Clinical and prognostic values of urinary alpha1-microglobulin as a tubular marker in acute heart failure

Ishiwata, Sayaki; Matsue, Yuya; Nakamura, Yutaka; Dotare, Taishi; Sunayama, Tsutomu; Suda, Shoko; Yatsu, Shoichiro; Kato, Takao; Hiki, Masaru; Kasai, Takatoshi; Minamino, Tohru

doi:10.1016/j.ijcard.2021.06.041

Cited by 7 publications

(7 citation statements)

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“…To our knowledge, this study was the first one to employ machine learning algorithms in conjunction with existing indicators readily available in rural areas to construct an early warning model for CKD. In constructing the model, demographic information, physical examination and blood biochemical were taken as the explanatory variables; ACR (7) and MCR (24), early CKD screening parameters, collected and Contributions of explanatory variables to the XGBoost algorithm. The "Gain" means the relative contribution of the corresponding feature to the model calculated by taking the contribution of each feature to each tree in the model.…”

Section: Discussionmentioning

confidence: 99%

“…To our knowledge, this study was the first one to employ machine learning algorithms in conjunction with existing indicators readily available in rural areas to construct an early warning model for CKD. In constructing the model, demographic information, physical examination and blood biochemical were taken as the explanatory variables; ACR ( 7 ) and MCR ( 24 ), early CKD screening parameters, collected and calculated from urine samples were taken as response variables. This study suggested that machine learning approaches show good performance in achieving early CKD-aided screening, as reflected by excellent Accuracy, Sensitivity, Specificity and AUC.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province

et al. 2023

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IntroductionChronic kidney disease (CKD) is a progressive disease with high incidence but early imperceptible symptoms. Since China’s rural areas are subject to inadequate medical check-ups and single disease screening programme, it could easily translate into end-stage renal failure. This study aimed to construct an early warning model for CKD tailored to impoverished areas by employing machine learning (ML) algorithms with easily accessible parameters from ten rural areas in Shanxi Province, thereby, promoting a forward shift of treatment time and improving patients’ quality of life.MethodsFrom April to November 2019, CKD opportunistic screening was carried out in 10 rural areas in Shanxi Province. First, general information, physical examination data, blood and urine specimens were collected from 13,550 subjects. Afterward, feature selection of explanatory variables was performed using LASSO regression, and target datasets were balanced using the SMOTE (synthetic minority over-sampling technique) algorithm, i.e., albuminuria-to-creatinine ratio (ACR) and α1-microglobulin-to-creatinine ratio (MCR). Next, Bagging, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were employed for classification of ACR outcomes and MCR outcomes, respectively.Results12,330 rural residents were included in this study, with 20 explanatory variables. The cases with increased ACR and increased MCR represented 1,587 (12.8%) and 1,456 (11.8%), respectively. After conducting LASSO, 14 and 15 explanatory variables remained in these two datasets, respectively. Bagging, RF, and XGBoost performed well in classification, with the AUC reaching 0.74, 0.87, 0.87, 0.89 for ACR outcomes and 0.75, 0.88, 0.89, 0.90 for MCR outcomes. The five variables contributing most to the classification of ACR outcomes and MCR outcomes constituted SBP, TG, TC, and Hcy, DBP and age, TG, SBP, Hcy and FPG, respectively. Overall, the machine learning algorithms could emerge as a warning model for CKD.ConclusionML algorithms in conjunction with rural accessible indexes boast good performance in classification, which allows for an early warning model for CKD. This model could help achieve large-scale population screening for CKD in poverty-stricken areas and should be promoted to improve the quality of life and reduce the mortality rate.

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Section: Discussionmentioning

confidence: 99%

“…To our knowledge, this study was the first one to employ machine learning algorithms in conjunction with existing indicators readily available in rural areas to construct an early warning model for CKD. In constructing the model, demographic information, physical examination and blood biochemical were taken as the explanatory variables; ACR ( 7 ) and MCR ( 24 ), early CKD screening parameters, collected and calculated from urine samples were taken as response variables. This study suggested that machine learning approaches show good performance in achieving early CKD-aided screening, as reflected by excellent Accuracy, Sensitivity, Specificity and AUC.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province

et al. 2023

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“…34 Urine alpha-1m has been examined in adult studies where higher levels have been shown to be associated with coronary artery disease, heart failure, and CVD incidence. [34][35][36] In a retrospective observational study of adults with acute heart failure, Ishiwata et al found that higher urine alpha-1m levels were associated with a…”

Section: Discussionmentioning

confidence: 99%

“… 34 Urine alpha-1m has been examined in adult studies where higher levels have been shown to be associated with coronary artery disease, heart failure, and CVD incidence. 34 – 36 In a retrospective observational study of adults with acute heart failure, Ishiwata et al found that higher urine alpha-1m levels were associated with a higher left ventricular ejection fraction ( P = 0.002). 36 This previous research does not explain why higher alpha-1m concentrations were associated with a lower prevalence of LVH in our study.…”

