Rotbain Curovic Viktor, Roy Neil, Hansen Tine W, Luiza Caramori M, Cherney David Z, De Boer Ian H, Emanuele Mary Ann, Hirsch Irl B, Lingvay Ildiko, Mcgill Janet B, Polsky Sarit, Pop-Busui Rodica, Sigal Ronald J, Tuttle Katherine R, Umpierrez Guillermo E, Wallia Amisha, Rosas Sylvia E, Rossing Peter
Steno Diabetes Center Copenhagen, Herlev, Denmark.
Joslin Diabetes Center, Boston, MA, USA.
Diabetes Res Clin Pract. 2022 Nov;193:110119. doi: 10.1016/j.diabres.2022.110119. Epub 2022 Oct 17.
Baseline risk variables and visit-to-visit variability (VV) of systolic blood pressure (SBP), HbA, serum creatinine, and uric acid (UA) are potential risk markers of kidney function decline in type 1 diabetes.
Post-hoc analysis of a double-blind randomized placebo-controlled clinical trial investigating allopurinol's effect on iohexol-derived glomerular filtration rate (iGFR) in type 1 diabetes with elevated UA. Primary outcome was iGFR change over three years. Linear regression with backwards selection of baseline clinical variables was performed to identify an optimized model forecasting iGFR change. Furthermore, VVs of SBP, HbA, serum creatinine, and UA were calculated using measurements from the run-in period; thereafter assessed by linear regression, with iGFR change as the dependent variable.
404 participants were included in the primary analyses. In the optimized baseline variable model, higher HbA, SBP, iGFR, albuminuria, and heart rate, and mineralocorticoid receptor antagonist prescription were associated with greater iGFR decline. Higher VV of SBP was associated with greater iGFR decline (adjusted β (ml/min/1.73 m/50 % increase): -0.79, p = 0.01).
We identified several risk markers for faster iGFR decline in a high-risk population with type 1 diabetes. While further research is needed, our results indicate possible new and clinically feasible measures to risk stratify for DKD in type 1 diabetes.
收缩压(SBP)、糖化血红蛋白(HbA)、血清肌酐和尿酸(UA)的基线风险变量以及逐次就诊变异性(VV)是1型糖尿病患者肾功能下降的潜在风险标志物。
一项双盲随机安慰剂对照临床试验的事后分析,该试验研究别嘌醇对尿酸升高的1型糖尿病患者碘海醇衍生的肾小球滤过率(iGFR)的影响。主要结局是三年期间iGFR的变化。采用对基线临床变量进行向后选择的线性回归来确定预测iGFR变化的优化模型。此外,使用导入期的测量值计算SBP、HbA、血清肌酐和UA的VV;然后以iGFR变化作为因变量,通过线性回归进行评估。
404名参与者纳入了主要分析。在优化的基线变量模型中,较高的HbA、SBP、iGFR、蛋白尿和心率以及盐皮质激素受体拮抗剂处方与更大的iGFR下降相关。SBP的较高VV与更大的iGFR下降相关(调整后的β(ml/min/1.73m/50%增加):-0.79,p = 0.01)。
我们在1型糖尿病高危人群中确定了几个iGFR快速下降的风险标志物。虽然需要进一步研究,但我们的结果表明可能有新的且临床上可行的措施对1型糖尿病患者糖尿病肾病进行风险分层。