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

Glycoprotein Acetyls Associate With Intraglomerular Hemodynamic Dysfunction, Albuminuria, Central Adiposity, and Insulin Resistance in Youth With Type 1 Diabetes

OBJECTIVES

Glycoprotein acetyls (GlycA's) are biomarkers of systemic inflammation and cardiovascular disease, yet little is known about their role in type 1 diabetes (T1D). In this study we examined the associations among GlycA's, central adiposity, insulin resistance, and early kidney injury in youth with T1D.

METHODS

Glomerular filtration rate and renal plasma flow by iohexol and p-aminohippurate clearance, urine albumin-to-creatinine ratio (UACR), central adiposity by dual-energy x-ray absorptiometry, and estimated insulin sensitivity were assessed in 50 youth with T1D (16±3.0 years of age, 50% female, glycated hemoglobin 8.7%±1.3%, T1D duration 5.7±2.6 years). Concentrations of GlycA were quantified by targeted nuclear magnetic resonance spectroscopy. Correlation and multivariable linear regression analyses were performed.

RESULTS

GlycA's were higher in girls vs boys (1.05±0.26 vs 0.84±0.15 mmol/L, p=0.001) and in participants living with overweight/obesity vs normal weight (1.12±0.23 vs 0.87±0.20 mmol/L, p=0.0004). GlycA's correlated positively with estimated intraglomerular pressure (r=0.52, p=0.001), UACR (r=0.53, p<0.0001), and trunk mass (r=0.45, p=0.001), and inversely with estimated insulin sensitivity (r=-0.36, p=0.01). All relationships remained significant after adjustment for age, sex, and glycated hemoglobin.

CONCLUSIONS

As biomarkers of inflammation, GlycA's were higher in girls and those with overweight or obese body habitus in T1D. GlycA's associated with parameters of early kidney dysfunction, central adiposity, and insulin resistance.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

Protective effect of phospholipids in lipoproteins against diabetic kidney disease: A Mendelian randomization analysis

BACKGROUND

The etiology of diabetic kidney disease is complex, and the role of lipoproteins and their lipid components in the development of the disease cannot be ignored. However, phospholipids are an essential component, and no Mendelian randomization studies have yet been conducted to examine potential causal associations between phospholipids and diabetic kidney disease.

METHODS

Relevant exposure and outcome datasets were obtained through the GWAS public database. The exposure datasets included various phospholipids, including those in LDL, IDL, VLDL, and HDL. IVW methods were the primary analytical approach. The accuracy of the results was validated by conducting heterogeneity, MR pleiotropy, and F-statistic tests. MR-PRESSO analysis was utilized to identify and exclude outliers.

RESULTS

Phospholipids in intermediate-density lipoprotein (OR: 0.8439; 95% CI: 0.7268-0.9798), phospholipids in large low- density lipoprotein (OR: 0.7913; 95% CI: 0.6703-0.9341), phospholipids in low- density lipoprotein (after removing outliers, OR: 0.788; 95% CI: 0.6698-0.9271), phospholipids in medium low- density lipoprotein (OR: 0.7682; 95% CI: 0.634-0.931), and phospholipids in small low-density lipoprotein (after removing outliers, OR: 0.8044; 95% CI: 0.6952-0.9309) were found to be protective factors.

CONCLUSIONS

This study found that a higher proportion of phospholipids in intermediate-density lipoprotein and the various subfractions of low-density lipoprotein, including large LDL, medium LDL, and small LDL, is associated with a lower risk of developing diabetic kidney disease.

Sex differences in kidney metabolism may reflect sex-dependent outcomes in human diabetic kidney disease

Diabetic kidney disease (DKD) is the main cause of chronic kidney disease (CKD) and progresses faster in males than in females. We identify sex-based differences in kidney metabolism and in the blood metabolome of male and female individuals with diabetes. Primary human proximal tubular epithelial cells (PTECs) from healthy males displayed increased mitochondrial respiration, oxidative stress, apoptosis, and greater injury when exposed to high glucose compared with PTECs from healthy females. Male human PTECs showed increased glucose and glutamine fluxes to the TCA cycle, whereas female human PTECs showed increased pyruvate content. The male human PTEC phenotype was enhanced by dihydrotestosterone and mediated by the transcription factor HNF4A and histone demethylase KDM6A. In mice where sex chromosomes either matched or did not match gonadal sex, male gonadal sex contributed to the kidney metabolism differences between males and females. A blood metabolomics analysis in a cohort of adolescents with or without diabetes showed increased TCA cycle metabolites in males. In a second cohort of adults with diabetes, females without DKD had higher serum pyruvate concentrations than did males with or without DKD. Serum pyruvate concentrations positively correlated with the estimated glomerular filtration rate, a measure of kidney function, and negatively correlated with all-cause mortality in this cohort. In a third cohort of adults with CKD, male sex and diabetes were associated with increased plasma TCA cycle metabolites, which correlated with all-cause mortality. These findings suggest that differences in male and female kidney metabolism may contribute to sex-dependent outcomes in DKD.

