Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts.

BACKGROUND: Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model. METHODS: In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes-namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae. FINDINGS: Among 95 935 participants, 35 749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14 599 [41%] were male and 21 150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60 186 were from the UK Biobank (median age 62 [15] years; 25 031 [42%] male and 35 155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94-0·95; sensitivity of 0·94 [0·94-0·95] and specificity of 0·82 [0·81-0·83]) and 0·93 (0·92-0·93; sensitivity of 0·90 [0·89-0·90] and specificity of 0·88 [0·87-0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91-0·91; sensitivity of 0·84 [0·83-0·84] and specificity of 0·83 [0·82-0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0-1·0], 0·2% prevalence; decile 6: 11 [3·9-31], 3·1% prevalence; and decile 10: 56 [20-158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines. INTERPRETATION: Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals. FUNDING: National Institutes of Health.

A prospective randomized feasibility trial comparing angiography and angiography with intravascular ultrasound for treatment of hemodialysis access failures.

OBJECTIVE: Hemodialysis accesses suffer from limited primary patency requiring frequent interventions, revisions, or even abandonment. Prolongation of access life and usability with minimization of these adverse events is paramount. Endovascular methods are established first-line interventions for failing arteriovenous access and treatment of venous outflow stenoses. The Primary goal of this feasibility study was to evaluate intravascular ultrasound (IVUS) during interventional treatments on outcomes in those undergoing angiography for failing hemodialysis access. Secondary goals were to determine differences between IVUS and angiography on vessel and lesion characteristics and impact on treatment. METHODS: In this prospective, randomized controlled trial, patients scheduled for angiography to evaluate and treat a failing hemodialysis access were randomized to use of angiography (DSA) alone or angiography plus IVUS (DSA + IVUS). Patients were treated by a standardized protocol and seen in follow-up at 2 weeks, and every 3 months for 2 years or until a study endpoint was reached. Measurement of vessel diameters, % stenosis, lesion length, and study endpoints (AV access thrombosis, re-intervention, or surgical revision) were recorded. RESULTS: A total of 55 subjects were enrolled, 27 in the DSA cohort and 28 in the DSA + IVUS cohort. There were 41 treated lesions in each group. Freedom from the composite endpoint of AV access thrombosis or re-intervention was 46.3% in the DSA cohort and 61.0% in the DSA + IVUS cohort (p = 0.27). Diameter measurements matched between the two imaging modalities only 9 times out of 41 total comparison measures. In pre-treatment lesions with >80% stenosis, IVUS had a greater tendency than DSA to underestimate the severity of stenosis, whereas in pre-treatment lesions with 50-80% stenosis, DSA was more likely than IVUS to underestimate the severity of stenosis. Post-treatment % stenosis had mean difference of -7.5% between DSA versus DSA + IVUS cohorts. In five lesions with <30% stenosis measured by angiogram, IVUS led to treatment escalation. CONCLUSION: In the interventional treatment of failing angioaccess, IVUS and angiography differ in the vast majority of cases in measurement of vessel diameter. A significant number of patients were found to have suboptimal therapeutic response by IVUS only, which led to an escalation in treatment, and in over one-third of cases, the IVUS results led to a change in treatment plan. The improved patency rates in the IVUS group was not statistically significant in this small population but should be further investigated in a larger trial.

Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury.

BACKGROUND/AIMS: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. METHODS: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. RESULTS: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52-84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67-0.73), followed by MLP 0.59 (0.54-0.64), LR 0.57 (0.52-0.62), SVM 0.51 (0.46-0.56), AdaBoost 0.51 (0.46-0.55), RF 0.44 (0.39-0.48), and XGBoost 0.43 (CI 0.38-0.47). CONCLUSIONS: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.

Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New informatics approaches including natural language processing (NLP) can be utilized to identify symptoms from narrative clinical documentation. Here we utilized NLP to identify seven patient symptoms from notes of maintenance hemodialysis patients of the BioMe Biobank and validated our findings using a separate cohort and the MIMIC-III database. NLP performance was compared for symptom detection with International Classification of Diseases (ICD)-9/10 codes and the performance of both methods were validated against manual chart review. From 1034 and 519 hemodialysis patients within BioMe and MIMIC-III databases, respectively, the most frequently identified symptoms by NLP were fatigue, pain, and nausea/vomiting. In BioMe, sensitivity for NLP (0.85 - 0.99) was higher than for ICD codes (0.09 - 0.59) for all symptoms with similar results in the BioMe validation cohort and MIMIC-III. ICD codes were significantly more specific for nausea/vomiting in BioMe and more specific for fatigue, depression, and pain in the MIMIC-III database. A majority of patients in both cohorts had four or more symptoms. Patients with more symptoms identified by NLP, ICD, and chart review had more clinical encounters. NLP had higher specificity in inpatient notes but higher sensitivity in outpatient notes and performed similarly across pain severity subgroups. Thus, NLP had higher sensitivity compared to ICD codes for identification of seven common hemodialysis-related symptoms, with comparable specificity between the two methods. Hence, NLP may be useful for the high-throughput identification of patient-centered outcomes when using electronic health records.

