The prevalence of obesity is growing worldwide so that nearly a third of the global population is being overweight or obese(Reference Chooi, Ding and Magkos1,Reference Fruh2) . The WHO also indicated that over half of Iranian adults suffer from overweight and obesity, contributing to several diseases, including diabetes, CVD and cancer(Reference Engin3). Dietary habits are one of the modifiable risk factors for obesity that might be associated with an increase in cardiovascular events(Reference Barbosa, Vasconcelos and Correia4,Reference Wang, Masters and Bai5) . For instance, following the Mediterranean diet, which is known for higher intake of plant-based diets and a lower intake of animal foods, results in weight loss in obese people(Reference D’Innocenzo, Biagi and Lanari6,Reference Meslier, Laiola and Roager7) .
The circadian timing of behavioural rhythms and physiological functions, such as energy intake, sleep-wake cycle and physical activity, has shown to play a major role in body weight regulation(Reference Baron, Reid and Kern8–Reference Willis, Creasy and Honas10). The circadian rhythms in approximately all mammalian cells are generated by the circadian clocks, including the master clocks in the suprachiasmatic nucleus of the hypothalamus(Reference Beersma and Gordijn11,Reference Reppert and Weaver12) . Although circadian rhythms are entrained to follow the 24 h by external light-dark cycle, there are interindividual differences in the circadian rhythms, somewhat due to genetics(Reference Barclay, Watson and Buchwald13,Reference Koskenvuo, Hublin and Partinen14) . Chronotype is a biological feature that comprises interindividual disparities in the circadian phase attributed to light-dark transition and affects daily activities(Reference Baehr, Revelle and Eastman15,Reference Roenneberg, Kuehnle and Juda16) .
It should be noted that synchronising physiological and environmental functions like eating behaviours can be affected by chronotypes(Reference Meijer, Colwell and Rohling17,Reference Reutrakul, Hood and Crowley18) . Chronotype based on diurnal preferences could be ranged from extreme morning and evening types(Reference Horne and Östberg19). Evening types were more prone to have unhealthier habits, such as lower physical activity levels, than morning types(Reference Nauha, Jurvelin and Ala-Mursula20). Moreover, evening types also tend to have higher risks for diabetes(Reference Merikanto, Lahti and Puolijoki21) and all-cause mortality(Reference Knutson and Von Schantz22). Maukonen et al., in a population-based study on adults (n 1097), demonstrated that evening types prefer to higher intake of soft drinks and lower intake of cereals, vegetables and whole fruits than morning types(Reference Maukonen, Kanerva and Partonen23). Mirghani et al., in a small-scale study (n 169), indicated that evening types were more probably to be breakfast skippers, as well as later dinner intake over 8 weeks among medical students(Reference Mirghani, Albalawi and Alali24). Findings from a Turkish study among 142 university students indicated that evening types had different habits of nutrition like breakfast skipping, eating big portions of foods and higher intake of low-quality foods compared with morning types(Reference Toktaş, Erman and Mert25). Furthermore, the US small-scale study among 137 college students indicated that evening types were more prone to have higher weight gain and BMI over 8 weeks of follow-up(Reference Culnan, Kloss and Grandner26). Additionally, some social rhythms (e.g. working schedules) may force mainly evening chronotypes to differ from their intrinsic chronotype. This misalignment between biological and social times is known as social jet lag(Reference Wittmann, Dinich and Merrow27). Besides, social jet lag has been related to poor health behaviours and increased risk of obesity(Reference Wittmann, Dinich and Merrow27,Reference Roenneberg, Allebrandt and Merrow28) . However, the relation between chronotype and obesity has been contradictory(Reference Culnan, Kloss and Grandner26,Reference Roenneberg, Allebrandt and Merrow28) . There are insufficient data on the association between chronotype, eating habits and anthropometric measures among adults in developing countries that might have caused some bias in figuring out the particular features of adult obesity. Therefore, we aimed to explore whether chronotypes are linked to anthropometric measures in Iranian adults. In addition, the association of chronotype with eating habits was also evaluated to see whether the relation is different across chronotypes.
