Pickering John W, Endre Zoltán H
Department of Medicine, University of Otago Christchurch, Christchurch, New Zealand.
Department of Nephrology, Prince of Wales Clinical School, University of New South Wales, Sydney, Australia.
PLoS One. 2014 Jul 9;9(7):e101288. doi: 10.1371/journal.pone.0101288. eCollection 2014.
Factors which modify the excretion profiles of acute kidney injury biomarkers are difficult to measure. To facilitate biomarker choice and interpretation we modelled key modifying factors: extent of hyperfiltration or reduced glomerular filtration rate, structural damage, and reduced nephron number. The time-courses of pre-formed, induced (upregulated), and filtered biomarker concentrations were modelled in single nephrons, then combined to construct three multiple-nephron models: a healthy kidney with normal nephron number, a non-diabetic hyperfiltering kidney with reduced nephron number but maintained total glomerular filtration rate, and a chronic kidney disease kidney with reduced nephron number and reduced glomerular filtration rate. Time-courses for each model were derived for acute kidney injury scenarios of structural damage and/or reduced nephron number. The model predicted that pre-formed biomarkers would respond quickest to injury with a brief period of elevation, which would be easily missed in clinical scenarios. Induced biomarker time-courses would be influenced by biomarker-specific physiology and the balance between insult severity (which increased single nephron excretion), the number of remaining nephrons (reduced total excretion), and the extent of glomerular filtration rate reduction (increased concentration). Filtered biomarkers have the longest time-course because plasma levels increased following glomerular filtration rate decrease. Peak concentration and profile depended on the extent of damage to the reabsorption mechanism and recovery rate. Rapid recovery may be detected through a rapid reduction in urinary concentration. For all biomarkers, impaired hyperfiltration substantially increased concentration, especially with chronic kidney disease. For clinical validation of these model-derived predictions the clinical biomarker of choice will depend on timing in relation to renal insult and interpretation will require the pre-insult nephron number (renal mass) and detection of hyperfiltration.
影响急性肾损伤生物标志物排泄谱的因素难以测量。为便于生物标志物的选择和解读,我们对关键影响因素进行了建模:超滤程度或肾小球滤过率降低、结构损伤以及肾单位数量减少。在单个肾单位中对预先形成的、诱导型(上调)和滤过型生物标志物浓度的时间进程进行建模,然后将其合并以构建三个多肾单位模型:肾单位数量正常的健康肾脏、肾单位数量减少但总肾小球滤过率维持不变的非糖尿病超滤肾脏以及肾单位数量减少且肾小球滤过率降低的慢性肾脏病肾脏。针对结构损伤和/或肾单位数量减少的急性肾损伤情况,得出每个模型的时间进程。该模型预测,预先形成的生物标志物对损伤的反应最快,会有一段短暂的升高期,这在临床情况下很容易被忽略。诱导型生物标志物的时间进程将受到生物标志物特异性生理学以及损伤严重程度(增加单个肾单位排泄)、剩余肾单位数量(减少总排泄)和肾小球滤过率降低程度(增加浓度)之间平衡的影响。滤过型生物标志物的时间进程最长,因为随着肾小球滤过率降低,血浆水平会升高。峰值浓度和曲线取决于对重吸收机制的损伤程度和恢复速率。通过尿浓度的快速降低可能检测到快速恢复。对于所有生物标志物,超滤受损会大幅增加浓度,尤其是在慢性肾脏病中。对于这些模型推导预测的临床验证而言,选择的临床生物标志物将取决于与肾损伤相关的时间,解读将需要损伤前的肾单位数量(肾质量)以及超滤的检测。