Also available as PDF (figures and tables appear in the PDF).
Introduction The global prevalence of obesity has reached epidemic proportions. Given the negative strain that obesity and associated chronic diseases, such as type 2 diabetes, put on the healthcare system and the economy, disease management has begun evolving to help individuals change their behaviors. Obesity is often difficult to treat and even harder to maintain. Past studies have failed to show weight loss maintenance over long periods after interventions. To overcome the complexity of obesity, a multifaceted precision care treatment approach should be adopted. Aim The aim of this case study was to assess the health benefits and weight loss journey of a cohabiting Caucasian heterosexual married couple using the Digbi Health personalized obesity management program. A personalized integrative nutrition plan is created based on one’s genetic and gut microbiome obesity risk profile and incorporates daily digital tracking and lifestyle coaching. Never before has a program offered personalized data including genetic, gut microbiome and lifestyle coaching to help people understand the best plan to lose weight and keep it off long term. Method The male subject achieved a total change in weight loss of 15.94%, as well as a reduction in A1C and blood pressure levels and the female subject achieved a 13.65% change in weight loss over a period of four months. The couple have still been able to maintain their weight loss goals four months after completing the program, stating their individual and personalized approach gave them the tools long-term to maintain. Conclusion A supportive environment for cohabiting couples following a personalized weight loss program based on their genetic and gut microbiome profile may help with weight loss and long-term maintenance.
Keywords: Diabetes; Gut microbiome; Obesity; Diet; Physical exercise; Overweight; Body mass index (BMI); Couples; Hypertension
The global prevalence of obesity has reached epidemic proportions. Given the negative strain that obesity and associated chronic
diseases, such as type 2 diabetes, put on the healthcare system and the economy, disease management has begun evolving to help
individuals change their behaviors. Obesity is often difficult to treat and even harder to maintain. Past studies have failed to show
weight loss maintenance over long periods after interventions. To overcome the complexity of obesity, a multifaceted precision care
treatment approach should be adopted.
The aim of this case study was to assess the health benefits and weight loss journey of a cohabiting Caucasian heterosexual married
couple using the Digbi Health personalized obesity management program. A personalized integrative nutrition plan is created based
on one’s genetic and gut microbiome obesity risk profile and incorporates daily digital tracking and lifestyle coaching. Never before
has a program offered personalized data including genetic, gut microbiome and lifestyle coaching to help people understand the best
plan to lose weight and keep it off long term.
The male subject achieved a total change in weight loss of 15.94%, as well as a reduction in A1C and blood pressure levels and the
female subject achieved a 13.65% change in weight loss over a period of four months. The couple have still been able to maintain
their weight loss goals four months after completing the program,
tools long-term to maintain.
A supportive environment for cohabiting couples following a personalized weight loss program based on their genetic and gut mi-
crobiome profile may help with weight loss and long-term maintenance.
Diabetes; Gut microbiome; Obesity; Diet; Physical exercise; Overweight; Body mass index (BMI); Couples; Hypertension.
besity is a multifactorial disease arising from an at-risk ge-
netic profile, and environmental risk factors, such as
O physical inactivity, insufficient sleep, excessive caloric intake,
medications, socioeconomic status, endocrine disruptors and the
1-5 gastrointestinal microbiome.1-5
cc Copyright 2019 by Singh-Rambiritch S. This is an open-access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0),
which allows to copy, redistribute, remix, transform, and reproduce in any medium or format, even commercially, provided the original work is properly cited. stating their individual and personalized approach gave them the
Some emerging strategies to help individuals success-
fully change their health behaviors include using personalized
health programs that incorporate one’s physiological information
and lifestyle coaching.6 Researchers have also recently begun
to explore whether relationships and social support networks
can be leveraged to help individuals achieve healthier lifestyles,
weight loss and improve health outcomes.7
According to the World Health Organization (WHO),
overweight and obesity are defined as abnormal or excessive fat
accumulation that presents a risk to health, including diabetes and
cardiovascular disease.8 The National Heart, Lung, and Blood In-
stitute (NHLBI) define a body mass index (BMI) of more than 25
as overweight and an individual is considered obese if the BMI is
above 30, and severely obese if the BMI is above 40.9 Over 70% of
adults in the United States were considered overweight or obese in
the Centers for Disease Control and Prevention (CDC) 2015-2016
report.10 Today almost 2 out of 3 adults are considered overweight
or obese.11
The 2018 data brief on the National Health and Nutrition
Examination Survey (NHANES) showed that 49.1% of U.S. adults
tried to lose weight in the 2013-2016 time period. A higher per-
centage of women (56.4%) than men (41.7%) tried to lose weight.
