Personalized Strategy of Obesity Prevention and Management Based on the Analysis of Pathogenetic, Genetic, and Microbiotic Factors

Abstract

This chapter reviews the issue of overweight management, which is one of the major challenges faced by most countries today. The causes of obesity include genetic and epigenetic factors, a lack of physical activity, eating disorders, and gut microbiota status. Physical exercise is the main means of prevention and management of overweight and obesity. The effectiveness of exercise programs for obese people typically varies around 80%, but it can be increased by taking into account biochemical, genetic, epigenetic, and microbiome markers, which allows choosing the most appropriate type of exercise according to individual characteristics. The pathogenetic preconditions for reducing exercise tolerance were examined based on the existing imbalance of adipokines, cytokines, and incretins. The association between genotype and weight loss induced by different diets and types of exercise was discussed along with obesity epigenetic markers. The effects of dietary choice on the microbiome composition and its contribution to the development of systemic inflammation in obese people were assessed. The weight management exercise program for middle-aged women was presented. The structure and value of the factors that determine the physical condition of overweight middle-aged women were described. These data provide the basis for designing a sound exercise program for weight management.

1. Introduction

Obesity is one of the modern world challenges, which has become epidemic and has negatively affected population health. Obesity and overweight, which in most cases accompany metabolic syndrome and type 2 diabetes, are among the independent risk factors for overall mortality, including death from cardiovascular disease and cancer [1]. In recent years, special attention has been given to the sedentary lifestyle of people of almost all ages, as it is proven that lack of physical activity is an independent significant factor in deteriorating health. A meta-analysis of studies examining the relationship between physical activity and overall mortality showed that a higher level of total physical activity of any intensity and less time spent in sedentary behaviors are associated with a significant reduction in the risk of overall mortality [2].

In addition to physical inactivity, eating disorders, and intestinal microbiota status, the causes of obesity include genetic factors, such as gene polymorphisms and epigenetic modifications. Weight loss training programs are the major means of obesity prevention, cardiovascular fitness improvement, and body mass reduction. However, the effectiveness of these programs ranges from 79–83%. The outcomes of exercise training aimed at weight loss in overweight and obese adults depend on individual characteristics of the body, such as morphological and metabolic features, developed under the influence of hereditary and environmental factors throughout life. A number of informative biochemical, genetic, epigenetic, and microbiotic markers can be used to determine the most effective mode of exercise training according to individual metabolic characteristics.

The current situation in the world only exacerbates it drawing attention to the increased risk of COVID-19-related death in overweight and obese people. The problem of being overweight in light of the current epidemic situation is further exacerbated as the quarantine restrictions, which are periodically imposed by state and municipal authorities, contribute to the spread of hypodynamia among the population. The stress provoked by the epidemic situation triggers destructive eating behaviors with excessive calorie density. Hypodynamia and excessive calorie density make them gain weight even more and lead to greater health risks. The urgency of this problem stipulates the need to develop a personalized approach to designing physical exercise programs for obese people and assess the impact of individual markers of obesity on the effectiveness of such programs.

2. Imbalance of adipokines, cytokines, and incretins as a pathogenetic risk factor for reduced exercise tolerance

The progressive increase in obesity prevalence during the second half of the last century has put adipose tissue at the center of scientific interest. It is now seen not only as a passive reservoir for the accumulation of energy reserves but as a metabolically active endocrine organ that secretes hormones and cytokines. Adipose tissue cytokines and adipokines are involved in the regulation of many vital processes, the imbalance of which results in the development of insulin resistance, metabolic syndrome, diabetes, and cardiovascular complications. Many studies confirm that the presence of metabolic syndrome or any of its components correlates with the level of pro-inflammatory cytokines [3].

Our studies showed a significant increase in IL-6 (interleukin 6), TNF-α (tumor necrosis factor alpha), and OPG (osteoprotegerin) in subjects with overweight and obesity irrespective of the presence of carbohydrate metabolism disorders confirming the significant role of overweight and obesity in the development of nonspecific inflammation, one of the main pathogenetic risk factors for metabolic disorders development (Table 1). We wanted to pay special attention to a new cytokine identified in obese people.

Cytokine	No diabetes mellitus (n = 50)				Type 2 diabetes mellitus (n = 58)
	No obesity (n = 19)	Obesity (n = 31)	t	p	No obesity (n = 20)	Obesity (n = 38)	t	p
TNF-alpha	12.1 ± 1.2	22.0 ± 1.4	5.2	0.001	13.6 ± 0.5	24.8 ± 1.5	7.3	0.001
IL-1	13.6 ± 2.3	11.5 ± 0.6	0.9	0.433	10.5 ± 0.4	12.1 ± 1.0	1.6	0.127
IL-6	17.4 ± 3.3	36.4 ± 0.7	5.6	0.001	13.5 ± 2.7	38.1 ± 2.0	7.3	0.001
S IL-6R	294.6 ± 31.3	333.4 ± 17.6	1.1	0.317	365.0 ± 54.2	354.1 ± 25.2	1.3	0.252
Osteoprotegerin	8.7 ± 0.8	23.9 ± 2.2	6.6	0.001	7.4 ± 1.3	23.4 ± 2.6	5.5	0.001

Table 1.

