A plasma fatty acid profile associated to type 2 diabetes development: from the CORDIOPREV study

Purpose: The prevalence of type 2 diabetes mellitus (T2DM) is increasing worldwide. For this reason, it is essential to identify biomarkers for the early detection of T2DM risk and/or for a better prognosis of T2DM. We aimed to identify a plasma fatty acid (FA) profile associated with T2DM development. Methods: We included 462 coronary heart disease patients from the CORDIOPREV study without T2DM at baseline. Of these, 107 patients developed T2DM according to the American Diabetes Association (ADA) diagnosis criteria after a median follow-up of 60 months. We performed a random classification of patients in a training set, used to build a FA Score, and a Validation set, in which we tested the FA Score. Results: FA selection with the highest prediction power was performed by random survival forest in the Training set, which yielded 4 out of the 24 FA: myristic, petroselinic, α-linolenic and arachidonic acids. We built a FA Score with the selected FA and observed that patients with a higher score presented a greater risk of T2DM development, with an HR of 3.15 (95% CI 2.04–3.37) in the Training set, and an HR of 2.14 (95% CI 1.50–2.84) in the Validation set, per standard deviation (SD) increase. Moreover, patients with a higher FA Score presented lower insulin sensitivity and higher hepatic insulin resistance (p < 0.05). Conclusión: Our results suggest that a detrimental FA plasma profile precedes the development of T2DM in patients with coronary heart disease, and that this FA profile can, therefore, be used as a predictive biomarker

An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study

Introduction. A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results. A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusión. These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease

The gut microbiota as a predictive factor associated with the development and remission of type 2 diabetes mellitus in patients with coronary heart disease: from CORDIOPREV study