Methods
Study population
This is a cross-sectional study that was conducted on 850 adults (20–59 years old) from September 2018 to February 2019. Study participants were recruited by the method of classified two-stage cluster sampling from five districts of the Tehran province, including North, South, West and east, and the urban core. The subjects were randomly chosen from five districts (forty health houses), and then the number of subjects in each centre was obtained in the following manner: total sample size (850)/the number of health houses (40). The community health centres (health houses) cover approximately 6000–10 000 patients and consist of physicians and health technicians(Reference Tavassoli29). These centres are accountable for elective as well as emergency case management. The primary function of these centres is to provide health care services to the community it serves(Reference Azizi, Gouya and Vazirian30).
Eligible subjects were 20–59 years old, satisfied to participate in the study, with no major health problems, living in Tehran city and members of health houses. Individuals with diabetes, cancer, chronic kidney and liver disease, CVD, rheumatoid arthritis, Parkinson and other chronic diseases were excluded, as were those with pregnancy or lactation status.
The study was affirmed by the Medical Ethics Committee of the Tehran University of Medical Sciences, Tehran, Iran (Ethic number: IR.TUMS.MEDICINE.REC.1400·446) and written informed consent was provided by all individuals who participated in the study.
Chronotype assessment
Horne and Ostberg developed morningness–eveningness questionnaire (MEQ). It contains nineteen questions that were related to daily performance and sleep time preferences(Reference Horne and Östberg19). The range of scores varies from 16 to 86. The higher scores demonstrate a propensity towards morningness and the lower scores demonstrate a tendency towards eveningness. The validity and reliability of the MEQ for evaluating the chronotypes were established and found to be appropriate(Reference Rahafar, Sadeghi Jojeili and Sadeghpour31). We first divided subjects into three groups, according to their chronotypes (morning-, intermediate- and evening-type chronotypes)(Reference Mozafari, Tabaraie and Tahrodi32). With this classifying method, we had a small number of people in the evening group (n 15) compared with the other two groups (morning type (n 437) and intermediate type (n 400)) which made it difficult to compare the three groups due to a decrease in statistical power. Thus, we divided the people into two groups (intermediate/evening (score range: 16–58) v. morning types (score range: 59–86))(Reference Mozafari, Tabaraie and Tahrodi32). By conducting this procedure, 138 men and 128 women were morning types and 299 men and 285 women were intermediate/evening types.
Anthropometric measures
Height was measured with a sensitivity of 0·1 cm, applying a stadiometer (Seca 206), unshod. Weight was measured with a sensitivity of 0·1 kg, applying digital scales (Seca 808) with light clothing. BMI was calculated as weight (kg) divided by height square (m2). Waist circumference (WC) was assessed among lower rib and iliac crest, in the exhaled state, with light clothing, applying a tape metre (Seca 201)(Reference Kouchi33). Waist to hip ratio (WHR) was calculated by dividing WC (cm) to hip circumference (cm). Waist to height ratio (WHtR) was calculated as WC (cm) divided by height (cm).
Visceral adiposity index (VAI) was dependent on WC (cm) and BMI (kg/m2) and two biochemical factors (TAG (mg/dl) and HDL-cholesterol (mg/dl)). VAI has a different formula for men and women. This index was used to indicate the visceral fat(Reference Baveicy, Mostafaei and Darbandi34).
$${Men:VAI = \left( {{{WC} \over {39 \cdot 68 + \left( {1 \cdot 88\; \times BMI} \right)}}} \right) \times \left( {{{TG} \over {1 \cdot 03}}} \right) \times \left( {{{1 \cdot 31} \over {HDL}}} \right)}$$
$${Women:VAI = \left( {{{WC} \over {39 \cdot 58 + \left( {1 \cdot 89\; \times BMI} \right)}}} \right) \times \left( {{{TG} \over {0 \cdot 81}}} \right) \times \left( {{{1 \cdot 52} \over {HDL}}} \right)}$$
Body roundness index was a good expression for body fat and dependent on WC (cm) and height (cm)(Reference Baveicy, Mostafaei and Darbandi34).
