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Different domains of self-reported physical activity and risk of type 2 diabetes in a population-based Swedish cohort: the Malmö diet and Cancer study.

基于人群的瑞典队列中自我报告的体力活动和 2 型糖尿病风险的不同领域: 马尔默饮食和癌症研究。

  • 影响因子:2.94
  • DOI:10.1186/s12889-020-8344-2
  • 作者列表:"Mutie PM","Drake I","Ericson U","Teleka S","Schulz CA","Stocks T","Sonestedt E
  • 发表时间:2020-02-21
Abstract

BACKGROUND:While a dose-response relationship between physical activity and risk of diabetes has been demonstrated, few studies have assessed the relative importance of different measures of physical activity on diabetes risk. The aim was to examine the association between different self-reported measures of physical activity and risk of type 2 diabetes in a prospective cohort study. METHODS:Out of 26,615 adults (45-74 years, 60% women) in the population-based Swedish Malmö Diet and Cancer Study cohort, 3791 type 2 diabetes cases were identified from registers during 17 years of follow-up. Leisure-time (17 activities), occupational and domestic physical activity were assessed through a questionnaire, and these and total physical activity were investigated in relation to type 2 diabetes risk. RESULTS:All physical activity measures showed weak to modest associations with type 2 diabetes risk. The strongest association was found in the lower end of leisure-time physical activity in dose-response analysis at levels approximately below 22 MET-hrs/week (300 min/week) representing around 40% of the population. Compared with the lowest quintile, the moderate leisure-time physical activity category had a 28% (95% CI: 0.71, 0.87) decreased risk of type 2 diabetes. Total physical activity showed a similar, but weaker, association with diabetes risk as to that of leisure-time physical activity. Domestic physical activity was positively and linearly related to diabetes risk, HR = 1.11 (95% CI: 0.99, 1.25) comparing highest to lowest quintile. There was no association between occupational physical activity and diabetes risk. CONCLUSION:A curvilinear association was observed between leisure-time physical activity and risk of diabetes. Beyond a threshold level of approximately 22 MET-hrs/week or 300 min/week, no additional risk reduction was observed with increase in physical activity.

摘要

背景: 虽然体力活动和糖尿病风险之间的剂量-反应关系已经被证明,但很少有研究评估不同的体力活动对糖尿病风险的相对重要性。目的是在一项前瞻性队列研究中检查不同自我报告的体力活动指标与 2 型糖尿病风险之间的相关性。 方法: 在基于人群的瑞典马尔默饮食和癌症研究队列中的 26,615 名成年人 (45-74 岁,60% 名女性) 中, 在 17 年的随访中,从登记中确定了 3791 例 2 型糖尿病病例。通过问卷调查评估休闲时间 (17 项活动) 、职业和家庭体力活动,并调查这些和总体力活动与 2 型糖尿病风险的关系。 结果: 所有体力活动指标均显示出弱至中度与 2 型糖尿病风险的相关性。在剂量反应分析中的休闲时间体力活动的低端发现了最强的相关性,水平大约低于 22 MET-hrs/week (300 min/week) 约占人口的 40%。与最低五分位数相比,中等休闲时间体力活动类别的 2 型糖尿病风险降低了 28% (95% CI: 0.71,0.87)。总体力活动与休闲体力活动与糖尿病风险的相关性相似,但较弱。国内体力活动与糖尿病风险呈正相关和线性关系,hr = 1.11 (95% CI: 0.99,1.25),比较最高至最低五分位数。职业体力活动与糖尿病风险之间没有关联。 结论: 休闲体力活动与糖尿病风险之间存在曲线关联。超过约 22 MET-hrs/week 或 300 min/week 的阈值水平,未观察到随体力活动增加而额外的风险降低。

关键词: 糖尿病 体力活动
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影响因子:2.86
发表时间:2020-01-08
来源期刊:Acta Diabetologica
DOI:10.1007/s00592-019-01469-5
作者列表:["Benhalima, Katrien","Crombrugge, Paul","Moyson, Carolien","Verhaeghe, Johan","Vandeginste, Sofie","Verlaenen, Hilde","Vercammen, Chris","Maes, Toon","Dufraimont, Els","Block, Christophe","Jacquemyn, Yves","Mekahli, Farah","Clippel, Katrien","Den Bruel, Annick","Loccufier, Anne","Laenen, Annouschka","Minschart, Caro","Devlieger, Roland","Mathieu, Chantal"]

METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.

关键词: 暂无
翻译标题与摘要 下载文献
影响因子:19.14
发表时间:2020-01-01
来源期刊:Nature Medicine
DOI:10.1038/s41591-019-0724-8
作者列表:["Artzi, Nitzan Shalom","Shilo, Smadar","Hadar, Eran","Rossman, Hagai","Barbash-Hazan, Shiri","Ben-Haroush, Avi","Balicer, Ran D.","Feldman, Becca","Wiznitzer, Arnon","Segal, Eran"]

METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.

关键词: 暂无
翻译标题与摘要 下载文献
影响因子:4.34
发表时间:2020-01-27
DOI:10.1128/AAC.01777-19
作者列表:["Lowes DJ","Hevener KE","Peters BM"]

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