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Edit｜Wang Duoyu Typesetting｜Shuichengwen With economic development and improvement of living standards, overweight/obesity has become a major public health problem worldwide
.
According to statistics from the World Health Organization (WHO), nearly 2 billion people are overweight or obese in the world.
From 1975 to 2016, the global obesity rate has nearly tripled.
Every year, overweight or obesity causes 2.
8 million deaths
.
In fact, obesity is not only a feature, but also a disease.
Obese people not only have inconvenience in life and decreased exercise capacity, but are also more susceptible to metabolic diseases and cardiovascular and cerebrovascular diseases.
In addition, many studies have shown that obesity is associated with more than ten kinds of cancers.
The increased risk of disease is related to the decreased prognosis and survival rate
.
In recent years, various forms of weight loss interventions have been gradually applied in clinical practice and written into guidelines.
Life>
.
For a long time, low carbohydrate diets (LCD) have received the most attention as a dietary intervention mode for weight loss treatment
.
However, in different studies, the weight loss effects of LCD intervention are quite different.
At present, there is not enough evidence to explain this heterogeneity.
This is also a difficult point in the field of medical weight management
.
On September 15, 2021, the team of Professor Hong Chen and Professor Sun Jia from the Department of Endocrinology and Metabolism, Zhujiang Hospital of Southern Medical University, and the team of Professor Liwei Xie from the Gut Microecology and Health Team of Guangdong Institute of Microbiology, published the title: Gut in the journal Microbiology Spectrum microbiota serves a clinical research report of a predictable outcome of short-term low-carbohydrate diet (LCD) intervention for patients with obesity
.
This study reported for the first time that the baseline characteristics of the intestinal flora are the determinants of the short-term low-carbohydrate diet (LCD) weight loss effect of overweight and obese people.
The study constructed an artificial neural network based on the baseline characteristics of the intestinal flora to predict the effect of LCD weight loss.
(Artificial Neural Networks, ANN) model, this research provides brand new strategies and methods for clinical medical weight management and intervention
.
The study included 51 18-65-year-old male or female subjects who met the diagnostic criteria for overweight/obesity (no antibiotics or drugs were used in the first 3 months of the clinical trial).
The subjects were randomized into groups and were divided into different groups.
The energy-restricted normal diet group (Normal diet, ND) and the non-calorie-restricted low-carbohydrate diet group (LCD)
.
The diet intervention time was 12 weeks
.
In order to ensure the LCD diet structure, the LCD group adopted standardized nutrition bars (gifted by Guangzhou Nanda Feite Nutrition and Health Consulting Co.
, Ltd.
) to replace the daily staple food for lunch and dinner.
The number of other foods is not limited, and overeating is avoided
.
At the time of enrollment (ie baseline) and 12 weeks after the intervention, venous blood and stool samples were collected.
The blood samples were used for the detection of blood biochemical indicators such as glucose and lipid metabolism, liver and kidney function, and the stool samples were used for intestinal flora 16S rDNA amplicon sequencing, through 16S rDNA amplicon sequencing, a total of 2.
47 million high-quality Reads were obtained (Figure 1A)
.
The diet of the subjects was monitored through a 24-hour diet 3 days a week
.
During the entire study period, the average proportion of carbohydrate intake in the normal diet group was about 50%, and the proportion in the LCD group was about 20% (Figure 1B-D).
Although calorie intake was not restricted, the average energy intake of the low-carbon group was about 50%.
Enrollment was significantly lower than that in the normal diet group.
The 12-week LCD intervention significantly improved the subjects’ body parameters such as BMI, waist circumference, waist circumference, body fat percentage, and visceral fat area (Figure 1E)
.
Figure 1 Research overview and changes in clinical indicators of body weight In addition to different weight loss results, different dietary components may affect the composition and diversity of the intestinal flora, but apart from changes in the overall composition and phylum level, previous studies have not Draw a constructive conclusion to guide the clinical trial of weight loss under LCD.
Therefore, we analyzed the intestinal flora sequencing data, and adopted 5-fold cross-validation and random forest algorithm, taking into account the minimum sum of error rate and standard deviation To ensure the highest accuracy and stability, we analyzed the 16S rDNA sequence data of subjects in the ND and LCD groups before and after the test to identify potential flora biomarkers
.