Mean cytokine levels (pg/mL) in obese patients.

Note: p—statistical significance of the difference between the groups with and without obesity.

The glycoprotein OPG, which belongs to the tumor necrosis factor receptor superfamily, was initially identified as an inhibitor of bone resorption, however other important regulatory functions of OPG have been later identified. The OPG/RANKL (NF-κB ligand receptor) axis has been shown to play a significant regulatory role in the skeletal, immune, and vascular systems, including vascular calcification that accompanies atherosclerosis. Elevated OPG levels are associated with the incidence and prevalence of coronary heart disease. Moreover, increased OPG is considered as an independent risk factor for overall vascular mortality in obese adults and IR (insulin resistant) [4]. Our data showed that the OPG level was the most sensitive proinflammatory cytokine. This is confirmed by other authors. Kotanidou et al. (2018) reported that obese individuals show an increase in serum OPG already during puberty, and a key factor in the regulation of OPG is IR, which accompanies even the initial metabolic disorders on the background of excessive body weight [5].

We also investigated the levels of some hormones, which we believe play a key role in the regulation of appetite, neurophysiological relationships between the gut-brain axis and muscles, and physical activity (Table 2).

Hormone	No diabetes mellitus (n = 50)				Type 2 diabetes mellitus (n = 58)
	No obesity (n = 19)	Obesity (n = 31)	t	p	No obesity (n = 20)	Obesity (n = 38)	t	p
Insulin (μUnits/mL)	5.5 ± 0.7	8.9 ± 0.3	5.1	0.001	6.4 ± 0.3	14.3 ± 1.4	5.4	0.001
Leptin (ng/mL)	18.2 ± 4.4	32.3 ± 3.8	2.4	0.020	18.4 ± 6.2	39.8 ± 3.9	2.6	0.014
Cortisol (nmol/L)	427.6 ± 45.7	461.5 ± 41.5	0.5	0.632	425.7 ± 43.9	440.9 ± 26.5	0.3	0.759

Table 2.

Mean hormone levels in obese patients.

Note: p—statistical significance of the difference between the groups with and without obesity.

The most significant changes were observed in insulin and leptin. Hyperinsulinemia as a consequence of insulin resistance and hyperleptinemia are the most common hormonal disorders in the background of being overweight. These data are confirmed by numerous studies. Leptin is one of the most well-known adipose tissue hormones; it is very sensitive to energy consumption, especially in energy-deficient conditions. It has been shown that a reduction in the serum leptin was observed before fat loss even after 2–3 days of a low-calorie diet. Decreased leptin levels can cause a number of biological and hormonal responses, including the decreased activity of the sympathetic nervous system, hypothalamic gonadotropin-releasing hormones, IGF-I (insulin growth factor I), GH (increased production of growth hormone), and ACTH (adrenocorticotropic hormone). Conversely, hyperleptinemia, which is common in overweight people, is associated with leptin resistance. At night, the leptin level is 30% higher [4]. In the presence of obesity, the level of leptin is increased many times, and a 10% decrease in body weight decreases its blood content by 50%. According to modern ideas, leptin sends a signal to the hypothalamus by activating specific leptin receptors, which are located in different parts of the brain – hypothalamus, cerebellum, cortex, hippocampus, thalamus, and vascular plexus of the cerebral capillary endothelium [6].

An important regulator of leptin secretion is hyperinsulinemia. Adipocytes produce leptin in response to increased postprandial insulin levels in healthy people and patients with metabolic syndrome. Based on numerous studies, we can conclude that the feedback mechanism between the level of insulin secretion by β-cells and fat and muscle cells utilizing glucose depends only on leptin action because leptin receptors are present in the islets of Langerhans of the pancreas, so there is a direct relationship between the concentration of insulin and its effect on the secretion of hormones by adipocytes. Normally, due to the increase in insulin secretion, production of leptin increases that inhibits the secretion and release of insulin by the feedback mechanism [7].

It should be noted that in our study we did not find a significant difference in cortisol levels between patients with normal body weight and obesity. However, it is well known that over time, elevated glucocorticoid levels can lead to increased skeletal muscle protein breakdown, adipose tissue lipolysis, and hepatic gluconeogenesis accompanied by decreased glucose utilization. These effects increase circulating blood glucose levels, thus contributing to insulin resistance and hyperinsulinemia.

