Next Article in Journal
Continuous Monitoring of Interstitial Fluid Glucose Responses to Endurance Exercise with Different Levels of Carbohydrate Intake
Next Article in Special Issue
Impact of a Food Rebalancing Program Associated with Plant-Derived Food Supplements on the Biometric, Behavioral, and Biological Parameters of Obese Subjects
Previous Article in Journal
Examining the Association between Coffee Intake and the Risk of Developing Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 22
10.3390/nu15224744
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Short-Term Effects of Weight-Loss Meal Replacement Programs with Various Macronutrient Distributions on Gut Microbiome and Metabolic Parameters: A Pilot Study

by
Seungmin Song
1,2,†,
Jinyoung Shon
1,2,†,
Woo-ri Yang
3,
Han-Bit Kang
3,
Keun-Ha Kim
3,
Ju-Yeon Park
3,
Sanghoo Lee
4,
Sae Yun Baik
5,
Kyoung-Ryul Lee
4,5 and
Yoon Jung Park
1,2,*
1
Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
2
Department of Nutritional Science and Food Management, Ewha Womans University, Seoul 03760, Republic of Korea
3
Hyundai Greenfood Greating Laboratory, Yongin-si 16827, Republic of Korea
4
SCL Healthcare Inc., Yongin-si 16954, Republic of Korea
5
Hanaro Medical Foundation, Seoul 03159, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Nutrients 2023, 15(22), 4744; https://doi.org/10.3390/nu15224744
Submission received: 6 October 2023 / Revised: 21 October 2023 / Accepted: 31 October 2023 / Published: 10 November 2023
(This article belongs to the Special Issue Diet and Nutrition Approaches in Obesity Treatment)
Browse Figures
Versions Notes

Abstract

:
It has emerged the gut microbiome is crucially linked to metabolic health and obesity. Macronutrient distribution has been discussed as a key parameter in weight-loss programs, but little is known about its impact on the gut microbiome. We investigated the effects of weight-loss meal replacement programs with different macronutrient ratios on the gut microbiota and metabolic parameters in subjects with overweight and obesity. Three low-calorie meal replacement programs with different ratios of carbohydrates, proteins, and lipids were designed: a balanced diet (Group B, 60:15:30), a high-lipid−low-carbohydrate diet (Group F, 35:20:55), and a protein-enriched diet (Group P, 40:25:35). Sixty overweight or obese participants were provided with the meals twice daily for 3 weeks. In all groups, diet intervention resulted in reduced body weight and BMI. The relative abundance of Bacteroidetes and Firmicutes phyla decreased and increased, respectively, which increased the Firmicutes/Bacteroidetes (F/B) ratio in all subjects, particularly in Groups B and P. Alpha- and beta-diversity were augmented at the phylum level in Group P. In conclusion, short-term interventions with weight-loss meal replacement programs increased butyrate-producing bacteria and the F/B ratio. Moreover, the protein-enriched diet significantly increased alpha- and beta-diversity compared to the balanced diet and the high-lipid−low-carbohydrate diet.

1. Introduction

About one-third of the world’s population is classified as overweight or obese, making it one of the biggest public health problems in the world [1]. Obesity is caused by excessive energy accumulation, and is one factor that increases the risk of chronic diseases, such as type 2 diabetes mellitus, cardiovascular disease, and cancer [2]. In the last two decades, functional compounds from plant and animal sources have been extensively explored to develop effective strategies for preventing these chronic diseases. However, there is a concern about the toxicity of high-dose functional foods during long-term and continuous consumption [3,4,5]. To mitigate this risk, meal-based diet intervention has emerged as a safe strategy for weight loss, and its optimal dietary pattern and macronutrient distribution have been explored in addition to calorie restriction. In the last few years, limited mobility due to coronavirus disease 2019 (COVID-19) has greatly affected eating habits, with dramatic increases in food delivery and the consumption of ready-made meals or home meal replacements [6,7]. Together with limited mobility, the changes in food choice have, at least in part, increased the risk of obesity and chronic disease. This situation has shed light on meal replacement programs based on healthy eating principles [8,9]. In our study, we provided weight-loss meal replacement programs with different macronutrient distributions to patients with obesity or overweight and investigated the effects on metabolic regulation and gut microbiome changes.
Macronutrient composition has long been debated in weight-loss dietary plans. Traditionally, dietary advice has focused on limiting fat intake to prevent obesity and chronic disease [10]. More recent research has shown that processed carbohydrates may also contribute to metabolic problems, leading to an increased interest in high-fat−low-carbohydrate diets, often accompanied by high protein, like the ketogenic diet [11]. In a systemic-review study, low-carbohydrate, high-protein, and Mediterranean diets were reported to be more effective in weight loss than low-fat and low-glycemic diets and moderate-fat diets with limited energy-maintained weight loss [12]. Although the debate is not conclusive on the optimal ratio of macronutrients, various strategies for metabolic health have been devised [13]. Intriguingly, multiple mouse studies on ad libitum feeding with various ratios of macronutrients showed that high-carbohydrate−low-protein diets yielded more positive results in metabolic regulation and longevity, despite an increase in body weight, compared to high-protein−low-carbohydrate diets [14]. The results indicate that further investigation is required to understand the effects of macronutrient distribution in diets on metabolic changes.
Evidence shows that the gut microbiome is a key factor influencing the risk of obesity and a useful biomarker for metabolic health [15,16,17]. Microbiome research is becoming the cornerstone of identifying individual variability in response to lifestyle or nutritional interventions for precision nutrition, with many studies being conducted on the effect of specific dietary factors on the diversity of the gut microbiome [18,19,20]. The most representative gut bacteria in humans are Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, and Verrucomicrobia [17,21]. Bacteroidetes are gram-negative bacteria that ferment polysaccharides and indigestible carbohydrates, producing short-chain fatty acids (SCFAs). The SCFAs offer numerous beneficial effects in the gut [22,23]. Predominant genera within this group include Bacteroides and Prevotella. On the other hand, Firmicutes are a gram-positive bacteria characterized by their rigid or semi-rigid cell walls. They encompass over 200 different genera, including Lactobacillus, Bacillus, Clostridium, Enterococcus, and Ruminicoccus. Firmicutes are crucial in understanding the relationship between the gut microbiota and human health. Their enhanced ability to ferment and metabolize carbohydrates and lipids may link to the development of obesity [23,24]. Dietary compounds, such as fiber and fat, are one of the important modulators of gut microbiome composition [25]. Dietary fiber is a functional food component that can greatly impact the composition, diversity, and abundance of bacteria in the microbiome. Moreover, changes in the composition of the gut microbiome due to an increase in dietary fiber intake are concomitant with the relative expansion of specific bacterial populations [26].
Dysbiosis of the gut microbiome has been proven to be caused by diet-induced obesity, and clinical studies have shown that changes in the Firmicutes/Bacteroidetes (F/B) ratio are closely linked to obesity [27,28]. In several in vivo studies, changes in the abundance and physiological changes of the gut microbiome occurred during a high-fat diet. After 2-month-old male Sprague Dawley (SD) rats received a high-fat diet (60% fat) for 1 month, colonic macroscopic damage was significantly greater compared to the low-fat diet (10% fat) group, and the level of leptin was significantly increased [29]. In male C57BL/6NCrl mice fed with a high-fat diet (60% fat) for 12 weeks, changes in the diversity of dominant gut bacteria, bile acid and bilirubin metabolism, and amino acid and monosaccharide metabolism were observed [30]. Furthermore, in male SD rats that received a high-fat diet (60% fat) for 8 weeks, the F/B ratio increased, and metabolic functions, such as lipid and carbohydrate metabolism and glycan biosynthesis, differed from mice that received the normal diet (10% fat) [31]. The ketogenic diet, which is low in carbohydrates and high in fat, decreased Bifidobacterium due to an increase in ketone bodies, especially beta-hydroxybutyrate, which lowers inflammatory cell levels in intestinal fat and visceral fat, reducing insulin resistance and obesity [32]. In this context, a high-fat diet and a ketogenic diet not only change the phenotype, but also gut microbiome diversity and abundance, concomitant with physiologically metabolic changes. However, little is known about how diets with various macronutrient distributions affect gut microbiota. This study aimed to investigate the short-term effects of diet interventions with weight-loss meal replacement programs of various ratios of macronutrients on metabolic parameters and gut microbiota in subjects who are overweight or obesity.

2. Materials and Methods

2.1. Study Population

This study recruited a total of 60 people by screening for customers at food service establishments operated by Hyundai Green Food in Seoul and Gyeonggi, Republic of Korea. As shown in Figure 1A, the subjects were assigned to three subgroups of 20 people each: Group B, Group F, and Group P. Subsequently, two participants in Group F and one in Group P were eliminated, and two in Group B and one in Group P were eliminated after the wash-out period (1 week) before diet intervention. After 3 weeks of diet intervention, 47 participants were finally analyzed by excluding subjects who had microbiome data outliers (i.e., microbiome data too low to read) or had consumed alcohol on 7 or more days during the intervention time. The exclusion criteria were as follows: (1) hypersensitive to certain foods or ingredients or have experienced severe food allergy reactions; (2) participated in other clinical studies 1 month prior to their first visit; (3) difficulty using smartphones; (4) judged by the researcher to be inappropriate to participate in this study; (5) difficulty taking appropriate samples during the study. All of the subjects agreed to participate in this study, signed a written consent form, and were adults aged between 20 and 65 years with a BMI ≥ 23 kg/m2. The elimination criteria were as follows: (1) if the diet is uncomfortable or unsatisfactory; (2) if there is an allergic reaction or other abnormal reaction after eating; (3) if the dietary intake rate is less than 50% and the dietary record is insufficient; (4) if sample collection is not possible; (5) if subjects find it impossible to participate in or continue the study. Participants were assigned using stratified randomization based on BMI. Body weight was recorded at the preliminary visit. This study was approved by the Ethics Committee of the Hanaro Medical Foundation (HMFIRB No. HNR2021-02, approval date 21 October 2021).

