Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations

Barrios, Clara; Zierer, Jonas; Würtz, Peter; Haller, Toomas; Metspalu, Andres; Gieger, Christian; Thorand, Barbara; Meisinger, Christa; Waldenberger, Mélanie; Raitakari, Olli T.; Lehtimäki, Terho; Otero, Sol; Rodríguez, Eva; Pedro-Botet, J; Kähönen, Mika; Ala‐Korpela, Mika; Kastenmüller, Gabi; Spector, Tim D.; Pascual, Julio; Menni, Cristina

doi:10.1038/s41598-018-33507-7

Cited by 45 publications

(55 citation statements)

References 56 publications

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“…Cholesterol and phospholipids in and particle concentrations of HDL subclasses, HDL particle size, and apolipoprotein A1 were positively associated with eGFR (P FDR < 0.05). 143 (19) 142 (18) 156 21137 17148 (19) 143 (19) HbA 83 (16) 73 (14) 88 1492 (14) 82 (16) UACR 167 (17) 128 (15) 59 (14) 120 1826 (18) 500 (16) History of CVD a , n (%)…”

Section: Cross-sectional Associations Between Metabolites and Egfrmentioning

confidence: 99%

“…165 (18) 219 (26) 100 (24) 78 1225 (17) 587 ( 263 (26) 175 (21) 47 (11) 87 1313 (9) 585 (19) Categorical outcomes eGFR 30-60 mL/min/1.73m 2 , n (%) 120 (12) 69 (8) 76 1822 36 4293 (9) eGFR ≤ 30 ml/min/1.73m 2 , n (%)…”

Section: Cross-sectional Associations Between Metabolites and Egfrmentioning

confidence: 99%

“…Two recent European studies, using NMR metabolomics in T2D, demonstrated that tyrosine is a marker of microvascular complications, but DKD was not investigated separately (17). Second, Barrios et al demonstrated associations of several metabolites, mainly lipids, amino acids, and energy metabolites, with measures of kidney function and incident DKD in 926 persons with T2D and 4838 persons without diabetes (18), although only taking into account a limited number of relevant confounders.…”

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confidence: 99%

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Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

Tofte

Vogelzangs

Mook‐Kanamori

et al. 2020

The Journal of Clinical Endocrinology &Amp; Metabolism

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Context There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease. Objective To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D). Design 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts. Main outcome measures Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. Results In total, 125 metabolites were significantly associated (PFDR = 1.5×10–32 − 0.046; β = −11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after. Conclusions This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation.

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Section: Cross-sectional Associations Between Metabolites and Egfrmentioning

confidence: 99%

Section: Cross-sectional Associations Between Metabolites and Egfrmentioning

confidence: 99%

mentioning

confidence: 99%

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Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

Tofte

Vogelzangs

Mook‐Kanamori

et al. 2020

The Journal of Clinical Endocrinology &Amp; Metabolism

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“…We calculated that 54 principal components explained 99% of the variation in the metabolic measures. Alternative methods have yielded a similar number of independent tests in the NMR metabolite data [19,20]. Hence, we inferred statistical significance at meta-analysis p value <0.0009 (0.05/54).…”

Section: Statistical Analysesmentioning

confidence: 90%

“…Ari V. Ahola-Olli 1,2,3 & Linda Mustelin 3,4,5 & Maria Kalimeri 4 & Johannes Kettunen 6,7,8 & Jari Jokelainen 6,9 & Juha Auvinen 6,9,10 & Katri Puukka 11,12,13 & Aki S. Havulinna 3,14 & Terho Lehtimäki 15,16 & Mika Kähönen 15,17 & Markus Juonala 18 & Sirkka Keinänen-Kiukaanniemi 6,9,19,20 & Veikko Salomaa 14 & Markus Perola 3,14,21 & Marjo-Riitta Järvelin 6,9,22,23,24 & Mika Ala-Korpela 7,8,25,26,27,28 & Olli Raitakari 1,29 & Peter Würtz 4,30 1…”

Section: Data Availabilityunclassified

Circulating Metabolites and the Risk of Type 2 Diabetes—A Prospective Study of 10,938 Young Adults from Four Finnish Cohorts

Ahola‐Olli¹,

Mustelin²,

Kettunen³

et al. 2018

Diabetes

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Aims/hypothesis Metabolomics technologies have identified numerous blood biomarkers for type 2 diabetes risk in case−control studies of middle-aged and older individuals. We aimed to validate existing and identify novel metabolic biomarkers predictive of future diabetes in large cohorts of young adults. Methods NMR metabolomics was used to quantify 229 circulating metabolic measures in 11,896 individuals from four Finnish observational cohorts (baseline age 24-45 years). Associations between baseline metabolites and risk of developing diabetes during 8-15 years of follow-up (392 incident cases) were adjusted for sex, age, BMI and fasting glucose. Prospective metabolite associations were also tested with fasting glucose, 2 h glucose and HOMA-IR at follow-up. Results Out of 229 metabolic measures, 113 were associated with incident type 2 diabetes in meta-analysis of the four cohorts (ORs per 1 SD: 0.59-1.50; p< 0.0009). Among the strongest biomarkers of diabetes risk were branched-chain and aromatic amino acids (OR 1.31-1.33) and triacylglycerol within VLDL particles (OR 1.33-1.50), as well as linoleic n-6 fatty acid (OR 0.75) and non-esterified cholesterol in large HDL particles (OR 0.59). The metabolic biomarkers were more strongly associated with deterioration in post-load glucose and insulin resistance than with future fasting hyperglycaemia. A multi-metabolite score comprised of phenylalanine, non-esterified cholesterol in large HDL and the ratio of cholesteryl ester to total lipid in large VLDL was associated with future diabetes risk (OR 10.1 comparing individuals in upper vs lower fifth of the multi-metabolite score) in one of the cohorts (mean age 31 years). Conclusions/interpretation Metabolic biomarkers across multiple molecular pathways are already predictive of the long-term risk of diabetes in young adults. Comprehensive metabolic profiling may help to target preventive interventions for young asymptomatic individuals at increased risk.

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Serum metabolomics detected by LDI‐TOF‐MS can be used to distinguish between diabetic patients with and without diabetic kidney disease

et al. 2023

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Diabetic kidney disease (DKD) is an important cause of end‐stage renal disease with changes in metabolic characteristics. The objective of this study was to study changes in serum metabolic characteristics in patients with DKD and to examine metabolite panels to distinguish DKD from diabetes with matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry(MALDI‐TOF‐MS). We recruited 40 type II diabetes mellitus (T2DM) patients with or without DKD from a single center for a cross‐sectional study. Serum metabolic profiling was performed with MALDI‐TOF‐MS using a vertical silicon nanowire array. Differential metabolites between DKD and diabetes patients were selected, and their relevance to the clinical parameters of DKD was analyzed. We applied machine learning methods to the differential metabolite panels to distinguish DKD patients from diabetes patients. Twenty‐four differential serum metabolites between DKD patients and diabetes patients were identified, which were mainly enriched in butyrate metabolism, TCA cycle, and alanine, aspartate, and glutamate metabolism. Among the metabolites, l‐kynurenine was positively correlated with urinary microalbumin, urinary microalbumin/creatinine ratio (UACR), creatinine, and urea nitrogen content. l‐Serine, pimelic acid, 5‐methylfuran‐2‐carboxylic acid, 4‐methylbenzaldehyde, and dihydrouracil were associated with the estimated glomerular filtration rate (eGFR). The panel of differential metabolites could be used to distinguish between DKD and diabetes patients with an AUC value reaching 0.9899–0.9949. Among the differential metabolites, l‐kynurenine was related to the progression of DKD. The differential metabolites exhibited excellent performance at distinguishing between DKD and diabetes. This study provides a novel direction for metabolomics‐based clinical detection of DKD.

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Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations

Cited by 45 publications

References 56 publications

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

Circulating Metabolites and the Risk of Type 2 Diabetes—A Prospective Study of 10,938 Young Adults from Four Finnish Cohorts

Serum metabolomics detected by LDI‐TOF‐MS can be used to distinguish between diabetic patients with and without diabetic kidney disease

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