Introduction: Single-nucleotide polymorphism (SNP) rs9939609 in the FTO gene has been associated with dietary intake and appetite traits, mainly in participants with obesity; however, it remains widely unexplored in normal weight participants. Thus, the aims of this study were (1) to compare the changes in subjective appetite sensations, ghrelin, and insulin concentrations according to the SNP rs9939609 T>A in FTO and (2) to compare dietary intake between rs9939609 genotype groups in normal weight young participants. Methods: We conducted a quasi-experimental study involving 88 normal weight participants to analyze subjective perception of appetite, hormonal response for hunger and satiety, and dietary intake according to the rs9939609 SNP. Participants received a standardized single breakfast. Visual analogue scales (VAS) were utilized for assessing the subjective perception of appetite at fasting and immediately after breakfast and at 30, 60, 90, and 120 min postprandially. Glucose, lipid profile, ghrelin, and insulin were measured at fasting and at 120 min after breakfast. Dietary intake was assessed with a 3-day food record. The SNP was determined by allelic discrimination with TaqMan probes. To compare dietetic, biochemical, and the subjective appetite sensations, Student t test, ANCOVA test, and the repeated measures ANOVA were used. The linear regression model and the linear mixed model were used for the association analysis. Pearson correlation was used to test the correlation between two quantitative variables. Results: A total of 88 people participated, 81.8% were female, with a mean body mass index of 21.8 ± 2.0 kg/m2 and a mean age of 20.6 ± 2.0. Genotype frequencies of the rs9939609 SNP were 52% for the TT allele and 48% for the TA/AA. The subjective perception of appetite named hunger, fullness, satiety, desire to eat, and prospective food consumption were similar between genotypes of the rs9939609. Participants with the TA/AA genotype showed a higher intake of added sugar (p = 0.039) than TT participants. No differences were found in ghrelin, insulin, glucose, or lipid parameters between genotypes. Conclusion: Carriers of the A allele from FTO gene SNP rs9939609 may have an increased preference for foods, specifically for added sugars.

Energy regulation corresponds to a homeostatic system in which one of the main regulating organs is the brain, more specifically the hypothalamus, where hormonal signals about nutritional status are received [1]. This mediation is provided by biochemical signaling between the endocrine and nervous systems through the action of gastrointestinal peptides and metabolic hormones like insulin [2]. Ghrelin, both in its acylated and deacylated forms, is found circulating in the blood, which is hypothalamic mediated and has been positioned as a key factor in the initiation of a meal [3, 4]. On the other hand, insulin is considered a hormone with anorexigenic functions, and its levels increase rapidly after a meal [5]. In people with a normal weight, fasting ghrelin and insulin levels of 1,625.66 pg/mL and 6.93 μU/mL, respectively, were observed. After receiving a standardized breakfast, the observed levels were 1,537.27 pg/mL for ghrelin and 10.69 μU/m for insulin [6].

A meta-analysis reported that normal weight subjects had higher total ghrelin concentrations under the fasting and postprandial conditions compared to people with obesity. The same was observed for postprandial PYY and hunger, where subjects without obesity showed higher values. Besides, no differences were found in active ghrelin, GLP-1, CKK, and fullness [7]. In another study, lean weight was classified as resistant or susceptible to obesity, and it was found that both groups have similar ghrelin levels after a standardized meal, but the susceptible to obesity group have higher scores in dietary restrain and disinhibition. The authors stated that the two groups had a different response to similar ghrelin levels, perhaps due to a different response in eating behavior and present dieting patterns [8].

Current dietary pattern in Mexico reflects a typical Western diet with high saturated fats, sodium, added sugar, calorie-dense foods, and ultra-processed and palatable foods. Western of Mexico is not the exception where the same pattern has been reported [9], but the reasons for this preference and selection of this type of food are not clear [10]. Understanding the above is relevant because the consumption of these nutrients has been associated with the development of obesity and other non-communicable diseases [11]. Nowadays, the obesogenic environment characterized by a high availability of energy-dense foods interacts with genetic variants which in turn favor the development of obesity [12, 13]. Consequently, the growth of obesity rates has also been linked to genetic factors due to the presence of single-nucleotide polymorphism (SNP) that can affect the regulation of food intake, energy balance, eating behavior, preference for certain types of food, and appetite regulation [14, 15].

One of the most studied SNPs is the rs9939609 T>A, which is in the fat mass and obesity-related (FTO) gene [16]. FTO is mainly expressed in the hypothalamus, and the allele A of the rs9939609 has been associated with higher energy intake, appetite, and basal metabolic rate [17‒19]. Carriage of the A allele has been associated with predisposition to obesity in children, adolescents, and adults [20‒22]. The FTO rs9939609 has been associated with a major energy intake [23], and since its greatest expression is in the hypothalamus it has been proposed that this genetic variant is involved in the development of obesity through the appetite regulation mechanisms in the hypothalamus [24, 25].

Most of the studies related to the influence of genetic variants and the dysregulation of appetite and appetite-related hormones have focused on participants with obesity [16‒19]; however, their association in subjects with normal weight is less studied. This is important because the presence of genetic variants and the dysregulation of hormones involved in appetite regulation may lead to an increased reward response to palatable foods like those with high content of simple sugars and saturated fats [26].

Therefore, we hypothesized that carriers of the A allele present a major response to appetite and dietary intake. Thus, the aims of this study were to (1) to compare the changes in subjective appetite sensations, ghrelin, and insulin concentrations according to the SNP rs9939609 T>A in FTO and (2) to compare dietary intake between rs9939609 genotype groups in normal weight young participants.

Participants

This study was conducted in the city of Guadalajara, Mexico, at the “Instituto de Nutrigenética y Nutrigenómica Traslacional” of the Universidad de Guadalajara. The recruitment period spanned from February 2019 to March 2020. The sampling method used was a convenience sampling. Call for participants was carried out through the distribution of flyers and advertising on social media. Inclusion criteria were male and female aged 18–25 years old, body mass index between 18.5 and 24.9 kg/m2, habit of eating breakfast, being available to attend 1 day for 3 h during the morning and be from Western Mexico. Participants in this study were young adults whose category corresponds to an age range of 18–25 years who are at a normal and predictable point of biological and psychological maturation [27]. The age range of this study was considered based on previous studies that show that after 25 years of age, life processes such as having a job, living with a partner, or having children can affect eating behavior [28, 29]. Exclusion criteria were being a vegan or vegetarian person, previous history of chronic diseases, having an impaired taste, having performed strenuous physical activity 24 h before the study, being a pregnant or breastfeeding female, taking medications that could interfere with their appetite, lipid-lowering drugs, or use of contraceptives.

A clinical history to evaluate scholar degree, marriage status, smoking, alcohol consumption, and sleep habits was applied, also place of birth of the participants, their parents, and their grandparents was asked.

