New Energy-sensing Metabolites: Beneficial Effects on Metabolic Health in Obesity Comparing Diary Caloric Restriction vs Intermittent Fasting. A Randomized Cross-over Study
Phase: NA
Conditions: Obesity, Insulin Sensitivity/Resistance, Diabetes Type 2
Sponsor: Institut Investigacio Sanitaria Pere Virgili

 
 
 
 
 
 
 
 
 
TITLE: 
 
 
New energy-sensing metabolites: Beneficial effects on metabolic health in obesity 
comparing diary   caloric restriction vs intermittent fasting. A randomized cross-over 
study 
 
 
 
 
 
 
 
 
 
 
 
Date of the document (approved by Ethical Committee)  
 
January 28th 2021           

2020 
HEALTH RESEARCH PROJECTS 
 
 
  European Union 
 
 
 
 
 
 
 
 
 
 
TITLE: 
New energy-sensing metabolites: Beneficial effects on metabolic health in obesity comparing 
diary caloric restriction vs intermittent fasting. A randomized cross-over study 
 
 
 
 
ABSTRACT (Objectives and Methodology of the Project) 
(Please only use the space provided below) 
 
 
Application No 
PI20/00338 
 
General integrated goal of the coordinated project: 
To elucidate the role of succinate and other metabolites derived from the intestinal microbiota such as Short Chain Fatty Acids 
(SCFAs), as energy sensing metabolites in the context of obesity and type 2 diabetes (T2D). 
Specific objectives of Subproject 1 (SP1): 1a. - To investigate whether intermittent fasting (IF) is better than Continued Daily 
Caloric Restriction (DCR) in terms of metabolic improvement through the study of: 1) the dynamics of gastrointestinal 
hormones and energy sensing metabolites, 2) the intestinal microbiome, 3) variability on succinate and SCFAs, MCFAs and 
Biliary Acid after weight loss; 1b. - To characterize the functional changes of adipose tissue mesenchymal derived stem cells 
(ASCs) in IF and DCR conditions according to the BMI of the donor (ex vivo study); 1c.- To investigate the expression and 
secretory profile of ASCs according calorie restriction conditions. 
Methodology: clinical study: randomized, cross-over design, study participants (n=15) will consume either lifestyle 
recommendations for a healthy Mediterranean diet under a continued caloric restriction diet (DCR) or will undertake an 
intermittent (IF) protocol. Clinical, anthropometrical and functional studies. Metabolomics for gut derived metabolites in 
plasma. Enteroendocrine gastrointestinal dynamics. Metagenomic analysis.  

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HEALTH RESEARCH PROJECT APPLICATION FORM 
BACKGROUND AND STATE-OF-THE-ART 
 
Project's aim, background and state-of-the-art of the scientific and technical knowledge, and national or international 
groups working in the same specific or related lines. 
Please state references in the next item: Relevant references 
Max. 3 pages (15,700 characters) 
Obesity and type 2 diabetes (T2D) are directly responsible for a high percentage of mortality among “non- 
communicable diseases” (https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases), with 
more than 2 million deaths annually worldwide. Their prevalence, despite all efforts to prevent and treat them, continue 
to increase relentlessly, becoming one of the main health and social emergencies for the healthcare systems. There are 
many causes of this epidemic, and better understanding the reasons that lead to them is critical to better and more 
efficiently prevent and treat these diseases. Weight control is a crucial element involved in both diseases (obesity and 
T2D) and despite being the subject of study for decades, its management remains elusive in most interventions. 
 
Many elements are involved in weight control, ranging from (a) alterations of the energy balance with high inter- 
individual variability [1,2] in terms of metabolic flexibility defining “thrifty” and “non-thrifty” phenotypes [3], (b) 
hormone mediators such as FGF21 identified in animal models and suggested in associative studies of obese patients 
[4], (c) changes in adaptive thermogenesis although the impact of brown tissue in humans in energy expenditure is 
limited, (d) genetic factors with an effect on the overall result of energy expenditure, and (e) energy sensors responsible 
for establishing a direct link between energy expenditure and intake [5-8]. In this latter area, new elements are 
emerging as relevant players with a high potential to become useful tools both to better understand the 
pathophysiological process and to delineate useful therapeutic approaches. 
 
