MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
BMC Med. 2022 May 3;20(1):159. doi: 10.1186/s12916-022-02354-9.
Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors.
Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.
During median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]) and relative (15.0% [10.5-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).
When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.
有效的 2 型糖尿病（T2D）靶向预防取决于疾病风险的准确预测。我们评估了代谢组学分析在超越传统危险因素的情况下改善 T2D 风险预测的作用。
在没有糖尿病且未服用降脂药物的 65684 名英国生物库参与者的基线血浆样本中进行了基于核磁共振（NMR）的代谢组学分析。在一个包含 50519 名参与者的亚组中，所有相关协变量（社会人口统计学特征、父母糖尿病史、包括饮食因素在内的生活方式、人体测量学指标和禁食时间）的数据都可用，Cox 回归得出了 143 个个体代谢生物标志物（包括脂质、脂蛋白、脂肪酸、氨基酸、酮体和其他低分子量代谢生物标志物）和 11 个代谢生物标志物主成分（PC）（解释了个体生物标志物总方差的 90%）与事件性 T2D 关联的调整后的危险比。这 11 个 PCs 被添加到全研究人群的 T2D 风险预测模型中，并评估了风险判别（C 统计量）和再分类（连续净重新分类改善 [NRI]、综合判别指数 [IDI]）的措施。
在中位随访 11.9 年（IQR 11.1-12.6）期间，在进行多次测试后，在 50519 名参与者（1211 例 T2D 事件）中，有 90 个代谢生物标志物与 T2D 风险呈独立相关，在进一步调整 HbA1c 后，有 76 个标志物与 T2D 风险呈相关（假发现率控制的 p < 0.01）。总的来说，有 8 个代谢生物标志物 PCs 与 T2D 呈独立相关。在 65684 名全部参与者中，有 1719 人患上了 T2D，在包括年龄、性别、父母糖尿病史、体重指数和 HbA1c 在内的既定风险预测模型中添加 PCs，提高了 T2D 风险预测的 C 统计量（从 0.802（95%CI 0.791-0.812）增加到 0.830（0.822-0.841））、连续 NRI（0.44（0.38-0.49））和相对（15.0%（10.5-20.4%））和绝对（1.5（1.0-1.9））IDI。当将代谢生物标志物 PCs 添加到另外还包括腰围、血压和血浆脂质浓度的更全面的既定 T2D 风险预测模型中时，观察到了更适度的改善（C 统计量，0.829（0.819-0.838）至 0.837（0.831-0.848）；连续 NRI，0.22（0.17-0.28）；相对 IDI，6.3%（4.1-9.8%）；绝对 IDI，0.7（0.4-1.1））。
当与传统危险因素一起使用时，循环 NMR 基于代谢物的生物标志物适度提高了 T2D 的风险预测。
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