|Year : 2022 | Volume
| Issue : 1 | Page : 51
Interaction of cry1 gene polymorphisms and dominant food patterns on obesity: A cross-sectional study
Hadith Tangestani1, Hadi Emamat2, Mir Saeed Yekaninejad3, Mohsen Alipour4, Seyed Ali Keshavarz5, Khadijeh Mirzaei6
1 Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran; Department of Nutrition, Faculty of Health and Nutrition, Bushehr University of Medical Sciences, Bushehr, Iran
2 Student Research Committee, PhD Candidate in Nutrition Sciences, Department and Faculty of Clinical Nutrition Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran
4 Cellular and Molecular Biology Research Center, School of Advanced, Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IRAN (ATM, SBMU), Tehran, Iran
5 Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
6 Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran
|Date of Submission||16-Jun-2020|
|Date of Acceptance||03-Sep-2020|
|Date of Web Publication||05-Apr-2022|
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), P.O. Box 14155-6117, Tehran
Source of Support: None, Conflict of Interest: None
Background: Evidence suggests that there is some relationship between circadian clock gene variants and obesity. However, there are few examinations supporting this observation in human subjects. This study was aimed to investigate the interaction between Cry1 circadian gene polymorphism and major dietary patterns on obesity measurements. Methods: Healthy overweight and obese women aged 18–53 years old were recruited from health centers in Tehran, Iran by a multistage cluster random sampling method (n = 377). Major dietary patterns were elicited after assessing the intake of 16 food groups using a valid and reliable 147-item food frequency questionnaire (FFQ). Anthropometric measurements were performed for each and every participant. Body composition was analyzed using bioelectrical impedance analysis (BIA). Socio-demographic and physical activity data were also collected by a validated Farsi demographic questionnaire and the international physical activity questionnaire (IPAQ). The Cry1 rs2287161 polymorphism were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Generalized linear models were used for interaction analysis. Results: Two major dietary patterns, including healthy and unhealthy dietary pattern (HDP and UDP, respectively) were determined using factor analysis. Our study showed a significant higher weight (P = 0.003), body mass index (BMI) (P = 0.042), hip circumference (P = 0.052), and body fat mass (P = 0.028) in carriers of C allele compared with G allele. Moreover, a significant gene-diet interaction was observed between being a carrier of C allele and BMI (P = 0.099 for CC genotype; P = 0.1 for CG genotype) and fat mass (P = 0.1 for CG genotype). Conclusions: The current study suggests a significant interaction of Cry1 rs2287161 gene polymorphisms in people following a healthy dietary pattern on BMI and fat mass among carriers of C allele compared to carriers of G allele.
Keywords: Circadian clocks, diet; dietary patterns and Cry1 gene polymorphisms, fat body, gene-environment interaction, obesity
|How to cite this article:|
Tangestani H, Emamat H, Yekaninejad MS, Alipour M, Keshavarz SA, Mirzaei K. Interaction of cry1 gene polymorphisms and dominant food patterns on obesity: A cross-sectional study. Int J Prev Med 2022;13:51
|How to cite this URL:|
Tangestani H, Emamat H, Yekaninejad MS, Alipour M, Keshavarz SA, Mirzaei K. Interaction of cry1 gene polymorphisms and dominant food patterns on obesity: A cross-sectional study. Int J Prev Med [serial online] 2022 [cited 2022 May 19];13:51. Available from: https://www.ijpvmjournal.net/text.asp?2022/13/1/51/342569
| Introduction|| |
The prevalence of obesity, as a multifactorial metabolic disorder, is rapidly increasing worldwide, and particularly in the Middle East., It is predictable that by 2030, half of the population will be obese. The obesity epidemic is associated not only with increased mortality, but also with lower quality of life, increased needs of professional health care, and dramatically increased health care costs., The causes of obesity are divided into two main categories of genetic and environmental factors, such as diet, physical activity, and so on; however, it may be more prudent to assume that a combination of both factors plays the major role. Therefore, gene-environment interaction could be regarded as the most potent pathological exposure determining the risk of obesity in individuals.,
One of the factors affecting the body metabolism, and thus possibly obesity, is the body circadian clock. In mammals, the circadian rhythm affects almost all aspects of physiology and behavior, including sleep cycle, cardiovascular activity, endocrine system, body temperature, kidney activity, gastrointestinal physiology, and hepatic metabolism., At the molecular level, the circadian clock is regulated by a network of transcription and translation factors; these factors, in turn, are regulated by positive and negative feedback pathways that exist in the suprachiasmatic nucleus of hypothalamus. The main components of the positive limb are Clock and Bmal1 and the major components of the negative limb are period (Per) and cryptochromes (Cry). The cryptochromes family, including cry1 and cry2, play an important role in regulating the circadian rhythm. Studies on mutant animals have linked the disruption of circadian clock rhythm to metabolic abnormalities and obesity.,, Studies at the human level are very limited. One study on Chinese population have supported the association between some clock gene polymorphisms and abdominal obesity.
