Authors:

Ranjan Sinha, Dashyanng Kachru, Roshni Ray Ricchetti, Simitha SinghRambiritch, Karthik Marimuthu Muthukumar, Vidhya Singaravel, Carmel Irudayanathan, Chandana Reddy-Sinha, Imran Junaid, Garima Sharma, Catherine Airey, Patricia Alice Francis-Lyon

Submitted to: Journal of Medical Internet Research on: November

Background:

In this age of global COVID-19 pandemic, the urgency of addressing an epidemic of obesity and associated inflammatory illnesses has come to the fore. Studies have demonstrated that interactions between single nucleotide polymorphisms (SNPs) and lifestyle interventions like food and exercise may vary metabolic outcomes, contributing to obesity and therapeutic response. However, there is a paucity of research relating outcomes from digital therapeutics to inclusion of genetic data in care interventions.

Objective:

This study aims to describe and model weight loss of subjects enrolled in a precision digital weight loss program informed by machine learning analysis of subject data, including genomic. It was hypothesized that weight loss models would exhibit better fit when incorporating genomic data than utilizing demographic and engagement variables alone.

Methods:

A cohort of 393 participants enrolled in Digbi Health’s personalized digital care program for 120 days was analyzed retrospectively. Care protocol included the use of subject genomic and gut microbiome data informing precision coaching by mobile app and personal coach. Two linear regression models of weight loss in this cohort (pounds lost, percentage lost) as a function of demographic and behavioral engagement variables were fit. Genomic-enhanced models were built by adding 197 SNPs from subject genomic data as predictors, then refitting, employing Lasso regression on SNPs for variable selection. Success/failure logistic regression models were also fit, with and without genomic data.

Results:

72% of subjects in this cohort lost weight, while 17% maintained stable weight. 142 subjects lost 5% within 120 days. Models describe the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. The addition of genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13 respectively. The logistic model improved pseudo R2 from 0.193 to 0.285. Gender, engagement and specific SNPs were significantly associated with weight loss.

SNPs within genes involved in metabolic pathways that process food and regulate storage of fat were associated with weight loss in this cohort. This included rs17300539_G (insulin resistance, monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, cholesterol metabolism), and rs4074995_A (calcium-potassium transport, serum calcium levels). Models described greater average weight loss for subjects having more of these risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

Conclusions:

Adding genomic information in modeling outcomes of a digital precision weight loss program greatly enhanced model accuracy. Interpretable weight loss models pointed to efficacy of coaching informed by subjects’ genomic risk, accompanied by active engagement of subjects in their own success. While large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss utilizing genetic risk, with digitally delivered recommendations alongside health-coaching to improve intervention efficacy.

INTRODUCTION

The global death toll of COVID-19 has eclipsed 1 million cases 1 . Obesity, following age, has emerged as the most critical risk factor in morbidity, hospitalizations, and complications 2 . The prevalence of obesity in the United States and in other Western countries has seen a sharp increase in the last two decades. Since the early 1960s when a little over 10% of Americans were obese, that proportion has grown to 42.4% of adults [3] . Moreover, the prevalence of obesity is higher in minority communities: 49.6% of non-Hispanic Blacks and 44.8% of Hispanic Americans are obese, compared with 42.2% of non-Hispanic Whites. These same minority communities are experiencing disproportionate COVID-19 driven mortality, likely linked, at least in part, to the heightened prevalence of obesity [4] . Although a precise cause of obesity has yet to be discovered, several factors have been linked to its development [5] . In particular, biology interacts with behavior and demographics (such as socioeconomic status or ethnic/cultural cuisine) to influence obesity risk [6] . Obesityassociated biological factors include, but are far from limited to, genetics and epigenetics, microbiome composition, age, circadian rhythm disruption, pharmaceutical interactions, and comorbidities and their management [6,7]

The rapid increase in obesity prevalence has coincided with sociological factors such as generally reduced physical activity alongside a rise in the consumption of highly processed, high-calorie but nutrient poor foodstuffs. However, these obesogenic conditions have not uniformly affected the population. Instead, a notable proportion of the population is still able to remain at a healthy weight, indicating that the heterogeneous response to obesogenic conditions may result, in part, from individual innate protection from these conditions, possibly conferred by genetic makeup [8].

Most current clinical interventions for obesity management focus on lifestyle and dietary adaptation with varying levels of professional guidance and involvement, short or long-term pharmaceutical therapies, and also bariatric surgery [9] . Individual responses to these therapeutic interventions are confoundingly (for clinicians and subjects alike) heterogeneous for multifactorial reasons [10], making imperative the need for personalized, precision medicine courses of treatment. Most Americans (63%) have made serious efforts towards weight loss over the course of their lives, and almost a third are in the process of trying to lose weight [11]. In 2014, commercial weight loss services were a $2.5 billion market consisting primarily of the following market shares - Weight Watchers (45%), NutriSystem (14%), and Jenny Craig (13%) - but the long-term effectiveness of various commercial “calorie-restriction” based weight loss programs is unclear [12,13]. (Table 1)

Personalizing Weight Loss Interventions

Recent research has elucidated mechanisms of food-derived biomarkers, allowing for stratification based on metabolic profiles (i.e.: the capacity to uniquely metabolize or respond to given food products). This permits targeting of personalized nutrition to groups that are better characterized [8,15,16] . For example, given that low-grade inflammation has been implicated in insulin resistance, mediating inflammation via targeted dietary approaches is a precision nutrition intervention [17,18] .

Advances have already been made in the early intervention and risk assessment of obese subjects by designing therapies based on unique genetic predisposition and risk. Environmental interventions such as diet and exercise can trigger epigenetic changes, altering gene expression in metabolic pathways. Recent research indicates that physical activity and high fat diets may alter DNA ‐ methylation patterns in skeletal muscle and adipose tissue [19,20,21], influencing weight management [8] . Eventually, researchers hope to elucidate the genetic patterns that influence individual obesity and concomitant illness susceptibility, risk of progression, and response to therapy, to afford subjects optimal treatment [22] .

EPIGENETICS AND ITS ROLE IN OBESITY

Even as science illuminates many genetic risk factors of complex metabolic diseases such as obesity and Type-2 diabetes [23,24,25,26,27], these genetic variants account for only a fraction of BMI variation [25] . The “missing” heritability of obesity might be at least partially explained by interactions between genetics and environmental factors [28.] In particular, specific gene variants may influence sensitivity to certain environmental factors so that exposure to these factors in susceptible individuals can contribute to disease. Because obese individuals are characterized by considerable heterogeneity within the spectrum of clinical obesity, supporting gene-diet interaction and precision nutrition in different subtypes of obesity is imperative [29,30,31,32,33].

Bariatric surgery is a weight loss option for subjects with severe and complex obesity for whom dietary interventions or digital therapeutics have been less than successful [34, 35, 36]. Genetics may be a significant predictor of weight loss following Roux-en-Y gastric bypass (RYGB) surgery [37], but few genetic variants have been characterized to date [38, 39].

The Role Of Diet In Obesity

While obesity can, in some cases, be linked to excessive appetite and food consumption, these behaviors may have a genetic component, and even food preferences themselves may have a genetic basis [40, 41]. The FTO locus rs9939609, for example, has been associated with reduced satiety [42] , increased caloric and fat intake [43, 44], and a propensity to consume calorie dense foods ‐ [45, 43.] TAS2R38 genotype differentiates potential super-, medium-, and non-tasters of bitter tasting thiourea ‐ compounds. These different bitter-tasting profiles appear predictive of differential dietary preferences, and in particular, non-tasters were observed to have higher BMIs [46]. Considered together and alongside other evidence, this research implies that body weight and BMI may be affected by genetic variations regarding food preferences, tendencies, and eating behaviors. Elucidating how food intake and body metrics are mediated by genetics is made challenging by the difficulty of reproducing results across varying populations as well as the complexity of identifying causal interactions [47, 48, 49.] Research using randomized controlled trials and large sample-sized biobanks with the electronic health records will better characterize how diet and genetics interact to mediate health outcomes [50, 51].

