Variants of the Cry 1 Gene May Inuence the Effect of Fat Intake on Resting Metabolic Rate In Overweight and Obese Women: A Cross-sectional Study

Background: Previous studies have shown that the minor allele (C allele) for Cry 1 rs2287161, may be associated with increased risk of cardiovascular diseases (CVDs). Low resting metabolic rate (RMR) caused by the diet has been shown to have, potentially, unfavorable effects on obesity. This study sought to investigate the interactions between the Cry 1 Gene and fat intake on RMR in overweight and obese women. Methods: This comparative cross-sectional study was conducted on 377 Iranian women with overweight and obesity. A food frequency questionnaire (FFQ), with 147 items, was used to assess dietary intake. Individuals were categorized into two groups based on the rs2287161 genotype. Body composition, dietary intake, and RMR were assessed for all participants. Results: There was a signicant difference between genotypes for FBS (P=0.04), fat free mass (FFM) (P=0.0009), RMR per FFM (P =0.05), RMR per body mass index (BMI) (P=0.02), and RMR deviation (P=0.01). Our ndings also showed signicant interactions between total fat and C allele carrier group on RMR per kg, RMR per body surface area (BSA), RMR per FFM, and RMR deviation (P for interaction <0.1), in addition to a signicant interaction between CC+CG group genotype and PUFA intake on RMR per BMI (P for interaction =0.009) and RMR per kg (P for interaction=0.02) and RMR per BSA (P=0.07), compared to the GG group, after control for confounder factors. Conclusion: These results highlight that dietary compositions, gene variants, and their interaction, should be acutely considered in lower RMR. Utilizing an ergonomically designed mask, a small portion of breathed air was conducted through the volume ow sensor. The RMR is evaluated by measuring the amount of O 2 consumed and CO 2 produced. The RMR was assessed in the morning, after a comfortable night’s sleep, and following a 10–12 hour fast. Subjects were asked to avoid caffeine or alcohol consumption and severe intensity exercise for a day before. After reclining in a steady-state and a supine position in a quiet room, the RMR was measured, continuously, for 30 min. The respiratory exchange ratio and oxygen uptake (VO 2 ) were analyzed within the middle 20 min of the resting period. Predictive RMR was determined using the Harris-Benedict equation, which considers the weight, height, and age of participants (45). Participants were classied to two groups, low and high RMR, based on median values for; RMR per body surface area (BSA) (854.50), RMR deviation (-8.00), RMR per BMI (50.90), and RMR per FFM (33.73), and 20 kcal/24h /kg for RMR per kg. minor allele: G) was genotyped by polymerase chain reaction-restricted length polymorphism (PCR–RFLP) technique. PCR applied the following primers: forward 5′-GGAACAGTGATTGGCTCTATCT − 3′; reverse 5′-GGTCCTCGGTCTCAAGAAG-3′. PCR reactions were performed in a nal volume of 20 µl include of 2 µl primers, 1 µl extracted DNA,7 µl distilled water, and 10 µl Taq DNA Polymerase Master Mix (Amplicon; Denmark) with the next conditions in a DNA thermocycler: The DNA templates were denatured at 94° C for 4 min; amplication contained of 35 cycles at 94°C, 58°C and 72°C (each stage for 30 s), with a nal extension at 72°C for 7 min. Amplied DNA (10 microliters) was mixed with 2 microliters of DRI restriction enzyme (Thermo Fisher Scientic; USA) at 37 ° C. To ensure the PCR process and amplication of the desired parts, PCR products electrophoresis was performed on agarose gel. Fragments including three possible genotypes were then determined: uncut homozygous GG (107 bp), cut heterozygous GC (107,48 and 226 bp), and cut homozygous CC (155 and 226 bp). In order to examine the interactions between fat intake, SFA, PUFA intake, and Cry 1 polymorphisms on RMR, the participants were grouped based on Cry 1 polymorphisms: group 1 with GG (rs2287161) genotype (n 107), group 2 or C allele carrier group with CC and GC genotype (n 270).


