Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2022 Jan;65(1):140-149.
doi: 10.1007/s00125-021-05584-3. Epub 2021 Oct 22.

Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes

Affiliations
Observational Study

Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes

Stefan Mutter et al. Diabetologia. 2022 Jan.

Abstract

Aims/hypothesis: This prospective, observational study examines associations between 51 urinary metabolites and risk of progression of diabetic nephropathy in individuals with type 1 diabetes by employing an automated NMR metabolomics technique suitable for large-scale urine sample collections.

Methods: We collected 24-h urine samples for 2670 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study and measured metabolite concentrations by NMR. Individuals were followed up for 9.0 ± 5.0 years until their first sign of progression of diabetic nephropathy, end-stage kidney disease or study end. Cox regressions were performed on the entire study population (overall progression), on 1999 individuals with normoalbuminuria and 347 individuals with macroalbuminuria at baseline.

Results: Seven urinary metabolites were associated with overall progression after adjustment for baseline albuminuria and chronic kidney disease stage (p < 8 × 10-4): leucine (HR 1.47 [95% CI 1.30, 1.66] per 1-SD creatinine-scaled metabolite concentration), valine (1.38 [1.22, 1.56]), isoleucine (1.33 [1.18, 1.50]), pseudouridine (1.25 [1.11, 1.42]), threonine (1.27 [1.11, 1.46]) and citrate (0.84 [0.75, 0.93]). 2-Hydroxyisobutyrate was associated with overall progression (1.30 [1.16, 1.45]) and also progression from normoalbuminuria (1.56 [1.25, 1.95]). Six amino acids and pyroglutamate were associated with progression from macroalbuminuria.

Conclusions/interpretation: Branched-chain amino acids and other urinary metabolites were associated with the progression of diabetic nephropathy on top of baseline albuminuria and chronic kidney disease. We found differences in associations for overall progression and progression from normo- and macroalbuminuria. These novel discoveries illustrate the utility of analysing urinary metabolites in entire population cohorts.

Keywords: Diabetic nephropathy; Metabolite profiling; NMR; Progression; Type 1 diabetes.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Standardised HRs and 95% CIs for urinary metabolites that were significantly associated with incidence of (overall) progression after accounting for multiple testing (p < 0.001) in all 2670 individuals. Urine metabolites were scaled to creatinine and log-transformed. The analysis was adjusted for sex and baseline age, year of diabetes diagnosis, baseline glycaemic control (HbA1c > 58.5 mmol/mol or 7.5%) and baseline CKD stage and albuminuria class. HRs were scaled to SD units. The proportional hazard assumption was tested with Schoenfeld residuals, and follow-up times were split when violated. FU, follow-up; y, year
Fig. 2
Fig. 2
Standardised HRs and 95% CIs for urinary metabolites that were significantly associated with incidence of progression to ESKD after accounting for multiple testing (p < 0.001) in 347 individuals with macroalbuminuria. Urine metabolites were scaled to creatinine and log-transformed. The analysis was adjusted for sex and baseline age, year of diabetes diagnosis, baseline glycaemic control (HbA1c > 58.5 mmol/mol or 7.5%) and baseline CKD stage and albuminuria class. HRs were scaled to SD units. The proportional hazard assumption was tested with Schoenfeld residuals, and follow-up times were split when violated

References

    1. Harjutsalo V, Katoh S, Sarti C, Tajima N, Tuomilehto J. Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes. 2004;53(9):2449–2454. doi: 10.2337/diabetes.53.9.2449. - DOI - PubMed
    1. Yaribeygi H, Maleki M, Sathyapalan T, Sahebkar A. The effect of C-peptide on diabetic nephropathy: a review of molecular mechanisms. Life Sci. 2019;237:116950. doi: 10.1016/j.lfs.2019.116950. - DOI - PubMed
    1. Tynkkynen T, Wang Q, Ekholm J, et al. Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics. Int J Epidemiol. 2019;48(3):978–993. doi: 10.1093/ije/dyy287. - DOI - PMC - PubMed
    1. Mäkinen V-P, Tynkkynen T, Soininen P, et al. Metabolic diversity of progressive kidney disease in 325 patients with type 1 diabetes (the FinnDiane study) J Proteome Res. 2012;11(3):1782–1790. doi: 10.1021/pr201036j. - DOI - PubMed
    1. Schlosser P, Li Y, Sekula P, et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet. 2020;52(2):167–176. doi: 10.1038/s41588-019-0567-8. - DOI - PMC - PubMed

Publication types