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

Received: 26 October 2020 Revised: 27 January 2021 Accepted: 31 January 2021 Published online: 4 April 2021 DOI: 10.1002/ctm2.326 LETTER TO EDITOR A microbiota-based predictive model for type 2 diabetes remission induced by dietary intervention: From the CORDIOPREV study Dear Editor, Type 2 diabetes is widely considered as a chronic, progressive disease that is a consequence of the seemingly inexorable decline in β-cell function.1However, recent studies have demonstrated that in the early stages of development, it may be reversible. Indeed, in this regard, primary support derived from patients undergoing bariatric surgery has provided the earliest evidence of type 2 diabetes remission. In fact, the normalization of plasma glucose levels can occur in some patients just days after bariatric surgical intervention, and even before achieving significant weight loss.2This observation points to a relevant glucoregulatory role of the gastrointestinal tract. It has been proposed that a number of different, but not mutually exclusive, potential mechanisms may contribute toward this effect, including changes in bile acid metabolism, gastrointestinal tract nutrient sensing and glucose utilization, incretins, and gut microbiota.3 Recent studies have shown that it is possible to induce type 2 diabetes remission by weight loss with calorie restriction interventions.4Depletion of the gut microbiota with antibiotic treatment and fecal microbiota transplantation suggest that the gut microbiota plays a causal role in the beneficial effects of calorie restriction, especially by lowering body weight and hepatic lipid accumulation.5 Furthermore, it has been observed that calorie restriction and diabetes remission are associated with an improvement of gut permeability and a reduction in inflammatory and endotoxemia biomarkers.6,7 Thus, it is noteworthy that the two approaches that, to date, are known to enable type 2 diabetes remission have plausibly suggested that a role is played by the gut microbiota. This idea is further strengthened by the reported association between this disease and the gut microbiota. Alterations in the gut microbiota of patients with type 2 diabetes have been described,8which adds to the potential causal relationship between the gut microbiome and This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics impaired glucose metabolism, a notion which is supported by studies based on fecal transfer in patients with metabolic syndrome.9 Lifestyle modifications, including the implementation of healthy diets, have been shown to have a beneficial effect on type 2 diabetes prevention.10 In particular, it has been suggested that the impact of dietary intervention on metabolism is associated with baseline gut microbiota composition. Hence, microbiome biomarkers could potentially be used to identify subjects who might benefit from specific dietary interventions.11 Our study, conducted in 110 newly diagnosed type 2 diabetes patients with coronary heart disease (CHD) within the Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention (CORDIOPREV) study, evaluated whether baseline gut microbiota composition, in addition to the classic type 2 diabetes risk-associated variables, improves the identification of patients who underwent type 2 diabetes remission achieved by two dietary models (low-fat or Mediterranean diet) after a 5-year follow-up (responders, n=44) and those who did not respond to the dietary intervention (nonresponders, n=66), with both groups presenting the same adherence to both diets, and without promoting changes in lifestyle such as weight loss or medication. We observed a higher weight, body mass index, waist circumference and glucose levels, and a lower disposition index at baseline in the nonresponders than in the responders (p<0.05) (Tables 1and 2). First of all, Linear discriminant analysis Effect Size (LEfSe) (Figure 1) showed that gut microbiota in the responders group was characterized by the Ruminococcus genus of the Lachnospiraceae family. In contrast, the baseline gut microbiota in the nonresponders group was enriched