|
1
|
Wang M, Pang Y, Guo Y, Tian L, Liu Y, Shen
C, Liu M, Meng Y, Cai Z, Wang Y and Zhao W: Metabolic
reprogramming: A novel therapeutic target in diabetic kidney
disease. Front Pharmacol. 13:9706012022. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Bonner R, Albajrami O, Hudspeth J and
Upadhyay A: Diabetic kidney disease. Prim Care. 47:645–659. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Scilletta S, Di Marco M, Miano N,
Filippello A, Di Mauro S, Scamporrino A, Musmeci M, Coppolino G, Di
Giacomo Barbagallo F, Bosco G, et al: Update on diabetic kidney
disease (DKD): Focus on non-albuminuric DKD and cardiovascular
risk. Biomolecules. 13:7522023. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Sun H, Saeedi P, Karuranga S, Pinkepank M,
Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC, et
al: IDF diabetes atlas: Global, regional and country-level diabetes
prevalence estimates for 2021 and projections for 2045. Diabetes
Res Clin Pract. 183:1091192022. View Article : Google Scholar
|
|
5
|
Forbes JM and Fotheringham AK: Vascular
complications in diabetes: Old messages, new thoughts.
Diabetologia. 60:2129–2138. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Wang Y, Gu S, Xie Z, Xu Z, He W, Chen Y,
Jin J and He Q: Trends and disparities in the burden of chronic
kidney disease due to type 2 diabetes in China from 1990 to 2021: A
population-based study. J Diabetes. 17:e700842025. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
A/L B Vasanth Rao VR, Tan SH, Candasamy M
and Bhattamisra SK: Diabetic nephropathy: An update on pathogenesis
and drug development. Diabetes Metab Syndr. 13:754–762. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Qi C, Mao X, Zhang Z and Wu H:
Classification and differential diagnosis of diabetic nephropathy.
J Diabetes Res. 2017:86371382017. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Petrazzuolo A, Sabiu G, Assi E, Maestroni
A, Pastore I, Lunati ME, Montefusco L, Loretelli C, Rossi G, Ben
Nasr M, et al: Broadening horizons in mechanisms, management, and
treatment of diabetic kidney disease. Pharmacol Res.
190:1067102023. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Naaman SC and Bakris GL: Diabetic
nephropathy: Update on pillars of therapy slowing progression.
Diabetes Care. 46:1574–1586. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Forst T, Mathieu C, Giorgino F, Wheeler
DC, Papanas N, Schmieder RE, Halabi A, Schnell O, Streckbein M and
Tuttle KR: New strategies to improve clinical outcomes for diabetic
kidney disease. BMC Med. 20:3372022. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Wu J, Lu AD, Zhang LP, Zuo YX and Jia YP:
Study of clinical outcome and prognosis in pediatric core binding
factor-acute myeloid leukemia. Zhonghua Xue Ye Xue Za Zhi.
40:52–57. 2019.In Chinese. PubMed/NCBI
|
|
13
|
MacIsaac RJ, Ekinci EI and Jerums G:
'Progressive diabetic nephropathy. How useful is microalbuminuria?:
Contra'. Kidney Int. 86:50–57. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Vučić Lovrenčić M, Božičević S and Smirčić
Duvnjak L: Diagnostic challenges of diabetic kidney disease.
Biochem Med (Zagreb). 33:0305012023.
|
|
15
|
Tan KS, McDonald S and Hoy W: The
diagnostic performance of a clinical diagnosis of diabetic kidney
disease. Life (Basel). 13:14922023.PubMed/NCBI
|
|
16
|
Wang N and Zhang C: Recent advances in the
management of diabetic kidney disease: Slowing progression. Int J
Mol Sci. 25:30862024. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Fang Z, Liu R, Xie J and He JC: Molecular
mechanism of renal lipid accumulation in diabetic kidney disease. J
Cell Mol Med. 28:e183642024. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Du S, Zhai L, Ye S, Wang L, Liu M and Tan
M: In-depth urinary and exosome proteome profiling analysis
identifies novel biomarkers for diabetic kidney disease. Sci China
Life Sci. 66:2587–2603. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Hou Q and Yi B: The role of long
non-coding RNAs in the development of diabetic kidney disease and
the involved clinical application. Diabetes Metab Res Rev.
