Time courses of urinary creatinine excretion, measured creatinine clearance and estimated glomerular filtration rate over 30 days of ICU admission

Purpose: Baseline urinary creatinine excretion (UCE) is associated with ICU outcome, but its time course is not known. Materials and methods: We determined changes in UCE, plasma creatinine, measured creatinine clearance (mCC) and estimated glomerular filtration (eGFR) in patients with an ICU-stay 30d without acute kidney injury stage 3. The Cockcroft-Gault, MDRD (modification of diet in renal disease) and CKD-EPI (chronic kidney disease epidemiology collaboration) equations were used. Results: In 248 patients with 5143 UCEs hospital mortality was 24%. Over 30d, UCE absolutely decreased in male survivors and non-survivors and female survivors and nonsurvivors by 0.19, 0.16, 0.10 and 0.05 mmol/d/d (all P < 0.001). Relative decreases in UCE were similar in all four groups: 1.3, 1.4, 1.2 and 0.9%/d respectively. Over 30d, mCC remained unchanged, but eGFR rose by 31% (CKD-EPI) and 73% (MDRD) and creatinine clearance estimated by Cockcroft-Gault by 59% (all P < 0.001). Conclusions: Over 1 month of ICU stay, UCE declined by 1%/d which may correspond to an equivalent decline in muscle mass. These rates of UCE decrease were similar in survivors, non-survivors, males and females underscoring the intransigent nature of this process. In contrast to measured creatinine clearance, estimates of eGFR progressively rose during ICU stay. (c) 2020 Published by Elsevier Inc

Metformin Preconditioning and Postconditioning to Reduce Ischemia Reperfusion Injury in an IsolatedEx VivoRat and Porcine Kidney Normothermic Machine Perfusion Model

Metformin may act renoprotective prior to kidney transplantation by reducing ischemia-reperfusion injury (IRI). This study examined whether metformin preconditioning and postconditioning duringex vivonormothermic machine perfusion (NMP) of rat and porcine kidneys affect IRI. In the rat study, saline or 300 mg/kg metformin was administered orally twice on the day before nephrectomy. After 15 minutes of warm ischemia, kidneys were preserved with static cold storage for 24 hours. Thereafter, 90 minutes of NMP was performed with the addition of saline or metformin (30 or 300 mg/L). In the porcine study, after 30 minutes of warm ischemia, kidneys were preserved for 3 hours with oxygenated hypothermic machine perfusion. Subsequently, increasing doses of metformin were added during 4 hours of NMP. Metformin preconditioning of rat kidneys led to decreased injury perfusate biomarkers and reduced proteinuria. Postconditioning of rat kidneys resulted, dose-dependently, in less tubular cell necrosis and vacuolation. Heat shock protein 70 expression was increased in metformin-treated porcine kidneys. In all studies, creatinine clearance was not affected. In conclusion, both metformin preconditioning and postconditioning can be done safely and improved rat and porcine kidney quality. Because the effects are minor, it is unknown which strategy might result in improved organ quality after transplantation

Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion

Because cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl—experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood–brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease

Increasing metformin concentrations and its excretion in both rat and porcine ex vivo normothermic kidney perfusion model