Procedure

This study used a quasi-experimental design, where participants attended an intervention session in which they received a standardized breakfast. One week prior to attending the intervention, participants attended to the laboratory for an anthropometric and body composition assessment and at the end of the session were given instructions on how to fill out a 3-day consumption diary. The day of intervention participants arrived at the laboratory with a minimum fasting time of 8–10 h. Participants had 30 min to finish the breakfast. During fasting state and at 120 min after breakfast, a blood sample was taken to determine biochemical parameters. Additionally, visual analogue scales (VAS) of appetite were answered during fasting state, at the end of breakfast, and every 30 min until completing 120 min.

Breakfast Design

The breakfast design has been previously reported [30]. The breakfast was prepared at the “CUCSINE, Laboratorio de Gestión de Servicios de Alimentos, Centro de Universitario de Ciencias de la Salud, Universidad de Guadalajara,” and consisted of 526 kilocalories of which 43% were carbohydrates, 36% were fat, and 21% were protein. To know how much the breakfast contributed to the energy requirement of the participants, the total energy requirements was calculated as the sum of resting metabolic rate using Cunningham formula [31] + thermogenic food effect + factor of physical activity.

Body Composition, Anthropometrics, and Blood Pressure Measurements

Body composition was measured in the morning during fasting state using an InBody 370 equipment (Biospace Co., Seoul, Korea, 250 kg capacity, 0.1 kg precision). Body composition variables included total body water, lean mass, fat-free mass, fat mass, skeletal muscle mass, percentage of fat mass, and mineral mass. For height, a SECA stadiometer (SECA® stadiometer, SECA GMBH & Co., Hamburg, Germany; model 213) was used with an accuracy of 0.1 cm. For waist and hip circumference, a Lufkin Rosscraft® tape (Lufkin Rosscraft® metal tape measure, Houston, TX, USA; model W606, range 0–200 cm, accurate to 0.1 cm) was used. The waist circumference was measured at the narrowest level between the costal border and the iliac crest, and the hip circumference was measured at the maximum level of the buttock protrusion [32]. The classification of normal and elevated body fat percentage was done considering the age and sex and of the participants [33]. Besides, male and female were classified as with normal or elevated waist circumference according to cut-off points specific for sex [34].

Blood pressure was taken after the participants had rested for 15 min. An Omron Automatic arm digital blood pressure monitor (HEM-7130 Omron Healthcare Co., Ltd., Kyoto, Japan) was used. The participants remained seated with their backs touching the back of the chair, resting their arms on a horizontal surface, and without crossing their legs.

Dietary Intake Assessment

Participants were instructed to fill out a dietary record to register all food and beverages consumed for 3 days (one weekend day, one weekday, and 1 day before the intervention). To identify food portions, participants were shown food replicas from Nasco to use as reference (Atkinson, WI, USA). Dietary data were analyzed with Nutritionist Pro™ software version 8.1 (Axxya Systems, Woodinville, WA, USA) which uses the United States Department of Agriculture (USDA) food composition tables. The dietary records also allowed obtaining information regarding the energy consumption of the last previous meal (dinner time) before the participation in this study.

For the dietary analysis presented in this study, 6 main food groups were identified. The starch/bread group includes foods such as grains, cereals, pastas, beans, peas, lentils, high starch vegetables such as corn and potatoes as well as breads. The meat listing is characterized by a portion of 7 g of protein and is subdivided according to the amount of fat that the portion of food may contain: very lean, lean, medium-fat, and high-fat. The group of vegetables with an average content of less than 5 g of carbohydrates and the list of fruits with an average of 15 g of carbohydrates. The milk group is divided into three according to their fat content, skim, low-fat, and whole, and the fat group with an average fat content of 5 g.

Available carbohydrate has been defined as the sum of total sugars (glucose, fructose, galactose, sucrose, maltose, lactose), oligosaccharides, and complex carbohydrates (dextrin, starch, and glycogen), these are carbohydrates that are digested and absorbed and are glucogenic in humans [35]; while total sugars include all sugars found naturally in food. Added sugars were also considered as those that were added to the food during its processing and are included in total sugars [36].

Subjective Appetite Sensations

For the assessment of appetite sensations, VAS were used, which consist of a 100-mm straight line with the phrase “not at all” at one end and at the other end the phrase “extremely.” Participants were asked to mark with a thin vertical line according to their level of sensation at that moment when answering the following questions: How hungry are you? How full are you? How satisfied are you? How strong is your desire to eat? How much do you think you could eat right now? [37, 38]. VAS were presented in Spanish language to the participants.

Blood Samples and Biochemical Determinations

A peripheral blood sample was collected at baseline (8–10 h of fasting) and 120 min after consuming a standardized breakfast. Blood samples were centrifuged at 2,399 g to obtain serum for the determination of glucose, total cholesterol, high-density lipoprotein (HDL-c), and triglycerides in the VITROS 350 Chemistry equipment (Ortho-Clinical Diagnostics, Johnson and Johnson Services Inc., Rochester, NY, USA). For the determination of low-density lipoprotein cholesterol (LDL-c), the Friedewald formula was used except when triglyceride levels were higher than 400 mg/dL [39]. Calculation of very-LDL-c was performed from total cholesterol minus the sum of LDL-c + HDL-c.

For ghrelin serum levels the RayBio® brand KIT, model Human GHRL/Ghrelin ELISA Kit catalog number ELH-GHRL was used (RayBiotech, Norcross, GA, USA). The reading was performed on the Multiskan Sky Microplate Spectrophotometer (Thermo Fisher Scientific Inc., Singapore). Total ghrelin has been the most reported in published articles, so for comparison purposes it was used in this protocol [40]. Insulin serum levels were determined by chemiluminescent immunoassay technique. Insulin kit (REF 310360) DiaSorin S.p.A (Via Crescentino snc-1340 Saluggia [VC] Italy) was used in the equipment of the LIAISON® (Saluggia VC, Italy).

Nucleic Acids Extraction and Genotyping

The extraction of gDNA from peripheral blood was performed with the High Pure PCR Template Preparation Kit (Roche Diagnostics, Indianapolis, IN, USA). To assess the purity of the gDNA samples, the 260/280 ratio was determined with the Multiskan™ SkyHigh Microplate Spectrophotometer (Thermo Fisher Scientific Inc., Singapore). Genotyping of FTO SNP (rs9939609) was performed using TaqMan® probes (context sequence GGT​TCC​TTG​CGA​CTG​CTG​TGA​ATT​TA/TGT​GAT​GCA​CTT​GGA​TAG​TCT​CTG​TT, catalog number 4351376, Thermo Fisher Scientific, Waltham, MA, USA) for allelic discrimination. The real-time PCR (polymerase chain reaction) technique was carried out using a Light Cycler® 96 (Roche Diagnostics, Mannheim, Germany) with the following conditions: a pre-incubation step at 95°C for 10 min and then 40 cycles each consisting of 15 s at 95°C and 60 s at 60°C. For a 10 μL reaction, 5 μL of FastStart TaqMan® Probe Master (2x), 1 μL of TaqMan® probe (20x), 2.5 μL of gDNA (20 ng/μL), and 1.5 μL of water PCR grade were used. For quality control, 20% of the samples were genotyped twice and positive and negative controls were also used.