Systematic identification of the role of gut microbiota in mental disorders: a TwinsUK cohort study

Mental disorders are complex disorders influenced by multiple genetic, environmental, and biological factors. Specific microbiota imbalances seem to affect mental health status. However, the mechanisms by which microbiota disturbances impact the presence of depression, stress, anxiety, and eating disorders remain poorly understood. Currently, there are no robust biomarkers identified. We proposed a novel pyramid-layer design to accurately identify microbial/metabolomic signatures underlying mental disorders in the TwinsUK registry. Monozygotic and dizygotic twins discordant for mental disorders were screened, in a pairwise manner, for differentially abundant bacterial genera and circulating metabolites. In addition, multivariate analyses were performed, accounting for individual-level confounders. Our pyramid-layer study design allowed us to overcome the limitations of cross-sectional study designs with significant confounder effects and resulted in an association of the abundance of genus Parabacteroides with the diagnosis of mental disorders. Future research should explore the potential role of Parabacteroides as a mediator of mental health status. Our results indicate the potential role of the microbiome as a modifier in mental disorders that might contribute to the development of novel methodologies to assess personal risk and intervention strategies.

Sodium fluoride preserves blood metabolite integrity for biomarker discovery in large-scale, multi-site metabolomics investigations

Background: Metabolite profiling of blood by nuclear magnetic resonance (NMR) is invaluable to clinical biomarker discovery. To ensure robustness, biomarkers require validation in large cohorts and across multiple centres. However, collection procedures are known to impact on the stability of biofluids that may, in turn, degrade biomarker signals. We trialled three blood collection tubes with the aim of solving technical challenges due to preanalytical variation in blood metabolite levels that are common in cohort studies. Methods: We first investigated global NMR-based metabolite variability between biobanks, including the large-scale UK Biobank and TwinsUK biobank of the general UK population, and more targeted biobanks derived from multicentre clinical trials relating to inflammatory bowel disease. We then compared the blood metabolome of 12 healthy adult volunteers when collected into either sodium fluoride/potassium oxalate, lithium heparin, or serum blood tubes using different pre-processing parameters. Results: Preanalytical variation in the method of blood collection strongly influences metabolite composition within and between biobanks. This variability can largely be attributed to glucose and lactate. In the healthy control cohort, the fluoride oxalate collection tube prevented fluctuation in glucose and lactate levels for 24 hours at either 4 °C or room temperature (20 °C). Conclusions: Blood collection into a fluoride oxalate collection tube appears to preserve the blood metabolome with delayed processing up to 24 hours at 4 °C. This method may be considered as an alternative when rapid processing is not feasible.

Insights into serum metabolic biomarkers for early detection of incident diabetic kidney disease in Chinese patients with type 2 diabetes by random forest

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD) worldwide. Early detection is critical for the risk stratification and early intervention of progressive DKD. Serum creatinine (sCr) and urine output are used to assess kidney function, but these markers are limited by their delayed changes following kidney pathology, and lacking of both sensitivity and accuracy. Hence, it is essential to illustrate potential diagnostic indicators to enhance the precise prediction of early DKD. A total of 194 Chinese individuals include 30 healthy participants (Stage 0) and 164 incidents with type 2 diabetes (T2D) spanning from DKD's Stage 1a to 4 were recruited and their serums were subjected for untargeted metabolomic analysis. Random forest (RF), a machine learning approach, together with univariate linear regression (ULR) and multivariate linear regression (MvLR) analysis were applied to characterize the features of untargeted metabolites of DKD patients and to identify candidate DKD biomarkers. Our results indicate that 2-(α-D-mannopyranosyl)-L-tryptophan (ADT), succinyladenosine (SAdo), pseudouridine and N,N,N-trimethyl-L-alanyl-L-proline betaine (L-L-TMAP) were associated with the development of DKD, in particular, the latter three that were significantly elevated in Stage 2-4 T2D incidents. Each of the four metabolites in combination with sCr achieves better performance than sCr alone with area under the receiver operating characteristic curve (AUC) of 0.81-0.91 in predicting DKD stages. An average of 3.9 years follow-up study of another cohort including 106 Stage 2-3 patients suggested that "urinary albumin-to-creatinine ratio (UACR) + ADT + SAdo" can be utilized for better prognosis evaluation of early DKD (average AUC = 0.9502) than UACR without sexual difference.

Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD

RATIONALE & OBJECTIVE

Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study.

STUDY DESIGN

Observational cohort study.

SETTING & PARTICIPANTS

A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy.

EXPOSURE

Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy.

OUTCOME

Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records.

ANALYTICAL APPROACH

Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals.

RESULTS

We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD.

LIMITATIONS

Potential underreporting of CKD cases because of case identification via electronic health records.

CONCLUSIONS

Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD.

PLAIN-LANGUAGE SUMMARY

The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

Exploration of Metabolomic Markers Associated With Declining Kidney Function in People With Type 2 Diabetes Mellitus

Background

Metabolomics, the study of small molecules in biological systems, can provide valuable insights into kidney dysfunction in people with type 2 diabetes mellitus (T2DM), but prospective studies are scarce. We investigated the association between metabolites and kidney function decline in people with T2DM.

