A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome

Abstract

The present study aimed to explore the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predict an appropriate dosage schedule using a machine-learning approach. Data were obtained from literature mining and the rates of body weight change from the initial values were selected as the therapeutic index. The maximal effect (Emax) model was built up as the machine-learning model. A total of 242 patients with PCOS were included for analysis. In the machine-learning model, the Emax of carnitine supplementation on body weight was −3.92%, the ET50 was 3.6 weeks, and the treatment times to realize 25%, 50%, 75%, and 80% (plateau) Emax of carnitine supplementation on body weight were 1.2, 3.6, 10.8, and 14.4 weeks, respectively. In addition, no significant relationship of dose-response was found in the dosage range of carnitine supplementation used in the present study, indicating the lower limit of carnitine supplementation dosage, 250 mg/day, could be used as a suitable dosage. The present study first explored the effect of carnitine supplementation on body weight in patients with PCOS, and in order to realize the optimal therapeutic effect, carnitine supplementation needs 250 mg/day for at least 14.4 weeks.

Effects of dapagliflozin on body weight in patients with type 2 diabetes mellitus: Evidence‑based practice

“…When the OFV was increased (>6.63; χ 2 , α=0.01, d.f.=1), significance was achieved in the final model. Our previous studies were mainly based on the methodology used in the present study, and indicated that the present method was reliable and acceptable (32)(33)(34)(35).…”