The Role of Physical Activity in Obesity

Exercise that can stave off weight gain and promote weight maintenance has been well established through research [52, 53, 54, 55]. Evidence suggests that body weight, as well as waist: hip ratio and BMI, share a significant association with adherence to an aerobic exercise intervention [56]. Interestingly, propensity for exercise appears to be heritable, at least in part, with studies estimating this heritability to range from 9% to up to almost 80% [57]. MC4R genes appear to be associated with physical inactivity [58], and yet other genes may share associations with adherence and tolerance to physical activity regimens [56 ].

Gut Microbiome and its role in obesity

The human gastrointestinal tract hosts millions of commensal microorganisms comprising the gut microbiome, which acts as a virtual endocrine organ regulating nutrient production and metabolism, satiety, and even energy homeostasis [59, 8] . These microbes are intrinsically linked to host health, as they are implicated in nutrient processing and metabolism, pathogen displacement, vitamin synthesis, and body weight regulation [60]. Researchers and clinicians have been studying alterations of the gut microbiome in individuals, as perturbations in gut biome appear to underlie the pathophysiology of obesity and associated comorbidities such as Type-2 diabetes and metabolic syndrome [61, 62] . Microbiome profiling for nutritional intervention is gaining prominence as a key feature of precision nutrition.

Research on the impact of specific dietary factors on microbiome Research on the impact of specific dietary factors on microbiome diversity can guide interventions focused on optimizing gut microbial composition [63]. For example, variation in the LCT region, associated with response to dairy intake, appears to be associated with abundance of the gut microbiome Bifidobacterium [64]. In particular, variations in LCT were found to be predictive of obesity based modulating dairy lactose and milk intake [65], indicating that shifts in gut microbiota across LCT genotypes could be tied to caloric extraction of ingested food [65]. Like specific genes, specific bacterial species are also directly implicated in the etiology of obesity. Methanobrevibacter smithii, for example, can itself metabolize dietary substrates or metabolic byproducts of other bacteria, thereby promoting weight gain [66].

Further evidence ties both an individual’s genetics and diet to microbiome composition because lower microbial diversity appears to be associated with excess weight gain [67]. Even in early childhood, disruptions in the gut biome can have long lasting influence on adult body weight ‐ [68] . Moreover, nutritional interventions such as administering prebiotics and probiotics to manipulate gut microbiota that promote or are refractory to weight loss show potential as obesity interventions but require further study [69]. Weight loss, whether mediated by diet or via bariatric surgery, can alter the gut biome in ways that affect efficacy of various weight loss strategies [70, 71] . An interesting feature of bariatric surgery is that it appears to induce obesity associated gut microbiota to shift toward lean ‐ microbiome phenotypes [72].

Behavioral and Digital Interventions in Obesity

As the obesity epidemic continues to proliferate, new digital programs available on websites and/or as smartphone applications are being leveraged to promote weight loss [73] . Digital programs are agile in that they can easily be modified to reflect the latest research and best practices in a rapidly changing field; they are more cost-effective than traditional, in-person programs, and are also more easily scalable, increasing their reach [74]. Resources can include activity trackers, videos, logs, device communication, and third-party application compatibility [73] . Additionally, research indicates [75, 76, 77, 78] that remotely administered programs can result in significant weight loss.

Digital programs have the availability to provide personalization to address the plethora of needs presented by subjects [79]. Individuals partaking in such programs are still able to leverage interpersonal relationships. Digital health coaching, for example, allows subjects to discuss their weight-loss journey via any number of communication platforms [79]. According to research, both in-person as well as telehealth coaching relationships are effective in motivating overweight individuals to work towards weight loss [80]. In a recent study of more than 600 participants in a smartphone-based weight loss program with a coaching component, subjects lost, on average, more than 7% of their body weight, successfully passing the 5% weight loss marker that many in-person programs set [79] .

The multifactorial nature of obesity is reflected in the myriad heritable, behavioral, and environmental factors that can lead to obesity risk [47]. The most successful interventions are likely to be those that leverage current findings across the full spectrum of obesity related risk factors: dietary interventions accounting for both genetic markers of food sensitivities, metabolic predispositions, and behavioral risk as well as those geared towards optimizing gut microbial diversity and composition; physical activity measures taken in consideration of genetic risk profiles; and behavioral modifications undertaken via digital care [59]. The precision nutrition program offered by Digbi Health aims to account for these various factors in delivering a personalized course of obesity intervention [59] .

METHODS:

Recruitment:

For this study, we identified all Digbi Health participants who enrolled between June 2019 to June 2020, had been in the program for at least 120 days, and had been genotyped by Digbi. DNA and gut microbiome kits. These kits had been shipped to 443 participants, out of whom 393 mailed back their samples for processing, thereby yielding a cohort size of 393. From among these participants, 315 individuals self-identified as female, 77 individuals as male, and 1 individual declined to state. All participants self-enrolled for the Digbi Health program via a large California-based insurance payor wellness program. The qualifying criteria to join the program were BMI>25 with a comorbidity (e.g., pre-diabetes, diabetes, cardiovascular disease, hypertension, etc.) or BMI>30 regardless of comorbidities. The dataset included data from each participant’s first 120 days in the program. This Digbi Health anonymized and retrospective research study was exempted from full review by the Ethical and Independent Review Services West Coast Board, Corte Madera, California, reference 20149-01. All participants agreed to the Digbi Health terms and conditions and privacy policy when enrolling in the program.

Intervention:

Digbi Health is a next-generation, prescription-grade, digital therapeutic platform that uses artificial intelligence (AI) to analyze genetics, gut bacteria, lifestyle habits, socioeconomic and behavioral risk patterns to create evidence-based personalized nutrition, fitness, sleep and stress management program proven to reduce weight, reverse weight-related inflammatory gut, musculoskeletal, cardiovascular and insulin-related illnesses. The Digital precision care interventions are delivered via online or mobile app in order to expand the accessibility, safety and effectiveness of health care. Digbi Health’s DNA and gut microbiome-based health program is geared primarily toward individuals who are overweight or obese, with or without a comorbidity, and functions as a weight loss management tool. The program is currently covered by a large California based health insurance payor for their qualifying members through its obesity management wellness platform.

Upon enrolling in the Digbi Health program, participants were provided with online login credentials and were mailed a Bluetooth compatible digital weighing scale and saliva and stool biosampling kits. App usage consisted of daily tracking of weight (via the Bluetooth scale), dietary intake (uploading photographs of all food items consumed), and tracking wellness associated metrics (sleep quality and quantity, exercise type and duration, stress and meditation, energy levels, cravings, and recommended foods consumed/avoided).

Sample collection:

Individual’s DNA was self-collected via provided buccal swab [Mawi Technologies iSwab DNA collection kit, Model no. ISWAB-DNA-1200]. Saliva DNA extraction, purification, and genotyping using Affymetrix’s Direct to Consumer Array version 2.0 (“DTC”) on the Affymetrix GeneTitan is all by Akesogen Laboratories in Atlanta, GA. The results presented in the genetics section of the report were determined by the number of markers and risk genotypes present in the genomic raw data, and the Digbi Health reports were loaded into the app. Individuals’ gut microbiome was selfcollected via provided fecal swab [Mawi Technologies iSWAB Microbiome collection kit, Model no. ISWAB-MBF-1200]. Sample processing and 16S rRNA-targeted next generation sequencing was performed at Akesogen Laboratories in Atlanta, GA. Sequence data was processed using an opensource microbiome analysis pipeline and publicly available database capable of detecting various microbes in the gut with high specificity. Based on analysis of these genetic and gut microbiome risk profiles, the Digbi Health Total Wellness Report was generated, and the results were systematically reviewed with the participants 1:1 by the health coach over a 4-month period at regular, predetermined, weekly and bi-weekly intervals.

Genetic report:

Our genetic report consists of two sections, gene nutrition and gene fitness. The gene nutrition report analyzes participants’ genotypes that have been shown to influence nutritional traits such as diet and weight management, micronutrient requirements, food intolerances and sensitivities and several other attributes relevant to nutritional well-being. For each of these traits, participants are assigned a “High,” “Medium,” or “Low” risk score based on the number of risk alleles detected, and health coaches guide interventions based on these potential risks (for example, suggesting someone high risk for gluten intolerance eliminate dietary gluten or someone with medium risk reduce consumption). The degree of risk associated with any specific SNP was determined by the presence of 0, 1, or 2 risk alleles. There are also often several individual SNPs that may contribute to a single trait or function--and some of these SNPs might increase risk for a trait, while others may decrease it. In our gene reports, we take into consideration as many SNPs as possible when determining risk of a particular trait.

