Kastrati Lum, Groothof Dion, Quezada-Pinedo Hugo G, Raeisi-Dehkordi Hamidreza, Bally Lia, De Borst Martin H, Bakker Stephan J L, Vidal Pedro-Marques, Eisenga Michele F, Muka Taulant
Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Bern, Switzerland; Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism UDEM, Inselspital, Bern University Hospital, University of Bern, Switzerland.
Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, 9713, GZ, Groningen, the Netherlands.
Maturitas. 2024 Jan;179:107872. doi: 10.1016/j.maturitas.2023.107872. Epub 2023 Nov 4.
To examine the association of iron biomarkers with menopausal status and assess whether these biomarkers can help differentiate menopausal status beyond age.
In this cross-sectional study we included 1679 women from the CoLaus and 2133 from the PREVEND cohorts, with CoLaus used as primary cohort and PREVEND for replication. Ferritin, transferrin, iron, and transferrin saturation (TSAT) were used to assess iron status. Hepcidin and soluble transferrin receptor were assessed only in PREVEND. Menopausal status was self-reported and defined as menopausal or non-menopausal. Logistic regressions were used to explore the association of these iron biomarkers with menopause status. Sensitivity, specificity, area under the receiver operating characteristic curves (AUC), positive and negative predictive values as well as cut-off points for the iron biomarkers were calculated. The model with the highest AUC was defined as the best.
In the CoLaus and PREVEND cohorts, respectively, 513 (30.6 %) and 988 (46.3 %) women were postmenopausal. Ferritin (OR, 2.20; 95 % CI 1.72-2.90), transferrin (OR, 0.03; 95 % CI 0.01-0.10), and TSAT (OR, 1.28; 95 % CI 1.06-1.54) were significantly associated with menopausal status in CoLaus, with the findings replicated in PREVEND. AUC of age alone was 0.971. The best model resulted from combining age, ferritin, and transferrin, with an AUC of 0.976, and sensitivity and specificity of 87.1 % and 96.5 %, respectively. Adding transferrin and ferritin to a model with age improved menopause classification by up to 7.5 %. In PREVEND, a model with age and hepcidin outperformed a model with age, ferritin, and transferrin.
Iron biomarkers were consistently associated with menopausal status in both cohorts, and modestly improved a model with age alone for differentiating menopause status. Our findings on hepcidin need replication.
研究铁生物标志物与绝经状态之间的关联，并评估这些生物标志物是否有助于在年龄之外区分绝经状态。
在这项横断面研究中，我们纳入了来自CoLaus队列的1679名女性和来自PREVEND队列的2133名女性，以CoLaus作为主要队列，PREVEND用于重复验证。使用铁蛋白、转铁蛋白、铁和转铁蛋白饱和度（TSAT）来评估铁状态。仅在PREVEND中评估了铁调素和可溶性转铁蛋白受体。绝经状态通过自我报告确定，分为绝经或未绝经。使用逻辑回归来探讨这些铁生物标志物与绝经状态之间的关联。计算了铁生物标志物的敏感性、特异性、受试者工作特征曲线下面积（AUC）、阳性和阴性预测值以及截断点。AUC最高的模型被定义为最佳模型。
在CoLaus和PREVEND队列中，分别有513名（30.6%）和988名（46.3%）女性处于绝经后状态。在CoLaus中，铁蛋白（比值比[OR]，2.20；95%置信区间[CI]1.72 - 2.90）、转铁蛋白（OR，0.03；95%CI 0.01 - 0.10）和TSAT（OR，1.28；95%CI 1.06 - 1.54）与绝经状态显著相关，这些结果在PREVEND中得到重复验证。仅年龄的AUC为0.971。最佳模型是将年龄、铁蛋白和转铁蛋白相结合，AUC为0.976，敏感性和特异性分别为87.1%和96.5%。在仅包含年龄的模型中加入转铁蛋白和铁蛋白可使绝经分类改善高达7.5%。在PREVEND中，包含年龄和铁调素的模型优于包含年龄、铁蛋白和转铁蛋白的模型。
在两个队列中，铁生物标志物均与绝经状态持续相关，并适度改善了仅基于年龄的区分绝经状态的模型。我们关于铁调素的研究结果需要重复验证。
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