = 1.36 [95% CI, 1.00-1.86; p=0.05]) and was nullified after exclusion of patients with pre-existing severe renal disease (OR = 1.16 [95% CI, 0.74-1.81; p=0.52]). Conclusions: APOL1 high-risk genotypes were associated with an increased risk of sepsis; however, this increased risk was attributable predominantly to pre-existing severe renal disease. Funding: This study was supported by R01GM120523 (QF), R01HL163854 (QF), R35GM131770 (CMS), HL133786 (WQW), and Vanderbilt Faculty Research Scholar Fund (QF). The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU which is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the CTSA grant UL1TR0004from NCATS/NIH. Additional funding provided by the NIH through grants P50GM115305 and U19HL065962. The authors wish to acknowledge the expert technical support of the VANTAGE and VANGARD core facilities, supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA068485) and Vanderbilt Vision Center (P30 EY08126). The funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

When the body is fighting off an infection, the processes it uses to protect itself can sometimes overreact. This results in a condition known as sepsis which can cause life-threatening damage to multiple organs. In the United States, Black patients are 60-80% more likely to develop sepsis compared to individuals who identify as White; differences remain even after accounting for socio-economic status and presence of other illnesses. Recent work has suggested that two variants of the APOL1 gene which are almost exclusively found in people with African ancestry may be a contributing factor to this disparity. These 'high-risk' genetic variants have also been shown to increase the likelihood of kidney diseases. It is therefore possible that the elevated chance of sepsis is not directly linked to these variations of APOL1, but rather is the result of patients already having reduced kidney function. To understand the relationship between APOL1 and sepsis, Jiang et al. analyzed data from patients admitted to Vanderbilt University Medical Centre in the United States between 2000 and 2020. This included 2,242 patients who identified as Black and had been hospitalized with an infection. The analyses showed that 16% of these individuals were carriers of the APOL1 high-risk variants. The high-risk patients were more likely to experience sepsis and demonstrate kidney damage. But other organs commonly damaged by sepsis were not affected more in these individuals compared to the other 84% of patients who did not have these variants. Furthermore, when individuals with pre-existing kidney diseases were removed from this high-risk group, the increased likelihood of sepsis was no longer prominent. These findings suggest that the APOL1 variants do not directly increase the risk of sepsis, and this association is primarily due to patients with these genetic variations being more susceptible to kidney diseases. There are new drugs under development targeting the APOL1 variants. While these may provide protection against kidney diseases, they are unlikely to be successful at preventing or treating sepsis once a patient has been hospitalized with an infection.

Identification of novel biomarkers for the prediction of subclinical coronary artery atherosclerosis in patients with rheumatoid arthritis: an exploratory analysis.

BACKGROUND: Cardiovascular (CV) risk estimation calculators for the general population underperform in patients with rheumatoid arthritis (RA). The purpose of this study was to identify relevant protein biomarkers that could be added to traditional CV risk calculators to improve the capacity of coronary artery calcification (CAC) prediction in individuals with RA. In a second step, we quantify the improvement of this prediction of CAC when these circulating biomarkers are added to standard risk scores. METHODS: A panel of 141 serum and plasma proteins, which represent a broad base of both CV and RA biology, were evaluated and prioritized as candidate biomarkers. Of these, 39 proteins were selected and measured by commercial ELISA or quantitative mass spectroscopy in 561 individuals with RA in whom a measure of CAC and frozen sera were available. The patients were randomly split 50:50 into a training/validation cohort. Discrimination (using area under the receiver operator characteristic curves) and re-classification (through net reclassification improvement and integrated discrimination improvement calculation) analyses were performed first in the training cohort and replicated in the validation cohort, to estimate the increase in prediction accuracy for CAC using the ACA/AHA (American College of Cardiology and the American Heart Association) score with, compared to without, addition of these circulating biomarkers. RESULTS: The model containing ACC/AHA score plus cytokines (osteopontin, cartilage glycoprotein-39, cystatin C, and chemokine (C-C motif) ligand 18) and plus quantitative mass spectroscopy biomarkers (serpin D1, paraoxonase, and clusterin) had a statistically significant positive net reclassifications index and integrated discrimination improvement for the prediction of CAC, using ACC/AHA score without any biomarkers as the reference category. These results were confirmed in the validation cohort. CONCLUSION: In this exploratory analysis, the addition of several circulating CV and RA biomarkers to a standard CV risk calculator yielded significant improvements in discrimination and reclassification for the presence of CAC in individuals with RA.