$$BRI = 364 \cdot 2 - 365 \cdot 5\; \times \;\sqrt {1 - \left( {{{{{\left( {{{WC} \over {2 \pi }}} \right)}^2}} \over {{{\left( {0 \cdot 5\;height} \right)}^2}}}} \right)} $$
Body adiposity index (BAI) was a direct indicator of body fatness and independent of further adjustment for other characteristics like sex and age. BAI was also dependent on hip circumference (cm) and height (m)(Reference Freedman, Thornton and Pi-Sunyer35).
$$BAI = {{Hip\;circumferemce\;\left( {cm} \right)} \over {Height\;{{\left( m \right)}^{1 \cdot 5}}}} - 18$$
Clinical assessment
After 8–12 h of fasting, 10 ml of blood samples was collected from all participants for blood sample collection. Serum and blood samples were centrifuged, poured into clean cryotubes and stored at –80°C until the analysis was performed. HDL was assessed by applying the cholesterol oxidase phenol-amino-pyrine method and TAG was assessed by applying the enzymatic method, based on glycerol-3-phosphate oxidase phenol-amino-pyrene with the automatic machine (Selectra E, Vitalab) with inter- and intra-assay coefficient variances lower than 10 %.
Dietary assessment
The participant’s intake of food and drinks, in the past 24 h, was captured by trained dietitians using three non-consecutive days 24-h dietary recalls (24HR), which were structured and included two weekdays and one weekend day. The first 24HR were recorded by face-to-face interview and the other two 24HR were recorded by telephone interview. The 24HR was a standardised five-pass approach originated from the USA Department of Agriculture (USDA)(Reference Subar, Kipnis and Troiano36). Then, the nutrients intake of individuals was analysed by Nutritionist IV software.
Participants were also asked to report the definite time of the largest and smallest energy contents of meals and of all other snacks. The number of main meals and snacks that were consumed per day and eating breakfast was recorded. Breakfast was marked as a meal consumed before 11.00 hours, lunch between 11.00 and 16.00 hours and dinner between 17.00 and 23.00 hours(Reference Kahleova, Lloren and Mashchak9,Reference Huang, Roberts and Howarth37) .
Covariates
In accordance with inclusion and exclusion criteria, participants were selected and interviewed to gather data on demographics, menopause, physical activity, smoking status and supplement intake. We indicated widely validated International Physical Activity Questionnaire to measure physical activity of the subjects. Then, subjects were divided into three categories based on metabolic equivalents (MET), defined as very low (< 600 MET-min/week), low (600–3000 MET-min/week), moderate and high (> 3000 MET-min/week)(Reference Wareham, Jakes and Rennie38). Blood pressure was assessed two times, in the seated position, after a 10–15-min rest, using a digital sphygmomanometer (Beurer, BC 08), and the average of systolic and diastolic blood pressure was measured.
Statistical analysis
The statistical analysis was conducted applying SPSS version 26 (IBM). For comparison of the general characteristics, according to chronotypes, an independent sample t test and χ 2 test were performed for continuous and categorical variables, respectively. One-way ANOVA and χ 2 tests were used for continuous and categorical variables to present the differences between eating habits, according to chronotypes. We also used an independent sample t test to compare differences between chronotypes and anthropometric measures. In addition, we separately analysed data for men and women since there were discrepancies by sex on eating behaviours and anthropometric measures.
