Joint Effects of Low Ambient Temperature and Nocturnal Eating Behavior Contributing to Glycolipid Metabolism Disorder by Changing Gut Microbiota Structure

Joint Effects of Low Ambient Temperature and Nocturnal Eating Behavior Contributing to Glycolipid Metabolism Disorder by Changing Gut Microbiota Structure | 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 Article Joint Effects of Low Ambient Temperature and Nocturnal Eating Behavior Contributing to Glycolipid Metabolism Disorder by Changing Gut Microbiota Structure Yinglan Shi, Zhaoxia Huang, Xiaofang Tang, Yongqin Zhang, Die Shi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6258553/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Diabetes has emerged as a prominent public health issue significantly impacting human health, evidenced by a consistent annual increase in patient cases. Winter plays a pivotal role in either initiating or exacerbating diabetes, although the precise mechanisms underlying this phenomenon remain unclear. Our previous work was consistent with the findings, indicating an intriguing potential link between glycolipid metabolism and the circadian clock, such as ambient temperature and dietary rhythms. So in this research, we endeavor to delve into the intricate relationship among ambient temperature, diurnal dietary rhythms, and glycolipid metabolism via animal experiments, ultimately aiming to shed light on the potential mechanisms through which the circadian clock may initiate or exacerbate diabetes. Methods: Thirty-six healthy rats were randomly assigned to four groups(N=9), with each group exposed to a unique combination of temperature (25°C or 16°C) and time-restricted feeding schedules (8:00 ~ 20:00 or 20:00 ~ 8:00). After a 10-day experimental period, we assayed the levels of fasting insulin (FINS), adiponectin, cortisol, leptin, and other homeostatic energy substances in serum. Furthermore, we investigated the neurotransmitter content in serum, blood metabolic profiles, and alterations in gut microbiota. Results: Notably, exposure to low temperatures elevated the food consumption and body mass of the rats, whereas nocturnal eating syndromes contributed to hyperinsulinemia and insulin resistance, subsequently improving microbial imbalances. In the experiment, the low-temperature nocturnal eating group rats showed a notable decrease in the relative abundances of Bacteroidetes and Actinobacteria (P < 0.05). Serum metabolite analysis revealed that both ambient temperature and dietary rhythm affect glucose, lipids, and amino acid metabolism. Neurotransmitters and blood lipid profile changes can cause an intestinal flora imbalance. Conclusion: Our study indicates that glycolipid metabolism disorders are caused by low temperatures and nocturnal eating, possibly due to changes in gut microbiota and neurotransmitter levels. Increasing ambient temperature and managing gut microbiota in winter may help prevent and treat diabetes. Health sciences/Diseases/Endocrine system and metabolic diseases/Metabolic syndrome Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Low-Temperature Stress Circadian Rhythm Disruption Time-Restricted Feeding Intestinal Microbiota Glycolipid Metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlight 1. Low ambient temperature and nocturnal eating might induce disorders in glycolipid metabolism. 2. Disorders of glycolipid metabolism induced by low-ambient temperature are associated with alterations in the intestinal microbiota. 1 Introduction Diabetes has emerged as a salient global health problem in the 21st century. The International Diabetes Federation (IDF) projected that the population of diabetes patients will increase to 643 - 783 million by 2030 - 2045 [1] . Diabetes can contribute to microvascular complications [2] , affecting the kidneys, eyes, and nervous system, and poses risks for life-threatening conditions like, diabetic nephropathy, heart disease, and stroke, raising mortality and morbidity rates these complications can impair cognitive function, physical health, and social life quality [3] . Managing blood glucose can be particularly challenging for individuals with diabetes in winter, potentially leading to new complications [4] . Therefore, further research is crucial to explore how environmental temperature affects glycolipid metabolism and understand the mechanisms behind metabolic disorders in cold conditions. Winter's low temperatures trigger the body's stress response, activating the sympathetic nervous system increasing catecholamine secretion, and raising blood glucose levels [5] . In diabetics, cold can worsen blood pressure and glucose levels [6, 7] . Prolonged cold exposure may lead to diseases like myocardial infarction (MI), and cerebral issues [8] . Low temperatures significantly stimulate the sympathetic nervous system, increasing epinephrine release and accelerating liver glycogen breakdown. This process reduces glucose uptake by tissues like muscle, raising blood sugar levels. Otherwise, cold environments can increase cravings for high-fat, high-sugar foods, and decrease physical activity, worsening blood sugar levels [9, 10] . Studies show short-term cold stress affects glucose metabolism and might aid weight loss, but the impact of prolonged winter on glycolipid metabolism is unclear. Additionally, plentiful winter food can lead to poor habits affecting metabolism [11,12] . Life pressure and fast-paced technology are also altering daily habits and rest patterns. People who eat late at night or early in the morning often disrupt their circadian rhythms and gut microbiota [13] , affecting insulin secretion and glycolipid metabolism. We propose that both environmental temperature and circadian dietary rhythms influence glycolipid metabolism, though the exact mechanisms are unclear. Diabetic patients may experience worsened symptoms in cold environments due to these factors. This paper seeks to investigate the effects of low temperature and altered circadian dietary rhythms, on glycolipid metabolism, as well as their potential to induce glycolipid metabolism disorders in rats. Additionally, this paper aims to examine the influence of glycolipid metabolism, energy balance, neurotransmitters, gut microbiota, and metabolites on the progression of type 2 diabetes. Our ultimate goal is to elucidate the underlying pathological mechanisms and identify novel therapeutic strategies for metabolic diseases in the foreseeable future. 2 Materials and Methods 2.1 Animals and Experimental Design The experimental subjects consisted of male SPF SD rats, aged 6 weeks and weighing 180.0 ± 10.0 g, which were procured from Changsha Tianqin Biotechnology Co., Ltd. All animal experiments and procedures received approval from the Experimental Animal Ethics Review Committee of Guizhou University of Chinese Medicine (approval number: 20210188). Meanwhile, all experiments were strictly carried out following the relevant protocols and in compliance with the ARRIVE guidelines. Feed the standard diet for one week, after which 36 rats will be randomly divided into four groups (N=9). The groups include: the CRD25 group, which was maintained at 25°C and fed from 8:00 ~ 20:00; the CRN25 group, which was maintained at 25°C and fed from 20:00 ~ 8:00; the CRD16 group, which was maintained at 16°C and fed from 8:00 ~ 20:00; and the CRN16 group, which was maintained at 16°C and fed from 20:00~8:00(The experimental protocol is comprehensively outlined in Figure 1A.). The experiment was conducted for 10 days, during which the basic animals' weight and food intake were meticulously recorded daily throughout the study period. after the experiment, fresh fecal samples were collected and subsequently stored at -80°C in a refrigerator for future analysis of alterations in the intestinal microbiome and metabolites. Blood was collected from the abdominal aorta of rats and centrifuged at 1200xg for 15 minutes to obtain serum samples, which were then stored at -80°C for subsequent analysis. Subsequently, the rats were euthanized. All these rats were deeply anesthetized by intraperitoneal injection (2% sodium pentobarbital, 100 mg/kg) and killed. The liver, kidneys, epididymis, and subcutaneous adipose tissues were collected, weighed, and stored at -80°C for further analysis. 2.2 Detection of Oral Glucose Tolerance Test and Insulin Resistance-Related Indicators Before the oral glucose tolerance test (OGTT), rats underwent a 12-hour fasting period. A glucose solution at a concentration of 2 g/kg was administered intragastrically, and blood samples were collected from the tail vein for measurement at 0, 30, 60, and 120min post-administration using a glucometer (Jiangsu Yuyue Medical Equipment & Supply Co., Ltd., Danyang, China). A line graph is constructed with the measured blood glucose concentration represented on the vertical axis and the corresponding measurement time displayed on the horizontal axis. The area under the curve (AUC) is calculated to quantify the cumulative changes in blood glucose response. The parameter for the homeostasis model assessment of insulin resistance (HOMA-IR) is calculated as follows: HOMA-IR = FINS (mU/L) × FBG (mmol/L) / 22.5. The HOMA insulin sensitivity (HOMA-IS) index is defined as 1/(FBG × FINS) [14] . The serum levels of fasting insulin (FINS), adiponectin, cortisol, indices related to insulin resistance, and leptin were measured using an ELISA kit. 2.3 Biochemical Analysis Serum triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein cholesterol (LDL-C) levels were measured using an automated biochemical analyzer. Additionally, the effects of environmental temperature and dietary habits on serum lipid profiles and blood glucose levels were analyzed. 2.4 Microbial Analysis of the Gut Microbiota Total genomic DNA was extracted from fecal samples utilizing the MOBIO PowerSoil® DNA Extraction Kit (MOBIO Laboratories, Inc., Carlsbad, CA, USA). Subsequently, amplification and sequencing of the hypervariable regions V3-V4 of the 16S rRNA gene were performed on the Illumina Novaseq 6000 platform. The sequencing process was carried out by Beijing BoMaiKe Biotechnology Co., Ltd. The sequence reads was clustered utilizing USEARCH software at a similarity threshold of 97.0%, resulting in the identification of operational taxonomic units (OTUs) [15] . Subsequently, the QIIME software was utilized to analyze the composition of the intestinal microbiota in each sample across various taxonomic levels (phylum, class, order, family, genus, and species) and to assess species abundance at different hierarchical classifications. An analysis of alpha and beta diversity was conducted to elucidate the diversity and structure of the intestinal microbiota. The β diversity analysis includes non-metric multidimensional scaling (NMDS) based on the Bray–Curtis algorithm, as well as principal coordinate analysis (PCoA). Additionally, ANOSIM analysis is utilized to assess the significance of community differences among groups [16] . LEfSe (Linear Discriminant Analysis Effect Size) is utilized to identify biomarkers that exhibit statistically significant differences across various groups. 2.5 Analysis of Monoamine Neurotransmitters in the Serum The concentration of neurotransmitters in the serum was quantitatively analyzed using high-performance liquid chromatography (HPLC). A suitable volume of the serum sample was collected, and methanol was added to precipitate proteins. The mixture was then subjected to centrifugation at 10,000g. Following this, the supernatant was carefully removed, evaporated to dryness under a nitrogen stream, and reconstituted to a final volume of 0.5 mL with methanol. An appropriate aliquot of this solution was filtered through a needle filter into a sample vial equipped with a liner for subsequent measurement. Detection and analysis were conducted using a Rigol L3000 high-performance liquid chromatograph equipped with a Kromasil C18 reversed-phase column (250 mm × 4.6 mm, 5 μm). The mobile phase consisted of methanol as phase A and a 0.1 mol/L aqueous potassium dihydrogen phosphate solution as phase B. An injection volume of 10 μL was employed, with a flow rate set at 1 mL/min and the column maintained at a temperature of 30°C. The retention time was established at 40min. A fluorescence detector was utilized, with an excitation wavelength of 278 nm and an emission wavelength of 338 nm. 2.6 Untargeted Metabolomic Analysis In each group, 100 μl of serum samples were collected and thawed on ice. The samples underwent extraction, drying, centrifugation, and other necessary procedures. The resulting supernatants were transferred to clean vials for liquid chromatography-mass spectrometry (LC/MS) analysis. Before detection, the quality control (QC) samples were prepared by mixing with 10 μl of each sample. The analysis was conducted using a UHPLC-QTOF-MS system, employing a mobile phase consisting of 0.1% formic acid aqueous solution (A) and 0.1% formic acid acetonitrile (B) for gradient elution. The injection volume utilized was 1 μl [17] . Simultaneously, the Waters Xevo G2-XS QTOF high-resolution mass spectrometer was utilized to acquire mass spectrometry data. Furthermore, principal component analysis (PCA) model score plots were generated to examine the differences among the sample groups. The volcano plot was employed to assess the overall trends in metabolite content across each group, statistically evaluate the significance of observed differences, and calculate variable importance in projection (VIP) values. Meanwhile, bubble plots and heat maps were generated to analyze the associated metabolite pathways and the quantities of differential metabolites. 