1. Introducción o motivación de la tesis. La prevalencia de la diabetes mellitus tipo 2 (DMT2) se ha incrementado en las últimas décadas suponiendo un grave problema de salud mundial. Ante esta situación, es crucial identificar los pacientes de alto riesgo, para evitar el desarrollo de la enfermedad, así como aquellos subgrupos a los que se pueden aplicar con éxito recomendaciones dietéticas para prevenir o remitir la diabetes, o bien, requieren otro tipo de tratamiento. Esto es especialmente importante en pacientes con enfermedad coronaria (CHD), pues la presencia simultánea de ambas enfermedades aumenta significativamente el riesgo de desarrollar un nuevo evento cardiovascular y, por tanto, un incremento en la mortalidad. En este sentido, el uso de técnicas computacionales automáticas para el reconocimiento de patrones a partir de un conjunto complejo de datos (en inglés, “machine learning”) son prometedoras y podrían ayudar a identificar nuevos factores asociados al riesgo o la remisión mejorando la predicción. La evidencia científica actual sugiere que la microbiota intestinal podría influir en la fisiopatología de la DMT2, por su potencial impacto en la inflamación y metabolismo del huésped. Recientemente, alteraciones de la microbiota intestinal han sido identificadas no solo en pacientes con DMT2, cuya microbiota podría estar afectada por la medicación hipoglucemiante, sino también en los pacientes con DMT2 sin tratamiento y prediabetes, sugiriéndose la existencia de patrones específicos de microbiota asociados a la DMT2. No obstante, se necesita una mayor evidencia sobre un posible papel causal o valor predictivo de la microbiota intestinal. En este sentido, sería de interés estudios prospectivos para la búsqueda de patrones microbianos que pudieran mejorar la predicción de la enfermedad. Es por ello, que en este trabajo se ha analizado la microbiota intestinal de los pacientes años antes del desarrollo de DMT2 abordando dicha investigación. Por otra parte, hay una variación significativa en las respuestas interindividuales a las intervenciones dietéticas. La dieta es uno de los aspectos esenciales de las modificaciones en el estilo de vida recomendadas para el tratamiento y prevención de la DMT2, siendo esta una alternativa al tratamiento farmacológico, a menos que el nivel de hiperglucemia requiera un tratamiento médico inmediato. Por tanto, es importante descubrir perfiles clínicos y/o bilógicos que se asocien con la eficacia de las estrategias dietéticas, con los cuales se podrían identificar que individuos se beneficiaran en mayor medida de su consumo para prevenir o revertir la enfermedad. Además, la predicción de la respuesta específica a una dieta determinada permitiría realizar tratamientos nutricionales individualizados. La interacción entre dieta y microbiota intestinal como determinante del estado de salud cada vez está siendo más investigada. Recientemente, se ha sugerido que la respuesta metabólica a una intervención dietética por parte del organismo huésped está asociado con la composición de la microbiota intestinal. Por ello, la composición de la microbiota intestinal podría servir para predecir la capacidad de respuesta a las intervenciones dietéticas, lo que a su vez tendría una relevancia potencial en el campo de la nutrición personalizada. Basándonos en estos antecedentes, nuestra hipótesis es que patrones específicos de microbiota intestinal podrían ayudar a identificar aquellos pacientes que se beneficiarían de intervenciones dietéticas saludables experimentando una remisión de la DMT2, así como alteraciones de la microbiota intestinal podrían preceder al desarrollo de diabetes y potencialmente mejorar la predicción del riesgo de desarrollo de DMT2. Adicionalmente, podría determinar el riesgo específico de DMT2 asociado al consumo de distintas dietas. 2. Contenido de la investigación. Objetivo principal. Evaluar el potencial de la composición de la microbiota intestinal como factor predictivo asociado al desarrollo y la remisión de la DMT2 con el consumo de dos modelos dietas saludables (la dieta baja en grasa (LF) y la dieta Mediterránea (Med)), así como perfiles de riesgo específicos para cada una de las dietas, en pacientes con CHD del estudio CORDIOPREV. Objetivos específicos: 1. Evaluar si la composición de la microbiota intestinal, combinada con las variables clásicas asociadas al riesgo de DMT2, puede mejorar la predicción de la remisión de la DMT2 inducida por el consumo de dos dietas saludables, LF o Med, en pacientes con DMT2 de reciente diagnóstico y CHD del estudio CORDIOPREV. 2. Estudiar si la composición de la microbiota intestinal, combinada con las variables clínicas de riesgo, podría mejorar la predicción de casos incidentes de DMT2 en pacientes con CHD, en el marco del estudio CORDIOPREV. 3. Explorar las diferencias entre la microbiota intestinal basal de los pacientes que desarrollaron DMT2 mientras consumían una dieta LF y los que consumían la dieta Med y, evaluar los riesgos específicos para cada dieta que permitan valorar o predecir el riesgo individual de DMT2 asociado al consumo de cada una de las dietas en el marco del estudio CORDIOPREV. Participantes, diseño y metodología. Esta tesis se ha desarrollado en el marco del estudio CORDIOPREV, un estudio prospectivo, randomizado y controlado realizado en 1002 pacientes con CHD, asignados aleatoriamente a uno de los dos modelos de dieta (dieta Med o LF) durante un período de siete años de seguimiento. Publicación 1: En este trabajo se incluyeron todos los pacientes con DMT2 de reciente diagnóstico y sin tratamiento hipoglucemiante al inicio del estudio CORDIOPREV (n=190). Se analizó la microbiota intestinal basal mediante el gen de ARNr 16S en aquellos pacientes de los que se disponía de muestra fecal y que no habían recibido tratamiento antibiótico en los tres meses anteriores a su recogida (n=110). En 73 pacientes de ellos (44 con muestra fecal viable) la DMT2 remitió durante el periodo de seguimiento de 5 años. Se realizaron modelos predictivos usando el algoritmo de aprendizaje automático “Random Forest”, análisis de curvas ROC y de regresión de Cox. Además, se determinó los niveles plasmáticos de LPS en ayunas y tras 4h de una prueba de sobrecarga lipídica al inicio del estudio. Publicación 2: En este trabajo se incluyeron todos los pacientes sin DMT2 al inicio del estudio CORDIOPREV (n=462) y se analizaron todos aquellos con disponibilidad de muestra fecal basal que no habían consumido antibióticos en los tres meses previos a su recogida (n=273). De entre ellos, 107 pacientes (64 con muestra fecal viable) desarrollaron DMT2 de acuerdo con los criterios de diagnóstico