Next Article in Journal
Emerging Targets in Non-Small Cell Lung Cancer
Next Article in Special Issue
Molecular Therapeutics for Diabetic Kidney Disease: An Update
Previous Article in Journal
Assembly of mTORC3 Involves Binding of ETV7 to Two Separate Sequences in the mTOR Kinase Domain
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Novel Metabolites Associated with Decreased GFR in Finnish Men: A 12-Year Follow-Up of the METSIM Cohort

by
Lilian Fernandes Silva
1,2,
Jagadish Vangipurapu
1,3,
Anniina Oravilahti
1 and
Markku Laakso
1,4,*
1
Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70211 Kuopio, Finland
2
Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
3
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
4
Department of Medicine, Kuopio University Hospital, 70200 Kuopio, Finland
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(18), 10044; https://doi.org/10.3390/ijms251810044
Submission received: 24 August 2024 / Revised: 11 September 2024 / Accepted: 14 September 2024 / Published: 18 September 2024
(This article belongs to the Special Issue Molecular Therapeutics for Diabetes and Related Complications)

Abstract

Identification of the individuals having impaired kidney function is essential in preventing the complications of this disease. We measured 1009 metabolites at the baseline study in 10,159 Finnish men of the METSIM cohort and associated the metabolites with an estimated glomerular filtration rate (eGFR). A total of 7090 men participated in the 12-year follow-up study. Non-targeted metabolomics profiling was performed at Metabolon, Inc. (Morrisville, NC, USA) on EDTA plasma samples obtained after overnight fasting. We applied liquid chromatography mass spectrometry (LC-MS/MS) to identify the metabolites (the Metabolon DiscoveryHD4 platform). We performed association analyses between the eGFR and metabolites using linear regression adjusted for confounding factors. We found 108 metabolites significantly associated with a decrease in eGFR, and 28 of them were novel, including 12 amino acids, 8 xenobiotics, 5 lipids, 1 nucleotide, 1 peptide, and 1 partially characterized molecule. The most significant associations were with five amino acids, N-acetylmethionine, N-acetylvaline, gamma-carboxyglutamate, 3-methylglutaryl-carnitine, and pro-line. We identified 28 novel metabolites associated with decreased eGFR in the 12-year follow-up study of the METSIM cohort. These findings provide novel insights into the role of metabolites and metabolic pathways involved in the decline of kidney function.

1. Introduction

Chronic kidney disease (CKD) affects approximately 10% of the Western countries’ population [1]. Glomerular filtration rate (GFR) is accepted as the best marker of impaired kidney function, calculated as an estimated GFR (eGFR) [2]. Diabetes is a major risk factor for impaired kidney function [3], but also age, sex, hypertension, obesity, increased total triglycerides, and smoking are risk factors for CKD [4]. During the last few years, genome-wide association studies identified hundreds of genetic variants for kidney diseases [5,6,7]. Interestingly, a recent study identified genetic variants in the individuals with and without diabetes and reported that a majority of eGFR loci were similar in individuals with and without diabetes [8]. Genetic studies advanced our understanding of CKD, but they explain only a portion of the disease progression. Additionally, clinical markers often detect an impairment in kidney function only at later stages.
The first studies aiming to identify metabolites associated with eGFR had a small size and included only a low number of metabolites [9,10,11,12,13]. Grams et al. [14] included 587 participants in their study, and identified five metabolites (16-hydroxypalmitate, kynurenate, homovanillate sulphate, N2,N2-dimethylguanosine, and hippurate) associated with CKD. Lin et al. performed a large metabolome-wide association study, including 640 metabolites in 3906 participants of the Hispanic Community Health Study/Study of Latinos. They identified 404 eGFR-metabolite associations and found 79 novel associations [15], where amino acids and xenobiotics were the most frequent metabolites associated with eGFR. Recently, two studies reported several metabolites associated with CKD [16,17].
Identification of novel metabolites offers additional insights into the underlying biochemical processes that are lacking in genetic or clinical markers. Metabolites can serve as early biomarkers and potentially modifiable risk factors. Early identification of individuals having impaired kidney function is essential in the prevention of CKD and its complications. However, previous studies aiming to identify metabolites associated with a decrease in eGFR were cross-sectional and included a small number of participants and metabolites. Furthermore, many of these studies never addressed the long-term progression of CKD, leaving the gaps in the understanding of the changes in metabolites over time. Our population-based study included 10,159 participants having 1009 metabolites measured at baseline. A total of 7090 participated in a 12-year follow-up. Our study is the largest study identifying novel metabolites associated with a decline in eGFR during a long follow-up. Therefore, our study has a good statistical power to reveal new metabolic pathways for impaired kidney function.

2. Results

2.1. Baseline Characteristics

We included in our study 10,159 METSIM participants. Table 1 shows the baseline characteristics of the participants according to their glucose tolerance. These groups differed significantly in age, systolic blood pressure, BMI, total triglycerides, fasting glucose, HbA1c, fasting plasma insulin, eGFR, urine albumin, and high-sensitivity C-reactive protein (hs-CPR). The difference between the three groups was statistically significant but not clinically relevant eGFR (87.9 in the NGT group, 88.6 and 86.1 in the T2D group).

2.2. Metabolites in Participants with Decreased and Normal eGFR

We compared metabolite concentrations between the participants having eGFR < 80 and eGFR ≥ 80 ml/min/1.73 m2 and found statistically significant (p < 5 × 10−5) differences in 586 metabolites. The top significant 100 associations are shown in Table S1. The most significant differences (p < 1.1 × 10−350) between the two groups were in 1-methylhistidine, 1-methyl-4-imidazoleacetate, 2,3-dihydroxy-5-methylthio-4-pentenoate (DMTPA), creatinine, hydroxy asparagine, N,N,N-trimethyl-alanylproline betaine, N-acetylalanine, and pseudouridine.