2.7 Statistical Analysis Statistical analyses were conducted using SPSS version 20.0. A one-way analysis of variance (ANOVA) was utilized to assess the statistical differences among various groups, followed by the Tukey-Kramer post hoc test. Conduct additional significant statistical tests utilizing R software (version 3.4.1) compatible with Windows operating systems. Meanwhile, the data were analyzed and visualized using GraphPad Prism version 8.0.1, with results presented as mean ± standard deviation. Values with p < 0.05 were considered statistically significant. 3 Results 3.1 Low Temperature Leads to a Significant Increase in Body Weight and Food Intake of Rats Fed at Night To achieve the assessment of alterations in the physiological indicators of healthy Sprague-Dawley (SD) rats induced by low-temperature exposure and dietary rhythm, we systematically recorded the daily food intake (Figure 1B) and body weight (Figure 1C, P < 0.01) across four groups of rats throughout the experimental duration (Figure 1A). Initially, body weights were comparable across all groups. By the end of the study, rats exposed to low temperatures (CRD16 and CRN16) exhibited a significant increase in both body weight and food intake compared to those maintained at a comfortable temperature (CRD25 and CRN25). Additionally, we analyzed the weight gain of the rats before and after the experiment (Figure 1D, P < 0.01). The final weight gains for the CRD25, CRN25, CRD16, and CRN16 groups were 49.0 ± 18.4 g, 48.0 ± 25.2 g, 62.8 ± 22.4 g, and 101.3 ± 17.0 g, respectively. Notably, the increase in weight observed in the night-time restricted group under low-temperature conditions (CRN16) was particularly remarkable. Moreover, ambient temperature may have an impact on the weight of perirenal fat in rats, as depicted in Figure 1E (P < 0.01). The perirenal fat weight in the CRD16 and CRN16 groups exhibited a significant increase compared to the CRD25 and CRN25 groups, with the most pronounced increase observed in the CRN16 group. However, no statistically significant differences were observed in testicular fat weight among the four groups (Figure 1F, P > 0.05). Histological examination of liver and pancreatic tissues using hematoxylin and eosin (HE) staining, revealed no significant pathological alterations. Our findings suggest that exposure to low temperatures and alterations in dietary rhythms can enhance food intake in rats, leading to excessive fat accumulation and abnormal weight gain. To elucidate the specific effects of low temperature on glycolipid metabolism, further investigations involving glucose tolerance tests and insulin-related indicators are warranted. 3.2 Nighttime Eating Might Result in Increased Glucose Intolerance and Insulin Resistance. To investigate the impact of low-temperature exposure and time-restricted feeding on glycolipid metabolism, we employed the oral glucose tolerance test (OGTT) to assess glucose tolerance in rats. Our results indicated that blood glucose levels were higher in both the CRN16 and CRN25 groups compared to the CRD25 and CRD16 groups, with a particularly notable increase observed in the CRN16 group (Figure 2A). Additionally, analysis of the area under the curve (AUC) demonstrated a significant elevation in the AUC value for the CRN16 group (Figure 2B, P < 0.05). These findings suggest that the nighttime feeding behavior of rats under low-temperature conditions may enhance glucose tolerance. Numerous studies have established a strong association between glycolipid metabolism disorders, diabetes, and insulin resistance. In comparison to the CRD25 group, the CRN16 group exhibited significantly higher fasting insulin (FINS) levels (P < 0.01) and homeostatic model assessment of insulin resistance (HOMA-IR) index (P < 0.05), alongside a significant reduction in the homeostatic model assessment of insulin sensitivity (HOMA-IS) index (P < 0.05). Under low-temperature conditions, the FINS level in the CRN16 group was significantly elevated compared to the CRD16 group. Furthermore, there was a significant increase in the HOMA-IR index and a notable decrease in the HOMA-IS index (Figure 2C-E). When compared to the CRD25 and CRN25 groups, the CRD16 group of rats demonstrated significantly higher adiponectin levels and notably lower cortisol levels. However, no significant differences in leptin levels across the four groups (Figure 2F-H). Analyzing the effects of diurnal dietary rhythms and environmental temperatures on blood insulin levels and insulin resistance indicators in rats, it was found that rats subjected to time-restricted feeding during nighttime developed hyperinsulinemia and exhibited signs of insulin resistance. 3.3 Low-Temperature Treatment May Lead to Disturbances in Glycolipid Metabolism within Rat Serum To investigate the effects of diurnal dietary rhythms and low-temperature treatment on blood glucose and lipid levels (Figure 2A, Figure 3), an automated biochemical analyzer was employed to evaluate the fluctuations in serum blood glucose and lipid levels for each group. The study’s findings indicated that as the temperature decreased, the night-feeding rats in both the CRN25 and CRN16 groups experienced a significant increase in blood glucose levels (P≤0.01). Concurrently, there was a significant increase in serum total cholesterol, lipid levels, and high-density lipoprotein levels. In comparison to the other three groups, the serum triglyceride (TG) level in the CRN16 group was significantly higher (P ≤ 0.05), with no significant correlation observed with temperature. Figure 3D suggests a potential relationship between serum low-density lipoprotein cholesterol (LDL-C) levels and environmental temperature. However, this association was not statistically significant. Overall, nocturnal feeding may impede glucose circulation within the human body, while low temperatures could exacerbate glycolipid metabolic disorders associated with nocturnal feeding. Given that disturbances in glycolipid metabolism can lead to impaired intestinal function, alterations in gut microbiota, and abnormal bile acid profiles, we conducted a comprehensive analysis of the differences in intestinal flora species among the various groups. Additionally, we examined the effects of low-temperature treatment and time-restricted diets on intestinal microorganisms. 3.4 Low Temperatures Result in Abnormal Alterations of the Intestinal Flora in Rats. The variations in intestinal flora species among the experimental groups were examined utilizing Linear Discriminant Analysis Effect Size (LEfSe) analysis. The effects of low-temperature exposure and nocturnal feeding on the richness and evenness of intestinal flora were evaluated using the Chao1, Shannon, and Simpson indices (Figure 4). Compared to the CRD25 group, the CRN25 group exhibited a significant reduction in both the Chao1 and Shannon indices (P < 0.05). Similarly, when compared to the CRD16 group, the CRN16 group also exhibited a significant decrease in these indices (P < 0.05). Notably, no significant variations were detected in the Simpson index among the four groups. These findings suggest that nocturnal dietary patterns can lead to a substantial reduction in the alpha diversity of the microbial community. Furthermore, the β diversity of the microbial community in the CRN16 group was significantly reduced compared to the CRN25 group, suggesting that low-temperature environments impose a certain degree of disturbance on the intestinal flora. To further investigate the impact of environmental temperature on intestinal microbial flora, we analyzed the relative abundances of microorganisms at the phylum level to elucidate variations within the intestinal microbiota. Firmicutes and Bacteroidetes are the predominant phyla involved in the production of short-chain fatty acids (SCFAs) within the intestinal microbiota. Our findings revealed that the relative abundance of Firmicutes remained largely consistent across all four experimental groups. The study demonstrated that, compared to the CRD25 group, the CRN25 group exhibited a significant reduction in the relative abundances of Bacteroidetes and Actinobacteria (P < 0.05), while the Firmicutes/Bacteroidetes ratio showed a marked increase (P < 0.01). Meanwhile, when compared to the CRD16 group, the CRN16 group displayed a significant decrease in the relative abundances of Bacteroidetes and Actinobacteria (P < 0.05), accompanied by a significant reduction in the Firmicutes/Bacteroidetes ratio (P < 0.01). The intestinal microbiome balance in the CRD16 group was disrupted, with notable alterations observed within the Lactobacillus genus. In conclusion, there is a notable correlation between glycolipid metabolism disorders and the proliferation of Lactobacillus. Future research will involve a comprehensive analysis of the intestinal microbiota in rats, with a focus on assessing correlations between species exhibiting significant differences and relevant physiological indicators. We aim to evaluate the impact of diurnal dietary rhythms and environmental temperatures on the intestinal microbiota of rats by analyzing inter-group differences. Our study conducted a detailed examination of the correlations between intestinal bacterial composition, focusing on the top 15 bacterial genera with higher abundances (Figure 5A). The study revealed significant differences in the intestinal microbiota among the four groups. Notably, compared to the CRN25 group, the CRN16 group exhibited markedly elevated relative abundances of g_Lactobacillus and g_Allobaculum, while the relative abundances of g_norank_f_Muribaculaceae, B_Blautia, and other lactobacilli exhibited a notable decrease. These results suggest that both low-temperature environments and dietary rhythms can induce specific disturbances in the intestinal microbiota (Figure 5B). Linear Discriminant Analysis (LDA) revealed that the CRD25 group exhibited a significant enrichment of g__unclassified_f__Lachnospiraceae, whereas the CRN25 group was predominantly enriched with Firmicutes Lachnospiraceae and Proteobacteria. In contrast, the CRD16 group showed enrichment of Firmicutes Lactobacillus, whereas the CRN16 group was primarily enriched with p__Bacteroidetes. Significant variations were observed in the quantities of intestinal Lactobacillus species, including Lactobacillus reuteri and Lactobacillus faecis, among others (Figure 5C). Correlation heatmap analysis revealed positive correlations among five Lactobacillus species, including Lactobacillus reuteri and Lactobacillus faecis, among others. Notably, four specific Lactobacillus species demonstrated a negative correlation with the abundance of the majority of the intestinal microbiota (Figure 5D). Evidence suggests that low-temperature environments and nocturnal feeding habits may favorably influence the intestinal colonization of most lactobacilli, potentially contributing to a decrease in the overall abundance of the intestinal microbiota. Therefore, the disruption of glycolipid metabolism induced by a low-temperature environment may be intricately associated with the specific strains of lactobacilli colonizing the intestine. 