OBJECTIVE Identifying patients who may experience decreased or increased mortality risk from intensive glycemic therapy for type 2 diabetes remains an important clinical challenge. We sought to identify characteristics of patients at high cardiovascular risk with decreased or increased mortality risk from glycemic therapy for type 2 diabetes using new methods to identify complex combinations of treatment effect modifiers.

RESEARCH DESIGN AND METHODS The machine learning method of gradient forest analysis was applied to understand the variation in all-cause mortality within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251), whose participants were 40–79 years old with type 2 diabetes, hemoglobin A_1c (HbA_1c) ≥7.5% (58 mmol/mol), cardiovascular disease (CVD) or multiple CVD risk factors, and randomized to target HbA_1c <6.0% (42 mmol/mol; intensive) or 7.0–7.9% (53–63 mmol/mol; standard). Covariates included demographics, BMI, hemoglobin glycosylation index (HGI; observed minus expected HbA_1c derived from prerandomization fasting plasma glucose), other biomarkers, history, and medications.

RESULTS The analysis identified four groups defined by age, BMI, and HGI with varied risk for mortality under intensive glycemic therapy. The lowest risk group (HGI <0.44, BMI <30 kg/m², age <61 years) had an absolute mortality risk decrease of 2.3% attributable to intensive therapy (95% CI 0.2 to 4.5, P = 0.038; number needed to treat: 43), whereas the highest risk group (HGI ≥0.44) had an absolute mortality risk increase of 3.7% attributable to intensive therapy (95% CI 1.5 to 6.0; P < 0.001; number needed to harm: 27).

CONCLUSIONS Age, BMI, and HGI may help individualize prediction of the benefit and harm from intensive glycemic therapy.

目的识别 2 型糖尿病强化血糖治疗可能降低或增加死亡风险的患者仍然是一个重要的临床挑战。我们试图使用新方法来确定治疗效果调节剂的复杂组合，从而确定心血管高风险患者的特征，这些患者因 2 型糖尿病的血糖治疗而死亡风险降低或增加。

研究设计和方法应用梯度森林分析的机器学习方法来了解控制糖尿病心血管风险行动 (ACCORD) 试验 ( N = 10,251) 中全因死亡率的变化，受试者年龄为 40-79 岁患有 2 型糖尿病、血红蛋白 A _1c (HbA _1c ) ≥7.5% (58 mmol/mol)、心血管疾病 (CVD) 或多种 CVD 危险因素，并随机分配至目标 HbA _1c <6.0% (42 mmol/mol；强化）或 7.0–7.9%（53–63 mmol/mol；标准）。协变量包括人口统计数据、BMI、血红蛋白糖基化指数（HGI；观察值减去随机化前空腹血糖得出的预期 HbA _1c ）、其他生物标志物、病史和药物治疗。

结果分析根据年龄、BMI 和 HGI 确定了四组在强化血糖治疗下具有不同死亡风险的组。最低风险组（HGI <0.44、BMI <30 kg/m ² 、年龄 <61 岁）由于强化治疗，绝对死亡风险降低了 2.3%（95% CI 0.2 至 4.5， P = 0.038；数量需要治疗：43），而最高风险组（HGI ≥0.44）由于强化治疗，绝对死亡风险增加了 3.7%（95% CI 1.5 至 6.0； P < 0.001；需要伤害的人数：27） 。

结论年龄、BMI 和 HGI 可能有助于个体化预测强化血糖治疗的益处和危害。