Statistical Analysis

The sample size was calculated according to the dominant model of the FTO SNP. The results of Dougkas et al. [14] were used, in which a significant difference was observed in the average appetite measurement. Therefore, to ensure a power of 80%, a confidence level of 0.05 and an effect size of 0.7, a sample of 27 subjects per group (TT vs. TA + AA) were needed. For descriptive statistics, variables are presented as mean ± standard deviation. Qualitative variables are expressed in frequency or percentage. The distribution of genotypes was verified by Hardy-Weinberg equilibrium (χ2 was used). The comparison of body composition, anthropometric, and biochemical data between genotypes was realized using the Student t test or χ2 test as appropriate. Nutritional variables were comparing with the ANCOVA test, adjusting by kilocalories. Subjective appetite sensations were compared with repeated measure ANOVA with one inter-subject factor (genotype). In addition, the correlation between VAS and ghrelin concentrations was analyzed with Pearson correlation coefficient. Finally, the linear regression model was used to analyze the variables that contribute to the ghrelin concentrations, and the linear mixed model was used to estimate between-subject variation (genotypes) and within-subject variation (fasted and fed) on ghrelin levels. Basal and final measures of biochemical variables were compared with Student t test for related variables or Wilcoxon. The data were entered into a Microsoft Excel spreadsheet and then imported into the SPSS v.20 (Armonk, NY, USA). A p < 0.05 was considered statistically significant. The graphs were made with GraphPad software version 9.3.1.

General Characteristics

A total of 88 persons participated, of which 81.8% were female. The participants had a mean body mass index of 21.8 ± 2.0 kg/m2, a mean age of 20.6 ± 2.0 years old and they did 26.9 ± 32.0 min of physical activity per day. All participants were single and university students of which 55.2% did exercise, only 8.1% smoked, and 38.6% consumed alcohol.

The frequencies of TT, TA, and AA genotypes of the rs9939609 SNP were 52%, 43%, and 5%, respectively. The allele frequencies were 26% and 74% for the A and T alleles, respectively. The population was in Hardy-Weinberg equilibrium (p = 0.22). Due to the low frequency of the AA genotype, the dominant model was used to the comparison between variables. Anthropometric and body composition variables between genotypes were analyzed but no differences were found among them (show in online suppl. Table S1; for all online suppl. material, see https://doi.org/10.1159/000534741).

Dietary Intake

Dietary variables were adjusted according to total energy consumed (shown in Table 1). It was observed that carriers of one or two copies of the risk allele had a higher consumption of added sugars compared to carriers of the TT genotype. This result is shown in Figure 1. None of the other dietary variables were different between genotypes. Moreover, according to the energy requirements of the participants, the standardized breakfast provided 31.1 ± 3.4% of their daily requirement.

Table 1.

Dietary intake in FTO gene SNP carriers (rs9939609) according to the dominant model

TT (n = 45)TA+AA (n = 43)p value
Macronutrients 
 *Energy 2,207.4±900.0 1,928.7±591.3 0.09 
 *Proteins, % 18.2±4.4 18.5±4.9 0.83 
 *Lipids, % 37.4±7.1 37.12±8.0 0.83 
 Saturated fatty acids, g 26.4±1.7 28.6±1.7 0.38 
 Monounsaturated fatty acids, g 32.2±1.7 33.1±1.8 0.67 
 Polyunsaturated fatty acids, g 17.2±1.2 18.1±1.3 0.61 
 Cholesterol, mg 315.3±22.9 333.9±23.2 0.57 
 *Carbohydrates, % 47.6±6.4 47.9±9.5 0.85 
 Available carbohydrates, g 219.7±6.2 217.9±6.3 0.84 
 Total dietary fiber, g 28.5±1.5 26.5±1.6 0.37 
 Total sugar, g 80.1±6.5 88.2±6.6 0.38 
 Added sugars, g 5.8±3.1 15±3.1 0.04 
 Alcohol, g 1.6±0.6 0.9±0.6 0.40 
Dietary exchange groups 
 Starch/bread (portions) 7.6±0.4 7.9±0.4 0.61 
 Fat (portions) 5.6±0.6 5.6±0.6 0.98 
 Fruits (portions) 2.1±0.3 1.8±0.3 0.40 
 Lean meat (portions) 1.5±0.3 2.8±0.3 0.07 
 High-fat meat (portion) 0.5±0.1 0.5±0.1 0.88 
 Medium-fat meat (portions) 2±0.4 2.9±0.4 0.18 
 Very lean meat (portions) 1.1±0.2 0.5±0.2 0.09 
 Whole milk (portions) 0.4±0.1 0.6±0.1 0.25 
 Skim milk (portions) 0.3±0.4 0.1±0.3 0.08 
 Vegetables (portions) 1.9±0.2 1.8±0.2 0.73 
TT (n = 45)TA+AA (n = 43)p value
Macronutrients 
 *Energy 2,207.4±900.0 1,928.7±591.3 0.09 
 *Proteins, % 18.2±4.4 18.5±4.9 0.83 
 *Lipids, % 37.4±7.1 37.12±8.0 0.83 
 Saturated fatty acids, g 26.4±1.7 28.6±1.7 0.38 
 Monounsaturated fatty acids, g 32.2±1.7 33.1±1.8 0.67 
 Polyunsaturated fatty acids, g 17.2±1.2 18.1±1.3 0.61 
 Cholesterol, mg 315.3±22.9 333.9±23.2 0.57 
 *Carbohydrates, % 47.6±6.4 47.9±9.5 0.85 
 Available carbohydrates, g 219.7±6.2 217.9±6.3 0.84 
 Total dietary fiber, g 28.5±1.5 26.5±1.6 0.37 
 Total sugar, g 80.1±6.5 88.2±6.6 0.38 
 Added sugars, g 5.8±3.1 15±3.1 0.04 
 Alcohol, g 1.6±0.6 0.9±0.6 0.40 
Dietary exchange groups 
 Starch/bread (portions) 7.6±0.4 7.9±0.4 0.61 
 Fat (portions) 5.6±0.6 5.6±0.6 0.98 
 Fruits (portions) 2.1±0.3 1.8±0.3 0.40 
 Lean meat (portions) 1.5±0.3 2.8±0.3 0.07 
 High-fat meat (portion) 0.5±0.1 0.5±0.1 0.88 
 Medium-fat meat (portions) 2±0.4 2.9±0.4 0.18 
 Very lean meat (portions) 1.1±0.2 0.5±0.2 0.09 
 Whole milk (portions) 0.4±0.1 0.6±0.1 0.25 
 Skim milk (portions) 0.3±0.4 0.1±0.3 0.08 
 Vegetables (portions) 1.9±0.2 1.8±0.2 0.73 

Results are shown as estimated mean ± standard error or as mean ± standard deviation.

The ANCOVA test was used to comparisons, adjusting by energy intake.