BMI was classified into two groups, including underweight/normal weight (< 25 kg/m2) and overweight/obese (≥ 25 kg/m2)(Reference Shah and Braverman39). We used sex-specific cut-points to classify WC and WHR. WC was classified into two groups as < 90 v. ≥ 90 in males and < 80 v. ≥ 80 cm in females(Reference Ahmad, Adam and Nawi40).WHR was classified into two groups, including < 0·95 v. ≥ 0·95 in males and < 0·80 v. ≥ 0·80 in females(Reference Lean, Han and Morrison41). WHtR was classified into two groups as < 0·5 v. ≥ 0·5(Reference Browning, Hsieh and Ashwell42). VAI was divided into two categories based on the prediction of the metabolic syndrome: <4·11 v. ≥ 4·11 in males and < 4·28 v. ≥ 4·28 in females. Body roundness index was classified into two groups as <4·75 v. ≥ 4·75 in males and < 6·17 v. ≥ 6·17 in females(Reference Baveicy, Mostafaei and Darbandi34). BAI was classified into two groups using cut-points based on population-based BMI cut-off values as < 25·6 v. ≥ 25·6 in males and < 37·7 v. ≥ 37·7 in females(Reference Gupta and Kapoor43).
We also categorised specific eating behaviours so that we classified fruit and vegetable intake into two groups as < 206 v. ≥ 206 g/d since the intake of fruit and vegetable was lower (on overage: 2·58 servings/d (206 g/d)) than the WHO recommended guidelines (5 servings/d) among Iranian adults(Reference Esteghamati, Noshad and Nazeri44). Responses of having more than 3 d/week eating breakfast classified as ‘having breakfast’ and less than 3 d/week eating breakfast classified as ‘skipping breakfast’. Tea consumption was classified into two groups, including (< 480 v. ≥ 480 g/d)(Reference Naveed45). Soft drinks, fast food and processed meats were classified into two groups based on median split (soft drinks: < 9·3 v. ≥ 9·3 g/d, fast food: < 29 v. ≥ 29 g/week and processed meats: < 1 v. ≥ 1 g/d). OR and 95 % CI were achieved using logistic regression to investigate the association of the chronotype (independent variable) with anthropometric measures (BMI, WC, WHR, WHtR, VAI, VRI and BAI) (dependent variables). Moreover, logistic regression was conducted to evaluate the association between chronotype (independent variable) and specific eating habits (dependent variables). The risk was described in an adjusted model for age, energy intake, marital status, smoking, education, occupation, sleep duration, physical activity, supplement intake and menopause. The statistical significance level was accepted at 0·05 for all analyses.
Results
Characteristics of participants
Eight hundred fifteen adults were incorporated in the study (68·7 % females; 44·5 (sd 11) years). In Table 1, the general characteristics of participants according to chronotypes were reported. Morning- and intermediate/evening-type chronotypes accounted for 51·4 and 48·6 % of the total individuals, respectively. Intermediate/evening-type chronotypes were shown to have a lower education of diploma (53 %), employed (49·9 %) and smokers (11·6 %) compared with morning types. The mean of TAG and HDL among intermediate/evening types was 146 (sd 80) mg/dl and 49·9 (sd 10·4) mg/dl. Moreover, the mean of TAG and HDL among morning types was 140 (sd 80) mg/dl and 49·7 (sd 9·91) mg/dl, respectively. In addition, the interviewed males and females did not differ significantly in age and the other general characteristics except for SBP and DBP among males, based on their chronotypes. So, intermediate/evening-type males had higher levels of SBP and DBP than morning types (PSBP = 0·03 and PDBP = 0·04).
Table 1. General characteristics of participants according to chronotypes in men and women
(Numbers and percentages; mean values and standard deviations)
DBP, diastolic blood pressure; SBP, systolic blood pressure.
* Calculated by χ 2 and independent sample t test for qualitative and quantitative variables, respectively and P-value < 0·05 indicates significant level.
