The axis of complement C1 and nucleolus in antinuclear autoimmunity.

Antinuclear autoantibodies (ANA) are heterogeneous self-reactive antibodies that target the chromatin network, the speckled, the nucleoli, and other nuclear regions. The immunological aberration for ANA production remains partially understood, but ANA are known to be pathogenic, especially, in systemic lupus erythematosus (SLE). Most SLE patients exhibit a highly polygenic disease involving multiple organs, but in rare complement C1q, C1r, or C1s deficiencies, the disease can become largely monogenic. Increasing evidence point to intrinsic autoimmunogenicity of the nuclei. Necrotic cells release fragmented chromatins as nucleosomes and the alarmin HMGB1 is associated with the nucleosomes to activate TLRs and confer anti-chromatin autoimmunogenecity. In speckled regions, the major ANA targets Sm/RNP and SSA/Ro contain snRNAs that confer autoimmunogenecity to Sm/RNP and SSA/Ro antigens. Recently, three GAR/RGG-containing alarmins have been identified in the nucleolus that helps explain its high autoimmunogenicity. Interestingly, C1q binds to the nucleoli exposed by necrotic cells to cause protease C1r and C1s activation. C1s cleaves HMGB1 to inactive its alarmin activity. C1 proteases also degrade many nucleolar autoantigens including nucleolin, a major GAR/RGG-containing autoantigen and alarmin. It appears that the different nuclear regions are intrinsically autoimmunogenic by containing autoantigens and alarmins. However, the extracellular complement C1 complex function to dampen nuclear autoimmunogenecity by degrading these nuclear proteins.

A five-safes approach to a secure and scalable genomics data repository.

Genomic researchers increasingly utilize commercial cloud service providers (CSPs) to manage data and analytics needs. CSPs allow researchers to grow Information Technology (IT) infrastructure on demand to overcome bottlenecks when combining large datasets. However, without adequate security controls, the risk of unauthorized access may be higher for data stored on the cloud. Additionally, regulators are mandating data access patterns and specific security protocols for the storage and use of genomic data. While CSP provides tools for security and regulatory compliance, building the necessary controls required for cloud solutions is not trivial. Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud-native genomics data repository and analytics platform that implements a "five-safes" framework to provide security and governance controls to data contributors and users, leveraging CSP for sharing and analysis of genomic datasets without the risk of security breaches or running afoul of regulations.

Dual rheumatoid factor and anti-cyclic citrullinated peptide antibody positivity affects the manifestations of rheumatoid arthritis.

Introduction: Rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (ACPA) are used in the diagnosis and prognostication of rheumatoid arthritis (RA). We wanted to determine the specific contributions of RF and ACPA to the biological nature of RA and whether they act synergistically. Methods: We identified 731 patients from our prospective multi-ethnic RA cohort and categorised them into four groups: ACPA-positive, RF-positive, doubly positive and doubly negative. We compared the demographics, Disease Activity Score-28, Health Assessment Questionnaire score, quality of life using Short Form 36 and the use of prednisolone and disease-modifying antirheumatic drugs (DMARDs) of these patient groups. Results: Four hundred and ninety-one patients (67.2%) were ACPA+RF+, 54 (7.4%) were ACPA+RF-, 82 (11.2%) were ACPA-RF+ and 104 (14.2%) were ACPA-RF-. Mean disease duration before the study entry was not different in the four groups. Patients with older age of onset were less likely to be positive for RF and ACPA. Fewer ACPA+RF+ patients were in remission compared to those in the other groups (P < 0.05). Erythrocyte sedimentation rate (ESR) was higher at study entry in the ACPA+RF+ group (40.4 mm/h vs. 30.6-30.9 mm/h, P < 0.05). Prednisolone and number of DMARDs used were higher in the ACPA+RF+ group compared to the doubly negative group. There were no differences in the functional status and quality of life. Conclusions: RA patients who were positive for both ACPA and RF had lower remission rate, higher baseline ESR and required more corticosteroid and DMARD treatment compared to those who were singly positive or doubly negative. Being doubly positive confers a worse outcome to RA patients.

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

BACKGROUND: The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease. METHODS: This study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature. RESULTS: Through robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10-16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed ( https://xistance.shinyapps.io/prs-ra/ ) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs. CONCLUSIONS: These findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

The Singapore National Precision Medicine Strategy.

Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.

Analysis of clinically relevant variants from ancestrally diverse Asian genomes.

Asian populations are under-represented in human genomics research. Here, we characterize clinically significant genetic variation in 9051 genomes representing East Asian, South Asian, and severely under-represented Austronesian-speaking Southeast Asian ancestries. We observe disparate genetic risk burden attributable to ancestry-specific recurrent variants and identify individuals with variants specific to ancestries discordant to their self-reported ethnicity, mostly due to cryptic admixture. About 27% of severe recessive disorder genes with appreciable carrier frequencies in Asians are missed by carrier screening panels, and we estimate 0.5% Asian couples at-risk of having an affected child. Prevalence of medically-actionable variant carriers is 3.4% and a further 1.6% harbour variants with potential for pathogenic classification upon additional clinical/experimental evidence. We profile 23 pharmacogenes with high-confidence gene-drug associations and find 22.4% of Asians at-risk of Centers for Disease Control and Prevention Tier 1 genetic conditions concurrently harbour pharmacogenetic variants with actionable phenotypes, highlighting the benefits of pre-emptive pharmacogenomics. Our findings illuminate the diversity in genetic disease epidemiology and opportunities for precision medicine for a large, diverse Asian population.

TPMT and NUDT15 testing for thiopurine therapy: A major tertiary hospital experience and lessons learned.

Variants in thiopurine methyltransferase (TPMT) and nudix hydrolase 15 (NUDT15) are associated with an accumulation of cytotoxic metabolites leading to increased risk of drug-related toxicity with standard doses of thiopurine drugs. We established TPMT and NUDT15 genetic testing for clinical use and evaluated the utilization, service outcomes and potential value of multi-gene PGx testing for 210 patients that underwent pharmacogenetics (PGx) testing for thiopurine therapy with the aim to optimize service delivery for future prescribing. The test was most commonly ordered for Gastroenterology (40.0%) and Neurology (31.4%), with an average turnaround time of 2 days. Following testing, 24.3% patients were identified as intermediate or poor metabolizers, resulting in 51 recommendations for a drug or dose change in thiopurine therapy, which were implemented in 28 (54.9%) patients. In the remaining patients, 14 were not adjusted and 9 had no data available. Focusing on drug gene interactions available for testing in our laboratory, multi-gene PGx results would present opportunities for treatment optimization for at least 33.8% of these patients who were on 2 or more concurrent medications with actionable PGx guidance. However, the use of PGx panel testing in clinical practice will require the development of guidelines and education as revealed by a survey with the test providers. The evaluation demonstrated successful implementation of single gene PGx testing and this experience guides the transition to a pre-emptive multi-gene testing approach that provides the opportunity to improve clinical care.

Pregnancy loss and the risk of rheumatoid arthritis in Chinese women: findings from the China Kadoorie biobank.

Considering the female preponderance of rheumatoid arthritis (RA), and disease onset typically after the reproductive years, pregnancy and childbirth may play a role in the aetiology of the disease. Adverse outcomes of pregnancy have been found to precede the diagnosis of autoimmune diseases, including RA, but the evidence is scant and inconsistent. Therefore, we investigate whether pregnancy loss is associated with the risk of RA in Chinese women. Data from the China Kadoorie Biobank, conducted by the University of Oxford and the Chinese Centre for Disease Control and Prevention, of 299,629 Chinese women who had been pregnant were used. Multivariable logistic regression and stratified analyses were employed to analyse the association between types of pregnancy loss with the risk of RA. Pregnancy loss was significantly associated with increased risk of RA (OR 1.12, 95% CI 1.06-1.18), specifically, spontaneous (OR 1.11, 95% CI 1.03-1.20) and induced abortions (OR 1.11, 95% CI 1.06-1.17). There was no significant association between stillbirth and the risk of RA (OR 1.07, 95% CI 0.97-1.18). The risk of developing RA increases with the number of pregnancy losses: one loss confers an OR of 1.09 (95% CI 1.03-1.16), two an OR of 1.13 (95% CI 1.05-1.20), three or more an OR of 1.19 (95% CI 1.10-1.28) and OR of 1.06 (95% CI 1.03-1.08) for each additional. Spontaneous and induced abortions are associated with an increased risk of RA in Chinese women.

