A Combinatorial Mouse Model of Circadian Disruption and Dietary Stress for Investigating Lifestyle-Accelerated Aging Pathophysiology

Summary

Here, we present a protocol to establish a mouse model of disordered circadian rhythm plus high-fat, high-sugar (HFHS) diet, which mimics unhealthy lifestyles and induces metabolic disorders and aging phenotypes.

Abstract

The goal of this protocol is to establish a standardized mouse model that recapitulates the multifactorial damage induced by modern, unhealthy lifestyles. To this end, we developed a novel C57BL/6J model combining randomized circadian rhythm disruption and a high-fat, high-sugar (HFHS) diet, with single-factor HFHS and circadian disruption groups as controls. Compared with controls, the composite model showed more severe metabolic disorders, including increased body weight, elevated fasting glucose and lipid levels, and marked hepatic steatosis, as well as obvious cognitive and motor impairments. The composite model also exhibited more significant accelerated aging, as confirmed by the frailty index and hematoxylin and eosin (HE) staining. This integrated model reliably mimics lifestyle-related metabolic-cognitive decline and premature aging. This step-by-step reproducible protocol supports mechanistic studies on lifestyle-mediated aging and the evaluation of targeted therapeutic strategies.

Introduction

Accelerated modern lifestyles increasingly expose populations to rhythm-disrupting behaviors and obesogenic diets, with compelling evidence linking these factors to systemic functional decline and chronic disease pathogenesis¹. Epidemiological data reveal striking comorbidities: shift workers show a high prevalence of metabolic syndrome markers, while long-term intake of a high-fat and high-sugar diet is strongly associated with an elevated risk of dementia^2,3. These insults converge to drive multiorgan pathology—metabolic dysfunction frequently coincides with accelerated cognitive decline and cardiovascular remodeling, demonstrating bidirectional crosstalk between peripheral and central deterioration. Despite recognized clinical synergies, standardized preclinical models capturing the integrated pathophysiology of lifestyle-induced organ decline remain underdeveloped.

At the molecular level, chronic circadian misalignment initiates a pathological cascade originating in the suprachiasmatic nucleus, disrupting glutamatergic signaling to the paraventricular hypothalamus and blunting corticotropin-releasing hormone pulsatility. This impairs hypothalamic-pituitary-adrenal (HPA) axis negative feedback⁴, causing glucocorticoid receptor desensitization and sustained cortisol elevation. Consequent hepatic gluconeogenesis activation and skeletal muscle insulin receptor substrate-1 phosphorylation suppression collectively drive peripheral insulin resistance, establishing a metabolic foundation for hyperglycemia and dyslipidemia^5,6. Concurrently, dietary saturated fatty acids permeate the compromised blood-brain barrier via downregulation of endothelial tight junction proteins^7,8, activating microglial TLR4/NF-κB signaling. This triggers TNF-α/IL-1β-mediated neuroinflammation⁹. The key point is that these two unhealthy lifestyle factors do not act independently but rather synergistically. However, there remains a significant lack of systematic research on their combined exposure in the current literature. Therefore, it is meaningful to conduct modeling studies integrating circadian rhythm disruption with high-fat and high-sugar diets as composite factors.

Despite their utility in studying isolated aging mechanisms, current murine models fail to recapitulate the integrated pathophysiology of lifestyle-induced deterioration. Natural aging exhibits pronounced inter-individual variability and requires prolonged timelines incompatible with experimental efficiency. Pharmacological agents like doxorubicin primarily induce DNA damage and acute senescence without replicating chronic metabolic dysregulation¹⁰. Genetically modified models target single molecular pathways, overlooking systemic neuroendocrine-metabolic crosstalk^11,12. D-galactose administration, though inducing oxidative stress, inadequately mimics circadian disruption's impact on glucocorticoid rhythmicity or dietary lipid-mediated neuroinflammation¹³. Critically, single-factor interventions cannot capture synergistic multiorgan decline patterns observed clinically. This translational gap necessitates an advanced combinatorial platform simultaneously incorporating chronic circadian disruption and obesogenic dietary stressors to faithfully accelerate interdependent neurodegeneration, cardiac dysfunction, and metabolic impairment.