3.5 The Impact of Low-temperature Environment on the Content of Monoamine Neurotransmitters in Serum Given the fundamental role of monoamine neurotransmitters in regulating appetite and energy intake-owing to their ability to induce satiety or stimulate food cravings-we propose the existence of a complex and closely interconnected relationship between monoamine neurotransmitters and glycolipid metabolism. Based on this, our research aims to investigate the effects of time-restricted dietary strategies and low-temperature treatments on plasma concentrations of monoamine neurotransmitters (Figure 6, Table S1-S2). Our findings indicate that exposure to a low-temperature environment significantly elevates melatonin (MT) secretion while concurrently reducing plasma dopamine levels. Although plasma norepinephrine (NE) levels increase in response to decreased temperature, the magnitude of this increase is not statistically significant. The plasma concentration of 5-hydroxytryptamine (5-HT) did not demonstrate significant differences across the groups. A potential correlation may exist among the circadian rhythms of diet and sleep, environmental temperature, and the levels of peripheral melatonin and dopamine. We conducted an additional analysis to examine the associations between the gut microbiome and both neurotransmitters and lipids in the serum (Figure 7). As depicted in Figure 7, a notable positive correlation was observed between Lactobacillus and neurotransmitters. During these investigations, we identified a novel lactic acid bacterium, Reuteri, and examined its relationship with other lactic acid bacteria and triglycerides (P < 0.001). Furthermore, a significant positive correlation was identified between four types of lactic acid bacteria and dopamine levels (P < 0.01). Additionally, three species of lactobacilli exhibited a notable positive correlation with blood glucose levels (P < 0.01). In conclusion, a nocturnal diet, in conjunction with exposure to low-temperature environments, may exacerbate disorders related to glycolipid metabolism. This combination has the potential to disrupt the normal secretion of monoamine neurotransmitters and adversely affect the biological rhythms of tissues and organs. Future research will entail a comprehensive analysis of the types and variation characteristics of metabolites present in serum, aiming to elucidate their roles and interactions, as well as the impact of environmental temperature and time-restricted dietary patterns on glycolipid metabolism. 3.6 The Effect of Low-Temperature Treatment on Blood Metabolite Levels To examine the effects of a low ambient temperature environment on serum metabolites, we analyzed the PCA results, which revealed that inter-sample variations within the CRN25 group were relatively minor. In contrast, the greater distances between samples in the other three groups indicated more significant differences among them. Further investigation into the disparities among the various treatment groups (Figure 7A), demonstrates that the OPLS-DA model results clearly distinguish between CRD25 and CRD16, whereas the discriminatory power for the other two groups is comparatively limited (Figure 7B). Additionally, the Venn diagram indicates that a total of 1062 OUTs were identified across the four groups, with 977 being common among them (Figure 7C). Through the classification of metabolites using KEGG, it was found that compounds such as lipids, peptides, hormones, and neurotransmitters were relatively abundant. Specific compounds such as fatty acids, eicosanoids, glycolipids, and phospholipids were identified within the lipid category. The classification of metabolic pathways for these compounds indicates that the majority are associated with Metabolism, Organismal Systems, and Human Diseases. Notably, the highest number of compounds is associated with Amino Acid Metabolism and Lipid Metabolism. This finding further supports the notion that low-temperature treatment and modifications in dietary rhythms can significantly influence the levels and composition of metabolites in mouse serum. Subsequently, we will conduct a comprehensive analysis of metabolites that exhibit significant differences and investigate the potential relationships between these differential metabolites and glycolipid metabolism. Volcano plots were utilized to assess the overall trends in metabolite content across the four groups, evaluate the statistical significance of observed differences, and identify metabolites that exhibited a VIP > 1, P < 0.05, and fold change (FC) ≥ 2 as being significantly influenced by PC (Figure 8A, Figure S1). In comparison to the CRN25 group, the CRD25 group exhibited upregulation of 76 metabolites and downregulation of 110 metabolites. Relative to the CRD16 group, the CRD25 group demonstrated upregulation of 34 metabolites and downregulation of 107 metabolites. When compared to the CRN16 group, 17 metabolites were upregulated and 39 were downregulated in the CRD25 group. About the CRD16 group, the CRN16 group showed upregulation of 79 metabolites and downregulation of 110 metabolites (Figure 8A-D). The volcano plot findings facilitated the generation of a hierarchical clustering heatmap of the selected differential metabolites, which illustrated significant variations in metabolite levels across the four groups (Figure 8E). To further explore the effects of low-temperature treatment on metabolite-related metabolic pathways, we selected the top 20 significant pathways for KEGG pathway enrichment analysis. This enrichment analysis was conducted using the annotation results of differential metabolites in conjunction with the hypergeometric test from cluster Profiler, and a bubble plot was subsequently generated. The intensity of the blue color in the points depicted in the figure correlates with the significance of enrichment, while the size of each point corresponds to the number of differentially enriched metabolites. The metabolic pathways exhibiting relatively high levels of enrichment primarily include Phenylalanine metabolism, Caffeine metabolism, Sphingolipid signaling pathway, Cholinergic synapse, Fatty acid degradation, African trypanosomiasis, Serotonergic synapse, alpha-linolenic acid metabolism, and Arginine and proline metabolism (Figure 8F). These findings further substantiate that low-temperature treatment may modify the metabolic profile characteristics associated with glycolipid and amino acid metabolism in rat serum. 3.7 Correlation Analysis of the Gut Microbiota with Glycolipid Metabolism and Neurotransmitters Based on the aforementioned research findings, it has been identified that the combined effects of a low-temperature environment and nighttime dietary habits can lead to disturbances in glycolipid metabolism. Furthermore, there appears to be a significant association among these factors, as well as with gut microbiota, neurotransmitters, and serum metabolites. To conduct a comprehensive analysis of the relationships between gut microbiota, neurotransmitters, and the serum lipid metabolome (Figure 9), we generated a correlation heatmap. Our analysis revealed a notable positive correlation between the gut microbiota and both neurotransmitter levels and lipid profiles in the serum. Specifically, the relationship between Lactobacillus species and triglycerides was significant (P < 0.001). Lactobacillus exhibits a notable positive correlation with blood sugar, dopamine, FINS, serum glucose (Serum_GLU), serum triglycerides (Serum_TG), GC, and the HOMA-IR index. Additionally, 5-HTA, HOMA-IGI, HOMA-IS, MT, serum LDL cholesterol (Serum_LDL_C), and total cholesterol (Serum_TC) showed significant positive correlations with multiple Lactobacillus species. Furthermore, three Lactobacillus-related factors (P < 0.01) exhibited a significant positive correlation with blood glucose levels. These research findings revealed a significant and close association between intestinal lactobacilli and levels of dopamine, triglycerides, and blood glucose. Given the substantial volume of data represented in the correlation heatmap, we subsequently filtered this data and employed Cytoscape software to visually represent the refined correlation data in the form of a network graph. 3.8 Co-expression Analysis To facilitate a more intuitive comprehension of the intricate correlations among serum indicators, neurotransmitters, gut microbiota, and metabolites (Figure 10), we utilized Spearman's rank correlation analysis and visualized the correlation data using Cytoscape software. Our findings revealed that the majority of serum glycolipid metabolism indicators (AUC, GC, FINS, HOMA-IR, Serum_GLU, Dopa), exhibited significant negative correlations with various lactobacilli species (g__Allobaculum, g__Parasutterella, g__norank_f__Erysipelotrichaceae, g__Blautia, g__Faecalibacterium, g__Romboutsia). In contrast, g__Bifidobacterium showed a significant positive correlation with MT. Furthermore, Serum_TG demonstrated a notable positive correlation with g__Lactobacillus. Additionally, it was identified that eight types of lactobacilli were closely associated with bile acid metabolism; among these species, both g__Lactobacillus and g__Faecalibacterium displayed significant positive correlations with [2-hydroxy-3-(phenoxycarbonyl)phenyl] oxidanesulfonic acid. A bidirectional regulatory relationship exists between bile acids and the microbiota, where the intestinal microbial community can be restructured through the modulation of bile acids. Consequently, these findings reveal that a low-temperature environment coupled with disrupted dietary rhythms, induces disturbances in glycolipid metabolism, gut microbiota, neurotransmitters, bile acids, and other metabolites. These disruptions ultimately disturb the physiological equilibrium in rats, leading to the development of various metabolic diseases. 4. Discussion Previous studies have demonstrated that short-term exposure to low temperatures can enhance glucose transport in the intestine and liver, thereby modulating glycolipid metabolism [18,19] . Additionally, time-restricted eating (TRE) strategies have been shown to improve metabolic health [20] . Both low-temperature exposure and TRE are increasingly recognized as innovative strategies and targets for managing metabolic diseases. However, our research has demonstrated that prolonged exposure to low temperatures combined with improper time-restricted eating can result in glycolipid metabolism disorders. This phenomenon appears to share mechanisms with the onset or exacerbation of diabetes during winter months and may be closely linked to alterations in energy homeostasis molecules, neurotransmitters, and the intestinal microbiota. 4.1 Low-Temperature Environments and Nocturnal Eating Might Induce Disorders in Glycolipid Metabolism. Seasonal variations in glycolipid metabolism among animals are intricately linked to growth and reproductive processes. During autumn, many animals consume a substantial amount of high-calorie and high-fat foods, facilitating the accumulation of body fat necessary for sustaining physiological activities and maintaining stable body temperature throughout the challenging winter months [21, 22] . Concurrently, the low-temperature characteristic of winter can impede both glucose uptake by muscles and insulin secretion [23] . In response to extremely low temperatures, mammals activate thermoregulatory adaptive mechanisms to maintain core body temperature, such as heat production through Brown Adipose Tissue (BAT) or other thermogenic pathways. They also strategically adjust fat accumulation according to their specific conditions, ensuring no detrimental effects on health [24] . Nonetheless, with the rapid advancement of technology, mammals can now regulate their body temperature by modifying their dietary patterns and meal timings. This adaptation contradicts the organism's inherent physiological needs, resulting in significant disruptions to its biological circadian rhythm and subsequently contributing to obesity, diabetes, and other metabolic disorders [25-27] . Through meticulous monitoring of changes in glycolipid metabolism over 24 hours, it was revealed that nocturnal eating significantly elevates blood glucose and lipid levels. This phenomenon has implications for various diseases, including diabetes, obesity, and hyperlipidemia. These findings are consistent with our previous research results [28] . In the present study, following the intervention of environmental temperature and circadian eating rhythm, we observed a significant increase in body weight among SD rats exposed to a low-temperature environment. Additionally, serum levels of blood glucose and lipids were markedly elevated in both the low-temperature environment group and the nighttime time-restricted feeding group (Figure 1), leading to the development of insulin resistance (Figure 2). This study reveals that the combined effects of a low-temperature environment and nighttime time-restricted eating can rapidly lead mammals to consume excessive amounts of food. This behavior leads to substantial fat accumulation, which exacerbates disorders related to glycolipid metabolism, and ultimately precipitates early symptoms of type 2 diabetes. Research indicates that insulin levels within the organism undergo fluctuations over 24 hours. Prolonged exposure to a low-temperature environment stimulates an increase in insulin secretion, thereby inducing a sensation of hunger [29] . Under the combined influence of hunger and decreased body temperature, the organism is more inclined to consume a substantial amount of high-calorie foods, imposing additional stress on insulin-secreting tissues such as the pancreas. This stress may significantly contribute to pancreatic injury, potentially resulting in insulin resistance. 