2.3. Effects of Glucose Tolerance on Metabolic Profile

We analyzed the associations of eGFR with metabolites in different subgroups of glucose tolerance (n = 1 057 in each group matched for age and BMI). Participants with NGT had 379 statistically significant associations with the metabolites, participants with prediabetes had 474 significant associations, and participants with T2D had 378 significant associations. Table S1 presents the 100 most significant metabolites in the participants with T2D, prediabetes, and NGT. Independently of the glucose tolerance, all metabolites were associated with a decrease in eGFR. The Venn diagram (Figure 1) shows that the participants in the different glucose tolerance groups shared 78% of the 100 most significant metabolite associations, 11 of the metabolites were found only in the NGT group, 7 in the prediabetes group, and 12 in the T2D group.

2.4. Metabolites Associated with a Decrease in eGFR

We performed linear regression to associate 1009 metabolites with eGFR at baseline without adjustment for confounding factors, adjusted for baseline eGFR (Model 1), and adjusted for baseline eGFR, age, BMI, smoking, fasting glucose, total triglycerides, and systolic blood pressure (Model 2) (Table S2). All metabolites listed in Table S2 had p < 5 × 10−5 in all models. Adjustment for the baseline eGFR (Model 1) substantially decreased beta and p values. In Model 2 beta and p value further deceased but the decreases were relatively small.
We found 108 metabolites significantly associated with a decrease in eGFR (Table S2), and 28 of them were novel (Table 2). The 10 most statistically significant metabolites associated with decreased eGFR were six amino acids, creatinine, hydroxyasparagine, N,N,N-trimethyl-alanylproline betaine, N-acetylalanine, N-acetylserine, C-glycosyltryptophan, and N-formylmethionine; a nucleotide pseudouridine; xenobiotics erythritol; and carbohydrate erythronate.
Among the novel 28 metabolites decreasing eGFR, 12 were amino acids, 5 lipids, 1 nucleotide, 1 peptide, 8 xenobiotics, and 1 a partially characterized molecule. Among the amino acids, the three most significant imverse associations were with N-acetylmethionine (beta = −0.087, p = 5.5 × 10−24), N-acetylvaline (beta = −0.082, p = 2.6 × 10−21), and γ-carboxyglutamate (beta = −0.065, p = 2.6 × 10−14). Among the lipids, the two most significant inverse associations were with 11beta-hydroxyetiocholanolone (beta = −0.050, p = 4.0 × 10−7), and 2-methylmalonylcarnitive C4-DC (beta = -0.042, p = 3.1.0 × 10−6), and among the xenobiotics for 2,3-dihydroxyisovalerate (beta = −0.048, p = 6.8 × 10−6, and (S)-a-amino-omega-caprolactam (beta = −0.050, p = 1.0 × 10−8).

2.5. Genetic Variants Associated with Novel Metabolites

We identified nine genetic variants significantly associated with the novel metabolites (Table 3). The most significant associations were with 5-methyluridine, glycine, proline, and N-acetylmethionine. Each of the nine genetic variants were associated with at least three different metabolites, suggesting pleiotropy of these genes. Importantly, none of these genetic variants were significantly associated with a decrease in eGFR, indicating that the effects of the metabolites on eGFR were not explained by genetic factors.