INTRODUCTION: Metformin can accumulate and cause lactic acidosis in patients with renal insufficiency. Metformin is known to inhibit mitochondria, while renal secretion of the drug by proximal tubules indirectly requires energy. We investigated whether addition of metformin before or during ex vivo isolated normothermic machine perfusion (NMP) of porcine and rat kidneys affects its elimination.RESEARCH DESIGN AND METHODS: First, Lewis rats were pretreated with metformin or saline the day before nephrectomy. Subsequently, NMP of the kidney was performed for 90 min. Metformin was added to the perfusion fluid in one of three different concentrations (none, 30 mg/L or 300 mg/L). Second, metformin was added in increasing doses to the perfusion fluid during 4 hours of NMP of porcine kidneys. Metformin concentration was determined in the perfusion fluid and urine by liquid chromatography-tandem mass spectrometry.RESULTS: Metformin clearance was approximately 4-5 times higher than creatinine clearance in both models, underscoring secretion of the drug. Metformin clearance at the end of NMP in rat kidneys perfused with 30 mg/L was lower than in metformin pretreated rats without the addition of metformin during perfusion (both p≤0.05), but kidneys perfused with 300 mg/L trended toward lower metformin clearance (p=0.06). Creatinine clearance was not different between treatment groups. During NMP of porcine kidneys, metformin clearance peaked at 90 min of NMP (18.2±13.7 mL/min/100 g). Thereafter, metformin clearance declined, while creatinine clearance remained stable. This observation can be explained by saturation of metformin transporters with a Michaelis-Menten constant (95% CI) of 23.0 (10.0 to 52.3) mg/L.CONCLUSIONS: Metformin was secreted during NMP of both rat and porcine kidneys. Excretion of metformin decreased under increasing concentrations of metformin, which might be explained by saturation of metformin transporters rather than a self-inhibitory effect. It remains unknown whether a self-inhibitory effect contributes to metformin accumulation in humans with longer exposure times.</p

Objective assessment of dietary patterns using metabolic phenotyping: a randomized, controlled, crossover trial

Background: The burden of non-communicable diseases, such as obesity, diabetes, coronary heart disease and cancer, can be reduced by the consumption of healthy diets. Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, many of them influenced by food intake. We aim to classify people according to dietary behaviour and enhance dietary reporting using metabolic profiling of urine. Methods: To develop metabolite models from 19 healthy volunteers who attended a clinical research unit for four day periods on four occasions. We used the World Health Organisation’s healthy eating guidelines (increase fruits, vegetables, wholegrains, dietary fibre and decrease fats, sugars, and salt) to develop four dietary interventions lasting for four days each that ranged from a diet associated with a low to high risk of developing non-communicable disease. Urine samples were measured by 1H-NMR spectroscopy. This study is registered as an International Standard Randomized Controlled Trial, number ISRCTN 43087333. INTERMAP U.K. (n=225) and a healthy-eating Danish cohort (n=66) were used as free-living validation datasets. Findings: There was clear separation between the urinary metabolite profiles of the four diets. We also demonstrated significant stepwise differences in metabolite levels between the lowest and highest metabolic risk diets and developed metabolite models for each diet. Application of the derived metabolite models to independent cohorts confirmed the association between urinary metabolic and dietary profiles in INTERMAP (P<0•001) and the Danish cohort (P<0•001). Interpretation: Urinary metabolite models, developed in a highly controlled environment, can classify groups of free-living people into consumers of dietary profiles associated with lower or higher non-communicable disease risk based on multivariate metabolite patterns. This approach enables objective monitoring of dietary patterns in population settings and enhances validity of dietary reporting. Funding: National Institute for Health Research (NIHR) and Medical Research Council (MRC)

Integrated fecal microbiome–metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS) several modalities of biological and clinical data must be combined. We aimed to identify profiles of faecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Faecal metabolites were measured using proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy and gut microbiome using Shotgun Metagenomic Sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HC) with extensive clinical, biological and phenotype information. Data were analysed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve (AUC) of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on faecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavourable stress-response related to gastrointestinal symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI Direct study

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n=403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n=458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariate regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred was associated with healthier diets high in wholegrain (β=0.004 g, p=0.02 and β=0.003 g, p=0.03) and lower energy intake (β=-0.0002 kcal, p=0.04 and β=-0.0002 kcal, p=0.003), and saturated fat (β=-0.03 g, p<.0001 and β=-0.03 g, p<.0001), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and improved lipid profiles HDL-cholesterol (β=0.07 mmol/L, p<.0001), (β=0.08 mmol/L, p=0.0002), and triglycerides (β=-0.1 mmol/L, p=0.003), (β=-0.2 mmol/L, p=0.0002), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat content (β=-0.74 %, p<.0001), and lower fasting concentrations of HbA1c (β=-0.9mmol/mol, p=0.02), glucose (β=-0.2 mmol/L, p=0.01) and insulin (β=-11.0 pmol/mol, p=0.01). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, p=0.03) and insulin (β=-9.2 pmol/mol, p=0.04) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data