2.2. Diet Design

We constructed three meal plans with different ratios of calories from carbohydrate, lipid, and protein sources (Figure 1B). The balanced diet had a carbohydrate calorie ratio of ≥55% (Group B, n = 16), the high-lipid−low-carbohydrate diet had a lipid calorie ratio of ≥50% (Group F, n = 14), and the protein-enriched diet had a protein calorie ratio of ≥25% (Group P, n = 17). According to the 2020 Dietary Reference Intakes for Koreans, the daily energy intake of those aged 30~49 is 2200 kcal. The intervention diets provided in our study aimed at about 1100 kcal for two out of three meals, and the other meal was freely consumed. The balanced diet consisted of 4~5 kinds of side dishes, the high-lipid−low-carbohydrate diet was served in the form of salad, and the protein-enriched diet was served in the form of a one-dish meal, and it was composed differently for each meal, so it did not overlap for a week. Experimental diets were eaten for a total of 3 weeks (21 days), and two meals (lunch and dinner) with snacks were provided per day. Participants were asked to eat at regular times every day, and snacks were eaten freely during the day. As shown in Table 1, to minimize the difference according to calories in all three diets, the calories per meal were adjusted to 530 kcal to provide 1060 kcal ± 5% per day.

2.3. Clinical Assessments

As shown in Figure 1B, body measurements (weight, height, waist circumference, and BMI) were determined using an InBody 720 (Biospace Co., Seoul, Republic of Korea) at the preliminary visit (−1 week), visit 1 (0 week), and visit 2 (3 weeks). The diet survey was conducted using the Recommended Food Score (RFS) and Meats, Eggs, Dairy, Fried foods, fat in baked goods, Convenience foods, fats added at the Table, and Snacks (MEDFICTS) during visit 1. Sleep duration and exercise were investigated at visit 1 and 2. Meal and lifestyle education was assessed at the preliminary visit and visit 1, and a food diary was prepared for 3 days before visits 1 and 2. In the food diary, wake-up/bedtime, exercise, drinking, and smoking were also recorded. For biochemical markers, blood was collected after 12 h of fasting during visits 1 and 2 and processed for serum and plasma.

2.4. Microbiome Analysis

Subjects were asked to take a stool sample at visits 1 and 2, and it was stored at −80 °C. Fusion primers targeting the V3–V4 region of the bacterial 16S rRNA gene were selected for PCR amplification, using 341 forward and 805 reverse primers. Low-quality sequences were filtered using the DADA2 pipeline in the QIIME 2 package version 2021.4 (https://qiime2.org, accessed on 20 September 2022), and the amplicon sequence variants were generated. Taxonomy analysis was performed to confirm the relative frequency of the subjects’ gut microbiome at the phylum, family, and genus levels, and analysis of the alpha- and beta-diversity were performed using QIIME 2.

2.5. Statistical Analysis

We performed one-way ANOVA to confirm that there were no significant differences in lifestyle, anthropometric, and biochemical indicators, and diet intake among the three groups at pre- and post-diet intervention. A paired t-test was performed to confirm significant differences in lifestyle indicators, anthropometric indicators, biochemical indicators, dietary intake, and gut microbiome data at the phylum level and in the F/B ratio pre- and post-diet intervention, and all were analyzed using SAS 9.4 (SAS Institute, Cary, NC, USA). A violin plot representing alpha-diversity, PCoA plot representing beta-diversity, and heatmap representing Spearman’s rank order correlation analysis were constructed using R software version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria). Significant differences in alpha-diversity at pre- and post-diet intervention in each group were analyzed with the Kruskal−Wallis test using the R program, and beta-diversity at pre-and post-diet intervention in the total subjects and each group were analyzed using permutational multivariate analysis (PERMANOVA) using QIIME 2.

3. Results

3.1. Characteristics of the Subjects

A total of 47 subjects (45 men, 2 women) participated in this study, including 16 in Group B, 14 in Group F, and 17 in Group P. Lifestyle, anthropometric, and biochemical indicators were investigated before diet intervention with the weight-loss meal replacement programs (Table 2). The mean age of the total subjects was 36.0 ± 4.3, physical activity was 124.6 ± 86.5 kcal/day, and sleep duration was 6.60 ± 0.73 h/day. From the dietary history of the total subjects, the mean RFS score was 19.9 ± 8.7, and the mean MEDFICTS score was 66.5 ± 11.8. The mean body weight of the total subjects was 81.9 ± 12.2 kg, with a mean BMI of 26.7 ± 3.0 kg/m2, and a mean waist-to-hip ratio (WHR) of 0.9 ± 0.1. The mean values for biochemical markers related to liver function of the total subjects were gamma-glutamyl transferase (γ-GTP) 29.6 ± 19.5 IU/L, total bilirubin 0.7 ± 0.3 mg/dL, aspartate aminotransferase (AST) 26.9 ± 13.6 IU/L, alanine aminotransferase (ALT) 30.4 ± 20.2 IU/L, lactate dehydrogenase (LDH) 189.6 ± 30.4 IU/L, and alkaline phosphatase (ALP) 63.6 ± 13.2 U/L. Further analyses of the total subjects showed the mean level of creatinine, associated with kidney function, was 0.9 ± 0.1 mg/dL, and the lipid profile showed mean levels of 129.2 ± 63.1 mg/dL for triglycerides (TG), 203.3 ± 29.0 mg/dL for total cholesterol (T-Cho), 51.9 ± 9.7 mg/dL for high-density lipoprotein (HDL), and 139.4 ± 26.8 mg/dL for low-density lipoprotein (LDL). When comparing the values of biochemical markers with normal values, there were no clinical problems and no indicators with significant differences among the three groups.

3.2. Weight-Loss Meal Replacement Program Effects on Anthropometric and Biochemical Indicators

There were no significant differences in the diet intake and percentage of calories among the three groups before diet intervention. As shown in Table 3, the daily calorie intake after diet intervention in the total subjects was significantly decreased −569.1 ± 431.1 kcal/day, carbohydrate −62.5 ± 55.9 g/day, protein −18.4 ± 20.9 g/day, and fat −14.8 ± 23.7 g/day. In Group B, the percentage of calories from carbohydrates increased by 11.2 ± 7.2%, and the percentage of calories from protein and fat decreased. The daily intake of fat in Group F increased by 11.0 ± 13.4 g/day, and the percentage of calories increased by 15.5 ± 5.8%. In Group P, the daily intake of calories, carbohydrate, and fat decreased significantly, but the protein remained the same, and the percentage of calories increased significantly, by 5.2 ± 3.1%.
After the 3-week weight-loss meal replacement program, changes in lifestyle, anthropometric, and biochemical indicators were investigated (Table 4). Among the anthropometric factors, body weight, BMI, fat-free mass (FFM), and relative body weight (RBW) significantly decreased in the total subjects and each of the three groups, and WHR increased, albeit not significantly. Among the biochemical markers related to liver function, the levels of ALP and γ-GTP after diet intervention were significantly lowered in the total subjects and in each of the three groups. After diet intervention, the ALT level was significantly lower in the total subjects (−4.8 ± 17.2 IU/L), and the LDH level was lower in the total subjects (−17.1 ± 31.8 IU/L) and Group F (−27.9 ± 39.1 IU/L). However, total bilirubin was significantly higher in the total subjects (0.1 ± 0.3 mg/dL) and Group B (0.2 ± 0.3 mg/dL) after diet intervention, and the level of liver function markers in the total subjects was lowered except for total bilirubin, indicating that the weight-loss meal replacement program improved liver function. Among the lipid profile markers, the levels of TG and T-Cho were significantly lower in the total subjects, especially T-Cho in Group P (−11.1 ± 15.9 mg/dL). Conversely, the HDL levels decreased, especially in Group B (−4.4 ± 6.9 mg/dL) and Group P (−4.4 ± 5.0 mg/dL), and the LDL levels increased. This is consistent with another study in which the HDL level temporarily decreased during a short-term, very-low-calorie diet (VLCD) but returned after adaptation to the intervention diet [33].
In addition, after diet intervention, the total protein level was significantly lowered in the total subjects (−0.2 ± 0.3 g/dL) and in Group F (−0.2 ± 0.2 g/dL) and Group P (−0.2 ± 0.3 g/dL), and high-sensitivity C-reactive protein (hsCRP) was significantly decreased in the total subjects (−0.7 ± 1.3 mg/L) and Group F (−0.7 ± 1.0 mg/L). Leptin and adiponectin were lowered in the total subjects. In particualr, leptin was significantly lowered in Group F (−3.9 ± 5.7 ng/mL) and Group P (−2.0 ± 2.2 ng/mL), and fibroblast growth factor 21 (FGF21) was significantly lowered in the total subjects (−39.0 ± 79.1 pg/mL) and Group F (−56.3 ± 86.7 pg/mL).

