Distinct approaches to inhibit fructose-induced obesity

doi:10.21203/rs.3.rs-1964767/v1

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Distinct approaches to inhibit fructose-induced obesity

https://doi.org/10.21203/rs.3.rs-1964767/v1

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Background

Obesity is primarily a consequence of eating disorder. Drugs have been confirmed effective for weight loss more or less related to the functional connectivity in neural networks and metabolic pattern. Functional connectivity, amplitudes of neural signals, and the major ways of ATP productions were analyzed to discover the mechanisms of Metformin and Dapagliflozin.

Methods

Male ob/ob mice were divided into high-fat-diet fed (HFD), high-fat-diet fed with Metformin, and high-fat-diet fed with Dapagliflozin groups. Functional connectivity amplitude of low-frequency signal fluctuations and rCBV quantification were statistically analyzed in the linear mixed model, meanwhile, metabolic pattern of intestinal cells was also tested.

Results

Our results showed that Bold signaling responses, functional connectivity, and rCBV quantification tended to be attenuated in the Metformin-treated group compared to the HFD and Dapagliflozin groups. And only Dapagliflozin prevented fructose-induced hyper survival of intestinal cells and hypertrophy of intestinal villus by reducing glycolysis levels. Both Metformin and Dapagliflozin are effective for weight loss.

Conclusions

Our findings showed distinct mechanisms by which Dapagliflozin and Metformin inhibit obesity related eating disorders, combined use of both drugs Dapagliflozin and Metformin may be more beneficial for clinical improvement in fructose-induced obesity.

Obesity

Metformin

Dapagliflozin

Neural networks

Intestinal villus

Obesity is a growing global health problem, with more and more people dying from being overweight rather than underweight.^1,2 Even though obesity is preventable, its prevalence rate sustainedly rise, associated with increased risk of cardiovascular disease, diabetes and cancer.^3,4 To combat obesity with appropriate treatment, it is necessary to get more insights into the different mechanisms of reasons underlying the development of overweight. As reported the neural transmissions involved between multiple brain areas are associated with the regulation in disorders.^5–7. Wright and colleagues has reported that with measurement of resting-state functional magnetic resonance imaging (rs-fMRI), fasting and satiation were associated with the changes in functional connectivity of insula and hypothalamus.^6,7 Avery et al. has also reported that starvation was associated with a significant functional connectivity of the left orbitofrontal cortex (OFC) to limbic regions.^8–12 According to these studies, brain areas are functionally implicated in the regulation of eating disorders.^{13–17,18−25}

Fructose contributes to eating addiction, and fructose consumption has also been reported to be associated with increased global morbidity and mortality leading by obesity and cancer. The metabolism of dietary fructose is transported by glucose transporter type 5 (GLUT5), and then phosphorylated to form fructose 1-phosphate (F1P) in the epithelium of small intestine. When F1P accumulating to a high level, it may enhance the survival of intestinal cells and increase the length of intestinal villus in mouse models.^26–28 As villus length increases, the surface area of the gut expands, then nutrient and lipid absorption increases which results obesity in mice.

Glycolysis is an important metabolic pathway that converts glucose to pyruvate, then pyruvate enters the tricarboxylic acid (TCA) cycle in mitochondria to produce adenosine-triphosphate (ATP) through oxidative phosphorylation. The Pentose phosphate pathway (PPP) which has been reported with function of anti-reactive oxygen species (anti-ROS) is upregulated when glycolysis is inhibited. Fructose metabolism is distinct from glucose, which is uniquely metabolized to fructose 1-phosphate (F1P),¹³ then F1P can be subsequently metabolized to link with glucose metabolism pathway. Previous studies have indicated that fructose promotes hypoxic cell survival in the intestine and F1P is an important component within this process.^14–16 During the fructose metabolism, fructose-derived F1P is structurally similar to the endogenous regulator of PKM2, fructose 1,6-bisphosphate (FBP). The difference is F1P competitively binds to the active site and stabilizes PKM2 in an inactive state, which continuously downregulate glycolysis, activate PPP and alleviate ROS to improve cell survival.²⁹

Metformin is an anti-hyperglycemic drug that is beneficial in the treatment of Diabetes. Emerging evidence suggests that Metformin affects the excitability of various neurons to mediate weight-loss.³⁰ Dapagliflozin, a sodium-glucose transporter 2 inhibitor, which is a novel glucose-lowering medication for diabetes, has recently been reported to be effective in controlling body weight and reducing overweight in both human and animal obesity models, however, the mechanisms have not been completely elucidated.³¹

In our study, we designed a modified eating disorder mice model using a high fat and fructose diet and explored more detailed mechanisms through resting-state fMRI studies and metabolomics to discover better drug compatibility to help people with obesity related to eating disorder in the clinic.