The exception are the variables marked with an * in which the Student t test was used.

A p value <0.05 was considered statistically significant.

g, grams.

Fig. 1.

Comparison of added sugars consumption between rs9939609 genotypes. The comparisons were realized with the ANCOVA adjusted by energy intake. A p value <0.05 was considered statistically significant. g, grams.

Fig. 1.

Comparison of added sugars consumption between rs9939609 genotypes. The comparisons were realized with the ANCOVA adjusted by energy intake. A p value <0.05 was considered statistically significant. g, grams.

Close modal

Biochemical Variables According to the SNP rs9939609 on FTO Gene

The assessment of the orexigenic hormone ghrelin and anorexigenic hormone insulin did not show differences between participants with the TT or the TA + AA genotypes (Fig. 2a–d). Because ghrelin levels are affected by several variables, linear regression models were performed considering that basal concentrations of ghrelin were influenced by sex, age, smoking habit, hours of sleep, fat mass, and energy intake of the previous meal (the dinner the day before of the procedures); and in a second analysis, the SNP in FTO was also included. Nevertheless, only sex was a significant variable related to basal ghrelin levels (shown in online suppl. Table S2). The variables that were considered to affect postprandial ghrelin levels were sex, age, smoking habit, hours of sleep, fat mass, and the percentage of contribution of the standardized breakfast. In another model, the SNP was also added, but in both cases only sex and age were significantly associated with this hungry hormone (shown in online suppl. Table S3). Besides, the variation of ghrelin levels in the fasted and fed states between genotypes did not show any difference (shown in online suppl. Table S4). As expected, the glucose and lipid parameters increased significantly in the postprandial period compared with the fasting state. But when comparing these values between SNP rs9939609 genotypes, no significant differences were found (shown in Table 2).

Fig. 2.

Comparisons of ghrelin between genotypes at fasting (a, b) 120 min after breakfast and insulin at fasting (c, d) 120 min after breakfast. The Student t test was used to analyze the comparisons. A p value <0.05 was considered statistically significant. µIU, micro-international units; mL, milliliters.

Fig. 2.

Comparisons of ghrelin between genotypes at fasting (a, b) 120 min after breakfast and insulin at fasting (c, d) 120 min after breakfast. The Student t test was used to analyze the comparisons. A p value <0.05 was considered statistically significant. µIU, micro-international units; mL, milliliters.

Close modal
Table 2.

Biochemical analysis in FTO gene SNP carriers (rs9939609) according to the dominant model

Biochemical variablesTotal (n = 87)TT (n = 44)TA+AA (n = 43)p value1
Fasted glucose, mg/dL 89.7±11.2 90.6±11.2 88.8±11.2 0.44 
120-min postprandial glucose, mg/dL 88.4±13.7 88.0±15.5 88.7±11.8 0.82 
p value2 0.40 0.34 0.96  
Fasted total cholesterol, mg/dL 143.1±26.6 141.6±25.4 144.7±28.1 0.59 
120-min postprandial total cholesterol, mg/dL 156.4±34.6 151.1±35.3 161.8±35.4 0.15 
p value2 <0.01 0.04 <0.01  
Fasted triglycerides, mg/dL 79.4±30.0 79.8±31.0 79.0±29.2 0.91 
120-min postprandial triglycerides, mg/dL 106.6±46.2 102.0±44.3 161.8±33.4 0.35 
p value2 <0.01 <0.01 <0.01  
Fasted HDL, mg/dL 48.7±11.2 48.31±10.9 49.1±11.6 0.74 
120-min postprandial HDL, mg/dL 51.7±14.0 50.59±15.9 52.86±11.8 0.45 
p value2 0.02 0.30 0.01  
Fasted LDL, mg/dL 77.5±22.1 77.4±20.3 77.7±24.1 0.95 
120-min postprandial LDL, mg/dL 83.3±24.5 80.0±24.4 86.7±24.5 0.21 
p value2 0.003 0.272 0.005  
Fasted VLDL, mg/dL 15.8±6.0 15.9±6.3 15.8±5.9 0.93 
120-min postprandial VLDL, mg/dL 21.3±9.3 20.4±8.9 22.3±9.6 0.34 
p value2 <0.01 <0.01 <0.01  
Biochemical variablesTotal (n = 87)TT (n = 44)TA+AA (n = 43)p value1
Fasted glucose, mg/dL 89.7±11.2 90.6±11.2 88.8±11.2 0.44 
120-min postprandial glucose, mg/dL 88.4±13.7 88.0±15.5 88.7±11.8 0.82 
p value2 0.40 0.34 0.96  
Fasted total cholesterol, mg/dL 143.1±26.6 141.6±25.4 144.7±28.1 0.59 
120-min postprandial total cholesterol, mg/dL 156.4±34.6 151.1±35.3 161.8±35.4 0.15 
p value2 <0.01 0.04 <0.01  
Fasted triglycerides, mg/dL 79.4±30.0 79.8±31.0 79.0±29.2 0.91 
120-min postprandial triglycerides, mg/dL 106.6±46.2 102.0±44.3 161.8±33.4 0.35 
p value2 <0.01 <0.01 <0.01  
Fasted HDL, mg/dL 48.7±11.2 48.31±10.9 49.1±11.6 0.74 
120-min postprandial HDL, mg/dL 51.7±14.0 50.59±15.9 52.86±11.8 0.45 
p value2 0.02 0.30 0.01  
Fasted LDL, mg/dL 77.5±22.1 77.4±20.3 77.7±24.1 0.95 
120-min postprandial LDL, mg/dL 83.3±24.5 80.0±24.4 86.7±24.5 0.21 
p value2 0.003 0.272 0.005  
Fasted VLDL, mg/dL 15.8±6.0 15.9±6.3 15.8±5.9 0.93 
120-min postprandial VLDL, mg/dL 21.3±9.3 20.4±8.9 22.3±9.6 0.34 
p value2 <0.01 <0.01 <0.01  

Results are shown as mean ± standard deviation.

A p value <0.05 was considered statistically significant.

HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; VLDL-c, very low-density lipoprotein cholesterol; mg/dL, milligrams per deciliter.

1The Student t test for independent groups was used for the analysis.

2The Student t test for dependent groups was used for the analysis.

Subjective Appetite Assessment

The perception of appetite sensations of hunger, fullness, satiety, desire to eat, and prospective food consumption was similar between carries of the TT and the TA + AA in all the time points that were evaluated (shown in Table 3). The correlations between ghrelin and appetite sensations measured at fasting and at 120 min postprandially were not significant either (shown in online suppl. Table S5).

Table 3.

