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Efficacy to biologics in rheumatoid arthritis (RA) patients is variable and is likely influenced by each patient's circulating drug levels. Using modelling and simulation, the aim of this study was to investigate whether adalimumab and etanercept biosimilar dosing intervals can be altered to achieve therapeutic drug levels at a faster/similar time compared to the recommended interval. RA patients starting subcutaneous Amgevita or Benepali (adalimumab and etanercept biosimilars, respectively) were recruited and underwent sparse serum sampling for drug concentrations. Drug levels were measured using commercially available kits. Pharmacokinetic data were analysed using a population approach (popPK) and potential covariates were investigated in models. Models were compared using goodness-of-fit criteria. Final models were selected and used to simulate alternative dosing intervals. Ten RA patients starting the adalimumab biosimilar and six patients starting the etanercept biosimilar were recruited. One-compartment PK models were used to describe the popPK models for both drugs; no significant covariates were found. Typical individual parameter estimates were used to simulate altered dosing intervals for both drugs. A simulation of dosing the etanercept biosimilar at a lower rate of every 10 days reached steady-state concentrations earlier than the usual dosing rate of every 7 days. Simulations of altered dosing intervals could form the basis for future personalised dosing studies, potentially saving costs whilst increasing efficacy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives Biologic DMARDs (bDMARDs) are widely used in patients with RA, but response to bDMARDs is heterogeneous. The objective of this work was to identify pretreatment proteomic biomarkers associated with RA clinical outcome measures in patients starting bDMARDs. Methods Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to generate spectral maps of sera from patients with RA before and after 3 months of treatment with the bDMARD etanercept. Protein levels were regressed against RA clinical outcome measures, i.e. 28-joint DAS (DAS28) and its subcomponents and DAS28 <2.6 (i.e. remission). The proteins with the strongest evidence for association were analysed in an independent, replication dataset. Finally, subnetwork analysis was carried out using the Disease Module Detection algorithm and biological plausibility of identified proteins was assessed by enrichment analysis. Results A total of 180 patients with RA were included in the discovery dataset and 58 in the validation dataset from a UK-based prospective multicentre study. Ten individual proteins were found to be significantly associated with RA clinical outcome measures. The association of T-complex protein 1 subunit η with DAS28 remission was replicated in an independent cohort. Subnetwork analysis of the 10 proteins from the regression analysis identified the ontological theme, with the strongest associations being with acute phase and acute inflammatory responses. Conclusion This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response to this drug, one of which was replicated in an independent cohort. [ABSTRACT FROM AUTHOR]

Background and Aims: There is limited research surrounding dual maternal use of cigarettes and electronic cigarettes (e-cigarettes). We aimed to assess predictors of maternal quitting of cigarettes, e-cigarettes, and both during late pregnancy. Materials and Methods: We analyzed dual use (n = 4,006) and exclusive e-cigarette use (n = 1,685) among mothers using data from the 2016 to 2019 phase of the Pregnancy Risk Assessment Monitoring Systems (PRAMS), a nationally representative sample of the United States. Dual use and exclusive e-cigarette use were defined based on use reported during the 3 months before pregnancy and quitting was assessed during the last 3 months of pregnancy. Multinomial and binomial logistic regression models estimated the odds ratios and 95% confidence intervals for predictors of quitting status among mothers who reported dual use and exclusive e-cigarette use, respectively. Separate predictor analyses were conducted in the dual and exclusive e-cigarette use groups to see predictors of quitting e-cigarettes, cigarettes, or both. Results: The highest proportion of mothers who used cigarettes and e-cigarettes before pregnancy quit both during late pregnancy (46.2%), followed by those who quit e-cigarette use only (26.5%) and those who quit cigarette use only (6.6%). Among mothers who reported dual use, those who were African American or Asian, of Hispanic ethnicity, consumed alcohol before pregnancy, had higher education, were married, had diabetes, had higher annual household income, had nongovernmental health insurance, had more prenatal care visits, had a higher frequency of e-cigarette use before pregnancy, had a lower frequency of cigarette use before pregnancy, and smoked hookah around pregnancy had a higher likelihood of quitting both cigarette and e-cigarette use during late pregnancy. Conclusions: Quitting use of cigarettes and/or e-cigarettes was fairly common among mothers who reported dual use or e-cigarette use only. Sociodemographics, pregnancy characteristics, and use of other tobacco products predicted quitting use of both cigarettes and e-cigarettes during late pregnancy. [ABSTRACT FROM AUTHOR]