40:e38092024. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Zhang H, Zuo JJ, Dong SS, Lan Y, Wu CW,
Mao GY and Zheng C: Identification of potential serum metabolic
biomarkers of diabetic kidney disease: A widely targeted
metabolomics study. J Diabetes Res. 2020:30490982020. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Matys U, Bachorzewska-Gajewska H, Malyszko
J and Dobrzycki S: Assessment of kidney function in diabetic
patients. Is there a role for new biomarkers NGAL, cystatin C and
KIM-1? Adv Med Sci. 58:353–561. 2013. View Article : Google Scholar
|
|
22
|
Valent Morić B, Šamija I, La Grasta,
Sabolić L, Unić A and Miler M: Is the urinary neutrophil
gelatinase-associated lipocalin concentration in children and
adolescents with type 1 diabetes mellitus different from that in
healthy children? Biochem Med (Zagreb). 34:0207092024.
|
|
23
|
Sharma K, Ix JH, Mathew AV, Cho M,
Pflueger A, Dunn SR, Francos B, Sharma S, Falkner B, McGowan TA, et
al: Pirfenidone for diabetic nephropathy. J Am Soc Nephrol.
22:1144–1151. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Jones D: Setbacks shadow microRNA
therapies in the clinic. Nat Biotechnol. 36:909–910. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Panduru NM, Sandholm N, Forsblom C,
Saraheimo M, Dahlström EH, Thorn LM, Gordin D, Tolonen N, Wadén J,
Harjutsalo V, et al: Kidney injury molecule-1 and the loss of
kidney function in diabetic nephropathy: A likely causal link in
patients with type 1 diabetes. Diabetes Care. 38:1130–1137. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Liao X, Zhu Y and Xue C: Diagnostic value
of serum cystatin C for diabetic nephropathy: A meta-analysis. BMC
Endocr Disord. 22:1492022. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Luo Y, Zhang W and Qin G: Metabolomics in
diabetic nephropathy: Unveiling novel biomarkers for diagnosis
(Review). Mol Med Rep. 30:1562024. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Langfelder P and Horvath S: WGCNA: An R
package for weighted correlation network analysis. BMC
Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Li B, Ye S, Fan Y, Lin Y, Li S, Peng H,
Diao H and Chen W: Identification of novel key genes and potential
candidate small molecule drugs in diabetic kidney disease using
comprehensive bioinformatics analysis. Front Genet. 13:9345552022.
View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Greener JG, Kandathil SM, Moffat L and
Jones DT: A guide to machine learning for biologists. Nat Rev Mol
Cell Biol. 23:40–55. 2022. View Article : Google Scholar
|
|
31
|
Handelman GS, Kok HK, Chandra RV, Razavi
AH, Lee MJ and Asadi H: eDoctor: Machine learning and the future of
medicine. J Intern Med. 284:603–619. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Choi RY, Coyner AS, Kalpathy-Cramer J,
Chiang MF and Campbell JP: Introduction to machine learning, neural
networks, and deep learning. Transl Vis Sci Technol.
9:142020.PubMed/NCBI
|
|
33
|
Obuchowski NA and Bullen JA: Receiver
operating characteristic (ROC) curves: Review of methods with
applications in diagnostic medicine. Phys Med Biol. 63:07TR012018.
View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Xu M, Zhou H, Hu P, Pan Y, Wang S, Liu L
and Liu X: Identification and validation of immune and oxidative
stress-related diagnostic markers for diabetic nephropathy by WGCNA
and machine learning. Front Immunol. 14:10845312023. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Barrett T, Wilhite SE, Ledoux P,
Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH,
Sherman PM, Holko M, et al: NCBI GEO: Archive for functional
genomics data sets-update. Nucleic Acids Res. 41:D991–D995. 2013.
View Article : Google Scholar :
|
|
36
|
Woroniecka KI, Park ASD, Mohtat D, Thomas
DB, Pullman JM and Susztak K: Transcriptome analysis of human
diabetic kidney disease. Diabetes. 60:2354–2369. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Na J, Sweetwyne MT, Park AS, Susztak K and
Cagan RL: Diet-induced podocyte dysfunction in drosophila and
mammals. Cell Rep. 12:636–647. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW,
Shi W and Smyth GK: limma powers differential expression analyses
for RNA-sequencing and microarray studies. Nucleic Acids Res.