Mice

All mice were purchased from Shanghai Model Organisms Center. Mice were fed with a standard laboratory diet and high fat diet (D09100310 Rodent Diets With 40 kcal% Fat-Palm Oil 20 kcal% Fructose and 2% Cholesterol) with free access to food and water. All mice were kept in a temperature (22 ± 1℃), humidity (65%-70%) controlled room, with a 12 h light-dark cycle. All mice were anesthetized by inhalation of isoflurane (1.5%-2%) and then euthanized by cervical dislocation at the end of the experiments. Only male mice were used in the study because estrogen might affect the results of the experiment.

In vivo MRI acquisition

We performed in vivo MRI measurements using a BioSpec 94/30 USR Bruker Germany, 9.4 T horizontal bore Magnetic Resonance (MR) system equipped with a 100 mT/m gradient coil (Agilent).

Anatomical MRI data

Non-uniformity correction was performed on anatomical images using n3, and brain masks were obtained by applying the Brain Extraction Tool to anatomical images, both provided by FSL (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl, version 5.0.9).

Resting-state fMRI data processing

All rs-fMRI images were corrected for subject motion using MCFLIRT, and image intensity non-uniformity correction was performed using n3 (solely for registration purposes).

Resting-state fMRI data analysis-Whole-brain connectivity maps

Rs-fMRI data were band-pass filtered between 0.01 and 0.1 Hz using 3dFourier as provided by AFNI (Functional Neuroimages Analysis, Version Debian-16.2.07, https://afni.nimh.nih.gov/).

Resting-state fMRI data analysis-ROI analyses

Functional connectivity matrices were obtained from the Fisher-transformed z’ of Pearson correlation coefficients r between ROIs using FSL and MATLAB (The MathWorks Inc., version R2014a).

Resting-state fMRI data analysis-Fractional amplitude of low-frequency fluctuations

As a measure of the magnitude of brain activity, we assessed the fractional amplitude of low-frequency fluctuations (fALFFs), calculated from the rs-fMRI BOLD signals as the ratio of the power of each frequency in the low-frequency range (0.01–0.1 Hz) to that of the entire frequency range. The fALFF has been shown more sensitive than ALFF and can also correct for physiological noise.

rCBV quantification and functional connectivity analysis

Regional fMRI responses to the medicine treatments were quantified in the form of relative CBV (rCBV) in regions of interest as Galbusera et al. previously described.

Histological analysis

Scanned H&E images of small intestine from each trial were downloaded from Histowiz as Scan Scope Virtual Slide (SVS) files and divided into separate files for duodenum, proximal jejunum, distal jejunum and ileum.

Immunohistochemistry and immunofluorescence

Immunohistochemistry was performed on formalin-fixed, paraffin-embedded tissues. TUNEL staining was performed on formalin-fixed, paraffin-embedded tissues by Histowiz. Hypoxyprobe, HP1-100Kit was used for Pimonidazole staining.

Biochemical analysis

For the measurement of hepatic triglyceride, frozen liver was measured using calorimetric assay (Stanbio). The assay kit was also used to measure serum triglyceride.

Metabolomics analysis

Samples (intestine) were isolated and analyzed by LC-MS for quantitative metabolome analysis.

Statistics Analysis

At least three independent experiments were presented as mean ± SD for each data. To determine the differences among groups at a single time point, data were tested using either 2-tailed, unpaired, Student’s t test or 1-way ANOVA followed by Tukey’s multiple comparisons test. To determine the differences groups at multiple time points, data were tested using 2-way ANOVA followed by Bonferroni’s multiple comparisons test. All analyses were performed by GraphPad Prism 8.0 software, and only differences with P-value less than 0.05 were considered statistically significant.

Comparable fMRI reactivity in Metformin and Dapagliflozin treatment mice groups

To explore the result of Metformin administration in vivo, we quantified then compared the Bold signal responses in Metformin group, control group and Dapagliflozin group (Fig. 1). Firstly, we tested the Bold signal responses of control group after glucose treatment to define the time of absorption reacted to the neural network system. Then we determine to choose 10 mins, 20 mins and 30 mins as checkpoints, depending on the change of Bold signal (Fig. 2A). The Bold signal responses across cortico-limbic regions from control group and Dapagliflozin group were observed significantly larger than Metformin groups, no matter quantified either at voxel-level or in volumes of interests (Fig. 2B). The results suggested that Metformin treatment decreased Bold signal responses among PFC, Hypo, VTA, vHPC, OFC, Thal and NAc areas compared to control and Dapagliflozin groups, so Metformin may functionally associate with the regulation of eating disorders by reducing the reward system in brain.

Metformin treatment reduced functional connectivity within cortico-limbic region

To map the network of mouse brain among the three mice groups, we performed covariance-based inter-regional rCBV mapping within representative cortical and subcortical areas using voxel-wise seed-based correlations.¹⁷ After probing the functional connectivity between regions among different groups, our analysis revealed a significant decrease in functional connectivity among PFC, Hypo, VTA, vHPC, OFC, Thal and NAc areas in Metformin treatment group compared with the other two groups. Functional connectivity mapping of Hypo showed a low connectivity to VTA, vHPC and NAc (t-test, p < 0.05, FWER cluster-corrected with p < 0.05), simultaneously, functional probing of VTA showed a low connectivity to vHPC (t-test, p < 0.05, FWER cluster-corrected with p < 0.05) and vHPC showed a low connectivity to Thal. Further seed-based mapping of the prefrontal cortex, showed foci of a significant low connectivity between this region and OFC (t-test, p < 0.05, FWER cluster-corrected, with cluster defining threshold of p < 0.05). Collectively, these results demonstrate that Metformin treatment reduced the functional connectivity between limbic and prefrontal regions caused by HFD (Fig. 3). Taken all together, our findings indicated that aberrantly strong functional connectivity associated with HFD was revoked by treatment with Metformin which beneficially affected obesity related to eating disorders.

Dapagliflozin treatment enhances intestinal cell survival and nutrient absorption

Fructose promotes IECs survive longer through altering metabolic pattern.^{26, 35–37} To determine whether the medical effects of weight-loss were related to revoke over-long intestinal villi caused by fructose, the mean intestinal villus length was quantified using a high-throughput, unbiased image-segmentation-based approach in H&E staining slides in control, Metformin and Dapagliflozin groups. After that, intestinal tissues and serum were tested through biochemical analysis. After analysis, our results showed that the length of intestinal villus in the duodenum and proximal jejunum increased in Metformin group and control group compared to Dapagliflozin group (Fig. 4) which was correlated with increased weight and lipid absorption (Fig. 5). We also performed TUNEL experiments, which showed more apoptosis in Dapagliflozin group (Fig. 6). Our results indicated that the intestinal villi of Metformin-treated group were similar to control group, however, more than twice as long as Dapagliflozin-treated group and the length of intestinal villi is associated with increased weight and lipid absorption.

Dapagliflozin treatment change metabolic pattern of intestinal cell

In our study, the quantitative metabolomics of intestinal tissue indicated that in the three groups, glycolysis, PPP and TCA were the most affected metabolic pathways. The ratio of pyruvate to phosphoenolpyruvate (PEP) which indirectly reflected the activity of PKM2 increased and F1P was also found decreased in Dapagliflozin treatment group. Simultaneously, the abundances of intermediate metabolites related to glycolysis increased, especially, the hallmark metabolite of glycolysis, lactate was obviously increased compared with the other mice groups (Fig. 7). Pimonidazole staining which indicated the hypoxic areas (O₂ less than 10mmHg within tissue) showed an increasing hypoxic area in intestinal tissues from Dapagliflozin treatment group (Fig. 8). According to above results, the metabolic pattern of IECs from isolated intestinal tissue in Dapagliflozin group has more glycolysis than other groups (Fig. 9), and the enhancement of glycolysis revoked the longer survival of IECs caused by fructose.

Obesity is a growing global health problem, more and more people die from over-weight than under-weight, so, a multidisciplinary clinic was established for the treatment of severe obesity and its related comorbidities. Current clinical weight loss methods include bariatric surgery, dietary therapy and T2D pharmacological therapy. Those qualifying patients offered bariatric surgery only achieved at average appropriate weight loss (EBWL of 10%).³⁸ Also, those patients after a year of dietary therapy, 46% did not achieve the desired weight loss. However, those patients with diabetes and obesity could benefit from the use of T2D pharmacological treatment as recommended in 2015 by the Endocrine and Obesity Society,³⁹ such as Metformin and Dapagliflozin.

Metformin is the first-line glucose-lowering medication recommended by almost all international guidelines.⁴⁰ Several studies have demonstrated that Metformin has a positive impact on body weight by reducing appetite with consequent decreased caloric intake. The impact on appetite regulation depends on the directly central effect of the drug and indirectly on its gastrointestinal side effects, such as nausea, bloating, diarrhea and dysgeusia.^30,40 Increased secretion of the weight-loss-promoting incretin glucagon-like peptide 1 (GLP-1) and the anorectic hormone peptide YY (PYY) is thought to be an neuropeptides to suppress orexigenic hypothalamic-related (AgRP) neurons.³⁷

The sodium-dependent glucose cotransporter proteins 1 and 2 (SGLT1/2) in the proximal renal tubule of the kidney regulate renal glucose reabsorption. SGLT2 transporter has been reported to induce approximatively the 90% of reabsorption, while the remaining 10% is reabsorbed by SLGT1 transporter.³⁵ Through inhibiting these co-transporters, such as Dapagliflozin, a large amount of glucose, ranging from 50 to 100 g daily, and the corresponding caloric equivalent (200–400 kcal/diet) are pharmacologically forced into urinary excretion, so the whole-body metabolism must undergo adaptive changes involving glucose fluxes, hormonal responses, fuel selection, and energy expenditure. In particular, Dapagliflozin induces an increasing in the glucagon/insulin ratio, resulting in an increased lipid mobilization. Furthermore, Dapagliflozin reduces serum leptin and increases adiponectin concentration, which are mechanisms contributing to lipolysis, weight loss and reduction of adipose accumulation in the myocardium.³⁶ Pharmacological blockade of renal glucose reabsorption reduces fasting and postprandial glycemia both acutely and chronically without causing hypoglycemia, with improvement of both β cell function and insulin sensitivity.⁴⁷ The long-term deficit of glucose induces an increase in lipid oxidation, thereby maintaining energy balance, with reduction in fat mass.³⁸

So far, there has been seldom discussion related to the dysregulation of brain reward systems in food addiction. In our study, we firstly explored whether T2D medicine treatments could help people with obesity lose weight through modulating the activation of brain reward systems in neural networks. Basing on the functional connectivity and rCBV quantification analysis, our data revealed a trend toward lower functional connectivity and rCBV quantification in Metformin group in prefrontal and limbic regions areas which might be the key for inhibit eating disorder related obesity such as PFC, Hypo, VTA, vHPC, OFC, Thal and NAc. In addition, our results also firstly indicated the effects of T2D medicine on intestinal villus length and intestinal absorption influenced by fructose. In our study, the activity of PKM2 and the abundances of intermediate metabolites associated with glycolysis was increased in Dapagliflozin group which suggesting that the metabolic pattern of IECs has more glycolysis than the control and Metformin groups. Enhanced glycolysis eliminated the longer survival of IECs caused by fructose, as increasing glycolysis inhibited the PPP pathway, which produces anti-ROS to protect IECs. Therefore, Dapagliflozin decreases intestinal villus length and reduce the surface area of the gut for absorbing nutrient and adiposity then mediate the weight-loss.

Even in our study, we have revealed a new mechanism on how Dapagliflozin and Metformin inhibit obesity, there are still no clinical data to support our explorations. Meanwhile, the effects of anesthesia on intrinsic functional connectivity and amplitude were inevitable in our study when process the fMRI.

In conclusion, our findings showed that Dapagliflozin and Metformin inhibit eating disorder related weightloss by different mechanisms, therefore, we consider that use Dapagliflozin and Metformin in combination might be more beneficial in improving eating disorder related obesity in clinical in the future. Moreover, because Dapagliflozin has a special effect on revoking more absorption induced by fructose, it may be especially useful for those obese people who are addicted to sweets.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

All protocols in this study were approved by the Committee on the Ethics of Animal Experiments of Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital (Approval No. 20011141), in compliance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (Eighth Edition).

CONFLICT OF INTEREST

None declared.

SOURCES OF FUNDING

This work was supported by the grants from the Natural Science Foundation of China (grant number 82070366), and the Key Project supported by Medical Science and Technology Development Foundation, Nanjing Department of Health (grant number ZKX20018).

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Version 1

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You are reading this latest preprint version

Distinct approaches to inhibit fructose-induced obesity

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

Mice

In vivo MRI acquisition

Anatomical MRI data

Resting-state fMRI data processing

Resting-state fMRI data analysis-Whole-brain connectivity maps

Resting-state fMRI data analysis-ROI analyses

Resting-state fMRI data analysis-Fractional amplitude of low-frequency fluctuations

rCBV quantification and functional connectivity analysis

Histological analysis

Immunohistochemistry and immunofluorescence

Biochemical analysis

Metabolomics analysis

Statistics Analysis

Results

Comparable fMRI reactivity in Metformin and Dapagliflozin treatment mice groups

Metformin treatment reduced functional connectivity within cortico-limbic region

Dapagliflozin treatment enhances intestinal cell survival and nutrient absorption

Dapagliflozin treatment change metabolic pattern of intestinal cell

Discussion

Conclusion

Declarations

References

Status:

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