Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
ISSN: 1303 - 2968   
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©Journal of Sports Science and Medicine (2024) 23, 236 - 257   DOI: https://doi.org/10.52082/jssm.2024.236

Review article
Genetics of Exercise and Diet-Induced Fat Loss Efficiency: A Systematic Review
Aleksandra Bojarczuk1, , Emiliya S. Egorova2, Magdalena Dzitkowska-Zabielska1, Ildus I. Ahmetov2,3,4,5  
Author Information
1 Faculty of Physical Culture, Gdansk University of Physical Education and Sport, Gdansk, Poland
2 Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, Kazan, Russia
3 Sports Genetics Laboratory, St Petersburg Research Institute of Physical Culture, St. Petersburg, Russia
4 Center for Phygital Education and Innovative Sports Technologies, Plekhanov Russian University of Economics, Moscow, Russia
5 Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK

Aleksandra Bojarczuk
✉ Faculty of Physical Culture, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
Email: aleksandra.bojarczuk@awf.gda.pl
Publish Date
Received: 08-01-2024
Accepted: 21-02-2024
Published (online): 01-03-2024
 
ABSTRACT

Physical exercise and dieting are well-known and effective methods for fat loss and improving cardiovascular health. However, different individuals often react differently to the same exercise regimen or dietary plan. While specific individuals may undergo substantial fat loss, others may observe only limited effects. A wide range of inter-individual variability in weight gain and changes in body composition induced by physical exercises and diets led to an investigation into the genetic factors that may contribute to the individual variations in such responses. This systematic review aimed at identifying the genetic markers associated with fat loss resulting from diet or exercise. A search of the current literature was performed using the PubMed database. Forty-seven articles met the inclusion criteria when assessing genetic markers associated with weight loss efficiency in response to different types of exercises and diets. Overall, we identified 30 genetic markers of fat-loss efficiency in response to different kinds of diets and 24 in response to exercise. Most studies (n = 46) used the candidate gene approach. We should aspire to the customized selection of exercise and dietary plans for each individual to prevent and treat obesity.

Key words: SNP, physical activity, weight loss, dieting


           Key Points
  • Inter-individual differences play a crucial role in determining body weight and shaping the body's response to changes in diet and engagement in physical activity.
  • The review identified 30 genetic markers associated with fat-loss efficiency in response to various diets and 24 markers in response to exercise.
  • In the future, the focus should be on tailoring the choice of diet and exercise types to individual genetic characteristics to prevent and treat obesity.

INTRODUCTION

Weight loss encompasses factors such as body mass index (BMI), body weight, waist circumference, and data on absolute fat mass and fat mass percentage. On the other hand, fat loss involves explicit data related to absolute fat mass and fat mass percentage, including measurements for different body areas like the whole body and visceral fat. Therefore, it can be understood that fat loss is a component of overall weight loss.

Weight and fat loss, a pervasive global concern, present a formidable challenge for most individuals, with a high risk of regaining lost weight. The intricacies of weight management involve multifaceted environmental factors, encompassing nutrition, physical activity levels, lifestyle choices (Serio et al., 2023), and psychological, social, and medical contributors. Calorie-dense foods, identified through various studies, contribute to excessive calorie intake, exacerbating weight-related challenges (Camachoa and Ruppela, 2017; Romieu et al., 2017; Hall et al., 2019). Moreover, the decline in physical activity levels, notably lower than that of our ancestors, emerges as a significant factor contributing to weight gain (Chaput and Tremblay, 2009). Fundamental principles of weight loss involve reducing calorie intake and strategies like intermittent fasting. However, these approaches may not significantly affect body fat ratio or lipid profile. Instead, they tend to induce short-term weight loss (Ooi and Pak, 2019). The intricate dynamics of weight regulation become evident in the short term, where a decrease in energy intake is often counteracted by mechanisms lowering metabolic rate and increasing calorie intake, contributing to weight regain (Benton and Young, 2017). However, the persistent pattern of achieving less weight loss than anticipated or desired, followed by subsequent weight regain, underscores the complexity of the body's response to changes in energy balance (Evert and Franz, 2017). Therefore, incorporating long-term regular physical exercise into a weight loss strategy is deemed crucial, creating a more significant caloric deficit and a positive influence on metabolic rate and overall body composition, promoting a more sustainable and practical approach to weight management (Cox, 2017).

Interestingly, modifications in diet and exercise do not always lead to weight loss success. The consequence of fat gain is obesity, which plays a pivotal role in the development of significant chronic conditions, including cardiometabolic diseases, specific cancers, type 2 diabetes, and metabolic syndrome (Scully et al., 2021). Nevertheless, not all individuals residing in obesogenic settings develop the condition, indicating that the reaction to environmental influences is also influenced by additional factors, such as genetic predispositions (Loos and Yeo, 2022). Similarly, among Olympic athletes, there are individuals with obesity. There is compelling evidence of a significant influence of genetic factors on weight control, as indicated by numerous studies (McPherson, 2007; Albuquerque et al., 2017; Sivasubramanian and Malhotra, 2023). In exploring the complexities of weight management, it becomes evident that genetic predispositions play a substantial role alongside individual efforts. Recent research has revealed a complex interaction between genes and the response to fat loss induced by exercise or food intake. Genetic factors influence the body's reactions to fat loss through aerobic exercise and dieting in recreational and professional athletes. In the context of this review, the term "genetic factors" refers specifically to single nucleotide polymorphisms (SNPs).

The rationale for this review stems from the alarming increase in obesity rates over the last four decades. Upon an in-depth examination of the extensive literature, it is evident that individual research papers contribute valuable components to the overarching puzzle. Therefore, synthesizing these findings is imperative to construct a coherent narrative, with particular consideration given to insights derived from studies conducted in 2023. The primary goal of this systematic review is to recognize the genetic markers linked to fat loss induced by diet and exercise.

Biological significance of SNPs

The simplest and most common genetic alterations in the human genome involve changing in a single base pair. This type of alteration can be a Single Nucleotide Polymorphism (SNP), or a point variant. It is known that when a variant occurs in less than 1% of the population, it is considered a mutation, while when its incidence exceeds 1%, it is regarded as a polymorphism (Karki et al., 2015). SNPs occurring in coding regions can influence the amino acid sequence of a protein. This might result in a change in the protein's structure or improper folding, ultimately leading to a complete lack or significant limitation of its biological activity. SNPs occurring in non-coding regions might be necessary for the organism's physiology, especially by influencing gene expression regulation (Robert and Pelletier, 2018). SNPs are considered the most valuable markers for diagnosing or predicting diseases due to their widespread occurrence, ease of analysis, low genotyping costs, and the possibility of conducting association studies based on statistical and bioinformatic tools (Srinivasan et al., 2016). Until two years ago, almost 60 Genome-Wide Association Studies (GWAS) had revealed over 1100 distinct loci linked to various obesity-related traits (Buniello et al., 2019). Recently, 55 SNPs associated with sarcopenic obesity have been identified. The risk alleles of most of these SNPs were also associated with low physical activity in the European-ancestry UK Biobank (Semenova et al., 2023). Identifying novel polymorphisms influencing the variability in fat loss efficiency is expected to yield valuable information, establishing it as a practical tool for clinicians and physical activity coaches.

Diet and weight loss

To lose one pound per week, one must restrict 500 kcal per day. Most weight-loss diet strategies limit caloric intake in some way. There are many different diets. Described below are the most popular. Very-low-calorie diets (modified fats) allow for approximately 800 kcal per day, while some go as low as 400 to 500 kcal per day. They are intended to produce faster weight loss than traditional low-calorie diets. The latter allows for 1,000 to 1,200 kcal per day. Low-calorie diets are seen to be safer than very low-calorie regimens, and some research and reports claim that they offer better long-term benefits (Heymsfield et al., 2003; Howard et al., 2006). Low-fat diets limit fat consumption to 20% to 30% of total daily calories. However, a low-fat diet without a reduction in total caloric intake will not promote weight loss (Baker, 2006). Controversially, cutting down on calories might not lead to weight loss. This is because when calorie intake is reduced, the body compensates by either lowering its metabolic rate or increasing food consumption (Benton and Young, 2017). Some low-fat and very low-fat diets (in which fat accounts for up to 15% of calories ingested) have a higher proportion of complex carbohydrates (Seid and Rosenbaum, 2019). Individuals who substitute fat calories with high-fiber, low-calorie fruits and vegetables will consume fewer calories and lose weight. A recent study compared the Ornish diet, a vegetarian diet with 10% fat calories, to three other popular diets: a low-carbohydrate diet (Atkins diet), a moderate-carbohydrate diet (Zone diet), and a calorie-restricted diet (Weight Watchers) (Dansinger et al., 2005). A moderate-fat, low-calorie diet (also known as a Mediterranean-style diet) permits the intake of up to 35% of calories from fats while restricting carbohydrates and proteins (Kim, 2021). A randomized controlled trial investigating whether the consumption of fats affects weight loss revealed an average weight loss of 4.1 kg in individuals adhering to the Mediterranean-style diet, demonstrating better long-term participation and adherence. The findings were based on an 18-month evaluation comparing a Mediterranean-style diet (with 35% of calories from fat) and a low-fat diet (with 20% of calories from fat) (McManus et al., 2009). Next, low-carbohydrate, high-protein diets typically allow only 20 to 90 g of carbohydrates per day, with no restrictions on protein or fat (Kushner and Doerfler, 2008). When carbohydrate consumption is reduced, glycogen stores and lipids are used as energy sources, affecting the body's metabolism and resulting in initial weight loss due to ketosis and diuresis (Ashtary-Larky et al., 2022). However, there is no single, universally effective diet for promoting weight loss. In the short term, high-protein, low-carbohydrate diets and intermittent fasting are recommended for achieving significant initial weight loss and can be considered a kick-start. Caution is advised due to potential adverse effects. In the long term, evidence suggests that various diets result in comparable weight loss, and the key to success lies in adhering to the chosen diet. Ultimately, it is crucial to adopt a diet that establishes a negative energy balance and prioritizes consuming high-quality foods to enhance overall health (Freire, 2020).

