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Anemia in Diabetic Patients Reflects Severe Tubulointerstitial Injury and Aids in Clinically Predicting a Diagnosis of Diabetic Nephropathy.

ABSTRACT: Objective A kidney biopsy is generally performed in diabetic patients to discriminate between diabetic nephropathy (DN) and non-diabetic kidney disease (NDKD) and to provide more specific treatments. This study investigated the impact of anemia on the renal pathology and the clinical course in patients who underwent a kidney biopsy. Methods We reviewed 81 patients with type 2 diabetes who underwent a percutaneous kidney biopsy. Patients were classified into two groups: isolated DN (DN group, n=30) and NDKD alone or concurrent DN (NDKD group, n=51) groups. The laboratory and pathological findings and clinical courses were investigated. Results In the NDKD group, membranous nephropathy was the most common finding (23.5%), followed by IgA nephropathy (17.6%) and crescentic glomerulonephritis (13.7%). In the logistic regression analysis, the absence of severe hematuria and presence of anemia were significantly associated with a diagnosis of DN. Akaike's information criterion (AIC) and net reclassification improvement (NRI) analyses revealed improved predictive performance by adding anemia to the conventional factors (AIC 100.152 to 91.844; NRI 27.0%). The tissues of patients in the DN group demonstrated more severe interstitial fibrosis and tubular atrophy (IF/TA) than those in the NDKD group (p<0.05) regardless of the rate of global glomerulosclerosis, and IF/TA was related to the prevalence of anemia (odds ratio: 7.31, 95% confidence interval: 2.33-23.00, p<0.01) according to a multivariable regression analysis. Furthermore, the isolated DN group demonstrated a poorer prognosis than the NDKD group. Conclusion DN is associated with anemia because of severe IF/TA regardless of the renal function, and anemia helps clinician discriminate clinically between isolated DN and NDKD.

SUBMITTER: Ito K

PROVIDER: S-EPMC8170246 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionKey genes involved in tubulointerstitial injury may influence the development and progression of diabetic nephropathy (DN). We investigated whether complement-related genes are linked to the mechanism underlying tubulointerstitial injury in DN.MethodsWe analyzed the microarray data of 17 tubulointerstitial tissue samples from DN patients and 21 normal controls from the Gene Expression Omnibus. A gene co-expression network was constructed, and genes were divided into modules by weighted gene co-expression network analysis (WGCNA). We also investigated the association of C3 and C1q deposits in kidney tissues with a composite outcome of end-stage renal disease or a 50% reduction in the estimated glomerular filtration rate (eGFR) in DN patients. Finally, we performed immunohistochemical analyses of C3, C1q, C5b-9, mannose-binding lectin (MBL), and factor B in kidney tissues.ResultsNine co-expression modules were constructed using 12,075 genes from the 38 human tubulointerstitial tissue samples. Black module with more genes was positively correlated with tubulointerstitial injury in DN. C3, one of the top 10 genes in tubulointerstitial injury, was verified in an independent dataset; C3 was significantly overexpressed in tubulointerstitial tissue from patients with DN compared to the normal controls. The mRNA level of C3 in renal tubulointerstitium was negatively correlated with eGFR in DN patients (r = -0.75; p = 0.001). Analysis of the follow-up data of 54 DN patients demonstrated that codeposits of C3 and C1q in kidney tissues were independently associated with the renal outcome in DN (hazard ratio, 2.3, 95% confidence interval, 1.01-5.2, p < 0.05). Immunohistochemical analysis showed that patients with higher C1q, C3, C5b-9, MBL, or factor B expression in renal tubulointerstitium were more likely to progress to kidney failure.ConclusionLocal complement activation of the classical, lectin and alternative pathways appears linked to tubulointerstitial injury and disease progression in DN.

Project description:AimsThe tendency to develop diabetic nephropathy is, in part, genetically determined, however this genetic risk is largely undefined. In this proof-of-concept study, we tested the hypothesis that combined analysis of multiple genetic variants can improve prediction.MethodsBased on previous reports, we selected 27 SNPs in 15 genes from metabolic pathways involved in the pathogenesis of diabetic nephropathy and genotyped them in 1274 Ashkenazi or Sephardic Jewish patients with Type 1 or Type 2 diabetes of >10 years duration. A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population. The model was validated by using random "training" (75%) and "test" (25%) subgroups of the original population and by applying the model to an independent dataset of 848 Ashkenazi patients.ResultsThe logistic model based on 5 SNPs in 5 genes (HSPG2, NOS3, ADIPOR2, AGER, and CCL5) and 5 conventional variables (age, sex, ethnicity, diabetes type and duration), and allowing for all possible two-way interactions, predicted nephropathy in our initial population (C-statistic = 0.672) better than a model based on conventional variables only (C = 0.569). In the independent replication dataset, although the C-statistic of the genetic model decreased (0.576), it remained highly associated with diabetic nephropathy (χ(2) = 17.79, p<0.0001). In the replication dataset, the model based on conventional variables only was not associated with nephropathy (χ(2) = 3.2673, p = 0.07).ConclusionIn this proof-of-concept study, we developed and validated a genetic model in the Ashkenazi/Sephardic population predicting nephropathy more effectively than a similarly constructed non-genetic model. Further testing is required to determine if this modeling approach, using an optimally selected panel of genetic markers, can provide clinically useful prediction and if generic models can be developed for use across multiple ethnic groups or if population-specific models are required.

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Anemia in Diabetic Patients Reflects Severe Tubulointerstitial Injury and Aids in Clinically Predicting a Diagnosis of Diabetic Nephropathy.

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