Department of Clinical Sciences, Lund University, Malmö, Sweden.
Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark.
Diabetes Care. 2022 May 1;45(5):1260-1267. doi: 10.2337/dc21-2402.
Obesity is a key risk factor for type 2 diabetes; however, up to 20% of patients are normal weight. Our aim was to identify metabolite patterns reproducibly predictive of BMI and subsequently to test whether lean individuals who carry an obese metabolome are at hidden high risk of obesity-related diseases, such as type 2 diabetes.
Levels of 108 metabolites were measured in plasma samples of 7,663 individuals from two Swedish and one Italian population-based cohort. Ridge regression was used to predict BMI using the metabolites. Individuals with a predicted BMI either >5 kg/m2 higher (overestimated) or lower (underestimated) than their actual BMI were characterized as outliers and further investigated for obesity-related risk factors and future risk of type 2 diabetes and mortality.
The metabolome could predict BMI in all cohorts (r2 = 0.48, 0.26, and 0.19). The overestimated group had a BMI similar to individuals correctly predicted as normal weight, had a similar waist circumference, were not more likely to change weight over time, but had a two times higher risk of future type 2 diabetes and an 80% increased risk of all-cause mortality. These associations remained after adjustments for obesity-related risk factors and lifestyle parameters.
We found that lean individuals with an obesity-related metabolome have an increased risk for type 2 diabetes and all-cause mortality compared with lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals to initiate lifestyle and pharmacological interventions.
肥胖是 2 型糖尿病的一个关键风险因素;然而,多达 20%的患者体重正常。我们的目的是确定可重复预测 BMI 的代谢物模式,然后检验携带肥胖代谢组的瘦个体是否存在肥胖相关疾病(如 2 型糖尿病)的隐藏高风险。
在来自瑞典两个和意大利一个基于人群的队列的 7663 名个体的血浆样本中测量了 108 种代谢物的水平。使用代谢物进行岭回归来预测 BMI。预测 BMI 高于(高估)或低于(低估)实际 BMI 5kg/m2 以上的个体被视为离群值,并进一步研究其与肥胖相关的危险因素以及未来 2 型糖尿病和死亡率的风险。
代谢组可在所有队列中预测 BMI(r2=0.48、0.26 和 0.19)。高估组的 BMI 与预测为正常体重的个体相似,腰围相似,体重随时间变化的可能性不大,但未来发生 2 型糖尿病的风险高出两倍,全因死亡率增加 80%。这些关联在调整肥胖相关危险因素和生活方式参数后仍然存在。
我们发现,与具有健康代谢组的瘦个体相比,具有肥胖相关代谢组的瘦个体发生 2 型糖尿病和全因死亡率的风险增加。代谢组学可用于识别隐藏的高危个体,以启动生活方式和药物干预。