Subjective sensations of appetite in FTO gene SNP carriers (rs9939609) according to the dominant model

−30 min0 min30 min60 min90 min120 minp value
Hunger, mm 
 TT 58 (52–64.1) 4.1 (2–6.3) 11.1 (6.1–16.1) 21.2 (15.5–26.7) 29.2 (22.7–35.8) 36.8 (29.8–43.8) 0.81 
 TA + AA 59 (53.4–65.8) 3.6 (1.4–5.9) 9.9 (4.8–15) 17.4 (11.6–23.3) 25 (18.3–31.7) 37.2 (30–44.3)  
Fullness, mm 
 TT 12 (8–15.9) 82.6 (77.1–88) 75.1 (68.2–81.9) 66.9 (59.2–73.8) 55.8 (48.1–63.5) 44.7 (36.8–52.7) 0.41 
 TA + AA 13.1 (9.1–17.2) 85.1 (79.6–90.6) 73.5 (66.5–80.6) 60.1 (53–68) 54.5 (46.6–62.3) 49.5 (41.4–57.6)  
Satiety, mm 
 TT 61.6 (54.3–67.8) 5.2 (1.9–8.4) 12.2 (7.9–16.5) 21.7 (16.2–27.2) 31.4 (24.4–38.4) 41.2 (33.8–48.7) 0.81 
 TA + AA 65.2 (58.5–72) 6.6 (3.3–9.9) 10.4 (5.9–14.8) 21 (15.4–26.7) 29 (21.9–36.1) 40.6 (33–48.2)  
Desire to eat, mm 
 TT 61.2 (54.3–67.8) 5.6 (1.9–8.4) 12.2 (7.9–16.5) 21.7 (16.2–27.2) 31.4 (24.4–38.4) 41.2 (33.8–48.7) 0.82 
 TA + AA 65.2 (58.4–72) 6.6 (3.3–9.8) 10.4 (5.9–14.8) 21 (15.4–26.7) 29 (21.9–36.1) 40.6 (33–48.2)  
Prospective food consumption, mm 
 TT 69.2 (64–74.4) 8.5 (4–13) 16.3 (10.9–21.6) 25.8 (19.4–32.2) 33.3 (26.2–40.4) 42.2 (34.9–49.5) 0.79 
 TA + AA 67.3 (62–72.6) 12.6 (7.9–17.8) 16.3 (10.8–21.8) 24.6 (18–31.1) 32.3 (25–39.6) 42.8 (35.4–50.3) 
−30 min0 min30 min60 min90 min120 minp value
Hunger, mm 
 TT 58 (52–64.1) 4.1 (2–6.3) 11.1 (6.1–16.1) 21.2 (15.5–26.7) 29.2 (22.7–35.8) 36.8 (29.8–43.8) 0.81 
 TA + AA 59 (53.4–65.8) 3.6 (1.4–5.9) 9.9 (4.8–15) 17.4 (11.6–23.3) 25 (18.3–31.7) 37.2 (30–44.3)  
Fullness, mm 
 TT 12 (8–15.9) 82.6 (77.1–88) 75.1 (68.2–81.9) 66.9 (59.2–73.8) 55.8 (48.1–63.5) 44.7 (36.8–52.7) 0.41 
 TA + AA 13.1 (9.1–17.2) 85.1 (79.6–90.6) 73.5 (66.5–80.6) 60.1 (53–68) 54.5 (46.6–62.3) 49.5 (41.4–57.6)  
Satiety, mm 
 TT 61.6 (54.3–67.8) 5.2 (1.9–8.4) 12.2 (7.9–16.5) 21.7 (16.2–27.2) 31.4 (24.4–38.4) 41.2 (33.8–48.7) 0.81 
 TA + AA 65.2 (58.5–72) 6.6 (3.3–9.9) 10.4 (5.9–14.8) 21 (15.4–26.7) 29 (21.9–36.1) 40.6 (33–48.2)  
Desire to eat, mm 
 TT 61.2 (54.3–67.8) 5.6 (1.9–8.4) 12.2 (7.9–16.5) 21.7 (16.2–27.2) 31.4 (24.4–38.4) 41.2 (33.8–48.7) 0.82 
 TA + AA 65.2 (58.4–72) 6.6 (3.3–9.8) 10.4 (5.9–14.8) 21 (15.4–26.7) 29 (21.9–36.1) 40.6 (33–48.2)  
Prospective food consumption, mm 
 TT 69.2 (64–74.4) 8.5 (4–13) 16.3 (10.9–21.6) 25.8 (19.4–32.2) 33.3 (26.2–40.4) 42.2 (34.9–49.5) 0.79 
 TA + AA 67.3 (62–72.6) 12.6 (7.9–17.8) 16.3 (10.8–21.8) 24.6 (18–31.1) 32.3 (25–39.6) 42.8 (35.4–50.3) 

Values are shown as means and 95% confidence intervals.

The repeated measures ANOVA model was used with one inter-subject factor (genotype).

A p value <0.05 was considered statistically significant.

In this study related to the rs9939609 T>A on FTO gene and appetite traits in normal weight participants, we found a higher intake of added sugars in carriers of the TA + AA genotypes versus the TT genotype. This finding coincides with other studies in which a higher intake of carbohydrates has been reported, but the classification of the type of carbohydrates was not described [17, 41]. Nevertheless, the study of Brunkwall et al. [19] found a higher consumption of pastry and bakery products in carriers of the A allele, which would coincide with what was reported in our study since these foods are characterized by a high content of added sugars.

These differences in the type of food consumed may be related to food choices that are constituted by two important components: environment and genetic predispositions which lead to food selection and predispose to diet quality [42]; therefore, such preferences are important drivers of the determination of micronutrient and macronutrient intake [43]. The high consumption of sugars characterizes the Western diet, which may increase the risk of metabolic disorders, obesity, and cardiovascular events [44]. The increased presence of this dietary pattern has been observed in an Emirati population where carriers of the A allele have shown an adherence to that pattern 2.41 times higher compared to carriers of the TT genotype [45]. According to this study, it is possible that the presence of the A allele may be a factor in determining food preferences, but more research is needed to confirm this food behavior in other populations, including the Mexican population.

Furthermore, it is important to highlight that there were no differences between genotypes in the dietary exchanges of breads/starch and fruits, this could indicate that the group with the risk allele chose foods with higher sugar density (gm/serving) while consuming the same number of servings. This result supports the fact that the FTO mechanism is biased toward taste preference rather than increased intake and satiety.

In agreement with the results of our study and previous one, it is clear the importance of the genetics in the type of nutrients intake. This may be related to the fact that the taste for sweet flavors and the frequency of sweet food consumption have a heritability of 49–53% [46]. In that sense, a GWAS study found an association between a SNP on FTO and total sugar intake, which highlighted the importance of the genetic variations of genes involve in central mechanisms of hunger and in the intake of sweet food [47, 48]. Besides, other studies in participants with obesity have also reported a higher preference for calorie-dense foods and an increased reward-related brain response to images of fattening foods in participants with the AA genotype of the rs9939609 compared with TA or TT subjects [49].