Clinical consequences of a polygenic predisposition to benign lower white blood cell counts: Consequences of benign WBC count genetics.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is undefined. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio=0.55 per standard deviation increase in PGSWBC [95%CI, 0.30 - 0.94], p=0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n=1,724, hazard ratio [HR]=0.78 [0.69 - 0.88], p=4.0×10-5) or immunosuppressant (n=354, HR=0.61 [0.38 - 0.99], p=0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n=1,466, HR=0.62 [0.44 - 0.87], p=0.006). Collectively, these findings suggest that a WBC count polygenic score identifies individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Concurrent Gabapentin and Opioid Use and Risk of Mortality in Medicare Recipients with Non-Cancer Pain.

Gabapentin is prescribed for pain and is perceived as safe generally. However, gabapentin can cause respiratory depression, exacerbated by concomitant central nervous system depressants (e.g., opioids), a concern for vulnerable populations. We compared mortality rates among new users of either gabapentin or duloxetine with or without concurrent opioids in the 20% Medicare sample. We conducted a new-user design retrospective cohort study, in Medicare enrollees ages 65-89 years with noncancer chronic pain and no severe illness who filled prescriptions between 2015 and 2018 for gabapentin (n = 233,060) or duloxetine (n = 34,009). Daily opioid doses, estimated in morphine milligram equivalents (MMEs), were classified into none, low (0 < MME < 50), and high (≥ 50 MME), based on Centers for Disease Control and Prevention (CDC) recommendations. The outcomes were all-cause mortality (primary) and out-of-hospital mortality (secondary). We used inverse probability of treatment weighting to adjust for differences between gabapentin and duloxetine users. During 116,707 person-years of follow-up, 1,379 patients died. All-cause mortality rate in gabapentin users was 12.16 per 1,000 person-years vs. 9.94 per 1,000 in duloxetine users. Risks were similar for users with no concurrent opioids (adjusted hazard ratio (aHR) = 1.03, 95% confidence interval (CI): 0.80-1.31) or low-dose daily opioids (aHR = 1.06, 95% CI: 0.63-1.76). However, gabapentin users receiving concurrent high-dose daily opioids had an increased rate of all-cause mortality compared with duloxetine users on high-dose opioids (aHR = 2.03, 95% CI: 1.19-3.46). Out-of-hospital mortality yielded similar results. In this retrospective cohort study of Medicare beneficiaries, concurrent use of high-dose opioids and gabapentin was associated with a higher all-cause mortality risk than that for concurrent use of high-dose opioids and duloxetine.

Clinical diagnoses associated with a positive antinuclear antibody test in patients with and without autoimmune disease.