In recent years, there has been increasing information about the effects of incretins, which are a group of gut-derived hormones, on the regulation of body weight. Modern pharmacological therapy of obesity is associated with the introduction of GLP-1 (glucagon-like peptide-1) agonists, the level of which is significantly reduced in overweight and obese people irrespective of disorders of carbohydrate metabolism. Incretin hormones, such as GLP-1 and GIP (glucose-dependent insulinotropic peptide), are secreted from the gastrointestinal tract into the portal circulatory system in response to nutrients. In a nutrient-dependent manner, incretins have been shown to contribute to lowering blood glucose levels by increasing insulin secretion, decreasing glucagon secretion, and decreasing the rate of gastric emptying. The main effect of GLP-1 is glucose-dependent stimulation of insulin secretion by pancreatic beta cells. GLP-1 slows down the rate of gastric emptying, which helps to reduce fluctuations in postprandial glycemia [8, 9]. GLP-1 also enhances the feeling of satiety and reduces food intake, by providing prolonged stimulation of mechanoreceptors and satiation receptors. Decreased food intake may be mediated by a direct effect of GLP-1 on sensory neurons located in the upper gastrointestinal tract or by a direct effect on the central nervous system because GLP-1 receptors are present in the hypothalamic centers that regulate food intake.

Physical activity combined with a balanced diet is the basis for the prevention and normalization of weight gain and obesity. No modern guidelines for weight correction, impaired carbohydrate metabolism, prevention, and treatment of cardiovascular complications can be provided without a primary emphasis on the need for physical activity. At least 150 min per week of moderate-intensity physical activity is the minimum required to ensure active metabolism of basic, carbohydrate and fat metabolism. For example, the American Diabetes Association currently provides the following recommendations for physical activity/exercise for people with carbohydrate metabolism disorders. Daily exercise, or at least not allowing more than 2 days to elapse between exercise sessions, is recommended to enhance insulin action. Adults with type 2 diabetes should ideally perform both aerobic and resistance exercise training for optimal glycemic and health outcomes. Structured lifestyle interventions that include at least 150 min/week of physical activity and dietary changes resulting in weight loss of 5–7% are recommended to prevent or delay the onset of type 2 diabetes in populations at high risk and with prediabetes [10]. To date, the influence of physical activity on the pathogenetic risk factors of overweight and obesity has been proven, including optimization of the secretion of adipose tissue hormones, incretins and reduction of low-gradient nonspecific inflammation [11, 12].

The most convincing data on the effects of physical activity on adipose tissue hormones concern leptin. It has been shown that short-term exercise does not affect leptin levels in healthy people. However, longer and more intense exercises (≥60 min), which are associated with increased energy expenditure (≥800 kcal), lead to a decrease in leptin levels [13, 14]. In general, lifestyle changes that result in weight loss contribute to the normalization of serum insulin and leptin levels [15].

An inverse relationship was also found between physical activity and proinflammatory cytokine levels in obesity, diabetes, and metabolic syndrome. It is believed that the positive effect of exercise, which is partly mediated by changes in the profile of adipokines, is an increase in anti-inflammatory cytokines with a decrease in pro-inflammatory ones. This effect was described at the level of gene expression, protein ligands, and receptor binding [16]. For example, exercises increase insulin sensitivity by lowering TNF-α, C-reactive protein, and increasing adiponectin. Interleukin-6 is the first cytokine that appears in the bloodstream during exercise, and its levels increase exponentially in response to exercise [17]. The increase in IL-6 levels in plasma caused by exercise correlates with the muscle mass, as well as with the mode, duration and, especially, the intensity of exercise. Infusion of recombinant human IL-6 (rhIL-6) in humans simulates the IL-6 response to exercise and prevents an increase in plasma TNF-α [18]. Inhibition of IL-6-induced TNF-α production has also been shown in cultured human monocytes. Furthermore, IL-6 stimulates the release of other anti-inflammatory cytokines, including IL-10 and IL-1Ra. These and other experiments suggest that the anti-inflammatory effects of exercise are partly mediated by IL-6 levels [19].

It has also been shown that normal physical activity can affect glucose-induced GLP-1 secretion [20]. The more time spent in physical activity, the more pronounced is the glucose-induced GLP-1 response irrespective of insulin sensitivity. This indicates a positive effect of normal moderate-intensity physical activity on GLP-1 secretion that may help improve glucose regulation and reduce the risk of type 2 diabetes [21, 22]. Therefore, physical activity contributes not only to weight normalization but also improves metabolic disorders characteristic of obese and overweight people.

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, high-protein diet) [27], although different methods of creating a calorie deficit may be chosen depending on an individual’s genetic profile (Figure 1).

Figure 1.

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

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 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 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.

3.1 Personalization of health-promoting fitness programs for young women based on PPARG gene polymorphism

To personalize health-promoting fitness programs for young women, a study was conducted that included an assessment of women’s physical fitness before and after the implementation of two 4-month health-promoting fitness programs (aerobic and resistance workouts). The personalized approach presupposed genotyping women according to Pro/Ala polymorphism of the PPARG gene. At the beginning of the pedagogical experiment, we formed two groups of women, the experimental group 1 (EG1) (resistance training, n = 24) and the experimental group 2 (EG2) (aerobic training, n = 20).