Opioid receptor-dependent sex differences in synaptic plasticity in the hippocampal mossy fiber pathway of the adult rat.

The mossy fiber (MF) pathway is critical to hippocampal function and influenced by gonadal hormones. Physiological data are limited, so we asked whether basal transmission and long-term potentiation (LTP) differed in slices of adult male and female rats. The results showed small sex differences in basal transmission but striking sex differences in opioid receptor sensitivity and LTP. When slices were made from females on proestrous morning, when serum levels of 17ß-estradiol peak, the nonspecific opioid receptor antagonist naloxone (1 µm) enhanced MF transmission but there was no effect in males, suggesting preferential opioid receptor-dependent inhibition in females when 17ß-estradiol levels are elevated. The µ-opioid receptor (MOR) antagonist Cys2,Tyr3,Orn5,Pen7-amide (CTOP; 300 nm) had a similar effect but the δ-opioid receptor (DOR) antagonist naltrindole (NTI; 1 µm) did not, implicating MORs in female MF transmission. The GABAB receptor antagonist saclofen (200 µm) occluded effects of CTOP but the GABAA receptor antagonist bicuculline (10 µm) did not. For LTP, a low-frequency (LF) protocol was used because higher frequencies elicited hyperexcitability in females. Proestrous females exhibited LF-LTP but males did not, suggesting a lower threshold for synaptic plasticity when 17ß-estradiol is elevated. NTI blocked LF-LTP in proestrous females, but CTOP did not. Electron microscopy revealed more DOR-labeled spines of pyramidal cells in proestrous females than males. Therefore, we suggest that increased postsynaptic DORs mediate LF-LTP in proestrous females. The results show strong MOR regulation of MF transmission only in females and identify a novel DOR-dependent form of MF LTP specific to proestrus.

Testosterone depletion in adult male rats increases mossy fiber transmission, LTP, and sprouting in area CA3 of hippocampus.

Androgens have dramatic effects on neuronal structure and function in hippocampus. However, androgen depletion does not always lead to hippocampal impairment. To address this apparent paradox, we evaluated the hippocampus of adult male rats after gonadectomy (Gdx) or sham surgery. Surprisingly, Gdx rats showed increased synaptic transmission and long-term potentiation of the mossy fiber (MF) pathway. Gdx rats also exhibited increased excitability and MF sprouting. We then addressed the possible underlying mechanisms and found that Gdx induced a long-lasting upregulation of MF BDNF immunoreactivity. Antagonism of Trk receptors, which bind neurotrophins, such as BDNF, reversed the increase in MF transmission, excitability, and long-term potentiation in Gdx rats, but there were no effects of Trk antagonism in sham controls. To determine which androgens were responsible, the effects of testosterone metabolites DHT and 5α-androstane-3α,17ß-diol were examined. Exposure of slices to 50 nm DHT decreased the effects of Gdx on MF transmission, but 50 nm 5α-androstane-3α,17ß-diol had no effect. Remarkably, there was no effect of DHT in control males. The data suggest that a Trk- and androgen receptor-sensitive form of MF transmission and synaptic plasticity emerges after Gdx. We suggest that androgens may normally be important in area CA3 to prevent hyperexcitability and aberrant axon outgrowth but limit MF synaptic transmission and some forms of plasticity. The results also suggest a potential explanation for the maintenance of hippocampal-dependent cognitive function after androgen depletion: a reduction in androgens may lead to compensatory upregulation of MF transmission and plasticity.

Genome-first evaluation with exome sequence and clinical data uncovers underdiagnosed genetic disorders in a large healthcare system.