Introduction
The prevalence of overweight and obesity has increased such that almost one-third of the global population is now categorized as overweight or obese (1,2). Globally, obesity is almost 50% more prevalent among women (3,4), primarily attributed due to a 3-5% lower resting metabolic rate (RMR) compared to men (5). Overweight and obesity may be de ned by an abnormal or excessive fat accumulation that leads to health impairment (6). Moreover, obesity is associated with diabetes mellitus, cardiovascular diseases (CVDs), Alzheimer's disease, osteoarthritis, obstructive sleep apnea syndrome, and some types of cancer (7)(8)(9), and is, generally, the culmination of a complex interaction of genetic, environmental, and lifestyle factors, e.g. low physical activity, energy-dense foods, and a high-fat diet (10)(11)(12)(13).
Resting metabolic rate is considered a major predictor for future body weight gain (14,15). Indeed, RMR accounts for 60-75% of total energy expenditure (16), and it is highly determined by body composition, speci cally fat-free mass (FFM) (17). A signi cant reduction in FFM has a negative in uence on RMR, and increases susceptibility to weight regain (18,19). Energy intake and FFM are strongly linked (20), and, by extension, RMR is associated with energy intake (21).
For example, a low-glycemic-load diet has been shown to attenuate the decline of resting energy expenditure (REE) during weight loss (22,23). Indeed, as adipose tissue increases in overweight and obese adults, fat mass (FM) poses a greater in uence on RMR (24). Different body composition indices, such as weight, lean body mass, and body cell mass are inter-related (25), thus, various ratios of RMR are used among individuals with different body sizes. One of these ratios is RMR per body mass index (RMR/BMI), and because the FM/FFM proportions of the same BMIs may vary (26), RMR per kilogram of FFM (RMR/FFM) (27) is also used to describe metabolic changes (28). In addition, RMR per kilogram (kg) of body weight (RMR/BW) (27) is also measured, and cut-offs determined for RMR/BW predict the risk of obesity and its complications (29).
The human circadian clock is responsible for the coordination between energy intake and expenditure based on changes in external factors including sunset/sunrise, physical activity, and dietary intake (30)(31)(32). At the molecular level, the central circadian clock consists of Clock (circadian locomotor output cycles kaput), Bmal1 (brain and muscle Arnt like protein-1), Per (period)1,2,3, and Cry1,2 (cryptochrome) genes (33). According to experimental studies, Cry1 plays a major role in lipid metabolism (34). Indeed, hepatic depletion of CRY proteins increases circulating glucose, and their overexpression leads to a Assessment of high fat intake A semi-quantitative, standard food frequency questionnaire (FFQ) was used to assess dietary intake, which was previously validated and adapted for this population (43).The FFQ included 147 foods commonly consumed by Iranians, which were de ned by standard serving sizes for each food item. FFQ data were collected through face-to-face interviews by trained interviewers at the health centers in Tehran. The software program, Nutritionist IV, was used for nutrient analysis, and was modi ed for Iranian foods (44).
To calculate fat intake, we rst adjusted fat intake to energy, and then the associated percentage was calculated as the total daily caloric intake, where above 30% was de ned as high fat intake and < 30% de ned as low fat intake. Also, for saturated fatty acid (SFA) and poly-saturated fatty acid (PUFA), medians of SFA and PUFA intake were applied in statistical analysis.

Anthropometric Measurements
Weight was measured using a digital weighing scale, where participants wore light indoor clothing, were unshod, and recorded to the nearest 100 g. Height was measured to the nearest 0.5 cm while participants were in the normal standing position, without shoes, using a standard stadiometer. Waist circumference (WC) was measured at the umbilicus and recorded to the nearest 0.5 cm. A plastic tape measure was used to assess and hip circumferences (HC), to the nearest 0.5cm, then, the ratio between waist and hip (WHR) circumferences was calculated. BMI was computed from the height and weight data, using the standard, weight (kg)/height 2 (m 2 ), equation.

Resting metabolic rate (RMR) Measurement
Resting metabolic rate was measured for all participants by a trained and experienced nutritionist using indirect calorimetry (spirometer METALYZERR 3B-R3, Cortex Biophysik GmbH, Leipzig, Germany). According to the manufacturer's instructions, gas ventilation and exchange was calibrated before each test. Highresolution spiroergometric systems, with an infrared sensor, were used for CO 2 evaluation and an amperometric solid electrolyte sensor for O 2 evaluation, which were both recorded continuously through breath-by-breath gas analysis. Utilizing an ergonomically designed mask, a small portion of breathed air was conducted through the volume ow sensor. The RMR is evaluated by measuring the amount of O 2 consumed and CO 2 produced. The RMR was assessed in the morning, after a comfortable night's sleep, and following a 10-12 hour fast. Subjects were asked to avoid caffeine or alcohol consumption and severe intensity exercise for a day before. After reclining in a steady-state and a supine position in a quiet room, the RMR was measured, continuously, for 30 min.
The respiratory exchange ratio and oxygen uptake (VO 2 ) were analyzed within the middle 20 min of the resting period. Predictive RMR was determined using the Harris-Benedict equation, which considers the weight, height, and age of participants (45). Participants were classi ed to two groups, low and high RMR, based on median values for; RMR per body surface area (BSA) (854.50), RMR deviation (-8.00), RMR per BMI (50.90), and RMR per FFM (33.73), and 20 kcal/24h /kg for RMR per kg.