Physical exercise in fat loss

Aerobic training

The metabolism of fat is possible only at a slow rate of change and exclusively under aerobic conditions (van Loon et al., 2003). Triacylglycerol (TAG) is the stored fat in adipocytes and striated muscle. It comprises a glycerol molecule linked to three chains of fatty acids (FAs). The intracellular mechanism involved in freeing FAs from the glycerol backbone is referred to as lipolysis. Following this process, liberated FAs are released into the bloodstream and conveyed to actively contracting muscles for subsequent oxidation (Purdom et al., 2018). If exercise intensity is maintained below 65% of the maximal oxygen uptake (VO2max), prolonged exercise can theoretically be sustained due to the oxidation of endogenous triacylglycerol (TAG) stores. Nevertheless, as exercise intensities exceed approximately 65% of VO2max, the oxidation of FAs diminishes, resulting in an increased reliance on carbohydrates for energy (Brooks and Mercier, 1994; Venables et al., 2005). During moderate-intensity exercise, approximately 50% of the total energy expenditure is derived from fat oxidation, where plasma-free fatty acids (FFA) serve as the principal source of fat (Martin et al., 1993; Romijn et al., 1993). The FA oxidation rate in trained male cyclists during exercise of moderate intensity constitutes 50% of the overall fat oxidation (van Loon et al., 2001). FAs derived from adipose tissue, muscle lipid droplets, and dietary sources constitute the primary energy substrate during exercise within the intensity range of 45% to 65% of the maximal oxygen uptake (VO2max) (Purdom et al., 2018; Muscella et al., 2020). Aerobic training involves various exercises lasting from a few minutes to several hours. Common aerobic exercises include jogging, running, cycling, swimming, brisk walking, dancing, Nordic walking, and aerobics classes.

A solitary attempt to burn fat should be prolonged and avoid excessive intensity (van Loon et al., 2001). This is attributed to the fact that the initiation of lipolysis is a relatively gradual process (Duncan et al., 2007; Lass et al., 2011). In a randomized, controlled study, it was also demonstrated that among overweight and obese participants who engaged in supervised exercise five days a week over 10 months while maintaining their usual diet, aerobic exercises alone led to significant weight loss in both men and women. Moreover, the differences in weight loss from baseline to the 10th month between men and women within the groups were not statistically significant, suggesting that gender does not influence weight reduction (Donnelly et al., 2013). More specifically, the weight loss from baseline to the end of the program was 4.3% for the group exercising with an intensity of 400 kcal per session and 5.7% for the group exercising with an intensity of 600 kcal per session, compared to a weight gain of 0.5% in the control group (Donnelly et al., 2013). For example, Weiss et al. (2017) illustrated not only successful weight reduction (7% over 16.8 weeks) solely through exercise but also the conservation of lean body mass (LBM) and enhancement in VO2max in contrast to weight loss achieved by an equivalent energy deficit through calorie restriction alone. The latter approach led to a decline in lean body mass and a reduction in VO2max (Weiss et al., 2017).

Nevertheless, the literature is not unequivocal regarding aerobic exercise and fat burning. This is elegantly reviewed by Harris and Kuo (2021) (Harris and Kuo, 2021). The classic theory of fat burning is commonly used to explain the reduction of abdominal fat resulting from exercise training. This theory is based on the idea that exercise, an energy-consuming activity, will enhance the oxidation of fatty acids from abdominal fat stores compared to a sedentary state, thereby contributing to the fat loss observed in exercise training (Abbasi, 2019). This theory appears supported by enhanced lipolysis with raised circulating fatty acids and increased oxygen consumption during exercise (Mora-Rodriguez and Coyle, 2000). Yet, the absolute energy contribution from plasma fatty acids, assuming all are from adipose tissue, diminishes as exercise intensity rises (from 25 to 85% of VO2max) and aligns with a reduction in tissue fatty acid uptake during exercise (Romijn et al., 1993). Moreover, the augmented energy expenditure, particularly during high-intensity exercise, primarily stems from the fuel stored in skeletal muscle, predominantly glycogen, rather than adipose tissue (fatty acids) (Romijn et al., 1993). Surprisingly, neither aerobic exercise nor resistance exercise leads to an increase in 24-hour fatty acid oxidation, as reported by Melanson et al. (2002) (Melanson et al., 2002). Increased lipolysis, combined with elevated circulating fatty acids and increased oxygen consumption during aerobic exercise, seems to contribute to fat tissue loss (Harris and Kuo, 2021). Many clinical studies reveal a paradox between the fat-burning process and the effect on fat tissue loss. For instance, Willis et al. (2012) conducted a study where they compared the effects of aerobic exercise (equivalent to a caloric expenditure of 12 miles per week), resistance exercise (performed three days per week), and a combination of both on changes in body mass. This investigation involved individuals with a sedentary history and without diabetes, falling within the BMI range of 25–35 kg/m2. Following an 8-month trial, weight loss and reduction in fat mass were more pronounced with aerobic training compared to resistance training (1.76 kg vs. 0.83 kg for the aerobic and resistance groups, respectively) (Willis et al., 2012). However, a 15-week sprint training, mainly relying on anaerobic metabolism, effectively leads to a reduction in abdominal fat tissue, while moderate-intensity training relying on aerobic metabolism, with similar energy expenditure (60% VO2max, about 200 kcal, three times a week), did not result in fat tissue reduction in young women (Trapp et al., 2008).

Therefore, exploring alternative theories explaining the effects of fat tissue loss during exercise is essential to establish a robust scientific foundation for designing effective training programs aimed at fat tissue reduction. In clarification, a repetitive and focused training regimen initiates the activation of specific genes, leading to transcription that produces mRNA complementary to DNA. Subsequently, during the second phase of gene expression, known as translation, a polypeptide chain is synthesized on the generated mRNA by sequentially attaching amino acids, forming a protein. Understanding the significance of including complete proteins in the diet is crucial. These proteins release exogenous amino acids through digestion, which are supplied through food since the body cannot synthesize them independently. These amino acids play a crucial role in translation (Humińska-Lisowska et al., 2021). In this light, it can be said that various genetic and environmental factors influence response to aerobic training. In terms of genetics, the idea of variability among individuals in their responsiveness to exercise training was introduced by Bouchard and his group (Bouchard and Rankinen, 2001; Rankinen and Bouchard, 2008; Bouchard et al., 2011b; Bouchard et al., 2011a; Bouchard, 2012). The HERITAGE Family Study, which involved 742 healthy but sedentary participants completing a highly standardized, meticulously monitored, laboratory-based endurance-training program for 20 weeks, provided the most comprehensive data on individual variations in response to the training (Bouchard et al., 1999; Bouchard and Rankinen, 2001).

Resistance training

Resistance training (RT) stimulates the enlargement of skeletal muscles. This occurs as a physiological response involving the structural reconstruction of muscle tissue, leading to the expansion of muscle fibers and, ultimately, an increase in the cross-sectional area of the entire muscle (Booth and Thomason, 1991). Contemporary weight loss and maintenance recommendations incorporate resistance training into the prescribed workout routine. Nevertheless, few studies have directly evaluated the effects of comparable aerobic and resistance training periods on overweight individuals' adult body mass and fat mass. According to Donnelly et al. (2009) guidelines on body weight reduction and maintenance, resistance training (RT) has been linked to a potential decrease in fat mass (Donnelly et al., 2009). The authors claim that RT does not contribute to weight loss improvement, but it might lead to an increase in fat-free mass and more significant fat mass loss (Donnelly et al., 2009). As Willis et al. (2012) discussed, guidelines might occasionally cause practitioners, fitness experts, and laypeople to misinterpret the quality of the data supporting RT's ability to cause weight loss and fat mass loss (Willis et al., 2012). Some publications suggest that RT has been proven to decrease fat mass. Nevertheless, a thorough examination of the available literature indicates that randomized controlled trials do not provide conclusive evidence (Castaneda et al., 2002; Schmitz et al., 2003; 2007; Sigal et al., 2007). One of the studies aiming to compare the advantages of resistance training in contrast to aerobic exercise and the combination of both, specifically focusing on their effects on body composition measures such as total body mass and fat mass, was The Study of a Targeted Risk Reduction Intervention through Defined Exercise randomized trial (STRRIDE AT/RT) (Willis et al., 2012). The trial sought to compare alterations in body composition resulting from equivalent durations of resistance training, aerobic training, or a combination of both in individuals who did not have diabetes, were previously inactive, and were overweight or obese. The key findings of the study can be summarised as follows: 1) Engaging in a significant amount of RT alone did not lead to a reduction in body mass or fat mass; 2) prescribed amounts of aerobic training (AT) were notably more effective than RT in decreasing measures of body fat and body mass; and 3) the combination of aerobic and resistance training did not result in an additional effect in reducing fat mass or body mass when compared to aerobic training alone. Consequently, the combined training modes did not demonstrate synergy or interference but instead appeared to have a linear impact when body composition measures were considered outcome variables. Considering the trade-off between time commitment and health benefits, AT seems to be the optimal exercise mode for reducing fat and body mass. On the other hand, incorporating resistance training RT becomes essential for middle-aged, overweight, or obese individuals seeking to increase lean mass (Willis et al., 2012). An issue in the literature exploring the impact of resistance training (RT) on weight and fat loss is the common practice of combining RT with other interventions. In a recent meta-analysis (Lopez et al., 2022), the authors reported that 56 out of 114 studies, accounting for 49.1%, incorporated RT, followed by a combination of RT and AT (51 out of 114 studies, representing 44.7%). Including RT and AT alongside caloric restriction was observed in 7.0% of the studies, and the combination of resistance training and caloric restriction was present in 5.3% (Lopez et al., 2022). Debate continues about the best strategies for the reduction in weight and fat. However, there is no straightforward answer to whether resistance training burns fat. For the mobilization of triglycerides from fat tissue to occur, the body must be in a caloric deficit. This was the primary conclusion drawn from the review. The authors concluded that resistance-based exercise programs prove effective and should be incorporated into a comprehensive therapy regimen when caloric restriction is employed for adults dealing with overweight or obesity. Given their comparable impact on fat and weight loss and distinctive influence on lean mass, prioritizing resistance training over aerobic exercise alone is advisable in formulating a multicomponent fat loss prescription for individuals struggling with overweight or obesity (Lopez et al., 2022).

