1. Journal Club 埼玉医科大学 総合医療センター 内分泌・糖尿病内科 Department of Endocrinology and Diabetes, Saitama Medical Center, Saitama Medical University 松田 昌文 Matsuda, Masafumi 2011 年 12 月 8 日 8:30-8:55 ８階 医局 Wadden TA, Volger S, Sarwer DB, Vetter ML, Tsai AG, Berkowitz RI, Kumanyika S, Schmitz KH, Diewald LK, Barg R, Chittams J, Moore RH. A two-year randomized trial of obesity treatment in primary care practice. N Engl J Med. 2011 Nov 24;365(21):1969-79. Epub 2011 Nov 14. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011 Nov 28;343:d7163. doi: 10.1136/bmj.d7163.

2. weight reduction program

3. the Departments of Psychiatry (T.A.W., S.V., D.B.S., M.L.V., R.I.B., L.K.D., R.H.M.), Surgery (D.B.S.), and Medicine (M.L.V.) and the Center for Clinical Epidemiology and Biostatistics (S.K., K.H.S., J.C., R.H.M.), the Perelman School of Medicine at the University of Pennsylvania; the Department of Child and Adolescent Psychiatry, Children’s Hospital of Philadelphia (R.I.B.); and Penn Medicine, Clinical Care Associates (R.B.) — all in Philadelphia; and the Division of General Internal Medicine and Center for Human Nutrition, University of Colorado, Aurora (A.G.T.) N Engl J Med 2011;365:1969-79.

4. Background Calls for primary care providers (PCPs) to offer obese patients behavioral weight-loss counseling have not been accompanied by adequate guidance on how such care could be delivered. This randomized trial compared weight loss during a 2-year period in response to three lifestyle interventions, all delivered by PCPs in collaboration with auxiliary health professionals (lifestyle coaches) in their practices.

5. Methods We randomly assigned 390 obese adults in six primary care practices to one of three types of intervention: usual care, consisting of quarterly PCP visits that included education about weight management; brief lifestyle counseling, consisting of quarterly PCP visits combined with brief monthly sessions with lifestyle coaches who instructed participants about behavioral weight control; or enhanced brief lifestyle counseling, which provided the same care as described for the previous intervention but included meal replacements or weight-loss medication (orlistat or sibutramine), chosen by the participants in consultation with the PCPs, to potentially increase weight loss.

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22. Results Of the 390 participants, 86% completed the 2-year trial, at which time, the mean (±SE) weight loss with usual care, brief lifestyle counseling, and enhanced brief lifestyle counseling was 1.7±0.7, 2.9±0.7, and 4.6±0.7 kg, respectively. Initial weight decreased at least 5% in 21.5%, 26.0%, and 34.9% of the participants in the three groups, respectively. Enhanced lifestyle counseling was superior to usual care on both these measures of success (P = 0.003 and P = 0.02, respectively), with no other significant differences among the groups. The benefits of enhanced lifestyle counseling remained even after participants given sibutramine were excluded from the analyses. There were no significant differences between the intervention groups in the occurrence of serious adverse events.

23. Conclusions Enhanced weight-loss counseling helps about one third of obese patients achieve longterm, clinically meaningful weight loss. (Funded by the National Heart, Lung, and Blood Institute; POWER-UP ClinicalTrials.gov number, NCT00826774.)

24. Message/Comments 肥満患者 390 人を対象に、プライマリ・ケ ア提供者による減量カウンセリングの効果 を 2 年間の無作為化試験で比較。減量平均 値は医師の指導のみ、医師＋生活改善コー チの指導、医師＋コーチの指導＋代用食 / 減量薬の各群で、 1.7±0.7 、 2.9±0.7 、 4.6±0.7kg だった。減量薬服用者を分析 から除外しても減量効果が見られた。

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26. http://cvrisk.mvm.ed.ac.uk/calculator/calc.asp

27. http://aricnews.net/DiabRisk/DiabRC1.html

30. Objective To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice.

31. Design Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes.

71. Results 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse.

72. Conclusion Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.

73. What is already known on this topic The many known risk factors for type 2 diabetes can be combined in statistical models to produce risk scores What this study adds Dozens of risk models and scores for diabetes have been developed and validated in different settings Sociodemographic and clinical data were much better predictors of diabetes risk than genetic markers Research on this topic is beginning to shift from developing new statistical risk models to considering the use and impact of risk scores in the real world

74. Message/Comments Medline 、 Cochrane などのデータベースか ら抽出した論文の、開発または検証された 2 型糖尿病のリスク予測モデルおよびスコア 94 件をシステマティックレビューで検討。「そ の多くは日常的に使用できない検査が必要だ ったり、特定の使用者や明確な用途を想定し ていなかったりするため滅多に使用されてい ない」と著者らは結論している。 で、日本人のモデルは？（引用されているの は米国にいる日本人のモデル）

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