Urinary Metabolite
Urinary metabolite involves the analysis of various small molecules present in urine, providing a snapshot of an individual’s metabolic state. These measurements can reflect systemic (blood) biomarker levels, or indicate specific changes in kidney function, such as alterations in glomerular filtration rate, increased leakage of molecules into the urine, or changes in tubular reabsorption. Metabolites can also originate directly from kidney or urinary system tissues.[1] The non-invasive nature and accessibility of urine samples make them a valuable source for biomarker discovery and health monitoring.
Biological Basis
Section titled “Biological Basis”The kidney plays a central role in filtering blood and excreting waste products, making urinary metabolites a direct reflection of renal processes. Genetic factors significantly influence the concentrations of these metabolites. Genome-wide association studies (GWAS) have identified hundreds of genomic regions (loci) associated with urinary metabolite levels.[1] For instance, studies have found 622 genomic intervals linked to 1399 metabolites.[1]and 240 genomic intervals (metabolite quantitative trait loci or mQTLs) associated with urinary metabolite concentrations.[2] These genetic insights illuminate mechanisms related to detoxification and excretion in humans.[2]Research indicates that genetic effects on metabolite levels can be more pronounced in patients with chronic kidney disease (CKD), though these effects are also relevant in individuals with normal kidney function.[2]The molecular impact of kidney function on urinary metabolites is evident, with identified genetic variants often associated with kidney disease traits such as estimated glomerular filtration rate (eGFR) and CKD.[1]Mendelian randomization studies further suggest a causal relationship, where kidney health directly influences urinary metabolite concentrations.[1]Heritability analyses demonstrate that a significant proportion of the variation in urinary metabolite levels is attributable to additive genetic effects.[3]
Clinical Relevance
Section titled “Clinical Relevance”Urinary metabolites serve as molecular phenotypes that can drive clinical phenotypes and predict disease progression.[4]Dissecting the genetic mechanisms underlying urinary metabolite concentrations offers molecular insights into kidney function and opens avenues for causally assessing the link between metabolites, disease risk factors, and disease outcomes.[1]The identification of robust genetic variants associated with urinary metabolites is particularly valuable, as these variants can act as genetic instruments in Mendelian randomization analyses. This approach helps to infer causality between metabolites and disease outcomes, offering more reliable results by allowing for methods that account for pleiotropic effects.[1]Beyond kidney health, urinary metabolite profiling has been linked to the risk of diabetic nephropathy progression and provides a quantitative epidemiological approach to renal-cardiometabolic biomarkers.[5]
Social Importance
Section titled “Social Importance”The study of urinary metabolites holds significant social importance due to its potential to advance personalized medicine and public health. The non-invasive nature of urine collection makes it a more accessible and patient-friendly method for biomarker sampling compared to blood, facilitating large-scale population studies and broader health monitoring efforts.[1] Understanding the genetic determinants of urinary metabolites contributes to a deeper understanding of human metabolic individuality.[6]This knowledge can lead to the development of novel diagnostic tools for early disease detection, more targeted therapeutic strategies, and personalized health interventions, ultimately improving health outcomes across diverse populations.
Methodological and Scope
Section titled “Methodological and Scope”The study employed Nuclear Magnetic Resonance (NMR) spectroscopy to quantify 54 urinary metabolites.[1]While this platform enabled a large sample size, its coverage is notably less comprehensive compared to mass spectrometry-based methods, which can detect hundreds or even thousands of metabolites.[1], [2] This limited scope means that many other potentially relevant urinary metabolites and their genetic influences remain uncharacterized, offering only a partial view of the overall urinary metabolome and its genetic architecture. The restriction to a targeted panel may overlook critical pathways or biomarkers that are not captured by the current methodology.
Despite rigorous quality control, some metabolites exhibited high levels of missingness across cohorts.[1] Although this missingness was considered random, it directly impacted the statistical power to detect genetic associations and conduct Mendelian Randomization analyses.[1] Such data gaps can compromise the reliability of findings and may limit the applicability of certain metabolites as robust biomarkers, particularly if the missingness is related to biological or technical factors not fully accounted for.
