**3. Role of genetic factors in weight loss efficiency**

The common reasons are believed to be unhealthy eating habits and low physical activity, but undoubtedly body weight is also influenced by genetic factors that account for 40–70% variance in BMI [23]. Besides, there are marked inter-individual differences in terms of weight loss even if energy consumption and expenditure are supervised [24].

A plethora of studies has shown that SNPs (single nucleotide polymorphisms) of certain genes are associated with weight, waist circumference, distribution, and types of fat tissue. Moreover, an accumulating number of epidemiological evidence indicate that genetic pressure could be one of the leading factors in the obesity epidemic spread due to the assortative mating and increased number of offspring in individuals with higher BMI [25]. Thus, obesity-associated genetic variants could become more common in the population, thus demanding new weight-loss strategies that take into consideration the interaction between genetic factors, diet, and physical activity. Only a rare number of genetic mutations can lead to unavoidable obesity, hence, the impact of genetics on BMI could be diminished by environmental and behavioral factors [25]. Identification of genetic factors determining individual susceptibility to weight loss can be used to choose the appropriate intensity and mode of weight loss program intervention and diet macronutrient composition or to suggest alternative treatment methods, such as surgery or pharmacological intervention.

Obesity is an extreme form of fat tissue accumulation. According to thermodynamics laws, even low continuous excess of calories income over calories expenditure causes obesity development [26], although the reasons for increased or decreased dietary intake could be different.

The long-term efficiency of weight loss is considerably determined by dietary habits that include nutritional composition, timing, quantity, and quality of food intake. The genetic predictors influencing the nervous system function define nutritional behavior and can lead to monogenic obesity forms. For example, SNPs of the *MC4R (*melanocortin 4 receptor*)*, *BDNF (*brain-derived neurotrophic factor*)*, and *FTO* (fat mass and obesity-associated) genes are associated with hyperphagia and increased fat macronutrient intake [25]. From this perspective, designing an individual nutrition

plan taking into consideration genetically predetermined dietary habits is important to improve long-term adherence to the prescribed diet.

All successful weight loss programs are focused on reducing caloric intake through the alteration of macronutrient composition (i.e., low-carb vs. low-fat diet, highprotein diet) [27], although different methods of creating a calorie deficit may be chosen depending on an individual's genetic profile (**Figure 1**).

*FTO* is one of the most studied obesity-related genes and its genetic variants are associated with higher total energy and fat intake, reduced satiety, and craving for calories dense food [26, 28], which partially can be explained by a higher level of appetite-related hormones (ghrelin and leptin) [29]. High-protein diets could be a useful tool in managing satiety [27] and reduction of appetite-related hormones in case of *FTO*-associated obesity. For example, carriers of risk allele *FTO* rs1558902 experience a greater weight loss in response to a 2-year high-protein diet intervention program; and, generally, higher content of protein in a diet is preventive against weight gain for this risk allele carrier [30]. Furthermore, the carriers of rs1558902 SNP experience more metabolic benefits from high-fat diets compared to low-fat diets [31]. Putting together this data allows for designing a diet plan for the rs1558902 risk allele carriers with the highest adherence (high protein), weight loss, and health improvement (high fat) properties.

Several other SNPs are also associated with a greater weight loss in response to a high-fat diet plan, including *HNF1A* (hepatocyte nuclear factor-1 alpha) gene rs7957197 minor T allele, *TNF-α* gene rs1800629 minor A allele, and *CYP2R1 (*cytochrome P450 2R1*)* gene rs10741657 minor A allele [32]. The last association may suggest that a high amount of dietary high-quality fat also contains other essential nutrients, such as vitamin D, that could be beneficial for people prone to its deficiency. Similarly, monounsaturated fats are well known for their anti-inflammatory properties in obesity [33] and a diet plan full of these fats is beneficial for IL-6 polymorphism rs1800795 associated with the higher inflammatory process [32].

