CLOCK 3111 T/C SNP Interacts with evening preference, appetite hormones, late eating and sleep reduction for obesity and food intake in obese Iranian adults

doi:10.21203/rs.3.rs-2958003/v1

Background

Previous studies have shown that the Circadian locomotor output cycles protein kaput (CLOCK) gene (rs1801260) variant may be associated with obesity risk. Moreover, lifestyle and biochemical parameters have been shown to elicit favorable effects on the obesity risk potentially. Therefore, this study seeks to investigate the effect of lifestyle, biochemical parameters, and CLOCK interaction on food intake and risk of obesity.

Methods

This cross-sectional study comprised 403 overweight and/or obese subjects aged 20–50 from Iran. The CLOCK rs1801260 data was measured by the PCR-RFLP method. Dietary intake, food timing, sleep duration, appetite, and chronotype were assessed by using validated questionnaires. Ghrelin and Glucagon-like peptide-1 (GLP-1) were measured by radioimmunoassay in plasma samples. Participants were also divided into three groups based on rs1801260 genotype. Univariate linear regression models were used to assess the interaction between CLOCK and study parameters on body weight, and logistic regression models were used for interaction terms between CLOCK and study parameters on food intakes.

Results

After controlling confounding factors, our findings showed significant interactions between the C-allele carrier group with chronotype (Pinteraction = 0.048), appetite (Pinteraction = 0.035), lunch time (Pinteraction = 0.016), dinner time (Pinteraction = 0.047), GLP-1 (Pinteraction = 0.035), and ghrelin (Pinteraction = 0.022) on obesity. Also, there was a significant interaction between evening type, high appetite, short sleep and late lunch with C-allele on food intake.

Conclusion

The results of the present study indicate that differences in sleep, appetite hormones, eating behaviors and chronotype influence the risk of obesity differently by CLOCK genotype. These results highlight that diet, gene variants, lifestyle factors, and their interaction should be considered in obesity risk assessment.

CLOCK gene

obesity

circadian rhythm

food timing

sleep

the appetite hormone

Obesity, a chronic metabolic disorder, is spreading in adults and children epidemically and is associated with many diseases, including cardiovascular disease, diabetes, hypertension, cancers, apnea, and sleep-disordered breathing [1]. In 2016, the World Health Organization (WHO) announced that more than 1.9 billion adults were overweight, and about 650 million were obese [2, 3]. The current prevalence of obesity is related to diet and physical activity changes. Recent research, however, has shown that changes in our daily behavioral patterns, such as morning-evening life, eating, and sleeping times, can play a significant role in the prevalence of obesity [4–6].

In industrialized countries, the amount of sleep per day has decreased by 1.5 hours in the last century, coinciding with a significant increase in obesity [7]. Although these observed correlations do not support a causal relationship, additional evidence based on data from night shift staff and people with sleep disabilities suggests that sleep disorders and consequent changes in circadian rhythms may play a significant role in the etiology of obesity [8, 9]. Evidence indicates that sleep affects ghrelin [10]. Experimental studies of sleep restriction have also reported adverse effects on the levels of appetite-related hormones such as ghrelin, leptin, starvation, total energy intake, and weight [11]. In most living organisms, biological processes are organized into a 24-hour cycle. These regular daily physiological adjustments, known as circadian clocks, are repeated every 24 hours to coordinate vital processes. The clock works by expressing several clock genes that may enable or disable the clock to display an overall 24-hour pattern. In the human biological clock, the CLOCK gene, which is an essential element in the positive regulatory part, participates in metabolic regulation [11, 12]. Thus, CLOCK disorder may affect different pathways of metabolic homeostasis. Previous studies have shown that carriers of the C allele of the single nucleotide polymorphism CLOCK 3111T / C (rs1801260) are of the evening type compared to the TT carriers [11]. Also, sleep restriction increased plasma ghrelin levels, altered eating behaviours, and increased energy and fat intake in C allele carriers may explain part of the increase in body mass index (BMI) and decrease in weight loss in dietary C allele carriers [4, 13].

Although dietary intake is one of the influencing factors to obesity, studies on the relationship between dietary intake and the CLOCK 3111T / C gene and behavioural factors are limited. Previous studies have investigated the relationship between behavioral and biochemical factors with the CLOCK gene, but have not investigated the interaction of these factors with the CLOCK polymorphism on obesity and food intake. Therefore, in the present study, we evaluated the interaction of these factors with CLOCK 3111T / C on obesity and food intake in obese and overweight people for the first time.

Study papulation

For this cross-sectional study, 403 overweight and obese subjects, with a BMI in the range of 25–40 kg/m² and aged 20–50 years, were recruited from the community health center of Zahedan University of Medical Sciences using a convenience sample method. Subjects with the following conditions were not included in our study; pregnancy, lactation, menopause, history of diseases including; diabetes, renal, hepatic diseases, thyroid, polycystic ovary syndrome, cancers, and stroke. Furthermore, participants taking glucose- and lipid-lowering medications, users of drugs affecting body weight, or total calorie intakes not in the range between 800 and 4200 (kcal/day), were excluded. The data were collected from June to October 2019. All participants signed the informed consent form, prior to study commencement, which was based on the approval of the Ethics Committee of the Tehran University of Medical Sciences (TUMS) (NO: IR.TUMS.VCR.REC.1398.260).