BackgroundObesity is an important risk factor for many chronic diseases. Mobile health interventions such as smartphone apps can potentially provide a convenient low-cost addition to other obesity reduction strategies. ObjectiveThis study aimed to estimate the impacts on quality-adjusted life-years (QALYs) gained and health system costs over the remainder of the life span of the New Zealand population (N=4.4 million) for a smartphone app promotion intervention in 1 calendar year (2011) using currently available apps for weight loss. MethodsThe intervention was a national mass media promotion of selected smartphone apps for weight loss compared with no dedicated promotion. A multistate life table model including 14 body mass index–related diseases was used to estimate QALYs gained and health systems costs. A lifetime horizon, 3% discount rate, and health system perspective were used. The proportion of the target population receiving the intervention (1.36%) was calculated using the best evidence for the proportion who have access to smartphones, are likely to see the mass media campaign promoting the app, are likely to download a weight loss app, and are likely to continue using this app. ResultsIn the base-case model, the smartphone app promotion intervention generated 29 QALYs (95% uncertainty interval, UI: 14-52) and cost the health system US $1.6 million (95% UI: 1.1-2.0 million) with the standard download rate. Under plausible assumptions, QALYs increased to 59 (95% UI: 27-107) and costs decreased to US $1.2 million (95% UI: 0.5-1.8) when standard download rates were doubled. Costs per QALY gained were US $53,600 for the standard download rate and US $20,100 when download rates were doubled. On the basis of a threshold of US $30,000 per QALY, this intervention was cost-effective for Māori when the standard download rates were increased by 50% and also for the total population when download rates were doubled. ConclusionsIn this modeling study, the mass media promotion of a smartphone app for weight loss produced relatively small health gains on a population level and was of borderline cost-effectiveness for the total population. Nevertheless, the scope for this type of intervention may expand with increasing smartphone use, more easy-to-use and effective apps becoming available, and with recommendations to use such apps being integrated into dietary counseling by health workers.

The sedimentary core (late Miocene to early Pliocene) retrieved from the Nicobar Fan (Site-U1480) was used to determine organic matter provenances, paleodeposition, and paleoclimate conditions on the hinterland. Total organic carbon (Corg), nitrogen (Ntotal), and Corg/Ntotal vary from 0.11 to 0.58%, 0.01 to 0.08%, and 2.5 to 33.0, separately. High Corg/Ntotal values (11.0 to 33.0) are observed between 367.53 and 718.05 mbsf depths, suggesting highly land-derived OM. The carbon (δ13Corg: −22.95 to −27.58‰) and nitrogen (δ15N: + 0.40 to + 2.51‰) isotope values suggest that OM was mostly derived from mixed terrestrial C3 plants and freshwater-phytoplanktons, with some marine OM. Palynofacies analysis also indicates that during the late Miocene, majority of OM is of terrestrial origin with little of marine origin. The spore-pollens are represented by Pinus, Picea, Abies, and Cerdus pollen, as well as trilete, and monolete spores, transported through major rivers like Irrawaddy and Salween from the surrounding Himalayan regions. Two facies assemblages have been recognized. Assemblage I: ~100% opaque phytoclasts at depths of 718.05mbsf, 969.78mbsf, and 1291.77mbsf indicate an oxidizing environment and existence of wildfires in the hinterland, which further point towards increased aridity during the late Miocene period. Assemblage II: non-opaque phytoclasts (avg. ~66.68%) are dominant over opaque phytoclasts (avg. ~25.82%) with few palynomorphs, suggesting enhanced sediment supply into the Nicobar Fan region. The dinoflagellate cysts are low in abundance, mostly represented by Spiniferites spp. and Operculodinium spp. Their productivity is relatively high during the late Miocene and declines in early Pliocene period, indicating a shift in monsoon conditions. [ABSTRACT FROM AUTHOR]