Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity.

Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease-disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease-disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients' multiple chronic conditions, their risk stratification and personalization of treatment strategies.

Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage.

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson-Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines.

muSignAl: An algorithm to search for multiple omic signatures with similar predictive performance.

Multidimensional omic datasets often have correlated features leading to the possibility of discovering multiple biological signatures with similar predictive performance for a phenotype. However, their exploration is limited by low sample size and the exponential nature of the combinatorial search leading to high computational cost. To address these issues, we have developed an algorithm muSignAl (multiple signature algorithm) which selects multiple signatures with similar predictive performance while systematically bypassing the requirement of exploring all the combinations of features. We demonstrated the workflow of this algorithm with an example of proteomics dataset. muSignAl is applicable in various bioinformatics-driven explorations, such as understanding the relationship between multiple biological feature sets and phenotypes, and discovery and development of biomarker panels while providing the opportunity of optimising their development cost with the help of equally good multiple signatures. Source code of muSignAl is freely available at https://github.com/ShuklaLab/muSignAl.

ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.

The Need for Standardizing Diagnosis, Treatment and Clinical Care of Cholecystitis and Biliary Colic in Gallbladder Disease.

Gallstones affect 20% of the Western population and will grow in clinical significance as obesity and metabolic diseases become more prevalent. Gallbladder removal (cholecystectomy) is a common treatment for diseases caused by gallstones, with 1.2 million surgeries in the US each year, each costing USD 10,000. Gallbladder disease has a significant impact on the logistics and economics of healthcare. We discuss the two most common presentations of gallbladder disease (biliary colic and cholecystitis) and their pathophysiology, risk factors, signs and symptoms. We discuss the factors that affect clinical care, including diagnosis, treatment outcomes, surgical risk factors, quality of life and cost-efficacy. We highlight the importance of standardised guidelines and objective scoring systems in improving quality, consistency and compatibility across healthcare providers and in improving patient outcomes, collaborative opportunities and the cost-effectiveness of treatment. Guidelines and scoring only exist in select areas of the care pathway. Opportunities exist elsewhere in the care pathway.

Anti-tumour necrosis factor-alpha response associated with combined CD226 and HLA-DRB1[*]0404 haplotype in rheumatoid arthritis.

OBJECTIVES: Predicting response to anti-tumour necrosis factor alpha (anti-TNFα) drugs at baseline remains an elusive goal in rheumatoid arthritis (RA) management. The purpose of this study was to determine if baseline genetic variants of PTPRC, AFF3, myD228, CHUK, MTHFR1, MTHFR2, CD226 and a number of KIR and HLA alleles could predict response to anti-TNF-α in rheumatoid arthritis patients. METHODS: Peripheral blood samples were collected from 238 RA patients treated with anti-TNFα drugs. Genotyping was performed using biochip array technology by Randox Laboratories Ltd. and sequence specific polymerase chain reaction. Linear regression analysis was performed to investigate the role of these genotypes in predicting response to treatment, as defined by European League Against Rheumatism (EULAR) response classification and absolute change in disease activity score (DAS28). RESULTS: Of 238 RA patients analysed, 50.4% received adalimumab, 29.7% received etanercept, 14.8% received infliximab, 3.4% certoluzimab and 1.7% golimumab. The MTHFR1 variant rs1801133 was significantly associated with the EULAR response, p=0.044. Patients with the HLA-DRB1*0404 allele displayed a significantly larger reduction in DAS28 compared to non-carriers (mean -2.22, -1.67 respectively, p=0.033). CD226 rs763361 was the only SNP variant significantly associated with ΔDAS28 (p=0.029). CONCLUSIONS: This study has investigated individual allele associations with reductions in DAS28 across a range of anti-TNFα treatments. A combined predictive model indicates that patients with the HLA-DRB1*0404 allele and without the CD226 rs763361 polymorphism exhibit the largest reduction in DAS28 after anti-TNF-α treatment.

Exploring the Involvement of NLRP3 and IL-1ß in Osteoarthritis of the Hand: Results from a Pilot Study.