General, anthropometric and physical activity assessments

We collected general information, such as age, level of education, marital status, and history of weight loss in past years using standard questionnaires. Also, anthropometric measures including height (m) and weight (kg) were evaluated by using a digital scale (Seca, Germany) with light clothing and no shoes on. WC and hip circumference (HC) were measured according to standard protocols, and waist-to-hip ratio (WHR) was calculated as WC/HC. Finally, we computed BMI by dividing weight (kg) by the square of height (meters) (BMI = weight/height (kg/m 2)). Obesity and overweight were BMI 30–40 kg/m2 and BMI 25-29.9 kg/m2, respectively [14].

The International Physical Activity Questionnaire (IPAQ) short form was used to assess physical activity and was classified as follows: low < 600, moderate (600–3500), and high (> 3500) (MET-h/wk). The reliability and validity of the IPAQ have previously been evaluated in Iranian adolescents [15].

Genotyping

At the beginning of the study, 10 cc blood was collected from each patient. Genomic DNA was extracted from whole blood using the GeneAll, Exgene™Cell SV kit (Gene All, Korea) based on the constructor's protocol. DNA fragment containing a thymine-to-cytosine (T / C) substitution in CLOCK gene was genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) as follows. PCR amplification of the genomic DNA fragment for CLOCK was performed by the forward primer 5′- GGG AAA GTT CCA GCA GTT-3′ and reverse primer 5′- ATC CAG GCA CCT AAA ACA-3′( Copenhagen, Denmark)[16]. The PCR was performed in a total of 20 µl containing 0.6 µl of each primer, 0.8 µl of DNA, 10 µl of Taq DNA Polymerase 2×MasterMix (Ampliqon, Germany). The amplification protocol is considered of an initial denaturation step at 95°C for 5 min, followed by 35 cycles of denaturation at 95°C for 30 s, annealing at 53°C for 30 s, and extension at 72°C for 30 s, and final extension at 72°C for 5 min. Digestion was performed with 10.5 µl of each PCR product, incubated with 0.15 µl of Bsp 12861 (10 U/µl, Fermentas, Germany) and 1.5 µl of 10X buffer sdul (in total 21.15 µl reaction) over-night at 37°C then the reaction was stopped at 0°C for 5 min. The digested product was then subjected to electrophoresis on 2% agarose gel (Boehringer Manheim GmbH, Mannheim, Germany), stained with green viewer (Pars Tous, Iran) and visualized on a Gel Doc-system (U.V.P Company, Cambridge, UK). A 50 base pair (bp) Ladder (Fermentas, Germany) was used to determine the Length of the digested products. The T allele appeared as a 167 bp fragment after electrophoresis, while the C allele was cleaved by the recorded enzyme and appeared as 38 bp and 129 bp fragments.

Ghrelin and GLP-1

Blood samples were taken from all subjects in the morning after 12 to 14 hours of fasting and centrifuged at 4^oC, and the plasma was stored at -80^oC for further analysis. Plasma GLP-1 and ghrelin samples were measured by radioimmunoassay (Linco Research, St. Charles, MO).

Dietary intake and timing of food intake assessment

Dietary intakes and food timing were assessed by expert dietitians using a valid 3-day food record [17]. The participants were trained by expert dietitians in how to complete the 3-day food record Questionnaire. The participants were also asked to record every detail and quantities of their consumer foods and timing of food intake in six meals (breakfast, lunch, dinner, and three snacks) for three days (two weekdays and one weekend) in the questionnaire. Specifically, participants recorded the start time and end time, and duration of individual food intake periods during 2 weekdays and 1 weekend. After recording three days of their diets, they were supposed to visit the laboratory at the end of the week. The questionnaires were delivered and analyzed. Total energy and dietary nutrients were assessed by the Iranian Food Composition Table (FCT) and N4 software.

Chronotype questionnaire

Subjects completed 19-item morning/evening questionnaire (MEQ; score range: 16–86) of Horne and Ostberg [18], during the follow-up period. Based on this score, individuals are categorized was neutral (53–64 of score), morning (above 64 of score) or evening (under 53 of score) types. Morning-evening typology is a way to characterize subjects depending on individual differences in waking/sleeping patterns and the time of the day people report best performance.

Some people are “night owls” and like to stay up late at night and sleep late in the morning (evening types), whereas others are “early birds” and prefer to go to bed early and arise with the break of dawn (morning-types) [19]. The reliability and validity of the MEQ questionnaire were assessed in Iranian population [20].

Sleep duration

The duration of normal sleep was estimated using a questionnaire [4, 11]. The total duration of weekly sleep was calculated as [(min weekdays x 5) + (min weekend days’ x 2)]/7.

Assessments of appetite

Visual Analog Scales (VAS) were filled to a length of 100 mm to assess appetite (satiety, satiety, hunger, and possible food intake). An overall appetite suppression score was calculated based on the four appetite parameters using the following formula (satiety + fullness + [100 – hunger] + [100 – prospective food consumption])/4, with 0 indicating higher appetite/less satiety and 100 indicating lower appetite/more satiety (primary assessment of the self-reported appetite sensations) [21]. Reliability and validity of the VAS questionnaire were assessed in Iranian population [22].

