HbA1c and Aortic Calcification Index as Noninvasive Predictors of Pre-Existing Histopathological Damages in Living Donor Kidney Transplantation.
Kosuke TanakaShigeyoshi YamanagaYuji HidakaSho NishidaKohei KinoshitaAkari KabaToshinori IshizukaSatoshi HamanoueKenji OkumuraChiaki KawabataMariko ToyodaAsami TakedaAkira MiyataMasayuki KashimaHiroshi YokomizoPublished in: Journal of clinical medicine (2020)
We previously reported that allografts from living donors may have pre-existing histopathological damages, defined as the combination of interstitial fibrosis (ci), tubular atrophy (ct), and arteriolar hyalinosis (ah) scores of ≧1, according to the Banff classification. We examined preoperative characteristics to identify whether the degree of these damages was related to metabolic syndrome-related factors of donors. We conducted a single-center cross-sectional analysis including 183 living kidney donors. Donors were divided into two groups: chronic change (ci + ct ≧ 1 ∩ ah ≧ 1, n = 27) and control (n = 156). Preoperative characteristics, including age, sex, blood pressure, hemoglobin A1c (HbA1c), aortic calcification index (ACI), and psoas muscle index (PMI), were analyzed. Comparing the groups, the baseline estimated glomerular filtration rate was not significantly different; however, we observed a significant difference for ACI (p = 0.009). HbA1c (p = 0.016) and ACI (p = 0.006) were independent risk factors to predict pre-existing histopathological damages, whereas PMI was not. HbA1c correlated with ct scores (p = 0.035), and ACI correlated with ci (p = 0.005), ct (p = 0.021), and ah (p = 0.017). HbA1c and ACI may serve as preoperative markers for identifying pre-existing damages on the kidneys of living donors.
Keyphrases
- kidney transplantation
- image quality
- dual energy
- computed tomography
- contrast enhanced
- metabolic syndrome
- blood pressure
- patients undergoing
- risk factors
- cross sectional
- aortic valve
- chronic kidney disease
- positron emission tomography
- magnetic resonance imaging
- left ventricular
- machine learning
- pulmonary artery
- heart failure
- skeletal muscle
- adipose tissue
- type diabetes
- coronary artery
- hypertensive patients
- pulmonary arterial hypertension
- weight loss
- cardiovascular risk factors
- glycemic control
- high glucose