The central melanocortin system critically regulates energy balance by influencing food intake and energy expenditure. Moreover, the MC4R pathway seems to be involved in glycaemic homeostasis, independently of its effects on body weight and composition(22). This study demonstrated that the effects of MC4R genetic variants on body mass, body composition, as well as some comorbidities of obesity, depend on dietary factors. Results from previous studies have suggested that MC4R SNPs may influence dietary habits, which may explain the higher body mass and body fat content. However, our study participants did not differ by genotype in daily energy and diet macronutrient intake or daily physical activity levels. Since we did not observe these genotype differences, we can exclude the impact of different macronutrient intake on MC4R gene expression and activation of melanocortin pathways, as has been previously reported by Lauria et. al(36).
In the present study, we observed associations between SNPs in rs17782313 with BMI and fasting blood glucose levels. Moreover, we found that carriers of CC genotypes presented with lower subcutaneous fat content, higher visceral fat content and higher VAT/SAT ratio compared to TT genotype carriers. The metabolic consequences of these differences include higher fasting blood glucose and triglycerides levels, which we have also observed in this group. Our results are in line with a previous study by Qi et al.(28), who found that carrying a C-allele is associated with increased BMI and 14% increased risk of type 2 diabetes. Zobel et al.(45) also found associations between rs17782313 genotypes and BMI, but did not observe any differences in glucose homeostasis. In our previous study, consisting of participants from the same cohort group, we observed differences in body fat distribution without finding any differences in BMI between genotypes(31). These discrepancies may have arisen from differences in the groups chosen for analysis. Here, it is important to note that we choose our study group based on the availability of data to analyse in order to maximize the number of study participants included in every analysis. This prompted us to other factors that might influence our results. We did not observe any differences in daily dietary intake between genotypes, but we found metabolic disturbances that are significantly different in individuals from the upper dietary protein intake, so in those, whose daily percentage of energy intake from protein exceeds 18%. It is worth to notice that the mean energy value of diets from upper quantiles of protein intake was significantly lower compared to the lower intake quantiles across all studied genotypes examined. Our results are in contrast with findings from Qi et al.(28), who observed significant differences between rs17782313 genotypes in total energy, fat and protein intake, as well as association with BMI, which was independent of dietary intake. However, only women were included in that study, which may partially explain the different results. This is important to consider because there are both significant sex differences in dietary intakes as well as sexually dimorphic effects of diet on health(46). In another study, Hasselbalch et al.(34) did not observe any significant associations between these SNPs near MC4R, food intake and food preferences. Actually, we could not exclude the genotype differences also in the participants in the lower protein intake quantiles, since we could not compare it due to too few CC genotype carriers in the lower protein intake quantiles group. Moreover, we have noted higher body fat mass content also among individuals carrying CT and TT genotypes in the upper protein intake quantiles, compared to the same genotypes in the lower quantiles. Therefore, we constructed linear regression models and found that the increase of percentage of energy derived from proteins in CC carriers of rs17782313 was associated with higher BMI and body fat content, as well as higher visceral fat content and VAT/SAT ratio. The distribution of body fat tissue is of crucial importance, since visceral adipocytes are more metabolically active and may lead to the insulin resistance (IR) development. VAT is associated not only with metabolic disturbances but also with all-cause mortality(47, 48). Moreover, higher VAT/SAT ratios, observed in AA genotype carriers, may be associated with increased metabolic and cardiovascular risk, independent from BMI and visceral fat content(49). Results from analyses of protective TT genotype carriers revealed that being in the upper quantiles of fat intake was associated with lower subcutaneous fat deposition but higher visceral fat content and VAT/SAT ratios. Intriguingly, carriers of the protective TT genotype in the upper carbohydrate intake quantiles had significantly lower body weight, waist circumference, fasting insulin levels, as well as HOMA-IR, what indicate higher insulin sensitivity of these individuals when in their diet more than 48% of energy was derived from carbohydrates. This is one of the most crucial observations of our study, which is salient in light of current interest in carbohydrate-restricted diets among the general population. Surprisingly, we did not observe any differences, which would be dependent on percentages of energy derived from dietary fat and carbohydrates in CC genotype carriers. It suggests that the impact of dietary fat and carbohydrates intake may not be crucial for the risk of obesity, in carriers of this high-risk genotype. The practical implication of these results may be that individuals with the CC genotype should avoid diets with > 18% of daily energy from protein, while the total dietary fat in the diets of TT genotype carriers should provide less than 30% of daily energy intake, and more than 48% of energy should be derived from carbohydrates. Ortega-Azorin et al.(50) found that adherence to the Mediterranean diet was effective in reducing risk of type 2 diabetes in carriers of the risk variant alleles of MC4R rs17782313. These observations may support our results if we consider that the Mediterranean diet is a predominantly plant-based diet, in which animal proteins (especially from meat) are consumed in very small amounts and come mostly from nuts, fish, and dairy sources(51, 52). Additionally, the Mediterranean diet includes fat comprising between 35% and 45% of energy intake, in which monounsaturated fatty acids (MUFAs) provide at least 50% of total fat content, mostly from the consumption of extra virgin olive oil(51).
