Body Fat Area as a Predictive Marker of New-Onset Diabetes Mellitus After Kidney Transplantation

First Testing of Literature-Based Models for Predicting Increase in Body Weight and Adipose Tissue Mass After Kidney Transplantation

Introduction: Weight gain is a risk factor for poor clinical outcomes following kidney transplantation. Research Question: This study's aim was a first testing of 2 models to identify patients early after kidney transplantation who are at risk for weight gain and increase in adipose tissue mass in the first year after kidney transplantation. Design: The literature-based models were evaluated on longitudinal data of 88, respectively 79 kidney transplant recipients via ordinary and Firth regression, using gains ≥ 5% in weight and adipose tissue mass respectively as primary and secondary endpoints. Results: The models included physical activity, smoking cessation at time of kidney transplantation, self-reported health status, depressive symptomatology, gender, age, education, baseline body mass index and baseline trunk fat as predictors. Area under the curve was 0.797 (95%-CI 0.702 to 0.893) for the weight model and 0.767 (95%-CI 0.656 to 0.878) for the adipose tissue mass model-showing good, respectively fair discriminative ability. For weight gain ≥ 5%, main risk factors were smoking cessation at time of transplantation (OR 16.425, 95%-CI 1.737-155.288) and better self-reported baseline health state (OR 1.068 for each 1-unit increase, 95%-CI 1.012-1.128). For the adipose tissue mass gain ≥ 5%, main risk factor was overweight/obesity (BMI ≥ 25) at baseline (odds ratio 7.659, 95%-CI 1.789-32.789). Conclusions: The models have potential to assess patients' risk for weight or adipose tissue mass gain during the year after transplantation, but further testing is needed before implementation in clinical practice.

LASSO-derived nomogram predicting new-onset diabetes mellitus in patients with kidney disease receiving immunosuppressive drugs

WHAT IS KNOWN AND OBJECTIVE

Patients with kidney disease receiving immunosuppressive drugs (ISDs) (tacrolimus, cyclosporine and glucocorticoids) have a high risk of developing new-onset diabetes mellitus (NODM). We aimed to establish a precise and convenient model for predicting NODM in patients receiving immunosuppressive drugs.

METHODS

This retrospective study recruited 1883 patients receiving ISDs between January 2010 and October 2018. The occurrence of NODM was the primary endpoint. The patients were randomly divided into training (n = 1318) and validation cohorts (n = 565) at a 7:3 ratio. A nomogram was established based on a least absolute shrinkage and selection operator (LASSO)-derived logistic regression model. The nomogram's discrimination and calibration abilities were evaluated in both cohorts using the Hosmer-Lemeshow test and calibration curves. Decision curve analysis (DCA) was used to evaluate the net benefit of the predictive efficacy.

RESULTS AND DISCUSSION

Amongst the 1883 patients with kidney disease receiving immunosuppressive drugs, 375 (28.5%) and 169 (29.9%) developed NODM in the training (n = 1318) and validation cohorts (n = 565), respectively. Nine clinic predictors were included in this LASSO-derived nomogram, which is easy to be operated clinically. The discriminative ability, determined by the area under the receiver operating characteristic curve (AUC), was 0.816 (95% confidence interval [CI] 0.790-0.841) and 0.831 (95%CI 0.796-0.867) in the training and validation cohorts, respectively. Calibration was confirmed with the Hosmer-Lemeshow test in the training and validation cohorts (p = 0.238, p = 0.751, respectively).

WHAT IS NEW AND CONCLUSION

Nearly one-third of patients with kidney disease receiving immunosuppressive drugs developed NODM. The nomogram established in this study may aid in predicting the occurrence of NODM in patients with kidney disease receiving immunosuppressive drugs.