in the Porphyromonadaceae family and Parabacteroides genus. However, the bacterial richness and diversity assessed by the main αdiversity indexes were similar between groups, and no significant differences were found. Clin. Transl. Med. 2021;11:e326. wileyonlinelibrary.com/journal/ctm2 1of8 https://doi.org/10.1002/ctm2.326 2of8 VALS-DELGADO et al. TABLE 1 Baseline characteristics of the newly diagnosed type 2 diabetes population for type 2 diabetes mellitus remission study and of the population with analyzed gut microbiota Responders Nonresponders Responders†Nonresponders† (n=73) (n=110) pvalue (n=44) (n=66) pvalue Men/women 60/13 92/18 0.799 36/8 56/10 0.674 Age (years) 60.8 ±1.0 59.3 ±0.9 0.252 60.1 ±1.3 57.7 ±1.2 0.198 Weight (kg) 80.2 ±1.3 88.4 ±1.4 <0.001 78.2 ±1.7 88.0 ±1.8 <0.001 Body mass index (kg/m2)29.9 ±0.4 32.1 ±0.4 0.001 29.2 ±0.6 31.7 ±0.5 0.002 Waist circumference (cm) 101 ±1108±1<0.001 99 ±1.3 108 ±1.3 <0.001 Systolic blood pressure (mm Hg) 137 ±3138 ±20.717 137 ±3135 ±20.518 Diastolic blood pressure (mm Hg) 76.6 ±1.6 77.1 ±1.0 0.755 76.4 ±2.1 76.5 ±1.4 0.945 Triglycerides (mmol/L) 1.48 ±0.10 1.69 ±0.07 0.090 1.60 ±0.15 1.71 ±0.09 0.478 Total cholesterol (mmol/L) 4.16 ±0.08 4.31 ±0.08 0.203 4.24 ±0.11 4.29 ±0.11 0.726 HDL-cholesterol (mmol/L) 1.11 ±0.03 1.06 ±0.02 0.141 1.12 ±0.05 1.04 ±0.03 0.116 LDL cholesterol (mmol/L) 2.31 ±0.07 2.42 ±0.07 0.302 2.32 ±0.10 2.35 ±0.09 0.828 C-reactive protein (nmol/L) 37.1 ±5.3 33.5 ±3.6 0.558 37.8 ±6.8 32.2 ±4.5 0.480 HbA1c (mmol/mol) 47.8 ±0.9 50.7 ±0.9 0.032 48.1 ±1.2 50.7 ±1.3 0.176 HbA1c (%) 6.53 ±0.08 6.79 ±0.08 0.032 6.55 ±0.11 6.79 ±0.12 0.176 Glucose (mmol/L) 5.50 ±0.09 6.58 ±0.14 <0.001 5.59 ±0.12 6.47 ±0.20 0.001 Insulin (nmol/L) 64.4 ±5.5 93.3 ±7.8 0.007 64.8 ±7.1 94.8 ±11.9 0.058 HOMA-IR 3.49 ±0.42 4.84 ±0.32 0.010 3.93 ±0.65 4.76 ±0.45 0.281 Insulin sensitivity index 3.16 ±0.20 2.37 ±0.12 <0.001 3.02 ±0.26 2.47 ±0.16 0.063 Insulinogenic index 0.70 ±0.19 0.68 ±0.14 0.921 0.89 ±0.26 0.80 ±0.22 0.795 Hepatic insulin resistance index 1421 ±168 1970 ±129 0.009 1616 ±263 1943 ±182 0.293 Muscle insulin sensitivity index (×102) 1.93 ±0.22 2.20 ±0.25 0.452 1.74 ±0.29 2.23 ±0.35 0.313 Disposition index 0.68 ±0.06 0.43 ±0.02 <0.001 0.65 ±0.05 0.46 ±0.03 0.002 Smoking (%) 11.4 16.7 0.440 12.3 11.8 0.917 Hypertension (%) 57.5 65.5 0.279 61.4 60.6 0.936 History of peripheral vascular disease (%) 0.0 4.5 0.065 0.0 6.1 0.096 History of stroke or TIA (%) 4.1 4.5 0.888 4.5 4.5 1.000 History of myocardial infarction (%) 53.4 55.5 0.787 56.8 57.6 0.937 History of anginá (%) 40.0 39.7 0.970 38.6 36.4 0.809 History of PCI (%) 93.2 98.2 0.082 90.9 98.5 0.062 History of CABG (%) 5.5 2.7 0.342 6.8 1.5 0.146 Baseline medication (%) Anti-aggregates 94.5 96.4 0.550 95.5 100 0.080 Beta-blockers 57.5 65.5 0.279 56.8 68.2 0.225 ACE inhibitors 13.7 17.3 0.517 13.6 12.1 0.815 Diuretics 39.7 41.8 0.778 29.5 37.9 0.368 Angiotensin-II receptor blockers (ARBs) 20.5 23.6 0.624 22.7 19.7 0.702 Calcium antagonists 11.0 19.1 0.140 13.6 19.7 0.410 Nitrates 15.1 7.3 0.090 11.4 9.1 0.697 (Continues) VALS-DELGADO et al. 3of8 TABLE 1 (Continued) Responders Nonresponders Responders†Nonresponders† (n=73) (n=110) pvalue (n=44) (n=66) pvalue Anti-arrhythmics 2.7 0.9 0.340 2.3 1.5 0.771 Oral anticoagulants 1.4 0.9 0.769 0.0 1.5 0.412 Statins 86.3 88.2 0.707 95.5 95.5 1.000 Other hypolipidemics 6.8 7.3 0.913 6.8 6.1 0.873 Proton pump inhibitors 83.6 82.7 0.883 86.4 89.4 0.630 Tranquilizers 6.8 8.2 0.740 9.1 10.6 0.795 Abbreviations: CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention; TIA, transient ischemic attack. Our study was conducted in 183 newly diagnosed type 2 diabetes patients, 110 from which had available feces samples and had not received antibiotic treatment within 3 months before sample collection. Data are mean ±SEM. Responders group: patients who reverted from type 2 diabetes after 5 years of dietary intervention follow-up. Nonresponders group: patients who remained with type 2 diabetes after 5 years of follow-up. Responders†: patients who reverted from type 2 diabetes after 5 years of dietary intervention follow-up to which we have the availability of