The gene fitness report analyzes SNPs studied in conjunction with fitness regimes, exercise motivation and ability to develop various types of muscle fibers. This section of the report also analyzes potential inflammatory response to exercise, including endurance, strength, and flexibility training. As in the gene nutrition section, each trait is assigned a “High,” “Medium,” or “Low” risk score based on SNP data, and health coaches guide participants through recommendations for healthy exercise.

Gut microbiome report:

In addition to using genetic risk profiles to guide the course of subjects’ precision care, the Digbi Health program also analyzes gut microbiome profiles (collected from stool swab sampling) to guide the course of care. The Digbi Health Total Wellness Report (gut microbiome section) looks at overall microbiome diversity as an indicator of gut health [81], and also looks at the abundance of various probiotic and anti-inflammatory microbes. Certain probiotic bacteria are associated with healthy digestion, metabolism, and immunity. A high abundance of associated bacteria such as Akkermansia, Bacteroides, Bifidobacterium, Christensenellaceae amongst others helps to combat weight gain, inflammation, regulates weight, appetite, fat accumulation [82, 83].

In the report, an individual’s obesity risk profile is represented by an obesity risk score for each individual. The Digbi Health Total Wellness Report includes information on the presence, absence, and relative proportions of gut microbes associated with health and various conditions. These microbes are identified in the report as either as negatively or positively associated with health outcomes by taking into account a large number of peer-reviewed studies. Positively associated microbes found to be underrepresented in a subjects’ sample are considered “high risk,” and dietary interventions are recommended to improve microbial diversity and probiotic representation; if those same microbes are found to be well-represented, then the status is “low risk”. Similarly, negatively associated microbes found to be underrepresented in a subject are “low risk” whereas if overrepresented, they are then “high risk” and dietary interventions are recommended to decrease levels of those organisms.

Lifestyle:

The Digbi Health program is a 120-day program which uses body metrics, gut microbiome and genetic profiles, and personalized health-coaching to manage weight loss. Participants use the Digbi Health app to track 10 key lifestyle and wellness markers (weight, sleep, hunger, cravings, stress, meditation, superfoods, morning energy, foods to avoid, and exercise) on a daily basis, take photos of the food they consume, and are assigned a health coach who works personally with the participant through 12 guided sessions at various intervals to interpret the personalized wellness reports generated from sampling participants’ DNA and gut microbiota. The reports also provide a breakdown of obesity risk based on individuals’ genetic and gut microbiome profiles. The program is geared toward participants losing at least 5% of their baseline body weight by Day 90 of the 120-day program. To achieve this goal, the program seeks to nudge participants towards making incremental lifestyle changes focused around reducing sugar consumption, timing meals to optimize insulin sensitivity, reducing systemic inflammation by identifying possibly inflammatory and antiinflammatory nutrients via genetic testing, establishing a base level of physical activity and doing so in a manner that reduces inflammation, optimizing gut health based on microbiome testing, and most importantly, making these behavioral modifications supported by health coaching and the app so that the changes are sustainable long-term. The genetic profile of Digbi Health users identifies several nutrients/foods that have associations with obesity, comorbidity, or inflammatory risk (e.g., gluten sensitivity, lactose tolerance, caffeine sensitivity, fatty acid metabolism, blood pressure response to salt or riboflavin intake, reduced insulin resistance with exercise, etc.).

Statistical analysis:

The data from our cohort of 393 participants over their first 120 days in the Digbi Health personalized digital weight loss program was analyzed retrospectively. Interpretable regression models (linear, logistic) were built and visualizations generated using R. Two linear regression models of weight loss in this cohort (pounds lost, percentage lost) as a function of demographic and behavioral engagement variables were fit. Genomic-enhanced models were built by refitting demographic/engagement models with added genomic variables. These 197 additional predictors were from Digbi-curated panels of SNPs associated with obesity, fitness, nutrient metabolism, and inflammatory markers. Each SNP value was encoded for each participant as their number of risk alleles (0, 1, or 2). Lasso regression was employed for SNP variable selection. One subject did not identify gender, so was excluded from all models, resulting in 392 observations included in each of the 4 linear regression models.

Success/failure logistic regression models were also fit, with and without genomic data. Success was defined as 5% or greater weight loss, failure as weight gain or negligible change (less than 2 lbs weight change). Removed from this model were observations of subjects who were only partially successful, having lost weight but not reached the milestone of 5% weight loss. This resulted in inclusion of 251 of the cohort subjects in the logistic models, both genomic-enhanced and demographic/engagement only.

Insignificant variables were removed from each model, resulting in 6 final interpretable models, half containing demographic and behavioral engagement variables only, while the remaining three were genomic-enhanced.

Demographic variables included gender, age, and baseline BMI. Behavioral engagement variables included number of coaching sessions completed, number of weight entries, and number of food posts. Since the number of food posts and number of weight entries were highly correlated (Pearson correlation =.98), each regression model could include one, but not both. In order to incorporate both variables in modeling, number of food posts was retained as a predictor for the linear models, while number of weight entries was kept as a predictor for the logistic models.

For genomic-enhanced models, SNP variables were imputed to most frequent value (mode). SNPs with greater than 10% missing information, high (>=80%) Pearson correlation with another variable, or zero variance were removed, resulting in 124 SNPs remaining for linear and 122 SNPs remaining for logistic model variable selection by lasso. The SNPs with non-zero coefficients after lasso regularization for that particular outcome variable (pounds lost, percentage weight loss, successful weight loss) then served as predictors, along with the 3 above demographic variables and 2 engagement variables (number of coaching sessions completed along with either number of weight entries or number of food posts).

RESULTS

393 participants were included for this study aiming to describe and model the weight loss of participants enrolled in the Digbi Health program for 120 days. 315 were female and 77 male, and one participant declined to state (Figure 1 A). 283 (72%) of them lost weight compared with 42 (11%) who gained weight, while for 68 (17%) participants weight remained within normal fluctuations (Figure 1B). 142 participants lost ≥ 5% of their baseline body weight within the first 120 days. Weight loss was defined as according to Table 2. See Table S1 and Figures S1A and S1B in the Appendix for full distribution of baseline and end points by Obesity class.

As hypothesized, the addition of genomic predictors substantially improved the fit of weight loss models. For linear regression weight loss models (pounds lost and percent) the addition of genomic data improved mean squared error (MSE) from 70 to 60 and 16 to 13 respectively, while the logistic success/fail model improved pseudo R2 from 0.193 to 0.285.

Figure 2 depicts weight loss distribution by gender. The difference in percent weight loss for males and females was found to be statistically significant (Table S3). Males lost, on average, about 4% more weight than females. Gender was significant to all linear regression models (Tables S3-S6), but not to the logistic success/fail model (Table S7 and S8), as both women and men succeeded in 5% weight loss within 120 days.

Unsurprisingly, baseline BMI was significant to both pounds lost linear models (Table S4, S5). but not to any other model. Subject age was not significant to any of the models. Increased completion of coaching sessions was significantly associated with increased weight loss in all regression models (Tables S3-S8). The two highly correlated engagement variables, number of weight entries and number of food posts, were significant to all models in which they were considered (as described above, weight entries were in logistic models, while food posts were in linear models). (See Tables S3-S8.)

In addition to the demographic/engagement variables described above, the genomic-enhanced models identified 6 SNPs were significant to the logistic model (Table S8), 11 SNPs to the linear weight loss percentage model (Table S6), and 10 SNPs to the linear pounds lost model (Table S4). Of the SNPs found significant to the linear models, 8 SNPs were common in both genomic-enhanced linear models (Tables S4, S6). Three notable SNPs that were found to be strongly associated with change in body weight: rs17300539_G, rs2016520_C, and rs4074995_A were further explored.