The findings of our study also show that an AA genotype of rs12970134 was associated with increased BMI, body fat mass, WHR and fasting blood glucose levels in our participants. These observations are in line with our previous findings(31) as well as results from a study by Zobel et al.(45), who found that the A-allele was associated with obesity, morbid obesity and abdominal obesity. Moreover, AA genotype carriers following a diet with > 18% of total energy from protein not only had higher BMI, body fat mass, subcutaneous fat content and waist circumference but also higher visceral fat volume, higher fasting blood glucose, triglycerides and total cholesterol levels, even if the energy value of the diet from the upper quantiles of protein intake was significantly lower compared to the diet from the lower quantiles of protein intake. Moreover, participants with the AA genotype had higher values of metabolic parameters, which were mostly independent from dietary carbohydrate and fat content. However, we also observed that GG genotype carriers from upper quantiles of protein intake had higher body fat percentages, higher fasting insulin levels and higher HOMA-IR values compared to the same genotype carriers from the lower quantiles of protein intake. This suggests that a diet providing > 18% of energy from protein may have disadvantageous effects independent from MC4R rs12970134 genotypes, promoting insulin resistance in individuals with the protective genotype even when the total energy intake is lower. Because we could not analyse these associations between AA genotypes from the lower quantiles of protein intake due to insufficient numbers, we constructed regression models which showed an increase of percentage of daily energy delivered from protein in AA genotype was associated with significantly lower skeletal muscle mass and subcutaneous fat mass content but higher visceral fat content and VAT/SAT ratios. The effects of visceral body fat deposition have been mentioned above, but in addition, reduction of skeletal muscle mass, may lead to development of type 2 diabetes (53), which we have previously reported on based on results from 5-years observation of our 1000PLUS Cohort Study group(39). In the present study, the AG and GG genotypes carriers had lower subcutaneous body fat and higher visceral body fat content when the percentage of daily energy from fat exceeded 30%. Koochakpoor et al.(30) noted that the risk of abdominal obesity increases across quantiles of total fat intake, but in A allele carriers, and this association was not significant for GG homozygotes participants. However, the authors also reported that the association of A allele carriers in rs12970134 with metabolic syndrome was modulated by saturated fatty acid intake, which was not analysed in our study and may help explain the different results obtained. Moreover, similar to the previously described loci near the MC4R gene, AG and GG genotype carries in rs12970134 with a dietary intake of more than 48% of energy from carbohydrates had lower body weight, waist circumference, fasting insulin levels and HOMA-IR values compared to same genotype carriers from the lower quantiles of carbohydrate intake. Intriguingly, among the AA genotype carriers we did not observe any associations between investigated parameters and dietary carbohydrates content, suggesting that dietary carbohydrates may not affect the risk of obesity in high-risk genotype carriers. However, in carriers of the protective genotypes, dietary carbohydrate intake providing more than 48% of total energy may have even beneficial metabolic effects. Wang et al.(33) found that in overweight and obese children, rs12970134 is associated with appetite and beverage intake, which could indicate that rs12970134 SNPs may possibly increase adiposity by affecting eating behaviours. Nevertheless, in our study we observed higher BMI and body fat content without noting any differences in daily energy and macronutrient intake between genotypes.
The other two SNPs in rs633265 and rs1350341 investigated in our study were associated with blood glucose levels at 30 minutes of OGTT. We have previously observed that these genetic variants are associated with body fat distribution without any differences in BMI and total body fat content(31). In our previously described dietary intervention sub-study, we also found differences in postprandial glucose utilization. Comparing genotypes revealed that GG genotypes of rs633265 carriers had significantly lower BMI values than homozygous carriers with the risk allele T. When we included dietary factors in the analysis, GG genotype participants with daily protein intake lower than 18% of total energy had lower blood glucose levels at 30 and at 60 minutes of OGTT as well as lower HbA1c. Moreover, these participants also had lower fasting blood glucose levels, and 30 minutes of OGTT, lower HbA1c, and higher HOMA-B values when the percentage of energy derived from carbohydrates was less than 48%, indicating higher insulin sensitivity and better β-cell function. Carriers of the protective genotype GG had higher body fat percentages when > 18% of daily energy was derived from protein, even if the total energy intake in diets from the upper quantiles of protein intake was significantly lower compared to the lower quantiles. In GG individuals from the upper quantile of protein intake, we also observed higher fasting insulin levels, HOMA-IR, volume of subcutaneous fat tissue and waist circumference, which can indicate higher insulin resistance. Surprisingly, lower body weight and waist circumference were observed when more than 48% of energy of the GG genotype carriers’ diet was derived from carbohydrates, compared to lower quantiles of carbohydrate intake.
