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. 2024 Apr 23:18:1368552.
doi: 10.3389/fnins.2024.1368552. eCollection 2024.

Probucol mitigates high-fat diet-induced cognitive and social impairments by regulating brain redox and insulin resistance

Affiliations

Probucol mitigates high-fat diet-induced cognitive and social impairments by regulating brain redox and insulin resistance

Han-Ming Wu et al. Front Neurosci. .

Abstract

Probucol has been utilized as a cholesterol-lowering drug with antioxidative properties. However, the impact and fundamental mechanisms of probucol in obesity-related cognitive decline are unclear. In this study, male C57BL/6J mice were allocated to a normal chow diet (NCD) group or a high-fat diet (HFD) group, followed by administration of probucol to half of the mice on the HFD regimen. Subsequently, the mice were subjected to a series of behavioral assessments, alongside the measurement of metabolic and redox parameters. Notably, probucol treatment effectively alleviates cognitive and social impairments induced by HFD in mice, while exhibiting no discernible influence on mood-related behaviors. Notably, the beneficial effects of probucol arise independently of rectifying obesity or restoring systemic glucose and lipid homeostasis, as evidenced by the lack of changes in body weight, serum cholesterol levels, blood glucose, hyperinsulinemia, systemic insulin resistance, and oxidative stress. Instead, probucol could regulate the levels of nitric oxide and superoxide-generating proteins, and it could specifically alleviate HFD-induced hippocampal insulin resistance. These findings shed light on the potential role of probucol in modulating obesity-related cognitive decline and urge reevaluation of the underlying mechanisms by which probucol exerts its beneficial effects.

Keywords: high-fat diet; insulin resistance; probucol; redox homeostasis; social behavior; spatial cognition.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Probucol counteracts HFD-induced deficits in spatial and social cognition. (A) Graphic workflow of the study. C57BL/6 J mice were assigned to either the NCD group or the HFD group for 8 weeks. Subsequently, HFD-fed mice were divided into two groups. One group received drinking water supplemented with probucol, while the other HFD group and the NCD group maintained their original diet without any supplementary treatment. After 12 weeks of probucol intervention, mice were subjected to Y maze, elevated plus maze test, three-chamber social approach task, Morris water maze test, and forced swim test, sequentially. Mice were sacrificed after the behavioral tests. (B,C) Performance of mice in navigation training of the Morris swim navigation task. The training was stopped when more than 90% of mice found the platform within 30 s for two consecutive days. Data of latency to target are shown in (B) as mean ± SEM (n = 11 or 12 mice for each group, and 4 starting points per mouse). Ordinary two-way ANOVA (p < 0.0001), followed by Holm-Sidak’s multiple comparisons test. The area under the curve (AUC) of the latency to target of each mouse is illustrated in (C) as individual values with median ± 95% CI. Kruskal-Wallis test (p = 0.0003), followed by Dunn’s multiple comparisons test. (D) Representative trajectories of mice on day 4 of the navigation training showing latency to the platform. (E–G) Performance of mice in the probe trials of the Morris swim navigation task. After reaching the standard of navigation training, the probe trials were conducted on mice described in A by removing the hidden platform. Data are expressed as individual values with median ± 95% CI (n = 11 or 12 mice for each group, and 3 starting regions per mouse). Time in quadrant, ordinary one-way ANOVA (p = 0.0085), followed by Tukey’s multiple comparisons test. Mean distance to targets, ordinary one-way ANOVA (p = 0.0118), followed by Tukey’s multiple comparisons test. Target crossing number, Kruskal-Wallis test (p = 0.0204), followed by Dunn’s multiple comparisons test. (H) Representative full trajectories of mice in the probe trials of the Morris swim navigation task. (I,J) Social interaction of mice in the three-chamber social approach task. Stranger and Empty in the sociability task indicate the cage with a novel stranger male mouse and an empty cage, respectively. Data are presented as individual values with median ± 95% CI (n = 11 or 12 mice for each group). Time around cage, two-way repeated measure (RM) ANOVA (cage, p = 0.0069; group, p = 0.1689), followed by Sidak’s multiple comparisons test. Distance around cage, two-way RM ANOVA (cage, p = 0.0073; group, p = 0.0341), followed by Sidak’s multiple comparisons test. (K) Representative trajectories of HFD-fed mice and probucol treated mice in the three-chamber social approach task. p < 0.05 was indicative of statistical significance.
Figure 2
Figure 2
Probucol has no effect on anxiety and depression-like behaviors in HFD-fed mice. (A–G) Performance of mice in the elevated plus maze test. The data are expressed as individual values with median ± 95% CI (n = 11 or 12 mice for each group). Head dipping, Kruskal-Wallis test (p = 0.0044), followed by Dunn’s multiple comparisons test. Time in open arms, Kruskal-Wallis test (p = 0.0154), followed by Dunn’s multiple comparisons test. Distance in open arms, Kruskal-Wallis test (p = 0.0137), followed by Dunn’s multiple comparisons test. Entries in open arms, Kruskal-Wallis test (p = 0.0278), followed by Dunn’s multiple comparisons test. Entries in closed arms, Kruskal-Wallis test (p = 0.4304). Time in closed arms, ordinary one-way ANOVA (p = 0.1647). Distance in closed arms, ordinary one-way ANOVA (p = 0.0173), followed by Tukey’s multiple comparison. (H,I) Performance of mice in the forced swim test. The immobility duration and global activity are presented as individual values with median ± 95% CI (n = 11 or 12 mice for each group). Immobility duration, Kruskal-Wallis test (p = 0.0155), followed by Dunn’s multiple comparisons test. Global activity, Kruskal-Wallis test (p = 0.0127), followed by Dunn’s multiple comparisons test. p < 0.05 was indicative of statistical significance.
Figure 3
Figure 3
Probucol has no metabolic beneficial effects on HFD fed mice. (A) Probucol treatment has no effect on the body weight of mice. Data are presented as median ± 95% confidence interval (CI), and the slopes of the growth curves are compared by simply linear regression. (B–F) Probucol treatment has no effect on metabolic parameters of mice. The mass of gWAT and HOMA-IR of mice that received probucol for 5 weeks were analyzed (n = 7–8 mice for each group). The other parameters were analyzed for mice treated with probucol for 12 weeks (n = 12 mice for each group). Blood of mice was collected after 6 or 8 h fasting. Data are expressed as individual values with median ± 95% CI. The difference in gWAT mass between groups was compared by Brown-Forsythe ANOVA (p < 0.0001) followed by Tamhane’s T2 multiple comparisons test. Blood glucose, ordinary one-way ANOVA (p < 0.0001), followed by Tukey’s multiple comparisons test. HOMA-IR, Brown-Forsythe ANOVA (p = 0.0005), followed by Tamhane’s T2 multiple comparisons test. Total cholesterol, Kruskal-Wallis test (p = 0.0081) followed by Dunn’s multiple comparisons test. LDL-cholesterol, Kruskal-Wallis test (p < 0.0001) followed by Dunn’s multiple comparisons test. (G) Brain weights of mice. Brains dissected from mice were weighted and shown as individual values with median ± 95% CI (n = 11 or 12 mice for each group). Kruskal-Wallis test (p = 0.4531). (H) The correlations between body weights and the brain mass of HFD-fed mice and probucol treated mice. The degree of correlations was measured by Pearson’s correlation. p < 0.05 was indicative of statistical significance.
Figure 4
Figure 4
Influences of probucol feeding on mice redox status. (A,B) The levels of oxLDL (A) and MDA (B) in mice. Data are presented as individual values with median ± 95% CI (n = 7 or 8 mice per group for oxLDL, n = 11 mice per group for MDA). OxLDL, Kruskal-Wallis test (p = 0.0080), followed by Dunn’s multiple comparisons test. MDA, Kruskal-Wallis test (p < 0.0001), followed by Dunn’s multiple comparisons test. (C–F) The levels of T-GSH (C), GSH (D), GSSH (E), and the GSH:GSSH ratio (F) in mice. Data are presented as individual values with median ± 95% CI (n = 9 or 11 mice for each group). T-GSH, Kruskal-Wallis test (p = 0.0026), followed by Dunn’s multiple comparisons test. GSH, Kruskal-Wallis test (p = 0.0171), followed by Dunn’s multiple comparisons test. GSSH, Kruskal-Wallis test (p = 0.3849). GSH/GSSH, Kruskal-Wallis test (p = 0.1144). p < 0.05 was indicative of statistical significance.
Figure 5
Figure 5
Probucol alleviates hippocampal insulin resistance and differentially regulates radical species in cerebral cortex of mice. (A–F) Western blot analysis of lysates of cerebral cortex and hippocampus from male mice with or without probucol administration for 12 weeks. The levels of proteins were quantified in the left (n = 4 mice per group). Cortex iNOS, ordinary one-way ANOVA (p = 0.0296), followed by Tukey’s multiple comparisons test. Cortex NOX2, ordinary one-way ANOVA (p = 0.0099), followed by Tukey’s multiple comparisons test. Hippocampal iNOS, ordinary one-way ANOVA (p = 0.0025), followed by Tukey’s multiple comparisons test. Hippocampal NOX2, ordinary one-way ANOVA (p = 0.2109). (G–L) Western blot analysis of AKT phosphorylation and PSD95 in the brain extracts of male mice with or without probucol administration for 5 weeks (n = 4 mice per group). Cortex p-AKT, ordinary one-way ANOVA (p = 0.9565). Cortex PSD95, ordinary one-way ANOVA (p = 0.0547). Hippocampus p-AKT, ordinary one-way ANOVA (p = 0.0179), followed by Tukey’s multiple comparisons test. Hippocampus PSD95, ordinary one-way ANOVA (p = 0.0435), followed by Tukey’s multiple comparisons test. p < 0.05 was indicative of statistical significance.

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Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was financially supported by the National Key Research and Development Program of China (#2022YFA0806500), the National Natural Science Foundation of China (#31822027 and #82088102), the Fundamental Research Funds for the Central Universities (#20720210110), the Natural Science Foundation of Fujian Province of China (2021J011356), the Science and Technology Program of Xiamen (3502Z20224ZD1006) and XMU Training Program of Innovation and Entrepreneurship for Undergraduates (#2020Y1023).

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