Healthy lifestyle, metabolomics and incident type 2 diabetes in a population-based cohort from Spain

Background: The contribution of metabolomic factors to the association of healthy lifestyle with type 2 diabetes risk is unknown. We assessed the association of a composite measure of lifestyle with plasma metabolite profiles and incident type 2 diabetes, and whether relevant metabolites can explain the prospective association between healthy lifestyle and incident type 2 diabetes. Methods: A Healthy Lifestyle Score (HLS) (5-point scale including diet, physical activity, smoking status, alcohol consumption and BMI) was estimated in 1016 Hortega Study participants, who had targeted plasma metabolomic determinations at baseline examination in 2001-2003, and were followed-up to 2015 to ascertain incident type 2 diabetes. Results: The HLS was cross-sectionally associated with 32 (out of 49) plasma metabolites (2.5% false discovery rate). In the subset of 830 participants without prevalent type 2 diabetes, the rate ratio (RR) and rate difference (RD) of incident type 2 diabetes (n cases = 51) per one-point increase in HLS was, respectively, 0.69 (95% CI, 0.51, 0.93), and - 8.23 (95% CI, - 16.34, - 0.13)/10,000 person-years. In single-metabolite models, most of the HLS-related metabolites were prospectively associated with incident type 2 diabetes. In probit Bayesian Kernel Machine Regression, these prospective associations were mostly driven by medium HDL particle concentration and phenylpropionate, followed by small LDL particle concentration, which jointly accounted for ~ 50% of the HLS-related decrease in incident type 2 diabetes. Conclusions: The HLS showed a strong inverse association with incident type 2 diabetes, which was largely explained by plasma metabolites measured years before the clinical diagnosis.This work was supported by the Strategic Action for Research in Health sciences [PI10/0082, PI13/01848, PI14/00874, PI16/01402, PI11/00726, PI16/609, PI16/1512, PI18/287, PI19/319 and PI20/00896], the GUTMOM Project (JPI-A Healthy Diet for a Healthy Life INTIMIC-085, State Secretary of R + D + I PCIN-2017-117), the Cátedra de Epidemiología y Control del Riesgo Cardiovascular at UAM (#820024), the State Agency for Research (PID2019-108973RB-C21 and C22), the Valencia Government (GRUPOS 03/101; PROMETEO/2009/029 and ACOMP/2013/039), the Castilla-Leon Government (GRS/279/A/08) and European Network of Excellence Ingenious Hypercare (EPSS- 037093) from the European Commission; CIBER Fisiopatología Obesidad y Nutrición (CIBEROBN) (CIBER-02-08-2009, CB06/03 and CB12/03/30016). MSP holds a Ramón y Cajal contract (RYC-2018-025069-I) from the Ministry of Science, Innovation and Universities. MDV holds a “Predoctoral Training in Health Research” contract (FI20/00162) from the Carlos III Health Institute. MGP and ADR received the support of a fellowship from “la Caixa” Foundation (ID 100010434, fellowship codes LCF/BQ/IN18/11660001, and LCF/BQ/DR19/11740016, respectively). PO received the support of a Sara Borrell contract from the Carlos III Health Institute (reference CD16/00255). The Strategic Action for Research in Health Sciences, CIBEROBN are initiatives from Carlos III Health Institute Madrid and co-funded by the European Social Fund “The ESF - investing in your future”. The State Agency for Research and Carlos III Health Institute belong to the Spanish Ministry of Science and Innovation. The funding bodies had no role in the study design, data collection and analysis, interpretation of results, manuscript preparation or in the decision to submit this manuscript for publication.S

High content and dispersion of Gd in bimodal porous silica: T2 contrast agents under ultra-high magnetic fields

Silica-based UVM-7-type bimodal mesoporous materials with high gadolinium content (∞ ≥ Si/Gd ≥ 13) have been synthesized through a one-pot surfactant-assisted procedure from hydroalcoholic solution using a cationic surfactant as template, and starting from atrane complexes of Gd and Si as inorganic precursors. The novel synthetic pathway developed in the study preserves the UVM-7-type architecture while optimizing the dispersion of the Gd-guest species at the nanoscale and even at atomic level. It has been determined that the number of Gd atoms forming clusters is always less than 10. The behaviour under exposure to ultra-high magnetic fields reveals a significant increase in the transversal relaxivity value when compared with related materials in the bibliography. Their activity as T2 instead of T1 contrast agents is discussed and explained considering the high Gd-dispersion and concentration, nature of the materials as well as due to the high magnetic fields used, typical of MRM studies. The absence of toxicity has been confirmed in preliminary cell cultures “in vitro” and the degradation of the solids studied under biological conditions. Results suggest that the atrane route could be a suitable synthesis approach for the preparation of Gd containing contrast agents