Hand osteoarthritis (HOA) includes different subsets; a particular and uncommon form is erosive HOA (EHOA). Interleukin- (IL-) 1ß plays a crucial role in the pathogenesis of osteoarthritis (OA); it is synthesized as an inactive precursor which requires the intervention of a cytosolic multiprotein complex, named inflammasome, for its activation. The aim of this study was to investigate the involvement of IL-1ß and the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome in patients with EHOA and nonerosive HOA (NEHOA) compared to healthy controls. In particular, we evaluated the gene expression of IL-1ß and NLRP3, the serum levels of IL-1ß, IL-6, IL-17, and tumor necrosis factor- (TNF-) α, and the protein levels of IL-1ß and NLRP3. We also assessed the relationships between IL-1ß and NLRP3 and clinical, laboratory, and radiological findings. Fifty-four patients with HOA (25 EHOA and 29 NEHOA) and 20 healthy subjects were included in the study. Peripheral blood mononuclear cell (PBMC) gene and protein expressions of IL-1ß and NLRP3 were quantified by quantitative real-time PCR and western blot. IL-1ß, IL-6, IL-17, and TNF-α serum levels were determined by ELISA. IL-1ß gene expression was significantly reduced (p = 0.0208) in EHOA compared to healthy controls. NLRP3 protein levels were significantly increased in the NEHOA group versus the control (p = 0.0063) and EHOA groups (p = 0.0038). IL-1ß serum levels were not significantly different across the groups; IL-6, IL-17, and TNF-α were not detectable in any sample. IL-1ß concentrations were negatively correlated with the Kellgren-Lawrence score in the whole population (r = -0.446; p = 0.0008) and in NEHOA (r = -0.608; p = 0.004), while IL-1ß gene expression was positively correlated with the number of joint swellings in the EHOA group (r = 0.512; p = 0.011). Taken together, our results, showing poorly detectable IL-1ß concentrations and minimal inflammasome activity in the PBMCs of HOA patients, suggest a low grade of systemic inflammation in HOA. This evidence does not preclude a possible involvement of these factors at the local level.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual.

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Towards Stratified Medicine in Plasma Cell Myeloma.

Plasma cell myeloma is a clinically heterogeneous malignancy accounting for approximately one to 2% of newly diagnosed cases of cancer worldwide. Treatment options, in addition to long-established cytotoxic drugs, include autologous stem cell transplant, immune modulators, proteasome inhibitors and monoclonal antibodies, plus further targeted therapies currently in clinical trials. Whilst treatment decisions are mostly based on a patient's age, fitness, including the presence of co-morbidities, and tumour burden, significant scope exists for better risk stratification, sub-classification of disease, and predictors of response to specific therapies. Clinical staging, recurring acquired cytogenetic aberrations, and serum biomarkers such as ß-2 microglobulin, and free light chains are in widespread use but often fail to predict the disease progression or inform treatment decision making. Recent scientific advances have provided considerable insight into the biology of myeloma. For example, gene expression profiling is already making a contribution to enhanced understanding of the biology of the disease whilst Next Generation Sequencing has revealed great genomic complexity and heterogeneity. Pathways involved in the oncogenesis, proliferation of the tumour and its resistance to apoptosis are being unravelled. Furthermore, knowledge of the tumour cell surface and its interactions with bystander cells and the bone marrow stroma enhance this understanding and provide novel targets for cell and antibody-based therapies. This review will discuss the development in understanding of the biology of the tumour cell and its environment in the bone marrow, the implementation of new therapeutic options contributing to significantly improved outcomes, and the progression towards more personalised medicine in this disorder.

Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity: Protocol for a Retrospective Observational Study.

BACKGROUND: Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression. OBJECTIVE: The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19. METHODS: The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk. RESULTS: An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes. CONCLUSIONS: This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50733.

Analysis of SARS-CoV-2 antibody seroprevalence in Northern Ireland during 2020-2021.

Background: With the spread of SARS-CoV-2 impacting upon public health directly and socioeconomically, further information was required to inform policy decisions designed to limit virus spread during the pandemic. This study sought to contribute to serosurveillance work within Northern Ireland to track SARS-CoV-2 progression and guide health strategy. Methods: Sera/plasma samples from clinical biochemistry laboratories were analysed for anti-SARS-CoV-2 antibodies. Samples were assessed using an Elecsys anti-SARS-CoV-2 or anti-SARS-CoV-2 S ECLIA (Roche) on an automated cobas e 801 analyser. Samples were also assessed via an anti-SARS-CoV-2 ELISA (Euroimmun). A subset of samples assessed via the Elecsys anti-SARS-CoV-2 ECLIA were subsequently analysed in an ACE2 pseudoneutralisation assay using a V-PLEX SARS-CoV-2 Panel 7 for IgG and ACE2 (Meso Scale Diagnostics). Results: Across three testing rounds (June-July 2020, November-December 2020 and June-July 2021 (rounds 1-3 respectively)), 4844 residual sera/plasma specimens were assayed for anti-SARS-CoV-2 antibodies. Seropositivity rates increased across the study, peaking at 11.6 % (95 % CI 10.4 %-13.0 %) during round 3. Varying trends in SARS-CoV-2 seropositivity were noted based on demographic factors. For instance, highest rates of seropositivity shifted from older to younger demographics across the study period. In round 3, Alpha (B.1.1.7) variant neutralising antibodies were most frequently detected across age groups, with median concentration of anti-spike protein antibodies elevated in 50-69 year olds and anti-S1 RBD antibodies elevated in 70+ year olds, relative to other age groups. Conclusions: With seropositivity rates of <15 % across the assessment period, it can be concluded that the significant proportion of the Northern Ireland population had not yet naturally contracted the virus by mid-2021.

