Multi-omics biomarkers of metabolic homeostasis of risk factors associated to non-communicable diseases

Les malalties no transmissibles, com l'obesitat, la síndrome metabòlica, les malalties cardiovasculars, el càncer i les malalties neurodegeneratives, es consideren malalties multifactorials. Per aquesta raó, s'ha proposat que l'aparició d’aquestes malalties es deu a un desequilibri de processos globals. El seguiment d'aquests processos obre la porta a la possibilitat de modular-los i, per tant, prevenir-los mitjançant el disseny d'intervencions/tractaments personalitzats més precisos. No obstant això, els biomarcadors actuals no tenen la capacitat d'avaluar les alteracions primerenques que podrien conduir al desenvolupament de la malaltia, la qual cosa posa de manifest la necessitat de definir nous biomarcadors. Per tant, en el present treball es presenta una signatura metabòlica característica de processos específics obtinguda mitjançant l'ús de tecnologies òmiques: disfunció de carbohidrats, hiperlipèmia, hipertensió i dysbiosis intestinal, com a representatius de l'estrès metabòlic; l'estrès inflamatori; l'estrès oxidatiu i l'estrès psicològic. Per això, s'han desenvolupat diferents models animals i s'ha avaluat el perfil metabòlic dels diferents factors de risc d'interès en plasma i orina. Els resultats indiquen que els lípids i els intermediaris del cicle del TCA són els metabòlits més prometedors del perfil metabòlic. En tots els factors de risc, els diacilglicerols (DG) són els biomarcadors lipídics amb major impacte: en concret, el DG 36:4 i el DG 34:2 vinculen els factors de risc amb el metabolisme de l'àcid araquidònic. En inflamació, estrès oxidatiu i psicològic, l'altre protagonista és el cicle del TCA a causa del seu paper clau en el mitocondri amb l'alfa-cetoglutarat com l'intermediari més prometedor. En conseqüència, el perfil metabòlic presentat és una eina potencial per al seguiment dels factors de risc i podria obrir una finestra per a orientar l'aparició de malalties i intentar prevenir-les i tractar-les.Las enfermedades no transmisibles, como la obesidad, el síndrome metabólico, las enfermedades cardiovasculares, el cáncer y las enfermedades neurodegenerativas, se consideran enfermedades multifactoriales. Por esta razón, se ha propuesto que la aparición de estas enfermedades se debe a un desequilibrio de procesos globales. El seguimiento de estos procesos abre la puerta a la posibilidad de modularlos y, por lo tanto, prevenirlos mediante el diseño de intervenciones/tratamientos personalizados más precisos. Sin embargo, los biomarcadores actuales no tienen la capacidad de evaluar las alteraciones tempranas que podrían conducir al desarrollo de la enfermedad, lo que pone de manifiesto la necesidad de definir nuevos biomarcadores. Por lo tanto, en el presente trabajo se presenta una firma metabólica característica de procesos específicos obtenida mediante el uso de tecnologías ómicas: disfunción de carbohidratos, hiperlipidemia, hipertensión y disbiosis intestinal, como representativos del estrés metabólico; el estrés inflamatorio; el estrés oxidativo y el estrés psicológico. Para ello se han desarrollado diferentes modelos animales y se ha evaluado el perfil metabólico de los diferentes factores de riesgo de interés en plasma y orina. Los resultados indicaron que los lípidos y los intermediarios del ciclo del TCA son los metabolitos más prometedores del perfil metabólico. En todos los factores de riesgo, los diacilgliceroles (DG) son el biomarcador lipídico con mayor impacto: en concreto, el DG 36:4 y el DG 34:2 vinculan los factores de riesgo con el metabolismo del ácido araquidónico. En inflamación, estrés oxidativo y psicológico, el otro protagonista es el ciclo del TCA debido a su papel clave en la mitocondria con el alfa-cetoglutarato como el intermediario más prometedor. En consecuencia, el perfil metabólico presentado es una herramienta potencial para el seguimiento de los factores de riesgo y podría abrir una ventana para orientar la aparición de enfermedades e intentar prevenirlas y tratarlas.Non-communicable diseases, such as obesity, metabolic syndrome, cardiovascular diseases, cancer and neurodegenerative diseases, are considered multifactorial diseases. For this reason, it has been proposed that the occurrence of these diseases is due to an imbalance of overarching processes (i.e., metabolic, inflammatory, oxidative, and psychological stress). Monitoring these overarching processes opens the door to the possibility of modulating them, and thus preventing the occurrence of different process through the design of more precise personalised interventions or treatments. However, current biomarkers of disease cannot assess early alterations that could lead to the development of disease, highlighting the need to define new biomarkers. Thus, the present work presents a characteristic metabolic signature for the detection of specific processes using omic technologies: carbohydrate dysfunction, hyperlipidaemia, hypertension and intestinal dysbiosis, as representative of metabolic stress; inflammatory stress; oxidative stress and psychological stress. For this purpose, different animal models have been developed and the metabolic profile in plasma and urine has been evaluated in the different risk factors of interest. The results indicated that lipids and TCA cycle intermediates are the most promising metabolites of the metabolic profile. In all the risk factors, diacylglycerols (DG) are the lipidic biomarker with the greatest impact on metabolic profiles: specifically, DG 36:4 and DG 34:2 linking risk factors to arachidonic acid metabolism. In inflammation, oxidative and psychological stress, the other protagonist is the TCA cycle due to its multiple roles in mitochondrial metabolism: being alpha-ketoglutarate one of the most promising intermediate. In consequence, the presented metabolic profile is a potential tool for the monitoring of risk factors and could open a window to target the onset of diseases and try to prevent and treat them

Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study

Hypertriglyceridemia; Inhibiting lipoprotein lipaseHipertrigliceridèmia; Inhibició de la lipoproteïna lipasaHipertrigliceridemia; Inhibición de la lipoproteína lipasaHypertriglyceridemia (HTG) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). One of the multiple origins of HTG alteration is impaired lipoprotein lipase (LPL) activity, which is an emerging target for HTG treatment. We hypothesised that early, even mild, alterations in LPL activity might result in an identifiable metabolomic signature. The aim of the present study was to assess whether a metabolic signature of altered LPL activity in a preclinical model can be identified in humans. A preclinical LPL-dependent model of HTG was developed using a single intraperitoneal injection of poloxamer 407 (P407) in male Wistar rats. A rat metabolomics signature was identified, which led to a predictive model developed using machine learning techniques. The predictive model was applied to 140 humans classified according to clinical guidelines as (1) normal, less than 1.7 mmol/L; (2) risk of HTG, above 1.7 mmol/L. Injection of P407 in rats induced HTG by effectively inhibiting plasma LPL activity. Significantly responsive metabolites (i.e. specific triacylglycerols, diacylglycerols, phosphatidylcholines, cholesterol esters and lysophospholipids) were used to generate a predictive model. Healthy human volunteers with the impaired predictive LPL signature had statistically higher levels of TG, TC, LDL and APOB than those without the impaired LPL signature. The application of predictive metabolomic models based on mechanistic preclinical research may be considered as a strategy to stratify subjects with HTG of different origins. This approach may be of interest for precision medicine and nutritional approaches.This research was financially supported by the Catalan Government through the funding grant ACCIÓ-Eurecat (PRIV2019-PREVENTOMICS) and by the Centre for the Development of Industrial Technology (CDTI) of the Spanish Ministry of Science and Innovation under grant agreement: TECNOMIFOOD project. CER-20191010

A Pilot Study for Metabolic Profiling of Obesity-Associated Microbial Gut Dysbiosis in Male Wistar Rats