3. Discussion

We measured 1009 metabolites with LC-MS/MS in 10,188 participants of the METSIM study. Our study reports several novel findings. We found that glucose tolerance did not have a major effect on the metabolite profile at baseline. Among the top 100 metabolites associated with eGFR, 78 were identical in participants with normal glucose tolerance, pre-diabetes, and diabetes (Figure 1). Our results suggest that the metabolic pathways leading to a decrease in eGFR are largely independent of glucose tolerance. This observation agrees with a previous study reporting that the majority of the eGFR loci were similar in the individuals with and without diabetes [8].
We found several statistically significant associations of the metabolites with a decrease in eGFR in the 12-year follow-up of the METSIM cohort. Of the 108 metabolites associated with a decrease in eGFR, 28 were novel (Table 2). We also replicated metabolite associations with decreased eGFR reported in previous studies [13,18,19,20,21,22,23,24,25,26]. The metabolic pathways independent from glucose highlights the complexity in the progression of CKD. Our findings suggest that non-glucose pathways, including amino acids, lipids, and xenobiotics, have independent effects on kidney function. However, our findings do not change the current treatment of patients with diabetes having impaired kidney function. Medication lowering hyperglycaemia remains the primary treatment in patients with diabetes and CKD.
We found three novel associations of N-acetylated amino acids (N-acetylmethionine, N-acetylvaline, and N-acetyltaurine) with a decrease in eGFR. N-acetylated amino acids are uremic toxins [27]. Aminoacylase-1 (ACY1) enzyme converts acetylated amino acids into free amino acids, and therefore the individuals having impaired activity of ACY1 or a mutation in the ACYL1 gene have increased concentrations of acetylated amino acids in blood and urine [28,29,30,31,32,33]. N-acetylated amino acids are uremic toxins that accumulate in the blood due to impaired kidney function (27). These toxins disrupt cellular processes, promote inflammation, and induce oxidative stress, worsening CKD progression (27–32). This highlights the potential of the metabolites to identify therapeutic targets for the prevention of CKD progression.
Amino acid γ-carboxyglutamate was significantly associated with a decrease in eGFR. γ-carboxyglutamate is a calcification inhibitor [34]. Atherosclerotic and vascular calcification are closely linked to the vitamin K-dependent protein matrix γ-carboxyglutamate. Vitamin K antagonists, including warfarin, are associated with increased calcification of renal and other arteries [34,35]. Coronary artery calcification was previously associated with a decline in eGFR [36]. Increased levels of γ-carboxyglutamate may represent a compensatory response to counteract vascular calcification as kidney function declines.
We report three novel associations of N-lactoyl-amino acids (N-lactoylvaline, N-lactoylisoleucine, and N-lactoylphenylalanine) with a decrease in eGFR. N-lactoylphenylalanine concentrations are increased in patients with phenylketonuria [37]. These patients have increased oxidative stress leading to tubulointerstitial disease, impaired kidney function, proteinuria, and arterial hypertension [38,39]. N-lactoylvaline and N-lactoylisoleucine were found in the urine of a patient with maple syrup urine disease [40], which is associated with nephrotic syndrome [41].
We also found that the nucleoside 5-methyluridine (ribothymidine), an endogenous methylated nucleoside, decreased eGFR. This finding was previously reported in rats with CKD [42]. Altered DNA methylation modulates the expression of pro-inflammatory and pro-fibrotic genes, stimulating renal disease progression [43]. High concentrations of homocysteine, hypoxia, and inflammation alter the epigenetic regulation of gene expression in CKD, impacting eGFR [43].
Eight of the 28 novel metabolites impairing eGFR were xenobiotics, chemical substances within an organism that are not naturally produced. Xenobiotics are food components, plant constituents, pesticides, industrial chemicals, environmental pollutants, or benzoate metabolites. An organic compound (S)-a-amino-omega-caprolactam is a uremic solute previously shown to impair kidney function [44]. 3-methyl catechol sulfate, a marker of current smoking and coffee consumption [45], decreased eGFR in our study. We also showed that genetic variants were not associated with xenobiotics, suggesting that decreased eGFR is largely regulated also by lifestyle and environmental factors.
Our findings highlight multiple metabolic pathways associated with a decrease in eGFR. We identified 28 novel metabolites among amino acids, lipids, nucleotides, peptides, and xenobiotics associated with decreased eGFR. Eight xenobiotics were associated with a decrease in eGFR, showing that non-genetic factors, including benzoate pathway, food components, and plants play a significant role in kidney dysfunction, demonstrating the influence of environmental factors on eGFR. Additionally, the effects of N-lactoyl-amino acids and 5-methyluridine show a potential for epigenetic regulation of kidney function. Overall, our novel findings provide valuable insights into the complex biochemical interactions affecting kidney function and pave the way for future studies to explore metabolic pathways on kidney function in diverse populations.
The strength of our study is that the METSIM study is the largest randomly selected population-based cohort identifying metabolites associated with a decrease in eGFR by applying the LC-MS/MS analysis method. Additionally, we followed our cohort for 12 years, and at baseline and follow-up, the metabolites identified were inversely associated with eGFR, increasing the credibility of our findings. We applied a conservative statistical significance threshold in all analyses to obtain reliable conclusions.
Our study has several limitations. The homogeneity of the study population (middle-aged and elderly Finnish men) limits the generalizability of our findings. Therefore, the replication of our findings in more diverse populations, such as women and non-European cohorts, is needed. Our study was an observational study, and therefore, we cannot establish causality between the metabolites and the decline of kidney function. Additionally, there may be unmeasured confounders, such as medication use or environmental factors, having effects on our findings. The LC-MS/MS platform provides a broad metabolite coverage, but the results are not fully generalizable to other metabolomics platforms.
In vitro and animal studies could help to establish causal relationships between the metabolites and the decline of the kidney function. Incorporating these findings into CKD risk prediction models may improve early detection and personalized treatment. Additionally, targeting specific metabolic pathways, for example, those involving uremic toxins, could reveal novel therapeutic approaches. Expanding the approach to include other omics data, such as genomics and proteomics, could further enhance our understanding of CKD progression.

4. Materials and Methods

4.1. Study Population and Laboratory Measurements

The METabolic Syndrome in Men (METSIM) study includes 10,197 men, aged from 45 to 73 years at baseline, and randomly selected from the population register of Kuopio, Eastern Finland. The METSIM study was approved by the Ethics Committee of the Kuopio University Hospital, Finland. All participants provided written informed consent.
The design and methods of the METSIM study were previously described in detail [46,47]. A total of 10,159 men were included in the current study, 3034 had normal glucose tolerance (NGT, fasting glucose < 6.1 mmol/L, 2-hour glucose < 7.8 mmol/L), 5715 prediabetes [impaired fasting glucose (6.1–6.9 mmol/L) or impaired glucose tolerance (7.8 to 11.0 mmol/L) or both], and 1410 T2D, [fasting glucose ≥ 7.0 mmol/L, or 2-hour glucose ≥ 11.1 mmol/L or glycated hemoglobin A1c (HbA1c) ≥ 6.5%] according to the American Diabetes Association classification [48]. BMI was calculated as weight divided by height squared. Smoking status was defined as current smoking (yes/no). All participants, excluding participants with T2D at baseline, underwent a 2-hour oral glucose tolerance test (75 g of glucose), and samples for plasma glucose and insulin were drawn at 0, 30, and 120 min. Other laboratory measurements were previously explained [46]. eGFR was calculated using the CKD-Epi equation [49].

4.2. Metabolomics

Non-targeted metabolomics profiling was performed at Metabolon, Inc. (Morrisville, NC, USA) on EDTA plasma samples obtained after overnight fasting, as previously described in detail [47,50]. We applied liquid chromatography mass spectrometry (LC-MS/MS) to identify the metabolites (the Metabolon DiscoveryHD4 platform). The LC-MS/MS platform was chosen for its high sensitivity, specificity, and broad dynamic range, making it ideal for detecting a wide variety of metabolites. Compared to other platforms, especially proton NMR, LC-MS/MS offers superior sensitivity, allowing for the identification of subtle metabolic changes, which is crucial in discovering early biomarkers for kidney function decline. Although limitations such as ion suppression and complex data processing exist for LC-MS/MS, its advantages in sensitivity and metabolite coverage makes it the best choice for this study. All samples were processed together for peak quantification and data scaling. We quantified raw mass spectrometry peaks for each metabolite using the area under the curve, and evaluated overall process variability by the median relative standard deviation for endogenous metabolites present in all 20 technical replicates in each batch. We adjusted for variation caused by day-to-day instrument tuning differences and columns used for biochemical extraction by scaling the raw peak quantifications to the median for each metabolite by the Metabolon batch.

