Abstract
Background/Aim: This study examined the effects of tocotrienols (TT) in conjunction with statin on glucose homeostasis, bone microstructure, gut microbiome, and systemic and liver inflammatory markers in obese C57BL/6J mice. Materials and Methods: Forty male C57BL/6J mice were fed a high-fat diet (HFD) and assigned into four groups in a 2 (no statin vs. 120 mg statin/kg diet)×2 (no TT vs. 400 mg TT/kg diet) factorial design for 14 weeks. Results: Statin and TT improved glucose tolerance only when each was given alone, and only statin supplementation decreased insulin resistance. Consistently, only statin supplementation decreased serum insulin levels and HOMA-IR. Pancreatic insulin was also increased with statin treatment. Statin and TT, alone or in combination, reduced the levels of serum IL-6, but only TT attenuated the increased serum leptin levels induced by a HFD. Statin supplementation increased bone area/total area and connectivity density at LV-4, while TT supplementation increased bone area/total area and trabecular number, but decreased trabecular separation at the distal femur. Statin supplementation, but not TT, reduced hepatic inflammatory cytokine gene expression. Neither TT supplementation nor statin supplementation statistically altered microbiome species evenness or richness. However, they altered the relative abundance of certain microbiome species. Most notably, both TT and statin supplementation increased the relative abundance of Lachnospiraceae UCG-006. Conclusion: TT and statin collectively benefit bone microstructure, glucose homeostasis, and microbial ecology in obese mice. Such changes may be, in part, associated with suppression of inflammation in the host.
Type 2 diabetes mellites (T2DM) is characterized by peripheral insulin resistance with a variable degree of hyperglycemia, hyperinsulinemia, hypertriglyceridemia, and impaired insulin secretion after metabolic challenge with glucose (1). Hyperglycemia can stimulate free radical production and induce oxidative stress to promote inflammation. Excessive oxidative stress and chronic inflammation result in impaired insulin signaling, compromised skeletal muscle glucose transport, and insulin resistance in T2DM (2, 3). Such oxidative stress and chronic inflammation also have detrimental effects on skeletal muscle, pancreatic beta cells, and bone, causing an imbalance in bone metabolism and bone loss (4).
T2DM is often associated with intestinal flora disorders and dysfunction of multiple organs. Metabolites of the intestinal flora, such as bile acids, short-chain fatty acids, and amino acids may improve insulin sensitivity associated with T2DM and benefit metabolic and immune homeostasis (5). For example, T2DM patients have a lower proportion of butyrate-producing Clostridiales (Roseburia and Faecalibacterium prausnitzii) and a greater proportion of Clostridiales that do not produce butyrate (6). The levels of E. coli and Proteobacteria are also increased in the fecal microbiota of T2DM patients (7). In T2DM, toll-like receptor activation and recognition of microorganisms from the intestinal lumen may trigger inflammatory responses, induce the phosphorylation of serine residues in insulin receptor substrate-1 (IRS-1), and reduce insulin sensitivity (8). Thus, gut microbiota composition may have important implications for metabolite production, inflammation activation, and insulin resistance in T2DM.
T2DM is an important risk factor for cardiovascular disease (CVD). Statins have been prescribed in patients with prediabetes or T2DM to reduce the risk factors for CVD (9). The effects of statin on glucose homeostasis in T2DM depend on the type and dosage of statin. For example, lovastatin lowered the fasting blood glucose levels in obese mice by improving insulin sensitivity, but it did not affect serum insulin levels (10). Clinically, moderate-dose pitavastatin improved glycemic control in T2DM patients, whereas high-dose atorvastatin had an opposite effect on T2DM patients (11). Regarding statin’s impacts on gut microbiota, the hypolipidemic effect of simvastatin in obese mice was mediated by modulation of gut microbiota with a suppression of bile acids synthesized from cholesterol (12). However, the effects of statin on T2DM-associated disorders, such as glucose intolerance, insulin resistance, detrimental bone microstructure, and gut dysbiosis are poorly understood.
Dietary bioactive compounds are prominent modifiers of gut microbiota composition. The modulation of gut microbiome via bioactive compounds provides a novel therapeutic target for improving chronic inflammation and insulin resistance. Among different dietary bioactive compounds, tocotrienols (TT), a subfamily of vitamin E, possess potent anti-oxidant and anti-inflammatory activities. In nature, TTs exist in four isomers, alpha (α), beta (b), gamma (γ) and delta (d) at different compositions, and the order of anti-oxidant capacity is d-TT > γ-TT > b-TT=α-TT (13). Unlike tocopherols, another subfamily of vitamin E found in most plant species, TTs can only be found in certain plants, such as annatto, palm, grains, and nuts (14). Compared to tocopherols, TTs have shown superior antioxidant and anti-inflammatory properties and ameliorate aging-related CVD and its associated morbidities (15). We reported that annatto-extracted TT, consisting of 90% d-TT, 10% γ-TT and no detectable tocopherol, improved glucose homeostasis and mitigated the deterioration of bone microarchitecture in obese male mice (4). We also showed that dietary TT supplementation increased the Bacteroidetes/Firmicutes ratio in the mouse cecum. The number of bacteria that belong to the Clostridiales order, especially the Oscillospira genus (Firmicutes phylum), was halved, whereas the number of S24-7 family members (Bacteroidetes phylum) was increased (16).