A further analysis of all the genus screened by the random forest model in baseline and week 12 data found that the relative abundance of Ruminococcaceae Oscillospira and Porphyromonadaceae Parabacteroides increased significantly after the 12-week LCD intervention, and the difference was statistically significant (p< 0.
05) (Figure 2)
.
According to existing research reports, these two species of bacteria are involved in the production of butyrate in the intestine, suggesting that there may be other factors affecting weight changes during the process of LCD intervention in weight loss
.
Figure 2 The correlation between short-term LCD intervention and specific flora markers is to further analyze the weight loss of each subject.
According to the cluster stratification of the weight loss parameters BMI, waist circumference, WHR, BFR and VFA, each component is divided into Two subgroups: the moderate weight loss group (MG) and the distinct weight loss group (DG) (Figure 3A)
.
Under the conditions of LCD intervention, the energy intake and the proportion of carbohydrates in the diet of the two subgroups were almost the same, but the weight loss indicators of the subjects in the significant subgroup decreased more significantly, suggesting that the individualized differences in weight loss effects may be affected by other factors.
Impact (Figure 3E-F)
.
Figure 3 There are individual differences in the weight loss of the two dietary interventions in each subgroup.
The above results indicate that the LCD intervention has a better weight loss effect, but there are individual differences
.
Therefore, this study further analyzed the intestinal flora data of the two subgroups, and further explored whether there are potential factors related to the flora that caused the difference in weight loss between the two subgroups in this diet
.
In further subgroup analysis, we used the co-occurrence network at the genus level to further analyze the interaction between the intestinal flora in the LCD subgroup and found that after 12 weeks of LCD intervention, although the two subgroups LCD_DG and LCD_MG The network interaction complexity of the network has decreased, but LCD_DG showed a denser, more extensive and richer network interaction complexity than LCD_MG in the baseline and at the 12th week
.
The above results indicate that, in addition to the differences in the composition and diversity of the flora, the differences between the structure of the flora and the complexity of the interaction of the flora may be an important reason for the individual differences in weight loss effects (Figure 4C-F)
.
In the low-carbon subgroup, analysis by the random forest model algorithm found that the baseline relative abundance of Bacteroidaceae Bacteroides was statistically different between the two subgroups of the low-carbon diet (Figure 4I).
According to the linear regression analysis, we found that the baseline relative abundance of Bacteroidaceae Bacteroides The baseline relative abundance of Bacillus is positively correlated with the short-term low-carbon diet weight loss effect (Figure 4J-N)
.
Based on the above results, the ROC model was established based on the baseline relative abundance of the low-carbon subgroup of Bacteroides.
The ROC model reflects the susceptibility of each data point on the curve to the same signal stimulus, and comprehensively reflects the sensitivity and specificity of the variables
.
In this study, the ROC model AUC value reached 73.
2%, suggesting that the baseline relative abundance of Bacteroides has a certain predictive value for the short-term low-carbohydrate diet weight loss effect (Figure 4O)
.
Figure 4 Intestinal flora is an important factor affecting the weight loss effect of LCD.
Because the flora in the human intestine is not an independent individual, there are intricate connections between bacteria
.
Therefore, this research introduces the artificial neural network model (ANN).
ANN is a more powerful deep learning model that is trained and used to simulate biological neural networks for complex data analysis.
ANN is based on biological neural networks.
The basic principles of the network imitate the human brain structure and the external stimulus response mechanism, and build a model based on the knowledge of network topology.
It has the functions of associative memory, classification and recognition, optimized calculation, and nonlinear mapping
.
In recent years, more and more medical researches apply ANN to the processing of complex data
.
We incorporated the change values and ratios of the weight loss parameters of the LCD group into the ANN model based on the baseline relative abundance of the overall intestinal flora of the group, and obtained a higher predictive model determination coefficient (R2), which also indicates the prediction of ANN The effect is better than the linear model, suggesting that the prediction effect is better (Figure 5)
.
Figure 5 ANN model for predicting the effect of LCD weight loss.