Assessment of other variables
The International Physical Activity Questionnaire (IPAQ) was used to assess Physical Activity (PA), and was reported as metabolic equivalent hours per week (METs h/week) (46). Activity levels were classi ed into low (< 600METs), moderate (600-3000 METs), and high (≥ 3000 METs) levels, according to the IPAQ scoring protocol. A demographic questionnaire (information on age, marital status, education, economic and job status) at study commencement.
DNA extraction and sequencing of the gene The Cry 1 gene primer was selected based on a previous study (47). All participants from whom deoxyribonucleic acid (DNA) samples were accessible, were evaluated to be genotyped for the rs2287161. According to the manufacturer's protocol, we extracted genomic DNA from blood samples with the use of the Mini Columns, Type G kit (GeneALL, Exgene) The concentration and quality of the extracted DNA were assessed by the use of a Nano Drop ND-2000 spectrometer. The rs2287161 (major allele: C; minor allele: G) was genotyped by polymerase chain reaction-restricted length polymorphism (PCR-RFLP) technique. PCR applied the following primers: forward 5′-GGAACAGTGATTGGCTCTATCT − 3′; reverse 5′-GGTCCTCGGTCTCAAGAAG-3′. PCR reactions were performed in a nal volume of 20 µl include of 2 µl primers, 1 µl extracted DNA,7 µl distilled water, and 10 µl Taq DNA Polymerase Master Mix (Amplicon; Denmark) with the next conditions in a DNA thermocycler: The DNA templates were denatured at 94° C for 4 min; ampli cation contained of 35 cycles at 94°C, 58°C and 72°C (each stage for 30 s), with a nal extension at 72°C for 7 min. Ampli ed DNA (10 microliters) was mixed with 2 microliters of DRI restriction enzyme (Thermo Fisher Scienti c; USA) at 37 ° C. To ensure the PCR process and ampli cation of the desired parts, PCR products electrophoresis was performed on agarose gel. Fragments including three possible genotypes were then determined: uncut homozygous GG (107 bp), cut heterozygous GC (107,48 and 226 bp), and cut homozygous CC (155 and 226 bp). In order to examine the interactions between fat intake, SFA, PUFA intake, and Cry 1 polymorphisms on RMR, the participants were grouped based on Cry 1 polymorphisms: group 1 with GG (rs2287161) genotype (n = 107), group 2 or C allele carrier group with CC and GC genotype (n = 270).

Laboratory tests
All samples were collected, after 10-12 hours fasting, at the laboratory of the school of Nutritional and Dietetics at TUMS. Fasting serum glucose, insulin, total cholesterol, triglyceride (TG), low density lipoprotein (LDL), and high density lipoprotein (HDL) were measured from blood samples. IR was calculated by the homeostatic model assessment(HOMA) according to the following equation: HOMA-IR= [fastingplasma glucose (mmol/l) * fasting plasma insulin (mIU/l)]/22.5

Statistical analysis
The Hardy-Weinberg equilibrium and comparison of categorical variables were assessed with the χ2 test. Descriptive statistics, including the mean (standard deviation) and frequency summaries, were used to describe the study population. Comparisons between groups were made using the independent t-test for continuous variables and chi-square test for categorical variables. Moreover, age, BMI, IPAQ, and energy intake-adjusted analyses were performed using general linear models. Moreover, to analyze the potential interactions between genotype and diet intake, and the genotype and fat, PUFA or SFA, an interaction term of genotype x fat, SFA or PUFA dietary intake on types of RMR was included in the binary logestic regression. Data were analyzed using IBM SPSS version 23 (SPSS, Chicago, IL, USA). P < 0.05 was considered statistically signi cant, but for interactions, P < 0.1 was considered signi cant.