Within each age group, 20 and over, 20-39, 40-59 and 60 and over,
the percentage of women trying to lose weight was higher within
each age group and overall for all age groups. The percentage of
adults who tried to lose weight increased with increasing BMI, with
the highest percentage of men and women trying to lose weight in
the obese category. The results showed that 66.7% of adults with
obesity, 49.0% of overweight adults, and 26.5% of underweight
or normal weight adults tried to lose weight. The most commonly
reported methods used amongst adults to lose weight were exercis-
ing (62.9%) and eating less food (62.9%), followed by consuming
more fruits, vegetables, and salads (50.4%). Participants could re-
port more than one method used to lose weight. Most individuals
(88.3%), reported using two or more methods.12
Obesity is associated with major health risks.1,13 Individu-
als are motivated for a variety of reasons to lose weight, including
improving health and appearance. On the basis of these epidemic
dimensions, the issue of overweight and obesity cannot be taken
lightly. There is currently an abundance of research demonstrat-
ing that an individuals’ genetic and gut microbiome makeups are
intrinsically linked to their metabolisms; for example, people with
certain genotypes are able to lose weight faster when on diets high
Table 1. Past Studies Conducted amongst Couples Focusing on Behavioral Change Outcomes
Longitudinal study in 2015 by Jackson Researcher looked at the influence
18 behavior on health behavioral change in 3722 activity and weight loss, if their partner does too, and
et al couples that were either married or cohabiting with a stronger effect than if the partner had been
Literature review study in 2016 by Mechanisms Study (NORMS).They
19 impact of obesogenic relationships Perry et al
Literature review study in 2007 by Researchers analyzed data of almost 12,000 couples Respondents whose partner reports poor health are
20 using data from the annual Netherlands Health almost three times more likely to report poor health
Monden C Interview Survey (NethHIS)
Cross sectional literature study of
ARIC cohort in 2016 by Cobb et al 3,889 spousal pairs in fat compared to peers with different genetics. Others respond
better to short bursts of intense activity as opposed to long ses-
14-16 sions of moderate activity. Certain ethnic groups, for example,
populations of Asian or African origin, have shown a propensity
for obesity and diabetes even when following a diet deemed nutri-
tious by the United States Department of Agriculture (USDA).17
Following dietary recommendations curated to an individual’s
biology may help to optimize people’s health.15 Even more so, is
the support from a spouse when one embarks on a personalized
weight loss program.
Couples, especially cohabiting couples, are known to
be a strong influence on each other’s health, and concordance in
health behavior increases over time. Several studies suggest couple-
focused interventions for health behavior change may be more ef-
18-21 fective than individual interventions (Table 1).
Studies often suggest that adoption of diet modification
and physical exercise in one individual may spread to proximate
others, such as partners, creating a social environment charac-
terized by mutual reinforcement of healthy behavior and weight
18-20 loss. Given the amount of time cohabiting couples spend with
one another, it is logical to assume that modeling of health behav-
iors would occur, such as high-levels of sedentary behavior and
low-levels of physical activity. Also, a strong attachment between
couples may heighten identification with his or her experiences and
18,19 the motivation to conform. The other issue to take into consid-
eration, is that couples are often eating the same food together, but
the same foods may be wrong for the individual genetic profiles
which could end up resulting in poor weight loss maintenance. 1,13
Involving partners in behavior change interventions and
encouraging partners to lose weight together may help improve
health outcomes. Having couples adopt healthy behaviors togeth-
er may be a promising approach, especially when weight gain is
not a consequence of medications or comorbid conditions. Hav-
ing positive reinforcement from a partner or spouse is invaluable
18,19 when tackling obesity and weight loss. Using a program that
Men and women are more likely to make a positive
of a partner's health behavior change, such as increased physical
consistently healthy in that domain.