Population-based genomic screening may help diagnose individuals with disease-risk variants. Here, we perform a genome-first evaluation for nine disorders in 29,039 participants with linked exome sequences and electronic health records (EHRs). We identify 614 individuals with 303 pathogenic/likely pathogenic or predicted loss-of-function (P/LP/LoF) variants, yielding 644 observations; 487 observations (76%) lack a corresponding clinical diagnosis in the EHR. Upon further investigation, 75 clinically undiagnosed observations (15%) have evidence of symptomatic untreated disease, including familial hypercholesterolemia (3 of 6 [50%] undiagnosed observations with disease evidence) and breast cancer (23 of 106 [22%]). These genetic findings enable targeted phenotyping that reveals new diagnoses in previously undiagnosed individuals. Disease yield is greater with variants in penetrant genes for which disease is observed in carriers in an independent cohort. The prevalence of P/LP/LoF variants exceeds that of clinical diagnoses, and some clinically undiagnosed carriers are discovered to have disease. These results highlight the potential of population-based genomic screening.

Muesli Intake May Protect Against Coronary Artery Disease: Mendelian Randomization on 13 Dietary Traits.

BACKGROUND: Diet is a key modifiable risk factor of coronary artery disease (CAD). However, the causal effects of specific dietary traits on CAD risk remain unclear. With the expansion of dietary data in population biobanks, Mendelian randomization (MR) could help enable the efficient estimation of causality in diet-disease associations. OBJECTIVES: The primary goal was to test causality for 13 common dietary traits on CAD risk using a systematic 2-sample MR framework. A secondary goal was to identify plasma metabolites mediating diet-CAD associations suspected to be causal. METHODS: Cross-sectional genetic and dietary data on up to 420,531 UK Biobank and 184,305 CARDIoGRAMplusC4D individuals of European ancestry were used in 2-sample MR. The primary analysis used fixed effect inverse-variance weighted regression, while sensitivity analyses used weighted median estimation, MR-Egger regression, and MR-Pleiotropy Residual Sum and Outlier. RESULTS: Genetic variants serving as proxies for muesli intake were negatively associated with CAD risk (OR: 0.74; 95% CI: 0.65-0.84; P = 5.385 × 10-4). Sensitivity analyses using weighted median estimation supported this with a significant association in the same direction. Additionally, we identified higher plasma acetate levels as a potential mediator (OR: 0.03; 95% CI: 0.01-0.12; P = 1.15 × 10-4). CONCLUSIONS: Muesli, a mixture of oats, seeds, nuts, dried fruit, and milk, may causally reduce CAD risk. Circulating levels of acetate, a gut microbiota-derived short-chain fatty acid, could be mediating its cardioprotective effects. These findings highlight the role of gut flora in cardiovascular health and help prioritize randomized trials on dietary interventions for CAD.

Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.

Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disease progression, severity and underdiagnosis on this spectrum and could enhance genetic discovery efforts for CAD. Here we tested associations of rare and ultrarare coding variants with the in silico score for CAD in the UK Biobank, All of Us Research Program and BioMe Biobank. We identified associations in 17 genes; of these, 14 show at least moderate levels of prior genetic, biological and/or clinical support for CAD. We also observed an excess of ultrarare coding variants in 321 aggregated CAD genes, suggesting more ultrarare variant associations await discovery. These results expand our understanding of the genetic etiology of CAD and illustrate how digital markers can enhance genetic association investigations for complex diseases.

Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications.

Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases. The top 0.83%, 0.28% and 0.19% of the GPS conferred a 5.3-, 9.9- and 11.0-fold increased effect of having an indication, respectively. In addition, we observed that targets in the top 0.28% of the score were 1.7-, 3.7- and 8.8-fold more likely to advance from phase I to phases II, III and IV, respectively. Complementary to the GPS, we incorporated the direction of genetic effect and drug mechanism into a directional version of the score called the GPS with direction of effect. We applied our method to 19,365 protein-coding genes and 399 drug indications and made all results available through a web portal.

Expression of c-fos in hilar mossy cells of the dentate gyrus in vivo.

Granule cells (GCs) of the dentate gyrus (DG) are considered to be quiescent--they rarely fire action potentials. In contrast, the other glutamatergic cell type in the DG, hilar mossy cells (MCs) often have a high level of spontaneous activity based on recordings in hippocampal slices. MCs project to GCs, so activity in MCs could play an important role in activating GCs. Therefore, we investigated whether MCs were active under basal conditions in vivo, using the immediate early gene c-fos as a tool. We hypothesized that MCs would exhibit c-fos expression even if rats were examined randomly, under normal housing conditions. Therefore, adult male rats were perfused shortly after removal from their home cage and transfer to the laboratory. Remarkably, most c-fos immunoreactivity (ir) was in the hilus, especially temporal hippocampus. C-fos-ir hilar cells co-expressed GluR2/3, suggesting that they were MCs. C-fos-ir MCs were robust even when the animal was habituated to the investigator and laboratory where they were euthanized. However, c-fos-ir in dorsal MCs was reduced under these circumstances, suggesting that ventral and dorsal MCs are functionally distinct. Interestingly, there was an inverse relationship between MC and GC layer c-fos expression, with little c-fos expression in the GC layer in ventral sections where MC expression was strong, and the opposite in dorsal hippocampus. The results support the hypothesis that a subset of hilar MCs are spontaneously active in vivo and provide other DG neurons with tonic depolarizing input.

