The application of personal genetic information for improved weight loss

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Masterthesis

The application of personal genetic information for improved weight loss

Submitted by

Florian Schneebauer, MSc

For the academic degree of Master of Science

(MSc)

at the

Medical University of Graz

Under the supervision of

Ao.Univ.-Prof. Mag. Dr. Dr. Erwin Petek Daniel Wallerstorfer, PhD, BSc

Salzburg, 19.08.2021

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Statutory Declaration

I declare on my honor that I have written this master-thesis independently and without assistance, that no sources other than those cited were used, and that the sources used verbatim or in substance have been marked as such.

Florian Schneebauer, MSc eh Salzburg, 19.08.2021

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Abstract

Background. The genetic contribution to obesity and adiposity is widely accepted and estimated to be in the range of 45 to 90 %. The 12th update of the Human Obesity Gene Map lists 253 quantitative trait loci involved in body weight regulation, including rare, high penetrance monogenic causes for severe obesity. Most of these loci have diagnostic value only and do not influence the treatment of overweight and obesity. Only a fraction of these loci are known to influence the body’s response to certain weight-loss strategies, but these bear the potential for clinical application of genetic information to the affected individuals benefit in preventing or treating obesity. We hypothesize, that evaluating specific genetic predispositions of individuals to determine responses to weight-loss strategies may enable the creation of an effective, individual weight management program.

Methods. Initially, scientific literature was scanned for evidence of common genetic variations with known obesity-related gene-diet and gene-environment interactions including fat content of the diet, carbohydrate content of the diet, exercise as a weight-loss strategy, and calorie reduction as a weight-loss strategy. 303 individuals ranging from normal body weight (BMI between 20 and 24.99 kg/m²) to severe obesity (BMI above 40 kg/m²) were genotyped for 7 single nucleotide polymorphisms (SNPs). Participants adhered to a 3-week intervention program (a genetically personalized weight management strategy including an individual strategy guide). The weight-loss effectiveness of the personalized program was recorded.

Results. Participants reduced their body weight following the personalized intervention program by an average of 3.60 kg (mean -3.60±1.90 s.d.) over 3 weeks.

People of different initial body weights responded differently well to the intervention program.

Discussion. Using genetic information for personalized weight-loss strategies appears to result in an effective nutrition and exercise program for effective weight loss. Further studies may be warranted to assess the exact effect of individual genetic variants on the effectiveness of various weight management strategies.

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Zusammenfassung

Hintergrund. Der genetische Einfluss auf Fettleibigkeit und Adipositas ist bereits gut dokumentiert und wird auf 45 bis 90 % geschätzt. Das 12. Update der Human Obesity Gene Map listet aktuell 253 Loci, die an der Regulierung des Körpergewichts beteiligt sind, einschließlich seltener monogener Ursachen mit hoher Penetranz für schwere Fettleibigkeit. Die meisten dieser Loci haben jedoch nur diagnostischen Wert und keinen Einfluss auf die Behandlung von Übergewicht.

Obwohl nur ein Bruchteil dieser Loci die Reaktion des Körpers auf bestimmte Strategien zur Gewichtsabnahme beeinflussen können, bergen diese das Potenzial für klinische Anwendungen genetischer Informationen zur Prävention und Behandlung von Fettleibigkeit. Wir gehen davon aus, dass die Analyse spezifischer genetischer Prädispositionen von Individuen mit Einfluss auf Gewichtsverluststrategien die Erstellung eines effektiven, individuellen Gewichtsmanagementprogramms ermöglicht.

Methoden. Zunächst wurde eine wissenschaftliche Literaturrecherche durchgeführt, um genetische Variationen mit bekannten Übergewicht-assoziierten Gen-Umwelt-Interaktionen einschließlich Fettgehalt in der Nahrung, Kohlenhydratgehalt in der Nahrung, Sport als Strategie zur Gewichtsreduktion und Kalorienreduktion als Strategie zur Gewichtsreduktion zu identifizieren. Bei 303 Personen mit normalem Körpergewicht (BMI zwischen 20 und 24,99) bis zu schwerer Fettleibigkeit (BMI über 40) wurden 7 Einzelnukleotidpolymorphismen (SNPs) genotypisiert. Alle Teilnehmer durchliefen ein 3-wöchiges Interventionsprogramm (eine genetisch personalisierte Gewichtsmanagementstrategie einschließlich eines individuellen Leitfadens). Die Wirksamkeit des personalisierten Programms in Bezug auf die Gewichtsabnahme wurde dokumentiert.

Ergebnisse. Die Teilnehmer reduzierten ihr Körpergewicht mit Hilfe des personalisierten Interventionsprogramms um durchschnittlich 3,60 kg (Mittelwert - 3.60±1.90 s.d.) in 3 Wochen. Personen mit unterschiedlichem Ausgangsgewicht reagierten unterschiedlich gut auf das Interventionsprogramm.

Diskussion. Die Verwendung genetischer Informationen für eine personalisierte Strategie zur Gewichtsabnahme scheint ein effektives Ernährungs- und

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Sportprogramm zur effektiven Gewichtsabnahme zu ermöglichen. Weitere Studien sind nötig, um die genaue Wirkung einzelner genetischer Variationen auf die Wirksamkeit verschiedener Gewichtsmanagementstrategien beurteilen zu können.

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Abbreviations

ADRB2 Adrenoceptor Beta 2 ADRB3 Adrenoceptor Beta 3 AGRP Agouti-related protein APOA2 Apolipoprotein A2 APOA5 Apolipoprotein A5 BBS Bardet-Biedl syndrome

BDNF Brain-derived neurotrophic factor BMI Body mass index

BMR Basal metabolic rate CRP C-reactive protein CVD Cardiovascular disease EE Energy expenditure

EI Energy intake

FABP2 Fatty acid-binding protein 2

FTO Fat mass and obesity-associated protein GBD Global Burden of Disease study

GERD Gastroesophageal reflux disease GWAS Genome-wide association studies HR Hazard ratio

LEPR Leptin receptor

NAFLD Nonalcoholic fatty liver disease NIH National Institutes of Health NPY Neuropeptide Y

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PPARG Peroxisome proliferator-activated receptor gamma PRS Polygenic risk score

PWS Prader-Willi syndrome SIM1 Single-minded 1

SNP Single nucleotide polymorphism T2D Type 2 diabetes

TRKB Tyrosine kinase receptor WC Waist circumference

WHO World Health Organisation WHR Waist to hip circumference ratio α-MSH α melanocyte-stimulating hormone

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List of tables and figures

Figure 1: Share of adults that are overweight or obese, 2016 Figure 2: Obesity-related comorbidities

Figure 3: Number of deaths worldwide by risk factor, 2017

Figure 4: Benefits of weight loss on obesity-related comorbidities Figure 5: The leptin/melanocortin pathway

Figure 6: Effect sizes and allele frequencies BMI associated SNPs Figure 7: Weight loss in 3 weeks based on gender

Figure 8: Weight loss in 3 weeks based on BMI Figure 9: Weight loss in 3 weeks based on age group

Table 1: BMI classification (WHO) Table 2: Wild-type and variant alleles Table 3: Genotype/phenotype correlation Table 4: Baseline Characteristics

Table 5: Allele-specific genotyping assays

Table 6: Macronutrient balance assigned by fat- and carb-score

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1. Introduction

Obesity and overweight are some of the leading causes of preventable death worldwide and escalate in epidemic proportions. While the number of undernourished people has fallen, the proportion of overweight people has tripled in the last 40 years, meaning that in most countries of the world more people die from being obese than from being underweight [1]. According to the World Health Organisation (WHO), more than 1.9 billion adults and 340 million children and adolescents aged 5-19 were overweight or obese in 2016 [2, 3]. This implies that currently, more than 39% of the world's adult population is overweight or obese.