A high-fat western-style diet is a contributing risk factor for developing metabolic disorders, such as T2DM, CVD, dyslipidemia, low-grade chronic inflammation, and excessive oxidative stress. In addition to these metabolic disorders, excessive fat consumption contributes to gut dysbiosis (17) and bone microstructure destruction in HFD-induced obese animals (18). To date, the combination of statin and TT on glucose homeostasis, bone microstructure, gut microbiota, and associated molecular mechanisms in obese mice has not been studied. Therefore, in the present study, we investigated the combined effects of lovastatin and TT on glucose homeostasis, bone microstructure, and gut microbiome in obese male mice. We hypothesized that supplementation of statin and TT in the diet would additively or synergistically improve bone microstructure, glucose homeostasis, and gut microbiome composition in obese male mice. Such changes may be mediated, in part, through the suppression of inflammation.
Materials and Methods
Animals and treatments. Forty-eight 6-week-old male C57BL/6J mice (Jackson Laboratory, Bar Harbor, ME, USA) were fed chow diet and distilled water ad libitum for 5 days. After acclimatization, mice were weighed, randomly stratified by weight, and assigned to 4 groups (n=12/group): HFD, Statin, T400, and T400/Statin groups from a factorial design of 2 (no statin vs. 120 mg lovastatin/kg diet)×2 (no TT vs. 400 mg TT/kg diet). Throughout the 14-week study period, all mice had free access to water and diet. Mice were fed an HFD consisting of 20%, 21% and 58% of energy from carbohydrates, protein, and fat. TT (gift from American River Nutrition, Inc., Hadley, MA, USA) was extracted from annatto oil to 70% purity, containing 90% δ -TT, 10% γ -TT, and free of tocopherol. TT was premixed with tocopherol-stripped soybean oil (Dytes Inc., Bethlehem, PA, USA) before being added to HFD. Statin was in the form of lovastatin from Merck (Rahway, NJ, USA) The TT dosage at 400 mg/kg diet in mice corresponds to approximately 160 mg daily for a 70-kg body weight human. The statin dosage at 120 mg/kg diet in mice corresponds to approximately 70 mg daily for a 70-kg body weight human.
Mice were housed in cages of four and maintained at a controlled temperature of 21±2°C with a 12 h light-dark cycle. Each treatment group had three cages of mice. All animals were observed daily for clinical signs of disease. Body weight, food intake, and water consumption were recorded weekly. All conditions and handling of the animals were approved by the Texas Tech University Health Sciences Center Institutional Animal Care and Use Committee (IACUC#16015). All experiments were performed in accordance with the relevant guidelines and regulations.
Insulin and glucose tolerance assessment. After 12 to 13 weeks of treatment, the mice were fasted for 4 h and insulin or glucose tolerance tests were performed. For both glucose tolerance tests (GTT) and insulin tolerance tests (ITT) blood was collected from the tail vein and baseline glucose levels were measured using a glucometer (One touch ultra mini, Life Scan, Wayne, PA, USA). For GTT, 2 mg/g body weight of glucose was injected intraperitoneally and blood glucose levels were measured at 15, 30, 60, and 120 min following injection. For ITT, 1 U/kg body weight of insulin (Humulin, Abbott, Chicago, IL, USA) was injected intraperitoneally and blood glucose levels were analyzed at 15, 30, 60, and 120 min following injection. The trapezoidal method was used to calculate total area under the curve (AUC) for both GTT and ITT.
Sample collection. At the end of the experiment, animals were fasted for 4 hours and blood was collected from mice anesthetized with isoflurane. Pancreases were stored in acetic acid at −80°C prior to insulin extraction for ELISA or fixed in Z-fix (AnaTech Ltd., Battle Creek, MI, USA) at room temperature and embedded in paraffin for histological assessment. Femur and lumbar vertebrae-4 (LV-4) were harvested and cleaned of adhering soft tissues and stored in 70% ethanol at 4°C for later bone microstructure analyses. Liver and colon feces specimens were harvested and stored at −80°C for later analyses. Blood samples were centrifuged at 1,500×g for 20 min and serum samples were obtained and kept at −80°C until analyzed.