4.3. Selection of Genetic Variants Decreasing Glomerular Filtration Rate

We identified genetic variants associated with a decrease in eGFR from previously published studies and the GWAS Catalog (The NHGRI-EBI Catalog of human genome-wide association studies (https://www.ebi.ac.uk/gwas/—accessed on 4 July 2024) in individuals of European ancestry. Altogether, 117 genes were found to be associated with impaired eGFR.

4.4. Statistical Analysis

We conducted statistical analyses using IBM SPSS Statistics, version 29. We log-transformed all continuous variables except for age and follow-up time to correct for their skewed distribution. We performed association analyses between the eGFR and metabolites using linear regression adjusted for confounding factors (Model 1, adjustment for eGFR at baseline, Model 2, adjustment for eGFR at baseline, age, BMI, smoking, systolic blood pressure, fasting glucose, and total triglycerides). The variables in Model 2 (age, BMI, smoking, systolic blood pressure, fasting glucose, and triglycerides) were selected because they are well-known risk factors for both CKD progression and metabolic changes. These variables were chosen to reduce bias and ensure that the associations between metabolites and eGFR are not influenced by these factors. We give the results as standardized beta coefficients and p values with the metabolite as a dependent variable. We used one-way ANOVA to assess the differences in clinical traits and metabolites between the two groups at baseline. We applied the Bonferroni correction to determine statistical significance for the metabolites identified (p < 5.0 × 10−5).

5. Conclusions

We measured 1009 metabolites in 10,159 Finnish men of the METSIM cohort and associated the metabolites with eGFR in the 12-year follow-up study. We found 108 metabolites significantly associated with a decrease in eGFR, and 28 of them were novel, including especially amino acids, xenobiotics, and lipids, showing that hyperglycaemia is not the only cause for impaired eGFR. Our findings provide novel insights into the role of metabolites and metabolic pathways involved in the decline of kidney function.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms251810044/s1.

Author Contributions

Conceptualization: L.F.S. and M.L.; methodology: A.O., J.V., L.F.S. and M.L.; investigation: L.F.S., J.V., A.O. and M.L.; visualization: L.F.S. and A.O.; funding acquisition: M.L.; project administration: M.L.; supervision: M.L.; writing—original draft: L.F.S., J.V., A.O. and M.L. All authors have read and agreed to the published version of the manuscript.

Funding

The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreements no. 115372 EMIF (to M.L), and no. 115974 BEAt-DKD (to M.L.). This Joint Undertaking received support from the European Union’s 7th Framework (EMIF) resp. Horizon 2020 (BEAt-DKD) research and innovation programmes and EFPIA, with JDRF (BEAt-DKD). Academy of Finland grant no. 321428 (ML). Centre of Excellence of Cardiovascular and Metabolic Diseases, the Academy of Finland grant no. 271961 (ML). Sigrid Juselius Foundation grant (ML). Finnish Foundation for Cardiovascular Research grant (ML). Kuopio University Hospital grant (ML).

