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The use of medicinal plant extracts and their isolated bioactive compounds for the management of diabetes mellitus has been tremendously increased in recent decades. The present study aimed at providing in-depth information on medicinal flora that has been widely used in the Sri Lankan traditional healthcare system for the management of diabetes mellitus. The data of this review article were obtained from published articles from January 2000 to September 2020 in scientific databases of PubMed, Web of Science, and Google Scholar. In this review, a total number of 18 medicinal plants with the antidiabetic activity were expressed, and their isolated antidiabetic active compounds were highlighted as new drug leads. Results of the reported studies revealed that medicinal plants exert a potent antidiabetic activity via both in vitro and in vivo study settings. However, bioactive compounds and antidiabetic mechanism (s) of action of many of the reported medicinal plants have not been isolated/elucidated the structure in detail, to date. Reported antidiabetic medicinal plants with other properties such as antioxidant and antihyperlipidemic activities deliver new entities for the development of antidiabetic agents with multiple therapeutic targets. This is a comprehensive review on potential antidiabetic activities of the Sri Lankan medicinal plants that have been widely used in the traditional healthcare system. The information presented here would fill the gap between the use of them by traditional healers in the traditional medicine healthcare system in Sri Lanka and their potency for development of new drug entities in future.

Background Insulin resistance (IR) has been considered as a therapeutic target in the management of type 2 diabetes mellitus (T2DM). Readily available, simple and low cost measures to identify individuals with IR is of utmost importance for clinicians to plan optimal management strategies. Research on the associations between surrogate markers of IR and routine clinical and lipid parameters have not been carried out in Sri Lanka, a developing country with rising burden of T2DM with inadequate resources. Therefore, we aimed to study the utility of readily available clinical parameters such as age, body mass index (BMI), waist circumference (WC) and triglyceride to high density lipoprotein cholesterol ratio (TG/HDL-C) in the fasting lipid profile in predicting IR in a cohort of patients with newly diagnosed T2DM in Sri Lanka. Methods and findings We conducted a community based cross sectional study involving of 147 patients (age 30–60 years) with newly diagnosed T2DM in a suburban locality in Galle district, Sri Lanka. Data on age, BMI, WC, fasting plasma glucose (FPG) concentration, fasting insulin concentration and serum lipid profile were collected from each subject. The indirect IR indices namely homeostasis model assessment (HOMA), quantitative insulin sensitivity check index (QUICKI) and McAuley index (MCA) were estimated. Both clinical and biochemical parameters across the lowest and the highest fasting insulin quartiles were compared using independent sample t-test. Linear correlation analysis was performed to assess the correlation between selected clinical parameters and indirect IR indices. The area under the receiver operating characteristic (ROC) curve was obtained to calculate optimal cut-off values for the clinical markers to differentiate IR. BMI (p<0.001) and WC (p = 0.01) were significantly increased whereas age (p = 0.06) was decreased and TG/HDL-C (p = 0.28) was increased across the insulin quartiles. BMI and WC were significantly correlated (p<0.05) with HOMA, QUICKI and MCA. Out of the clinical parameters, age showed a borderline significant correlation with QUICKI and TG/HDL-C showed a significant correlation only with MCA. The area under ROC of BMI was 0.728 (95% CI 0.648–0.809; p<0.001) and for WC, it was 0.646 (95% CI 0.559–0.734; p = 0.003). The optimized cut-off value for BMI and WC were 24.91 kg/m2 and 81.5 cm respectively to differentiate the patients with IR or ID. Study limitations include small sample size due to recruitment of patients only from a limited geographical locality of the country and not totally excluding of the possibility of inclusion of some patients with slowly progressive type 1 DM or Latent onset diabetes of adulthood from the study population. Conclusions The results revealed that there was a significant positive correlation between BMI, WC and HOMA while a significant negative correlation with QUICKI and MCA among the cohort of patients with newly diagnosed T2DM. The cut-off values of BMI and WC as 24.91 kg/m2 and 81.5 cm respectively could be used as simple clinical parameters to identify IR in newly diagnosed patients with T2DM. Our results could be beneficial in rational decision making in the management of newly diagnosed patients with T2DM in limited resource settings.

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