de la ADA tras una mediana de seguimiento de 60 meses. La microbiota intestinal basal fue analizada mediante secuenciación del gen de ARNr 16S y se crearon modelos predictivos usando el algoritmo de aprendizaje automático “Random Forest”. Los modelos fueron evaluados mediante análisis de curvas ROC y el riesgo de desarrollar DMT2 en función de la microbiota inicial mediante análisis de Cox. Publicación 3: En este trabajo se incluyeron todos los pacientes no diagnosticados con DMT2 al inicio del estudio CORDIOPREV (n=462) con disponibilidad de muestra fecal basal y sin recibir tratamiento con antibióticos el mes previo su recogida (n=319), aleatorizados para recibir una dieta LF (n=148, de los cuales 28 desarrollaron diabetes) o la dieta Med (n=171, de los cuales 41 desarrollaron diabetes). Se analizó la microbiota intestinal mediante la secuenciación del gen de ARNr 16S, se crearon estimaciones del riesgo basadas en la microbiota intestinal para cada dieta utilizando dos enfoques predictivos (modelos RSF y Lasso) y, se evaluó el riesgo de DMT2 tras una mediana de seguimiento de 60 meses mediante análisis Cox. Resultados. Publicación 1: El modelo de predicción de remisión de DMT2 construido exclusivamente con las variables asociadas al riesgo de DMT2 (variables incluidas en el test de riesgo FINDRISC y test de riesgo de la ADA, HDL, triglicéridos y HbA1c) mostró un área bajo la curva ROC de 0,698±0,152, mientras que en el modelo generado al combinarlas con los datos de composición basal de la microbiota intestinal el poder de predicción mejoró, aumentando el área a 0,822±0,135. Además, la estimación de respuesta a la intervención con una escala de clasificación numérica (en inglés “score”) basada en la microbiota intestinal, que incluía los diez géneros más discriminantes entre aquellos pacientes que remitieron y aquellos que continuaron siendo diabéticos, reveló una mayor probabilidad de remisión de la DMT2 asociada a valores elevados de la escala de clasificación numérica en comparación con valores bajos de dicha escala (Hazard Ratio: 3,195; IC del 95%: 1,37-7,48). Además, esta escala de clasificación se asoció negativamente con el aumento de LPS postprandial (χ2=0,015). Publicación 2: Identificamos un perfil de microbiota intestinal asociado al desarrollo de DMT2 mediante un modelo predictivo basado en la composición de la microbiota intestinal. La combinación de los datos de la microbiota intestinal a los parámetros clínicos (variables incluidas en el test de riesgo FINDRISC y el test de la ADA, HDL, triglicéridos y HbA1c) mejoró la predicción del desarrollo de DMT2, aumentando el área bajo la curva ROC de 0,632 a 0,946 en el conjunto de datos de prueba. Además, una escala de clasificación numérica (en inglés “score”) basada en la microbiota intestinal, que incluía los diez géneros más discriminantes, estimó la probabilidad de desarrollar DMT2 en los pacientes de manera que valores elevados en la escala se asociaron con una mayor probabilidad del desarrollo de DMT2 en comparación con valores bajos (HR: 3,22, IC del 95%: 1,56-6,66). Publicación 3: El análisis lineal discriminante del tamaño del efecto (LEfSe) mostró una microbiota intestinal inicial diferente entre los pacientes que desarrollaron DMT2 consumiendo la dieta LF y la Med. Así mismo, la estimación del riesgo basada en la microbiota asoció un mayor riesgo de desarrollo de DMT2 con una mayor abundancia de Paraprevotella, y una menor de Gammaproteobacterias y B. unformis (HR=3,66 puntuación RSF-LF, 3,15 puntuación Lasso-LF; IC 95%) cuando una dieta LF fue consumida. Por el contrario, una mayor abundancia de Saccharibacterias, Betaproteobacterias y Prevotella se asoció al riesgo de DMT2 (HR=4,00 puntuación RSF-Med, 3,45 puntuación Lasso-Med; IC 95%) cuando se consumió una dieta Med. 3. Conclusión. Nuestro estudio mostró el potencial papel de la microbiota intestinal como factor predictivo asociado al desarrollo y la remisión de la DMT2 en pacientes con CHD, mejorando la predicción de las variables clínicas de riesgo cuando se construyen modelos predictivos combinando datos de la microbiota intestinal y variables clínicas. Además, nuestros resultados suponen el punto de partida para el desarrollo de herramientas clínicas basadas en el uso de escalas de clasificación numérica (en inglés “score”), que evalúen la probabilidad de riesgo o remisión, así como para determinar el modelo dietético más adecuado para prevenir la DMT2.1. Introduction or dissertation background. The prevalence of type 2 diabetes mellitus (T2DM) has been increasing steadily in recent years and has become a major worldwide health problem. In view of this fact, it is essential to identify high-risk patients, in order to prevent the development of the disease, also discriminate between those groups of patients who can successfully prevent or reverse diabetes by dietary recommendations and those whose require another type of treatment. This is especially important in patients with coronary heart disease (CHD), as patients with concurrent CHD and T2DM have a significantly higher risk of developing a new cardiovascular event and, therefore, an increased mortality risk than those without T2DM. In this regard, the use of machine learning algorithms is promising and could help to identify new factors associated with the risk or remission of T2DM to improve their prediction. The current evidence suggests that the gut microbiota could play a role in the pathophysiology of T2DM, because of its potential impact on inflammation and host metabolism. Recently, alterations of the gut microbiota have been identified not only in patients with T2DM, whose microbiota could be modified by antidiabetic drugs, but also in T2DM patients without treatment and prediabetics, suggesting the possibility of specific gut microbiota patterns associated with T2DM. However, more evidence is needed about the role as a causal driving factor and predictive value of the gut microbiota. In this sense, prospective studies might be useful to identify gut microbiota profiles that could improve the disease prediction. For this reason, this doctoral thesis analyzed the gut microbiota of the patients years before the development of T2DM, focusing on this respect. On the other hand, different interindividual responses to dietary interventions has been observed. Diet is of paramount importance in the management and prevention of T2DM, being an alternative to drug treatment, unless the level of hyperglycemia requires immediate medical treatment. Therefore, it is important to find clinical and/or biologic profiles that may determine the success of the dietary strategies, which could enable us to