Dietary modification is the major environmental factor worthy of note. Studies have suggested a connection between dietary intake, circadian preference (morning, intermediate, or evening-type) and obesity; it seems that individuals with late preference (evening-type) are more likely to pursue unhealthy dietary habits, including skipping the breakfast, lower consumption of fruits and vegetables, higher consumption of sugar and alcohol, and so on, as compared to individuals with morning preference.,, However, the various interactions between different nutrients, the circadian rhythm, and the epigenetic factors involved cannot be understood thoroughly using a single foodstuff; thus, a complete dietary pattern is more reasonable to be taken into account.
Findings have supported the relationship between circadian clock, diet, and obesity. To the best of our knowledge, there is no research to map out the interaction of clock gene polymorphism and dietary pattern on obesity. The current study aimed to examine the relationship between interaction of Cry1 gene polymorphism rs2287161 and dietary patterns on obesity.
| Methods|| |
A cross-sectional design was adopted and the study was conducted between Feb 2018 and May 2019. Study participants were recruited from overweight and obese women referred to 21 health centers of Tehran, Iran by a multistage cluster random sampling method. The inclusion criteria included: (1) having a body mass index (BMI) between 25 to 40 kg/m2, and (2) an age range of over 18 years. Participants were excluded if they (1) had entered the menopause, (2) were pregnant, (3) had a history of cardiovascular disease, diabetes, cancer, kidney disease, or thyroid disease, (4) used nutritional supplements over the last three mounts, or (5) had followed any kind of weight loss regimen over the past year. The sample size for this study was calculated according to Peduzzi method using a binary logistic regression model. The research protocol was fully explained to study participants and after signing a written consent, 377 overweight and obese women entered the research. The measurements and evaluations were done in the laboratory of the Faculty of Nutritional Science and Dietetics, Tehran University of Medical Science (TUMS). The protocol of study was approved by the ethics committee of TUMS (IR.TUMS.VCR.REC.1398.051).
Weight was measured in minimal clothing without shoes using a digital scale (Seca 808; Hamburg, Germany) with a sensitivity of 0.1 kg. Height was determined by a stadiometer (Seca) with a sensitivity of 0.5 cm. BMI was calculated as weight in kilograms divided by the height squared in meters. Waist circumference was measured by an unstreached tape between the lowest gear and the iliac crest and in expiratory state. Then the waist to hip ratio (WHR) was calculated for each person.
A validated Farsi demographic questionare, including measures of education level, employment status, smoking, family size, economic situation, and income was used in order to obtain necessary demographic data.
A a valid and reliable semi-quantitative 147-item food frequency questionnaire (FFQ) was used to assess dietary intake. The participants were asked by a trained questioner to report their amount and frequency of food intake on a daily (e.g., bread), weekly (e.g., meat), monthly (e.g., fish), or yearly (e.g., some seasonal fruits) basis. The portion sizes reported by study subjects were converted to grams per day and were enterned in the Nutritionist IV software for further analysis (version 7; N-Squared Computing, Salem, OR, USA). The validity and reliability of the questionnaire were assessed in the Tehran Lipid and Glucose Cohort.
Body composition analysis
Body composition analysis was assessed using bioelectrical impedance analysis (BIA) Inbody 770 Scanner (Inbody Co., Seoul, Korea). According to the implementation protocol, the study participants were asked to avoid wearing too many clothes, carrying metal jewelry, watch or electronic devices, and eating and exercising for at least four hours before the assessment. Moreover, they were asked to urinate before the measurements. Each participant stood on the device in bare feet and held the handles for some seconds until the device notified the end of the analysis.
