Association of urinary albumin excretion with all-cause and cardiovascular mortality among patients with rheumatoid arthritis: a national prospective study.

Background: Rheumatoid arthritis (RA) patients suffering from chronic renal insufficiency tend to exhibit subtle manifestations at the beginning. Urine albumin to creatinine ratio (ACR) is a sensitive indicator for early assessment of renal function. However, it is unclear whether it serves as an independent risk factor influencing the prognosis of RA patients. Methods: National Health and Nutrition Examination Survey (NHANES) data from 2009-2018 were included. Kaplan-Meier (K-M) curves were plotted to compare the cumulative survival probability of RA patients with different urinary albumin excretion. The association of ACR with mortality among RA patients was investigated with Cox regression model, restricted cubic spline (RCS) and stratified analyses. The prognostic efficacy of ACR and estimated glomerular filtration rate (eGFR) was evaluated by receiver operating characteristic (ROC) curves. Results: The Cox regression model adjusted with covariates showed a 53% (HR 1.53, 95% CI 1.06-2.21) increase in all-cause mortality and a statistically non-significant increase in cardiovascular disease (CVD) mortality in RA patients with microalbuminuria (30mg/g ≤ACR<300mg/g). ACR≥300mg/g was associated with an increase in all-cause mortality (HR 2.62, 95% CI 1.55-4.45) and CVD mortality (HR 5.67, 95% CI 1.96-16.39). RCS demonstrated a nonlinear correlation between ACR and all-cause mortality in RA patients with microalbuminuria. Subgroup analysis showed that CVD mortality was higher in RA patients with microalbuminuria characterized by the following features: female, other ethnicity, eGFR≥60 ml/min/1.73 m2, hypertension or hyperlipidemia. Compared with eGFR, ACR provided better prognostic efficacy than eGFR with higher values of the area under the curve (AUC) for all-cause mortality (AUC=0.683, 95% CI 0.613-0.754) and CVD mortality (AUC=0.681, 95% CI 0.541-0.820). Conclusion: ACR is an independent risk factor affecting the prognosis of RA patients. The all-cause mortality was increased in RA patients with albuminuria. There was an upward trend in the CVD mortality of those with macroalbuminuria when ACR increased.

The promise of Synovial Joint-on-a-Chip in rheumatoid arthritis.

Rheumatoid arthritis (RA) affects millions of people worldwide, but there are limited drugs available to treat it, so acquiring a more comprehensive comprehension of the underlying reasons and mechanisms behind inflammation is crucial, as well as developing novel therapeutic approaches to manage it and mitigate or forestall associated harm. It is evident that current in vitro models cannot faithfully replicate all aspects of joint diseases, which makes them ineffective as tools for disease research and drug testing. Organ-on-a-chip (OoC) technology is an innovative platform that can mimic the microenvironment and physiological state of living tissues more realistically than traditional methods by simulating the spatial arrangement of cells and interorgan communication. This technology allows for the precise control of fluid flow, nutrient exchange, and the transmission of physicochemical signals, such as bioelectrical, mechanical stimulation and shear force. In addition, the integration of cutting-edge technologies like sensors, 3D printing, and artificial intelligence enhances the capabilities of these models. Here, we delve into OoC models with a particular focus on Synovial Joints-on-a-Chip, where we outline their structure and function, highlighting the potential of the model to advance our understanding of RA. We integrate the actual evidence regarding various OoC models and their possible integration for multisystem disease study in RA research for the first time and introduce the prospects and opportunities of the chip in RA etiology and pathological mechanism research, drug research, disease prevention and human precision medicine. Although many challenges remain, OoC holds great promise as an in vitro model that approaches physiology and dynamics.

The U-shape relationship between insulin resistance-related indexes and chronic kidney disease: a retrospective cohort study from National Health and Nutrition Examination Survey 2007-2016.

BACKGROUND: There is ongoing debate on the correlation between chronic kidney disease (CKD) and insulin resistance (IR)-related indices. Our objective was to explore the prognostic ability of IR-related indexes for the prevalence of CKD, as well as the mortality from all causes and cardiovascular disease (CVD) in CKD patients. METHODS: The data used in this study came from the National Health and Nutrition Examination Survey (NHANES). Binary logistic regression analysis, Cox proportional hazards model, and restricted cubic spline (RCS) were used to analyze the relationship between IR-related indexes, including metabolic score of IR (METS-IR), homeostatic model assessment for IR (HOMA-IR), triglyceride glucose index (TyG), triglyceride glucose-waist-to-height ratio (TyG-WHtR), triglyceride glucose-body mass index (TyG-BMI), with CKD and its all-cause mortality and CVD mortality. Subgroup analysis was performed to test the stability of the results. Finally, the predictive power of IR-related indexes for CKD was tested by the receiver operating characteristic (ROC) curve. RESULTS: Among the recruited 10,660 participants, 15.42% were CKD patients. All IR-related indexes were found to be nonlinearly correlated to the prevalence of CKD in the study. When the TyG index was higher than 9.05, it was positively associated with CKD (OR: 1.77, 95% CI 1.44-2.18). Moreover, increased TyG-WHtR level was correlated with a greater prevalence of CKD when it was higher than 4.3 (OR: 1.31, 95% CI 1.19-1.45). Other IR-related indexes (METS-IR, HOMA-IR, and TyG-BMI) showed fewer notable correlations with CKD. The association of IR-related indexes and the prevalence of CKD remained consistent in most subgroups (P for interactions > 0.05). TyG-WHtR was also the predictor of all-cause mortality in CKD patients (HR: 1.34, 95% CI 1.14-1.58), while other IR-related indexes were not correlated with the all-cause mortality or CVD mortality in CKD patients (P > 0.05). Otherwise, ROC curves showed that TyG-WHtR had more robust diagnostic efficacy than other IR-related indexes (METS-IR, HOMA-IR, TyG, and TyG-BMI) in predicting CKD (area under the curve: 0.630, 95% CI 0.615-0.644). CONCLUSIONS: IR-related biomarkers (METS-IR, HOMA-IR, TyG, and TyG-BMI) were positively correlated with the prevalence of CKD. Moreover, TyG-WHtR enhanced CKD and its all-cause mortality prediction. In patients with elevated levels of IR-related indexes, the early detection and intervention of IR may reduce the occurrence of CKD and the prognosis of CKD patients.