Obesity is one of the most incident and concerning disease worldwide. Definite strategies to prevent obesity and related complications remain elusive. Among the risk factors of the onset of obesity, gut microbiota might play an important role in the pathogenesis of the disease, and it has received extensive attention because it affects the host metabolism. In this study, we aimed to define a metabolic profile of the segregated obesity-associated gut dysbiosis risk factor. The study of the metabolome, in an obesity-associated gut dysbiosis model, provides a relevant way for the discrimination on the different biomarkers in the obesity onset. Thus, we developed a model of this obesity risk factors through the transference of gut microbiota from obese to non-obese male Wistar rats and performed a subsequent metabolic analysis in the receptor rats. Our results showed alterations in the lipid metabolism in plasma and in the phenylalanine metabolism in urine. In consequence, we have identified metabolic changes characterized by: (1) an increase in DG:34:2 in plasma, a decrease in hippurate, (2) an increase in 3-HPPA, and (3) an increase in o-coumaric acid. Hereby, we propose these metabolites as a metabolic profile associated to a segregated dysbiosis state related to obesity disease

Alterations in Metabolome and Microbiome Associated with an Early Stress Stage in Male Wistar Rats: A Multi-Omics Approach

Stress disorders have dramatically increased in recent decades becoming the most prevalent psychiatric disorder in the United States and Europe. However, the diagnosis of stress disorders is currently based on symptom checklist and psychological questionnaires, thus making the identification of candidate biomarkers necessary to gain better insights into this pathology and its related metabolic alterations. Regarding the identification of potential biomarkers, omic profiling and metabolic footprint arise as promising approaches to recognize early biochemical changes in such disease and provide opportunities for the development of integrative candidate biomarkers. Here, we studied plasma and urine metabolites together with metagenomics in a 3 days Chronic Unpredictable Mild Stress (3d CUMS) animal approach that aims to focus on the early stress period of a well-established depression model. The multi-omics integration showed a profile composed by a signature of eight plasma metabolites, six urine metabolites and five microbes. Specifically, threonic acid, malic acid, alpha-ketoglutarate, succinic acid and cholesterol were proposed as key metabolites that could serve as key potential biomarkers in plasma metabolome of early stages of stress. Such findings targeted the threonic acid metabolism and the tricarboxylic acid (TCA) cycle as important pathways in early stress. Additionally, an increase in opportunistic microbes as virus of the Herpesvirales was observed in the microbiota as an effect of the primary stress stages. Our results provide an experimental biochemical characterization of the early stage of CUMS accompanied by a subsequent omic profiling and a metabolic footprinting that provide potential candidate biomarkers

A Pilot Study for Metabolic Profiling of Obesity-Associated Microbial Gut Dysbiosis in Male Wistar Rats

Obesity is one of the most incident and concerning disease worldwide. Definite strategies to prevent obesity and related complications remain elusive. Among the risk factors of the onset of obesity, gut microbiota might play an important role in the pathogenesis of the disease, and it has received extensive attention because it affects the host metabolism. In this study, we aimed to define a metabolic profile of the segregated obesity-associated gut dysbiosis risk factor. The study of the metabolome, in an obesity-associated gut dysbiosis model, provides a relevant way for the discrimination on the different biomarkers in the obesity onset. Thus, we developed a model of this obesity risk factors through the transference of gut microbiota from obese to non-obese male Wistar rats and performed a subsequent metabolic analysis in the receptor rats. Our results showed alterations in the lipid metabolism in plasma and in the phenylalanine metabolism in urine. In consequence, we have identified metabolic changes characterized by: (1) an increase in DG:34:2 in plasma, a decrease in hippurate, (2) an increase in 3-HPPA, and (3) an increase in o-coumaric acid. Hereby, we propose these metabolites as a metabolic profile associated to a segregated dysbiosis state related to obesity disease

Alterations in Metabolome and Microbiome Associated with an Early Stress Stage in Male Wistar Rats: A Multi-Omics Approach