identify those individuals who would benefit to a greater extent from their consumption to prevent or reverse the disease. In addition, the prediction of the specific response to a given diet might help to recommend individualized nutritional treatments. The studies about the interaction between diet and gut microbiota as determinant of health status is on rise. Recently, it has been suggested that the metabolic response to a dietary intervention by the host organism is associated with the composition of the gut microbiota. Thus, the composition of the gut microbiota might predict responsiveness to dietary interventions, which could be relevance for personalized nutrition. Based on this background, we hypothesize that certain gut microbiota profiles might be useful to identify those patients who would benefit from healthy dietary interventions achieving T2DM remission, likewise, alterations of the gut microbiota might precede the T2DM development and potentially improve the prediction of T2DM development risk. Moreover, it might determine the specific risk of T2DM associated with the consumption of different diets. 2. Research content. Main objective. The aim was to evaluate the potential of the gut microbiota composition as a predictive factor associated with the development and remission of T2DM by the consumption of two healthy dietary models (the low-fat diet (LF) and the Mediterranean diet (Med)), besides the diet-specific T2DM risk profiles for each diet, in patients with CHD from the CORDIOPREV study. Specific objectives 1. To evaluate whether baseline gut microbiota composition, in addition to T2DM risk-associated classical variables, may improve the prediction of remission achieved by two dietary models (LF or Med diets) in newly-diagnosed type 2 diabetes patients with coronary heart disease (CHD) within the CORDIOPREV study. 2. To evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of T2DM in patients with CHD, within the CORDIOPREV study. 3. To explore the differences between the baseline gut microbiota of patients who developed T2DM while consuming a LF diet and those consuming the Med diet and, evaluate microbiota-based diet-specific risk scores in order to assess the individual T2DM risk associated with the consumption of a LF or a Med diet, within the CORDIOPREV study. Participants, design and methodology. This doctoral thesis has been conducted in the framework of the CORDIOPREV study, a prospective, randomized, controlled study including 1002 patients with CHD, randomly assigned to one of two dietary models (Med or LF diet) during a seven-year follow-up period. Paper 1: All the patients from the CORDIOPREV study newly-diagnosed with type 2 diabetes at baseline (n=190) were included. Gut microbiota was analyzed by 16S rRNA gene in those patients whose fecal sample was available and who had not received antibiotic treatment within three months before baseline sample collection (n=110). 44 of these reverted during the 5-year follow-up. Predictive models were built using the Random Forest machine learning algorithm. We also performed ROC curves and Cox regression analysis. In addition, LPS levels were determined at fasting and after 4h of a lipid overload test at baseline. Paper 2: All the patients from the CORDIOPREV study without T2DM at baseline were included (n = 462). The gut microbiota composition was determined by 16S rRNA gene sequencing in those patients whose fecal sample was available and who had not received antibiotic treatment within three months before baseline sample collection (n=273). Overall, 107 (64 with available fecal sample) patients developed T2DM after a median of 60 months. The predictive models were built using Random Forest algorithm and hold-out method. The models were evaluated by ROC analysis and the risk of T2DM development was analyzed by Cox analysis. Paper 3: All the patients from the CORDIOPREV study without T2DM at baseline (n = 462) whose fecal sample were available and who had not received treatment with antibiotics within a month before baseline sample collection were included (n=319), randomized to receive either LF diet (n=148, of which 28 developed diabetes) or Med diet (n=171, of which 41 developed diabetes). The gut microbiota was analyzed by 16S rRNA sequencing. Microbiome-based risk scores were built for each diet using two predictive approaches (RSF and Lasso models) and the risk of T2DM after a median follow-up of 60 months was assessed by Cox analysis. Results Paper 1: The model built exclusively with T2DM risk-associated classical variables showed an area under the curve of 0.698±0.152, whereas the addition of the baseline microbiome yielded 0.822±0.135. Furthermore, a microbiota-based response prediction score, including the ten most highly discriminant genera between those patients who reverted of T2DM and those who remained diabetic, revealed a greater likelihood of T2DM remission associated with a high score compared with a low score (Hazard Ratio: 3.195, 95% CI: 1.37-7.48). Moreover, this score was negatively associated with the postprandial LPS increase (χ2=0.015). Paper 2: A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the ADA, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiota-based risk score including the ten most discriminant genera, was associated with the probability of T2DM development. A higher probability of development of T2DM was associated with a high score compared with a low score (HR: 3.22, 95% CI: 1.56-6.66). Paper 3: Linear discriminant analysis effect size (LEfSe) analysis showed a different baseline gut microbiota in patients who developed T2DM consuming LF and Med diets. The scores built showed that a higher abundance of Paraprevotella, and lower abundance of Gammaproteobacteria and B.uniformis were associated with T2DM risk (HR=3.66 RSF-LF score, 3.15 Lasso-LF score; 95% CI) when a LF diet was consumed. In contrast, higher abundances of Saccharibacteria, Betaproteobacteria, and Prevotella were associated with T2DM risk (HR=4.00 RSF-Med score, 3.45 Lasso-Med score; 95% CI) when a Med diet was consumed. 3. Conclusion. Our study showed the potential role of the gut microbiota as a predictive factor associated with the development and remission of T2DM in patients with CHD, improving the prediction of clinical risk variables when predictive models are built combining the gut microbiota data and clinical variables. Furthermore, our results might provide a major foothold for the development of clinical tools based on gut microbiota scores to assess the probability of T2DM risk or remission, in addition to recommending the dietary model which is consistent with a lower risk

