Innovative Statistical Model Uncover Effective Herbal Medicines Among Personalized Treatment Plans in Persian Medicine: A Small-Scale Study in Type 2 Diabetes.

Abstract

Objectives: In holistic medicine, developing personalized treatment plans is challenging due to the multitude of possible therapy combinations. This study introduces the use of a statistical approach to identify the most effective herbal medicines prescribed in Persian medicine (PM) in a small-scale sample of patients with type 2 diabetes mellitus (T2DM). Methods: This prospective observational cohort study was conducted with 47 patients with T2DM referred to Behesht Clinic in Tehran, Iran. A physician prescribed individualized PM treatment for T2DM and related systemic issues. The fasting blood sugar (FBS) level of each patient was recorded at initial and two follow-up visits, with visit intervals and treatment modifications determined by patient health status. Patients who completed two follow-up visits were included in the final analysis (n = 27). Data were analyzed using R software. A general linear model was assumed for the mean response, along with an exponential covariance pattern model, to manage irregularly timed measurements. Results: Two fitted models showed that, after adjusting for confounders, the use of the "Diabetes Capsule" significantly reduced the average FBS by 17.14 mmol/L (p = 0.046). For each unit increase in the consumption of "Diabetes Capsule" or "Hab-e-Amber Momiai," the average FBS decreased by 15.22 mmol/L (p = 0.015) and 14.14 mmol/L (p = 0.047), respectively. Conclusion: It is possible to observe which medications are most effective, even when treatments are applied in a holistic and personalized fashion. Preliminary studies such as these may identify promising products for testing in clinical trials conducted under standardized conditions, to inform initial choices for future personalized treatments.