4.2 The Disorders of Glycolipid Metabolism Induced by the Low-Temperature Environment are Associated with Alterations in the Intestinal Microbiota. The gut microbiota is often referred to as the "second genome" due to its vital role in maintaining the host's health. It is predominantly composed of Bacteroidetes and Firmicutes, with additional contributions from Proteobacteria, Actinobacteria, Fusobacteria, and Verrucomicrobia. Notably, Firmicutes account for 64% of the total gut microbiota [30, 31] . The composition and structure of the gut microbiota are influenced by various external factors, including high-fat diets and circadian rhythm disorders [32] . Research has demonstrated that disruptions in circadian clock genes, such as Bmal1, in murine models can result in impaired glucose tolerance and diminished insulin secretion [33] . Furthermore, these disruptions lead to significant alterations in the circadian rhythmicity of the intestinal microbiota, affecting both bacterial abundance and composition [34, 34] . These findings suggest a potential interplay between the host's circadian clock and the rhythmicity of the gut microbiota. Disruption of the circadian rhythm system can modify the intestinal microbial community, potentially disturbing host metabolism, energy homeostasis, and inflammatory pathways, thereby contributing to the development of metabolic syndrome. This study found that variations in environmental temperature and circadian rhythm notably decreased the abundance of intestinal microbiota (Figure 4), influenced glucose transport and glycolysis within the intestinal microbiota (Figure 6), and induced downregulation of genes associated with insulin resistance signaling pathways in both the intestinal wall and surrounding tissues (such as INSR, which encodes the insulin receptor) (Figure 8). Recent studies have demonstrated that organisms characterized by a low microbial gene count (LGC) are more predisposed to increased body fat, insulin resistance, dyslipidemia, and pronounced inflammatory phenotypes, suggesting a strong association between LGC and metabolic [36] . Furthermore, lactobacilli within the intestinal microbiota have been implicated in glycolipid metabolism. Specifically, Lactobacillus fermentum and Lactobacillus esophagitis have demonstrated potential in regulating weight gain in mice and reducing the risk of type 2 diabetes [37] . Notably, an increase in Lactobacillus acidophilus, Lactobacillus casei, and Lactobacillus rhamnosus has been observed in individuals with diabetes [38] , suggesting that these bacteria may influence glycolipid metabolism through intricate interactions. This study also identified a relatively high abundance of the genus Lactobacillus, which was associated with low-temperature environments and dietary circadian rhythms. Furthermore, a significant negative correlation was observed between the abundance of Lactobacillus and other bacterial groups (Figure 5D). Consequently, this study further elucidates that the dysregulation of intestinal flora may serve as a pivotal connection between low-temperature environments and altered dietary rhythms, ultimately contributing to disturbances in glycolipid metabolism. 4.3 The Glycolipid Metabolism Disorder Induced by the Low-Temperature Environment Might be Related to the Alterations in Peripheral Neurotransmitters Induced by the Intestinal Microbiota. An increasing number of studies have revealed the bidirectional interactions within the intestinal milieu, encompassing the intestinal epithelium, the mucosal immune system, and the intestinal microbiota, in conjunction with the enteric nervous system [39] . The intestinal microbiota possesses the ability to activate the enteric nervous system through metabolic by-products, modulate the secretion profile of enteric nerve metabolites, and influence neurotransmitter synthesis in both central and peripheral nervous systems [40] . Consequently, it can impact biological rhythms [41] and the expression of circadian rhythm genes such as Clock [42] . The intestinal microbiota plays a crucial role in regulating the secretion rhythm of energy homeostasis regulators [43] , including ghrelin, leptin, insulin, glucagon-like peptide 1 (GLP-1), and adiponectin [44] . In our research, we found that both low-temperature environments and nocturnal dietary habits significantly influence the accumulation of melatonin and dopamine in serum. Further analysis revealed that low environmental temperature exerts a more pronounced regulatory effect on dopamine accumulation (Figure 4). We conducted a correlation analysis involving neurotransmitters, plasma lipid profiles, and gut microbiota (Figure 9). Notably, five species of Lactobacillus microorganisms, including Lactobacillus reuteri, exhibited a significant positive correlation with triglyceride levels (P < 0.001). Furthermore, three species of Lactobacillus microorganisms exhibited a highly significant positive correlation with blood glucose levels (P < 0.01) (Figure 7). Additionally, four species of Lactobacillus, including Lactobacillus reuteri, demonstrated a significant negative correlation with the intestinal microbiota (P < 0.01) (Figure 8). These findings suggest an interrelationship among Lactobacillus, dopamine, and glycolipid metabolism. It has been reported that the genus Lactobacillus is capable of synthesizing a diverse array of neurotransmitters, including dopamine and norepinephrine, among others [45] . The presence of dopaminergic neurons and dopamine transporter proteins within the intestinal lamina propria potentially plays a crucial role in facilitating dopamine transport and activating dopaminergic neurons [46] . These findings suggest that specific Lactobacillus microorganisms can modulate the physiological activities of organisms through the secretion of neurotransmitters, including dopamine. Meanwhile, Lactobacillus casei has been demonstrated to mitigate depressive-like behaviors in rats and to affect the plasma levels of dopamine (DA), norepinephrine (NE), and serotonin (5-HT), highlighting the intricate relationship between Lactobacillus microorganisms and neurotransmitters dynamics within the brain [47] . In summary, low environmental temperature may reduce the biodiversity of the intestinal flora, intervene in the accumulation of peripheral neurotransmitters such as dopamine, alter gene expression in tissues such as the pancreas, and disturb the biological rhythms of tissues/organs. 5 Conclusions In summary, this study has revealed that the synergistic effects of low-temperature environments and circadian dietary rhythms contribute to increased food intake, promote excessive fat deposition, exacerbate glucose and lipid metabolic disorders, and precipitate the early onset of metabolic diseases, including type 2 diabetes. Furthermore, exposure to low environmental temperature significantly diminishes the biodiversity of the intestinal microbiota, disrupts bile acid metabolism and the synthesis of peripheral neurotransmitters (such as dopamine), and disturbs the biological rhythms of tissues and organs. Consequently, the strategic regulation of environmental temperature and the implementation of time-restricted dietary interventions are anticipated to emerge as innovative and effective strategies for the management of type 2 diabetes. Declarations Author Contributions: Yinglan Shi and Zhaoxia Huang: Investigation, Methodology, Software, Writing-original draft, Writing-review & editing, Validation. Xiaofang Tang: Investigation, Methodology, Validation. Yongqin Zhang: Visualization, Data curation, Investigation. Die Shi: Conceptualization, Supervision. Jing Chen: Software. Qingxue Wang: Conceptualization, Methodology, Supervision, Writing-review & editing, Project administration. Jun Li: Resources, Project administration. Funding: This work was supported by the National Natural Science Foundation of China (NO.82060163 and 82160167), the scientific and technological research project of traditional Chinese medicine and ethnic medicine of Guizhou Provincial Administration of traditional Chinese medicine (QZYY-2022-004) Institutional Review Board Statement: The Committee on the Ethics of Animal Experiments of Guizhou University of Traditional Chinese Medicine approved all the animal experiments (Permission number: 20210188). Data Availability Statement: All the data generated or analyzed throughout this study are encompassed in the text and supplementary files. They can also be obtained from the corresponding author upon reasonable request. Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Ruze R, Liu T, Zou X, Song J, Chen Y, Xu R, Yin X, Xu Q. 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Neurosci Lett . 2022 Mar 23;774:136474.[PubMed] Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6258553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":537610857,"identity":"345a31b4-991e-44dd-b18b-5ef34e9b3ecc","order_by":0,"name":"Yinglan Shi","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yinglan","middleName":"","lastName":"Shi","suffix":""},{"id":537610859,"identity":"c4528d18-c5e9-49bc-bb56-e2ce2776bc76","order_by":1,"name":"Zhaoxia Huang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhaoxia","middleName":"","lastName":"Huang","suffix":""},{"id":537610862,"identity":"cad9ef3f-3811-4362-b9df-533e7ae3d5ae","order_by":2,"name":"Xiaofang Tang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaofang","middleName":"","lastName":"Tang","suffix":""},{"id":537610865,"identity":"6a1012c6-91aa-40b7-9788-605fb4e7f78f","order_by":3,"name":"Yongqin Zhang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongqin","middleName":"","lastName":"Zhang","suffix":""},{"id":537610869,"identity":"894f719a-d33d-4ce7-ab49-c6a7509801cb","order_by":4,"name":"Die Shi","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Die","middleName":"","lastName":"Shi","suffix":""},{"id":537610870,"identity":"5e2b2c1f-c44a-4b34-a763-0e1b9f35236c","order_by":5,"name":"Jing Chen","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""},{"id":537610874,"identity":"0f30ba94-abe2-488f-a258-aea8947df61d","order_by":6,"name":"Qingxue Wang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qingxue","middleName":"","lastName":"Wang","suffix":""},{"id":537610876,"identity":"49fc07ba-fab0-44f4-bce7-63d72f939e4d","order_by":7,"name":"Jun Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYHACZiC24eHnbyBNS5qM5IwDpGk5bGPQkECkevnZzYcNPu45z2PAcIDxw8ccIrQY3DmWnDjj2W0ec+YGZsmZ24jRIpFjfJjnwG0ey4YDbMy8xGiRn5H/+fCfA+d4DA4kEKmF4UYOczLDgQMkaAH6xdiw50Ayj+SMg83E+QUYYo8lfhyws+fnbz744SNRDpOAsxgbiFGPomUUjIJRMApGAQ4AACZbNu+U4tHFAAAAAElFTkSuQmCC","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-19 06:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6258553/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6258553/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95500508,"identity":"b2818f19-737b-42b8-ad0b-8356f21bdd86","added_by":"auto","created_at":"2025-11-10 05:22:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176020,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of low-temperature treatment on Physiological Indexes of the night-eating rats (A) experimental scheme (n=9); (B) Food intake/day/animal; (C) Body weight; (D) Weight gain; (E) Perirenal fat weight; (F) Peritesticular fat weight.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/fadf54464f8da912d8bd2196.jpeg"},{"id":95528043,"identity":"48a51a62-6d69-4b37-87ed-9100b828842e","added_by":"auto","created_at":"2025-11-10 10:15:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178849,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of low temperature and time-restricted eating on insulin resistance-related indicators in rats. (A) Oral glucose tolerance test (OGTT) curve of rats; (B) Area under the curve (AUC); (C) Serum fasting insulin (FINS) levels; (D) Homeostasis model assessment of insulin resistance (HOMA-IR); (E) Insulin sensitivity (HOMA-IS) index; (F) Adiponectin levels; (G) Cortisol levels; (H) Leptin levels.