Molecular pathways that drive diabetic kidney disease

Journal of Clinical Investigation, Journal Year: 2023, Volume and Issue: 133(4)

Published: Feb. 14, 2023

Kidney disease is a major driver of mortality among patients with diabetes and diabetic kidney (DKD) responsible for close to half all chronic cases. DKD usually develops in genetically susceptible individual as result poor metabolic (glycemic) control. Molecular genetic studies indicate the key role podocytes endothelial cells driving albuminuria early diabetes. Proximal tubule changes show strong association glomerular filtration rate. Hyperglycemia represents cellular stress by altering metabolism imposing an excess workload requiring energy oxygen proximal cells. Changes induce adaptive hypertrophy reorganization actin cytoskeleton. Later, mitochondrial defects contribute increased oxidative activation inflammatory pathways, causing progressive function decline fibrosis. Blockade renin-angiotensin system or sodium-glucose cotransporter associated protection slowing decline. Newly identified molecular pathways could provide basis development much-needed novel therapeutics.

Language: Английский

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Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease

Diabetes & Metabolism Journal, Journal Year: 2022, Volume and Issue: 46(2), P. 181 - 197

Published: March 25, 2022

Although diabetic kidney disease (DKD) remains the leading cause of end-stage eventually requiring chronic replacement therapy, prevalence DKD has failed to decline over past 30 years. In order reduce prevalence, extensive research been ongoing improve prediction onset and progression. most commonly used markers are albuminuria estimated glomerular filtration rate, their limitations have encouraged researchers search for novel biomarkers that could risk stratification. Considering is a complex process involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, damage fibrosis, many capture one specific mechanism developed. Moreover, increasing use high-throughput omic approaches analyze biological samples include proteomics, metabolomics, transcriptomics emerged strong tool in biomarker discovery. This review will first describe recent advances understanding pathophysiology DKD, second, current clinical well status multiple potential with respect protein biomarkers, transcriptomics.

Language: Английский

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Abnormal lactate metabolism is linked to albuminuria and kidney injury in diabetic nephropathy

Kidney International, Journal Year: 2023, Volume and Issue: 104(6), P. 1135 - 1149

Published: Oct. 16, 2023

Language: Английский

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Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e41065 - e41065

Published: March 28, 2024

Background Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major microvascular complications, contributing significantly to morbidity, disability, mortality worldwide. The the eye, having similar structures physiological pathogenic features, may experience metabolic changes in diabetes. Objective This study aimed use machine learning (ML) methods integrated with data identify biomarkers associated DKD DR a multiethnic Asian population diabetes, as well improve performance of detection models beyond traditional risk factors. Methods We used ML algorithms (logistic regression [LR] Least Absolute Shrinkage Selection Operator gradient-boosting decision tree) analyze 2772 adults diabetes from Singapore Epidemiology Eye Diseases study, population-based cross-sectional conducted (2004-2011). From 220 circulating metabolites 19 factors, we selected most important variables (defined an estimated glomerular filtration rate <60 mL/min/1.73 m2) Early Treatment Retinopathy Study severity level ≥20). were developed based on variable selection results externally validated sample 5843 participants UK biobank (2007-2010). Machine-learned model (area under receiver operating characteristic curve [AUC] 95% CI, sensitivity, specificity) was compared that LR adjusted for age, sex, duration, hemoglobin A1c, systolic blood pressure, BMI. Results had median age 61.7 (IQR 53.5-69.4) years, 49.1% (1361/2772) being women, 20.2% (555/2753) DKD, 25.4% (685/2693) DR. 61.0 55.0-65.0) 35.8% (2090/5843) 6.7% (374/5570) 6.1% (355/5843) identified insulin usage, tyrosine factors both additionally cardiovascular history, antihypertensive medication use, 3 (lactate, citrate, cholesterol esters total lipids ratio intermediate-density lipoprotein), while glucose, pulse alanine. outperformed internal (AUC 0.838 vs 0.743 0.790 0.764 DR) external validation 0.791 0.691 0.778 0.760 DR). Conclusions highlighted detecting integration biomedical big enables biomarker discovery improves

Language: Английский

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Biomarkers: New Advances in Diabetic Nephropathy

Natural Product Communications, Journal Year: 2025, Volume and Issue: 20(2)

Published: Feb. 1, 2025

Diabetic nephropathy (DN) is the leading cause of uremia and clinical mortality, characterized by progressive deterioration kidney structure function due to prolonged exposure hyperglycemia. This condition often necessitates renal replacement therapy for patients with end-stage-renal-disease (ESRD). Consequently, early detection, diagnosis, treatment DN are crucial mitigate disease progression, enhance patient outcomes, maintain a good quality life. Exploring relevant biomarkers diagnosis holds significant importance. In recent years, numerous researchers have identified various novel associated diabetic nephropathy, which critical predicting both onset progression disease. article aims provide comprehensive overview related DN.