3.3. Changes in Gut Microbiome by Diet Intervention

To investigate the effects of diet intervention on the subjects, the fecal microbiota was characterized by 16S rRNA gene sequencing. The compositions of the gut microbiome at the phylum, family, and genus levels and the F/B ratio are presented in Figure 2A–D and Table 5. The main bacterial phyla were Bacteroidetes and Firmicutes, followed by Proteobacteria and Actinobacteria. In the total subjects (Figure 2A), the relative abundance of Bacteroidetes decreased and Firmicutes increased. Post diet intervention, the significant changes at the family level (Figure 2B) were the decrease in Bacteroidaceae and the increases in Ruminococcaceae and Lachnospiraceae. At the genus level (Figure 2C), a significant decrease in Bacteroides and increases in Faecalibacterium and Blautia were most noteworthy. As shown in Figure 2D,E and Table 5, Bacteroidetes decreased and Firmicutes increased after diet intervention in all three groups, resulting in augmented F/B ratios. The F/B ratio significantly increased by 0.7 ± 1.0% in the total subjects, 0.6 ± 0.8% in Group B, and 0.7 ± 0.9% in Group P. Proteobacteria showed no significant difference between pre- and post-diet intervention, whereas Actinobacteria increased significantly in the total subjects (0.5 ± 1.2%) after diet intervention.
To investigate the alpha-diversity in each group pre and post diet intervention, observed features and the Shannon index were used to evaluate the species richness of the gut microbiome, and the species evenness was also analyzed (Figure 3). In the total subjects (Figure 3A,C,E), species richness increased significantly post diet intervention compared to pre diet intervention (observed features p = 0.003, Shannon index p = 0.0032), but there was no significant difference in species evenness (p = 0.0910). When comparing the alpha-diversity pre- and post-diet intervention by group, there was no significant difference in all groups in species evenness (Figure 3F). In the species richness analysis, there was a significant difference in the observed features in Group F (p = 0.004) and Group P (p = 0.025) and a significant difference in the Shannon index only in Group F (p = 0.035). Therefore, there was no difference in the evenness of the gut microbiome after the weight-loss meal replacement program, but a significant difference in microbiome diversity had occurred in both Group F and Group P.
Beta-diversity was measured using weighted UniFrac to assess differences in microbiome composition pre and post diet intervention of the total subjects, and significant differences were analyzed by PERMANOVA (Figure 4). There was a significant difference in the microbiome composition pre and post diet intervention in the total subjects (Figure 4A, p = 0.019). Figure 4B–D shows beta-diversity pre and post diet intervention in each group. Only Group P (Figure 4D, p = 0.004) showed a significant difference in the pre- to post-intervention change in the composition of the gut microbiome, not Group B (p = 0.056, Figure 4B) or Group F (p = 0.066, Figure 4C).

3.4. Correlation between Microbiome and Lifestyle, Anthropometric, Biochemical, and Diet Markers

Spearman’s rank order correlation was computed to assess the correlation of the gut microbiome with lifestyle, anthropometric, biochemical, and diet markers (Figure 5). In Figure 5A, total bilirubin showed a negative correlation with Bacteroidetes and a positive correlation with Firmicutes. Accordingly, the F/B ratio also showed a positive correlation. In addition, Proteobacteria showed a strong negative correlation with creatinine. Among the diet markers, calories (kcal) and fat (g/day) showed positive correlations with Bacteroidetes, and fat (g/day) showed a significant negative correlation with the F/B ratio. Figure 5B reveals the changes in other markers related to the gut microbiome. Creatinine showed a negative correlation with Bacteroidetes and a positive correlation with Firmicutes, while the lipid profile (T-Cho, HDL, and LDL), leptin, and adiponectin showed positive correlations with Bacteroidetes and negative correlations with Firmicutes. Accordingly, the F/B ratio showed a correlation in the same direction as Firmicutes. A significant result in the change of the diet markers was the negative correlation between carbohydrate (g/day) and Proteobacteria.
In the total subjects, the correlation between the change in the F/B ratio and the change in sleep duration, one of the lifestyle markers, was significant (r = 0.23, Figure 6A). Likewise, the correlation between the change in the F/B ratio and the change in the biochemical indicators was significant for creatinine (r = 0.43), HDL-cholesterol (r = −0.36), and adiponectin (r = −0.31) in the total subjects (Figure 6B–D).

4. Discussion

In this study, we characterized anthropometric and biochemical parameters, diet intake, and microbiome data changes mediated by different weight-loss meal replacement programs with different proportions of macronutrients (carbohydrate, fat, and protein) in subjects who were overweight or obesity. Among the results for changes in the gut microbiome, changes in the F/B ratio, known as an obesity marker, were observed. While an increase in the F/B ratio is known to be related to weight gain [24,34], clinical studies have stated that it is difficult to determine health status from the F/B ratio, and a correlation of the F/B ratio with obesity has not been identified [35,36]. In this study, the F/B ratio increased in all groups, which was coincident with similar studies [35,36,37,38], even though body weight was lost due to calorie-restricted diets. Bacteroidetes decreased by 14.3% at the phylum level (p < 0.0001) due to a 13.3% decrease in Bacteroidaceae at the family level (p < 0.0001) and a 13.2% decrease in Bacteroides at the genus level (p = 0.0002). Firmicutes increased by 13.6% at the phylum level (p < 0.0001) due to a 3.1% increase in Ruminococcaceae at the family level (p = 0.0389), a 7.7% increase in Lachnospiraceae (p < 0.0001) at the family level, and a 3.3% increase in Faecalibacterium at the genus level (p = 0.0079). Ruminococcaceae and Lachnospiraceae are butyrate-producing bacteria belonging to the Firmicutes phylum. Butyrate, produced by most of the complex carbohydrates and plant polysaccharides consumed by humans, is one of the SCFAs, which are important metabolites for maintaining intestinal homeostasis.
Butyrate-producing bacteria can promote a beneficial metabolism by fermenting dietary fibers in the intestinal lumen [39,40,41]. Dietary fiber shapes the gut microbiota composition, and fermentable dietary fiber increases the intestinal production of SCFA, which may help prevent obesity [42,43]. In our study, fiber intake was significantly increased in the total subjects (4.3 ± 7.0, p = 0.0001, Table S1) and Group B (10.9 ± 5.3, p < 0.0001, Table S1) after diet intervention. In the mouse model, when a refined high-fat diet (rHFD) was ingested, body weight and fat mass increased significantly compared to mice that ingested chow and a refined low-fat diet (rLFD), but there was no significant difference in F/B ratio between rLFD and rHFD. The reason is the different fiber content in these two diets [37]. Furthermore, research has shown a higher F/B ratio, lower lipid level, and less liver fat accumulation in mice that ingested a purified-fiber diet compared to a purified-starch diet [38]. Our results highlight the increased F/B ratio, accompanied by the positive health outcomes. This may be attributed to a notable rise in butyrate-producing Firmicutes within the gut microbiota, stemming from a higher fiber intake in the calorie-restricted intervention diets. Another potential explanation relates to energy adaptation. Firmicutes are known to be more adept than Bacteroidetes at extracting energy from food [42,44]. Given the short duration of our intervention, our findings may depict the microbiome’s adaptive phase, becoming more efficient in energy utilization under calorie-restricted diets. This notion is reinforced by the consistent changes in the F/B ratio across the three groups, all of which followed energy-restricted meal plans.
Alpha- and beta-diversity analyses at the phylum level were performed to compare the diversity of intestinal microorganisms within or between groups. Microbial diversity in the intestine is associated with health status, and when diversity is reduced, disease conditions, such as inflammatory bowel disease, may occur [45]. A low-diversity microbiome is suggestive of dysbiosis and has been linked to being overweight or obesity [46,47,48]. Our study confirmed that weight and BMI decreased in subjects who were overweight or obesity during a calorie-restricted diet. Moreover, alpha-diversity (observed features, Shannon index) increased in the total subjects and Group F and Group P. Compared to the balanced diet, which had a uniform proportion of macronutrients, the high-lipid–low-carbohydrate diet, which had a 50~60% increase in fat content, and the protein-enriched diet, which had a 25~30%, increase in protein content, are thought to have increased species richness (alpha-diversity) because these diets included fruits, salads, and nuts. In particular, fruits, salads, and nuts were provided daily in the high-lipid–low-carbohydrate diet and more frequently than in the protein-enriched diet.
A diet rich in fruits and vegetables has been suggested to be associated with changes in intestinal microbial composition and is known to play an important role in improving intrinsic ecology [49,50]. Higher intakes of fruit have been associated with greater gut microbiome diversity and a lower risk of type 2 diabetes and are supported by dietary recommendations for increasing fruit consumption to prevent this disease [49]. In addition, the Mediterranean diet, a plant-based diet that emphasizes fruits, vegetables, and nuts, is favorably associated with an increased abundance of Ruminococcaceae and Lachnospiraceae families [51], which can be associated with an increase in the F/B ratio after intervention with the Mediterranean diet. Alpha-diversity (Shannon index) was significantly increased by a calorie-restricted high-protein (30% protein) diet group compared to a calorie-restricted normal protein (15% protein) diet, indicating that a high-protein diet can modulate the gut microbiome in obesity [52]. In our study, it was found that the species richness increased in the groups that consumed fruits and vegetables (Group F and P), suggesting that this was an important difference compared to Group B, and alpha-diversity increased significantly (observed features and Shannon index), especially in subjects that consumed a lot of fruits and vegetables (Group F). Conversely, only Group P induced a significant difference in the pre- and post-diet intervention change in beta-diversity. Therefore, the protein-enriched diet seems to affect alpha- and beta-diversity more than the others. While the macronutrient distribution did have an impact on microbial diversity, the significant shifts we noted in the microbiome seemed to be more linked to the weight loss diet than to the specific diet composition. Even though the three meal plans differed in the magnitude of changes observed, key alterations, like those in the F/B ratio, were consistently seen across all groups following the intervention.
The correlation of the microbiome markers (Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria) with the F/B ratio and lifestyle, anthropometric, biochemical, and diet markers was analyzed using Spearman’s rank order. As shown in Figure 5A, total bilirubin and creatinine, as biochemical markers, and intake of calories (kcal) and fat (g/day), as diet markers, were highly related to the status of the static microbiome. Total bilirubin showed a negative correlation with Bacteroidetes and positive correlations with Firmicutes and the F/B ratio. Bilirubin, a component of the heme catabolism pathway essential for liver function, is conjugated and secreted into the bile by the liver and catabolized by microbes in the intestine to urobilinoids before excretion. There are four bacteria known to reduce bilirubin to urobilinoids: Clostridium ramosum, Clostridium perfringens, Clostridium difficile, and Bacteroides fragilis, which are essential for normal gut function or can play an important role in the development of intestinal disease [53]. Of the four bacteria, Clostridium spp. belong to the phylum Firmicutes [54,55,56] and Bacteroides spp. belong to Bacteroidetes [57]. In our study, although the above bacteria were not included in the top 15 at the genus level, the relative abundance of Firmicutes increased and Bacteroidetes decreased at the phylum level, supporting the role of the intestinal microbiota in bile acid metabolism. In addition, looking at the markers related to liver function in Table 4, the levels in the total subjects and each group decreased after diet intervention except for total bilirubin, which increased in the total subjects, Group B, and Group F. Similarly, serum bilirubin levels increased by 18~45% when people with overweight or obesity lost weight after a short-term, calorie-restricted diet [58]. It has been reported that an increase in serum bilirubin levels in people who are overweight or obesity can protect other organs and directly affect fat tissue and fat secretion patterns, thereby improving various obesity-mediated or obesity-related diseases [58,59].
In Figure 5B, when looking at markers highly associated with microbiome shifts induced by diet intervention, most were biochemical markers of lipid profiles, especially T-Cho, HDL-cholesterol, and LDL-cholesterol. In addition, creatinine, leptin, adiponectin, sleep duration (a lifestyle marker), and carbohydrate (g/day; a diet marker) showed significant correlations. It is not known how serum creatinine with the highest correlation relates to microbial bacteria, which should be further studied. In addition, the lipid profile showed a high correlation, which can be related to a short diet intervention. Table 4 shows that the HDL levels decreased after a calorie-restricted diet in all groups. Although calorie-restricted diets are generally expected to increase HDL levels, other studies have shown that VLCDs reduce weight and, as a temporary phenomenon, HDL levels also decrease before returning to pre-VLCD levels or improving [33]. Similar to our study, a 28-day diet intervention of 800 kcal/day showed significant weight loss, but the level of HDL was 1.05 ± 0.04 mmol/L before diet intervention and decreased to 0.09 ± 0.03 mmol/L after the intervention [60].
This pilot study has proposed a potential benefit of the effects of macronutrient intake patterns on the gut microbiome based on a short-term meal-based intervention. However, several points should be considered when designing future research on the optimal macronutrient distributions for health maintenance. While our weight-loss meal replacement programs reduced body weight or BMI in people who were overweight or obesity during short-term diet intervention, further study is needed to evaluate whether that effect is temporary or permanent. In addition, weight-loss effects and changes in HDL-cholesterol levels should be checked even after long-term intervention. Another limitation is that the sample size was less than 20 in each of the three groups, which was insufficient to be representative of the population who are overweight or obesity. A small sample size can reveal clinically significant results, but because other statistically significant results may be insufficient [61], it is necessary to increase the sample size in further studies to determine the effective ratio of macronutrient intake. There was also no information on the micronutrients in our designed weight-loss meals. In this regard, when diet intervention is performed in vivo, the same raw materials are used for the diets, but in a clinical study, the dietary food items used to achieve the macronutrient distribution ratio are different. As the macronutrient distribution ratio is changed, the food composition changes, especially in micronutrients, but this was not considered in our study. Additionally, as shown in Table S2, according to the actual intake of micronutrients consumed by the subjects, some nutrients did not reach the recommended nutrient intake. Future studies on meal plans should consider micronutrients in study design and analysis.