People observe and may adopt the behaviors of Researchers analyzed data of 401 families from the those around them, whether healthy or unhealthy.
Networks and Obesity: Relationships and
examined the Research suggests that a person who makes a on individual healthy behavior change has a larger impact on behaviors and outcomes looking at three potential others’ positive health behaviors and that adoption social mechanisms influencing obesity; normative of exercise or diet modifications in one individual is body size, social control and behavior modeling likely to spread to others.
than respondents whose partner is in good health.
As the men or women’s BMIs in the study increased,
their spouses BMIs also increased. Spousal BMI simi- Researchers analyzed the data of BMI changes in 21 larities might be driven by the impact of living in a
shared obesogenic environment and by the influence
of one spouse on the other.
sets a person up for weight loss success through personalized nu-
trition, such as the Digbi Health program, is a key component to
improving health and weight loss maintenance. In the instance of
the couple in this case report, this was found to be true.
A 58-year Caucasian male with type 2 diabetes and hypertension
was not happy with his primary care physician’s management of
his chronic conditions. He had been receiving treatment from the
same physician for many years and decided to make a change. His
new physician wanted him to make dietary changes to reduce his
weight and increase his physical activity routine.
At the time of the appointment with his new physician he
reported the following:
• Weight=251 pounds
• BMI=35
• A1c level=9.4
• BP reading=140/90
• Diabetes medication: Januvia; Metformin 500 mg twice a day
• Hypertension medication: Atenolol; Terazosin; Valsartan
• Acid reflux medication: Omeprazole
• Other medications: Aspirin; Multivitamins
The patient also reported suffering from sleep apnea, di-
gestive disorders and acid reflux. He also complained of fatigue
and reported that he had an injured ankle at the time so increasing
physical activity would be difficult. The doctor advised incorporat-
ing a fitness routine and referred him to a fitness center close to his
house.
After an initial assessment at the fitness center his fitness
expert advised him to use the Digbi Health personalized obesity
management program. Due to his injured ankle, he was not able to
start a full fitness routine, but started a low intensity cardio routine
consisting of light walking daily. He signed up for the Digbi Health
program. Next, he sampled his deoxyribonucleic acid (DNA) and
gut microbiome and started working with the Digbi Health coach-
es. His spouse, a 65-year-old Caucasian female, whom he had been
married to for over five years, did not suffer from any chronic
conditions, but she did take omeprazole for heartburn and had
her right knee replaced 3-years prior. Occasionally she experienced
slight knee and joint pain. She reported gaining over 15 pounds in
the last five years and wanted to lose weight to look and feel better.
At the time of the initial appointment at the fitness cen-
ter, she weighed 197 pounds and had a BMI of 32.8. She reported
trying a variety of diets over the years with quick results, but often
resulted in gaining the weight back. Being a supportive spouse, and
wanting to make a change herself, she decided to also sign up for
the Digbi Health program and go through the program together
with her husband.
The Digbi Health program is a 24-week program which
uses metabolic markers, body metrics, gut microbiome and genetic profiles to offer more than a one-size-fits-all food and lifestyle
recommendation weight loss program. Participants use the Digbi
Health app to input 10 key lifestyle and wellness markers, includ-
ing weight, sleep, hunger, cravings, stress, meditation, superfoods,
morning energy, foods to avoid and exercise on a daily basis and
take photos of the food they consume.
Individuals will receive a personalized wellness report af-
ter sampling their blood, DNA and gut microbiome. The report
provides a better understanding of an individual’s unique biology
and metabolism and how it interacts with food and lifestyle to im-
pact your health and quality longevity. It also provides a breakdown
of obesity risk based on an individual’s DNA and gut microbiome
profile.22 The program takes the guesswork out of healthy eating
for an individual’s body, lifestyle, gender and age.