Study population characteristics
The present study was conducted on 377 obese and overweight Iranian women, of which, 70.8% were married, 36 (20), respectively. Also, the mean intake of total dietary fat intake was 95.13±35.17 gr, SFA 28.40±7.43 gr, and PUFA 20.08±7.57gr, respectively. The overall prevalence of rs2287161 genotypes in participants for CC+ CG and GG was 66.8% and 26.5%, respectively.

Study participant characteristics between genotype of rs1333048
A total of 377 Iranian overweight and obese women were categorized based on rs2287161genotypes and divided into two groups according to risk allele(C): CG + GC genotype (n = 270), GG genotype (n = 107).
Association between general characteristics of participants in three grouped of SFA (gr/d), PUFA (gr/d), and fat intake (gr/d) among the population General characteristics of participants, such as body composition, biochemical assessment, RMR measurement, and others among lower vs. higher than the median of total fat, trans fatty acid (TFA), and polyunsaturated fatty acid (PUFA) intake, are presented in Table 2. P-values for all variables were reported before the adjustment in the crude model by one-way analysis of variance (ANOVA), and after adjustment with potential confounders as age, BMI, physical activity, and energy intake using analysis of covariance (ANCOVA) ( Table 2).

General characteristics of participants among SFA intake categories
In the crude model, in body composition variables there were signi cant mean differences for BFM (P = 0.04), WC(P = 0.02), and in biochemical variables; TG(P = < 0.001). Among SFA categories, there was a signi cant mean difference for marriage status (P = 0.02). After adjusting for potential confounders, women with higher intake of SFA had signi cantly higher mean HOMA-IR (P = 0.02), and LDL(P = 0.02), all other variables were no longer signi cant after adjustment. Regarding other variables related to general characteristics, there were no signi cant differences noted (all P > 0.05).

General characteristics of participants among PUFA intake categories
There was a signi cant difference in cholesterol between lower and higher PUFA intake categories before adjustment (P = 0.05), but after controlling for confounders, this association was not present. There were no signi cant differences in terms of other biochemical assessments, body composition, RMR measurement, education level, economic status, marital status, rs2287161 genotypes, physical activity, and job-status (all P > 0.05) ( Table 2).

General characteristics of participants among total fat intake category
There were signi cant differences in age (P = 0.05), TG (P = 0.05), height (P = 0.02), and marriage status (P = 0.02) between lower and higher total fat intake categories in the crude model, but after controlling for confounders (age, BMI, physical activity and total energy intake), these variables were no longer signi cant(P > 0.05). There were no signi cant differences for the remaining variables before and after adjustment (P > 0.05) ( Table 2).