Figure 1 shows the worldwide distribution of overweight adults indicating that the proportion of overweight individuals tends to be higher in wealthy countries with more than 60% [4].

Figure 1: Share of adults that are overweight or obese, 2016 [4]

However, obesity is not only a health issue. In the USA alone, the annual economic burden caused by obesity is estimated at around 100 billion US dollars [5].

Overall, overwhelming evidence proves that obesity can be categorized as one of the most severe public health issues to date demonstrating the need for effective prevention and intervention programs [6].

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1.1. Definitions

To define whether a person is underweight, normal weight, overweight, or obese, scientific studies usually use three markers: the body mass index (BMI), the waist circumference (WC), and the waist to hip circumference ratio (WHR) [7].

The WHR can be calculated using the quotient of waist and hip circumference. The value allows assessing the individual likelihood of body fat distribution. The typical male fat distribution results in a typically high WHR value, while the female fat distribution tends to be typically low. If the WHR value is above 0.9 in men and above 0.85 in women, an increased risk of obesity can be assumed. Measuring the WC alone (waist circumference at the level of the navel) is also suitable for assessing the risk of obesity. If the waist circumference is above 88 cm in females and 102 cm in males, this indicates a predominantly intra-abdominal fat distribution [8].

However, the most commonly used measure is the BMI, a statistical index derived from height and weight to provide an estimate of body fat. The calculation takes the individual’s weight (kg), divided by the squared height (m²) [9]:

BMI = weight (kg)/ height² (m²)

Table 1: BMI classification (WHO) [10]

BMI (kg/m²) CLASSIFICATION

< 16.5 Severely underweight

< 18.5 Underweight

>= 18.5 to 24.9 Normal weight

>= 25 to 29.9 Overweight

>= 30 to 34.9

Obese

Obesity class I

>= 35 to 39.9 Obesity class II (severe obesity)

>= 40 Obesity class III (morbid obesity)

For White, Hispanic, and Black adult individuals, the WHO and the National Institutes of Health (NIH) define overweight as a BMI between 25 and 29.9, a BMI greater or equal to 30 is defined as obesity [10]. However, it has to be taken into account, that individual variations do exist and that the BMI does not always correlate with the health status of an individual.

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1.2. Obesity-related mortality and comorbidities

In regards to clinical practice, obesity is not only reducing the quality of life, it is also a well-known major risk factor and contributor to the development of a broad range of diseases. Numerous large, long-term epidemiological studies including millions of participants already support the association between obesity and higher all-cause mortality rates broadly consistent over several different populations [11-13]. For example, in a meta-analysis of 239 prospective studies with over 10 million participants from Europe, Asia, North America, Australia, and New Zealand the authors reported a hazard ratio (HR) of 1.45 for obesity class 1, 1.94 for obesity class 2, and 2.76 for obesity class 3 relative to normal weight subjects [12].

Furthermore, being overweight is associated with a variety of comorbidities. A systematic review conducted by Yuen et al. from 2016 revealed nearly 200 diseases associated with obesity, many of which increase with a rise in BMI [14, 15].

The most common obesity-related comorbidities are cardiovascular diseases (CVD), diabetes, kidney diseases, dyslipidemia, hypertension, non-alcoholic fatty liver disease, reproductive dysfunction, musculoskeletal disorders, psychiatric conditions as well as cancer (Figure 2) [6, 16, 17].

Figure 2: Obesity-related comorbidities [18]

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In the Global Burden of Disease (GBD) study, which represents the most comprehensive epidemiological database analyzing causes and risk factors for death, data from 67.8 million people were analyzed to quantify the disease burden related to BMI. The GBD showed that in 2017 the annual number of deaths attributed to obesity was 4.72 million making it the fifth largest risk factor for death following high blood pressure, smoking, high blood sugar, and air pollution [19]

Figure 3: Number of deaths worldwide by risk factor, 2017 [4]

The results showed that over 60% of deaths associated with an increased BMI were due to CVD with 992.4 per 100,000 people making CVD the number one reason for increased BMI-associated deaths followed by diabetes, kidney diseases, and neoplasms. These 4 comorbidities represented 89.3% of all increased-BMI- associated disability-adjusted life years (DALYs) [20].

The association of increased weight and the pathogenesis of type 2 diabetes has been reported in multiple studies and was consistent across different populations [21]. It has been shown that adipose tissue modulates metabolism by releasing factors like hormones, glycerol, fatty acids, and proinflammatory cytokines which are linked to the occurrence of insulin resistance, one major factor in the early development of T2D [22]. A meta‐analysis from 2009 revealed a 7-fold higher T2D risk in obese men and a 12-fold higher T2D risk in obese women compared with

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normal-weight individuals pointing out the importance of a weight management program in T2D prevention [23].

Studies have shown that a weight reduction of 5-10% in obese individuals can significantly improve the risk of obesity-associated comorbidities [24]. Improved health-related outcomes associated with weight loss were reported in individuals suffering from type 2 diabetes (T2D), dyslipidemia, hyperglycemia, osteoarthritis, stress incontinence, gastroesophageal reflux disease (GERD), hypertension, polycystic ovary syndrome (PCOS), nonalcoholic fatty liver disease (NAFLD) and obstructive sleep apnea (OSA) [24]. Figure 4 shows the beneficial effects of investigated weight loss ranges on obesity-associated comorbidities [25].

Figure 4: Benefits of weight loss on obesity-related comorbidities [25].

1.3. Etiology

Obesity is a multifactorial diseasewhich means that many different factors can play together in the development of the disease. In addition to diet and exercise habits, psychological and social components, as well as genetic and neurobiological factors, play an essential role. Overall scientists agree that obesity arises from an imbalance between energy expenditure (EE) and energy intake (EI) [25].

EE is the amount of energy that individuals need to maintain their life processes including breathing, circulating blood, food digestion, as well as exercising. In theory, the total energy expenditure of an individual is composed of the basal metabolic rate (BMR), the amount of energy used while resting, plus an activity factor which is composed of exercise and non-exercise activity thermogenesis. This defines the total amount of energy that an organism consumes per day [26]. Any

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additional calories would have to be worked off through physical activity, otherwise, they end up in storage and cause overweight.

