|
| |
 |
|
| ORIGINAL ARTICLE |
|
| Year : 2022 | Volume
: 65
| Issue : 6 | Page : 301-310 |
|
The role of exercise intensity on fatty liver in rats
Xueyan Gu1, Xiaocui Ma2, Limin Mo3, Qiyu Wang3
1 Department of Sports and Nutrition, Kunsan National University, Gunsan, Korea; Research Institute of Microbiology, Jiangxi Academy of Sciences, Nanchang, China
2 Henan Key Laboratory of Pediatric Genetics and Metabolic Diseases, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
3 Research Institute of Microbiology, Jiangxi Academy of Sciences, Nanchang, China
| Date of Submission | 25-Jul-2022 |
| Date of Decision | 29-Sep-2022 |
| Date of Acceptance | 13-Oct-2022 |
| Date of Web Publication | 26-Dec-2022 |
Correspondence Address:
Dr. Qiyu Wang
Research Institute of Microbiology, Jiangxi Academy of Sciences, Nanchang, Jiangxi
China
 Source of Support: None, Conflict of Interest: None  | 3 |
DOI: 10.4103/0304-4920.365461
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease and is often caused by obesity. Currently, moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT) are two effective treatments for reducing fat mass in patients with obesity and NAFLD. However, the comparative fat-reducing effects and underlying molecular mechanisms of MICT and HIIT remain unclear. This comprehensive study was performed on male Wistar rats treated with standard diet, high-fat diet, MICT, and HIIT to explore their comparative fat-reducing effects and corresponding molecular mechanisms. HIIT had a greater effect on hepatic vacuolation density and lipid content reduction than MICT, and triglyceride and total cholesterol levels in the serum and the liver demonstrated different sensitivities to different exercise training programs. At the molecular level, both MICT and HIIT altered the processes of fatty acid synthesis, fatty acid transport, fatty acid β-oxidation, and cholesterol synthesis, wherein the transcriptional and translational levels of signaling molecules peroxisome proliferator-activated receptors (PPARs) regulating fatty acid and cholesterol synthesis were strongly changed. Moreover, the metabolic pathways of amino acids, bile acids, and carbohydrates were also affected according to transcriptome analysis, and the changes in the above-mentioned processes in the HIIT group were greater than those in the MICT group. In combination with the search tool for the retrieval of interacting genes/proteins (STRING) analysis and the role of PPARs in lipid metabolism, as well as the expression pattern of PPARs in the MICT and HIIT groups, the MICT-and HIIT-induced fat loss was mediated by the PPAR pathway, causing feedback responses in fatty acid, steroid, amino acid, bile acid, and carbohydrate metabolism, and HIIT had a better fat-reducing effect, which may be initiated by PPAR-α. This study provides a theoretical basis for targeted therapy of patients with obesity and NAFLD.
Keywords: Fat reduction, high-intensity interval training, moderate-intensity continuous training, nonalcoholic fatty liver disease, obesity, peroxisome proliferator-activated receptor
How to cite this article:
Gu X, Ma X, Mo L, Wang Q. The role of exercise intensity on fatty liver in rats. Chin J Physiol 2022;65:301-10 |
| Introduction | |  |
Obesity is conventionally defined as excessive accumulation or abnormal distribution of body fat, causing health prejudice.[1] Each year, 2.8 million people die from obesity and overweight.[2] According to the World Health Organization statistics, worldwide obesity has doubled since 1980, and 600 million in 1.9 billion adults were obese in 2014.[3],[4] It is estimated that one in five adults worldwide will be obese by 2025.[5] Thus, obesity is a major public health concern. The prevalence of obesity is closely correlated with the prevalence and severity of nonalcoholic fatty liver disease (NAFLD).[6] NAFLD is characterized by steatosis accompanied by steatohepatitis,[7] which may eventually lead to cirrhosis and hepatocellular carcinoma.[8] The prevalence of NAFLD is estimated to be 25%–30% worldwide, accounting for 90% of patients with morbid obesity.[6] Obesity and the occurrence of metabolic complications such as NAFLD have drawn more attention to health. Therefore, searching for suitable interventions and therapeutic strategies is important.