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/d69b7d53a15764e45ab8925c.jpeg"},{"id":95528063,"identity":"a0757115-0aa7-43fe-a867-55e8cef70106","added_by":"auto","created_at":"2025-11-10 10:15:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91748,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of hypothermia and time-restricted eating on serum lipids and blood glucose. (A) Serum triglycerides (TG), (B) total cholesterol (TC), (C) high-density lipoprotein (HDL), and (D) low-density lipoprotein cholesterol (LDL-C).\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/67123812f67192c393bd0674.jpeg"},{"id":95528986,"identity":"42fde694-7511-4fa9-988d-d36390fc742b","added_by":"auto","created_at":"2025-11-10 10:16:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":190826,"visible":true,"origin":"","legend":"\u003cp\u003erat gut microbiota analysis. Alpha diversity analysis (A) Chao l, (B) Shannon and (C) Simpson index, (D) PC3 index, (E) NMDS2 index, (F) relative abundance at phylum level in each group, and (G-J) relative abundance of Lactobacillus in rats.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/bebd72564db5522b77c04ccd.jpeg"},{"id":95500511,"identity":"4a465e5a-85db-44f7-ae94-0b83b16c5279","added_by":"auto","created_at":"2025-11-10 05:22:57","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":301503,"visible":true,"origin":"","legend":"\u003cp\u003eIn-depth analysis of rat gut microbiota. (A) Flora analysis of the top 15 gut microbes, (B) significantly different species. (C) Linear discriminant analysis effect size (LEfSe) analysis. (D) Correlation analysis of intestinal microbes.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/009cf7ea6415ea6fbdaa4ec1.jpeg"},{"id":95528524,"identity":"109e5887-1c8d-4dcb-8306-1e315cbb02f4","added_by":"auto","created_at":"2025-11-10 10:16:14","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":78360,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of hypothermia and time-restricted eating on monoamine neurotransmitters (A) dopamine, (B) MT: melatonin, (C) NE: norepinephrine, (D) 5-HT:5-hydroxytryptamine.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/481ce8156b9b687132066e47.jpeg"},{"id":95529109,"identity":"c5f392f9-ff60-4846-8a27-43ea23f0d2ac","added_by":"auto","created_at":"2025-11-10 10:16:46","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":181488,"visible":true,"origin":"","legend":"\u003cp\u003echanges in the metabolome in serum. (A) Score plot of PCA model, (B) score plot of OPLS-DA model among groups, (C) Venn plot, (D) metabolite classification based on KEGG, (E) metabolic pathways of compounds by KEGG enrichment analysis\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/bf9c8494fa54e81e76188d0c.jpeg"},{"id":95500517,"identity":"36af2c55-a169-42b6-a914-f7925f6011ea","added_by":"auto","created_at":"2025-11-10 05:22:57","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":257843,"visible":true,"origin":"","legend":"\u003cp\u003echanges in metabolic components in serum. (A-D) volcano map (E) Heatmap. (F) KEGG bubble chart\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/82f8e809a784941c751a145c.jpeg"},{"id":95500518,"identity":"cdf25987-aa89-4331-8b09-d02675e82340","added_by":"auto","created_at":"2025-11-10 05:22:57","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":339898,"visible":true,"origin":"","legend":"\u003cp\u003ecorrelation analysis of neurotransmitters, blood lipids, and gut microbiota levels. The x-axis shows biochemical indicators and neurotransmitters, and the y-axis shows intestinal microbes.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/dd8ada34e8a13d9e4a97294c.jpeg"},{"id":95500515,"identity":"9fdc492c-4f5b-4dee-b724-884dffda5318","added_by":"auto","created_at":"2025-11-10 05:22:57","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":555586,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation network analysis (showing the correlation of significant differences between specific gut microbiota, CO-regulated metabolites in feces, and obesity-related biomarkers); The red line represents a positive correlation and the green line represents a negative correlation. Correlations were calculated with Spearman rank correlation. Screening data （Hr＞0.5， P＜0.05），|r|\u0026gt;0.5， Network maps were generated using Cytoscape.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/33ecbbc4813303a5f697d1b3.png"},{"id":95655685,"identity":"6efe89a2-51bf-4319-aa61-5aea3c335293","added_by":"auto","created_at":"2025-11-11 16:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3632145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/978904c0-7015-49ac-befb-a35e27e50edc.pdf"},{"id":95500512,"identity":"d00c4d87-c6f5-40ac-8476-cdc54dd04d61","added_by":"auto","created_at":"2025-11-10 05:22:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":390335,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6258553/v1/01b53b5dfe9e745ad1885747.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint Effects of Low Ambient Temperature and Nocturnal Eating Behavior Contributing to Glycolipid Metabolism Disorder by Changing Gut Microbiota Structure","fulltext":[{"header":"Highlight","content":"\u003cp\u003e1. Low ambient temperature and nocturnal eating might induce disorders in glycolipid metabolism. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Disorders of glycolipid metabolism induced by low-ambient temperature are associated with alterations in the intestinal microbiota.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eDiabetes has emerged as a salient global health problem in the 21st century. The International Diabetes Federation (IDF) projected that the population of diabetes patients will increase to 643 - 783 million by 2030 - 2045\u003csup\u003e\u0026nbsp;[1]\u003c/sup\u003e. Diabetes can contribute to microvascular complications \u003csup\u003e[2]\u003c/sup\u003e, affecting the kidneys, eyes, and nervous system, and poses risks for life-threatening conditions like, diabetic nephropathy, heart disease, and stroke, raising mortality and morbidity rates these complications can impair cognitive function, physical health, and social life quality \u003csup\u003e[3]\u003c/sup\u003e. Managing blood glucose can be particularly challenging for individuals with diabetes in winter, potentially leading to new complications \u003csup\u003e[4]\u003c/sup\u003e. Therefore, further research is crucial to explore how environmental temperature affects glycolipid metabolism and understand the mechanisms behind metabolic disorders in cold conditions.\u003c/p\u003e\n\u003cp\u003eWinter\u0026apos;s low temperatures trigger the body\u0026apos;s stress response, activating the sympathetic nervous system increasing catecholamine secretion, and raising blood glucose levels \u003csup\u003e[5]\u003c/sup\u003e. In diabetics, cold can worsen blood pressure and glucose levels\u003csup\u003e\u0026nbsp;[6, 7]\u003c/sup\u003e. Prolonged cold exposure may lead to diseases like myocardial infarction (MI), and cerebral issues \u003csup\u003e[8]\u003c/sup\u003e. Low temperatures significantly stimulate the sympathetic nervous system, increasing epinephrine release and accelerating liver glycogen breakdown. This process reduces glucose uptake by tissues like muscle, raising blood sugar levels. Otherwise, cold environments can increase cravings for high-fat, high-sugar foods, and decrease physical activity, worsening blood sugar levels \u003csup\u003e[9, 10]\u003c/sup\u003e. Studies show short-term cold stress affects glucose metabolism and might aid weight loss, but the impact of prolonged winter on glycolipid metabolism is unclear. Additionally, plentiful winter food can lead to poor habits affecting metabolism \u003csup\u003e[11,12]\u003c/sup\u003e. Life pressure and fast-paced technology are also altering daily habits and rest patterns. People who eat late at night or early in the morning often disrupt their circadian rhythms and gut microbiota \u003csup\u003e[13]\u003c/sup\u003e, affecting insulin secretion and glycolipid metabolism. We propose that both environmental temperature and circadian dietary rhythms influence glycolipid metabolism, though the exact mechanisms are unclear. Diabetic patients may experience worsened symptoms in cold environments due to these factors.\u003c/p\u003e\n\u003cp\u003eThis paper seeks to investigate the effects of low temperature and altered circadian dietary rhythms, on glycolipid metabolism, as well as their potential to induce glycolipid metabolism disorders in rats. Additionally, this paper aims to examine the influence of glycolipid metabolism, energy balance, neurotransmitters, gut microbiota, and metabolites on the progression of type 2 diabetes. Our ultimate goal is to elucidate the underlying pathological mechanisms and identify novel therapeutic strategies for metabolic diseases in the foreseeable future.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003ch2\u003e2.1 Animals and Experimental Design\u003c/h2\u003e\n\u003cp\u003eThe experimental subjects consisted of male SPF SD rats, aged 6 weeks and weighing 180.0 \u0026plusmn; 10.0 g, which were procured from Changsha Tianqin Biotechnology Co., Ltd. All animal experiments and procedures received approval from the Experimental Animal Ethics Review Committee of Guizhou University of Chinese Medicine (approval number: 20210188). Meanwhile, all experiments were strictly carried out following the relevant protocols and in compliance with the ARRIVE guidelines. Feed the standard diet for one week, after which 36 rats will be randomly divided into four groups (N=9). The groups include: the CRD25 group, which was maintained at 25\u0026deg;C and fed from 8:00 ~ 20:00; the CRN25 group, which was maintained at 25\u0026deg;C and fed from 20:00 ~ 8:00; the CRD16 group, which was maintained at 16\u0026deg;C and fed from 8:00 ~ 20:00; and the CRN16 group, which was maintained at 16\u0026deg;C and fed from 20:00~8:00(The experimental protocol is comprehensively outlined in Figure 1A.). The experiment was conducted for 10 days, during which the basic animals\u0026apos; weight and food intake were meticulously recorded daily throughout the study period.\u0026nbsp;after the experiment, fresh fecal samples were collected and subsequently stored at -80\u0026deg;C in a refrigerator for future analysis of alterations in the intestinal microbiome and metabolites. Blood was collected from the abdominal aorta of rats and centrifuged at 1200xg for 15 minutes to obtain serum samples, which were then stored at -80\u0026deg;C for subsequent analysis. Subsequently, the rats were euthanized. All these rats were deeply anesthetized by intraperitoneal injection (2% sodium pentobarbital, 100\u0026nbsp;mg/kg) and killed. The liver, kidneys, epididymis, and subcutaneous adipose tissues were collected, weighed, and stored at -80\u0026deg;C for further analysis.\u003c/p\u003e\n\u003ch2\u003e2.2 Detection of\u0026nbsp;Oral\u0026nbsp;Glucose\u0026nbsp;Tolerance\u0026nbsp;Test\u0026nbsp;and\u0026nbsp;Insulin\u0026nbsp;Resistance-Related\u0026nbsp;Indicators\u003c/h2\u003e\n\u003cp\u003eBefore the oral glucose tolerance test (OGTT), rats underwent a 12-hour fasting period. A glucose solution at a concentration of 2 g/kg was administered intragastrically, and blood samples were collected from the tail vein for measurement at 0, 30, 60, and 120min post-administration using a glucometer (Jiangsu Yuyue Medical Equipment \u0026amp; Supply Co., Ltd., Danyang, China).\u0026nbsp;A line graph is constructed with the measured blood glucose concentration represented on the vertical axis and the corresponding measurement time displayed on the horizontal axis. The area under the curve (AUC) is calculated to quantify the cumulative changes in blood glucose response. The parameter for the homeostasis model assessment of insulin resistance (HOMA-IR) is calculated as follows: HOMA-IR = FINS (mU/L) \u0026times; FBG (mmol/L) / 22.5. The HOMA insulin sensitivity (HOMA-IS) index is defined as 1/(FBG \u0026times; FINS)\u003csup\u003e\u0026nbsp;[14]\u003c/sup\u003e.\u0026nbsp;The serum levels of fasting insulin (FINS), adiponectin, cortisol, indices related to insulin resistance, and leptin were measured using an ELISA kit.