Statistical analysis

Statistical analysis was performed using SPSS software for Windows (version 26) (Chicago, IL, USA). The normality of variable distribution was examined by the Kolmogorov–Smirnov test. A chi-square test was used to assess the Hardy–Weinberg equilibrium. The relation between variables and CLOCK genotypes was analyzed in papulation study. One-way ANOVA and Kruskal-Wallis were used to compare the continuous variables between different genotypes with normal and without normal distribution, respectively.

The difference between the distribution of macronutrients (energy adjusting by residual method) and energy was calculated with regard to age and sex using analysis of covariance (ANCOVA) method according to the three genotypic groups of CLOCK polymorphism after adjustment. The categorical variables were analyzed using chi-square test.

For the genetic analyses, minor C allele carriers (CC + CT) were compared against major T allele homozygotes (TT). To study CLOCK 3111T/C*chronotype interactions, CLOCK 3111T/C*appetite interactions, CLOCK 3111T/C*food timing interaction, CLOCK 3111T/C*sleep duration interaction and CLOCK 3111T/C*appetite hormones for body weight, we used univariate linear regression models adjusted for age and gender when needed (ANCOVA).To investigate the interaction between the CLOCK 3111T/C variants and behavioral and hormonal factors on food intake, logistic regression models were used for interaction terms in addition to the potential confounders. The logistic regression results were presented as odds ratio (OR) and 95% CI. P-value ˂ 0.05 was considered statistically significant.

Study population characteristics

This cross-sectional comparative study was performed on 403 participants classified as overweight or obese. The means and standard deviation (SD) of age, weight, BMI, and WC of individuals were; 36.5 ± 8.7 years, 85.8 ± 10.6 kg, 30.2 ± 3.1 kg/m2, and 99.01 ± 8.8 cm, respectively (data not shown). The alleles frequency for CLOCK (rs1801260) in the study was 37% C and 63 %T. The distribution of the CLOCK rs1801260 genotypes (TT, TC, and CC) was in the Hardy–Weinberg equilibrium (P = 0.76).

Association between population characteristics, biochemical parameters, behavioral parameters, and rs1801260 genotypes

A total of 403 Iranian subjects were categorized based on rs1801260 genotypes, and divided into three groups: TT (n= 158), CT (n= 192) and CC genotype (n= 53) (Table 1). ANOVA was used to assess the difference in anthropometric measurements, biochemical parameters and behavioral parameters across genotypes. The results of the study revealed that the mean BMI and ghrelin were lower in TT genotype and mean breakfast, lunch and dinner time were significantly earlier in TT genotype, compared with individuals in the CT and CC genotypes (P<0.05), which remained significant even after adjustment for BMI, age, sex, energy intake, and physical activity. Moreover, there was a significant difference between groups for GLP-1 (P=0.02), appetite score, morning- evening score and sleep duration (P˂0.001) before and after adjustment for confounding factors (BMI, sex, age, total energy intake, and physical activity) (Table 1).

Dietary intake of study population according to rs1801260 genotypes

The dietary intake of the participants in rs1801260 genotypes is shown in Table 2. The results of the comparison showed that mean energy, carbohydrate and fat were lower in subjects with TT genotype, compared with participants in the CT and CC genotypes (P˂0.05), before and after adjusting for energy intake.

Interaction between CLOCK gene (rs1801260) and behavior and hormonal parameters on obesity

Using univariate linear regression models, the interaction between CLOCK polymorphism (rs1801260) and behavioral and hormonal parameters on the risk of obesity were examined.

We reviewed CLOCK 3111T/C in the context of morning-evening chronotype. When patients were classified into the three different chronotypes (morning, evening and intermediate) with MEQ, a significant interaction was found between CLOCK 3111T/C and the individual chronotype for body weight (p=0.048). Further stratified analyses between morning- and evening-type subjects showed that among carriers of the risk allele C, evening-type subjects were more obese (body weight) than morning-type patients (p=0.03) (Figure 1.A). Differences were maintained after adjusting for age and gender (p=0.04). However, no significant difference was observed between T-carriers in the morning and evening chronotype in terms of obesity parameters (p=0.56, and after adjusting for age and gender p=0.62).

After adjusting for age and sex, a significant interaction was observed between appetite and CLOCK 3111T/C with weight (p = 0.035) in that C-allele carriers with high appetite were significantly more obese than individuals with T-allele carriers (p = 0.03); whereas, there was no significant difference between the T-allele and C-allele carriers who had low appetite (p = 0.28) after adjusting for age and sex (p = 0.38) (Figure 1B). There was also no significant interaction between sleep duration and CLOCK 3111T/C with weight (p = 0.123) (Figure 1C).

