RESUMEN
PURPOSE: Evidence-based guidelines for the management of polycystic ovary syndrome (PCOS) recommend clinical laboratories use liquid chromatography-tandem mass spectrometry (LC-MS/MS) for diagnosing biochemical hyperandrogenism. However, automated immunoassays are still mostly used in routine laboratories worldwide. Another hurdle for PCOS phenotyping in the clinical setting is ultrasound assessment of polycystic ovarian morphology. We address the impact of using state-of-the-art (LC-MS/MS) and of an anti-müllerian hormone (AMH) assay on the diagnosis of PCOS in routine practice. METHODS: In a cross-sectional study, we included 359 premenopausal women consecutively evaluated because of symptoms of functional androgen excess or hyperandrogenemia, and finally diagnosed with PCOS. Patients were submitted to routine phenotyping based on serum androgen measurements by immunoassays and an ovarian ultrasound when necessary. Samples of all patients were also assayed by LC-MS/MS for hyperandrogenemia and for circulating AMH. RESULTS: The observed agreement between immunoassays and LC-MS/MS in identifying hyperandrogenemia was poor [78.0%; k(95%CI): 0.366 (0.283;0.449)]. The observed agreement between ultrasound and increased AMH was 27.3% [(95%CI): 0.060 (0.005; 0.115)]. Using LC-MS/MS changed PCOS phenotypes in 60(15.8%) patients. Fifty-two (18.3%) individuals with hyperandrogenemia by routine immunoassays no longer presented with androgen excess by LC-MS/MS. Overall diagnostic agreement between routine assessment using immunoassays and ultrasound and that derived from LC-MS/MS and the addition of AMH to US was moderate [weighted κ (linear weights): 0.512 (0.416;0.608)]. CONCLUSIONS: Immunoassays used in routine practice are unacceptably inaccurate for phenotyping women with PCOS. Our data cast some doubts upon the interchangeability of serum AMH and ultrasound examination for the diagnosis of PCOS.
RESUMEN
AIMS: To assess if advanced characterization of serum glycoprotein and lipoprotein profile, measured by proton nuclear magnetic resonance spectroscopy (1H-NMRS) improves a predictive clinical model of cardioautonomic neuropathy (CAN) in subjects with type 1 diabetes (T1D). METHODS: Cross-sectional study (ClinicalTrials.gov Identifier: NCT04950634). CAN was diagnosed using Ewing's score. Advanced characterization of macromolecular complexes including glycoprotein and lipoprotein profiles in serum samples were measured by 1H-NMRS. We addressed the relationships between these biomarkers and CAN using correlation and regression analyses. Diagnostic performance was assessed by analyzing their areas under the receiver operating characteristic curves (AUCROC). RESULTS: Three hundred and twenty-three patients were included (46% female, mean age and duration of diabetes of 41 ± 13 years and 19 ± 11 years, respectively). The overall prevalence of CAN was 28% [95% confidence interval (95%CI): 23; 33]. Glycoproteins such as N-acetylglucosamine/galactosamine and sialic acid showed strong correlations with inflammatory markers such as high-sensitive C-reactive protein, fibrinogen, IL-10, IL-6, and TNF-α. On the contrary, we did not find any association between the former and CAN. A stepwise binary logistic regression model (R2 = 0.078; P = 0.003) retained intermediate-density lipoprotein-triglycerides (IDL-TG) [ß:0.082 (95%CI: 0.005; 0.160); P = 0.039], high-density lipoprotein-triglycerides (HDL-TGL)/HDL-Cholesterol [ß:3.633 (95%CI: 0.873; 6.394); P = 0.010], and large-HDL particle number [ß: 3.710 (95%CI: 0.677; 6.744); P = 0.001] as statistically significant determinants of CAN. Adding these lipoprotein particles to a clinical prediction model of CAN that included age, duration of diabetes, and A1c enhanced its diagnostic performance, improving AUCROC from 0.546 (95%CI: 0.404; 0.688) to 0.728 (95%CI: 0.616; 0.840). CONCLUSIONS: When added to clinical variables, 1H-NMRS-lipoprotein particle profiles may be helpful to identify those patients with T1D at risk of CAN.