The mechanisms by which the A allele carries showed a higher consumption of added sugars are not yet clear, but one hypothesis may be related to the hypothalamus which is the main site of FTO gene expression gene and studies have reported a greater expression when the A allele is present [50]. In addition, the hypothalamus is the main brain area where the regulation of dietary intake takes place, and it is connected to signals of the reward system and a motivational neurocircuit that are involved in the modification of eating behavior. Therefore, it has been proposed that dysregulation of these systems may lead to overeating, where foods would be an excellent reward, mainly those rich in added sugars, even in the absence of physiological hunger [51]. In this line, normal weight participants with the AA genotype have rated food images high in calories as more pleasing than TT subject, which could be related to different response in brain areas of hedonic hunger [49, 52].

Appetite-regulating hormone levels showed no differences between genotypes, which may be explained by the calorie-dependent response of ghrelin [53] because of the differences in the percentage of calories provided by the standardized breakfast. On the other hand, VAS have demonstrated to be valid for the assessment of subjective appetite sensation [38, 54‒56]. Several studies in participants with overweight or with obesity have reported a different response in perceived appetite between FTO genotypes [14, 20, 57], but in this study no differences were detected. This discrepancy could be partially explained for an interaction between the SNP and body fat mass. In fact, the A allele leads to a higher expression of FTO [52] and more body fat accumulation [58]. In that sense, it has been demonstrated that participants with obesity and normal weight do not refer to the same state when they use the term hunger; hence, the amount of body fat could mediate the different responses in the subjective perception of appetite. Karra et al. [52] also found differences in hunger between FTO genotypes in normal weight participants, but they matched the participants for total and visceral fat. Another possible explanation is that FTO could be involved in hedonic hunger [49, 52] which refers to the motivation to consume palatable foods in the absence of an energy deficit [59], contrary to physiological hunger based on the prolonged absence of energy which is presented through homeostatic gut-brain signaling [60, 61], and the VAS and ghrelin hormone that we measured only assess the latter.

Limitations of this study include its quasi-experimental design and the fact that we were not able to assess the postintervention dietary intake. Although most of the studies in which this SNP has been related to food preferences have been carried out in people with obesity, our study shows similar results, which coincides with a westernized dietary pattern. To expand the information about the effects of rs9939609 on satiety, it is important to consider its relationship with other appetite-related hormones such as PYY, CCK, and GLP-1. As part of the limitations, we show a higher participation of females who tend to under-report their dietary intake; however, previous studies showed that females have a greater interest in participating in health studies, maybe due to their willingness to participate in the community [62]. Besides, the economic constraints of the participants as college students may have influenced their food choices.

In conclusion, this research shows that young adults with normal weight carrying the A allele have a higher consumption of added sugars, even when the appetite hormones and subjective sensations of appetite were similar between genotypes. More studies are needed to clarify the food choices according to the rs9939609 in participants with normal weight and its susceptibility to development obesity in early stages.

This study was submitted for registration and approval by the Research, Ethics, and Biosafety Committees of the University Center of Health Sciences of the University of Guadalajara, which was approved and has the registration number CI-03619. All procedures were conducted in accordance with the World Medical Association Declaration of Helsinki. All participants read and signed the informed consent form for their participation.

The authors have no conflicts of interest to declare.

This research was supported by a grant from “PRODEP” (NPTC 2020, UDG-PTC-1600), “PRO-SNI” (Universidad de Guadalajara, Number 248576, 2020).

A.M.-J.: methodology, formal analysis, investigation, data curation, writing – original draft, visualization, project administration. E.M.-L.: conceptualization, methodology, resources, writing – original draft, writing – review and editing, supervision, project administration, funding acquisition. T.S.-M.: conceptualization, methodology, investigation, data curation, writing – review and editing, visualization, project administration. L.M.-V.: methodology, investigation, data curation, writing – original draft, visualization. R.R.-E.: data curation, writing – original draft, writing – review and editing, visualization. M.S.-V.: methodology, data curation, writing – original draft, visualization. R.T.-V.: conceptualization, formal analysis, data curation, writing – original draft, visualization. N.T.-C.: conceptualization, methodology, formal analysis, resources, data curation, writing – original draft, visualization.

The data supporting the conclusions of this study are not publicly available due to confidentiality but can be requested from the corresponding author.