BACKGROUND: Antinuclear antibodies (ANA) are antibodies present in several autoimmune disorders. However, a large proportion of the general population (20%) also have a positive test; very few of these individuals will develop an autoimmune disease, and the clinical impact of a positive ANA in them is not known. Thus, we test the hypothesis that ANA + test reflects a state of immune dysregulation that alters risk for some clinical disorders in individuals without an autoimmune disease. METHODS: We performed high throughput association analyses in a case-control study using real world data from the de-identified electronic health record (EHR) system from Vanderbilt University Medical Center. The study population included individuals with an ANA titer ≥ 1:80 at any time (ANA +) and those with negative results (ANA-). The cohort was stratified into sub-cohorts of individuals with and without an autoimmune disease. A phenome-wide association study (PheWAS) adjusted by sex, year of birth, race, and length of follow-up was performed in the study cohort and in the sub-cohorts. As secondary analyses, only clinical diagnoses after ANA testing were included in the analyses. RESULTS: The cohort included 70,043 individuals: 49,546 without and 20,497 with an autoimmune disease, 26,579 were ANA + and 43,464 ANA-. In the study cohort and the sub-cohort with autoimmune disease, ANA + was associated (P ≤ 5 × 10-5) with 88 and 136 clinical diagnoses respectively, including lupus (OR ≥ 5.4, P ≤ 7.8 × 10-202) and other autoimmune diseases and complications. In the sub-cohort without autoimmune diseases, ANA + was associated with increased risk of Raynaud's syndrome (OR ≥ 2.1) and alveolar/perialveolar-related pneumopathies (OR ≥ 1.4) and decreased risk of hepatitis C, tobacco use disorders, mood disorders, convulsions, fever of unknown origin, and substance abuse disorders (OR ≤ 0.8). Analyses including only diagnoses after ANA testing yielded similar results. CONCLUSION: A positive ANA test, in addition to known associations with autoimmune diseases, Raynaud's phenomenon, and idiopathic fibrosing alveolitis related disorders, is associated with decreased prevalence of several non-autoimmune diseases.

Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets.

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets.

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

Risk for Bleeding-Related Hospitalizations During Use of Amiodarone With Apixaban or Rivaroxaban in Patients With Atrial Fibrillation : A Retrospective Cohort Study.

BACKGROUND: Amiodarone, the most effective antiarrhythmic drug in atrial fibrillation, inhibits apixaban and rivaroxaban elimination, thus possibly increasing anticoagulant-related risk for bleeding. OBJECTIVE: For patients receiving apixaban or rivaroxaban, to compare risk for bleeding-related hospitalizations during treatment with amiodarone versus flecainide or sotalol, antiarrhythmic drugs that do not inhibit these anticoagulants' elimination. DESIGN: Retrospective cohort study. SETTING: U.S. Medicare beneficiaries aged 65 years or older. PATIENTS: Patients with atrial fibrillation began anticoagulant use between 1 January 2012 and 30 November 2018 and subsequently initiated treatment with study antiarrhythmic drugs. MEASUREMENTS: Time to event for bleeding-related hospitalizations (primary outcome) and ischemic stroke, systemic embolism, and death with or without recent (past 30 days) evidence of bleeding (secondary outcomes), adjusted with propensity score overlap weighting. RESULTS: There were 91 590 patients (mean age, 76.3 years; 52.5% female) initiating use of study anticoagulants and antiarrhythmic drugs, 54 977 with amiodarone and 36 613 with flecainide or sotalol. Risk for bleeding-related hospitalizations increased with amiodarone use (rate difference [RD], 17.5 events [95% CI, 12.0 to 23.0 events] per 1000 person-years; hazard ratio [HR], 1.44 [CI, 1.27 to 1.63]). Incidence of ischemic stroke or systemic embolism did not increase (RD, -2.1 events [CI, -4.7 to 0.4 events] per 1000 person-years; HR, 0.80 [CI, 0.62 to 1.03]). The risk for death with recent evidence of bleeding (RD, 9.1 events [CI, 5.8 to 12.3 events] per 1000 person-years; HR, 1.66 [CI, 1.35 to 2.03]) was greater than that for other deaths (RD, 5.6 events [CI, 0.5 to 10.6 events] per 1000 person-years; HR, 1.15 [CI, 1.00 to 1.31]) (HR comparison: P = 0.003). The increased incidence of bleeding-related hospitalizations for rivaroxaban (RD, 28.0 events [CI, 18.4 to 37.6 events] per 1000 person-years) was greater than that for apixaban (RD, 9.1 events [CI, 2.8 to 15.3 events] per 1000 person-years) (P = 0.001). LIMITATION: Possible residual confounding. CONCLUSION: In this retrospective cohort study, patients aged 65 years or older with atrial fibrillation treated with amiodarone during apixaban or rivaroxaban use had greater risk for bleeding-related hospitalizations than those treated with flecainide or sotalol. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.

