The association between methylation levels of targeted genes and albuminuria in patients with early diabetic kidney disease

Aldemir, Ozgur; Turgut, Faruk; Gökçe, Cumali

doi:10.1080/0886022x.2017.1358180

Cited by 28 publications

(25 citation statements)

References 21 publications

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“…27 Besides, these methods were also used to predict the risk of drug noncompliance in Chinese patients with inflammatory rheumatism. 2,2 To further verify the stability of this prediction model, the whole data set was used for internal verification, and the C index was obtained. Note: Data are presented as n (%), mean±SD or median (IQR).…”

Section: Discussionmentioning

confidence: 99%

“…DN and DR not only impose a heavy financial burden associated with medical treatment but also seriously affect the quality of life of the patients and increases the risk of death of T2DM patients. 2 DN and DR are closely related. Some studies have shown that the prevalence of DR in patients with DN confirmed by biopsy was 48%, 9 and the prevalence of DN in patients with confirmed DR was 35.6%.…”

Section: Introductionmentioning

confidence: 98%

“…Diabetic nephropathy (DN) and diabetic retinopathy (DR) are two typical microvascular complications of T2DM. DN is progressive renal failure characterized by persistent proteinuria and hypertension, [2][3][4] which is one of the most common complications of diabetes 5 and the major pathogenesis of end-stage renal disease (ESRD) in developed countries. DR patients lose their vision due to retinal detachment, preretinal or vitreous haemorrhage, neovascular glaucoma, macular degeneration or capillary perfusion.…”

Section: Introductionmentioning

confidence: 99%

“…7 In China, the incidence of DN in patients with T2DM is 20% to 40%. 2 The global incidence of DR is nearly 30%. 8 In 2010, the number of patients with DR was 126.6 million worldwide, and this number is expected to increase by approximately 50.87% in 20 years.…”

Section: Introductionmentioning

confidence: 99%

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<p>Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study</p>

Shi¹,

Niu²,

Wu³

et al. 2020

DMSO

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Purpose: This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample. Methods: We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model. Results: The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice. Conclusion: This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

<p>Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study</p>

Shi¹,

Niu²,

Wu³

et al. 2020

DMSO

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“…Previous studies exploring blood-derived DNA methylation used an EWAS approach for DKD focused on the 27K array (Sapienza et al, 2011), the mitochondrial genome (Swan et al, 2015) or renal function decline in 181 Pima Indians with diabetes (Qiu et al, 2018). Targeted DNA methylation studies have been conducted using blood-derived DNA (Aldemir et al, 2017;Smyth et al, 2018), cell models of DKD (Brennan et al, 2010;Li et al, 2019) and an EWAS reported on kidney biopsy samples from 91 individuals of whom 45% had diabetes (Gluck et al, 2019) and 11 individuals with diabetes (Ko et al, 2013). The case and control population employed in this study has >60% power to detect a true positive (defined as detected CpGs with a meaningful difference in mean blood derived DNA methylation ±0.2 with a false discovery rate P ≤ 0.05) association using pwrEWAS (Graw et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

DNA Methylation Associated With Diabetic Kidney Disease in Blood-Derived DNA

Smyth

Patterson

Swan

et al. 2020

Front. Cell Dev. Biol.

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A subset of individuals with type 1 diabetes will develop diabetic kidney disease (DKD). DKD is heritable and large-scale genome-wide association studies have begun to identify genetic factors that influence DKD. Complementary to genetic factors, we know that a person's epigenetic profile is also altered with DKD. This study reports analysis of DNA methylation, a major epigenetic feature, evaluating methylome-wide loci for association with DKD. Unique features (n = 485,577; 482,421 CpG probes) were evaluated in blood-derived DNA from carefully phenotyped White European individuals diagnosed with type 1 diabetes with (cases) or without (controls) DKD (n = 677 samples). Explicitly, 150 cases were compared to 100 controls using the 450K array, with subsequent analysis using data previously generated for a further 96 cases and 96 controls on the 27K array, and de novo methylation data generated for replication in 139 cases and 96 controls. Following stringent quality control, raw data were quantile normalized and beta values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls; resultant P-values for array-based data were adjusted for multiple testing. Genes with significantly increased (hypermethylated) and/or decreased (hypomethylated) levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways. Twenty-two loci demonstrated statistically significant fold changes associated with DKD and additional support for these associated loci was sought using independent samples derived from patients recruited with similar inclusion criteria. Markers associated with CCNL1 and ZNF187 genes are supported as differentially regulated loci (P < 10 −8), with evidence also presented for AFF3, which has been identified from a meta-analysis and subsequent replication of genomewide association studies. Further supporting evidence for differential gene expression in CCNL1 and ZNF187 is presented from kidney biopsy and blood-derived RNA in people with and without kidney disease from NephroSeq. Evidence confirming that methylation sites influence the development of DKD may aid risk prediction tools and stimulate research to identify epigenomic therapies which might be clinically useful for this disease.

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Epigenetic Risk Profile of Diabetic Kidney Disease in High-Risk Populations

Natarajan²,

Chen³

2019

Curr Diab Rep

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Purpose of Review-Epigenetic variations have been shown to reveal vulnerability to diabetes and its complications. Although it has become clear that metabolic derangements, especially hyperglycemia, can impose a long-term metabolic memory that predisposes to diabetic complications, the underlying mechanisms remain to be understood. It has been suggested that epigenetics (e.g. histone modification, DNA methylation, and non-coding RNAs) help link metabolic disruption to aberrancies related to diabetic kidney disease (DKD). In this review we discuss the key findings and advances made in the epigenetic risk profile of DKD and provide perspectives on the emerging topics that implicate epigenetics in DKD.Recent Findings-Epigenetic profiles can be profoundly altered in patients with diabetes, in circulating blood cells as well as in renal tissues. These changes provide useful insight into the mechanisms of diabetic kidney injury and progressive kidney dysfunction.Summary-Increasing evidence supports the role of epigenetic regulation in DKD. More studies are needed to elucidate the mechanism and importance of epigenetic changes in the initiation and progression of DKD and to further explore their diagnostic and therapeutic potential in the clinical management of patients with diabetes who have a high risk for DKD.

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The association between methylation levels of targeted genes and albuminuria in patients with early diabetic kidney disease

Abstract: The present study provided evidence that hypomethylation of TIMP-2 and AKR1B1 genes can be associated with albuminuria in patients with early DN. We may speculate that the hypomethylation of TIMP-2 and AKR1B1 genes may be an early surrogate marker of DN.

Cited by 28 publications

References 21 publications

<p>Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study</p>

<p>Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study</p>

DNA Methylation Associated With Diabetic Kidney Disease in Blood-Derived DNA

Epigenetic Risk Profile of Diabetic Kidney Disease in High-Risk Populations

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