This study also investigated the interaction of CLOCK 3111T/C and meal timing with obesity. The results showed that there was no significant interaction between the breakfast time and polymorphism of CLOCK with weight (p = 0.452). Moreover, there was no significant relationship between breakfast timing (before and after 9 am) in C-allele carriers (p = 0.32) with weight after adjustment for age and sex (p = 0.38) and in T-allele carriers (p = 0.21) with weight after adjustment for age and sex (p = 0.26) (Figure 1D). There was a significant interaction between CLOCK 3111T/C and lunch time and obesity (p = 0.016) in those C-allele carriers taking their lunch after 3 pm were significantly more obese than those taking their lunch before 3 pm (p = 0.008) after adjustment for age and sex (p = 0.01) whereas, there was no significant difference between the T-allele carriers who taking their lunch before or after 3 pm (p = 0.12) after adjusting for age and sex (p = 0.22) (Figure 1E). There was a significant interaction between CLOCK 3111T/C and dinner time with obesity (p = 0.047). Obesity (body weight) was more prevalent among C-allele carriers taking dinner after 9 pm than those taking dinner before 9 pm (p = 0.03) after adjustment for age and sex (p = 0.05) while, there was no significant difference between the T-allele carriers who received their dinner before or after 9 pm (p = 0.15) after adjusting for age and sex (p = 0.29) (Figure 1F).

There was a significant interaction between the appetite hormones and CLOCK 3111T/C with body weight. In that, body weight in C-allele carriers with low GLP-1 level was significantly higher than in individuals with T-allele (p = 0.039) after adjustment for age and sex (p = 0.044) (Figure 1G). Moreover, the prevalence of obesity was significantly higher in C-allele carriers with a high ghrelin level than those with T-allele carriers (p = 0.041) after adjustment for age and sex (p = 0.048) (Figure 1H).

Interaction between CLOCK gene (rs1801260) and behavior and hormonal parameters on food intake

Using logistic regression models, interaction terms between CLOCK polymorphism (rs1801260) and behavioral and hormonal parameters on food intake was examined.

The results from the analysis of the interaction between 3111T/C CLOCK and chronotype on food intake showed that participants with the C-allele who stay up late in the night had approximately 3.8 times increased odds of high carbohydrate intake than those T-allele carriers (95%CI: 1.89 to 8.06; P: 0.039). Moreover, C-allele carriers with high appetite had increased risk of high energy intake (OR: 3.06; 95%CI: 1.18 to 7.93; P:0.021) compared with T-allele carriers. Moreover, the odds of fat intake was higher in C-allele carriers with high appetite (OR: 1.31; 95%CI: 1.01 to 5.37; P: 0.041) than T-allele carriers. The interaction between sleep duration and 3111T/C CLOCK on food intake showed that participants with C-allele carriers and short sleep had 73% decreased risk of protein intake than those T-allele carriers (95%CI: 0.1 to 0.71; P: 0.008). Moreover, the odds of fat intake in C-allele carriers was 34% higher than those T-allele carriers (OR: 1.34; 95%CI: 1.03 to 3.89; P: 0.029) (Table 3).

The interaction between food timing and CLOCK 3111T/C on food intake was also investigated. Analyses showed an interaction only between C-allele carriers who ate lunch late (after 3 pm) and the food intake. In that, the odds of energy intake in this group was 98% higher than in T-allele carriers (95%CI: 1.05 to 4.80; P: 0.036). There was no significant interaction between the breakfast and dinner time and polymorphism of CLOCK on food intake (p > 0.05). The interaction of appetite hormone and 3111T/C CLOCK on food intake was not significant (p > 0.05) (Table 3).

To our knowledge, this is the first study to examine the interactions between genetic variants of the CLOCK gene with behavioral and hormonal factors on food intake and risk of obesity. A positive interaction was observed between the evening type, high appetite, late lunch and dinner, decreased GLP-1 and increased ghrelin on obesity in minor alleles carriers (CT + CC) compared to non-carriers. The findings of this study provide further insight into the possible relationship between CLOCK and behavioral factors. Therefore, by modifying nutrition-related behaviors in people who carry the C allele, we can reduce or even eliminate the detrimental effect of genetic type on the prevalence of obesity. The results of our previous study also showed that there is a significant difference between physical activity, BMI, appetite, sleep duration, food timing, level of appetite hormones and energy intake, carbohydrates, and fats in CLOCK genotypic groups [23].

The body's circadian clock genes have already been studied with some factors related to diet and obesity. For example, some CLOCK gene polymorphisms, such as rs3749474, rs4580704, and rs1801260, were associated with variables related to obesity, energy expenditure, and BMI [24]. Monteleon et al. [25] also reported a significant positive relationship between CC genotype and BMI. In addition to the direct links between circadian clock genes and obesity, these genes may be indirectly associated with regulating fat and glucose metabolism in peripheral organs such as adipose tissue and muscle with reduced obesity and metabolic syndrome [26]. A study by Turek et al. [27] showed that mutant mice homozygote for the CLOCK gene have highly attenuated diurnal feeding rhythm, are hyperphagic and obese, and develop a metabolic syndrome associated with hyperleptinemia, hyperlipidemia, hepatic steatosis, hyperglycemia, and hyperinsulinemia.

