Jiang Yingxin Celia, Lai Kaitao, Muirhead Roslyn Patricia, Chung Long Hoa, Huang Yu, James Elizaveta, Liu Xin Tracy, Wu Julian, Atkinson Fiona S, Yan Shuang, Fogelholm Mikael, Raben Anne, Don Anthony Simon, Sun Jing, Brand-Miller Jennie Cecile, Qi Yanfei
Centenary Institute, The University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; ANZAC Research Institute, The University of Sydney, Sydney, New South Wales, Australia.
Am J Clin Nutr. 2024 Oct;120(4):864-878. doi: 10.1016/j.ajcnut.2024.08.015. Epub 2024 Aug 23.
Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk.
This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss.
We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses.
We quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance.
Newly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice. The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).
通过生活方式干预实现体重减轻，尤其是低能量饮食，对超重相关的糖尿病前期人群具有改善血糖的益处。然而，这些个体中有超过50%在体重减轻后未能实现血糖正常。循环脂质具有评估饮食影响和预测糖尿病风险的潜力。
本研究旨在确定可作为饮食诱导体重减轻后个体血糖变化的评估或预测生物标志物的血清脂质。
我们研究了104名超重相关的糖尿病前期参与者，他们在8周内通过低能量饮食减轻了≥8%的体重。在饮食干预前后对血清样本进行了高覆盖脂质组学分析。使用差异脂质丰度比较和偏最小二乘判别分析评估脂质组重新校准。通过Spearman相关性和集成机器学习模型的Bootstrap森林确定脂质变化与临床特征之间的关联。使用Bootstrap森林分析评估预测体重减轻后血糖参数变化的基线脂质。
我们定量了439种血清脂质种类和9种相关有机酸。饮食干预显著降低了二酰甘油、神经酰胺、溶血磷脂和醚连接的磷脂酰乙醇胺。相反，酰基肉碱、短链脂肪酸、有机酸和醚连接的磷脂酰胆碱显著增加。某些脂质种类（如含饱和和单不饱和脂肪酸的甘油脂质、基于鞘氨二烯的超长链鞘脂和有机酸）的变化与临床血糖参数密切相关。六种基线生物活性鞘脂主要预测空腹血糖的变化。此外，一些基线脂质种类，主要是二酰甘油和甘油三酯，可预测糖化血红蛋白、胰岛素和胰岛素抵抗稳态模型评估的临床变化。
新发现的血清脂质组改变以及脂质-临床变量的相关变化表明与饮食介导的血糖控制相关的广泛代谢重编程。血糖结果的新型脂质预测指标可促进对体重减轻代谢反应较差的糖尿病前期个体进行早期分层，从而制定出超越一刀切的生活方式改变建议的更具针对性的干预策略。PREVIEW生活方式干预研究已在clinicaltrials.gov上注册，注册号为NCT01777893（https://clinicaltrials.gov/study/NCT01777893）。
