Recent research has revealed that obesity is not simply caused by overeating or lack of exercise, but that genetic factors play a major role. Certain genes affect metabolism, appetite, and fat accumulation, which means that some people are more likely to gain weight than others, even if they eat the same diet.
Genetic testing can help you find a diet method and lifestyle improvement plan that suits your constitution. This article explains in detail the main genes involved in obesity and their effects, how to use genetic testing, and scientifically based countermeasures.
Twin studies have shown that the heritability of obesity is approximately 40-70% (Stunkard et al., 1990), indicating that the risk of obesity is largely determined not only by environmental factors but also by genetic factors.
Genetic factors that influence obesity include:
Differences in energy metabolism (high and low basal metabolic rate)
Appetite regulation (how easily you feel hungry)
Ease of fat accumulation (lipid metabolism ability)
Effects of exercise (efficiency of fat burning through exercise)
The FTO (Fat mass and obesity-associated) gene is a representative gene involved in obesity. Mutations in this gene increase appetite, especially the tendency to prefer high-calorie foods (Loos & Bouchard, 2008).
Type AA : Strong appetite and high carbohydrate intake.
AT type : Moderately affected.
TT type : Appetite is easily controlled.
2. MC4R gene (appetite and energy expenditure)
The MC4R (melanocortin 4 receptor) gene is involved in regulating satiety and energy expenditure. People with this mutation are less likely to feel full, which makes them more likely to overeat.
3. ADRB2 gene (fat burning and exercise effects)
The ADRB2 (β2 adrenergic receptor) gene is a gene that influences the efficiency of fat burning. Mutations in this gene affect the effectiveness of aerobic exercise.
Glu27Glu type : Highly effective in burning fat through aerobic exercise.
Gln27Gln type : Fat burning efficiency is low, and exercise is less effective.
4. UCP1 gene (thermogenesis and fat burning)
The UCP1 (uncoupling protein 1) gene affects mitochondria in fat cells and regulates energy consumption through heat production. Mutations in the UCP1 gene decrease basal metabolism, making people more likely to gain weight.
ADRB2 mutation present (poor fat burning) : Prioritize strength training over aerobic exercise.
UCP1 mutation (low basal metabolic rate) : Short-term high-intensity training (HIIT) is effective.
MC4R mutation (strong appetite) : Light exercise before meals will prevent a sudden rise in blood sugar levels.
3. Genetic testing and the latest research trends
Use of Polygenic Risk Scores (PRS)
Recent research has advanced the technology of using polygenic risk scores (PRS) to comprehensively analyze multiple genetic information and precisely assess individual obesity risk (Shadrina et al., 2018).
Integrating AI and genetic data
Research is also underway to utilize AI technology to combine genetic information and lifestyle data to propose personalized measures to combat obesity.
4. Specific measures for obesity prevention and weight management using genetic testing
By utilizing genetic information, it is possible to find an obesity prevention method that suits your constitution. Here, we will explain in detail scientifically based measures for improving diet, exercise, and lifestyle habits.
1. Genetic Type-Specific Meal Plans
(1) People with a strong appetite and a tendency to overeat (FTO/MC4R gene mutations)
People with this type of diet tend to have difficulty feeling full and controlling their eating habits.
Countermeasure
Order your meals accordingly : Eating fiber-rich vegetables first will help prevent blood sugar spikes and keep you feeling fuller for longer.
Increase high-protein foods : Protein increases satiety, so actively consume meat, fish, and beans.
Small meals: Eat 5-6 small meals a day instead of 3 meals a day to curb hunger.
Foods to avoid
High GI foods such as white rice and white bread (which spike blood sugar levels and increase hunger)
Fast food and processed foods (high in salt and fat, which increase appetite).
People with this type of diet tend to experience a sudden rise in blood sugar levels when they eat carbohydrates, which are then easily stored as fat.
Countermeasure
Choose low-GI foods : Focus on brown rice, oatmeal, and whole-wheat bread.
Eat protein with your meals : Combining it with meat, fish, and eggs, rather than just carbohydrates, will help prevent blood sugar levels from rising.
Snack on nuts and cheese : Choose snacks that help stabilize blood sugar levels.
Foods to avoid
White rice, bread, and sugary snacks.
Sugary juices and carbonated drinks (which spike blood sugar levels).
5. Potential for future obesity countermeasures using genetic testing
1. Obesity prevention using AI and big data
Using AI technology, genetic information and dietary and exercise data are analyzed to automatically generate the optimal diet plan for each individual.
Works in conjunction with wearable devices to monitor energy consumption and the effects of diet in real time.
2. Advances in gene editing technology and obesity prevention
Research is underway to use CRISPR technology to correct genetic mutations that confer risk of obesity.
It is hoped that new medical technologies will be developed to suppress the expression of obesity-related genes.
6. The latest trend in personalized diets using genetic testing
Personalized dieting using genetic testing is attracting attention as a new, evidence-based approach. Unlike conventional dieting methods such as calorie restriction and increased exercise, its major feature is that it identifies a person’s constitution at the genetic level and allows them to select the most effective method.
1. Types of diet programs that utilize genetic information
(1) DNA diet (dietary management according to genotype)
The DNA diet is a method of optimizing nutritional balance and dietary content based on an individual’s genetic information.