Lifestyle modification is the cornerstone of NAFLD management, and targeting obesity is a rational option.[9] Since the concept of "exercise as medicine" was proposed by the American College of Sports Medicine in 2007, exercise, as a cost-effective way to manage weight and health, has become a basic strategy for the prevention and treatment of overweight and obesity.[10] Currently, moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT) are the two most frequently used methods for fat loss.[11] In adults with overweight and obesity, both HIIT and MICT elicited significant reductions in whole-body fat mass and waist circumference,[12] and HIIT demonstrated predominant and time-efficient performance.[13] A group of obese young women (body fat percentage ≥30%) were subjected to a 12-week intervention of MICT and HIIT, the reductions in the abdominal visceral fat area resulting from HIIT were greater in comparison with MICT.[14] After a conduction with 12 weeks of MICT and HIIT on 35 inactive adults (age 54.6 ± 1.4 years, 54% male; body mass index 35.9 ± 0.9 kg/m2) with obesity and type 2 diabetes, liver fat and glycemic were greatly improved.[15] A meta-analysis involving 333 participants showed that HIIT was beneficial for promoting a reduction in liver fat.[16] Several clinical trials have all indicated that MICT and HIIT are effective in decreasing adipocytokines, intrahepatic lipids, and NAFLD risk, and HIIT had a better performance.[17],[18],[19]
Triglyceride (TG) and total cholesterol (T-CHO) are the two main forms of lipids that are significantly elevated in patients with obesity and NAFLD.[20] TG metabolism is highly dependent on fatty acid synthesis, fatty acid transport, and fatty acid β-oxidation, and steroid synthesis is a vital step in cholesterol metabolism.[21],[22] The expression of genes associated with fatty acid and cholesterol synthesis is regulated by peroxisome proliferator-activated receptors (PPARs).[23],[24],[25] PPARs include PPAR-α, PPAR-β, and PPAR-γ, of which PPAR-α and PPAR-γ are the most highly expressed in hepatocytes for regulating lipid metabolism, while PPAR-β-mediated lipid metabolism mainly occurs in extrahepatic organs.[23],[26] Abnormal performances in the processes of fatty acid synthesis, fatty acid transport, fatty acid β-oxidation, and steroid synthesis in obese rats, were often accompanied by alterations in PPAR expression.[27],[28],[29] Moreover, the metabolism of amino acids, bile acids, and carbohydrates is also participating in lipid homeostasis regulation. Amino acids can be transformed into α-ketoacids through deamination, and the latter are then converted into fatty acids, taking part in lipid metabolism.[30],[31] Bile acids can activate the activity of pancreatic lipase, promoting the digestion and hydrolysis of fat into fatty acids, finally reducing lipids.[32] Carbohydrates are the raw materials for lipid synthesis, and these two can be converted into each other.[33] Current evidence suggests that MICT and HIIT could induce insulin sensitization to improve glucose metabolism, activate the PERK-ATF4-CHOP signaling pathway to reduce ER stress in hepatic cells, increase leptin level to promote organismal metabolism, increase mitochondrial respiration to accelerate fatty acid oxidation, and ultimately to achieve fat loss, therein, HIIT had a better performance in the above processes.[34],[35],[36],[37],[38] However, assessment of fat-reducing effects in NAFLD under MICT and HIIT by normalizing lipid-related biochemical indicators is still lacking. Moreover, the corresponding molecular mechanism on lipid loss under MICT and HIIT is not fully understood, such as the role of PPAR pathway in MICT and HIIT-induced fat loss.
Wistar rats are sensitive to various nutrients and are suitable for research on various nutritional and metabolic diseases.[39] This study applied multiple approaches, including morphological examination, biochemical measurement, quantitative real-time polymerase chain reaction (qRT-PCR), transcriptome analysis, and western blotting to systemically investigate the comparative fat loss effects and underlying molecular mechanisms of MICT and HIIT in male Wistar rats with NAFLD. This study aims to explore a better exercise training intervention mode and corresponding molecular mechanism, which contributes to providing a theoretical basis for prevention and targeted therapy of obesity and NAFLD.
| Materials and Methods | | |
Ethical approval
All experimental procedures involving animals were approved by the Independent Animal Care and Use Committee of the Jiangxi Academy of Sciences for Laboratory Animal Use in Research (Approval No. 2019–023).
Animal model and experimental design
Sixty (8-week-old) healthy male Wistar rats (203.2 ± 12.8 g) were purchased from Beijing Weitong Lihua Laboratory Animal Technology Co., Ltd. The rats (three rats per cage) were housed under standard conditions: constant temperature (22°C ± 2°C), free access to food and water, and reversed 12-h light/dark cycle at an air humidity of 40%–60%.
As illustrated in [Supplementary Figure S1], after 1 week of acclimatization, the rats were randomly divided into two groups: standard diet (D12450B, Research Diets) (SD, n = 12) and high-fat diet (HFD) (D12492, Research Diets) (HFD, n = 48) groups, which were fed for 8 weeks. After being fed HFD for 8 weeks, 43 obese rats were obtained from the HFD group, whose body weight exceeded 10% of that of the SD group rats.[40] The obese rats were further randomly distributed into three experimental groups: the HFD group, which was fed an HFD for 12 weeks without exercise training (n = 12); MICT group with a 12-week HFD and MICT (n = 12); and the HIIT group with a 12-week HFD and HIIT (n = 12). The rats in the SD group were fed an SD.
Exercise training protocol
One week of adaptive exercise training was performed before MICT and HIIT on a treadmill (DSPT-208) with a slope of 0°, speed of 10–15 m/min and frequency of 20 min/d. After the adaptation, MICT and HIIT were performed for 12 weeks. The MICT group performed continuous treadmill exercise at 60% VO2max intensity for 50 min/day for 5 days/week. HIIT consisted of four phases, including warm-up, high-intensity exercise, high-intensity interval, and recovery; the detailed parameters are presented in [Supplementary Table S1].. Exercise intensity was re-determined based on the speed corresponding to the measured VO2max at the end of adaption, 4th week, and 8th week and the treadmill slope was set to 10° in both MICT and HIIT. The exercise training programs and VO2max measurement methods were determined according to previous studies.[41],[42]
Sample collection
After the exercise training, the rats were fasted for 24 h, anesthetized with 10% chloral hydrate, and euthanized. Whole blood samples were immediately collected, statically settled, and centrifuged at 3,000×g at 4°C for 10 min to obtain the serum. Aliquots were stored at −80°C until used for biochemical analysis. The liver samples were simultaneously dissected, collected, and weighed. The right lobes of the liver samples were fixed in 4% paraformaldehyde for further histopathological examination, and additional liver samples were immediately frozen in liquid nitrogen and stored at −80°C for biochemical analyses.
Hepatic histopathological examination
The fixed liver samples (n = 12) in 4% paraformaldehyde were dehydrated using an ethanol gradient, clarified with xylene, and embedded in paraffin wax. A series of 4 μm sections were cut using a rotary microtome (Leica, RM 2245) and placed on glass microscope slides. The mounted sections were stained with hematoxylin and eosin for light microscopy (Leica M165FC). Liver vacuolation density was determined using the ImageJ software (https://imagej.nih.gov/ij/).
