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女性尿中类固醇谱提示多囊卵巢综合征的诊断特征:一项考虑被忽视类固醇代谢物的初步研究。

Urinary steroid profiling in women hints at a diagnostic signature of the polycystic ovary syndrome: A pilot study considering neglected steroid metabolites.

机构信息

Department of Nephrology and Hypertension and Department of BioMedical Research, Inselspital, University Hospital, University of Bern, Bern, Switzerland.

Pediatric Endocrinology and Diabetology, Department of Pediatrics and Department of BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

出版信息

PLoS One. 2018 Oct 11;13(10):e0203903. doi: 10.1371/journal.pone.0203903. eCollection 2018.

DOI:10.1371/journal.pone.0203903
PMID:30308019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6181287/
Abstract

BACKGROUND

Although the polycystic ovary syndrome (PCOS) is the most common endocrine disorder in women with vast metabolic consequences, its etiology remains unknown and its diagnosis is still made by exclusion. This study aimed at characterizing a large number of urinary steroid hormone metabolites and enzyme activities in women with and without PCOS in order to test their value for diagnosing PCOS.

METHODS

Comparative steroid profiling of 24h urine collections using an established in-house gas-chromatography mass spectrometry method. Data were collected mostly prospectively. Patients were recruited in university hospitals in Switzerland. Participants were 41 women diagnosed with PCOS according to the current criteria of the Androgen Excess and PCOS Society Task Force and 66 healthy controls. Steroid profiles of women with PCOS were compared to healthy controls for absolute metabolite excretion and for substrate to product conversion ratios. The AUC for over 1.5 million combinations of metabolites was calculated in order to maximize the diagnostic accuracy in patients with PCOS. Sensitivity, specificity, PPV, and NPV were indicated for the best combinations containing 2, 3 or 4 steroid metabolites.

RESULTS

The best single discriminating steroid was androstanediol. The best combination to diagnose PCOS contained four of the forty measured metabolites, namely androstanediol, estriol, cortisol and 20βDHcortisone with AUC 0.961 (95% CI 0.926 to 0.995), sensitivity 90.2% (95% CI 76.9 to 97.3), specificity 90.8% (95% CI 81.0 to 96.5), PPV 86.0% (95% CI 72.1 to 94.7), and NPV 93.7% (95% CI 84.5 to 98.2).

CONCLUSION

PCOS shows a specific 24h urinary steroid profile, if neglected metabolites are included in the analysis and non-conventional data analysis applied. PCOS does not share a profile with hyperandrogenic forms of congenital adrenal hyperplasias due to single steroid enzyme deficiencies. Thus PCOS diagnosis by exclusion may no longer be warranted. Whether these findings also apply to spot urine and serum, remains to be tested as a next step towards routine clinical applicability.

摘要

背景

多囊卵巢综合征(PCOS)是女性最常见的内分泌紊乱疾病,其具有广泛的代谢后果,但病因仍不清楚,其诊断仍然是通过排除法进行的。本研究旨在对患有和不患有 PCOS 的女性的大量尿甾体激素代谢物和酶活性进行特征描述,以测试其对 PCOS 的诊断价值。

方法

使用已建立的内部气相色谱-质谱法对 24 小时尿液采集进行比较甾体谱分析。数据主要是前瞻性收集的。患者在瑞士的大学医院招募。参与者包括 41 名根据雄激素过多和 PCOS 协会工作组的当前标准诊断为 PCOS 的女性和 66 名健康对照者。将 PCOS 患者的甾体谱与健康对照者进行比较,以比较代谢产物的绝对排泄量和底物与产物的转化比。计算了超过 150 万个代谢物组合的 AUC,以最大限度地提高 PCOS 患者的诊断准确性。为包含 2、3 或 4 种甾体代谢物的最佳组合提供了灵敏度、特异性、PPV 和 NPV。

结果

最佳的单一鉴别甾体是雄烷二醇。诊断 PCOS 的最佳组合包含四十种测量代谢物中的四种,即雄烷二醇、雌三醇、皮质醇和 20βDH 皮质酮,AUC 为 0.961(95%CI 0.926 至 0.995),灵敏度为 90.2%(95%CI 76.9 至 97.3),特异性为 90.8%(95%CI 81.0 至 96.5),PPV 为 86.0%(95%CI 72.1 至 94.7),NPV 为 93.7%(95%CI 84.5 至 98.2)。

结论

如果在分析中忽略代谢物并应用非传统数据分析,则 PCOS 显示出特定的 24 小时尿甾体谱。由于单一甾体酶缺乏,PCOS 与先天性肾上腺增生的高雄激素形式不同,因此排除 PCOS 的诊断可能不再必要。这些发现是否也适用于点尿和血清,仍有待进一步测试,以迈向常规临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e5/6181287/f3640836ad0f/pone.0203903.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e5/6181287/ca389035ae76/pone.0203903.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e5/6181287/f148746133ed/pone.0203903.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e5/6181287/f3640836ad0f/pone.0203903.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e5/6181287/ca389035ae76/pone.0203903.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e5/6181287/f148746133ed/pone.0203903.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e5/6181287/f3640836ad0f/pone.0203903.g003.jpg

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