Physical activity assessment
The International Physical Activity Questionnaire (IPAQ) was used to evaluate physical activity. The questionnaire has showed the acceptable validity and reliability in previous studies., This questionnaire describes physical activity at three levels, including intense (e.g., football and running), moderate (e.g., cycling at medium speed), and low (e.g., walking). Study subjects were asked to answer the questions based on the duration and frequency of the activity during the previous week. The number obtained from the physical activity questionnaire indicated the intensity of the activity: <600 (MET-h/wk), 600-3500 (MET-h/wk), and >3500 (MET-h/wk) which were interpreted as low, moderate, and severe physical activity levels; respectively.
Venous blood samples (10 mL) were obtained from each study participant fasting overnight for 10 to 12 hours at the Nutrition and Biochemistry laboratory of School of Nutritional Sciences and Dietetics, TUMS. Blood samples were collected in EDTA containing tubes. After resting for 30 minutes in room temperature, samples were stored in -20°C until the genotyping analysis.
DND extractionand genotyping
About 3 mL of prepheral blood samples were obtained from all study subjects. Genomic DNA was isolated from prepheral whole blood leukocytes using GeneAll Mini Columns Type kit (GeneAll, South Korea). The extracted DNA was used to determin the rs2287161 single-nucleotide polymorphism (SNP) located between cry1 and MTERF2 gene. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was conducted on the rs2287161 SNP using forward: 5′-GGAACAGTGATTGGCTCTATCT -3′ and reverse primer: 5′-GGTCCTCGGTCTCAAGAAG-3′. The amplication proccess was performed in 5 steps, including 4 minutes initial denaturation (94°C), 30 seconds denaturation (94°C), 30 seconds annealing (58°C), 30 seconds extension (72°C), and 5 minutes final extension (72°C); denaturation, annealing, and extension were done for 35 cycles. PCR products were digested using BseYI (New England Biolabs, Cataloge number: R0635S) which yielded two cuts and three fragments of 108, 50, and 226 base pairs (bps); and one cut and two fragments of 156 and 226 bps in the presence of G and C alleles, respectively.
Findings of the study were analyzed using the SPSS (version 20). Data were reported as means and standard deviations. Kolmogrov–Smirnov was used to verify the normality of data distribution. ANOVA test was used to compare the data related to anthropometric and body composition, based on dietary patterns and genotypes. In order to use this tset, participants that followed each dietary pattern were catogorized into three groups (tertiles), then the effect of each dietary pattern on the relevant variables was analyzed. We took advantage of the post hoc method (Tukey) to observe the differences in the variables of concern between groups. In the next step, anthropometric and body composition variables based on dietary patterns and genotypes were compared through ANCOVA test adjusted for age, BMI, calorie intake, and physical activity. P value <0.05 was considered as significant for all the tests mentioned above.
The interaction of Cry1 gene polymorphisms and major dietary patterns on anthropometric and body composition measurements were verified by Generalized Linear Models. In this model, the studied outcomes, including BMI and body fat mass, were considered as response variables (dependent variable); while Cry1 genotypes and each dietary pattern were regarded as factor variables, and age and calorie intake as covariates. In interaction models, P values <0.1 were considered as statistically significant.
| Results|| |
The general charachteristics of study participants are listed in [Table 1]. Four hundred four overweight and obese women enterd the study, but due to lack of information, some participants were not included in the analysis. Three hundred seventy-seven overweight, obese, and apparently healthy women with an age range of 18 to 53 and mean (±SD) of 36.64 (±9.02) years participated in the study. The mean (±SD) of height, weight, BMI, body fat mass, and fat free mass of subjects were 161.05 (± 5.82) cm, 79.90 (± 10.73) kg, 30.81 (± 3.8) kg/m2, 33.73 (± 7.47) kg, and 45.98 (± 5.2) kg, respectively.