A machine learning model identifies patients in need of autoimmune disease testing using electronic health records.

Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.

Phenome-wide Mendelian randomization study of plasma triglyceride levels and 2600 disease traits.

Background: Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH, and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems. Methods: Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p<0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR (MVMR) controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels. Results: Our results identified seven disease traits reaching Bonferroni-corrected significance in both the discovery (p<1.92 × 10-5) and replication analyses (p<2.26 × 10-4), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p=2.47 × 10-13). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p<0.05), identifying plasma TG levels as a novel potential risk factor for nine non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p=1.17 × 10-5). Conclusions: Taking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies. Funding: RD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).

Impairment of select forms of spatial memory and neurotrophin-dependent synaptic plasticity by deletion of glial aquaporin-4.

Aquaporin-4 (AQP4) is the major water channel in the CNS and is primarily expressed in astrocytes. Little is known about the potential for AQP4 to influence synaptic plasticity, although many studies have shown that it regulates the response of the CNS to injury. Therefore, we evaluated long-term potentiation (LTP) and long-term depression (LTD) in AQP4 knock-out (KO) and wild-type mice. KO mice exhibited a selective defect in LTP and LTD without a change in basal transmission or short-term plasticity. Interestingly, the impairment in LTP in KO mice was specific for the type of LTP that depends on the neurotrophin BDNF, which is induced by stimulation at theta rhythm [theta-burst stimulation (TBS)-LTP], but there was no impairment in a form of LTP that is BDNF independent, induced by high-frequency stimulation. LTD was also impaired in KO mice, which was rescued by a scavenger of BDNF or blockade of Trk receptors. TrkB receptors, which mediate effects of BDNF on TBS-LTP, were not altered in KO mice, but p75NTR, the receptor that binds all neurotrophins and has been implicated in some types of LTD, was decreased. The KO mice also exhibited a cognitive defect, which suggests a new role for AQP4 and astrocytes in normal cognitive function. This defect was evident using a test for location-specific object memory but not Morris water maze or contextual fear conditioning. The results suggest that AQP4 channels in astrocytes play an unanticipated role in neurotrophin-dependent plasticity and influence behavior.

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank.

Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank. We identified 9151 colocalized genes for 1411 phenotypes across 48 tissues. Then, we constructed bipartite networks using the colocalized signals in each tissue, and showed that the majority of links were observed in a single tissue. We applied the biLouvain clustering algorithm in each tissue-specific network to identify co-clusters of genes and phenotypes. We observed significant enrichments of these co-clusters with known biological and functional gene classes. Overall, the phenome-wide map provides links between genes, phenotypes and tissues, and can yield biological and clinical discoveries.

Acetylcholine α7 nicotinic and dopamine D2 receptors are targeted to many of the same postsynaptic dendrites and astrocytes in the rodent prefrontal cortex.

The alpha-7 nicotinic acetylcholine receptor (α7nAChR) and the dopamine D(2) receptor (D(2) R) are both implicated in attentional processes and cognition, mediated in part through the prefrontal cortex (PFC). We examined the dual electron microscopic immunolabeling of α7nAChR and either D(2) R or the vesicular acetylcholine transporter (VAChT) in rodent PFC to assess convergent functional activation sites. Immunoreactivity (ir) for α7nAChR and/or D(2) R was seen in the same as well as separate neuronal and glial profiles. At least half of the dually labeled profiles were somata and dendrites, while most labeled axon terminals expressed only D(2) R-ir. The D(2) R-labeled terminals were without synaptic specializations or formed inhibitory or excitatory-type synapses with somatodendritic profiles, some of which expressed the α7nAChR and/or D(2) R. Astrocytic glial processes comprised the majority of nonsomatodendritic α7nAChR or α7nAChR and D(2) R-labeled profiles. Glial processes containing α7nAChR-ir were frequently located near VAChT-labeled terminals and also showed perisynaptic and perivascular associations. We conclude that in rodent PFC α7nACh and D(2) R activation can dually modulate (1) postsynaptic dendritic responses within the same or separate but synaptically linked neurons in which the D(2) R has the predominately presynaptic distribution, and (2) astrocytic signaling that may be crucial for synaptic transmission and functional hyperemia.