5. Conclusions

Our study demonstrated that a moderate calorie-restricted meal intervention with various macronutrient intake patterns significantly reduced body weight, BMI, and the levels of TG and T-Cho, and improved hepatic metabolic parameters, except total bilirubin. The changes in the parameters were not significantly different between the macronutrient patterns. The intervention increased the intestinal distribution ratio of butyrate-producing bacteria among the most abundant Firmicutes in the body and elevated the F/B ratio, regardless of different macronutrient ratios. Furthermore, it increased the microbiome diversity, in particular, after intervention with the protein-enriched diet, compared to the high-lipid–low-carbohydrate diet and that with the balanced diet. The findings suggested that microbiome alterations may be more sensitive to dietary macronutrient patterns than other anthropometric and biochemical measures. Future studies should focus on long-term interventions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15224744/s1, Table S1: Micronutrient change data of subjects.; Table S2: Micronutrient intakes of subjects in the diet intervention change data of subjects.

Author Contributions

Conceptualization, J.-Y.P., S.L., S.Y.B., K.-R.L. and Y.J.P.; investigation, S.S., J.S., W.-r.Y., H.-B.K., K.-H.K., J.-Y.P., S.L., S.Y.B. and Y.J.P.; data curation, S.S. and J.S.; resource, J.-Y.P., S.L., S.Y.B. and K.-R.L.; writing—original draft preparation and writing—review and editing, S.S. and Y.J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Research Foundation (NRF) funded by the Korean government (2021R1A2C2012578) to Y.J.P.

Institutional Review Board Statement

The study was approved by the Ethics Committee of the Hanaro Medical Foundation (HMFIRB No. HNR2021-02, approval date 21 October 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors acknowledge Hyundai Green Food Co., Ltd. and SCL Healthcare Inc. for experimental materials and analytical supports, respectively. We appreciate the supports from the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Republic of Korea) and the National Research Foundation of Korea (NRF-5199990614253, Education Research Center for 4IR-Based Health Care) for S.S. and J.S.

Conflicts of Interest

Y.J.P. reports a consulting fee from Hyundai Green Food Co., Ltd. All other authors have no conflict of interest relevant to this study.