Energy sensing metabolites: 
 
There is increasing evidence that there is a direct relationship between energy expenditure and intake, as a key element 
in determining daily energy balance [7,8], through what is known as energy-sensing mechanisms [7]. These mechanisms, 
which are still little known, may include biological mediators such as certain hormones like myokines, adipokines and 
metabolites such as glucose itself, bile acids or free fatty acids with a central action on intake regulatory centers [9]. 
The maladjustment between nutrient availability and cell energy requirements is a key factor contributing to the 
development of obesity and T2D. Dynamic exchanges of intra- and extracellular metabolites are crucial to duly 
integrate and coordinate the biological network in cells, particularly the concentration of nutrients and intermediate 
metabolites [10]. 
 
Succinate may have a new role in energy sensing: 
 
There is wide evidence that succinate is a pleiotropic metabolite that works not only as an energy intermediate but also 
as a signalling metabolite both in cytosol, but also and extracellularly through its cognate receptor SUCNR1 [11]. 
Although it has been long been described as a danger signal, recently, in the context of energy homeostasis, succinate 
has shown to be an antilipolytic factor [12], a potent activator of thermogenesis [13] and a regulator of intestinal 
gluconeogenesis [14, 15]. Recently, we have demonstrated that succinate acts as a new signaling metabolite that 
controls the resolution of inflammation, a physiological circuit that is broken in obesity. Thus, we have observed that 
SUCNR1 deficiency in macrophages induces in vivo inflammation, glucose intolerance and cell metabolic stress [16]. 
High levels of circulating succinate have been mainly described in pathological conditions [17-20], including obesity 
and T2D [17,18]. Therefore, it is not unreasonable to suggest that obesity may be associated with a resistance to 
succinate as occurs with other hormones such as insulin or leptin [21,22], favoring a vicious circle of higher succinate 
levels, greater succinate resistance, which has already been proven in terms of its effects on the resolution of 
 
 
 
Application No.: 
PI20/00338 

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inflammation [16]. However, circulating succinate does not only increase in diseases and data from previous studies 
also describe higher levels of succinate in some physiological conditions, such as enduring exercise [23]. 
Therefore, despite all the available information, the physiological role of succinate in the energy balance and in the 
pathophysiology of obesity and related diseases needs to be clarified. In fact, the main source of succinate circulating 
levels is so far unknown, but there is strong evidence pointing to the intestine as one of the relevant contributors to 
blood levels, through differences in the composition of the microbiota [18]. 
 
Circulating succinate as a biomarker of metabolic health: 
 
This metabolite is an excellent marker of cell state as it occupies a key position in the metabolism, being the only 
mediator between the tri-carboxylic acid cycle and the mitochondrial respiration chain through complex I. Succinate is 
a good marker of cell damage and circulating levels have been used as predictors of poor prognosis in critically ill 
patients, and as a marker of ischemia and inflammation. When mitochondrial dysfunction occurs, as has been proved 
in metabolically active tissues in obesity and T2D, mitochondrial succinate levels increase, and subsequently 
extracellular levels could also increase [24, 25]. Thus, in a pathological context circulating succinate might be sourced 
from both damaged tissues and gut microbiota. 
Our results [26] point to succinate as a metabolite capable of representing at the same time various components of the 
metabolic state, offering a broader vision of energy homeostasis, contained in a single, easily measurable marker. 
 
Succinate, a metabolite regulated by dietary intake: 
 
Metabolomic analysis of plasma after a meal shows an increase in succinate, pyruvate, and lactate, indicating a 
postprandial shift in the body's energy source from β-oxidation to glycolysis [27]. Interestingly and similarly to 
what happens with some gastrointestinal hormones, we have seen that this metabolite presents a response after food 
intake and that this response is dependent on both the type of nutrient and the route of administration, indicating 
that it has a clear nutritional regulation. Thus, we have observed that there is a peak in circulating levels 60' after a 
standard meal test, which is reproduced after administering glucose orally, unlike what happens when it is 
administered intravenously, (preliminary data submitted in Diabetes Care 2020, annex figure 1). This indicates that the 
higher levels of circulating succinate after food ingestion are not only consequence of glycolysis and intestine play a key 
role. 
 
The physiological significance of this dynamic of succinate related to intake requires further investigation. In this regard, 
the new concept that energy sensing metabolites act as signaling molecules with extracellular functions beyond energy 
obtention is gaining prominence. In fact, it has been postulated for some molecules such as short- chain fatty acids 
(SCFAs) from the intestinal microbiome, which can regulate the energy metabolism in the host by exerting an autocrine-
paracrine signal on adipose tissue with an anti-lipolytic effect [28,29] mediated by the attenuation of the effect of 
hormone-sensitive lipase (HSL), besides other metabolic effects. 
 
Effect of weight loss on succinate levels: 
 
In addition to intake and dysmetabolic situations, circulating levels are also likely to undergo changes after weight loss. 
Severely obese patients, with and without dysglycemia, are able to reverse high circulating levels after weight loss [26]. 
In these patients, decreased circulating levels are paralleled with a restoration of the “physiological” response after 
food ingestion observed in   non-obese subjects (Submitted in Diabetes Care, figure 2). Interestingly, this behavior is 
highly like what we observe with incretin hormones after a meal ingestion. This supports the conceptual hypothesis 
that succinate is a good marker of metabolic health, which can be especially useful as a biomarker in certain 
circumstances as we have already noted in the case of diabetes remission following metabolic surgery [26]. 
 
In summary, among the mechanisms involved in weight control, energy sensing metabolites are opening new paths  to 
help better understand the concept of metabolic flexibility and propose new therapeutic approaches in diseases such 
as obesity and diabetes. In this field, typically intracellular energy metabolites such as succinate are now being 
considered relevant in their extracellular signalling function through their specific receptor in metabolically sensitive 

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 tissues. To date we have sufficient preliminary evidence supporting this function, with data that clearly point to a 
nutritional response and a more-than-likely intervention of the intestinal flora in plasma regulation. 
 
Specific proposal subproject 1: 
Weight loss 
Daily calorie restriction diets (DCR)and more recently, diets that incorporate intermittent fasting (IF), are suitable 
methods that can reduce obesity and its associated comorbidities including T2D [30]. Both (DCR and IF), have shown 
similar efficacy in improving body composition, reducing cardiovascular risk factors, improving glycemic control, insulin 
sensitivity and insulin secretion. The benefits of hypocaloric diets are dependent on weight loss capacity and on the 
degree of fat mass reduction. However, diets involving IF, particularly those adapted to the individual's circadian 
rhythm (eTRF early-time-restricted-feeding), can achieve similar benefits even without weight loss [31,32]. 
 
Metabolic switch 
 
During fasting periods, triglycerides are broken down to fatty acids that are converted by the liver and gut epithelial 
cells in ketone bodies during both b-oxidation to acetyl -coenzyme A (CoA) and/or conversion of ketogenic amino acids, 
and are released into the bloodstream to provide metabolic fuel to various organs [33]. Ketone bodies begin their 
increase in plasma within 8 to 12 hours after the onset of fasting, reaching levels as high as 2 to 5 mM by 24 hours [34]. 
Ketone bodies are not just fuel used during periods of fasting but also act as signaling molecules with relevant cellular 
and organ effects. Ketone bodies regulate the expression and activity of many proteins and molecules. These include 
peroxisome proliferator -activated receptor γ coactivator 1α (PGC-1α), nicotinamide adenine dinucleotide (NAD+), 
fibroblast growth factor-21, sirtuins, poly-adenosine diphosphate [ADP] -ribose) polymerase 1 (PARP1) and ADP ribosyl 
cyclase (CD38) [35-39], among others. All of these changes drive the switch on systemic metabolism. 
 
Health benefits of intermittent fasting 
 
As commented above, many studies have indicated that several of the benefits of IF are dissociated from its effects 
on weight loss. In the recent work by Sutton EF et al. in pre-diabetic patients, all classic metabolic parameters except 
triglyceride levels improved during intermittent fasting, without weight loss [40]. Most organ systems respond to IF to 
enable the organism to overcome the challenge and restore homeostasis. Intermittent fasting induces an adaptive 
stress response leading to an increase in cellular DNA repair mechanisms, protein quality control, antioxidant defenses, 
mitochondrial biogenesis and autophagy, and inflammation [41]. Although we do not fully understand the precise 
mechanisms, the beneficial effects of IF have been attributed to a metabolic switching and an improvement in the 
cellular response to a persisting stress. Notably, although it is widely thought that changes in the microbiome might 
explain some of the beneficial effects of IF, nowadays no in-depth analysis have been done to address the effects on 
gastrointestinal hormones, energy sensors and the microbiome. Studies in animal models demonstrate an 
improvement in systemic inflammatory parameters and in adipose tissue with improved thermogenic [42] response 
and the restoration of the dynamics in intestinal flora improving metabolic [43] health, when subjected to IF. We know 
today that commensal bacteria and their metabolites have the potential to exert pro- or anti-inflammatory effects 
regulating the immune response in the intestinal wall [44]. Finally, these changes may have systemic effects, as already 
described in some autoimmune and neurological [45] diseases. Continued low-calorie dieting has clear systemic anti-
inflammatory effects [46], although changes in the microbiome are less clear. In our series of patients put on continued 
low-calorie dieting for 6 months with effective weight loss, an improvement is noted in all metabolic parameters but 
with great interindividual variability in the microbiome between before and after weight loss (preliminary data 
attached, figure 3). Changes during IF have only been explored in animal models with an increase in the relative 
abundance of bacteria associated with the synthesis and degradation of ketone bodies and metabolic pathways 
involved in the glutathione metabolism [47], which could favour antioxidant pathways that would eventually influence 
the host. 

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We think that the recovery of intestinal microbiome following an IF diet in obese patients, could favor an anti- 
inflammatory metabolic profile in the microbiome that will eventually be transmitted to the host. The 
recovery in the response of the gastrointestinal hormones and in metabolic sensors such as succinate or 
SCFAs could be behind the differences between the two types of calorie restriction. 

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BACKGROUND AND STATE-OF-THE-ART. RELEVANT REFERENCES 
 
Please list references of the quotes included in the previous item: background and state-of-the-art. 
(Max. 1 page) 
[1]. Schlogl M, et al. Diabetes 2015;64:3680-3689 
[2]. Rynders CA, et al. Obesity (Silver Spring) 2017;25:873-880 
[3]. Reinhardt M, et al. Diabetes 2015;64:2859-2867 
[4]. Laeger T, et al. J Clin Invest 2014;124:3913-3922 
[5]. MarlattKL, et al. Curr Obes Rep 2017;6:389-396. 
[6]. Kleinendorst L, et al. Exp Suppl 2019;111:419-441 
[7]. Piaggi P, et al. J Endocrinol Invest 2018 Jan;41(1):83-89 
[8]. Hall KD and Guo J. Gastroenterology 2017;152:1718-1727 
[9]. Chinag JYL and Ferrel JM. Annu Rev Nutr 2019 Aug 21;39:175-200 
[10]. Hui S, et al. Nature 2017 Nov 2;551(7678):115-118 
[11]. Gilissen J, et al. Pharmacol Ther 2016 Mar;159:56-65 
[12]. Regard JB,  et al. Cell 2008; 135(3): 561-71 
[13]. Mills EL, et a. Nature 2018 Aug;560(7716):102-106 
[14]. De Vadder, et al. Cell Metab 2016 Jul 12;24(1):151-7 
[15]. Wang K, et al. Cell Repor 2019 Jan 2;26(1):222-235.e5 
[16]. Keiran N, et al. Nat Immunol 2019 May;20(5):581-592 
[17]. van Diepen JA, et al. Diabetologia 2017 Jul;60(7):1304-1313 
[18]. Serena C, et al. ISMEJ 2018 Jun;12(7):1642-1657 
[19]. Sadagopan N,et al. Am J Hyperth 2007 Nov;20(11):1209-15 
[20]. Aguiar CJ, et al. Cell Commun Signal 2014 Dec 24;12:78 
[21]. Thomas DD, et al. J Endocr Soc. 2019 Jul 24;3(9):1727-1747 
[22]. Andreoli MF, et al. J Endocrinol 2019 Jun 1;241(3):R81-R96 
[23]. Hochachka PW, et al. Eur J Appl Physiol Occup Physiol 1976 Sep 23;35(4):235-42 
[24]. Correa PR, et al. J Hepatol 2007;47:262 -269 
[25]. Schrauwen P and Hesselink MK. Diabetologia 2008;51:1694 -1697 
[26]. Ceperuelo-Mallafré V, et al. Diabetes Care 2019 Oct;42(10):1956-1965 
[27]. AahanaShrestha, et al. EurJNutr 2017;56:671 -681 
[28]. Jocken JWE, et al. Front Endocrinol (Lausanne) 2017; 8: 372 
[29]. Kim KN, et al. Nutrients 2019 Oct 18;11(10) 
[30]. Marianna P, et al. Rev Endocr Metab Disord 2019 Dec 6 
[31]. Sutton EF, et al. Cell Metab 2018 Jun 5;27(6):1212-1221.e3 
[32]. Zubrzycki A, et al. J Physiol Pharmacol 2018 Oct;69(5) 
[33]. Puchalska P, et al. Cell Metab 2017 25,262 -284 
[34]. Patel S, et al. Cell Metab 2019; 29(3): 707e8-718e8 
[35]. Newman JC, et al. Rev Nutr 2017; 37: 51-76 
[36]. Fisher FM, et al. Rev Physiol 2016; 78: 223-41 
[37]. Iman GC, et al. Cell Metab 2008; 8: 169-74 
[38]. Imai SI, et al. NPJ Aging Mech Dis 2016; 2:16017 
[39]. Lee HC, et al. Annu Rev Pharmacol Toxicol 2001; 41: 317-45 
[40]. Sutton EF, et al. Cell Metab.2018 Jun 5;27(6):1212-1221.e3 
[41]. Anton SD, et al. Obesity (Silver Spring) 2018; 26: 254-68 
[42]. Kim K-H, et al. Cell Res 2017 Nov;27(11):1309 -26 
[43]. Zarrinpar A, et al. Cell Metab. 2014; 20, 1006 -1017 
[44]. Furusawa Y, et al. Seminars in immunopathology 2015; 37, 17 -25 
[45]. Jangi S, et al. Nature communications 2015; 7, 12015 
[46]. Meydani SN, et al. Aging 2016; 8, 1416 -1431 
[47]. Cignarella F, et al. Cell Metab 2018 Jun 5;27(6):1222-1235.e6 
Application No.: 
PI20/00338 

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HEALTH RESEARCH PROJECT APPLICATION FORM 
HYPOTHESIS AND OBJECTIVES 
(Please only use the space provided below) 
 
HYPOTHESIS 
Summary of evidence: 
 
Energy sensing metabolites are emerging as being crucial in the relationship between energy expenditure and intake. 
They are responsible for the proper integration and coordination of the biological network in cells, particularly the 
concentration of nutrients and intermediate metabolites, key elements in the pathogenesis of obesity and T2D. 
 
 
 
OBJECTIVES 
General integrated goal of the coordinated project: 
To elucidate the role of succinate and other metabolites derived from the intestinal microbiota such as SCFAs, as 
energy sensing metabolites in the context of obesity and T2D. 
 
Specific objectives of Subproject 1 (SP1) 
 
1a. - To investigate whether IF is better than DCR in terms of metabolic improvement through the study of: 
1) the dynamics of gastrointestinal hormones and energy sensing metabolites 
2) the intestinal microbiome 
3) variability on succinate and SCFAs MCFAs and BA after weight loss 
. 
Application No.: 
PI20/00338 

2020 
HEALTH RESEARCH PROJECTS 
 
 
  European Union 
 
 
 
 
HEALTH RESEARCH PROJECT APPLICATION FORM 
METHODOLOGY 
 
Design, study subjects, variables, data collection and analysis, study limitations. 
 
 
Task 1: 
Human study protocol: 
A pilot clinical trial in human study participants will be assayed. Participants for intermittent fasting (IF) will be asked 
to fast for 24 hours on two days of the week (2/5 protocol). 
1A.-Design: 
Utilizing a randomized, cross-over design, study participants (n=15) will consume either lifestyle recommendations 
for a healthy Mediterranean diet under a continued daily caloric restriction diet (DCR) or will undertake an 
intermittent (IF) protocol. Each study period will be 8 weeks - total study period will be 16 weeks + a 4-week washout 
period between dietary exposures. The study participants will be adults who have obesity with a Body Mass Index 
(BMI)25 kg/m2 and 40 kg/m2 and have no contraindications for intermittent fasting (see inclusion and exclusion 
criteria below). 
DCR protocol is detailed in the annex 
IF protocol is detailed in the annex 
Setting of the study: Endocrinology Service of Hospital Universitary Joan XXIII and Clinical Trial Unit of the IISPV. 
A call for participation to the study will be performed at the local media and social networks, according to the 
recommendations of the IISPV Ethics Committee (these calls have been already done for previous studies by our group 
with a good acceptance). 
Inclusion criteria: a) White men and women between 18 and 65 years of age; b) BMI range between 25 and 40 kg/ 
m2; c) Absence of underlying pathology in medical and physical examination, except for those related to excess weight; 
d) Signature of the informed consent for participation in the study. 
Exclusion criteria: a) Serious systemic disease not related to obesity, such as cancer, kidney, or severe liver disease. 
b) Systemic diseases with intrinsic inflammatory activity (autoimmune diseases such as rheumatoid arthritis and 
asthma); c) Pregnancy and lactation; d) Vegetarians or subjects subjected to an irregular diet; e) Patients with 
severe eating disorders; f) Patients with clinical symptoms and signs of infection in the previous month; g) Patients 
with chronic anti-inflammatory steroid treatments and/or nonsteroidal anti-inflammatory drugs; h) Recent antibiotic 
treatment; i) Psychiatric history; j) Uncontrolled alcoholism or drug abuse. 
Sample size: 
See annex 
1A.a- Procedure: 
For the IF protocol: 
Subjects will meet a registered dietitian at the beginning of the study and every two weeks to learn how to follow the 
IF regimen at home. During each counseling session, the dietitian will work with the subject to develop individualized 
fast day meal plans. These plans will include menus, portion sizes and food lists that can be consistent with their food 
preferences and prescribed calorie levels for the fast day. During these sessions, subjects will also be instructed as to 
how to make healthy food choices on the ad libitum food intake days. 
For the DCR protocol: 
Subjects will meet a registered dietitian at the beginning of the study and every two weeks to learn how to prepare 
and follow an adequate caloric restriction diet. During each counseling session, the dietitian will work with the subject 
to develop individualized meal plans. These plans will include menus, portion sizes, and food lists that are consistent 
with their food preferences and prescribed calorie levels for the rest of the week. 
1A.b-Adherence to the IF diet: 
Subjects will be asked to report any extra food item consumed on the fast day that did not comply with their 
prescribed plan by using the extra food record (efr). The efr will be reviewed by the study dietitian at each visit. If 
the efr indicates that the subject ate an extra food item on a fast day, that day will be labelled as “not adherent”. If  
 
Application No.: 
PI20/00338 

2020 
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the efr reveals that the subject did not eat any extra food item, that day will be labelled as “adherent”. Adherence 
data will be assessed each week as 1) total adherence (number of days adherent with diet) and 2) percentage 
adherence calculated by applying the following formula: 
% Adherence = no. fast days adherent/ no. fast days in week x 100. 
1A.c-Clinical/anthropometrical and analytical variables: 
Routine clinical and analytical evaluation: (blood count, routine biochemistry, family and personal history, 
anthropometric measurements (weight, height, waist and hip circumference), frequency of food (previous week) 
questionnaires, and chronic treatments if any. The evaluation of physical activity and quality of life will be recorded 
according to validated questionnaires. 
1B.-Analytical and functional test variables: 
The degree of insulin resistance will be calculated according to the formula: HOMA-IR: [glucose (mmol / L) x insulin 
(mU / L)] / 22.5. Serum and plasma aliquots will be collected before and after each period of dietary intervention 
(for analytical determinations), and also stool samples for the study of microbiota. 
1B.a-Metabolites and circulating factors: 
Circulating succinate levels will be determined with EnzyChromTM Succinate Assay Kit (BioAssay Systems, USA). Serum 
short chain fatty acids (SCFAs) including acetic (C2), propionic (C3), butyric (C4) and formic acids will be analyzed by 
chromatography-mass spectrometry according to the methodology published by Lotti C et al [5]; Medium chain fatty 
acids (MCFAs) will also be quantified by chromatography in a separate run. Serum primary and secondary biliary acids 
(BA) will be quantified by the liquid chromatography -tandem mass spectrometry (LC/MS/ MS) method on the 
Ciberdem Metabolomics Platform http://www. iispv.cat/support_plataform/metabolomica. html. 
1B.b-Meal Test and study of gastrointestinal hormones: 
Before and after each restriction diet (DCR and IF diet) at the beginning ant at the end of each period (4 test). 200ml 
administration of Isosource Energy (Novartis, Switzerland) containing 398kcal (50% carbohydrates, 15% proteins and 
35% lipids). Extraction at 0, 15, 30, 60 and 120 minutes.Determination of Plasma concentrations of glucose, insulin, 
GLP-1 andusing the Elisa kit (EZGLP1T-36K EMD Millipore, ThermoFisher) and succinate (EnzyChromTM Succinate 
Assay Kit (BioAssay Systems, USA) 
 
1C-Microbial composition/metagenomic analysis: 
Fresh stools will be collected before and after dietary intervention, which will be immediately frozen and stored at 
-80ºC until they are processed. Three days before sample collection, all patients will be placed on the same diet. They 
will be asked to complete a questionnaire. The microbial composition will be processed by Illumina Sequencing [6-9]. 
Briefly, we will obtain the microbial genomic DNA and the total RNA extraction; the sequencing of 16S rDNA and 16S 
rRNA; metagenomic sequencing; purification, amplification, and sequencing of mRNA; metagenomic and 
metatranscriptomics analysis [10]; protein extraction, separations and identification and data processing. All the 
sequences will be deposited in "the European Bioinformatics Institute database", with an access number. 
Metagenomic analysis will be done in collaboration with the team of Andres Moya from the Institute of Integrative 
Systems Biology at the Universitat de Valencia, with whom we actively collaborate. 
 
Statistical analysis, Study limitations and contingency plan and References: 
See annex 

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HEALTH RESEARCH PROJECT APPLICATION FORM 
STRATEGIC FRAMEWORK 
(Please only use the space provided below) 
 
1. Project's capacity of approaching the objectives and priorities of the Societal Challenge of Health, Demographic 
Change and Well-being of the Spanish Strategy for Science, Technology and Innovation. 
2. Relevance of the proposal for clinical-translational research. 
 
1. This project is part of the research oriented towards the challenges of society, whose goal is to improve the citizens' 
health encompassing the investigation of the most prevalent diseases. Specifically, the proposal focuses on two 
pathologies that are highly prevalent in the Spanish population: obesity and T2D. Both pathologies are in a phase of 
progressive increase of epidemic characteristics, (Prevalence of diabetes mellitus and impaired glucose regulation in 
Spain: the Di@bet.es Study. Diabetologia. 2012 Jan; 55 (1): 88-93 and Incidence of diabetes mellitus in Spain: results 
of the nation-wide cohort di@bet.es study. Scientific Reports. 2020 in press). Furthermore, the proposed research 
combines clinical aspects with biological process research related to the mechanisms involved in obesity and type 2 
diabetes. 
 
In this sense, the proposal submitted to this call may allow obtaining useful information in the field of Biotechnology 
with transfer capacity to clinical setting. Another relevant aspect outlined in the priorities of the Health challenge, is 
the development of personalized medicine, a relevant challenge that will result in benefits both for the patient, 
essentially, and for the national health system itself, optimizing therapies and individualizing the best treatment 
options in each case. Thus, the identification of predictive patterns of differential behavior of patients with T2D under 
metabolic surgery may help to simplify the choice of the surgical approach, according to a more consistent algorithm 
than the ones used so far. Previous data from our group, published in first line international journals, supports the 
validity of our hypothesis in this sense. Furthermore, the study in non-morbid obese patients helps us to cover a wide 
number of patients who usually ask clinicians for a good, efficacious, and safe dietary regimen to be healthier and feel 
better. The approach submitted will give us new clues about the efficacy of two common dietary protocols analysing 
new players in the weight loss mechanisms.  
Application No.: 
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AVAILABLE RESOURCES 
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HEALTH RESEARCH PROJECT APPLICATION FORM 
ANNEXES 
 
ANNEXES (TEXT) 
Max. 3 pages (15,700 characters) 
 
The DCR protocol will be a daily reduction of 500 - 1000 kcal according to each individual's resting metabolic rate [1]; 
with a nutrient composition: < 30% of calories from fat, 15 - 25% from protein, 45   - 50% from carbohydrates [2]. 
The IF protocol will involve fasting for two days (non-consecutive) out of seven, with the fasting days separated by 
at least one day. A fasting day would entail the consumption of ~ 400-600 kcal at the evening meal (between 12,00 
and 15,00 hours to ensure that each subject was undergoing the same duration of fasting), but no other caloric intake, 
and the participants can consume their desired intake on the other five days. The nutrient composition of the 
prescribed fasting day meal will be approximately: Total Fat: 13 g (60% mono or polyunsaturated fat), Protein 25 g, 
Carbohydrate 60 g and Fiber 10 g. During fasting days, participants may consume water, tea or coffee (may contain 
milk max 10cc or cream, max 5 cc), unsweetened beverages such as iced coffee or tea, or a clear soup of vegetable 
broth. 
 
Sample Size: 
We have considered this trial as a pilot study. In this sense and following the recommendations on the calculation 
of the sample size for pilot studies [3], we have considered that a sample of at least 15 subjects per subproject 
(considering a dropout of 20% during follow up) will be sufficient to obtain valid exploratory data that allow us, in 
future, to carry out larger epidemiological studies [4]. 
 
Statistical analysis: 
Strategy of the main statistical analysis: The data will be shown as percentages (categorical variables), means +/- 
standard deviation (continuous variables with normal distribution) or median with interquartile range (continuous 
variables with 
non-normal distribution). The normality of the variables will be analysed with the Kolmogorov-Smirnoff test. To 
perform the Comparisons of each pre and post intervention group test for paired data will be used. To compare 
variables The McNemar test (paired data) will be used categorically / qualitatively. The continuous variables of normal 
distribution will be analysed with the Student t-test for paired data. For continuous variables that do not follow a 
normal distribution, first we will try to normalize by means of their logarithmic transformation to use parametric tests; 
if not, non-parametric tests for paired data (Wilcoxon T) will be used. The increase in concentrations of GLP-1 and 
succinate in response to dynamic functional tests will be measured by calculating the area under the curve. The 
correlations between the variables will be analysed with the Pearson or Spearman correlation coefficients, when 
appropriate. Multivariate analysis will be used to predict the clinical and/or functional variables that determine the 
improvement of clinical and metabolic variables after each diet protocol. Possible confusing variables or effect 
modifiers (interaction) will be selected based on clinical criteria (data from previous literature) and in function of the 
result of the univariant analysis (p <0.20). All calculations will be performed with the STATA software package (v13.1 
for Mac; StataCorp LP, College Station, Texas) and a value of bilateral p <0.05 will be used as significant. 
 
References of methodology: 
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[2]. Goday A, et al. Nutr Diabetes 2016; 6: e230 
[3]. Hertzog MA, et al. Res Nurs Health 2008; 31:180-91 
[4]. Kimmelman J, et al. PLoS biology 2014;12:e1001863 
[5]. Lotti C, et al. Anal Bioanal Chem 2017 Sep; 409(23):5555-5567 
[6]. Vinaixa M, et al. Plos One 2011;6:e29052 
Application No.: 
PI20/00338 

[7]. Perez-Cobas AE, et al. Gut 2013; 62: 1591-601 
[8]. Cardona S, et al. BMC Microbiol 2012; 12:158 
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