Immunology of psoriasis.

The skin is the front line of defense against insult and injury and contains many epidermal and immune elements that comprise the skin-associated lymphoid tissue (SALT). The reaction of these components to injury allows an effective cutaneous response to restore homeostasis. Psoriasis vulgaris is the best-understood and most accessible human disease that is mediated by T cells and dendritic cells. Inflammatory myeloid dendritic cells release IL-23 and IL-12 to activate IL-17-producing T cells, Th1 cells, and Th22 cells to produce abundant psoriatic cytokines IL-17, IFN-γ, TNF, and IL-22. These cytokines mediate effects on keratinocytes to amplify psoriatic inflammation. Therapeutic studies with anticytokine antibodies have shown the importance of the key cytokines IL-23, TNF, and IL-17 in this process. We discuss the genetic background of psoriasis and its relationship to immune function, specifically genetic mutations, key PSORS loci, single nucleotide polymorphisms, and the skin transcriptome. The association between comorbidities and psoriasis is reviewed by correlating the skin transcriptome and serum proteins. Psoriasis-related cytokine-response pathways are considered in the context of the transcriptome of different mouse models. This approach offers a model for other inflammatory skin and autoimmune diseases.

COVID-19-neutralizing antibodies predict disease severity and survival.

Coronavirus disease 2019 (COVID-19) exhibits variable symptom severity ranging from asymptomatic to life-threatening, yet the relationship between severity and the humoral immune response is poorly understood. We examined antibody responses in 113 COVID-19 patients and found that severe cases resulting in intubation or death exhibited increased inflammatory markers, lymphopenia, pro-inflammatory cytokines, and high anti-receptor binding domain (RBD) antibody levels. Although anti-RBD immunoglobulin G (IgG) levels generally correlated with neutralization titer, quantitation of neutralization potency revealed that high potency was a predictor of survival. In addition to neutralization of wild-type SARS-CoV-2, patient sera were also able to neutralize the recently emerged SARS-CoV-2 mutant D614G, suggesting cross-protection from reinfection by either strain. However, SARS-CoV-2 sera generally lacked cross-neutralization to a highly homologous pre-emergent bat coronavirus, WIV1-CoV, which has not yet crossed the species barrier. These results highlight the importance of neutralizing humoral immunity on disease progression and the need to develop broadly protective interventions to prevent future coronavirus pandemics.

Immunology of SARS-CoV-2 infection in children.

Children and adolescents exhibit a broad range of clinical outcomes from SARS-CoV-2 infection, with the majority having minimal to mild symptoms. Additionally, some succumb to a severe hyperinflammatory post-infectious complication called multisystem inflammatory syndrome in children (MIS-C), predominantly affecting previously healthy individuals. Studies characterizing the immunological differences associated with these clinical outcomes have identified pathways important for host immunity to SARS-CoV-2 and innate modulators of disease severity. In this Review, we delineate the immunological mechanisms underlying the spectrum of pediatric immune response to SARS-CoV-2 infection in comparison with that of adults.

Health system-scale language models are all-purpose prediction engines.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

Elevated Plasmin(ogen) as a Common Risk Factor for COVID-19 Susceptibility.

Patients with hypertension, diabetes, coronary heart disease, cerebrovascular illness, chronic obstructive pulmonary disease, and kidney dysfunction have worse clinical outcomes when infected with SARS-CoV-2, for unknown reasons. The purpose of this review is to summarize the evidence for the existence of elevated plasmin(ogen) in COVID-19 patients with these comorbid conditions. Plasmin, and other proteases, may cleave a newly inserted furin site in the S protein of SARS-CoV-2, extracellularly, which increases its infectivity and virulence. Hyperfibrinolysis associated with plasmin leads to elevated D-dimer in severe patients. The plasmin(ogen) system may prove a promising therapeutic target for combating COVID-19.

Molecular bases of comorbidities: present and future perspectives.

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis.

Multimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases and robust evidence for association-signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals and identify high-confidence effector genes, including FTO and IRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.

Determining treatment tolerance and fitness for intensive chemotherapy in older adults with AML: a call to action.