Bone microarchitecture measurement with μ-CT. The LV-4 and right femur were scanned using micro-computed tomography (μCT) (Scanco μCT 40; SCANCO Medical AG, Wangen-Brüttisellen, Switzerland) following Cao et al. (19) and the recommended guidelines for μCT scanning (20). The entire trabecular bone between the cranial and the caudal area of the LV-4 was scanned and analyzed. For the femoral trabecular bone, the volume of interest (VOI) comprised 100 cross-sectional slices of the secondary spongiosa starting from about 0.1 mm distal to growth plate. For assessment of cortical indexes, the VOI included 100 slices at the femoral mid-diaphysis between the top of the femur head and the bottom of the lateral and medial condyles. All scans were performed in 1024×1024 matrix resulting in an isotropic voxel resolution of 12 mm3. An integration time of 300 milliseconds per projection was used. Bone nomenclature was based on Parfitt et al. (21). Trabecular parameters in both LV-4 and femur included trabecular bone volume per unit bone area/total volume (BV/TV, %), number (Tb.N, 1/mm), separation (Tb.Sp, mm), and thickness (Tb.Th, mm), structure model index (SMI) and trabecular connectivity density (Conn.Dn, mm−3). Cortical parameters in femur included bone area/total area (B.Ar/T.Ar, mm2), medullary area (Me.Ar, mm2), and cortical thickness (Ct.Th, mm). The operator performing the scans and analysis was blinded to treatments.
Biomarker measurement. The concentrations of resistin, leptin, and IL-6 were determined in the adipose tissue homogenate using a commercial multiplexing system (Luminex-MagPix, Luminex Corporation, Austin, TX, USA).
Insulin, HOMA-IR and HOMA-B measurement. Cellular insulin content of the pancreas was determined by acetic acid extraction followed by mouse insulin ELISA (EMD Millipore Co., Billerica, MA, USA). Serum insulin (n=7 per treatment group) was also quantified using the same mouse insulin ELISA kit. HOMA-IR (homeostatic model assessment for insulin resistance) to estimate insulin resistance and HOMA-β (homeostasis model assessment of β-cell function) to estimate beta cell function were calculated as follows:
Analysis of pancreatic tissue. After 14 weeks of treatment, pancreases (n=4 per treatment group) were collected for histological assessment. Tissue was fixed in Z-fix, embedded in paraffin and tissue sections were immunostained as described previously (22). Primary antibodies were guinea pig anti-insulin (diluted 1:1,000; Dako Agilent Pathology Solutions, Santa Clara, CA, USA) and mouse anti-glucagon (diluted 1:5,000; Sigma, St. Louis, MO, USA). Appropriate biotinylated secondary antibodies and avidin–biotin–enzyme complexes were purchased from Vector Laboratories (Burlingame, CA, USA). Diaminobenzidine as the chromogen was purchased from BioGenex (Fermont, CA, USA). Tissue sections were counterstained with hematoxylin.
Liver mRNA expression. RNA was extracted from hepatic tissue using TRI reagent (Sigma-Aldrich,). RNA was quantified using the Nanodrop ND 1000 Spectrophotometer (Thermo Scientific, Waltham, MA, USA). Two μg RNA was reverse transcribed to produce cDNA. Gene expression was assessed by RT-PCR (Applied Biosystems, Foster City, CA, USA) using SYBR Green. mRNA levels were normalized to cyclophilin expression and analyzed using the 2−ΔΔCT method. Cyclophilin expression levels did not change with diet or in response to treatment group. Primer sequences are as follows: IL-1β, forward 5′-TGCCACCTTTTGACAGTGATG-3′ and reverse 5′-TTCTTGTGACCCTGAGCGAC-3′. IL-2, forward 5′-CATGCAGCT CGCATCCTGT-3′; reverse 5′-AAGTGGGGCTTGAAGTGGG-3′, interferon-γ (IFN-γ), forward 5′-GAGGTCAACAACCCACAGGT-3′; reverse 5′-GGGACAATCTCTTCCCCACC-3′. Tumor necrosis factor-α (TNF-α), forward 5′-TAGCCCACGTCGTAGCAAAC-3′; reverse 5′-ACCCTGAGCCATAATCCCCT-3′. Cyclophilin, forward 5′-CTTCGAGCTGTTTGCAGACAAAGT-3′; reverse 5′-AGATGCC AGGACCTGTATGCT-3′.