Population and Study Design Generalizability
Section titled “Population and Study Design Generalizability”The meta-analysis combined data from a Finnish cohort of individuals with type 1 diabetes and two Scottish general population cohorts.[1] Significant age differences existed between the type 1 diabetes cohort (mean age 37.7 years) and the general population cohorts (mean ages 49.8 and 55.8 years).[1]While overall concordance was observed, heterogeneity in heritability estimates for six metabolites and discrepant effect sizes for nine lead signals, including specific associations in the type 1 diabetes cohort for the glycineGM2Aassociation, indicate that genetic effects may not be uniformly generalizable across populations with different disease statuses or age structures.[1] Furthermore, the reliance on European ancestry populations for both the study cohorts and the instrumental variables used in Mendelian Randomization.[1] restricts the generalizability of these findings to other global populations, where genetic architectures and environmental exposures may differ substantially.
For several Mendelian Randomization (MR) analyses, particularly those investigating the causal effect of certain urinary metabolites on health outcomes, the findings were based on only one or two genetic variants.[1] This limited number of genetic instruments precludes the use of more robust MR methods designed to account for pleiotropic effects, where a single genetic variant influences multiple traits.[1] Consequently, the causal inferences drawn from these specific analyses may be less reliable and more susceptible to unmeasured confounding or pleiotropy, highlighting the need for discovery of additional, independent genetic instruments for stronger causal evidence.
Unexplained Biological Variation and Confounding
Section titled “Unexplained Biological Variation and Confounding”While the study identified significant heritability for 27 of the 54 urinary metabolites, ranging from 6% to 36%.[1] a substantial portion of the variation in these metabolites remains unexplained by the measured genetic factors. For the other 27 metabolites, no significant heritability was detected.[1]This “missing heritability” suggests that non-genetic factors, including environmental exposures, lifestyle, diet, gut microbiome interactions, or unmeasured rare genetic variants, play a significant role in shaping urinary metabolite profiles. The complex interplay of these factors, including potential gene-environment interactions, was not fully explored and represents an ongoing knowledge gap in understanding the full determinants of urinary metabolome variability.
The study successfully cataloged numerous genetic associations for urinary metabolites.[1] providing a foundation for future research. However, the exact biological mechanisms through which many of these genetic variants influence metabolite levels, especially those located outside gene coding regions.[1]require further elucidation. While these variants can serve as genetic instruments for Mendelian Randomization to infer causality with disease outcomes, the direct biological and clinical relevance of many newly identified associations still needs to be fully established.[1]This necessitates further functional genomic studies and clinical validation to translate these genetic insights into a comprehensive understanding of human health and disease.
Variants
Section titled “Variants”Genetic variations play a crucial role in shaping an individual’s unique metabolic profile, influencing the production and excretion of various urinary metabolites. Several genes, including PYROXD2, ALMS1, and NAT8, are associated with N-acetylated compounds and other key metabolites. For instance, variants in PYROXD2, such as rs2147896 , rs11598867 , and rs4488133 , are strongly linked to urinary trimethylamine levels. PYROXD2codes for pyridine nucleotide-disulphide oxidoreductase domain 2, and its variants, particularlyrs2147896 , have shown significant associations with trimethylamine in urine, suggesting a role in its metabolism or excretion.[4] Similarly, rs11598867 also associates with trimethylamine, indicating a consistent genetic influence on this metabolite.[7]Trimethylamine is a gut microbiome-derived metabolite, and its urinary levels can be indicative of gut health and conditions like trimethylaminuria.