The differences between dietary intakes and eating habits according to chronotypes
The differences between eating habits, according to chronotypes, are presented in Table 2. Males with the intermediate/evening-type chronotype were indicated to have a lower intake of total fibre (P = 0·01) than morning types after adjustment for covariates. Also, intermediate/evening-type males were indicated to have a habit of eating snacks during the day (P = 0·02) than morning types. Of 584 females, those with intermediate/evening-type chronotypes were significantly more likely to eat breakfast later (P < 0·001) after covariates adjustment. However, our analysis did not present any significant difference for the other eating habits among the two groups.
Table 2. The differences between, dietary intakes and eating habits according to chronotypes in men and women
(Mean values and standard deviations)
* Adjusted for age and enery intake.
** Adjusted for age.
† Calculated by χ 2 and one-way ANOVA for qualitative and quantitative variables, respectively and P-value < 0·05 indicates significant level.
Logistic regression demonstrated no significant relation between chronotype and specific eating habits in men and women after controlling for confounders (all P > 0·05) (online Supplementary Table S1).
The anthropometric measures of participants according to chronotypes
Table 3 presents the anthropometric measures of participants according to chronotypes. No significant differences were observed between chronotypes and anthropometric measures in both sexes (all P > 0·05).
Table 3. The anthropometric measures of participants according to chronotypes
(Mean values and standard deviations)
BAI, body adiposity index; BRI, body roundness index; VAI, visceral adiposity index; WC, waist circumference; WHR, waist to hip ratio; WHtR, waist to height ratio.
† Calculated by independent sample t test, and P-value < 0·05 indicates significant level.
We used logistic regression to investigate the association between chronotypes and anthropometric measures (obesity and abdominal obesity measures), based on BMI, WC, WHR, WHtR, VAI, VRI and BAI (Table 4). Logistic regression revealed that intermediate/evening chronotypes have no significant relation with an increase in anthropometric measures after adjusting for potential covariates, including age, energy intake, marital status, smoking, education, occupation, sleep duration, physical activity, supplement intake and menopause in both sexes (all P > 0·05).
Table 4. The association between anthropometric measures (obesity and abdominal obesity measures) and chronotypes in men and women (Odds ratios and 95 % confidence intervals)
BAI, body adiposity index; BRI, body roundness index; ref, reference; VAI, visceral adiposity index; WC, waist circumference; WHR, waist to hip ratio; WHtR, waist to height ratio.
Cut-points for anthropometric measures/indexes: BMI (≥ 25 v. < 25 kg/m2), WC (men ≥ 90 v. < 90 cm, women ≥ 80 v. < 80 cm), WHR (men ≥ 0·95 v. < 0·95, women ≥ 0·8 v. < 0·8), WHtR (≥ 0·5 v. < 0·5), VAI (men ≥ 4·11 v. < 4·11, women ≥ 4·28 v. < 4·28), BRI (men ≥ 4·75 v. < 4·75, women ≥ 6·17 v. > 6·17) and BAI (men ≥ 25·6 v. < 25·6, women ≥ 37·7 v. < 37·7).
* Calculated by logistic regression and P-value < 0·05 indicates significant level.
† Adjusted for age, energy intake, marital status, smoking, education, occupation, sleep duration, physical activity, supplement intake and menopause.
Discussion
The current study aimed to explore the association of chronotype and eating habits with anthropometric measures in Iranian adults. However, we found no significant relation between chronotype and following specific eating habits. Moreover, no obvious differences were observed between chronotypes and anthropometric measures and indexes. These findings suggest that intermediate/evening-type chronotypes might not be significantly related to higher anthropometric measures and following unhealthy eating habits among Iranian adults.
Chronotypes and eating habits
Our study indicated no significant relation between chronotypes and specific eating habits after controlling for covariates. In contrast, a review study that analysed data from thirty-six studies indicated that evening types followed an unhealthier diet associated with obesity. Therefore, these people were more likely to fail in weight loss programmes(Reference Mazri, Manaf and Shahar46). In addition, previous studies demonstrated that evening-type chronotype was related to binge eating(Reference Harb, Levandovski and Oliveira47) and greater intake of energy(Reference Maukonen, Kanerva and Partonen48), mainly from lower intake of fruits and vegetables(Reference Baron, Reid and Kern8) and higher intake of fast food(Reference Fleig and Randler49).