Microbial community profiling using 16S rRNA amplicon sequencing. Genomic DNA was extracted from the cecal contents using the MO BIO PowerSoil DNA Isolation kit (Qiagen, Gaithersburg, MD, USA) with a few modifications. The cecal contents (20-25 mg) were processed in 96-well plates with recommended beads and buffers. The sealed plates were shaken horizontally at 20 rpm for 20 min using the MO BIO shaker. The remaining steps were performed as directed by the manufacturer. The extracted DNA was quantitated spectrophotometrically and stored at −20°C. Fifty nanograms of genomic DNA were utilized for the amplification of the V4 variable region of the 16S rRNA gene using 515F/806R primers. Forward and reverse primers were dual-indexed, as described by Kozich et al., to accommodate the multiplexing of up to 384 samples per run (23). Paired-end sequencing (2×250 bp) of pooled amplicons was carried out on an Illumina MiSeq (Illumina, Inc., San Diego, CA, USA) with ~30% of PhiX DNA (24).
Statistical analysis. Results of glucose homeostasis (GTT, ITT, insulin, HOMA-IR, and HOMA-β), hepatic pro-inflammatory gene expression, and bone parameters are presented as mean±standard error of the mean (SEM) and analyzed by two-way analysis of variance (ANOVA, with two factors: no TT vs. TT and no statin vs. statin) followed by Fisher’s Least Significant Difference (LSD) post-hoc test with SigmaStat software, version 14.0 (Systat Software, Inc., San Jose, CA, USA). A significance level of p<0.05 applies to all statistical tests.
Microbiome data analysis. Raw sequencing data has been deposited under BioProject PRJNA982402 in the National Center for Biotechnology Information (NCBI) BioProject database. 16S rRNA amplicon data was analyzed using QIIME 2. In brief, reads were filtered, denoised, and merged. DADA2 was used to identify features or exact amplicon sequence variants (ASVs). On average, ~11K reads were retained after filtering per sample for downstream analyses. For taxonomy assignment, Silva database release 138. LOCOM (Logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control) was performed for compositional analysis and compared the abundance of taxa between groups. Results were regarded as significant when p<0.05. Visualization was performed using software R Statistics version 4.0.5 (Shake and Throw) (The R Foundation, Indianapolis, IN, USA).
Results
GTT and ITT. The effect of TT and statin supplementation on glucose homeostasis was assessed by GTT and ITT (Figure 1). For GTT results in the first 60 min after glucose administration, there was significant interaction between statin and TT (p=0.004) with an order of HFD group=T400/Statin group>T400 group>Statin group (Figure 1A). However, this was not sustained at 120 min because neither statin nor TT affected blood glucose (Figure 1A). The AUC data for GTT had a similar pattern showing an interaction between statin and TT (p=0.002) (Figure 1B).
Effect of annatto-extracted tocotrienols and statin on GTT blood glucose (A), GTT AUC (B), ITT blood glucose (C), and ITT AUC (D). Group assignment includes HFD group, a high-fat diet group; Statin group, statin supplementation at 120 mg/kg diet in a high-fat diet; T400 group, annatto-extracted TT supplementation at 400 mg/kg diet in a high-fat diet; and T400/Statin group, combination group. GTT: glucose tolerance test; ITT: insulin tolerance test; AUC: area under curve. Having different letters (x and y for statin effect; A and B for interaction effect) are significantly different by two-way ANOVA and Fisher’s LSD test (p<0.05). Values are mean (n=12/group) with their standard error of mean (SEM) represented by vertical bars.
The ITT results showed that (i) only statin supplementation lowered blood glucose concentrations starting at 15 min after insulin administration, and the impact of statin continued for the whole 120 min (all p<0.05); (ii) TT supplementation tended to increase the blood glucose level during the ITT at 120 min, although this TT effect did not reach statistical significance (p=0.058); and (iii) there was no interaction in blood glucose between statin and TT across time (Figure 1C). The AUC for ITT results shows only statin supplementation lowered AUC levels (p<0.001) (Figure 1D).
Insulin and HOMA. Only statin supplementation, not TT supplementation, reduced serum insulin concentrations, HOMA-IR, and HOMA-β in obese mice (Table I). In contrast, statin supplementation increased the pancreas insulin concentrations.
Effect of tocotrienols and statin supplementation on insulin and HOMA in obese mice.
Histological analysis of the pancreas for insulin-producing islet beta cells (insulin, Figure 2A-D) or glucagon-producing alpha cells (glucagon, Figure 2E-H) via immunohistochemistry indicated that the pancreatic islets contained normal proportions of both types of cells regardless of treatment.
Immunohistochemical assessment of pancreatic tissue in mice after statin and tocotrienol supplementation. Pancreatic tissues (n=4) were sectioned and immunostained for insulin (A-D) and glucagon (E-H). All sections were counterstained with hematoxylin. Group assignment includes HFD group (A, E); Statin group, statin supplementation at 120 mg/kg diet in a high-fat diet (B, F); T400 group, annatto-extracted TT supplementation at 400 mg/kg diet in a high-fat diet (C, G); and T400/Statin group, combination group (D, H).