However, despite the increased satiety properties, a high-fat, high-protein plan is not a universal approach. Often the carriers of genes variants that impair insulin

**Figure 1.**

*The interplay between genetic factors, diet, physical activity, and obesity development.*

### *Personalized Strategy of Obesity Prevention and Management Based on the Analysis… DOI: http://dx.doi.org/10.5772/intechopen.105094*

secretion benefit more from low-fat diets. For instance, the *ADCY3* (adenylate cyclase 3) gene codes for an adenylate cyclase protein, which is responsible for the formation of cyclic AMP, a secondary messenger involved in insulin secretion, decreased mTOR signaling and lipogenesis, and promoted thermogenesis and fatty acid oxidation. The minor allele of *ADCY3* rs10182181 SNP has been shown to correlate with increased BMI. The carriers of this allele lost more weight with low-fat diets in comparison to high-protein diets [34]. Such improvement can be explained by a higher percentage of carbohydrates in a diet and its positive influence on insulin signal function. The same is known for the two *TCF7L2* (transcription factor 7-like 2) SNPs rs7903146 and rs7901695—the individuals with these risk alleles for type 2 diabetes undergo lower weight loss with a high-fat diet [32, 35, 36] and experience more metabolic benefits from lower protein intake. Likewise, the carriers of the *MTNR1B* (melatonin receptor 1B) risk allele rs10830963, which is associated with increased fasting glucose and type 2 diabetes, experience greater weight loss with a low-fat diet [37]. Similar results are obtained with the *IRS1* (insulin receptor substrate 1) genetic variant related to insulin resistance and increased BMI. Those with the SNP risk allele rs2943641 lose more weight with the high-carbohydrate low-fat diet, while the low-carbohydrate high-fat diet is more suitable for the noncarriers [38].

Additionally, different macronutrient compositions should be considered for the carriers of different *PPARG2* (peroxisome proliferator-activated receptor gamma) rs1801282 and *PPM1K* (protein phosphatase 1 K) rs1440581 polymorphisms. The GG and GC genotypes (obesity-related G allele carriers) of the *PPARG2* gene lose more fat following a low-fat diet, whereas individuals with the CC genotype experience greater weight loss with a high-fat nutrition plan [39]. Although *PPM1K* is the gene coding for a protein participating in the branched-chain amino acid metabolism, its SNP rs1440581 T allele correlates with higher fasting glucose and higher BMI and is associated with greater fat loss following a high-fat diet [32], while a low-fat diet has more benefits for individuals with the CC genotype [40].

Moreover, the composition of dietary fats, that is, the proportion of saturated, poly-, and monounsaturated fats, can be critical for a successful weight loss for the specific genotypes. *ADIPOQ* is a gene that encodes for adiponectin, the most ubiquitous protein hormone that is secreted by the adipose tissue. The level of serum adiponectin is correlated with BMI and has 80% heritability [41]. Its expression can be affected by the rs17300539 SNP located in the proximal promoter region. A risk variant of this gene is associated with higher BMI, although the genetic impact on body weight becomes negligible when the level of dietary intake of MUFA (monounsaturated fatty acids) is reduced to less than 13% of energy intake [28]. However, for another *ADIPOQ* SNP rs266729 located in the promoter region, two studies revealed no benefits for weight loss in the minor risk allele carriers with different body compositions [41, 42]. Although individuals with all genotypes showed an improvement in body weight and parameters of glucose homeostasis, non-risk allele carriers showed better response.

The timing of meals can also be an important part of weight-loss strategies since some of the circadian rhythm signaling proteins are associated with increased BMI and diabetes. For example, G allele of *MTNR1B* (melatonin receptor 1B) rs10830963 was found to be associated with higher BMI and higher resting glucose levels, however, the association was more significant in early sleep timing compared to late sleep timing [43]. This allele was associated with elevated melatonin levels early in the morning, thus simultaneous food intake additionally elevates blood glucose levels and may disturb circadian rhythm regulation. Similarly, weight loss resistance is the

case for carriers of the C allele of *CLOCK* (basic helix–loop–helix-PAS transcription factor) SNP rs1801260 is also associated with higher activity in the second half of the day [44]. This data suggests the disturbance of the natural circadian rhythm due to modern lifestyle. This knowledge could help align meal timing with the natural cycle so that carriers of the risk allele could experience more weight loss by scheduling their breakfast later in the day.