Two major dietary patterns which were later entitled as healthy dietary pattern (HDP) and unhealthy dietary pattern (UDP), were determined by factor analysis based on eigenvalues greater than one. To extract these two dietary patterns based on 16 food groups, the principal component analysis (PCA) was done. HDP was charachterized as consuming higher amounts of vegetables, fruits, fruit juices, dried fruits, dairy, fish, poultry, red meat, eggs; and lower amounts of unhealthy fats, grains, high-energy drinks, sweets, desserts, and industrial juices. In UDP, participants were more likely to consume higher quantities of processed meats, organ meats, fast foods, sauces, high-energy drinks, sweets, desserts, industrial juices; and lower quantities of legumes. To simplify the reading of loading factor matrix, the values lower than 0.3 were discarded. Food groups and loading factors are presented in [Table 2].
Each dietary pattern was divided into tertiles for further comparison in dietary adherence. Higher adherance to HDP was associated with lower percent body fat (P = 0.036), and higher free fat mass (P = 0.049); after adjusment for age, physical activity, and calorie intake. Moreover, significant differences across the tertiles of UDP were observed for age (P < 0.001), height (P = 0.058), fat free mass (P = 0.014), and total body water (P = 0.025) ; however, except for age (P = 0.004), these dicrepancies disappeared after being adjusted for age, physical activity, and calorie intake [Table 3].
|Table 3: Characteristics of study participants according to adherence to HDP and UDP|
Click here to view
Distribution of Cry1 rs2287161 polymorphism was GG (28.4%), CG (39.3%), and CC (32.4%); and overall 51.98% and 48.02% for C and G allele, respectively [Table 4]. Significant differences across Cry1 rs2287161 polymorphism were observed for weight (P < 0.001), BMI (P < 0.001), waist circumference (P = 0.013), hip circumference (P = 0.013), body fat mass (P = 0.002), visceral fat level (P = 0.04), and total body water (P = 0.014). After adjusting for age, physical activity, and calorie intake, significant associations between being a carrier of GG genotype and lower weight (P = 0.003), BMI (P = 0.042), hip circumference (P = 0.052), and body fat mass (P = 0.028) were observed.
|Table 4: Characteristics of study participants according to cry1 rs2287161 genotype|
Click here to view
As the interaction of each dietary pattern (HDP and UDP) and Cry1 gene polymorphisms with body fat mass was verified, we found that in individuals carrying at least one risk allele (CC or CG genotype) with the highest adherence to the HDP, when compared with those with no risk allele (GG genotype) with the lowest adherence to HDP, body fat mass was significantly lower (P = 0.099 for CC; P = 0.1 for CG).
Similar gene-diet interactions were observed for BMI. BMI was significantly lower in those carrying the genotype CC (P = 0.15) when they had the highest adherence to a HDP. Also, BMI was significantly lower in highest adherence to HDP in the participants carrying CG (P = 0.1). When the interaction of UDP and Cry1 gene genotype on body fat mass and BMI was examined, we were not able to find any significant association. [Table 5] presents the data regarding the interaction of HDP and UDP with Cry1 on BMI and body fat mass.
| Discussion|| |
The present study addressed the relationship between interaction of Cry1 gene polymorphism rs2287161 and dietary patterns on obesity and body composition. After adjusting for confounding variables, we were able to observe a significant positive association between being a carrier of CC genotype and having a higher weight, BMI, waist circumference, hip circumference, fat mass, and visceral fat. Additionally, the gene-diet interaction revealed, individuals with CC and CG genotypes who had significantly higher mean fat mass and other obesity-related measurements, while following HDP, had about 3.5 kg less fat mass, compared with GG carriers. The present study, for the first time, has investigated the association of the Cry1 rs2287161 polymorphism with fat mass and obesity-related components.
The circadian clock genes have already been studied in relation to diet and obesity. For example, some CLOCK gene SNPs, such as rs3749474, rs1554483, rs4864548, rs4580704, and rs1801260, were already associated with obesity, energy intake, and BMI., In our study, carriers of risk allele C who were more obese, have less weight when adhering to HDP compared with people homozygous for G allele. This study showed that improving adherence to HDP might alter the effect of genetic polymorphisms on obesity in carriers of the risk allele more than in individuals having a lower-risk genetic background. Similar findings were observed in a cohort study in the population of Nurses' Health Study (n = 8828) and the Health Professionals Follow-up Study (n = 5218). Also paralleled with our findings, a meta-analysis of ten studies has shown that in people predisposed to obesity carrying an FTO-gene polymorphism, diet and lifestyle interventions may cause greater weight loss compared with non-carriers.