References

  1. Hunter, E.; Avenell, A.; Maheshwari, A.; Stadler, G.; Best, D. The effectiveness of weight-loss lifestyle interventions for improving fertility in women and men with overweight or obesity and infertility: A systematic review update of evidence from randomized controlled trials. Obes. Rev. 2021, 22, e13325. [Google Scholar] [CrossRef] [PubMed]
  2. Guan, Q.; Wang, Z.; Cao, J.; Dong, Y.; Chen, Y. Mechanisms of Melatonin in Obesity: A Review. Int. J. Mol. Sci. 2021, 23, 218. [Google Scholar] [CrossRef] [PubMed]
  3. Hasler, C.M. Functional foods: Benefits, concerns and challenges-a position paper from the american council on science and health. J. Nutr. 2002, 132, 3772–3781. [Google Scholar] [CrossRef]
  4. Alissa, E.M.; Ferns, G.A. Functional foods and nutraceuticals in the primary prevention of cardiovascular diseases. J. Nutr. Metab. 2012, 2012, 569486. [Google Scholar] [CrossRef]
  5. Glei, M.; Matuschek, M.; Steiner, C.; Böhm, V.; Persin, C.; Pool-Zobel, B.L. Initial in vitro toxicity testing of functional foods rich in catechins and anthocyanins in human cells. Toxicol. In Vitro 2003, 17, 723–729. [Google Scholar] [CrossRef]
  6. Skalkos, D.; Kalyva, Z.C. Exploring the Impact of COVID-19 Pandemic on Food Choice Motives: A Systematic Review. Sustainability 2023, 15, 1606. [Google Scholar] [CrossRef]
  7. Urhan, M.; Okut Aysin, E. Nutritional and health behaviour predictors of the weight gain during the COVID-19 pandemic. Eur. J. Nutr. 2022, 61, 2993–3002. [Google Scholar] [CrossRef] [PubMed]
  8. Jeong, Y.; Lee, E.; Park, Y.J.; Kim, Y.; Kwon, O.; Kim, Y. A Review of Recent Evidence from Meal-Based Diet Interventions and Clinical Biomarkers for Improvement of Glucose Regulation. Prev. Nutr. Food Sci. 2020, 25, 9–24. [Google Scholar] [CrossRef] [PubMed]
  9. Kim, J.Y. Optimal Diet Strategies for Weight Loss and Weight Loss Maintenance. J. Obes. Metab. Syndr. 2021, 30, 20–31. [Google Scholar] [CrossRef]
  10. Astrup, A.; Grunwald, G.K.; Melanson, E.L.; Saris, W.H.; Hill, J.O. The role of low-fat diets in body weight control: A meta-analysis of ad libitum dietary intervention studies. Int. J. Obes. Relat. Metab. Disord. 2000, 24, 1545–1552. [Google Scholar] [CrossRef]
  11. Zhu, H.; Bi, D.; Zhang, Y.; Kong, C.; Du, J.; Wu, X.; Wei, Q.; Qin, H. Ketogenic diet for human diseases: The underlying mechanisms and potential for clinical implementations. Signal Transduct. Target. Ther. 2022, 7, 11. [Google Scholar] [CrossRef] [PubMed]
  12. Ajala, O.; English, P.; Pinkney, J. Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes. Am. J. Clin. Nutr. 2013, 97, 505–516. [Google Scholar] [CrossRef] [PubMed]
  13. Wali, J.A.; Solon-Biet, S.M.; Freire, T.; Brandon, A.E. Macronutrient Determinants of Obesity, Insulin Resistance and Metabolic Health. Biology 2021, 10, 336. [Google Scholar] [CrossRef] [PubMed]
  14. Solon-Biet, S.M.; McMahon, A.C.; Ballard, J.W.O.; Ruohonen, K.; Wu, L.E.; Cogger, V.C.; Warren, A.; Huang, X.; Pichaud, N.; Melvin, R.G.; et al. The Ratio of Macronutrients, Not Caloric Intake, Dictates Cardiometabolic Health, Aging, and Longevity in Ad Libitum-Fed Mice. Cell Metab. 2020, 31, 654. [Google Scholar] [CrossRef]
  15. Moreno-Indias, I.; Cardona, F.; Tinahones, F.J.; Queipo-Ortuño, M.I. Impact of the gut microbiota on the development of obesity and type 2 diabetes mellitus. Front. Microbiol. 2014, 5, 190. [Google Scholar] [CrossRef]
  16. Muscogiuri, G.; Cantone, E.; Cassarano, S.; Tuccinardi, D.; Barrea, L.; Savastano, S.; Colao, A. Gut microbiota: A new path to treat obesity. Int. J. Obes. Suppl. 2019, 9, 10–19. [Google Scholar] [CrossRef]
  17. Cunningham, A.L.; Stephens, J.W.; Harris, D.A. A review on gut microbiota: A central factor in the pathophysiology of obesity. Lipids Health Dis. 2021, 20, 65. [Google Scholar] [CrossRef]
  18. de Toro-Martín, J.; Arsenault, B.J.; Després, J.P.; Vohl, M.C. Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome. Nutrients 2017, 9, 913. [Google Scholar] [CrossRef]
  19. Zeevi, D.; Korem, T.; Zmora, N.; Israeli, D.; Rothschild, D.; Weinberger, A.; Ben-Yacov, O.; Lador, D.; Avnit-Sagi, T.; Lotan-Pompan, M.; et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell 2015, 163, 1079–1094. [Google Scholar] [CrossRef]
  20. Berry, S.E.; Valdes, A.M.; Drew, D.A.; Asnicar, F.; Mazidi, M.; Wolf, J.; Capdevila, J.; Hadjigeorgiou, G.; Davies, R.; Al Khatib, H.; et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 2020, 26, 964–973. [Google Scholar] [CrossRef]
  21. Kootte, R.S.; Vrieze, A.; Holleman, F.; Dallinga-Thie, G.M.; Zoetendal, E.G.; de Vos, W.M.; Groen, A.K.; Hoekstra, J.B.; Stroes, E.S.; Nieuwdorp, M. The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes. Metab. 2012, 14, 112–120. [Google Scholar] [CrossRef] [PubMed]
  22. Johnson, E.L.; Heaver, S.L.; Walters, W.A.; Ley, R.E. Microbiome and metabolic disease: Revisiting the bacterial phylum Bacteroidetes. J. Mol. Med. 2017, 95, 1–8. [Google Scholar] [CrossRef] [PubMed]
  23. Rinninella, E.; Raoul, P.; Cintoni, M.; Franceschi, F.; Miggiano, G.A.D.; Gasbarrini, A.; Mele, M.C. What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. Microorganisms 2019, 7, 14. [Google Scholar] [CrossRef] [PubMed]
  24. Stojanov, S.; Berlec, A.; Štrukelj, B. The Influence of Probiotics on the Firmicutes/Bacteroidetes Ratio in the Treatment of Obesity and Inflammatory Bowel disease. Microorganisms 2020, 8, 1715. [Google Scholar] [CrossRef]
  25. Shin, J.H.; Ahn, Y.J.; Chung, W.-H.; Lim, M.Y.; Hong, S.; Kim, J.-H.; Park, M.H.; Nam, Y.-D. Effect of Saengshik Supplementation on the Gut Microbial Composition of Healthy Korean Adults: A Single-Group Pilot Study. Front. Nutr. 2021, 8, 743620. [Google Scholar] [CrossRef]
  26. Cronin, P.; Joyce, S.A.; O’Toole, P.W.; O’Connor, E.M. Dietary Fibre Modulates the Gut Microbiota. Nutrients 2021, 13, 1655. [Google Scholar] [CrossRef]
  27. Murphy, E.A.; Velazquez, K.T.; Herbert, K.M. Influence of high-fat diet on gut microbiota: A driving force for chronic disease risk. Curr. Opin. Clin. Nutr. Metab. Care 2015, 18, 515–520. [Google Scholar] [CrossRef]
  28. Ley, R.E.; Bäckhed, F.; Turnbaugh, P.; Lozupone, C.A.; Knight, R.D.; Gordon, J.I. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. USA 2005, 102, 11070–11075. [Google Scholar] [CrossRef]
  29. Chompre, G.; Sambolin, L.; Cruz, M.L.; Sanchez, R.; Rodriguez, Y.; Rodríguez-Santiago, R.E.; Yamamura, Y.; Appleyard, C.B. A one month high fat diet disrupts the gut microbiome and integrity of the colon inducing adiposity and behavioral despair in male Sprague Dawley rats. Heliyon 2022, 8, e11194. [Google Scholar] [CrossRef]
  30. Daniel, H.; Gholami, A.M.; Berry, D.; Desmarchelier, C.; Hahne, H.; Loh, G.; Mondot, S.; Lepage, P.; Rothballer, M.; Walker, A.; et al. High-fat diet alters gut microbiota physiology in mice. ISME J. 2014, 8, 295–308. [Google Scholar] [CrossRef]
  31. Wang, Y.; Yao, W.; Li, B.; Qian, S.; Wei, B.; Gong, S.; Wang, J.; Liu, M.; Wei, M. Nuciferine modulates the gut microbiota and prevents obesity in high-fat diet-fed rats. Exp. Mol. Med. 2020, 52, 1959–1975. [Google Scholar] [CrossRef]
  32. Ang, Q.Y.; Alexander, M.; Newman, J.C.; Tian, Y.; Cai, J.; Upadhyay, V.; Turnbaugh, J.A.; Verdin, E.; Hall, K.D.; Leibel, R.L.; et al. Ketogenic Diets Alter the Gut Microbiome Resulting in Decreased Intestinal Th17 Cells. Cell 2020, 181, 1263–1275.e16. [Google Scholar] [CrossRef]
  33. Rolland, C.; Broom, I. The Effects of Very-Low-Calorie Diets on HDL: A Review. Cholesterol 2011, 2011, 306278. [Google Scholar] [CrossRef] [PubMed]
  34. Houtman, T.A.; Eckermann, H.A.; Smidt, H.; de Weerth, C. Gut microbiota and BMI throughout childhood: The role of firmicutes, bacteroidetes, and short-chain fatty acid producers. Sci. Rep. 2022, 12, 3140. [Google Scholar] [CrossRef] [PubMed]
  35. Schwiertz, A.; Taras, D.; Schäfer, K.; Beijer, S.; Bos, N.A.; Donus, C.; Hardt, P.D. Microbiota and SCFA in lean and overweight healthy subjects. Obesity 2010, 18, 190–195. [Google Scholar] [CrossRef]
  36. David, L.A.; Maurice, C.F.; Carmody, R.N.; Gootenberg, D.B.; Button, J.E.; Wolfe, B.E.; Ling, A.V.; Devlin, A.S.; Varma, Y.; Fischbach, M.A.; et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014, 505, 559–563. [Google Scholar] [CrossRef] [PubMed]