The results of the study showed the dependence of young women’s physical fitness on their genotype for the above-mentioned polymorphism as a result of undergoing fitness programs by them. Resistance workouts caused significant changes in body composition, with a slight decrease in body weight and girth for women with Pro/Ala and Ala/Ala genotypes, while among women with Pro/Pro genotype, there was a significant decrease in body weight and girth. The level of physical fitness in women with Pro/Pro genotype increased by 11.1%, and in women with Pro/Ala and Ala/Ala genotype by 10.5% (p < 0.05) under the influence of the strength fitness program.

The patterns of the changes in the parameters of physical condition differed between the women with Pro/Pro genotype and Pro/Ala and Ala/Ala genotypes, who participated in aerobic training. Aerobic training resulted in significant changes in the bodyweight of young women in both subgroups.

Identification of genetic markers allows applying a differentiated approach in the development of fitness programs, which stipulates choosing the structure of the program, the ratio of aerobic and resistance activities, exercise intensity, and pulse regimes depending on the polymorphism of the PPARG gene [52].

3.2 Microbiome and obesity

The global obesity epidemic has stimulated a great interest in studying the effects of microbial metabolome on the metabolic profile and maintenance of the energy homeostasis of an individual. The study by Turnbaugh et al. was one of the first that showed the relationship between the gut microbiota and increased body weight [53]. These and other similar findings formed a basis for the development of ideas about the mechanisms of microbiome influence on metabolic pathways.

The introduction of new technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics, and bioinformatics, in the last decade, has revolutionized and expanded the understanding of the structure and function of the human microbiome, as well as of its key role in regulating metabolic processes in the macroorganism, absorption of nutrients, endogenous synthesis of essential enzymes, vitamins, and biologically active compounds [54].

3.3 Functional significance of microbial enterotypes

The microbiome consists of about 100 trillion microorganisms that exist in a symbiotic relationship with human hosts [55] and can be classified into four enterotypes: Bacteroides, Prevotella, Ruminococcus, and Firmicutes, which have different dominant classifications, pathways, functions, and correlations between coexisting genera. The Bacteroides enterotype metabolizes carbohydrates and proteins with enzymes involved in glycolysis and pentose phosphate pathways. Prevotella and Ruminococcus contribute to the transport and absorption of monosaccharides by enriching the membrane and binding gut mucin to hydrolyze it. Enterotypes use different strategies to obtain energy from substrates present in the gut ecosystem. The specific composition of enterotypes responds to special mechanisms of metabolism of carbohydrates, amino acids, and fatty acids, which determine the frequency of obesity and obesity-related metabolic diseases [56].

16S rRNA microbiome sequencing identified the relationship between microbial diversity and different physiopathological conditions and allowed to observe the behavior of different types and genera of bacteria in combination with different phenotypes, different types of diets, and, in particular, obesity [57]. It is assumed that the microbiome can regulate the extraction of energy substrates from food and energy balance of the body, thus promoting the development of obesity or protecting against it. This hypothesis was confirmed in a study by Gordon, which reported an increase in fat content in the body of gnotobiotic (germfree) rats after fecal transplantation from obese rats [58]. Some studies have shown an increase in the percentage of Firmicutes and a decrease in the percentage of Bacteroidetes in obese humans compared to humans and underweight rats [59], while the others did not find significant changes in microbial composition between the two groups, and some even reported the opposite results. The relationship between the gut microbiome and metabolic disorders was first proven in the laboratory of Jeffrey I. Gordon at the Washington University School of Medicine in St. Louis. The authors demonstrated that leptin-resistant mice, characterized by increased appetite and obesity, have a deficiency of Bacteroidetes and an increased relative proportion of Firmicutes compared with control animals [60].

3.4 Relationship between obesity and the ratio of Bacteroides and Firmicutes in fecal samples

Dysbiosis in obesity is often characterized by a decrease in microbial diversity, changes in the relative numbers of major enterotypes, such as Firmicutes and Bacteroidetes, and/or an increase in pathogenic microorganisms. In a study of 18 obese male volunteers, the percentage of total fecal bacteria identified as Bacteroides did not differ between obese subjects and a normal weight control group [57]. These results contrast with similar studies but are quite consistent taking into account significant inter-individual differences.

Despite the disagreement, the ratio of Firmicutes to Bacteroidetes was studied and associated with susceptibility to disease [60], particularly an increase in the number of Firmicutes and a decrease in Bacteroidetes were observed in obese patients and type 2 diabetics [61]. The ratio of Firmicutes and Bacteroidetes in the fecal samples of healthy adults was 10/1 and in obese patients was 100/1. Thus, obesity was shown to be associated with an increase in the number of Firmicutes and a decrease in fecal Bacteroidetes [62]. Also, the predominance of Firmicutes in the gut microbiota was constantly observed in obese subjects in the study of Lei et al. [60], and the number of Proteobacteria was related to a large number of genera Bacteroides, Prevotella, and Ruminococcus that is positively correlated with a healthy intestinal microbiota. Some authors believe that an important factor associated with obesity is not the ratio of Bacteroidetes to Firmicutes in the gut microbiota, but the amount of short-chain fatty acids produced by it [63].

