The research has been led by Dr. Sohini Sengupta and her team of experts after analyzing nearly 2,000 samples.
India, September 19th, 2023: Redcliffe Labs, one of India’s fastest-growing omnichannel diagnostics service providers, announces groundbreaking achievement in women’s health research led by Dr. Sohini Sengupta, the Medical Laboratory Director. It is the first-ever study on Indian women for establishing age-specific reference intervals for Anti-Mullerian Hormone (AMH) and defining an AMH diagnostic cut-off for Polycystic Ovarian Syndrome (PCOS). The results will facilitate diagnosis accuracy and treatment of PCOS, a condition that affects approximately 11.34% of women in India.
Dr. Sohini Sengupta and her team of experts meticulously analyzed nearly 2,000 samples obtained from Indian women. The study suggests the optimal cut-off point of AMH for PCOS diagnosis in the Indian female population is 7.51ng/ml. With an impressive sensitivity of 99.4% and specificity of 95.5%, this diagnostic cut-off provides a highly accurate tool for healthcare professionals to swiftly and accurately identify PCOS in their patients. This innovative approach holds the potential to expedite PCOS diagnoses and facilitate timely interventions and treatments.
Healthcare providers in India have relied on AMH reference intervals and diagnostic thresholds derived from data collected from Caucasian women for years. Redcliffe Lab’s research is tailored specifically to the Indian female population and will provide healthcare professionals with a robust tool for better management and treatment of PCOS. This pioneering research has been published in the renowned International Journal of Reproduction, Contraception, Obstetrics, and Gynecology (IJRCOG).
Dr. Sohini Sengupta, Medical Laboratory Director at Redcliffe Labs, said, “Our findings represent a significant leap toward population-specific healthcare in India. Not only will they facilitate diagnosis and treatment of PCOS, but they also lay the groundwork for future research focused on localized medical data.”