Skip to content
2000
Volume 32, Issue 2
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286
file format text download EPUB
file format pdf download PDF
side by side viewer icon HTML

Abstract

Objective

In this study, the causation between serum metabolites and the risk of Diabetic Nephropathy (DN) was investigated by means of a Mendelian Randomization (MR) analysis.

Methods

Our data on diabetic nephropathy were obtained from the IEU OpenGWAS Project database, while serum metabolite data originated came from the GWAS summary statistics by Chen . The Inverse Variance Weighted (IVW) method was the main analysis approach, with Weighted Median (WME) and MR-Egger regression serving as supplementary approaches to construing the causalities between serum metabolites and the DN risk. In addition to the MR-Egger regression intercept, Cochran's Q test was utilized for sensitivity analysis, with values used as the metric to assess the results.

Results

In total, 14 SNPs regarding serum metabolites were chosen as Instrumental Variables (IVs). The IVW results indicated that levels of Behenoylcarnitine (C22), Arachidoylcarnitine (C20), and the ratio of 5-methylthioadenosine (MTA) to phosphate exerted a positive causal effect on the DN risk. Conversely, levels of 5-hydroxylysine, Butyrylglycine, 1-stearoyl-glycerophosphocholine (18:0), Isobutyrylglycine, 1-stearoyl-2-oleoyl-GPE (18:0/18:1), N2,N5-diacetylornithine, 2-butenoylglycine, 3-hydroxybutyroylglycine, N-acetyl-isoputreanine, the ratio of Arginine to Ornithine, and the ratio of Aspartate to Mannose exerted a negative impact of causality on the DN risk. By identifying these serum metabolites, high-risk patients can be recognized in the early stages of diabetic nephropathy, enabling preventive measures or delaying its progression. These findings also provide a solid foundation for further research into the underlying etiology of diabetic nephropathy.

Conclusion

The translation of serum metabolites into clinical applications for DN aims to utilize changes in serum metabolites as biomarkers for early diagnosis, thereby monitoring the progression of DN and providing a foundation for personalized treatment. For instance, the development of serum metabolite diagnostic kits could be used for early detection and prevention of DN. Changes in metabolites can help identify different stages of DN.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128377862250429045226
2026-05-12
2026-02-20

  • From This Site

    /content/journals/cpd/10.2174/0113816128377862250429045226
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cpd/32/2/CPD-32-2-04.html?itemId=/content/journals/cpd/10.2174/0113816128377862250429045226&mimeType=html&fmt=ahah