METHODS

Literature research strategies

The selection criteria were previously described in detail in our previous work (Egorova and Ahmetov, 2023). We have adopted methodology from our systematic review to minimize bias in selecting current articles for the current review and to implement an effective bibliographic research strategy (Egorova and Ahmetov, 2023). Briefly, PubMed was searched for relevant publications following PRISMA guidelines. The analysis included English-language literature without date restrictions, using keywords like "polymorphism," "SNP," "genotype," "diet," "nutrition," "physical activity," "physical exercises," "training," "weight loss," and "fat loss." However, the PubMed database was searched for works indexed from 1997 to no later than 30 November 2023.

Studies selection

This review included intervention studies with specified diet or exercise details, duration, evidence of genetic polymorphism, and changes in anthropometric parameters or body composition. Exclusion criteria encompassed non-interventional longitudinal studies, non-human studies, and those involving subjects with severe illnesses such as cancer. Additionally, studies with children, athletes, pregnant and lactating women, and surgical patients were excluded (Egorova and Ahmetov, 2023).

Data extraction

Studies retrieved from the databases were extracted using Microsoft Excel 2016 and automatically screened for duplicates. The remaining works, post-duplicate removal, underwent a two-stage evaluation based on title, abstract, and full text. Full works were assessed for eligibility according to the specified inclusion and exclusion criteria.

Quality assessment and risk of bias

The bias risk assessment in randomized controlled trials was conducted using a modified and validated version of the Cochrane Collaboration tool (Higgins et al., 2011). The study evaluation criteria were as follows: method of randomization of study participants into groups and concealment of the randomization sequence; blinding of study subjects, medical staff, and investigators evaluating the effect of the intervention; missing outcome data; incomplete reporting of results; and other sources of bias (for example, conflict of interest). Each criterion's risk of bias was evaluated and categorized as low, high, or unclear.

Bias risk for the publication of cohort studies was evaluated using the Newcastle–Ottawa quality rating scale (Wells et al., 2000). The assessment criteria for the research encompassed cohort design, cohort comparability, and outcome assessment, with eight subitems contributing to a maximum score of 9. Publications were categorized based on their final scores: studies with a risk of bias graded as high (0-5 points), studies with an average risk of bias (6-7 points), and studies deemed to have a low risk of bias (8-9 points). Furthermore, the methodological quality of the publications was appraised following criteria deemed crucial for studies exploring genetic associations (Campbell and Rudan, 2002; Dietrich et al., 2019). The assessment of study quality was grounded in eight distinct criteria, encompassing the primary objective of interaction, statistical testing for interaction, adjustments for multiple testing, considerations for ethnicity or population stratification, Hardy-Weinberg equilibrium examination, baseline group similarity testing, sample size or power analysis, and a comprehensive specification of the study procedure (Supplementary Tables ). Assigning positive (1 point), neutral (0 points), or negative (-1 point) ratings to each criterion, the cumulative score for each publication ranged from -8 to 8 points. Consequently, publications achieving scores between 6 and 8 points were categorized as exhibiting high methodological quality, those scoring between 2 and 5 were deemed moderate quality, and those scoring between -8 and 1 were classified as low quality.

RESULTS

Selection and characterization of studies

The literature search revealed 4187 publications (Figure 1). In addition, 22 publications were included in the analysis found by searching for similar articles and not by searching for keywords. After removing 2969 duplicates, 1240 articles were screened based on the title. Next, 286 full-text articles were read for detailed evaluation, of which 238 articles were excluded. The reasons for study exclusion were: no fat mass loss outcomes (n = 155); no significant differences in changes in fat mass between genotypes (n = 34); non-interventional research (n = 17); no gene-diet or gene-training interaction in studies (n = 13); unspecified intervention parameters (n = 5); medical or surgical interventions (n = 4); studies that did not have uniform intervention parameters for all subjects (n = 3); testing of athletes (n = 3) and children and adolescents (n = 2); unspecified level of p-value (n = 1); and no full-text available (n = 1).

This resulted in 48 articles being included in the systematic review. The timeframe of the publications encompassed the years 1997 to 2023. The collective count of genetic polymorphisms identified in the selected articles amounted to 48. All interventions and genetic screenings involved 12434 participants, with sample sizes ranging from 24 to 1004. The duration of the interventions varied from four weeks to two years. There was variability among the studies regarding participant characteristics, dietary intervention types, physical activity (PA) types, and intervention durations (Table 1 and Table 2).

Study quality and risk of bias

Applying the Cochrane Collaboration tool (Higgins et al., 2011), 13 randomized controlled trials were appraised with a low risk of bias, while one randomized controlled trial was classified as having a moderate risk (Supplementary Tables). The rationale behind the lower rating for randomized controlled trials is attributed to the inherent challenges of blinding both patients and staff due to the nature of lifestyle interventions. The Newcastle–Ottawa quality rating scale (Wells et al., 2000) was employed to assess bias in nonrandomized controlled trials, revealing 30 studies with a low risk of bias and 4 studies with a moderate risk (Supplementary Tables). The lower quality ratings were attributed to insufficient information on additional criteria for cohort comparability, specifically, statistically significant differences in diet adherence and the level of physical activity among subjects. The evaluation of the analytical quality in genetics studies, employing a tailored scale for investigations into the correlation between genes and diet/exercise, classified 30 publications as high-quality. In contrast, the quality of 18 articles was deemed moderate (Supplementary Tables). The primary factors contributing to the lower quality of these publications were the absence of correction for multiple testing, inadequate correction for population stratification, and insufficient size of the study sample.

Genetic markers associated with weight loss efficiency in response to different types of diets

There is ongoing research into genetic markers associated with reduction efficiency in response to diverse diets. According to several studies, genetic variables might alter an individual's responsiveness to specific dietary interventions. For example, certain genes may influence how the body processes fats or carbohydrates, influencing weight loss outcomes. However, the field is complex, and the interplay between genetics and diet response is not fully understood. Individual differences in weight loss success are believed to be influenced by numerous genes.

Weight loss encompasses parameters such as BMI, body weight, waist circumference, absolute fat mass, and fat mass percentage data. Fat loss refers explicitly to the reduction in absolute fat mass and fat mass percentage data, including measurements for the entire body and specific areas like visceral fat. Therefore, it is essential to understand that fat loss is a component of overall weight loss. We intended to compose a review specifically focusing on fat loss. Therefore, we omitted publications that included information about BMI, weight, and waist circumference and retained those mentioning absolute or relative fat mass. Some research works incorporate various measurements, such as weight and fat data. In such cases, we kept those in Table 1 and Table 2, showing key findings. Table 1 summarizes SNPs associated with fat loss efficiency in response to different types of diets.

Genetic markers associated with exercise-induced weight loss efficiency

Not only have genetic changes associated with obesity and the response to physical activity been identified, but significant progress has also been made in research into the links between genes, physical activity, and their interactions to understand how these factors affect the body’s composition. Breakthrough research has come from the Bouchard laboratory. Along with collaborators, they described 23 autosomal genes that significantly interacted with physical activity or exercise, influencing body composition (Rankinen et al., 2001; Bray et al., 2009). However, research is ongoing on genetic markers associated with exercise-induced weight loss and fat loss efficiency. In fact, over the past ten years, significant advancements have been made in pinpointing genetic loci linked to obesity through genome-wide association studies (GWAS) (Fox et al., 2012; Vogelezang et al., 2015; Yoneyama et al., 2017; Yengo et al., 2018; Wong et al., 2022). As highlighted in our recent review, by increasing the sample size to encompass several million individuals in the foreseeable future, it is expected that numerous additional common genetic variants (occurring more than 5% of the time) will be uncovered, accounting for up to 30% of the variance in BMI. Simultaneously, low-frequency (1-5%) and rare genetic variations (≤1%) are expected to account for the remaining portion of the BMI's heritability ((Egorova and Ahmetov, 2023) referring to (Locke et al., 2015; Ge et al., 2018; Wainschtein et al., 2022)). Table 2 summarises the genetic variants associated with exercise-induced fat loss efficiency.

DISCUSSION

The current systematic review included 47 studies investigating the association between genetic variants and the efficacy of weight loss. Among them, 27 studies explored the impact of diet on fat loss, while 20 studies evaluated the influence of exercise. The selected studies spanned a substantial timeframe of 26 years. This systematic review discloses findings from 27 studies investigating the association between 30 genetic markers and fat loss in response to dietary interventions. Furthermore, examining 20 studies within the exercise contexts identified an association between 24 genetic markers and fat loss in response to exercise.

