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人体测量指标和代谢率与乳腺癌风险的关系(美国)

Anthropometric measures and metabolic rate in association with risk of breast cancer (United States).

作者信息

Freni S C, Eberhardt M S, Turturro A, Hine R J

机构信息

US Food and Drug Administration, National Center for Toxicological Research, Jefferson, AR, USA.

出版信息

Cancer Causes Control. 1996 May;7(3):358-65. doi: 10.1007/BF00052942.

Abstract

To investigate whether cancer risk-reduction seen in calorie-restricted animals also applies to breast cancer in women, we have analyzed data from the first National Health and Nutrition Examination Survey in the United States and subsequent follow-up surveys. During the follow-up of one to 155 months, 182 out of 7,622 women developed breast cancer. Due to biased under-reporting of dietary intake, the analysis did not examine calorie intake as an exposure variable, but rather focused on anthropometric measures and metabolic rate as biomarkers of nutritional balance. Multiple Cox regression analysis showed elevated odds ratios (OR) for height, elbow width, and skinfolds among postmenopausal women. ORs for the fifth quintile were 2.0 (95 percent confidence interval [CI] = 1.0-3.8), 2.3 (CI = 1.2-4.7), and 2.0 (CI = 1.0-4.0), respectively. Weight (OR = 2.5, CI = 1.2-5.1) and resting metabolic rate (OR = 2.0, CI = 1.0-4.0) were significant relative to the second quintile. Bitrochanteric breadth, sitting height, body fat, body mass index, or combination variables were not associated with cancer risk. It was concluded that in the analysis of breast cancer data, skeletal measures ought to be considered as routine potential confounders, and that using measured rather than estimated metabolic rates may improve risk prediction.

摘要

为了研究热量限制动物中所观察到的癌症风险降低情况是否也适用于女性乳腺癌,我们分析了美国首次全国健康与营养检查调查及后续随访调查的数据。在1至155个月的随访期间,7622名女性中有182人患乳腺癌。由于饮食摄入量报告存在偏差,该分析未将热量摄入作为暴露变量进行研究,而是将重点放在人体测量指标和代谢率上,将其作为营养平衡的生物标志物。多因素Cox回归分析显示,绝经后女性的身高、肘宽和皮褶厚度的优势比(OR)升高。第五分位数的OR分别为2.0(95%置信区间[CI]=1.0 - 3.8)、2.3(CI = 1.2 - 4.7)和2.0(CI = 1.0 - 4.0)。相对于第二分位数,体重(OR = 2.5,CI = 1.2 - 5.1)和静息代谢率(OR = 2.0,CI = 1.0 - 4.0)具有显著性。大转子间宽度、坐高、体脂、体重指数或综合变量与癌症风险无关。研究得出结论,在乳腺癌数据分析中,骨骼测量指标应被视为常规潜在混杂因素,并且使用实测而非估计的代谢率可能会改善风险预测。

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