Studies of the interaction of other genes associated with the circadian clock regulation and environmental factors are very limited. However, one study has been shown that CLOCK rs1801260 SNP was associated with greater weight loss and poor adherence to the Mediterranean diet; there was also an interaction between carrying this SNP with saturated fatty acids intake which in turn modified waist circumference and overnight feeding. Animal studies have also shown that saturated fat alters the correlation between SNPs in clock genes and obesity. They have suggested that animals fed saturated fat faced circadian disruption, obesity, and metabolic syndrome.,
In the present study, we observed that the C allele in Cry1 rs2287161 SNP modifies the impact of an HDP, consisting lower saturated fat intake, which leads to lower BMI and fat mass. Although the mechanisms of this association are not well understood, some studies have suggested roles of circadian clock genes in regulation of lipid metabolism and adipogenesis., Moreover, in animal models high-fat diet is shown to alter the clock gene expression and circadian oscillation balance.,
Many circadian clock genes were studied in relation to emotion, emotional feeding, and obesity., Garaulet et al. investigated the interaction between the CLOCK 3111T/C genotype and emotional eating behavior on the effectiveness of a weight loss program. The study found that among carriers of the minor C allele, individuals with “emotional eating” lost less weight than the “non-emotional eating”. Likewise, Soria et al. showed that there was a significant association between different circadian clock gene SNPs and mental disorders, such as depression and bipolar disorder. They found that in the carriers of the C allele of the Cry1 rs2287161 gene, the risk of mental disorders is up to three times higher than homozygous carriers for the G allele. On the other hand, many studies have found positive and significant relationships between mental disorders, such as depression and obesity,, which may be one of the probable hypotheses explaining the association between clock and obesity genes. Moreover, studies on animals have revealed that higher transcription of orexigenic hormones and polyphagia, like orexin and ghrelin, which may in turn result in obesity, are observed more frequently in CLOCK-mutant mice., Based on these observations, the hormonal pathway could also be one of the regulatory pathways that may explain the association between CLOCK gene polymorphisms, obesity-related genotypes, and obesity.
This study has some limitations. First, the cross-sectional design of the study does not allow us to derive a causal relationship between the studied variables. Second, we were not able to assure the health status of the study participants, in part due to lack of a screening method. Third, the gender specificity of the study population does not permit an extrapolation to the whole society. Fourth, the use of FFQ is mostly associated with various biases. For example, misclassification bias may occur with regard to the classification of food items into groups according to the researcher's idea. However, it is the first study determines the frequency of Cry1 rs2287161 gene genotype in Iranian population. Moreover, to our best of knowledge, this is the first study investigating the interaction of major dietary patterns and Cry1 rs2287161 polymorphism on obesity-related parameters paving the way for other studies with larger populations or interventional designs.
| Conclusion|| |
Our study showed significant differences between Cry1 rs2287161 variants and obesity-related measurements. We found that Cry1 rs2287161 gene polymorphisms interact with an HDP probably resulting to a lower BMI and fat mass among carriers of C allele compared to G allele. From another point of view, the current study indicated that improving adherence to HDP may be more beneficial to women with higher risk for obesity compared with those having a lower-risk genetic profile.
Abbreviations: Cry: Cryptochromes, Per: period, BMI: body mass index, WHR: Waist to hip ratio, FFQ: food frequency questionnaire, IPAQ: International Physical Activity Questionnaire, PCR-RFLP: Polymerase chain reaction-restriction fragment length polymorphism, HDP: healthy dietary pattern, UDP: unhealthy dietary pattern, SNP: single-nucleotide polymorphism.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
We are grateful to our co-workers. This study was supported by Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Collaboration NRF. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet 2016;387:1377-96.
Nair SC, Sheikh SM, Ibrahim H. Higher physician body mass index is associated with increased weight bias in an Arab Country with high prevalence of obesity. Int J Prev Med 2019;10:93.