To address this issue, we developed a C57BL/6J mouse model integrating stochastic circadian disruption with a high-fat and high-sugar diet to recapitulate synergistic contemporary lifestyle insults. Subjecting mice to rotating light schedules combined with sustained obesogenic feeding induced significantly exacerbated metabolic derangements, including elevated adiposity, fasting hyperglycemia, and dyslipidemia. Cognitive assessments revealed pronounced deficits in spatial and recognition memory, while locomotor tests indicated reduced endurance. Crucially, longitudinal quantification of the frailty index demonstrated accelerated accumulation of age-related deficits. Our dual-challenge paradigm thus robustly recapitulates multifaceted accelerated aging, providing a validated novel platform to study lifestyle-driven pathophysiology.

Protocol

All experimental procedures were approved by the Animal Research Ethics Committee of Hebei Yiling Pharmaceutical Research Institute (Approval No: N2024043). Thirty-two 8-month-old male C57BL/6J mice (see Table of Materials) were housed in a specific pathogen-free (SPF) animal facility affiliated with the New Drug Evaluation Center, Hebei Yiling Pharmaceutical Research Institute. The rearing environment was maintained under standardized conditions: ambient temperature ranged from 20–26 °C, relative humidity was controlled at 40%–70%, and a 12-h light/12-h dark cycle was implemented. The mice had free access to a standard laboratory rodent diet and sterilized drinking water.

1. Establishment of a combinatorial mouse model of circadian disruption and dietary stress (Figure 1)

NOTE: Animals that died unexpectedly during the experiment, exhibited severe weight loss, or failed to complete the modeling procedure were excluded from the final analysis. The initial number of animals was 8 per group, and the final valid sample size was 5 per group.

1. Prepare a high-fat/high-sugar (HFHS) diet (see Table of Materials) and an independent room equipped with a programmable timer for flexible light-dark cycle control prior to the experiment.
   NOTE: Store the diet at 4 °C or -20 °C to prevent oxidation; bring it to room temperature before use to avoid condensation affecting its composition.
2. Animal adaptation and random grouping
   1. House all 8‑month‑old mice under standard conditions for a 7‑day adaptive feeding period before initiating the experiment.
   2. Randomly assign mice into four groups based on body weight (n = 8 per group) after the adaptation period: Control group, combined stress model (Model) group, HFHS group, and circadian rhythm disruption (CR) stress group.
3. Administer an 8-month intervention of the HFHS diet combined with circadian rhythm disruption to the combined stress model group. Feed the HFHS group with an HFHS diet alone for the same duration.
4. Expose the CR group to circadian rhythm disruption alone. Feed the control group with a standard maintenance diet throughout the entire experiment. Provide free access to drinking water for all animals.
   NOTE: The 8-month intervention can be paused temporarily (e.g., for routine health checks of mice) without affecting the experimental outcome, and the intervention can be resumed after the check is completed.
5. Circadian rhythm disruption protocol (Figure 2A,B)
   1. Use a programmable timer (see Table of Materials) to flexibly control the weekly light-dark cycle. Apply four distinct light regimes: rapid light-dark switching (alternating light and dark every 7 min for 2 consecutive hours daily), reverse light-dark cycle (lights off at 8:00 and on at 20:00 daily), constant light (24-h illumination), and constant dark (24-h darkness).
   2. Generate a random sequence using statistical software (R language, with a fixed random seed) at the start of each week. Randomly assign the four light modes to each day of the week to ensure that each mode is applied consecutively for 1–2 days.
6. Provide the HFHS diet to the mice ad libitum, and monitor the food intake weekly. The detailed composition of the HFHS diet is presented in Table 1.
   NOTE: Inspecting the food supply is required to prevent underfeeding or food deprivation.
7. Weigh mice weekly at a fixed time point (9:00 AM on Thursdays) using an electronic balance during the 8-month intervention period. Record all body weight data in detail.
   NOTE: Perform the weighing process rapidly (each session completed in less than 30 s) to minimize stress and disruption to the circadian rhythm.

2. Novel object recognition

1. Place the mice in a 60 cm × 60 cm × 40 cm test box one day before training, and allow them to freely explore for 5 min to acclimate to the test environment.
   NOTE: Remove feces and urine from the box after each trial. Wipe the box with 75% ethanol to eliminate residual odors.
2. Place two identical cylinders (A and B) in the bottom-left and bottom-right corners of the test box, respectively. Introduce each mouse into the box facing away from the objects and allow the mouse to explore freely for 5 min.
   1. Randomly alternate the left‑right positions of the two cylinders across subjects to prevent positional bias.
   2. After the 5-min exploration training, pause the experiment for up to 23 h, and complete the subsequent novel object replacement step (step 2.3) within 24 h after training to ensure the validity of the recognition test.
3. Replace object A in the bottom-left corner with a novel cube (object C) 24 h after training. Activate the overhead camera. Place the mouse into the box and record behavior for 5 min.
   NOTE: Use a novel object that is similar in size to the familiar objects but distinctly different in material, color, or texture to ensure that the mouse can recognize its novelty.
4. Analyze the videos using the video tracking system (see Table of Materials).
   1. Define exploration as the mouse orienting its snout toward or within 2 cm of an object. Record the time spent exploring the familiar object (TB) and the novel object (TC).
   2. Calculate a recognition index according to the following formula: Recognition index = TC / (TB + TC) × 100%.
      NOTE: Keep the analyst blinded to the experimental groups to avoid subjective bias; systematically exclude non-exploratory behaviors such as climbing or gnawing from the exploration time records.

3. Grip strength test

1. Measure the forelimb grip strength of the mice using a grip strength meter¹⁴ (see Table of Materials). Rotate the force gauge 90 degrees vertically and fix it securely to a metal stand before each test session to ensure complete immobilization of the system.
2. Gently hold the mouse by the tail base. Allow the forepaws to grasp the bar naturally. Zero the gauge after the reading stabilizes, then pull the tail horizontally backward at a constant, slow speed until the mouse releases its grip.
3. Record the maximum peak force (kgf) at the moment of release as the grip strength. Perform three consecutive trials per mouse per day. Use the average of three measurements for subsequent analysis.
   NOTE: Keep the pulling speed uniform and sufficiently slow to allow the mouse to generate full resistance against the pull.
4. Exclude any trial from analysis if one of the following occurs: the mouse uses only one forepaw, the hindlimbs contact the bar or contribute to grip, the mouse twists its body during pulling, or the bar is released without active resistance.
   NOTE: Flag ambiguous or outlier trials for review. Repeat measurements when necessary.

4. Frailty index (FI)

NOTE: Calibrate all equipment parameters to standardized settings prior to testing. Limit the total assessment time for each mouse to under 30 min to avoid fatigue-induced phenotypic changes. Conduct all tests within a fixed morning time window (9:00 AM–12:00 PM) to minimize the effects of circadian rhythm.

1. Fast the mice for 12 h with free access to water prior to testing to minimize the acute effects of food intake on physiological measurements.
2. Move the mice to the testing environment 30 min before the assessment to acclimate them and reduce stress-related interference.
3. Exclude mice with severe pathological conditions such as tumors, infections, or limb deformities to prevent confounding effects on frailty assessment.
4. Evaluate the frailty index based on 31 phenotypic deficit indicators across five domains: motor function, nutritional status, sensory function, spontaneous activity and behavior, and physiological defects^15,16.
   NOTE: The specific 31 assessment items cover five domains, including: motor function (7 items such as decreased grip strength, abnormal gait, and reduced climbing ability), nutritional status (6 items such as weight loss, decreased body condition score, and reduced food intake), sensory function (5 items such as visual impairment, hearing impairment, and tactile dullness), spontaneous activity and behavior (7 items such as reduced spontaneous activity, decreased grooming behavior, and reduced exploratory behavior), and physiological defects (6 items such as eye defects, ear defects, and skin defects). All items adopt a binary scoring method (0 = no defect, 1 = with defect), and the final frailty index = total number of defective items / 31.
5. Assign two researchers who are blinded to the experimental groups to perform all evaluations independently.
6. Score each indicator as 0 (no deficit) or 1 (deficit) according to predefined criteria covering grip strength, gait speed, body weight change, coat condition, sensory response, spontaneous activity capacity, and physical abnormalities.

5. Fasting blood glucose detection

NOTE: Fast mice for 12 h with free access to water to minimize acute feeding-related effects on blood glucose. Perform measurements between 9:00 AM–12:00 PM to reduce circadian fluctuations. Calibrate the glucometer with a standard glucose solution pre-experiment for inter-batch consistency.

1. Gently restrain the mice, disinfect the tail tips with 75% ethanol. Make a 1–2 mm incision in the tail veins using a sterile disposable lancet. Discard the first drop of blood to avoid tissue-fluid contamination.
2. Collect a small volume of capillary blood and analyze it immediately using a portable glucometer (see Table of Materials) with compatible test strips. Test each sample in duplicate to ensure data accuracy.

6. Blood lipid detection included total cholesterol (TC)

NOTE: Exclude samples with severe hemolysis or turbidity from analysis. Repeat measurements with a coefficient of variation > 10% among triplicates.

1. Place collected blood in non-anticoagulant tubes. Allow to clot naturally at room temperature for 2 h. Centrifuge samples at 1000 × g for 15 min at 4 °C to separate serum.
   NOTE: Ensure the centrifuge is properly balanced before operation and close the safety inner lid securely.
2. Carefully aspirate the upper serum layer, aliquot it, and store immediately at -80 °C to prevent lipid oxidation.
   NOTE: After serum aliquoting and storage at -80 °C, the experiment can be paused for several days to weeks; the subsequent total cholesterol detection (step 6.3) can be performed when the test kit and equipment are ready, ensuring the serum is not repeatedly frozen and thawed.
3. Measure serum total cholesterol levels using a commercial Greiss's TC test kit (see Table of Materials) according to the manufacturer's instructions. Test each sample in triplicate and run standard reference samples concurrently for calibration.

7. Hematoxylin-eosin staining (HE staining)

1. Anesthetize mice with sodium pentobarbital (20 mg/kg) post-behavioral tests.
2. Collect blood via retro-orbital venous plexus. Dissect tissues and fix in 4% paraformaldehyde for 48 h.
   NOTE: After tissue fixation in 4% paraformaldehyde for 48 h, the experiment can be paused; fixed tissues can be stored in 4% paraformaldehyde at 4 °C for up to 1 week before proceeding to the dehydration step (step 7.3).
3. Dehydrate fixed tissues with an automated processor using a graded ethanol series (60%, 70%, 90% ethanol for 1 h each; 95%, 100% ethanol for 2 h each).
   CAUTION: Ethanol is a flammable liquid with a low flash point. Keep away from open flames, heat sources, and sparks during storage and operation. Use in a well-ventilated area, wear non-static gloves and a lab coat. Avoid large-volume storage in the experimental area; store in a dedicated flammable liquid cabinet.
4. Clear dehydrated tissues in xylene for 2 h, then infiltrate with paraffin at 60 °C for 3 h (see Table of Materials). Embed tissues oriented to standard anatomical planes.
   NOTE: Clearing time should not be excessively long to avoid tissue brittleness; maintain a constant paraffin temperature during infiltration to prevent crystallization. After paraffin embedding, the embedded tissue blocks can be stored at room temperature for several months, and the sectioning step (step 7.5) can be performed when needed.
5. Once paraffin blocks solidify completely, cut 4 µm continuous sections with a paraffin slicer (see Table of Materials), transfer onto poly-L-lysine-coated slides, air-dry at room temperature, then seal for storage.
   NOTE: After section mounting and air-drying, slides can be stored at room temperature in a dust-free environment for up to 1 month before proceeding to the staining step (step 7.6).
6. Stain sections with an automatic staining system (see Table of Materials).
   1. Deparaffinize in xylene I and xylene II (10 min each).
   2. Rehydrate via a descending ethanol series (100%, 95%, 80%, 2 min each), then rinse with distilled water.
   3. Stain with hematoxylin for 3–8 min until nuclei are clearly visible, rinse under running water to promote bluing, and counterstain with eosin for 1–3 min to achieve adequate cytoplasmic pink coloration.
7. Dehydrate stained sections in absolute alcohol, clear in xylene, and mount with neutral balsam for slide scanner analysis (see Table of Materials).

8. Oil Red O staining

1. Embed fresh liver tissues in optimal cutting temperature (OTC) compound (see Table of Materials) within 30 min of collection.
   NOTE: After embedding fresh liver tissues in OTC compound, the embedded tissues can be stored at -80 °C for up to 1 month before sectioning (step 8.3), avoiding repeated freezing and thawing.
2. Place embedded tissues in a cryostat and allow them to freeze completely before sectioning.
3. Set cryostat temperature to -22 °C and cut fully frozen tissue blocks into consecutive 8 µm-thick sections.
4. Transfer sections directly onto uncoated glass slides and gently flatten to ensure tight adhesion between sections and slides.
5. Fix slides with neutral formalin at room temperature for 10 min. Immediately after fixation, rinse sections in 60% isopropanol for 5 s.
6. Place pretreated sections in a humidity chamber, add Oil Red O staining solution (see Table of Materials), and incubate for 20 minutes.
7. After staining, differentiate sections with 60% isopropanol until lipid droplet and interstitial tissue boundaries are clearly distinguishable.
   NOTE: Closely monitor sections during differentiation to prevent over-differentiation, which causes faded staining.
8. Examine differentiated stained sections under a light microscope to observe hepatic lipid deposition-related histopathological changes.
   NOTE: After staining and differentiation, sections can be stored at 4 °C for up to 3 days before microscopic examination, ensuring the staining effect is not affected.

9. Statistical analysis

1. Express all experimental data as mean ±± standard error of the mean (SEM)
2. Apply the Shapiro-Wilk test to verify if the data conforms to a normal distribution. Perform the Levene test to evaluate homogeneity of variance across groups if normality is confirmed.
   NOTE: Apply nonparametric tests directly if the data does not meet the normality assumption.
3. Use one-way analysis of variance (ANOVA) to test the statistical significance of overall differences among multiple groups, and perform pairwise comparisons using Fisher's least significant difference (LSD) test.
   NOTE: Use Welch ANOVA instead when the variances are heterogeneous.
4. Use a two-tailed Student's t-test for comparisons between two independent sample groups.
5. Define statistical significance as a two-sided p-value < 0.05. Perform all statistical analyses using statistical analysis software.

Results

Synergistic exacerbation of metabolic pathology in dual-exposure cohorts
Longitudinal body weight monitoring revealed distinct growth trajectories among cohorts. The model group and the HFHS group exhibited accelerated weight gain compared to both the Control and CR groups. Notably, the CR group demonstrated modest acceleration relative to Control (Figure 3A). Further evidence of metabolic dysfunction was provided by the observation that both fasting blood glucose and bl...

Discussion

Contemporary research has unequivocally confirmed that circadian disruption and a high-fat, high-sugar diet are two independent drivers of systemic functional decline in the organism. Our data demonstrates that the pathological damage induced by the combined action of these two factors is far more severe than the simple additive effect of either alone. Compared with the single-factor intervention groups, the composite model group showed no significant difference in adiposity relative to the HFHS group, but exhibited a ma...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ANY-maze	Science	SA225
Accu-Chek Active Blood Glucose Monitor	Switzerland, Roche Diagnostics	accu check active
C75BL/6J mice	BEIJING HFK BIOSCIENCE CO.,LTD	No.110324241101250228
Grip strength meter	USA, Columbus Instruments	1027CSM-E54
High-fat and high-sucrose diet	China, Xietong Biotechnology Co., Ltd.	XT303
HistoCore MULTICUT - Semi-Automated Rotary Microtome	Germany, Leica	RM2245
Oil Red O solution	China, Wuhan Servicebio Technology Co., Ltd.	G1260
Optimal cutting temperature compound	Japan, Sakura	4583
Programmable Timer Socket	China, GONEO	GND-1
Tissue Tech Prisma Plus	Japan, Sakura	20B2X00014000034
Tissue-Tek TEC 6 Embedding Module	Japan, Sakura	M01-021E-02
Total Cholesterol (TC) Assay Kit	China, Suzhou Grace Biotechnology Co., Ltd.	G0909W