\u003c/p\u003e\n\u003ch2\u003e2.3 Biochemical Analysis\u003c/h2\u003e\n\u003cp\u003eSerum triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein cholesterol (LDL-C) levels were measured using an automated biochemical analyzer. Additionally, the effects of environmental temperature and dietary habits on serum lipid profiles and blood glucose levels were analyzed.\u003c/p\u003e\n\u003ch2\u003e2.4 Microbial Analysis of the Gut Microbiota\u003c/h2\u003e\n\u003cp\u003eTotal genomic DNA was extracted from fecal samples utilizing the MOBIO PowerSoil\u0026reg; DNA Extraction Kit (MOBIO Laboratories, Inc., Carlsbad, CA, USA). Subsequently, amplification and sequencing of the hypervariable regions V3-V4 of the 16S rRNA gene were performed on the Illumina Novaseq 6000 platform. The sequencing process was carried out by Beijing BoMaiKe Biotechnology Co., Ltd. The sequence reads was clustered utilizing USEARCH software at a similarity threshold of 97.0%, resulting in the identification of operational taxonomic units (OTUs) \u003csup\u003e[15]\u003c/sup\u003e.\u0026nbsp;Subsequently, the QIIME software was utilized to analyze the composition of the intestinal microbiota in each sample across various taxonomic levels (phylum, class, order, family, genus, and species) and to assess species abundance at different hierarchical classifications. An analysis of alpha and beta diversity was conducted to elucidate the diversity and structure of the intestinal microbiota. The \u0026beta; diversity analysis includes non-metric multidimensional scaling (NMDS) based on the Bray\u0026ndash;Curtis algorithm, as well as principal coordinate analysis (PCoA). Additionally, ANOSIM analysis is utilized to assess the significance of community differences among groups \u003csup\u003e[16]\u003c/sup\u003e. LEfSe (Linear Discriminant Analysis Effect Size) is utilized to identify biomarkers that exhibit statistically significant differences across various groups.\u003c/p\u003e\n\u003ch2\u003e2.5 Analysis of Monoamine Neurotransmitters in\u0026nbsp;the Serum\u003c/h2\u003e\n\u003cp\u003eThe concentration of neurotransmitters in the serum was quantitatively analyzed using high-performance liquid chromatography (HPLC). A suitable volume of the serum sample was collected, and methanol was added to precipitate proteins. The mixture was then subjected to centrifugation at 10,000g. Following this, the supernatant was carefully removed, evaporated to dryness under a nitrogen stream, and reconstituted to a final volume of 0.5 mL with methanol. An appropriate aliquot of this solution was filtered through a needle filter into a sample vial equipped with a liner for subsequent measurement. Detection and analysis were conducted using a Rigol L3000 high-performance liquid chromatograph equipped with a Kromasil C18 reversed-phase column (250 mm \u0026times; 4.6 mm, 5 \u0026mu;m). The mobile phase consisted of methanol as phase A and a 0.1 mol/L aqueous potassium dihydrogen phosphate solution as phase B. An injection volume of 10 \u0026mu;L was employed, with a flow rate set at 1 mL/min and the column maintained at a temperature of 30\u0026deg;C. The retention time was established at 40min. A fluorescence detector was utilized, with an excitation wavelength of 278 nm and an emission wavelength of 338 nm.\u003c/p\u003e\n\u003ch2\u003e2.6 Untargeted Metabolomic Analysis\u003c/h2\u003e\n\u003cp\u003eIn each group, 100 \u0026mu;l of serum samples were collected and thawed on ice. The samples underwent extraction, drying, centrifugation, and other necessary procedures. The resulting supernatants were transferred to clean vials for liquid chromatography-mass spectrometry (LC/MS) analysis. Before detection, the quality control (QC) samples were prepared by mixing with 10 \u0026mu;l of each sample. The analysis was conducted using a UHPLC-QTOF-MS system, employing a mobile phase consisting of 0.1% formic acid aqueous solution (A) and 0.1% formic acid acetonitrile (B) for gradient elution. The injection volume utilized was 1 \u0026mu;l \u003csup\u003e[17]\u003c/sup\u003e. Simultaneously, the Waters Xevo G2-XS QTOF high-resolution mass spectrometer was utilized to acquire mass spectrometry data. Furthermore, principal component analysis (PCA) model score plots were generated to examine the differences among the sample groups. The volcano plot was employed to assess the overall trends in metabolite content across each group, statistically evaluate the significance of observed differences, and calculate variable importance in projection (VIP) values. Meanwhile, bubble plots and heat maps were generated to analyze the associated metabolite pathways and the quantities of differential metabolites.\u003c/p\u003e\n\u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analyses were conducted using SPSS version 20.0. A one-way analysis of variance (ANOVA) was utilized to assess the statistical differences among various groups, followed by the Tukey-Kramer post hoc test. Conduct additional significant statistical tests utilizing R software (version 3.4.1) compatible with Windows operating systems. Meanwhile, the data were analyzed and visualized using GraphPad Prism version 8.0.1, with results presented as mean \u0026plusmn; standard deviation. Values with p \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Low\u0026nbsp;Temperature\u0026nbsp;Leads to a\u0026nbsp;Significant\u0026nbsp;Increase in\u0026nbsp;Body\u0026nbsp;Weight and\u0026nbsp;Food\u0026nbsp;Intake of\u0026nbsp;Rats\u0026nbsp;Fed at\u0026nbsp;Night\u003c/h2\u003e\n\u003cp\u003eTo achieve the assessment of alterations in the physiological indicators of healthy Sprague-Dawley (SD) rats induced by low-temperature exposure and dietary rhythm, we systematically recorded the daily food intake (Figure 1B) and body weight (Figure 1C, P \u0026lt; 0.01) across four groups of rats throughout the experimental duration (Figure 1A). Initially, body weights were comparable across all groups. By the end of the study, rats exposed to low temperatures (CRD16 and CRN16) exhibited a significant increase in both body weight and food intake compared to those maintained at a comfortable temperature (CRD25 and CRN25). Additionally, we analyzed the weight gain of the rats before and after the experiment (Figure 1D, P \u0026lt; 0.01). The final weight gains for the CRD25, CRN25, CRD16, and CRN16 groups were 49.0 \u0026plusmn; 18.4 g, 48.0 \u0026plusmn; 25.2 g, 62.8 \u0026plusmn; 22.4 g, and 101.3 \u0026plusmn; 17.0 g, respectively. Notably, the increase in weight observed in the night-time restricted group under low-temperature conditions (CRN16) was particularly remarkable. Moreover, ambient temperature may have an impact on the weight of perirenal fat in rats, as depicted in Figure 1E (P \u0026lt; 0.01). The perirenal fat weight in the CRD16 and CRN16 groups exhibited a significant increase compared to the CRD25 and CRN25 groups, with the most pronounced increase observed in the CRN16 group. However, no statistically significant differences were observed in testicular fat weight among the four groups (Figure 1F, P \u0026gt; 0.05).\u0026nbsp;Histological examination of liver and pancreatic tissues using hematoxylin and eosin (HE) staining, revealed no significant pathological alterations. Our findings suggest that exposure to low temperatures and alterations in dietary rhythms can enhance food intake in rats, leading to excessive fat accumulation and abnormal weight gain. To elucidate the specific effects of low temperature on glycolipid metabolism, further investigations involving glucose tolerance tests and insulin-related indicators are warranted.\u003c/p\u003e\n\u003ch2\u003e3.2 Nighttime\u0026nbsp;Eating\u0026nbsp;Might\u0026nbsp;Result in\u0026nbsp;Increased\u0026nbsp;Glucose\u0026nbsp;Intolerance and\u0026nbsp;Insulin\u0026nbsp;Resistance.\u003c/h2\u003e\n\u003cp\u003eTo investigate the impact of low-temperature exposure and time-restricted feeding on glycolipid metabolism, we employed the oral glucose tolerance test (OGTT) to assess glucose tolerance in rats. Our results indicated that blood glucose levels were higher in both the CRN16 and CRN25 groups compared to the CRD25 and CRD16 groups, with a particularly notable increase observed in the CRN16 group (Figure 2A). Additionally, analysis of the area under the curve (AUC) demonstrated a significant elevation in the AUC value for the CRN16 group (Figure 2B, P \u0026lt; 0.05). These findings suggest that the nighttime feeding behavior of rats under low-temperature conditions may enhance glucose tolerance. Numerous studies have established a strong association between glycolipid metabolism disorders, diabetes, and insulin resistance. In comparison to the CRD25 group, the CRN16 group exhibited significantly higher fasting insulin (FINS) levels (P \u0026lt; 0.01) and homeostatic model assessment of insulin resistance (HOMA-IR) index (P \u0026lt; 0.05), alongside a significant reduction in the homeostatic model assessment of insulin sensitivity (HOMA-IS) index (P \u0026lt; 0.05). Under low-temperature conditions, the FINS level in the CRN16 group was significantly elevated compared to the CRD16 group. Furthermore, there was a significant increase in the HOMA-IR index and a notable decrease in the HOMA-IS index (Figure 2C-E). \u0026nbsp;When compared to the CRD25 and CRN25 groups, the CRD16 group of rats demonstrated significantly higher adiponectin levels and notably lower cortisol levels. However, no significant differences in leptin levels across the four groups (Figure 2F-H). Analyzing the effects of diurnal dietary rhythms and environmental temperatures on blood insulin levels and insulin resistance indicators in rats, it was found that rats subjected to time-restricted feeding during nighttime developed hyperinsulinemia and exhibited signs of insulin resistance.\u003c/p\u003e\n\u003ch2\u003e3.3 Low-Temperature\u0026nbsp;Treatment\u0026nbsp;May\u0026nbsp;Lead to\u0026nbsp;Disturbances in\u0026nbsp;Glycolipid\u0026nbsp;Metabolism within\u0026nbsp;Rat\u0026nbsp;Serum\u003c/h2\u003e\n\u003cp\u003eTo investigate the effects of diurnal dietary rhythms and low-temperature treatment on blood glucose and lipid levels (Figure 2A, Figure 3), an automated biochemical analyzer was employed to evaluate the fluctuations in serum blood glucose and lipid levels for each group. The study\u0026rsquo;s findings indicated that as the temperature decreased, the night-feeding rats in both the CRN25 and CRN16 groups experienced a significant increase in blood glucose levels (P\u0026le;0.01). Concurrently, there was a significant increase in serum total cholesterol, lipid levels, and high-density lipoprotein levels. In comparison to the other three groups, the serum triglyceride (TG) level in the CRN16 group was significantly higher (P \u0026le; 0.05), with no significant correlation observed with temperature. Figure 3D suggests a potential relationship between serum low-density lipoprotein cholesterol (LDL-C) levels and environmental temperature. However, this association was not statistically significant. Overall, nocturnal feeding may impede glucose circulation within the human body, while low temperatures could exacerbate glycolipid metabolic disorders associated with nocturnal feeding. Given that disturbances in glycolipid metabolism can lead to impaired intestinal function, alterations in gut microbiota, and abnormal bile acid profiles, we conducted a comprehensive analysis of the differences in intestinal flora species among the various groups. Additionally, we examined the effects of low-temperature treatment and time-restricted diets on intestinal microorganisms.