In comparison, morning-type chronotypes were more prone to have cognitive restrain and were more prone to have lower disinhibition and inclination to hunger(Reference Schubert and Randler50). Beaulieu et al., in a study of forty-four adults (aged 18–25 years), indicated that morning-type individuals had a lower tendency for higher intake of fast food and had a smaller appetite than evening-type individuals(Reference Beaulieu, Oustric and Alkahtani51). Thus, previous studies have shown that evening types have adverse health behaviours. Although we combined intermediate- and evening-type groups due to the small number of evening types (n 15), our results were more likely to reflect just the differences between intermediate and morning types and it might not capture the evening-type chronotype. Moreover, this discrepancy with the previous study might indicate that not everyone takes advantage of having a morning-type chronotype. Moreover, our study did not consider the following eating habits, including food addiction, watching TV during meals, binge eating and eating duration, which might be the factors that caused our relationship to be insignificant.
Chronotypes and anthropometric measures
We observed no significant relationship between chronotype and anthropometric measures after controlling for covariates. A finding from a cohort study involving 390 healthy young adults indicated no significant relationship between chronotype and anthropometric outcomes, including BMI, WHR and WHtR(Reference McMahon, Burch and Youngstedt52). Also, a Spanish cohort study among 4243 adults with 3·5 years of follow-up indicated no relation between total energy intake in the evening and weight gain(Reference Hermengildo, López-García and García-Esquinas53). The majority of other studies investigating the relation between chronotype and anthropometric outcomes indicated that evening types had higher BMI and were more likely to gain weight than morning types(Reference Culnan, Kloss and Grandner26,Reference Wang54) .
A study by Amicis et al., in a cross-sectional study among 416 European adults, demonstrated that for a 1 point increment in reduced MEQ score, evening types had 0·5 cm greater visceral fat and 2 cm greater WC than morning types(Reference De Amicis, Galasso and Leone55). BAI, body roundness index and VAI are new indices that effectively predict cardiovascular events and metabolic abnormalities(Reference Baveicy, Mostafaei and Darbandi34,Reference Bennasar-Veny, Lopez-Gonzalez and Tauler56) . However, we did not find any association between chronotype and these new indexes. Plausible explanations for this discrepancy might be due to the cross-sectional design of our study or differences in individual characteristics. For instance, young adults are likely to have raised tolerance to shift works(Reference Saksvik, Bjorvatn and Hetland57). Moreover, most of our participants were women and overweight. It should be noted that women and overweight individuals are more likely to underreport their weight compared with normal body weight individuals and men(Reference Hirvonen, Männistö and Roos58). In addition, some methodological problems, such as different dietary assessment methods and variations in assessing and classifying chronotypes (the fact intermediate- and evening-type groups were combined due to the small number of evening types), might lead to inconsistent findings. In other words, diurnal preferences were investigated using MEQ(Reference Horne and Östberg19). However, most of our participants were of the age to be a working population. Hence, the MEQ might not reflect what they were actually doing in terms of their habits as it relates to chronotype.
As mentioned previously, evening types are exposed to following unhealthier food intake(Reference Maukonen, Kanerva and Partonen23,Reference Kanerva, Kronholm and Partonen59) and are at higher risk of obesity(Reference Culnan, Kloss and Grandner26), type 2 diabetes(Reference Merikanto, Lahti and Puolijoki21) and all-cause mortality(Reference Knutson and Von Schantz22) than morning types. One possible mechanism underlying the relation between chronotype and obesity risk might be increased plasma C-reactive protein level(Reference de Punder, Heim and Entringer60). C-reactive protein level is greater in the evening chronotype(Reference Yu, Yun and Ahn61), an impact that might be possibly described by alterations in the activity of the autonomic nervous system(Reference Roeser, Obergfell and Meule62). In addition, evening chronotype is related to greater cortisol response to a standardised laboratory stressor. Also, there was a positive relation between cortisol response and obesity(Reference de Punder, Heim and Entringer60). Lipid metabolism in the liver and adipose tissue is regulated by glucocorticoids that stimulate hepatic gluconeogenesis and cause short-term insulin resistance(Reference Hitze, Hubold and van Dyken63) and thus, it promotes abdominal visceral fat mass as well as weight gain through elevated cortisol secretion(Reference Geiker, Astrup and Hjorth64). Altogether, these effects could cause metabolic dysfunction in evening type chronotypes(Reference de Punder, Heim and Entringer60). Considering such chronotype-related factors, more studies are needed to elucidate this association.