fecal sample. Nonresponders†: patients who remained with type 2 diabetes after 5 years of follow-up to which we have the availability of fecal sample. p-values were calculated by one-way ANOVA. Gender p-value by chi-square analysis. TABLE 2 Baseline characteristics of the newly diagnosed type 2 diabetes population for type 2 diabetes mellitus remission study compared with the population with analyzed gut microbiota Responders Responders†Nonresponders Nonresponders† (n=73) (n=44) pvalue (n=110) (n=66) pvalue Men/women 60/13 36/8 0.959 92/18 56/10 0.831 Age (years) 60.8 ±1.0 60.1 ±1.3 0.649 59.3 ±0.9 57.7 ±1.2 0.304 Weight (kg) 80.2 ±1.3 78.2 ±1.7 0.341 88.4 ±1.4 88.0 ±1.8 0.835 Body mass index (kg/m2)29.9 ±0.4 29.2 ±0.6 0.361 32.1 ±0.4 31.7 ±0.5 0.639 Waist circumference (cm) 101 ±199±10.304108±1108±10.725 Triglycerides (mmol/L) 1.48 ±0.10 1.60 ±0.15 0.515 1.69 ±0.07 1.71 ±0.09 0.868 Total cholesterol (mmol/L) 4.16 ±0.08 4.24 ±0.11 0.543 4.31 ±0.08 4.29 ±0.11 0.895 HDL cholesterol (mmol/L) 1.11 ±0.03 1.12 ±0.05 0.838 1.06 ±0.2 1.04 ±0.03 0.667 LDL cholesterol (mmol/L) 2.31 ±0.07 2.32 ±0.10 0.916 2.42 ±0.07 2.35 ±0.09 0.570 C-reactive protein (nmol/L) 37.1 ±5.3 37.8 ±6.8 0.938 33.5 ±3.6 32.2 ±4.5 0.832 HbA1c (mmol/mol) 47.8 ±0.9 48.1 ±1.2 0.870 50.7 ±0.9 50.7 ±1.3 0.992 HbA1c (%) 6.53 ±0.08 6.55 ±0.11 0.870 6.79 ±0.08 6.79 ±0.12 0.992 Glucose (mmol/L) 5.50 ±0.09 5.59 ±0.12 0.561 6.58 ±0.14 6.47 ±0.20 0.649 Insulin (nmol/L) 64.4 ±5.5 64.8 ±7.1 0.968 93.3 ±7.8 94.8 ±11.9 0.912 HOMA-IR 3.49 ±0.42 3.93 ±0.65 0.555 4.84 ±0.32 4.76 ±0.45 0.886 Insulin sensitivity index 3.16 ±0.20 3.02 ±0.26 0.666 2.37 ±0.12 2.47 ±0.16 0.594 Insulinogenic index 0.70 ±0.19 0.89 ±0.26 0.549 0.68 ±0.14 0.80 ±0.22 0.621 Hepatic insulin resistance index 1421 ±168 1616 ±263 0.512 1970 ±129 1943 ±182 0.904 Muscle insulin sensitivity index (×102) 1.93 ±0.22 1.74 ±0.29 0.583 2.20 ±0.25 2.23 ±0.35 0.920 Disposition index 0.68 ±0.06 0.65 ±0.05 0.713 0.43 ±0.02 0.46 ±0.03 0.940 Our study was conducted in 183 newly diagnosed type 2 diabetes patients, 110 from which had available feces samples and had not received antibiotic treatment within 3 months before sample collection. Data are mean ±SEM. Responders: patients who reverted from type 2 diabetes after 5 years of dietary intervention follow-up. Responders†: patients who reverted from type 2 diabetes after 5 years of dietary intervention follow-up to which we have availability of fecal sample. Nonresponders: patients who remained with type 2 diabetes after 5 years of follow-up. Nonresponders†: patients who remained with type 2 diabetes after 5 years of follow-up to which we have availabilityof fecal sample. p-Values were calculated by one-way ANOVA. Gender pvalue by chi-square analysis. Significant differences (p<0.05). 4of8 VALS-DELGADO et al. FIGURE 1 Differently abundant taxa identified using Linear discriminant analysis Effect Size (LEfSe) analysis. The most differently abundant taxa between the groups of study are represented in a bar graph according to the LDA score (log 10) and in a taxonomic cladogram. Only taxa with p<0.05 and an LDA score significant threshold | >2| are shown. The responders group (green color) was characterized by the Ruminococcus genus of the Lachnospiraceae family. Nonresponders group was enriched in the Porphyromonadaceae family and Parabacteroides genus (red color). In the taxonomic cladogram, each successive circle represents a different phylogenetic level. The order (from the center outwards) is phylum, class, family, and genus levels. Differing taxa are listed on the right-hand side of the cladogram Emerging evidence suggests that the host’s metabolic response to a nutritional or dietary intervention depends on microbiome composition. In fact, a recent study showed that the gut microbiota, together with clinical, anthropometric and lifestyle data, enables us to make an accurate prediction of the postprandial glucose individual response to different foods.11 Moreover, this prediction was demonstrated to be useful for designing personalized dietary interventions aimed at reducing postprandial glucose.11 In order to evaluate the potential of gut