Studies have shown that different polymorphisms in our circadian machine are related to diet and obesity-related behaviors. Garaulet’s study in humans also showed that various polymorphisms of CLOCK are associated with BMI, energy intake, and obesity variables. Carriers of minor alleles in rs3749474 and rs1801260 polymorphisms consume more energy (250 kcal) and higher fat (5–10 g) per day and, at the same time, are fatter than carriers of major alleles (3 kg) [13]. Experimental models have reported that high-fat diets, especially saturated fats, alter circadian rhythms with light and lead to metabolic abnormalities such as obesity and insulin resistance [28]. Garaulet's study indicated that individuals carrying the C minor allele who were resistant to weight loss received less MUFA (Monounsaturated fatty acids) and more SFA than those who experienced moderate to high weight loss during the weight loss program [29]. Aligned with the results of previous studies, the current study showed that C allele increases food intake through interaction with high-risk behavioral parameters such as short sleep, high appetite, evening type and delay in consuming main meals.

However, the results of several studies were inconsistent with the results of the current study. A clinical study showed that the amount of energy and carbohydrate intake between C allele carriers and TT genotype was not statistically significant [11]. Also, the study by Garaulet et al. showed that the carriers of minor (T) allele of rs3749474 polymorphism received higher energy than the CC genotype. In comparison, in rs4580704 polymorphism, they received lower energy. However, there was no statistically significant relationship between rs1801260 polymorphism and energy intake [13], while a significant relationship was observed between CLOCK polymorphisms, especially 3111T / C, and plasma adiponectin levels. It has been reported that adiponectin can play a role in energy intake by activating AMP-activated protein kinase (AMPK), which modulates eating-related activities in the hypothalamus [30, 31]. In another study, individuals with recessive alleles for single-nucleotide polymorphism (SNP) of the Sirtuin 1 (SIRT1) and CLOCK genes showed higher resistance to weight loss and less weekly weight loss than homozygotes for both original alleles. Carbohydrate intake in this group was significantly less than those who showed moderate to high weight loss during the study [29].

These differences may be due to the participation of other types of that gene or genes in different populations or the heterogeneity of phenotypes in which the number of individuals does not represent the factors under study [32, 33]. Also, the method of evaluating food intake in the present study with other studies can be the reason for the different results. The present study used the 7-day feeding recording method to evaluate food intake. In contrast, in other studies, the 24-hour feed recall method and Food Frequency Questionnaire (FFQ) was used, which is an innovation compared to other studies.

Among the behavioral factors that can cause obesity is inadequate sleep (short sleep duration and/or poor sleep quality), which depends on the cycle of darkness and light (circadian rhythm) [34, 35]. Sleep disorders can alter the brain functions involved in controlling appetite, leading to overeating and obesity [32]. Sleep deprivation is associated with decreased insulin sensitivity, obesity, and higher BMI. Some mechanisms were introduced for this relationship. For example, in people who sleep less, the serum levels of appetite suppressants such as leptin and appetite enhancer such as ghrelin decrease and increase, respectively, and they have more time to eat during the day and night. In addition, studies have shown that short sleep is associated with changes in eating behaviors [11, 36, 37]. In a meta-analysis performed by Allebrandt [38], it was shown that sleep time in Allele A carriers was shorter in SNP rs1264507. At the same time, a significant interaction was observed between this SNP and PUFA (Polyunsaturated fatty acids) intake so that allele carriers Minors A who have prolonged sleep are associated with increased PUFA intake.

On the other hand, in a cohort study, no significant relationship was observed between rs11932595 and rs6843722 polymorphisms and sleep duration [32]. Our study introduced a new finding on the mechanism of rs1801260 polymorphism and sleep duration in obesity, which is an increase in fat intake and a decrease in protein intake in C-carrier individuals with less than 8 hours of sleep per day. The results of the present study suggest that increasing sleep hours may be a protective factor against obesity due to CLOCK polymorphisms.

Various studies show that analyzing the circadian rhythm of individuals before starting treatment will be useful in predicting future weight loss [39, 40]. Along with the present study, Garaulet found that people with minor C alleles have a chronotype of vigilance, reduced sleep duration, increased morning fatigue, decreased morning mental function, a preference for night work, and reduced physical activity [11].

A review study also found that evening people had habits such as skipping breakfast, overeating at night, consuming less protein and vegetables, and increasing consumption of sucrose, sweets, coffee, and alcohol [41]. Our study also showed that interaction of evening type and C allele is associated with obesity and increased carbohydrate intake.

A new aspect to consider in dietary interventions is meal timing. If we consider that food is the external coordinator of our environmental clock and unusual eating time may disrupt our circadian system, then "when" we eat in addition to "what we eat" plays a vital role in the treatment of obesity [42, 43]. In this regard, a study showed that eating late may affect the success of weight-loss treatment and lead to a decrease in its effectiveness [44]. The results of the Teixeira et al. study showed that people who wake up early in the morning eat their breakfast-lunch earlier than normal and night owls, and night owls have a better chance of skipping breakfast and gaining weight [45]. Another study on people with a history of bariatric surgery showed that night owls ate their lunch and dinner later than those who woke up early, and their initial weight and BMI were higher [4, 46]. Yazdaninezhad also showed that night owls consume lunch and snacks later than those who wake up early in the morning [47]. In the mentioned studies, only the relationship between mealtimes based on the circadian rhythm of people has been studied, and only one study has investigated the relationship between CLOCK polymorphisms and eating time. The results of this study are inconsistent with our study. The results show that the frequency of minor G allele of rs4580704 CLOCK polymorphism in slow-eating people is significantly higher than in fast-eating people and no correlation was observed between other CLOCK polymorphisms such as rs1801260 and food consumption time [48]. Eating late through metabolic changes such as decreased glucose tolerance, reduced resting energy and reduced fat oxidation causes weight gain [49–51] and the new findings of the present study showed that eating late in C allele carriers increased energy intake and obesity.