  37. Dalby, M.J.; Ross, A.W.; Walker, A.W.; Morgan, P.J. Dietary Uncoupling of Gut Microbiota and Energy Harvesting from Obesity and Glucose Tolerance in Mice. Cell Rep. 2017, 21, 1521–1533. [Google Scholar] [CrossRef] [PubMed]
  38. Schipke, J.; Brandenberger, C.; Vital, M.; Mühlfeld, C. Starch and Fiber Contents of Purified Control Diets Differentially Affect Hepatic Lipid Homeostasis and Gut Microbiota Composition. Front. Nutr. 2022, 9, 915082. [Google Scholar] [CrossRef]
  39. Parada Venegas, D.; De la Fuente, M.K.; Landskron, G.; González, M.J.; Quera, R.; Dijkstra, G.; Harmsen, H.J.M.; Faber, K.N.; Hermoso, M.A. Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for Inflammatory Bowel Diseases. Front. Immunol. 2019, 10, 277. [Google Scholar] [CrossRef] [PubMed]
  40. Gasaly, N.; Hermoso, M.A.; Gotteland, M. Butyrate and the Fine-Tuning of Colonic Homeostasis: Implication for Inflammatory Bowel Diseases. Int. J. Mol. Sci. 2021, 22, 3061. [Google Scholar] [CrossRef]
  41. Zhu, L.B.; Zhang, Y.C.; Huang, H.H.; Lin, J. Prospects for clinical applications of butyrate-producing bacteria. World J. Clin. Pediatr. 2021, 10, 84–92. [Google Scholar] [CrossRef] [PubMed]
  42. Magne, F.; Gotteland, M.; Gauthier, L.; Zazueta, A.; Pesoa, S.; Navarrete, P.; Balamurugan, R. The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients? Nutrients 2020, 12, 1474. [Google Scholar] [CrossRef] [PubMed]
  43. Wang, B.; Kong, Q.; Li, X.; Zhao, J.; Zhang, H.; Chen, W.; Wang, G. A High-Fat Diet Increases Gut Microbiota Biodiversity and Energy Expenditure Due to Nutrient Difference. Nutrients 2020, 12, 3197. [Google Scholar] [CrossRef] [PubMed]
  44. Ley, R.E.; Turnbaugh, P.J.; Klein, S.; Gordon, J.I. Microbial ecology: Human gut microbes associated with obesity. Nature 2006, 444, 1022–1023. [Google Scholar] [CrossRef]
  45. Gilbert, J.A.; Quinn, R.A.; Debelius, J.; Xu, Z.Z.; Morton, J.; Garg, N.; Jansson, J.K.; Dorrestein, P.C.; Knight, R. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 2016, 535, 94–103. [Google Scholar] [CrossRef]
  46. Stanislawski, M.A.; Dabelea, D.; Lange, L.A.; Wagner, B.D.; Lozupone, C.A. Gut microbiota phenotypes of obesity. NPJ Biofilms Microbiomes 2019, 5, 18. [Google Scholar] [CrossRef]
  47. Pinart, M.; Dötsch, A.; Schlicht, K.; Laudes, M.; Bouwman, J.; Forslund, S.K.; Pischon, T.; Nimptsch, K. Gut Microbiome Composition in Obese and Non-Obese Persons: A Systematic Review and Meta-Analysis. Nutrients 2021, 14, 12. [Google Scholar] [CrossRef]
  48. Duan, M.; Wang, Y.; Zhang, Q.; Zou, R.; Guo, M.; Zheng, H. Characteristics of gut microbiota in people with obesity. PLoS ONE 2021, 16, e0255446. [Google Scholar] [CrossRef]
  49. Jiang, Z.; Sun, T.Y.; He, Y.; Gou, W.; Zuo, L.S.; Fu, Y.; Miao, Z.; Shuai, M.; Xu, F.; Xiao, C.; et al. Dietary fruit and vegetable intake, gut microbiota, and type 2 diabetes: Results from two large human cohort studies. BMC Med. 2020, 18, 371. [Google Scholar] [CrossRef]
  50. Partula, V.; Mondot, S.; Torres, M.J.; Kesse-Guyot, E.; Deschasaux, M.; Assmann, K.; Latino-Martel, P.; Buscail, C.; Julia, C.; Galan, P.; et al. Associations between usual diet and gut microbiota composition: Results from the Milieu Intérieur cross-sectional study. Am. J. Clin. Nutr. 2019, 109, 1472–1483. [Google Scholar] [CrossRef]
  51. Gil-Cardoso, K.; Ginés, I.; Pinent, M.; Ardévol, A.; Blay, M.; Terra, X. Effects of flavonoids on intestinal inflammation, barrier integrity and changes in gut microbiota during diet-induced obesity. Nutr. Res. Rev. 2016, 29, 234–248. [Google Scholar] [CrossRef]
  52. Dong, T.S.; Luu, K.; Lagishetty, V.; Sedighian, F.; Woo, S.L.; Dreskin, B.W.; Katzka, W.; Chang, C.; Zhou, Y.; Arias-Jayo, N.; et al. A High Protein Calorie Restriction Diet Alters the Gut Microbiome in Obesity. Nutrients 2020, 12, 3221. [Google Scholar] [CrossRef] [PubMed]
  53. Hamoud, A.R.; Weaver, L.; Stec, D.E.; Hinds, T.D., Jr. Bilirubin in the Liver-Gut Signaling Axis. Trends Endocrinol. Metab. 2018, 29, 140–150. [Google Scholar] [CrossRef] [PubMed]
  54. Woting, A.; Pfeiffer, N.; Loh, G.; Klaus, S.; Blaut, M. Clostridium ramosum promotes high-fat diet-induced obesity in gnotobiotic mouse models. mBio 2014, 5, e01530-14. [Google Scholar] [CrossRef] [PubMed]
  55. Wang, B.; Zhou, Y.; Mao, Y.; Gong, L.; Li, X.; Xu, S.; Wang, F.; Guo, Q.; Zhang, H.; Li, W. Dietary Supplementation With Lactobacillus plantarum Ameliorates Compromise of Growth Performance by Modulating Short-Chain Fatty Acids and Intestinal Dysbiosis in Broilers Under Clostridium perfringens Challenge. Front. Nutr. 2021, 8, 706148. [Google Scholar] [CrossRef]
  56. Bien, J.; Palagani, V.; Bozko, P. The intestinal microbiota dysbiosis and Clostridium difficile infection: Is there a relationship with inflammatory bowel disease? Therap. Adv. Gastroenterol. 2013, 6, 53–68. [Google Scholar] [CrossRef]
  57. Lukiw, W.J. Bacteroides fragilis Lipopolysaccharide and Inflammatory Signaling in Alzheimer’s Disease. Front. Microbiol. 2016, 7, 1544. [Google Scholar] [CrossRef]
  58. Žiberna, L.; Jenko-Pražnikar, Z.; Petelin, A. Serum Bilirubin Levels in Overweight and Obese Individuals: The Importance of Anti-Inflammatory and Antioxidant Responses. Antioxidants 2021, 10, 1352. [Google Scholar] [CrossRef]
  59. Eremiasova, L.; Hubacek, J.A.; Danzig, V.; Adamkova, V.; Mrazova, L.; Pitha, J.; Lanska, V.; Cífková, R.; Vitek, L. Serum Bilirubin in the Czech Population-Relationship to the Risk of Myocardial Infarction in Males. Circ. J. 2020, 84, 1779–1785. [Google Scholar] [CrossRef]
  60. Clément, K.; Viguerie, N.; Poitou, C.; Carette, C.; Pelloux, V.; Curat, C.A.; Sicard, A.; Rome, S.; Benis, A.; Zucker, J.D.; et al. Weight loss regulates inflammation-related genes in white adipose tissue of obese subjects. FASEB J. 2004, 18, 1657–1669. [Google Scholar] [CrossRef]
  61. Page, P. Beyond statistical significance: Clinical interpretation of rehabilitation research literature. Int. J. Sports Phys. Ther. 2014, 9, 726–736. [Google Scholar] [PubMed]
Nutrients 15 04744 g001aNutrients 15 04744 g001b
Figure 1. The flow of subjects’ selection and schematic representation of study design. (A) A flow diagram of subjects’ selection. There were 60 subjects in the weight-loss meal replacement program, and 47 subjects were finally analyzed. (B) Anthropometric measurements, dietary surveys, and blood and feces were sampled at visit 1 and visit 2 for subjects with BMI ≥ 23 kg/m2. The 3-week diet intervention was conducted with a balanced diet, a high-lipid–low-carbohydrate diet, and a protein-enriched diet.
Figure 1. The flow of subjects’ selection and schematic representation of study design. (A) A flow diagram of subjects’ selection. There were 60 subjects in the weight-loss meal replacement program, and 47 subjects were finally analyzed. (B) Anthropometric measurements, dietary surveys, and blood and feces were sampled at visit 1 and visit 2 for subjects with BMI ≥ 23 kg/m2. The 3-week diet intervention was conducted with a balanced diet, a high-lipid–low-carbohydrate diet, and a protein-enriched diet.
Nutrients 15 04744 g001aNutrients 15 04744 g001b
Nutrients 15 04744 g002aNutrients 15 04744 g002b
Figure 2. Changes in gut microbiome by diet intervention in Group B, F, and P. The relative abundance of (A) phylum level, (B) family level, (C) genus level, and (D) Firmicutes-to-Bacteroidetes (F/B) ratio at pre- and post-intervention in total subjects, and (E) F/B ratio at pre- and post-intervention by Group B, F, and P. In (A), Firmicutes and Bacteroidetes were used to obtain the F/B ratio of (D,E). Classified microbiome levels with relative abundances above a cutoff level of 0.2% are indicated. Gray bars represent microbiome with a relative abundance of <0.2%. Data presented as mean ± standard error. Total (n = 47), Group B (n = 16), Group F (n = 14), and Group P (n = 17). Statistical significance was measured using a paired t-test (p < 0.05). Asterisk (*) indicated a microbiome showing a significant difference.
Figure 2. Changes in gut microbiome by diet intervention in Group B, F, and P. The relative abundance of (A) phylum level, (B) family level, (C) genus level, and (D) Firmicutes-to-Bacteroidetes (F/B) ratio at pre- and post-intervention in total subjects, and (E) F/B ratio at pre- and post-intervention by Group B, F, and P. In (A), Firmicutes and Bacteroidetes were used to obtain the F/B ratio of (D,E). Classified microbiome levels with relative abundances above a cutoff level of 0.2% are indicated. Gray bars represent microbiome with a relative abundance of <0.2%. Data presented as mean ± standard error. Total (n = 47), Group B (n = 16), Group F (n = 14), and Group P (n = 17). Statistical significance was measured using a paired t-test (p < 0.05). Asterisk (*) indicated a microbiome showing a significant difference.
Nutrients 15 04744 g002aNutrients 15 04744 g002b
Nutrients 15 04744 g003aNutrients 15 04744 g003b
Figure 3. Changes in gut bacterial alpha-diversity at the phylum level by diet intervention in Group B, F, and P. Alpha-diversity violin plots to visualize the difference in microbiota at the phylum level between pre- and post-diet intervention. Alpha-diversity comparisons of (A,B) observed features, (C,D) Shannon index, and (E,F) species evenness in the fecal microbiota among Group B, F, and P (Kruskal−Wallis test). There was no significant difference in the change (post−pre) values among the three groups.
Figure 3. Changes in gut bacterial alpha-diversity at the phylum level by diet intervention in Group B, F, and P. Alpha-diversity violin plots to visualize the difference in microbiota at the phylum level between pre- and post-diet intervention. Alpha-diversity comparisons of (A,B) observed features, (C,D) Shannon index, and (E,F) species evenness in the fecal microbiota among Group B, F, and P (Kruskal−Wallis test). There was no significant difference in the change (post−pre) values among the three groups.
Nutrients 15 04744 g003aNutrients 15 04744 g003b
Nutrients 15 04744 g004aNutrients 15 04744 g004b
Figure 4. Changes in gut bacterial beta-diversity at the phylum level by diet intervention in Group B, F, and P. PCoA plots of beta-diversity derived using weighted UniFrac to visualize the difference in microbiota at the phylum level between pre- and post-diet intervention for total subjects (A), Group B (B), Group F (C), and Group P (D). The statistical significance of weighted UniFrac was measured using permutational analysis of variance (PERMANOVA).
Figure 4. Changes in gut bacterial beta-diversity at the phylum level by diet intervention in Group B, F, and P. PCoA plots of beta-diversity derived using weighted UniFrac to visualize the difference in microbiota at the phylum level between pre- and post-diet intervention for total subjects (A), Group B (B), Group F (C), and Group P (D). The statistical significance of weighted UniFrac was measured using permutational analysis of variance (PERMANOVA).
Nutrients 15 04744 g004aNutrients 15 04744 g004b
Nutrients 15 04744 g005
Figure 5. Spearman’s rank order correlation analysis of lifestyle, anthropometric, biochemical, diet, and microbiome. Spearman’s rank order correlation matrix of a total of 41 significant observations, including lifestyle (red), anthropometric (yellow), biochemical (green), diet (blue), and microbiome data among Group B, F, and P. (A) Spearman’s rank order correlation determined between lifestyle, anthropometric, biochemical, diet, and microbiome indicators to assess subjects’ pre- and post-diet intervention status. (B) Spearman’s rank order correlation of diet intervention-induced changes (post−pre) in lifestyle, anthropometric, biochemical, diet, and microbiome indicators to assess the subjects’ change status. BMI, body mass index; γ-GTP, gamma-glutamyl transferase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; ALP, alkaline phosphatase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; FGF21, fibroblast growth factor 21; TMAO, trimethylamine-N-oxide; NAD; nicotinamide adenine dinucleotide. Asterisk (*) means variables with a correlation of 0.25 ≥ or ≤−0.25.
Figure 5. Spearman’s rank order correlation analysis of lifestyle, anthropometric, biochemical, diet, and microbiome. Spearman’s rank order correlation matrix of a total of 41 significant observations, including lifestyle (red), anthropometric (yellow), biochemical (green), diet (blue), and microbiome data among Group B, F, and P. (A) Spearman’s rank order correlation determined between lifestyle, anthropometric, biochemical, diet, and microbiome indicators to assess subjects’ pre- and post-diet intervention status. (B) Spearman’s rank order correlation of diet intervention-induced changes (post−pre) in lifestyle, anthropometric, biochemical, diet, and microbiome indicators to assess the subjects’ change status. BMI, body mass index; γ-GTP, gamma-glutamyl transferase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; ALP, alkaline phosphatase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; FGF21, fibroblast growth factor 21; TMAO, trimethylamine-N-oxide; NAD; nicotinamide adenine dinucleotide. Asterisk (*) means variables with a correlation of 0.25 ≥ or ≤−0.25.
Nutrients 15 04744 g005
Nutrients 15 04744 g006
Figure 6. Scatter plots and Spearman’s rank correlation coefficient between change in F/B ratio and change in sleep duration, creatinine, HDL-cholesterol, and adiponectin. To investigate Spearman’s rank correlation coefficient between the change in F/B ratio and change in sleep duration, creatinine, HDL-cholesterol, and adiponectin in the total subjects, it was indicated by a scatter plot. Scatter plot of (A) sleep duration, (B) creatinine, (C) HDL-cholesterol, and (D) adiponectin, the parameters that were most closely related to the F/B ratio according to Figure 5B.
Figure 6. Scatter plots and Spearman’s rank correlation coefficient between change in F/B ratio and change in sleep duration, creatinine, HDL-cholesterol, and adiponectin. To investigate Spearman’s rank correlation coefficient between the change in F/B ratio and change in sleep duration, creatinine, HDL-cholesterol, and adiponectin in the total subjects, it was indicated by a scatter plot. Scatter plot of (A) sleep duration, (B) creatinine, (C) HDL-cholesterol, and (D) adiponectin, the parameters that were most closely related to the F/B ratio according to Figure 5B.
Nutrients 15 04744 g006
Table 1. Composition of the three intervention diets.
Table 1. Composition of the three intervention diets.
Energy (kcal/Day)Carbohydrate (g/Day)Protein (g/Day)Fat (g/Day)
Group B1044155
(59.3%)		39
(14.9%)		33
(28.5%)
Group F111793
(33.3%)		49
(17.5%)		66
(53.6%)
Group P1038107
(41.2%)		67
(25.7%)		40
(34.5%)
Group B, balanced diet; Group F, high-lipid–low-carbohydrate diet; Group P, protein-enriched diet. Each value in parentheses is energy from carbohydrate, fat, and protein.
Table 2. Baseline characteristics of the three groups of subjects in the dietary intervention study.
Table 2. Baseline characteristics of the three groups of subjects in the dietary intervention study.
Total
(n = 47)		Diet Interventionp-Value
Group B
(n = 16)		Group F
(n = 14)		Group P
(n = 17)
Age (years)36.0 ± 4.336.4 ± 5.235.6 ± 3.935.8 ± 3.80.8626
Male, n (%)45 (95.7)15 (93.8)13 (92.9)17 (100)
Physical activity (kcal/day)124.6 ± 86.5144.1 ± 73.8120.3 ± 91.8109.8 ± 94.60.5201
Sleep duration (hours)6.60 ± 0.736.65 ± 0.936.54 ± 0.606.60 ± 0.640.9108
Dietary history
 RFS *19.9 ± 8.719.0 ± 9.818.1 ± 7.922.2 ± 8.20.3912
 MEDFICTS **66.5 ± 11.862.3 ± 10.365.9 ± 13.171.1 ± 10.90.0951
Anthropometric parameters
 Height (cm)174.9 ± 5.9174.1 ± 5.8175.4 ± 6.6175.2 ± 5.90.8157
 BW (kg)81.9 ± 12.279.9 ± 10.484.6 ± 15.481.5 ± 11.20.5821
 BMI (kg/m2)26.7 ± 3.026.3 ± 2.327.3 ± 3.526.6 ± 3.30.6461
 FM (%)25.3 ± 5.925.9 ± 6.226.2 ± 5.024.0 ± 6.40.5505
 FFM (%)60.8 ± 7.259.0 ± 6.462.1 ± 9.261.5 ± 6.10.4515
 SMM (kg)34.4 ± 4.333.3 ± 3.935.2 ± 5.534.8 ± 3.70.4475
 RBW (%)21.3 ± 13.319.7 ± 10.024.0 ± 14.620.7 ± 15.20.6662
 WHR0.9 ± 0.10.9 ± 0.10.9 ± 0.10.9 ± 0.10.4716
Biochemical parameters
Liver function
 γ-GTP (IU/L)29.6 ± 19.528.9 ± 22.829.9 ± 17.029.9 ± 19.20.9877
 Total Bilirubin (mg/dL)0.7 ± 0.30.7 ± 0.20.6 ± 0.30.8 ± 0.40.2370
 AST (IU/L)26.9 ± 13.622.8 ± 10.733.6 ± 18.325.2 ± 9.80.0727
 ALT (IU/L)30.4 ± 20.227.7 ± 24.235.3 ± 19.028.8 ± 17.40.5570
 LDH (IU/L)189.6 ± 30.4185.8 ± 31.2195.6 ± 35.6188.1 ± 26.00.6659
 ALP (U/L)63.6 ± 13.264.1 ± 12.763.4 ± 16.063.3 ± 12.10.9816
Kidney function
 Creatinine (mg/dL)0.9 ± 0.10.9 ± 0.10.9 ± 0.20.9 ± 0.10.9449
Lipid profiles
 TG (mg/dL)129.2 ± 63.1120.4 ± 65.2129.3 ± 58.1137.4 ± 67.60.7505
 T-Cho (mg/dL)203.3 ± 29.0 202.6 ± 30.4200.0 ± 32.3206.7 ± 26.00.8144
 HDL (mg/dL)51.9 ± 9.756.2 ± 9.348.1 ± 9.250.9 ± 9.30.0599
 LDL (mg/dL)139.4 ± 26.8135.5 ± 27.8139.1 ± 29.8143.2 ± 24.20.7176