microbiota composition as a predictive factor of type 2 diabetes remission, we built several random forest classifier models, which were evaluated using 10-fold cross-validation method. These analyses showed that the addition of the microbiome to the classic variables associated with diabetes risk improved our ability to differentiate between those responder individuals who would benefit from the consumption of two dietary models (low-fat or Mediterranean diet) and those whose diabetes would remain despite the dietary intervention. These models had a sensitivity of 83% and specificity of 66% (Figure 2A) and a sensitivity of 78% and specificity of 68% (Figure S1), respectively. We also evaluated the putative role of the microbiome in evaluating the probability of dietary consumption-induced type 2 diabetes remission by COX regressions analysis. To do this, we built a response prediction score for patients based on their microbiome profile, using the 10 bacterial taxa with the highest importance in the predictive microbiome-based model, as assessed by mean decrease accuracy (MDA, Figure 2B). We evaluated the probability of type 2 diabetes remission by COX regressions of the score generated by categorizing patients into (ascending) tertiles of the microbiome-based response prediction score value (Figure 3A). A large number of trials have demonstrated the effectiveness of weight loss or carbohydrate restriction diets, based on a change in macronutrients, to induce diabetes remission,12 in which the ability of β-cells to recover glucose-stimulated insulin secretion seems to be crucial.4 This suggests that the microbiota profile is associated with β-cell functionality, as evidenced by the lack of baseline differences between responders and nonresponders when patients were classified by ascending tertiles, with the exception of the disposition index (Table S4). In addition, we evaluated the relationship between the groups defined according to their microbiome-based response prediction score by ascending tertiles, with the tertiles of the postprandial fold-change of endotoxin lipopolysaccharide (LPS) plasma levels using the chi-square test. An inverse relationship was observed between the response prediction score and the postprandial increase of LPS. Thus, in the group of patients categorized as high-response prediction score, we found a higher percentage of patients with a low postprandial increase in LPS. The intermediate-response prediction score group was characterized by a higher percentage of patients with an intermediate postprandial increase in LPS and the lowresponse prediction score group was composed of a higher percentage of patients with a high postprandial increase in LPS (χ2=0.015) (Figure 3B). Our study has certain limitations, such as the specific geographical area to which the population belongs, and the fact that type 2 diabetes remission was not the primary endpoint of the CORDIOPREV trial, which includes patients with CHD, thus limiting our findings to individuals with this comorbidity. In conclusion, our results suggest that there is a gut microbiota profile associated with type 2 diabetes remission and provide compelling evidence of a potential role of the microbiome as a predictive factor for responders to diet-induced type 2 diabetes remission in newly diagnosed patients with CHD. Further studies are needed to assess gut microbiota–diet interaction in the host metabolism response and to understand the exact impact of this relationship on type 2 diabetes remission. VALS-DELGADO et al. 5of8 FIGURE 2 Random forest predictive models for diet consumption-induced type 2 diabetes remission. (A) Multivariate Receiver operating characteristic (ROC) curve analysis based on the random forest classification models built. Data are mean ±standard deviation (SD) and ROC curves are an average of the predictive models using a cross-validation method. The classic variables were the clinical parameters known to be risk factors for type 2 diabetes development: gender, age, body mass index, high-density lipoprotein (HDL), triglycerides, glycosylated hemoglobin, type A1C (HbA1c), physical activity, dietary