Methods

The Edinburgh Type 2 Diabetes Study, a population-based cohort of 1066 men and women aged 60 to 75 years with T2DM. We measured 149 serum metabolites at baseline and investigated individual associations with baseline estimated glomerular filtration rate (eGFR), incident chronic kidney disease [CKD; eGFR <60 mL/min/(1.73 m)2], and decliner status (5% eGFR decline per year).

Results

At baseline, mean eGFR was 77.5 mL/min/(1.73 m)2 (n = 1058), and 216 individuals had evidence of CKD. Of those without CKD, 155 developed CKD over a median 7-year follow-up. Eighty-eight metabolites were significantly associated with baseline eGFR (β range -4.08 to 3.92; PFDR < 0.001). Very low density lipoproteins, triglycerides, amino acids (AAs), glycoprotein acetyls, and fatty acids showed inverse associations, while cholesterol and phospholipids in high-density lipoproteins exhibited positive associations. AA isoleucine, apolipoprotein A1, and total cholines were not only associated with baseline kidney measures (PFDR < 0.05) but also showed stable, nominally significant association with incident CKD and decline.

Conclusion

Our study revealed widespread changes within the metabolomic profile of CKD, particularly in lipoproteins and their lipid compounds. We identified a smaller number of individual metabolites that are specifically associated with kidney function decline. Replication studies are needed to confirm the longitudinal findings and explore if metabolic signals at baseline can predict kidney decline.

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

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.

Metabolomic and gut microbiome profiles across the spectrum of community-based COVID and non-COVID disease

Whilst most individuals with SARS-CoV-2 infection have relatively mild disease, managed in the community, it was noted early in the pandemic that individuals with cardiovascular risk factors were more likely to experience severe acute disease, requiring hospitalisation. As the pandemic has progressed, increasing concern has also developed over long symptom duration in many individuals after SARS-CoV-2 infection, including among the majority who are managed acutely in the community. Risk factors for long symptom duration, including biological variables, are still poorly defined. Here, we examine post-illness metabolomic profiles, using nuclear magnetic resonance (Nightingale Health Oyj), and gut-microbiome profiles, using shotgun metagenomic sequencing (Illumina Inc), in 2561 community-dwelling participants with SARS-CoV-2. Illness duration ranged from asymptomatic (n = 307) to Post-COVID Syndrome (n = 180), and included participants with prolonged non-COVID-19 illnesses (n = 287). We also assess a pre-established metabolomic biomarker score, previously associated with hospitalisation for both acute pneumonia and severe acute COVID-19 illness, for its association with illness duration. We found an atherogenic-dyslipidaemic metabolic profile, including biomarkers such as fatty acids and cholesterol, was associated with longer duration of illness, both in individuals with and without SARS-CoV-2 infection. Greater values of a pre-existing metabolomic biomarker score also associated with longer duration of illness, regardless of SARS-CoV-2 infection. We found no association between illness duration and gut microbiome profiles in convalescence. This highlights the potential role of cardiometabolic dysfunction in relation to the experience of long duration symptoms after symptoms of acute infection, both COVID-19 as well as other illnesses.

Progress in genetics of type 2 diabetes and diabetic complications

Type 2 diabetes results from a complex interaction between genetic and environmental factors. Precision medicine for type 2 diabetes using genetic data is expected to predict the risk of developing diabetes and complications and to predict the effects of medications and life-style intervention more accurately for individuals. Genome-wide association studies (GWAS) have been conducted in European and Asian populations and new genetic loci have been identified that modulate the risk of developing type 2 diabetes. Novel loci were discovered by GWAS in diabetic complications with increasing sample sizes. Large-scale genome-wide association analysis and polygenic risk scores using biobank information is making it possible to predict the development of type 2 diabetes. In the ADVANCE clinical trial of type 2 diabetes, a multi-polygenic risk score was useful to predict diabetic complications and their response to treatment. Proteomics and metabolomics studies have been conducted and have revealed the associations between type 2 diabetes and inflammatory signals and amino acid synthesis. Using multi-omics analysis, comprehensive molecular mechanisms have been elucidated to guide the development of targeted therapy for type 2 diabetes and diabetic complications.

Variants in the Control Region of Mitochondrial Genome Associated with type 2 Diabetes in a Cohort of Mexican Mestizos

BACKGROUND

According to the International Diabetes Federation, Mexico is seventh place in the prevalence of type 2 diabetes (T2D) worldwide. Mitochondrial DNA variant association studies in multifactorial diseases like T2D are scarce in Mexican populations.

AIM OF THE STUDY

The objective of this study was to analyze the association between 18 variants in the mtDNA control region and T2D and related metabolic traits in a Mexican mestizo population from Mexico City.

METHODS

This study included 1001 participants divided into 477 cases with T2D and 524 healthy controls aged between 42 and 62 years and 18 mtDNA variants with frequencies >15%.