A set of miRNAs predicts T2DM remission in patients with coronary heart disease: from the CORDIOPREV study

MicroRNAs (miRNAs) regulate the expression of genes associated with the development of diseases, including type 2 diabetes mellitus (T2DM). However, the use of miRNAs to predict T2DM remission has been poorly studied. Therefore, we aimed to investigate whether circulating miRNAs could be used to predict the probability of T2DM remission in patients with coronary heart disease. We included the newly diagnosed T2DM (n = 190) of the 1,002 patients from the CORDIOPREV study. Seventy-three patients reverted from T2DM after 5 years of dietary intervention with a low-fat or Mediterranean diet. Plasma levels of 56 miRNAs were measured by OpenArray. Generalized linear model, receiver operating characteristic (ROC), Cox regression, and pathway analyses were performed. ROC analysis based on clinical variables showed an area under the curve (AUC) of 0.66. After a linear regression analysis, seven miRNAs were identified as the most important variables in the group’s differentiation. The addition of these miRNAs to clinical variables showed an AUC of 0.79. Cox regression analysis using a T2DM remission score including miRNAs showed that high-score patients have a higher probability of T2DM remission (hazard ratio [HR]low versus high, 4.44). Finally, 26 genes involved in 10 pathways were related to the miRNAs. We have identified miRNAs (hsa-let-7b, hsa-miR-101, hsa-miR-130b-3p, hsa-miR-27a, hsa-miR-30a-5p, hsa-miR-375, and hsa-miR-486) that contribute to the prediction of T2DM remission in patients with coronary heart disease.S