consumption of fruit and vegetables, use of antihypertensive medication, and family history of diabetes; oral glucose tolerance test (OGTT)-derived indexes: homeostatic model assessment of insulin resistance, disposition index, hepatic insulin resistance index, insulin sensitivity index, muscle insulin sensitivity index, and insulinogenic index. The models were adjusted by the diet consumed (low-fat or Mediterranean diet) and intensity of statin treatment including these variables in all the models. AUC, the area under the ROC curve. (B) Variable importance values of the microbiome model, which included all the genera present in the patients’ gut microbiota. The variable importance values are represented by the mean of the decrease in accuracy of the models when these taxa are removed. A higher mean decrease in accuracy or bar lengths indicates the greater importance of the variable. The top 10 most discriminant genera are highlighted with a square ACKNOWLEDGMENTS 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. We want 6of8 VALS-DELGADO et al. FIGURE 3 Type 2 diabetes remission according to the microbiome score. (A) Probability of type 2 diabetes remission by the dietary intervention (consumption of low-fat or Mediterranean diet) by COX regression according to the microbiome-based response prediction score. The microbiome-based response prediction score was built by adding up the mean decrease accuracy for the 10 most discriminant bacterial taxa, with a positive or negative value, according to the above protective or risk tertiles and disregarding the tertiles with neutral effect (added as 0). The detrimental or beneficial role was determined by observing a higher mean baseline abundance in nonresponders or responders, respectively. In this way, for a detrimental genus: tertile 1 was scored as 1 (protective), tertile 2 as 0 (neutral effect), and tertile 3 as -1 (risk), and the opposite for a beneficial genus: tertile 1 as -1 (risk), tertile 2 as 0 (neutral effect), and tertile 3 as 1 (protective). The data represent the response prediction score values by tertiles (ascending), T1: low-response prediction score (solid line); T2: intermediate-response prediction score (short dash); T3: high-response prediction score (long dash). *The model was adjusted by age, body mass index, gender, diet, high-density lipoprotein (HDL), triglycerides, and intensity of statin treatment. †This model was adjusted by the above variables as well as by smoking, history of myocardial infarction, history of stroke or transient ischemic attack, history of peripheral vascular disease, hypertension, history of coronary artery bypass grafting, history of percutaneous coronary intervention. (B) Relationship between microbiome-based response prediction score and postprandial increase of lipopolysaccharide (LPS) plasma levels. The relationship between the groups defined according to the microbiome-based response prediction score by ascending tertiles and the tertiles of the postprandial fold change of LPS plasma levels was analyzed using chi-square analysis VALS-DELGADO et al. 7of8 to thank the Córdoba branch of the Biobank of the Sistema Sanitario Público de Andalucía (Andalusia, Spain) for providing the human biological samples. We would also like to thank Jose Andrés Morales Martínez for his technical assistance, as well as the EASP (Escuela Andaluza de Salud Publica), Granada, Spain, which performed the randomization process for this study. CONFLICT OF INTEREST The authors declare that there is no conflict of interest. ETHICS APPROVAL AND CONSENT TO PARTICIPATE The trial protocol and all amendments were approved by the Reina Sofia University Hospital Ethics Committee, following the Helsinki declaration and good clinical practices. All patients signed an informed consent to participate in the study. AUTHOR CONTRIBUTIONS Cristina Vals-Delgado and Antonio Camargo wrote the draft manuscript. Juan F. Alcala-Diaz and Irene Roncero- Ramos collected data and performed the classification of participants Cristina Vals-Delgado, Irene Roncero-Ramos, Francisco M. Gutierrez-Mariscal, Silvia de la Cruz-Ares performed the experiments. Juan F. Alcala-Diaz, Ana Leon-Acuña, and Juan L. Romero-Cabrera performed the medical revisions of participants and clinical databases. Cristina Vals-Delgado and Helena Molina-Abril performed the statistical analysis. Cristina Vals-Delgado, Antonio Camargo, and Jose Lopez-Miranda interpreted the data and contributed to the discussion. Antonio Camargo, Irene Roncero-Ramos, Ben van Ommen, Justo P. Castaño, Jose M. Ordovas, Pablo Perez-Martinez, Javier Delgado-Lista, and Jose Lopez-Miranda contributed to the writing of the manuscript and revised it critically for important intellectual content. Antonio Camargo and Jose Lopez-Miranda are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. DATA AVAILABILITY STATEMENT The sequences obtained in this study are open available in the NCBI Sequence Read Archive (SRA) repository at https://www.ncbi.nlm.nih.gov/sra/PRJNA612599, reference number PRJNA612599. The clinical datasets from the CORDIOPREV study used during and/or analyzed during the current study are available from the corresponding authors upon reasonable request. Cristina Vals-Delgado1,2,3,4,# Juan F. Alcala-Diaz1,2,3,4,# Irene Roncero-Ramos1,2,3,4 Ana Leon-Acuña1,2,3,4 Helena Molina-Abril5 Francisco M. Gutierrez-Mariscal1,2,3,4 Juan L. Romero-Cabrera1,2,3,4 Silvia de la Cruz-Ares1,2,3,4 Ben van Ommen6 Justo P. Castaño4,7 Jose M. Ordovas8,9 Pablo Perez-Martinez1,2,3,4 Javier Delgado-Lista1,2,3,4 Antonio Camargo1,2,3,4,* Jose Lopez-Miranda1,2,3,4,* 1Lipids and Atherosclerosis Unit, Internal Medicine Unit, Reina Sofia University Hospital, Cordoba, Spain 2Department of Medicine, University of Cordoba, Cordoba, Spain 3Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Cordoba, Spain 4CIBER Fisiopatologia de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Cordoba, Spain 5Department of Applied Mathematics I, University of Seville, Seville, Spain 6Netherlands Institute for Applied Science (TNO), Research Group Microbiology & Systems Biology, Zeist, The Netherlands 7Department of Cell Biology, Physiology and ImmunologyUniversity of Cordoba, Córdoba, Spain 8Nutrition and Genomics Laboratory, J.M.-US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA 9IMDEA Alimentacion, Madrid, Spain. CNIC, Madrid, Spain Correspondence Jose López-Miranda and Antonio Camargo, Department of Medicine, Lipids and Atherosclerosis Unit, GC9 Nutrigenomics, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Av. Menendez Pidal, s/n. 14004 Córdoba, Spain. Email: [email protected]; antonio[email protected] #Cristina Vals-Delgado and Juan F. Alcala-Diaz contributed equally to this work. *Antonio Camargo and Jose Lopez-Miranda contributed equally to this work. Clinical Trials.gov.Identifier: NCT00924937; Date of registration June 19, 2009; https://clinicaltrials.gov/ct2/show/NCT00924937. 8of8 VALS-DELGADO et al. ORCID CristinaVals-Delgado https://orcid.org/0000-0002- 7564-5941 JuanF.Alcala-Diaz https://orcid.org/0000-0002-4572- 3611 IreneRoncero-Ramos https://orcid.org/0000-0002- 3098-7788 AnaLeon-Acuña https://orcid.org/0000-0003-1396-2631 HelenaMolina-Abril https://orcid.org/0000-0001-5595- 132X Francisco M. Gutierrez-Mariscal https://orcid.org/0000- 0003-3353-2188 JuanL.Romero-Cabrera https://orcid.org/0000-0001- 6459-3536 SilviadelaCruz-Ares https://orcid.org/0000-0002-9013- 228X BenvanOmmen https://orcid.org/0000-0002-0124-9428 JustoP. Castaño https://orcid.org/0000-0002-3145-7287 JoseM. Ordovas https://orcid.org/0000-0002-7581-5680 PabloPerez-Martinez https://orcid.org/0000-0001-7716- 8117 JavierDelgado-Lista https://orcid.org/0000-0002-2982- 2716 AntonioCamargo https://orcid.org/0000-0002-0415- 4184 JoseLopez-Miranda https://orcid.org/0000-0002-8844- 0718 REFERENCES 1. Steven S, Hollingsworth KG, Al-Mrabeh A, et al. Very lowcalorie diet and 6 months of weight stability in type 2 diabetes: pathophysiological changes in responders and nonresponders. Diabetes Care. 2016;39:808-815. 2. Kirwan JP, Münzberg H, Berthoud H-R. Mechanisms responsible for metabolic improvements of bariatric surgeries. Diabetes. 2018;67:1043-1044. 3. Batterham RL, Cummings DE. 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