1.
Murphy
KG
,
Bloom
SR
.
Gut hormones and the regulation of energy homeostasis
.
Nature
.
2006
;
444
(
7121
):
854
9
.
2.
Little
TJ
,
McKie
S
,
Jones
RB
,
D’Amato
M
,
Smith
C
,
Kiss
O
.
Mapping glucose-mediated gut-to-brain signalling pathways in humans
.
Neuroimage
.
2014
;
96
(
96
):
1
11
.
3.
Howick
K
,
Griffin
BT
,
Cryan
JF
,
Schellekens
H
.
From belly to brain: targeting the ghrelin receptor in appetite and food intake regulation
.
Int J Mol Sci
.
2017
;
18
(
2
):
273
.
4.
Patterson
M
,
Bloom
SR
,
Gardiner
JV
.
Ghrelin and appetite control in humans: potential application in the treatment of obesity
.
Peptides
.
2011
;
32
(
11
):
2290
4
.
5.
Austin
J
,
Marks
D
.
Hormonal regulators of appetite
.
Int J Pediatr Endocrinol
.
2009
;
2009
:
141753
9
.
6.
Korek
E
,
Krauss
H
,
Gibas-Dorna
M
,
Kupsz
J
,
Piątek
M
,
Piątek
J
.
Fasting and postprandial levels of ghrelin, leptin and insulin in lean, obese and anorexic subjects
.
Prz Gastroenterol
.
2013
;
8
(
6
):
383
9
.
7.
Aukan
MI
,
Nymo
S
,
Haagensli Ollestad
K
,
Akersveen Boyesen
G
,
DeBenedictis
JN
,
Rehfeld
JF
.
Differences in gastrointestinal hormones and appetite ratings among obesity classes: gastrointestinal hormones and appetite in obesity
.
Appetite
.
2022
171
.
8.
Brown
RC
,
McLay-Cooke
RT
,
Richardson
SL
,
Williams
SM
,
Grattan
DR
,
Chisholm
AWAH
.
Appetite response among those susceptible or resistant to obesity
.
Int J Endocrinol
.
2014
;
2014
:
512013
.
9.
Campos-Pérez
W
,
González-Becerra
K
,
Ramos-lópez
O
,
Silva-gómez
JA
,
Barrón-cabrera
E
,
Roman
S
.
Same dietary but different physical activity pattern in normal-weight and overweight Mexican subjects
.
Food Nutr Res
.
2016
;
4
(
11
):
729
35
.
10.
Bohara
SS
,
Thapa
K
,
Bhatt
LD
,
Dhami
SS
,
Wagle
S
.
Determinants of junk food consumption among adolescents in pokhara valley, Nepal
.
Front Nutr
.
2021
;
8
:
644650
.
11.
Sánchez-Pimienta
TG
,
Batis
C
,
Lutter
CK
,
Rivera
JA
.
Sugar-sweetened beverages are the main sources of added sugar intake in the Mexican population
.
J Nutr
.
2016
146
9
1888S
96S
.
12.
Reddon
H
,
Guéant
JL
,
Meyre
D
.
The importance of gene-environment interactions in human obesity
.
Clin Sci
.
2016
;
130
(
18
):
1571
97
.
13.
Llewellyn
CH
.
Genetic susceptibility to the “obesogenic” environment: the role of eating behavior in obesity and an appetite for change
.
Am J Clin Nutr
.
2018
;
108
(
3
):
429
30
.
14.
Dougkas
A
,
Yaqoob
P
,
Givens
DI
,
Reynolds
CK
,
Minihane
AM
.
The impact of obesity-related SNP on appetite and energy intake
.
Br J Nutr
.
2013
;
110
(
6
):
1151
6
.
15.
Espinoza García
AS
,
Martínez Moreno
AG
,
Reyes Castillo
Z
.
The role of ghrelin and leptin in feeding behavior: genetic and molecular evidence
.
Endocrinol Diabetes Nutr
.
2021
;
68
(
9
):
654
63
.
16.
Ursu
RI
,
Badiu
C
,
Cucu
N
,
Ursu
GF
,
Craciunescu
I
,
Severin
E
.
The study of the rs9939609 FTO gene polymorphism in association with obesity and the management of obesity in a Romanian cohort
.
J Med Life
.
2015
;
8
(
2
):
232
8
.
17.
Saber-Ayad
M
,
Manzoor
S
,
Radwan
H
,
Hammoudeh
S
,
Wardeh
R
,
Ashraf
A
.
The FTO genetic variants are associated with dietary intake and body mass index amongst Emirati population
.
PLoS One
.
2019
;
14
(
10
):
e0223808
.
18.
Mehrdad
M
,
Doaei
S
,
Gholamalizadeh
M
,
Eftekhari
MH
.
The association between FTO genotype with macronutrients and calorie intake in overweight adults
.
Lipids Health Dis
.
2020
;
19
(
1
):
197
6
.
19.
Brunkwall
L
,
Ericson
U
,
Hellstrand
S
,
Gullberg
B
,
Orho-melander
M
,
Sonestedt
E
.
Genetic variation in the fat mass and obesity-associated gene (FTO) in association with food preferences in healthy adults
.
Food Nutr Res
.
2013
;
57
(
1
):
20028
8
.
20.
Magno
FCCM
,
Guaraná
HC
,
Fonseca
ACP
,
Cabello
GMK
,
Carneiro
JRI
,
Pedrosa
AP
.
Influence of FTO rs9939609 polymorphism on appetite, ghrelin, leptin, IL6, TNFα levels, and food intake of women with morbid obesity
.
Diabetes Metab Syndr Obes
.
2018
;
11
:
199
207
.
21.
Huang
T
,
Qi
Q
,
Li
Y
,
Hu
FB
,
Bray
GA
,
Sacks
FM
.
FTO genotype, dietary protein, and change in appetite: the Preventing Overweight Using Novel Dietary Strategies trial
.
Am J Clin
.
2014
;
99
(
5
):
1126
30
.
22.
Jiang
Y
,
Mei
H
,
Lin
Q
,
Wang
J
,
Liu
S
,
Wang
G
.
Interaction effects of FTO rs9939609 polymorphism and lifestyle factors on obesity indices in early adolescence
.
Obes Res Clin Pract
.
2019
;
13
(
4
):
352
7
.
23.
Yeo
GSH
.
The role of the FTO (Fat Mass and Obesity Related) locus in regulating body size and composition
.
Mol Cell Endocrinol
.
2014
397
1–2
34
41
.
24.
Lappalainen
T
,
Lindström
J
,
Paananen
J
,
Eriksson
JG
,
Karhunen
L
,
Tuomilehto
J
.
Association of the fat mass and obesity-associated (FTO) gene variant (rs9939609) with dietary intake in the Finnish Diabetes Prevention Study
.
Br J Nutr
.
2012
;
108
(
10
):
1859
65
.
25.
Wardle
J
,
Llewellyn
C
,
Sanderson
S
,
Plomin
R
.
The FTO gene and measured food intake in children
.
Int J Obes
.
2009
;
33
(
1
):
42
5
.
26.
Frank
GKW
,
Shott
ME
,
Keffler
C
,
Cornier
MA
.
Extremes of eating are associated with reduced neural taste discrimination
.
Int J Eat Disord
.
2016
;
49
(
6
):
603
12
.
27.
Bonnie
RJ
,
Stroud
C
,
Breiner
H
National Research Council (U.S.)
Committee on improving the health S, national research council (U.S.)
Board on children Y, institute of medicine (U.S.)
Investing in the health and well-being of young adults
1st ed.
Washington (DC)
National Academies of Sciences, Engineering, and Medicine; The National Academies Press
2015
. p.
501
.
28.
Abdella
HM
,
Farssi
HOE
,
Broom
DR
,
Hadden
DA
,
Dalton
CF
.
Eating behaviours and food cravings; influence of age, sex, BMI and FTO genotype
.
Nutrients
.
2019
;
11
(
2
):
337
.
29.
Burke
V
,
Beilin
LJ
,
Dunbar
D
,
Kevan
M
.
Changes in health-related behaviours and cardiovascular risk factors in young adults: associations with living with a partner
.
Prev Med
.
2004
;
39
(
4
):
722
30
.
30.
Sanchez-Murguia
T
,
Torres-Castillo
N
,
Magaña-de la Vega
L
,
Rodríguez-Reyes
SC
,
Campos-Pérez
W
,
Martínez-López
E
.
Role of Leu72Met of GHRL and Gln223Arg of LEPR variants on food intake, subjective appetite, and hunger-satiety hormones
.
Nutrients
.
2022
;