RESULTS

Association analyses matched by age and sex showed differences in the distribution between cases and controls for variants m.315_316insC (p = 1.18 × 10^-6), m.489T>C (p = 0.009), m.16362T>C (p = 0.001), and m.16519T>C (p = 0.004). The associations between T2D and variants m.315_316ins (OR = 6.13, CI = 3.42-10.97, p = 1.97 × 10^-6), m.489T>C (OR = 1.45, CI = 1.00-2.11, p = 0.006), m.16362T>C (OR = 2.17, CI = 1.57-3.00, p = 0.001), and m.16519T>C (OR = 1.69, CI = 1.23-2.33, p = 0.006) were significant after performing logistic regression models adjusted for age, sex, and diastolic blood pressure. Metabolic traits in the control group through linear regressions, adjusted for age, sex and BMI, and corrected for multiple comparisons showed nominal association between glucose and variants m.263A>G (p <0.050), m.16183A>C (p <0.010), m.16189T>C (p <0.020), and m.16223C>T (p <0.024); triglycerides, and cholesterol and variant m.309_310insC (p <0.010 and p <0.050 respectively); urea, and creatinine, and variant m.315_316insC (p <0.007, and p <0.004 respectively); diastolic blood pressure and variants m.235A>G (p <0.016), m.263A>G (p <0.013), m.315_316insC (p <0.043), and m.16111C>T (p <0.022).

CONCLUSION

These results demonstrate a strong association between variant m.315_316insC and T2D and a nominal association with T2D traits.

Triglyceride content of lipoprotein subclasses and kidney hemodynamic function and injury in adolescents with type 1 diabetes

AIMS

Elevated triglycerides (TG) are associated with development and progression of kidney disease, and TG distributions across lipoprotein subclasses predict kidney dysfunction in adults with type 1 diabetes (T1D). Little is known regarding these relationships in youth.

METHODS

In this single center study conducted from October 2018-2019, lipid constituents from lipoprotein subclasses were quantified by targeted nuclear magnetic resonance spectroscopy. Glomerular filtration rate (GFR), renal plasma flow (RPF), afferent arteriolar resistance (RA), efferent arteriolar resistance (RE), intraglomerular pressure (PGLO), urine albumin-to-creatinine ratio (UACR), and chitinase-3-like protein 1 (YKL-40), a marker of kidney tubule injury, were assessed. Cross-sectional relationships were assessed by correlation and multivariable linear regression (adjusted for age, sex, HbA1c) models.

RESULTS

Fifty youth with T1D (age 16 ± 3 years, 50 % female, HbA1c 8.7 ± 1.3 %, T1D duration 5.7 ± 2.6 years) were included. Very-low-density lipoprotein (VLDL)-TG concentrations correlated and associated with intraglomerular hemodynamic function markers including GFR, PGLO, UACR, as did small low-density lipoprotein (LDL)-TG and small high-density lipoprotein (HDL)-TG. YKL-40 correlated with all lipoprotein subclasses.

CONCLUSION

TG within lipoprotein subclasses, particularly VLDL, associated with PGLO, GFR, albuminuria, and YKL-40. Lipid perturbations may serve as novel targets to mitigate early kidney disease.

Apt interpretation of comprehensive lipoprotein data in large-scale epidemiology: disclosure of fundamental structural and metabolic relationships

BACKGROUND

Quantitative lipoprotein analytics using nuclear magnetic resonance (NMR) spectroscopy is currently commonplace in large-scale studies. One methodology has become widespread and is currently being utilized also in large biobanks. It allows the comprehensive characterization of 14 lipoprotein subclasses, clinical lipids, apolipoprotein A-I and B. The details of these data are conceptualized here in relation to lipoprotein metabolism with particular attention on the fundamental characteristics of subclass particle numbers, lipid concentrations and compositional measures.

METHODS AND RESULTS

The NMR methodology was applied to fasting serum samples from Northern Finland Birth Cohorts 1966 and 1986 with 5651 and 5605 participants, respectively. All results were highly consistent between the cohorts. Circulating lipid concentrations in a particular lipoprotein subclass arise predominantly as the result of the circulating number of those subclass particles. The spherical lipoprotein particle shape, with a radially oriented surface monolayer, imposes size-dependent biophysical constraints for the lipid composition of individual subclass particles and inherently restricts the accommodation of metabolic changes via compositional modifications. The new finding that the relationship between lipoprotein subclass particle concentrations and the particle size is log-linear reveals that circulating lipoprotein particles are also under rather strict metabolic constraints for both their absolute and relative concentrations.

CONCLUSIONS

The fundamental structural and metabolic relationships between lipoprotein subclasses elucidated in this study empower detailed interpretation of lipoprotein metabolism. Understanding the intricate details of these extensive data is important for the precise interpretation of novel therapeutic opportunities and for fully utilizing the potential of forthcoming analyses of genetic and metabolic data in large biobanks.