43:e472015. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Subramanian A, Tamayo P, Mootha VK,
Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub
TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A
knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Gene Ontology Consortium: Gene ontology
consortium: Going forward. Nucleic Acids Res. 43:D1049–D1056. 2015.
View Article : Google Scholar :
|
|
41
|
Kanehisa M, Furumichi M, Sato Y, Matsuura
Y and Ishiguro-Watanabe M: KEGG: Biological systems database as a
model of the real world. Nucleic Acids Res. 53:D672–D677. 2025.
View Article : Google Scholar :
|
|
42
|
Iranzad R and Liu X: A review of random
forest-based feature selection methods for data science education
and applications. Int J Data Sci Anal. 20:197–211. 2025. View Article : Google Scholar
|
|
43
|
Fernández-Delgado M, Sirsat MS, Cernadas
E, Alawadi S, Barro S and Febrero-Bande M: An extensive
experimental survey of regression methods. Neural Netw. 111:11–34.
2019. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Sanz H, Valim C, Vegas E, Oller JM and
Reverter F: SVM-RFE: Selection and visualization of the most
relevant features through non-linear kernels. BMC Bioinformatics.
19:4322018. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Yang YY, Gao ZX, Mao ZH, Liu DW, Liu ZS
and Wu P: Identification of ULK1 as a novel mitophagy-related gene
in diabetic nephropathy. Front Endocrinol (Lausanne).
13:10794652023. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Newman AM, Steen CB, Liu CL, Gentles AJ,
Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA,
Steiner D, et al: Determining cell type abundance and expression
from bulk tissues with digital cytometry. Nat Biotechnol.
37:773–782. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Suriano F, Vieira-Silva S, Falony G,
Roumain M, Paquot A, Pelicaen R, Régnier M, Delzenne NM, Raes J,
Muccioli GG, et al: Novel insights into the genetically obese
(ob/ob) and diabetic (db/db) mice: Two sides of the same coin.
Microbiome. 9:1472021. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Paller MS, Hoidal JR and Ferris TF: Oxygen
free radicals in ischemic acute renal failure in the rat. J Clin
Invest. 74:1156–1164. 1984. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Livak KJ and Schmittgen TD: Analysis of
relative gene expression data using real-time quantitative PCR and
the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001.
View Article : Google Scholar
|
|
50
|
Kidney Disease: Improving Global Outcomes
(KDIGO) Diabetes Work Group: KDIGO 2022 clinical practice guideline
for diabetes management in chronic kidney disease. Kidney Int.
102(5S): S1–S127. 2022. View Article : Google Scholar
|
|
51
|
Jung CY and Yoo TH: Pathophysiologic
mechanisms and potential biomarkers in diabetic kidney disease.
Diabetes Metab J. 46:181–197. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Tuttle KR, Bakris GL, Bilous RW, Chiang
JL, de Boer IH, Goldstein-Fuchs J, Hirsch IB, Kalantar-Zadeh K,
Narva AS, Navaneethan SD, et al: Diabetic kidney disease: A report
from an ADA consensus conference. Diabetes Care. 37:2864–2883.
2014. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Tuttle KR, Bakris GL, Bilous RW, Chiang
JL, de Boer IH, Goldstein-Fuchs J, Hirsch IB, Kalantar-Zadeh K,
Narva AS, Navaneethan SD, et al: Diabetic kidney disease: A report
from an ADA consensus conference. Am J Kidney Dis. 64:510–533.
2014. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Li J, Guo K, Qiu J, Xue S, Pi L, Li X,
Huang G, Xie Z and Zhou Z: Epidemiological status, development
trends, and risk factors of disability-adjusted life years due to
diabetic kidney disease: A systematic analysis of global burden of
disease study 2021. Chin Med J (Engl). 138:568–578. 2025.
View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Limonte CP, Kretzler M, Pennathur S,
Pop-Busui R and de Boer IH: Present and future directions in
diabetic kidney disease. J Diabetes Complications. 36:1083572022.
View Article : Google Scholar : PubMed/NCBI
|
|
56
|
McKnight AJ, McKay GJ and Maxwell AP:
Genetic and epigenetic risk factors for diabetic kidney disease.