Institutional Review Board Statement

The METSIM study was approved by the Ethics Committee of the Kuopio University Hospital, Finland. All participants provided written informed consent.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author M.L. upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sekula, P.; Goek, O.-N.; Quaye, L.; Barrios, C.; Levey, A.S.; Römisch-Margl, W.; Menni, C.; Yet, I.; Gieger, C.; Inker, L.A.; et al. A Metabolome-Wide Association Study of Kidney Function and Disease in the General Population. J. Am. Soc. Nephrol. 2016, 27, 1175–1188. [Google Scholar] [CrossRef] [PubMed]
  2. Levey, A.S.; Stevens, L.A.; Schmid, C.H.; Zhang, Y.L.; Castro, A.F., 3rd; Feldman, H.I.; Kusek, J.W.; Eggers, P.; Van Lente, F.; Greene, T.; et al. A New Equation to Estimate Glomerular Filtration Rate. Ann. Intern. Med. 2009, 150, 604–612. [Google Scholar] [CrossRef] [PubMed]
  3. Baumeister, S.E.; Böger, C.A.; Krämer, B.K.; Döring, A.; Eheberg, D.; Fischer, B.; John, J.; Koenig, W.; Meisinger, C. Effect of Chronic Kidney Disease and Comorbid Conditions on Health Care Costs: A 10-Year Observational Study in a General Population. Am. J. Nephrol. 2009, 31, 222–229. [Google Scholar] [CrossRef] [PubMed]
  4. Kazancioğlu, R. Risk factors for chronic kidney disease: An update. Kidney Int. Suppl. 2013, 3, 368–371. [Google Scholar] [CrossRef] [PubMed]
  5. Wuttke, M.; Li, Y.; Li, M.; Sieber, K.B.; Feitosa, M.F.; Gorski, M.; Tin, A.; Wang, L.; Chu, A.Y.; Hoppmann, A.; et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat. Genet. 2019, 51, 957–972. [Google Scholar] [CrossRef]
  6. Wuttke, M.; König, E.; Katsara, M.-A.; Kirsten, H.; Farahani, S.K.; Teumer, A.; Li, Y.; Lang, M.; Göcmen, B.; Pattaro, C.; et al. Imputation-powered whole-exome analysis identifies genes associated with kidney function and disease in the UK Biobank. Nat. Commun. 2023, 14, 1–16. [Google Scholar] [CrossRef]
  7. Gorski, M.; Rasheed, H.; Teumer, A.; Thomas, L.F.; Graham, S.E.; Sveinbjornsson, G.; Winkler, T.W.; Günther, F.; Stark, K.J.; Chai, J.-F.; et al. Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies. Kidney Int. 2022, 102, 624–639. [Google Scholar] [CrossRef]
  8. Winkler, T.W.; Rasheed, H.; Teumer, A.; Gorski, M.; Rowan, B.X.; Stanzick, K.J.; Thomas, L.F.; Tin, A.; Hoppmann, A.; Chu, A.Y.; et al. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals. Commun. Biol. 2022, 5, 1–20. [Google Scholar] [CrossRef]
  9. Titan, S.; Venturini, G.; Padilha, K.; Tavares, G.; Zatz, R.; Bensenor, I.; Lotufo, P.; Rhee, E.; Thadhani, R.; Pereira, A. Metabolites related to eGFR: Evaluation of candidate molecules for GFR estimation using untargeted metabolomics. Clin. Chim. Acta 2018, 489, 242–248. [Google Scholar] [CrossRef]
  10. Lee, H.; Jang, H.B.; Yoo, M.-G.; Park, S.I.; Lee, H.-J. Amino Acid Metabolites Associated with Chronic Kidney Disease: An Eight-Year Follow-Up Korean Epidemiology Study. Biomedicines 2020, 8, 222. [Google Scholar] [CrossRef]
  11. Lee, S.; Han, M.; Moon, S.; Kim, K.; An, W.J.; Ryu, H.; Oh, K.-H.; Park, S.K. Identifying Genetic Variants and Metabolites Associated with Rapid Estimated Glomerular Filtration Rate Decline in Korea Based on Genome–Metabolomic Integrative Analysis. Metabolites 2022, 12, 1139. [Google Scholar] [CrossRef] [PubMed]
  12. Peng, H.; Liu, X.; Aoieong, C.; Tou, T.; Tsai, T.; Ngai, K.; I Cheang, H.; Liu, Z.; Liu, P.; Zhu, H. Identification of Metabolite Markers Associated with Kidney Function. J. Immunol. Res. 2022, 2022, 1–9. [Google Scholar] [CrossRef]
  13. Peng, H.; Liu, X.; Ieong, C.A.; Tou, T.; Tsai, T.; Zhu, H.; Liu, Z.; Liu, P. A Metabolomics study of metabolites associated with the glomerular filtration rate. BMC Nephrol. 2023, 24, 1–12. [Google Scholar] [CrossRef]
  14. Grams, M.E.; Tin, A.; Rebholz, C.M.; Shafi, T.; Köttgen, A.; Perrone, R.D.; Sarnak, M.J.; Inker, L.A.; Levey, A.S.; Coresh, J. Metabolomic Alterations Associated with Cause of CKD. Clin. J. Am. Soc. Nephrol. 2017, 12, 1787–1794. [Google Scholar] [CrossRef] [PubMed]
  15. Lin, B.M.; Zhang, Y.; Yu, B.; Boerwinkle, E.; Thygarajan, B.; Yunes, M.; Daviglus, M.L.; Qi, Q.; Kaplan, R.; Lash, J.; et al. Metabolome-wide association study of estimated glomerular filtration rates in Hispanics. Kidney Int. 2021, 101, 144–151. [Google Scholar] [CrossRef] [PubMed]
  16. Schlosser, P.; Scherer, N.; Grundner-Culemann, F.; Monteiro-Martins, S.; Haug, S.; Steinbrenner, I.; Uluvar, B.; Wuttke, M.; Cheng, Y.; Ekici, A.B.; et al. Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine. Nat. Genet. 2023, 55, 995–1008. [Google Scholar] [CrossRef] [PubMed]
  17. Nierenberg, J.L.; He, J.; Li, C.; Gu, X.; Shi, M.; Razavi, A.C.; Mi, X.; Li, S.; Bazzano, L.A.; Anderson, A.H.; et al. Novel associations between blood metabolites and kidney function among Bogalusa Heart Study and Multi-Ethnic Study of Atherosclerosis participants. Metabolomics 2019, 15, 149. [Google Scholar] [CrossRef]