3.5 Microbiota-derived metabolites

Microbial metabolites are able to affect the metabolic functions of the host and play a key role in the pathophysiology of metabolic diseases. Bacteria of the gut microbiome produce a large number of enzymes that catalyze the depolymerization of complex carbohydrates as well as the degradation of indigestible components of chyme to SCFA (short-chain fatty acids), which are not only energy substrates but can also serve as messengers participating in the immune and systemic inflammatory response [64] and affecting intestinal motility and vascular tone. Many human and animal studies have shown a clear relationship between gut microflora, SCFAs, and obesity.

Bile acids are also one of the most important microbial products with bioactivity in stimulating the secretion of gut hormones, as intestinal bacteria are involved in the deconjugation of bile acids, which are endogenous ligands of the FXR (nuclear Farnezoid receptor), found in various tissues, such as liver, intestines, kidneys, adipose tissue, and immune cells. Bile acid signaling through FXR plays a role in maintaining lipid and glucose homeostasis. Studies have shown that FXR-deficient mice have impaired insulin signaling with impaired regulation of glucose homeostasis and elevated blood cholesterol and triglyceride levels [65]. Among the microbiota-derived metabolites, TMAO (trimethyl N-oxide) should also be mentioned, which is an important modulating factor in various diseases and significantly affects platelet hyperactivity, abnormal plasma lipid levels, obesity, and insulin resistance [66].

However, the main end product of the hydrolysis of indigestible carbohydrates is SCFA, that is, acetic (acetate), propionic (propionate), and butyric (butyric) acids, which are the most common and make up >95% of the total content of SCFAs and are produced in an approximate molar ratio of 60:20:20, reaching a combined concentration of more than 100 mM in the intestinal lumen [67], and act as the main energy supply for intestinal epithelial cells and, therefore, can increase the protection of the mucous barrier [68]. As the primary metabolic end product, gram-negative Bacteroidetes produce acetate and propionate, while the type Firmicutes produce mostly butyrate [69]. Several animal and human studies have found elevated concentrations of SCFAs in feces (particularly propionate) in obese individuals compared to normal-weight subjects [70] and, at the same time, recent data suggest that butyrate and propionate may promote healthy metabolism by activating IGN (intestinal gluconeogenesis) [71], which plays a dual role in maintaining energy homeostasis—regulating food intake and increasing insulin sensitivity. Propionate can directly initiate gut-brain communication by acting as an agonist of FFAR3 (free fatty acid receptor 3) to induce IGN with a positive effect on host physiology [65]. SCFAs are not only involved in energy metabolism but also perform a signaling function by activating GPRs (G-protein bound receptors) or FFAR2 (free fatty receptor 2). As reported, propionic acid is the most powerful activator of this receptor [53]. GPRs are expressed in most cells of the gastrointestinal tract, as well as in adipose tissue and immune cells. High-level expression of this receptor was found in the endocrine L-cells of the ileum and colon, which produce GLP-1 and PYY (peptide YY), and, in this way, SCFAs can modulate the secretion of incretins and regulate the onset of satiety and appetite, thus affecting the metabolic mechanisms of obesity [72].

3.6 The role of dietary intervention

Accumulated data from numerous meta-analyzes shows that macronutrients, especially proteins, fats, and insoluble fiber, have a profound effect on the structure, function, and secretion of gut microbiota-derived metabolites that modulate multiple metabolic and inflammatory pathways. Genetic studies [73] have highlighted the importance of host genotype in determining the relative numbers of certain microbiome groups but found that Bacteroidetes can be influenced by host genetics, meaning that most environmental factors (including diet) determine their relative numbers by epigenetic influence.

Hyperphagia is common in obese individuals and refers to excessive calorie intake compared to the energy needed to maintain body weight, and it has been suggested that Bacteroidetes numbers are sensitive to this condition. Jumpertz et al. conducted an inpatient study of obese and normal body weight subjects, who were randomly assigned to a diet to maintain weight or to a hypercaloric diet (2400 and 3400 kcal/day, respectively). In subjects with normal body weight, hypercaloric diet results in a decrease in the level of Bacteroidetes in fecal samples by 20% simultaneously with an increase in energy intake by approximately 150 kcal [74]. A similar observation was made in the Finnish monozygotic twin’s study, where a hypercaloric diet was also associated with a decrease in the number of Bacteroides [75]. Interestingly, gastric bypass surgery results in an increase in Bacteroides that may be due to a reduction in caloric load rather than weight loss [76].