Rs17300539 is located in the promoter region of the ADIPOZ gene encoding adiponectin [84]. The high risk allele has been associated with insulin resistance whereas the low risk allele may be associated with protection from weight regain post weight loss intervention [85]. Moreover, the high risk allele has been associated with higher weight, BMI, and waist and hip circumferences. However, genotype‐ related differences in BMI became undetectable in interaction with a diet that is low, below the median (i.e. - less than 13% of energy intake) in mono-unsaturated fats (MUFAs) [86]. This led researchers to pose the possibility of moderating high risk with dietary intervention of reducing MUFAs for those with the risk allele(s). Of 392 subjects, 334 were homozygous for the high risk allele (G), 54 were heterozygous for the risk allele, and 4 were homozygous for the low risk allele (Figure 3).

The regression models are interpretable models describing weight loss in this cohort, and may be visualized to gain insight on variables found to be significant. Figures 4 and S3 depict relationships of engagement variables and rs17300539 to weight loss in the genomic-enhanced weight loss percent model. These plots reveal the least squares fit of weight loss percent for females (panel A) and males (panel B) as the two visualized predictors are varied while holding all other model variables constant. (SNPs were held constant at their most frequent (mode) values, while engagement variables were held constant at their gender-specific means, except for coaching sessions completed, which was fixed at its gender-specific median). The visualizations permit us to see model relationships of particular predictors as they impact the outcome variable. For example, the weight loss (%) model fit to this cohort describes the average male having 2 risk alleles who posts zero food photos as losing 3.5% of body weight, while the average male with the same genomic risk who posts 975 food photos loses 8.75% of body weight (Figure 4). The coefficients of the fitted predictors reveal that in this model, for every 100 additional food posts subjects lose, on average an additional 0.60% weight while holding all other model predictors constant (Table S6).

In this model, for each additional risk allele (G) of rs17300539 subjects lose, on average an additional 1.09% weight while holding all other model predictors constant (Table S6). We see that as the number of risk alleles of rs17300539 increases from 0 to 1 to 2, so does percentage weight loss as a function of greater behavioral engagement measured both in number of completed coaching sessions (Figure S3) and number of food photos posted (Figure 4). In essence, subjects in this cohort who were at higher risk lost a greater percentage of weight compared to their lower risk counterparts. Moreover, the percentage of weight loss increased in proportion to greater behavioral engagement.

Rs2016520 is a variant of the PPARD gene, responsible for encoding a protein implicated in fat metabolism and baseline cholesterol levels87. This SNP has been shown, in women, to be associated with muscle development and blood cholesterol reduction after a 12-week exercise regime88. High risk alleles predisposed women to less weight loss upon exercise88. Of 392 subjects, 20 were homozygous for the high risk allele (C) of SNP rs2016520, 127 were heterozygous for the risk allele, 244 were homozygous for the low risk allele, and 1 had no available data (Figure 5).

Similarly to Figures 4 and S3, Figures 6, 7 and S4 reveal the least squares fit of weight loss in pounds for females (panel A) and males (panel B) as the two visualized predictors are varied while holding all other model variables constant. The weight loss pounds model fit to this cohort describes the average female having 2 risk alleles who posts 975 food photos loses 21 lbs, while the average female with the same genomic risk who posts 0 food photos loses only 8 lbs (Figure S4 A). Similarly for males, we see 25 lbs lost on average with 975 food posts, only 13 lbs with zero food posts. (Figure S4 B). Figures 6, 7 and S4 show that as the number of risk alleles increases from 0 to 2, so does pounds of weight loss with respect to engagement, when engagement is measured either as number of coaching sessions or as the number of food photos posted in the Digbi Health app. Moreover, as seen in Figure 7, a higher baseline BMI is also associated with more pounds lost. Again, males lost more weight than females for each risk group of this SNP.

The rs4074995 SNP has been implicated in calcium-potassium regulation89; it is located within the RGS14 gene and is associated with both serum phosphate90 and serum calcium91 levels. In particular, each copy of the A allele is correlated with an increase in serum calcium concentrations91. For the rs4074995_A SNP, of the 251 subjects, 25 were homozygous for the high risk allele (A), 67 were heterozygous for the risk allele, 159 were homozygous for the low risk allele. The sample size of 251 was smaller than for the above linear models because rs4074995 was chosen to be highlighted as a predictor of the genomic-enhanced logistic regression (success vs failure) model, which was fit to a subset of the cohort that experienced success, defined as 5% or greater weight loss, or failure, defined as weight gain or negligible change (less than 2 lbs weight change).

As seen in Figures 9 and S5, as the risk alleles of SNP rs4074995 increase from 0 to 1 to 2, there is an increase in the success score, which is the likelihood of this model assigning the particular observation to the success class. Similarly to the other models, we see that increased number of coaching sessions completed are associated with a sharp rise in success score. Notably, however, upon highest engagement, the effect of risk status diminishes (those with 0, 1, or 2 risk alleles were about equally likely to achieve weight loss upon highest engagement).

DISCUSSION

A group of 393 subjects underwent lifestyle changes over 120 days through the Digbi Health program - a precision digital care program applying machine learning analytics to genetic and microbiome profiles, demographics and self-reported lifestyle habits; delivering care through app and weekly health coaching check-ins. Over the duration of the program, patients’ genomic and gut microbiome data pertinent to weight loss (from Digbi-curated panels) were provided and translated into lifestyle recommendations, recipes, etc.. Of the 393 participants, 72% (283) lost weight, whereas 11% (42) gained 2 or more pounds. Of those who lost weight, 50% (142) were able to lose 5% or more over 120 days.

Interpretable linear regression models of weight loss in this cohort (pounds lost, percentage lost) as a function of demographic and behavioral engagement variables were fit. Genomic-enhanced models were also built by adding subject genomic data as predictors. Interpretable success/failure logistic regression models were also fit, with and without genomic data. The addition of genomic predictors substantially improved the fit of all models.

The fitted models were examined for insights on the weight loss journey of this cohort. Gender, engagement, and specific SNP risk alleles were significantly associated with weight loss success. The models described greater average weight loss in our cohort for subjects having more of certain risk alleles. Here we consider how success in weight loss may be obtained in the face of greater genetic risk factors. Notably, Digbi Health precision coaching for lifestyle modification is personalized to these genetic risks, and patients report realizing success that was previously unattainable after being empowered by knowledge of their genetic and microbiome risk factors, accompanied by advice on lifestyle modifications to address these risks.

We profiled three of those genetic markers (see Results) to illustrate the relationship of their associated traits with the personalized recommendations delivered by the Digbi Health app and coaching staff. The profiled SNPs were associated with circulating adiponectin and response to dietary monounsaturated fat consumption, fat metabolism and baseline cholesterol levels, and serum calcium levels and calcium-potassium metabolism were strongly associated with weight loss success.

As an example of personalized dietary advice delivered by both app and coach for program participants who are at genetic risk of weight gain, we consider the advice delivered to participants having different genetic outlook as regards rs17300539, a risk allele for weight gain with high monounsaturated fats intake, This SNP is depicted in visualizations of our linear model for weight loss percent ( Figure 4 and S3). As reported above, subjects in this cohort who were at higher risk lost a greater percentage of weight compared to their lower risk counterparts, and percentage of weight losses correlated to greater behavioral engagement. This finding can be explained in that those with high risk for this trait were advised by both app and human coaching to avoid MUFA consumption as much as possible (contrary to conventional wisdom that these - olive oils, almond oils, etc. - are comparatively healthy fats). They were advised instead to shift to consumption of polyunsaturated or saturated fats, depending on their genotypes [92]. Moreover, this SNP is associated with insulin resistance, and parts of the Digbi Health nutrition plan (ex: intermittent fasting and reducing processed carbohydrate consumption) would be expected to lessen insulin resistance, addressing a risk associated with this SNP, thereby helping with weight loss [85].

The linear regression pounds lost models found an association between higher baseline BMI and increased weight loss in this cohort (Figure 7). For each 1-unit increase in baseline BMI, subjects lost an additional 0.2 lbs on average, while holding the other variables in the model constant. This finding could be encouraging to new subjects at higher BMI, who may have attempted weight loss with other programs, but without much success. In Figure 6, we see that men of this cohort at the highest baseline BMI, who had 2 risk alleles for rs2016520_C and completed 5 coaching sessions lost an average of 21 lbs. Women with this same risk outlook and behavioral engagement lost, on average, 16.5 lbs. When compared to subjects of the same gender, baseline BMI, number of coaching sessions, and genomic outlook for all SNPs except rs2016520_C, subjects in this dataset lost 2.4 lbs, on average, over their treatment for each additional risk allele they had of rs2016520_C.

