従来の遺伝子検査では、1つまたは数種類の遺伝子変異を分析するのが一般的でした。しかし、近年では**ポリジェニックリスクスコア（PRS）**という新たなアプローチが登場し、複数の遺伝子変異を統合的に評価することで、より正確な肥満リスクを予測できるようになっています（Shadrina et al., 2018）。
It has been scientifically proven that obesity is not simply caused by overeating or lack of exercise, but that genetic factors also play a major role. Recent research has shown that specific genes are involved in fat accumulation, metabolism, and appetite regulation, making it possible to implement prevention and countermeasures tailored to each individual’s constitution.
By utilizing genetic information, you can find the diet and exercise methods that are best suited to you and more effectively prevent and manage obesity. This article provides a detailed explanation of the major types of genes related to obesity, dietary and exercise methods based on these, and the latest obesity countermeasures using genetic testing.
Twin studies have shown that the heritability of obesity is 40-70% (Stunkard et al., 1990), indicating that obesity is largely determined by genetic as well as environmental factors.
The genetic influence on obesity can be categorized into the following main factors:
Energy metabolism (basal metabolic rate)
Appetite regulation (how easily you feel full or hungry)
Ease of fat accumulation (tendency to increase fat cells)
Effects of exercise (fat burning efficiency and ease of muscle building)
The FTO gene is involved in appetite and energy metabolism, and mutations in the gene have been reported to increase the tendency to overeat (Loos & Bouchard, 2008).
Type AA : They have a strong appetite and tend to prefer high-calorie foods.
AT type : Moderately affected.
TT type : Appetite is easily controlled.
2. MC4R gene (regulates satiety)
The MC4R gene is associated with neurotransmitters that regulate satiety and appetite. Mutations in the gene can make it harder for people to feel full, which can lead to overeating.
3. ADRB2 gene (fat burning and exercise effects)
The ADRB2 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 (heat production and energy expenditure)
The UCP1 gene affects the mitochondria of fat cells, increasing energy consumption. Mutations in the UCP1 gene decrease basal metabolism, making it easier for body fat to accumulate.
People with this type of diet tend to experience a sudden rise in blood sugar levels when they consume carbohydrates, which are then easily stored as fat.
Recommended foods : brown rice, whole wheat bread, oatmeal (low GI foods).
Foods to avoid : White rice, white bread, and foods high in sugar.
Solution : Eat plenty of dietary fiber to prevent a sudden rise in blood sugar levels.
(2) Type with poor lipid metabolism (PPARG/APOA2 gene mutations)
People with this type metabolize saturated fatty acids slowly, and a high-fat diet is likely to lead to increased body fat.
Recommended foods : olive oil, nuts, and oily fish (unsaturated fatty acids).
Foods to avoid : butter, fried foods, and processed meats.
Solution : Reduce fat intake and choose healthy fats.
3. The latest obesity countermeasures using genetic testing
1. Integrating AI and genetic data
Services are emerging that use AI technology to comprehensively analyze genetic information and diet and exercise data to propose individually optimized diet plans.
“DNAfit” : Personalized diet and exercise plans based on genetic data.
“ZOE” : Integrates intestinal bacteria and genetic data to suggest dietary recommendations based on blood sugar levels and lipid metabolism.
4. Future obesity prevention and countermeasures using genetic information
In recent years, advances in genetic research have made it possible to assess obesity risk in more detail and implement personalized prevention and countermeasures. In the future, integration with AI technology and biotechnology will likely lead to even more advanced health management. This chapter explains the latest research trends and the future of obesity countermeasures.
1. The evolution of personalized diets using genetic information
(1) ポリジェニックリスクスコア（PRS）による精密なリスク評価
While traditional genetic testing typically analyzes one or a few gene mutations, a new approach known as polygenic risk scores (PRSs) has emerged in recent years, which allows for more accurate prediction of obesity risk by comprehensively assessing multiple gene mutations (Shadrina et al., 2018).
PRS usage examples
Dozens to hundreds of obesity-related genes, such as FTO, MC4R, PPARG, and ADRB2, are combined to assess individual risk.
Dietary and exercise plans are individually optimized, and appropriate measures are implemented according to risk.
(2) Customized diet based on genotype
Genetic testing is increasingly being used in diet programs, with personalized approaches including:
People who are suited to carbohydrate restriction (TCF7L2 mutation)
A diet centered on low GI foods is recommended to prevent sudden rises in blood sugar levels.
People who are suited to a high-protein diet (with UCP1 mutation)
Focus on protein intake to increase muscle mass and improve basal metabolism.
People for whom healthy fats are important (with PPARG mutations)
3. Interaction between genetic information and intestinal bacteria
Gut bacteria are known to have a significant impact on the risk of obesity. Integrating genetic information and gut bacteria data will enable more effective measures to combat obesity.
(1) The relationship between intestinal bacterial balance and obesity
Obese individuals tend to have more bacteria in the phylum Firmicutes and fewer bacteria in the phylum Bacteroidetes (Turnbaugh et al., 2006).
Supports weight management by analyzing the balance of genetic information and intestinal bacteria and providing appropriate probiotics (lactic acid bacteria and bifidobacteria).
(2) AI-based recommendations for optimizing intestinal bacteria balance
The state of your intestinal bacteria is measured and AI automatically adjusts the optimal meal plan.
**Example: Services such as “Viome”** integrate gut bacteria data with genetic information to suggest personalized dietary programs.
6. Next-generation obesity prevention and health management using genetic information
Advances in genetic research are making obesity prevention and management more precise and personalized. Going forward, we anticipate the integration of AI technology, biotechnology, and personalized nutrition to provide optimal obesity countermeasures. This chapter provides a detailed look at cutting-edge technologies in next-generation obesity management and their practical applications.