The hypothesis of ghrelin regulation by circadian clocks has been demonstrated by mouse models so that in the absence of circadian clocks, ghrelin is no longer expressed rhythmically [11]. In their study of GLP-1 as a missing link in the metabolic clock, Brubaker and colleagues introduced GLP-1 as a new component of the environmental metabolic clock. They showed that the secretion of GLP-1 from intestinal L cells in humans and mice follows a rhythmic pattern that can be disrupted by factors such as constant exposure to light, eating a Western diet, and eating at the wrong time. The rhythm of GLP-1 secretion results in disruption of the body's metabolic processes, such as glucose intolerance and food intake [52]. In addition, according to studies [53, 54], high carbohydrate intake in individuals carrying the minor C allele may be another factor in lowering GLP-1 levels. In the present study, the relationship between GLP-1 satiety hormone and the CLOCK gene was evaluated for the first time and our results showed that high appetite interaction in C allele carriers increases the incidence of obesity along with increasing energy and fat intake.

Several limitations of the current study should also be considered. First, due to the cross-sectional nature of this study, causality cannot be inferred. Nevertheless, these results can provide a hypothesis for prospective studies to evaluate and confirm the real causal relation. Secondly, since a small sample size may limit statistical power, the results of the present study with a relatively small sample size should be interpreted with caution until replicated in large longitudinal studies. Third, underreporting dietary intakes commonly observed in obese individuals may lead to potential bias and null results [55]. However, the analysis did not include subjects with extreme dietary intake values. On the other hand, the current study, like other observational studies, is prone to residual confounding due to unknown or unmeasured confounders [56].

Despite the limitations discussed above, this is the first attempt to study the interaction between CLOCK rs1801260 polymorphisms and behavioral and hormonal factors on obesity and food intake, according to our knowledge. Identification of these gene-behavioral factors' interactions could be crucial in planning appropriate personalized nutritional advice for the prevention and management of obesity and its related consequences.

As a result, significant positive interactions were found between evening type, high appetite, late lunch, late dinner, low GLP-1 levels and high levels of ghrelin and the CLOCK risk allele group regarding the risk of obesity versus the TT genotype. Therefore, modification of nutrition-related behavioral patterns has favorable effects on obesity risk factors on minor allele genotypes. Finally, this highlights the usefulness of using genotypes in planning and modifying behavioral patterns. Thus, these findings are integrated with the patient's genetic history to provide more relevant and customized nutritional recommendations for people who are overweight or obese and to prevent or reduce obesity and related diseases.

AMPK

AMP-activated protein kinase

ANCOVA

Analysis of Covariance

BMI

Body mass index

bp

base pair

CLOCK

Circadian locomotor output cycles protein kaput

FCT

Food composition table

FFQ

Food frequency questionnaire

GLP-1

Glucagon-like peptide-1

HC

Hip circumference

IPAQ

International physical activity questionnaire

MEQ

Morning-evening questionnaire

MUFA

Monounsaturated fatty acids

OR

Odds ratio

PCR

Polymerase chain reaction

PUFA

Polyunsaturated fatty acids

RFLP

Restriction fragment length polymorphism

SD

Standard deviation

SFA

Saturated fatty acid

SIRT1

Sirtuin 1

SNP

Single-nucleotide polymorphism

VAS

Visual analog scales

WC

Waist circumference

WHO

World health organization

WHR

Waist-to-hip ratio.

Acknowledgments

We would like to thank the Research Council of Tehran University of Medical Sciences (40783) for their financial support and all the subjects who participated in this study.

Authors’ contributions

S.R. conceptualized and designed the study, analyzed and interpreted the data, prepared the manuscript and approved the final manuscript as submitted. M.Q. analyzed and interpreted the data and drafted the initial manuscript, and approved the final manuscript as submitted. A.N. designed the study and approved the final manuscript as submitted. H.P. supervised the project, critically revised the manuscript and approved the final manuscript as submitted.

Funding

This work was supported by the Research Council of Tehran University of Medical Sciences (TUMS) (40783).

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

All participants signed a written, informed consent form. The study was approved by Ethics Committee of Tehran University of Medical Sciences (NO: IR.TUMS.VCR.REC.1398.260), in compliance with the principles of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

¹Department of cellular - Molecular Nutrition, School of Nutrition Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran. ²Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran.³Cellular and Molecular Research Center, Resistant Tuberculosis Institute and Department of Genetics, Zahedan University of Medical Sciences, Zahedan, Iran. ⁴Department of cellular - Molecular Nutrition, School of Nutrition Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.