ABSTRACT: Determining fitness for intensive chemotherapy in an older adult with acute myeloid leukemia (AML) is an unanswered age-old question. Geriatric assessment captures any variation in multidimensional health, which can influence treatment tolerance. A prospective study is necessary to validate fitness criteria, determine whether geriatric assessment-based fitness performs superiorly to other criteria, and what components of geriatric assessment are associated with treatment tolerance. A validation study should enroll diverse patients from both academic and community centers and patients receiving intensive and lower-intensity chemotherapy. Geriatric assessment should include at minimum measures of comorbidity burden, cognition, physical function, and emotional health, which in previous smaller studies have shown to be associated with mortality in AML. These assessments should be completed before or within a few days of initiation of chemotherapy to reduce the influence of chemotherapy on the assessment results. Treatment tolerance has been measured by rates of toxicities in patients with solid malignancies; however, during the initial treatment of AML, rates of toxicities are very high regardless of treatment intensity. Early mortality, frequently used in previous studies, can provide a highly consequential and easily identifiable measure of treatment tolerance. The key end point to assess treatment tolerance, thus, should include early mortality. Other end points may include decline in function and quality of life and treatment modifications or cessation due to toxicities. Validating fitness criteria can guide treatment selection and supportive care interventions and are crucial to guide fitness-based trial eligibility, inform the interpretation of trial results, and facilitate drug labeling.

Cardiac and Renal Comorbidities in Aging People Living With HIV.

Contemporary World Health Organization data indicates that ≈39 million people are living with the human immunodeficiency virus. Of these, 24 million have been reported to have successfully accessed combination antiretroviral therapy. In 1996, the World Health Organization endorsed the widespread use of combination antiretroviral therapy, transforming human immunodeficiency virus infection from being a life-threatening disease to a chronic illness characterized by multiple comorbidities. The increased access to combination antiretroviral therapy has translated to people living with human immunodeficiency virus (PLWH) no longer having a reduced life expectancy. Although aging as a biological process increases exposure to oxidative stress and subsequent systemic inflammation, this effect is likely enhanced in PLWH as they age. This narrative review engages the intricate interplay between human immunodeficiency virus associated chronic inflammation, combination antiretroviral therapy, and cardiac and renal comorbidities development in aging PLWH. We examine the evolving demographic profile of PLWH, emphasizing the increasing prevalence of aging individuals within this population. A central focus of the review discusses the pathophysiological mechanisms that underpin the heightened susceptibility of PLWH to renal and cardiac diseases as they age.

Noncoding RNAs improve the predictive power of network medicine.

Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.

Role of the intestinal microbiota in contributing to weight disorders and associated comorbidities.

SUMMARYThe gut microbiota is a major factor contributing to the regulation of energy homeostasis and has been linked to both excessive body weight and accumulation of fat mass (i.e., overweight, obesity) or body weight loss, weakness, muscle atrophy, and fat depletion (i.e., cachexia). These syndromes are characterized by multiple metabolic dysfunctions including abnormal regulation of food reward and intake, energy storage, and low-grade inflammation. Given the increasing worldwide prevalence of obesity, cachexia, and associated metabolic disorders, novel therapeutic strategies are needed. Among the different mechanisms explaining how the gut microbiota is capable of influencing host metabolism and energy balance, numerous studies have investigated the complex interactions existing between nutrition, gut microbes, and their metabolites. In this review, we discuss how gut microbes and different microbiota-derived metabolites regulate host metabolism. We describe the role of the gut barrier function in the onset of inflammation in this context. We explore the importance of the gut-to-brain axis in the regulation of energy homeostasis and glucose metabolism but also the key role played by the liver. Finally, we present specific key examples of how using targeted approaches such as prebiotics and probiotics might affect specific metabolites, their signaling pathways, and their interactions with the host and reflect on the challenges to move from bench to bedside.

Evolving mechanisms and presentations of cardiovascular disease in people with HIV: implications for management.

People with HIV (PWH) are at elevated risk for cardiovascular diseases (CVDs), including myocardial infarction, heart failure, and sudden cardiac death, among other CVD manifestations. Chronic immune dysregulation resulting in persistent inflammation is common among PWH, particularly those with sustained viremia and impaired CD4+ T cell recovery. This inflammatory milieu is a major contributor to CVDs among PWH, in concert with common comorbidities (such as dyslipidemia and smoking) and, to a lesser extent, off-target effects of antiretroviral therapy. In this review, we discuss the clinical and mechanistic evidence surrounding heightened CVD risks among PWH, implications for specific CVD manifestations, and practical guidance for management in the setting of evolving data.

Neuronal and Molecular Mechanisms Underlying Chronic Pain and Depression Comorbidity in the Paraventricular Thalamus.

