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Lifetime Number of Ovulatory Cycles and Risks of Ovarian and Endometrial Cancer Among Postmenopausal Women

机译:绝经后妇女的终生排卵周期数和卵巢癌和子宫内膜癌的风险

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摘要

Previous studies have shown that a greater number of ovulatory cycles, cumulatively summed as lifetime number of ovulatory cycles (LOC), increases ovarian cancer risk, but there is no uniform algorithm with which to compute LOC. The association between LOC and endometrial cancer is less certain. Accordingly, we identified 14 different LOC algorithms in a literature review and calculated LOCs in the Polish Cancer Study (2001–2003). We evaluated the associations of LOC with ovarian and endometrial cancer risks using unconditional logistic regression, with and without adjustment for individual risk factors used in the LOC computations. Our analysis included 302 ovarian cancer cases with 1,356 controls and 532 endometrial cancer cases with 1,286 controls. We found a high correlation between LOC values among the combined controls (r ≥ 0.88) and identified 5 groups of similar LOC algorithms. A LOC value in the highest quartile was associated with ovarian cancer risk as computed by 2 algorithms (odds ratio (OR) = 2.22 (95% confidence interval (CI): 1.07, 4.62) and OR = 2.44 (95% CI: 1.22, 4.87)) and with endometrial cancer risk as computed by 1 algorithm (OR = 1.95, 95% CI: 1.11, 3.44). LOC algorithms using a core set of variables widely available in epidemiologic studies may be independently associated with risk of gynecological cancers beyond the contribution of the individual risk factors, such as ages at menopause and menarche.
机译:先前的研究表明,更多的排卵周期(累计为一生的排卵周期(LOC)数)会增加卵巢癌风险,但是目前尚无统一的算法来计算LOC。 LOC与子宫内膜癌之间的关联尚不确定。因此,我们在文献综述中确定了14种不同的LOC算法,并在“波兰癌症研究”(2001-2003年)中计算了LOC。我们使用无条件逻辑回归来评估LOC与卵巢癌和子宫内膜癌风险之间的关联,并在不调整LOC计算中使用的单个风险因素的情况下进行评估。我们的分析包括302例卵巢癌病例和1,356例对照,以及532例子宫内膜癌病例和1,286例对照。我们发现组合控件之间的LOC值之间存在高度相关性(r≥0.88),并确定了5组相似的LOC算法。根据2种算法计算,最高四分位数的LOC值与卵巢癌风险相关(比值比(OR)= 2.22(95%置信区间(CI):1.07、4.62)和OR = 2.44(95%CI:1.22, 4.87)),并采用1种算法计算得出的子宫内膜癌风险(OR = 1.95,95%CI:1.11,3.44)。使用流行病学研究中广泛使用的一组核心变量的LOC算法可能会与妇科癌症的风险独立相关,而不受诸如更年期和初潮年龄等单个风险因素的影响。

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