Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects
Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects
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DOI：
10.3945/ajcn.117.156216
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发表时间：
2017-09-01
影响因子：
7.1
通讯作者：
Valsesia, Armand
中科院分区：
文献类型：
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作者：
Armenise, Claudia;Lefebvre, Gregory;Valsesia, Armand
背景：低热量饮食 (LCD) 可减少脂肪量过剩，提高胰岛素敏感性，并改变脂肪组织 (AT) 基因表达，但与临床结果的关系仍不清楚。目的：我们评估了 LCD 期间 AT 转录组的变化，以及 LCD 终止时和 LCD 后 6 个月与体重和血糖结果的关联。设计：使用 RNA 测序 (RNAseq)，我们分析了多中心、受控饮食干预中 191 名肥胖、非糖尿病患者的 AT 转录组变化。表达变化与 LCD 8 周（800-1000 kcal/d）和 LCD 后 6 个月的结果相关。结果通过使用定量逆转录酶聚合酶链反应在同一队列的 350 名受试者中得到验证。构建统计模型对体重维持者或血糖改善者进行分类。结果：通过 RNAseq 分析，我们鉴定了 LCD 后差异表达的 1173 个基因，其中 350 个和 33 个基因分别与体重指数（BMI；单位为 kg/m(2)）和松田指数值的变化相关，而 29 个基因与两个终点相关。通路分析强调了脂质和葡萄糖代谢的富集。构建分类模型来识别体重保持者。基于临床基线变量的模型无法实现任何分类（验证 AUC：0.50；95% CI：0.36，0.64）。然而，LCD 期间的临床变化使模型具有更好的性能（AUC：0.73；95% CI：0.60，0.87]）。向该模型添加基线表达显着提高了性能（AUC：0.87；95% CI：0.77、0.96；Delong's P = 0.012）。进行了类似的分析以对血糖改善良好的受试者进行分类。基于基线和基于 LCD 的临床模型产生相似的性能（最佳 AUC：0.73；95% CI：0.60、0.86）。 LCD 期间添加表达变化可显着改善性能（AUC：0.80；95% CI：0.69、0.92；P = 0.058）。结论：本研究在一大群肥胖非糖尿病患者中调查了 LCD 后 AT 转录组的变化。基因表达与临床变量相结合使我们能够区分体重和血糖反应者与无反应者。这些潜在的生物标志物可以帮助临床医生了解受试者间的变异性并更好地预测饮食干预的成功。
Background: A low-calorie diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression, yet the relation with clinical outcomes remains unclear.Objective: We evaluated AT transcriptome alterations during an LCD and the association with weight and glycemic outcomes both at LCD termination and 6 mo after the LCD.Design: Using RNA sequencing (RNAseq), we analyzed transcriptome changes in AT from 191 obese, nondiabetic patients within a multicenter, controlled dietary intervention. Expression changes were associated with outcomes after an 8-wk LCD (800-1000 kcal/d) and 6 mo after the LCD. Results were validated by using quantitative reverse transcriptase-polymerase chain reaction in 350 subjects from the same cohort. Statistical models were constructed to classify weight maintainers or glycemic improvers.Results: With RNAseq analyses, we identified 1173 genes that were differentially expressed after the LCD, of which 350 and 33 were associated with changes in body mass index (BMI; in kg/m(2)) and Matsuda index values, respectively, whereas 29 genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Classification models were constructed to identify weight maintainers. A model based on clinical baseline variables could not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64). However, clinical changes during the LCD yielded better performance of the model (AUC: 0.73; 95% CI: 0.60, 0.87]). Adding baseline expression to this model improved the performance significantly (AUC: 0.87; 95% CI: 0.77, 0.96; Delong's P = 0.012). Similar analyses were performed to classify subjects with good glycemic improvements. Baseline-and LCD-based clinical models yielded similar performance (best AUC: 0.73; 95% CI: 0.60, 0.86). The addition of expression changes during the LCD improved the performance substantially (AUC: 0.80; 95% CI: 0.69, 0.92; P = 0.058).Conclusions: This study investigated AT transcriptome alterations after an LCD in a large cohort of obese, nondiabetic patients. Gene expression combined with clinical variables enabled us to distinguish weight and glycemic responders from nonresponders. These potential biomarkers may help clinicians understand intersubject variability and better predict the success of dietary interventions.