Predicting Flare in Patients With Rheumatoid Arthritis in Biologic Induced Remission, on Tapering, and on Stable Therapy.

OBJECTIVE: The tapering of biologic disease-modifying antirheumatic drug (b-DMARD) therapy for patients with rheumatoid arthritis (RA) in stable remission is frequently undertaken, but specific guidance on how to successfully taper is lacking. The objective of this study is to identify predictors of flare in patients in stable b-DMARD-induced clinical remission, who did or did not follow structured b-DMARD tapering. METHODS: Patients with RA receiving b-DMARD treatment who had achieved sustained remission according to a Disease Activity Score in 28 joints using the C-reactive protein level (DAS28-CRP) <2.6 for ≥6 months were offered tapering. Clinical, ultrasound (US) (total power Doppler [PD]/grayscale abnormalities), CD4+ T cell subsets, and patient-reported outcomes (PROs) were collected at inclusion. The primary endpoint was the occurrence of flare (loss of DAS28-CRP remission) over 12 months. Logistic regression analyses identified predictors of flare. Dichotomization into high/low-risk groups was based on 80% specificity using the area under the receiving operator curve (AUROC). RESULTS: Of 63 patients choosing tapering, 23 (37%) flared compared with 12 of 60 (20%) on stable treatment (P = 0.043). All patients who flared regained remission upon reinstating treatment. In the tapering group, flare was associated with lower regulatory T cell (Treg) (P < 0.0001) and higher CRP levels (P < 0.0001), erythrocyte sedimentation rate (P < 0.035), and inflammation-related cells (IRCs) (P = 0.054); stepwise modeling selected Tregs (odds ratio [OR] = 0.350, P = 0.004), IRCs (OR = 1.871, P = 0.007), and CRP level (OR = 1.577, P = 0.004) with 81.7% accuracy and AUROC = 0.890. In the continued therapy group, modeling retained the tender joint count, total PD, and visual analog scale pain score, with 82.1% accuracy and AUROC = 0.899. Most patients in the study were considered low risk of flare (80 of 123 patients [65%]). Only 5 of 37 (13.5%) of the low-risk patients who tapered flared, which was notable compared with the continued therapy group (20% flare). CONCLUSION: Flare on tapering b-DMARDs was predicted by lower Tregs and elevated inflammation biomarkers (IRCs/CRP level); flare on continued b-DMARDs was associated with raised pain parameters and US inflammation. Knowledge of these biomarkers should improve outcomes by targeted selection for tapering, and by increased monitoring of those on continued therapy predicted to flare.

Lymphocyte subset phenotyping for the prediction of progression to inflammatory arthritis in anti-citrullinated-peptide antibody-positive at-risk individuals.

OBJECTIVES: Inflammatory arthritis (IA) is considered the last stage of a disease continuum, where features of systemic autoimmunity can appear years before clinical synovitis. Time to progression to IA varies considerably between at-risk individuals, therefore the identification of biomarkers predictive of progression is of major importance. We previously reported on the value of three CD4+T-cell subsets as biomarkers of progression. Here, we aim to establish the value of 18 lymphocyte subsets (LS) for predicting progression to IA. METHODS: Participants were recruited based on a new musculoskeletal complaint and being positive for anti-citrullinated-peptide Antibody. Progression (over 10 years) was defined as the development of clinical synovitis. LS analysis was performed for lymphocyte lineages, naïve/memory subsets, inflammation-related cells (IRC), and regulatory cells (Treg/B-reg). Modelling used Logistic/Cox regressions. RESULTS: Of 210 patients included, 93 (44%) progressed to IA, 41/93 (44%) within 12 months (rapid progressors). 5/18 LS were associated with progression (Treg/CD4-naïve/IRC (adjusted p < 0.0001), CD8 (p = 0.021), B-reg (p = 0.015)) and 3 trends (NK-cells/memory-B-cells/plasmablasts).Unsupervised hierarchical clustering using these 8 subsets segregated 3 clusters of patients, one cluster being enriched (63/109(58%)) and one poor (10/45(22%)) in progressors.Combining all clinical and LS variables, forward logistic regression predicted progression with accuracy=85.7% and AUC=0.911, selecting smoking/Rheumatoid-Factor/HLA-Shared-Epitope/Tender-Joint-Count-78 and Treg/CD4-naive/CD8/NK-cells/B-reg/plasmablasts.To predict rapid progression, a Cox regression was performed resulting in a model combining smoking/rheumatoid factor and IRC/CD4-naïve/Treg/NK-cells/CD8+T-cells (AUC=0.794). CONCLUSION: Overall, progression was predicted by specific LS, suggesting potential triggers for events leading to the development of IA, while rapid progression was associated with a different set of subsets.

Predicting Inflammatory Arthritis in At-Risk Persons: Development of Scores for Risk Stratification.