Other common obesity risk factors are nutrient-poor food choices (e.g. sweetened beverages), sedentariness, little or excess sleep, psychological conditions, or specific medication [27].

Therefore, lifestyle factors such as too little exercise and too many ingested calories are well-known risk factors for the development of obesity and overweight [28].

However, many individuals lead this lifestyle and never develop obesity even in an obesogenic environment, which hints at underlying genetic factors playing a vital role.

1.4. Genetics of obesity

Twin studies have shown that besides environmental factors it is estimated that 45

% to 90 % of obesity is inherited, hence caused by genetic variations [29-31]. In an early study by Stunkard et al., which compared the BMI of twins raised either together or separately, it was shown that inherited factors have a greater impact than the childhood environment [30].

1.4.1. Monogenetic obesity

However, only in rare cases, obesity occurs according to a clear inheritance pattern, caused by a high impact monogenetic mutation. Monogenic obesity is a rare disease characterized by a severe early-onset in combination with endocrine disorders caused by a single mutation. This type of monogenetic obesity accounts for about 7.3% of all severe early-onset obesity cases [32].

Mutations in the Leptin (LEP), Leptin Receptor (LEPR), Single-minded homolog 1 (SIM1), Proopiomelanocortin (POMC), Brain-derived neurotrophic factor (BDNF), Tropomyosin receptor kinase B (TrkB), and the Proprotein-Convertase 1 (PC1) genes represent the most common cause of monogenic obesity by influencing the leptin-melanocortin signaling pathway. The leptin-melanocortin signal cascade regulates energy expenditure as well as energy intake which both control the energy balance in the hypothalamus (Figure 5). [33].

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Another rare genetic form of obesity, called syndromic obesity, corresponds to severe obesity in addition to other phenotypes like intellectual disability, anatomical and developmental abnormalities. The most commonly linked forms are the Prader- Willi (PRS) and the Bardet-Biedl (BBS) syndrome. The melanocortin-4-receptor (MC4R)-linked obesity belongs to the group of oligogenic obesity and is characterized by a variable penetrance, which partly depends on environmental factors. It is estimated that 2-3% of obesity cases belong to this group [33, 34]. It is estimated that monogenic mutations are responsible for approximately 5-7% of all severe obesity cases [35-37].

1.4.2. Polygenic obesity

However, the vast majority of studies have pointed toward a complex multifactorial disease, triggered by a combination of several genetic variations, lifestyle factors, and environmental factors. Using genome-wide association studies (GWAS)

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researchers have already identified hundreds of genetic variations associated with overweight, increased BMI, waist circumference, and waist to hip ratio [38, 39]. The last update of the Human Obesity Gene Map cites 253 genetic loci involved in clinical obesity [40]. The advantage of genome-wide association studies is the hypothesis-free approach allowing the identification of new loci involved in the regulation of body weight without knowing their exact function or pathway.

Following this approach, researchers have already identified numerous variants associated with increased weight, BMI, waist circumference, and other overweight- related anthropometric parameters. The first two loci associated with obesity were intronic variations in the fat mass and obesity-associated gene (FTO) and a variant downstream of MC4R, both published in Nature Genetics [41, 42].

Over the next years, FTO became the most popular obesity risk increasing locus and has been studied in multiple populations. Although the exact causal mechanism between genetic variations in the FTO gene and an increased BMI has not yet been fully clarified, it has been shown that FTO plays a part in regulating feeding behavior and energy expenditure [43].

The first GWAS publications analyzing obesity-associated variations were published in 2009 and identified 16 loci in total [44-46]. In 2014 Qi et al. reported 30 genomic loci associated with increased BMI. The mean effect per allele was 0.17 (0.06–0.39) kg/m2 per allele (Figure 6).

A more recent meta-analysis of genome-wide association studies conducted by Yengo et al. in 2018 examined a cohort of more than 700 000 European individuals and was able to identify a total of 941 near-independent SNPs associated with BMI (751 SNPs located within loci not previously identified) [47]. These results demonstrated, as previously predicted, the strong polygenic background in the development of obesity.

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Figure 6: Effect sizes and allele frequencies BMI associated SNPs [48].

1.4.3. Polygenic risk scores

One possibility to use the knowledge about these risk-increasing variants is the so- called polygenic risk score (PRS). A PRS can assess the individual heritable risk of developing a certain disease by calculating the total number of risk-increasing variations of a person and compare the score with the average population. As PRSs are simple to analyze and calculate, their potential in clinical medicine is vast.

Multiple studies suggest that PRSs could be part of clinical risk tools in the near future [49]. In an interesting recent study from 2019, Khera et al. derived a PRS comprised of over 2 million common genetic variations and validated it on over 300 000 individuals from four different cohorts. The authors defined the top 10% of the PRS distribution as high PRS carriers and reported an increase of 8 kg in weight when compared with non-carriers. Furthermore, a high PRS was associated with a

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4.2, 6.6, and 14.4-fold increased risk of BMI ≥ 40, 50, and 60 kg/m², respectively [50].

1.4.4. Gene-environment interactions

However, genetic testing of most of these variations or calculating an obesity risk PRS would only have a diagnostic value which gives an additional advantage in preventing but not in treating overweight in affected individuals. In addition, reviews have shown that PRS could only explain about 6% of the heritability of overweight and obesity, but as mentioned earlier, twin studies unveiled that heritability is significantly higher [47, 51].

Indeed, this so-called “missing heritability” can in part be explained by gene- environment interactions [52-54]. Therefore, multiple studies already focused on these interactions and the individual effectiveness of eating behavior and lifestyle interventions based on genetic variations. Three recent individual reviews tried to summarize the results on obesity-related gene-diet and gene-environment interactions from multiple observational and randomized control studies. The authors of all three reviews agreed that increasing evidence shows the importance of gene-environment interactions in the pathogenesis of obesity and supporting the hypothesis that these interactions can explain the missing heritability [52-54].

But what gene-environment effects exactly have been investigated so far? For example, studies have shown that two individuals following the same weight-loss strategy such as increased exercise and decreased calory intake, may experience wildly different results [55].

Until now studies revealed associations of obesity-related polymorphisms with macronutrients, micronutrients, specific foods, diet patterns, exercise, and other lifestyle and environmental factors [56-72].

The most studied strategies include the adaption of macronutrient intake as well as physical activity and calorie reduction as a weight-loss intervention program [48]. It is plausible to examine these strategies more closely, as it has already been shown that changes in the areas of diet and physical activity are ideally suited to successfully lose weight or maintain it. The largest prospective study analyzing long- term successful weight loss maintenance called the National Weight Control

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Registry (NWCR), tracked over 10000 individuals and analyzed lifestyle factors associated with achieving and maintaining weight loss. 98% of successful participants reported modified food intake and 94% reported increased physical activity [25].