Dietary intake of study population according to rs2287161 genotypes
The dietary intake of the participants across two groups of risk allele genotype as GG and GC + CG are shown in Table 3. Dietary intake of macronutrients and the other micronutrients, such as vitamins and minerals, were signi cant in the crude or adjusted models (P < 0.05), Table 3.
The interactions between the intake of total fat, SFA, and PUFA intake, and rs2287161 genotypes on the different type of RMR The interaction between total fat, SFA, and PUFA intake and Cry 1 polymorphism (rs2287161) gene on the different types of RMR was performed using binary logistic regression model analysis, in Table 4. For this analysis, the GG genotype and categories of lower intake of total fat, PUFA, SFA were considered as reference groups. Interaction between different types of RMRs across total fat intake category In the crude models, there was no signi cant interaction between CC + CG group genotypes and high fat intake on odds of RMR per kg compared to the GG group (β:-0.65, OR:0.51; 95% CI:0.19-1.35, P = 0.18) but in Model 1, after adjusting for potential confounders, such as education level, BMI, marriage status, age, history of weight loss in past year, total energy intake, economic status, respiratory quotient (RQ), and physical activity, the association changed to a signi cant interaction (β:-1.55, OR: 0.21, 95%CI: 0.04-0.98, P = 0.02), indicating that participants with risk allele(C) of rs228716 genotype group (CC + CG) and higher intake of total fat were at a 79% lower odds for higher RMR per kg compared to participants with no allele risk(GG) and a lower intake of fat. The RMR per BSA variable in the crude model did not yield a signi cant interaction (β: -0.97, OR: 0.55, 95%CI: 0.13-1.18, P = 0.28), yet, after controlling for confounders, a signi cant interaction was found (β:-1.49, OR: 0.28, 95%CI: 0.05-0.92, P = 0.08), such that the group with the risk allele with higher fat intake had 72% lower odds for higher RMR per BSA compared to no risk allele group. In addition, RMR per FFM was not signi cant in the crude model (β: -0.59, OR: 0.55, 95%CI: 0.18-1.64, P = 0.28), but, in the adjusted model, a signi cant interaction was found (β: -1.24, OR: 0.28, 95%CI:0.07-1.16, P = 0.08). Thus, the CC + CG group with a higher intake of total fat compared to the GG group had 72% lower odds for higher RMR per FFM. Moreover, in the crude model, there was no signi cant interaction between the allele risk group (CC + CG) in comparison with the reference group (GG) on RMR deviation from normal (β:-0.77, OR: 0.46, 95%CI: 0.15-1.39, P = 0.17), however, after controlling for confounders, a signi cant interaction was found (β:-1.19, OR: 0.30, 95%CI: 0.07-1.24, P = 0.09), indicating that there were 70% lower odds for higher RMR deviation from normal in CC + CG group with higher intake of total fat intake, compared to the GG group (Table 4, Fig. 1). No signi cant interaction was found between RMR per BMI and total fat intake (  Fig. 2), indicating that individuals in the risk allele group with higher intake of PUFA intake had 71% lower odds for a higher RMR per BSA compared to the GG group.
There was a signi cant interaction between PUFA intake with risk allele (C) genotype group (CC + CG) on RMR per MBI in the crude model (β: -1.38, OR:0.25 CI:0.08-0.73; P = 0.01), and this remained signi cant after adjustment for potential confounders and lead to decreased odds (β: -1.97, OR:0.13 CI:0.03-0.61; P = 0.009). Accordingly, this equated to an 87% reduction in the odds of higher RMR per BMI in individuals in the risk allele group (CC + CG) and with higher intake of PUFA intake, compared to participants with no allele risk (GG) and a lower intake of PUFA (  (Table 4).

Discussion
The current cross-sectional study was conducted among overweight and obese adult women to investigate the interactions between the CRY1 gene and fat intake on RMR. An important factor which can signi cantly in uence obesity is dietary intake; however, recent research has indicated that genetic differences and variants in the human genome may alter energy expenditure and body weight (48). Therefore, we hypothesized that individuals with the CC + GC genotypes may have lower RMR compare to individuals with GG genotypes and a high-fat diet may interact with this association. Accordingly, based on our results, participants who consumed a diet consisting of more than 30% from fat with CC + GC genotype had lower RMR compare to subjects who consumed less than 30% from fat with GG genotype. We did not etect any signi cant interaction between different types of RMRs across SFA and PUFA categories.
Genetic pro le is an informative factor in the etiopathogenesis of obesity. In addition to gene polymorphisms, which effect on adipogenesis, there are some gene polymorphisms which can alter the regulation and level of energy balance (49). A two year randomized weight-loss diet trial found a signi cant relationship between Cry 2 rs2287161and changes of RMR (50). Moreover, in the mentioned study, it was found that dietary fat intake modi ed the effect of CRY2 in changes in respiratory quotient (RQ), a parameter of fuel utilization.
A 3-month low-calorie-diet interventional study among women who were at the risk of gestational diabetes revealed that carriers of G allele presented less body weight loss and less improvement in insulin secretion, HOMA-IR, and insulin sensitivity than counterparts who were non-carriers of the G allele (51). Indeed, previous research has shown that presence of the G risk allele was linked to decreased insulin secretion and sensitivity (52). In a study consisting of African-American pregnant women, it was indicated that participants who were C allele carriers have lower fat intake than non-carriers (53). Recently, Moradi et al. (54) posited that dietary fat intake may have an effect on RMR and RMR/FFM among obese women. Indeed, Moradi et al reported that the AA genotype of rs11290186 had a positive association with PUFAs intake, even after adjustment for energy intake. Moreover, there was an interaction between total fat and SFAs intake with the PPARGC1A genotypes, and, in line with the present study, the authors found that women with a fat intake of more than 30% of calories/day had lower RMR, as well as RMR/FFM (54).
The principal mechanism of the impact of gene variants on lipid metabolism, weight changes, and RMR level is unknown. However, animal studies have demonstrated the effect of fat on expression of clock gene mRNA, lipogenic genes, and circadian balance (55,56). High fat intake is known to induce a decrease of the mRNA, which is needed for several different enzymes, including glutathione synthetase, superoxide dismutase, and glutathione peroxidase (57). A high-fat diet can elicit the hyperacetylation of proteins, which is related to impaired mitochondrial function (58). Moreover, after a high-fat diet, hyperinsulinemia and insulin resistance can occur through glucagon-like peptide-1 signaling, which is related to reducing metabolic thermogenesis and energy expenditure reduction (59).
In the present study, we did not nd any signi cant interaction between different types of RMRs across PUFA categories. It has previously been shown that the higher intake of PUFA is bene cial for glycemic indices and lipid pro le among individuals who carried G allele (60). Moreover, a meta-analysis on feeding trials revealed that PUFA had some effect in improving insulin resistance (61). Also, PUFA can, reportedly, alleviate the in ammation of adipose tissue and oxidative stress (62). Indeed, the extant literature indicates that the composition of dietary fat is important in insulin-related processes and probably RMR. Therefore, more studies among different genders and age groups are needed to better elucidate the importance, and the manipulation, of dietary fat composition.
To the best of our knowledge, this is the rst investigation on the association between GC genotypes, dietary fat, and the level of RMR in overweight and obese women. However, notwithstanding the novelty of the present study, several limitations should be considered in the interpretation of the results, including the small number of participants, considering just one gender, and the cross-sectional nature of the study. Indeed, it is, therefore, advocated that cohort studies, that include both genders, be conducted; in addition to appropriately powered sample sizes.