A diet high in refined and processed foods, red meat, and sugary drinks, combined with low fiber, fruit, and vegetable intake, correlates positively with the development of metabolic diseases, such as diabetes and obesity, both of which are associated with low-grade systemic inflammation and endotoxemia due to decreased commensal microbiota [77]. In obese people, a high-protein, low-carbohydrate diet combined with caloric restriction has been reported to result in increased quantities of branched-chain fatty acids, reduced butyrate, and reduced Roseburia/Eubacterium rectale [78]. On the other hand, a diet with a high percentage of fat and sucrose led to a decrease in the diversity of gut microbiota, metabolic dysfunction, and an increase in the number of opportunistic pathogens [79]. The study by de Wit et al. [80] showed that a diet high in fat (45% energy from fat) with palm oil resulted in reduced fat absorption and increased concentration of fat in the feces compared to a diet with the addition of olive or safflower oil. The increase in the concentration of fecal fat in the palm oil group was accompanied by a decrease in microbial diversity, an increase in the ratio of Firmicutes to Bacteroidetes, and an increase in the expression of lipid-related genes in the mucosa that can be considered a sign of dysbiosis [80].

Preclinical studies showed that a high-fat diet can increase the proportion of gram-negative bacteria while reducing the number of gram-positive E. rectale/Clostridium coccoides and Bifidobacterium [81]. There is evidence that the use of emulsifiers to improve the sensory properties of food has increased in the production of low-fat foods, in part due to innovations in specialized products for health-conscious consumers. It has been reported that emulsifiers can potentially increase virulence factors and thus the pro-inflammatory potential of the microbiota and contribute to low-grade inflammation, which may promote colon carcinogenesis [82]. According to epidemiological studies showing that high protein intake from plant sources and dairy products is associated with protection against obesity [83], rats fed dietary soya as a source of protein had a lower body weight than rats fed beef, pork, or turkey [84]. A recent review of human and animal studies examining the effects of soy feeding on the microbiome found that consumption of soy products increased the numbers of Bifidobacterium and Lactobacilli and altered the ratio between Firmicutes and Bacteroidetes [85]. A study of dietary interventions in obese and overweight individuals showed that a large number of gut microbiota taxa increased due to a high-fiber diet with a low content of animal fats that improved the clinical symptoms associated with obesity [86].

Similarly, in a recent randomized clinical trial, obese individuals that were randomly assigned to a Mediterranean diet for a 2-year period displayed an increase in the genera Bacteroides, Prevotella, and Faecalibacterium and the genera Roseburia, Ruminococcus as well as in Parabacteroides distasonis and Faecalibacterium, which are known for their saccharolytic activity and ability to metabolize carbohydrates to short-chain fatty acids [87]. In another study, adherence to a Mediterranean diet characterized by high consumption of vegetables, legumes, and fruits was associated with the enrichment of Bacteroidetes and increased levels of SCFAs in feces. In contrast, nonadherence to the Mediterranean diet was associated with an increase in Ruminococcus and Streptococcus, and higher concentrations of TMAO (trimethylamine N-oxide) [88]. Furthermore, a recent meta-analysis of 12 randomized controlled trials involving 609 overweight and obese adult participants showed that consumption of isolated soluble fiber resulted in a reduction in BMI, body weight by 2.52 kg, fat deposits by 0.41%, fasting glucose by 0.17 mmol/L, and fasting insulin by 15.88 pg./mL compared to placebo treatment [89]. Numerous clinical trials examining the effect of the Mediterranean diet pattern on metabolic syndrome as well as a meta-analysis of findings from eight studies of more than 10,000 participants and five studies have reported positive effects of the Mediterranean diet pattern [90]. Some of these benefits include decreased waist circumference (−0.42 cm), increased serum HDL cholesterol (1.17 mg/dL), decreased serum TGs (−6.14 mg/dL), decreased systolic (−2.35 mm Hg) and diastolic (−1.58 mm Hg) blood pressure, and decreased blood glucose (−3.89 mg/dL) in participants who were instructed to consume a Mediterranean diet pattern compared with those who were not given instructions to change their diet [91].

4. Health-enhancing physical exercise for the management of excessive body weight in middle-aged women

Solving the issue of overweight by workouts in gyms and fitness studios is complicated by periodic bans and quarantine restrictions on the operation of the latter, so the search for an alternative becomes particularly relevant. In light of the mentioned facts, designing independent preventive and health-enhancing workouts aimed at body weight management and their further implementation for middle-aged women is a full-fledged alternative for group exercise classes. The need for scientific substantiation of the criteria for assessing the effectiveness of independent workouts aimed at body weight management in middle-aged women determined the direction of our research.

The treatment of overweight in middle-aged women includes planning, managing, and controlling certain indicators to achieve the basic goal, that is, effective management of body weight. Body weight measurements are already criteria that allow for evaluation of the effectiveness of a body weight management program. However, our research allows us to suggest a more comprehensive approach to the organization of the management of excessive body weight using physical exercise [92]. We have identified and substantiated a list of indicators of physical condition, which, in our opinion, should serve as benchmarks in the planning, management, and control of excessive body weight in middle-aged women. Furthermore, we developed the recommendations that include the most effective parameters of physical exercise workouts aimed at body weight management in middle-aged women. Taken together, the proposed approach to planning, managing, and controlling excessive body weight can be seen as an original strategy for improving health in middle-aged women.

