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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, stating their individual and personalized approach gave them the tools long-term to maintain.
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.
Diabetes; Gut microbiome; Obesity; Diet; Physical exercise; Overweight; Body mass index (BMI); Couples; Hypertension.
The obesity epidemic has been largely attributed to changes in lifestyle habits established over the past three decades. These changes are mainly attributed to excessive nutrition and decline in physical activity as well as additional factors such as reduced intestinal microbiota diversity, sleep duration, endocrine disruptors, and reduced variability of the ambient temperature. However, the obesogenic environment is not sufficient to determine the presence of obesity, it is necessary that the lifestyle becomes associated with a personal predisposition for the phenotype to emerge. In this article, we review the main forms of monogenic and syndromic obesity, as well as a historical summary of the search for the genes that add up to confer greater risk for the development of polygenic obesity.
We carried out a PubMed search, along with ExcerptaMedica database (EMBASE)/Cochrane library, Web Sciences for the Medical Subject Headings (MeSH) terms “obesity’’ AND “genetics” for the past 5-years.
We found a total of 14057 articles pertaining to obesity and genetics together of which we selected 92 articles for this review after getting articles after searching cross references.
Studies with twins and adopted children show that 55 to 80% of the variation of body mass index (BMI) is attributed to genetic factors. According to the genetic criteria, obesity can be classified as A) Monogenic – when a mutated gene is responsible for the phenotype; B) Syndromic – when a set of specific symptoms are present and a small group of genes is involved; usually the term is used to describe obese patients with cognitive delay, dysmorphic features, organ-specific abnormalities, hyperphagia, and/or other signs of hypothalamic dysfunction; C) Polygenic – also called “common” obesity, present in up to 95% of cases. Many genes add up to give a greater risk to the individual, and if associated with some habits culminates in obesity. In spite of its great relevance, the search for the genes that raise the risk of obesity has not been easy. It is still a challenge for the scientific community to separate the genetic element from the environmental component in the etiology of this disease. Individuals more susceptible to excessive adiposity may carry risk variants in the genes that influence appetite control, the regulation of cellular machinery, lipid metabolism and adipogenesis, the energy expenditure, insulin signaling, and inflammation.
Obesity; Genetics; Polygenic; Monogenic; Syndromic; Polymorphism.
While there are some studies on sleep and physical activity, little is known regarding the associations between sleep and sedentary behavior. This study investigated the associations between sleep, sedentary behavior, and physical activity among young adults.
Cross-sectional data from 124 undergraduate students were included in the analysis (age=21±1 years). Both accelerometer-based and self-report assessments of sleep were included; physical activity and sedentary behavior were assessed by accelerometers. Participants were asked to fill out sleep questionnaires and wear accelerometers for 7 days. Pearson correlations, partial correlations, and analysis of covariance (ANCOVA) analyses were performed to investigate the relationships between sleep, sedentary behavior, and physical activity.
After adjusting for age, gender, percent body fat, educational level, and monthly allowance, prolonged sedentary time was correlated with a shorter sleep onset latency (r=-0.19, p=0.04), shorter time in bed (r=-0.43, p<0.001), and shorter sleep duration (r=-0.38, p<0.001). Moderate-to-vigorous physical activity (MVPA) was positively correlated with sleep onset latency (r=0.43, p<0.001). Sedentary behavior and MVPA were not correlated with sleep quality or daytime sleepiness. After further categorizing sleep duration into three subgroups, individuals with ≤6 hours (p<0.001) of sleep spent more time being sedentary than did those with 6-7 hours (p<0.001) and ≥7 hours (p=0.007) of sleep. Individuals with 6-7 hours of sleep had a higher level of MVPA than did those with ≥7 hours of sleep.
Improving the duration of sleep may be a viable approach to help reduce sedentary behavior among young adults. Future studies with longitudinal designs are needed to further investigate the directionality of these associations and their potential mediators and moderators.
Accelerometer; Sleep; Sedentary; Physical activity.
Department of Nutrition
School of Public Health and Health Sciences
University of Massachusetts, Amherst
Department of Health Sciences
College of Public Health
East Tennessee State University
P. O. Box 70673 Johnson City, TN 37614, USA
Chair ISAFA www.isafa.info
Qatar Olympics Committee Professorial Chair in Sport Science
College of Arts and Sciences
Al Tarfa, Doha 2713
Department of Nutrition
School of Public health
Iran University of Medical Sciences