Tissue-specific genetic features inform prediction of drug side effects in clinical trials.

Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features-tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint-confer a 2.6-fold greater risk of side effects, compared to genes without such features. The presence of eQTL in multiple tissues resulted in more unique phenotypes driven by GWA loci, suggesting that drugs delivered to multiple tissues can induce several side effects. We demonstrate the utility of PheWAS and eQTL data from multiple tissues for informing drug side effect prediction and highlight the need for tissue-specific drug delivery.

Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement. RESULTS: We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4). CONCLUSIONS: Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

Rate of Correction of Hypernatremia and Health Outcomes in Critically Ill Patients.

BACKGROUND AND OBJECTIVES: Hypernatremia is common in hospitalized, critically ill patients. Although there are no clear guidelines on sodium correction rate for hypernatremia, some studies suggest a reduction rate not to exceed 0.5 mmol/L per hour. However, the data supporting this recommendation and the optimal rate of hypernatremia correction in hospitalized adults are unclear. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We assessed the association of hypernatremia correction rates with neurologic outcomes and mortality in critically ill patients with hypernatremia at admission and those that developed hypernatremia during hospitalization. We used data from the Medical Information Mart for Intensive Care-III and identified patients with hypernatremia (serum sodium level >155 mmol/L) on admission (n=122) and hospital-acquired (n=327). We calculated different ranges of rapid correction rates (>0.5 mmol/L per hour overall and >8, >10, and >12 mmol/L per 24 hours) and utilized logistic regression to generate adjusted odds ratios (aOR) with 95% confidence intervals (95% CIs) to examine association with outcomes. RESULTS: We had complete data on 122 patients with severe hypernatremia on admission and 327 patients who developed hospital-acquired hypernatremia. The difference in in-hospital 30-day mortality proportion between rapid (>0.5 mmol/L per hour) and slower (≤0.5 mmol/L per hour) correction rates were not significant either in patients with hypernatremia at admission with rapid versus slow correction (25% versus 28%; P=0.80) or in patients with hospital-acquired hypernatremia with rapid versus slow correction (44% versus 40%; P=0.50). There was no difference in aOR of mortality for rapid versus slow correction in either admission (aOR, 1.3; 95% CI, 0.5 to 3.7) or hospital-acquired hypernatremia (aOR, 1.3; 95% CI, 0.8 to 2.3). Manual chart review of all suspected chronic hypernatremia patients, which included all 122 with hypernatremia at admission, 128 of the 327 hospital-acquired hypernatremia, and an additional 28 patients with ICD-9 codes for cerebral edema, seizures and/or alteration of consciousness, did not reveal a single case of cerebral edema attributable to rapid hyprnatremia correction. CONCLUSIONS: We did not find any evidence that rapid correction of hypernatremia is associated with a higher risk for mortality, seizure, alteration of consciousness, and/or cerebral edema in critically ill adult patients with either admission or hospital-acquired hypernatremia.

Acute restraint stress decreases c-fos immunoreactivity in hilar mossy cells of the adult dentate gyrus.

Although a great deal of information is available about the circuitry of the mossy cells (MCs) of the dentate gyrus (DG) hilus, their activity in vivo is not clear. The immediate early gene c-fos can be used to gain insight into the activity of MCs in vivo, because c-fos protein expression reflects increased neuronal activity. In prior work, it was identified that control rats that were perfusion-fixed after removal from their home cage exhibited c-fos immunoreactivity (ir) in the DG in a spatially stereotyped pattern: ventral MCs and dorsal granule cells (GCs) expressed c-fos protein (Duffy et al., Hippocampus 23:649-655, 2013). In this study, we hypothesized that restraint stress would alter c-fos-ir, because MCs express glucocorticoid type 2 receptors and the DG is considered to be involved in behaviors related to stress or anxiety. We show that acute restraint using a transparent nose cone for just 10 min led to reduced c-fos-ir in ventral MCs compared to control rats. In these comparisons, c-fos-ir was evaluated 30 min after the 10 min-long period of restraint, and if evaluation was later than 30 min c-fos-ir was no longer suppressed. Granule cells (GCs) also showed suppressed c-fos-ir after acute restraint, but it was different than MCs, because the suppression persisted for over 30 min after the restraint. We conclude that c-fos protein expression is rapidly and transiently reduced in ventral hilar MCs after a brief period of restraint, and suppressed longer in dorsal GCs.