Most publications (n = 22) examine the efficacy of a traditional low-calorie diet. The goal of a hypocaloric diet is to create a calorie deficit, meaning that the energy intake is lower than the energy expenditure. This deficit is believed to prompt the body to use stored energy, typically fat, leading to weight loss. This approach represents the most straightforward and efficient method for weight loss (Koliaki and Katsilambros, 2022), and, for that reason, it is probably most often used in research. Nevertheless, the nutritional composition of a diet can impact diverse physiological aspects. This includes hormonal levels (Ryan and Seeley, 2013; Kim et al., 2021), metabolic pathways (Moszak et al., 2020), gene expression (Mierziak et al., 2021), and the composition of the gut microbiome (Singh et al., 2017). Therefore, this emphasizes the importance of conducting studies that explore the effectiveness of diets with varying macronutrient compositions for weight loss, taking into account the genetic status of individuals.

Individuals exhibit genetic variability, and specific alleles or genotypes might be more advantageous regarding fat loss efficiency in response to specific dietary intervenetions. Favorable alleles are those genetic variants linked to a more favorable outcome, such as efficient fat loss, in response to particular diets. These alleles might be associated with enhanced metabolism, improved nutrient utilization, or better responses to specific dietary components. Table 1 presents these alleles. Certain alleles or genotypes might also be linked to more efficient utilization of fat as an energy source during physical activity. As in the case of diet-induced fat loss, understanding an individual's genetic profile, including the presence of favorable alleles, can facilitate personalized exercise recommendations. Tailoring exercise routines based on genetic information seems a very attractive way to optimize individuals' fat loss outcomes and overall fitness. The favorable alleles related to exercise-induced fat loss are demonstrated in Table 2. The analyzed publications exhibited different SNPs for the same genes in most cases. However, among the studied genes in the context of diet-induced fat loss, for the FTO gene, 2 polymorphisms were identified, with one of them, rs9939609, appearing in 2 publications (de Luis et al., 2015a; 2015b), and the other, rs1558902, in one publication (Zhang et al., 2012).

Interestingly, there is no difference in weight loss outcomes between a high-protein/low-carbohydrate diet and a standard hypocaloric diet. The findings indicate an association between the FTO variant rs9939609 and weight loss reduction following hypocaloric diet interventions (de Luis et al., 2015a). In the case of MTNR1B and rs1083096, two studies indicated that carriers of the CC genotype, compared to carriers of the G allele, were more effective in fat loss (de Luis et al., 2020a, 2020b). However, inconsistent findings were also observed. For instance, a study by Phares et al. (2004) with 70 individuals carrying the G allele of ADRB3 rs4994 showed an effective reduction in body fat mass (Phares et al., 2004). In contrast, de Luis et al. (2007) demonstrated the highest efficiency for carriers with the AA genotype (de Luis et al., 2007).

Notably, most of the genetic variants identified in intervention studies were revealed through the candidate-gene approach, constrained by the current understanding researchers possess regarding the biology of obesity. Specifically, in the 27 publications studied in Table 1 and the 20 publications studied in Table 2, genome-wide association studies (GWAS) were used only once in Table 2 and in Bojarczuk et al., (2022). This underscores the existing limitations in our knowledge of genetic markers influencing body weight and the physiological response to physical activity and macronutrient intake. Association studies are the most commonly used in genetic analysis in sports. However, they have two weaknesses - they rely on the analysis of candidate genes (somewhat closing themselves off to other potentially unrelated markers with the examined trait). GWAS, instead, can assess hundreds of thousands of SNPs, and it is not hypothesis-driven (Tam et al., 2019; Loos and Yeo, 2022) To overcome these limitations and gain a more comprehensive understanding, extensive GWAS, replication studies, and meta-analyses are crucial (Egorova and Ahmetov, 2023). To avoid false positive results in association studies, studies of this type should undergo repetition in so-called replication studies (Kraft et al., 2009), either in subgroups of the same population (internal replication) or in additional groups of athletes and non-athletes of diverse ethnic backgrounds (external replication) (Liu et al., 2008).

Furthermore, genetic markers explain the observed differences in how individuals respond to weight-loss interventions. Understanding these markers allows researchers and clinicians to tailor interventions for better outcomes. The ultimate goal is, therefore, to use genetic information to develop personalized strategies for weight loss. This involves recommending specific diets and exercise plans based on an individual's genetic profile.

Our methods involved a meticulous approach to ensure the inclusion of studies directly relevant to investigating genetic factors influencing fat loss outcomes. However, we do not exclude the possibility that our systematic review might have limitations, such as potential biases in individual studies (patient selection, performance evaluation, measurement) or publication biases (Yuan and Hunt, 2009; Vrabel, 2015). When assessing the literature, crucial factors include variables such as diet duration. For example, in the study of Hamada et al. (2011), the intervention period was relatively brief, and the dietary method was homogeneous. Thus, it is intriguing to explore whether the genetic effects identified in such a study manifest over the long term and/or with different dietary interventions (Hamada et al., 2011). Another primary source of uncertainty is the nature of the exercise, duration, and intensity. For instance, in (Phares et al., 2004), the participants were subjected to aerobic exercises of moderate intensity for 24 weeks. Similar to the publication (Hamada et al., 2011), the effects of long-term exercise training have not been determined (Phares et al., 2004). The subjects’ recruited characteristics are also important considerations. This is because the body composition is determined by, e.g., age, sex, ethnicity, height, and weight. Despite adjustments for variables like age, sex, ethnicity, height, diabetes status, smoking, dietary intake, and physical activity, the heritability of percentage fat mass, whole-body fat mass, and whole-body lean mass (fat-free mass) persist at a notably high level (Bojarczuk et al., 2022). A key aspect of the response to training or diet is the nutritional status (normal, overweight, or obese). Not everyone within a specific weight category will respond the same way to dietary changes. Next, the training status also matters. Changes in traits are always more pronounced in untrained individuals than in their trained counterparts (Bojarczuk et al., 2022). As described, several methodological considerations regarding the response training or diet can significantly affect experimental outcomes. Moreover, systematic reviews have no rules regarding the sample size requirements (Ferrari, 2015). However, we used articles with representative sample sizes.

CONCLUSION

In conclusion, this comprehensive analysis highlights the intricate interplay between genetic factors and the effectiveness of weight loss strategies, shedding light on potential markers linked to fat loss in dietary and exercise interventions. The approach presented here identified 30 genetic markers associated with the efficiency of fat loss in reaction to dietary interventions and 24 markers in response to physical activity. If advancements are made in this field, a methodology could be developed to tailor the selection of diet and exercise based on genetics to prevent and treat obesity.

ACKNOWLEDGEMENTS

The other authors declare no potential or actual conflicts of interest. The present study complies with the current laws of the country in which it was performed. The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author, who was an organizer of the study.

AUTHOR BIOGRAPHY

Journal of Sports Science and Medicine Aleksandra Bojarczuk
Employment: Faculty of Physical Culture, Department of Biochemistry, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
Degree: Ph.D.
Research interests: Health maintenance, Gene variants and exercise-induced fat loss, Genetics of obesity
E-mail: aleksandra.bojarczuk@awf.gda.pl
 

Journal of Sports Science and Medicine Emiliya S. Egorova
Employment: Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, Russia
Degree: Ph.D. Student
Research interests: Genetics of obesity, Genotype-exercise interactions, Genotype-diet interactions
E-mail: jastspring@yandex.ru
 

Journal of Sports Science and Medicine Magdalena Dzitkowska-Zabielska
Employment: Faculty of Physical Culture, Department of Biochemistry, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
Degree: Ph.D.
Research interests: Biochemical adaptations to physical exercise, Sports genetics
E-mail: magdalena-dzitkowska-zabielska@awf.gda.pl
 

Journal of Sports Science and Medicine Ildus I. Ahmetov
Employment: Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, Russia; Sports Genetics Laboratory, St Petersburg Research Institute of Physical Culture, Russia; Center for Phygital Education and Innovative Sports Technologies, Plekhanov Russian University of Economics, Russia; Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, UK
Degree: Ph.D., DSc
Research interests: Sports genomics and epigenomics; Nutrigenetics and obesity; Exercise biochemistry and physiology; Aging, Sarcopenia and longevity; Social and behavioral genomics
E-mail: i.akhmetov@ljmu.ac.uk
 