Genetic type with poor glucose metabolism (TCF7L2 mutation)
Recommended diet : Focus on low GI foods (brown rice, oatmeal, whole wheat bread).
Foods to avoid : Sugary snacks, refined carbohydrates (white rice, white bread).
Genetic type with poor fat metabolism (PPARG/APOA2 mutations)
Dietary recommendations : Consume a moderate amount of healthy fats (olive oil, nuts, fish).
Foods to avoid : butter, fried foods, and processed meats (bacon, sausages).
(2) Genotype-specific exercise programs
Your genes determine whether strength training or aerobic exercise is more effective for you.
A gene for excellent endurance (ACTN3 R577X gene)
Recommended exercise : jogging, cycling, and long aerobic exercise.
A gene that excels at muscle growth (ACTN3 RR type)
Recommended exercises : weight training, high-intensity interval training (HIIT）。
7. Utilizing genetic information for public health and obesity prevention
Measures to combat obesity using genetic information are beginning to have an impact not only on the individual level, but also on national and corporate health policies.
1. National health policy utilizing genetic data
In the United States and Finland , personalized nutritional guidance using genetic information has been introduced.
In Japan, research is underway into specific health guidance using genetic testing .
2. Corporate Health Management and the Introduction of Genetic Testing
A major company has introduced genetic testing as part of its employee health management program, and has begun efforts to reduce the risk of lifestyle-related diseases.
Example: There is a growing trend in company cafeterias to offer menus based on genetic information.
9. The future of obesity prevention using genetic information
Obesity prevention measures using genetic testing are currently evolving and are predicted to become even more personalized in the future, enabling more effective health management through early detection of obesity risk, optimization of individualized diet plans, and advances in medical technology.
1. Integration of genetic information and personalized medicine
(1) Advancement of genetic risk assessment for obesity
**Polygenic Risk Score (PRS)** combines multiple genetic data to more accurately assess obesity risk.
Example: Comprehensive analysis of multiple gene mutations such as FTO, MC4R, PPARG, and ADRB2 to quantify risk levels.
This allows for early intervention and precise customization of dietary management and exercise plans.
(2) Popularization of personalized diets using AI
AI analyzes genetic information, blood sugar data, and intestinal bacteria data to suggest optimal diet and exercise plans.
For example, services like ZOE and Lumen measure metabolic data in real time and provide personalized meal plans.
(3) Diet programs provided by medical institutions using genetic testing
There are an increasing number of cases where hospitals and clinics are providing dietary advice based on genetic testing.
We offer individual programs that take into account the risk of lifestyle-related diseases such as diabetes and high blood pressure.
3. Integrating genetic testing with wearable devices
(1) Real-time obesity risk management
Combine genetic information with a smartwatch (Apple Watch, Fitbit, etc.) to monitor your health in real time.
AI analyzes each individual’s metabolic state based on changes in blood sugar levels, body temperature, and heart rate, and suggests optimal diet and exercise.
(2) Food selection using genetic information
You can now use a smartphone app to select foods that suit your genes at supermarkets and restaurants .
For example, a service is currently under development that will allow you to scan a barcode and see whether a food is genetically suitable.
(3) Integrating genetic data with mental health
Predict the impact of stress on obesity using genetic data and propose optimal interventions to reduce stress .
For example, people with the 5-HTTLPR gene mutation are recommended to practice mindfulness and take certain nutrients (tryptophan) to manage stress.
11. Potential for new obesity countermeasures using genetic information
Advances in genetic testing have dramatically improved the accuracy of obesity risk assessment and personalized dieting. The introduction of more advanced technology in the future will likely make obesity prevention and management even more effective. This chapter provides a detailed explanation of the latest obesity countermeasures that utilize genetic information.
1. New nutritional therapy utilizing genetic information
(1) “Smart Food” using genetic data
In recent years, development of specific foods based on genetic information has been progressing.
Development of low-sugar, high-protein foods
A food product that prevents sudden rises in blood sugar levels has been developed for people with a genetic mutation (TCF7L2 mutation) that causes poor sugar metabolism.
Example: Developing pasta and rice using carbohydrates with a low GI value.
Foods enriched with healthy fats
Foods rich in omega-3 fatty acids are becoming popular for people who have difficulty metabolizing lipids (PPARG mutations).
Examples: Omega-3 enriched eggs, yogurt rich in EPA and DHA.
(2) Providing nutritional supplements for each gene
DNA-based vitamin and mineral supplementation
For example, for people with a VDR gene mutation that results in poor vitamin D metabolism, supplements with increased absorption are recommended.
Heme iron supplements are provided to people with the HFE gene mutation, which causes poor iron absorption.
Genetic testing allows for a scientific analysis of the causes of obesity and allows individuals to select diet and lifestyle options that suit their constitution. Genes such as FTO, MC4R, and PPARG are known to affect appetite, fat metabolism, and exercise effects, making personalized nutrition and exercise plans important.
Furthermore, advances in AI and gene editing technology are expected to enable even more precise obesity prevention and treatment in the future. By utilizing genetic information and providing evidence-based health management, more effective measures against obesity will be realized.