Patients with chronic pain often develop comorbid depressive symptoms, which makes the pain symptoms more complicated and refractory. However, the underlying mechanisms are poorly known. Here, in a repeated complete Freund's adjuvant (CFA) male mouse model, we reported a specific regulatory role of the paraventricular thalamic nucleus (PVT) glutamatergic neurons, particularly the anterior PVT (PVA) neurons, in mediating chronic pain and depression comorbidity (CDC). Our c-Fos protein staining observed increased PVA neuronal activity in CFA-CDC mice. In wild-type mice, chemogenetic activation of PVA glutamatergic neurons was sufficient to decrease the 50% paw withdrawal thresholds (50% PWTs), while depressive-like behaviors evaluated with immobile time in tail suspension test (TST) and forced swim test (FST) could only be achieved by repeated chemogenetic activation. Chemogenetic inhibition of PVA glutamatergic neurons reversed the decreased 50% PWTs in CFA mice without depressive-like symptoms and the increased TST and FST immobility in CFA-CDC mice. Surprisingly, in CFA-CDC mice, chemogenetically inhibiting PVA glutamatergic neurons failed to reverse the decrease of 50% PWTs, which could be restored by rapid-onset antidepressant S-ketamine. Further behavioral tests in chronic restraint stress mice and CFA pain mice indicated that PVA glutamatergic neuron inhibition and S-ketamine independently alleviate sensory and affective pain. Molecular profiling and pharmacological studies revealed the 5-hydroxytryptamine receptor 1D (Htr1d) in CFA pain-related PVT engram neurons as a potential target for treating CDC. These findings identified novel CDC neuronal and molecular mechanisms in the PVT and provided insight into the complicated pain neuropathology under a comorbid state with depression and related drug development.

Insula→Amygdala and Insula→Thalamus Pathways Are Involved in Comorbid Chronic Pain and Depression-Like Behavior in Mice.

The comorbidity of chronic pain and depression poses tremendous challenges for the treatment of either one because they exacerbate each other with unknown mechanisms. As the posterior insular cortex (PIC) integrates multiple somatosensory and emotional information and is implicated in either chronic pain or depression, we hypothesize that the PIC and its projections may contribute to the pathophysiology of comorbid chronic pain and depression. We show that PIC neurons were readily activated by mechanical, thermal, aversive, and stressful and appetitive stimulation in naive and neuropathic pain male mice subjected to spared nerve injury (SNI). Optogenetic activation of PIC neurons induced hyperalgesia and conditioned place aversion in naive mice, whereas inhibition of these neurons led to analgesia, conditioned place preference (CPP), and antidepressant effect in both naive and SNI mice. Combining neuronal tracing, optogenetics, and electrophysiological techniques, we found that the monosynaptic glutamatergic projections from the PIC to the basolateral amygdala (BLA) and the ventromedial nucleus (VM) of the thalamus mimicked PIC neurons in pain modulation in naive mice; in SNI mice, both projections were enhanced accompanied by hyperactivity of PIC, BLA, and VM neurons and inhibition of these projections led to analgesia, CPP, and antidepressant-like effect. The present study suggests that potentiation of the PIC→BLA and PIC→VM projections may be important pathophysiological bases for hyperalgesia and depression-like behavior in neuropathic pain and reversing the potentiation may be a promising therapeutic strategy for comorbid chronic pain and depression.

Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.

BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80-0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case-control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.

Heart failure with preserved ejection fraction: everything the clinician needs to know.

Heart failure with preserved ejection fraction (HFpEF) is increasingly recognised and diagnosed in clinical practice, a trend driven by an ageing population and a rise in contributing comorbidities, such as obesity and diabetes. Representing at least half of all heart failure cases, HFpEF is recognised as a complex clinical syndrome. Its diagnosis and management are challenging due to its diverse pathophysiology, varied epidemiological patterns, and evolving diagnostic and treatment approaches. This Seminar synthesises the latest insights on HFpEF, integrating findings from recent clinical trials, epidemiological research, and the latest guideline recommendations. We delve into the definition, pathogenesis, epidemiology, diagnostic criteria, and management strategies (non-pharmacological and pharmacological) for HFpEF. We highlight ongoing clinical trials and future developments in the field. Specifically, this Seminar offers practical guidance tailored for primary care practitioners, generalists, and cardiologists who do not specialise in heart failure, simplifying the complexities in the diagnosis and management of HFpEF. We provide practical, evidence-based recommendations, emphasising the importance of addressing comorbidities and integrating the latest pharmacological treatments, such as SGLT2 inhibitors.

Early Remdesivir to Prevent Progression to Severe Covid-19 in Outpatients.

