Lipo-prostaglandin E1 improves renal hypoxia evaluated by BOLD-MRI in patients with diabetic kidney disease

Li, Zhicheng; Cai, Yu-Zhe; Tang, Zi Kang; Zuo, Panli; Liu, Rongbo; Liu, Fang

doi:10.1016/j.clinimag.2018.04.015

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Notably, participants who had hypoalbuminemia were also susceptible to being anemic, which could accelerate their kidney damage [ 26 ]. Indeed, our group and others had identified hypoxia-mediated pathways as potential therapeutic targets [ 27 ]. Since activation of hypoxia-inducible factor (HIF) prevents diabetes-induced tissue hypoxia, proteinuria, and renal tubular interstitial fibrosis by protecting mitochondrial function, HIF improving renal oxygen homeostasis might be a new target for DKD treatment [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease

et al. 2022

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Aims Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in patients with diabetes. Identification of risk factors involved in the progression of DKD to ESRD is expected to result in early detection and appropriate intervention and improve prognosis. Therefore, this study aimed to establish a risk prediction model for ESRD resulting from DKD in patients with type 2 diabetes mellitus (T2DM). Methods Between January 2008 and July 2019, a total of 390 Chinese patients with T2DM and DKD confirmed by percutaneous renal biopsy were enrolled and followed up for at least 1 year. Four machine learning algorithms (gradient boosting machine, support vector machine, logistic regression, and random forest (RF)) were used to identify the critical clinical and pathological features and to build a risk prediction model for ESRD. Results There were 158 renal outcome events (ESRD) (40.51%) during the 3-year median follow up. The RF algorithm showed the best performance at predicting progression to ESRD, showing the highest AUC (0.90) and ACC (82.65%). The RF algorithm identified five major factors: Cystatin-C, serum albumin (sAlb), hemoglobin (Hb), 24-hour urine urinary total protein, and estimated glomerular filtration rate. A nomogram according to the aforementioned five predictive factors was constructed to predict the incidence of ESRD. Conclusion Machine learning algorithms can efficiently predict the incident ESRD in DKD participants. Compared with the previous models, the importance of sAlb and Hb were highlighted in the current model. Highlights What is already known? Identification of risk factors for the progression of DKD to ESRD is expected to improve the prognosis by early detection and appropriate intervention. What this study has found? Machine learning algorithms were used to construct a risk prediction model of ESRD in patients with T2DM and DKD. The major predictive factors were found to be CysC, sAlb, Hb, eGFR, and UTP. What are the implications of the study? In contrast with the treatment of participants with early-phase T2DM with or without mild kidney damage, major emphasis should be placed on indicators of kidney function, nutrition, anemia, and proteinuria for participants with T2DM and advanced DKD to delay ESRD, rather than age, sex, and control of hypertension and glycemia.

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Section: Discussionmentioning

confidence: 99%

Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease

et al. 2022

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“…Previous studies of the pharmacological activities of PGE1 mainly focused on its effect on vasodilation. These studies showed that PGE1 can increase peripheral blood flow in heart, kidney, liver, and eyes in patients with peripheral vascular disease (Shin et al, 2012; Zhao et al, 2013; Yucel et al, 2015; Steigerwalt et al, 2016; Li et al, 2018). Emerging evidence has also shown that PGE1 activates angiogenesis and fibrinolysis, inhibits platelet aggregation, fibrinogenesis, inflammatory cell activity and proliferation (Schror and Hohlfeld, 2004; Huang et al, 2008; Gao et al, 2010).…”

Section: Introductionmentioning

confidence: 98%

Prostaglandin E1 Alleviates Cognitive Dysfunction in Chronic Cerebral Hypoperfusion Rats by Improving Hemodynamics

Xie

Chen

et al. 2019

Front. Neurosci.

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Compensatory vascular mechanisms can restore cerebral blood flow (CBF) but fail to protect against chronic cerebral hypoperfusion (CCH)-mediated neuronal damage and cognitive impairment. Prostaglandin E1 (PGE1) is known as a vasodilator to protect against ischemic injury in animal models, but its protective role in CCH remains unclear. To determine the effect of PGE1 on cerebral hemodynamics and cognitive functions in CCH, bilateral common carotid artery occlusion (BCCAO) was used to mimic CCH in rats, which were subsequently intravenously injected with PGE1 daily for 2 weeks. Magnetic resonance imaging, immunofluorescence staining and Morris water maze (MWM) were used to evaluate CBF, angiogenesis, and cognitive functions, respectively. We found that PGE1 treatment significantly restored CBF by enhancing vertebral artery dilation. In addition, PGE1 treatment increased the number of microvascular endothelial cells and neuronal cells in the hippocampus, and decreased the numbers of astrocyte and apoptotic cells. In the MWM test, we further showed that the escape latency of CCH rats was significantly reduced after PGE1 treatment. Our results suggest that PGE1 ameliorates cognitive dysfunction in CCH rats by enhancing CBF recovery, sustaining angiogenesis, and reducing astrocyte activation and neuronal loss.

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“…A major advantage of using BOLD to indirectly assess renal tissue oxygenation lies in its non-invasive nature compared to direct oxygen pressure measurement using microelectrodes as the only existing alternative and not acceptable for use in volunteers or patients. BOLD has been used to explore the potential renoprotective role of drug interventions, such as GLP-1 agonists [97], lipo-prostaglandin E1 (Lipo-PGE1) [124], or SGLT2i [125,126] (see also Table 1). A single 50 mg dose of dapagliflozin, a medication that inhibits the uptake of sodium and glucose from the filtrate, decreased cortical R 2 * with no change in renal perfusion and RBF, potentially due to reduced workload because of the reduced sodium re-uptake [73] (see Table 1).…”

Section: Oxygenation (Bold)mentioning

confidence: 99%

Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease

Friedli

Baid-Agrawal

Unwin

et al. 2023

JCM

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Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.

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Lipo-prostaglandin E1 improves renal hypoxia evaluated by BOLD-MRI in patients with diabetic kidney disease

Cited by 6 publications

References 28 publications

Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease

Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease

Prostaglandin E1 Alleviates Cognitive Dysfunction in Chronic Cerebral Hypoperfusion Rats by Improving Hemodynamics

Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease

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