  18. Wang, F.; Sun, L.; Sun, Q.; Liang, L.; Gao, X.; Li, R.; Pan, A.; Li, H.; Deng, Y.; Hu, F.B.; et al. Associations of Plasma Amino Acid and Acylcarnitine Profiles with Incident Reduced Glomerular Filtration Rate. Clin. J. Am. Soc. Nephrol. 2018, 13, 560–568. [Google Scholar] [CrossRef]
  19. Kwan, B.; Fuhrer, T.; Zhang, J.; Darshi, M.; Van Espen, B.; Montemayor, D.; de Boer, I.H.; Dobre, M.; Hsu, C.-Y.; Kelly, T.N.; et al. Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study. Am. J. Kidney Dis. 2020, 76, 511–520. [Google Scholar] [CrossRef]
  20. Wen, D.; Zheng, Z.; Surapaneni, A.; Yu, B.; Zhou, L.; Zhou, W.; Xie, D.; Shou, H.; Avila-Pacheco, J.; Kalim, S.; et al. Metabolite profiling of CKD progression in the chronic renal insufficiency cohort study. J. Clin. Investig. 2022, 7. [Google Scholar] [CrossRef]
  21. Bernard, L.; Zhou, L.; Surapaneni, A.; Chen, J.; Rebholz, C.M.; Coresh, J.; Yu, B.; Boerwinkle, E.; Schlosser, P.; Grams, M.E. Serum Metabolites and Kidney Outcomes: The Atherosclerosis Risk in Communities Study. Kidney Med. 2022, 4, 100522. [Google Scholar] [CrossRef] [PubMed]
  22. Guo, X.; Peng, H.; Liu, P.; Tang, L.; Fang, J.; AoIeong, C.; Tou, T.; Tsai, T.; Liu, X. Novel Metabolites to Improve Glomerular Filtration Rate Estimation. Kidney Blood Press. Res. 2023, 48, 287–296. [Google Scholar] [CrossRef] [PubMed]
  23. Au, A.Y.M.; Mantik, K.; Bahadory, F.; Stathakis, P.; Guiney, H.; Erlich, J.; Walker, R.; Poulton, R.; Horvath, A.R.; Endre, Z.H. Plasma arginine metabolites in health and chronic kidney disease. Nephrol. Dial. Transplant. 2023, 38, 2767–2775. [Google Scholar] [CrossRef]
  24. Liu, J.-J.; Ching, J.; Wee, H.N.; Liu, S.; Gurung, R.L.; Lee, J.; M., Y.; Zheng, H.; Lee, L.S.; Ang, K.; et al. Plasma Tryptophan-Kynurenine Pathway Metabolites and Risk for Progression to End-Stage Kidney Disease in Patients With Type 2 Diabetes. Diabetes Care 2023, 46, 2223–2231. [Google Scholar] [CrossRef]
  25. Hou, Y.; Xiao, Z.; Zhu, Y.; Li, Y.; Liu, Q.; Wang, Z. Blood metabolites and chronic kidney disease: A Mendelian randomization study. BMC Med Genom. 2024, 17, 1–14. [Google Scholar] [CrossRef]
  26. Luo, Y.; Zhang, W.; Qin, G. Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review). Mol. Med. Rep. 2024, 30, 1–13. [Google Scholar] [CrossRef]
  27. Wishart, D.S.; Guo, A.; Oler, E.; Wang, F.; Anjum, A.; Peters, H.; Dizon, R.; Sayeeda, Z.; Tian, S.; Lee, B.L.; et al. HMDB 5.0: The Human Metabolome Database for 2022. Nucleic Acids Res. 2021, 50, D622–D631. [Google Scholar] [CrossRef]
  28. Stelzer, G.; Rosen, R.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; et al. The GeneCards Suite: From gene data mining to disease genome sequence analysis. Curr. Protoc. Bioinform. 2016, 54, 1.30.1–13033. [Google Scholar] [CrossRef] [PubMed]
  29. Jellum, E.; Horn, L.; Thoresen, O.; A Kvittingen, E.; Stokke, O. Urinary excretion of N-acetyl amino acids in patients with some inborn errors of amino acid metabolism. Scand. J. Clin. Lab. Investigation. Suppl. 1986, 184, 21–26. [Google Scholar]
  30. Engelke, U.F.H.; Sass, J.O.; Van Coster, R.N.; Gerlo, E.; Olbrich, H.; Krywawych, S.; Calvin, J.; Hart, C.; Omran, H.; Wevers, R.A. NMR spectroscopy of aminoacylase 1 deficiency, a novel inborn error of metabolism. NMR Biomed. 2008, 21, 138–147. [Google Scholar] [CrossRef]
  31. Okajima, K.; Inoue, M.; Morino, Y. Studies on the mechanism for renal elimination of N-acetylphenylalanine: Its pathophysiologic significance in phenylketonuria. J. Lab. Clin. Med. 1985, 105, 132–138. [Google Scholar] [PubMed]
  32. Sass, J.O.; Mohr, V.; Olbrich, H.; Engelke, U.; Horvath, J.; Fliegauf, M.; Loges, N.T.; Schweitzer-Krantz, S.; Moebus, R.; Weiler, P.; et al. Mutations in ACY1, the Gene Encoding Aminoacylase 1, Cause a Novel Inborn Error of Metabolism. Am. J. Hum. Genet. 2006, 78, 401–409. [Google Scholar] [CrossRef]
  33. Coster, V.; Gerlo, E.; Giardina, T.; Engelke, U.; Smet, J.; De Praeter, C.; Meersschaut, V.; De Meirleir, L.; Seneca, S.; Devreese, B.; et al. Aminoacylase I deficiency: A novel inborn error of metabolism. Biochem. Biophys. Res. Commun. 2005, 338, 1322–1326. [Google Scholar] [CrossRef]
  34. Luo, G.; Ducy, P.; McKee, M.D.; Pinero, G.J.; Loyer, E.; Behringer, R.R.; Karsenty, G. Spontaneous calcification of arteries and cartilage in mice lacking matrix GLA protein. Nature 1997, 386, 78–81. [Google Scholar] [CrossRef]
  35. Chatrou, M.L.; Winckers, K.; Hackeng, T.M.; Reutelingsperger, C.P.; Schurgers, L.J. Vascular calcification: The price to pay for anticoagulation therapy with vitamin K-antagonists. Blood Rev. 2012, 26, 155–166. [Google Scholar] [CrossRef] [PubMed]
  36. Budoff, M.J.; Rader, D.J.; Reilly, M.P.; Mohler, E.R., 3rd; Lash, J.; Yang, W.; Rosen, L.; Glenn, M.; Teal, V.; Feldman, H.I.; et al. Relationship of estimated GFR and coronary artery calcification in the CRIC (Chronic Renal Insufficiency Cohort) Study. Am. J. Kidney Dis. 2011, 58, 519–526. [Google Scholar] [CrossRef]