While dietary lifestyle changes are a key weight loss factor, individuals experience more metabolic and health benefits when those are combined with physical activity intervention [24]. Although WHO recommends a minimal 150 min per week of moderate exercise for health improvement, a number of studies have shown that such amount of physical activity is not sufficient for clinically significant weight loss, thus indicating the need for increased physical activity to 225–420 min per week. It is decidedly more likely to lose more weight with increased physical activity, however, the optimal level of physical exercise still should be determined (**Figure 1**). Furthermore, various types of exercises are available, for example, anaerobic, aerobic, and interval training, all of which affect body composition and metabolism in different ways and could be beneficial to different genotypes [45].

The PPAR (peroxisome proliferator-activated receptors) family genes are involved in lipolysis and lipogenesis, the efficiency of energy utilization, and mitochondrial biogenesis [45]. Some SNPs located in these genes are often associated with different weight loss outcomes in response to physical activity intervention. According to our data, SNPs (rs12629751, rs9833097) of the *PPARG* gene are associated with greater fat mass loss and improvement in cardiometabolic health. Another member of the peroxisome receptors family *PPARGC1A* is induced by physical activity and associated with an increased lipid oxidation rate. Our results showed that the polymorphism of this gene rs17650401 is correlated with the efficiency of fat mass loss following a moderate exercise intervention program [45].

As was mentioned, different types of physical activity would be beneficial for individuals with different genotypes (**Figure 1**). Well-known *PPARG* polymorphism Pro12Ala has no or even negative effects on weight loss in response to the aerobic training program [46]. Moreover, individuals with the high-risk SNP of *PPARD* gene rs2016520 also showed less weight loss after a moderate aerobic exercise program [47]. The 12Ala allele is associated with strong abilities and is responsible for the transition to an anaerobic energy supply during exercise [48], so carriers of this obesity-related allele can benefit more from the anaerobic intervention programs or high-intensity interval training. This is the case for the obesity-related SNP rs1885988 of the MTIF3 (mitochondrial translational initiation factor 3) gene, where intensive lifestyle interventions lead to more weight loss in risk-allele carriers [49]*.*

Interestingly, the predisposition to physical activity seems to be heritable. The majority of genes responsible for this trait are involved in behavior control, mood, and reward pathway function. For instance, *MC4R* genes are associated with a low level of physical activity [50], therefore, the risk-allele carriers could be discouraged with high-intensity or high-impact exercise. In contrast, some polymorphisms are related to exercise adherence and even correlated with exercise dose (rs6314 *HTR2A, 5-*hydroxytryptamine receptor 2A), duration (rs5946015 *HTR2C* and rs3758653 *DRD4,* dopamine receptor D4), and intensity (rs1801412 *HTR2C*) [51], consequently, they can determine a better outcome from the higher level of physical activity.

Plenty of studies has indicated that there is a wide inter-individual variation in response to diet and physical activity weight-loss programs. Personalization of dietary and physical activity recommendations could be a powerful tool for planning the

*Personalized Strategy of Obesity Prevention and Management Based on the Analysis… DOI: http://dx.doi.org/10.5772/intechopen.105094*

most appropriate weight loss plan taking into account subjective feeling of satiety (*FTO* variants), metabolic characteristics (polymorphisms associated with disrupted insulin signaling), meal schedule (*MTNR1B* and *CLOCK*), required level and type of physical activity (*PPAR* family genes), and long-term adherence to designed physical activity plan (MC4R, HTR genes, DRD4). Altogether genotyping data could be used for managing and preventing obesity with a higher level of success.