Duloxetine, Gabapentin, and the Risk for Acute Myocardial Infarction, Stroke, and Out-of-Hospital Death in Medicare Beneficiaries With Non-Cancer Pain.

OBJECTIVE: Duloxetine is a serotonin-norepinephrine reuptake inhibitor prescribed for musculoskeletal and other forms of chronic pain. Its dual pharmacologic properties have the potential to either raise or lower cardiovascular risk: adrenergic activity may increase the risk for acute myocardial infarction (AMI) and stroke, but antiplatelet activity may decrease risk. Gabapentin is another nonopioid medication used to treat pain, which is not thought to have adrenergic/antiplatelet effects. With the current emphasis on the use of nonopioid medications to treat patients with chronic pain, assessing cardiovascular risks associated with these medications among high-risk patients is important. MATERIALS AND METHODS: We conducted a retrospective cohort study among a 20% sample of Medicare enrollees, aged 65 to 89, with chronic pain who were new users between 2015 and 2018 of either duloxetine (n = 34,009) or gabapentin (n = 233,060). We excluded individuals with cancer or other life-threatening conditions at study drug initiation. The primary outcome was a composite of AMI, stroke, and out-of-hospital mortality. We adjusted for comorbidity differences with time-dependent inverse probability of treatment weighting. RESULTS: During 115,668 person-years of follow-up, 2361 patients had the composite primary outcome; the rate among new users of duloxetine was 16.7/1000 person-years compared with new users of gabapentin (21.1/1000 person-years), adjusted hazard ratio = 0.98 (95% CI: 0.83, 1.16). Results were similar for the individual components of the composite outcome as well as in analyses stratified by demographic and clinical characteristics. DISCUSSION: In summary, cohort Medicare patients with non-cancer pain beginning treatment with duloxetine had rates of AMI, stroke, and out-of-hospital mortality comparable to those who initiated gabapentin.

Genetic susceptibility for autoimmune diseases and white blood cell count.

Some autoimmune (AI) conditions affect white blood cell (WBC) counts. Whether a genetic predisposition to AI disease associates with WBC counts in populations expected to have low numbers of AI cases is not known. We developed genetic instruments for 7 AI diseases using genome-wide association study summary statistics. Two-sample inverse variance weighted regression (IVWR) was used to determine associations between each instrument and WBC counts. Effect size represents change in transformed WBC counts per change in log odds-ratio of the disease. For AI diseases with significant associations by IVWR, polygenic risk scores (PRS) were used to test for associations with measured WBC counts in individuals of European ancestry in a community-based (ARIC, n = 8926), and a medical-center derived cohort (BioVU, n = 40,461). The IVWR analyses revealed significant associations between 3 AI diseases and WBC counts: systemic lupus erythematous (Beta = - 0.05 [95% CI, - 0.06, - 0.03]), multiple sclerosis (Beta = - 0.06 [- 0.10, - 0.03]), and rheumatoid arthritis (Beta = 0.02 [0.01, 0.03]). PRS for these diseases showed associations with measured WBC counts in ARIC and BioVU. Effect sizes tended to be larger among females, consistent with the known higher prevalence of these diseases among this group. This study shows that genetic predisposition to systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis was associated with WBC counts, even in populations expected to have very low numbers of disease cases.

Association between APOL1 risk variants and the occurrence of sepsis in Black patients hospitalized with infections: a retrospective cohort study.

