人体测量指标及其与2型糖尿病发病的关联:哪种指标最适合预测?四项德国基于人群的队列研究的汇总分析及与一项全国性队列研究的比较。
Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study.
作者信息
Hartwig Saskia, Kluttig Alexander, Tiller Daniel, Fricke Julia, Müller Grit, Schipf Sabine, Völzke Henry, Schunk Michaela, Meisinger Christa, Schienkiewitz Anja, Heidemann Christin, Moebus Susanne, Pechlivanis Sonali, Werdan Karl, Kuss Oliver, Tamayo Teresa, Haerting Johannes, Greiser Karin Halina
机构信息
Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany.
出版信息
BMJ Open. 2016 Jan 20;6(1):e009266. doi: 10.1136/bmjopen-2015-009266.
OBJECTIVE
To compare the association between different anthropometric measurements and incident type 2 diabetes mellitus (T2DM) and to assess their predictive ability in different regions of Germany.
METHODS
Data of 10,258 participants from 4 prospective population-based cohorts were pooled to assess the association of body weight, body mass index (BMI), waist circumference (WC), waist-to-hip-ratio (WHR) and waist-to-height-ratio (WHtR) with incident T2DM by calculating HRs of the crude, adjusted and standardised markers, as well as providing receiver operator characteristic (ROC) curves. Differences between HRs and ROCs for the different anthropometric markers were calculated to compare their predictive ability. In addition, data of 3105 participants from the nationwide survey were analysed separately using the same methods to provide a nationally representative comparison.
RESULTS
Strong associations were found for each anthropometric marker and incidence of T2DM. Among the standardised anthropometric measures, we found the strongest effect on incident T2DM for WC and WHtR in the pooled sample (HR for 1 SD difference in WC 1.97, 95% CI 1.75 to 2.22, HR for WHtR 1.93, 95% CI 1.71 to 2.17 in women) and in female DEGS participants (HR for WC 2.24, 95% CI 1.91 to 2.63, HR for WHtR 2.10, 95% CI 1.81 to 2.44), whereas the strongest association in men was found for WHR among DEGS participants (HR 2.29, 95% CI 1.89 to 2.78). ROC analysis showed WHtR to be the strongest predictor for incident T2DM. Differences in HR and ROCs between the different markers confirmed WC and WHtR to be the best predictors of incident T2DM. Findings were consistent across study regions and age groups (<65 vs ≥ 65 years).
CONCLUSIONS
We found stronger associations between anthropometric markers that reflect abdominal obesity (ie, WC and WHtR) and incident T2DM than for BMI and weight. The use of these measurements in risk prediction should be encouraged.
目的
比较不同人体测量指标与2型糖尿病(T2DM)发病之间的关联,并评估它们在德国不同地区的预测能力。
方法
汇总来自4个基于人群的前瞻性队列研究的10258名参与者的数据,通过计算粗标记、调整后标记和标准化标记的风险比(HR),以及绘制受试者工作特征(ROC)曲线,来评估体重、体重指数(BMI)、腰围(WC)、腰臀比(WHR)和腰高比(WHtR)与T2DM发病之间的关联。计算不同人体测量指标的HR和ROC之间的差异,以比较它们的预测能力。此外,使用相同方法对来自全国性调查的3105名参与者的数据进行单独分析,以提供具有全国代表性的比较。
结果
发现每种人体测量指标与T2DM发病率之间均存在强关联。在标准化人体测量指标中,我们发现合并样本中WC和WHtR对T2DM发病的影响最强(WC每增加1个标准差,HR为1.97,95%置信区间为1.75至2.22;女性WHtR每增加1个标准差,HR为1.93,95%置信区间为1.71至2.17),在德国国民健康访谈与体检调查(DEGS)的女性参与者中也是如此(WC的HR为2.24,95%置信区间为1.91至2.63;WHtR的HR为2.10,95%置信区间为1.81至2.44),而在DEGS的男性参与者中,WHR与T2DM发病的关联最强(HR为2.29,95%置信区间为1.89至2.78)。ROC分析表明,WHtR是T2DM发病的最强预测指标。不同指标之间HR和ROC的差异证实,WC和WHtR是T2DM发病的最佳预测指标。研究结果在不同研究地区和年龄组(<65岁与≥65岁)中均一致。
结论
我们发现,反映腹部肥胖的人体测量指标(即WC和WHtR)与T2DM发病之间的关联比BMI和体重更强。应鼓励在风险预测中使用这些测量指标。
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