Language: Английский

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High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study

American Journal of Nephrology, Journal Year: 2022, Volume and Issue: 53(2-3), P. 215 - 225

Published: Jan. 1, 2022

<b><i>Introduction:</i></b> Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis urine samples from 995 CRIC participants with diabetes state-of-the-art statistical modeling, we aimed to identify metabolites DKD <b><i>Methods:</i></b> Urine (<i>N</i> = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, stringent criteria used eliminate noisy compounds, resulting in 698 annotated ions. Utilizing the metabolites’ ion along clinical data (demographics, blood pressure, HbA1c, eGFR, albuminuria), developed univariate multivariate models eGFR slope using penalized (lasso) random forest models. Final tested on time-to-ESKD (end-stage disease) via cross-validated C-statistics. We also conducted pathway enrichment a targeted subset metabolites. <b><i>Results:</i></b> Six selected 9–30 variables. In adjusted ESKD model highest C-statistic, valine (or betaine) 3-(4-methyl-3-pentenyl)thiophene associated (<i>p</i> &#x3c; 0.05) 44% 65% higher hazard per doubling abundance, respectively. Also, 13 (of 15) amino acids, including betaine, confirmed analysis. Enrichment revealed pathways implicated cardiometabolic disease. <b><i>Conclusions:</i></b> Using diverse sample, high-throughput assay, followed analysis, rigorous reduce false discovery, identified several If replicated independent cohorts, our findings inform risk stratification treatment strategies patients DKD.

Language: Английский

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Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Sept. 29, 2022

Abstract Diabetic kidney disease is the main cause of end-stage renal worldwide. The prediction clinical course patients with diabetic remains difficult, despite identification potential biomarkers; therefore, novel biomarkers are needed to predict progression disease. We conducted non-targeted metabolomics using plasma and urine whose estimated glomerular filtration rate was between 30 60 mL/min/1.73 m 2 . analyzed how changed over time (up months) detect rapid decliners function. Conventional logistic analysis suggested that only one metabolite, urinary 1-methylpyridin-1-ium (NMP), a promising biomarker. then applied deep learning method identify physiological parameters in an explainable manner. narrowed down 3388 variables 50 two regression models, piecewise linear handcrafted regression, both which examined utility biomarker combinations. Our analysis, based on method, identified systolic blood pressure albumin-to-creatinine ratio, six metabolites, three unidentified metabolites including NMP, as biomarkers. This research suggests machine can could otherwise escape conventional statistical method.

Language: Английский

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Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective

Life Sciences, Journal Year: 2023, Volume and Issue: 316, P. 121414 - 121414

Published: Jan. 19, 2023

Language: Английский

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Elucidating ascorbate and aldarate metabolism pathway characteristics via integration of untargeted metabolomics and transcriptomics of the kidney of high-fat diet-fed obese mice

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(4), P. e0300705 - e0300705

Published: April 11, 2024

Obesity is a major independent risk factor for chronic kidney disease and can activate renal oxidative stress injury. Ascorbate aldarate metabolism an important carbohydrate metabolic pathway that protects cells from damage. However the effect of on this still unclear. Therefore, primary objective study was to investigate ascorbate in kidneys high-fat diet-fed obese mice determine effects stress. Male C57BL/6J were fed diet 12 weeks induce obesity. Subsequently, non-targeted metabolomics profiling used identify metabolites tissues mice, followed by RNA sequencing using transcriptomic methods. The integrated analysis transcriptomics revealed alterations these mice. diet-induced obesity resulted notable changes, including thinning glomerular basement membrane, podocyte morphology, increase Metabolomics 649 positive-ion mode, 470 negative-ion mode. Additionally, 659 differentially expressed genes (DEGs) identified which 34 upregulated 625 downregulated. Integrated analyses two DEGs 13 differential pathway. expression levels ugt1a9 ugt2b1 downregulated, level tissue reduced. Thus, injury induced affects regulation emerged as potential marker predicting damage due

Language: Английский

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Exposome and Metabolome Analysis of Sugarcane Workers Reveals Predictors of Kidney Injury

Kidney International Reports, Journal Year: 2024, Volume and Issue: 9(5), P. 1458 - 1472

Published: Feb. 6, 2024

IntroductionSugarcane workers are exposed to potentially hazardous agrochemicals, including pesticides, heavy metals, and silica. Such occupational exposures present health risks have been implicated in a high rate of kidney disease seen these workers.MethodsTo investigate potential biomarkers mechanisms that could explain chronic (CKD) among this worker population, paired urine samples were collected from sugarcane cutters at the beginning end harvest season Guatemala. Workers then separated into 2 groups, namely those with or without function decline (KFD) across season. Urine groups underwent elemental analysis untargeted metabolomics.ResultsUrine profiles demonstrated increases silicon, certain phosphorus levels all workers, whereas metals remained low. The KFD group had reduction estimated glomerular filtration (eGFR) season; however, injury marker 1 did not significantly change. Cross-harvest metabolomic found trends fatty acid accumulation, perturbed amino metabolism, presence other known signs impaired function.ConclusionSilica pesticides elevated KFD. Future work should determine whether long-term exposure silica multiple seasons contributes CKD workers. Overall, results confirmed occurring may provide insight early warning help increased incidence agricultural

Language: Английский

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