Section: Discussionmentioning

confidence: 99%

Associations of Biomarkers of Kidney Tubule Health, Injury, and Inflammation with Left Ventricular Hypertrophy in Children with CKD

Jiang¹,

Greenberg²,

Abraham³

et al. 2023

Kidney360

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Background: Left ventricular hypertrophy (LVH) is common in children with CKD and is associated with an increased risk of cardiovascular disease and mortality. We have shown that several plasma and urine biomarkers are associated with increased risk of CKD progression. As CKD is associated with LVH, we sought to investigate the association between the biomarkers and LVH. Methods: In the CKiD Cohort Study, children aged 6 months to 16 years old with an eGFR of 30-90 ml/min/1.73m2 were enrolled at 54 centers in the US and Canada. We measured plasma biomarkers KIM-1, TNFR-1, TNFR-2, and suPAR and urine KIM-1, MCP-1, YKL-40, alpha-1m, and EGF in stored plasma and urine collected 5 months after enrollment. Echocardiograms were performed one year after enrollment. We assessed the cross-sectional association between the log2 biomarker levels and LVH (left ventricular mass index greater than or equal to the 95th percentile) using a Poisson regression model, adjusted for age, sex, race, body mass index, hypertension, glomerular diagnosis, urine Pr/Cr, and eGFR at study entry. Results: Among the 504 children, LVH prevalence was 12% (n=59) one year after enrollment. In a multivariable-adjusted model, higher plasma and urine KIM-1 and urine MCP-1 concentrations were associated with a higher prevalence of LVH (plasma KIM-1 prevalence ratio [PR] per log2: 1.27, 95% CI: 1.02-1.58; urine KIM-1 PR: 1.21, 95% CI: 1.11-1.48; and urine MCP-1 PR: 1.18, 95% CI: 1.04-1.34). After multivariable adjustment for covariates, lower urine alpha-1m was also associated with a higher prevalence of LVH (PR: 0.90, 95% CI: 0.82-0.99). Conclusions: Higher plasma and urine KIM-1, urine MCP-1, and lower urine alpha-1m were each associated with LVH prevalence in children with CKD. These biomarkers may better inform risk and help elucidate the pathophysiology of LVH in pediatric CKD.

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“…15 As daprodustat is primarily adjusted to achieve a haemoglobin level of 11.5-12.5 g/dl, we estimated the post-treatment difference between the two groups to be 0.75 g/dl at 16 weeks. Among patients enrolled in the JEDI-AHF, a retrospective registry database of patients with HF requiring hospitalization at the Juntendo University Department of Cardiology, 16 haemoglobin levels were measured at two time points, with a time interval of 16 ± 2 weeks in 31 patients with HF who met the inclusion criteria of this study were evaluated. The standard deviations of the haemoglobin levels of the first and second measurements were 0.78 and 0.95 g/dl, respectively, and the correlation coefficient of these two measurements was 0.54.…”

Section: Sample Size Calculationsmentioning

confidence: 99%

Daprodustat for anaemia in patients with heart failure and chronic kidney disease: A randomized controlled study

et al. 2022

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Aims Hypoxia‐inducible factor‐prolyl hydroxylase (HIF‐PH) inhibitors have been developed for the treatment of renal anaemia; however, no study has evaluated the safety and efficacy of HIF‐PH inhibitors in patients with heart failure (HF). This study was designed to evaluate the safety and efficacy of daprodustat, a HIF‐PH inhibitor, in patients with HF and renal anaemia. Methods and results We designed a pilot, multi‐centre, open‐label, randomized controlled study, in which 50 patients with HF complicated with chronic kidney disease and anaemia will be randomized 1:1 to either the daprodustat or control group at seven sites in Japan. Study entry requires New York Heart Association Class II HF symptoms or a history of hospitalization due to HF, an estimated glomerular filtration rate of <60 mL/min/1.73 m2, and a haemoglobin level of 7.5 to <11.0 g/dl. Patients randomized to the daprodustat group will be treated with oral daprodustat, and the dose will be uptitrated according to the changes in the haemoglobin level from previous visits. In this study, we will evaluate the impact of HIF‐PH inhibitors on cardiac function using advanced cardiovascular imaging modalities, including cardiac magnetic resonance imaging. The primary outcome is the haemoglobin level at 16 weeks of randomization, and all adverse events will be recorded and evaluated for any association with daprodustat treatment. Conclusion Considering the hypothetical upside and downside of using HIF‐PH inhibitors in anaemic patients with HF and chronic kidney disease, and because there are virtually no safe and effective treatments for patients with anaemia not caused by iron deficiency, our study results will contribute significantly to this field.

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Clinical and prognostic values of urinary alpha1-microglobulin as a tubular marker in acute heart failure

Cited by 7 publications

References 36 publications

Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province

Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province

Associations of Biomarkers of Kidney Tubule Health, Injury, and Inflammation with Left Ventricular Hypertrophy in Children with CKD

Daprodustat for anaemia in patients with heart failure and chronic kidney disease: A randomized controlled study

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