1.5. Macronutrient distribution of the diet

As described before, a widely accepted assumption in weight management is that obesity is solely a product of suboptimal energy balance between EE and EI, namely more calories are consumed than are expended, leading to a caloric surplus that ends up in storage [73, 74]. The assumption is, that all calories that are ingested add directly to the energy balance, which would mean that all people would gain the same amount of calories from a fatty meal, but as discussed, several scientific studies appear to contradict this assumption suspecting a gene-diet interaction based on macronutrient consumption.

1.5.1. Fat content 1.5.1.1. FABP2 rs1799883

Numerous studies have shown the association of genetic variations in the fatty acid- binding protein 2 (FABP2) gene with type 2 diabetes, insulin sensitivity, as well as increased triglyceride-rich lipoproteins (TRL) by regulating intracellular transport of dietary long-chain fatty acids and fatty acid metabolism [75-80]. Especially carriers of the 54Thr allele at polymorphism rs1799883 seem to react with insulin resistance to a high-fat diet which can lead to higher blood pressure as well as overweight [81].

A study performed by Martinez-Lopez et al. analyzing 109 overweighted subjects showed that carriers of the Thr54 allele following a diet including decreased saturated fat intake exhibit a significantly (p < 0.05) better response in terms of weight (-7.5 versus -4.2 kg), BMI (-2.1 versus -1.2 kg/m²), waist-to-hip ratio(-0.04 versus -0.02), waist circumference (-7.6 versus -5.2 cm), and C-reactive protein (CRP) (-1.4 versus -0.76 mg/L) changes compared to Ala54 homozygous individuals [82].

Similarly, De Luis et al. examined the effect of applying a hypocaloric (1520 kcal), low-fat (25% lipids) diet to 69 obese, nondiabetic subjects in a prospective way.

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Weight loss was associated with different metabolic responses, depending on the FABP2 rs1799883 genotype [83].

Similar results could be found by the same author in another prospective study on 122 obese patients, showing different biochemical responses on insulin and leptin levels during weight loss in Thr54 allele carriers [84].

Furthermore, De Luis et al. confirmed these results a third time in 2015 in a cohort of 193 obese individuals. Again, metabolic response to weight loss was different with no decrease of glucose, LDL, and insulin levels in Thr54 carriers [85].

1.5.1.2. PPARG rs1801282

Another gene associated with increased BMI, overweight, and obesity is the peroxisome proliferator-activated receptor gamma (PPARG) gene, a member of the PPAR subfamily. The PPAR-gamma protein regulates adipocyte differentiation and is involved in the development of diseases like diabetes, atherosclerosis, and cancer and is a critical transcriptional regulator of adipogenesis [86-89]. However, it has also been shown that the gene plays a role in the physiological responses to dietary fat intake.

Animal studies using PPARG heterozygous null mice receiving a high-fat diet were able to show significantly less weight and fat mass gain compared to the wild-type mice [90, 91].

A study conducted by Memisoglu et al. investigated the genotype effect of PPARG Pro12Ala, dietary fat intake, and mean BMI in 2141 human subjects. The authors were able to show that high total fat intake in combination with the Pro/Pro genotype led to a significantly higher BMI compared to low fat intake (27.3 versus 25.4 kg/m², respectively; p < 0.0001). An effect that was not reproducible in Ala12 carriers [92].

Similar results have been obtained by Robitaille et al. in a group of 720 participants in the Québec Family Study (QFS) where the waist circumference was increased in combination with a high-fat diet. Interestingly, the effect was only seen in carriers of the Pro/Pro homozygous genotype [93].

However, observations on the effect of Pro12Ala genotypes are not always consistent as conflicting results have been reported supporting the need for further research [94, 95].

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1.5.1.3. FTO rs9939609

One of the best-studied genes related to obesity is the fat mass and obesity- associated (FTO) gene. The relation of genetic variations in the FTO gene and obesity, as well as increased BMI, has been confirmed in multiple studies [96-99].

A large study conducted by Frayling et al. including 38 759 participants in 13 cohorts confirmed the association of FTO rs9939609 concluding that homozygosity for the risk allele led to an increased weight of about 3kg on average and a 1.67-fold higher likelihood of obesity compared to the wild-type individuals [100].

Even though the mechanism behind this effect is not fully understood, multiple studies have been shown the role of the gene in regulating the energy balance and intake [101, 102]. Especially, A-allele carriers seem to have higher total energy intake, in particular, derived from dietary fat [103-105].

This observation has been confirmed by the HELENA cross-sectional study in 652 individuals. The A-allele of rs9939609 was only associated with adiposity indices if the dietary fat intake was between 30% and 35% of total macronutrient intake supporting the assumption that low-fat diets can reduce the predisposition to overweight in A-allele carriers [106].

The same effect has been shown previously by two other studies. AA genotype participants showed a higher BMI compared to wild-type allele carriers only in combination with a high-fat diet. No BMI association has been found with low-fat diets [59, 107].

1.5.1.4. APOA2 rs5082

Another obesity-linked genetic variation is rs5082 in the Apolipoprotein A2 (APOA2) gene coding for the second most important high-density lipoprotein (HDL) [108].

Several studies indicate a correlation between rs5082, dietary saturated fat intake, and overweight. In particular, it has been shown that CC individuals do have a higher risk for obesity compared to T-allele carriers as well as a higher total fat intake [109].

Later, Corella et al. replicated this gene-diet interaction in 3 independent populations. The authors showed an increased BMI in CC genotype individuals

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compared to T-allele carriers only in combination with high saturated fat intake in all 3 populations [110].

The results were replicated by the same group in 2011 as well as by another group in 2015 [111, 112].

1.5.1.5. APOA5 rs662799

Apolipoprotein A5 (APOA5) plays an important role in lipid metabolism by regulating plasma triglyceride levels. Genetic variations in this gene have been associated with hypertriglyceridemia and cardiovascular diseases [113-115].

Concerning weight loss, Aberle et al. examined the effect of short-term fat restriction on BMI in regards to the APOA5 rs662799 genotype. The study group consisted of 606 overweight men and it has been shown that BMI reduction was significantly higher in C-allele carriers compared to the wild-type allele [116].

In contrast, Corella et al. showed in a cohort of 2280 individuals (1073 men and 1207 women) participating in the “Framingham Offspring Study” an association of BMI and total fat intake in subjects carrying the T/T genotype but not in C-allele carriers. Increased total fat intake led to increased BMI [117].

These results were consistent with a study conducted by Sanchez-Moreno et al.

showing a positive association of high dietary fat intake and high BMI in T-allele carriers but not in C-allele carriers supporting the hypothesis that C-allele carriers may be protected from obesity, even when consuming a high-fat diet [118].

1.5.2. Carbohydrate content 1.5.2.1. ADRB2 rs1042714

Few attempts have been made to examine the influence of carbohydrate intake and weight loss. ADRB2, also known as Adrenoceptor Beta 2, encodes the beta-2- adrenergic receptor and plays an important role in the regulation of the cardiac, pulmonary, vascular, endocrine, and central nervous systems [119]. The protein can be found in myocytes and plays a crucial role in its regulation and it has been shown that genetic variations within this gene can influence BMI, waist circumference, and visceral obesity [120-123].