 
REFERENCES
Journal of Sports Science and Medicine Abbasi J. (2019) For fat burning, interval training beats continuous exercise. JAMA 321, 2151-2152.  Crossref
Journal of Sports Science and Medicine Abete I., Goyenechea E., Crujeiras A. B., Martínez J. A. (2009) Inflammatory State and Stress Condition in Weight-lowering Lys109Arg LEPR Gene Polymorphism Carriers. Archives of Medical Research 40, 306-310.  Crossref
Journal of Sports Science and Medicine Albuquerque D., Nóbrega C., Manco L., Padez C. (2017) The contribution of genetics and environment to obesity. British Medical Bulletin 123, 159-173.  Crossref
Journal of Sports Science and Medicine Andrade-Mayorga O., Díaz E., Salazar L. A. (2021) Effects of Four Lipid Metabolism-Related Polymorphisms on Body Composition Improvements After 12 Weeks of High-Intensity Interval Training and Dietary Energy Restriction in Overweight/Obese Adult Women: A Pilot Study. Frontiers in Physiology 12, 712787.  Crossref
Journal of Sports Science and Medicine Ashtary-Larky D., Bagheri R., Bavi H., Baker J. S., Moro T., Mancin L., Paoli A. (2022) Ketogenic diets, physical activity and body composition: a review. British Journal of Nutrition 127, 1898-1920.  Crossref
Journal of Sports Science and Medicine Østergård T., Ek J., Hamid Y., Saltin B., Pedersen O. B., Hansen T., Schmitz O. (2005) Influence of the PPAR-gamma2 Pro12Ala and ACE I/D polymorphisms on insulin sensitivity and training effects in healthy offspring of type 2 diabetic subjects. Hormone and Metabolic Research 37, 99-105.  Crossref
Journal of Sports Science and Medicine Baker B. (2006) Weight loss and diet plans. The American Journal of Nursing 106, 52-59.  Crossref
Journal of Sports Science and Medicine Benton D., Young H. A. (2017) Reducing Calorie Intake May Not Help You Lose Body Weight. Perspectives on Psychological Science 12, 703-714.  Crossref
Journal of Sports Science and Medicine Bojarczuk A., Boulygina E. A., Dzitkowska-Zabielska M., Łubkowska B., Leońska-Duniec A., Egorova E. S., Semenova E. A., Andryushchenko L. B., Larin A. K., Generozov E. V, Cięszczyk P., Ahmetov I. I. (2022) Genome-Wide Association Study of Exercise-Induced Fat Loss Efficiency. Genes (Basel) 13, 1975.  Crossref
Journal of Sports Science and Medicine Booth F. W., Thomason D. B. (1991) Molecular and cellular adaptation of muscle in response to exercise: Perspectives of various models. Physiological Reviews 71, 541-585.  Crossref
Journal of Sports Science and Medicine Bouchard C. (2012) Genomic predictors of trainability. Experimental Physiology 97, 3473-52.  Crossref
Journal of Sports Science and Medicine Bouchard C., Ping A., Rice T., Skinner J. S., Wilmore J. H., Gagnon J., Pérusse L., Leon A. S., Rao D. C. (1999) Familial aggregation of VO2max response to exercise training: results from the HERITAGE Family Study. Journal of Applied Physiology (1985) 87, 1003-1008.  Crossref
Journal of Sports Science and Medicine Bouchard C., Rankinen T. (2001) Individual differences in response to regular physical activity. Medicine and Science in Sports and Exercise 33, S446-S451.  Crossref
Journal of Sports Science and Medicine Bouchard C., Rankinen T., Timmons J. A. (2011b) Genomics and genetics in the biology of adaptation to exercise. Comprehensive Physiology 1, 1603-1648.  Crossref
Journal of Sports Science and Medicine Bouchard C., Sarzynski M. A., Rice T. K., Kraus W. E., Church T. S., Sung Y. J., Rao D. C., Rankinen T. (2011a) Genomic predictors of the maximal O2 uptake response to standardized exercise training programs. Journal of Applied Physiology (1985) 110, 1160-11701008.  Crossref
Journal of Sports Science and Medicine Bray M. S., Hagberg J. M., Pérusse L., Rankinen T., Roth S. M., Wolfarth B., Bouchard C. (2009) The human gene map for performance and health-related fitness phenotypes: The 2006-2007 Update. Medicine and Science in Sports and Exercise 41, 35-73.  Crossref
Journal of Sports Science and Medicine Brooks G. A., Mercier J. (1994) Balance of carbohydrate and lipid utilization during exercise: the “crossover” concept. Journal of Applied Physiology (1985) 76, 2253-2261.  Crossref
Journal of Sports Science and Medicine Buniello A., Macarthur J. A. L., Cerezo M., Harris L. W., Hayhurst J., Malangone C., McMahon A., Morales J., Mountjoy E., Sollis E., Suveges D., Vrousgou O., Whetzel P. L., Amode R., Guillen J. A., Riat H. S., Trevanion S. J., Hall P., Junkins H. (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Research 47, 1005-1012.  Crossref
Journal of Sports Science and Medicine Camachoa S., Ruppela A. (2017) Is the calorie concept a real solution to the obesity epidemic?. Global Health Action 10, 1289650.  Crossref
Journal of Sports Science and Medicine Cameron J. D., Riou M. È., Tesson F., Goldfield G. S., Rabasa-Lhoret R., Brochu M., Doucet É. (2013) The TaqIA RFLP is associated with attenuated intervention-induced body weight loss and increased carbohydrate intake in post-menopausal obese women. Appetite 60, 111-116.  Crossref
Journal of Sports Science and Medicine Campbell H., Rudan I. (2002) Interpretation of genetic association studies in complex disease. The Pharmacogenomics Journal 2, 349-360.  Crossref
Journal of Sports Science and Medicine Castaneda C., Layne J. E., Munoz-Orians L., Gordon P. L., Walsmith J., Foldvari M., Roubenoff R., Tucker K. L., Nelson M. E. (2002) A randomized controlled trial of resistance exercise training to improve glycemic control in older adults with type 2 diabetes. Diabetes Care 25, 2335-2341.  Crossref
Journal of Sports Science and Medicine Cha M. H., Kim K. S., Suh D., Yoon Y. (2007) Effects of genetic polymorphism of uncoupling protein 2 on body fat and calorie restriction-induced changes. Hereditas 144, 222-227.  Crossref
Journal of Sports Science and Medicine Cha M. H., Shin H. D., Kim K. S., Lee B. H., Yoon Y. (2006) The effects of uncoupling protein 3 haplotypes on obesity phenotypes and very low-energy diet-induced changes among overweight Korean female subjects. Metabolism 55, 578-586.  Crossref
Journal of Sports Science and Medicine Chaput J.P., Tremblay A. (2009) Obesity and physical inactivity: the relevance of reconsidering the notion of sedentariness. Obesity Facts 2, 249-254.  Crossref
Journal of Sports Science and Medicine Chmurzynska A., Muzsik A., Krzyżanowska-Jankowska P., Mądry E., Walkowiak J., Bajerska J. (2019) PPARG and FTO polymorphism can modulate the outcomes of a central European diet and a Mediterranean diet in centrally obese postmenopausal women. Nutrition Research 69, 94-100.  Crossref
Journal of Sports Science and Medicine Cox C. E. (2017) Role of physical activity for weight loss and weight maintenance. Diabetes Spectrum 30, 157-160.  Crossref
Journal of Sports Science and Medicine Dansinger M. L., Gleason J. A., Griffith J. L., Selker H. P., Schaefer E. J. (2005) Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial. JAMA 293, 43-53.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Aller R., Izaola O., Pacheco D. (2015b) Role of rs9939609 FTO gene variant in weight loss, insulin resistance and metabolic parameters after a high monounsaturated vs a high polyunsaturated fat hypocaloric diets. Nutricion Hospitalaria 32, 175-181.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Aller R., Izaola O., Primo D., Urdiales S., Romero E. (2015a) Effects of a High-Protein/Low-Carbohydrate Diet versus a Standard Hypocaloric Diet on Weight and Cardiovascular Risk Factors: Role of a Genetic Variation in the rs9939609 FTO Gene Variant. Journal of Nutrigenetics and Nutrigenomics 8, 128-136.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Aller R., Izaola O., Sagrado M. G., Conde R. (2006) Influence of ALA54THR polymorphism of fatty acid binding protein 2 on lifestyle modification response in obese subjects. Annals of Nutrition and Metabolism 50, 354-360.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Aller R., Izaola O., Sagrado M. G., Conde R. (2008) Modulation of adipocytokines response and weight loss secondary to a hypocaloric diet in obese patients by -55CT polymorphism of UCP3 gene. Hormones and Metabolic Research 40, 214-218.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Gonzalez Sagrado M., Aller R., Izaola O., Conde R. (2007) Influence of the Trp64Arg polymorphism in the beta 3 adrenoreceptor gene on insulin resistance, adipocytokine response, and weight loss secondary to lifestyle modification in obese patients. European Journal of Internal Medicine 18, 587-592.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Izaola O., Primo D., Aller R. (2020a) A circadian rhythm-related MTNR1B genetic variant (rs10830963) modulate body weight change and insulin resistance after 9 months of a high protein/low carbohydrate vs a standard hypocaloric diet. Journal of Diabetes and its Complications 34, 107534.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Izaola O., Primo D., Aller R. (2020b) Dietary-fat effect of the rs10830963 polymorphism in MTNR1B on insulin resistance in response to 3 months weight-loss diets. Endocrinologia, Diabetes y Nutrition (Engl Ed) 67, 43-52.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Izaola O., Primo D., Gómez J. J. L. (2023) Role of beta-2 adrenergic receptor polymorphism (rs1042714) on body weight and glucose metabolism response to a meal-replacement hypocaloric diet. Nutrition 116, 112170.  Crossref