A Diet-Dependent Microbiota Profile Associated with Incident Type 2 Diabetes: From the CORDIOPREV Study

Scope The differences between the baseline gut microbiota of patients who developed type 2 diabetes (T2D) consuming a low‐fat (LF) or a Mediterranean (Med) diet are explored and risk scores are developed to predict the individual risk of developing T2D associated with the consumption of LF or Med diet. Methods and Results All the patients from the CORDIOPREV study without T2D at baseline (n = 462) whose fecal sample are available, are included. Gut microbiota is analyzed by 16S sequencing and the risk of T2D after a median follow‐up of 60 months assessed by Cox analysis. Linear discriminant analysis effect size (LEfSe) analysis shows a different baseline gut microbiota in patients who developed T2D consuming LF and Med diets. A higher abundance of Paraprevotella, and lower Gammaproteobacteria and B. uniformis are associated with T2D risk when an LF diet is consumed. In contrast, higher abundances of Saccharibacteria, Betaproteobacteria, and Prevotella are associated with T2D risk when a Med diet is consumed. Conclusion The results suggest that different interactions between the microbiome and dietary patterns may partially determine the risk of T2D development, which may be used for selecting personalized dietary models to prevent T2D.Agencia de Innovación y Desarrollo de Andalucía. Grant Number: CVI‐7450 Instituto de Salud Carlos III. Grant Numbers: CP14/00114, CPII19/00007, DTS19/00007, FIS PI13/00023, PI16/01777, PI19/00299, PIE 14/00031, PIE14/00005 European Regional Development Fund. Grant Number: na Secretaría de Estado de Investigación, Desarrollo e Innovación. Grant Number: AGL2015‐67896‐

Owning a Pet Is Associated with Changes in the Composition of Gut Microbiota and Could Influence the Risk of Metabolic Disorders in Humans

Pet ownership positively influences clinical outcomes in cardiovascular prevention. Additionally, cardiovascular disease (CVD) has been previously linked to microbiota dysbiosis. We evaluated the influence of owning a pet and its relationship with the intestinal microbiota. We analyzed the gut microbiota from 162 coronary patients from the CORDIOPREV study (NCT00924937) according to whether they owned pets (n = 83) or not (n = 79). The pet-owner group was further divided according to whether they owned dogs only (n = 28) or not (n = 55). A 7-item pet-owners test score was used. Patients who owned pets had less risk of metabolic syndrome (MetS) (OR = 0.462) and obesity (OR = 0.519) and were younger (p < 0.001) than patients who did not own pets. Additionally, patients who owned dogs had less risk of MetS (OR = 0.378) and obesity (OR = 0.418) and were younger (p < 0.001) than patients who did not own pets. A preponderance of the genera Serratia and Coprococcus was found in the group of owners, while the genera Ruminococcus, an unknown genus of Enterobacteriaceae and Anaerotruncus were preponderant in the group of non-owners. In patients who owned dogs, Methanobrevibacter and two more genera, Coprococcus and Oscillospira, were more common. Our study suggests that the prevalence of MetS and obesity in CVD patients is lower in pet owners, and that pet ownership could be a protective factor against MetS through the shaping of the gut microbiota. Thus, owning a pet could be considered as a protective factor against cardiometabolic diseases

A microbiota-based predictive model for type 2 diabetes remission induced by dietary intervention: From the CORDIOPREV study.

The CIBEROBN is an initiative of the Instituto de Salud Carlos III, Madrid, Spain. The CORDIOPREV study is supported by Ministerio de Economia y Competitividad, Spain, under grants AGL2012/39615, PIE14/00005, and PIE14/00031 to J.L.‐M.; AGL2015‐67896‐P to J.L.‐M. and A.C.; CP14/00114 to A.C.; PI19/00299 to A.C.; DTS19/00007 to A.C.; FIS PI13/00023 to J.D.‐L., PI16/01777 to F.P.‐J. and P.P.‐M.; Fundacion Patrimonio Comunal Olivarero, Junta de Andalucía (Consejería de Salud, Consejeria de Agricultura y Pesca, Consejería de Innovacion, Ciencia y Empresa), Diputaciones de Jaen y Cordoba, Centro de Excelencia en Investigación sobre Aceite de Oliva y Salud and Ministerio de Medio Ambiente, Medio Rural y Marino, Gobierno de España; Consejeria de Innovación, Ciencia y Empresa, Proyectos de Investigación de Excelencia, Junta de Andalucía under grant CVI‐7450 to J.L.‐M.; and by the Fondo Europeo de Desarrollo Regional (FEDER). Antonio Camargo is supported by an ISCIII research contract (Programa Miguel‐Servet CP14/00114 and CPII19/00007). J.M.O. is supported by the US Department of Agriculture, under agreement no. 8050‐51000‐098‐00D.S