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To assess the role of dietary macronutrient intake and the risk of obesity in regards to the ADRB2 rs1042714 genotype, Martinez et al. conducted a case-control study on a group of 159 overweight subjects and 154 controls. The study results demonstrated that subjects with the Glu27 allele had a nearly 3-fold higher likelihood of being overweight (OR: 2.56) if they obtained more than 49 % of their daily calories in the form of carbohydrates [124].

1.6. Weight loss strategies

Following the energy balance rationale, one approach to reduce body weight is to increase energy expenditure through increased physical activity and reduce the ingested amounts of calories respectively [73]. However, in addition to different responses to specific macronutrient distributions based on genetic variation (gene- diet interactions), it has also been shown that the response to lifestyle interventions like sports or calorie restriction can also affect body weight regulation depending on individual genetic variations.

1.6.1. Weight loss through exercise 1.6.1.1. ADRB3 rs4994

Adrenoceptor Beta 3 also belongs to the family of beta-adrenergic receptors and represents the main lipolytic receptor in white adipose tissues. The tryptophane to arginine variation decreases the activation of cyclic adenosine monophosphate (cAMP) and lipolytic glycerol formation [125].

Several studies have already shown the association of the Arg64 allele with increased BMI and body fat [126, 127]. However, Sakane et al. firstly described the association of an ADRB3 Trp64Arg variant and physical activity in 61 obese T2D patients. To investigate the effects of the Trp64Arg variation all patients took part in a 3-month weight reduction program consisting of a change in diet and exercise.

Depending on the ADRB3 genotype, they had a higher decrease in weight,waist- to-hip ratio (WHR) insulin resistance index, and HbA1c levels as a result. Carriers of the variant allele lost significantly less bodyweight following the same intervention program than carriers of the wild-type allele, even though they invested an equal amount of effort into the weight loss strategy. The authors hypothesized that the effect may be based on lower lipolytic response in adipose tissues caused by the

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genetic variation [128, 129]. In a more recent study, Sakane et al. reproduced his findings analyzing 112 subjects with impaired glucose tolerance undergoing a 6‐

month intervention program. In addition to the increased weight loss of wild-type carriers, the authors reported a higher increase in high‐density lipoprotein (HDL) cholesterol levels [55].

In line with these findings, Shiwaku et al. demonstrated a significant difference in anthropometric parameters including weight loss based on the ADRB3 rs4994 genotype in a 3-month intervention trial of 76 perimenopausal women. Again, carriers of the wild-type allele lost more weight compared to the variant allele [130].

Another notable prospective study conducted by De Luis et al. successfully replicated the association of ADRB3 rs4994 and improved weight loss for wild-type carriers in combination with an exercise intervention program consisting of an aerobic exercise at least three times a week [131].

Another interesting study done by Morita et al. examined fat oxidation during exercise in young healthy Japanese men. It has been shown that carriers of the Arg64 allele had decreased fat oxidation during aerobic exercise [132].

In contrast to these findings, other intervention studies found no associations between genotypes and weight loss [133, 134]. However, Tchernof et al. showed a 43% lower reduction of visceral fat in variant allele carries compared to the wild-type [135].

1.6.1.2. PPARG rs1801282

Individual variability in exercise responses has also been reported for the PPARG rs1801282 polymorphism. As described earlier the transcription factor regulates lipid uptake and stimulates adipogenesis by fat cells [136].

In 2005 Østergård et al. investigated the influence of the PPARG rs1801282 polymorphism on insulin sensitivity and maximal aerobic capacity induced by exercise in a cohort of 48 subjects including 29 relatives of type 2 diabetes patients.

The group underwent a 10-week aerobic training program consisting of 45min ergometer training at 70% of VO2max 3 times a week. The authors reported an increased weight loss in Ala12 carriers (−1.8 ± 1.8 kg versus −0.3 ± 1.4 kg) and a

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greater improvement in insulin sensitivity compared to the wild-type genotype respectively. However, the effect was only significant in the offspring group [137].

In the same year, Lindi et al. researched the association of the PPARG variant with the risk of type 2 diabetes in a cohort of 522 individuals with impaired glucose tolerance (IGT) participating in the course of the Finnish Diabetes Prevention Study.

The intervention group followed individual dietary advice and a guided exercise program. The researchers showed that during the follow-up subjects of the intervention group who were homozygous for the Ala12 allele decreased their weight significantly more than other genotypes [138].

In 2008 another research group led by Kilpeläinen et al. studied the association of 7 PPARG variations with type 2 diabetes and the interaction with exercise intervention. They reported an increased risk of the development of type 2 diabetes for Ala12 carriers. However, this effect could be modified by physical activity [72].

The findings were supported in a study conducted by Delahanty et al. analyzing 3234 overweight individuals showing that patients with the Ala12 allele had greater weight loss in response to a 150min per week physical activity program [139].

1.6.1.3. FTO rs9939609

In 2008 a research group led by Andreasen et al. investigate the association of the FTO rs9939609 variation on the risk of developing overweight, type 2 diabetes, and anthropometric parameters including obesity-related measures, fasting serum lipids, plasma glucose, and serum insulin in large study samples of danish subjects.

Physical activity was evaluated by a self-reported questionnaire. First of all, the study confirmed the association of the A-allele with increased body weight, waist circumference, and BMI. Secondly, the study demonstrated a significant BMI increase (1.95 ± 0.33 kg/m2) as a consequence of physical inactivity in carriers of the homozygous AA genotype but not in T-allele carriers indicating a genotype related effect of a sedentary lifestyle [140].

Similarly, Ruiz et al. tried to assess whether the effect of increased anthropometric parameters like BMI, body fat percentage, waist circumference can be attenuated by increased physical activity. The authors studied a cohort of 752 European adolescents and concluded that the described negative effect of the risk allele was

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much lower if the individual met the goal of 60 minutes per day of moderate to high physical activity [141].

An independent study conducted by Lee et al. in 2010 investigated the same effect in a Korean cohort of 711 children and 8842 adults classified in 3 subgroups (moderately inactive n = 3286, moderately active n= 2136, and active n= 3420) based on their physical activity status. In line with previous findings, the authors reported an association between the level of physical activity and the deleterious effect of the FTO rs9939609 polymorphism in both children and adults. A-allele carriers had a significantly higher BMI in the inactive and moderate active subgroups. The effect could not be detected in the active subgroup indicating that the deleterious effect of the FTO rs9939609 variant can be overcome by meeting the daily physical activity [142].

The findings were supported again in a large study among 22 799 Swedish adults conducted by Sonestedt et al. investigating the effect of the rs9939609 on fat intake, physical activity, fat mass, lean mass, and mortality. The authors concluded that A- allele carriers may benefit from an increased level of physical activity to decreased the percentage of body fat [143].