] [Full text]
Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B, et al
. Obesity and severe obesity forecasts through 2030. Am J Prev Med 2012;42:563-70.
Sepúlveda J, Murray C. The state of global health in 2014. Science 2014;345:1275-8.
Haidari F, Abiri B, Haghighizadeh M-H, Kayedani GA, Birgani NK. Association of hematological parameters with obesity-induced inflammation among young females in Ahvaz, South-West of Iran. Int J Prev Med 2020;11:55. [Full text]
Milagro F, Mansego M, De Miguel C, Martinez J. Dietary factors, epigenetic modifications and obesity outcomes: Progresses and perspectives. Mol Aspects Med 2013;34:782-812.
Lavebratt C, Almgren M, Ekström T. Epigenetic regulation in obesity. Int J Obes 2012;36:757-65.
Qi L. Gene–diet interaction and weight loss. Curr Opin Lipidol 2014;25:27-34.
Ripperger JA, Schibler U. Circadian regulation of gene expression in animals. Curr Opin Cell Biol 2001;13:357-62.
Lowrey PL, Takahashi JS. Mammalian circadian biology: Elucidating genome-wide levels of temporal organization. Annu Rev Genomics Hum Genet 2004;5:407-41.
Moynihan Ramsey K, Marcheva B, Kohsaka A, Bass J. The clockwork of metabolism. Annu Rev Nutr 2007;27:219-40.
Zhang EE, Liu Y, Dentin R, Pongsawakul PY, Liu AC, Hirota T, et al
. Cryptochrome mediates circadian regulation of cAMP signaling and hepatic gluconeogenesis. Nature Med 2010;16:1152-6.
Ando H, Yanagihara H, Hayashi Y, Obi Y, Tsuruoka S, Takamura T, et al
. Rhythmic messenger ribonucleic acid expression of clock genes and adipocytokines in mouse visceral adipose tissue. Endocrinology 2005;146:5631-6.
Turek FW, Joshu C, Kohsaka A, Lin E, Ivanova G, McDearmon E, et al
. Obesity and metabolic syndrome in circadian Clock mutant mice. Science 2005;308:1043-5.
Ye D, Cai S, Jiang X, Ding Y, Chen K, Fan C, et al
. Associations of polymorphisms in circadian genes with abdominal obesity in Chinese adult population. Obes Res Clin Pract 2016;10:S133-41.
Reutrakul S, Hood MM, Crowley SJ, Morgan MK, Teodori M, Knutson KL. The relationship between breakfast skipping, chronotype, and glycemic control in type 2 diabetes. Chronobiol Int 2014;31:64-71.
Kanerva N, Kronholm E, Partonen T, Ovaskainen ML, Kaartinen NE, Konttinen H, et al.
Tendency toward eveningness is associated with unhealthy dietary habits. Chronobiol Int 2012;29:920-7.
Patterson F, Malone SK, Lozano A, Grandner MA, Hanlon AL. Smoking, screen-based sedentary behavior, and diet associated with habitual sleep duration and chronotype: Data from the UK Biobank. Ann Behav Med 2016;50:715-26.
Newby P, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: A review. Nutr Rev 2004;62:177-203.
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373-9.
Garmaroudi GR, Moradi A. Socio-economic status in Iran: A study of measurement index. Payesh (Health Monitor) 2010;9:137-44.
Asghari G, Rezazadeh A, Hosseini-Esfahani F, Mehrabi Y, Mirmiran P, Azizi F. Reliability, comparative validity and stability of dietary patterns derived from an FFQ in the Tehran Lipid and Glucose Study. Br J Nutr 2012;108:1109-17.
Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. Int J Behav Nutr Phys Act 2011;8:115.
Moghaddam MB, Aghdam FB, Jafarabadi MA, Allahverdipour H, Nikookheslat SD, Safarpour S. The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: Content and construct validity, factor structure, internal consistency and stability. World Appl Sci J 2012;18:1073-80.
Committee IR. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. Available from: http://www ipaq ki se/scoring.pdf
. 2005. [Last accessed on 2022 Aug 03].
Garaulet M, Lee Y-C, Shen J, Parnell LD, Arnett DK, Tsai MY, et al.