BACKGROUND: Remdesivir improves clinical outcomes in patients hospitalized with moderate-to-severe coronavirus disease 2019 (Covid-19). Whether the use of remdesivir in symptomatic, nonhospitalized patients with Covid-19 who are at high risk for disease progression prevents hospitalization is uncertain. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving nonhospitalized patients with Covid-19 who had symptom onset within the previous 7 days and who had at least one risk factor for disease progression (age ≥60 years, obesity, or certain coexisting medical conditions). Patients were randomly assigned to receive intravenous remdesivir (200 mg on day 1 and 100 mg on days 2 and 3) or placebo. The primary efficacy end point was a composite of Covid-19-related hospitalization or death from any cause by day 28. The primary safety end point was any adverse event. A secondary end point was a composite of a Covid-19-related medically attended visit or death from any cause by day 28. RESULTS: A total of 562 patients who underwent randomization and received at least one dose of remdesivir or placebo were included in the analyses: 279 patients in the remdesivir group and 283 in the placebo group. The mean age was 50 years, 47.9% of the patients were women, and 41.8% were Hispanic or Latinx. The most common coexisting conditions were diabetes mellitus (61.6%), obesity (55.2%), and hypertension (47.7%). Covid-19-related hospitalization or death from any cause occurred in 2 patients (0.7%) in the remdesivir group and in 15 (5.3%) in the placebo group (hazard ratio, 0.13; 95% confidence interval [CI], 0.03 to 0.59; P = 0.008). A total of 4 of 246 patients (1.6%) in the remdesivir group and 21 of 252 (8.3%) in the placebo group had a Covid-19-related medically attended visit by day 28 (hazard ratio, 0.19; 95% CI, 0.07 to 0.56). No patients had died by day 28. Adverse events occurred in 42.3% of the patients in the remdesivir group and in 46.3% of those in the placebo group. CONCLUSIONS: Among nonhospitalized patients who were at high risk for Covid-19 progression, a 3-day course of remdesivir had an acceptable safety profile and resulted in an 87% lower risk of hospitalization or death than placebo. (Funded by Gilead Sciences; PINETREE ClinicalTrials.gov number, NCT04501952; EudraCT number, 2020-003510-12.).

Incidence and Prediction of Unrelated Mortality After Successful Endoscopic Eradication Therapy for Barrett's Neoplasia.

BACKGROUND & AIMS: Follow-up (FU) strategies after endoscopic eradication therapy (EET) for Barrett's neoplasia do not consider the risk of mortality from causes other than esophageal adenocarcinoma (EAC). We aimed to evaluate this risk during long-term FU, and to assess whether the Charlson Comorbidity Index (CCI) can predict mortality. METHODS: We included all patients with successful EET from the nationwide Barrett registry in the Netherlands. Data were merged with National Statistics for accurate mortality data. We evaluated annual mortality rates (AMRs, per 1000 person-years) and standardized mortality ratio for other-cause mortality. Performance of the CCI was evaluated by discrimination and calibration. RESULTS: We included 1154 patients with a mean age of 64 years (±9). During median 59 months (p25-p75 37-91; total 6375 person-years), 154 patients (13%) died from other causes than EAC (AMR, 24.1; 95% CI, 20.5-28.2), most commonly non-EAC cancers (n = 58), cardiovascular (n = 31), or pulmonary diseases (n = 26). Four patients died from recurrent EAC (AMR, 0.5; 95% CI, 0.1-1.4). Compared with the general Dutch population, mortality was significantly increased for patients in the lowest 3 age quartiles (ie, age <71 years). Validation of CCI in our population showed good discrimination (Concordance statistic, 0.78; 95% CI, 0.72-0.84) and fair calibration. CONCLUSION: The other-cause mortality risk after successful EET was more than 40 times higher (48; 95% CI, 15-99) than the risk of EAC-related mortality. Our findings reveal that younger post-EET patients exhibit a significantly reduced life expectancy when compared with the general population. Furthermore, they emphasize the strong predictive ability of CCI for long-term mortality after EET. This straightforward scoring system can inform decisions regarding personalized FU, including appropriate cessation timing. (NL7039).

Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions.

MOTIVATION: The emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA). RESULTS: Here, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with C19, as well as modification of risk factor impact by hyperlipidemia (HL) on severe C19. RTDA found higher-order relationships between RAAS pathway and severe C19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, HL, chronic kidney failure, and disproportionately affecting Black individuals. RTDA combined with CuNA (cumulant-based network analysis) yielded a higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and HL, of patients with severe bouts of C19. Where single variable association tests with outcome can struggle, TDA's higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as C19 work in concert. AVAILABILITY AND IMPLEMENTATION: Code for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.