Gene-environment interaction analysis of redox-related metals and genetic variants with plasma metabolic patterns in a general population from Spain: The Hortega Study

Background: Limited studies have evaluated the joint influence of redox-related metals and genetic variation on metabolic pathways. We analyzed the association of 11 metals with metabolic patterns, and the interacting role of candidate genetic variants, in 1145 participants from the Hortega Study, a population-based sample from Spain. Methods: Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu), selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS, respectively. We summarized 54 plasma metabolites, measured with targeted NMR, by estimating metabolic principal components (mPC). Redox-related SNPs (N = 291) were measured by oligo-ligation assay. Results: In our study, the association with metabolic principal component (mPC) 1 (reflecting non-essential and essential amino acids, including branched chain, and bacterial co-metabolism versus fatty acids and VLDL subclasses) was positive for Se and Zn, but inverse for Cu, arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2 (reflecting essential amino acids, including aromatic, and bacterial co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3 (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse for Co. The association for mPC4 (reflecting HDL subclasses) was positive for Sb, but inverse for plasma Zn. These associations were mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of interactions with genetic variants associated to enriched endocrine, cardiovascular and neurological pathways. Conclusions: Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several metabolic patterns involved in chronic disease. Carriers of redox-related variants may have differential susceptibility to metabolic alterations associated to excessive exposure to metals.This work was supported by the Strategic Action for Research in Health sciences [CP12/03080, PI15/00071, PI10/0082, PI13/01848, PI14/00874, PI16/01402, PI21/00506 and PI11/00726], CIBER Fisio patología Obesidad y Nutrición (CIBEROBN) (CIBER-02-08-2009, CB06/03 and CB12/03/30,016), the State Agency for Research (PID2019-108973RB- C21 and C22), the Valencia Government (GRUPOS 03/101; PROMETEO/2009/029 and ACOMP/2013/039, IDI FEDER/2021/072 and GRISOLIAP/2021/119), the Castilla-Leon Government (GRS/279/A/08) and European Network of Excellence Ingenious Hypercare (EPSS-037093) from the European Commission. The Strategic Action for Research in Health sciences, CIBERDEM and CIBEROBN are initiatives from Carlos III Health Institute Madrid and cofunded with European Funds for Regional Development (FEDER). The State Agency for Research and Carlos III Health Institute belong to the Spanish Ministry of Science and Innovation. ADR received the support of a fellowship from “la Caixa” Foundation (ID 100010434) (fellowship code “LCF/BQ/DR19/11740016”). MGP received the support of a fellowship from “la Caixa” Foundation (ID 100010434, fellowship code LCFLCF/BQ/DI18/11660001). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.S

Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit.

Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by (1)H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a (1)H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA

ROC curves constructed with the logistic regression model and frequency table for discrimination between survivor and non-survivor patients in predicting 30-days mortality.

<p>(A) ROC curve based on the metabolomics scores evolution values (AUC 0.85; p<0.05, 95% CI 0.68–1). (B) ROC curve based on the SOFA evolution values (AUC 0.78; p<0.05, 95% CI 0.6–0.9). (C) Comparison of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC) and area under (AUC) the ROC curve of SOFA and metabolomic predictive models. The numbers in parenthesis represent the confidence interval in 95%.</p

Box plots showing representative metabolite changes between survivor and non-survivor patients.

<p>(A) Box plots from Urine-0h (B) Box plots from Urine-24h samples. Boxes denote interquartile ranges, lines denote medians, and whiskers denote 10th and 90th percentiles. Levels are expressed as area of the metabolite of interest divided with respect total aliphatic spectral area.*p < 0.05; **p < 0.01;***p < 0.001.</p

PLS-DA score plot for discrimination between survivor (open squares) and non-survivor patients (black circles).

<p>(A) PLS-DA score plot constructed with Urine-0h samples obtained at the admission to the ICU. (B) PLS-DA score plot constructed with Urine-24h samples obtained at 24h after admission to the ICU. (C) Loading plot of the PLS-DA score plot constructed with Urine-0h samples.</p