The ALMS1 gene, which encodes the Alström syndrome protein 1, is involved in diverse cellular functions including ciliary transport and cell cycle regulation. Variants within ALMS1, such as rs6711001 , rs6546861 , rs6546847 , rs10195357 , rs7607014 , rs10201159 , and rs111540621 , have been associated with urinary N-acetylated compounds.[4] These associations highlight a potential link between ALMS1 function and the metabolic pathways involving N-acetylation, which is a key detoxification process. Adjacent to ALMS1, the NAT8 gene, represented by variants like rs10178409 , rs4547554 , and rs13538 , is highly expressed in the kidney and putatively encodes an N-acetyltransferase.[7] The variant rs10178409 specifically associates with N-acetylaspartate and other N-acetylated compounds, aligning with NAT8’s enzymatic function.[7] Variations in NAT8have also been linked to chronic kidney disease and glomerular filtration rate, underscoring its importance in renal function and metabolite excretion.[7] Another significant gene influencing urinary metabolites is AGXT2(alanine-glyoxylate aminotransferase 2), a mitochondrial enzyme predominantly found in the kidney and liver.AGXT2 is crucial for the metabolism of various amino acids, including the conversion of (R)-3-aminoisobutyric acid. Variants such as rs37369 , rs40200 , and rs7717823 in AGXT2 are strongly associated with urinary 3-aminoisobutyrate levels.[7] The rs37369 variant is considered a likely causative factor for Beta-aminoisobutyric aciduria, a condition characterized by elevated urinary 3-aminoisobutyrate.[7] Furthermore, the NAT2 gene, represented by rs35246381 , rs4921914 , and rs1495741 , encodes an arylamine N-acetyltransferase that plays a vital role in drug metabolism and detoxification. NAT2variants are known to influence acetylation phenotypes and have been associated with the urinary formate-succinate ratio.[4] These genetic differences in NAT2 can significantly alter an individual’s capacity to process certain drugs and environmental toxins, impacting their urinary metabolic profile.
Genes involved in fatty acid metabolism, such as ACADL and ACADS, also exhibit variants that influence metabolite levels. ACADL (acyl-CoA dehydrogenase long chain) and ACADS (acyl-CoA dehydrogenase short chain) encode mitochondrial enzymes critical for the breakdown of long-chain and short-chain fatty acids, respectively. The variant rs2286963 in ACADL and rs1799958 and rs3916 in ACADS have been linked to metabolite concentrations in serum, suggesting a broader impact on systemic metabolism.[4] While specific urinary associations for these variants are still being elucidated, their known roles in fatty acid oxidation imply that genetic variations could affect the urinary excretion of acylcarnitines and other related metabolites. Additionally, the GLYATL3 gene, with its variant rs12190495 , belongs to a family of enzymes that conjugate glycine to various compounds. This process is essential for detoxification, leading to the urinary excretion of glycine conjugates. Variations inGLYATL3 likely modulate the efficiency of this conjugation, thereby influencing the urinary excretion patterns of a range of endogenous and exogenous substances.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs2147896 rs11598867 rs4488133 | PYROXD2 | urinary metabolite metabolite N-methylpipecolate N2-acetyl,N6-methyllysine N6-methyllysine |
| rs10201159 rs10178409 rs111540621 | ALMS1 - NAT8 | 2-aminooctanoate metabolite N-acetyl-3-methylhistidine N-acetylglutamine N-acetylarginine |
| rs37369 rs40200 rs7717823 | AGXT2 | serum dimethylarginine amount metabolite urinary metabolite protein X-12117 |
| rs6711001 rs6546861 | ALMS1 | N-acetylleucine N-acetylhistidine 1-Methylhistidine methionine sulfone N6-acetyllysine |
| rs35246381 rs4921914 rs1495741 | NAT2 - PSD3 | urinary metabolite triglyceride , physical activity cholesterol:totallipids ratio, high density lipoprotein cholesterol free cholesterol:totallipids ratio, intermediate density lipoprotein apolipoprotein M |
| rs2286963 | ACADL | metabolite serum metabolite level X-13431 C9 carnitine X-23641 |
| rs12190495 | GLYATL3 | vitamin B12 metabolite urinary metabolite |
| rs1799958 rs3916 | ACADS | serum metabolite level butyrylcarnitine methylsuccinate oxaloacetic acid ethylmalonate |
| rs4547554 rs13538 | NAT8, ALMS1P1, ALMS1P1 | N-acetyltyrosine N-acetyl-2-aminooctanoate methionine sulfone N-acetylleucine metabolite |
| rs6546847 rs10195357 rs7607014 | ALMS1 | urinary metabolite N-acetyl-2-aminooctanoate N-acetylarginine N-acetylglutamine |
Urinary Metabolites: Definition and Biological Role
Section titled “Urinary Metabolites: Definition and Biological Role”Urinary metabolites are small molecules found in urine that serve as valuable biomarkers, reflecting either systemic (blood) levels or localized changes within the renal and urinary systems.[1] Unlike blood-based biomarkers, urinary metabolites offer a more accessible and often less tightly regulated source of material, providing insights into physiological states.[1] Their concentrations can indicate alterations in kidney function, such as changes in glomerular filtration rate, increased leakage of molecules into the urine, or modified tubular reabsorption back into the blood, as well as molecules originating directly from kidney or urinary system tissues.[1]For instance, specific urinary metabolites like 1-methylnicotinamide, 3-hydroxyhippurate, 4-deoxythreonate, quinic acid, trigonelline, and ethanolamine have been linked to kidney function markers or body mass index (BMI).[1]