We observed similar associations with protein intake in homozygous carriers of protective G allele in rs1350341. Moreover, carriers of GG genotype had lower blood glucose levels at 30 minutes of OGTT also when less than 48% of total energy was derived from carbohydrates, and when more than 30% of total daily energy was derived from fat. GG genotype carriers in the upper protein intake quantiles had higher BMI values and body fat percentages, even if the total energy content in diets from upper quantiles was significantly lower compared to the lower quantiles of protein intake. When > 18% of total energy intake of GG genotype individuals was derived from protein, participants had a higher volume of subcutaneous fat tissue, waist and hip circumference, as well as higher fasting insulin levels and HOMA-IR values, indicating lower insulin sensitivity. Body weight and waist circumference were lower when GG genotype carriers followed a diet with more than 48% of total energy coming from carbohydrates, compared with lower carbohydrate intake quantiles. Our previous studies showed that GG genotype carriers had significantly higher glucose utilization after high-carbohydrate meal intake(31). Taken together, we can hypothesize that a mechanism that protects these individuals from de novo lipogenesis and fat deposition may be at play not only after high-carbohydrate meal intake but also when carbohydrates provide more that 48% of daily energy. Similar to the previously described rs633265, we did not find any other studies that we could compare our results with, because we likely analysed associations between both rs633265 and rs1350341 and dietary factors for the first time.
In summary, recommendation to decrease dietary fat < 30% is appropriate for MC4R carriers of protective genotypes, but in carriers of MC4R risk genotypes dietary total fat intake does not seem to affect metabolic parameters. In individuals carrying the protective genotypes, better metabolic effects can be expected when these subjects follow diets of more than 48% of energy from coming carbohydrates, which does not seem to influence the impact of MC4R high-risk genotype variants on obesity and related comorbidities. A previous study by Butler et al.(54) suggested that MC4R regulates metabolic and behavioural responses to high-protein and low-fat intake. In our study participants, carriers of genotypes that predispose individuals to obesity seem to be affected by dietary proteins, and with increasing dietary protein intake the adverse effects may be induced, even if total energy intake is reduced. Additionally, Huang et al.(55) found that dietary protein intake significantly modifies the effects of MC4R on changes in appetite. Furthermore, carriers of high-risk genotypes may experience greater increases in appetite and craving when consuming a high-protein weight-loss diet. Because our study population had a low number of risk variant carriers in the lower protein intake quantiles, our sample was too small to conduct analysis and obtain reliable results. However, we may hypothesize that decreasing dietary protein in this group may have beneficial metabolic effects. Nevertheless, we conclude that protein intake that provides > 18% of total energy may have negative metabolic consequences. On the other hand, the impact of protective genotypes investigated in rs633265 and rs1350341 may have more beneficial effects on obesity-related comorbidities, especially glucose homeostasis, if dietary protein is maintained at less than 18% of total energy intake. This is a crucial observation from our study, since dietary proteins are considered to be a highly satiating nutrient(56), which may also improve the glycaemic response by stimulating anorexigenic gastrointestinal peptides secretion (including glucagon-like peptide-1, GLP-1)(57). Since MC4R functions are affected by anorexigenic hormones, and GLP-1 receptor agonists (such as liraglutide) have been recommended as an effective treatment for the most common form of monogenic obesity caused by mutations in the MC4R gene(58), we might expect that exposure to high-protein diets may modulate MC4R metabolic effects. However, we could not expect that in this manner, although it has been observed in animal model that a low-protein diet is associated with significantly lower MC4R gene expression in PVN (paraventricular nucleus)(59).
Further studies are needed to investigate the possible mechanisms of noted differences, as well as studies investigating whether reducing dietary protein intake provides any metabolic benefits for people at high genetic risk of obesity, carrying a high-risk genetic variant of the MC4R gene. Nevertheless, it is important to note that some of these genetic variations are located close to non-coding regions of DNA, and potential associations between adiposity-related traits and non-coding variations are still not clear. Non-coding regions are very intriguing, but mechanisms for these associations have yet to be clarified(60, 61).
To the best of our knowledge, this is the first study to investigate interactions between these four common MC4R genetic variants and macronutrient intake as well as the effect of these relationships on obesity, body fat content and obesity-related comorbidities in the context of a large population study. Further studies are needed to determine whether our findings are generalizable to other ethnic groups. Even though the best models to test gene × environment interactions are randomized clinical trials, the number of participants in such studies are limited, and there is always a risk of false-positive findings. Therefore, replication in larger and more diverse populations is needed to verify findings. A major strength of our study is that it is based on a relatively large population-based sample, including women and men with a wide age range. The dietary information in this study was based on self-reported three-day diaries of food intake. This may be a major limitation in our study, because it has been demonstrated that people, especially obese individuals, tend to underreport their food intake(62). However, dietary questionnaires and diaries are the only tools that are currently available for large population studies. The other important limitation is that for the daily physical activity evaluation we could not use accelerometers, nevertheless, the long version of IPAQ questionnaire is a validated method to verify physical activity level, with a high reproducibility. In addition, advanced statistical methods are required to dissect and analyse complicated relationships and interactions between factors such as macronutrient intake and genetic risk.