In general, the current research shows that in overweight/obese people, short-term LCD intervention without calorie restriction has a significant weight loss effect without significant adverse effects
.
There are individual differences in short-term LCD weight loss.
The relative abundance of Bacteroidaceae Bacteroides at baseline before LCD intervention is positively correlated with the short-term LCD intervention weight loss effect
.
Finally, this study constructed a high-precision ANN prediction model based on the relative abundance of the intestinal flora at the baseline.
Through the ANN prediction model, it was found that the baseline relative abundance of the intestinal flora can be used as a predictor of the individualized weight loss effect before LCD intervention.
, It has important guiding significance for clinical medicine weight management
.
Based on the results of this research, in clinical medicine weight management, the relative abundance of Bacteroidaceae Bacteroides in the intestine is low, but the overweight/obese subjects who hope to lose weight through LCD may increase the weight loss of LCD by supplementing the corresponding probiotics.
Efficacy.
At present, our research group is working with the research team of Xie Liwei from the Institute of Microbiology of Guangdong Academy of Sciences to carry out clinical weight loss research on the combined use of probiotics and low-carbon diet to further explore the strategies and ideas of medical weight management.
Let us look forward to the updated research results
.
The main authors of this study are Zhang Susu, a physician in the Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University; Co-first author Wu Peili, a PhD candidate in the Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University; Tian Ye, a Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University Professor Chen Hong and researcher Xie Liwei from the State Key Laboratory of Applied Microbiology in South China jointly cultivated master students; Liu Bingdong was a joint training of PhD students by Professor Pan Jiyang from the Department of Psychiatry at the First Affiliated Hospital of Jinan University and researcher Xie Liwei from the State Key Laboratory of Applied Microbiology in South China
.
The corresponding author of this article is Professor Sun Jia from the Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, and the co-corresponding authors are Professor Chen Hong from the Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, and Researcher PI Xie Liwei from the Intestinal Microecology and Health Team, Institute of Microbiology, Guangdong Academy of Sciences
.
Paper link: https://pubmed.
ncbi.
nlm.
nih.
gov/34523948/ open for reprinting, welcome to forward to Moments and WeChat groups
.
According to statistics from the World Health Organization (WHO), nearly 2 billion people are overweight or obese in the world.
From 1975 to 2016, the global obesity rate has nearly tripled.
Every year, overweight or obesity causes 2.
8 million deaths
.
In fact, obesity is not only a feature, but also a disease.
Obese people not only have inconvenience in life and decreased exercise capacity, but are also more susceptible to metabolic diseases and cardiovascular and cerebrovascular diseases.
In addition, many studies have shown that obesity is associated with more than ten kinds of cancers.
The increased risk of disease is related to the decreased prognosis and survival rate
.
In recent years, various forms of weight loss interventions have been gradually applied in clinical practice and written into guidelines.
Life>
.
For a long time, low carbohydrate diets (LCD) have received the most attention as a dietary intervention mode for weight loss treatment
.
However, in different studies, the weight loss effects of LCD intervention are quite different.
At present, there is not enough evidence to explain this heterogeneity.
This is also a difficult point in the field of medical weight management
.
On September 15, 2021, the team of Professor Hong Chen and Professor Sun Jia from the Department of Endocrinology and Metabolism, Zhujiang Hospital of Southern Medical University, and the team of Professor Liwei Xie from the Gut Microecology and Health Team of Guangdong Institute of Microbiology, published the title: Gut in the journal Microbiology Spectrum microbiota serves a clinical research report of a predictable outcome of short-term low-carbohydrate diet (LCD) intervention for patients with obesity
.
This study reported for the first time that the baseline characteristics of the intestinal flora are the determinants of the short-term low-carbohydrate diet (LCD) weight loss effect of overweight and obese people.
The study constructed an artificial neural network based on the baseline characteristics of the intestinal flora to predict the effect of LCD weight loss.
(Artificial Neural Networks, ANN) model, this research provides brand new strategies and methods for clinical medical weight management and intervention
.
The study included 51 18-65-year-old male or female subjects who met the diagnostic criteria for overweight/obesity (no antibiotics or drugs were used in the first 3 months of the clinical trial).