This SNP is poorly characterized in the general population, but studies associate it with BMI and waist circumference amongst Han Chinese93 as well as with cholesterol metabolism94. This latter association drives Digbi Health’s recommendation that subjects presenting with the high risk allele limit cholesterol consumption. The association between the number of risk alleles of rs2016520_C and increased weight loss in this dataset may indicate the efficacy of Digbi Health’s data driven coaching. We see in Figure 6B that a man of average baseline BMI and most frequent genomic outlook for all except rs2016520_C lost 20 lbs, on average, if he had 12 coaching sessions, but a comparable man lost only 11.25 lbs on average if he completed only 1 coaching session over the course of treatment.

Calcium is an essential mineral critical to vascular function, muscle function, neurotransmission, cell signaling, and hormone secretion [95]. Serum calcium levels tend not to respond directly to dietary calcium intake, and instead the body relies upon reservoirs in bone tissue to maintain consistent calcium concentrations [95]. Recent research has emerged tying higher serum calcium levels to development of insulin resistance and cardiovascular hypertension [96]. High serum calcium levels have long been correlated with obesity [97]. Figure 9 depicts the success/fail logistic regression model of the associations between the number of coaching sessions completed and rs4074995_A with successful weight loss while holding all other variables in the model constant at their most frequent number of risk alleles. As with the linear model, in this Digbi Health treatment cohort, increasing total coaching sessions was associated with higher success in losing weight. Those at high risk for excess serum calcium levels were especially encouraged to embrace intermittent fasting and carbohydrate avoidance to combat insulin resistance. This may explain their higher success in achieving 5% or greater weight loss (Figure S5). We see that for subjects with more risk alleles of the rs4074995 SNP, success in weight loss increased with more coaching, although it is not as pronounced in those with minimum (0) risk alleles. It may be that success for those with more risk alleles was not as heavily dependent on more coaching sessions, as the app itself conveys the pertinent dietary advice.

Additionally, our data strongly indicate that behavioral engagement, particularly coaching, contributed to weight loss success. Participants experienced, on average, 0.37% more weight loss with each additional coaching session, while holding all other model variables constant (see Table S6). All models found weight loss to be significantly associated with behavioral engagement with the program and app.(numbers of coaching sessions completed, weight entries logged, and food photos logged as predictors). Food photos and weight tracking showed more than 98% correlation with each other, and both were significantly associated with weight loss success (see Tables S3-S8). Prior research has shown that regular engagement with digital weight loss platforms and regular tracking of weight is associated with greater weight loss success [98] .

We hypothesize that successful weight loss was achieved in adhering to data-driven dietary recommendations that departed from conventional nutritional weight loss advice. Of those 11% of the subject population who gained weight, there was a notable lack of engagement in the program. Those who gained weight, as compared with their counterparts who lost weight, tended to not engage in coaching and regularly use the Digbi Health app to log body weight and post food photos of meals. Those who checked in with the coach regularly and logged into the app frequently to post weight and food photos were more likely to lose weight than those who did not.

Coaching sessions completed, along with other behavioral engagement variables, differed between subjects who lost weight and those who gained, while baseline weight and BMI did not. The density plots of Figure 10 fairly compare distributions of the two groups: although many more people lost weight than gained, the area under the curve of each group is uniform at 1. Figure 10 A illustrates the distributions of completed coaching sessions for those who lost weight (Blue) vs those who gained weight (red). The difference is striking: only a fraction of those who failed to lose weight completed at least 5 (the mean and median) coaching sessions, while those who succeeded generally completed 5 or more. All 3 measures of engagement were significantly higher in subjects who lost weight (Blue) vs those who gained weight (Red). These distributions are visualized in Figure 10 and the statistically significant differences in means were confirmed by Welch Two Sample t-test. A) coaching sessions (P<.001), B) number of weight entries (P<.001), and C) number of food posts (P<.001). In contrast, however, Figure S6 and depicts no statistical difference in means in A) baseline weight and B) baseline BMI, confirmed by Welch Two Sample t-test (P=.64 and P=.42 respectively.) between subjects who lost (Blue) vs. gained (Red) weight. (Less than 2lbs gain or loss was considered negligible and were therefore excluded from analysis. Engagement variables were summed over the study period of 120 Days.)

Figure 10* : Engagement Variable Distributions differ by Weight Loss Group. Measures of engagement were higher in subjects who lost weight (Blue) vs those who gained weight (Red). Statistical difference in means confirmed by Welch Two Sample t-test A) coaching sessions (P<.001), B) number of weight entries (P<.001), and C) number of food posts (P<.001). Less than 2lbs gain or loss was considered negligible and excluded from this figure. Engagement variables were summed over the study period of 120 Days.

A notable feature of this cohort is that females are grossly overrepresented - a feature that is not specific to the demographics of obesity. Although globally more women are obese than men, that disparity is driven in large part by demographics of of the Middle East (particularly Africa and the Middle East). In western countries, it is in fact men who are more likely to be obese [99], which is not reflected in our sample. Instead, our participant demographics may be more reflective of individual self-image. Women appear both more likely to perceive themselves as overweight as well as more likely to attempt weight loss [100]. Our sample size was non-randomly chosen - participants opted into an insurance provided weight loss program.

CONCLUSION

Over the last two decades the obesity epidemic has coincided with a dramatic change in unhealthy eating habits, a sedentary lifestyle, and physical inactivity. In the U.S, more than 40% of the adult population is now overweight or obese. Hereditary predisposition to obesity may have interacted with the obesogenic environment and contributed even further towards the epidemic. The recent accumulation of genomic and lifestyle data has led to the demonstration of possible effects of gene– environmental interactions on obesity [101]. Data from dietary intervention trials indicate that genetic variants, particularly those linked to obesity, metabolism and nutrient consumption, may significantly alter changes in adiposity and metabolic response to nutritional interventions and promote effective weight loss [59].

In the foreseeable future, work to incorporate data on genes, eating patterns, metabolites and gut microbiome into weight loss interventions will be one of the most promising fields of precision care and may allow for the generation of predictable weight loss models based on an individual metabolomic profile. Precision nutrition is individually tailored to enable effective weight loss and prevent chronic diseases on the basis of genomic history, habitual consumption of food and drink, intake of nutrients (especially those that contribute to disease risks), and also metabolomics, microbiome, and other omics profiles of a person [59].

While using precision medicine to target heterogeneous conditions may seem counter-intuitive, it is the heterogeneous nature of conditions such as obesity and metabolic illness that make them such potent targets for intervention, impacting the greatest number of people8 . Identifying obese subpopulations genetically predisposed to favorably or unfavorably respond to a given weight loss intervention could be targeted accordingly.

Few studies have, to-date investigated metabolomic functioning, lifestyle and behavioral mechanisms and gut microbiome which can affect obesity and health at the interface between genetic variation and the environment. The Digbi Health digital precision weight loss program operates at this interface. While more large-scale studies will be needed to validate these findings, the analysis and modeling presented here appear to support dietary precision interventions considering genetic predisposition to disease and genetic variants defining dietary preference and metabolic risk. Additionally, our results point to the efficacy of coaching that empowers and actively engages participants in their own success.

In the foreseeable future, work to incorporate data on genes, eating patterns, metabolites and gut microbiome into weight loss interventions will be a promising field of precision care and may allow for the generation of predictable weight loss models based on individuals’ metabolomic profiles.

Funding:

The study has been funded by Digbi Health, Mountain View, California, USA

Conflicts of Interests:

Digbi Health is sponsoring this study and the PI and study staff have a financial interest in the company.

