Background. Extensive studies have been carried out to investigate the association between obesity and the risk of rheumatoid arthritis (RA); however, the results of the current reported original studies remain inconsistent. This study aimed to clarify the relationship between body mass index and rheumatoid arthritis by conducting an updated overall and dose-response meta-analysis. Methods. The relevant literature was searched using the PubMed and Embase databases (through 20 September 2018) to identify all eligible published studies. Random-effect models and dose-response meta-analyses were used to estimate the pooled risk ratio (RR) with a 95% confidence interval (CI). Subgroup analyses were also conducted based on the characteristics of the participants. Sensitivity analyses and publication bias tests were also performed to explore potential heterogeneity and bias in the meta-analysis. Results. Sixteen studies that included a total of 406,584 participants were included in the meta-analysis. Compared to participants with normal weight, the pooled RRs of rheumatoid arthritis were 1.12 (95% CI, 1.04-1.20) in overweight and 1.23 (95% CI, 1.09-1.39) in obese participants. There was evidence of a nonlinear relationship between body mass index (BMI) and RA (P for nonlinearity less than 0.001 in the overall meta-analysis, P for nonlinearity=0.025 in the case-control studies, P for nonlinearity=0.0029 in the cohort studies). No significant heterogeneity was found among studies (I2=10.9% for overweight and I2=45.5% for obesity). Conclusion. The overall and dose-response meta-analysis showed that increased BMI was associated with an increased risk for rheumatoid arthritis, which might present a prevention strategy for the prevention or control of rheumatoid arthritis. The nonlinear relationship between BMI and RA might present a personal prevention strategy for RA.
Background The prevalence of diabetes, constituted chiefly by type 2 diabetes mellitus (T2DM), is a global public health threat. Suboptimal health status (SHS), a physical state between health and disease, might contribute to the progression or development of T2DM. Methods We conducted a prospective cohort study, based on the China Suboptimal Health Cohort Study (COACS), to understand the impact of SHS on the progress of T2DM. We examined associations between SHS and T2DM outcomes using multivariable logistic regression models and constructed predictive models for T2DM onset based on SHS. Results A total of 61 participants developed T2DM after an average of 3.1 years of follow-up. Participants with higher SHS scores had more T2DM outcomes (p = 0.036). Moreover, compared with the lowest quartile of SHS scores, participants with fourth, third, and second quartile SHS scores were found to be associated with a 1.7-fold, 1.6-fold, and 1.5-fold risk of developing T2DM, respectively. The predictive model constructed with SHS had higher discriminatory power (AUC = 0.848) than the model without SHS (AUC = 0.795). Conclusions The present study suggests that a higher SHS score is associated with a higher incidence of T2DM. SHS is a new independent risk factor for T2DM and has the capability to act as a predictive tool for T2DM onset. The evaluation of SHS combined with the analysis of modifiable risk factors for SHS allows the risk stratification of T2DM, which may consequently contribute to the prevention of T2DM development. These findings might require further validation in a longer-term follow-up study.
BackgroundThe objective of this study was to assess the diagnostic value of platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), and neutrophil/lymphocyte ratio (NLR) as biomarkers in patients with rheumatoid arthritis (RA) and rheumatoid arthritis-associated interstitial lung disease (RA-ILD).Material/MethodsDemographic and laboratory data were acquired for 198 RA and 103 RA-ILD patients and 290 healthy controls. The subjects were categorized into female and male groups and further subcategorized based on age into <60 years and ≥60 years subgroups. One-way analysis of variance (ANOVA), receiver operating characteristics (ROC), Pearson analysis, multiple linear regression analysis, and logistic regression analysis were performed to analyze the association of PLR, NLR, and LMR with RA and RA-ILD.ResultsMean PLR and NLR were lowest in the control group, followed by the RA and RA-ILD groups (p<0.05). Mean LMR was lowest in the RA-ILD group, followed by the RA and control groups (p<0.05). The area under the ROC (AUROC) values of the PLR to distinguish between RA and controls, RA-ILD and controls, and RA-ILD and RA were 0.676, 0.776, and 0.650, respectively (p<0.001). Multiple linear regression analysis suggested a significantly positive association between the level of PLR and the level of DAS28 (p<0.001). The odds ratio of PLR was 1.101 for RA (p=0.023) and 1.217 for RA-ILD (p<0.001) when compared to the controls.ConclusionsPLR may be applied as a new biomarker for predicting and diagnosing RA and RA-ILD and for distinguishing RA-ILD patients from RA patients and healthy subjects.
Background: Epidemiological studies observing inconsistent associations of telomere length (TL) with ischemic stroke (IS) are susceptible to bias according to reverse causation and residual confounding. We aimed to assess the causal association between TL, IS, and the subtypes of IS, including large artery stroke (LAS), small vessel stroke (SVS), and cardioembolic stroke (CES) by performing a series of two-sample Mendelian randomization (MR) approaches. Methods: Seven single nucleotide polymorphisms (SNPs) were involved as candidate instrumental variables (IVs), summarized from a genome-wide meta-analysis including 37,684 participants of European descent. We analyzed the largest ever genome-wide association studies of stroke in Europe from the MEGASTROKE collaboration with 40,585 stroke cases and 406,111 controls. The weighted median (WM), the penalized weighted median (PWM), the inverse variance weighted (IVW), the penalized inverse variance weighted (PIVW), the robust inverse variance weighted (RIVW), and the Mendelian randomization-Egger (MR-Egger) methods were conducted for the MR analysis to estimate a causal effect and detect the directional pleiotropy. Results: No significant association between genetically determined TL with overall IS, LAS, or CES were found (all p > 0.05). SVS was associated with TL by the RIVW method (odds ratio (OR) = 0.72, 95% confidence interval (CI): 0.54–0.97, p = 0.028), after excluding rs9420907, rs10936599, and rs2736100. Conclusions: By a series of causal inference approaches using SNPs as IVs, no strong evidence to support the causal effect of shorter TL on IS and its subtypes were found.
Objective The present study aimed to explore the association between SUA and NAFLD in women with different menstrual statuses. Methods A total of 6043 women were selected from the Jidong and Kailuan communities for inclusion in the present study. The SUA levels of participants were divided into quartiles. NAFLD was determined by abdominal ultrasonography. Data from laboratory tests and clinical examination were collected, and basic information was obtained from standardized questionnaires. The menstrual status was stratified into menstrual period, menopause transition period, and postmenopause. Multivariate logistic regression models were used to determine the relationship between menstrual status, SUA, and NAFLD. Results The levels of SUA in subjects with NAFLD in the menstrual period, menopause transition period, and postmenopause were 268.0 ± 71.1, 265.6 ± 67.8, and 286.7 ± 75.8 (mmol/L), respectively, and were higher than those in subjects without NAFLD. The adjusted odds ratios (ORs) with 95% confidence interval (CI) for NAFLD among participants in the menopause transition period and postmenopausal period were 1.10 (0.89–1.37) and 1.28 (1.04–1.58), respectively, compared with the menstrual period women. Compared to the lowest quartile of SUA, the adjusted ORs with 95% CI of the highest quartile for NAFLD were 2.24 (1.69–2.99) for females in the menstrual period, 1.92 (1.10–3.37) for females in the menopause transition period, and 1.47 (1.06–2.03) for females in postmenopause. Conclusions Menstrual status was significantly correlated with NAFLD. High levels of SUA were associated with NAFLD in females during the three menstrual periods.
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