\u003c/p\u003e\n\u003ch2\u003e3.4 Low\u0026nbsp;Temperatures\u0026nbsp;Result in\u0026nbsp;Abnormal\u0026nbsp;Alterations of the\u0026nbsp;Intestinal\u0026nbsp;Flora in\u0026nbsp;Rats. \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe variations in intestinal flora species among the experimental groups were examined utilizing Linear Discriminant Analysis Effect Size (LEfSe) analysis. The effects of low-temperature exposure and nocturnal feeding on the richness and evenness of intestinal flora were evaluated using the Chao1, Shannon, and Simpson indices (Figure 4). Compared to the CRD25 group, the CRN25 group exhibited a significant reduction in both the Chao1 and Shannon indices (P \u0026lt; 0.05). Similarly, when compared to the CRD16 group, the CRN16 group also exhibited a significant decrease in these indices (P \u0026lt; 0.05). Notably, no significant variations were detected in the Simpson index among the four groups. These findings suggest that nocturnal dietary patterns can lead to a substantial reduction in the alpha diversity of the microbial community. Furthermore, the \u0026beta; diversity of the microbial community in the CRN16 group was significantly reduced compared to the CRN25 group, suggesting that low-temperature environments impose a certain degree of disturbance on the intestinal flora. To further investigate the impact of environmental temperature on intestinal microbial flora, we analyzed the relative abundances of microorganisms at the phylum level to elucidate variations within the intestinal microbiota. \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003eand \u003cem\u003eBacteroidetes\u003c/em\u003e are the predominant phyla involved in the production of short-chain fatty acids (SCFAs) within the intestinal microbiota. Our findings revealed that the relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e remained largely consistent across all four experimental groups. The study demonstrated that, compared to the CRD25 group, the CRN25 group exhibited a significant reduction in the relative abundances of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u0026nbsp;\u003c/em\u003e(P \u0026lt; 0.05), while the \u003cem\u003eFirmicutes/Bacteroidetes\u003c/em\u003e ratio showed a marked increase (P \u0026lt; 0.01). Meanwhile, when compared to the CRD16 group, the CRN16 group displayed a significant decrease in the relative abundances of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e (P \u0026lt; 0.05), accompanied by a significant reduction in the \u003cem\u003eFirmicutes/Bacteroidetes\u003c/em\u003e ratio (P \u0026lt; 0.01). The intestinal microbiome balance in the CRD16 group was disrupted, with notable alterations observed within the Lactobacillus genus. In conclusion, there is a notable correlation between glycolipid metabolism disorders and the proliferation of Lactobacillus. Future research will involve a comprehensive analysis of the intestinal microbiota in rats, with a focus on assessing correlations between species exhibiting significant differences and relevant physiological indicators.\u003c/p\u003e\n\u003cp\u003eWe aim to evaluate the impact of diurnal dietary rhythms and environmental temperatures on the intestinal microbiota of rats by analyzing inter-group differences. Our study conducted a detailed examination of the correlations between intestinal bacterial composition, focusing on the top 15 bacterial genera with higher abundances (Figure 5A). The study revealed significant differences in the intestinal microbiota among the four groups. Notably, compared to the CRN25 group, the CRN16 group exhibited markedly elevated relative abundances of g_Lactobacillus and g_Allobaculum, while the relative abundances of g_norank_f_Muribaculaceae, B_Blautia, and other lactobacilli exhibited a notable decrease. These results suggest that both low-temperature environments and dietary rhythms can induce specific disturbances in the intestinal microbiota (Figure 5B). Linear Discriminant Analysis (LDA) revealed that the CRD25 group exhibited a significant enrichment of g__unclassified_f__Lachnospiraceae, whereas the CRN25 group was predominantly enriched with Firmicutes Lachnospiraceae and Proteobacteria. In contrast, the CRD16 group showed enrichment of Firmicutes Lactobacillus, whereas the CRN16 group was primarily enriched with p__Bacteroidetes. Significant variations were observed in the quantities of intestinal Lactobacillus species, including Lactobacillus reuteri and Lactobacillus faecis, among others (Figure 5C). Correlation heatmap analysis revealed positive correlations among five Lactobacillus species, including Lactobacillus reuteri and Lactobacillus faecis, among others. Notably, four specific Lactobacillus species demonstrated a negative correlation with the abundance of the majority of the intestinal microbiota (Figure 5D). Evidence suggests that low-temperature environments and nocturnal feeding habits may favorably influence the intestinal colonization of most lactobacilli, potentially contributing to a decrease in the overall abundance of the intestinal microbiota. Therefore, the disruption of glycolipid metabolism induced by a low-temperature environment may be intricately associated with the specific strains of lactobacilli colonizing the intestine.\u003c/p\u003e\n\u003ch2\u003e3.5 The Impact of Low-temperature Environment on the Content of Monoamine Neurotransmitters in Serum\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eGiven the fundamental role of monoamine neurotransmitters in regulating appetite and energy intake-owing to their ability to induce satiety or stimulate food cravings-we propose the existence of a complex and closely interconnected relationship between monoamine neurotransmitters and glycolipid metabolism. Based on this, our research aims to investigate the effects of time-restricted dietary strategies and low-temperature treatments on plasma concentrations of monoamine neurotransmitters (Figure 6, Table S1-S2). Our findings indicate that exposure to a low-temperature environment significantly elevates melatonin (MT) secretion while concurrently reducing plasma dopamine levels. Although plasma norepinephrine (NE) levels increase in response to decreased temperature, the magnitude of this increase is not statistically significant. The plasma concentration of 5-hydroxytryptamine (5-HT) did not demonstrate significant differences across the groups. A potential correlation may exist among the circadian rhythms of diet and sleep, environmental temperature, and the levels of peripheral melatonin and dopamine. We conducted an additional analysis to examine the associations between the gut microbiome and both neurotransmitters and lipids in the serum (Figure 7). As depicted in Figure 7, a notable positive correlation was observed between Lactobacillus and neurotransmitters. During these investigations, we identified a novel lactic acid bacterium, Reuteri, and examined its relationship with other lactic acid bacteria and triglycerides (P \u0026lt; 0.001). Furthermore, a significant positive correlation was identified between four types of lactic acid bacteria and dopamine levels (P \u0026lt; 0.01). Additionally, three species of lactobacilli exhibited a notable positive correlation with blood glucose levels (P \u0026lt; 0.01). In conclusion, a nocturnal diet, in conjunction with exposure to low-temperature environments, may exacerbate disorders related to glycolipid metabolism. This combination has the potential to disrupt the normal secretion of monoamine neurotransmitters and adversely affect the biological rhythms of tissues and organs. Future research will entail a comprehensive analysis of the types and variation characteristics of metabolites present in serum, aiming to elucidate their roles and interactions, as well as the impact of environmental temperature and time-restricted dietary patterns on glycolipid metabolism.\u003c/p\u003e\n\u003ch2\u003e3.6 The Effect of Low-Temperature Treatment on Blood Metabolite Levels\u003c/h2\u003e\n\u003cp\u003eTo examine the effects of a low ambient temperature environment on serum metabolites, we analyzed the PCA results, which revealed that inter-sample variations within the CRN25 group were relatively minor. In contrast, the greater distances between samples in the other three groups indicated more significant differences among them. Further investigation into the disparities among the various treatment groups (Figure 7A), demonstrates that the OPLS-DA model results clearly distinguish between CRD25 and CRD16, whereas the discriminatory power for the other two groups is comparatively limited (Figure 7B). Additionally, the Venn diagram indicates that a total of 1062 OUTs were identified across the four groups, with 977 being common among them (Figure 7C). Through the classification of metabolites using KEGG, it was found that compounds such as lipids, peptides, hormones, and neurotransmitters were relatively abundant. Specific compounds such as fatty acids, eicosanoids, glycolipids, and phospholipids were identified within the lipid category.\u0026nbsp;The classification of metabolic pathways for these compounds indicates that the majority are associated with Metabolism, Organismal Systems, and Human Diseases. Notably, the highest number of compounds is associated with Amino Acid Metabolism and Lipid Metabolism.\u0026nbsp;This finding further supports the notion that low-temperature treatment and modifications in dietary rhythms can significantly influence the levels and composition of metabolites in mouse serum.\u0026nbsp;Subsequently, we will conduct a comprehensive analysis of metabolites that exhibit significant differences and investigate the potential relationships between these differential metabolites and glycolipid metabolism.\u003c/p\u003e\n\u003cp\u003eVolcano plots were utilized to assess the overall trends in metabolite content across the four groups, evaluate the statistical significance of observed differences, and identify metabolites that exhibited a VIP \u0026gt; 1, P \u0026lt; 0.05, and fold change (FC) \u0026ge; 2 as being significantly influenced by PC (Figure 8A, Figure S1). In comparison to the CRN25 group, the CRD25 group exhibited upregulation of 76 metabolites and downregulation of 110 metabolites. Relative to the CRD16 group, the CRD25 group demonstrated upregulation of 34 metabolites and downregulation of 107 metabolites. When compared to the CRN16 group, 17 metabolites were upregulated and 39 were downregulated in the CRD25 group. About the CRD16 group, the CRN16 group showed upregulation of 79 metabolites and downregulation of 110 metabolites (Figure 8A-D). The volcano plot findings facilitated the generation of a hierarchical clustering heatmap of the selected differential metabolites, which illustrated significant variations in metabolite levels across the four groups (Figure 8E).\u0026nbsp;To further explore the effects of low-temperature treatment on metabolite-related metabolic pathways, we selected the top 20 significant pathways for KEGG pathway enrichment analysis. This enrichment analysis was conducted using the annotation results of differential metabolites in conjunction with the hypergeometric test from cluster Profiler, and a bubble plot was subsequently generated. The intensity of the blue color in the points depicted in the figure correlates with the significance of enrichment, while the size of each point corresponds to the number of differentially enriched metabolites. The metabolic pathways exhibiting relatively high levels of enrichment primarily include Phenylalanine metabolism, Caffeine metabolism, Sphingolipid signaling pathway, Cholinergic synapse, Fatty acid degradation, African trypanosomiasis, Serotonergic synapse, alpha-linolenic acid metabolism, and Arginine and proline metabolism (Figure 8F). These findings further substantiate that low-temperature treatment may modify the metabolic profile characteristics associated with glycolipid and amino acid metabolism in rat serum.\u003c/p\u003e\n\u003ch2\u003e3.7 Correlation\u0026nbsp;Analysis of the\u0026nbsp;Gut\u0026nbsp;Microbiota with\u0026nbsp;Glycolipid\u0026nbsp;Metabolism and\u0026nbsp;Neurotransmitters\u003c/h2\u003e\n\u003cp\u003eBased on the aforementioned research findings, it has been identified that the combined effects of a low-temperature environment and nighttime dietary habits can lead to disturbances in glycolipid metabolism. Furthermore, there appears to be a significant association among these factors, as well as with gut microbiota, neurotransmitters, and serum metabolites. To conduct a comprehensive analysis of the relationships between gut microbiota, neurotransmitters, and the serum lipid metabolome (Figure 9), we generated a correlation heatmap. Our analysis revealed a notable positive correlation between the gut microbiota and both neurotransmitter levels and lipid profiles in the serum. Specifically, the relationship between Lactobacillus species and triglycerides was significant (P \u0026lt; 0.001). Lactobacillus exhibits a notable positive correlation with blood sugar, dopamine, FINS, serum glucose (Serum_GLU), serum triglycerides (Serum_TG), GC, and the HOMA-IR index. Additionally, 5-HTA, HOMA-IGI, HOMA-IS, MT, serum LDL cholesterol (Serum_LDL_C), and total cholesterol (Serum_TC) showed significant positive correlations with multiple Lactobacillus species. Furthermore, three Lactobacillus-related factors (P \u0026lt; 0.01) exhibited a significant positive correlation with blood glucose levels. These research findings revealed a significant and close association between intestinal lactobacilli and levels of dopamine, triglycerides, and blood glucose. Given the substantial volume of data represented in the correlation heatmap, we subsequently filtered this data and employed Cytoscape software to visually represent the refined correlation data in the form of a network graph.