The subjects were randomized into groups and were divided into different groups.
The energy-restricted normal diet group (Normal diet, ND) and the non-calorie-restricted low-carbohydrate diet group (LCD)
.
The diet intervention time was 12 weeks
.
In order to ensure the LCD diet structure, the LCD group adopted standardized nutrition bars (gifted by Guangzhou Nanda Feite Nutrition and Health Consulting Co.
, Ltd.
) to replace the daily staple food for lunch and dinner.
The number of other foods is not limited, and overeating is avoided
.
At the time of enrollment (ie baseline) and 12 weeks after the intervention, venous blood and stool samples were collected.
The blood samples were used for the detection of blood biochemical indicators such as glucose and lipid metabolism, liver and kidney function, and the stool samples were used for intestinal flora 16S rDNA amplicon sequencing, through 16S rDNA amplicon sequencing, a total of 2.
47 million high-quality Reads were obtained (Figure 1A)
.
The diet of the subjects was monitored through a 24-hour diet 3 days a week
.
During the entire study period, the average proportion of carbohydrate intake in the normal diet group was about 50%, and the proportion in the LCD group was about 20% (Figure 1B-D).
Although calorie intake was not restricted, the average energy intake of the low-carbon group was about 50%.
Enrollment was significantly lower than that in the normal diet group.
The 12-week LCD intervention significantly improved the subjects’ body parameters such as BMI, waist circumference, waist circumference, body fat percentage, and visceral fat area (Figure 1E)
.
Figure 1 Research overview and changes in clinical indicators of body weight In addition to different weight loss results, different dietary components may affect the composition and diversity of the intestinal flora, but apart from changes in the overall composition and phylum level, previous studies have not Draw a constructive conclusion to guide the clinical trial of weight loss under LCD.
Therefore, we analyzed the intestinal flora sequencing data, and adopted 5-fold cross-validation and random forest algorithm, taking into account the minimum sum of error rate and standard deviation To ensure the highest accuracy and stability, we analyzed the 16S rDNA sequence data of subjects in the ND and LCD groups before and after the test to identify potential flora biomarkers
.
A further analysis of all the genus screened by the random forest model in baseline and week 12 data found that the relative abundance of Ruminococcaceae Oscillospira and Porphyromonadaceae Parabacteroides increased significantly after the 12-week LCD intervention, and the difference was statistically significant (p< 0.
05) (Figure 2)
.
According to existing research reports, these two species of bacteria are involved in the production of butyrate in the intestine, suggesting that there may be other factors affecting weight changes during the process of LCD intervention in weight loss
.
Figure 2 The correlation between short-term LCD intervention and specific flora markers is to further analyze the weight loss of each subject.
According to the cluster stratification of the weight loss parameters BMI, waist circumference, WHR, BFR and VFA, each component is divided into Two subgroups: the moderate weight loss group (MG) and the distinct weight loss group (DG) (Figure 3A)
.
Under the conditions of LCD intervention, the energy intake and the proportion of carbohydrates in the diet of the two subgroups were almost the same, but the weight loss indicators of the subjects in the significant subgroup decreased more significantly, suggesting that the individualized differences in weight loss effects may be affected by other factors.
Impact (Figure 3E-F)
.
Figure 3 There are individual differences in the weight loss of the two dietary interventions in each subgroup.
The above results indicate that the LCD intervention has a better weight loss effect, but there are individual differences
.
Therefore, this study further analyzed the intestinal flora data of the two subgroups, and further explored whether there are potential factors related to the flora that caused the difference in weight loss between the two subgroups in this diet
.
In further subgroup analysis, we used the co-occurrence network at the genus level to further analyze the interaction between the intestinal flora in the LCD subgroup and found that after 12 weeks of LCD intervention, although the two subgroups LCD_DG and LCD_MG The network interaction complexity of the network has decreased, but LCD_DG showed a denser, more extensive and richer network interaction complexity than LCD_MG in the baseline and at the 12th week
.
The above results indicate that, in addition to the differences in the composition and diversity of the flora, the differences between the structure of the flora and the complexity of the interaction of the flora may be an important reason for the individual differences in weight loss effects (Figure 4C-F)
.