Biochemical measurement
Rat liver samples were homogenized on ice in 10 volumes of ice-cold phosphate buffer solution (50 mM, pH 7.0) using an electric homogenizer. The homogenates were centrifuged at 3,000×g for 10 min at 4°C. The obtained supernatants (n = 12) and serum samples (n = 12) were used to determine TG and T-CHO levels. In addition, the inflammatory factor of interleukin-8 (IL-8) and fibrosis biomarker of hyaluronic acid (HA) in serum samples were determined to reflect the status of NAFLD.[43],[44] All biochemical indicators were determined by using commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) according to the manufacturer's instructions.
Integrated biomarker response analysis
Different biomarkers exhibit different sensitivities and asynchronous responses when subjected to different intervention modes.[45] The integrated biomarker response (IBR) analysis allows the synchronization and normalization of various indicators to reflect bioreflection. According to previously reported algorithms (1)–(4),[46] hepatic/serum TG and T-CHO were monitored and converted into IBR values to compare the fat-reducing effects under different exercise training modes. A star map is presented, where each radius corresponds to the | Ai | value at each test level.
Xi and X0 are the individual biomarker data and the mean reference data, respectively, and μ and σ represent the general mean and standard deviation of Yi, respectively. Zi represents the ratio of Yi − μ to σ, and Ai is the difference between Zi and Z0.
Gene expression analysis
The livers excised from every two rats in each intervention group served as one sample, and a total of six samples from each treatment group were used for gene expression analysis. Total RNA extraction, cDNA synthesis, and qRT-PCR were performed according to previously described methods.[47] The gene-specific primer sequences involved in lipid metabolism are listed in [Supplementary Table S2]. β-actin was used as the reference gene for normalization because its expression was stable under our exposure conditions. The fold-change in target gene expression was analyzed using the 2−ΔΔCt method.
Illumina sequencing and transcriptomic analysis
According to the RNA quality assessment, five RNA samples (n = 5) from each treatment group were used for transcriptomic analysis. Briefly, the RNA-seq transcriptome library was prepared using an Illumina TruSeq RNA sample preparation kit (San Diego, CA, USA). After sequencing the paired-ends using the Illumina HiSeq xten/NovaSeq 6000 sequencer, clean reads were trimmed, and raw data were generated for further analysis. Differentially expressed genes (DEGs) were determined by EdgeR using the criteria of | fold change | >2, FDR <0.05, and Q ≤ 0.05, according to gene transcription abundance. Principal component analysis (PCA) was performed using OmicStudio tools (https://www.omicstudio.cn/tool) based on the transcription profile. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed at Bonferroni-corrected P ≤ 0.05 compared with the whole-transcriptome background.
STRING analysis
The interaction network of lipid metabolism-related DEGs identified by qRT-PCR was analyzed using STRING (V11.0) at default settings (https://string-db.org/). Connecting lines represent the determined protein–protein associations based on curated databases, experimental confirmation, gene neighborhood, and co-expression.
Western blotting
Livers excised from three rats in each intervention group served as one sample, and a total of four samples (n = 4) from each treatment group were used for the immunoblot detection of PPAR-α and PPAR-γ. Sodium dodecyl sulfate polyacrylamide gel electrophoresis and immunoblotting were performed as described previously.[48] The relative expression levels of PPAR-α and PPAR-γ were normalized to GAPDH using the ImageJ software (https://imagej.nih.gov/ij/). The antibodies used in this study are listed in [Supplementary Table S3].
Statistical analysis
All data are presented as mean ± standard error of the mean. Statistical analyses were performed using the GraphPad Prism software (version 7.0, GraphPad Software Inc., San Diego, CA, USA). Significant differences were evaluated using a one-way analysis of variance, followed by Duncan's test. Statistical significance was set at P < 0.05.
| Results | | |
Exercise training improves the histopathological performance of NAFLD
HFD significantly induced NAFLD, characterized by the presence of increased in liver vacuolation density by 7.41 fold compared with that in the SD group [Figure 1]. MICT and HIIT prominently reduced the HFD-induced hepatic vacuolation density, with the extents of 53.43% and 70.85%, respectively [Figure 1]. Therein, the HIIT group demonstrated better performance in liver vacuolation density reduction than the MICT group; however, the liver vacuolation densities in the HIIT and MICT groups were still higher than those in the SD group [Figure 1]. | Figure 1: Histopathological examination of nonalcoholic fatty liver under different exercise training strategies. (a) H and E staining of liver sections. Scale bar = 100 μm. (b) Corresponding statistics of liver vacuolation density in (a). The green arrowhead indicates the hepatic vacuoles. Significant differences (P < 0.05) are indicated with different lowercase letters among different groups. SD: Standard diet, HFD: High-fat diet, MICT: Moderate-intensity continuous training, HIIT: High-intensity interval training.
Click here to view |
Exercise training alleviates lipid accumulation in the liver and serum
The HFD group had significantly increased TG and T-CHO levels in the liver and serum by 115.13%, 82.57%, 51.02%, and 74.63%, respectively, than the SD group [Figure 2]a, [Figure 2]b, [Figure 2]c, [Figure 2]d. Compared with the HFD groups, the HIIT demonstrated a significant lipid-reducing effect, with a decrease of 27.09% and 31.85% in liver TG and T-CHO, respectively [Figure 2]a and [Figure 2]b, and a decrease of 20.95% and 27.35% in serum TG and T-CHO, respectively [Figure 2]c and [Figure 2]d. MICT only promoted the reduction of hepatic T-CHO and serum TG content, whereas liver TG and serum T-CHO content were not significantly changed, although with a slight decrease [Figure 2]a, [Figure 2]b, [Figure 2]c, [Figure 2]d. The IBR analysis demonstrated that both MICT and HIIT had fat-reducing effects, and that HIIT possessed the best fat-reducing capability [Figure 2]e. An in-depth analysis revealed that serum T-CHO, TG, and liver T-CHO were more sensitive to HIIT than liver TG, and that liver T-CHO and serum TG were more responsive to MICT than liver TG and serum T-CHO [Figure 2]e. Furthermore, HIIT exerted a greater effect on serum T-CHO and hepatic TG reduction than MICT [Figure 2]e. | Figure 2: TG and T-CHO content assessment in the liver and serum of rats with NAFLD. The content of hepatic TG (a), T-CHO (b), serum TG (c), and T-CHO (d) in SD, HFD, MICT, and HIIT-treated rats. (e) The integrated biomarker response analysis for fat reduction effects under different exercise training programs, in which each radius represents Ai value. Significant differences (P < 0.05) are indicated with different lowercase letters among different groups. TG: Triglyceride, T-CHO: Total cholesterol, NAFLD: Nonalcoholic fatty liver disease, SD: Standard diet, HFD: High-fat diet, MICT: Moderate-intensity continuous training.