The Roles of Fatty Acids and Apolipoproteins in the Kidneys

The kidneys are organs that require energy from the metabolism of fatty acids and glucose; several studies have shown that the kidneys are metabolically active tissues with an estimated energy requirement similar to that of the heart. The kidneys may regulate the normal and pathological function of circulating lipids in the body, and their glomerular filtration barrier prevents large molecules or large lipoprotein particles from being filtered into pre-urine. Given the permeable nature of the kidneys, renal lipid metabolism plays an important role in affecting the rest of the body and the kidneys. Lipid metabolism in the kidneys is important because of the exchange of free fatty acids and apolipoproteins from the peripheral circulation. Apolipoproteins have important roles in the transport and metabolism of lipids within the glomeruli and renal tubules. Indeed, evidence indicates that apolipoproteins have multiple functions in regulating lipid import, transport, synthesis, storage, oxidation and export, and they are important for normal physiological function. Apolipoproteins are also risk factors for several renal diseases; for example, apolipoprotein L polymorphisms induce kidney diseases. Furthermore, renal apolipoprotein gene expression is substantially regulated under various physiological and disease conditions. This review is aimed at describing recent clinical and basic studies on the major roles and functions of apolipoproteins in the kidneys.

Serum Metabolomics Benefits Discrimination Kidney Disease Development in Type 2 Diabetes Patients

Background

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, raising a considerable burden worldwide. Recognizing novel biomarkers by metabolomics can shed light on new biochemical insight to benefit DKD diagnostics and therapeutics. We hypothesized that serum metabolites can serve as biomarkers in the progression of DKD.

Methods

A cross-sectional study of 1,043 plasma metabolites by untargeted LC/MS among 89 participants identified associations between proteinuria severity and metabolites difference. Pathway analysis from differently expressed metabolites was used to determine perturbed metabolism pathways. The results were replicated in an independent, cross-sectional cohort of 83 individuals. Correlation and prediction values were used to examine the association between plasma metabolites level and proteinuria amount.

Results

Diabetes, and diabetic kidney disease with different ranges of proteinuria have shown different metabolites patterns. Cysteine and methionine metabolism pathway, and Taurine and hypotaurine metabolism pathway were distinguishable in the existence of DKD in DC (diabetes controls without kidney disease), and DKD with different ranges of proteinuria. Two interesting tetrapeptides (Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro) circulating levels were elevated with the DKD proteinuria progression.

Conclusions

These findings underscore that serum metabolomics provide us biochemical perspectives to identify some clinically relevant physiopathologic biomarkers of DKD progression.

The GenoDiabMar Registry: A Collaborative Research Platform of Type 2 Diabetes Patients

The GenoDiabMar registry is a prospective study that aims to provide data on demographic, biochemical, and clinical changes in type 2 diabetic (T2D) patients attending real medical outpatient consultations. This registry is also used to find new biomarkers related to the micro- and macrovascular complications of T2D, with a particular focus on diabetic nephropathy. With this purpose, longitudinal serum and urine samples, DNA banking, and data on 227 metabolomics profiles, 77 immunoglobulin G glycomics traits, and other emerging biomarkers were recorded in this cohort. In this study, we show a detailed longitudinal description of the clinical and analytical parameters of this registry, with a special focus on the progress of renal function and cardiovascular events. The main objective is to analyze whether there are differential risk factors for renal function deterioration between sexes, as well as to analyze cardiovascular events and mortality in this population. In total, 650 patients with a median age of 69 (14) with different grades of chronic kidney disease—G1−G2 (eGFR > 90−60 mL/min/1.73 m2) 50.3%, G3 (eGFR; 59−30 mL/min/1.73 m2) 31.4%, G4 (eGFR; 29−15 mL/min/1.73 m2) 10.8%, and G5 (eGFR < 15 mL/min/1.73 m2) 7.5%—were followed up for 4.7 (0.65) years. Regardless of albuminuria, women lost 0.93 (0.40−1.46) fewer glomerular filtration units per year than men. A total of 17% of the participants experienced rapid deterioration of renal function, 75.2% of whom were men, with differential risk factors between sexes—severe macroalbuminuria > 300 mg/g for men OR [IQ] 2.40 [1.29:4.44] and concomitant peripheral vascular disease 3.32 [1.10:9.57] for women. Overall mortality of 23% was detected (38% of which was due to cardiovascular etiology). We showed that kidney function declined faster in men, with different risk factors compared to women. Patients with T2D and kidney involvement have very high mortality and an important cardiovascular burden. This cohort is proposed as a great tool for scientific collaboration for studies, whether they are focused on T2D, or whether they are interested in comparing differential markers between diabetic and non-diabetic populations.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

Metabolome-wide association study of estimated glomerular filtration rates in Hispanics

Circulating metabolites are by-products of endogenous metabolism or exogenous sources and may inform disease states. Our study aimed to identify the source of variability in the association of metabolites with estimated glomerular filtration rate (eGFR) in Hispanics/Latinos with low chronic kidney disease prevalence by testing the association of 640 metabolites in 3,906 participants of the Hispanic Community Health Study/Study of Latinos. Metabolites were quantified in fasting serum through non-targeted mass spectrometry analysis. eGFR was regressed on inverse normally transformed metabolites in models accounting for study design and covariates. To identify the source of variation on eGFR associations, we tested the interaction of metabolites with lifestyle and clinical risk factors, and results were integrated with genotypes to identify metabolite genetic regulation. The mean age was 46 years, 43% were men, 22% were current smokers, 47% had a Caribbean Hispanic background, 19% had diabetes and the mean cohort eGFR was 96.4 ml/min/1.73 m². We identified 404 eGFR-metabolite associations (False Discovery Rate under 0.05). Of these, 69 were previously reported, and 79 were novel associations with eGFR replicated in one or more published studies. There were significant interactions with lifestyle and clinical risk factors, with larger differences in eGFR-metabolite associations within strata of age, urine albumin to creatinine ratio, diabetes and Hispanic/Latino background. Several newly identified metabolites were genetically regulated, and variants were located at genomic regions previously associated with eGFR. Thus, our results suggest complex mechanisms contribute to the association of eGFR with metabolites and provide new insights into these associations.