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

Killer immunoglobulin-like receptor and human leukocyte antigen-C genotypes in rheumatoid arthritis primary responders and non-responders to anti-TNF-α therapy.

The identification of patients who will respond to anti-tumor necrosis factor alpha (anti-TNF-α) therapy will improve the efficacy, safety, and economic impact of these agents. We investigated whether killer cell immunoglobulin-like receptor (KIR) genes are related to response to anti-TNF-α therapy in patients with rheumatoid arthritis (RA). Sixty-four RA patients and 100 healthy controls were genotyped for 16 KIR genes and human leukocyte antigen-C (HLA-C) group 1/2 using polymerase chain reaction sequence-specific oligonucleotide probes (PCR-SSOP). Each patient received anti-TNF-α therapy (adalimumab, etanercept, or infliximab), and clinical responses were evaluated after 3 months using the disease activity score in 28 joints (DAS28). We investigated the correlations between the carriership of KIR genes, HLA-C group 1/2 genes, and clinical data with response to therapy. Patients responding to therapy showed a significantly higher frequency of KIR2DS2/KIR2DL2 (67.7% R vs. 33.3% NR; P = 0.012). A positive clinical outcome was associated with an activating KIR-HLA genotype; KIR2DS2 (+) HLA-C group 1/2 homozygous. Inversely, non-response was associated with the relatively inhibitory KIR2DS2 (-) HLA-C group 1/2 heterozygous genotype. The KIR and HLA-C genotype of an RA patient may provide predictive information for response to anti-TNF-α therapy.

Simvastatin is associated with superior lipid and glycaemic control to atorvastatin and reduced levels of incident Type 2 diabetes, in men and women, in the UK Biobank.

INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of mortality in people with Type 2 diabetes mellitus (T2DM). Statins reduce low-density lipoproteins and positively affect CVD outcomes. Statin type and dose have differential effects on glycaemia and risk of incident T2DM; however, the impact of gender, and of individual drugs within the statin class, remains unclear. AIM: To compare effects of simvastatin and atorvastatin on lipid and glycaemic control in men and women with and without T2DM, and their association with incident T2DM. METHODS: The effect of simvastatin and atorvastatin on lipid and glycaemic control was assessed in the T2DM DiaStrat cohort. Prescribed medications, gender, age, BMI, diabetes duration, blood lipid profile and HbA1c were extracted from Electronic Care Record, and compared in men and women prescribed simvastatin and atorvastatin. Analyses were replicated in the UKBiobank in those with and without T2DM. The association of simvastatin and atorvastatin with incident T2DM was also investigated in the UKBiobank. Cohorts where matched for age, BMI and diabetes duration in men and women, in the UKBioBank analysis, where possible. RESULTS: Simvastatin was associated with better LDL (1.6 ± 0.6 vs 2.1 ± 0.9 mmol/L, p < .01) and total cholesterol (3.6 ± 0.7 vs 4.2 ± 1.0 mmol/L, p < .05), and glycaemic control (62 ± 17 vs 67 ± 19 mmol/mol, p < .059) than atorvastatin specifically in women in the DiaStrat cohort. In the UKBiobank, both men and women prescribed simvastatin had better LDL (Women: 2.6 ± 0.6 vs 2.6 ± 0.7 mmol/L, p < .05; Men: 2.4 ± 0.6 vs 2.4 ± 0.6, p < .01) and glycaemic control (Women:54 ± 14 vs 56 ± 15mmol/mol, p < .05; Men, 54 ± 14 vs 55 ± 15 mmol/mol, p < .01) than those prescribed atorvastatin. Simvastatin was also associated with reduced risk of incident T2DM in both men and women (p < .0001) in the UKBiobank. CONCLUSIONS: Simvastatin is associated with superior lipid and glycaemic control to atorvastatin in those with and without T2DM, and with fewer incident T2DM cases. Given the importance of lipid and glycaemic control in preventing secondary complications of T2DM, these findings may help inform prescribing practices.

Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19.

We investigated the association between a wide range of comorbidities and COVID-19 in-hospital mortality and assessed the influence of multi morbidity on the risk of COVID-19-related death using a large, regional cohort of 6036 hospitalized patients. This retrospective cohort study was conducted using Patient Administration System Admissions and Discharges data. The International Classification of Diseases 10th edition (ICD-10) diagnosis codes were used to identify common comorbidities and the outcome measure. Individuals with lymphoma (odds ratio [OR], 2.78;95% CI,1.64-4.74), metastatic cancer (OR, 2.17; 95% CI,1.25-3.77), solid tumour without metastasis (OR, 1.67; 95% CI,1.16-2.41), liver disease (OR: 2.50, 95% CI,1.53-4.07), congestive heart failure (OR, 1.69; 95% CI,1.32-2.15), chronic obstructive pulmonary disease (OR, 1.43; 95% CI,1.18-1.72), obesity (OR, 5.28; 95% CI,2.92-9.52), renal disease (OR, 1.81; 95% CI,1.51-2.19), and dementia (OR, 1.44; 95% CI,1.17-1.76) were at increased risk of COVID-19 mortality. Asthma was associated with a lower risk of death compared to non-asthma controls (OR, 0.60; 95% CI,0.42-0.86). Individuals with two (OR, 1.79; 95% CI, 1.47-2.20; P < 0.001), and three or more comorbidities (OR, 1.80; 95% CI, 1.43-2.27; P < 0.001) were at increasingly higher risk of death when compared to those with no underlying conditions. Furthermore, multi morbidity patterns were analysed by identifying clusters of conditions in hospitalised COVID-19 patients using k-mode clustering, an unsupervised machine learning technique. Six patient clusters were identified, with recognisable co-occurrences of COVID-19 with different combinations of diseases, namely, cardiovascular (100%) and renal (15.6%) diseases in patient Cluster 1; mental and neurological disorders (100%) with metabolic and endocrine diseases (19.3%) in patient Cluster 2; respiratory (100%) and cardiovascular (15.0%) diseases in patient Cluster 3, cancer (5.9%) with genitourinary (9.0%) as well as metabolic and endocrine diseases (9.6%) in patient Cluster 4; metabolic and endocrine diseases (100%) and cardiovascular diseases (69.1%) in patient Cluster 5; mental and neurological disorders (100%) with cardiovascular diseases (100%) in patient Cluster 6. The highest mortality of 29.4% was reported in Cluster 6.

The Mediating Roles of Mental Health and Substance Use on Suicidal Behavior Among Undergraduate Students With ADHD.

OBJECTIVE: To evaluate the prevalence of suicidal ideation (SI), plans and attempts, and non-suicidal self-injury (NSSI) among students with attention deficit hyperactivity disorder (ADHD). Furthermore, we explored the mediating effects of depression, anxiety, alcohol and substance use on the association between ADHD and suicidal behaviors and NSSI. METHOD: Participants were first-year undergraduate students (n = 1,829) recruited as part of the World Mental Health International College Student Initiative. Participants completed validated clinical measures online. RESULTS: The prevalence of suicide behaviors and NSSI were significantly higher among students with ADHD than those without. Mediation analyses indicated that ADHD directly and indirectly increased suicidal behaviors and NSSI. While ADHD increased suicidal behaviors and NSSI through depression, ADHD and the co-variates age and gender also had indirect effects on suicidal behaviors via substance use. CONCLUSIONS: Specific predictors of risk were identified for students with ADHD which may inform the development of more targeted mental health and suicide prevention strategies across campuses.

Suicidal behaviours and mental health disorders among students commencing college.

The increase in psychological disorders and suicidal behaviour in students is a reason for growing concern. Some may start university with pre-existing problems, while others develop problems during this time. It is important to evaluate mental health and wellbeing early, identifying those at risk. The aim of this study was to compare mental health problems and help-seeking behaviour between students in Northern Ireland (NI) and the Republic of Ireland (ROI). Whilst geographically proximate, the institutions span a cross-border region with distinct education and healthcare systems. First-year undergraduate students (n = 1828) were recruited in September 2019 as part of the World Mental Health International College Student Initiative. Suicidal behaviour, mental health and substance disorders were investigated using the World Mental Health- Composite International Diagnostic Interview. Prevalence of disorders was high, with more ROI students experiencing problems than NI students. Students were significantly more likely to experience mental health problems if they were female (p<0.001), non-heterosexual (p<0.0001), and over the age of 21 (p<0.0001). These findings show that many students are starting university with high levels of psychopathology and suicidal behaviour, highlighting the importance of early intervention which may need to be tailored to different student populations.