The following methods were used to address the objectives of the study—theoretical analysis of special scientific and methodological literature; anthropometric, physiological, and pedagogical methods; and mathematical statistics. The physiological research methods used were as follows—assessment of adaptation potential, assessment of the level of physical health using the Apanasenko technique, and measurement of vital capacity (VL) using ergospirometric system Oxycom Pro. Oxygen saturation was measured with a pulse oximeter Beurer PO 80 (Germany). To assess the level of physical fitness and evaluate the maximum oxygen consumption (VO2max) as well as the aerobic and anaerobic thresholds, cardiopulmonary exercise tests were performed with a treadmill (LE-200 CE, Jaeger, Germany). Pedagogical testing included performing several physical fitness tests from the Eurofit battery to determine the level of physical condition. The results of the study were subjected to statistical data analysis using conventional tools of Statistica 10.0 statistical software.

The data obtained during the study allowed to identify “problematic” indicators of physical and functional condition (such as signs of visceral obesity, risks of metabolic syndrome, hypertension, tachycardia, and an unsatisfactory level of adaptation potential) in overweight women. The identification of “problematic” indicators, in turn, allowed to provide recommendations on the most desirable profile of the organization of fitness classes for middle-aged women, that is, preventive and health-enhancing classes. Among the different variations of the means used in the modern fitness industry, the programs with preventive and health-enhancing goals are the most expedient in the framework of the management of excessive body weight. The validity of our recommendations was also confirmed by the results of our study of the motivational priorities of fitness classes in middle-aged women (n = 105). Good health was indicated as a priority motive for the participation in physical exercise classes by 82.7% of respondents. Moreover, a low level of quality of life in the parameter of health was observed in the studied contingent according to the Scale of Quality of Life (SF-36). Summing up, we can conclude that the most appropriate profile for organizations of fitness classes is preventive and health-enhancing classes.

Factor analysis was used to develop the most informative criteria for assessing the effectiveness of independent preventive and health-enhancing workouts for overweight middle-aged women. As a result of the analysis, four factors were identified that account for 81.4% of the total variance in the original data. We found that the largest factor loadings (42.1% of the total variance of the sample) had indicators that characterize physical development. This factor 1 included 14 indicators—chest circumference (CC) at inhalation (r = 0.875 at p < 0.01); chest circumference (CC) at exhalation (r = 0.848 at p < 0.01); relative muscle mass (r = 0.777 at p < 0.01); chest excursion (r = 0.768 at p < 0.01); and basal metabolic rate (r = 0.711 at p < 0.01). The following indicators had negative factor loading—abdomen circumference (r = −0.927 at p < 0.01); waist circumference (r = −0.926 at р < 0.01); waist-to-hip ratio (WHR) (r = −0.922 at р < 0.01); CC (r = 0.893 at р < 0.01); the waist-to-height ratio (WHtR) (r = −0.884 at р < 0.01); body weight (r = −0.820 at р < 0.01); BMI (r = −0.807 at р < 0.01); and hip circumference (r = −0.732 at р < 0.01). The second most important factor had a 21.2% contribution to the total variance, and identified 10 indicators that characterize the capacity of the aerobic energy supply and functional state. This factor showed a statistically significant direct correlation with the following indicators: VO₂max (r = 0.945 at р < 0.01); VC (mL) (r = 0.791 at р < 0.01); VC (mL/kg) (r = 0.715 at р < 0.01); and IPC (r = 0.714 at р < 0.01). The following indicators had negative factor loadings on the second factor: AP (r = −0.936 at р < 0.01); HR recovery time after 20 squats in 30 s (min) (r = −0.837 at р < 0.01); Robinson index (r = −0.832 at р < 0.01); BPsys (r = −0.824 at р < 0.01); Bayevsky’s stress index (r = −0.820 at р < 0.01); resting heart rate (r = −0.812 at р < 0.01); and BPdia (r = −0.806 at р< 0.01). The third factor had a 9.8% contribution to the total variance and consisted of six indicators that characterize the endurance and strength abilities. The fourth factor, which had an 8.3% contribution to the total variance, was formed by the indicators that characterize coordination abilities.

Taking into account the specifics of the studied contingent and based on the data of factor analysis, we selected five indicators that are recommended to use for the assessment of the effectiveness of independent preventive and health-enhancing exercise workouts for middle-aged women—waist circumference, abdomen circumference, waist-to-hip ratio, adaptive potential, and maximum oxygen consumption. To assess the informativeness of the selected indicators, we performed a correlation analysis to identify significant relationships.