1. Next-generation nutrition planning using genetic information
(1) Real-time dietary management using AI and genetic information
AI integrates genetic data, intestinal bacteria data, and real-time blood glucose level information to automatically generate a daily meal plan.
**Example: Services such as “ZOE” and “Nutrigenomix”** offer personalized diet optimization based on genetic data.
(2) Development of customized foods based on genotype
Functional food manufacturers are accelerating their efforts to provide foods based on genetic data.
For example, foods enriched with omega-3, low GI foods specifically designed for blood sugar management, and high protein foods are individually designed.
(3) Realizing personalized meals using 3D food printers
Technology is underway to combine genetic information with real-time health data to create meals containing optimal nutrients using a 3D food printer .
NASA is testing a customized diet for astronauts that uses genetic data.
2. Optimizing movement planning using genetic information
(1) Designing fitness programs based on genotype
Based on the results of your genetic testing, we will create the most effective exercise program for you , adjusting the balance between strength training and aerobic exercise .
For example, people with genes suitable for endurance (ACTN3 XX type) should focus on aerobic exercise, while people with genes suitable for muscle building (ACTN3 RR type) should focus on weight training.
(2) Integration of smart clothing and genetic information
Smart wear (wearable devices) analyze genetic data and measure the fat burning effect of exercise in real time.
For example, “DNAFit” uses AI to suggest optimal workouts based on your genes.
3. Anti-obesity measures by adjusting genetic information and hormone balance
(1) Metabolism-related genes and hormone regulation
Since the accumulation of body fat is strongly influenced by hormonal balance, attention is being paid to hormone regulation based on genetic information.
For example, people with FTO gene mutations tend to have reduced secretion of leptin (an appetite-suppressing hormone), so they need to supplement this with a specific diet.
(2) AI-based hormone level monitoring and adjustment
The wearable device monitors hormone balance and suggests optimization of diet, exercise, and supplements.
Example: Personalized supplements that balance blood sugar and cortisol (the stress hormone) are now available.
5. Advances in health management for society as a whole using genetic information
(1) Corporate health management using genetic data
Companies are increasingly using genetic information to manage the health of their employees, preventing lifestyle-related diseases and improving work efficiency.
Example: An increasing number of companies are using genetic testing data from their employees to provide optimal health management programs.
(2) Introduction of genetic nutrition into school education
School lunches will be optimized and health guidance tailored to children’s genetic types will be introduced.
Examples include providing appropriate diets for children with specific genotypes and customizing obesity prevention programs.
7. The future and challenges of obesity prevention using genetic information
Advances in genetic research and technology are ushering in an era in which obesity prevention and management are becoming more precise and personalized. Instead of the one-size-fits-all diet and health management methods of the past, approaches that optimize diet, exercise, and lifestyle based on individual genetic information are becoming more widespread. This article takes a closer look at the future of obesity prevention and the challenges that come with it.
1. Genetic information and the evolution of personalized nutrition
(1) Completely personalized meals: Integrating genetics, AI, and food technology
By combining genetic information with AI, the optimal nutrition plan for each individual is automatically created.
Dynamically adjust your diet in conjunction with real-time blood sugar, hormone balance, and metabolic data.
For example, “DNAfit” and “Nutrigenomix” calculate nutritional information based on genetic data and suggest daily meal plans.
(2) Optimizing food choices using genetic data
You can use a smartphone app to select foods that suit your genes at supermarkets and restaurants.
By scanning the barcode of ingredients, it is instantly determined whether the food is genetically suitable.
Example: In Finland, a service is being developed that optimizes food choices based on genetic information.
(3) Development of functional foods using genetic information
Functional foods enriched with specific nutrients based on genotype are now available.
Example：
A vitamin D fortified food with increased absorption rate for people with VDR gene mutations that reduce vitamin D absorption.
We have developed low GI foods for people with the TCF7L2 mutation, who have difficulty metabolizing carbohydrates。
8. Challenges and future prospects for using genetic information to combat obesity
While the use of genetic information to prevent and combat obesity holds great promise, there are also some challenges. As technology advances, it is hoped that these challenges will be overcome and more effective health management will become possible.
1. Limitations and precautions of genetic testing
(1) Genetic information alone cannot completely predict obesity
Obesity is not only influenced by genes, but also by environmental factors and lifestyle.
It is important not to rely too much on the results of genetic testing and to take comprehensive health management measures.
(2) Scientific evidence must be established
Some genetic testing services are not based on sufficient scientific evidence.
As research into genetic nutrition advances, more accurate data analysis will become possible.
3. Future obesity prevention measures using genetic information
(1) Integration of genetic information and IoT
Advances in AI and smart devices are enabling real-time health monitoring.
It works in conjunction with smartwatches and blood glucose sensors to provide dietary and exercise advice tailored to each individual’s metabolic state.
(2) Development of gene editing technology and obesity treatment
Research is underway into treatments that utilize CRISPR technology to regulate the activity of obesity risk genes.
This may potentially reduce the underlying risk of obesity.
In the future, obesity countermeasures that utilize genetic information will likely contribute to improving health management not only at the individual level, but also for society as a whole. By balancing technological advances with overcoming ethical challenges, we can look forward to a future in which more effective and fair obesity countermeasures will be realized.
Utilizing genetic information allows for individual assessment of obesity risk and more effective prevention and countermeasures. Genes such as FTO, MC4R, and PPARG affect appetite, fat metabolism, and exercise effects, making personalized diet and exercise plans important.
Furthermore, advances in AI and IoT technology are expected to lead to a future in which health data can be analyzed in real time, enabling optimal health management. However, privacy protection and the establishment of scientific evidence are necessary, and a balance must be struck between future technological developments and ethical issues.