In the low-carbon subgroup, analysis by the random forest model algorithm found that the baseline relative abundance of Bacteroidaceae Bacteroides was statistically different between the two subgroups of the low-carbon diet (Figure 4I).
According to the linear regression analysis, we found that the baseline relative abundance of Bacteroidaceae Bacteroides The baseline relative abundance of Bacillus is positively correlated with the short-term low-carbon diet weight loss effect (Figure 4J-N)
.
Based on the above results, the ROC model was established based on the baseline relative abundance of the low-carbon subgroup of Bacteroides.
The ROC model reflects the susceptibility of each data point on the curve to the same signal stimulus, and comprehensively reflects the sensitivity and specificity of the variables
.
In this study, the ROC model AUC value reached 73.
2%, suggesting that the baseline relative abundance of Bacteroides has a certain predictive value for the short-term low-carbohydrate diet weight loss effect (Figure 4O)
.
Figure 4 Intestinal flora is an important factor affecting the weight loss effect of LCD.
Because the flora in the human intestine is not an independent individual, there are intricate connections between bacteria
.
Therefore, this research introduces the artificial neural network model (ANN).
ANN is a more powerful deep learning model that is trained and used to simulate biological neural networks for complex data analysis.
ANN is based on biological neural networks.
The basic principles of the network imitate the human brain structure and the external stimulus response mechanism, and build a model based on the knowledge of network topology.
It has the functions of associative memory, classification and recognition, optimized calculation, and nonlinear mapping
.
In recent years, more and more medical researches apply ANN to the processing of complex data
.
We incorporated the change values and ratios of the weight loss parameters of the LCD group into the ANN model based on the baseline relative abundance of the overall intestinal flora of the group, and obtained a higher predictive model determination coefficient (R2), which also indicates the prediction of ANN The effect is better than the linear model, suggesting that the prediction effect is better (Figure 5)
.
Figure 5 ANN model for predicting the effect of LCD weight loss.
In general, the current research shows that in overweight/obese people, short-term LCD intervention without calorie restriction has a significant weight loss effect without significant adverse effects
.
There are individual differences in short-term LCD weight loss.
The relative abundance of Bacteroidaceae Bacteroides at baseline before LCD intervention is positively correlated with the short-term LCD intervention weight loss effect
.
Finally, this study constructed a high-precision ANN prediction model based on the relative abundance of the intestinal flora at the baseline.
Through the ANN prediction model, it was found that the baseline relative abundance of the intestinal flora can be used as a predictor of the individualized weight loss effect before LCD intervention.
, It has important guiding significance for clinical medicine weight management
.
Based on the results of this research, in clinical medicine weight management, the relative abundance of Bacteroidaceae Bacteroides in the intestine is low, but the overweight/obese subjects who hope to lose weight through LCD may increase the weight loss of LCD by supplementing the corresponding probiotics.
Efficacy.
At present, our research group is working with the research team of Xie Liwei from the Institute of Microbiology of Guangdong Academy of Sciences to carry out clinical weight loss research on the combined use of probiotics and low-carbon diet to further explore the strategies and ideas of medical weight management.
Let us look forward to the updated research results
.
The main authors of this study are Zhang Susu, a physician in the Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University; Co-first author Wu Peili, a PhD candidate in the Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University; Tian Ye, a Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University Professor Chen Hong and researcher Xie Liwei from the State Key Laboratory of Applied Microbiology in South China jointly cultivated master students; Liu Bingdong was a joint training of PhD students by Professor Pan Jiyang from the Department of Psychiatry at the First Affiliated Hospital of Jinan University and researcher Xie Liwei from the State Key Laboratory of Applied Microbiology in South China
.
The corresponding author of this article is Professor Sun Jia from the Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, and the co-corresponding authors are Professor Chen Hong from the Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, and Researcher PI Xie Liwei from the Intestinal Microecology and Health Team, Institute of Microbiology, Guangdong Academy of Sciences
.
Paper link: https://pubmed.
ncbi.
nlm.
nih.
gov/34523948/ open for reprinting, welcome to forward to Moments and WeChat groups