References:

1. COVID-19 Map. Johns Hopkins Coronavirus Resource Center. Accessed October 19, 2020. https://coronavirus.jhu.edu/map.html
2. Cunningham JW, Vaduganathan M, Claggett BL, et al. Clinical Outcomes in Young US Adults Hospitalized With COVID-19. JAMA Intern Med. Published online September 9, 2020. doi:10.1001/jamainternmed.2020.5313
3. Products - Data Briefs - Number 360 - February 2020. Published June 26, 2020. Accessed October 20, 2020.https://www.cdc.gov/nchs/products/databriefs/db360.htm
4. Townsend MJ, Kyle TK, Stanford FC. Outcomes of COVID-19: disparities in obesity and by ethnicity/race. Int J Obes. 2020;44(9):1807-1809. doi:10.1038/s41366-020-0635-2
5. Apovian CM. Obesity: definition, comorbidities, causes, and burden. Am J Manag Care. 2016;22(7 Suppl):s176-185.
6. McAllister EJ, Dhurandhar NV, Keith SW, et al. Ten putative contributors to the obesity epidemic. Crit Rev Food Sci Nutr. 2009;49(10):868-913. doi:10.1080/10408390903372599
7. Apovian CM, Aronne LJ, Bessesen DH, et al. Pharmacological Management of Obesity: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2015;100(2):342- 362. doi:10.1210/jc.2014-3415
8. Bray MS, Loos RJF, McCaffery JM, et al. NIH working group report—using genomic information to guide weight management: From universal to precision treatment. Obesity. 2016;24(1):14-22. doi:10.1002/oby.21381
9. Joo JK, Lee KS. Pharmacotherapy for Obesity. J Menopausal Med. 2014;20(3):90. doi:10.6118/jmm.2014.20.3.90
10. Severin R, Sabbahi A, Mahmoud AM, Arena R, Phillips SA. Precision Medicine in Weight Loss and Healthy Living. Prog Cardiovasc Dis. 2019;62(1):15-20. doi:10.1016/j.pcad.2018.12.012
11. Gallup Poll Social Series: Health and Healthcare. Accessed October 20, 2020. https://news.gallup.com/poll/150986/Lose-Weight-Americans-Rely-Dieting-Exercise.aspx
12. Effects of Comprehensive Lifestyle Modification on Blood Pressure Control: Main Results of the PREMIER Clinical Trial. JAMA. 2003;289(16). doi:10.1001/jama.289.16.2083
13. Diabetes Prevention Program Research Group, Knowler WC, Fowler SE, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet Lond Engl. 2009;374(9702):1677-1686. doi:10.1016/S0140-6736(09)61457-4
14. Jenny Craig vs. Nutrisystem vs. Weight Watchers | ConsumerAffairs. Accessed October 20, 2020. https://www.consumeraffairs.com/health/jenny-craig-vs-nutrisystem-vs-weightwatchers.html
15. Brennan L. Metabolomics in nutrition research–a powerful window into nutritional metabolism. Newsholme P, ed. Essays Biochem. 2016;60(5):451-458. doi:10.1042/EBC20160029
16. Allam-Ndoul B, Guénard F, Garneau V, et al. Association between Metabolite Profiles, Metabolic Syndrome and Obesity Status. Nutrients. 2016;8(6):324. doi:10.3390/nu8060324
17. Bakker GC, van Erk MJ, Pellis L, et al. An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach. Am J Clin Nutr. 2010;91(4):1044-1059. doi:10.3945/ajcn.2009.28822
18. Paquette M, Medina Larqué AS, Weisnagel SJ, et al. Strawberry and cranberry polyphenols improve insulin sensitivity in insulin-resistant, non-diabetic adults: a parallel, double-blind, controlled and randomised clinical trial. Br J Nutr. 2017;117(4):519-531. doi:10.1017/S0007114517000393
19. Rönn T, Volkov P, Davegårdh C, et al. A Six Months Exercise Intervention Influences the Genome-wide DNA Methylation Pattern in Human Adipose Tissue. Greally JM, ed. PLoS Genet. 2013;9(6):e1003572. doi:10.1371/journal.pgen.1003572
20. Nitert MD, Dayeh T, Volkov P, et al. Impact of an Exercise Intervention on DNA Methylation in Skeletal Muscle From First-Degree Relatives of Patients With Type 2 Diabetes. Diabetes. 2012;61(12):3322-3332. doi:10.2337/db11-1653
21. Jacobsen SC, Gillberg L, Bork-Jensen J, et al. Young men with low birthweight exhibit decreased plasticity of genome-wide muscle DNA methylation by high-fat overfeeding. Diabetologia. 2014;57(6):1154-1158. doi:10.1007/s00125-014-3198-8
22. Rodgers GP, Collins FS. Precision Nutrition—the Answer to “What to Eat to Stay Healthy.” JAMA. 2020;324(8):735. doi:10.1001/jama.2020.13601
23. MAGIC, on behalf of Procardis Consortium, Speliotes EK, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937-948. doi:10.1038/ng.686
24. Fall T, Ingelsson E. Genome-wide association studies of obesity and metabolic syndrome. Mol Cell Endocrinol. 2014;382(1):740-757. doi:10.1016/j.mce.2012.08.018
25. The LifeLines Cohort Study, The ADIPOGen Consortium, The AGEN-BMI Working Group, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197-206. doi:10.1038/nature14177
26. DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, Scott RA, Lagou V, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012;44(9):991-1005. doi:10.1038/ng.2385
27. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41-47. doi:10.1038/nature18642
28. Hunter DJ. Gene–environment interactions in human diseases. Nat Rev Genet. 2005;6(4):287- 298. doi:10.1038/nrg1578
29. Fabbrini E, Yoshino J, Yoshino M, et al. Metabolically normal obese people are protected from adverse effects following weight gain. J Clin Invest. 2015;125(2):787-795. doi:10.1172/JCI78425
30. Heianza Y, Arase Y, Tsuji H, et al. Metabolically Healthy Obesity, Presence or Absence of Fatty Liver, and Risk of Type 2 Diabetes in Japanese Individuals: Toranomon Hospital Health Management Center Study 20 (TOPICS 20). J Clin Endocrinol Metab. 2014;99(8):2952-2960. doi:10.1210/jc.2013-4427
31. Roberson LL, Aneni EC, Maziak W, et al. Beyond BMI: The “Metabolically healthy obese” phenotype & its association with clinical/subclinical cardiovascular disease and all-cause mortality -- a systematic review. BMC Public Health. 2014;14(1):14. doi:10.1186/1471-2458- 14-14
32. Zheng R, Zhou D, Zhu Y. The long-term prognosis of cardiovascular disease and all-cause mortality for metabolically healthy obesity: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(10):1024-1031. doi:10.1136/jech-2015-206948
33. Bell JA, Kivimaki M, Hamer M. Metabolically healthy obesity and risk of incident type 2 diabetes: a meta analysis of prospective cohort studies. ‐ Obes Rev. 2014;15(6):504-515. doi:10.1111/obr.12157
34. Courcoulas AP, Yanovski SZ, Bonds D, et al. Long-term Outcomes of Bariatric Surgery: A National Institutes of Health Symposium. JAMA Surg. 2014;149(12):1323. doi:10.1001/jamasurg.2014.2440
35. Madsbad S, Dirksen C, Holst JJ. Mechanisms of changes in glucose metabolism and bodyweight after bariatric surgery. Lancet Diabetes Endocrinol. 2014;2(2):152-164. doi:10.1016/S2213-8587(13)70218-3
36. Carlsson LMS, Sjöholm K, Karlsson C, et al. Long-term incidence of microvascular disease after bariatric surgery or usual care in patients with obesity, stratified by baseline glycaemic status: a post-hoc analysis of participants from the Swedish Obese Subjects study. Lancet Diabetes Endocrinol. 2017;5(4):271-279. doi:10.1016/S2213-8587(17)30061-X