Analytical Approaches and Data Processing
Section titled “Analytical Approaches and Data Processing”The profiling of urinary metabolites often employs advanced analytical techniques, with Nuclear Magnetic Resonance (NMR) spectroscopy being a prominent method.[1] This approach can be non-targeted, aiming to identify and quantify a broad spectrum of metabolites without prior assumptions about their biological relevance or specific metabolic pathways.[3] Urine samples are typically collected over 24 hours or as pooled samples to capture an average of daily metabolic fluctuations, which can be more representative than single spot urine samples.[3] To ensure accurate comparisons across samples, metabolite concentrations undergo various normalization steps, including division by urinary creatinine concentration to account for urine volume, median fold change methods, probabilistic quotient normalization, and logarithmic or inverse normal transformations.[1]
Classification Systems and Genetic Terminology
Section titled “Classification Systems and Genetic Terminology”Urinary metabolites are classified based on their chemical structure, metabolic pathways, or origin, such as being categorized into specific metabolic classes—for example, 44 metabolites classified into 22 distinct metabolic classes in some studies.[3] Another classification distinguishes between non-xenobiotic (endogenous) metabolites and xenobiotic metabolites, which are often excluded or treated differently in analyses due to their external origin.[2]The study of the genetic influences on these metabolites utilizes specialized terminology, including “urinary metabolomics” for the comprehensive study of urinary metabolite profiles, and “biomarkers” for measurable indicators of biological states.[1] Key terms in genetic research include “metabolite quantitative trait loci” (mQTLs), referring to genomic regions associated with variations in metabolite concentrations, which are often identified through “Genome-Wide Association Studies” (GWAS).[3]Additionally, “Mendelian Randomization” (MR) is a technique employed to infer causal relationships between genetic variants, urinary metabolite levels, and various health traits.[1]
Integrated Clinical Evaluation and Differential Diagnosis
Section titled “Integrated Clinical Evaluation and Differential Diagnosis”Urinary metabolite analysis complements traditional clinical assessments by offering molecular insights into disease states. For instance, the exclusion of individuals with end-stage kidney disease (ESKD) or very low estimated glomerular filtration rate (eGFR <10 mL/min/1.73m2) during studies ensures that metabolite profiles are not confounded by severe renal impairment and can aid in accurate diagnosis of conditions like type 1 diabetes.[1]The identification of specific urinary metabolites, such as 1-methylnicotinamide as a potential early marker for metabolic disease, suggests their utility in early detection and distinguishing various metabolic dysfunctions.[1] Furthermore, investigating the genetic regulation of urinary metabolites can reveal underlying biological pathways, thereby supporting the understanding and differentiation of complex diseases from observational findings.[1]
Advanced Laboratory Profiling for Metabolite Detection
Section titled “Advanced Laboratory Profiling for Metabolite Detection”The diagnostic landscape of urinary metabolites relies heavily on advanced laboratory techniques, primarily Nuclear Magnetic Resonance (NMR) metabolomics, which offers a high-throughput method for profiling a wide range of urinary compounds.[1] Studies have successfully quantified numerous metabolites, with platforms capable of detecting 54 urinary metabolites and exhibiting a mean coefficient of variation of 11% across measurements, indicating good analytical precision.[1]Complementary approaches include targeted mass spectrometry (MS)-based assays, such as using the AbsoluteIDQ p180 kit for serum metabolites, and non-targeted MS approaches for broader urinary metabolite coverage.[3] To ensure accuracy and account for variations in urine volume, metabolite levels are typically normalized, often by dividing by urinary creatinine concentrations or using probabilistic quotient normalization based on endogenous metabolites.[1]
Genetic Insights and Renal Function Markers
