Does testosterone influence the association between sleep and frailty in men: results from the European Male Aging Study.

BACKGROUND: Previous studies have suggested an association between sleep disturbance and frailty. The mechanism is unknown, although it has been suggested that hormonal factors may play a role. METHODS: The aim was to determine the association between sleep duration, sleep quality and frailty, and to determine whether testosterone influenced this association. Males aged 40-79 years were recruited from eight European centres to the European Male Aging Study (EMAS). Subjects completed an interviewer-assisted questionnaire including questions regarding sleep quality and duration. Sleep quality was scored 0-20 and categorised as 0-4, 5-9, 10-14, and 15-20, with higher scores indicating poorer quality. A 39-component frailty index (FI) was constructed. Total testosterone levels were measured. The association between sleep duration, sleep quality and the FI was assessed using negative binomial regression, with adjustment for putative confounders including testosterone level. RESULTS: Two thousand three hundred ninety-three participants contributed data to the analysis. The mean age was 63.3 years and mean sleep duration was 7.01 h. The mean frailty index was 0.15. Mean testosterone levels declined with decreasing sleep quality. After adjustment, compared to those with a sleep score of 0-4, the FI was 57% (95% CI 38%, 78%) higher among those with a sleep score of 15-20. After adjustment compared to those with normal sleep duration (6-9 h), those with a short (< 6 h) and long (≥ 9 h) sleep duration had a 16% (95% CI 6%, 28%) and 11% (95% CI 0%, 23%) higher FI, respectively. Adjustment for testosterone did not influence the strength of either association. CONCLUSION: Frailty is associated with impaired sleep quality and sleep duration. The association cannot, however, be explained by variation in testosterone levels.

Prevalence and predictors of adverse events with methotrexate mono- and combination-therapy for rheumatoid arthritis: a systematic review.

OBJECTIVES: This systematic review aims to summarize rates of adverse events (AEs) in patients with RA or inflammatory arthritis starting MTX as monotherapy or in combination with other csDMARDs, and to identify reported predictors of AEs. METHODS: Three databases were searched for studies reporting AEs in MTX-naïve patients with RA. Randomized controlled trials (RCTs) and observational cohort studies were included. Prevalence rates of AEs were pooled using random effects meta-analysis, stratified by study design. RESULTS: Forty-six articles (34 RCTs and 12 observational studies) were identified. The pooled prevalence of total AEs was 80.1% in RCTs (95% CI: 73.5, 85.9), compared with 23.1% in observational studies (95% CI: 12.3, 36.0). The pooled prevalence of serious AEs was 9.5% in RCTs (95% CI: 7.4, 11.7), and 2.1% in observational studies (95% CI: 1.0, 3.4). MTX discontinuation due to AEs was higher in observational studies (15.5%, 95% CI: 9.6, 22.3) compared with RCTs (6.7%, 95% CI: 4.7, 8.9). Gastrointestinal events were the most commonly reported AEs (pooled prevalence: 32.7%, 95% CI: 18.5, 48.7). Five studies examined predictors of AEs. RF status, BMI and HAQ score were associated with MTX discontinuation due to AEs; ACPA negativity, smoking and elevated creatinine were associated with increased risk of elevated liver enzymes. CONCLUSION: The review provides an up-to-date overview of the prevalence of AEs associated with MTX in patients with RA. The findings should be communicated to patients to help them make informed choices prior to commencing MTX.

Towards Personalized Medicine in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.

Genetics of rheumatoid arthritis.

In the past four decades, a plethora of genetic association studies have been carried out in cohorts of patients with rheumatoid arthritis. These studies have highlighted key aspects of disease pathogenesis and suggested causal mechanisms. In this review, we discuss major advances in our understanding of the genetic architecture of rheumatoid arthritis susceptibility, severity and treatment response and explain how genetics supports current models of disease pathogenesis and outcome. We outline future research directions, like Mendelian randomisation, and present a number of potential avenues for clinical translation, including risk and outcome prediction, patient stratification into treatment response groups and pharmacological applications.

Using Polygenic Risk Scores to Aid Diagnosis of Patients With Early Inflammatory Arthritis: Results From the Norfolk Arthritis Register.

OBJECTIVE: There is growing evidence that genetic data are of benefit in the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G-PROB) has been developed to aid diagnosis has not yet been tested in a real-world setting. Our aim was to assess whether G-PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a prospective observational cohort of patients presenting with early inflammatory arthritis. METHODS: Genotypes and clinician diagnoses were obtained from patients from NOAR. Six G-probabilities (0%-100%) were created for each patient based on known disease-associated odds ratios of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, spondyloarthropathy, gout, or "other diseases." Performance of the G-probabilities compared with clinician diagnosis was assessed. RESULTS: We tested G-PROB on 1,047 patients. Calibration of G-probabilities with clinician diagnosis was high, with regression coefficients of 1.047, where 1.00 is ideal. G-probabilities discriminated clinician diagnosis with pooled areas under the curve (95% confidence interval) of 0.85 (0.84-0.86). G-probabilities <5% corresponded to a negative predictive value of 96.0%, for which it was possible to suggest >2 unlikely diseases for 94% of patients and >3 for 53.7% of patients. G-probabilities >50% corresponded to a positive predictive value of 70.4%. In 55.7% of patients, the disease with the highest G-probability corresponded to clinician diagnosis. CONCLUSION: G-PROB converts complex genetic information into meaningful and interpretable conditional probabilities, which may be especially helpful at eliminating unlikely diagnoses in the rheumatology outpatient setting.

Chromosomal rearrangement-A rare cause of complement factor I associated atypical haemolytic uraemic syndrome.

Chromosomal rearrangements affecting the genes encoding complement factor H and the factor H related proteins have been described in aHUS patients. To date such disorders have not been described in other aHUS associated genes. We describe here a heterozygous 875,324bp deletion encompassing the gene (CFI) encoding complement factor I and ten other genes. The index case presented with aHUS and did not recover renal function. No abnormalities were detected on Sanger sequencing of CFI but a low factor I level led to a multiplex ligation-dependent probe amplification assay being undertaken. This showed a complete heterozygous deletion of CFI. The extent of the deletion and the breakpoint was defined. In the Newcastle aHUS cohort we have identified and report here 32 different CFI variants in 56 patients but to date this is the only deletion that we have identified. This finding although rare does suggest that screening for chromosomal rearrangements affecting CFI should be undertaken in all aHUS patients particularly if the factor I level is unexplainably low.