Acylcarnitines: Can They Be Biomarkers of Diabetic Nephropathy?

Diabetic nephropathy (DN), one of the most serious microvascular complications of diabetes mellitus (DM), may progress to end-stage renal disease (ESRD). Current biochemical biomarkers, such as urinary albumin excretion rate (UAER), have limitations for early screening and monitoring of DN. Recent studies have identified some metabolites as candidate biomarkers for early detection of DN. In this review, we summarize the role of dysregulated acylcarnitines (AcylCNs) in DN pathophysiology. Lower abundance of short- and medium-chain AcylCNs and higher long-chain AcylCNs often occurred in DM with normal albuminuria and microalbuminuria, compared with advanced stages of DN. The increase of long-chain AcylCNs was supposed to be an adaptive compensation in fat acids (FAs) oxidation in the early stage of DN. Conversely, the decrease of long-chain AcylCNs was due to incomplete oxidation of FAs in advanced stage of DN. Thus, AcylCNs may serve as sensitive biomarkers in predicting the risk of DN.

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

Association of Kidney Function With NMR-Quantified Lipids, Lipoproteins, and Metabolic Measures in Mexican Adults

CONTEXT

Chronic kidney disease (CKD) and diabetes are associated with dyslipidemia, metabolic abnormalities, and atherosclerotic risk. Nuclear magnetic resonance (NMR) spectroscopy provides much more detail on lipoproteins than traditional assays.

METHODS

In about 38 000 participants from the Mexico City Prospective Study, aged 35 to 84 years and not using lipid-lowering medication, NMR spectroscopy quantified plasma concentrations of lipoprotein particles, their lipidic compositions, and other metabolic measures. Linear regression related low estimated glomerular filtration rate (eGFR; <60 mL/min/1.73 m2) to each NMR measure after adjustment for confounders and for multiplicity. Analyses were done separately for those with and without diabetes.

RESULTS

Among the 38 081 participants (mean age 52 years, 64% women), low eGFR was present for 4.8% (306/6403) of those with diabetes and 1.2% (365/31 678) of those without diabetes. Among both those with and without diabetes, low eGFR was significantly associated with higher levels of 58 NMR measures, including apolipoprotein B (Apo-B), the particle numbers of most Apo-B containing lipoproteins, the cholesterol and triglycerides carried in these lipoproteins, several fatty acids, total cholines and phosphatidylcholine, citrate, glutamine, phenylalanine, β-OH-butyrate, and the inflammatory measure glycoprotein-A, and significantly lower levels of 13 NMR measures, including medium and small high-density lipoprotein particle measures, very low-density lipoprotein particle size, the ratio of saturated:total fatty acids, valine, tyrosine, and aceto-acetate.

CONCLUSIONS

In this Mexican population with high levels of adiposity and diabetes, low kidney function was associated with widespread alterations in lipidic and metabolic profiles, both in those with and without diabetes. These alterations may help explain the higher atherosclerotic risk experienced by people with CKD.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Circulating Levels of the Short-Chain Fatty Acid Acetate Mediate the Effect of the Gut Microbiome on Visceral Fat

Background

Acetate is a short-chain fatty acid (SCFA) produced by gut bacteria, which has been implicated in cardio-metabolic health. Here we examine the relationships of circulating acetate levels with gut microbiome composition and diversity and with visceral fat in a large population-based cohort.

Results

Microbiome alpha-diversity was positively correlated with circulating acetate levels (Shannon, Beta [95%CI] = 0.12 [0.06, 0.18], P = 0.002) after adjustment for covariates. Six serum acetate-associated bacterial genera were also identified, including positive correlations with Coprococcus, Barnesiella, Ruminococcus, and Ruminococcaceae NK4A21 and negative correlations were observed with Lachnoclostridium and Bacteroides. We also identified a correlation between visceral fat and serum acetate levels (Beta [95%CI] = −0.07 [−0.11, −0.04], P = 2.8 × 10^–4) and between visceral fat and Lachnoclostridium (Beta [95%CI] = 0.076 [0.042, 0.11], P = 1.44 × 10^–5). Formal mediation analysis revealed that acetate mediates ∼10% of the total effect of Lachnoclostridium on visceral fat. The taxonomic diversity showed that Lachnoclostridium and Coprococcus comprise at least 18 and 9 species, respectively, including novel bacterial species. By predicting the functional capabilities, we found that Coprococcus spp. present pathways involved in acetate production and metabolism of vitamins B, whereas we identified pathways related to the biosynthesis of trimethylamine (TMA) and CDP-diacylglycerol in Lachnoclostridium spp.

Conclusions

Our data indicates that gut microbiota composition and diversity may influence circulating acetate levels and that acetate might exert benefits on certain cardio-metabolic disease risk by decreasing visceral fat. Coprococcus may play an important role in host health by its production of vitamins B and SCFAs, whereas Lachnoclostridium might have an opposing effect by influencing negatively the circulating levels of acetate and being involved in the biosynthesis of detrimental lipid compounds.

Relationships Between Retinal Vascular Characteristics and Renal Function in Patients With Type 2 Diabetes Mellitus

Purpose

To develop a deep learning-based method to achieve vessel segmentation and measurement on fundus images, and explore the quantitative relationships between retinal vascular characteristics and the clinical indicators of renal function.

Methods

We recruited patients with type 2 diabetes mellitus with different stages of diabetic retinopathy (DR), collecting their fundus photographs and results of renal function tests. A deep learning framework for retinal vessel segmentation and measurement was developed. The correlation between the renal function indicators and the severity of DR were explored, then the correlation coefficients between indicators of renal function and retinal vascular characteristics were analyzed.

Results

We included 418 patients (eyes) with type 2 diabetes mellitus. The albumin to creatinine ratio, blood uric acid, blood creatinine, blood albumin, and estimated glomerular filtration rate were significantly correlated with the progression of DR (P < 0.05); no correlation existed in other metrics (P > 0.05). The fractal dimension was found to significantly correlate with most of the clinical parameters of renal function (P < 0.05).

Conclusions

The albumin to creatinine ratio, blood uric acid, blood creatinine, blood albumin, and estimated glomerular filtration rate have significant correlation with the progression of moderate to proliferative DR. Through deep learning-based vessel segmentation and measurement, the fractal dimension was found to significantly correlate with most clinical parameters of renal function.

Translational Relevance

Deep learning-based vessel segmentation and measurement on color fundus photographs could explore the relationships between retinal characteristics and renal function, facilitating earlier detection and intervention of type 2 diabetes mellitus complications.

Comprehensive metabolomic study of the response of HK-2 cells to hyperglycemic hypoxic diabetic-like milieu

Diabetic nephropathy (DN) is the leading cause of chronic kidney disease. Although hyperglycaemia has been determined as the most important risk factor, hypoxia also plays a relevant role in the development of this disease. In this work, a comprehensive metabolomic study of the response of HK-2 cells, a human cell line derived from normal proximal tubular epithelial cells, to hyperglycemic, hypoxic diabetic-like milieu has been performed. Cells simultaneously exposed to high glucose (25 mM) and hypoxia (1% O2) were compared to cells in control conditions (5.5 mM glucose/18.6% O2) at 48 h. The combination of advanced metabolomic platforms (GC-TOF MS, HILIC- and CSH-QExactive MS/MS), freely available metabolite annotation tools, novel databases and libraries, and stringent cut-off filters allowed the annotation of 733 metabolites intracellularly and 290 compounds in the extracellular medium. Advanced bioinformatics and statistical tools demonstrated that several pathways were significantly altered, including carbohydrate and pentose phosphate pathways, as well as arginine and proline metabolism. Other affected metabolites were found in purine and lipid metabolism, the protection against the osmotic stress and the prevention of the activation of the β-oxidation pathway. Overall, the effects of the combined exposure of HK-cells to high glucose and hypoxia are reasonably compatible with previous in vivo works.

High serum creatinine concentration is associated with metabolic perturbations in dogs

BACKGROUND

The kidneys have many essential metabolic functions, and metabolic disturbances during decreased renal function have not been studied extensively.

OBJECTIVES

To identify metabolic changes in blood samples with increased serum creatinine concentration, indicating decreased glomerular filtration.

ANIMALS

Clinical samples analyzed using a nuclear magnetic resonance (NMR) based metabolomics platform. The case group consisted of 23 samples with serum creatinine concentration >125 μmol/L, and the control group of 873 samples with serum creatinine concentration within the reference interval.

METHODS

Biomarker association with increased serum creatinine concentration was evaluated utilizing 3 statistical approaches: Wilcoxon rank-sum test, logistic regression analysis (false discovery rate (FDR)-corrected P-values), and random forest classification. Medians of the biomarkers were compared to reference intervals. A heatmap and box plots were used to represent the differences.

RESULTS

All 3 statistical approaches identified similar analytes associated with increased serum creatinine concentrations. The percentages of citrate, tyrosine, branched-chain amino acids, valine, leucine, albumin, linoleic acid and the ratio of phenylalanine to tyrosine differed significantly using all statistical approaches, acetate differed using the Wilcoxon test and random forest, docosapentaenoic acid percentage only using logistic regression (P < .05), and alanine only using random forest.

CONCLUSIONS AND CLINICAL IMPORTANCE

We identified several metabolic changes associated with increased serum creatinine concentrations, including prospective diagnostic markers and therapeutic targets. Further research is needed to verify the association of these changes with the clinical state of the dog. The NMR metabolomics test is a promising tool for improving diagnostic testing and management of renal diseases in dogs.

Serum apolipoprotein B/apolipoprotein A1 ratio is associated with the progression of diabetic kidney disease to renal replacement therapy

PURPOSE

The apolipoprotein B/apolipoprotein A1 (ApoB/ApoA1) ratio has been shown to be associated with cardiovascular disease risk and the progression of chronic kidney disease. The association between the ApoB/ApoA1 ratio and the progression of diabetic kidney disease (DKD) is not well studied.

METHODS

Patients with DKD were divided into four groups (< 0.63, ≥ 0.63 and < 0.85, ≥ 0.85 and < 1.15, ≥ 1.15) according to their ApoB/ApoA1 ratio. The association of the ApoB/ApoA1 ratio and progression of DKD to renal replacement therapy (RRT) were determined using Kaplan-Meier and Cox regression analyses.

RESULTS

The Kaplan-Meier analysis showed that the ≥ 1.15 group (log-rank = 15.771, P < 0.05) was significantly more likely to progress to RRT than the other three groups. Using univariate and multivariate regression analyses, we found that an ApoB/ApoA1 ratio of ≥ 1.15 was an independent predictor of DKD patients progressing to RRT.

CONCLUSION

An elevated ApoB/ApoA1 ratio of ≥ 1.15 was an independent predictor of DKD progression to RRT. A further study with a larger sample is needed to confirm the findings of the current study.

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

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.

Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts

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.

Combining the advantages of multilevel and orthogonal partial least squares data analysis for longitudinal metabolomics: Application to kidney transplantation

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.

TwinsUK: The UK Adult Twin Registry Update

TwinsUK is the largest cohort of community-dwelling adult twins in the UK. The registry comprises over 14,000 volunteer twins (14,838 including mixed, single and triplets); it is predominantly female (82%) and middle-aged (mean age 59). In addition, over 1800 parents and siblings of twins are registered volunteers. During the last 27 years, TwinsUK has collected numerous questionnaire responses, physical/cognitive measures and biological measures on over 8500 subjects. Data were collected alongside four comprehensive phenotyping clinical visits to the Department of Twin Research and Genetic Epidemiology, King’s College London. Such collection methods have resulted in very detailed longitudinal clinical, biochemical, behavioral, dietary and socioeconomic cohort characterization; it provides a multidisciplinary platform for the study of complex disease during the adult life course, including the process of healthy aging. The major strength of TwinsUK is the availability of several ‘omic’ technologies for a range of sample types from participants, which includes genomewide scans of single-nucleotide variants, next-generation sequencing, metabolomic profiles, microbiomics, exome sequencing, epigenetic markers, gene expression arrays, RNA sequencing and telomere length measures. TwinsUK facilitates and actively encourages sharing the ‘TwinsUK’ resource with the scientific community — interested researchers may request data via the TwinsUK website (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/) for their own use or future collaboration with the study team. In addition, further cohort data collection is planned via the Wellcome Open Research gateway (https://wellcomeopenresearch.org/gateways). The current article presents an up-to-date report on the application of technological advances, new study procedures in the cohort and future direction of TwinsUK.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Metabolomic Assessment Reveals Alteration in Polyols and Branched Chain Amino Acids Associated With Present and Future Renal Impairment in a Discovery Cohort of 637 Persons With Type 1 Diabetes

Background: Improved understanding of the pathophysiology causing diabetic kidney disease (DKD) is imperative. The aim of this study was to uncover associations between serum metabolites and renal outcomes. Methods: Non-targeted serum metabolomics analyses were performed in samples from 637 persons with type 1 diabetes using two-dimensional gas chromatography coupled to time-of-flight mass-spectrometry. Longitudinal data at follow-up (median 5.5 years) on renal events were obtained from national Danish health registries. A composite renal endpoint (n = 123) consisted of estimated glomerular filtration rate (eGFR) decline from baseline (≥30%), progression to end-stage renal disease and all-cause mortality. Metabolites with significant associations (p < 0.05) in any of the cross-sectional analyses with eGFR and albuminuria were analyzed for specific and composite endpoints. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. Results: A data-driven partial correlation analysis revealed a dense fabric of co-regulated metabolites and clinical variables dominated by eGFR. Ribonic acid and myo-inositol were inversely associated with eGFR, positively associated with macroalbuminuria (p < 0.02) and longitudinally associated with higher risk of eGFR decline ≥30% (HR 2.2-2.7, CI [1.3-4.3], p < 0.001). Ribonic acid was associated with a combined renal endpoint (HR 1.8, CI [1.3-2.3], p = 0.001). The hydroxy butyrate 3,4-dihydroxybutanoic acid was cross-sectionally associated with micro- and macroalbuminuria, urinary albumin excretion rate and inversely associated with eGFR (p < 0.04) while branched chain amino acids were associated with eGFR and lower risk of the combined renal endpoint (p < 0.02). Conclusions: Alterations in serum metabolites, particularly polyols and amino acids, were associated with renal endpoints in type 1 diabetes highlighting molecular pathways associated with progression of kidney disease. External validation is needed to further assess their roles and potentials as future therapeutic targets.