CLOCK genetic variation and metabolic syndrome risk: Modulation by monounsaturated fatty acids. Am J Clin Nutr 2009;90:1466-75.
Sánchez-Muniz F, Simón Martín C. Clock genes, chronodisruption, nutrition and obesity. Curr Res Diabetes Obes J 2017;3:1-6
Wang T, Heianza Y, Sun D, Huang T, Ma W, Rimm EB, et al
. Improving adherence to healthy dietary patterns, genetic risk, and long term weight gain: Gene-diet interaction analysis in two prospective cohort studies. BMJ 2018;360. doi: 10.1136/bmj.j5644.
Xiang L, Wu H, Pan A, Patel B, Xiang G, Qi L, et al.
FTO genotype and weight loss in diet and lifestyle interventions: A systematic review and meta-analysis. Am J Clin Nutr 2016;103:1162-70.
Lopez-Minguez J, Gómez-Abellán P, Garaulet M. Circadian rhythms, food timing and obesity. Proc Nutr Soc 2016;75:501-11.
Garaulet M, Tardido AE, Lee Y, Smith C, Parnell L, Ordovas J. SIRT1 and CLOCK 3111T>C combined genotype is associated with evening preference and weight loss resistance in a behavioral therapy treatment for obesity. Int J Obes 2012;36:1436-41.
Sherman H, Genzer Y, Cohen R, Chapnik N, Madar Z, Froy O. Timed high-fat diet resets circadian metabolism and prevents obesity. FASEB J 2012;26:3493-502.
Duez H, Staels B. Rev-erbα gives a time cue to metabolism. FEBS Lett 2008;582:19-25.
Yin L, Wu N, Curtin JC, Qatanani M, Szwergold NR, Reid RA, et al.
Rev-erbα, a heme sensor that coordinates metabolic and circadian pathways. Science 2007;318:1786-9.
Yanagihara H, Ando H, Hayashi Y, Obi Y, Fujimura A. High-fat feeding exerts minimal effects on rhythmic mRNA expression of clock genes in mouse peripheral tissues. Chronobiol Int 2006;23:905-14.
Cano P, Jiménez-Ortega V, Larrad A, Toso CFR, Cardinali DP, Esquifino AI. Effect of a high-fat diet on 24-h pattern of circulating levels of prolactin, luteinizing hormone, testosterone, corticosterone, thyroid-stimulating hormone and glucose, and pineal melatonin content, in rats. Endocrine 2008;33:118-25.
Garaulet M, Corbalán-Tutau MD, Madrid JA, Baraza JC, Parnell LD, Lee Y-C, et al.
PERIOD2 variants are associated with abdominal obesity, psycho-behavioral factors, and attrition in the dietary treatment of obesity. J Am Diet Assoc 2010;110:917-21.
Soria V, Martínez-Amorós È, Escaramís G, Valero J, Pérez-Egea R, García C, et al
. Differential association of circadian genes with mood disorders: CRY1 and NPAS2 are associated with unipolar major depression and CLOCK and VIP with bipolar disorder. Neuropsychopharmacology 2010;35:1279-89.
McElroy SL, Kotwal R, Malhotra S, Nelson EB, Keck PE Jr, Nemeroff CB. Are mood disorders and obesity related? A review for the mental health professional. J Clin Psychiatry 2004;65:634-51.
Mansur RB, Brietzke E, McIntyre RS. Is there a “metabolic-mood syndrome”? A review of the relationship between obesity and mood disorders. Neurosci Biobehav Rev 2015;52:89-104.
Oishi K, Ohkura N, Wakabayashi M, Shirai H, Sato K, Matsuda J, et al
. CLOCK is involved in obesity-induced disordered fibrinolysis in ob/ob mice by regulating PAI-1 gene expression. J Thromb Haemost 2006;4:1774-80.
Rudic RD, McNamara P, Curtis A-M, Boston RC, Panda S, Hogenesch JB, et al
. BMAL1 and CLOCK, two essential components of the circadian clock, are involved in glucose homeostasis. PLoS Biol 2004;2:e377.
Naska A, Lagiou A, Lagiou P. Dietary assessment methods in epidemiological research: Current state of the art and future prospects. F1000Research 2017;6:926.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]