References

  1. ChenJ. LiW. CaoJ. LuY. WangC. LuJ. Risk factors for carotid plaque formation in type 2 diabetes mellitus.J. Transl. Med.20242211810.1186/s12967‑023‑04836‑7 38178198
     [Google Scholar]
  2. HussainM.S. SrivastavaN. SinghG. KumarR. Long-term use of metformin and vitamin B12 deficiency in diabetes.Curr. Drug Saf.202420325827010.2174/0115748863308106240816044733 39206482
     [Google Scholar]
  3. AbdollahiS. YazdiM.H.H. MowlaviA.A. Erratum to: Surface guided 3DCRT in deep-inspiration breath hold for left sided breast cancer radiotherapy: Implementation and first clinical experience in Iran.Rep. Pract. Oncol. Radiother.202328343543610.5603/RPOR.a2023.0047 37795401
     [Google Scholar]
  4. AngariaaN. SainiS. HussainM.S. Natural polymer-based hydrogels: Versatile biomaterials for biomedical applications.Int. J. Polym. Mater.2023731171550156810.1080/00914037.2023.2301645
     [Google Scholar]
  5. BahlM.S.H.G. DineshK. Chronic calcific pancreatitis and its association with secondary diabetes mellitus.J. Endocrinol. Metab.2023133899510.14740/jem884
     [Google Scholar]
  6. BahlG. UpadhyayD.K. VarmaM. SinghR. DasS. HussainS. Persistent chronic calcific pancreatitis with intraductal calculi associated with secondary diabetes mellitus type 3 and diabetic ketoacidosis – A case report.Endocr. Regul.202458110110410.2478/enr‑2024‑0011 38656253
     [Google Scholar]
  7. LuH. GuoT. ZhangY. Endoplasmic reticulum stress-induced NLRP3 inflammasome activation as a novel mechanism of polystyrene microplastics (PS-MPs)-induced pulmonary inflammation in chickens.J. Zhejiang Univ. Sci. B202425323324310.1631/jzus.B2300409 38453637
     [Google Scholar]
  8. LuH. ZhangY. ZhangX. New insights into zinc alleviating renal toxicity of arsenic-exposed carp (Cyprinus carpio) through YAP-TFR/ROS signaling pathway.Pestic. Biochem. Physiol.202420510615310.1016/j.pestbp.2024.106153 39477605
     [Google Scholar]
  9. ZhangS. CaiY. MengC. The role of the microbiome in diabetes mellitus.Diabetes Res. Clin. Pract.202117210864510.1016/j.diabres.2020.108645 33359751
     [Google Scholar]
  10. BatchuP. NaldurtikerA. KouakouB. TerrillT.H. McCommonG.W. KannanG. Metabolomic exploration of the effects of habituation to livestock trailer and extended transportation in goats.Front. Mol. Biosci.20229102706910.3389/fmolb.2022.1027069 36465562
     [Google Scholar]
  11. WeiT. ZhaoL. JiaJ. Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice.Sci. Rep.2015511199810.1038/srep11998 26149603
     [Google Scholar]
  12. ZhaoT. ZhangH. ZhaoT. Intrarenal metabolomics reveals the association of local organic toxins with the progression of diabetic kidney disease.J. Pharm. Biomed. Anal.201260324310.1016/j.jpba.2011.11.010 22153801
     [Google Scholar]
  13. DunlopM. Aldose reductase and the role of the polyol pathway in diabetic nephropathy.Kidney Int.200058S3S1210.1046/j.1523‑1755.2000.07702.x 10997684
     [Google Scholar]
  14. WangL. ZhangL. YuY. WangY. NiuN. The protective effects of taurine against early renal injury in STZ-induced diabetic rats, correlated with inhibition of renal LOX-1-mediated ICAM-1 expression.Ren. Fail.200830876377110.1080/08860220802272563 18791949
     [Google Scholar]
  15. HirayamaA. NakashimaE. SugimotoM. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy.Anal. Bioanal. Chem.2012404103101310910.1007/s00216‑012‑6412‑x 23052862
     [Google Scholar]
  16. LawlorD.A. HarbordR.M. SterneJ.A.C. TimpsonN. Davey SmithG. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology.Stat. Med.20082781133116310.1002/sim.3034 17886233
     [Google Scholar]
  17. SongS. NiJ. SunY. Association of inflammatory cytokines with type 2 diabetes mellitus and diabetic nephropathy: A bidirectional Mendelian randomization study.Front. Med. (Lausanne)202411145975210.3389/fmed.2024.1459752 39574905
     [Google Scholar]
  18. ChangL. ZhouG. XiaJ. mGWAS-explorer 2.0: Causal analysis and interpretation of metabolite–phenotype associations.Metabolites202313782610.3390/metabo13070826 37512533
     [Google Scholar]
  19. YangL.Z. YangY. HongC. Systematic mendelian randomization exploring druggable genes for hemorrhagic strokes.Mol. Neurobiol.20256221359137210.1007/s12035‑024‑04336‑9 38977622
     [Google Scholar]
  20. FanZ. A two sample Mendelian randomization study of lipidome and lung cancer.J. Pharm. Biomed. Anal.202525211651410.1016/j.jpba.2024.116514 39405787
     [Google Scholar]
  21. WuJ. LiJ. YanR. GuoJ. Vitamin C and suicidal ideation: A cross-sectional and Mendelian randomization study.J. Affect. Disord.202536852853610.1016/j.jad.2024.09.062 39271073
     [Google Scholar]
  22. HarrisL. McDonaghE.M. ZhangX. Genome-wide association testing beyond SNPs.Nat. Rev. Genet.202526315617010.1038/s41576‑024‑00778‑y 39375560
     [Google Scholar]
  23. SongQ. HuT. LiangB. cascAGS: Comparative analysis of SNP calling methods for human genome data in the absence of gold standard.Interdiscip. Sci.202517111110.1007/s12539‑024‑00653‑8 39443427
     [Google Scholar]
  24. ChenY. LuT. Pettersson-KymmerU. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases.Nat. Genet.2023551445310.1038/s41588‑022‑01270‑1 36635386
     [Google Scholar]
  25. SakaueS. KanaiM. TanigawaY. A cross-population atlas of genetic associations for 220 human phenotypes.Nat. Genet.202153101415142410.1038/s41588‑021‑00931‑x 34594039
     [Google Scholar]
  26. LiZ. ChenY. KeH. Investigating the causal relationship between gut microbiota and Crohn’s disease: A Mendelian randomization study.Gastroenterology2024166235435510.1053/j.gastro.2023.08.047 37678500
     [Google Scholar]
  27. de LeeuwC. SavageJ. BucurI.G. HeskesT. PosthumaD. Understanding the assumptions underlying Mendelian randomization.Eur. J. Hum. Genet.202230665366010.1038/s41431‑022‑01038‑5 35082398
     [Google Scholar]
  28. XiangM. WangY. GaoZ. Exploring causal correlations between inflammatory cytokines and systemic lupus erythematosus: A Mendelian randomization.Front. Immunol.20231398572910.3389/fimmu.2022.985729 36741410
     [Google Scholar]
  29. PapadimitriouN. DimouN. TsilidisK.K. Physical activity and risks of breast and colorectal cancer: A Mendelian randomisation analysis.Nat. Commun.202011159710.1038/s41467‑020‑14389‑8 32001714
     [Google Scholar]
  30. LiuB. LyuL. ZhouW. Associations of the circulating levels of cytokines with risk of amyotrophic lateral sclerosis: A Mendelian randomization study.BMC Med.20232113910.1186/s12916‑023‑02736‑7 36737740
     [Google Scholar]
  31. ZouM. ZhangW. ShenL. XuY. ZhuY. Causal association between inflammatory bowel disease and herpes virus infections: A two-sample bidirectional Mendelian randomization study.Front. Immunol.202314120370710.3389/fimmu.2023.1203707 37465669
     [Google Scholar]
  32. BurgessS. ThompsonS.G. Interpreting findings from Mendelian randomization using the MR-Egger method.Eur. J. Epidemiol.201732537738910.1007/s10654‑017‑0255‑x 28527048
     [Google Scholar]
  33. LiK. LiuP. ZengY. LiuM. YeJ. ZhuL. Exploring the bidirectional causal association between Sleep Apnea Syndrome and Depression: A Mendelian randomization study involving gut microbiota, serum metabolites, and inflammatory factors.J. Affect. Disord.202436630831610.1016/j.jad.2024.08.153 39216644
     [Google Scholar]
  34. BottigliengoD. FocoL. SeiblerP. KleinC. KönigI.R. Del GrecoM.F. A Mendelian randomization study investigating the causal role of inflammation on Parkinson’s disease.Brain2022145103444345310.1093/brain/awac193 35656776
     [Google Scholar]
  35. BowdenJ. SmithG.D. BurgessS. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression.Int. J. Epidemiol.201544251252510.1093/ije/dyv080 26050253
     [Google Scholar]
  36. SuD. AiY. ZhuG. YangY. MaP. Genetically predicted circulating levels of cytokines and the risk of osteoarthritis: A mendelian randomization study.Front. Genet.202314113119810.3389/fgene.2023.1131198 36999058
     [Google Scholar]
  37. SunD. MaR. WangJ. WangY. YeQ. The causal relationship between sarcoidosis and autoimmune diseases: A bidirectional Mendelian randomization study in FinnGen.Front. Immunol.202415132512710.3389/fimmu.2024.1325127 38711527
     [Google Scholar]
  38. JiangH. HuD. WangJ. ZhangB. HeC. NingJ. Adiponectin and the risk of gastrointestinal cancers in East Asians: Mendelian randomization analysis.Cancer Med.202211122397240410.1002/cam4.4735 35384390
     [Google Scholar]
  39. BowdenJ. SmithG.D. HaycockP.C. BurgessS. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator.Genet. Epidemiol.201640430431410.1002/gepi.21965 27061298
     [Google Scholar]
  40. BowdenJ. Del GrecoM.F. MinelliC. SmithG.D. SheehanN.A. ThompsonJ.R. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: The role of the I2 statistic.Int. J. Epidemiol.2016456dyw22010.1093/ije/dyw220 27616674
     [Google Scholar]
  41. HayrehS.S. Ocular vascular occlusive disorders: Natural history of visual outcome.Prog. Retin. Eye Res.20144112510.1016/j.preteyeres.2014.04.001 24769221
     [Google Scholar]
  42. PierceB.L. BurgessS. Efficient design for Mendelian randomization studies: Subsample and 2-sample instrumental variable estimators.Am. J. Epidemiol.201317871177118410.1093/aje/kwt084 23863760
     [Google Scholar]
  43. LiL. RenQ. ZhengQ. Causal associations between gastroesophageal reflux disease and lung cancer risk: A Mendelian randomization study.Cancer Med.20231267552755910.1002/cam4.5498 36479899
     [Google Scholar]
  44. ZhangH. MaX. LiuW. Causal relationship between serum metabolites and juvenile idiopathic arthritis: A Mendelian randomization study.Pediatr. Rheumatol. Online J.20242215110.1186/s12969‑024‑00986‑0 38724970
     [Google Scholar]
  45. QiW. KeenanH.A. LiQ. Pyruvate kinase M2 activation may protect against the progression of diabetic glomerular pathology and mitochondrial dysfunction.Nat. Med.201723675376210.1038/nm.4328 28436957
     [Google Scholar]
  46. ZhangP.N. ZhouM.Q. GuoJ. Mitochondrial dysfunction and diabetic nephropathy: Nontraditional therapeutic opportunities.J. Diabetes Res.2021202111410.1155/2021/1010268 34926696
     [Google Scholar]
  47. KordalewskaM. MacioszekS. WawrzyniakR. Multiplatform metabolomics provides insight into the molecular basis of chronic kidney disease.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.20191117495710.1016/j.jchromb.2019.04.003 30999273
     [Google Scholar]
  48. PenaM.J. HeerspinkH.J.L. HellemonsM.E. Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with type 2 diabetes mellitus.Diabet. Med.20143191138114710.1111/dme.12447 24661264
     [Google Scholar]
  49. KelleyD.E. HeJ. MenshikovaE.V. RitovV.B. Dysfunction of mitochondria in human skeletal muscle in type 2 diabetes.Diabetes200251102944295010.2337/diabetes.51.10.2944 12351431
     [Google Scholar]
  50. KernerJ. MinklerP.E. LesnefskyE.J. HoppelC.L. Fatty acid chain elongation in palmitate-perfused working rat heart: mitochondrial acetyl-CoA is the source of two-carbon units for chain elongation.J. Biol. Chem.201428914102231023410.1074/jbc.M113.524314 24558043
     [Google Scholar]
  51. AvilaM.A. García-TrevijanoE.R. LuS.C. CorralesF.J. MatoJ.M. Methylthioadenosine.Int. J. Biochem. Cell Biol.200436112125213010.1016/j.biocel.2003.11.016 15313459
     [Google Scholar]
  52. TanY. LiuX. YangY. Metabolomics analysis reveals serum biomarkers in patients with diabetic sarcopenia.Front. Endocrinol. (Lausanne)202314111978210.3389/fendo.2023.1119782 37033246
     [Google Scholar]
  53. HerbertK.R. WilliamsG.M. CooperG.J.S. BrimbleM.A. Synthesis of glycosylated 5-hydroxylysine, an important amino acid present in collagen-like proteins such as adiponectin.Org. Biomol. Chem.20121061137114410.1039/c1ob06394d 22218394
     [Google Scholar]
  54. StanislausA. GuoK. LiL. Development of an isotope labeling ultra-high performance liquid chromatography mass spectrometric method for quantification of acylglycines in human urine.Anal. Chim. Acta201275016117210.1016/j.aca.2012.05.006 23062437
     [Google Scholar]
  55. SalekR.M. MaguireM.L. BentleyE. A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human.Physiol. Genomics20072929910810.1152/physiolgenomics.00194.2006 17190852
     [Google Scholar]
  56. LuoS. SurapaneniA. ZhengZ. NAT8 variants, N-acetylated amino acids, and progression of CKD.Clin. J. Am. Soc. Nephrol.2021161374710.2215/CJN.08600520 33380473
     [Google Scholar]
  57. ShiM. BazzanoL.A. HeJ. Novel serum metabolites associate with cognition phenotypes among Bogalusa Heart Study participants.Aging (Albany NY)201911145124513910.18632/aging.102107 31327759
     [Google Scholar]
  58. HuangY. SharmaR. FeigenbaumA. Arginine to ornithine ratio as a diagnostic marker in patients with positive newborn screening for hyperargininemia.Mol. Genet. Metab. Rep.20212710073510.1016/j.ymgmr.2021.100735 33732618
     [Google Scholar]
  59. LiL. ChenX. ZhangH. WangM. LuW. MRC2 promotes proliferation and inhibits apoptosis of diabetic nephropathy.Anal. Cell. Pathol. (Amst.)2021202111010.1155/2021/6619870 34012764
     [Google Scholar]
  60. HaydenM.R. The mighty mitochondria are unifying organelles and metabolic hubs in multiple organs of obesity, insulin resistance, metabolic syndrome, and type 2 diabetes: An observational ultrastructure study.Int. J. Mol. Sci.2022239482010.3390/ijms23094820 35563211
     [Google Scholar]
  61. AnH.J. KimJ.Y. KimW.H. HanS.M. ParkK.K. The protective effect of melittin on renal fibrosis in an animal model of unilateral ureteral obstruction.Molecules2016219113710.3390/molecules21091137 27618890
     [Google Scholar]
  62. YinY. ShanC. HanQ. Causal effects of human serum metabolites on occurrence and progress indicators of chronic kidney disease: A two-sample Mendelian randomization study.Front. Nutr.202410127407810.3389/fnut.2023.1274078 38260086
     [Google Scholar]
/content/journals/cpd/10.2174/0113816128377862250429045226
Loading
Mendelian Randomization Study on Serum Metabolites and Diabetic Nephropathy Risk: Identifying Potential Biomarkers for Early Intervention
Curr. Pharm. Des. 32, 143 (2026); https://doi.org/10.2174/0113816128377862250429045226
/content/journals/cpd/10.2174/0113816128377862250429045226
/content/journals/cpd/10.2174/0113816128377862250429045226
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

file format download Download

  • Article Type:     Research Article
Keyword(s): diabetic nephropathy; instrumental variables; mendelian randomization; metabolism; Serum metabolites; type 2 diabetes mellitus

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test