Strengths and limitations
To the best of our knowledge, this is the first study to evaluate the relationship between chronotype, eating habits and anthropometric measures among Iranian adults, which could design new policies to avoid obesity in the Iranian population. Clinical trial findings mentioned that adults involved in weight control programmes benefit more from a chronotype-related diet than traditional recommendations(Reference Galindo Muñoz, Gómez Gallego and Díaz Soler65). Additionally, we analysed data for men and women separately. In addition, our sample size was high and we also included new indices, which were related to cardiometabolic risks(Reference Baveicy, Mostafaei and Darbandi34,Reference Bennasar-Veny, Lopez-Gonzalez and Tauler56) and evaluated their relations with chronotype.
Our study had several limitations that needed to be acknowledged. First, we used 24HR for dietary assessment, which may have non-differential misreporting bias. Second, the cross-sectional design of this study may not find a true cause and effect relation between chronotype, eating habits and anthropometric measures. Furthermore, applying the USDA food composition table to estimate micro- and macronutrients might influence our results since USDA food composition table does not have the same nutrient content as the same food available in Iran. Also, some of the analyses that were conducted in our manuscript were post-hoc exploratory and not prospectively determined.
Moreover, we classified subjects into two groups based on their chronotypes (intermediate/evening and morning types). Besides, if we divided subjects into three groups, according to their chronotype (morning-, intermediate- and evening-type chronotype)(Reference Mozafari, Tabaraie and Tahrodi32), the evening group had only thirteen members compared with the other two groups (morning type (n 437) and intermediate type (n 400)), which caused the participants could not be divided balance; as a result, this led to a decrease in statistic power. As this study included a small number of evening types, it might not capture this particular group and thus the results more likely reflect just the differences between morning and intermediate types. Besides, this unusual distribution of chronotypes might be due to age of the participants because the average age of our participants was 44·7 years and this can be a factor that causes an imbalance between chronotypes distribution. For example, Paine et al., in a study of 2526 adults living in New Zealand (average age: 40 years), indicated that the mean score of the chronotype was 58·1(Reference Paine, Gander and Travier66). Moreover, another study of 526 French adults (average age: 51 years) showed that the mean score of chronotype was 59·6(Reference Taillard, Philip and Chastang67). On the other hand, a previous study showed that individuals tend to be more morning oriented with increases in the age(Reference Hur68). However, more than 50 % of the total variance is attributed to the heritability of morningness–eveningness(Reference Hur68).
Conclusion
Overall, our cross-sectional study did not indicate an association between chronotype and anthropometric measures and specific eating habits among Iranian adults. Further studies are needed, considering the limitations of our study and combined sleep disturbances with chronotypes, which might change our results.
Acknowledgements
Authors thanks all those who participated in this study.
We did not receive any funding for this study.
A. L. and M. M. collected the data. S. Z. M. performed the statistical analysis. S. Z. M., A. L. and M. M. wrote the first draft contribution from S. S. B. S. Z. M., A. L., M. M. and S. S. B. reviewed the draft. S. S. B. revised the manuscript for the final version. All of the authors are responsible for the final manuscript.
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.
Supplementary material
For supplementary material referred to in this article, please visit https://doi.org/10.1017/S0007114522001842