Click here to view |
Exercise training alters triglyceride and total cholesterol metabolism-related genes expression
TG metabolism is involved in fatty acid transport, fatty acid β-oxidation, and fatty acid synthesis, and steroid synthesis is a vital step in cholesterol degradation. The transcription levels of genes correlated with the above processes were subsequently quantified [Figure 3]. Compared with the HFD group, the HIIT group had significantly downregulated slc27a2, fabp1, ppar-α, and ppar-γ expression but increased acox1, cpt1a, cyp11a1, and hsd11b1 expression, while the MICT group had decreased slc27a2 and ppar-γ but increased cpt1a gene expression. No significant differences were observed in the expression of other lipid metabolism-related genes between the HFD and exercise groups. The genes related to steroid biosynthesis in both the MICT and HIIT groups demonstrated a trend of high expression compared with the HFD group. | Figure 3: Hepatic TG and T-CHO metabolism related genes expression quantification under different exercise training programs. Significant differences (P < 0.05) are indicated with different lowercase characters among different groups. TG: Triglyceride, T-CHO: Total cholesterol.
Click here to view |
Transcriptomic profile alteration under different exercise training
Hepatic transcriptomic sequencing was employed to globally probe and compare the molecular mechanisms of fat loss in NAFLD under different exercise training interventions. According to the PCA analysis, the hepatic transcriptional profile in the HFD group demonstrated an apparent deviation from the SD group, while the MICT and HIIT groups were close to the SD group [Figure 4]a. Compared with the HFD group, 431 and 597 DEGs were identified in the MICT and HIIT groups, respectively, and 219 were shared [Figure 4]b. Among these DEGs, 58 in the MICT group were involved in 18 lipid metabolism-related pathways, including the PPAR signaling pathway, fatty acid degradation, fatty acid elongation, and so on [Figure 4]c, [Supplementary Table S4]. In the HIIT group, 84 DEGs participated in 14 lipid metabolism-related pathways, such as the PPAR signaling pathway, fatty acid degradation, and steroid hormone biosynthesis and so on [Figure 4]d, [Supplementary Table S5]. The enrichment coefficients of the lipid metabolism-related pathways in the HIIT group were higher than those in the MICT group [Figure 4]c and [Figure 4]d. | Figure 4: Hepatic transcriptomic analysis under different exercise intervention modes. (a) PCA analysis for SD, HFD, MICT, and HIIT RNA samples. (b) Venn charts for DEGs of HFD versus MICT and HFD versus HIIT groups. (c-d) KEGG enrichment analysis for lipid metabolism related pathways in the HFD versus MICT (c) and HFD versus HIIT groups (d). SD: Standard diet, HFD: High-fat diet, MICT: Moderate-intensity continuous training, PCA: Principal component analysis, HIIT: High-intensity interval training, DEGs: Differentially expressed genes, KEGG: Kyoto Encyclopedia of Genes and Genomes.
Click here to view |
STRING analysis for lipid metabolism related differentially expressed genes
The STRING analysis revealed that ppar-α, ppar-γ, cpt1a, acox1, fabp1, slc27a2, Cyp11a1, and Hsd11b were located in the same interaction network [Figure 5]. | Figure 5: STRING analysis for lipid metabolism related DEGs identified by qRT-PCR. qRT-PCR: Quantitative real-time polymerase chain reaction.
Click here to view |
Exercise training changes peroxisome proliferator-activated receptors-α and peroxisome proliferator-activated receptors-γ expression
The translational levels of PPAR-α and PPAR-γ were consistent with their respective transcriptional levels [Figure 3] and [Figure 6]. The expression level of PPAR-α in the HFD group was significantly increased by 155% compared to that in the SD group, and its expression in the HIIT group was close to that in the SD group, which was 68% lower than that in the HFD group [Figure 6]a. Although no significant difference in the expression of PPAR-α was observed between the HIIT group and MICT group, the average decrease was high at 48% (P = 0.0647) [Figure 6]a. The PPAR-γ expression level in the HFD group was 125% higher than that in the SD group [Figure 6]b. Moreover, the expression levels of PPAR-γ in the MICT and HIIT groups were similar to those in the SD group and were 59.13% and 54.78% lower than those in the HFD group [Figure 6]b. | Figure 6: Immunoblot detection and relative expression level quantification of PPAR-α (a) and PPAR-γ (b) under different exercise training programs. Significant differences (P < 0.05) are indicated with different lowercase letters among different groups. PPAR: Peroxisome proliferator-activated receptor.