The waist circumference correlated with 28 of the studied parameters, and the correlation coefficients ranged from r = 0.210 at p < 0.05 to r = 0.852 at p < 0.001. The abdomen circumference correlated with 29 studied parameters, and the correlation coefficients ranged from r = −0.211 at p < 0.05 to r = 0.852 at p < 0.001. Waist-to-hip ratio (WHR) significantly correlated with 24 indicators of physical condition. The adaptation potential showed high correlations with 24 parameters and the correlations coefficients ranged from r = 0.222 at p < 0.05 to r = 0.902 at p < 0.001. Maximum oxygen consumption was significantly correlated with 18 parameters.

The indicators from the groups of the first and second factors had the highest loadings, so we consider them as criteria for effective planning, management, and control of body weight.

Furthermore, we experimentally substantiated methodological guidelines for the organization of fitness workouts for middle-aged women. We recommend doing 50–60 min of physical activity 3–4 times a week. Independent workouts should include exercise for the development of strength and general endurance with own body weight in the mode of alternating performance and aerobic exercise in the mode of continuous performance. Depending on the level of individual fitness, we recommend the percentage of special and general exercises ranged from 40–25% and 60 to 75%, respectively. The target heart rate zone for the aerobic part of workouts should be between 140 and 160 bpm for training activities and between 120 and 130 bpm for recovery activities. The intensity should range from 50–70% of VO₂max. Exercise load should be increased by increasing the coordination complexity of the exercises, the use of supersets of exercises, and circuit training.

We developed the plant for independent workouts that allowed to achieve a steady decrease in body weight and improved physical condition among the studied middle-aged women. After the study, we observed significant changes in the body weight, BMI, and body circumferences, as well as in the anthropometric measurements that indicate the harmony of the body: BMI decreased by 11.9%, waist circumference decreased by 12.1%, abdomen circumference decreased by 9.6%, and BW decreased by 10.6%. The data obtained showed the improvement of cardiopulmonary function and increase in the body’s adaptation potential, as well as the economization of cardiac pump function, which was evidenced by the changes in the heart rate, BPsys, and BPdia values. The risk of developing hypertension was reduced in the subjects as their BP values ranged from 90 to 130 mmHg for systolic pressure and from 65 to 80 mmHg for diastolic pressure. In women, there were observed statistically significant (p < 0.05; p < 0.01) improvements in lung vital capacity and heart rate recovery after dynamic exercise, which characterize the cardiopulmonary function. An increase of 10.8% in the mean group value of maximum oxygen consumption indicates an increase in the level of physical working capacity.

The results of the study demonstrate an example of an effective program for excessive body mass management in middle-aged women and allow to recommend a wider general use of the scientifically substantiated methodical guidelines for overweight management through health-enhancing physical activity for middle-aged women. Assessment of the obesity-related risks (Table 3) helps to establish a population that additionally benefits from the suggested weight-loss program. The annual blood check with selected hormone level determination and popularity of individual genetic tests may be practical tools for the advanced introduction of nutritional lifestyle changes and augmentation of physical activity. As well, specific identification of microbiome changes, shifts of its metabolite levels, and some genetic SNPs can be a basis of efficient individual dietary plan development.

Hormonal changes	High risk	leptin ↑, insulin ↑, GLP-1 ↓
	Moderate risk	IL-6 ↑, TNF-α ↑, OPG ↑, C-reactive protein ↑
Genetic variants	High risk	MC4R (rs17782313), BDNF (rs12291063), FTO (rs1558902), ADIPOQ (rs17300539, rs266729), PPARG (rs1801282)
	Moderate risk	ADCY3 (rs10182181), TCF7L2 (rs7903146, rs7901695), IRS1 (rs2943641), PPM1K (rs1440581), MTNR1B (rs10830963), PPARG (rs12629751, rs9833097), MTIF3 (rs1885988)
Microbiome composition	High risk	Firmicutes/Bacteroidetes ↑
	Moderate risk	Decreased diversity (Bacteroides, Prevotella, Ruminococcus, Faecalibacterium, Roseburia, Bifidobacterium ↓)
Intestinal metabolite changes	High risk	SCFA ↓, deconjugated bile acids ↓
	Moderate risk	branched-chain fatty acids ↑, TMAO ↑

Table 3.

Risk factors in developing obesity and resistance to weight-loss intervention programs.

5. Conclusions

One of the main ways of preventing the development of obesity, managing body weight, and improving the indicators of physical condition is properly organized physical activity and a balanced diet. The combination of a balanced diet with increased physical activity will reduce body weight, whereas a change in lifestyle will help to maintain the achieved result. Physical activity contributes not only to body weight normalization but also has positive effects on metabolic disorders characteristic of people with obesity and overweight. The effectiveness of physical activity can be significantly increased by taking into account a set of biochemical, genetic, and microbiome markers. The presented findings of clinical trials and meta-analyzes emphasize the complexity of interactions and the obvious relationships between genetic, epigenetic, metabolic factors and gut microbiota in the modulation of obesity and its complications. The application of a strategy of personalization of a highly effective health-enhancing exercise program, based on genetic, biochemical, and microbiome markers, will allow to achieve a high health-promoting effect and effectiveness of body weight management and prevent the development and progression of pathological conditions.