Bone microstructure properties. Table II lists the effects of TT and statin supplementation on bone microstructure properties. Based on the results of two-way ANOVA analysis, statin supplementation (main effect of statin: p=0.010) increased BV/TV and Conn.Dn at the trabecular bone of LV-4, whereas TT supplementation had no effect on LV-4 trabecular microstructure. In terms of trabecular microstructure at femur, both TT and statin supplementation increased BV/TV. TT supplementation tended to increase Tb.Th at the distal femur (the main effect of TT: p=0.095). Statin supplementation (the Statin and T400/Statin groups) increased Tb.N, while it decreased Tb.N at the distal femur. There were no significant interactions in trabecular microstructure of either LV-4 or distal femur. Regarding the cortical bone, (i) there was a significant interaction in B.Ar/T.Ar, the HFD group had a lower B.Ar/T.Ar than the other groups; and (ii) a significant interaction was found in Ct.Th value, with the HFD group exhibiting the lowest femur Ct.Th (T400 group≥T400/Statin≥Statin group>HFD group).
Effect of tocotrienols and statin supplementation on bone microstructure properties of LV-4 and femur in obese mice.
Microbiome composition. Average sequencing depth per sample was ~21,000 reads. ~11,000 non-chimeric reads were retained after filtering, denoising, and merging. Analysis was performed using QIIME2 version 2022.8. SILVA version 138 database was used for ASV feature taxonomy classification. Downstream analysis was performed in R version 4.0.5 (Shake and Throw). Based on Pielou’s evenness and Faith’s phylogenetic diversity indices, neither TT supplementation nor statin supplementation statistically significantly affected microbiome species evenness or richness (Figure 3) (ANOVA, p>0.1). Based on Bray–Curtis dissimilarity, overall PERMANOVA indicated differences in microbiome composition among the four groups (p<0.001). Pairwise PERMANOVA comparison showed a significant difference between all groups (p<0.05), except for HFD and T400 groups (p=0.12), suggesting T400 alone did not exhibit a strong impact on the global microbiome composition.
Effects of tocotrienols and statin supplementation on alpha diversity of the gut microbiome. Group assignment includes HFD group, a high-fat diet group; Statin group, statin supplementation at 120 mg/kg diet in a high-fat diet; T400 group, annatto-extracted TT supplementation at 400 mg/kg diet in a high-fat diet; and T400/Statin group, combination group. Kruskal-Wallis test followed by Dunn’s test was performed to determine statistical significance. No statistical significance was found.
To determine whether the abundance of the microbiome species was altered with different treatments, the Kruskal-Wallis Test was performed (p<0.05), followed by Dunn’s test (p<0.05). p-Values were adjusted using the Benjamini–Hochberg procedure. The heatmap (Figure 4) shows significant differences in each group vs. HFD (control) group. The heatmap colors represent the mean abundance of each taxon (Figure 4). The taxa were grouped based on their family as shown on the right side of the figure.
Effects of tocotrienols and statin supplementation on abundance of microbiome features in obese mice. Group assignment includes HFD group, a high-fat diet group; statin group, statin supplementation at 120 mg/kg diet in a high-fat diet; T400 group, annatto-extracted TT supplementation at 400 mg/kg diet in a high-fat diet; and T400/Statin group, combination group. Kruskal-Wallis test followed by Dunn’s test was performed to determine statistical significance compared to the HFD (control) group. *p<0.05; **p<0.01, and ***p<0.001.
To validate the alterations in the microbiome composition using another statistical method, we performed compositional analysis using LOCOM (A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control) (Figure 5). This analysis strengthened our findings stated above. For example, the Romboutsia genus of Peptostreptococcaceae family and the Roseburia genus of Lachnospiraceae family were increased in both T400 and T400/Statin groups. Lachnospiraceae UCG-006 was increased in Statin, T400, and T400/Statin groups vs. the HFD group. In contrast, Lactobacillus was decreased in both Statin and T400/Statin groups. Lachnoclostridium was decreased in the Statin group only. Acetatifactor was decreased in both T400 and T400/Statin groups.
Effects of tocotrienols and statin supplementation on gut microbiome species using compositional analysis. Group assignment includes the HFD group, a high-fat diet group; the statin group, statin supplementation at 120 mg/kg diet in a high-fat diet; the T400 group, annatto-extracted TT supplementation at 400 mg/kg diet in a high-fat diet; and the T400/Statin group, a combination group. LOCOM was used to determine statistical significance. All shown results have p<0.05. Labeled numbers (next to bars) indicate the adjusted p-values (p<0.1 was used as a cutoff here). The effect sizes resulting from the analysis and Benjamini-Hochberg-adjusted p-values are labeled at the base of each bar. Each group was compared to the HFD group. Blue bars indicate an increase in the treatment groups and red bars indicate an increase in the HFD group.
Serum resistin, leptin, and IL-6 concentration. Figure 6 illustrates the effects of TT, statin, and their interaction on serum resistin (Figure 6A), leptin (Figure 6B), and IL-6 (Figure 6C). Neither statin nor TT affected serum resistin level (Figure 6A). There was a significant interaction between statin and TT on serum leptin levels; the TT group had the lowest serum leptin and there was no difference among the other three groups (Figure 6B). A strong interaction between statin and TT was also observed for their effects on serum IL-6 levels, which followed the order of HFD group>the Statin group=the T400 group=the T4/Statin group (Figure 6C).
Effect of tocotrienols and statin supplementation on serum resistin (A), leptin (B), and IL-6 (C). Group assignment includes the HFD group, a high-fat diet group; the statin group, statin supplementation at 120 mg/kg diet in a high-fat diet; the T400 group, annatto-extracted TT supplementation at 400 mg/kg diet in a high-fat diet; and the T400/Statin group, a combination group. Different letters (A and B for interaction effect) indicate significant differences by two-way ANOVA and Fisher’s LSD test (p<0.05). Values are mean (n=10-12/group) with their standard error of mean (SEM) represented by vertical bars.
Expression of hepatic pro-inflammatory genes. Table III depicts the effects of TT, statin, and their interaction on the expression of the hepatic pro-inflammatory genes. Only statin supplementation, not TT supplementation, reduced the mRNA gene expression of IL-1β, IL-2, TNF-α, and IFN-γ in the livers of studied animals. There was no significant interaction between TT and statin administration.
Effect of tocotrienols and statin supplementation on hepatic pro-inflammatory gene expression in obese mice.
Discussion
Obesity is a contributory risk factor for diabetes associated with insulin resistance. In individuals with obesity, increased levels of non-esterified fatty acids, glycerol, hormones, and pro-inflammatory cytokines that are involved in the development of insulin resistance are released by adipose tissue. Diabetes is a chronic disease that occurs when either the pancreas does not produce enough insulin or when the body cannot effectively utilize the insulin it produces (25). Statins, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, are an important class of therapeutic agents used to control hyperlipidemia and prevent cardiovascular disease in diabetic and non-diabetic patients (26). In the current study, our findings indicate that GTT AUC changed with TT and statin supplementation with an interaction (Figure 1B). For instance, in the absence of TT, the statin-supplemented group (Statin group) had lower GTT AUC levels than that of the non-statin-supplemented group (HFD group), while in the presence of TT, the lowering effect of statin was not observed, resulting in no difference between T400 group and T400/statin group. Similarly, in the absence of statin, the TT-supplemented group (T400 group) had lower GTT AUC levels than that of the non-TT-supplemented group (HFD group), while in the presence of statin, the lowering effect of TT was not observed, leading to no differences between the Statin group and T400/Statin group. However, this study did not demonstrate additive effects for mice receiving TT in addition to lovastatin supplementation (the T400/Statin group). Therefore, our GTT AUC findings do not support our hypothesis that statin plus TT supplementation would have an additive or a synergistic beneficial effect on obesity-induced GTT AUC.
Obesity is associated with increased inflammation and oxidative stress that can lead to insulin resistance and T2DM. Here, we found that statin treatment decreased serum IL-6 and hepatic inflammatory cytokine production. Lim et al. also reported statin’s effect on decreasing inflammation and oxidative stress (27). This could lead to improved insulin sensitivity, decreased insulin resistance, and increased glucose tolerance. Indeed, in our study, statin treatment resulted in a decreased AUC for both GTT and ITT as well as a decrease in HOMA-IR, indicating decreased insulin resistance. Additionally, serum insulin levels were decreased, which was consistent with the reduced need for insulin to lower blood glucose. No changes were observed in pancreas alpha or beta cells, which was expected since normal islet morphology was observed in the HFD group without treatment. However, there was an increase in overall pancreas insulin levels. This may be due to a decrease in insulin secretion as observed by reduced serum insulin levels. Statin treatment has been associated with increased insulin resistance and the onset of T2DM. However, this depends on the type and dose of statin prescribed. For example, high intensity treatment with atorvastatin increased insulin resistance (28), while treatment with other statins or use of lower doses is not associated with increased insulin resistance. Overall, our study supports the editorial by Eliot suggesting that modification of the prescribed statin dose could provide both cardiovascular and diabetic benefits (29).
The findings of the present study demonstrate that mainly statin, not TT, supplementation improved glucose homeostasis in obese C57BL/6J mice, as shown by decreased ITT AUC, serum insulin, HOMA-IR, and HOMA-β, as well as increased pancreas insulin levels. These results corroborate those of Yu et al. who reported that early treatment with pravastatin prevented cardiovascular remodeling in the spontaneous T2DM model by retarding the progression of glucose intolerance, increasing expression of cardiac endothelial nitric oxide synthase (eNOS), and inhibiting over-expression of fibrogenic/proinflammatory cytokines (MCP-1) (30). In a recent review based on both preclinical and clinical studies, the authors concluded that diabetes affected statin effectiveness through shifts in pharmacokinetic parameters, such as clearance and biotransformation biomarkers at mRNA and protein levels. Plasma and serum concentrations of statins were accompanied by alterations in cellular activities, including decreased oxidative stress, Akt inhibition, as well as eNOS and phosphorylation reflected by changes in the adverse drug reaction profile of the different statins (26).
In this study, we show the beneficial effects of dietary bioactive compound (TT) and lipid-lowering compound (lovastatin) supplementation on bone microstructure in obese male C57BL/6J mice. The osteoprotective effects of TT supplementation on trabecular bones in obese male C57BL/6J mice is consistent with our previous investigation (4) and studies conducted by others (31-33). Previously, we demonstrated that TT supplementation increased serum bone formation marker, procollagen I intact N-terminal propeptide, trabecular BV/TV, Tb.N, Conn.Dn, and Ct.Th, and decreased serum bone resorption marker, collagen type I cross-linked C-telopeptide, Tb.Sp, and SMI in obese male C57BL/6 mice (4). Similar osteoprotective effects of TT have been reported in male animals experiencing osteoporosis induced by buserelin (31) or by metabolic syndrome (33), as well as female animals experiencing osteoporosis by ovariectomy (32). Regarding statins, the osteoprotective effect of statin supplementation supports our previous findings that (i) simvastatin (a form of statin) administration promotes the formation of new bone, increases bone density, and mitigates microstructure deterioration in ovariectomized rats (34, 35), and (ii) that simvastatin reverses the adverse effects of a HFD on titanium rod osseointegration in ovariectomized rats (36). Intriguingly, the findings of this study on cortical bone microstructure do not support our hypotheses that TT plus statin supplementation would synergistically mitigate obesity-induced bone microstructure deterioration. There was an interaction between TT and statin at femur mid-diaphysis (cortical bones), namely, B.Ar/T.Ar and Ct.Th; however, such interactive impact seems to be subtle (neither additive nor synergistic). It is likely that the TT dose employed in the present study is too marginal to have an effect on the cortical bone. In general, the cortical bone is less responsive than the trabecular bone to the TT treatment due to the smaller surface to volume ratio (37). Future studies are warranted to employ multiple doses of TT along with statin administration, or longer study duration, to investigate its potential additive or synergistic effects on bone microstructure.
Trabecular bone microstructure influences bone strength and fracture risk. Connectivity density is a topological measure that attempts to correlate biomechanical response with architecture. Trabecular low Conn.Dn has been reported in ovariectomized rats (38) and obese male C57BL/6J mice (39). In the present study, statin administration increased Conn.Dn and Tb.N, and decreased Tb.Sp, which is in agreement with the published studies showing a positive association between Conn.Dn and Tb.N, as well as a negative association between Conn.Dn and Tb.Sp in ex vivo studies (40, 41).
Bone loss is a process caused by the imbalance between bone formation and bone resorption. Excessive reactive oxygen species and pro-inflammatory cytokines induce bone resorption and suppress bone formation (42, 43). In this study, statin’s anti-osteoporotic effects are likely due in part to statin’s anti-inflammatory effect, as it lowered liver gene expression of IL-1β, IL-2, TNF-α, and IFN-γ. The anti-oxidative and anti-inflammatory potentials of statin in animals have been reported (44, 45). Simvastatin attenuates oxidative stress, NF-kB activation, and artery calcification in obese mice via down-regulation of TNF-α and TNF receptor 1 (44). Simvastatin induces intrinsic activation of AMPK in rat periodontal ligament cells that promote attenuation of inflammation via AMPK/MAPK/NF-kB pathways (45). Furthermore, Sharkawi et al. demonstrated that administration of lovastatin and anti-diabetics (metformin and gliclazide) had a combined hepatoprotective effect on streptozotocin-treated diabetic rats, as shown by increased antioxidant and anti-inflammatory capacities (46).
Rising evidence suggests that, in addition to the regulation of energy homeostasis, the adiposity hormone leptin also impacts glucose metabolism (47). Leptin regulates glycemia in addition to energy balance in both rodent models and clinical settings (48). Leptin therapy has been approved for the treatment of lipodystrophy and has glucose-lowering effects in rodent models of type 1 and type 2 diabetes (49). In an obese asthmatic mouse model, (i) pravastatin treatment decreased leptin expression in bronchoalveolar lavage fluid via down-regulation of p38/MAPK signaling in obese mice (50) and (ii) simvastatin treatment reduced the levels of glucose, lipid, leptin, and neutrophil percentage in obese mice (51). Additionally, TT supplementation reduces adipose leptin levels in obese mice compared to those without TT supplementation (16). The present study is the first to report that serum leptin levels in obese mice were modulated by TT and statin supplementation, as a result of the interaction between TT and statin (Figure 6B). For example, in the absence of TT there was no difference between the Statin group and the non-Statin group (the HFD group), while in the presence of TT the lowered serum leptin effect was not observed after the addition of statin (the T400/Statin group). Furthermore, in the absence of statin, there was no difference between the T400 group and the non-TT group (the HFD group) and in the presence of statin, there was also no difference between the Statin group and the non-TT group (the T400/Statin group). Similar to serum leptin, both TT and statin supplementation suppressed the production of serum IL-6 in obese mice, due to TT’s (16) and statin’s (52, 53) anti-inflammatory properties (Figure 6C). However, neither additive nor synergistic effect of TT and statin was observed in serum IL-6 suppression compared to TT or statin supplementation alone. Thus, serum leptin and IL-6 concentrations results do not support our hypothesis that statin and TT supplementation would act additively or synergistically to improve these parameters in obese C57BL/6J mice.
The effect of either TT or statin supplementation on the microbiome was relatively distinct. When combined together, the effect was possibly synergistic based on the species being altered. The decrease in Anaerotruncus was the strongest effect of statins. This was interesting because Anaerotruncus is associated with an HFD and linked to inflammation in animal models (54, 55). Therefore, it is hypothesized that this genus could be involved in the phenotype induced by statins. Lachnospiraceae UCG-006 was among the most notable changes due to its decrease in people with type 2 diabetes (56). The supplementation of statins or TT in the diet increased its relative abundance of Lachnospiraceae UCG-006. The findings of increased Lachnospiraceae UCG-006 due to statin or TT corroborates with Li et al. that probiotic mixture of Lactobacillus plantarum strains increased the abundance of Lachnospiraceae UCG-006 in the HFD-fed obese mice (57). However, its link to diabetes is yet to be closely examined. The observation of the decreased abundance of Romboutsia in T2DM mice agrees with Reitmeier et al. that the decreased abundance Romboutsia in T2DM individuals than non-T2DM individuals (58). Intriguingly, TT supplementation (the T400 group) or combined treatment (the T400/Statin group) increased its relative abundance of Romboutsia in T2DM mice. However, it is not known how it may be beneficial to the host, besides its production of the short-chain fatty acid butyrate. Overall, these results associate the statin and TT treatments with alterations in the gut microbiome in animals on HFD. While some of these changes are associated with physiological benefits, such as decreased pro-inflammatory markers, it is critical to follow up such observations with hypothesis-driven experiments to test the specific role of the microbiome in this context before implying causation.
Conclusion
Our data demonstrates tocotrienols and statins benefit bone microstructure, glucose homeostasis, and microbial ecology in obese mice. Such changes were mainly due to statin supplementation and may, in part, be associated with inflammation suppression in obese mice.
Acknowledgements
This study was funded by American River Nutrition, LLC., (Hadley, MA, USA) to CLS. GK and JMD were supported in part by The Ted Nash Long Life Foundation (JMD and GK) and The Robert A. Welch Foundation (JMD). UW and KS were supported in part by USDA-ARS Project 6026-51000-010-05S. The work of JJC was supported by the USDA Agricultural Research Service Project Plan #3062-51000-056-00D, as part of the author’s official duties. RGF was supported by the Agriculture and Food Research Initiative [grant no. 2019-67017-29257/project accession no. 1018642] from the USDA National Institute of Food and Agriculture. We thank Michael D. Tomson for the animal care, Latha Ramalingam for serum cytokine measurement, Kandis Wright for blood glucose test, and Jacob Lovett for editorial work. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The findings and conclusions in this manuscript are those of the authors and should not be construed to represent any official USDA of U.S. Government determination or policy.
Footnotes
Authors’ Contributions
CLS, UW, KS, MME, JMD, DMD, and JJC participated in study design. CLS, MME, and JMD wrote the manuscript. RSN and RGF participated in mRNA gene expression analysis and data interpretation. GK and JMD collected data on glucose homeostasis. UW, KS, MME, SVC, and BDP, participated in microbiota analysis and data interpretation. JJC participated in bone microstructure analysis and data interpretation. Authors reviewed the manuscript.
Conflicts of Interest
The Authors declare that there are no conflicts of interest in relation to this study.
- Received April 6, 2024.
- Revision received April 25, 2024.
- Accepted April 29, 2024.
- Copyright © 2024 The Author(s). Published by the International Institute of Anticancer Research.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
References
In this issue
Jump to section
Related Articles
Cited By...
- No citing articles found.