Stress disorders have dramatically increased in recent decades becoming the most prevalent psychiatric disorder in the United States and Europe. However, the diagnosis of stress disorders is currently based on symptom checklist and psychological questionnaires, thus making the identification of candidate biomarkers necessary to gain better insights into this pathology and its related metabolic alterations. Regarding the identification of potential biomarkers, omic profiling and metabolic footprint arise as promising approaches to recognize early biochemical changes in such disease and provide opportunities for the development of integrative candidate biomarkers. Here, we studied plasma and urine metabolites together with metagenomics in a 3 days Chronic Unpredictable Mild Stress (3d CUMS) animal approach that aims to focus on the early stress period of a well-established depression model. The multi-omics integration showed a profile composed by a signature of eight plasma metabolites, six urine metabolites and five microbes. Specifically, threonic acid, malic acid, alpha-ketoglutarate, succinic acid and cholesterol were proposed as key metabolites that could serve as key potential biomarkers in plasma metabolome of early stages of stress. Such findings targeted the threonic acid metabolism and the tricarboxylic acid (TCA) cycle as important pathways in early stress. Additionally, an increase in opportunistic microbes as virus of the Herpesvirales was observed in the microbiota as an effect of the primary stress stages. Our results provide an experimental biochemical characterization of the early stage of CUMS accompanied by a subsequent omic profiling and a metabolic footprinting that provide potential candidate biomarkers

Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study

Abstract Hypertriglyceridemia (HTG) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). One of the multiple origins of HTG alteration is impaired lipoprotein lipase (LPL) activity, which is an emerging target for HTG treatment. We hypothesised that early, even mild, alterations in LPL activity might result in an identifiable metabolomic signature. The aim of the present study was to assess whether a metabolic signature of altered LPL activity in a preclinical model can be identified in humans. A preclinical LPL-dependent model of HTG was developed using a single intraperitoneal injection of poloxamer 407 (P407) in male Wistar rats. A rat metabolomics signature was identified, which led to a predictive model developed using machine learning techniques. The predictive model was applied to 140 humans classified according to clinical guidelines as (1) normal, less than 1.7 mmol/L; (2) risk of HTG, above 1.7 mmol/L. Injection of P407 in rats induced HTG by effectively inhibiting plasma LPL activity. Significantly responsive metabolites (i.e. specific triacylglycerols, diacylglycerols, phosphatidylcholines, cholesterol esters and lysophospholipids) were used to generate a predictive model. Healthy human volunteers with the impaired predictive LPL signature had statistically higher levels of TG, TC, LDL and APOB than those without the impaired LPL signature. The application of predictive metabolomic models based on mechanistic preclinical research may be considered as a strategy to stratify subjects with HTG of different origins. This approach may be of interest for precision medicine and nutritional approaches

Relationship between method of anastomosis and anastomotic failure after right hemicolectomy and ileo-caecal resection: an international snapshot audit

Aim The anastomosis technique used following right-sided colonic resection is widely variable and may affect patient outcome. This study aimed to assess the association between leak and anastomosis technique (stapled vs handsewn). Method This was a prospective, multicentre, international audit including patients undergoing elective or emergency right hemicolectomy or ileo-caecal resection operations over a 2-month period in early 2015. The primary outcome measure was the presence of anastomotic leak within 30 days of surgery, determined using a prespecified definition. Mixed effects logistic regression models were used to assess the association between leak and anastomosis method, adjusting for patient, disease and operative cofactors, with centre included as a random-effect variable. Results This study included 3208 patients, of whom 78.4\% (n = 2515) underwent surgery for malignancy and 11.7\% (n = 375) underwent surgery for Crohn's disease. An anastomosis was performed in 94.8\% (n = 3041) of patients, which was handsewn in 38.9\% (n = 1183) and stapled in 61.1\% (n = 1858). Patients undergoing hand-sewn anastomosis were more likely to be emergency admissions (20.5\% handsewn vs 12.9\% stapled) and to undergo open surgery (54.7\% handsewn vs 36.6\% stapled). The overall anastomotic leak rate was 8.1\% (245/3041), which was similar following handsewn (7.4\%) and stapled (8.5\%) techniques (P = 0.3). After adjustment for cofactors, the odds of a leak were higher for stapled anastomosis (adjusted OR = 1.43; 95\% CI: 1.04-1.95; P = 0.03). Conclusion Despite being used in lower-risk patients, stapled anastomosis was associated with an increased anastomotic leak rate in this observational study. Further research is needed to define patient groups in whom a stapled anastomosis is safe