Evaluation of the prognostic nutritional index (PNI) as a tool for assessing disease activity in rheumatoid arthritis patients

OBJECTIVES

Rheumatoid arthritis (RA) is an autoimmune disease of unknown exact cause, characterized by chronic inflammation. The prognostic nutritional index (PNI), reflecting albumin concentration and lymphocyte count, is a newly established inflammation-based nutritional score. This study aimed to determine the relationship between PNI and disease activity in RA patients.

PATIENTS AND METHODS

This cross-sectional study included 138 RA patients who met the 2010 revised criteria of the American College of Rheumatology (ACR) for RA. PNI was calculated using the following formula: 10 × serum albumin value (g/dL) + 0.005 × total lymphocyte count in the peripheral blood (per mm3). The study population was divided into two groups: DAS28-ESR ≤ 3.2 (group 1 with remission and low disease activity) and DAS28-ESR > 3.2 (group 2 with moderate and high disease activity).

RESULTS

A total of 138 patients with a mean age of 52.1 years were recruited. While the female gender was more prevalent in both groups, it was significantly higher in group 2 (p < 0.05). Group 2 exhibited a lower PNI compared to those in group 1 (42.17 ± 3.46 vs. 44.02 ± 2.92; p = 0.001). Multivariate logistic regression analyses revealed that PNI was an independent predictor of disease activity (OR, 0.850; 95% CI, 0.735-0.983; p = 0.029). ROC curve analysis determined that the optimal cutoff value of PNI for disease activity was 43.01, with a sensitivity of 69.1% and specificity of 57.7% (AUC, 0.66; 95% CI, 0.57-0.75, p = 0.001).

CONCLUSION

This study demonstrates that the simple and readily available PNI could serve as an independent predictor of disease activity in rheumatoid arthritis patients. Key Points •The relationship between disease activity and the prognostic nutritional index, which is a nutritional indicator, in rheumatoid arthritis patients was investigated. •It has been shown that there is a connection between low PNI and high disease activity. •It has been shown that PNI can be used to evaluate disease severity with a simple calculation.

The role of smart devices and mobile application on the change in peak VO2 in patients with high cardiovascular risk: a sub-study of the LIGHT randomised clinical trial

BACKGROUND

This investigation aims to assess the influence of a mobile application and smart devices on cardiopulmonary exercise testing (CPET) over a one-year period in individuals who have high risk for cardiovascular disease.

METHODS

This is a post-hoc subgroup analysis of Lifestyle Intervention Using Mobile Technology in Patients with High Cardiovascular Risk: A Pragmatic Randomised Clinical Trial (LIGHT). In the intervention plus standard care standard standard care arms, 138 and 103 patients were recruited, respectively. The 1-year VO2 measurements were adjusted to the baseline VO2 measurements as the study's endpoint. VO2 measurements were taken for each subject during the randomisation and final CPET examinations.

RESULTS

The intervention plus standard care improved VO2 measurements by 1.1 (adjusted treatment effect 1.1, 95% confidence interval (CI): 0.8, 1.4, p < 0.001) compared to standard care following 1-year follow-up.

CONCLUSION

At a 1-year follow-up, the smart device and mobile application technologies increased VO2 measurements in individuals with high cardiovascular risk compared to conventional treatment alone.

Effect of a mobile application and smart devices on heart rate variability in diabetic patients with high cardiovascular risk: A sub-study of the LIGHT randomized clinical trial

BACKGROUND

This investigation aims to evaluate the effect of a mobile application and smart devices on frequency and time domains of heart rate variability (HRV) in diabetic patients in 1-year follow-up.

METHODS

This is post-hoc analysis of a diabetic subgroup of "Lifestyle Intervention usinG mobile technology in patients with high cardiovascular risk: a pragmatic randomized clinical Trial" (LIGHT). One hundred and nine and 118 patients were enrolled in two arms: the intervention plus usual care and the usual care arm. The study outcome was the 1-year HRV parameters adjusted to the baseline HRV parameters. HRV measures were recorded for every patient at the randomization and final visits with 24-hour Holter monitoring.

RESULTS

There was an improvement in the standard deviation of normal to normal (SDNN) R-R intervals 24-hour by 4.8 (adjusted treatment effect 4.8, 95% confidence interval [CI], 0.1-9.5; P = 0.044) in the intervention-plus-usual-care arm compared to usual care after a 1-year follow-up. The improvement was also experienced in other HRV time domains including standard deviation of the mean R-R intervals calculated over a 5-minute period, SDNN, square root of the mean squared difference of successive R-R intervals, and the percentage of the differences between adjacent normal R-R intervals exceeding 50 milliseconds. A significant enhancement was also detected in HRV frequency domains of total power low frequency and high frequency in the intervention plus usual care compared to usual care after a 1-year follow-up.

CONCLUSIONS

The mobile application and smart device technology compared to usual care alone improved HRV parameters in diabetic patients at 1-year follow-up.