  37. Jansen, R.S.; Addie, R.; Merkx, R.; Fish, A.; Mahakena, S.; Bleijerveld, O.B.; Altelaar, M.; Ijlst, L.; Wanders, R.J.; Borst, P.; et al. N-lactoyl-amino acids are ubiquitous metabolites that originate from CNDP2-mediated reverse proteolysis of lactate and amino acids. Proc. Natl. Acad. Sci. USA 2015, 112, 6601–6606. [Google Scholar] [CrossRef]
  38. Hennermann, J.B.; Roloff, S.; Gellermann, J.; Vollmer, I.; Windt, E.; Vetter, B.; Plöckinger, U.; Mönch, E.; Querfeld, U. Chronic kidney disease in adolescent and adult patients with phenylketonuria. J. Inherit. Metab. Dis. 2012, 36, 747–756. [Google Scholar] [CrossRef] [PubMed]
  39. Burton, B.K.; Jones, K.B.; Cederbaum, S.; Rohr, F.; Waisbren, S.; Irwin, D.E.; Kim, G.; Lilienstein, J.; Alvarez, I.; Jurecki, E.; et al. Prevalence of comorbid conditions among adult patients diagnosed with phenylketonuria. Mol. Genet. Metab. 2018, 125, 228–234. [Google Scholar] [CrossRef]
  40. Hagenfeldt, L.; Naglo, A. New conjugated urinary metabolites in intermediate type maple syrup urine disease. Clin. Chim. Acta 1987, 169, 77–83. [Google Scholar] [CrossRef]
  41. Maceda, E.B.G.; E Abadingo, M.; Magbanua-Calalo, C.J.; A Dator, M.; Resontoc, L.P.R.; De Castro-Hamoy, L.; Abacan, M.A.R.; Chiong, M.A.D.; Estrada, S.C. Maple syrup urine disease associated with nephrotic syndrome in a Filipino child. BMJ Case Rep. 2021, 14, e242689. [Google Scholar] [CrossRef] [PubMed]
  42. Zhang, Z.-H.; He, J.-Q.; Qin, W.-W.; Zhao, Y.-Y.; Tan, N.-H. Biomarkers of obstructive nephropathy using a metabolomics approach in rat. Chem. Interactions 2018, 296, 229–239. [Google Scholar] [CrossRef] [PubMed]
  43. Rysz, J.; Franczyk, B.; Rysz-Górzyńska, M.; Gluba-Brzózka, A. Are alterations in DNA methylation related to CKD development? Int. J. Mol. Sci. 2022, 23, 7108. [Google Scholar] [CrossRef] [PubMed]
  44. Ganesan, L.L.; O’brien, F.J.; Sirich, T.L.; Plummer, N.S.; Sheth, R.; Fajardo, C.; Brakeman, P.; Sutherland, S.M.; Meyer, T.W. Association of Plasma Uremic Solute Levels with Residual Kidney Function in Children on Peritoneal Dialysis. Clin. J. Am. Soc. Nephrol. 2021, 16, 1531–1538. [Google Scholar] [CrossRef]
  45. He, W.J.; Chen, J.; Razavi, A.C.; Hu, E.A.; Grams, M.E.; Yu, B.; Parikh, C.R.; Boerwinkle, E.; Bazzano, L.; Qi, L.; et al. Metabolites Associated with Coffee Consumption and Incident Chronic Kidney Disease. Clin. J. Am. Soc. Nephrol. 2021, 16, 1620–1629. [Google Scholar] [CrossRef]
  46. Laakso, M.; Kuusisto, J.; Stančáková, A.; Kuulasmaa, T.; Pajukanta, P.; Lusis, A.J.; Collins, F.S.; Mohlke, K.L.; Boehnke, M. The Metabolic Syndrome in Men study: A resource for studies of metabolic and cardiovascular diseases. J. Lipid Res. 2017, 58, 481–493. [Google Scholar] [CrossRef]
  47. Yin, X.; Chan, L.S.; Bose, D.; Jackson, A.U.; VandeHaar, P.; Locke, A.E.; Fuchsberger, C.; Stringham, H.M.; Welch, R.; Yu, K.; et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat. Commun. 2022, 13, 1–14. [Google Scholar] [CrossRef]
  48. Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 2010, 33, S62–S69. [CrossRef]
  49. Inker, L.A.; Eneanya, N.D.; Coresh, J.; Tighiouart, H.; Wang, D.; Sang, Y.; Crews, D.C.; Doria, A.; Estrella, M.M.; Froissart, M.; et al. New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race. N. Engl. J. Med. 2021, 385, 1737–1749. [Google Scholar] [CrossRef]
  50. Silva, L.F.; Vangipurapu, J.; Kuulasmaa, T.; Laakso, M. An intronic variant in the GCKR gene is associated with multiple lipids. Sci. Rep. 2019, 9, 10240. [Google Scholar] [CrossRef]
Figure 1. Venn diagram shows the top 100 most significant metabolites associated with eGFR across the participants with normal glucose tolerance (NGT), prediabetes, and type 2 diabetes (T2D).
Figure 1. Venn diagram shows the top 100 most significant metabolites associated with eGFR across the participants with normal glucose tolerance (NGT), prediabetes, and type 2 diabetes (T2D).
Ijms 25 10044 g001
Table 1. Baseline Characteristics of the Participants According to Glucose Tolerance.
Table 1. Baseline Characteristics of the Participants According to Glucose Tolerance.
MeasurementsNGT
(n = 3034)
Pre-Diabetes
(n = 5715)
T2D
(n = 1410)
p
Age (years)56.8 ± 6.957.4 ± 7.260.6 ± 6.71.1 × 10−63
Systolic blood pressure (mmHg)134.3 ± 15.9138.7 ± 16.2145.2 ± 18.12.1 × 10−93
Body mass index (kg/m2)25.8 ± 3.3827.4 ± 3.930.2 ± 5.21.1 × 10−247
Current smoking (%)18.018.417.20.606
Total triglycerides (mmol/l)1.22 ± 0.651.49 ± 1.081.90 ± 1.211.2 × 10−143
Fasting glucose (mmol/l)5.24 ± 0.245.97 ± 0.377.51 ± 2.01<1 × 10−250
HbA1C (%)5.59 ± 0.315.71 ± 0.346.58 ± 1.13<1 × 10−250
Fasting plasma insulin (mU/l)6.25 ± 4.119.32 ± 6.419.6 ± 28.5<1 × 10−250
Creatinine (umol/l)84.6 ± 15.983.4 ± 12.884. 6 ± 22.30.0003
eGFR (ml/min/1.73 m2)87.9 ± 12.388.6 ± 12.286.1 ± 14.54.5 × 10−10
Urine albumin (mg/l)18.4 ± 110.920.6 ± 82.593.5 ± 380.17.2 × 10−181
