Ketogenic Diets Induced Glucose Intolerance and Lipid Accumulation in Mice with Alterations in Gut Microbiota and Metabolites

The ketogenic diet with extremely high fat and very low carbohydrate levels is very popular in society today. Although it has beneficial effects on epilepsy and neurodegenerative diseases, how ketogenic diets impact host glucose and lipid metabolism and gut microbiota still needs further investigation.

plasma glucose in healthy men (7). Although it decreased triglycerides, a KD increased cholesterol and inflammatory markers in overweight/obese subjects after 4 weeks of dietary intervention (8). In a rodent trial, a KD reduced circulating glucose and lipids in both normal-weight and ob/ob mouse models (9) and improved glucose tolerance in diet-induced obese mice (10). Nevertheless, several studies found that a KD has negative effects, including hepatic steatosis, insulin resistance, and glucose intolerance (11)(12)(13)(14)(15)(16)(17), and a recent study showed that 2 to 4 months of KD feeding depleted adipose-resident gd T cells, which could restrain inflammation, resulting in impaired glucose homeostasis (18). Although the diets mentioned above are all called a "ketogenic diet," the differences in ingredients and dietary composition even in more controlled and standardized animal studies still exist. Therefore, clearly exploring the impact of a KD on glucose and lipid metabolism is needed.
The gut microbiota is critically influenced by diet and has been considered a key mediator linking diet and host physiology (19). Previous work found that a KD altered the structure of gut microbiota, and specific KD-associated bacteria could protect against seizures in mouse models of intractable epilepsy, which was further confirmed because the protective effects disappeared when mice were treated with antibiotics or reared germfree (20). Furthermore, a recent study showed that a KD decreased the abundance of Bifidobacterium, and KD-associated gut microbiota reduced the levels of intestinal proinflammatory Th17 cells (21). Therefore, whether the gut microbiota altered by a KD affects host glucose and lipid metabolism became an interesting question, as did whether microbe-associated metabolites also play a role.
Here, to evaluate the effects of KD on host glucose and lipid metabolism, gut microbiota, and microbe-associated metabolites, two kinds of ketogenic diets commonly used in mouse trials, type 1 (KDR) and type 2 (KDH), were investigated in mice. We found that KDR but not KDH induced insulin resistance and damage to glucose homeostasis, while KDH induced more fat accumulation in mice, which was associated with their distinct effects on the gut microbiota and metabolite profiles. Moreover, we showed that the differences in metabolic phenotypes in mice induced by these two kinds of KD may be due to the sources and proportions of fat in the diet.

RESULTS
Ketogenic diets induced glucose intolerance and lipid accumulation in mice. To observe how two kinds of ketogenic diets impacted physiological and metabolic consequences in mice, 8-week-old male C57BL/6J mice were randomly assigned into four groups: (i) fed ketogenic diet type 1 (KDR, caloric ratio of 89.5% fat, 0.1% carbohydrate, and 10.4% protein), (ii) fed control normal chow of KDR (NCR, caloric ratio of 10% fat, 70% carbohydrate, and 20% protein), (iii) fed ketogenic diet type 2 (KDH, caloric ratio of 91.3% fat, 1% carbohydrate, and 7.7% protein), and (iv) fed control normal chow of KDH (NCH, caloric ratio of 15.5% fat, 64.5% carbohydrate, and 20% protein). Details of these diets are shown in Table S1 in the supplemental material. b-Hydroxybutyrate concentrations in blood significantly increased in the mice fed the two kinds of KD, and there was no difference between the KDR and KDH groups (Fig. S1a).
First, we observed how these two kinds of KD affected glucose metabolism. We did oral glucose tolerance test (OGTT) after 2 and 5 weeks of dietary intervention and found that the KDR mice showed significantly higher fasting blood glucose levels than the NCR mice, but there was no significant difference between the mice in the KDH and NCH groups (Fig. 1a). Moreover, we found that the KDR mice but not KDH mice had slower glucose clearance than their control group during the OGTT ( Fig. 1b and  Fig. S1b). Although the secretion of insulin was similar among the four groups of mice, which was confirmed by fasting blood insulin levels and immunofluorescence staining for insulin in the pancreas ( Fig. 1c and d), the homeostatic model assessment for insulin resistance (HOMA-IR) index and insulin resistance index were significantly increased in the KDR mice ( Fig. 1e to g). These results suggested that KDR but not KDH induced insulin resistance and damage to glucose homeostasis in mice.
Next, we observed how these two kinds of KD impacted lipid metabolism. After 5 weeks of dietary intervention, the KDH mice had lower body weights (Fig. S1c) and higher weights of epididymal and retroperitoneal fat mass ( Fig. 1h and i) than the NCH mice. Furthermore, the KDH mice also exhibited excessive lipid accumulation in the liver with increased concentrations of liver total cholesterol and triglyceride compared to the NCH mice ( Fig. 1j and k). However, there was no significant difference in the above parameters between the mice in the KDR and NCR groups. Both the adipocyte size and nonalcoholic fatty liver disease (NAFLD) activity score significantly increased in the mice fed the two kinds of KD compared to their respective control groups, which and calculated histologic score (NAFLD activity score). Data are presented as the mean 6 SEM and analyzed using one-way ANOVA, followed by a Tukey post hoc test. *, P , 0.05; **, P , 0.01; ***, P , 0.001. n = 8 to 9 for both groups for all analyses except n = 5 for panel l.
Ketogenic Diets Induced Metabolic Disorders in Mice ® were estimated by hematoxylin and eosin (H&E)-stained epididymal fat and liver, and the KDH mice had significantly higher values than the KDR mice ( Fig. 1l and m). These results suggested that KDH induced more fat accumulation in mice than KDR.
Previous studies have shown that metabolic diseases, including type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD), which disrupt glucose and lipid metabolism, are characterized by an impaired and defective intestinal barrier (22). To determine whether intestinal permeability was impacted by two kinds of KD, we measured the expression of Zo-1, Occludin, Muc1, Muc2, and Muc3 in the colon. The KDR and KDH mice showed significantly lower expression of Occludin and Muc2 than their control groups, implying that the intestinal barrier was damaged in the mice fed with the two kinds of KD (Fig. S1d), which was further confirmed by immunohistochemistry staining for ZO-1 in the ileum (Fig. S1e). Moreover, we found that the serum level of lipopolysaccharide (LPS)-binding protein (LBP) was significantly enhanced in the mice fed with the two kinds of KD, implying increased intestinal permeability to LPS, and there was no difference between the KDR and KDH mice (Fig. S1f). These results suggested that both KDR and KDH impaired intestinal barrier function in mice.
Two kinds of ketogenic diets molded disparate gut microbiota in mice. By quantitative real-time PCR (qPCR) targeting the 16S rRNA gene, we found that the total bacterial loads in feces were similar among the four groups of mice (Fig. S2a). To analyze whether the structure of gut microbiota was modulated by these two kinds of KD, we sequenced the 16S rRNA gene V3-V4 region of fecal samples collected at the 5th week. After 5 weeks of intervention, the richness and diversity of gut microbiota, which were reflected by the numbers of observed amplicon sequence variants (ASVs) and the Shannon index, were significantly increased only in the KDH mice but not in the KDR mice compared to their control groups (Fig. S2b). Principal-coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distances showed clear separations of gut microbiota structure between the mice in the KDR and NCR groups, as well as the KDH and NCH groups (P , 0.01 with PERMANOVA, 9,999 permutations). Although the gut microbiota structures were more similar in the mice fed the two kinds of KD than in mice fed the two control diets, there was still a significant difference between the KDR and KDH mice ( Fig. 2a and b).
Changes in the metabolome in mice fed two kinds of ketogenic diets. To identify how these two kinds of KD affected metabolites of the gut microbiota in mice, the untargeted metabolome of the cecum content was analyzed by liquid chromatography-mass spectrometry (LC-MS), and principal-component analysis (PCA) showed that the metabolome profiles of the gut microbiota in the KDR and KDH mice were significantly different from those of their control groups. Notably, the metabolite profiles of the KDR and KDH mice clearly segregated in the first principal component (Fig. 3a).
From orthogonal projection to latent structure-discriminant analysis (OPLS-DA) models (Fig. S4), we identified differential metabolic features (variable importance in projection [VIP] . 1, P , 0.05) that included 127 metabolites between the KDR and NCR mice, 125 metabolites between the KDH and NCH mice, and 117 metabolites between the KDH and KDR mice (Data Set S1). A total of 156 of these metabolites were significantly correlated with host parameters related to glucose and lipid metabolism (Data Set S2). In addition, 85 of these 156 metabolites were significantly correlated with gut microbial variations (R 2 . 0.09, P , 0.05 of PERMANOVA) (Data Set S2). Among these 85 metabolites, suberic acid was significantly enhanced in both the KDR and KDH mice, and it positively correlated with parameters related to glucose intolerance and lipid accumulation. Moreover, nervonic acid and batyl alcohol, which were significantly decreased by both KDs, were negatively correlated with parameters related to glucose intolerance and lipid accumulation. Besides, two bile acids, deoxycholic acid (DCA) and chenodeoxycholate (CDCA), were positively correlated with glucose intolerance. They were increased in both the KDR and KDH mice, but the KDR mice had significantly higher levels than the KDH mice. 1H-indole-3-propanoic acid (IPA) and indole acetic acid (IAA), which are the products of the bacteria metabolizing tryptophan, were negatively correlated with lipid accumulation. They were decreased in the KDR and KDH mice, respectively. Furthermore, trans-2-hydroxycinnamic acid, which increased only in the KDR mice, was positively correlated with glucose intolerance, while isobutyric acid, which decreased in the KDH mice, was negatively correlated with lipid accumulation (Fig. 3b and c). Taken together, these results suggested that KD intervention changed the metabolites in the gut, and two types of KD also had different influences.
The sources and proportions of fat in the ketogenic diet affected the metabolic phenotypes in mice. The two kinds of KD induced different damage to metabolic phenotypes in mice, which was associated with gut microbiota and metabolite changes, although the components of macronutrients were virtually identical in KDR and KDH. Then, we customized a ketogenic diet called KD(89.5%) with the same proportions of fat as KDR and the same sources as KDH in nutrients to evaluate the influences of sources and proportions of fat in the diets on the metabolic phenotypes in mice. We obtained comparable results for parameters related to glucose and lipid metabolism for the mice fed KDH and NCH to those in the above-mentioned trial. Unlike the mice in the KDR group, which had dramatic damage to glucose metabolism, the KD(89.5%) mice had fasting blood glucose levels, glucose clearance, and insulin resistance indices similar to the KDH mice ( Fig. 4a to f). Furthermore, the KD(89.5%) mice exhibited an increase in epididymal and retroperitoneal fat mass compared to the NCH mice ( Fig. 4g to i), which was not observed between the mice in the KDR and NCR groups. Interestingly, the KD(89.5%) mice did not develop excessive lipid accumulation in the liver like the KDH mice ( Fig. 4j to l), indicating that only KDH, which contained 91.3% fat, induced lipid accumulation in the livers of mice. Taken together, these results suggested that the sources but not proportions of fat in the KD induced differences in metabolic phenotypes related to glucose metabolism, while both the sources and proportions induced differences in metabolic phenotypes related to lipid metabolism.  Based on the above results, we concluded that the sources of fat in ketogenic diets had important and significantly different effects on metabolic phenotypes in mice. Therefore, we quantified the concentrations of fatty acids in the three different ketogenic diets [KDR, KDH, and KD(89.5%)] and found that trans-fatty acids (TFAs) were dramatically increased in KDR, which was confirmed by the concentrations of trans-6-octadecenoic acid, trans-9-octadecenoic acid, and trans-11-octadecenoic acid, and there was no significant difference between KDH and KD(89.5%) (Fig. S5). Combining the results that KDR but not KDH induced insulin resistance and damaged glucose homeostasis in mice, we speculated that the trans-fatty acids (TFAs) in KDR may be an important suspect.
The gut bacteria and metabolites affected by the sources and proportions of fat in ketogenic diets. By 16S rRNA gene sequencing, we found that there were still significant differences in the gut microbiota between the mice in the KDH and KD (89.5%) groups (P , 0.05 with PERMANOVA, 9,999 permutations) (Fig. 5a). Next, we observed how the abundances of 87 ASVs affected by two kinds of KD in the abovementioned trial changed. Of the mice fed these two ketogenic diets with the same proportion but different sources of fat, the KD(89.5%) mice had an almost negligible abundance of Bacteroides (ASV8, ASV36, ASV42) and Ruminococcaceae (ASV60, ASV73), similar to the NCH mice but unlike the KDR mice, which showed significant enrichment in these microbes. However, the KD(89.5%) mice exhibited a significant increase in the abundance of Lachnospiraceae (ASV25, ASV160, ASV169), Intestinimonas (ASV96), and Oscillibacter (ASV202), and there was no significant difference between the KDR and NCR mice ( Fig. 5b and Fig. S6a). In addition, for these two ketogenic diets with the same source but different proportions of fat, although the abundances of most ASVs were similar between the KDH and KD(89.5%) mice, differences still existed. KD(89.5%) significantly increased the abundance of ASVs, including ASV25 (Lachnospiraceae), ASV26 (Lactococcus), and ASV63 (Roseburia), and decreased the abundance of ASV202 (Oscillibacter) compared to the KDH mice ( Fig. 5c and Fig. S6b). Taken together, we identified 10 ASVs and 4 ASVs that responded to the sources and proportions of fat in ketogenic diets, respectively. Thus, these 14 ASVs were considered key members that were affected by the sources and proportions of fat in the two kinds of KD.
From untargeted metabolome data, we showed that KDs affected many of the gut microbiota-related metabolites, such as short-chain fatty acids (SCFAs), tryptophan metabolites, and bile acids (BAs). Thus, we quantified the concentrations of 4 main tryptophan metabolites, 6 SCFAs, and 40 BAs with LC-MS or gas chromatography (GC)-MS. Consistent with the results in untargeted metabolome, we also observed decreased tryptophan metabolites in both the KDH and KD(89.5%) mice (Fig. 5d). Moreover, we found that the concentrations of SCFAs significantly decreased in the KDH and KD(89.5%) mice, and there was no significant difference between the mice of these two groups. This suggested that decreased SCFAs may be related to lack of carbohydrates in the KD (Fig. 5e). Furthermore, we observed the concentrations of 10 BAs were significantly different in the mice of different groups. Among them, DCA, which belongs to secondary BAs, was significantly increased in both the KDH and KD(89.5%) mice. Notably, the KDR mice had significantly higher DCA and CDCA levels than the KDH mice in untargeted metabolome, but there was no significant difference between the mice in the KD(89.5%) and KDH groups, suggesting that the differences of DCA and CDCA levels in the KDR and KDH mice may be due to different fat sources in the KD (Fig. 5f).
The ketogenic diet used in human studies still impaired metabolic health, gut microbiota, and metabolites in mice. Considering that the ketogenic diets used in the above two trials were nearly devoid of carbohydrates, the ketogenic diets used in human studies permit low carbohydrate consumption (approximately 5% to 10% of total caloric intake or below 50 g per day) (19). Thus, we customized another ketogenic diet called KD(72%) with 72% fat, 20% protein, and 8% carbohydrate. Notably, to eliminate the influence of TFAs in diet, KD(72%) used the same sources of nutrients as KDH. We observed increased b-hydroxybutyrate concentrations in the KD(72%) mice [NC, 0.44; KD(72%), 0.8 mmol/liter; P , 0.001]. Moreover, KD(72%) damaged metabolic health, which was mainly reflected in two aspects. First, although the fasting blood glucose level and glucose clearance during OGTT in the KD(72%) mice were similar to those in the NC group ( Fig. 6a and b), the secretion of insulin was significantly higher in the KD(72%) mice ( Fig. 6c and d), and the KD(72%) mice also had a higher HOMA-IR index and insulin resistance index (Fig. 6e and f). This suggests that insulin sensitivity Ketogenic Diets Induced Metabolic Disorders in Mice ® was reduced in the KD(72%) mice. Second, the KD(72%) mice showed significantly higher fat mass and larger adipocyte size than the NC mice ( Fig. 6g and h). To summarize, these results showed that KD(72%) induced insulin resistance and fat accumulation in mice.
By 16S rRNA gene sequencing, we found that the KD(72%) mice displayed significantly altered gut microbiota compared to the NC group (P , 0.01 with PERMANOVA, 9,999 permutations) (Fig. 7a). Although selected specific ASVs were not exactly the same (Fig. S7), KD (72%) still enhanced the abundance of Roseburia, Ruminococcaceae, and Lachnospiraceae and reduced the abundance of Turicibacter similarly to the KDs in the above-mentioned trials (Fig. 7b). Furthermore, significantly decreased SCFAs and increased BAs levels were also observed in the KD(72%) mice ( Fig. 7c and d). Although the concentration of 3-indoleacrylic For panels d to f, data are presented as the mean 6 SEM and analyzed using one-way ANOVA followed by a Tukey post hoc test. *, P , 0.05; **, P , 0.01; ***, P , 0.001. n = 8 to 9 for both groups for all analyses, except n = 5 to 6 for panel e. acid was significantly decreased in the KD(72%) mice, there was no significant difference in the concentrations of other tryptophan metabolites compared to those in the NC group (Fig. 7e). Taken together, these results suggested that KD(72%) impaired metabolic health, gut microbiota, and metabolites in mice.

DISCUSSION
In the current study, we showed that the impaired glucose and lipid metabolism in mice induced by KDs may be due to the sources and proportions of fat in these diets, which are associated with their distinct effects on gut microbiota and metabolites.
The KDs, regardless of the proportions and sources of fat, impaired glucose and lipid metabolism in mice in our study, which is in agreement with previous reports that KDs caused glucose intolerance, insulin resistance, hepatic steatosis, and fat accumulation (11)(12)(13)(14)(15)(16)(17). Several studies had reported the possible mechanisms of glucose intolerance and insulin resistance induced by KDs, such as the reduction in gd T cells that can restrain inflammation (18), or the decreased cell surface expression of GLUT4 in skeletal muscle, which reduces glucose uptake in the periphery (11). Regarding the hepatic lipid accumulation induced by KD, Schugar et al. reported that the synergistic effects of protein restriction and choline deficiency may be potential contributors (23). Notably, an impaired colonic mucus barrier has been suggested as one of the causes of metabolic damage (24), and the expression of genes related to the intestinal barrier (Occludin, Muc2) was significantly reduced by 5 weeks of KD consumption in our work. Although improved serum glucose and reduced weight were reported in short-term (1-week) KD studies (6,18,20), our work observed that extending feeding time with KD impaired host metabolism, especially in glucose tolerance and hepatic lipid Ketogenic Diets Induced Metabolic Disorders in Mice ® accumulation. The glucose and lipid metabolism disorders are a gradual accumulation process, and our work emphasizes that there is a risk of metabolic disorders on a longterm KD. Future work is still needed to identify the overall mechanism by which KDs impair glucose and lipid metabolism. Li et al.

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The destruction of glucose and lipid metabolism induced by KDs may be partly related to the changes in the gut microbiota and metabolites. In the current work, both two types of KD induced the changes of gut microbiota and metabolites. For example, we found that the increase in the abundance of many ASVs belonging to Ruminococcaceae and Lachnospiraceae was correlated with impaired lipid metabolism induced by two formulas of KD. Previously, a strain belonging to Lachnospiraceae was reported to induce the development of obesity and diabetes in germfree ob/ob mice (25). However, we also observed that the abundance of few ASVs belonging to Lachnospiraceae decreased in KD-fed mice, indicating that the response of gut microbiota to KD is species and strain specific, and this may explain our inconsistency with other studies (21). Furthermore, 3 ASVs belonging to Bacteroides were enriched only in our KDR mice, which could convert primary BAs into secondary BAs through the production of bile salt hydrolase (BSH) (26). Recent studies reported that transplantation of Bacteroides vulgatus resulted in insulin resistance in recipient mice (27). In addition, a high secondary bile acid DCA level was observed in our KDR mice. DCA could bind to farnesoid X receptor (FXR), and the activation of FXR could induce metabolic disorders (28). Thus, KDR induces severe glucose intolerance and insulin resistance in mice, which may be due to increased abundance of Bacteroides and high DCA level. Notably, we found that the abundance of Lactobacillus murinus was decreased in the mice with KD. In our previous studies, we found abundant Lactobacillus murinus in the mice with calorie restriction (CR) (29,30), which was significantly correlated with the improvement of midlife metabolic phenotypes. Moreover, we isolated a strain, L. murinus CR147, from the feces of CR mice and showed that this strain reduced intestinal permeability and systemic inflammatory markers in old microbiota-colonized mice (30). The beneficial effects of Lactobacillus murinus on glucose and lipid metabolism may be related to the ability to produce SCFAs and convert tryptophan into indole derivatives (31). SCFAs can protect the intestinal barrier by increasing the secretion of glucagonlike peptide-2 (GLP-2) (32) and exert anti-inflammatory effects by binding the G-protein-coupled receptor 41/43 (GPR41/43) (33). IAA was reported to play a role as aryl hydrocarbon receptor (AhR) in regulation of intestinal health (34). IPA can decrease intestinal permeability mediated by the pregnane X receptor (PXR) (35). The impaired intestinal barrier and an inflammatory condition have been regarded as a crucial pathological process underlying metabolic disorders (22,36). In our work, decreased levels of SCFAs and tryptophan metabolites were observed in the mice with KD. Therefore, the decreased abundance of Lactobacillus murinus induced by two formulas of KD may contribute to the impaired glucose and lipid metabolism. Our studies pointed out that candidate bacteria and their potential metabolites correlated with KD-induced metabolic disorders. In future studies, we should use these specific bacteria and their metabolites responding to KD to investigate its causality in metabolic disorders and involved mechanism.
More importantly, we showed that both the sources and proportions of fat in the KDs modified glucose and lipid metabolism in mice, suggesting that the controversial results from previous reports may be due to neglect of the composition of the KD. We found that KDR but not KDH induced glucose intolerance and insulin resistance. A recent study using the same KD as our KDR demonstrated that long-term KD feeding depleted adipose-resident gd T cells, leading to obesity and the loss of glycemic regulation in mice (18). Previous studies using a diet consistent with our KDH showed that KD reduced serum glucose in animal models (20,37). In our study, we analyzed the composition of these two formulas of KD and found that their sources of fat are very different. Therefore, future work in animal and clinical studies related to diet and nutritional intervention should consider not only the proportion of nutrients but also the source.
For the diets used in animal trials, the source of them may have an impact on the experimental results, especially in metabolic phenotypes. For example, KDR-induced severe glucose intolerance in mice may be due to high TFAs levels in fat. In epidemiological investigations, TFA intake was associated with an increased risk of cardiovascular disease and incidence of obesity and type 2 diabetes mellitus (38,39). And in a mouse model, high TFA intake resulted in decreased sensitivity of insulin receptor substrate 1 (IRS1) and downregulation of adipose triglyceride lipase (ATGL) expression in the liver (40). In an in vitro model, trans-9-octadecenoic acid may reduce the glucose uptake by cells through suppressing insulin-induced elevations of phosphorylated Akt levels and the fusion of GLUT4 storage vesicles (41). Our KDR (Research Diets D12369B) is a KD commonly used in animal trials, and the experimental results of the previous studies using this diet cannot be attributed to high fat content completely but may also be related to TFAs in fat.
Overall, our study demonstrated that the metabolic disorders induced by KDs are closely related to the source and proportion of fat in the diet, which may be associated with the changes of the gut microbiota and metabolites. Although KDs have beneficial effects on epilepsy and neurodegenerative diseases (1), according to our findings, KDs truly impair glucose and lipid metabolism. Thus, when using these diets for weight loss and the treatment of other conditions, the risk of glucose and lipid metabolism disorders should be kept in mind, and the sources and proportions of fat in these diets need to be further evaluated.

MATERIALS AND METHODS
Ethical approval. All animal experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Shanghai Jiao Tong University (no. 2018043 and 2018106).
Animal trial. The mice were purchased from SLAC, Inc. (Shanghai, China), and the experiments were performed at the animal center of Shanghai Jiao Tong University. All mice were kept under a standard 12-h light/dark cycle (lights on at 7:00 a.m.) and a temperature of 22°C 6 3°C.
(ii) Trial 2. After 2 weeks of acclimation, 8-week-old SPF C57BL/6J males were randomly assigned into three groups (9 mice per group) and fed ketogenic diets with two different proportions of fat or normal chow for 5 weeks: (i) fed normal chow (NCH), (ii) fed KDH, or (iii) fed a ketogenic diet containing 89.5% fat [KD(89.5%); 0.1% carbohydrate, 10.4% protein, 89.5% fat, 6.57 kcal/g; produced by FBSH Biotechnology Ltd., Shanghai, China]. The formulas of these diets are shown in Table S1. The mice were allowed ad libitum access to water and food. Both the KDH and KD(89.5%) mice were fed freshly prepared ketogenic diets every day. The body weight of the mice and food intake per cage (3 mice per cage) were measured every 3 days.
(iii) Trial 3. After 2 weeks of accommodation, 8-week-old SPF C57BL/6J males were randomly assigned into two groups (9 mice per group) and fed a ketogenic diet or normal chow for 5 weeks: (i) fed normal chow (NC; same as NCH in the above-mentioned trial) or (ii) fed a ketogenic diet containing 72% fat [KD(72%); 8% carbohydrate, 20% protein, 72% fat, 5.65 kcal/g; produced by FBSH Biotechnology Ltd., Shanghai, China]. The formulas of these diets are shown in Table S1. The mice were allowed ad libitum access to water and food. The KD(72%) mice were fed a freshly prepared ketogenic diet every day. The body weight of the mice and food intake per cage (3 mice per cage) were measured every 3 days.
Histopathology of eAT, liver, pancreas, and ileum. For epididymal adipose tissue (eAT) and liver, fresh tissue was fixed with 4% paraformaldehyde, embedded in paraffin, sectioned (4-mm thickness), and stained with hematoxylin and eosin (H&E) (Wuhan Servicebio Technology Ltd., Wuhan, China). Adipocyte size of eAT sections was assessed using Image-Pro Plus v6.0 (Media Cybernetics Inc., Silver Spring, MD, USA), and the mean cell area of adipocytes was determined in at least three discontinuous scans (.300 total adipocytes) in five random mice from each group. The histologic scores (NAFLD activity score) of liver sections were assessed as described previously (42). For the pancreas, fresh tissue was fixed with 4% paraformaldehyde, embedded in paraffin, sectioned (4-mm thickness), and stained for insulin (green) after standard indirect immunofluorescence staining. The islet areas were determined in at least three discontinuous scans under Â400 magnification and examined as the total islet areas divided by the total number of islets. For the ileum, immunohistochemistry staining for ZO-1 was performed as previously described (29). sequences per sample. The statistical significance of gut microbiota among different groups was assessed by permutational multivariate analysis of variance (PERMANOVA; 9,999 permutations). PERMANOVA clustering analysis was performed with Bray-Curtis distances. Differences were considered significant when Q was ,0.05.
Sparse partial least-squares discriminant analysis (sPLS-DA) (51) was performed using the "mixOmics" (v6.8.2) package (52) in R to identify the discriminative ASVs between different groups (NCR and KDR, NCH and KDH, and KDR and KDH). The optimal classification performance of the sPLS-DA model was assessed with the perf function using leave-one-out cross-validation with the smallest error rate. Variations in the relative abundance of selected ASVs in different groups were analyzed by the Mann-Whitney U test in MATLAB (R2017b). Differences were considered significant when P was ,0.05.
The visual presentation of the heatmap showing the relative abundance of selected ASVs in different groups was performed using the "pheatmap" package in R. Spearman correlations between ASV abundance and host parameters related to glucose and lipid metabolism were calculated using MATLAB. The P values were adjusted by false discovery rate (FDR) estimation using the Benjamini-Hochberg method (53). The visual presentation of correlations was performed using the "ggcorrplot" package in R. Correlations were considered significant when FDR was ,0.05.
Untargeted metabolomics study and data analysis. Cecum content samples (60 mg) were homogenized in 200 ml distilled water and vortexed. Next, 800 ml of methanol-acetonitrile solution (1:1, vol/vol) mixture was added to precipitate protein, pulverized by ultrasonic wave at low temperature for 30 min, and then kept at 220°C for 1 h. Next, samples were centrifuged at 14,000 relative centrifugal force (rcf) for 20 min at 4°C and stored at 280°C after removing the supernatant and vacuum drying. Quality control (QC) samples were prepared using the same methods. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) analysis of cecal contents in mice was performed by Shanghai Applied Protein Technology Ltd. (Shanghai, China) on the 1290 Infinity ultrahigh-performance liquid chromatography system (Agilent, Germany) coupled with a Triple TOF 5600 system (AB Sciex, USA). The raw data were converted into mzXML format using Proteo Wizard software, and peak alignment, retention time correction, and peak area extraction were performed using the XCMS program. Details of the data processing and analysis were performed as described previously (54).
Variable importance in projection (VIP) scores obtained from the orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model were used to assess the contribution of variables. Metabolites that had a VIP of .1 between different groups (NCR and KDR, NCH and KDH, and KDR and KDH) were defined as discriminating metabolites, and then these discriminating metabolite levels were compared between different groups using Student's t test statistical analysis. Only discriminating metabolites that had P of ,0.05 were defined as significant discriminative metabolites and identified by searching an in-house standard MS/MS library (using exact mass data [mass error # 25 ppm] or MS/MS spectra matching).
The visual presentation of the heatmap showing the standardized concentration of significant discriminative metabolites in different groups was performed using the "pheatmap" package in R. Spearman correlations between metabolite concentration and host parameters related to glucose and lipid metabolism were calculated using MATLAB. The P values were adjusted by FDR estimation. The visual presentation of correlations was performed using the "ggcorrplot" package in R. Correlations were considered significant with an FDR of ,0.05.
The "effect size" analysis strategy (55) was used to determine the biomarker metabolites affecting the gut microbiome. The adonis function of the "vegan" package in R was used to estimate the "one-toall" effect size (R 2 ) between each single variable of metabolites to the whole gut microbiome. Only variables with significant (P , 0.05, 999 permutations) effects and R 2 . 0.09 on the gut microbiome were considered later.
Targeted fatty acid quantitation. Gas chromatography-mass spectrometry (GC-MS) analysis of fatty acids in the three ketogenic diets [KDR, KDH, and KD(89.5%); 3 replicates for each diet] was performed by BioNovoGene Technology Ltd. (Suzhou, China) on a GC-MS-QP2010 Ultra system (Shimadzu, Japan). Data were analyzed with one-way ANOVA followed by a Tukey post hoc test. Samples (200 to 400 mg) were homogenized in a 1-ml N-hexane-isopropanol solution (3:2, vol/vol) mixture with 3-mm-diameter zirconia/silica steel beads, pulverized by ultrasonication for 30 min, and then centrifuged at 12,000 rpm for 5 min. Then, 500 ml of supernatant was added to 1 ml N-hexane. After repeating centrifugation for 5 min, 900 ml of supernatant was added to 400 ml methanol. After pulverizing in an ultrasonic wave for 5 min, 40 ml diazomethane was added and kept at room temperature for 15 min, and then centrifugation was repeated. Two hundred microliters of N-hexane was added after collecting the supernatant (200 ml) and vacuum drying, and the samples were centrifuged. Finally, the supernatant was collected and transferred into a GC vial for GC-MS analysis.
Targeted short-chain fatty acid (SCFA) quantitation. GC-MS analysis of SCFAs in the cecal contents of mice was performed on an Agilent 6890 (Agilent Technologies, CA, USA) with flame ionization, thermal conductivity detectors, and capillary columns. Data were analyzed with one-way ANOVA followed by a Tukey post hoc test. Then, 50 mg cecal contents was homogenized with 250 ml phosphate buffer and centrifuged at 16,000 Â g for 15 min at 4°C. The supernatants were filtered through 0.22-mm filters. Eighty microliters of supernatant was acidified by adding 40 ml of 50% (vol/vol) sulfuric acid. After vortexing and standing for 2 min, the organic acids were extracted by adding 160 ml of diethyl ether. Finally, the supernatant was collected for GC-MS analysis.
Targeted bile acid quantitation. Ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS) analysis of bile acids in the cecal contents of mice was performed on a Waters Acquity UPLC using an ethylene-bridged hybrid (BEH) C 18 column (1.7 mm, 2.1 Â 100 mm) coupled with a Triple TOF 4000 system equipped with an electrospray ionization (ESI) source operating in negative mode (AB Sciex, USA). Data were analyzed with one-way ANOVA followed by a Tukey post hoc test. Ten milligrams of cecal contents from mice was preweighed, mixed with 1 ml precooled methanol (220°C) and 100 mg glass beads, and shaken for 1 min at 25 Hz/s, and this process was repeated at least two times. Next, samples were pulverized by ultrasonic wave for 30 min and then centrifuged at 12,000 rpm for 10 min at 4°C. Methanol (580 ml) was added after collecting the supernatant (20 ml) and vortexed for 30 s. Finally, the supernatant was collected for LC-MS analysis.
Targeted tryptophan metabolite quantitation. UPLC-MS analysis of tryptophan metabolites in the feces of mice was performed on a Waters Acquity UPLC using an HSS T3 column (1.8 mm, 2.1 Â 100 mm) coupled with a Q Exactive mass spectrometer equipped with an ESI source operating in negative mode (Thermo, USA). Data were analyzed with one-way ANOVA followed by a Tukey post hoc test. Thirty milligrams of feces from mice was preweighed and mixed with 500 ml of 50% acetonitrile, followed by thorough homogenization. Next, samples were kept on ice for 30 min and centrifuged at 12,000 rpm for 10 min at 4°C. The supernatant was collected, and the above steps were repeated three times. Two hundred microliters of 50% acetonitrile was added after collecting the supernatant and vacuum drying and centrifuged at 12,000 rpm for 10 min. Finally, the supernatant was collected for LC-MS analysis.
Data availability. The raw Illumina sequence data generated during the current study have been deposited to the NCBI under BioProject accession no. PRJNA674951.

SUPPLEMENTAL MATERIAL
Supplemental material is available online only.