Section titled “Genetic Insights and Renal Function Markers”Genetic studies, particularly genome-wide association studies (GWAS) and metabolome-wide association studies (mGWAS), are instrumental in uncovering genetic variants associated with urinary metabolite concentrations, providing novel insights into human physiology and inborn errors of metabolism.[1] These genetic findings illuminate the mechanisms of detoxification and excretion, with hundreds of loci identified across various metabolites.[2]Specific urinary metabolites also serve as crucial biomarkers for assessing renal function and disease progression; for example, 10 urinary metabolites have been identified as predictive of diabetic kidney disease (DKD) progression in individuals with type 1 diabetes.[8]Concurrently, traditional measures of kidney function such as estimated glomerular filtration rate (eGFR), calculated using formulas like CKD-EPI, and urinary albumin excretion rate (AER) (categorized as normal, moderate, or severe albuminuria) provide essential clinical context, as alterations in kidney function directly impact urinary metabolite levels.[1]
Renal Physiology and Metabolite Homeostasis
Section titled “Renal Physiology and Metabolite Homeostasis”The kidneys play a central and dynamic role in maintaining systemic metabolic homeostasis by meticulously regulating the generation, breakdown, active reabsorption, and secretion of a wide array of metabolites products . This approach provides a less tightly regulated and more readily available source of biomarkers compared to blood, capturing changes related to glomerular filtration rate, tubular reabsorption, and kidney tissue health.[1] For instance, specific urinary metabolites have been identified as potential biomarkers for monitoring kidney health, including those affected by estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (UACR).[1]
Risk Stratification and Prognostic Value
Section titled “Risk Stratification and Prognostic Value”Urinary metabolite analysis holds significant promise for risk stratification and predicting disease progression and treatment response, contributing to personalized medicine approaches. Metabolic traits, as molecular phenotypes, can drive clinical phenotypes and predict disease progression.[4]For example, studies have identified ten specific urinary metabolites that are predictive of diabetic kidney disease (DKD) progression in individuals with type 1 diabetes.[1]Beyond kidney diseases, urinary metabolites have shown associations with various cardiometabolic biomarkers and conditions, including body mass index (BMI), which has been demonstrated to causally affect several urinary metabolite concentrations.[1] Such insights enable the identification of high-risk individuals and the development of targeted prevention strategies.
Insights into Disease Mechanisms and Comorbidities
Section titled “Insights into Disease Mechanisms and Comorbidities”Urinary metabolites provide valuable molecular insights into the underlying biological processes of diseases, their associated comorbidities, and the impact of kidney function. The genetic regulation of urinary metabolites can reveal key biological pathways, offering a deeper understanding of how these biomarkers are linked to human health.[1] The kidneys play a critical role in the generation, breakdown, active reabsorption, and secretion of metabolites, influencing their urinary concentrations.[2] This makes urinary profiling particularly informative for ADME (absorption, distribution, metabolism, excretion) processes, which are crucial for understanding drug handling and toxin clearance.[2]Furthermore, Mendelian randomization analyses suggest a causal relationship between kidney function (eGFR) and certain urinary metabolite concentrations, such as urinary ethanolamine, which may have a protective role.[1]This knowledge is vital for unraveling overlapping phenotypes and syndromic presentations, especially in complex conditions like chronic kidney disease, where information beyond filtration capacity, like N-acetylation, could predict disease course.[2]
Frequently Asked Questions About Urinary Metabolite
Section titled “Frequently Asked Questions About Urinary Metabolite”These questions address the most important and specific aspects of urinary metabolite based on current genetic research.
1. Why do some people seem to have “better” kidneys than others, even with similar lifestyles?
Section titled “1. Why do some people seem to have “better” kidneys than others, even with similar lifestyles?”Your genes significantly influence how your kidneys process and excrete substances. Studies show that a large portion of the variation in urinary metabolite levels, which reflect kidney function, is due to additive genetic effects. This means your individual genetic makeup can lead to different kidney efficiencies compared to others, even with similar habits.
2. Can my urine tell me if I’m at risk for kidney problems before I have symptoms?
Section titled “2. Can my urine tell me if I’m at risk for kidney problems before I have symptoms?”Yes, potentially. Urinary metabolites act as molecular phenotypes that can help predict disease progression. Identifying specific genetic variants associated with these metabolites offers early insights into your kidney function and potential risk for conditions like chronic kidney disease, often before you experience symptoms.