Conclusion
In summary, the present study revealed that the high-fat intake, with the CC + GC genotypes, may contribute to a lower RMR in overweight and obese women.
The present study highlights the important role of gene-diet interaction and the potential for personalized diet therapy based on genetic characteristics. Moreover, this study indicates important future research directions regarding the importance of genetic variants and their association with circadian rhythms and changes in energy expenditure. Further studies are needed to con rm the veracity our ndings and to clarify the precise mechanism(s) of action. Each participant was completely informed about the study protocol and provided a written and informed consent form before taking part in the study.

Availability of data and materials
The data that support the ndings of this study are available from Khadijeh Mirzaei but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Khadijeh Mirzaei. Figure 1 Interaction between dietary fat and Cry 1 genotypes on RMR disorder. Footnote of Figure 1-Percentage of Types of RMR disorder across GC&CC and CC genotypes base on low and high dietary fat (% energy) A) Percentage of RMR per kg disorder in low intake across GG and GC&CC genotypes were respiratory 35.5% and 29.5%, Percentage of RMR/kg disorder in high intake across GG and GC&CC genotypes were respiratory 64.5 and 70.5%. B) Percentage of RMR per BSA disorder in low intake across GG and GC&CC genotypes were respiratory 38.8% and 28.9%., Percentage of RMR per BSA disorder in high intake across GG and GC&CC genotypes were respiratory 61.2%%and 71.1%. C) Percentage of RMR Deviation disorder in low intake across GG and GC&CC genotypes were respiratory 36.6% and 27.8%., Percentage of RMR Deviation disorder in high intake across GG and GC&CC genotypes were respiratory 63.4% and 72.2%. D)

Figures
Percentage of RMR per FFM disorder in low intake across GG and GC&CC genotypes were respiratory 37.2% and 30.8%., Percentage of RMR per FFM disorder in high intake across GG and GC&CC genotypes were respiratory 62.8%and 69.2%. *P for interaction is for model adjusted (Potential confounders: education level, BMI, marriage status, age, history of weight loss in past year, energy intake, economic status, RQ, and physical activity) Figure 2 genotypes base on low and high dietary PUFA A) Percentage of RMR/ kg disorder in low intake across GG and GC&CC genotypes were respiratory 56.2% and 45.7%., Percentage of RMR/kg disorder in high intake across GG, GC&CC genotypes were respiratory 43.8% and 55.3%. B) Percentage of RMR per BMI disorder in low intake across GG and GC&CC genotypes were respiratory 55.1% and 38.9%., Percentage of RMR per BMI disorder in high intake across GG and GC&CC genotypes were respiratory 44.9%and 61.1%. C) Percentage of RMR per BSA disorder in low intake across GG and GC&CC genotypes were respiratory 60.2% and 45%., Percentage of RMR per BSA disorder in high intake across GG and GC&CC genotypes were respiratory 39.8%and 55%. *P for interaction is for model adjusted (Potential confounders: education level, BMI, marriage status, age, history of weight loss in past year, energy intake, economic status, RQ, and physical activity)