Others
 Glucose (mg/dL)92.3 ± 9.390.3 ± 7.094.6 ± 12.992.2 ± 7.70.4651
 Total protein (g/dL)7.4 ± 0.47.3 ± 0.47.4 ± 0.47.4 ± 0.30.4694
 Albumin (g/dL)4.8 ± 0.24.8 ± 0.24.7 ± 0.14.8 ± 0.20.0809
 hsCRP (mg/L)1.4 ± 1.61.9 ± 2.1 1.7 ± 1.60.7 ± 0.50.0819
 Leptin (ng/mL)12.1 ± 11.814.6 ± 16.713.4 ± 10.88.8 ± 4.80.3313
 Adiponectin (ng/mL)6919.7 ± 4066.96699.6 ± 3485.86761.7 ± 4218.07256.7 ± 4638.10.9153
 FGF21 (pg/mL)140.6 ± 96.2 136.3 ± 70.8154.1 ± 120.9133.5 ± 98.90.8240
 TMAO (umol/L)4.5 ± 5.55.5 ± 7.95.1 ± 5.02.9 ± 2.30.3550
 NAD (ug/mL)21.8 ± 3.022.2 ± 3.321.5 ± 3.621.8 ± 2.10.7839
All values are expressed as mean ± standard deviation. Significant differences in baseline values among Group B, F, and P were compared using one-way ANOVA. BW, body weight; BMI, body mass index; FM, fat mass; FFM, fat-free mass; SMM, skeletal muscle mass; RBW, relative body weight; WHR, waist-to-hip ratio; γ-GTP, gamma-glutamyl transferase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; ALP, alkaline phosphatase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; FGF21, fibroblast growth factor 21; TMAO, trimethylamine-N-oxide; NAD, nicotinamide adenine dinucleotide. * Recommended Food Score (RFS): the scores range from 0 to 47, and a higher score indicates a better-quality diet. ** Meats, Eggs, Dairy, Fried foods, fat in baked goods, Convenience foods, fats added at the Table, and Snacks (MEDFICTS): step 2 diet (score < 40, <7% of energy from saturated fat and <200 mg cholesterol), step 1 diet (score 40–69, 8–10% of energy from saturated fat and <300 mg cholesterol), high-fat diet (score > 70).
Table 3. Diet intake of subjects and diet intervention change data of subjects.
Table 3. Diet intake of subjects and diet intervention change data of subjects.
Total
(n = 47)		p-ValueDiet Interventionp-Value
(ANOVA)
Group B
(n = 16)		p-ValueGroup F
(n = 14)		p-ValueGroup P
(n = 17)		p-Value
Energy (kcal/day)−569.1 ± 376.6<0.0001−735.2 ± 441.0<0.0001−452.0 ± 343.60.0003−509.3 ± 293.7<0.00010.6353
Carbohydrate (g/day)−62.5 ± 55.9<0.0001−51.0 ± 64.60.0065−89.5 ± 47.9<0.0001−51.2 ± 48.10.00050.1739
Protein (g/day)−18.4 ± 20.9<0.0001−36.1 ± 17.5<0.0001−17.1 ± 17.10.0025−2.7 ± 12.60.38600.9641
Fat (g/day)−14.8 ± 23.7<0.0001−31.0 ± 20.3<0.000111.0 ± 13.40.0089−20.8 ± 14.3<0.00010.7501
Carbohydrate
(% of calorie)		1.0 ± 11.20.539711.2 ± 7.2<0.0001−10.3 ± 8.10.00040.7 ± 6.60.66010.1401
Protein (% of calorie)1.4 ± 3.80.0127−1.8 ± 1.90.00180.5 ± 1.40.20635.2 ± 3.1<0.00010.3230
Fat (% of calorie)3.2 ± 9.40.0233−3.1 ± 4.30.010315.5 ± 5.8<0.0001−0.9 ± 4.20.37740.1210
All values are expressed as mean ± standard deviation. The p-values comparing the specific timeline values at pre- and post-diet intervention in the total subjects, Group B, Group F, and Group P were measured using paired t-test. The p-values measured using ANOVA showed a significant difference in the pre-value among the three groups.
Table 4. Lifestyle, anthropometric, and biochemical change data of subjects.
Table 4. Lifestyle, anthropometric, and biochemical change data of subjects.
Total
(n = 47)		p-ValueDiet Intervention
Group B
(n = 16)		p-ValueGroup F
(n = 14)		p-ValueGroup P
(n = 17)		p-Value
Lifestyle parameters
Physical activity (kcal/day)−0.9 ± 73.70.93411.5 ± 54.00.9144−0.9 ± 1.80.463912.4 ± 67.60.4600
Sleep duration (hours)−0.1 ± 0.50.5345−0.1 ± 0.70.55760.1 ± 0.40.2435−0.1 ± 0.50.2534
Anthropometric parameters
BW (kg)−1.4 ± 1.3 <0.0001−1.2 ± 0.9<0.0001−1.8 ± 1.60.0012−1.1 ± 1.20.0013
BMI (kg/m2)−0.5 ± 0.4<0.0001−0.4 ± 0.3<0.0001−0.6 ± 0.50.0008−0.4 ± 0.40.0016
FM (%)−0.3 ± 1.00.0616−0.6 ± 1.10.0557−0.02 ± 0.80.9198−0.2 ± 1.00.4220
FFM (%)−0.8 ± 1.0<0.0001−0.5 ± 0.80.0335−1.3 ± 1.30.0021−0.7 ± 0.80.0015
SMM (kg)−0.4 ± 0.6<0.0001−0.3 ± 0.50.0827−0.8 ± 0.80.0028−0.4 ± 0.50.0089
RBW (%)−2.1 ± 1.9<0.0001−1.9 ± 1.40.0001−2.7 ± 2.30.0008−1.7 ± 1.90.0017
WHR0.003 ± 0.010.12950.001 ± 0.010.60910.003 ± 0.010.30190.004 ± 0.020.3109
Biochemical parameters
Liver function
γ-GTP (IU/L)−8.8 ± 10.0<0.0001−8.7 ± 9.80.0028−10.4 ± 11.40.0044−7.5 ± 9.60.0054
Total Bilirubin (mg/dL)0.1 ± 0.30.04130.2 ± 0.30.02160.1 ± 0.2 0.1134−0.01 ± 0.30.9060
AST (IU/L)−2.6 ± 18.20.3424−1.8 ± 5.20.1840−7.2 ± 19.90.19750.6 ± 24.10.9211
ALT (IU/L)−4.8 ± 12.8 0.0138−4.3 ± 10.70.1342−7.5 ± 14.30.0718−3.0 ± 13.60.3751
LDH (IU/L)−17.1 ± 31.80.0006−11.1 ± 25.20.0983−27.9 ± 39.10.0191−13.8 ± 30.30.0784
ALP (U/L)−6.8 ± 5.6<0.0001−5.3 ± 5.00.0007−9.9 ± 5.1<0.0001−5.8 ± 5.80.0008
Kidney function
Creatinine (mg/dL)−0.01 ± 0.10.3813−0.02 ± 0.10.3661−0.01 ± 0.10.7034−0.003 ± 0.10.8614
Lipid profiles
TG (mg/dL)−17.1 ± 40.090.0054−13.9 ± 28.40.0696−17.4 ± 48.80.2044−19.8 ± 43.70.0808
T-Cho (mg/dL)−4.8 ± 17.20.0642−1.6 ± 17.80.7206−0.6 ± 17.00.8895−11.1 ± 15.90.0109
HDL (mg/dL)−3.6 ± 5.3<0.0001−4.4 ± 6.90.0218−1.6 ± 2.70.0491−4.4 ± 5.00.0025
LDL (mg/dL)1.5 ± 16.30.53325.1 ± 16.20.22963.1 ± 16.70.4948−3.2 ± 15.80.4100
Others
Glucose (mg/dL)−0.3 ± 7.40.7977−1.5 ± 4.30.1792−0.5 ± 11.00.86781.1 ± 6.00.4807
Total protein (g/dL)−0.2 ± 0.30.0001−0.1 ± 0.30.2565−0.2 ± 0.20.0043−0.2 ± 0.30.0078
Albumin (g/dL)0.01 ± 0.10.65960.03 ± 0.10.45060.0 ± 0.11.00000.0 ± 0.21.0000
hsCRP (mg/L)−0.7 ± 1.30.0008−1.2 ± 1.90.0267−0.7 ± 1.00.0148−0.2 ± 0.40.0733
Leptin (ng/mL)−3.2 ± 4.8<0.0001−3.7 ± 5.80.0215−3.9 ± 5.70.0226−2.0 ± 2.20.0019
Adiponectin (ng/mL)−416.2 ± 1287.80.0317−271.6 ± 791.90.1903−227.0 ± 1450.2 0.5682−708.3 ± 1525.90.0737
FGF21 (pg/mL)−39.0 ± 79.10.0015−31.7 ± 64.40.0681−56.3 ± 86.70.0304−31.8 ± 87.40.1531
TMAO (umol/L)−0.4 ± 6.30.6497−0.9 ± 9.80.7295−1.6 ± 3.30.09191.0 ± 3.60.2846
NAD (ug/mL)−0.2 ± 2.40.64710.9 ± 3.00.2355−0.9 ± 1.80.0838−0.6 ± 1.90.2256
All values are expressed as mean ± standard deviation. Significant differences between pre- and post-values in Group B, F, and P were compared using paired t-test. BW, body weight; BMI, body mass index; FM, fat mass; FFM, fat-free mass; SMM, skeletal muscle mass; RBW, relative body weight; WHR, waist-to-hip ratio; γ-GTP, gamma-glutamyl transferase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; ALP, alkaline phosphatase; TG, triglyceride; T-Cho, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; FGF21, fibroblast growth factor 21; TMAO, trimethylamine-N-oxide; NAD, nicotinamide adenine dinucleotide.
Table 5. The percentage of major phyla in the gut microbiome of subjects.
Table 5. The percentage of major phyla in the gut microbiome of subjects.
Total
(n = 47)		p-ValueDiet Intervention
Group B
(n = 16)		p-ValueGroup F
(n = 14)		p-ValueGroup P
(n = 17)		p-Value
Bacteroidetes (%)−14.3 ± 17.2<0.0001−13.5 ± 15.40.0032−13.6 ± 20.30.0260−15.6 ± 16.90.0016
Firmicutes (%)13.6 ± 16.5<0.000112.5 ± 16.20.007312.3 ± 16.50.015115.6 ± 17.60.0022
Proteobacteria (%)0.2 ± 4.50.70450.4 ± 4.50.71780.8 ± 6.40.6325−0.4 ± 2.20.4698
Actinobacteria (%)0.5 ± 1.20.00870.5 ± 1.40.13800.5 ± 0.90.06470.4 ± 1.30.1965
F/B ratio (%)0.7 ± 1.0<0.00010.6 ± 0.80.00850.7 ± 1.50.07640.7 ± 0.90.0066
All values are expressed as mean ± standard deviation. The p-values comparing specific timeline values at pre- and post-diet intervention in total subjects, Group B, Group F, and Group P were measured using a paired t-test. F/B ratio; Firmicutes/Bacteroidetes ratio.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Song, S.; Shon, J.; Yang, W.-r.; Kang, H.-B.; Kim, K.-H.; Park, J.-Y.; Lee, S.; Baik, S.Y.; Lee, K.-R.; Park, Y.J. Short-Term Effects of Weight-Loss Meal Replacement Programs with Various Macronutrient Distributions on Gut Microbiome and Metabolic Parameters: A Pilot Study. Nutrients 2023, 15, 4744. https://doi.org/10.3390/nu15224744

AMA Style

Song S, Shon J, Yang W-r, Kang H-B, Kim K-H, Park J-Y, Lee S, Baik SY, Lee K-R, Park YJ. Short-Term Effects of Weight-Loss Meal Replacement Programs with Various Macronutrient Distributions on Gut Microbiome and Metabolic Parameters: A Pilot Study. Nutrients. 2023; 15(22):4744. https://doi.org/10.3390/nu15224744

Chicago/Turabian Style

Song, Seungmin, Jinyoung Shon, Woo-ri Yang, Han-Bit Kang, Keun-Ha Kim, Ju-Yeon Park, Sanghoo Lee, Sae Yun Baik, Kyoung-Ryul Lee, and Yoon Jung Park. 2023. "Short-Term Effects of Weight-Loss Meal Replacement Programs with Various Macronutrient Distributions on Gut Microbiome and Metabolic Parameters: A Pilot Study" Nutrients 15, no. 22: 4744. https://doi.org/10.3390/nu15224744

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop