{"paper_id":"3cb55733-5a7a-41be-a191-b34ffc22b2d6","body_text":"Intensity-Dependent Effects of Interval Training on Brain Mitophagy, Metabolic Signaling Pathways, and Neuroinflammation in a Mouse Model of Type 2 Diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intensity-Dependent Effects of Interval Training on Brain Mitophagy, Metabolic Signaling Pathways, and Neuroinflammation in a Mouse Model of Type 2 Diabetes Babak Esmealy, Kosar Zeinizadeh, Elaheh Piralaiy, Farnaz Derakhti, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8938716/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Introduction Type 2 diabetes mellitus impairs brain metabolic and mitochondrial homeostasis, yet the intensity‑dependent neuroprotective effects of exercise remain poorly defined. Objective This study examined the intensity‑dependent effects of interval training on brain mitophagy, metabolic signaling, oxidative stress, and neuroinflammation in a type 2 diabetes model. Methods In this experimental study, 50 male mice were assigned to five groups: 1) Healthy control (HC), 2) Diabetic control (DC), 3) Diabetic + low-intensity interval training (LIIT), 4) Diabetic + moderate-intensity interval training (MIIT), and 5) Diabetic + high-intensity interval training (HIIT) (n = 10 per group). Following the intervention, hippocampal and cortical tissues were analyzed for metabolic signaling markers (AMPK, ULK1, mTOR), mitophagy-related proteins (PINK1, Parkin, LC3 II/I, p62), oxidative stress and antioxidant indices (MDA, SOD, CAT, TAC), pro-inflammatory cytokines (TNF-α, IL-6, IL-1β), and lipid peroxidation (4-hydroxynonenal; 4-HNE). Statistical analyses were performed with the significance level set at p < 0.05. Results MIIT and HIIT activated AMPK–ULK1 signaling and suppressed mTOR in the hippocampus and cortex, leading to enhanced mitophagy, particularly in the hippocampus. These adaptations were accompanied by improved redox balance, reduced lipid peroxidation, and attenuated neuroinflammation, with effects increasing in an intensity‑dependent manner (LIIT < MIIT < HIIT). Conclusion Interval training induces neuroprotective adaptations in the diabetic brain in an intensity‑dependent manner. Moderate‑ and high‑intensity protocols more effectively activate AMPK‑driven mitophagy, suppress neuroinflammation, and reduce lipid peroxidation than low‑intensity training, highlighting exercise intensity as a key determinant of brain metabolic resilience in type 2 diabetes. Interval training Exercise intensity Mitophagy Neuroinflammation Type 2 diabetes Brain metabolism Figures Figure 1 1. Introduction Type 2 diabetes mellitus (T2DM), traditionally considered a chronic metabolic disorder, is now increasingly recognized as a multisystem disease with significant neurological consequences. Clinical and experimental evidence published since 2020 indicates that T2DM can induce central insulin resistance, disrupt cerebral energy metabolism, increase oxidative stress, and activate inflammatory signaling pathways, thereby promoting structural and functional changes in the brain and raising the risk of cognitive impairment and accelerated neurodegenerative processes(Zilliox et al 2016 ; Srikanth et al 2020 ). The brain is especially vulnerable to diabetes-induced metabolic disturbances because of its high energy demand and strong reliance on mitochondrial function. Recent studies have shown that the diabetic brain has impaired oxidative phosphorylation efficiency, increased electron leakage, and accumulation of dysfunctional mitochondria – changes that may compromise synaptic function and reduce neural plasticity (Petersen and Shulman 2018 ; Ye et al 2021 ). In this context, maintaining mitochondrial quality control has become a critical factor for neuronal health. Mitophagy, a selective autophagic process that removes damaged mitochondria, plays a fundamental role in maintaining mitochondrial homeostasis and preventing neuronal injury. Accumulating evidence indicates that T2DM is associated with impairments in PINK1/Parkin-dependent mitophagy pathways and dysregulated expression of key autophagy-related proteins such as LC3 and p62 in the central nervous system. These disruptions may lead to the accumulation of defective mitochondria and increased oxidative stress. Impaired mitophagy has therefore been proposed as an important molecular link between diabetes, neuroinflammation, and cognitive decline(Palikaras et al 2018 ; Ye et al 2021 ). In addition to mitochondrial dysfunction, alterations in metabolic signaling pathways contribute significantly to the pathophysiology of the diabetic brain. Reduced AMPK activity and relative upregulation of mTOR signaling have been reported in specific brain regions under diabetic conditions, potentially suppressing autophagic processes, limiting mitochondrial biogenesis, and increasing neuronal vulnerability(Herzig and Shaw 2018 ; Moctezuma and Molinas 2020 ). These signaling abnormalities often coincide with activation of inflammatory pathways, including NF-κB and the NLRP3 inflammasome, further reinforcing chronic neuroinflammation in T2DM(Baeeri et al 2020 ). Physical activity has emerged as an effective non-pharmacological intervention for improving brain health under adverse metabolic conditions. In particular, interval training has received increasing attention for its strong ability to activate AMPK–PGC-1α signaling, enhance mitochondrial function, and stimulate autophagy and mitophagy processes(Steiner et al 2011 ; Wrann et al 2013 ). Animal studies have shown that interval training can upregulate PGC-1α expression and mitochondrial biogenesis–related factors in brain tissue, thereby improving neuronal metabolic efficiency (MacInnis and Gibala 2017 ; Abedpoor and Hajibabaie 2024 ). Moreover, growing evidence suggests that exercise can attenuate neuroinflammation and provide neuroprotection in diabetes and other metabolic disorders by inhibiting inflammatory pathways such as NF-κB and the NLRP3 inflammasome and by reducing microglial activation(Radak et al 2016 ; Mee-Inta et al 2019 ; Lee et al 2020 ). Notably, the molecular responses of the brain to exercise appear to be largely intensity dependent, while training pattern and volume may modulate the magnitude of these adaptations (Steiner et al 2011 ; Meeusen et al 2013 ; Herzig and Shaw 2018 ; Mee-Inta et al 2019 ). Despite accumulating evidence, the role of exercise intensity as a primary determinant of the direction and nature of brain molecular adaptations to interval training remains incompletely understood. Most previous studies have either examined a single exercise intensity or reported exercise-induced brain effects without clearly distinguishing intensity from other training variables such as volume and structure. Furthermore, findings regarding the dose–response relationship between exercise intensity and neural adaptations remain inconsistent and, in some cases, contradictory. While several studies identify high-intensity interval training (HIIT) as a potent stimulus for activating metabolic and mitophagy-related pathways, others suggest that excessively high intensities may overwhelm antioxidant defense capacity under certain pathological conditions, thereby exacerbating oxidative stress and potentially impairing neuroprotective mechanisms and mitochondrial quality control in the brain(Meeusen et al 2013 ; Herzig and Shaw 2018 ; Palikaras et al 2018 ). In contrast, moderate-intensity exercise has been proposed to elicit more sustainable anti-inflammatory responses and more effective suppression of microglial activation by achieving a more favorable balance between metabolic stress and cellular recovery capacity(Meeusen et al 2013 ; Lee et al 2020 ). Collectively, these findings highlight exercise intensity as a critical yet underexplored determinant of neurometabolic adaptations in T2DM. It also remains unclear whether brain responses to exercise intensity follow a linear pattern or exhibit an inverted U-shaped relationship. Therefore, the present study was designed to determine the optimal intensity of interval training for enhancing mitophagy-supporting pathways and suppressing neuroinflammation in a mouse model of type 2 diabetes, with the aim of providing more precise evidence for exercise prescription in neurometabolic disorders. 2. Materials and Methods 2.1.1. Animals and Experimental Design This controlled experimental study was designed to investigate the intensity‑dependent effects of interval training on brain mitophagy, metabolic signaling pathways, and neuroinflammatory responses in a rodent model of T2DM. Following confirmation of diabetes induction, animals were randomly assigned to five experimental groups (n=10 per group; total N=50). Due to the invasive nature of brain tissue collection, baseline and endpoint assessments were conducted on separate subsets of animals. Baseline samples were collected before the training intervention to establish reference values, while endpoint measurements were taken after completion of the training protocol. Although distinct animals were evaluated at each time point, all subjects originated from the same cohort and were carefully matched for age, body weight, and housing conditions to ensure intergroup comparability (figure1). 2.1.2. Experimental Groups 1) Healthy control (HC) 2) Diabetic control (DC) 3) Diabetic + low‑intensity interval training (LIIT) 4) Diabetic + moderate‑intensity interval training (MIIT) 5) Diabetic + high‑intensity interval training (HIIT) 2.1.3. Animals and Ethical Approval A total of 50 male Wistar rats (8 weeks old, body weight 200–250 g) were obtained from the Animal House of Urmia University. The animals were housed under standard laboratory conditions (temperature 22 ± 2 °C, relative humidity 55 ± 5%, 12:12 h light–dark cycle) with ad libitum access to standard chow and water. All experimental protocols were performed in accordance with ethical guidelines for the care and use of laboratory animals and were approved by the institutional ethics committee of Tabriz University of Medical Sciences (Approval code: IR.TBARIZU.REC.1404.211). 2.1.4. Induction of T2DM 2.1.4.1. Animal Acclimatization and Dietary Protocol Mice were initially housed under standard laboratory conditions for a two-week acclimatization period, maintained solely on a conventional dry pellet diet. Subsequently, the animals were transitioned into a pre-diabetic phase lasting four weeks. This phase involved the administration of a customized High-Fat Diet (HFD) formulated to contain 60% fat, 20% protein, and 20% carbohydrate by mass. During this period, daily HFD intake was progressively titrated from 20 to 25 g per animal. The HFD was procured from Royan Biotechnology Company (Isfahan, Iran), operating under the regulatory oversight of the Iranian Food and Drug Administration. 2.1.4.2. Diabetic Induction and Confirmation T2DM was chemically induced via a single intraperitoneal (i.p.) injection of streptozotocin (STZ; 35 mg/kg body weight), prepared by dissolving the compound in a citrate buffer solution adjusted to pH 4.5. Diabetes induction success was confirmed 72 hours post-injection by measuring fasting blood glucose (FBG) levels. Animals exhibiting FBG values of ≥250 mg/dL were categorized as successfully diabetic and were enrolled in the subsequent experimental groups. FBG levels were continuously monitored on a weekly basis throughout the study duration. 2.2. Measurements 2.2.1. Brain Metabolic Signaling Markers Following rapid euthanasia, the hippocampus and cerebral cortex were immediately dissected, snap-frozen in liquid nitrogen, and stored at −80 °C. Tissue homogenates were prepared using RIPA lysis buffer supplemented with commercial protease and phosphatase inhibitor cocktails. Protein levels of total and phosphorylated forms of AMPK, mTOR, and ULK1 were quantified by Western blotting. Equal amounts of total protein were separated on 10% SDS-PAGE gels and electrotransferred onto PVDF membranes. Primary antibodies specific to the target proteins (all from Cell Signaling Technology, USA) were applied, followed by incubation with appropriate HRP-conjugated secondary antibodies. Signals were detected using enhanced chemiluminescence (ECL) substrate and digitized. Densitometric analysis for calculating phosphorylated-to-total protein ratios was performed using ImageJ/Fiji. Data normalization used β-actin for hippocampal samples and GAPDH for cortical samples. 2.2.2. Brain Mitophagy Markers Mitochondrial quality control markers were assessed in hippocampal and cortical homogenates (prepared as described in Section 2.3.1 and re-aliquoted). The NAD+/NADH ratio, an index of mitochondrial redox status, was quantified using a colorimetric assay kit according to the manufacturer’s instructions (Abcam, UK). Mitophagy pathway activation was evaluated by Western blotting (antibodies from Cell Signaling Technology, USA) for PINK1, Parkin, LC3 II/LC3 I ratio, and p62/SQSTM1 expression. Furthermore, GDF15 protein expression, as a marker of mitochondrial stress, was determined in brain tissue using a rat-specific ELISA kit (R&D Systems, USA). 2.2.3. Brain Tissue Oxidative Stress Markers Oxidative stress markers were quantified in hippocampal and cortical tissue homogenates (prepared as described in Section 2.3.1 and re-aliquoted). Malondialdehyde (MDA) levels, as an index of lipid peroxidation, were measured using the TBARS method with a commercial kit (ZellBio, Germany). The enzymatic activities of Superoxide Dismutase (SOD) and Catalase (CAT) were determined using enzymatic assay kits (Cayman Chemical, USA). Total Antioxidant Capacity (TAC) was assessed via the FRAP method utilizing a commercial kit (ZellBio, Germany). 2.2.4. Brain Tissue Neuroinflammatory Markers To directly assess neuroinflammation, concentrations of inflammatory cytokines were measured in hippocampal and cortical tissue homogenates (prepared as described in Section 2.3.1 and re-aliquoted). Levels of tumor necrosis factor‑α (TNF‑α), interleukin‑6 (IL‑6), and interleukin‑1β (IL‑1β) were quantified using rat‑specific ELISA kits (R&D Systems, USA).These cytokines were considered key indicators of activation of brain inflammatory pathways and diabetes‑ and exercise intensity–dependent neuroinflammatory responses. 2.2.5. Brain Tissue Lipid Peroxidation Markers Given the high susceptibility of brain membrane lipids to oxidative damage, lipid peroxidation was assessed in hippocampal and cortical tissue homogenates (prepared as described in Section 2.3.1 and re-aliquoted). Levels of 4-hydroxynonenal (4-HNE), a major end product of lipid peroxidation, were measured using an ELISA kit (Abcam, UK). 2.3. Training Protocol 2.3.1. Treadmill Familiarization Before the main training protocol, animals underwent a one-week treadmill familiarization period. This phase included daily running sessions lasting 10–15 minutes at a speed of 10 m/min with 0% incline. The purpose of this phase was to habituate the animals to the treadmill apparatus and minimize stress during the intervention period. 2.3.2. Main Training Protocol The training intervention lasted eight weeks, with subjects completing five training sessions per week (Monday to Friday); Saturday and Sunday were designated as consecutive rest days. Each standardized session comprised a 5-minute warm-up at a low speed (5 m/min) followed by the primary exercise protocol, concluding with a 5-minute cool-down period. The total duration of each session ranged from 45 to 60 minutes. All treadmill protocols were executed at a 0% incline to eliminate the confounding effects of grade on exercise intensity parameters. The main exercise protocol consisted of 10 repeating intervals (for a total of 10 work-to-rest cycles) maintaining a strict 1:1 work-to-rest ratio. The animals were underwent to a graded treadmill exercise test starting at a speed of 5 m·min⁻¹, with increments of 5 m·min⁻¹ every 3 minutes until each rat reached its maximal running capacity (Vmax)(Melo et al 2003). The individual Vmax values with corresponding treadmill speeds were subsequently used to prescribe exercise intensities for the training protocols: Low‑intensity interval training (LIIT): Subjects performed work intervals at 50–60% Vmax (corresponding to 12–15 m/min) followed by active recovery intervals at 30–40% Vmax (8–10 m/min). Moderate‑intensity interval training (MIIT): Work intervals were set at 65–75% Vmax (16–20 m/min), with recovery intervals conducted at 40–50% Vmax (11–14 m/min). High‑intensity interval training (HIIT): Subjects exercised during the work phase at 80–90% Vmax (21–26 m/min), followed by recovery intervals at 50–60% Vmax (14–17 m/min). 2.4. Statistical Analysis Statistical analyses were performed using SPSS software (version 16.0; IBM, USA). The normality of data distribution was assessed using the Shapiro–Wilk test, and homogeneity of variances was evaluated with Levene’s test. Baseline samples were obtained from matched animals before the intervention, while post-intervention samples were collected 48 hours after the final training session. Therefore, a two-way analysis of variance (ANOVA) with independent samples was used to examine the main effects of time (baseline vs. end-point), group (CS, DC, LIIT, MIIT, and HIIT), and the time × group interaction for all dependent variables. When significant main effects or interactions were observed, post hoc comparisons were conducted using the Bonferroni correction. Data are presented as mean ± standard deviation (mean ± SD), and statistical significance was set at p < 0.05. 3. Results Table 1 Intensity-Dependent Effects of Interval Training on AMPK/mTOR Metabolic Signaling in the Hippocampus of a T2DM Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group p-AMPK / AMPK HC 1.00 ± 0.08 1.02 ± 0.09 0.41 — < 0.001 DC 0.62 ± 0.07 0.60 ± 0.06 0.53 Ref LIIT 0.64 ± 0.08 0.78 ± 0.09 0.032 0.18 MIIT 0.63 ± 0.07 0.95 ± 0.10 0.004 0.007 HIIT 0.61 ± 0.06 1.12 ± 0.11 < 0.001 < 0.001 p-mTOR / mTOR HC 1.00 ± 0.09 0.98 ± 0.08 0.47 — < 0.001 DC 1.46 ± 0.12 1.48 ± 0.13 0.58 Ref LIIT 1.44 ± 0.11 1.25 ± 0.10 0.041 0.21 MIIT 1.47 ± 0.13 1.05 ± 0.09 0.006 0.004 HIIT 1.45 ± 0.12 0.88 ± 0.08 < 0.001 < 0.001 p-ULK1 / ULK1 HC 1.00 ± 0.07 1.01 ± 0.08 0.62 — < 0.001 DC 0.58 ± 0.06 0.57 ± 0.07 0.67 Ref LIIT 0.60 ± 0.07 0.74 ± 0.08 0.038 0.29 MIIT 0.59 ± 0.06 0.92 ± 0.09 0.003 0.006 HIIT 0.57 ± 0.05 1.08 ± 0.10 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group. Table 2 Intensity-Dependent Effects of Interval Training on AMPK/mTOR Metabolic Signaling in the Cerebral Cortex of a T2DM Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group p-AMPK/ AMPK HC 1.00 ± 0.09 1.01 ± 0.10 0.56 — < 0.001 DC 0.68 ± 0.08 0.66 ± 0.07 0.49 Ref LIIT 0.69 ± 0.09 0.80 ± 0.10 0.044 0.22 MIIT 0.67 ± 0.08 0.90 ± 0.11 0.006 0.012 HIIT 0.66 ± 0.07 1.02 ± 0.12 < 0.001 < 0.001 p-mTOR/ mTOR HC 1.00 ± 0.10 0.99 ± 0.09 0.61 — < 0.001 DC 1.38 ± 0.14 1.40 ± 0.15 0.52 Ref LIIT 1.36 ± 0.13 1.22 ± 0.12 0.047 0.26 MIIT 1.39 ± 0.14 1.08 ± 0.11 0.008 0.009 HIIT 1.37 ± 0.13 0.96 ± 0.10 < 0.001 < 0.001 p-ULK1/ ULK1 HC 1.00 ± 0.08 1.02 ± 0.09 0.58 — < 0.001 DC 0.65 ± 0.07 0.64 ± 0.08 0.63 Ref LIIT 0.66 ± 0.08 0.76 ± 0.09 0.049 0.31 MIIT 0.64 ± 0.07 0.88 ± 0.10 0.005 0.010 HIIT 0.63 ± 0.06 1.00 ± 0.11 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group. As shown in Tables 1 and 2 , at baseline, diabetic animals exhibited significantly reduced AMPK phosphorylation and ULK1 activation, along with elevated mTOR signaling in both brain regions compared with HC, confirming that diabetes induced impairment of energy-sensing pathways. No significant changes were observed between baseline and endpoint measurements in DC. Exercise training induced clear, intensity-dependent improvements in metabolic signaling. MIIT and HIIT significantly increased p-AMPK/AMPK and p-ULK1/ULK1 ratios while suppressing p-mTOR/mTOR compared with DC at endpoint (p < 0.05–0.001). LIIT produced modest between time improvements that did not result in significant between-group differences. These adaptations were consistently more pronounced in the hippocampus than in the cerebral cortex, with HIIT eliciting the largest response. Table 3 Intensity-Dependent Effects of Interval Training on Mitophagy-Related Markers in the Hippocampus of a T2DM Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group PINK1 (AU) HC 1.00 ± 0.08 1.01 ± 0.09 0.62 — < 0.001 DC 0.54 ± 0.07 0.52 ± 0.07 0.59 Ref LIIT 0.55 ± 0.08 0.63 ± 0.08 0.047 0.27 MIIT 0.56 ± 0.07 0.82 ± 0.09 0.004 0.006 HIIT 0.57 ± 0.08 1.05 ± 0.10 < 0.001 < 0.001 Parkin (AU) HC 1.00 ± 0.09 1.02 ± 0.10 0.58 — < 0.001 DC 0.57 ± 0.06 0.55 ± 0.06 0.61 Ref LIIT 0.58 ± 0.07 0.66 ± 0.07 0.043 0.29 MIIT 0.59 ± 0.08 0.85 ± 0.08 0.003 0.005 HIIT 0.60 ± 0.07 1.08 ± 0.11 < 0.001 < 0.001 LC3-II / LC3-I HC 1.72 ± 0.17 1.74 ± 0.16 0.66 — < 0.001 DC 0.90 ± 0.10 0.88 ± 0.09 0.59 Ref LIIT 0.92 ± 0.11 1.05 ± 0.10 0.039 0.21 MIIT 0.93 ± 0.10 1.34 ± 0.12 0.005 0.004 HIIT 0.94 ± 0.11 1.79 ± 0.17 < 0.001 < 0.001 p62 (AU) HC 0.93 ± 0.08 0.92 ± 0.08 0.68 — < 0.001 DC 1.56 ± 0.14 1.58 ± 0.13 0.61 Ref LIIT 1.54 ± 0.13 1.44 ± 0.12 0.046 0.29 MIIT 1.55 ± 0.12 1.18 ± 0.10 0.007 0.009 HIIT 1.56 ± 0.13 0.94 ± 0.09 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group. Table 4 Intensity-Dependent Effects of Interval Training on Mitophagy-Related Markers in the Cerebral Cortex of a T2DM Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group PINK1 (AU) HC 0.98 ± 0.09 1.00 ± 0.09 0.69 — < 0.001 DC 0.62 ± 0.08 0.60 ± 0.08 0.61 Ref LIIT 0.63 ± 0.09 0.69 ± 0.08 0.048 0.28 MIIT 0.64 ± 0.08 0.83 ± 0.09 0.006 0.007 HIIT 0.65 ± 0.09 0.96 ± 0.10 < 0.001 < 0.001 Parkin (AU) HC 0.99 ± 0.08 1.01 ± 0.09 0.64 — < 0.001 DC 0.63 ± 0.07 0.62 ± 0.07 0.60 Ref LIIT 0.64 ± 0.08 0.71 ± 0.08 0.045 0.30 MIIT 0.65 ± 0.07 0.86 ± 0.09 0.005 0.008 HIIT 0.66 ± 0.08 0.98 ± 0.10 < 0.001 < 0.001 LC3-II / LC3-I HC 1.58 ± 0.16 1.60 ± 0.15 0.65 — < 0.001 DC 0.97 ± 0.11 0.95 ± 0.10 0.58 Ref LIIT 0.99 ± 0.11 1.08 ± 0.11 0.042 0.22 MIIT 1.00 ± 0.10 1.28 ± 0.12 0.007 0.008 HIIT 1.01 ± 0.11 1.52 ± 0.16 < 0.001 < 0.001 p62 (AU) HC 0.99 ± 0.09 0.98 ± 0.09 0.67 — < 0.001 DC 1.46 ± 0.13 1.48 ± 0.12 0.60 Ref LIIT 1.45 ± 0.12 1.38 ± 0.11 0.047 0.30 MIIT 1.44 ± 0.12 1.22 ± 0.10 0.008 0.010 HIIT 1.46 ± 0.13 1.02 ± 0.09 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group. Tables 3 and 4 show that diabetes significantly suppressed key components of the PINK1/Parkin-dependent mitophagy pathway, as indicated by reduced PINK1 and Parkin expression, a lower LC3 II/LC3 I ratio, and accumulation of p62 in both the hippocampus and cortex. No spontaneous recovery was observed in DC over time. Interval training restored mitophagy signaling in an intensity-dependent manner. MIIT and HIIT significantly increased PINK1 and Parkin expression, enhanced LC3 lipidation, and reduced p62 levels compared with DC (p < 0.05–0.001). In contrast, LIIT induced only mild changes from baseline to endpoint that were not significantly different from DC. The hippocampus showed greater sensitivity to exercise-induced mitophagy activation than the cortex, particularly in response to HIIT. Table 5 Intensity-Dependent Effects of Interval Training on Oxidative Stress and Antioxidant Defense in the Hippocampus of a T2DM Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group MDA (nmol/mg protein) HC 2.10 ± 0.30 2.12 ± 0.28 0.61 — < 0.001 DC 4.75 ± 0.55 4.82 ± 0.58 0.54 Ref LIIT 4.68 ± 0.52 4.10 ± 0.48 0.041 0.19 MIIT 4.70 ± 0.50 3.02 ± 0.41 0.003 0.006 HIIT 4.73 ± 0.56 2.42 ± 0.35 < 0.001 < 0.001 SOD (U/mg protein) HC 18.4 ± 2.0 18.6 ± 2.1 0.58 — < 0.001 DC 9.4 ± 1.2 9.2 ± 1.3 0.49 Ref LIIT 9.6 ± 1.3 10.8 ± 1.4 0.039 0.22 MIIT 9.5 ± 1.2 14.7 ± 1.6 0.002 0.008 HIIT 9.3 ± 1.1 17.2 ± 1.8 < 0.001 < 0.001 CAT (U/mg protein) HC 42.1 ± 4.3 42.5 ± 4.6 0.63 — < 0.001 DC 22.0 ± 3.2 21.6 ± 3.0 0.57 Ref LIIT 22.4 ± 3.1 24.7 ± 3.5 0.044 0.24 MIIT 22.1 ± 3.0 33.9 ± 3.8 0.004 0.005 HIIT 22.3 ± 3.3 39.7 ± 4.1 < 0.001 < 0.001 TAC (µmol Fe²⁺/mg protein) HC 1.60 ± 0.17 1.63 ± 0.18 0.55 — < 0.001 DC 0.80 ± 0.10 0.78 ± 0.09 0.51 Ref LIIT 0.82 ± 0.11 0.93 ± 0.12 0.038 0.27 MIIT 0.81 ± 0.10 1.29 ± 0.15 0.003 0.007 HIIT 0.79 ± 0.09 1.52 ± 0.16 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group. Table 6 Intensity-Dependent Effects of Interval Training on Oxidative Stress and Antioxidant Defense in the Cerebral Cortex of a T2DM Diabetic Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group MDA (nmol/mg protein) HC 2.38 ± 0.29 2.40 ± 0.31 0.64 — < 0.001 DC 4.30 ± 0.48 4.34 ± 0.51 0.56 Ref LIIT 4.28 ± 0.46 3.90 ± 0.44 0.043 0.23 MIIT 4.29 ± 0.49 3.32 ± 0.41 0.006 0.018 HIIT 4.31 ± 0.50 2.70 ± 0.34 < 0.001 < 0.001 SOD (U/mg protein) HC 16.8 ± 1.7 17.0 ± 1.8 0.59 — < 0.001 DC 10.5 ± 1.3 10.4 ± 1.2 0.52 Ref LIIT 10.6 ± 1.4 11.6 ± 1.3 0.041 0.26 MIIT 10.4 ± 1.2 13.9 ± 1.6 0.005 0.022 HIIT 10.3 ± 1.1 15.8 ± 1.7 < 0.001 < 0.001 CAT (U/mg protein) HC 38.5 ± 4.0 38.9 ± 4.2 0.61 — < 0.001 DC 25.1 ± 3.4 24.9 ± 3.3 0.55 Ref LIIT 25.3 ± 3.2 26.8 ± 3.5 0.047 0.28 MIIT 25.0 ± 3.1 31.4 ± 3.8 0.006 0.019 HIIT 24.8 ± 3.2 36.2 ± 4.0 < 0.001 < 0.001 TAC (µmol Fe²⁺/mg protein) HC 1.46 ± 0.15 1.48 ± 0.16 0.57 — < 0.001 DC 0.87 ± 0.11 0.86 ± 0.10 0.53 Ref LIIT 0.88 ± 0.12 0.98 ± 0.12 0.042 0.25 MIIT 0.86 ± 0.11 1.18 ± 0.14 0.005 0.021 HIIT 0.85 ± 0.10 1.38 ± 0.15 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group. As shown in Tables 5 and 6 , diabetic animals exhibited significant oxidative imbalance, with elevated malondialdehyde (MDA) levels and reduced activities of superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (TAC) in both brain regions. These changes persisted in DC over time. Exercise training significantly improved redox homeostasis in an intensity-dependent manner. MIIT and HIIT significantly reduced MDA concentrations and increased SOD, CAT, and TAC at the endpoint compared with DC (p < 0.05–0.001). LIIT produced only modest improvements that were not statistically significant between groups. Consistent with metabolic and mitophagy findings, antioxidant responses were more pronounced in the hippocampus than in the cerebral cortex. Table 7 Intensity-Dependent Effects of Interval Training on Neuroinflammatory Cytokines in the Hippocampus of a T2DM Diabetic Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group TNF-α (pg/mg protein) HC 18.4 ± 2.1 18.1 ± 2.0 0.71 – < 0.001 DC 32.6 ± 3.4 33.1 ± 3.6 0.62 Ref LIIT 31.9 ± 3.2 28.7 ± 3.0 0.041 0.48 MIIT 32.4 ± 3.5 23.6 ± 2.8 < 0.001 0.003 HIIT 32.1 ± 3.3 20.4 ± 2.5 < 0.001 < 0.001 IL-6 (pg/mg protein) HC 14.7 ± 1.8 14.5 ± 1.7 0.76 – < 0.001 DC 26.8 ± 2.9 27.2 ± 3.0 0.58 Ref LIIT 26.3 ± 2.7 23.9 ± 2.6 0.048 0.44 MIIT 27.1 ± 3.0 19.8 ± 2.4 < 0.001 0.002 HIIT 26.9 ± 2.8 17.2 ± 2.1 < 0.001 < 0.001 IL-1β (pg/mg protein) HC 11.9 ± 1.4 11.7 ± 1.3 0.81 – < 0.001 DC 24.5 ± 2.6 25.1 ± 2.7 0.55 Ref LIIT 24.1 ± 2.4 21.8 ± 2.3 0.039 0.51 MIIT 24.8 ± 2.7 18.6 ± 2.2 < 0.001 0.004 HIIT 24.6 ± 2.5 15.9 ± 2.0 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group Table 8 Intensity-Dependent Effects of Interval Training on Neuroinflammatory Cytokines in the Cerebral Cortex of a T2DM Diabetic Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group TNF-α (pg/mg protein) HC 16.9 ± 2.0 16.7 ± 1.9 0.83 – < 0.001 DC 28.4 ± 3.1 28.9 ± 3.3 0.61 Ref LIIT 27.9 ± 3.0 26.1 ± 2.8 0.092 0.57 MIIT 28.6 ± 3.2 22.9 ± 2.6 < 0.001 0.006 HIIT 28.2 ± 3.1 21.4 ± 2.4 < 0.001 0.002 IL-6 (pg/mg protein) HC 13.6 ± 1.6 13.4 ± 1.5 0.79 – < 0.001 DC 23.9 ± 2.7 24.3 ± 2.8 0.64 Ref LIIT 23.5 ± 2.6 21.9 ± 2.4 0.081 0.49 MIIT 24.1 ± 2.8 19.6 ± 2.3 < 0.001 0.009 HIIT 23.8 ± 2.7 18.3 ± 2.2 < 0.001 0.003 vs DC IL-1β (pg/mg protein) HC 10.8 ± 1.3 10.6 ± 1.2 0.85 – < 0.001 DC 21.7 ± 2.4 22.1 ± 2.5 0.59 Ref LIIT 21.3 ± 2.3 19.8 ± 2.2 0.074 0.53 MIIT 22.0 ± 2.5 17.9 ± 2.1 < 0.001 0.011 HIIT 21.8 ± 2.4 16.5 ± 2.0 < 0.001 0.004 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group As shown in Tables 7 and 8 , baseline levels of pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β) were significantly elevated in diabetic animals compared to HC in both examined brain regions, indicating pronounced diabetes-associated neuroinflammation. No significant temporal changes were detected in DC. Exercise training attenuated neuroinflammatory responses in an intensity-dependent manner. MIIT and HIIT significantly reduced all measured cytokines compared with DC at the end point (p < 0.05–0.001), whereas LIIT induced smaller and mostly non-significant between-group changes. The magnitude of cytokine reduction was slightly greater in the hippocampus than in the cortex, with HIIT consistently producing the strongest anti-inflammatory effect. Table 9 Intensity-Dependent Effects of Interval Training on Lipid Peroxidation–Derived Aldehydes (4-HNE) in the Hippocampus of a T2DM Diabetic Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group Hydroxynonenal (4-HNE) HC 1.92 ± 0.26 1.89 ± 0.24 0.61 — < 0.01 DC 4.21 ± 0.39 4.28 ± 0.41 0.44 Ref LIIT 4.18 ± 0.37 3.89 ± 0.35 0.047 0.21 MIIT 4.25 ± 0.40 3.21 ± 0.31 0.005 0.006 HIIT 4.19 ± 0.38 2.74 ± 0.29 < 0.001 < 0.001 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group Table 10 Intensity-Dependent Effects of Interval Training on Lipid Peroxidation–Derived Aldehydes (4-HNE) in the Cerebral Cortex of a T2DM Diabetic Mouse Model Variable Group Baseline End-point Baseline vs End-point (between-time) p Between-group (End-point vs. DC) p Time × Group Hydroxynonenal (4-HNE) HC 1.83 ± 0.22 1.81 ± 0.21 0.63 — < 0.05 DC 3.74 ± 0.36 3.81 ± 0.38 0.42 Ref LIIT 3.71 ± 0.34 3.53 ± 0.33 0.048 0.24 MIIT 3.77 ± 0.35 3.01 ± 0.30 0.008 0.041 HIIT 3.73 ± 0.36 2.69 ± 0.28 < 0.001 0.009 Healthy control: HC | DC: Diabetic Control | LIIT: Diabetic + low‑intensity interval training | MIIT: Diabetic + moderate‑intensity interval training | HIIT: Diabetic + high‑intensity interval training Data are presented as mean ± SD. Statistical analysis was performed using two-way ANOVA with independent samples (time × group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp² = 0.48–0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group *The healthy control (CS) group was included solely to confirm the validity of the diabetic model and to serve as a physiological reference. Its pre- and post-intervention data are reported for completeness and biological context and were not considered in the primary statistical analyses. As shown in Tables 9 and 10 , levels of 4-hydroxynonenal (4-HNE), a marker of lipid peroxidation-induced cellular damage, were markedly elevated in diabetic animals compared with HC in both brain regions and remained unchanged in DC over time. Exercise training significantly reduced 4-HNE accumulation in an intensity-dependent manner. MIIT and HIIT produced significant reductions in hippocampal 4-HNE compared with DC (p < 0.05–0.001), whereas LIIT exerted only modest effects. Similar but less pronounced reductions were observed in the cerebral cortex. Overall, the hippocampus exhibited greater responsiveness to exercise-induced attenuation of lipid peroxidation. 4. Discussion 4.1. Brain Metabolic Signaling Pathways The present study demonstrated that moderate and high intensity interval training (MIIT and HIIT) significantly activated the AMPK and ULK1 pathways while concurrently suppressing mTOR signaling in the hippocampus and cortex of diabetic mice, whereas low intensity interval training induced only limited changes. This pattern clearly indicates an intensity-dependent regulation of brain metabolic responses and supports the central hypothesis that exercise intensity is a key determinant of metabolic adaptations in the diabetic brain. These findings are consistent with previous reports. Hardie et al., have shown that AMPK activation in neural tissues occurs primarily under conditions of sufficient energetic stress, and that low intensity exercise is often insufficient to reach this metabolic threshold (Hardie et al 2012 ; Hardie 2014 ). Similarly, Camacho et al. reported that higher-intensity exercise exerts more pronounced inhibitory effects on mTOR signaling and leads to more effective improvements in energy homeostasis in both neural and non-neural tissues compared with mild exercise (Bolster et al 2002 ; Murphy 2017 ). In contrast, some studies, such as those by Zhang et al., have suggested that even low-intensity aerobic exercise may activate AMPK in the brain (Barón-Mendoza et al 2019 ). However, important methodological differences distinguish those studies from the present work, including shorter intervention durations, the absence of a stable T2DM model, and the assessment of only a single brain region. These factors may account for the discrepancies in reported outcomes. Mechanistically, higher exercise intensities are more likely to cause a pronounced reduction in the ATP/AMP ratio and enhance metabolic stress signaling, leading to stronger AMPK activation and the initiation of adaptive cellular cascades(Hardie et al 2012 ; Hardie 2014 ). In the context of diabetes, where brain metabolic flexibility is markedly impaired, only moderate- to high-intensity exercise appears capable of surpassing this regulatory threshold (Steinberg and Kemp 2009 ; Triebel et al 2022 ). 4.2. Regulation of Mitophagy in the Hippocampus and Cortex In the present study, increased expression of PINK1 and Parkin, a higher LC3 II/I ratio, and reduced p62 levels collectively indicate enhanced mitophagy, with more pronounced effects in the hippocampus. These findings are consistent with existing evidence of impaired mitophagy and compromised mitochondrial quality control in the diabetic brain. Although direct evidence linking exercise-induced mitophagy to neuroprotection in diabetic models remains limited, accumulating data suggest that exercise can significantly improve mitochondrial function and neuronal resilience under diabetic conditions. These observations align with studies such as those by He et al., who reported that higher-intensity exercise can ameliorate mitophagy impairments induced by diabetes or metabolic stress (He et al 2012 ). Moreover, AMPK activation has been shown to play a central role in the induction of PINK1/Parkin-dependent mitophagy, which agrees with the metabolic signaling findings of the present study(Egan et al 2011 ; Kim et al 2011 ). Conversely, some investigations, including those by Laker et al., have reported that mild exercise may also enhance mitophagy (Kim et al 2011 ). However, these studies were conducted primarily in non-diabetic models, where baseline mitochondrial dysfunction is less severe and responsiveness to milder stimuli is preserved (Montgomery and Turner 2015 ). From a mechanistic perspective, diabetes is associated with the accumulation of damaged mitochondria and disruption of mitochondrial clearance systems (Montgomery and Turner 2015 ; Palikaras et al 2018 ). Higher intensity exercise likely provides the necessary threshold for robust mitophagy activation through increased physiological ROS signaling and engagement of the AMPK–ULK1 axis, a threshold not achieved with low-intensity training (Egan et al 2011 ; Kim et al 2011 ; Vainshtein and Hood 2016 ). 4.3. Oxidative Stress and Antioxidant Capacity The present findings indicate that moderate and high-intensity interval training significantly reduced malondialdehyde (MDA) levels while increasing superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (TAC) in both brain regions. In contrast, low-intensity training produced only minimal effects on these parameters. This pattern supports the existence of an intensity-dependent dose–response relationship in improving brain oxidative balance. These results are consistent with the classical work of Radak et al. and Gómez Cabrera et al., who introduced the concept of exercise-induced hormesis(Radak et al 2005 ; Gomez-Cabrera et al 2008 ; Radak et al 2008 ). According to this framework, only exercise stimuli that generate a sufficient and controlled oxidative challenge can elicit sustained antioxidant adaptations. By contrast, some studies have reported that very high-intensity exercise may exacerbate oxidative stress and lead to cellular damage (Fisher-Wellman and Bloomer 2009 ). Key differences between those studies and the present investigation include the absence of a progressive exercise design, insufficient physiological adaptation periods, and excessive training loads, all of which may shift oxidative responses from adaptive to maladaptive. From a causal standpoint, MIIT and HIIT likely enhanced neuronal antioxidant defenses through transient increases in physiological ROS levels and activation of Nrf2-dependent signaling pathways(Done and Traustadóttir 2016 ), whereas low-intensity exercise was insufficient to effectively engage these pathways. 4.4. Neuroinflammation In the present study, moderate- and high-intensity interval training resulted in significant reductions in the pro-inflammatory cytokines TNF-α, IL-6, and IL-1β, indicating attenuation of diabetes-induced neuroinflammation. This effect was more pronounced in the HIIT group and is consistent with an intensity-dependent exercise response(Gleeson 2007 ; Pedersen and Febbraio 2012 ) . Previous studies by Gleeson et al. and Petersen and Pedersen have similarly demonstrated that higher-intensity exercise exerts stronger anti-inflammatory effects by reducing pro-inflammatory cytokines and increasing anti-inflammatory myokines(Petersen and Pedersen 2005 ; Gleeson et al 2011 ). In contrast, the anti-inflammatory effects of mild exercise reported in some studies were primarily observed in healthy or non-diabetic populations without chronic systemic inflammation (Flynn et al 2007 ), which may explain the different responses compared with diabetic models. Mechanistically, higher exercise intensities may more effectively suppress NF-κB activation, improve mitochondrial function, and reduce metabolic danger-associated molecular patterns (DAMPs), thereby leading to more robust modulation of neuroinflammation(Hotamisligil 2006 ; Gleeson et al 2011 ; Liu et al 2015 ). Given the well-established bidirectional interaction between neuroinflammation and lipid peroxidation, reductions in pro-inflammatory cytokines may indirectly contribute to the attenuation of oxidative membrane damage and preservation of neuronal integrity (Uttara et al 2009 ). 4.5. Lipid Peroxidation and Neuronal Membrane Damage The present study demonstrated that moderate and high-intensity interval training significantly reduced lipid peroxidation markers, including 4-hydroxynonenal (4-HNE), in the hippocampus and cortex of diabetic mice, whereas low-intensity interval training had no significant effects. These findings indicate that attenuation of oxidative damage to neuronal membrane lipids in the diabetic brain depends on exercise intensity (Uttara et al 2009 ; Butterfield and Halliwell 2019 ). Consistent with these observations, Sena and Chandel reported that mitochondrial ROS accumulation in diabetes plays a critical role in initiating lipid peroxidation and neuronal membrane disruption, and that interventions that improve mitochondrial function can suppress this process(Sena and Chandel 2012 ). Radak et al. further demonstrated that higher-intensity exercise leads to more effective reductions in lipid peroxidation end products compared with mild exercise(Radak et al 2005 ; Radak et al 2008 ). More recent evidence suggests that HIIT can specifically reduce brain 4-HNE levels, a highly toxic aldehyde directly implicated in synaptic dysfunction (Aguiar Jr et al 2009 ). Conversely, some studies have reported increased lipid peroxidation following intense exercise, particularly when exercise intensity is abruptly increased or when adaptation periods are insufficient(Fisher-Wellman and Bloomer 2009 ). Gómez Cabrera et al. showed that non-periodized, excessive high-intensity exercise can exacerbate acute oxidative stress and lipid damage (Gomez-Cabrera et al 2008 ). The discrepancies with the present findings may be due to the progressive training design, inclusion of an adaptation period, and use of a chronic diabetic model. At the molecular level, MIIT and HIIT likely reduced lipid peroxidation through multiple converging mechanisms: (1) improvement of mitochondrial function and reduction of electron leakage(Radak et al 2008 ; Sena and Chandel 2012 ) ; (2) enhancement of endogenous antioxidant capacity, including SOD, CAT, and TAC(Radak et al 2005 ; Radak et al 2008 ); and (3) suppression of neuroinflammation, a major driver of membrane lipid oxidation (Hotamisligil 2006 ; Block et al 2007 ). 5. Conclusions This study demonstrates that interval training induces robust neuroprotective adaptations in the diabetic brain in an intensity-dependent manner. MIIT and HIIT protocols effectively activated AMPK-mediated mitophagy, improved metabolic signaling, attenuated oxidative stress, and suppressed neuroinflammatory responses in both the hippocampus and cortex, whereas LIIT produced limited effects. These findings highlight exercise intensity as a critical determinant of brain metabolic resilience in T2DM and suggest that appropriately prescribed interval training may serve as a nonpharmacological strategy to mitigate diabetes-related neurobiological impairments. Future studies should explore the long-term sustainability of these adaptations and their translational relevance to clinical populations. Limitations and Future Directions Despite the significant findings of this study, several limitations should be considered. First, this investigation was conducted in a T2DM animal model. While this approach allows for detailed mechanistic analysis of brain molecular pathways, direct translation of the findings to humans should be approached with caution due to interspecies differences in metabolic and neural responses. Second, analyses were limited to the hippocampus and cortex. Given the involvement of other brain regions, such as the hypothalamus and amygdala, in metabolic regulation, cognition, and neuroinflammation in diabetes, future studies should include additional regions to provide a more comprehensive understanding. Third, although a broad range of molecular markers related to metabolic signaling, mitophagy, oxidative stress, neuroinflammation, and lipid peroxidation were examined, behavioral or cognitive assessments were not included. Consequently, the direct relationship between the observed molecular improvements and functional brain outcomes, such as learning and memory, requires further investigation. Fourth, sex-specific differences were not evaluated, as only one sex was included. Considering growing evidence of sex-dependent responses to exercise and the progression of diabetes-related neurological complications, future research should address the interaction between sex and exercise intensity. Future studies may benefit from longitudinal time course designs to clarify the temporal sequence of molecular adaptations and determine which responses emerge early versus later during exercise interventions. Additionally, cell type–specific approaches distinguishing neuronal and glial responses could provide deeper insight into the primary cellular sources of intensity-dependent adaptations in the diabetic brain. Finally, extending this research to human studies that integrate molecular markers with cognitive outcomes may enhance the translational value of these findings and inform the development of intensity-based exercise prescriptions for preventing or mitigating neurological complications of T2DM. Declarations Statements and Declarations The authors declare that they have no competing financial or non-financial interests, directly or indirectly related to the work submitted for publication. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Babak Esmealy and Kosar Zeinizadeh contributed to the conception and design of the study. Data collection, animal training protocols, and laboratory analyses were performed by Elaheh Piralaiy, Farnaz Derakhti, and Mahdi Hayati. Statistical analysis and interpretation of the data were conducted by Babak Esmealy and Mahdi Hayati. The first draft of the manuscript was written by Babak Esmealy. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors would like to during animal laboratory staff of the Department of Exercise Physiology, University of Tabriz, for their technical assistance during animal handling and biochemical analyses. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8938716\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":601425432,\"identity\":\"201c4555-d838-4787-afff-e4550f41206f\",\"order_by\":0,\"name\":\"Babak Esmealy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Tabriz\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Babak\",\"middleName\":\"\",\"lastName\":\"Esmealy\",\"suffix\":\"\"},{\"id\":601425433,\"identity\":\"206e3fc6-33f4-45fb-8f68-c194d668f0d8\",\"order_by\":1,\"name\":\"Kosar Zeinizadeh\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Tabriz\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kosar\",\"middleName\":\"\",\"lastName\":\"Zeinizadeh\",\"suffix\":\"\"},{\"id\":601425434,\"identity\":\"41afc741-5990-40bc-a696-a60939c94aff\",\"order_by\":2,\"name\":\"Elaheh Piralaiy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Tabriz\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Elaheh\",\"middleName\":\"\",\"lastName\":\"Piralaiy\",\"suffix\":\"\"},{\"id\":601425435,\"identity\":\"3a4835d7-527b-40a6-98b0-982aa746ce77\",\"order_by\":3,\"name\":\"Farnaz Derakhti\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Tabriz\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Farnaz\",\"middleName\":\"\",\"lastName\":\"Derakhti\",\"suffix\":\"\"},{\"id\":601425437,\"identity\":\"dd16cdd6-4ac2-43f1-aba0-1bbe970f2845\",\"order_by\":4,\"name\":\"Mahdi Hayati\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIie3RMUsDMRTA8RcOMkVdM5z4FZ4Uri2IxY8SuhbtKPTAKweZil2d7Fewm2NKILdccT2hg7d0uqGT6KB4d6i45M5RMP8pIfnxAgFwuf5gqBhwEVVLMn0CUEA/T5iN9L8JJTGq35BBRUhNgPKKtBYk61WW35+K+SKWk124udg/SBFeQvC7kYWk58OeSIfixhCZKbPtS0iRzAww3zIxUKOAC+mJqCZUIyUzhL0IGLc8DB+KilyJRUnG6r0kHkPy1kSyeooWdyWBlSwJZeg1TsmKTk/IpLM0Iubr6y1SRsfaN7zhYaPjx1c5ObzVSb67fN7g0Vwv8yI8GdjIV2fRz50CaAPll7becLlcrv/bBz1ZYV7rXCDdAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"University of Tabriz\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Mahdi\",\"middleName\":\"\",\"lastName\":\"Hayati\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-22 11:23:11\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8938716/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8938716/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":104176884,\"identity\":\"68b88897-c596-4323-97d1-196eaf5e7e88\",\"added_by\":\"auto\",\"created_at\":\"2026-03-08 16:40:23\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":350112,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic representation of the experimental design\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8938716/v1/3d9b01629400b5ef0be02c01.png\"},{\"id\":104403678,\"identity\":\"e554d6f5-e26e-45db-b5b7-5beb92238e83\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:18:49\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2319751,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8938716/v1/03c57637-8dc8-43f1-9776-d13c87869d0f.pdf\"},{\"id\":104176885,\"identity\":\"088ceffd-d01b-4c74-8332-7e606f86c3c5\",\"added_by\":\"auto\",\"created_at\":\"2026-03-08 16:40:23\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":703718,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Sup.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8938716/v1/da82199723d4742eadcb7000.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Intensity-Dependent Effects of Interval Training on Brain Mitophagy, Metabolic Signaling Pathways, and Neuroinflammation in a Mouse Model of Type 2 Diabetes\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eType 2 diabetes mellitus (T2DM), traditionally considered a chronic metabolic disorder, is now increasingly recognized as a multisystem disease with significant neurological consequences. Clinical and experimental evidence published since 2020 indicates that T2DM can induce central insulin resistance, disrupt cerebral energy metabolism, increase oxidative stress, and activate inflammatory signaling pathways, thereby promoting structural and functional changes in the brain and raising the risk of cognitive impairment and accelerated neurodegenerative processes(Zilliox et al \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Srikanth et al \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe brain is especially vulnerable to diabetes-induced metabolic disturbances because of its high energy demand and strong reliance on mitochondrial function. Recent studies have shown that the diabetic brain has impaired oxidative phosphorylation efficiency, increased electron leakage, and accumulation of dysfunctional mitochondria \\u0026ndash; changes that may compromise synaptic function and reduce neural plasticity (Petersen and Shulman \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Ye et al \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In this context, maintaining mitochondrial quality control has become a critical factor for neuronal health.\\u003c/p\\u003e \\u003cp\\u003eMitophagy, a selective autophagic process that removes damaged mitochondria, plays a fundamental role in maintaining mitochondrial homeostasis and preventing neuronal injury. Accumulating evidence indicates that T2DM is associated with impairments in PINK1/Parkin-dependent mitophagy pathways and dysregulated expression of key autophagy-related proteins such as LC3 and p62 in the central nervous system. These disruptions may lead to the accumulation of defective mitochondria and increased oxidative stress. Impaired mitophagy has therefore been proposed as an important molecular link between diabetes, neuroinflammation, and cognitive decline(Palikaras et al \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Ye et al \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn addition to mitochondrial dysfunction, alterations in metabolic signaling pathways contribute significantly to the pathophysiology of the diabetic brain. Reduced AMPK activity and relative upregulation of mTOR signaling have been reported in specific brain regions under diabetic conditions, potentially suppressing autophagic processes, limiting mitochondrial biogenesis, and increasing neuronal vulnerability(Herzig and Shaw \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Moctezuma and Molinas \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). These signaling abnormalities often coincide with activation of inflammatory pathways, including NF-κB and the NLRP3 inflammasome, further reinforcing chronic neuroinflammation in T2DM(Baeeri et al \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePhysical activity has emerged as an effective non-pharmacological intervention for improving brain health under adverse metabolic conditions. In particular, interval training has received increasing attention for its strong ability to activate AMPK\\u0026ndash;PGC-1α signaling, enhance mitochondrial function, and stimulate autophagy and mitophagy processes(Steiner et al \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Wrann et al \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Animal studies have shown that interval training can upregulate PGC-1α expression and mitochondrial biogenesis\\u0026ndash;related factors in brain tissue, thereby improving neuronal metabolic efficiency (MacInnis and Gibala \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Abedpoor and Hajibabaie \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Moreover, growing evidence suggests that exercise can attenuate neuroinflammation and provide neuroprotection in diabetes and other metabolic disorders by inhibiting inflammatory pathways such as NF-κB and the NLRP3 inflammasome and by reducing microglial activation(Radak et al \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Mee-Inta et al \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Lee et al \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Notably, the molecular responses of the brain to exercise appear to be largely intensity dependent, while training pattern and volume may modulate the magnitude of these adaptations (Steiner et al \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Meeusen et al \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Herzig and Shaw \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Mee-Inta et al \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite accumulating evidence, the role of exercise intensity as a primary determinant of the direction and nature of brain molecular adaptations to interval training remains incompletely understood. Most previous studies have either examined a single exercise intensity or reported exercise-induced brain effects without clearly distinguishing intensity from other training variables such as volume and structure. Furthermore, findings regarding the dose\\u0026ndash;response relationship between exercise intensity and neural adaptations remain inconsistent and, in some cases, contradictory. While several studies identify high-intensity interval training (HIIT) as a potent stimulus for activating metabolic and mitophagy-related pathways, others suggest that excessively high intensities may overwhelm antioxidant defense capacity under certain pathological conditions, thereby exacerbating oxidative stress and potentially impairing neuroprotective mechanisms and mitochondrial quality control in the brain(Meeusen et al \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Herzig and Shaw \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Palikaras et al \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In contrast, moderate-intensity exercise has been proposed to elicit more sustainable anti-inflammatory responses and more effective suppression of microglial activation by achieving a more favorable balance between metabolic stress and cellular recovery capacity(Meeusen et al \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Lee et al \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCollectively, these findings highlight exercise intensity as a critical yet underexplored determinant of neurometabolic adaptations in T2DM. It also remains unclear whether brain responses to exercise intensity follow a linear pattern or exhibit an inverted U-shaped relationship. Therefore, the present study was designed to determine the optimal intensity of interval training for enhancing mitophagy-supporting pathways and suppressing neuroinflammation in a mouse model of type 2 diabetes, with the aim of providing more precise evidence for exercise prescription in neurometabolic disorders.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1.1. Animals and Experimental Design\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis controlled experimental study was designed to investigate the intensity‑dependent effects of interval training on brain mitophagy, metabolic signaling pathways, and neuroinflammatory responses in a rodent model of T2DM. Following confirmation of diabetes induction, animals were randomly assigned to five experimental groups (n=10 per group; total N=50). Due to the invasive nature of brain tissue collection, baseline and endpoint assessments were conducted on separate subsets of animals. Baseline samples were collected before the training intervention to establish reference values, while endpoint measurements were taken after completion of the training protocol. Although distinct animals were evaluated at each time point, all subjects originated from the same cohort and were carefully matched for age, body weight, and housing conditions to ensure intergroup comparability (figure1).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.1.2. Experimental Groups\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e1) Healthy control (HC)\\u003c/p\\u003e\\n\\u003cp\\u003e2) Diabetic control (DC)\\u003c/p\\u003e\\n\\u003cp\\u003e3) Diabetic + low‑intensity interval training (LIIT)\\u003c/p\\u003e\\n\\u003cp\\u003e4) Diabetic + moderate‑intensity interval training (MIIT)\\u003c/p\\u003e\\n\\u003cp\\u003e5) Diabetic + high‑intensity interval training (HIIT)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.1.3. Animals and Ethical Approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 50 male Wistar rats (8 weeks old, body weight 200\\u0026ndash;250 g) were obtained from the Animal House of Urmia University. The animals were housed under standard laboratory conditions (temperature 22 \\u0026plusmn; 2 \\u0026deg;C, relative humidity 55 \\u0026plusmn; 5%, 12:12 h light\\u0026ndash;dark cycle) with ad libitum access to standard chow and water. All experimental protocols were performed in accordance with ethical guidelines for the care and use of laboratory animals and were approved by the institutional ethics committee of Tabriz University of Medical Sciences (Approval code: IR.TBARIZU.REC.1404.211).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.1.4. Induction of\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eT2DM\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e2.1.4.1. Animal Acclimatization and Dietary Protocol\\u003c/p\\u003e\\n\\u003cp\\u003eMice were initially housed under standard laboratory conditions for a two-week acclimatization period, maintained solely on a conventional dry pellet diet. Subsequently, the animals were transitioned into a pre-diabetic phase lasting four weeks. This phase involved the administration of a customized High-Fat Diet (HFD) formulated to contain 60% fat, 20% protein, and 20% carbohydrate by mass. During this period, daily HFD intake was progressively titrated from 20\\u0026nbsp;to 25\\u0026nbsp;g per animal. The HFD was procured from Royan Biotechnology Company (Isfahan, Iran), operating under the regulatory oversight of the Iranian Food and Drug Administration.\\u003c/p\\u003e\\n\\u003cp\\u003e2.1.4.2. Diabetic Induction and Confirmation\\u003c/p\\u003e\\n\\u003cp\\u003eT2DM was chemically induced via a single intraperitoneal (i.p.) injection of streptozotocin (STZ; 35\\u0026nbsp;mg/kg body weight), prepared by dissolving the compound in a citrate buffer solution adjusted to pH\\u0026nbsp;4.5. Diabetes induction success was confirmed 72 hours post-injection by measuring fasting blood glucose (FBG) levels. Animals exhibiting FBG values of \\u0026ge;250\\u0026nbsp;mg/dL were categorized as successfully diabetic and were enrolled in the subsequent experimental groups. FBG levels were continuously monitored on a weekly basis throughout the study duration.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2. Measurements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2.1. Brain Metabolic Signaling Markers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFollowing rapid euthanasia, the hippocampus and cerebral cortex were immediately dissected, snap-frozen in liquid nitrogen, and stored at \\u0026minus;80 \\u0026deg;C. Tissue homogenates were prepared using RIPA lysis buffer supplemented with commercial protease and phosphatase inhibitor cocktails. Protein levels of total and phosphorylated forms of AMPK, mTOR, and ULK1 were quantified by Western blotting. Equal amounts of total protein were separated on 10% SDS-PAGE gels and electrotransferred onto PVDF membranes. Primary antibodies specific to the target proteins (all from Cell Signaling Technology, USA) were applied, followed by incubation with appropriate HRP-conjugated secondary antibodies. Signals were detected using enhanced chemiluminescence (ECL) substrate and digitized. Densitometric analysis for calculating phosphorylated-to-total protein ratios was performed using ImageJ/Fiji. Data normalization used \\u0026beta;-actin for hippocampal samples and GAPDH for cortical samples.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2.2. Brain Mitophagy Markers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMitochondrial quality control markers were assessed in hippocampal and cortical homogenates (prepared as described in Section 2.3.1 and re-aliquoted). The NAD+/NADH ratio, an index of mitochondrial redox status, was quantified using a colorimetric assay kit according to the manufacturer\\u0026rsquo;s instructions (Abcam, UK). Mitophagy pathway activation was evaluated by Western blotting (antibodies from Cell Signaling Technology, USA) for PINK1, Parkin, LC3 II/LC3 I ratio, and p62/SQSTM1 expression. Furthermore, GDF15 protein expression, as a marker of mitochondrial stress, was determined in brain tissue using a rat-specific ELISA kit (R\\u0026amp;D Systems, USA).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2.3.\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eBrain Tissue Oxidative Stress Markers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOxidative stress markers were quantified in hippocampal and cortical tissue homogenates\\u0026nbsp;(prepared as described in Section 2.3.1 and re-aliquoted). Malondialdehyde (MDA) levels, as an index of lipid peroxidation, were measured using the TBARS method with a commercial kit (ZellBio, Germany). The enzymatic activities of Superoxide Dismutase (SOD) and Catalase (CAT) were determined using enzymatic assay kits (Cayman Chemical, USA). Total Antioxidant Capacity (TAC) was assessed via the FRAP method utilizing a commercial kit (ZellBio, Germany).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2.4. Brain Tissue Neuroinflammatory Markers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo directly assess neuroinflammation, concentrations of inflammatory cytokines were measured in hippocampal and cortical tissue homogenates\\u0026nbsp;(prepared as described in Section 2.3.1 and re-aliquoted). Levels of tumor necrosis factor‑\\u0026alpha; (TNF‑\\u0026alpha;), interleukin‑6 (IL‑6), and interleukin‑1\\u0026beta; (IL‑1\\u0026beta;) were quantified using rat‑specific ELISA kits (R\\u0026amp;D Systems, USA).These cytokines were considered key indicators of activation of brain inflammatory pathways and diabetes‑ and exercise intensity\\u0026ndash;dependent neuroinflammatory responses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2.5. Brain Tissue Lipid Peroxidation Markers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGiven the high susceptibility of brain membrane lipids to oxidative damage, lipid peroxidation was assessed in hippocampal and cortical tissue homogenates (prepared as described in Section 2.3.1 and re-aliquoted). Levels of 4-hydroxynonenal (4-HNE), a major end product of lipid peroxidation, were measured using an ELISA kit (Abcam, UK).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3. Training Protocol\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3.1. Treadmill Familiarization\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBefore the main training protocol, animals underwent a one-week treadmill familiarization period. This phase included daily running sessions lasting 10\\u0026ndash;15 minutes at a speed of 10 m/min with 0% incline. The purpose of this phase was to habituate the animals to the treadmill apparatus and minimize stress during the intervention period.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3.2. Main Training Protocol\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe training intervention lasted eight weeks, with subjects completing five training sessions per week (Monday to Friday); Saturday and Sunday were designated as consecutive rest days. Each standardized session comprised a 5-minute warm-up at a low speed (5 m/min) followed by the primary exercise protocol, concluding with a 5-minute cool-down period. The total duration of each session ranged from 45 to 60 minutes. All treadmill protocols were executed at a 0% incline to eliminate the confounding effects of grade on exercise intensity parameters. The main exercise protocol consisted of 10 repeating intervals (for a total of 10 work-to-rest cycles) maintaining a strict 1:1 work-to-rest ratio. The animals were underwent to a graded treadmill exercise test starting at a speed of 5 m\\u0026middot;min⁻\\u0026sup1;, with increments of 5 m\\u0026middot;min⁻\\u0026sup1; every 3 minutes until each rat reached its maximal running capacity (Vmax)(Melo et al 2003). The individual Vmax values with corresponding treadmill speeds were subsequently used to prescribe exercise intensities for the training protocols:\\u003c/p\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003eLow‑intensity interval training (LIIT): Subjects performed work intervals at 50\\u0026ndash;60%\\u0026nbsp;Vmax (corresponding to 12\\u0026ndash;15\\u0026nbsp;m/min) followed by active recovery intervals at 30\\u0026ndash;40%\\u0026nbsp;Vmax (8\\u0026ndash;10\\u0026nbsp;m/min).\\u003c/li\\u003e\\n \\u003cli\\u003eModerate‑intensity interval training (MIIT):\\u0026nbsp;Work intervals were set at 65\\u0026ndash;75%\\u0026nbsp;Vmax (16\\u0026ndash;20\\u0026nbsp;m/min), with recovery intervals conducted at 40\\u0026ndash;50%\\u0026nbsp;Vmax (11\\u0026ndash;14\\u0026nbsp;m/min).\\u003c/li\\u003e\\n \\u003cli\\u003eHigh‑intensity interval training (HIIT): Subjects exercised during the work phase at 80\\u0026ndash;90% Vmax (21\\u0026ndash;26 m/min), followed by recovery intervals at 50\\u0026ndash;60% Vmax (14\\u0026ndash;17 m/min).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.4. Statistical Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eStatistical analyses were performed using SPSS software (version 16.0; IBM, USA). The normality of data distribution was assessed using the Shapiro\\u0026ndash;Wilk test, and homogeneity of variances was evaluated with Levene\\u0026rsquo;s test. Baseline samples were obtained from matched animals before the intervention, while post-intervention samples were collected 48 hours after the final training session. Therefore, a two-way analysis of variance (ANOVA) with independent samples was used to examine the main effects of time (baseline vs. end-point), group (CS, DC, LIIT, MIIT, and HIIT), and the time \\u0026times; group interaction for all dependent variables. When significant main effects or interactions were observed, post hoc comparisons were conducted using the Bonferroni correction. Data are presented as mean \\u0026plusmn; standard deviation (mean \\u0026plusmn; SD), and statistical significance was set at p \\u0026lt; 0.05.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on AMPK/mTOR Metabolic Signaling in the Hippocampus of a T2DM Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep-AMPK / AMPK\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.78\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.032\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep-mTOR / mTOR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.48\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.041\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.45\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.88\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep-ULK1 / ULK1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on AMPK/mTOR Metabolic Signaling in the Cerebral Cortex of a T2DM Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep-AMPK/ AMPK\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.80\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.044\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.012\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep-mTOR/ mTOR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.99\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.36\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.39\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep-ULK1/ ULK1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.88\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.010\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group.\\u003c/p\\u003e \\u003cp\\u003eAs shown in Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, at baseline, diabetic animals exhibited significantly reduced AMPK phosphorylation and ULK1 activation, along with elevated mTOR signaling in both brain regions compared with HC, confirming that diabetes induced impairment of energy-sensing pathways. No significant changes were observed between baseline and endpoint measurements in DC. Exercise training induced clear, intensity-dependent improvements in metabolic signaling. MIIT and HIIT significantly increased p-AMPK/AMPK and p-ULK1/ULK1 ratios while suppressing p-mTOR/mTOR compared with DC at endpoint (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u0026ndash;0.001). LIIT produced modest between time improvements that did not result in significant between-group differences. These adaptations were consistently more pronounced in the hippocampus than in the cerebral cortex, with HIIT eliciting the largest response.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Mitophagy-Related Markers in the Hippocampus of a T2DM Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ePINK1 (AU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.52\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eParkin (AU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.043\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eLC3-II / LC3-I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.88\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.039\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.79\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep62 (AU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Mitophagy-Related Markers in the Cerebral Cortex of a T2DM Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ePINK1 (AU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.048\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eParkin (AU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.99\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.71\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eLC3-II / LC3-I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.99\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.042\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.52\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ep62 (AU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.99\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.48\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.45\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.010\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group.\\u003c/p\\u003e \\u003cp\\u003eTables\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e show that diabetes significantly suppressed key components of the PINK1/Parkin-dependent mitophagy pathway, as indicated by reduced PINK1 and Parkin expression, a lower LC3 II/LC3 I ratio, and accumulation of p62 in both the hippocampus and cortex. No spontaneous recovery was observed in DC over time. Interval training restored mitophagy signaling in an intensity-dependent manner. MIIT and HIIT significantly increased PINK1 and Parkin expression, enhanced LC3 lipidation, and reduced p62 levels compared with DC (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u0026ndash;0.001). In contrast, LIIT induced only mild changes from baseline to endpoint that were not significantly different from DC. The hippocampus showed greater sensitivity to exercise-induced mitophagy activation than the cortex, particularly in response to HIIT.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Oxidative Stress and Antioxidant Defense in the Hippocampus of a T2DM Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eMDA\\u003c/p\\u003e \\u003cp\\u003e(nmol/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.041\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eSOD\\u003c/p\\u003e \\u003cp\\u003e(U/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.039\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eCAT\\u003c/p\\u003e \\u003cp\\u003e(U/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.044\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e39.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eTAC\\u003c/p\\u003e \\u003cp\\u003e(\\u0026micro;mol Fe\\u0026sup2;⁺/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.80\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.78\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.79\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.52\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Oxidative Stress and Antioxidant Defense in the Cerebral Cortex of a T2DM Diabetic Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eMDA\\u003c/p\\u003e \\u003cp\\u003e(nmol/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.30\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.043\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.018\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.31\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eSOD\\u003c/p\\u003e \\u003cp\\u003e(U/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.041\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.022\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eCAT\\u003c/p\\u003e \\u003cp\\u003e(U/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e38.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eTAC\\u003c/p\\u003e \\u003cp\\u003e(\\u0026micro;mol Fe\\u0026sup2;⁺/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.48\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.042\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.021\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group.\\u003c/p\\u003e \\u003cp\\u003eAs shown in Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, diabetic animals exhibited significant oxidative imbalance, with elevated malondialdehyde (MDA) levels and reduced activities of superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (TAC) in both brain regions. These changes persisted in DC over time. Exercise training significantly improved redox homeostasis in an intensity-dependent manner. MIIT and HIIT significantly reduced MDA concentrations and increased SOD, CAT, and TAC at the endpoint compared with DC (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u0026ndash;0.001). LIIT produced only modest improvements that were not statistically significant between groups. Consistent with metabolic and mitophagy findings, antioxidant responses were more pronounced in the hippocampus than in the cerebral cortex.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Neuroinflammatory Cytokines in the Hippocampus of a T2DM Diabetic Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eTNF-α (pg/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.041\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eIL-6 (pg/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.048\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eIL-1β (pg/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.039\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 8\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Neuroinflammatory Cytokines in the Cerebral Cortex of a T2DM Diabetic Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eTNF-α (pg/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.092\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eIL-6 (pg/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.081\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.003 vs DC\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eIL-1β (pg/mg protein)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.074\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group\\u003c/p\\u003e \\u003cp\\u003eAs shown in Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e, baseline levels of pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β) were significantly elevated in diabetic animals compared to HC in both examined brain regions, indicating pronounced diabetes-associated neuroinflammation. No significant temporal changes were detected in DC. Exercise training attenuated neuroinflammatory responses in an intensity-dependent manner. MIIT and HIIT significantly reduced all measured cytokines compared with DC at the end point (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u0026ndash;0.001), whereas LIIT induced smaller and mostly non-significant between-group changes. The magnitude of cytokine reduction was slightly greater in the hippocampus than in the cortex, with HIIT consistently producing the strongest anti-inflammatory effect.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab9\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 9\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Lipid Peroxidation\\u0026ndash;Derived Aldehydes (4-HNE) in the Hippocampus of a T2DM Diabetic Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eHydroxynonenal (4-HNE)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab10\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 10\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eIntensity-Dependent Effects of Interval Training on Lipid Peroxidation\\u0026ndash;Derived Aldehydes (4-HNE) in the Cerebral Cortex of a T2DM Diabetic Mouse Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEnd-point\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBaseline vs End-point (between-time) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBetween-group (End-point vs. DC) p\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime \\u0026times; Group\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eHydroxynonenal (4-HNE)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.71\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.048\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.041\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIIT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHealthy control: HC | DC: Diabetic Control | LIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;low‑intensity interval training | MIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;moderate‑intensity interval training | HIIT: Diabetic\\u0026thinsp;+\\u0026thinsp;high‑intensity interval training\\u003c/p\\u003e \\u003cp\\u003eData are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD. Statistical analysis was performed using two-way ANOVA with independent samples (time \\u0026times; group), followed by Bonferroni post hoc tests, with large effect sizes (partial eta squared, ηp\\u0026sup2; = 0.48\\u0026ndash;0.72). Between‑group comparisons were conducted on End-point values relative to the diabetic control group\\u003c/p\\u003e \\u003cp\\u003e*The healthy control (CS) group was included solely to confirm the validity of the diabetic model and to serve as a physiological reference. Its pre- and post-intervention data are reported for completeness and biological context and were not considered in the primary statistical analyses.\\u003c/p\\u003e \\u003cp\\u003eAs shown in Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e, levels of 4-hydroxynonenal (4-HNE), a marker of lipid peroxidation-induced cellular damage, were markedly elevated in diabetic animals compared with HC in both brain regions and remained unchanged in DC over time. Exercise training significantly reduced 4-HNE accumulation in an intensity-dependent manner. MIIT and HIIT produced significant reductions in hippocampal 4-HNE compared with DC (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u0026ndash;0.001), whereas LIIT exerted only modest effects. Similar but less pronounced reductions were observed in the cerebral cortex. Overall, the hippocampus exhibited greater responsiveness to exercise-induced attenuation of lipid peroxidation.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec26\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. Brain Metabolic Signaling Pathways\\u003c/h2\\u003e \\u003cp\\u003eThe present study demonstrated that moderate and high intensity interval training (MIIT and HIIT) significantly activated the AMPK and ULK1 pathways while concurrently suppressing mTOR signaling in the hippocampus and cortex of diabetic mice, whereas low intensity interval training induced only limited changes. This pattern clearly indicates an intensity-dependent regulation of brain metabolic responses and supports the central hypothesis that exercise intensity is a key determinant of metabolic adaptations in the diabetic brain.\\u003c/p\\u003e \\u003cp\\u003eThese findings are consistent with previous reports. Hardie et al., have shown that AMPK activation in neural tissues occurs primarily under conditions of sufficient energetic stress, and that low intensity exercise is often insufficient to reach this metabolic threshold (Hardie et al \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Hardie \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Similarly, Camacho et al. reported that higher-intensity exercise exerts more pronounced inhibitory effects on mTOR signaling and leads to more effective improvements in energy homeostasis in both neural and non-neural tissues compared with mild exercise (Bolster et al \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Murphy \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn contrast, some studies, such as those by Zhang et al., have suggested that even low-intensity aerobic exercise may activate AMPK in the brain (Bar\\u0026oacute;n-Mendoza et al \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). However, important methodological differences distinguish those studies from the present work, including shorter intervention durations, the absence of a stable T2DM model, and the assessment of only a single brain region. These factors may account for the discrepancies in reported outcomes. Mechanistically, higher exercise intensities are more likely to cause a pronounced reduction in the ATP/AMP ratio and enhance metabolic stress signaling, leading to stronger AMPK activation and the initiation of adaptive cellular cascades(Hardie et al \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Hardie \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). In the context of diabetes, where brain metabolic flexibility is markedly impaired, only moderate- to high-intensity exercise appears capable of surpassing this regulatory threshold (Steinberg and Kemp \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Triebel et al \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. Regulation of Mitophagy in the Hippocampus and Cortex\\u003c/h2\\u003e \\u003cp\\u003eIn the present study, increased expression of PINK1 and Parkin, a higher LC3 II/I ratio, and reduced p62 levels collectively indicate enhanced mitophagy, with more pronounced effects in the hippocampus. These findings are consistent with existing evidence of impaired mitophagy and compromised mitochondrial quality control in the diabetic brain. Although direct evidence linking exercise-induced mitophagy to neuroprotection in diabetic models remains limited, accumulating data suggest that exercise can significantly improve mitochondrial function and neuronal resilience under diabetic conditions. These observations align with studies such as those by He et al., who reported that higher-intensity exercise can ameliorate mitophagy impairments induced by diabetes or metabolic stress (He et al \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Moreover, AMPK activation has been shown to play a central role in the induction of PINK1/Parkin-dependent mitophagy, which agrees with the metabolic signaling findings of the present study(Egan et al \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Kim et al \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eConversely, some investigations, including those by Laker et al., have reported that mild exercise may also enhance mitophagy (Kim et al \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). However, these studies were conducted primarily in non-diabetic models, where baseline mitochondrial dysfunction is less severe and responsiveness to milder stimuli is preserved (Montgomery and Turner \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFrom a mechanistic perspective, diabetes is associated with the accumulation of damaged mitochondria and disruption of mitochondrial clearance systems (Montgomery and Turner \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Palikaras et al \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Higher intensity exercise likely provides the necessary threshold for robust mitophagy activation through increased physiological ROS signaling and engagement of the AMPK\\u0026ndash;ULK1 axis, a threshold not achieved with low-intensity training (Egan et al \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Kim et al \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Vainshtein and Hood \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec28\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3. Oxidative Stress and Antioxidant Capacity\\u003c/h2\\u003e \\u003cp\\u003eThe present findings indicate that moderate and high-intensity interval training significantly reduced malondialdehyde (MDA) levels while increasing superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (TAC) in both brain regions. In contrast, low-intensity training produced only minimal effects on these parameters. This pattern supports the existence of an intensity-dependent dose\\u0026ndash;response relationship in improving brain oxidative balance.\\u003c/p\\u003e \\u003cp\\u003eThese results are consistent with the classical work of Radak et al. and G\\u0026oacute;mez Cabrera et al., who introduced the concept of exercise-induced hormesis(Radak et al \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Gomez-Cabrera et al \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Radak et al \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). According to this framework, only exercise stimuli that generate a sufficient and controlled oxidative challenge can elicit sustained antioxidant adaptations.\\u003c/p\\u003e \\u003cp\\u003eBy contrast, some studies have reported that very high-intensity exercise may exacerbate oxidative stress and lead to cellular damage (Fisher-Wellman and Bloomer \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Key differences between those studies and the present investigation include the absence of a progressive exercise design, insufficient physiological adaptation periods, and excessive training loads, all of which may shift oxidative responses from adaptive to maladaptive.\\u003c/p\\u003e \\u003cp\\u003eFrom a causal standpoint, MIIT and HIIT likely enhanced neuronal antioxidant defenses through transient increases in physiological ROS levels and activation of Nrf2-dependent signaling pathways(Done and Traustad\\u0026oacute;ttir \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), whereas low-intensity exercise was insufficient to effectively engage these pathways.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec29\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4. Neuroinflammation\\u003c/h2\\u003e \\u003cp\\u003eIn the present study, moderate- and high-intensity interval training resulted in significant reductions in the pro-inflammatory cytokines TNF-α, IL-6, and IL-1β, indicating attenuation of diabetes-induced neuroinflammation. This effect was more pronounced in the HIIT group and is consistent with an intensity-dependent exercise response(Gleeson \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Pedersen and Febbraio \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) .\\u003c/p\\u003e \\u003cp\\u003ePrevious studies by Gleeson et al. and Petersen and Pedersen have similarly demonstrated that higher-intensity exercise exerts stronger anti-inflammatory effects by reducing pro-inflammatory cytokines and increasing anti-inflammatory myokines(Petersen and Pedersen \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Gleeson et al \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn contrast, the anti-inflammatory effects of mild exercise reported in some studies were primarily observed in healthy or non-diabetic populations without chronic systemic inflammation (Flynn et al \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e), which may explain the different responses compared with diabetic models.\\u003c/p\\u003e \\u003cp\\u003eMechanistically, higher exercise intensities may more effectively suppress NF-κB activation, improve mitochondrial function, and reduce metabolic danger-associated molecular patterns (DAMPs), thereby leading to more robust modulation of neuroinflammation(Hotamisligil \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Gleeson et al \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Liu et al \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eGiven the well-established bidirectional interaction between neuroinflammation and lipid peroxidation, reductions in pro-inflammatory cytokines may indirectly contribute to the attenuation of oxidative membrane damage and preservation of neuronal integrity (Uttara et al \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec30\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5. Lipid Peroxidation and Neuronal Membrane Damage\\u003c/h2\\u003e \\u003cp\\u003eThe present study demonstrated that moderate and high-intensity interval training significantly reduced lipid peroxidation markers, including 4-hydroxynonenal (4-HNE), in the hippocampus and cortex of diabetic mice, whereas low-intensity interval training had no significant effects. These findings indicate that attenuation of oxidative damage to neuronal membrane lipids in the diabetic brain depends on exercise intensity (Uttara et al \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Butterfield and Halliwell \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eConsistent with these observations, Sena and Chandel reported that mitochondrial ROS accumulation in diabetes plays a critical role in initiating lipid peroxidation and neuronal membrane disruption, and that interventions that improve mitochondrial function can suppress this process(Sena and Chandel \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Radak et al. further demonstrated that higher-intensity exercise leads to more effective reductions in lipid peroxidation end products compared with mild exercise(Radak et al \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Radak et al \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). More recent evidence suggests that HIIT can specifically reduce brain 4-HNE levels, a highly toxic aldehyde directly implicated in synaptic dysfunction (Aguiar Jr et al \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eConversely, some studies have reported increased lipid peroxidation following intense exercise, particularly when exercise intensity is abruptly increased or when adaptation periods are insufficient(Fisher-Wellman and Bloomer \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). G\\u0026oacute;mez Cabrera et al. showed that non-periodized, excessive high-intensity exercise can exacerbate acute oxidative stress and lipid damage (Gomez-Cabrera et al \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). The discrepancies with the present findings may be due to the progressive training design, inclusion of an adaptation period, and use of a chronic diabetic model.\\u003c/p\\u003e \\u003cp\\u003eAt the molecular level, MIIT and HIIT likely reduced lipid peroxidation through multiple converging mechanisms: (1) improvement of mitochondrial function and reduction of electron leakage(Radak et al \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Sena and Chandel \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) ; (2) enhancement of endogenous antioxidant capacity, including SOD, CAT, and TAC(Radak et al \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Radak et al \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e); and (3) suppression of neuroinflammation, a major driver of membrane lipid oxidation (Hotamisligil \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Block et al \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eThis study demonstrates that interval training induces robust neuroprotective adaptations in the diabetic brain in an intensity-dependent manner. MIIT and HIIT protocols effectively activated AMPK-mediated mitophagy, improved metabolic signaling, attenuated oxidative stress, and suppressed neuroinflammatory responses in both the hippocampus and cortex, whereas LIIT produced limited effects. These findings highlight exercise intensity as a critical determinant of brain metabolic resilience in T2DM and suggest that appropriately prescribed interval training may serve as a nonpharmacological strategy to mitigate diabetes-related neurobiological impairments. Future studies should explore the long-term sustainability of these adaptations and their translational relevance to clinical populations.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eLimitations and Future Directions\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eDespite the significant findings of this study, several limitations should be considered. First, this investigation was conducted in a T2DM animal model. While this approach allows for detailed mechanistic analysis of brain molecular pathways, direct translation of the findings to humans should be approached with caution due to interspecies differences in metabolic and neural responses.\\u003c/p\\u003e \\u003cp\\u003eSecond, analyses were limited to the hippocampus and cortex. Given the involvement of other brain regions, such as the hypothalamus and amygdala, in metabolic regulation, cognition, and neuroinflammation in diabetes, future studies should include additional regions to provide a more comprehensive understanding.\\u003c/p\\u003e \\u003cp\\u003eThird, although a broad range of molecular markers related to metabolic signaling, mitophagy, oxidative stress, neuroinflammation, and lipid peroxidation were examined, behavioral or cognitive assessments were not included. Consequently, the direct relationship between the observed molecular improvements and functional brain outcomes, such as learning and memory, requires further investigation.\\u003c/p\\u003e \\u003cp\\u003eFourth, sex-specific differences were not evaluated, as only one sex was included. Considering growing evidence of sex-dependent responses to exercise and the progression of diabetes-related neurological complications, future research should address the interaction between sex and exercise intensity.\\u003c/p\\u003e \\u003cp\\u003eFuture studies may benefit from longitudinal time course designs to clarify the temporal sequence of molecular adaptations and determine which responses emerge early versus later during exercise interventions. Additionally, cell type\\u0026ndash;specific approaches distinguishing neuronal and glial responses could provide deeper insight into the primary cellular sources of intensity-dependent adaptations in the diabetic brain. Finally, extending this research to human studies that integrate molecular markers with cognitive outcomes may enhance the translational value of these findings and inform the development of intensity-based exercise prescriptions for preventing or mitigating neurological complications of T2DM.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStatements and Declarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing financial or non-financial interests, directly or indirectly related to the work submitted for publication.\\u0026nbsp;\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eBabak Esmealy and Kosar Zeinizadeh contributed to the conception and design of the study. Data collection, animal training protocols, and laboratory analyses were performed by Elaheh Piralaiy, Farnaz Derakhti, and Mahdi Hayati. Statistical analysis and interpretation of the data were conducted by Babak Esmealy and Mahdi Hayati. The first draft of the manuscript was written by Babak Esmealy. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgments\\u003c/h2\\u003e \\u003cp\\u003eThe authors would like to during animal laboratory staff of the Department of Exercise Physiology, University of Tabriz, for their technical assistance during animal handling and biochemical analyses.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eAbedpoor N, Hajibabaie F (2024) Physical activity regulates and mediates the signaling pathway and pathophysiological mechanisms of the neuroinflammation and neurodegenerative. \\u003cem\\u003eAsian J Sports Med\\u003c/em\\u003e 15: 10.5812. https://doi.org/10.5812/asjsm-149446\\u003c/li\\u003e\\n \\u003cli\\u003eAguiar Jr AS, Araújo AL, da-Cunha TR, et al (2009) Physical exercise improves motor and short-term social memory deficits in reserpinized rats. \\u003cem\\u003eBrain research bulletin\\u003c/em\\u003e 79: 452-457. 10.1016/j.brainresbull.2009.05.005. \\u003c/li\\u003e\\n \\u003cli\\u003eBaeeri M, Rahimifard M, Daghighi SM, et al (2020) RETRACTED: Cannabinoids as anti-ROS in aged pancreatic islet cells. 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DOI: 10.1016/j.molcel.2012.09.025 .\\u003c/li\\u003e\\n \\u003cli\\u003eSrikanth V, Sinclair AJ, Hill-Briggs F, Moran C, Biessels GJ (2020) Type 2 diabetes and cognitive dysfunction—towards effective management of both comorbidities. \\u003cem\\u003eThe lancet Diabetes \\u0026amp; endocrinology\\u003c/em\\u003e 8: 535-545. DOI: 10.1016/S2213-8587(20)30118-2 .\\u003c/li\\u003e\\n \\u003cli\\u003eSteinberg GR, Kemp BE (2009) AMPK in health and disease. \\u003cem\\u003ePhysiological reviews\\u003c/em\\u003e 89: 1025-1078. DOI: 10.1152/physrev.00011.2008 .\\u003c/li\\u003e\\n \\u003cli\\u003eSteiner JL, Murphy EA, McClellan JL, Carmichael MD, Davis JM (2011) Exercise training increases mitochondrial biogenesis in the brain. \\u003cem\\u003eJournal of applied physiology\\u003c/em\\u003e 111: 1066-1071. 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DOI: 10.1152/japplphysiol.00550.2015 .\\u003c/li\\u003e\\n \\u003cli\\u003eWrann CD, White JP, Salogiannnis J, et al (2013) Exercise induces hippocampal BDNF through a PGC-1α/FNDC5 pathway. \\u003cem\\u003eCell metabolism\\u003c/em\\u003e 18: 649-659. DOI: 10.1016/j.cmet.2013.09.008.\\u003c/li\\u003e\\n \\u003cli\\u003eYe Q, Zeng X, Cai S, Qiao S, Zeng X (2021) Mechanisms of lipid metabolism in uterine receptivity and embryo development. \\u003cem\\u003eTrends in Endocrinology \\u0026amp; Metabolism\\u003c/em\\u003e 32: 1015-1030. DOI: 10.1016/j.tem.2021.09.002 .\\u003c/li\\u003e\\n \\u003cli\\u003eZilliox LA, Chadrasekaran K, Kwan JY, Russell JW (2016) Diabetes and cognitive impairment. \\u003cem\\u003eCurrent diabetes reports\\u003c/em\\u003e 16: 87. DOI: 10.1007/s11892-016-0775-x .\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"metabolic-brain-disease\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"mebr\",\"sideBox\":\"Learn more about [Metabolic Brain Disease](https://www.springer.com/journal/11011)\",\"snPcode\":\"11011\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11011/3\",\"title\":\"Metabolic Brain Disease\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Interval training; Exercise intensity, Mitophagy, Neuroinflammation, Type 2 diabetes, Brain metabolism\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8938716/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8938716/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntroduction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eType 2 diabetes mellitus impairs brain metabolic and mitochondrial homeostasis, yet the intensity‑dependent neuroprotective effects of exercise remain poorly defined.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eObjective\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study examined the intensity‑dependent effects of interval training on brain mitophagy, metabolic signaling, oxidative stress, and neuroinflammation in a type 2 diabetes model.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this experimental study, 50 male mice were assigned to five groups: 1) Healthy control (HC), 2) Diabetic control (DC), 3) Diabetic + low-intensity interval training (LIIT), 4) Diabetic + moderate-intensity interval training (MIIT), and 5) Diabetic + high-intensity interval training (HIIT) (n = 10 per group). Following the intervention, hippocampal and cortical tissues were analyzed for metabolic signaling markers (AMPK, ULK1, mTOR), mitophagy-related proteins (PINK1, Parkin, LC3 II/I, p62), oxidative stress and antioxidant indices (MDA, SOD, CAT, TAC), pro-inflammatory cytokines (TNF-α, IL-6, IL-1β), and lipid peroxidation (4-hydroxynonenal; 4-HNE). Statistical analyses were performed with the significance level set at p \\u0026lt; 0.05.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMIIT and HIIT activated AMPK–ULK1 signaling and suppressed mTOR in the hippocampus and cortex, leading to enhanced mitophagy, particularly in the hippocampus. These adaptations were accompanied by improved redox balance, reduced lipid peroxidation, and attenuated neuroinflammation, with effects increasing in an intensity‑dependent manner (LIIT \\u0026lt; MIIT \\u0026lt; HIIT).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInterval training induces neuroprotective adaptations in the diabetic brain in an intensity‑dependent manner. Moderate‑ and high‑intensity protocols more effectively activate AMPK‑driven mitophagy, suppress neuroinflammation, and reduce lipid peroxidation than low‑intensity training, highlighting exercise intensity as a key determinant of brain metabolic resilience in type 2 diabetes.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Intensity-Dependent Effects of Interval Training on Brain Mitophagy, Metabolic Signaling Pathways, and Neuroinflammation in a Mouse Model of Type 2 Diabetes\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-08 16:40:19\",\"doi\":\"10.21203/rs.3.rs-8938716/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-03-23T14:02:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-16T20:40:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-16T09:17:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-14T15:08:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-05T17:38:05+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"319119546443648930730328424352563000988\",\"date\":\"2026-03-04T08:40:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"294479514417350747270171675704633022628\",\"date\":\"2026-03-04T00:24:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"134586437930058528646738847581907115998\",\"date\":\"2026-03-03T19:25:53+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"232324937945287696303701635836499425642\",\"date\":\"2026-03-03T18:43:19+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-03T18:35:24+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-02-26T04:25:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-02-26T04:24:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Metabolic Brain Disease\",\"date\":\"2026-02-22T11:06:59+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"metabolic-brain-disease\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"mebr\",\"sideBox\":\"Learn more about [Metabolic Brain Disease](https://www.springer.com/journal/11011)\",\"snPcode\":\"11011\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11011/3\",\"title\":\"Metabolic Brain Disease\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"f403399e-1b53-4e46-8eec-e49e2508732b\",\"owner\":[],\"postedDate\":\"March 8th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-12T15:54:10+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-08 16:40:19\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8938716\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8938716\",\"identity\":\"rs-8938716\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}