Adv Chronic Kidney Dis. 21:287–296. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Brennan EP, Mohan M, Andrews D, Bose M and
Kantharidis P: Specialized pro-resolving mediators in diabetes:
Novel therapeutic strategies. Clin Sci (Lond). 133:2121–2141. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Kato M: Noncoding RNAs as therapeutic
targets in early stage diabetic kidney disease. Kidney Res Clin
Pract. 37:197–209. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Gupta S, Dominguez M and Golestaneh L:
Diabetic kidney disease: An update. Med Clin North Am. 107:689–705.
2023. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Ma X, Liu R, Xi X, Zhuo H and Gu Y: Global
burden of chronic kidney disease due to diabetes mellitus,
1990-2021 and projections to 2050. Front Endocrinol (Lausanne).
16:15130082025. View Article : Google Scholar
|
|
61
|
Rico-Fontalvo J, Aroca-Martinez G,
Daza-Arnedo R, Cabrales J, Rodriguez-Yanez T, Cardona-Blanco M,
Montejo-Hernández J, Rodelo Barrios D, Patiño-Patiño J and Osorio
Rodríguez E: Novel biomarkers of diabetic kidney disease.
Biomolecules. 13:6332023. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
Zhong M, Zhu E, Li N, Gong L, Xu H, Zhong
Y, Gong K, Jiang S, Wang X, Fei L, et al: Identification of
diagnostic markers related to oxidative stress and inflammatory
response in diabetic kidney disease by machine learning algorithms:
Evidence from human transcriptomic data and mouse experiments.
Front Endocrinol (Lausanne). 14:11343252023. View Article : Google Scholar : PubMed/NCBI
|
|
63
|
Chasset F and Arnaud L: Targeting
interferons and their pathways in systemic lupus erythematosus.
Autoimmun Rev. 17:44–52. 2018. View Article : Google Scholar
|
|
64
|
Cohen-Kedar S, Baram L, Elad H, Brazowski
E, Guzner-Gur H and Dotan I: Human intestinal epithelial cells
respond to β-glucans via Dectin-1 and Syk. Eur J Immunol.
44:3729–3740. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
65
|
Ulanova M, Duta F, Puttagunta L, Schreiber
AD and Befus AD: Spleen tyrosine kinase (Syk) as a novel target for
allergic asthma and rhinitis. Expert Opin Ther Targets. 9:901–921.
2005. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Chia XX, Ozols E, Nikolic-Paterson DJ and
Tesch GH: Spleen tyrosine kinase signaling in myeloid cells
promotes macrophage infiltration, glomerulosclerosis, and
interstitial fibrosis in diabetic kidney disease. Am J Physiol
Renal Physiol. 329:F872–F882. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Lin DW, Yang TM, Ho C, Shih YH, Lin CL and
Hsu YC: Targeting macrophages: Therapeutic approaches in diabetic
kidney disease. Int J Mol Sci. 25:43502024. View Article : Google Scholar : PubMed/NCBI
|
|
68
|
Zhang R, Qin C, Zhang J, Ren Honghong,
Wang Y, Wu Y, Zhao L, Wang J, Zhang J and Liu F: DNA
hypomethylation of Syk induces oxidative stress and apoptosis via
the PKCβ/P66shc signaling pathway in diabetic kidney disease. FASEB
J. 38:e235642024. View Article : Google Scholar
|
|
69
|
Piwkowska A, Rachubik P, Typiak M, Kulesza
T, Audzeyenka I, Saleem MA, Gruba N, Wysocka M, Lesner A and
Rogacka D: ADAM10 as a major activator of reactive oxygen species
production and klotho shedding in podocytes under diabetic
conditions. Biochem Pharmacol. 225:1163282024. View Article : Google Scholar : PubMed/NCBI
|
|
70
|
Gruba N, Piwkowska A and Lesner A: Initial
study of the detection of ADAM 10 in the urine of type-2 diabetic
patients. Bioorg Chem. 140:1068262023. View Article : Google Scholar : PubMed/NCBI
|
|
71
|
Sui C and Zhou D: ADAM metallopeptidase
domain 10 knockdown enables podocytes to resist high glucose
stimulation by inhibiting pyroptosis via MAPK pathway. Exp Ther
Med. 25:2602023. View Article : Google Scholar : PubMed/NCBI
|
|
72
|
Wang X, Tang D, Zou Y, Wu X, Chen Y, Li H,
Chen S, Shi Y and Niu H: A mitochondrial-targeted peptide
