ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Endocrine Abstracts (2019) 63 GP143 | DOI: 10.1530/endoabs.63.GP143

Impact of age, menstrual phase, menopausal status and metabolic profile on the definition of 12 circulating steroid reference intervals measured by LC-MS/MS in an Italian healthy adult female cohort

Marco Mezzullo, Guido Di Dalmazi, Alessia Fazzini, Daniela Ibarra Gasparini, Alessandra Gambineri, Uberto Pagotto & Flaminia Fanelli

Endocrinology Unit, Department of Medical and Surgical Sciences, Centre for Applied Biomedical Research (C.R.B.A.), S. Orsola-Malpighi Hospital, Alma Mater University of Bologna, Bologna, Italy.

Endocrine clinical practice strongly relies on normative data for the assessment of hormonal imbalance. However, the generation of robust steroids reference intervals (RIs) remains a challenge. As the steroids circulating levels are influenced by many procedural aspects related to blood collection (time, fasting state) and physio-pathological factors, such as age, sex, stress and metabolic status, the need for partitioning RIs is mandatory. Furthermore, unbiased healthy, drug-free cohorts for RI generation are scarce. After clinical examination we selected 333 drug and disease-free female volunteers aged 18-90y, with no signs of hyperandrogenism. Pre-menopausal women displayed regular menses. Blood withdrawal was standardized early in the morning, in fasting conditions and by 10min saline infusion. The steroid profile including cortisol (F) 11-deoxycortisol (11S), 17-hydroxyprogesterone (17OHP4), 17-hydroxypregnenolone (17OHP5), testosterone (T), androstenedione (A4), dheidroepiandrosterone (DHEA), dihydrotestosterone (DHT), progesterone (P4), corticosterone (B), estrone (E1) and estradiol (E2), was determined by two in-house LC-MS/MS methods previously validated by Certified Reference Materials and ring trials. We evaluated the impact of anthropometric and metabolic parameters over each steroid level by multiple stepwise regression. Then, we computed age-specific RIs for steroids impacted by age by fractional polynomial regression. To further refine RIs for steroid affected by metabolic parameters, we excluded subjects displaying abnormalities among BMI≥25.0kg/m2 and waist circumference >88 cm, systolic blood pressure≥140mmHg and/or diastolic blood pressure≥90mmHg, HOMA-IR>2.5, total-cholesterol/HDL-cholesterol>5 and triglycerides >150mg/dL. With the exception of F, 11S and B, aging was found in negative relationship with 17OHP4, 17HOP5, T, A4, DHEA, DHT, P4, E1 and E2 (all P<0.0001). BMI positively associated with E1 (P=0.002) while negatively associated with DHT (P=0.048). Waist circumference negatively associated with F, 11S and B (all P<0.0001). Triglycerides positively associated with F (P=0.001), 11S (P=0.007) and B (P=0.004), while negatively associated with E1 (P=0.019) and E2 (P=0.036). Finally, hypertension positively associated with 11S (P=0.032). According to regression results, we generated age-specific RIs on the entire cohort for A4, DHEA, T and 17OHP5. Age-independent RIs for F, 11S and B were computed by excluding subjects displaying metabolic abnormalities. Moreover, we applied additional partitioning considering the menstrual phase and menopausal status for 17OHP4, E1, E2, T and DHT. We provided LC-MS/MS-based, age, menstrual phase and metabolism-adjusted RIs fit for each steroid in a broad panel. The generated RIs represent a valuable tool in diagnostic work-up of steroidogenesis imbalances for women throughout the adult life, also providing insights into neglected steroid precursors and intermediates.