37. Hatoum IJ, Greenawalt DM, Cotsapas C, Reitman ML, Daly MJ, Kaplan LM. Heritability of the Weight Loss Response to Gastric Bypass Surgery. J Clin Endocrinol Metab. 2011;96(10):E1630-E1633. doi:10.1210/jc.2011-1130
38. Rinella ES, Still C, Shao Y, et al. Genome-wide Association of Single-Nucleotide Polymorphisms With Weight Loss Outcomes After Roux-en-Y Gastric Bypass Surgery. J Clin Endocrinol Metab. 2013;98(6):E1131-E1136. doi:10.1210/jc.2012-3421
39. Hatoum IJ, Greenawalt DM, Cotsapas C, Daly MJ, Reitman ML, Kaplan LM. Weight Loss after Gastric Bypass Is Associated with a Variant at 15q26.1. Am J Hum Genet. 2013;92(5):827-834. doi:10.1016/j.ajhg.2013.04.009
40. Breen F, Plomin R, Wardle J. Heritability of food preferences in young children . ☆ Physiol Behav. 2006;88(4-5):443-447. doi:10.1016/j.physbeh.2006.04.016
41. Törnwall O, Silventoinen K, Hiekkalinna T, Perola M, Tuorila H, Kaprio J. Identifying flavor preference subgroups. Genetic basis and related eating behavior traits. Appetite. 2014;75:1-10. doi:10.1016/j.appet.2013.11.020
42. Wardle J, Carnell S, Haworth CMA, Farooqi IS, O’Rahilly S, Plomin R. Obesity Associated Genetic Variation in FTO Is Associated with Diminished Satiety. J Clin Endocrinol Metab. 2008;93(9):3640-3643. doi:10.1210/jc.2008-0472
43. Timpson NJ, Emmett PM, Frayling TM, et al. The fat mass–and obesity-associated locus and dietary intake in children. Am J Clin Nutr. 2008;88(4):971-978. doi:10.1093/ajcn/88.4.971
44. McCaffery JM, Papandonatos GD, Peter I, et al. Obesity susceptibility loci and dietary intake in the Look AHEAD Trial. Am J Clin Nutr. 2012;95(6):1477-1486. doi:10.3945/ajcn.111.026955
45. Cecil JE, Tavendale R, Watt P, Hetherington MM, Palmer CNA. An Obesity-Associated FTO Gene Variant and Increased Energy Intake in Children. N Engl J Med. 2008;359(24):2558- 2566. doi:10.1056/NEJMoa0803839
46. Feeney E, O’Brien S, Scannell A, Markey A, Gibney ER. Genetic variation in taste perception: does it have a role in healthy eating? Proc Nutr Soc. 2011;70(1):135-143. doi:10.1017/S0029665110003976
47. Qi L. Gene–diet interaction and weight loss: Curr Opin Lipidol. 2014;25(1):27-34. doi:10.1097/MOL.0000000000000037
48. Qi L. Gene-Diet Interactions in Complex Disease: Current Findings and Relevance for Public Health. Curr Nutr Rep. 2012;1(4):222-227. doi:10.1007/s13668-012-0029-8
49. Qi L, Cho YA. Gene-environment interaction and obesity: Nutrition Reviews©, Vol. 66, No. 12. Nutr Rev. 2008;66(12):684-694. doi:10.1111/j.1753-4887.2008.00128.x
50. UK Biobank. Accessed October 20, 2020. https://www.ukbiobank.ac.uk/
51. Lv J, Yu C, Guo Y, et al. Adherence to Healthy Lifestyle and Cardiovascular Diseases in the Chinese Population. J Am Coll Cardiol. 2017;69(9):1116-1125. doi:10.1016/j.jacc.2016.11.076
52. Di Pietro L, Dziura J, Blair SN. Estimated change in physical activity level (PAL) and prediction of 5-year weight change in men: the Aerobics Center Longitudinal Study. Int J Obes. 2004;28(12):1541-1547. doi:10.1038/sj.ijo.0802821
53. Haapanen N, Miilunpalo S, Pasanen M, Oja P, Vuori I. Association between leisure time physical activity and 10-year body mass change among working-aged men and women. Int J Obes. 1997;21(4):288-296. doi:10.1038/sj.ijo.0800403
54. Schmitz K, Jacobs D, Leon A, Schreiner P, Sternfeld B. Physical activity and body weight: associations over ten years in the CARDIA study. Int J Obes. 2000;24(11):1475-1487. doi:10.1038/sj.ijo.0801415
55. Washburn RA, Szabo AN, Lambourne K, et al. Does the Method of Weight Loss Effect LongTerm Changes in Weight, Body Composition or Chronic Disease Risk Factors in Overweight or Obese Adults? A Systematic Review. Johannsen D, ed. PLoS ONE. 2014;9(10):e109849. doi:10.1371/journal.pone.0109849
56. Herring MP, Sailors MH, Bray MS. Genetic factors in exercise adoption, adherence and obesity. Obes Rev. 2014;15(1):29-39. doi:10.1111/obr.12089
57. Maia JAR, Thomis M, Beunen G. Genetic factors in physical activity levels. Am J Prev Med. 2002;23(2):87-91. doi:10.1016/S0749-3797(02)00478-6
58. Cai G, Cole SA, Butte N, et al. A Quantitative Trait Locus on Chromosome 18q for Physical Activity and Dietary Intake in Hispanic Children*. Obesity. 2006;14(9):1596-1604. doi:10.1038/oby.2006.184
59. de Toro-Martín J, Arsenault B, Després J-P, Vohl M-C. Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome. Nutrients. 2017;9(8):913. doi:10.3390/nu9080913
60. Reinehr T, Roth CL. The gut sensor as regulator of body weight. Endocrine. 2015;49(1):35-50. doi:10.1007/s12020-014-0518-1
61. Frost F, Storck LJ, Kacprowski T, et al. A structured weight loss program increases gut microbiota phylogenetic diversity and reduces levels of Collinsella in obese type 2 diabetics: A pilot study. Taheri S, ed. PLOS ONE. 2019;14(7):e0219489. doi:10.1371/journal.pone.0219489
62. Ridaura VK, Faith JJ, Rey FE, et al. Gut Microbiota from Twins Discordant for Obesity Modulate Metabolism in Mice. Science. 2013;341(6150):1241214-1241214. doi:10.1126/science.1241214
63. Kang JX. Gut Microbiota and Personalized Nutrition. J Nutr Nutr. 2013;6(2):I-II. doi:10.1159/000353144
64. Bonder MJ, Kurilshikov A, Tigchelaar EF, et al. The effect of host genetics on the gut microbiome. Nat Genet. 2016;48(11):1407-1412. doi:10.1038/ng.3663
65. Corella D, Arregui M, Coltell O, et al. Association of the LCT-13910C>T Polymorphism With Obesity and Its Modulation by Dairy Products in a Mediterranean Population. Obesity. 2011;19(8):1707-1714. doi:10.1038/oby.2010.320
66. Mathur R, Kim G, Morales W, et al. Intestinal Methanobrevibacter smithii but not total bacteria is related to diet-induced weight gain in rats: M. smithii Colonization and Weight Gain in Rats. Obesity. 2013;21(4):748-754. doi:10.1002/oby.20277
67. MetaHIT consortium, Le Chatelier E, Nielsen T, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500(7464):541-546. doi:10.1038/nature12506
68. Cox LM, Blaser MJ. Antibiotics in early life and obesity. Nat Rev Endocrinol. 2015;11(3):182- 190. doi:10.1038/nrendo.2014.210
69. Remely M, Tesar I, Hippe B, Gnauer S, Rust P, Haslberger AG. Gut microbiota composition correlates with changes in body fat content due to weight loss. Benef Microbes. 2015;6(4):431- 439. doi:10.3920/BM2014.0104
70. Jumpertz R, Le DS, Turnbaugh PJ, et al. Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. Am J Clin Nutr. 2011;94(1):58- 65. doi:10.3945/ajcn.110.010132
71. Sweeney TE, Morton JM. The Human Gut Microbiome: A Review of the Effect of Obesity and Surgically Induced Weight Loss. JAMA Surg. 2013;148(6):563. doi:10.1001/jamasurg.2013.5
72. Damms-Machado A, Mitra S, Schollenberger AE, et al. Effects of Surgical and Dietary Weight Loss Therapy for Obesity on Gut Microbiota Composition and Nutrient Absorption. BioMed Res Int. 2015;2015:1-12. doi:10.1155/2015/806248
73. Bacigalupo R, Cudd P, Littlewood C, Bissell P, Hawley MS, Buckley Woods H. Interventions employing mobile technology for overweight and obesity: an early systematic review of randomized controlled trials. Obes Rev. 2013;14(4):279-291. doi:10.1111/obr.12006
74. Krukowski RA, Tilford JM, Harvey-Berino J, West DS. Comparing behavioral weight loss modalities: incremental cost-effectiveness of an internet-based versus an in-person condition. Obes Silver Spring Md. 2011;19(8):1629-1635. doi:10.1038/oby.2010.341
75. Bray GA, Heisel WE, Afshin A, et al. The Science of Obesity Management: An Endocrine Society Scientific Statement. Endocr Rev. 2018;39(2):79-132. doi:10.1210/er.2017-00253
76. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults. J Am Coll Cardiol. 2014;63(25):2985- 3023. doi:10.1016/j.jacc.2013.11.004
77. Harvey-Berino J, West D, Krukowski R, et al. Internet delivered behavioral obesity treatment. Prev Med. 2010;51(2):123-128. doi:10.1016/j.ypmed.2010.04.018
78. Tate DF. Using Internet Technology to Deliver a Behavioral Weight Loss Program. JAMA. 2001;285(9):1172. doi:10.1001/jama.285.9.1172
79. Silberman JM, Kaur M, Sletteland J, Venkatesan A. Outcomes in a digital weight management intervention with one-on-one health coaching. Meyre D, ed. PLOS ONE. 2020;15(4):e0232221. doi:10.1371/journal.pone.0232221
80. Muñoz Obino KF, Aguiar Pereira C, Caron-Lienert RS. Coaching and barriers to weight loss: an integrative review. Diabetes Metab Syndr Obes Targets Ther. 2017;10:1-11. doi:10.2147/DMSO.S113874
81. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012;489(7415):220-230. doi:10.1038/nature11550
82. Digbi Health, Mountain View, CA, USA, Singh-Rambiritch S, Sinha R, et al. A Couple’s Personalized-Care Intervention for Weight-Loss and Diabetes based on DNA and Gut Biome Profiles: A Case Study. Obes Res – Open J. 2019;6(2):25-34. doi:10.17140/OROJ-6-139
83. Waters JL, Ley RE. The human gut bacteria Christensenellaceae are widespread, heritable, and associated with health. BMC Biol. 2019;17(1):83. doi:10.1186/s12915-019-0699-4
84. ADIPOQ - SNPedia. Accessed October 20, 2020. https://snpedia.com/index.php/ADIPOQ
85. Goyenechea E, Collins L, Parra D, et al. The − 11391 G/A Polymorphism of the Adiponectin Gene Promoter is Associated with Metabolic Syndrome Traits and the Outcome of an Energyrestricted Diet in Obese Subjects. Horm Metab Res. 2009;41(01):55-61. doi:10.1055/s-0028- 1087204
86. Warodomwichit D, Shen J, Arnett DK, et al. ADIPOQ Polymorphisms, Monounsaturated Fatty Acids, and Obesity Risk: The GOLDN Study. Obesity. 2009;17(3):510-517. doi:10.1038/oby.2008.583
87. Gallicchio L, Kalesan B, Huang H-Y, Strickland P, Hoffman SC, Helzlsouer KJ. Genetic Polymorphisms of Peroxisome Proliferator-Activated Receptors and the Risk of Cardiovascular Morbidity and Mortality in a Community-Based Cohort in Washington County, Maryland. PPAR Res. 2008;2008:1-9. doi:10.1155/2008/276581
88. Leońska-Duniec A, Cieszczyk P, Jastrzębski Z, et al. The polymorphisms of the PPARD gene modify post-training body mass and biochemical parameter changes in women. Böttcher Y, ed. PLOS ONE. 2018;13(8):e0202557. doi:10.1371/journal.pone.0202557
89. rs4074995 - SNPedia. Accessed October 20, 2020. https://www.snpedia.com/index.php/rs4074995
90. Kestenbaum B, Glazer NL, Köttgen A, et al. Common genetic variants associate with serum phosphorus concentration. J Am Soc Nephrol JASN. 2010;21(7):1223-1232. doi:10.1681/ASN.2009111104
91. O’Seaghdha CM, Wu H, Yang Q, et al. Meta-Analysis of Genome-Wide Association Studies Identifies Six New Loci for Serum Calcium Concentrations. Abecasis GR, ed. PLoS Genet.2013;9(9):e1003796. doi:10.1371/journal.pgen.1003796
92. 2015-2020 Dietary Guidelines | health.gov. Accessed October 20, 2020. https://health.gov/ourwork/food-nutrition/2015-2020-dietary-guidelines
93. Luo W, Chen F, Guo Z, Wu M, Zhou Z, Yao X. A population association study of PPAR δ gene rs2016520 and rs9794 polymorphisms and haplotypes with body mass index and waist circumference in a Chinese population. Ann Hum Biol. 2016;43(1):67-72. doi:10.3109/03014460.2015.1023847
94. Holzapfel J, Heun R, Lütjohann D, Jessen F, Maier W, Kölsch H. PPARD haplotype influences cholesterol metabolism but is no risk factor of Alzheimer’s disease. Neurosci Lett. 2006;408(1):57-61. doi:10.1016/j.neulet.2006.08.029
95. Office of Dietary Supplements - Calcium. Accessed October 20, 2020. https://ods.od.nih.gov/factsheets/Calcium-HealthProfessional/
96. Wu X, Han T, Gao J, et al. Association of Serum Calcium and Insulin Resistance With Hypertension Risk: A Prospective Population Based Study. ‐ J Am Heart Assoc. 2019;8(1). doi:10.1161/JAHA.118.009585
97. Dalfardi O, Jahandideh D, Omrani GHR. The Correlation of Serum Calcium Level and Obesity; Is There any Explanation? Galen Med J. 2013;2(1):26-31.
98. Chin SO, Keum C, Woo J, et al. Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Sci Rep. 2016;6(1):34563. doi:10.1038/srep34563
99. Kanter R, Caballero B. Global Gender Disparities in Obesity: A Review. Adv Nutr. 2012;3(4):491-498. doi:10.3945/an.112.002063
100. Lemon SC, Rosal MC, Zapka J, Borg A, Andersen V. Contributions of weight perceptions to weight loss attempts: Differences by body mass index and gender. Body Image. 2009;6(2):90- 96. doi:10.1016/j.bodyim.2008.11.004
101. Heianza Y, Qi L. Gene-Diet Interaction and Precision Nutrition in Obesity. Int J Mol Sci. 2017;18(4):787. doi:10.3390/ijms18040787
102. Wang TJ, Zhang F, Richards JB, et al. Common genetic determinants of vitamin D insufficiency: a genome-wide association study. Lancet Lond Engl. 2010;376(9736):180-188. doi:10.1016/S0140-6736(10)60588-0
103. Kaklamani VG, Sadim M, Hsi A, et al. Variants of the adiponectin and adiponectin receptor 1 genes and breast cancer risk. Cancer Res. 2008;68(9):3178-3184. doi:10.1158/0008- 5472.CAN-08-0533
104. Clarke R, Halsey J, Bennett D, Lewington S. Homocysteine and vascular disease: review of published results of the homocysteine-lowering trials. J Inherit Metab Dis. 2011;34(1):83-91. doi:10.1007/s10545-010-9235-y
105. Johnson JA, Zineh I, Puckett BJ, McGorray SP, Yarandi HN, Pauly DF. Beta 1-adrenergic receptor polymorphisms and antihypertensive response to metoprolol. Clin Pharmacol Ther. 2003;74(1):44-52. doi:10.1016/S0009-9236(03)00068-7
106. Fernando MMA, Stevens CR, Sabeti PC, et al. Identification of two independent risk factors for lupus within the MHC in United Kingdom families. PLoS Genet. 2007;3(11):e192. doi:10.1371/journal.pgen.0030192
107. van Heel DA, Franke L, Hunt KA, et al. A genome-wide association study for celiac disease identifies risk variants in the region harboring IL2 and IL21. Nat Genet. 2007;39(7):827-829. doi:10.1038/ng2058
108. Coffee and Caffeine Genetics Consortium, Cornelis MC, Byrne EM, et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol Psychiatry. 2015;20(5):647-656. doi:10.1038/mp.2014.107
109. Thorleifsson G, Walters GB, Gudbjartsson DF, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41(1):18-24. doi:10.1038/ng.274