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Table 1 Characteristics of the study population across rs1801260 genotypes

Variable	Genotype
	TT (n=158)	CT (n=192)	CC (n=53)	P-value	P-value^*
Demographic variables
Age (year)	36.5±8	36.4±8	37.1±8	0.90	0.91
Physical activity (MET-minutes/week)	1792±1134.1^ab	1334.5±1008.9^a	996.7±713.1^b	˂0.001	˂0.001
Anthropometric parameters
Weight (kg)	89.7±10	88±9	90.1±12	0.82	0.91
Height (cm)	165.1±7.7	164.6±7.1	164.2±8.6	0.62	0.42
BMI (kg/m²)	30.6±2^a	30.3±3	32.2±4^a	0.03	0.05
WC (cm)	97.3±8	99.7±9.1	100.7±8.7	0.81	0.84
WHR	0.94±0.1	0.95±0.07	0.96±0.07	0.52	0.24
Biochemical parameters
GLP-1 (pg/ml)	56.3±19.2^a	53.4±16.4	40.4±20.5^a	0.003	0.02
Ghrelin (ng/ml)	0.86±0.3^a	0.87±0.2	0.94±0.7^a	0.04	0.04
Behavioral parameters
VAS (score)	43.9±19.3^ab	32.1±21.3^a	21.7±18.2^b	˂0.001^†	˂0.001^†
Morning- evening type (score)	63.2±7.7^ab	51.2±6.5^a	47.5±6.8^b	˂0.001	˂0.001
Morning- evening categories (%)
Neutral	37.5 (59)	26.9 (52)	8.5 (5)	˂0.001	˂0.001
Morning	55.6 (88)	5.2 (10)	4.3 (2)
Evening	6.9 (11)	67.9 (130)	87.2 (46)
Sleep duration (h/day)	8.4±1^a	8.1±0.8	7.6±0.9^a	˂0.001	˂0.001
Sleep duration. Categories (%)
Short (˂ 6h)	2.1 (3)	0	2.1 (1)	˂0.001	˂0.001
Medium (6-8 h)	22.9 (35)	48.6 (93)	72.3 (38)
Long (˃ 8h)	75 (115)	51.4 (99)	25.5 (14)
Food timing (h)
Breakfast	7.8±0.9^ab	8.6±1^a	9.3±1^b	˂0.001	˂0.001
Lunch	13.7±0.7^ab	14.5±0.9^a	15.1±0.9^b	˂0.001	˂0.001
Dinner	20.2±0.8^ab	21±0.9^a	21.5±0.8^b	˂0.001	˂0.001
Qualitative variables
Married(%)	81.9 (129)	75 (144)	85.1 (45)	0.08	0.21
Males (%)	54.2 (86)	51.9 (100)	53.2 (28)	0.91	0.96
University graduate (%)	55.6 (88)	68.4 (131)	66 (35)	0.06	0.11
Current smoker (%)	2.8 (4)	4.2 (8)	2.1 (1)	0.69	0.43
Occupation (%)
Unemployed	44.4 (70)	42.9 (82)	64.8 (34)	0.91	0.55
Government employee	29.2 (46)	28.3 (54)	25.5 (14)
Worker	7.6 (12)	5.2 (10)	6.4 (3)
Self- employment	18.8 (30)	23.6 (46)	4 (2)

SD: Standard deviation; WHR: Waist hip ratio; WC: Waist circumference; VAS: Visual analog scale; BMI: Body mass index; GLP-1: Glucagon-like peptide-1; Variables are presented as mean ±SD for continuous variables and percent (%) for categorical variables. P from ANOVA for continuous variables and chi-square test for categorical variables. †P values were obtained from kruskal-wallis test. *P-value is found by ANCOVA and adjusted for age, sex, BMI, physical activity, and total energy intake (residual method), except for dietary energy intake, which was only adjusted for age, sex and BMI. a, b: Significant difference between genotype by ANOVA with Tukey's post hoc tests. Significant items with a P value ˂ 0.05 are bolded.

Table 2 Dietary intake of study population according to rs1801260 genotypes.