3. If my parents had kidney issues, will my urine tests show similar patterns?
Section titled “3. If my parents had kidney issues, will my urine tests show similar patterns?”It’s quite possible. Genetic factors strongly influence the concentrations of urinary metabolites, and a significant portion of this variation is heritable. If your parents had genetic predispositions affecting kidney function, you might inherit similar genetic influences that impact your own metabolite levels and kidney health patterns.
4. Is a urine test better than a blood test for understanding my metabolism?
Section titled “4. Is a urine test better than a blood test for understanding my metabolism?”Urine tests are incredibly valuable because they are non-invasive and easy to collect, making them excellent for widespread health monitoring. While blood samples reflect systemic levels, urine directly shows renal processes and changes in kidney function. This makes urine a unique and accessible source for understanding your metabolic state.
5. Could a urine test help me choose a more personalized diet or exercise plan?
Section titled “5. Could a urine test help me choose a more personalized diet or exercise plan?”Absolutely. Understanding your unique genetic determinants of urinary metabolites helps reveal your metabolic individuality. This knowledge can lead to the development of more targeted therapeutic strategies and personalized health interventions, including specific diet and exercise plans tailored to your distinct metabolic profile.
6. I have diabetes; does that change what my urine metabolites mean for my health?
Section titled “6. I have diabetes; does that change what my urine metabolites mean for my health?”Yes, it can. Research indicates that genetic effects on metabolite levels can be more pronounced in patients with chronic conditions like diabetes. Urinary metabolite profiling has been directly linked to the risk of diabetic nephropathy progression, meaning your diabetic status significantly influences what your metabolite profile signifies for your health.
7. Does my ethnic background affect what’s “normal” for my urine metabolites?
Section titled “7. Does my ethnic background affect what’s “normal” for my urine metabolites?”Yes, your ethnic background can matter. The primary research for these genetic insights has focused on populations of European ancestry. Different ethnic groups often have unique genetic variations that influence metabolic processes and kidney function, meaning what’s considered “normal” or risky for urinary metabolites might differ for you.
8. Why do doctors care about these tiny molecules in my pee?
Section titled “8. Why do doctors care about these tiny molecules in my pee?”Doctors care because these small molecules provide a valuable snapshot of your metabolic health. They directly reflect kidney function and overall metabolic state. By analyzing them, doctors gain molecular insights into your kidney health, identify disease risk factors, and can potentially use this information for early disease detection and personalized care.
9. Can my everyday health habits actually change what’s in my urine?
Section titled “9. Can my everyday health habits actually change what’s in my urine?”Yes, your daily health habits can definitely influence what’s found in your urine. While genetics set your baseline, your current metabolic state, which is affected by lifestyle, is reflected in your urinary metabolites. These measurements offer a dynamic view of how your body is functioning, indicating changes due to health habits or developing issues.
10. Can I change my genetic risk for kidney issues through diet or exercise?
Section titled “10. Can I change my genetic risk for kidney issues through diet or exercise?”While your genetic makeup largely influences your baseline kidney function and metabolite levels, lifestyle choices are still powerful. Understanding your genetic predispositions can guide personalized health interventions. This allows you to make targeted diet and exercise choices that can help mitigate risks and support kidney health, even with a genetic susceptibility.
This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.
Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.
References
Section titled “References”[1] Valo E et al. “Genome-wide characterization of 54 urinary metabolites reveals molecular impact of kidney function.” Nature Communications, 2025.
[2] Schlosser P et al. “Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans.” Nature Genetics, 2020.
[3] Calvo-Serra, Bàrbara, et al. “Urinary metabolite quantitative trait loci in children and their interaction with dietary factors.”Human Molecular Genetics, vol. 30, no. 1, 2021, pp. 1-13.
[4] Rueedi, Remo, et al. “Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links.”PLoS Genet, vol. 10, no. 2, 2014, p. e1004132.
[5] Julkunen, Heikki, et al. “Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank.”Nature Communications, vol. 14, no. 1, 2023, p. 604.
[6] Suhre, Karsten, et al. “Human metabolic individuality in biomedical and pharmaceutical research.” Nature, vol. 477, no. 7362, 2011, pp. 54-60.
[7] Raffler, J., et al. “Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality.” PLoS Genet, vol. 11, no. 9, 2015, e1005487.
[8] Mutter, Stefan, et al. “Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes.”Diabetologia, vol. 65, 2022, pp. 140–149.