Evaluation of Patients with Vaccine Allergies Prior to mRNA-Based COVID-19 Vaccination.

During the initial rollout of coronavirus disease 2019 (COVID-19) vaccination in Singapore, the Ministry of Health (MOH) issued a recommendation that patients with a history of any previous vaccine allergy be referred to an allergist for further review of their suitability to proceed with mRNA-based COVID-19 vaccines. Patients fulfilling the above criterion were divided into three groups: immediate reaction (Group A), delayed reaction (Group B) and no/irrelevant reaction (Group C). They were subjected to either a skin prick test (SPT) and intradermal test (IDT) with polyethylene glycol (PEG) or polysorbate-containing products; direct injection with the Pfizer BNT162b2 vaccine in the allergy clinic; or injection at community vaccination centres, respectively. Groups A and B were also invited to complete a questionnaire survey on post-vaccination reactions, and blood sampling pre-vaccination and 1 h after the first dose of the BNT162b2 vaccine to measure immunoglobulin (Ig) G, IgM and IgE antibodies to the Pfizer BNT162b2 vaccine via ELISA assays immobilised with the BNT162b2 vaccine, as well as levels of allergic cytokines interleukin (IL)-4 and IL-33, complement C5a and the endothelial activation marker intercellular adhesion molecule-1 (ICAM-1). Groups A and B comprised 62 (20.5%) patients each. In Group A, two subjects (3.2%) with equivocal IDT results tolerated both doses of the BNT162b2 vaccine without major allergic reactions. The remaining 60 (96.8%) in Group A and 62 (100%) in Group B completed both doses of BNT162b2 vaccination without major adverse reactions. Among the 99 who completed the questionnaire survey, 13 (13%) patients reported mild allergic reactions after the first dose of the vaccine. Immunoglobulin (Ig) G and M antibodies, but not IgE antibodies to the Pfizer BNT162b2 vaccine were detected in 67 subjects prior to vaccination. The presence of anti-Pfizer BNT162b2 IgG and IgM prior to vaccination did not result in major allergic reactions nor increases in Th2-related cytokines (IL-4, IL-33), complement activation products (C5a) or endothelial activation (ICAM-1). The majority of those with suspected reactions to non-COVID-19 polysorbate-containing vaccines tolerated the BNT162b2 vaccine. Excipient skin tests for PEG and polysorbate prior to vaccination are unnecessary.

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

BACKGROUND: Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients. METHODS: Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set. FINDINGS: Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients. INTERPRETATION: Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways. FUNDING: Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.

Machine learning using genetic and clinical data identifies a signature that robustly predicts methotrexate response in rheumatoid arthritis.

OBJECTIVE: To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. METHODS: MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. RESULTS: The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855-0.916; sensitivity: 0.715-0.892; and specificity: 0.733-0.862) and the unseen test set (area under the curve: 0.751-0.826; sensitivity: 0.581-0.839; and specificity: 0.641-0.923). CONCLUSION: Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.

Clinical utility of anti-DFS70 for identifying antinuclear antibody-positive patients without systemic autoimmune rheumatic disease.

INTRODUCTION: The antinuclear antibody (ANA) test is a screening test for systemic autoimmune rheumatic disease (SARD). We hypothesised that the presence of anti-DFS70 in ANA-positive samples was associated with a false-positive ANA test and negatively associated with SARD. METHODS: A retrospective analysis of patient samples received for ANA testing from 1 January 2016 to 30 June 2016 was performed. Patient samples underwent ANA testing via indirect immunofluorescence method and anti-DFS70 testing using enzyme-linked immunosorbent assay. RESULTS: Among a total of 645 ANA-positive samples, the majority (41.7%) were positive at a titre of 1:80. The commonest nuclear staining pattern (65.5%) was speckled. Only 9.5% of ANA-positive patients were diagnosed with SARD. Anti-DFS70 was found to be present in 10.0% of ANA-positive patients. The majority (51/59, 86.4%) of patients did not have SARD. Seven patients had positive ANA titre > 1:640, the presence of anti-double stranded DNA and/or anti-Ro60. The presence of anti-DFS70 in ANA-positive patients was not associated with the absence of SARD (Fisher's exact test, p = 0.245). CONCLUSION: The presence of anti-DFS70 was associated with a false-positive ANA test in 8.6% of our patients. Anti-DFS70 was not associated with the absence of SARD.

Metabolic syndrome and its effect on the outcomes of rheumatoid arthritis in a multi-ethnic cohort in Singapore.

INTRODUCTION: Over-expression of common inflammatory mediators in the metabolic syndrome (MetS) and in rheumatoid arthritis (RA) may lead to mutually adverse outcomes. AIM: We investigate the prevalence of MetS in a multi-ethnic population of RA patients and its effect on clinical and patient-reported outcomes. METHOD: Six hundred sixty RA (561 women) patients from a public-sector specialist clinic in a hospital in Singapore were assessed for MetS according to the 2009 Joint Consensus (JC) and the 2004 National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) definitions. Univariable and multivariable regression modelling were used to investigate the associations between patients' demographics with MetS and MetS with RA outcomes. RESULTS: The prevalence of MetS in our RA cohort was 49.4% and 44.9% according to the JC and NCEP ATP III definitions, respectively. The diagnosis of MetS was largely due to hypertriglyceridemia, hypertension, and obesity. MetS was associated with older age (OR 1.06 [95% CI 1.04-1.08]), Malay ethnicity (OR 1.78 [95% CI 1.02-3.09]), or Indian ethnicity (OR 3.07 [95% CI 1.68-5.59]). No significant associations between MetS and RA outcomes were observed. RA patients with MetS are more likely to suffer from stroke and ischemic heart disease. CONCLUSION: The prevalence of MetS in RA patients in Singapore was almost double that in the general population. MetS does not adversely affect RA outcomes but raises the risks of stroke and heart disease. RA patients, especially those older and of Indian and Malay ethnicities, should be routinely screened for MetS. Any MetS-defining condition should be actively controlled. Key Points • Approximately half of the RA sample from the Singapore RA population can be diagnosed with MetS. • Older patients, and patients of Malay and Indian ethnicities have higher odds of MetS. • MetS does not adversely affect RA outcomes but raises the risks of stroke and heart disease.

The impact of diabetes mellitus on treatment and outcomes of rheumatoid arthritis at 5-year follow-up: results from a multi-ethnic Asian cohort.

OBJECTIVES: We evaluated the impact of type 2 diabetes mellitus (T2DM) on RA treatment and outcomes in a longitudinal RA cohort. METHODS: We analysed data collected in the period 2001-2013 involving 583 RA patients, including demographics, diabetes diagnosis, clinical features, treatment, ACR functional class, HAQ, and quality-of-life measurement using the Short-Form 36. RESULTS: Seventy-seven (13.2%) of the RA patients had T2DM. DAS28 was not different in patients with T2DM at 5 years post-RA diagnosis. Fewer T2DM patients received MTX than those without T2DM (51% vs 80%, P < 0.001). Using univariate analysis, T2DM patients were more likely to experience poorer outcomes in terms of ACR functional status (P = 0.009), joint surgery (P = 0.007), knee arthroplasty (P < 0.001) and hospital admissions (P = 0.006). Multivariate regression analyses showed more knee arthroplasty (P = 0.047) in patients with T2DM. CONCLUSION: Fewer patients with T2DM received MTX compared with those without T2DM. Patients with RA and T2DM were at higher risk of knee arthroplasty than RA patients without T2DM.

DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data.

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

Pseudo-Anaphylactic Reactions to Pfizer BNT162b2 Vaccine: Report of 3 Cases of Anaphylaxis Post Pfizer BNT162b2 Vaccination.

Anaphylactic reactions were observed after Singapore's national coronavirus disease 2019 (COVID-19) vaccination programme started in December 2020. We report the clinical and laboratory features of three patients in our institution who developed anaphylactic reactions after receiving the Pifzer BNT162b2 vaccine. IgM and IgG antibodies, but not IgE antibodies to the Pfizer BNT162b2 vaccine, were detected in all subjects. Similarly, mild to high elevated levels of anti-polyethylene glycol (PEG) IgG (1035-19709 U/mL, vs. vaccine-naive < 265 U/mL, vaccine-tolerant < 785 U/mL) and IgM (1682-5310 U/mL, vs. vaccine-naive < 1011 U/mL, vaccine-tolerant < 1007 U/mL) were detected in two out of three patients via commercial ELISA. High levels of serum anaphylatoxin C3a (79.0 ± 6.3 µg/mL, mean ± SD, vs. normal < 10 µg/mL) were observed in all three patients during the acute phase of the reaction, while tryptase levels, a marker of mast cell activation, were not elevated. Finally, one patient with the highest levels of anti-PEG IgG, IgM, and anti-Pfizer BNT162b2 IgG and IgM exhibited an enhanced Th2 cytokine serum profile during an acute reaction, with high levels of IL-4 (45.7 pg/mL, vs. vaccine-naive/tolerant < 2.30 pg/mL), IL-33 (86.4 pg/mL, vs. vaccine-naive/tolerant < 5.51 pg/mL) and IL-10 (22.9 pg/mL, vs. vaccine-naive/tolerant < 12.49 pg/mL) diminishing over time following corticosteroid treatment. Taken together, we propose these cases of anaphylaxis described are driven by a complement activation-related pseudoallergy (CAPRA), rather than classical IgE-mediated mechanisms.

Comparison of mental-physical comorbidity, risk of death and mortality among patients with mental disorders - A retrospective cohort study.

AIM: To compare the risk of death, the prevalence of comorbid chronic physical illness and mortality among an Asian population of patients with mental disorders. METHODS: This was a retrospective data analysing of medical records of patients with schizophrenia, depression, anxiety, bipolar disorder, alcohol use disorder (AUD) or substance use disorder and the comorbid chronic physical illnesses. The hazard risk of death was calculated with Cox regression and compared between patients with and without comorbid chronic physical illness(es). Odds ratios of specific comorbid chronic physical illness were calculated with logistic regression and mean crude death rate was calculated for patients with different mental disorders. RESULTS: A total of 56,447 patients with mental disorders were included in the analysis. Compared to patients without comorbid physical illness, patients with mental-physical comorbidity were associated with a higher risk of death [2.36 (2.22-2.52); hazard ratio (95% CI)] and less estimated survival days [2157 (2142-2172) vs 2508 (2504-2513)]. Compared to other mental disorders, those with AUD had the highest prevalence of two or more comorbid chronic physical illnesses and associated with the highest odds of comorbid hypertension, diabetes mellitus, stroke, nephritis, chronic kidney disease, and cancer. The highest one-year crude death rate was similarly observed in patients with AUD. CONCLUSIONS: Mental-physical comorbidity was associated with a higher risk of death compared to patients with mental disorders only. The highest prevalence of mental-physical comorbidity and mortality were observed in patients with AUD. More attention and resources may be needed to tackle the burden of AUD.

Conversion among the 28-joint count activity indices for rheumatoid arthritis.

OBJECTIVE: Disease activity indices for rheumatoid arthritis (RA) are important in clinical practice and research. Although they are closely correlated, they are not in good agreement. We derived formulae to convert values from one of the four 28-joint count indices (disease activity score using erythrocyte sedimentation rate [DAS28-ESR], disease activity score using C-reactive protein [DAS28-CRP], clinical disease activity index [CDAI], and simple disease activity index [SDAI]) to any of the others. METHODS: We obtained data from 175 patients from our RA registry with concurrent CRP and ESR and established the nature of relationships between the indices using these data. Subsequently, we developed empiric conversion formulae. Furthermore, we developed new cutoff values for classifying disease activity to minimize the disparity among indices, using an iterative method. RESULTS: The relationships between DAS28-ESR and DAS28-CRP and between SDAI and CDAI were approximately linear; the others were quadratic. Quadratic equations approximated the relationship between DAS, SDAI, and CDAI, whereas natural logarithms function approximated the relationship between DAS28-ESR and DAS28-CRP. Patients are frequently categorized into inconsistent disease activity states with any two indices, with the disparity ranging from 9.7% to 40.6%. The new cutoff values were developed to minimize the discrepant activity state categorization, reducing the disparity range to 6.3%-32.6%. CONCLUSION: We derived empiric formulae that connect DAS28-ESR, DAS28-CRP, SDAI, and CDAI. Moreover, we developed new cutoff values to minimize the discrepant activity state categorization with different indices.