Journal of Sports Science and Medicine de Luis D. A., Mulero I., Primo D., Izaola O., Aller R. (2018b) Effects of polymorphism rs3123554 in the cannabinoid receptor gene type 2 (CB2R) on metabolic and adiposity parameters after weight loss with two hypocaloric diets. Diabetes Research and Clinical Practice 139, 339-347.  Crossref
Journal of Sports Science and Medicine de Luis Daniel Antonio, Fernández Ovalle H., Izaola O., Primo D., Aller R. (2018c) RS 10767664 gene variant in Brain Derived Neurotrophic Factor (BDNF) affect metabolic changes and insulin resistance after a standard hypocaloric diet. Journal of Diabetes and its Complications 32, 216-220.  Crossref
Journal of Sports Science and Medicine de Luis Daniel Antonio, Izaola O., Primo D., Aller R. (2018a) Association of the rs10830963 polymorphism in melatonin receptor type 1B (MTNR1B) with metabolic response after weight loss secondary to a hypocaloric diet based in Mediterranean style. Clinical Nutrition 37, 1563-1568.  Crossref
Journal of Sports Science and Medicine Dietrich S., Jacobs S., Zheng J. S., Meidtner K., Schwingshackl L., Schulze M. B. (2019) Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review. Obesity Reviews 20, 1557-1571.  Crossref
Journal of Sports Science and Medicine Donnelly J. E., Blair S. N., Jakicic J. M., Manore M. M., Rankin J. W., Smith B. K. (2009) Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Medicine and Science in Sports and Exercise 41, 459-471.  Crossref
Journal of Sports Science and Medicine Donnelly J. E., Honas effery J., Smith B. K., Mayo M. S., Gibson C. A., Sullivan D. K., Lee J., Herrmann S. D., Lambourne K., Washburn R. A. (2013) Aerobic exercise alone results in clinically significant weight loss for men and women: Midwest Exercise Trial-2. Obesity (Silver Spring) 21, 219-228.  Crossref
Journal of Sports Science and Medicine Duncan R. E., Ahmadian M., Jaworski K., Sarkadi-Nagy E., Sul H. S. (2007) Regulation of Lipolysis in Adipocytes Robin. Annual Review of Nutrition 27, 79-101.  Crossref
Journal of Sports Science and Medicine Egorova E. S., Ahmetov I. I. (2023) Genetic Polymorphisms Associated with the Efficiency of Weight Loss : A Systematic Review. Russian Journal of Genetics 59, 754-769.  Crossref
Journal of Sports Science and Medicine Evert A. B., Franz M. J. (2017) Why Weight Loss Maintenance Is Difficult. Diabetes Spectrum 30, 153-156.  Crossref
Journal of Sports Science and Medicine Ferrari R. (2015) Writing narrative literature reviews. Medical Writing 24, 230-235.  Crossref
Journal of Sports Science and Medicine Ficek K., Ciȩszczyk P., Leźnicka K., Kaczmarczyk M., Leońska-Duniec A. (2019) Novel associations between interleukin-15 polymorphisms and post-training changes of body composition parameters in young nonobese women. Frontiers in Physiology 10, 876.  Crossref
Journal of Sports Science and Medicine Fox C. S., Liu Y., White C. C., Feitosa M., Smith A. V., Heard-Costa N., Lohman K., Johnson A. D., Foster M. C., Greenawalt D. M., Griffin P., Ding J., Newman A. B., Tylavsky F., Miljkovic I., Kritchevsky S. B., Launer L., Garcia M., Eiriksdottir G. (2012) Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. Plos Genetics 8, e1002695.  Crossref
Journal of Sports Science and Medicine Franks P. W., Jablonski K. A., Delahanty L., Hanson R. L., Kahn S. E., Altshuler D., Knowler W. C., Florez J. C. (2007) The Pro12Ala variant at the peroxisome proliferator-activated receptor γ gene and change in obesity-related traits in the Diabetes Prevention Program. Diabetologia 50, 2451-2460.  Crossref
Journal of Sports Science and Medicine Freire R. (2020) Scientific evidence of diets for weight loss: Different macronutrient composition, intermittent fasting, and popular diets. Nutrition 69.  Crossref
Journal of Sports Science and Medicine Ge T., Chen C. Y., Neale B. M., Sabuncu M. R., Smoller J. W. (2018) Phenome-wide heritability analysis of the UK Biobank. Plos Genetics 14, e1007228.
Journal of Sports Science and Medicine Goni L., Riezu-Boj J. I., Milagro F. I., Corrales F. J., Ortiz L., Cuervo M., Martínez J. A. (2018) Interaction between an ADCY3 genetic variant and two weight-lowering diets affecting body fatness and body composition outcomes depending on macronutrient distribution: A randomized trial. Nutrients 10, 1-10.  Crossref
Journal of Sports Science and Medicine Grau K., Cauchi S., Holst C., Astrup A., Martinez J. A., Saris W. H. M., Blaak E. E., Oppert J. M., Arner P., Rössner S., Macdonald I. A., Klimcakova E., Langin D., Pedersen O., Froguel P., Sørensen T. I. A. (2010) TCF7L2 rs7903146-macronutrient interaction in obese individuals responses to a 10-wk randomized hypoenergetic diet. The American Journal of Clinical Nutrition 91, 472-479.  Crossref
Journal of Sports Science and Medicine Hall K. D., Ayuketah A., Brychta R., Cai H., Cassimatis T., Chen K. Y., Chung S. T., Costa E., Courville A., Darcey V., Fletcher L. A., Forde C. G., Gharib A. M., Guo J., Howard R., Joseph P. V., McGehee S., Ouwerkerk R., Raisinger K. (2019) Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metabolism 30, 67-77.  Crossref
Journal of Sports Science and Medicine Hamada T., Kotani K., Nagai N., Tsuzaki K., Sano Y., Matsuoka Y., Fujibayashi M., Kiyohara N., Tanaka S., Yoshimura M., Egawa K., Kitagawa Y., Kiso Y., Moritani T., Sakane N. (2011) Genetic polymorphisms of the renin-angiotensin system and obesity-related metabolic changes in response to low-energy diets in obese women. Nutrition 27, 34-39.  Crossref
Journal of Sports Science and Medicine Harris M. B., Kuo C. H. (2021) Scientific Challenges on Theory of Fat Burning by Exercise. Frontiers in Physiology 12, 685166-.  Crossref
Journal of Sports Science and Medicine Haupt A., Thamer C., Heni M., Ketterer C., Machann J., Schick F., Machicao F., Stefan N., Claussen C. D., Häring H. U., Fritsche A., Staiger H. (2010) Gene variants of TCF7L2 influence weight loss and body composition during lifestyle intervention in a population at risk for type 2 diabetes. Diabetes 59, 747-750.  Crossref
Journal of Sports Science and Medicine Heianza Y., Ma W., Huang T., Wang T., Zheng Y., Smith S. R., Bray G. A., Sacks F. M., Qi L. (2016) Macronutrient intake-associated FGF21 genotype modifies effects of weight-loss diets on 2-year changes of central adiposity and body composition: The POUNDS lost trial. Diabetes Care 39, 1909-1914.  Crossref
Journal of Sports Science and Medicine Heianza Y., Sun D., Ma W., Zheng Y., Champagne C. M., Bray G. A., Sacks F. M., Qi L. (2018) Gut-microbiome-related LCT genotype and 2-year changes in body composition and fat distribution: the POUNDS Lost Trial. International Journal of Obesity 42, 1565-1573.  Crossref
Journal of Sports Science and Medicine Heymsfield S. B., Van Mierlo C. A. J., Van Der Knaap H. C. M., Heo M., Frier H. I. (2003) Weight management using a meal replacement strategy: Meta and pooling analysis from six studies. International Journal of Obesity and Relatated Metabolic Disorders 27, 537-549.  Crossref
Journal of Sports Science and Medicine Higgins J. P. T., Altman D. G., Gøtzsche P. C., Jüni P., Moher D., Oxman A. D., Savović J., Schulz K. F., Weeks L., Sterne J. A. C. (2011) The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343.  Crossref
Journal of Sports Science and Medicine Howard B. V., Horn L., Van Hsia J., Manso J. E., Stefanick M. L., Wassertheil-Smoller S., Kuller L. H., LaCroix A. Z., Langer R. D., Lasser N. L., Lewis C. E., Limacher M. C., Margolis K. L., Mysiw W. J., Ockene J. K., Parker L. M., Perri M. G., Phillips L., Prentice R. L. (2006) Low-fat dietary pattern and risk of cardiovascular disease: the Women’s Health Initiative Randomized Controlled Dietary Modification Trial. JAMA 295, 655-666.  Crossref
Journal of Sports Science and Medicine Humińska-Lisowska, K., Mieszkowski, J., Michałowska-Sawczyn, M. and Cięszczyk, P. (2021) Adaptacja do treningu w odniesieniu do genetycznych determinant energetyki mięśni, in Genetyka Sportowa. I. PZWL Medical Publishing. (In Polish)
Journal of Sports Science and Medicine Jáuregui O.I., Gómez J.J.L., Martín D.P., Torres B.T., Hoyos E.G., Buigues A.O., Delgado E., 1 D.A. de L.R. (2020) ACYL-CoA synthetase long-chain 5 polymorphism is associated with weight loss and metabolic changes in response to a partial meal-replacement hypocaloric diet. Nutricion Hospitalaria 37, 757-762.  Crossref
Journal of Sports Science and Medicine Karki R., Pandya D., Elston R. C., Ferlini C. (2015) Defining “mutation” and “polymorphism” in the era of personal genomics. BMC Medical Genomics 8.  Crossref
Journal of Sports Science and Medicine Kim B. H., Joo Y., Kim M. S., Choe H. K., Tong Q., Kwon O. (2021) Effects of Intermittent Fasting on the Circulating Levels and Circadian Rhythms of Hormones. Endocrinol Metab (Seoul) 36, 745-756.  Crossref
Journal of Sports Science and Medicine Kim J. Y. (2021) Optimal diet strategies for weight loss and weight loss maintenance. Journal of Obesity & Metabolic Syndrome 30, 20-31.  Crossref
Journal of Sports Science and Medicine Koliaki C. C., Katsilambros N. L. (2022) Are the Modern Diets for the Treatment of Obesity Better than the Classical Ones?. Endocrines 3, 603-623.  Crossref
Journal of Sports Science and Medicine Kraft P., Zeggini E., Ioannidis J. P. A. (2009) Replication in genome-wide association studies. Statistical Science 24, 561-573.  Crossref
Journal of Sports Science and Medicine Kushner R. F., Doerfler B. (2008) Low-carbohydrate, high-protein diets revisited. Current Opinion in Gastroenterology 24, 198-203.  Crossref
Journal of Sports Science and Medicine Lass A., Zimmermann R., Oberer M., Zechner R. (2011) Lipolysis - A highly regulated multi-enzyme complex mediates the catabolism of cellular fat stores. Progress in Lipid Research 50, 14-27.  Crossref
Journal of Sports Science and Medicine Leońska-Duniec A., Jastrzębski Z., Jażdżewska A., Moska W., Lulińska-Kuklik E., Sawczuk M., Gubaydullina S. I., Shakirova A. T., Cięszczyk P., Maszczyk A., Ahmetov ldus I. (2018) Individual Responsiveness to Exercise-Induced Fat Loss and Improvement of Metabolic Profile in Young Women is Associated with Polymorphisms of Adrenergic Receptor Genes. Journal of Sports Science and Medicine 17, 134-144.  Pubmed
Journal of Sports Science and Medicine Lim K., Shin Y. A. (2014) Impact of UCP2 polymorphism on long-term exercise-mediated changes in adipocytokines and markers of metabolic syndrome. Aging Clinical and Experimental Research 26, 491-496.  Crossref
Journal of Sports Science and Medicine Lin X., Qi Q., Zheng Y., Huang T., Lathrop M., Zelenika D., Bray G. A., Sacks F. M., Liang L., Qi L. (2015) Neuropeptide y genotype, central obesity, and abdominal fat distribution: The POUNDS LOST trial. The American Journal of Clinical Nutrition 102, 514-519.  Crossref
Journal of Sports Science and Medicine Liu Y. J., Papasian C. J., Liu J. F., Hamilton J., Deng H. W. (2008) Is replication the gold standard for validating genome-wide association findings?. PloS One 3, e4037.  Crossref
Journal of Sports Science and Medicine Locke A., Kahali B., Berndt S., Justice A., Pers T., Day F. (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206.  Crossref
Journal of Sports Science and Medicine Loos R. J. F., Yeo G. S. H. (2022) The genetics of obesity: from discovery to biology. Nature Reviews Genetics 23, 120-133.  Crossref
Journal of Sports Science and Medicine Lopez P., Taaffe D. R., Galvão D. A., Newton R. U., Nonemacher E. R., Wendt V. M., Bassanesi R. N., Turella D. J. P., Rech A. (2022) Resistance training effectiveness on body composition and body weight outcomes in individuals with overweight and obesity across the lifespan: A systematic review and meta-analysis. Obesity Reviews 23, 1-25.  Crossref
Journal of Sports Science and Medicine Martin W. H., Dalsky G. P., Hurley B. F., Matthews D. E., Bier D. M., Hagberg J. M., Rogers M. A., King D. S., Holloszy J. O. (1993) Effect of endurance training on plasma free fatty acid turnover and oxidation during exercise. The American Journal of Physiology 265, E708-E714.  Crossref
Journal of Sports Science and Medicine Mazur I. I., Drozdovska S., Andrieieva O., Vinnichuk Y., Polishchuk A., Dosenko V., Andreev I., Pickering C., Ahmetov I. I. (2020) PPARGC1A gene polymorphism is associated with exercise-induced fat loss. Molecular Biology Reports 47, 7451-7457.  Crossref
Journal of Sports Science and Medicine McManus K., Antinoro L., Sacks F. (2009) A randomized controlled trial of a moderate-fat, low-energy diet compared with a low fat, low-energy diet for weight loss in overweight adults. Internation Journal of Obesity and Related Metabolic Disorders 25, 813-820.  Crossref
Journal of Sports Science and Medicine McPherson R. (2007) Genetic contributors to obesity. The Canadian Journal of Cardiology 23, 23-27.  Crossref
Journal of Sports Science and Medicine Melanson E. L., Sharp T. A., Seagle H. M., Donahoo W. T., Grunwald G. K., Peters J. C., Hamilton J. T., Hill J. O. (2002) Resistance and aerobic exercise have similar effects on 24-h nutrient oxidation. Medicine and Science in Sports and Exercise 34, 1793-1800.  Crossref
Journal of Sports Science and Medicine Mierziak J., Kostyn K., Boba A., Czemplik M., Kulma A., Wojtasik W. (2021) Influence of the bioactive diet components on the gene expression regulation. Nutrients 13, 1-33.  Crossref
Journal of Sports Science and Medicine Mora-Rodriguez R., Coyle E. F. (2000) Effects of plasma epinephrine on fat metabolism during exercise: interactions with exercise intensity. American Journal of Physiology. Endocrinology and Metabolism 278, 669-676.  Crossref
Journal of Sports Science and Medicine Moszak M., Szulińska M., Bogdański P. (2020) You are what you eat - the relationship between diet, microbiota, and metabolic disorders - A review. Nutrients 12, 1096.  Crossref
Journal of Sports Science and Medicine Muscella A., Stefàno E., Lunetti P., Capobianco L., Marsigliante S. (2020) The regulation of fat metabolism during aerobic exercise. Biomolecules 10, 1-29.  Crossref
Journal of Sports Science and Medicine Ooi S. L., Pak S. (2019) Short-term Intermittent Fasting for Weight Loss: A Case Report. Cureus 11, e4482.  Crossref
Journal of Sports Science and Medicine Orkunoglu-Suer F. E., Gordish-Dressman H., Clarkson P. M., Thompson P. D., Angelopoulos T. J., Gordon P. M., Moyna N. M., Pescatello L. S., Visich P. S., Zoeller R. F., Harmon B., Seip R. L., Hoffman E. P., Devaney J. M. (2008) INSIG2 gene polymorphism is associated with increased subcutaneous fat in women and poor response to resistance training in men. BMC Medical Genetics 9.  Crossref
Journal of Sports Science and Medicine Phares Dana A, Halverstadt A. A., Shuldiner A. R., Ferrell R. E., Douglass L. W., Ryan A. S., Goldberg A. P., Hagber J. M. (2004) Association between body fat response to exercise training and multilocus ADR genotypes. Obesity Research 12, 807-815.  Crossref
Journal of Sports Science and Medicine Purdom T., Kravitz L., Dokladny K., Mermier C. (2018) Understanding the factors that effect maximal fat oxidation. Journal of the International Society and Sports Nutriton 15.  Crossref
Journal of Sports Science and Medicine Rajkumar A., Lamothe G., Bolongo P., Harper M., Adamo K., Doucet É. (2016) Acyl-CoA synthetase long-chain 5 genotype is associated with body composition changes in response to lifestyle interventions in postmenopausal women with overweight and obesity: a genetic association study on cohorts Montréal-Ottawa New Emerging Team, and Complications Associated with Obesity. BMC Medical Genetics 17, 56.  Crossref
Journal of Sports Science and Medicine Ramos-Lopez O., Riezu-Boj J. I., Milagro F. I., Cuervo M., Goni L., Alfredo Martinez J. (2019) Models integrating genetic and lifestyle interactions on two adiposity phenotypes for personalized prescription of energy-restricted diets with different macronutrient distribution. Frontiers in Genetics 10.  Crossref
Journal of Sports Science and Medicine Rankinen T., Bouchard C. (2008) Gene-physical activity interactions: Overview of human studies. Obesity (Silver Spring) 16, 47-50.  Crossref
Journal of Sports Science and Medicine Rankinen T., Pérusse L., Rauramaa R., Rivera M. A., Wolfarth B., Bouchard C. (2001) The human gene map for performance and health-related fitness phenotypes. Medicine and Science in Sports and Exercise 33, 855-867.  Crossref
Journal of Sports Science and Medicine Rankinen T., Rice T., Teran-Garcia M., Rao D. C., Bouchard C. (2010) FTO genotype is associated with exercise training-induced changes in body composition. Obesity (Silver Spring) 18, 322-326.  Crossref
Journal of Sports Science and Medicine Renzo L., Di Rizzo M., Iacopino L., Sarlo F., Domino E., Jacoangeli F., Colica C., Sergi D., De Lorenzo A. (2013) Body composition phenotype: Italian Mediterranean Diet and C677T MTHFR gene polymorphism interaction. European Review for Medical and Pharmacological Sciences 17, 2555-6255.
Journal of Sports Science and Medicine Robert F., Pelletier J. (2018) Exploring the Impact of Single-Nucleotide Polymorphisms on Translation. Frontiers in Genetics 9, 507.  Crossref
Journal of Sports Science and Medicine Romieu I., Dossus L., Barquera S., Blottière H. M., Franks P. W., Gunter M., Hwalla N., Hursting S. D., Leitzmann M., Margetts B., Nishida C., Potischman N., Seidell J., Stepien M., Wang Y., Westerterp K., Winichagoon P., Wiseman M., Walter W. C. (2017) Energy balance and obesity: what are the main drivers?. Cancer Causes and Control 28, 247-258.  Crossref
Journal of Sports Science and Medicine Romijn J. A., Coyle E. F., Sidossis L. S., Gastaldelli A., Horowitz J. F., Endert E., Wolfe R. R. (1993) Regulation of endogenous fat and carbohydrate metabolism in relation to exercise intensity and duration. The American Journal of Physiology 265, 380-391.  Crossref
Journal of Sports Science and Medicine Ryan K. K., Seeley R. J. (2013) Food as a hormone. Science 339, 918-919.  Crossref
Journal of Sports Science and Medicine Schmitz K. H., Hannan P. J., Stovitz S. D., Bryan C. J., Warren M., Jensen M. D. (2007) Strength training and adiposity in premenopausal women: strong, healthy, and empowered study. The American Journal of Clinical Nutrition 86, 566-572.  Crossref