ameliorated podocyte apoptosis through a HOCl-alb-enhanced and
mitochondria-dependent signalling pathway in diabetic rats and in
vitro. J Enzyme Inhib Med Chem. 34:394–404. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
73
|
Zhang H, Hu J, Zhu J, Li Q and Fang L:
Machine learning-based metabolism-related genes signature and
immune infiltration landscape in diabetic nephropathy. Front
Endocrinol (Lausanne). 13:10269382022. View Article : Google Scholar : PubMed/NCBI
|
|
74
|
Hou G, Dong Y, Jiang Y, Zhao W, Zhou L,
Cao S and Li W: Immune inflammation and metabolic interactions in
the pathogenesis of diabetic nephropathy. Front Endocrinol
(Lausanne). 16:16025942025. View Article : Google Scholar : PubMed/NCBI
|
|
75
|
Chen XJ, Tang R, Zha J, Zeng L, Zhou L,
Liu Z, Yang D, Zeng M, Zhu X, Chen A, et al: A potential defensive
role of TIM-3 on T lymphocytes in the inflammatory involvement of
diabetic kidney disease. Front Immunol. 15:13652262024. View Article : Google Scholar : PubMed/NCBI
|
|
76
|
Moon JY, Jeong KH, Lee TW, Ihm CG, Lim SJ
and Lee SH: Aberrant recruitment and activation of T cells in
diabetic nephropathy. Am J Nephrol. 35:164–174. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
77
|
Crotty S: T follicular helper cell
differentiation, function, and roles in disease. Immunity.
41:529–542. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
78
|
Johnston RJ, Poholek AC, DiToro D, Yusuf
I, Eto D, Barnett B, Dent AL, Craft J and Crotty S: Bcl6 and
Blimp-1 are reciprocal and antagonistic regulators of T follicular
helper cell differentiation. Science. 325:1006–1010. 2009.
View Article : Google Scholar : PubMed/NCBI
|
|
79
|
Crotty S: Follicular helper CD4 T cells
(TFH). Annu Rev Immunol. 29:621–663. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
80
|
Zhang C, Xiao C, Wang P, Xu W, Zhang A, Li
Q and Xu X: The alteration of Th1/Th2/Th17/Treg paradigm in
patients with type 2 diabetes mellitus: Relationship with diabetic
nephropathy. Hum Immunol. 75:289–296. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
81
|
Wu CC, Chen JS, Lu KC, Chen CC, Lin SH,
Chu P, Sytwu HK and Lin YF: Aberrant cytokines/chemokines
production correlate with proteinuria in patients with overt
diabetic nephropathy. Clin Chim Acta. 411:700–704. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
82
|
Anand G, Vasanthakumar R, Mohan V, Babu S
and Aravindhan V: Increased IL-12 and decreased IL-33 serum levels
are associated with increased Th1 and suppressed Th2 cytokine
profile in patients with diabetic nephropathy (CURES-134). Int J
Clin Exp Pathol. 7:8008–8015. 2014.
|
|
83
|
Zamauskaite A, Yaqoob MM, Madrigal JA and
Cohen SB: The frequency of Th2 type cells increases with time on
peritoneal dialysis in patients with diabetic nephropathy. Eur
Cytokine Netw. 10:219–226. 1999.PubMed/NCBI
|
|
84
|
Spence A, Klementowicz JE, Bluestone JA
and Tang Q: Targeting Treg signaling for the treatment of
autoimmune diseases. Curr Opin Immunol. 37:11–20. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
85
|
Pichler R, Afkarian M, Dieter BP and
Tuttle KR: Immunity and inflammation in diabetic kidney disease:
Translating mechanisms to biomarkers and treatment targets. Am J
Physiol Renal Physiol. 312:F716–F731. 2017. View Article : Google Scholar
|
|
86
|
Gu QW, Sun Q, Wang J, Gu WS, Wang W and
Mao XM: Effects of glycemic variability on regulatory T cells in
patients with type 2 diabetes and kidney disease. Diabetes Metab
Syndr Obes. 16:2365–2375. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
87
|
Eller K, Kirsch A, Wolf AM, Sopper S,
Tagwerker A, Stanzl U, Wolf D, Patsch W, Rosenkranz AR and Eller P:
Potential role of regulatory T cells in reversing obesity-linked
insulin resistance and diabetic nephropathy. Diabetes.
60:2954–2962. 2011. View Article : Google Scholar : PubMed/NCBI
|