hs-CRP (mg/l)1.82 ± 2.962.13 ± 4.53.22 ± 6.073.4 × 10−40
Abbreviations: NGT, normal glucose tolerance; T2D, type 2 diabetes; HbA1C, hemoglobin A1C; eGFR, estimated glomerular filtration rate; and hs-CRP, high sensitivity C-reactive protein.
Table 2. Novel Metabolites Associated with a Decrease In eGFR.
Table 2. Novel Metabolites Associated with a Decrease In eGFR.
MetaboliteSub-ClassNBetap *Betap **
Amino acids
N-acetylmethionineMethionine, cysteine, taurine metabolism7080−0.334 1.4 × 10−183−0.087 5.5 × 10−24
N-acetylvalineLeucine, isoleucine, valine metabolism7082−0.343 1.0 × 10−194−0.082 2.6 × 10−21
γ-carboxyglutamateGlutamate metabolism6929−0.295 1.1 × 10−138−0.065 2.6 × 10−14
3-methylglutaryl-
carnitine (2)
Leucine, isoleucine, valine metabolism7001−0.257 1.1 × 10−105−0.058 5.8 × 10−12
ProlineUrea cycle; arginine proline metabolism.7081−0.107 1.3 × 10−19−0.048 3.9 × 10−9
Pro-hydroxy-proUrea cycle; arginine proline metabolism7079−0.155 1.9 × 10−39−0.047 5.2 × 10−9
4-guanidinobutanoateGuanidino acetamido metabolism7049−0.158 1.7 × 10−40−0.049 2.3 × 10−9
N-acetyltaurineMethionine, cysteine, taurine metabolism7048−0.208 1.4 × 10−69−0.041 7.6 × 10−7
Hydantoin-5-propionateHistidine metabolism6154−0.211 3.6 × 10−63−0.043 1.1 × 10−6
N-lactoylvalineLactoyl amino acid6781−0.182 2.5 × 10−51−0.043 3.1 × 10−6
N-lactoylisoleucineLactoyl amino acid5437−0.189 4.4 × 10−45−0.043 1.6 × 10−5
N-lactoylphenylalanineLactoyl amino acid7033−0.233 2.7 × 10−87−0.037 4.4 × 10−5
Lipids
11beta-hydroxy
etiocholanolone glucuronide
Androgenic steroids4891−0.204 2.9 × 10−47−0.050 4.0 × 10−7
3-decenoylcarnitineFatty acid metabolism5395−0.217 2.9 × 10−58−0.042 9.2 × 10−6
Cis-3,4-methylene heptanoylglycineFatty acid metabolism6825−0.161 5.2 × 10−41−0.038 4.8 × 10−6
2-methylmalonyl
carnitine (C4-DC)
Fatty acid metabolism 5827−0.235 8.0 × 10−74−0.042 3.1 × 10−6
PropionylglycineFatty acid metabolism3960−0.119 4.9 × 10−14−0.049 1.3 × 10−5
Nucleotide
5-methyluridine(ribothymidine)Pyrimidine metabolism7082−0.134 6.8 × 10−30−0.038 3.1 × 10−6
Peptide
PyroglutamylvalineModified peptides6398−0.202 7.7E × 10−60−0.051 2.6 × 10−9
Xenobiotics
2,3-dihydroxyisovalerateFood component/plant6998−0.206 3.8 × 10−68−0.048 6.8 × 10−9
(S)-a-amino-omega-caprolactamFood component/plant7007−0.296 1.3 × 10−141−0.050 1.0 × 10−8
3-methoxycatechol sulfate (2)Benzoate metabolism5379−0.185 2.0 × 10−42−0.044 1.9 × 10−6
3-methyl catechol sulfate (1)Benzoate metabolism7065−0.209 3.0 × 10−70−0.040 2.1 × 10−6
3-methoxycatechol sulfate (1)Benzoate metabolism6318−0.174 4.0 × 10−44−0.039 5.5 × 10−6
2-acetamidophenol sulfateFood component/plant5939−0.153 2.9 × 10−32−0.042 3.6 × 10−6
N-(2-furoyl)glycineFood component/plant5025−0.235 5.0 × 10−64−0.042 2.4 × 10−5
2-aminophenol sulfateFood component/plant7066−0.147 2.8 × 10−35−0.036 1.1 × 10−5
Other metabolites
Glutamine_degradantPartially characterized molecules7060−0.222 7.3 × 10−80−0.071 2.2 × 10−17
p *: non-adjusted; p **: adjusted for eGFR at baseline, age, BMI, smoking, fasting glucose, total triglycerides, and systolic blood pressure.
Table 3. The Variants of Nine Genes were Associated with the Novel Metabolites Related to a Decline in eGFR.
Table 3. The Variants of Nine Genes were Associated with the Novel Metabolites Related to a Decline in eGFR.
Gene-Variant Metabolitep
KLHDC7B-rs4701185-methyluridine9.9 × 10−199
CPS1-rs715Glycine8.1 × 10−90
AC007326.4-rs5992344Proline2.0 × 10−63
DOCK3-rs138144932N-acetylmethionine1.3 × 10−44
AOX1-rs7562507Hydantoin-5-propionate1.4 × 10−17
COLEC10-rs13264172Pro-hydroxy-pro3.5 × 10−10
MAGI1-rs2646762.3-dihydroxy-5-methylthio-4-penenoate2.9 × 10−8
DCBLD2-rs192423025Pyroglutamylvaline3.4 × 10−8
CNTNAP2-rs533473709γ-carboxyglutamate5.3 × 10−8
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Fernandes Silva, L.; Vangipurapu, J.; Oravilahti, A.; Laakso, M. Novel Metabolites Associated with Decreased GFR in Finnish Men: A 12-Year Follow-Up of the METSIM Cohort. Int. J. Mol. Sci. 2024, 25, 10044. https://doi.org/10.3390/ijms251810044

AMA Style

Fernandes Silva L, Vangipurapu J, Oravilahti A, Laakso M. Novel Metabolites Associated with Decreased GFR in Finnish Men: A 12-Year Follow-Up of the METSIM Cohort. International Journal of Molecular Sciences. 2024; 25(18):10044. https://doi.org/10.3390/ijms251810044

Chicago/Turabian Style

Fernandes Silva, Lilian, Jagadish Vangipurapu, Anniina Oravilahti, and Markku Laakso. 2024. "Novel Metabolites Associated with Decreased GFR in Finnish Men: A 12-Year Follow-Up of the METSIM Cohort" International Journal of Molecular Sciences 25, no. 18: 10044. https://doi.org/10.3390/ijms251810044

APA Style

Fernandes Silva, L., Vangipurapu, J., Oravilahti, A., & Laakso, M. (2024). Novel Metabolites Associated with Decreased GFR in Finnish Men: A 12-Year Follow-Up of the METSIM Cohort. International Journal of Molecular Sciences, 25(18), 10044. https://doi.org/10.3390/ijms251810044

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop