The prevalence of obesity continues to rise, predicted to become the biggest epidemic in history. According to the World Health Organization, around 2 billion adults are overweight; 50 million have obesity. Most of the world’s population lives in countries where obesity and overweight kill more people than underweight (1). Obesity is characterized by an excess of body fat, which can be measured by body composition analysis. Nevertheless, clinical obesity is classified based on the body mass index (BMI), expressed as the ratio of body weight in kilograms divided by the height in square meters (2, 3). Large population studies described the relationship between a higher BMI and increased mortality/morbidity risk (2, 4, 5). The BMI has been used to stratify patients into risk categories and to monitor changes in adiposity since it is an easy, affordable, and quick tool for clinical use, despite its limitations (6, 7).

Different obesity phenotypes are associated with additional cardiovascular risk (8, 9). Some of the phenotypes described in the literature are Metabolically Healthy Obesity, in which a high BMI is associated with an apparently healthy metabolic profile, lower visceral adipose tissue, high values of lean mass, and high cardiorespiratory fitness. This phenotype may transition to metabolically unhealthy obesity and is associated with an increased risk of cardiovascular diseases. Another phenotype is described as Metabolic Obesity with Normal Weight, in which individuals, despite having normal weight, present high values of visceral adipose tissue, lower insulin sensitivity, hyperinsulinemia, dyslipidemia, and increased plasma levels of pro-inflammatory cytokines. Normal weight obesity syndrome presents values of body fat mass above 30% with BMI values within the healthy range. Lastly, there is the Sarcopenic Obesity phenotype, characterized by low skeletal mass and increased fat mass percentage (10). Considering these different phenotypes, the BMI classification is insufficient to adequately stratify patients with obesity regarding types of adiposity, cardiovascular risk or adequacy of therapeutic interventions. In addition to the BMI, clinical practice guidelines suggest associating the measurement of the waist circumference as a reference for abdominal fat (11, 12), however, this measure does not differentiate subcutaneous from visceral fat deposition.

The knowledge regarding the etiology of obesity is still evolving; however, some gaps remain, since the translation of this knowledge into the treatment and management of this disease is yet to be successful in large-scale clinical programs (13). Obesity is a complex condition, therefore not to be considered a homogenous state, considering the noticeable heterogeneity observed among people that meet the current medical diagnostic criteria for obesity (BMI ≥ 30 kg/m²).

Obesity classes I and II encompass different health risks associated with factors that include body fat distribution, overall nutritional quality, physical activity levels, and cardiorespiratory fitness (14). Some people, although they have obesity, eat a nutritionally balanced diet and are physically active (15). Other individuals only show moderate obesity, however, when accompanied by visceral obesity, they can exhibit features of metabolic syndrome (16). These remarks underline the idea that BMI alone might not be sufficient to assess health risks, characterize, and diagnose obesity. Piché and colleagues (2) recently proposed that the singular term “obesity” does not properly define the diverse types of obesities, regarding adipose tissue type and function, body fat distribution, and patient lifestyle habits. These authors suggest that the concept of different “obesities” is more adequate to the clinical reality and to the distinct treatment challenges associated with different categories of obesity.

Current evidence indicates that there is a reciprocal interaction between metabolism and the circadian system. Primarily, a comprehensive neural network connects the suprachiasmatic nucleus (SCN) of the hypothalamus to the energetic centers implicated in, for example, food intake, sleep/wakefulness and energy expenditure (17). The effect of the circadian system on metabolism, mediated by the SCN and peripheral clocks, is reflected in the circadian fluctuations exhibited by several metabolically significant hormones (17, 18). Changes in hormonal rhythms might lead to internal malfunction and are related to metabolic dysfunctions that predispose the development of metabolic diseases such as obesity (18–22). While the SCN is mainly synchronized by light/dark cycles, peripheral clocks are entrained by feeding/fasting cycles (23, 24). Besides meal timing, nutrients can also induce phase shifts in the peripheral circadian clock. Hirao and colleagues found that a combination of carbohydrates and protein is necessary to change the mouse liver clocks (25). As reviewed by Oike and colleagues (26), nutrients such as glucose and amino acids quickly alter the expression of clock genes, particularly Per2 and Rev-erbα (27–30). Skipping breakfast or having a lower food intake in the first meal of the day alongside with high-caloric dinners impairs peripheral clock gene expression and results in higher daily glucose excursions in animals (31–33). A study conducted by Jakubowicz and colleagues on individuals with type 2 diabetes showed that calories consumed at breakfast or dinner affected the daily rhythm of postprandial glycaemic excursion and insulin levels (34). These authors also reported that skipping breakfast negatively affected clock and clock-controlled gene expression (35).

Despite the contribution in elucidating important mechanisms in chronobiology, the majority of the studies do not account for differences of circadian rhythmicity among individuals. Distinct chronotypes reflect different timings of circadian behavior, physiology, and even patterns of clock genes expression. Besides regulating sleep, the chronotype can have a major influence on appetite. Thus, the characterization of individual chronotypes (expression of circadian rhythmicity), as part of a patient phenotype, is gaining increasing importance for the development of novel and pinpointed nutritional interventions. A randomized clinical trial showed that a chronotype-adjusted diet was more effective than the traditional hypocaloric diet in the improvement of anthropometrical parameters in patients with obesity (36). Individual circadian variations may be valuable, and a critical first step, for providing information to design therapeutic strategies and to help “fine-tune” chronobiology interventions, including chrononutrition approaches (37). Research in this field is currently hindered by the fact that only a few studies assessed simultaneously the chronotype and nutrient intake and timing during the interventions. Furthermore, there is no standard method to objectively define chronotypes. Usually, this is determined by a combination of questionnaires, evaluation of clock genes expression and/or protein levels, determination of non-invasive clock outputs, such as core body temperature, and heterogeneous pools of biomarkers that act as readouts of the intrinsic circadian rhythms (38–41). Although there is an increasing interest in chronotype, as a novel dimension of nutrition and health, there is still a lack of consistent evidence regarding its relation to obesity and metabolic disturbances (42, 43). Researchers in this field highlight the need for more research and proper characterization of clock gene expression and circadian biomarkers in obesity. Furthermore, most studies have a high risk of bias and do not evaluate parameters related to metabolic dysfunction such as adiponectin, leptin, and insulin (42, 44).

The relationship between circadian rhythm disruption and obesity is complex and not fully understood. More studies are necessary to assess the role of meal schedules and dietary composition in the regulation and/or deregulation of peripheral clocks, especially including the biological differences in the circadian system through the assessment of individual chronotypes.

The present study aims to conduct an observational study on chronotype, and its relation to phenotype and dietary intake in patients with obesity, compared with healthy individuals. This study will contribute with scientific evidence to help future interventions based on chronobiology, specially chrononutrition.

In recent decades, there have been breakthroughs in obesity care and treatment. However, despite these advances, there is a major remaining challenge: improving the understanding of the heterogeneity of obesity. This knowledge is essential to determine the best approaches to screen, assess risk, and select personalized and individualized treatments. The findings of this study will provide a better understanding of obesity phenotypes in regard to their chronotype, dietary intake (including meal time and nutrient intake), and circadian and metabolic biomarkers.

The circadian system regulates metabolism through the secretion of hormones, such as cortisol and melatonin (63). Alterations in melatonin and cortisol rhythms, or their blunted secretion, are important attributes of circadian misalignment (64, 65). Melatonin is a major regulator of circadian rhythm, namely sleep–wake cycles and hormone secretion (66). The levels of this hormone typically rise at night and fall during the day, in synchrony with the body’s circadian rhythm. Studies have shown that melatonin can help regulate glucose homeostasis by improving insulin sensitivity and reducing insulin resistance (67, 68). In addition, melatonin has been shown to improve lipid metabolism by reducing the levels of triglycerides and cholesterol in the blood. It may also play a role in regulating appetite and body weight (68–70). Disruptions to the body’s circadian rhythm can lead to alterations in melatonin secretion patterns which can have negative effects on metabolism and overall health, as disrupted melatonin secretion has been associated with an increased risk of metabolic disorders such as type 2 diabetes and obesity. Cortisol is tightly regulated by the circadian system, showing an anti-phasic pattern of secretion compared to melatonin (71). Disturbances in the normal circadian patterns can promote alterations in cortisol secretion patterns, which might have a deleterious effect on metabolism. Cortisol is a catabolic hormone with several essential functions in the body, including the mobilization of glucose from glycogen, amino acids and fats in response to other hormonal signals (71). Prolonged cortisol release can promote insulin resistance and obesity can promote abnormal cortisol levels (66).

Circadian oscillations of gene expression also regulate whole-body metabolism (72). For example, Gaspar et al. showed that patients with obstructive sleep apnea, which is strongly related to obesity (73, 74), have different patterns of clock gene expression compared with healthy individuals (51), further highlighting the importance of assessing the circadian features in disease.

The importance of determining the circadian rhythm has been extensively reported; although it started more focused on pharmacological studies (40), this relevance has extended to nutrition. From this, the concept of chrononutrition emerged, mostly because peripheral circadian clocks are mainly synchronized by food intake (24, 31). Furthermore, the circadian clock also regulates nutrient challenges, through transcription factors that modulate the expression of genes with regulatory roles in nutrient pathways (37, 75) Therefore, there is the potential to improve metabolic homeostasis by coordinating behavioral changes with the body’s daily rhythm. The identification of genetic patterns and signatures in response to dietary intake may guide the advance of new therapeutic approaches.

Designing dietary interventions that account for the quality, quantity and timing of food intake could be an approach to modulate the circadian rhythm and clock-controlled genes and optimize the synchronization of central and peripheral clocks, with a positive impact on overall metabolism. Therefore, the characterization of circadian and dietary makers is a critical first step to underpin therapeutic strategies and to support the adjustment of chrononutritional approaches for the management of chronic diseases such as obesity. In the present study, caloric intake, macronutrients (proteins, carbohydrates, and lipids), and meal times (time of the first meal of the day, time of last meal of the day and overnight fasting period) will be evaluated, in order to assess the impact of dietary intake (timing and nutrients) on circadian rhythm markers.

The research protocol has the advantage of simultaneously evaluating dietary intake, circadian rhythm biomarkers, and metabolic biomarkers in blood samples, of both individuals with obesity and healthy individuals. Taking into account circadian rhythm dysfunction in obesity, a better understatement of the circadian clock might provide important clues regarding overall metabolism and disease progression, helping to design more fitting treatment approaches. Based on previous studies, we expect to find differences in the expression of circadian clock genes between the two groups, particularly, in the expression of BMAL1, PER2, CRY2, and REV-ERBα (51, 76–79).

To minimize the number of visits by participants to the study center, and to attempt to increase study adherence, the food diary, sleep diary, and physical exercise registry will be filled out through the mobile application NutriClock. Previous studies have shown increased satisfaction and preference for mobile applications for reporting dietary intake, compared with conventional methods (80), which is a particularly important observation supporting the methodology of our study. Also, as the data is collected through the NutriClock mobile application, it is immediately available to the researchers, preventing the participant from having to go to the study center to deliver the forms in paper format. Furthermore, if data is not being inputted into the mobile application, the participants receive notifications with reminders of the importance of the diaries. In the backoffice, the researcher is also able to detect immediately, through a color system, if the participants are not collecting data and can contact them, if necessary. The records inserted by participants are evaluated on a daily basis, and the system assigns one of four color levels to each component completed, such as the food or sleep diary. This evaluation is based on two criteria: the time elapsed since the start of registration and the number of registrations made. If the number of registrations exceeds 75% of the total days, the component is marked with a green tab. If the number is between 50% and 75%, the tab is yellow, while an orange tab indicates a number between 25% and 50%. A red tab is assigned if the number of registrations is less than 25%.

Some limitations of this study should be considered, including a potential low acceptance of the protocol, causing difficulties in recruitment, and a high drop-out rate. Therefore, a percentage of 20% of drop-out was taken into consideration when calculating the sample size. This anticipated difficulty in recruitment and acceptance of the study protocol is mostly related to the multiple sample collections, and the requirement of filling out a 3-day food diary, which could potentially be a burden for some participants. Initially, recruitment will be promoted in the Leiria (Portugal) area but may be extended to other areas of the central region of Portugal if necessary. Recruiting participants only in the Leiria area, although more convenient for logistical reasons, could also be considered a limitation, given that all study participants will represent the same geographical area and, therefore, we must be cautious in interpreting and extrapolating data. In addition, to mitigate drop-out, participants who complete the observational study will have the opportunity to have a nutrition education session with a registered nutritionist. Also, participants will have permanent access to all the data that they will introduce in the mobile application. Another potential limitation is related to social bias in the self-reporting questionnaires, especially, in the food diaries, which could lead to an underestimation of food intake (81, 82). The assessment of clock gene expression only at two-time points during the day also represents a pitfall of the study. The circadian rhythm integrity has been previously evaluated by looking at clock gene expression at four-time points in hospitalized patients, allowing a more robust interpretation of the results (50). This protocol is based on outpatient recruitment and collection of blood samples at two different time points during the day for outpatient volunteers carries a significant risk of increasing attrition of the study. To mitigate the risk of misinterpreting the data on circadian rhythm based on two-time point serum biomarkers and clock gene assessment, the Pittsburgh Sleep Quality Index and the Munich Chronotype Questionnaire were added to the protocol to broaden the information obtained by serological analysis.

In conclusion, this study will characterize and compare circadian rhythm biomarkers of healthy individuals and people with obesity, associating this information with dietary intake. The results of this study might provide new evidence to develop more targeted and personalized interventions for nutritionists, endocrinologists, and other healthcare professionals, for the treatment and prevention of obesity and its associated complications.

This is a research project that falls under the scope of Portuguese Law no. 21/2014 April 16 that covers all research with humans, including observational studies. This study will be carried out on adults over the age of 18 years. Adults who are unable to provide their informed consent will not be included in the study.

The research project was approved by the Ethics Committee of the Centre’s Regional Health Administration (ARS Centro) on 29th January 2021 (Proc no. 67-2020). The research will be carried out in strict compliance with the ethical principles of the Helsinki Declaration and obeying international, European, and national legislation, including the General Data Protection Regulation (Regulation (EU) 2016/679). The principles of autonomy, nonmaleficence and beneficence, fairness, and clear informed consent will be strictly respected. The researcher will assure that the informed consent document will be available to the volunteers before starting any procedure of the study. The recruitment will be gender-independent to minimize gender bias.

Biological samples will be protected by the principle of non-commercialization, commercial use, patenting, or any financial gain, and the principle of the non-susceptibility of patenting human genetic heritage. The samples will be used exclusively to support this research initiative and will be destroyed immediately after use.

The participants will not be identified by any of their data, to ensure the confidentiality and anonymity of the collected data, a unique ID number will be assigned to each participant. Only the Principal Investigator will have access to the list with the match between the participant’s ID and their data. This list will be in electronic format, with restricted access to its content and secured with a password, preventing confidential information from being accessed, printed, forwarded, or copied by an unauthorized person(s). Personal data collected in the context of this study, and shared between team members, will be kept in secure files, with restricted access, be they electronic or physical, and kept after the conclusion of this study, for the time defined by law.

Participants have the right to withdraw from the study at any time, without the need to provide a reason, if they wish not to do so. Participants will be assured that if they wish to withdraw from the study, their medical care will not be affected. Except if the participant indicates otherwise, any data collected up to the point of withdrawing consent will be included in the final analysis.

The results will be disseminated in group data, and individual results that might identify the participant will never be shown to the public. The results and deliverables of this research project will be available in academic health-related publications and through presentations in international scientific meetings.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by the Ethics Committee of the Centre’s Regional Health Administration. The patients/participants provided their written informed consent to participate in this study.

ML, RB, SC-S, and MG conceived and designed the study, contributed to the protocol development, and contributed to the manuscript by revising it critically for intellectual content. ML wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by Portuguese national funds provided by FCT—Fundação para a Ciência e Tecnologia, I.P., (UI/05704/2020), by the Portuguese Society of Diabetology through the award of the 2020 Emílio Peres Scholarship, and by the Faculty of Nutrition and Food Sciences of the University of Porto and LabbITR (LA/P/0064/2020). ML was supported by a PhD Scholarship from Fundação para a Ciência e Tecnologia (2021.07673.BD). MG was funded by national funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., under the Scientific Employment Stimulus—Institutional Call—CEECINST/00051/2018.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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