Journal of Sports Science and Medicine Schmitz K. H., Jensen M. D., Kugler K. C., Jeffery R. W., Leon A. S. (2003) Strength training for obesity prevention in midlife women. International Journal of Obesity and Related Metabolic Disorders 27, 326-333.  Crossref
Journal of Sports Science and Medicine Scully T., Ettela A., LeRoith D., Gallagher E. J. (2021) Obesity, Type 2 Diabetes, and Cancer Risk. Frontiers in Oncology 10, 615375.  Crossref
Journal of Sports Science and Medicine Seid H., Rosenbaum M. (2019) Low Carbohydrate and Low-Fat Diets: What We Don't Know and Why we Should Know It. Nutrients 11, 2749.  Crossref
Journal of Sports Science and Medicine Semenova E. A., Pranckevičienė E., Bondareva E. A., Gabdrakhmanova L. J., Ahmetov I. I. (2023) Identification and Characterization of Genomic Predictors of Sarcopenia and Sarcopenic Obesity Using UK Biobank Data. Nutrients 15, 758.  Crossref
Journal of Sports Science and Medicine Serio F., De Donno A., Valacchi G. (2023) Lifestyle, Nutrition, and Environmental Factors Influencing Health Benefits. International Journal of Environmental Research and Public Health 20, 5323.  Crossref
Journal of Sports Science and Medicine Sigal R. J., Kenny G. P., Boulé N. G., Wells G. A., Prud’homme D., Fortier M., Reid R. D., Tulloch H., Coyle D., Phillips P., Jennings A., Jaffey J. (2007) Effects of aerobic training, resistance training, or both on glycemic control in type 2 diabetes: A randomized trial. Annals of Internal Medicine 147, 357-369.  Crossref
Journal of Sports Science and Medicine Singh R. K., Chang H. W., Yan D., Lee K. M., Ucmak D., Wong K., Abrouk M., Farahnik B., Nakamura M., Zhu T. H., Bhutani T., Liao W. (2017) Influence of diet on the gut microbiome and implications for human health. Journal of Translational Medicine 15, 73.  Crossref
Journal of Sports Science and Medicine Sivasubramanian R., Malhotra S. (2023) Genetic Contributors to Obesity. Gastroenterology Clinics of North America 52, 323-332.  Crossref
Journal of Sports Science and Medicine Srinivasan S., Clements J. A., Batra J. (2016) Single nucleotide polymorphisms in clinics: Fantasy or reality for cancer?. Critical Reviews in Clinical Laboratory Sciences 53, 29-39.  Crossref
Journal of Sports Science and Medicine Suchanek P., Kralova-Lesna I., Poledne R., Lanska V., Hubacek J. A. (2011) An AHSG gene variant modulates basal metabolic rate and body composition development after a short-time lifestyle intervention. Neuroendocrinology Letters 32, 32-36.
Journal of Sports Science and Medicine Suchánek P., Lánská V., Hubáček J. A. (2015) Composition Changes in Adult Females after Lifestyle Intervention Are Influenced by the NYD-SP18 Variant. Central European Journal of Public Health 23, 19-22.  Crossref
Journal of Sports Science and Medicine Sun D., Heianza Y., Li X., Shang X., Smith S. R., Bray G. A., Sacks F. M., Qi L. (2018) Genetic, epigenetic and transcriptional variations at NFATC2IP locus with weight loss in response to diet interventions: The POUNDS Lost Trial. Diabetes. Obesity & Metabolism 20, 2298-2303.  Crossref
Journal of Sports Science and Medicine Tam V., Patel N., Turcotte M., Bossé Y., Paré G., Meyre D. (2019) Benefits and limitations of genome-wide association studies. Nature Reviews. Genetics 20, 467-484.  Crossref
Journal of Sports Science and Medicine Tchernof A., Starling R. D., Turner A., Shuldiner A. R., Walston J. D., Silver K., Poehlman E. T. (2000) Impaired capacity to lose visceral adipose tissue during weight reduction in obese postmenopausal women with the Trp64Arg beta3-adrenoceptor gene variant. Diabetes 49, 1709-1713.  Crossref
Journal of Sports Science and Medicine Thamer C., Machann J., Stefan N., Schäfer S. A., Machicao F., Staiger H., Laakso M., Böttcher M., Claussen C., Schick F., Fritsche A., Haring H.-U. (2008) Variations in PPARD determine the change in body composition during lifestyle intervention: a whole-body magnetic resonance study. Journal of Clinal Endocrinology and Metabolism 93, 1497-500.  Crossref
Journal of Sports Science and Medicine Trapp E. G., Chisholm D. J., Freund J., Boutcher S. H. (2008) The effects of high-intensity intermittent exercise training on fat loss and fasting insulin levels of young women. International Journal of Obesity 32, 684-691.  Crossref
Journal of Sports Science and Medicine Tworoger S. S., Chubak J., Aiello E. J., Yasui Y., Ulrich C. M., Farin F. M., Stapleton P. L., Irwin M. L., Potter J. D., Schwartz R. S., McTiernan A. (2004) The effect of CYP19 and COMT polymorphisms on exercise-induced fat loss in postmenopausal women. Obesity Research 12, 972-981.  Crossref
Journal of Sports Science and Medicine van Loon L. J. C., Koopman R., Stegen J. H. C. H., Wagenmakers A. J. M., Keizer H. A., Saris W. H. M. (2003) Intramyocellular lipids form an important substrate source during moderate intensity exercise in endurance-trained males in a fasted state. Journal of Physiology 553, 611-625.  Crossref
Journal of Sports Science and Medicine van Loon L. J. C., Van Greenhaff P. L., Saris W. H. M., Wagenmakers A. J. M. (2001) The effects of increasing exercise intensity on muscle fuel utilisation in humans. Journal of Physiology 536, 295-304.  Crossref
Journal of Sports Science and Medicine Venables M. C., Achten J., Jeukendrup A. E. (2005) Determinants of fat oxidation during exercise in healthy men and women: A cross-sectional study. Journal of Applied Physiology (1985) 98, 160-167.  Crossref
Journal of Sports Science and Medicine Vogelezang S., Monnereau C., Gaillard R., Renders C. M., Hofman A., Jaddoe V. W. V., Felix J. F. (2015) Adult adiposity susceptibility loci, early growth and general and abdominal fatness in childhood: The Generation R Study. International Journal of Obesity 39, 1001-1009.  Crossref
Journal of Sports Science and Medicine Vrabel M. (2015) Preferred reporting items for systematic reviews and meta-analyses. Oncology Nursing Forum 42, 552-554.  Crossref
Journal of Sports Science and Medicine Wainschtein P., Jain D., Zheng Z. (2022) Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nature Genetics 54, 263-273.  Crossref
Journal of Sports Science and Medicine Weiss E. P., Jordan R. C., Frese E. M., Albert S. G., Villareal D. T. (2017) Effects of Weight Loss on Lean Mass, Strength, Bone, and Aerobic Capacity. Medicine and Science in Sports and Exercise 49, 206-217.  Crossref
Journal of Sports Science and Medicine Wells, G., Shea, B., O'Connell, D., Peterson, J., Welch, V., Losos, M. and Tugwell, P. (2000) online, The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in metaanalyses. Available from URL: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
Journal of Sports Science and Medicine Willis L. H., Slentz C. A., Bateman L. A., Shields A. T., Piner L. W., Bales C. W., Houmard J. A., Kraus W. E. (2012) Effects of aerobic and/or resistance training on body mass and fat mass in overweight or obese adults. Journal of Applied Physiology (1985) 113, 1831-1837.  Crossref
Journal of Sports Science and Medicine Wong H. S. C., Tsai S. Y., Chu H. W., Lin M. R., Lin G. H., Tai Y. T., Shen C. Y., Chang W. C. (2022) Genome-wide association study identifies genetic risk loci for adiposity in a Taiwanese population. Plos Genetics 18, e1009952.  Crossref
Journal of Sports Science and Medicine Yengo L., Sidorenko J., Kemper K. E., Zheng Z., Wood A. R., Weedon M. N., Frayling T. M., Hirschhorn J., Yang J., Visscher P. M. (2018) Meta-analysis of genome-wide association studies for height and body mass index in ~700 000 individuals of European ancestry. Human Molecular Genetics 27, 3641-3649.  Crossref
Journal of Sports Science and Medicine Yoneyama S., Yao J., Guo X., Fernandez-Rhodes L.3, U. L., Boston J., Buzková P., Carlson C. S., Cheng I., Cochran B., Cooper R., Ehret G., Fornage M., Gong J., Gross M., Gu C. C., Haessler J., Haiman C. A., Hende B. (2017) Generalization and fine mapping of European ancestry-based central adiposity variants in African ancestry populations. International Journal of Obesity 41, 139-148.  Crossref
Journal of Sports Science and Medicine Yoon Y., Park B. L., Cha M. H., Kim K. S., Cheong H. S., Choi Y. H., Shin H. D. (2007) Effects of genetic polymorphisms of UCP2 and UCP3 on very low calorie diet-induced body fat reduction in Korean female subjects. Biochemical and Biophysical Research Communications 359, 451-456.  Crossref
Journal of Sports Science and Medicine Yuan Y., Hunt R. H. (2009) Systematic reviews: The good, the bad, and the ugly. The American Journal of Gastroenterology 104, 1086-1092.  Crossref
Journal of Sports Science and Medicine Zarebska A., Jastrzebski Z., Cieszczyk P., Leonska-Duniec A., Kotarska K., Kaczmarczyk M., Sawczuk M., Maciejewska-Karlowska A. (2014) The Pro12Ala polymorphism of the peroxisome proliferator-activated receptor gamma gene modifies the association of physical activity and body mass changes in Polish women. PAR Research 2014, 373782.  Crossref
Journal of Sports Science and Medicine Zhang X., Qi Q., Zhang C., Hu F. B., Sacks F. M., Qi L. (2012) FTO genotype and 2-year change in body composition and fat distribution in response to weight-loss diets: The POUNDS LOST trial. Diabetes 61, 3005-3011.  Crossref
 
 
 
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