Each individual is also assigned a lifestyle coach who will
work personally with the individual through 12 guided sessions at
various intervals. Both the husband and wife went through the full
coaching component of the program. The coaches worked with
the individuals based on their results and advised them on how to
best structure their diet. Individuals have the ability to view a per-
sonalized integrative nutrition plan based on their biophysiological
individuality and work with the Digbi Health lifestyle coaches to
produce modified behavioral changes and favorable weight loss re-
sults.
Personalized integrative nutrition,23 such as the personal-
ized plan from Digbi Health, is important because no two human
beings have the same genetic code. An individual’s genes not only
determine how one looks, height, skin type, but also influences dis-
ease risk, obesity risk, as well as one’s energy and immunity levels.
Genes play a key role in how effectively an individual is processing
carbohydrates, fat, protein and converting them into energy, creat-
ing vitamins, hormones and steroids.24 Our genes also play a role
in how we fight disease, metabolize medication one consumes and
body detoxification and rebuilding cells.25
The analysis of the saliva DNA sample and fecal gut microbiome
sample is completed after the samples are processed in clinical lab-
oratory improvement amendments (CLIA) accredited laboratory.
Fecal and saliva DNA extraction, purification, next-generation se-
quencing (NGS) library preparation and sequencing is performed
as per the Illumina standard protocol.
The outcome presented in the DNA report are determined by the
number of markers and risk genotypes present in the genome raw
data. Only a few human health conditions are dependent on a sin-
gle gene marker. The vast majority of human traits are influenced
by multiple gene markers. In addition, there is significant interac-
tion between genes and environment such as dietary and lifestyle
factors.
The outcome is a subjective measure and not a clinical
measure. It is the percentage of risk markers present in the ge-
nome data among the maximum possible risk markers in the ge-
nome data. The genetic risk score (GRS) is calculated by analyzing
a large number of risk variants. Unless several of these high-risk
variants are present in the data the outcome could be low or mod-
erate.
For the obesity risk DNA profile of an individual we pro-
file the genes that have been shown to influence nutritional traits
such as, diet and weight management, micronutrient requirements,
food intolerance and several other attributes relevant to nutritional
well-being. The word “likely” is used often in this study. “Likely”
means, it is more likely that one will see the outcome, but other
factors may modify it. The term “average” is also used in this study.
Average implies neither high nor low, rather an intermediate out-
come. For example, average likelihood of injury is an intermediate
level between high and low likelihood. Average can also be under-
stood in the context of “Normal” or “Typical” or “Moderate”.
The risk status of an individual’s markers is typically in-
dicated as low, moderate and high risk. Attributes that are advanta-
geous in nutritional well-being are indicated in green, or as a low
risk, and those are not advantageous, or high risk are in red. Mod-
erate or neutral outcomes are indicated in yellow.
Results and Incorporation of the DNA and Gut Microbiome Profile into the Digbi Health Program Treatment Plan
The 16s paired fastq reads of the gut microbiome samples were
quality checked and merged to generate the amplicons. Amplicons
were further processed to calculate the bacterial genus abundance
in each sample using automated data analysis pipeline based on the
Parallel-META 3 pipeline.27 The sample values are compared with
that of a healthy cohort value. The determined abundance of each
bacterial genus was further analyzed to compute the diversity index
using the in house developed python code. Further biochemical
annotation of identified bacterial genera was performed based on
our in-house database.
The associated bacteria are, Akkermansia, Bacteroides, Bi-
fidobacterium, Anaerostipes, Coprococcus, Roseburia, Eubacterium, Rumi-
nococcus and Christensenella. A higher abundance of associated bac-
teria helps to combat weight gain, inflammation, regulates weight,
appetite, fat accumulation. These are associated with weight loss
and leaner body types. A lower abundance of associated bacteria is
detrimental and increases the risk of gaining weight and increases
inflammation.
The negatively associated bacteria are Alistipes, Clostrid-
ium, Faecalibacterium, Lactobacillus, Prevotella and Blautia. A higher
abundance of negatively associated bacteria promotes weight gain,
inflammation and metabolic disorders. A lower abundance of neg-
atively bacteria is beneficial and reduces the risk of gaining weight
and inflammation (Tables 2 and 3).
Table 2. Percentile Range for High to Low Risk Associated Bacteria Used in the Digbi Health Database
High Risk 0 to 59.9% Low Abundance of Good Bacteria
Moderate Risk 60 to 79.9% Moderate Abundance
Low Risk 80%-99.9% Normal Abundance
Table 3. Percentile Range for High to Low Risk Negatively Associated Bacteria Used in the Digbi Health Database
High Abundance of Bad High Risk Above 80% Bacteria
Moderate Risk 60 to 79.9% Moderate Abundance
Low Risk 0 to 59.9% Low Abundance
Table 4. Male and Female Subject’s Obesity Risk (DNA) Profile
MAF, MC4R, MTCH2, SEC16B, NPC1,
LEP, PCSK1, NEGR1, KCTD15, STK33,
Tendency to gain weight BDNF, ADRB21, ADRB2, ADIPOQ, Moderate Risk
TMEM18, UCP3, ADRB1, UCP2,
GNPDA2, SH2B1, ETV5, FTO
Tendency to overeat FTO,TAS2R38, MC4R, DRD2, CLOCK Low Risk
Tendency to prefer fatty foods CD36, CD361 Moderate Risk
TAS1R2,TAS2R381,TAS1R21,
Tendency to prefer sweet foods TAS1R3,TAS2R38, GLUT2,TAS1R31, Moderate Risk
TAS2R382
Tendency to prefer bitter foods TAS2R38,TAS2R382,TAS2R381 Moderate Risk
SEC16B, RBJ, FTO1, FTO, FLJ35779, Carbohydrate intake and weight FAIM2, FABP2, AMY1, LRRN6C, Moderate Risk gain tendency TCF7L2
Saturated fats intake and weight FTO, APOA2 Low Risk gain tendency
Mono unsaturated fats intake NR1D1, ADIPOQ, PPARG High Risk and weight gain tendency
Poly unsaturated fats intake and BDNF Low Risk weight gain tendency
Protein intake and weight loss FTO Low Risk tendency
Tendency to regain weight TFAP2B, PPARG, ADIPOQ, BDNF Moderate Risk
Weight loss or weight gain High Risk INSIG2, FTO, ADRB2 with exercise (Unfavorable)
Exercise motivation BDNF Moderate Risk
Obesity Risk Index risk;
The male subject’s eating pattern prior to starting the program was
slightly high in carbohydrates and processed foods such as chips,
pretzels at snack times late in the evening and he would drink 35
cups of coffee per week.
The results revealed the male subject had a genetic mod-
erate obesity risk of 1.84 with 3 being the highest risk. According to
the genetic markers on genes NR1D1, ADIPOQ, PPARG assessed,
he showed a genetic tendency for a higher BMI on a monoun-
saturated fatty acids (MUFA) diet, but less likely to gain weight on
high polyunsaturated fatty acids (PUFA) diet according the mark-
ers assessed on the BDNF gene. His results also revealed he was
highly likely to lose weight with a low carbohydrate intake and high
protein intake according to the markers assessed on the follow-
ing genes SEC16B, RBJ, FTO1, FTO, FLJ35779, FAIM2, FABP2,
AMY1, LRRN6C and TCF7L2. According to the CYP1A2 gene,
the results revealed that the male subject was a slow metabolizer of
caffeine and was advised to reduce his coffee intake to two cups a
day to reduce inflammation and cardiac risk. The male subject did
significantly reduce his caffeine intake.
The report of the male subject in this case study also Male Subject Female Subject
Likely to gain weight Moderate Risk Likely to gain weight
Less likely to overeat Moderate Risk Likely to overeat
Likely to over consume Less likely to over consume Low Risk high fat foods high fat
Likely to over consume Highly likely to over consume High Risk sweet foods sweet foods
Normal bitter vegetable Normal bitter vegetable prefer- Moderate Risk preference ence
Likely to gain weight on Likely to gain weight on high carb Moderate Risk high carb intake intake
Less likely to gain weight Less likely to gain weight on high Low Risk on high SFA intake SFA intake
Highly likely to gain weight Highly likely to gain weight with High Risk with high MUFA intake high MUFA intake
Less likely to gain weight Less likely to gain weight with high Low Risk with high PUFA intake PUFA intake
Highly likely to lose weight Highly likely to lose weight on Low Risk on high protein intake high protein intake
Likely to regain weight Likely to regain weight after Moderate Risk after weight loss weight loss
May not experience much Low Risk Likely to experience higher weight
weight loss (favorable) loss
Moderate inclination Moderate inclination towards Moderate Risk towards exercise exercise
1.84 (1=Lowest 1.76 (1=Lowest
Moderate Risk risk; 3=Highest Moderate Risk
3=Highest risk) risk)
revealed that he was an unfavorable candidate to experience much
weight loss with exercise, based on his genetic markers. So, he was
advised to continue to exercise, but to focus on dietary means to
reduce weight. His diet included focusing on complex carbohy-
drates such as fruits, vegetables, legumes and whole grains, as well
as increasing intake in grape seed oil, chia seeds, tuna and mackerel
which are rich in polyunsaturated fatty acids (PUFA). The male
subject in this case study, had a moderate obesity risk according to
his gut microbiome report and his microbiome diversity score was
8.16 out of 10. A greater microbiome diversity score is associated
with good health.28 The microbiome diversity score is calculated by
normalizing the inverse form of Simpson’s Diversity Index to 10.
Scores range from 0 to 10, with 10 being the most diverse.
The female subject’s eating pattern prior to starting the program
was high in carbohydrates and processed foods such as chips, pret-
zels and protein bars. She also drank 14 cups of coffee a week
before starting the program.
After taking her sample the results revealed that she had
a genetic moderate obesity risk 1.76. Her results also revealed that
she had a genetic tendency for rapid weight regain after a weight
Table 5. Male and Female Subject’s Obesity Risk (Gut Microbiome) Profile
Akkermansia High Risk
Bacteroides Low Risk This genus has shown potential to combat weight gain Low Risk
Bifidobacterium High Risk This genus has shown potential to combat weight gain High Risk
Anaerostipes High Risk Associated with weight loss and leaner body types High Risk Associated with weight loss and leaner body types
Coprococcus High Risk Associated with weight loss and leaner body types High Risk Associated with weight loss and leaner body types
Roseburia Low Risk Associated with weight loss and leaner body types Low Risk Associated with weight loss and leaner body types
Eubacterium High Risk Regulates weight and appetite
Ruminococcus High Risk Regulates weight and appetite
Alistipes Low Risk
Clostridium Low Risk
Faecalibacterium Low Risk High abundance increases Obesity/metabolic disorder High Risk
Lactobacillus Low Risk High abundance increases Obesity/metabolic disorder Low Risk
Prevotella Low Risk High abundance increases Obesity/metabolic disorder Low Risk
Firmicutes/ Your gut microbiome’s Firmicutes/Bacteroidetes Ratio Your gut microbiome’s Firmicutes/Bacteroidetes 1.1:1 (ideal range is Bacteroidetes suggest moderate risk of chronic inflammatory risk- Ratio suggest moderate risk of chronic inflammatoless than 0.9:1) Ratio weight gain, aches and pains and headaches.
Obesity Risk 2 (1=Lowest risk;
Index 3=Highest Risk)
The markers are individual traits that ether positively or negatively associate with obesity based on
qualitative relative to the uBiome healthy cohort.
loss program according to the genetic markers on the genes ADI-
POQ, PPARG, TFAP2B and BDNF. She was advised to follow a
healthy lifestyle and eating pattern to ensure better weight main-
tenance. She was also advised to not skip breakfast, but rather to
eat a nutrient dense breakfast, avoid processed foods and sugar
sweetened beverages and include more whole grains, nuts, fruits
and vegetables in her diet. According to the genetic markers on
the genes TAS1R2, TAS2R381, TAS2R38, TAS2R382, GLUT2,
TAS1R21, TAS1R3, TAS1R31 her results revealed a genetic ten-
dency for lower sweet taste perception and overconsumption of
sweet foods. She was advised to drink more water and try to con-
sciously reduce intake of sweets, which she did, and this helped
with her weight loss.
Her genetic report also revealed a favorable response to
weight loss when engaging in regular exercise according to the ge-
netic markers assessed on the genes FTO, ADRB2, INSIG2. Inter-
estingly, similar to her husband, she also showed a genetic tendency
for a higher BMI on a MUFA diet, but less likely to gain weight on
high PUFA diet which would help to make meal planning and food
preparation together as a couple more aligned. Eating the right
food types for their genetic profiles helped with their weight loss.
The results of the first gut microbiome sample revealed
that the female subject in this case study had a high obesity risk and Male Subject Female Subject
This genus has shown potential to combat weight gain This genus has shown potential to combat weight High Risk and inflammation gain and inflammation
This genus has shown potential to combat weight
gain
This genus has shown potential to combat weight
gain
High Risk Regulates weight and appetite
High Risk Regulates weight and appetite
This genus has shown to promote weight gain and This genus has shown to promote weight gain and Low Risk inflammation inflammation
This genus has shown to promote weight gain and This genus has shown to promote weight gain and High Risk inflammation inflammation
High abundance increases Obesity/metabolic
disorder
High abundance increases Obesity/metabolic
disorder
High abundance increases Obesity/metabolic
disorder
1.69:1 (ideal range is
less than 0.9:1) ry risk-weight gain, aches and pains and headaches.
2.28 (1=Lowest risk; Moderate Risk High Risk 3=Highest Risk)
single nucleotide polymorphisms (SNPs) or bacterial genus abundance. Results are
her microbial diversity score was 8.22 out of 10.
After 16 weeks on the program the husband went from 251 pounds
to 207 pounds, achieving a 15.94% weight loss. He went from an
XXL size shirt to a large and lost four inches on his waist going
from a size 38 to 34-inch waist size. He also reported significant
health improvements after visiting his physician at the end of Au-
gust 2019:
• A1c level was 5.7
• BP reading was 109/80
• Total change in weight loss of 15.94%
• New BMI was 28.05 after program completion (reduced from
obese to overweight)
• Dosage change of his metformin-from 2 tablets daily to 1
tablet at night daily
• Stopped taking omeprazole due to relief of heartburn
symptoms
• The dosages of the other medication still remain the same
and will be reviewed by his physician in 6-months
Figure 1. Graph Showing Three-Month Weight Trend of Male Subject from the Start of the Program, Data from the Digbi Health App
Figure 3. Graph Showing Three-Month Weight Trend of Female Spouse from the Start of the Program, Data from the Digbi Health App sample 2. Christensenella limits fat accumulation. A significantly high increase
Figure 4. Graph Showing That the Probiotic Abundance was Relatively Unchanged between Sample 1 and Sample 2 of the Female Subject, Data from the Digbi Health App A Decrease in Abundance of Lactobacillus and Christensella were Noted in Sample 2. An Increase in Abundance of Akkermansia was Noted in Sample 2.
After completing 16 weeks on the program the husband
reported being easily able to maintain his weight because he knows
what foods are good for him and he knows the best way to prepare
his foods based on his genetic and gut microbiome profile. He felt
more energized and was able to incorporate strength training into
his fitness routine. He now goes to the gym daily (Figure 1).
A second gut fecal swab sample was taken by both the
husband and wife around the fourth month in the program and
the gut microbiome comparative profile was discussed during the
coaching session.
The results of the second gut microbiome sample re-
vealed that there was a substantial gain in the relative abundance
of “good” probiotic microbes in the gut and this resulted in a de-
creased obesity risk for the male subject. The bacterial microbial
diversity of the gut was largely unchanged (Figure 2).
The wife went from a starting weight of 197 to 170 and lost over
6 inches on her waist going from a size 16 to a size 10 and a total
change in weight loss of 13.65 percent. At the end of the program
she reported having more energy to take longer walks, around 45
minutes 4-5 times a week and has incorporated strength training
into her fitness routine when she goes to the gym, now three days
a week. Her genetic profile also reveals she is more likely to lose
weight with regular exercise. She also reported experiencing less
pain in her knees after the weight loss and she stopped taking the
omeprazole because she rarely suffers with heartburn (Figure 3).
The results of the second gut microbiome sample re-
vealed her bacterial microbial diversity of the gut was largely un-
changed, but her obesity risk moderately decreased, and her pro-
biotic abundance remained relatively unchanged. Her results also
revealed a reduced inflammation risk due to a substantial drop in the short chain fatty acid producing microbe butyrate. She was en-
couraged to increase her intake of fermented food and prebiotic
fiber-rich food including, chicory root, dandelion greens, Jerusa-
lem artichokes, lettuce, endive, asparagus, garlic, onions and leeks
help increase her probiotic abundance. Research has also revealed
that regular exercise can also improve your gut microbiome (Figure
4).29
A big challenge of obesity management is not weight loss, but
long-term weight loss maintenance.30 Weight loss maintenance is
often hindered by a complex interaction of biological, behavior-
al, and cognitive factors. The number of couple-based studies
were limited for this case study. More methodologically analyzed
couple-focused interventions are therefore required in order to de-
termine added benefits of couple-based interventions relative to
evidence-based individual interventions based on gut microbiome
and genetic profiles. Most obesity studies have either focused on
genetic profiles alone or gut microbiome alone, not a combina-
tion. Studies, ideally randomized controlled trials, are still needed
looking at a combination of DNA and gut microbiome together.
The Digbi Health program is the only program that provides the
individual with an obesity risk profile based on their genetics and
gut microbiome.
If one is equipped with all the tools needed for success-
ful maintenance and encouraged in a supportive environment, the
chances of regaining weight upon program completion is reduced.
The couple are now in the maintenance phase of their program
and have reported they both feel more energized, are exercising
more regularly, including playing golf together, spending more
time outdoors and feel very empowered to know how to prepare
the right foods for themselves and their bodies. They believe the
coaching sessions were invaluable because it made them feel ac-
countable for what they were eating and when they were eating.
They also reported that having a complete DNA and gut microbi-
ome profile was empowering to help them understand what foods
can be eaten and which ones should be avoided based on their
unique data. They believe that the Digbi Health App helps them to
stick to their goals and stay engaged and focused on their weight
loss journey to improved health. They also realized that following a
one-size-fits-all diet does not benefit individuals because everyone
is different. Five months after completing the program, they con-
tinue to maintain their weight.
This case study was limited to the assessment of one
couple who completed the Digbi Health program. The couple also
had a similar gene nutrition profile so food preparation and eating
the same foods benefited both for weight loss. More investigations
will need to be conducted to evaluate the potential of weight loss
amongst other cohabiting couples on the same program.
Since the sequencing of the human genome through the Human
Genome Project (HGP), the progress of “omics” techniques, in-
cluding gut microbiome analysis, personalized nutrition has been
extended to diseases mediated by gene-environment interactions
such as obesity and metabolic-related diseases such as type 2
diabetes. Growing studies have found that changes in adiposity,
sugar-related blood markers and metabolic response to low-calorie
weight loss diets might be modified by genetic variants related to
obesity, metabolic status, and preference to nutrients.
The gut microbiome has been identified in the past de-
cade as an important factor in obesity and diabetes and the mag-
nitude of its contribution to obesity and its related comorbidities
has aroused curiosity in the scientific community.25 Among the vast
quantity of factors attributed to obesity, environmental, dietary,
lifestyle, genetic, and others including the microbiome continues
to play an important role in the multifactorial approach to its man-
agement.1
In conclusion, we have come to realize there are many
factors that may help an individual lose weight. In particular co-
habiting couples’ close relationships, following a weight loss pro-
gram personalized to their gene and gut microbiome profile, may
provide a more supportive environment for overweight or obese
men and women to initiate the pursuit of weight loss and maintain
healthy dietary practices and increased physical activity.
We would like to acknowledge the Prescribe Fit team, a partner in
the Digbi Health Precision Care Network, for their referral of the
couple in this case study to the Digbi Health program.
Dr. Singh-Rambiritch reports that fees for the Digbi Health pro-
gram were collected from the couple through the institution, Pre-
scribe Fit, who is one of the Digbi Health partners that supplies
the program to at cost.