Although the same effect could be shown in other FTO polymorphisms, it has to be noted that other reports could not support this gene by environment interaction [144- 147].

Therefore, a research group of the Institute of Metabolic Science of Cambridge led by Kilpeläinen conducted a meta-study including 54 studies and 237434 subjects.

They grouped all individuals in one of 2 groups (inactive versus active) and found a 1.23-fold increased risk of obesity per A-allele. Similar to previous reports the data showed that the effect could be attenuated by 27% in physically active individuals.

The association was only significant in adults and not in children and adolescents [148].

Two more recent studies gave conflicting results. While Cho et al. reported a 2-fold reduced risk of obesity in A-allele carriers a study by West et al. failed to achieve significance [149].

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1.6.2. Weight loss through calorie reduction

Another approach to improving the calorie balance is to reduce the ingested amounts of calories, an approach termed “calorie restriction” (CR) [150]. Contrary to the common belief that ingested calories affect everyone equally, the efficiency of calorie reduction for weight loss appears to be influenced by genetic polymorphisms as investigated by the following studies.

1.6.2.1. ADRB2 rs1042714

As described before, the association between the ADRB2 gene including the rs1042714 polymorphism, and obesity has been replicated by numerous studies and finally been confirmed by Zhang et al. in a meta-analysis gathering data from 17 publications. The authors described 20% higher obesity rates in G-allele carriers [151].

The polymorphism also seems to affect the individual response to a calorie restriction diet. Ruiz et al. investigated the role of ADRB2 rs1042714 on anthropometric parameters like weight and BMI after a low caloric diet in a cohort of 83 obese women from northern Spain. The intervention provided 600 kcal less than the calculated resting metabolic rate (RMR) multiplied with an activity factor of 1.3 and lasted for 12 weeks. The outcome of the study was a significant interaction effect of ADRB2 Gln27Glu genotypes and weight loss response to the described diet. Glu allele carriers lost more weight (9.5 ± 2.9 s.d. versus 7.0 ± 3.5 s.d. %, respectively, p = 0.002) and bone-free lean mass (LM) (5.9 ± 2.7 s.d. versus 4.0 ± 2.7 s.d. %, respectively, p = 0.001) compared to the Gln/Gln group [152].

However, the association of a beneficial lean mass reduction in mutant allele carriers remained inconclusive as another study exhibited different results [153].

1.6.2.2. PPARG rs1801282

In an investigation conducted by Lindi et al. in 2002, 522 individuals with impaired glucose tolerance (IGT) were subjected to a 3-year multidisciplinary obesity treatment program consisting of individual dietary advice and a guided exercise program. The researchers showed that during the follow-up subjects of the intervention group who were homozygous for the Ala12 allele had significantly higher weight loss compared to the other genotypes [138].

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Along with these findings, Franks et al. analyzed data from the Diabetes Prevention Program (DPP), a multi-center randomized clinical trial investigating the effects of medications and lifestyle intervention on the risk of developing type 2 diabetes. The lifestyle intervention included a 500-1000 per day calorie reduction, exercise, and behavioral modification. In line with previous reports, G-allele carriers of the intervention group achieved a significantly greater weight reduction in body weight compared to C-allele carriers [154].

Similarly, Garaulet et al. investigated the association of the PPARG rs1801282 polymorphism and an intervention program including calorie reduction, exercise, nutritional education, and behavioral advice in 1465 healthy, overweight subjects aged from 20 to 65. The authors reported gene-diet interactions with beneficial effects for carriers of the Ala-allele [94].

Finally, Aller et al. in 2017 enrolled 587 individuals in an intervention program consisting of dietary advice, psychological counseling, and increased physical activity. The authors reported a polygenetic effect and higher weight loss after 12 months of intervention for subjects carrying the C/G or G/G genotype of PPARG2 rs1801282 polymorphism and the T/C genotype of TIMP4 rs3755724 polymorphism [155].

1.6.2.3. APOA5 rs662799

As mentioned before, Aberle et al. examined the effect of a short-term fat restriction diet on BMI in regards to the APOA5 rs662799 genotype. After subjects received dietary advice in 60-minute sessions, it has been shown that BMI reduction was significantly higher in C-allele carriers compared to the wild-type allele indicating a beneficial effect of fat- and calorie restriction [55].

In 2013, a Chinese meta-analysis of 51868 participants from Asia, Europe, and other ethnic groups tried to evaluate the association between APOA5 rs662799 and fasting lipid levels as well as Metabolic Syndrome. They reported significantly higher levels of total cholesterol (TC), triglycerides (TG), and LDL-cholesterol (LDL-C) in C-allele carriers [156].

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1.7. Summary of genotypes, and correlating phenotypes

The following tables summarize the phenotypes associated with the various genotypes and the analyzed polymorphisms (Table 2 and 3).

Table 2: Wild-type and variant alleles

GENE RSID Wild-Type allele Variant allele

FABP2 rs1799883 Ala (G) Thr (A)

PPARG rs1801282 Pro (C) Ala (G)

ADRB2 rs1042714 Gln (C) Glu (G)

ADRB3 rs4994 Trp (T) Arg (C)

APOA2 rs5082 T C

APOA5 rs662799 T C

FTO rs9939609 T A

Table 3: Genotype/phenotype correlation

Gene RSID Carb

sensitivity

Fat sensitivity

Exercise responder

Calorie- reduction responder

FABP2 rs1799883 A/G

A/A

PPARG rs1801282 C/C C/G

G/G

C/G G/G

ADRB2 rs1042714 C/G

G/G C/G

G/G

ADRB3 rs4994 T/T

APOA2 rs5082 C/C

APOA5 rs662799 T/T A/G

G/G

FTO rs9939609 T/A

A/A

T/A

A/A

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2. Materials and methods 2.1. Participants

Three hundred three subjects were recruited through advertisements on the internet and magazines to participate in a genetically customized 3-week weight loss intervention program. Inclusion criteria were (1) Age 18 years or older; (2) BMI above 18 kg/m²; (3) stable medical condition. A total of 303 participants, 17%

(51/303) male and 83% (252/303) female aged 19 to 86 satisfied all inclusion criteria. Table 4 displays the demographic details of the participants at baseline.

Table 4: Baseline Characteristics

Demographics Total, mean (SD) Male, mean (SD) Female, mean (SD)

Sample size (n) 303 51 (16.8%) 252 (83.2%)

Age (years) 43.3 (12.94) 41.74 (11.93) 43.61 (13.12) Weight, (kg) 84.31 (18.18) 100.02 (17.27) 81.13 (16.65) BMI (kg/m²) 29.52 (5.5) 30.66 (4.21) 29.29 (5.7)

At the beginning of the intervention program, 3 BMI groups were categorized according to the report of the WHO expert committee [157].

BMI 18-25 kg/m²: n = 58 BMI 25-30 kg/m²: n = 129 BMI >30 kg/m²: n = 116

For statistical analysis, the subjects were further grouped into 4 different age groups (0-35, 36-50, 51-65, >65).