Genotypes	TT (158) Mean ± SD	CT (192) Mean ± SD	CC (53) Mean ± SD	P-value	^*P-value
Macronutrient
Energy (kcal/day)	2096.5±437.7^ab	2232.4±416.9^a	2434.1±526.7^b	˂0.001	-
Carbohydrate (g/day)	310.1±80^a	322.8±66^b	355±92^ab	0.002	0.01
Protein (g/day)	82.8±25.2	82.2±20.5	87.9±25	0.30	0.44
Total Fat (g/day)	62.5±18^ab	77±24.2^a	84.4±22^b	˂0.001	˂0.001
Micronutrient
Trans.fat (gr/day)	0.0005±0.003	0.0006±0.002	0.0008±0.003	0.81	0.84
Cholesterol (gr/day)	251.12±122.3	264.67±221.1	281.23±114.1	0.42	0.45
SFA (gr/day)	27.01±12.1	26.05±11.2	28.78±13.9	0.55	0.59
PUFA (gr/day)	20.55±11.87	19.21±10.61	18.90±12.51	0.51	0.58
Linoleic (gr/day)	18.11±9.02	18.09±11.01	18.14±10.12	0.54	0.57
Linolenic (gr/day)	1.24±0.71	1.22±0.59	1.22±0.64	0.88	0.91
Total fiber (g/day)	49.21±24.12	52.11±24.10	47.23±21.50	0.49	0.56
Minerals
Phosphor (mg/day)	1792.44±612.4	1721.32±719.2	1699.29±751.3	0.46	0.49
Magnesium (mg/day)	471.75±169.2	465.77±171.6	476.70±177.5	0.61	0.65
Zinc (mg/day)	13.98±3.41	13.78±4.44	13.91±3.72	0.77	0.79
Copper (mg/day)	1.87±0.73	2.01±0.78	1.99±0.75	0.75	0.80
Calcium (mg/day)	1252.11±523.98	1265.61±548.23	1271.09±511.81	0.54	0.61
Iron (mg/day)	26.3±19.23	27.9±20.12	25.60±20.31	0.52	0.54
Sodium (mg/day)	4491.24±1737.9	4456.67±1759.5	4517.21±1754.2	0.64	0.72
Potassium (mg/day)	4428.17±1682.8	4565.41±1641.9	4383.81±1643.1	0.25	0.35
Selenium (mg/day)	128.51±34.1	128.01±44.8	126.56±46.11	0.59	0.68
Manganese (mg/day)	8.01±4.11	7.88±3.01	8.12±3.78	0.80	0.85
Vitamins
A (IU/day)	721.71±381.5	746.23±325.3	753.29±341.5	0.79	0.84
D (mg/day)	1.7±1.4	1.9±1.32	1.8±1.6	0.68	0.76
E (mg/day)	17.45±7.45	16.42±7.50	17.87±8.12	0.28	0.34
K (mg/day)	292.71±301.7	293.37±288.1	285.82±294.5	0.79	0.75
B1 (mg/day)	2.16±0.68	2.13±0.77	2.14±0.75	0.88	0.93
B2 (mg/day)	2.26±0.88	2.22±0.81	2.25±0.85	0.59	0.64
B3 (mg/day)	27.81±11.3	26.34±10.2	26.4±11.09	0.87	0.91
B6 (mg/day)	2.24±0.67	2.18±0.69	2.20±0.73	0.20	0.22
B9 (mg/day)	624.52±175.9	621.43±200.45	625.71±188.1	0.68	0.73
Biotin (mg/day)	38.76±14.2	37.26±16.4	37.72±16.2	0.81	0.86
C (mg/day)	185.67±134.2	190.34±119.2	188.41±122.7	0.41	0.45
Other variables
Caffeine (gr/day)	153.11±121.3	154.9±171.6	153.29±145.5	0.91	0.85

SFA, saturated fatty acid; PUFA, poly unsaturated fatty acid.

P-values from ANOVA.

*P -value reported after adjusting kcal with ANCOVA.

Significant items with a P value ˂ 0.05 are bolded.

Table 3 Interaction between CLOCK polymorphism and behavior and hormonal parameters on food intake.

Variables		Energy		Carbohydrate		Protein		Fat
	TT ^a	CT+CC	P_Interaction	CT+CC	P_Interaction	CT+CC	P_Interaction	CT+CC	P_Interaction
Evening type	1	2.89 (0.67, 12.32)	0.151	3.8 (1.89, 8.06)	0.039	1.67 (0.4, 6.89)	0.476	1.10 (0.26, 4.60)	0.889
High appetite	1	3.06 (1.18, 7.93)	0.021	1.77 (0.69, 4.50)	0.229	0.93 (0.37, 2.35)	0.892	1.31 (1.01, 5.37)	0.041
Short sleep	1	0.60 (0.23, 1.58)	0.307	0.71 (0.27, 1.85)	0.493	0.27 (0.10, 0.71)	0.008	1.34 (1.03, 3.89)	0.029
Late breakfast	1	1.03 (0.38, 2.78)	0.949	1.95 (0.71, 5.31)	0.191	1.45 (0.54, 3.89)	0.457	0.80 (0.29, 2.18)	0.669
Late lunch	1	1.98 (1.05, 4.80)	0.036	0.85 (0.21, 3.35)	0.817	1.55 (0.40, 5.94)	0.515	1.18 (0.30, 4.56)	0.805
Late dinner	1	1.02 (0.39, 2.53)	0.996	1.50 (0.60, 3.76)	0.384	1.24 (0.49, 3.11)	0.643	0.90 (0.35, 2.31)	0.833
High ghrelin	1	0.96 (0.15, 6.10)	0.968	2.51 (0.40, 15.76)	0.326	1.17 (0.18, 7.67)	0.866	0.90 (0.13, 5.89)	0.913
High GLP-1	1	0.29 (0.04, 1.94)	0.204	0.24 (0.03, 1.57)	0.138	1.52 (0.23, 9.91)	0.660	0.60 (0.09, 3.86)	0.592

GLP-1: Glucagon-like peptide-1; Data are presented as Odds ratio (OR) and 95 % confidence intervals (CI) after adjusted for age, gender and physical activity.

a Reference group

No competing interests reported.

CLOCK 3111 T/C SNP Interacts with evening preference, appetite hormones, late eating and sleep reduction for obesity and food intake in obese Iranian adults

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Study papulation

General, anthropometric and physical activity assessments

Genotyping

Ghrelin and GLP-1

Dietary intake and timing of food intake assessment

Chronotype questionnaire

Sleep duration

Assessments of appetite

Statistical analysis

Result

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Status:

Version 1