Chen Debbie C, Lu Kaiwei, Scherzer Rebecca, Lees Jennifer S, Rutherford Elaine, Mark Patrick B, Potok O Alison, Rifkin Dena E, Ix Joachim H, Shlipak Michael G, Estrella Michelle M
Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA.
Kidney Health Research Collaborative, San Francisco VA Health Care System & University of California, San Francisco, San Francisco, CA.
Kidney Med. 2024 Feb 16;6(4):100796. doi: 10.1016/j.xkme.2024.100796. eCollection 2024 Apr.
RATIONALE & OBJECTIVE: Large differences between estimated glomerular filtration rate (eGFR) based on cystatin C (eGFRcys) and creatinine (eGFRcr) occur commonly. A comprehensive evaluation of factors that contribute to these differences is needed to guide the interpretation of discrepant eGFR values.
Cohort study.
SETTING & PARTICIPANTS: 468,969 participants in the UK Biobank.
Candidate sociodemographic, lifestyle factors, comorbidities, medication usage, and physical and laboratory predictors.
eGFRdiff, defined as eGFRcys minus eGFRcr, categorized into 3 levels: lower eGFRcys (eGFRdiff, less than -15 mL/min/1.73 m), concordant eGFRcys and eGFRcr (eGFRdiff, -15 to < 15 mL/min/1.73 m), and lower eGFRcr (eGFRdiff, ≥15 mL/min/1.73 m).
Multinomial logistic regression models were constructed to identify predictors of lower eGFRcys or lower eGFRcr. We developed 2 prediction models comprising 375,175 participants: (1) a clinical model using clinically available variables and (2) an enriched model additionally including lifestyle variables. The models were internally validated in an additional 93,794 participants.
Mean ± standard deviation of eGFRcys was 88 ± 16 mL/min/1.73 m, and eGFRcr was 95 ± 13 mL/min/1.73 m; 25% and 5% of participants were in the lower eGFRcys and lower eGFRcr groups, respectively. In the multivariable enriched model, strong predictors of lower eGFRcys were older age, male sex, South Asian ethnicity, current smoker (vs never smoker), history of thyroid dysfunction, chronic inflammatory disease, steroid use, higher waist circumference and body fat, and urinary albumin-creatinine ratio >300 mg/g. Odds ratio estimates for these predictors were largely inverse of those in the lower eGFRcr group. The model's area under the curve was 0.75 in the validation set, with good calibration (1.00).
Limited generalizability.
This study highlights the multitude of demographic, lifestyle, and health characteristics that are associated with large eGFRdiff. The clinical model may identify individuals who are likely to have discrepant eGFR values and thus should be prioritized for cystatin C testing.
基于胱抑素C的估算肾小球滤过率（eGFRcys）与基于肌酐的估算肾小球滤过率（eGFRcr）之间通常存在较大差异。需要对导致这些差异的因素进行全面评估，以指导对不一致的eGFR值的解读。
队列研究。
英国生物银行的468,969名参与者。
候选的社会人口统计学、生活方式因素、合并症、药物使用以及身体和实验室预测指标。
eGFRdiff，定义为eGFRcys减去eGFRcr，分为3个水平：较低的eGFRcys（eGFRdiff＜-15 mL/min/1.73 m²）、一致的eGFRcys和eGFRcr（eGFRdiff为-15至＜15 mL/min/1.73 m²）以及较低的eGFRcr（eGFRdiff≥15 mL/min/1.73 m²）。
构建多项逻辑回归模型以识别较低的eGFRcys或较低的eGFRcr的预测指标。我们开发了2个预测模型，包含375,175名参与者：（1）一个使用临床可用变量的临床模型，以及（2）一个额外纳入生活方式变量的强化模型。这些模型在另外93,794名参与者中进行了内部验证。
eGFRcys的平均值±标准差为88±16 mL/min/1.73 m²，eGFRcr为95±13 mL/min/1.73 m²；分别有25%和5%的参与者属于较低的eGFRcys组和较低的eGFRcr组。在多变量强化模型中，较低的eGFRcys的强预测指标包括年龄较大、男性、南亚族裔、当前吸烟者（相对于从不吸烟者）、甲状腺功能障碍病史、慢性炎症性疾病、使用类固醇、较高的腰围和体脂以及尿白蛋白肌酐比值＞300 mg/g。这些预测指标的优势比估计在很大程度上与较低的eGFRcr组相反。验证集中模型的曲线下面积为0.75，校准良好（1.00）。
可推广性有限。
本研究强调了与较大的eGFRdiff相关的众多人口统计学、生活方式和健康特征。临床模型可识别可能具有不一致的eGFR值的个体，因此应优先进行胱抑素C检测。