Variations in psychological disorders, suicidality, and help-seeking behaviour among college students from different academic disciplines.

BACKGROUND: Elevated levels of suicidality, ADHD, mental ill-health and substance disorders are reported among college students globally, yet few receive treatment. Some faculties and courses appear to have more at-risk students than others. The current study aimed to determine if students commencing college in different academic disciplines were at a heightened risk for psychopathology, substance use disorders and suicidal behaviour, and examined variations in help-seeking behaviour. MATERIALS AND METHODS: The study utilised data collected from 1,829 first-year undergraduate students as part of the Student Psychological Intervention Trial (SPIT) which commenced in September 2019 across four Ulster University campuses in Northern Ireland and an Institute of Technology, in the North-West of Ireland. The SPIT study is part of the World Mental Health International College Student Initiative (WMH-ICS) which uses the WMH-CIDI to identify 12-month and lifetime disorders. RESULTS: Students from Life and Health Sciences reported the lowest rates of a range of psychological problems in the year prior to commencing college, while participants studying Arts and Humanities displayed the highest levels (e.g. depression 20.6%; social anxiety 38.8%). However, within faculty variations were found. For example, psychology students reported high rates, while nursing students reported low rates. Variations in help seeking behaviour were also revealed, with male students less likely to seek help. CONCLUSIONS: Detecting specific cohorts at risk of psychological disorders and suicidality is challenging. This study revealed that some academic disciplines have more vulnerable students than others, with many reluctant to seek help for their problems. It is important for educators to be aware of such issues and for colleges to provide information and support to students at risk. Tailored interventions and prevention strategies may be beneficial to address the needs of students from different disciplines.

CD169+ Monocyte and Regulatory T Cell Subsets Are Associated with Disease Activity in Rheumatoid Arthritis.

Disease activity in rheumatoid arthritis (RA) is influenced by activation of circulating and synovial immune cells. Regulatory T cells (Tregs) and monocytes are key cells that drive inflammation in RA. This study investigated if a relationship exists between disease activity in RA and circulating Treg and monocyte numbers and phenotypes. A potential sialic acid (Sia) mediated link between Tregs and monocytes was also probed in vitro. Peripheral blood mononuclear cells (PBMCs) were isolated from RA patient (n = 62) and healthy control (n = 21) blood using density gradient separation. Flow cytometry was used to count and phenotype Treg and monocyte subsets, and to sort healthy control Tregs for Sia cell culture experiments. The effects of Sia on activated Treg FoxP3 and NFκB expression was assessed by flow cytometry and concentrations of secreted TNFα, IL-10 and IFNγ determined by ELISA. High disease activity RA patients who were unresponsive to disease modifying anti-rheumatic drugs (n = 31), have significantly lower relative numbers (percentages) of CD4+CD25+CD127− Tregs (p < 0.01) and memory CD45RA−FoxP3+ Tregs (p < 0.01), compared to low disease activity responders (n = 24). Relative numbers of non-classical CD169+ monocytes are associated with disease activity in RA (p = 0.012). Sia reduced Treg expression of FoxP3, NFκB and cytokines in vitro. A strong association has been identified between non-classical CD169+ monocytes and post-treatment disease activity in RA. This study also indicates that Sia can reduce Treg activation and cytokine release. We postulate that such a reduction could be mediated by interaction with sialyted proteins captured by CD169+ monocytes.

Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic associated with substantial morbidity and mortality worldwide, with particular risk for severe disease and mortality in the elderly population. SARS-CoV-2 infection is driven by a pathological hyperinflammatory response which results in a dysregulated immune response. Current advancements in aging research indicates that aging pathways have fundamental roles in dictating healthspan in addition to lifespan. Our review discusses the aging immune system and highlights that senescence and aging together, play a central role in COVID-19 pathogenesis. In our review, we primarily focus on the immune system response to SARS-CoV-2 infection, the interconnection between severe COVID-19, immunosenescence, aging, vaccination, and the emerging problem of Long-COVID. We hope to highlight the importance of identifying specific senescent endotypes (or "sendotypes"), which can used as determinants of COVID-19 severity and mortality. Indeed, identified sendotypes could be therapeutically exploited for therapeutic intervention. We highlight that senolytics, which eliminate senescent cells, can target aging-associated pathways and therefore are proving attractive as potential therapeutic options to alleviate symptoms, prevent severe infection, and reduce mortality burden in COVID-19 and thus ultimately enhance healthspan.