14
(
10
):
2100
.
31.
Ferreira
S
,
Marroni
CA
,
Stein
JT
,
Rayn
R
,
Henz
AC
,
Schmidt
NP
.
Assessment of resting energy expenditure in patients with cirrhosis
.
World J Hepatol
.
2022
;
14
(
4
):
802
11
.
32.
ISAK
International Standar for AhthrAssesment
.
2001
.
33.
Gallagher
D
,
Heymsfield
SB
,
Heo
M
,
Jebb
SA
,
Murgatroyd
PR
,
Sakamoto
Y
.
Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index
.
Am J Clin Nutr
.
2000
;
72
(
3
):
694
701
.
34.
World Health Organization
Obesity: preventing and managing the global epidemic–report of a WHO consultation
World Health Organization
2000
. p.
253
.
35.
McCleary
BV
,
McLoughlin
C
,
Charmier
LMJ
,
McGeough
P
.
Measurement of available carbohydrates, digestible, and resistant starch in food ingredients and products
.
Cereal Chem
.
2020
;
97
(
1
):
114
37
.
36.
Food U, Administration D, for Food Safety C, Nutrition A
Added Sugars: now Listed on the Nutrition Facts Label_Azúcares añadidas: ahora incluidos en la etiqueta de información nutricional. [Internet]
. Available from: www.FDA.gov/NewNutritionFactsLabel.
37.
Sørensen
LB
,
Møller
P
,
Flint
A
,
Martens
M
,
Raben
A
.
Effect of sensory perception of foods on appetite and food intake: a review of studies on humans
.
Int J Obes
.
2003
;
27
(
10
):
1152
66
.
38.
Flint
A
,
Raben
A
,
Blundell
J
,
Astrup
A
.
Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single test meal studies
.
Int J Obes
.
2000
;
24
(
1
):
38
48
.
39.
Friedewald
WT
,
Levy
RI
,
Fredrickson
DS
.
Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge
.
Clin Chem
.
1972
;
18
(
6
):
499
502
.
40.
Garcia
JM
,
Garcia-Touza
M
,
Hijazi
RA
,
Taffet
G
,
Epner
D
,
Mann
D
.
Active ghrelin levels and active to total ghrelin ratio in cancer-induced cachexia
.
J Clin Endocrinol Metab
.
2005
;
90
(
5
):
2920
6
.
41.
Danaher
J
,
Stathis
CG
,
Cooke
MB
.
Similarities in metabolic flexibility and hunger hormone ghrelin exist between FTO gene variants in response to an acute dietary challenge
.
Nutrients
.
2019
;
11
(
10
):
2518
.
42.
Törnwall
O
,
Silventoinen
K
,
Hiekkalinna
T
,
Perola
M
,
Tuorila
H
,
Kaprio
J
.
Identifying flavor preference subgroups. Genetic basis and related eating behavior traits
.
Appetite
.
2014
;
75
:
1
10
.
43.
Herle
M
,
Smith
AD
,
Kininmonth
A
,
Llewellyn
C
.
The role of eating behaviours in genetic susceptibility to obesity
.
Curr Obes Rep
.
2020
;
9
(
4
):
512
21
.
44.
Satokari
R
.
High intake of sugar and the balance between pro-and anti-inflammatory gut bacteria
.
Nutrients
.
2020
;
12
:
1348
.
45.
Naja
F
,
Itani
L
,
Hammoudeh
S
,
Manzoor
S
,
Abbas
N
,
Radwan
H
.
Dietary patterns and their associations with the FTO and FGF21 gene variants among Emirati adults
.
Front Nutr
.
2021
;
8
:
668901
.
46.
Treur
JL
,
Boomsma
DI
,
Ligthart
L
,
Willemsen
G
,
Vink
JM
.
Heritability of high sugar consumption through drinks and the genetic correlation with substance use
.
Am J Clin
.
2016
;
104
(
4
):
1144
50
.
47.
Bachmanov
AA
,
Bosak
NP
,
Floriano
WB
,
Inoue
M
,
Li
X
,
Lin
C
.
Genetics of sweet taste preferences
.
Flavour Fragr J
.
2011
;
26
(
4
):
286
94
.
48.
Hwang
LD
,
Lin
C
,
Gharahkhani
P
,
Cuellar-Partida
G
,
Ong
JS
,
An
J
.
New insight into human sweet taste: a genome-wide association study of the perception and intake of sweet substances
.
Am J Clin
.
2019
;
109
(
6
):
1724
37
.
49.
Melhorn
SJ
,
Askren
MK
,
Chung
WK
,
Kratz
M
,
Bosch
TA
,
Tyagi
V
.
FTO genotype impacts food intake and corticolimbic activation
.
Am J Clin
.
2018
;
107
(
2
):
145
54
.
50.
Church
C
,
Moir
L
,
McMurray
F
,
Girard
C
,
Banks
GT
,
Teboul
L
.
Overexpression of Fto leads to increased food intake and results in obesity
.
Nat Genet
.
2010
;
42
(
12
):
1086
92
.
51.
Volkow
ND
,
Wang
GJ
,
Baler
RD
.
Reward, dopamine and the control of food intake: implications for obesity
.
Trends Cogn Sci
.
2011
;
15
:
37
46
.
52.
Karra
E
,
O’Daly
OG
,
Choudhury
AI
,
Yousseif
A
,
Millership
S
,
Neary
MT
.
A link between FTO, ghrelin, and impaired brain food-cue responsivity
.
J Clin Invest
.
2013
;
123
(
8
):
3539
51
.
53.
Callahan
HS
,
Cummings
DE
,
Pepe
MS
,
Breen
PA
,
Matthys
CC
,
Weigle
DS
.
Postprandial suppression of plasma ghrelin level is proportional to ingested caloric load but does not predict intermeal interval in humans
.
J Clin Endocrinol Metab
.
2004
;
89
(
3
):
1319
24
.
54.
Horner
KM
,
Byrne
NM
,
King
NA
.
Reproducibility of subjective appetite ratings and ad libitum test meal energy intake in overweight and obese males
.
Appetite
.
2014
;
81
:
116
22
.
55.
Gregersen
NT
,
Flint
A
,
Bitz
C
,
Blundell
JE
,
Raben
A
,
Astrup
A
.
Reproducibility and power of ad libitum energy intake assessed by repeated single meals
.
Am J Clin Nutr
.
2008
;
87
(
5
):
1277
81
.
56.
Tucker
AJ
,
Heap
S
,
Ingram
J
,
Law
M
,
Wright
AJ
.
Postprandial appetite ratings are reproducible and moderately related to total day energy intakes, but not ad libitum lunch energy intakes, in healthy young women
.
Appetite
.
2016
;
99
:
97
104
.
57.
Den Hoed
M
,
Westerterp-Plantenga
MS
,
Bouwman
FG
,
Mariman
ECM
,
Westerterp
KR
.
Postprandial responses in hunger and satiety are associated with the rs9939609 single nucleotide polymorphism in FTO
.
Am J Clin Nutr
.
2009
;
90
(
5
):
1426
32
.
58.
Frayling
TM
,
Timpson
NJ
,
Weedon
MN
,
Zeggini
E
,
Freathy
RM
,
Lindgren
CM
.
A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity
.
Science
.
2007
;
316
(
5826
):
889
94
.
59.
Mead
BR
,
Boyland
EJ
,
Christiansen
P
,
Halford
JCG
,
Jebb
SA
,
Ahern
AL
.
Associations between hedonic hunger and BMI during a two-year behavioural weight loss trial
.
PLoS One
.
2021
;
16
(
6
):
e0252110
.
60.
Simon
JJ
,
Wetzel
A
,
Sinno
MH
,
Skunde
M
,
Bendszus
M
,
Preissl
H
.
Integration of homeostatic signaling and food reward processing in the human brain
.
JCI Insight
.
2017
;
2
(
15
):
e92970
.
61.
Lowe
MR
,
Butryn
ML
.
Hedonic hunger: a new dimension of appetite
.
Physiol Behav
.
2007
;
91
(
4
):
432
9
.
62.
Otufowora
A
,
Liu
Y
,
Young
H
,
Egan
KL
,
Varma
DS
,
Striley
CW
.
Sex differences in willingness to participate in research based on study risk level among a community sample of African Americans in north central Florida
.
J Immigr Minor Health
.
2021
;
23
(
1
):
19
25
.