BACKGROUND: Inflammatory arthritis (IA) is an immune-related condition defined by the presence of clinical synovitis. Its most common form is rheumatoid arthritis. OBJECTIVE: To develop scores for predicting IA in at-risk persons using multidimensional biomarkers. DESIGN: Prospective observational cohort study. SETTING: Single-center, Leeds, United Kingdom. PARTICIPANTS: Persons with new musculoskeletal symptoms, a positive test result for anticitrullinated protein antibodies, and no clinical synovitis and followed for 48 weeks or more or until IA occurred. MEASUREMENTS: A simple score was developed using logistic regression, and a comprehensive score was developed using the least absolute shrinkage and selection operator Cox proportional hazards regression. Internal validation with bootstrapping was estimated, and a decision curve analysis was done. RESULTS: Of 455 participants, 32.5% (148 of 455) developed IA, and 15.4% (70 of 455) developed it within 1 year. The simple score identified 249 low-risk participants with a false negative rate of 5% (and 206 high-risk participants with a false-positive rate of 72%). The comprehensive score identified 119 high-risk participants with a false-positive rate of 29% (and 336 low-risk participants with a false-negative rate of 19%); 40% of high-risk participants developed IA within 1 year and 71% within 5 years. LIMITATIONS: External validation is required. Recruitment occurred over 13 years, with lower rates of IA in later years. There was geographic variation in laboratory testing and recruitment availability. CONCLUSION: The simple score identified persons at low risk for IA who were less likely to need secondary care. The comprehensive score identified high-risk persons who could benefit from risk stratification and preventive measures. Both scores may be useful in clinical care and should also be useful in clinical trials. PRIMARY FUNDING SOURCE: National Institute for Health and Care Research Leeds Biomedical Research Centre.

Predictors of poor glycemic control in adult with type 2 diabetes in South-Eastern Nigeria.

BACKGROUND: The study investigated predictors of poor glycemic control in type 2 diabetes (T2DM). METHODS: Data on demographics, anthropometric and clinical parameters were collected in a cross-section survey from 140 adults with T2DM, using standard tools/instruments. Glycated hemoglobin (A1C) was assessed as a measure of glycemic control. RESULTS: Majority (83.3%) had poor glycemic control status of which about 95% constitute the elderly. The elderly (OR= 5.90, 95% Cl: 1.66-20.96) were more likely associated with poor glycemic control than the non-elderly (p = 0.006). Adjustment for significant predictor variables: Age, waist-hip ratio (WHR), Fasting plasma glucose (FPG) and Systolic blood pressure (SBP), although attenuated the odds (OR= 5.00, 95% CI: 1.19-20.96) of poor glycemic control, it still remained significantly (p = 0.028) higher in the elderly. Patients outside tight FPG control significantly (p = 0.001) showed poor glycemic cotrol than those within tight FPG (OR= 17.39, 95%Cl: 5.83-51.90), even with attenuated OR (OR= 10.85, 95%Cl: 3.10-37.96) and (OR=12.08, 95%Cl: 3.64-40.09) when non-significant and significant predictor variables were accounted for, respectively. CONCLUSION: Age, WHR, FPG, and SBP were significantly associated with differences in glycemic control. The elderly and FPG outside tight control showed significantly increased odds of poor glycemic control status.

Investigation into Hypoglycemic, Antihyperlipidemic, and Renoprotective Potentials of Dennettia tripetala (Pepper Fruit) Seed in a Rat Model of Diabetes.

This study investigated the hypoglycemic, antihyperlipidemic, and renoprotective potentials of Dennettia tripetala (DT) in a rat model of diabetes. The hypoglycemic activity in crude methanol seed extract of DT (CMEDT) and methanol seed fraction of DT (MFDT) measured by glucose oxidase method was increased by 47.37% and 28.72%, respectively, after 8 hours of administration. After 10 days of treatment, CMEDT and MFDT gave a good glycemic control with the highest percentage reduction of 75.82% and 71.34% in glucose level, respectively, which is closely compared with 79.91% in glibenclamide. Using the enzymatic assay and Friedewald's equation, there was a significant reduction in serum level of total cholesterol (TC), triglyceride (TG), very-low-density lipoprotein (VLDL), and low-density lipoprotein (LDL) and a significant increase in high-density lipoprotein (HDL) (p < 0.05) following treatment with CMEDT and MFDT, when compared with the untreated group, although results varied in dosed groups, with high dose of MFDT showing a better lipid-lowering activity. High dose of MFDT improved lipid metabolism and increased percentage protection against atherogenesis by 44%. However, neither CMEDT nor MFDT ameliorated the renal biochemical alteration in urea and creatinine. Thus, the study demonstrates hypoglycemic and antihyperlipidemic potentials of DT seed in diabetes.