Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease
Abstract
:1. Introduction
2. Diabetic Kidney Disease: Definition and Prognosis
3. Complexities Underlying Diabetic Kidney Disease: Molecular Mechanisms of Damage
4. Old and Novel Treatments Available for Reducing Risk in Patients with Diabetes and CKD
5. Biomarkers and New Tools to Improve Individual Risk Prediction in Patients with Diabetes and CKD
6. Clinical and Genetic Predisposition to Individual Response to Therapies in Diabetes and CKD
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type 1 Diabetes | |||
---|---|---|---|
Prognosis | Source | Biomarker/Variable | Findings and Interpretation |
Single Biomarkers/Biomarkers family | |||
Tofte N et al. [64] | MR-proANP, NT-proBNP | They are associated with 2-fold increased risk of EKSD, CV events and all-cause mortality, regardless of the main traditional risk factors. | |
Costacou T et al. [65] | hs-cTnT | Blood levels of hs-cTnT were associated (with about 40% more risk for each unit increase) with CV events over time. | |
El Dayem SMA et al. [67] | Copeptin | Higher blood levels of copeptin are strictly associated with the development of atherosclerosis, arterial stiffness and kidney. damage. Patients with the highest levels of copeptin have concomitantly increased levels of albuminuria. | |
Nakano D et al. [71] | Urinary AGT | Urinary levels of ATG predict eGFR decline and ESKD, regardless of baseline levels of albuminuria. | |
Genomic findings | |||
Salem RM et al. [73] | Single nucleotide polymorphisms- 16 loci (e.g., SNP variant rs55703767) | SNP variant rs55703767 is responsible for a mutation in the collagen type IV alpha 3 chain (COL4A3). It was the variant with the strongest association with kidney damage and CKD progression. | |
Smyth LJ et al. [74] | DNA methylation patterns FKBP5-RUNX3-PIM1-ELMO1-LY0) | Polymorphisms in these genes have been associated with cardiovascular and kidney disease, ageing, tumor cell proliferation, TGF-β signaling and inflammatory-immune pathways. | |
Type 2 Diabetes | |||
Prognosis | Source | Biomarker/Variable | Findings and Interpretation |
Single Biomarkers/Biomarkers family | |||
Niewczas MA et al. [77] Waijer SW et al. [78] | TNFR-1/TNFR-2 | Their plasma levels are associated with an increased risk of CKD progression and ESKD. They may help to improve risk stratification of DKD patients and forecast ESKD even in the absence of proteinuria, thus testifying their possible predictive role in the earlier stages of CKD and in non-proteinuric phenotypes of CKD. | |
Nowak N et al. [80] Coca SG et al. [81] | KIM-1 | Promote kidney fibrosis and accelerate eGFR decline. Plasma KIM-1 level is associated with CKD progression strongly and independently of the TNFR-1 and -2 levels and both in patients with early and advanced DKD. | |
Luan HH et al. [84] Sen T et al. [83] | GDF-15 | GDF-15 increases in chronic conditions such as diabetes or CKD. Increased plasma levels are associated with higher risk for CV events. | |
Tang O et al. [87] | hs-cTnT/hs-cTnI | In DKD patients, the measurements of both biomarkers improve CV risk stratification. | |
Kammer M et al. [89] | NT-proBNP | Predict CV and kidney endpoints. | |
Velho G et al. [90] | Copeptin | High plasma levels were found to forecast the CKD progression (ESKD or doubling of serum creatinine). Such an association was strong and independent of a series of baseline covariates such as age, gender, eGFR and albuminuria. | |
Combination of multiple markers | |||
Roscioni et al. [93] | CKD273 | Panel of 273 urine peptides that predict the onset of albuminuria and CKD progression over time. | |
Genomic findings | |||
Vujkovic M et al. [95] | UMOD gene | Genetic variants in UMOD gene were associated with CKD development in a multiethnic analysis. From the same population, 13 variants predicted CV complications of type 2 diabetes patients | |
Ma J et al. [96] | Cubilin and Megalin genes | Polymorphisms in these genes modified ESKD risk in an African American population. | |
Treatment Response Markers | |||
Nichols G.A. et al. [105] Becker M.L. et al. [107] | Metformin | First line treatment for hyperglycaemia. In DKD patients were not contraindicated unless the kidney damage is advanced or conditions predisposing to lactic acidosis coexist. Clinical and pharmacogenetic factors explain the individual variation of the response to metformin. Genetic variants of the SLC22A1 and SLC47A1 gene influence both pharmacokinetic (PK) and pharmacodynamic (PD) behavior of metformin. | |
De Luis D.A. et al. [108] Ferreira M.C. et al. [110] Shu L. et al. [111] | GLP1-RA | Polymorphisms in the GLP1 receptor gene exert a different response to GLP1-RA. | |
Nagai K. Et al. [114] Hoeben E. et al. [115] Zimdahl H. et al. [116] | SGLT2 inhibitors | Novel drugs in the treatment of patients with diabetes and CKD. Some studies have highlighted a greater response in males than in females. Genetic plays a relevant role in determining the degree of response to SGLT2 inhibitors. | |
Cohen J.B. at al. [119] Kwaker Naak A.J et al. [121] Miao Y. et al. [122] Parving H.H. et al. [123] | ACE/ARB | Clinical and genetic reasons explain the variability in response to ACEi and ARBs. BMI and obesity, for example, are associated with a decreased response to these agents. An insertion (I) or deletion (D) polymorphism of the ACE gene modifies the activity of the systemic and renal renin-angiotensin-aldosterone system (RAAS) with a higher activity in patients with the D polymorphism. | |
Simon J.A. et al. [125] Elens L. et al. [126] | Statins | Statins work through the competitive inhibition of the enzyme 3-hydroxy-3-methylglutaryl-CoA reductase, lowering LDL cholesterol levels. A degree of individual variation in treatment effect has been found. Polymorphisms in the gene involved in the PK of statins are majorly modificatory of their individual response, particularly with respect to the cytochrome P450 expression. |
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Provenzano, M.; Maritati, F.; Abenavoli, C.; Bini, C.; Corradetti, V.; La Manna, G.; Comai, G. Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease. Int. J. Mol. Sci. 2022, 23, 5719. https://doi.org/10.3390/ijms23105719
Provenzano M, Maritati F, Abenavoli C, Bini C, Corradetti V, La Manna G, Comai G. Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease. International Journal of Molecular Sciences. 2022; 23(10):5719. https://doi.org/10.3390/ijms23105719
Chicago/Turabian StyleProvenzano, Michele, Federica Maritati, Chiara Abenavoli, Claudia Bini, Valeria Corradetti, Gaetano La Manna, and Giorgia Comai. 2022. "Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease" International Journal of Molecular Sciences 23, no. 10: 5719. https://doi.org/10.3390/ijms23105719