Background: Two risk variants in the apolipoprotein L1 gene ( APOL1 ) have been associated with increased susceptibility to sepsis in Black patients. However, it remains unclear whether APOL1 high-risk genotypes are associated with occurrence of either sepsis or sepsis-related phenotypes in patients hospitalized with infections, independent of their association with pre-existing severe renal disease. Methods: A retrospective cohort study of 2,242 Black patients hospitalized with infections. We assessed whether carriage of APOL1 high-risk genotypes was associated with the risk of sepsis and sepsis-related phenotypes in patients hospitalized with infections. The primary outcome was sepsis; secondary outcomes were short-term mortality and organ failure related to sepsis. Results: Of 2,242 Black patients hospitalized with infections, 565 developed sepsis. Patients with high-risk APOL1 genotypes had a significantly increased risk of sepsis (odds ratio [OR]=1.29 [95% CI, 1.00-1.67; p=0.047]); however, this association was not significant after adjustment for pre-existing severe renal disease (OR=1.14 [95% CI, 0.88-1.48; p=0.33]), nor after exclusion of those patients with pre-existing severe renal disease (OR=0.99 [95% CI, 0.70-1.39; p=0.95]. APOL1 high-risk genotypes were significantly associated with the renal dysfunction component of the Sepsis-3 criteria (OR=1.64 [95% CI, 1.21-2.22; p=0.001], but not with other sepsis-related organ dysfunction or short-term mortality. The association between high-risk APOL1 genotypes and sepsis-related renal dysfunction was markedly attenuated by adjusting for pre-existing severe renal disease (OR=1.36 [95% CI, 1.00-1.86; p=0.05]) and was nullified after exclusion of patients with pre-existing severe renal disease (OR=1.16 [95% CI, 0.74-1.81; p=0.52]). Conclusion: APOL1 high-risk genotypes were associated with an increased risk of sepsis; however, this increased risk was attributable predominantly to pre-existing severe renal disease. Funding: This study was supported by R01GM120523 (Q.F.), R01HL163854 (Q.F.), R35GM131770 (C.M.S.), HL133786 (W.Q.W.), and Vanderbilt Faculty Research Scholar Fund (Q.F.). The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU which is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the CTSA grant UL1TR0004from NCATS/NIH. Additional funding provided by the NIH through grants P50GM115305 and U19HL065962. The authors wish to acknowledge the expert technical support of the VANTAGE and VANGARD core facilities, supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA068485) and Vanderbilt Vision Center (P30 EY08126).The funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

The relationship between high density lipoprotein cholesterol and sepsis: A clinical and genetic approach.

Sepsis accounts for one in three hospital deaths. Higher concentrations of high-density lipoprotein cholesterol (HDL-C) are associated with apparent protection from sepsis, suggesting a potential therapeutic role for HDL-C or drugs, such as cholesteryl ester transport protein (CETP) inhibitors that increase HDL-C. However, these beneficial clinical associations might be due to confounding; genetic approaches can address this possibility. We identified 73,406 White adults admitted to Vanderbilt University Medical Center with infection; 11,612 had HDL-C levels, and 12,377 had genotype information from which we constructed polygenic risk scores (PRS) for HDL-C and the effect of CETP on HDL-C. We tested the associations between predictors (measured HDL-C, HDL-C PRS, CETP PRS, and rs1800777) and outcomes: sepsis, septic shock, respiratory failure, and in-hospital death. In unadjusted analyses, lower measured HDL-C concentrations were significantly associated with increased risk of sepsis (p = 2.4 × 10-23 ), septic shock (p = 4.1 × 10-12 ), respiratory failure (p = 2.8 × 10-8 ), and in-hospital death (p = 1.0 × 10-8 ). After adjustment (age, sex, electronic health record length, comorbidity score, LDL-C, triglycerides, and body mass index), these associations were markedly attenuated: sepsis (p = 2.6 × 10-3 ), septic shock (p = 8.1 × 10-3 ), respiratory failure (p = 0.11), and in-hospital death (p = 4.5 × 10-3 ). HDL-C PRS, CETP PRS, and rs1800777 significantly predicted HDL-C (p < 2 × 10-16 ), but none were associated with sepsis outcomes. Concordant findings were observed in 13,254 Black patients hospitalized with infections. Lower measured HDL-C levels were significantly associated with increased risk of sepsis and related outcomes in patients with infection, but a causal relationship is unlikely because no association was found between the HDL-C PRS or the CETP PRS and the risk of adverse sepsis outcomes.