The primary outcome measure was defined as mean weight loss over a 3 weeks intervention period.

2.2. Approvals, registrations, and patient consents

The aim and design of the study were explained to subjects before obtaining the written and informed consent. In a statement by the Salzburg ethics committee, it was concluded that the study consisted merely of typical lifestyle interventions is was hence not subject to ethics committee obligations.

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2.3. Literature search

To identify studies that reported gene-diet and gene-environment interactions, a literature search of published studies in the English language was performed in PubMed (National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health). Search terms included, inter alia, polymorphism, gene-diet, gene-environment, weight loss, fat, low-fat, carbohydrates, macronutrient, exercise, physical activity, calorie restriction, and diet.

2.4. Study design

Participants were genotyped and each subject received a personalized booklet including the individual results and personalized intervention recommendations based on the genotyping results in written form. The protocol consisted of a 3-week intervention period in which the participants had to follow the recommended intervention and monitor their perceived adherence to the nutritional advice, exercise advice, and daily body weight by written records. The records were collected, digitized, and analyzed statistically.

2.5. Sample preparation and DNA extraction

Participants were instructed to neither eat nor drink for at least 30 minutes before the buccal swab samples were collected using Sarstedt forensic swabs 80.629 (Sarstedt AG & CO. KG, Nümbrecht, Germany). The extraction procedure was carried out using the fully automated BioRobot Universal system using the QIAamp 96 DNA Swab BioRobot Kit (QIAGEN, Hilden, Germany) following the manufacturer’s instructions. DNA was eluted in 150 µl TE buffer (10 mM Tris-HCl, 0.1 mM EDTA, pH 8.0) and stored at 4°C until use.

2.6. Genotyping

Allele-specific genotyping was performed using the TaqMan® technology (Fisher Scientific, Foster City, CA, USA). Reactions were performed in 384 well reaction plates using the original TaqMan™ Genotyping Master Mix (Fisher Scientific, Foster City, CA, USA) according to the manufacturer’s instructions. 0.5 µl TaqMan assay was mixed with 2.5 µl nuclease-free water, 5 µl TaqMan™ Genotyping Master Mix,

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and 2 µl purified genomic DNA. 384 well plates were sealed using optical disposable adhesive foil. Plates were vortexed briefly (1000 rpm, 200 seconds) and centrifuged at 2000 rpm for 5 minutes to collect the reaction mix in the bottom of each well.

Thermal cycling was performed on a ViiA ® 7 Real-Time PCR System (Fisher Scientific, Foster City, CA, USA). Reactions were performed using the following conditions:

Table 5: PCR conditions

Step Temperature Length Cycles

Polymerase activation 95°C 10 minutes

Denaturation 95°C 15 seconds

Annealing/extension 60°C 60 seconds 40

The assays used are listed in Table 5.

Table 5: Allele-specific genotyping assays

Genotype calling was carried out using the ViiA™ 7 Software (Fisher Scientific, Foster City, CA, USA).

Gene RSID Context Sequence ThermoFisher Assay ID

FABP2 rs1799883

AAAACAACTTCAATGTTTC GAAAAG[C/T]GCTTGATTC

TTTGACTGTGAATTTA

C____761961_10

PPARG rs1801282

AACTCTGGGAGATTCTCCT ATTGAC[C/G]CAGAAAGCG

ATTCCTTCACTGATAC

C___1129864_10

ADRB2 rs1042714

TGCGCCGGACCACGACGT CACGCAG[C/G]AAAGGGA CGAGGTGTGGGTGGTGG

C___2084765_20

ADRB3 rs4994

GTCATGGTCTGGAGTCTC GGAGTCC[A/G]GGCGATG GCCACGATGACCAGCAGG

C___2215549_20

APOA2 rs5082

TGAGATCTGAGGTCCTTG GACTTGA[A/G]TGCAACAG

GAAGCAGGATTCCAAGT

C__11453334_10

APOA5 rs662799

GAGCCCCAGGAACTGGAG CGAAAGT[A/G]AGATTTGC CCCATGAGGAAAAGCTG

C___2310403_10

FTO rs9939609

GGTTCCTTGCGACTGCTG TGAATTT[A/T]GTGATGCA CTTGGATAGTCTCTGTT

C__30090620_10

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2.7. Determining diet type

As 5 of the analyzed polymorphisms independently influence the fat sensitivity of an individual, subjects were classified in one of 6 categories. The lowest fat-score of 0

% was assigned to individuals with the non-fat sensitive allele in all 5 polymorphisms. The highest fat-score of 100 % was assigned to individuals with fat sensitivity genotypes in all 5 polymorphisms. Any intermediate genotype received a score proportional to the number of fat-sensitive genotypes at these 5 loci.

As there is only one polymorphism influencing carbohydrate sensitivity, rs1042714 C/G, and G/G genotypes received a carb-score of 100 % and carriers of the homozygous C/C genotype received a carb-score of 0 %.

Taking different energy contributions from common recommendations, appropriate macronutrient balances were assigned to each genetic nutritional type based on fat- and carb-score as seen in Table 6 [158-160]. The final program ensured, that the macronutrient balance of each subject followed his/her assigned targets as defined.

Table 6: Macronutrient balance assigned by fat- and carb-score

Fat-Score 0% 20% 40% 60% 80% 100% >0% 0%

Carb-Score 100% 100% 100% 100% 100% 100% 0% 0%

Carbohydrates 45% 47% 49% 51% 53% 55% 65% 51%

Fat 35% 33% 31% 29% 27% 25% 20% 29%

Protein 20% 20% 20% 20% 20% 20% 15% 20%

2.8. Determining strategy type

To determine the relative effectiveness of exercise for weight loss as a weight-loss strategy based on the genetic profile, 5 different polymorphisms were taken into account.

The lowest exercise-score of 0 % was given to subjects carrying none of the exercise responder genotypes and the highest score of 100 % was given to subjects carrying all 3 of the exercise responder genotypes.

Similarly, the lowest calorie-score of 0 % was given to subjects carrying none of the calorie reduction responder genotypes and the highest score of 100 % was given to subjects carrying all 3 of the calorie reduction responder genotypes.

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To determine where the main focus of the weight loss strategy should be (exercise or calorie reduction or both), both scores were combined to a final percentage using the formula:

Final exercise score (%) = (100 * exercise-score) / (calorie-score + exercise-score) Final calorie score (%) = (100 * calorie-score) / (calorie-score + exercise-score)

2.9. Determining daily nutritional calories and exercise requirements

To estimate the basic metabolic rate at rest (BMR), the Mifflin St Jeor equation was used as follows [161]:

Males:

BMR (kcal / day) = 10 * weight (kg) + 6.25 * height (cm) – 5 * age (y) + 5 (kcal / day) Females:

BMR (kcal / day) = 10 * weight (kg) + 6.25 * height (cm) – 5 * age (y) - 161 (kcal / day) The total target daily calorie deficit for every subject was the smaller number of either 45 % of the basic metabolic rate at rest or 850 kcal. Calorie deficit was however split up between calorie reduction through diet and increased expenditure through exercise. The minimum calorie reduction (below the basic metabolic rate at rest) for every subject was 25 % of his/her BMR. The minimum expenditure of calories through additional exercise was 50 kcal. The remaining calories were then shared out among the two strategies according to the final exercise and final calorie scores.

The final daily calories to be ingested were then determined by the formula:

Daily kcal = BMR – (25 % of BMR) – (surplus calorie deficit * final calorie score) Daily exercise kcal = 50 kcal + (surplus calorie deficit * final exercise-score)

2.10. Lifestyle intervention

Subjects were given several different options to change their eating and exercise behavior according to the calculated recommendations. For one, they were given 8 complete daily menus that contained exactly the correct amount of daily calories according to the calculation described before and the correct macronutrient balance

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for their diet type which was assigned according to table 6. In addition, they were given a list of approximately 1900 different foods with known macronutrient distribution. The individual diet type was then applied to every single food from the list and the result were displayed as a bar chart showing how well the macronutrient balance of the food matches the assigned distribution according to the die type. To be able to obtain the recommended kcal per day, the list also contained a kcal per serving value.

Participants also had access to menu planning software that allowed them to create individual menus based on their diet type and kcal requirements. Participants were free to choose which diet intervention they preferred to use.

All the food item data like ingredients, macronutrient distribution, portion size, etc. is based on the "Bundeslebensmittelschlüssel (BLS)” (version 3) which is published by the federal ministry of Nutrition in Germany. The database contains research results of German Federal Research Centers and universities. In addition, analytical values compiled from nutritional science literature, international nutrient tables, and food-producing firms were used [162].

To reach their required level of calorie expenditure, participants were given a table of potential exercises together with the time they needed to perform these exercises to expend the required calories. Participants were free to choose which exercises to perform and on how many days per week to perform them, but they were required to reach the required calorie expenditure per day on average.

2.11. Statistical analysis

Values are expressed as the mean ± standard deviation. The 2-sample T-Test was used for comparison. Significance was determined as being a p-value smaller or equal to 0.05.

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3. Results

3.1. Weight loss through the intervention program

During the intervention program, 98.3% (n=289) subjects lost weight while 1.7%

(n=5) did not lose weight or gained weight. The mean weight loss over 3 weeks was 3.60 kg (mean -3.60±1.90 kg s.d.) ranging from an actual weight gain of 1.1 kg to a weight loss of 13 kg in 3 weeks. The mean decrease in BMI over 3 weeks was 1.28 kg/m² (mean -1.28±0.66 kg/m² s.d.) ranging from -4.61 to +0.41.

3.2. Comparative weight loss

3.2.1. Weight loss efficiency by gender

The mean weight loss over 3 weeks was 3.46 kg (mean -3.46±1.82 kg s.d.) in female subjects and 4.56 kg (mean -4.56±2.15 kg s.d.) in male subjects (Figure 7).

Therefore, on average male subjects lost significantly more weight compared to female subjects. (mean -4.56±2.15 kg s.d. versus -3.46±1.82 kg s.d., p=0.00117).

Therefore, the decreased in body weight was 1.3-fold higher in men.

Figure 7: Weight loss in 3 weeks based on gender

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

female male

weight loss (kg)

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3.2.2. Weight loss efficiency by BMI

Subjects with different entry BMI responded differently to the intervention program.

The mean weight loss over 3 weeks in normal-weight subjects with a BMI between 19 and 24, in overweight subjects with a BMI between 25 and 29, in obese subjects with a BMI above 30 were 2.86 kg (mean -2.86±1.43 kg s.d.), 3.45 kg (mean - 3.45±1.63 kg s.d.) and 4.26 kg (mean -4.26±2.22 kg s.d.), respectively (Figure 8).

Compared to normal-weight subjects, overweight subjects had a 1.2-fold (mean - 2.86±1.43 kg s.d. versus -3.45±1.63 kg s.d., p=0.0153), obese subjects a 1.5-fold (mean -2.86±1.43 kg s.d. versus -4.26±2.22 kg s.d., p<0,001) increased weight loss.

Figure 8: Weight loss in 3 weeks based on BMI

3.2.3. Weight loss efficiency by age

All age groups responded similarly to the intervention program (Figure 9). No significant difference could be observed in the 4 groups (mean -3.53±2.05 kg s.d.

versus -3.88±2.08 kg s.d. versus -3.51±1.64 kg s.d. versus -3.71±1.18 kg s.d., p>0.05).

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

normal BMI 18-25

overweight BMI 25-30

obese BMI >30

weight loss (kg)

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Figure 9: Weight loss in 3 weeks based on age group

3.3. Compliance

The mean compliance over the 3 weeks intervention program was calculated to be 83% (mean 83.37±13.79 % s.d.) ranging from 32 % to 100%. There was no significant gender-specific difference between women and men (mean 83.77±13.18

% s.d. versus 81.42±16.32 % s.d., p=0.34).

3.4. Success rates

Overall, 98.3 % (n = 298) of subjects lost weight by following the 3 weeks intervention program.

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

age 18-35 age 36-50 age 51-65 age > 65

weight loss (kg)

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4. Discussion

The results of this study concluded that the use of the described personalized intervention program represents an effective tool for short-term weight loss in all groups studied. At an average weight loss of 3.6 kg over 3 weeks among all 303 subjects studied, the results are above average compared to other behavioral weight loss studies published to date [163, 164].

For example, Finker et al. reviewed the results of 35 weight loss studies focusing on energy restriction and reported a weight loss range between 0.002 - 1.13 kg per week with 1.13 kg per week for the most effective approaches [165].

McKnight et al. recently published results of the “Fit for Life” weight loss and diabetes prevention program consisting of seminars on nutrition and lifestyle and weekly meetings in person. Over 12 weeks the average weight loss was 2.7 kg or 1.25 points of BMI among the 1200 people studied. By comparison, the intervention program described here led to a mean weight loss of 3.60 kg in 3 weeks, supporting the hypothesis of the effectiveness of the personalized approach [166].

Although the study results appear conclusive, the study design also bears some limitations, that need to be addressed. The intervention period of 3 weeks is too short to predict long long-term effectiveness. Studies have already demonstrated that the intervention length is usually negatively related to the weight loss effectiveness [165].

As the intervention program consists of well-known weight loss strategies namely calorie restriction and increased physical activity, which have further been adapted by reported gene-environment interactions, it can only be assumed but not confirmed that the effectiveness is a result of this adaptation.

Besides the influence of individual genetic factors, other aspects of the intervention may also contribute to greater weight loss when compared to standardized nutritional and lifestyle counseling. For one, the greater motivation to follow personalized recommendations is likely to play a significant role. The aspect of the requirement to take daily measurements and records is likely to also increase compliance compared to a purely informational counseling approach.

The application of personal genetic information for improved weight loss