QuartzBio SA, Geneva, Switzerland.
Nestlé Institute of Health Sciences, Lausanne, Switzerland.
Am J Clin Nutr. 2017 Sep;106(3):736-746. doi: 10.3945/ajcn.117.156216. Epub 2017 Aug 9.
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. 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. 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. 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) 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 = 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; = 0.058). 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. This trial was registered at clinicaltrials.gov as NCT00390637.
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)的基因表达,但与临床结果的关系仍不清楚。我们评估了 LCD 期间 AT 转录组的改变,以及与体重和血糖结果的关系,这些结果在 LCD 结束时和 LCD 结束后 6 个月时都有体现。我们使用 RNA 测序(RNAseq)分析了来自 191 名肥胖、非糖尿病患者的 AT 转录组的变化,这些患者来自一个多中心、对照饮食干预研究。表达变化与 8 周的 LCD(800-1000 卡路里/天)后的体重和血糖结果有关。结果在同一队列中的 350 名患者中使用定量逆转录聚合酶链反应进行了验证。构建统计模型来分类体重维持者或血糖改善者。通过 RNAseq 分析,我们鉴定了 1173 个在 LCD 后差异表达的基因,其中 350 个和 33 个与 BMI(kg/m)和 Matsuda 指数值的变化相关,而 29 个基因与这两个终点都相关。途径分析突出了脂质和葡萄糖代谢的富集。构建分类模型以识别体重维持者。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的性能更好(AUC:0.73;95%CI:0.60,0.87])。将基线表达添加到该模型中可显著提高性能(AUC:0.87;95%CI:0.77,0.96;Delong's = 0.012)。进行了类似的分析以分类血糖改善良好的受试者。基于基线和基于 LCD 的临床模型产生了类似的性能(最佳 AUC:0.73;95%CI:0.60,0.86)。在 LCD 期间添加表达变化大大提高了性能(AUC:0.80;95%CI:0.69,0.92; = 0.058)。本研究在一大群肥胖、非糖尿病患者中调查了 LCD 后 AT 转录组的改变。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非反应者。这些潜在的生物标志物可能有助于临床医生了解个体间的差异,并更好地预测饮食干预的成功。该试验在 clinicaltrials.gov 上注册为 NCT00390637。