Click here to view |
| Discussion | | |
Obesity is a chronic metabolic disease worldwide and is caused by unbalanced energy intake and consumption.[49] In the free diet state, an HFD typically provides higher calories, resulting in a significant increase in body weight, finally leading to obesity.[50] The liver is important detoxification and metabolic organ that serves as an important metabolic center responsible for maintaining normal lipid metabolism, vitamin metabolism, hormone metabolism, and other important physiological functions.[51] Obesity can cause excessive accumulation of fat in the liver, resulting in fatty liver disease, characterized by hepatic adipose infiltration and steatosis.[6] MICT and HIIT are effective methods for weight gain prevention and fat reduction.[11],[52] Compared to MICT, HIIT demonstrated better performance.[12],[29] However, systematic and in-depth comparative studies on the effects of fat loss in NAFLD and the corresponding mechanism of MICT and HIIT are lacking. This study applied various approaches to comprehensively compare the fat-reducing effect and molecular mechanism of MICT and HITT, which provided a theoretical basis for the targeted treatment of patients with obesity and NAFLD and the selection of the most effective way to manage weight.
Hepatic vacuolization is a main manifestation of NAFLD. Lipid droplets accumulate in the cytoplasm of hepatocytes, deforming the nucleus to form vacuoles.[53] In addition, TG and T-CHO levels become elevated with the occurrence of obesity and NAFLD.[20] A meta-analysis involving human studies has demonstrated that HIIT required less energy and time to elicit comparable improvements in liver fat than MICT.[19] A randomized clinical trial also confirmed this, revealing that only energy-matched MICT could achieve HIIT produced fasting insulin level and intrahepatic lipid reduction.[18] In mice, HIIT exhibited significantly lower body weight, percentage of fat mass, and smaller adipocyte size than MICT, ameliorating adiposity and related metabolic dysfunction induced by HFD and sedentary lifestyle.[29] In the present study, HIIT having a better effect on fat loss than MICT was demonstrated from the aspects of hepatic vacuolation density decrease and liver/serum TG and T-CHO content reduction [Figure 1] and [Figure 2]. Moreover, NAFLD can develop into nonalcoholic steatohepatitis (NASH), which can further progress to liver fibrosis.[44] IL-8 is a pro-inflammatory factor, and HA is an extracellular protein, which serve as sensitive indicators reflecting the degree of NASH and hepatic fibrosis, respectively.[43],[54] In the present study, HFD-induced NAFLD was accompanied by a significant increase in IL-1β content but no change in HA content [Supplementary Figure S2], indicating our established NAFLD rats had developed into NASH, but no fibrosis occurred in the liver. HIIT significantly reduced IL-1β content, promoting the recovery of liver inflammation, while only a slight decrease of IL-1β content occurred in MICT treatment group [Supplementary Figure S2]a.
A previous study conducted by Fredrickson et al. showed that obese rats with 16-week MICT and HIIT treatments displayed effectiveness in ameliorating NASH progression, with the presence of a decrease in liver mass, liver TG, liver inflammatory genes expression (mcp1, sele, icam1, il-1β, tnf-α, inos) and liver pro-fibrogenesis genes expression (col1a1, timp1, mmp2, tgfb1, acta2), and HIIT was more effective than MICT in improving liver inflammation and lipid biosynthesis.[55] Compared with our results with a 12-week exercise intervention, the longer-term duration of exercise training may be the main reason for the more significant improvement in NAFLD. The fact that different parameters under different exercise training programs improved obesity and NAFLD to varying degrees have been extensively reported.[13],[14],[34],[56] The strength and duration of exercise, as well as the conditions of subjects, may be key determinants of the magnitude of special physical functions.
Although HIIT exerted a better fat-reducing effect than MICT, the contribution of these two types of exercise to lipid content changes in fat reduction is unknown. IBR analysis is mainly used for the synchronization and normalization of various indicators upon suffering from different intervention modes to reflect overall bioreflection.[45],[46] To the best of our knowledge, this study is the first to demonstrate that serum TG and liver T-CHO were the most sensitive to exercise, and that serum T-CHO exhibited similarly high sensitivity to HIIT [Figure 2]e.
TG metabolism is involved in the fatty acid synthesis, transport, and β-oxidation.[57] PPARs are important signaling molecules that control fatty acid and cholesterol synthesis-related gene transcription.[23],[24],[25] Fatty acid transport is responsible for fatty acid uptake and activation, and ultimately participates in TG synthesis.[58] Fatty acid β-oxidation is a process in which long-chain fatty acids undergo continuous cyclic reactions to be completely oxidized to CO2 and H2O, finally releasing energy and achieving lipolysis.[57] The lower expression of fatty acid synthesis-related genes (ppar-α, ppar-γ) and fatty acid transport-related genes (slc27a2, fabp1), and higher expression of fatty acid β-oxidation related genes (acox1, cpt1a) in the HIIT group compared with the HFD group suggested that HIIT not only increased TG consumption but also decreased TG synthesis [Figure 3]. Steroids are degradation products of cholesterol, which is completed under the action of Cyp11a1, Hsd11b1, and Star.[59] The increased expression of cyp11a1, hsd11b1, and decreased expression of ppar-α, ppar-γ in the HIIT group compared with the HFD group suggested that cholesterol degradation was aggravated and cholesterol synthesis was reduced [Figure 3]. The MICT group presented a similar gene expression pattern as the HIIT group, although the magnitude of the change in gene expression was smaller than that in the HIIT group [Figure 3], which explained the lesser fat-reducing effect in the MICT group than in the HIIT group.
This conclusion was also confirmed by the transcriptome analysis. The fact that transcriptional profiles in the MICT and HIIT groups were similar to those in the SD group suggested that exercise greatly improved obesity status [Figure 4]a. The number of lipid metabolism-related DEGs in the HIIT vs. HFD intersected group was higher than that in the MICT versus HFD intersected group, indicating that HIIT was more effective in reversing obesity-induced negative effects at the molecular level [Figure 4]b. The KEGG pathway enrichment analysis also verified the superior performance of HIIT, as the enrichment coefficients of lipid metabolism-related pathways in the HIIT group were higher than those in the MICT group, even though the number of enriched lipid metabolism-related pathways in the HIIT group was slightly lower than that in the MICT group [Figure 4]c and [Figure 4]d. In addition to the identified PPAR pathway, fatty acid metabolism pathway, and steroid metabolism pathway by qRT-PCR, other pathways, including amino acid metabolism pathway, bile acid metabolism pathway, and glycometabolism pathway, were also enriched in the MICT and HIIT groups [Figure 4]c and [Figure 4]d, which demonstrated that exercise training could also achieve a fat-reducing effect by changing the amount and distribution of carbohydrates, bile acids, and amino acids. Carbohydrates and amino acids can alter the lipid content by acting on the tricarboxylic acid cycle,[60],[61] and bile acids can accelerate fat digestion and absorption by enhancing lipase activity.[62] Overall, MICT and HIIT may act on similar molecular signaling pathways to induce lipid reduction.
Considering the role of PPARs as signaling molecules in triggering fatty acid and cholesterol synthesis-related gene transcription,[23],[24],[25] and the occurrence of altered transcription of PPARs and PPAR pathway enrichment after exercise training interventions, STRING analysis was performed to visualize the interaction of our identified lipid metabolism-related DEGs. The results indicated that all lipid metabolism-related DEGs, including ppar-α, ppar-γ, cpt1a, acox1, fabp1, slc27a2, cyp11a1, and hsd11b were located in the same interaction network [Figure 5], suggesting that ppars were the first-level target genes in lipid metabolism alteration induced by HFD and exercise training. Other DEGs involved in fatty acid, steroid, amino acid, bile acid, and carbohydrate metabolism were subsequently regulated by positive and negative feedback mechanisms. In addition, the expression levels of PPAR-γ in the MICT and HIIT groups were consistent, while the expression level of PPAR-α in the HIIT group was lower than that in the MICT group [Figure 6], indicating that HIIT induced an excellent fat-reducing effect may be initialed by acting on PPAR-α, which then caused more intense feedback responses in fatty acids, steroids, amino acids, bile acids, and carbohydrate metabolism. This explains why the enrichment coefficients of the lipid metabolism-related pathways in the HIIT group were higher than those in the MICT group [Figure 4]c and [Figure 4]d.
| Conclusion | | |
Both HIIT and MICT can reduce fat by activating the PPAR pathway and altering the metabolism of fatty acids, steroids, amino acids, bile acids, and carbohydrates. HIIT exerted a better effect on lipid reduction than MICT, in which the fat-reducing effects may be initiated by acting on PPAR-α, triggering more intense feedback responses in fatty acid, steroid, amino acid, bile acid, and carbohydrate metabolism. This study deeply probed and compared MICT- and HIIT-induced fat loss effects and underlying molecular mechanisms, providing the theoretical basis for targeted therapy of patients with obesity and NAFLD and the selection of the most effective way to manage weight.
Acknowledgment
We would like to thank Editage (www.editage.cn) for English language editing.
Financial support and sponsorship
This research work was financially supported by the National Natural Science Foundation of China (32101372), Jiangxi Provincial Natural Science Foundation (20212BAB213040), and Medical Scientific and Technological Projects of Henan Province (LHGJ20210685).
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Mayoral LP, Andrade GM, Mayoral EP, Huerta TH, Canseco SP, Rodal Canales FJ, et al. Obesity subtypes, related biomarkers & heterogeneity. Indian J Med Res 2020;151:11-21.  [ PUBMED] [Full text] |
| 2. | Wiciński M, Gębalski J, Gołębiewski J, Malinowski B. Probiotics for the treatment of overweight and obesity in humans-a review of clinical trials. Microorganisms 2020;8:E1148. |
| 3. | Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism 2019;92:6-10. |
| 4. | World Health Organization. Obesity Data and Statistics. Geneva, Switzerland: World Health Organization; 2017. |
| 5. | Mohammed MS, Sendra S, Lloret J, Bosch I. Systems and WBANs for controlling obesity. J Healthc Eng 2018;2018:1564748. |
| 6. | Polyzos SA, Kountouras J, Mantzoros CS. Obesity and nonalcoholic fatty liver disease: From pathophysiology to therapeutics. Metabolism 2019;92:82-97. |
| 7. | Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ. Mechanisms of NAFLD development and therapeutic strategies. Nat Med 2018;24:908-22. |
| 8. | Perumpail BJ, Khan MA, Yoo ER, Cholankeril G, Kim D, Ahmed A. Clinical epidemiology and disease burden of nonalcoholic fatty liver disease. World J Gastroenterol 2017;23:8263-76. |
| 9. | Yin C, Li Z, Xiang Y, Peng H, Yang P, Yuan S, et al. Effect of intermittent fasting on non-alcoholic fatty liver disease: Systematic review and meta-analysis. Front Nutr 2021;8:709683. |
| 10. | Lobelo F, Stoutenberg M, Hutber A. The Exercise is Medicine Global Health Initiative: A 2014 update. Br J Sports Med 2014;48:1627-33. |
| 11. | Miguet M, Fearnbach NS, Metz L, Khammassi M, Julian V, Cardenoux C, et al. Effect of HIIT versus MICT on body composition and energy intake in dietary restrained and unrestrained adolescents with obesity. Appl Physiol Nutr Metab 2020;45:437-45. |
| 12. | Wewege M, van den Berg R, Ward RE, Keech A. The effects of high-intensity interval training vs. moderate-intensity continuous training on body composition in overweight and obese adults: A systematic review and meta-analysis. Obes Rev 2017;18:635-46. |
| 13. | Zhang H, Tong TK, Qiu W, Zhang X, Zhou S, Liu Y, et al. Comparable effects of high-intensity interval training and prolonged continuous exercise training on abdominal visceral fat reduction in obese young women. J Diabetes Res 2017;2017:5071740. |
| 14. | Zhang H, Tong TK, Kong Z, Shi Q, Liu Y, Nie J. Exercise training-induced visceral fat loss in obese women: The role of training intensity and modality. Scand J Med Sci Sports 2021;31:30-43. |
| 15. | Sabag A, Way KL, Sultana RN, Keating SE, Gerofi JA, Chuter VH, et al. The effect of a novel low-volume aerobic exercise intervention on liver fat in type 2 diabetes: A randomized controlled trial. Diabetes Care 2020;43:2371-8. |
| 16. | Khalafi M, Symonds ME. The impact of high intensity interval training on liver fat content in overweight or obese adults: A meta-analysis. Physiol Behav 2021;236:113416. |
| 17. | Khalafi M, Symonds ME. The impact of high-intensity interval training on inflammatory markers in metabolic disorders: A meta-analysis. Scand J Med Sci Sports 2020;30:2020-36. |
| 18. | Winn NC, Liu Y, Rector RS, Parks EJ, Ibdah JA, Kanaley JA. Energy-matched moderate and high intensity exercise training improves nonalcoholic fatty liver disease risk independent of changes in body mass or abdominal adiposity – A randomized trial. Metabolism 2018;78:128-40. |
| 19. | Sabag A, Barr L, Armour M, Armstrong A, Baker CJ, Twigg SM, et al. The effect of high-intensity interval training vs moderate-intensity continuous training on liver fat: A systematic review and meta-analysis. J Clin Endocrinol Metab 2022;107:862-81. |
| 20. | Wang CL, Liang L, Fu JF, Zou CC, Hong F, Xue JZ, et al. Effect of lifestyle intervention on non-alcoholic fatty liver disease in Chinese obese children. World J Gastroenterol 2008;14:1598-602. |
| 21. | Vergani L. Fatty acids and effects on in vitro and in vivo models of liver steatosis. Curr Med Chem 2019;26:3439-56. |
| 22. | Manna PR, Stetson CL, Slominski AT, Pruitt K. Role of the steroidogenic acute regulatory protein in health and disease. Endocrine 2016;51:7-21. |
| 23. | Mirza AZ, Althagafi II, Shamshad H. Role of PPAR receptor in different diseases and their ligands: Physiological importance and clinical implications. Eur J Med Chem 2019;166:502-13. |
| 24. | Xu BM, Xiao L, Kang CM, Ding L, Guo FX, Li P, et al. LncRNA AC096664.3/PPAR-γ/ABCG1-dependent signal transduction pathway contributes to the regulation of cholesterol homeostasis. J Cell Biochem 2019;120:13775-82. |
| 25. | Srivastava RA, Cefalu AB, Srivastava NS, Averna M. NPC1L1 and ABCG5/8 induction explain synergistic fecal cholesterol excretion in ob/ob mice co-treated with PPAR-α and LXR agonists. Mol Cell Biochem 2020;473:247-62. |
| 26. | Wang Y, Nakajima T, Gonzalez FJ, Tanaka N. PPARs as metabolic regulators in the liver: Lessons from liver-specific PPAR-Null mice. Int J Mol Sci 2020;21:E2061. |
| 27. | Gu W, Wang Y, Zeng L, Dong J, Bi Q, Yang X, et al. Polysaccharides from Polygonatum kingianum improve glucose and lipid metabolism in rats fed a high fat diet. Biomed Pharmacother 2020;125:109910. |
| 28. | Tung YT, Chen HL, Wu HS, Ho MH, Chong KY, Chen CM. Kefir peptides prevent hyperlipidemia and obesity in high-fat-diet-induced obese rats via lipid metabolism modulation. Mol Nutr Food Res 2018;62:1700505. |
| 29. | Wang N, Liu Y, Ma Y, Wen D. High-intensity interval versus moderate-intensity continuous training: Superior metabolic benefits in diet-induced obesity mice. Life Sci 2017;191:122-31. |
| 30. | Snyderman SE, Goldstein F, Sansaricq C, Norton PM. The relationship between the branched chain amino acids and their alpha-ketoacids in maple syrup urine disease. Pediatr Res 1984;18:851-3. |
| 31. | Gandomkar S, Hall M. Enzymatic oxidative cascade for oxofunctionalization of fatty acids in one-pot. Methods Mol Biol 2022;2397:321-39. |
| 32. | Lindquist S, Hernell O. Lipid digestion and absorption in early life: An update. Curr Opin Clin Nutr Metab Care 2010;13:314-20. |
| 33. | Binienda A, Twardowska A, Makaro A, Salaga M. Dietary carbohydrates and lipids in the pathogenesis of leaky gut syndrome: An overview. Int J Mol Sci 2020;21:E8368. |
| 34. | Ryan BJ, Schleh MW, Ahn C, Ludzki AC, Gillen JB, Varshney P, et al. Moderate-Intensity exercise and high-intensity interval training affect insulin sensitivity similarly in obese adults. J Clin Endocrinol Metab 2020;105:dgaa345. |
| 35. | Zhang Y, Liu X, Wei J, Liu X, Zhang N, Sheng L. HIIT and MICT attenuate high-fat diet-induced hepatic lipid accumulation and ER stress via the PERK-ATF4-CHOP signaling pathway. J Physiol Biochem 2022;78:641-52. |
| 36. | Sun L, Li FH, Li T, Min Z, Yang LD, Gao HE, et al. Effects of high-intensity interval training on adipose tissue lipolysis, inflammation, and metabolomics in aged rats. Pflugers Arch 2020;472:245-58. |
| 37. | Maillard F, Vazeille E, Sauvanet P, Sirvent P, Combaret L, Sourdrille A, et al. High intensity interval training promotes total and visceral fat mass loss in obese Zucker rats without modulating gut microbiota. PLoS One 2019;14:e0214660. |
| 38. | Li FH, Sun L, Zhu M, Li T, Gao HE, Wu DS, et al. Beneficial alterations in body composition, physical performance, oxidative stress, inflammatory markers, and adipocytokines induced by long-term high-intensity interval training in an aged rat model. Exp Gerontol 2018;113:150-62. |
| 39. | Aleixandre de Artiñano A, Miguel Castro M. Experimental rat models to study the metabolic syndrome. Br J Nutr 2009;102:1246-53. |
| 40. | Zhang XY, Guo CC, Yu YX, Xie L, Chang CQ. Establishment of high-fat diet-induced obesity and insulin resistance model in rats. Beijing Da Xue Xue Bao Yi Xue Ban 2020;52:557-63. |
| 41. | Bedford TG, Tipton CM, Wilson NC, Oppliger RA, Gisolfi CV. Maximum oxygen consumption of rats and its changes with various experimental procedures. J Appl Physiol Respir Environ Exerc Physiol 1979;47:1278-83. |
| 42. | Leandro CG, Levada AC, Hirabara SM, Manhães-de-Castro R, De-Castro CB, Curi R, et al. A program of moderate physical training for Wistar rats based on maximal oxygen consumption. J Strength Cond Res 2007;21:751-6. |
| 43. | Diehl AM, Day C. Cause, pathogenesis, and treatment of nonalcoholic steatohepatitis. N Engl J Med 2017;377:2063-72. |
| 44. | Liu YL, Zhang QZ, Wang YR, Fu LN, Han JS, Zhang J, et al. Astragaloside IV improves high-fat diet-induced hepatic steatosis in nonalcoholic fatty liver disease rats by regulating inflammatory factors level via TLR4/NF-κB signaling pathway. Front Pharmacol 2020;11:605064. |
| 45. | Beliaeff B, Burgeot T. Integrated biomarker response: A useful tool for ecological risk assessment. Environ Toxicol Chem 2002;21:1316-22. |
| 46. | Sanchez W, Burgeot T, Porcher JM. A novel "integrated biomarker response" calculation based on reference deviation concept. Environ Sci Pollut Res Int 2013;20:2721-5. |
| 47. | Huang J, Sun L, Mennigen JA, Liu Y, Liu S, Zhang M, et al. Developmental toxicity of the novel PFOS alternative OBS in developing zebrafish: An emphasis on cilia disruption. J Hazard Mater 2021;409:124491. |
| 48. | Wang Q, Peng Z, Long H, Deng X, Huang K. Polyubiquitylation of α-tubulin at K304 is required for flagellar disassembly in Chlamydomonas. J Cell Sci 2019;132:jcs229047. |
| 49. | Friedrichsen M, Breitschaft A, Tadayon S, Wizert A, Skovgaard D. The effect of semaglutide 2.4 mg once weekly on energy intake, appetite, control of eating, and gastric emptying in adults with obesity. Diabetes Obes Metab 2021;23:754-62. |
| 50. | Conway B, Rene A. Obesity as a disease: No lightweight matter. Obes Rev 2004;5:145-51. |
| 51. | Kmieć Z. Cooperation of liver cells in health and disease. Adv Anat Embryol Cell Biol 2001;161:I-151. |
| 52. | Wilson RA, Deasy W, Stathis CG, Hayes A, Cooke MB. Intermittent fasting with or without exercise prevents weight gain and improves lipids in diet-induced obese mice. Nutrients 2018;10:E346. |
| 53. | Ramot Y, Zandani G, Madar Z, Deshmukh S, Nyska A. Utilization of a deep learning algorithm for microscope-based fatty vacuole quantification in a fatty liver model in mice. Toxicol Pathol 2020;48:702-7. |
| 54. | Kozumi K, Kodama T, Murai H, Sakane S, Govaere O, Cockell S, et al. Transcriptomics identify thrombospondin-2 as a biomarker for NASH and advanced liver fibrosis. Hepatology 2021;74:2452-66. |
| 55. | Fredrickson G, Barrow F, Dietsche K, Parthiban P, Khan S, Robert S, et al. Exercise of high intensity ameliorates hepatic inflammation and the progression of NASH. Mol Metab 2021;53:101270. |
| 56. | Kalaki-Jouybari F, Shanaki M, Delfan M, Gorgani-Firouzjae S, Khakdan S. High-intensity interval training (HIIT) alleviated NAFLD feature via miR-122 induction in liver of high-fat high-fructose diet induced diabetic rats. Arch Physiol Biochem 2020;126:242-9. |
| 57. | Alves-Bezerra M, Cohen DE. Triglyceride metabolism in the liver. Compr Physiol 2017;8:1-8. |
| 58. | Gimeno RE. Fatty acid transport proteins. Curr Opin Lipidol 2007;18:271-6. |
| 59. | Miller WL, Auchus RJ. The molecular biology, biochemistry, and physiology of human steroidogenesis and its disorders. Endocr Rev 2011;32:81-151. |
| 60. | Jones JG. Hepatic glucose and lipid metabolism. Diabetologia 2016;59:1098-103. |
| 61. | Walker RP, Chen ZH, Famiani F. Gluconeogenesis in plants: A key interface between organic acid/amino acid/lipid and sugar metabolism. Molecules 2021;26:5129. |
| 62. | He X, McClorry S, Hernell O, Lönnerdal B, Slupsky CM. Digestion of human milk fat in healthy infants. Nutr Res 2020;83:15-29. |
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
|
Search Pubmed for