Metabolomics profiling in multi-ancestral individuals with type 2 diabetes in Singapore identified metabolites associated with renal function decline

Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY (2004) Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 351(13):1296–1305. https://doi.org/10.1056/NEJMoa041031

Article  CAS  PubMed  Google Scholar 

Magliano DJ, Boyko EJ, IDF Diabetes Atlas 10th edition scientific committee (2021) IDF Diabetes Atlas 2021. Available from: https://diabetesatlas.org/data/en/. Accessed 19 Sept 2024

Tong LL, Adler SG (2022) Diabetic kidney disease treatment: new perspectives. Kidney Res Clin Pract 41(Suppl 2):S63–S73. https://doi.org/10.23876/j.krcp.21.288

Article  PubMed  PubMed Central  Google Scholar 

Kume S, Maegawa H (2020) Lipotoxicity, nutrient-sensing signals, and autophagy in diabetic nephropathy. JMA J 3(2):87–94. https://doi.org/10.31662/jmaj.2020-0005

Article  PubMed  PubMed Central  Google Scholar 

Liu JJ, Ghosh S, Kovalik JP et al (2017) Profiling of plasma metabolites suggests altered mitochondrial fuel usage and remodeling of sphingolipid metabolism in individuals with type 2 diabetes and kidney disease. Kidney Int Rep 2(3):470–480. https://doi.org/10.1016/j.ekir.2016.12.003

Article  PubMed  Google Scholar 

Chen H, Chen L, Liu D et al (2017) Combined clinical phenotype and lipidomic analysis reveals the impact of chronic kidney disease on lipid metabolism. J Proteome Res 16(4):1566–1578. https://doi.org/10.1021/acs.jproteome.6b00956

Article  CAS  PubMed  Google Scholar 

Grams ME, Sang Y, Ballew SH et al (2019) Evaluating glomerular filtration rate slope as a surrogate end point for ESKD in clinical trials: an individual participant meta-analysis of observational data. J Am Soc Nephrol 30(9):1746–1755. https://doi.org/10.1681/ASN.2019010008

Article  CAS  PubMed  PubMed Central  Google Scholar 

Boucquemont J, Heinze G, Jager KJ, Oberbauer R, Leffondre K (2014) Regression methods for investigating risk factors of chronic kidney disease outcomes: the state of the art. BMC Nephrol 15:45. https://doi.org/10.1186/1471-2369-15-45

Article  PubMed  PubMed Central  Google Scholar 

Leffondre K, Boucquemont J, Tripepi G, Stel VS, Heinze G, Dunkler D (2015) Analysis of risk factors associated with renal function trajectory over time: a comparison of different statistical approaches. Nephrol Dial Transplant 30(8):1237–1243. https://doi.org/10.1093/ndt/gfu320

Article  CAS  PubMed  Google Scholar 

Qiao Y, Shin JI, Chen TK et al (2020) Association between renin-angiotensin system blockade discontinuation and all-cause mortality among persons with low estimated glomerular filtration rate. JAMA Intern Med 180(5):718–726. https://doi.org/10.1001/jamainternmed.2020.0193

Article  CAS  PubMed  PubMed Central  Google Scholar 

Inker LA, Collier W, Greene T et al (2023) A meta-analysis of GFR slope as a surrogate endpoint for kidney failure. Nat Med 29(7):1867–1876. https://doi.org/10.1038/s41591-023-02418-0

Article  CAS  PubMed  Google Scholar 

Zeng W, Beyene HB, Kuokkanen M et al (2022) Lipidomic profiling in the Strong Heart Study identified American Indians at risk of chronic kidney disease. Kidney Int 102(5):1154–1166. https://doi.org/10.1016/j.kint.2022.06.023

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yoshioka K, Hirakawa Y, Kurano M et al (2022) Lysophosphatidylcholine mediates fast decline in kidney function in diabetic kidney disease. Kidney Int 101(3):510–526. https://doi.org/10.1016/j.kint.2021.10.039

Article  CAS  PubMed  Google Scholar 

Hirakawa Y, Yoshioka K, Kojima K et al (2022) Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics. Sci Rep 12(1):16287. https://doi.org/10.1038/s41598-022-20638-1

Article  CAS  PubMed  PubMed Central  Google Scholar 

Afshinnia F, Nair V, Lin J et al (2019) Increased lipogenesis and impaired β-oxidation predict type 2 diabetic kidney disease progression in American Indians. JCI Insight 4(21):1–19. https://doi.org/10.1172/jci.insight.130317

Article  Google Scholar 

Lee S, Han M, Moon S et al (2022) Identifying genetic variants and metabolites associated with rapid estimated glomerular filtration rate decline in korea based on genome-metabolomic integrative analysis. Metabolites 12(11):1–13. https://doi.org/10.3390/metabo12111139

Article  CAS  Google Scholar 

Lanktree MB, Theriault S, Walsh M, Pare G (2018) HDL cholesterol, LDL cholesterol, and triglycerides as risk factors for CKD: a mendelian randomization study. Am J Kidney Dis 71(2):166–172. https://doi.org/10.1053/j.ajkd.2017.06.011

Article  CAS  PubMed  Google Scholar 

Zheng J, Zhang Y, Rasheed H et al (2022) Trans-ethnic Mendelian-randomization study reveals causal relationships between cardiometabolic factors and chronic kidney disease. Int J Epidemiol 50(6):1995–2010. https://doi.org/10.1093/ije/dyab203

Article  PubMed  Google Scholar 

Wang Y, Zhang L, Zhang W et al (2023) Understanding the relationship between circulating lipids and risk of chronic kidney disease: a prospective cohort study and large-scale genetic analyses. J Transl Med 21(1):671. https://doi.org/10.1186/s12967-023-04509-5

Article  CAS  PubMed  PubMed Central  Google Scholar 

Park S, Lee S, Kim Y et al (2022) Mendelian randomization reveals causal effects of kidney function on various biochemical parameters. Commun Biol 5(1):713. https://doi.org/10.1038/s42003-022-03659-4

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deng L, Hoh BP, Lu D et al (2015) Dissecting the genetic structure and admixture of four geographical Malay populations. Sci Rep 5:14375. https://doi.org/10.1038/srep14375

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liyanage T, Toyama T, Hockham C et al (2022) Prevalence of chronic kidney disease in Asia: a systematic review and analysis. BMJ Glob Health 7(1):1–9. https://doi.org/10.1136/bmjgh-2021-007525

Article  Google Scholar 

Epidemiology & Disease Control Division, Ministry of Health, Republic of Singapore (2022) National Population Health Survey 2022 (Household Interview and Health Examination). Available from: https://isomer-user-content.by.gov.sg/3/28c3b8f9-9216-46be-8fc9-b614098666a9/nphs-2022-survey-report_final.pdf. Accessed 19 Sept 2024

Wang J, Liu JJ, Gurung RL et al (2022) Clinical variable-based cluster analysis identifies novel subgroups with a distinct genetic signature, lipidomic pattern and cardio-renal risks in Asian patients with recent-onset type 2 diabetes. Diabetologia 65(12):2146–2156. https://doi.org/10.1007/s00125-022-05741-2

Article  CAS  PubMed  PubMed Central  Google Scholar 

Luo M, Tan LWL, Sim X et al (2020) Cohort profile: the Singapore diabetic cohort study. BMJ Open 10(5):e036443. https://doi.org/10.1136/bmjopen-2019-036443

Article  PubMed  PubMed Central  Google Scholar 

Liu JJ, Liu S, Saulnier PJ et al (2020) Association of urine haptoglobin with risk of all-cause and cause-specific mortality in individuals with type 2 diabetes: a transethnic collaborative work. Diabetes Care 43(3):625–633. https://doi.org/10.2337/dc19-1295

Article  CAS  PubMed  Google Scholar 

American Diabetes Association (2006) Diagnosis and classification of diabetes mellitus. Diabetes Care 29(Suppl 1):S43-48

Article  Google Scholar 

Levey AS, Stevens LA, Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604–612. https://doi.org/10.7326/0003-4819-150-9-200905050-00006

Article  PubMed  PubMed Central  Google Scholar 

Khwaja A (2012) KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 120(4):c179-184. https://doi.org/10.1159/000339789

Article  PubMed  Google Scholar 

Tai ES, Tan ML, Stevens RD et al (2010) Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 53(4):757–767. https://doi.org/10.1007/s00125-009-1637-8

Comments (0)

No login
gif