\u003c/p\u003e\n\u003ch2\u003e3.8 Co-expression\u0026nbsp;Analysis\u003c/h2\u003e\n\u003cp\u003eTo facilitate a more intuitive comprehension of the intricate correlations among serum indicators, neurotransmitters, gut microbiota, and metabolites (Figure 10), we utilized Spearman\u0026apos;s rank correlation analysis and visualized the correlation data using Cytoscape software. Our findings revealed that the majority of serum glycolipid metabolism indicators (AUC, GC, FINS, HOMA-IR, Serum_GLU, Dopa), exhibited significant negative correlations with various lactobacilli species (g__Allobaculum, g__Parasutterella, g__norank_f__Erysipelotrichaceae, g__Blautia, g__Faecalibacterium, g__Romboutsia). In contrast, g__Bifidobacterium showed a significant positive correlation with MT. Furthermore, Serum_TG demonstrated a notable positive correlation with g__Lactobacillus. Additionally, it was identified that eight types of lactobacilli were closely associated with bile acid metabolism; among these species, both g__Lactobacillus and g__Faecalibacterium displayed significant positive correlations with [2-hydroxy-3-(phenoxycarbonyl)phenyl] oxidanesulfonic acid. A bidirectional regulatory relationship exists between bile acids and the microbiota, where the intestinal microbial community can be restructured through the modulation of bile acids. Consequently, these findings reveal that a low-temperature environment coupled with disrupted dietary rhythms, induces disturbances in glycolipid metabolism, gut microbiota, neurotransmitters, bile acids, and other metabolites. These disruptions ultimately disturb the physiological equilibrium in rats, leading to the development of various metabolic diseases.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrevious studies have demonstrated that short-term exposure to low temperatures can enhance glucose transport in the intestine and liver, thereby modulating glycolipid metabolism \u003csup\u003e[18,19]\u003c/sup\u003e. Additionally, time-restricted eating (TRE) strategies have been shown to improve metabolic health \u003csup\u003e[20]\u003c/sup\u003e. Both low-temperature exposure and TRE are increasingly recognized as innovative strategies and targets for managing metabolic diseases. However, our research has demonstrated that prolonged exposure to low temperatures combined with improper time-restricted eating can result in glycolipid metabolism disorders. This phenomenon appears to share mechanisms with the onset or exacerbation of diabetes during winter months and may be closely linked to alterations in energy homeostasis molecules, neurotransmitters, and the intestinal microbiota.\u003c/p\u003e\n\u003ch2\u003e4.1 Low-Temperature\u0026nbsp;Environments and\u0026nbsp;Nocturnal\u0026nbsp;Eating\u0026nbsp;Might\u0026nbsp;Induce\u0026nbsp;Disorders in\u0026nbsp;Glycolipid\u0026nbsp;Metabolism. \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSeasonal variations in glycolipid metabolism among animals are intricately linked to growth and reproductive processes. During autumn, many animals consume a substantial amount of high-calorie and high-fat foods, facilitating the accumulation of body fat necessary for sustaining physiological activities and maintaining stable body temperature throughout the challenging winter months \u003csup\u003e[21, 22]\u003c/sup\u003e. Concurrently, the low-temperature characteristic of winter can impede both glucose uptake by muscles and insulin secretion \u003csup\u003e[23]\u003c/sup\u003e. In response to extremely low temperatures, mammals activate thermoregulatory adaptive mechanisms to maintain core body temperature, such as heat production through Brown Adipose Tissue (BAT) or other thermogenic pathways. They also strategically adjust fat accumulation according to their specific conditions, ensuring no detrimental effects on health\u003csup\u003e\u0026nbsp;[24]\u003c/sup\u003e. Nonetheless, with the rapid advancement of technology, mammals can now regulate their body temperature by modifying their dietary patterns and meal timings. This adaptation contradicts the organism\u0026apos;s inherent physiological needs, resulting in significant disruptions to its biological circadian rhythm and subsequently contributing to obesity, diabetes, and other metabolic disorders \u003csup\u003e[25-27]\u003c/sup\u003e. Through meticulous monitoring of changes in glycolipid metabolism over 24 hours, it was revealed that nocturnal eating significantly elevates blood glucose and lipid levels. This phenomenon has implications for various diseases, including diabetes, obesity, and hyperlipidemia. These findings are consistent with our previous research results \u003csup\u003e[28]\u003c/sup\u003e. In the present study, following the intervention of environmental temperature and circadian eating rhythm, we observed a significant increase in body weight among SD rats exposed to a low-temperature environment. Additionally, serum levels of blood glucose and lipids were markedly elevated in both the low-temperature environment group and the nighttime time-restricted feeding group (Figure 1), leading to the development of insulin resistance (Figure 2).\u0026nbsp;This study reveals that the combined effects of a low-temperature environment and nighttime time-restricted eating can rapidly lead mammals to consume excessive amounts of food. This behavior leads to substantial fat accumulation, which exacerbates disorders related to glycolipid metabolism, and ultimately precipitates early symptoms of type 2 diabetes.\u0026nbsp;Research indicates that insulin levels within the organism undergo fluctuations over 24 hours. Prolonged exposure to a low-temperature environment stimulates an increase in insulin secretion, thereby inducing a sensation of hunger \u003csup\u003e[29]\u003c/sup\u003e.\u0026nbsp;Under the combined influence of hunger and decreased body temperature, the organism is more inclined to consume a substantial amount of high-calorie foods, imposing additional stress on insulin-secreting tissues such as the pancreas.\u0026nbsp;This stress may significantly contribute to pancreatic injury, potentially resulting in insulin resistance.\u003c/p\u003e\n\u003ch2\u003e4.2 The\u0026nbsp;Disorders of\u0026nbsp;Glycolipid\u0026nbsp;Metabolism\u0026nbsp;Induced by the\u0026nbsp;Low-Temperature\u0026nbsp;Environment are\u0026nbsp;Associated with\u0026nbsp;Alterations in the\u0026nbsp;Intestinal\u0026nbsp;Microbiota.\u003c/h2\u003e\n\u003cp\u003eThe gut microbiota is often referred to as the \u0026quot;second genome\u0026quot; due to its vital role in maintaining the host\u0026apos;s health. It is predominantly composed of Bacteroidetes and Firmicutes, with additional contributions from Proteobacteria, Actinobacteria, Fusobacteria, and Verrucomicrobia. Notably, Firmicutes account for 64% of the total gut microbiota \u003csup\u003e[30, 31]\u003c/sup\u003e.\u0026nbsp;The composition and structure of the gut microbiota are influenced by various external factors, including high-fat diets and circadian rhythm disorders \u003csup\u003e[32]\u003c/sup\u003e. Research has demonstrated that disruptions in circadian clock genes, such as Bmal1, in murine models can result in impaired glucose tolerance and diminished insulin secretion\u003csup\u003e[33]\u003c/sup\u003e. Furthermore, these disruptions lead to significant alterations in the circadian rhythmicity of the intestinal microbiota, affecting both bacterial abundance and composition \u003csup\u003e[34, 34]\u003c/sup\u003e. These findings suggest a potential interplay between the host\u0026apos;s circadian clock and the rhythmicity of the gut microbiota. Disruption of the circadian rhythm system can modify the intestinal microbial community, potentially disturbing host metabolism, energy homeostasis, and inflammatory pathways, thereby contributing to the development of metabolic syndrome. This study found that variations in environmental temperature and circadian rhythm notably decreased the abundance of intestinal microbiota (Figure 4), influenced glucose transport and glycolysis within the intestinal microbiota (Figure 6), and induced downregulation of genes associated with insulin resistance signaling pathways in both the intestinal wall and surrounding tissues (such as INSR, which encodes the insulin receptor) (Figure 8). Recent studies have demonstrated that organisms characterized by a low microbial gene count (LGC) are more predisposed to increased body fat, insulin resistance, dyslipidemia, and pronounced inflammatory phenotypes, suggesting a strong association between LGC and metabolic \u003csup\u003e[36]\u003c/sup\u003e. Furthermore, lactobacilli within the intestinal microbiota have been implicated in glycolipid metabolism. Specifically, Lactobacillus fermentum and Lactobacillus esophagitis have demonstrated potential in regulating weight gain in mice and reducing the risk of type 2 diabetes \u003csup\u003e[37]\u003c/sup\u003e. Notably, an increase in Lactobacillus acidophilus, Lactobacillus casei, and Lactobacillus rhamnosus has been observed in individuals with diabetes \u003csup\u003e[38]\u003c/sup\u003e, suggesting that these bacteria may influence glycolipid metabolism through intricate interactions. This study also identified a relatively high abundance of the genus Lactobacillus, which was associated with low-temperature environments and dietary circadian rhythms. Furthermore, a significant negative correlation was observed between the abundance of Lactobacillus and other bacterial groups (Figure 5D). Consequently, this study further elucidates that the dysregulation of intestinal flora may serve as a pivotal connection between low-temperature environments and altered dietary rhythms, ultimately contributing to disturbances in glycolipid metabolism.\u003c/p\u003e\n\u003ch2\u003e4.3 The\u0026nbsp;Glycolipid\u0026nbsp;Metabolism\u0026nbsp;Disorder\u0026nbsp;Induced by the\u0026nbsp;Low-Temperature\u0026nbsp;Environment\u0026nbsp;Might be\u0026nbsp;Related to the\u0026nbsp;Alterations in\u0026nbsp;Peripheral\u0026nbsp;Neurotransmitters\u0026nbsp;Induced by the\u0026nbsp;Intestinal\u0026nbsp;Microbiota.\u003c/h2\u003e\n\u003cp\u003eAn increasing number of studies have revealed the bidirectional interactions within the intestinal milieu, encompassing the intestinal epithelium, the mucosal immune system, and the intestinal microbiota, in conjunction with the enteric nervous system \u003csup\u003e[39]\u003c/sup\u003e.\u0026nbsp;The intestinal microbiota possesses the ability to activate the enteric nervous system through metabolic by-products, modulate the secretion profile of enteric nerve metabolites, and influence neurotransmitter synthesis in both central and peripheral nervous systems \u003csup\u003e[40]\u003c/sup\u003e. Consequently, it can impact biological rhythms \u003csup\u003e[41]\u003c/sup\u003e and the expression of circadian rhythm genes such as Clock \u003csup\u003e[42]\u003c/sup\u003e. The intestinal microbiota plays a crucial role in regulating the secretion rhythm of energy homeostasis regulators \u003csup\u003e[43]\u003c/sup\u003e, including ghrelin, leptin, insulin, glucagon-like peptide 1 (GLP-1), and adiponectin \u003csup\u003e[44]\u003c/sup\u003e. In our research, we found that both low-temperature environments and nocturnal dietary habits significantly influence the accumulation of melatonin and dopamine in serum. Further analysis revealed that low environmental temperature exerts a more pronounced regulatory effect on dopamine accumulation (Figure 4). We conducted a correlation analysis involving neurotransmitters, plasma lipid profiles, and gut microbiota (Figure 9). Notably, five species of Lactobacillus microorganisms, including Lactobacillus reuteri, exhibited a significant positive correlation with triglyceride levels (P \u0026lt; 0.001). Furthermore, three species of Lactobacillus microorganisms exhibited a highly significant positive correlation with blood glucose levels (P \u0026lt; 0.01) (Figure 7). Additionally, four species of Lactobacillus, including Lactobacillus reuteri, demonstrated a significant negative correlation with the intestinal microbiota (P \u0026lt; 0.01) (Figure 8). These findings suggest an interrelationship among Lactobacillus, dopamine, and glycolipid metabolism. It has been reported that the genus Lactobacillus is capable of synthesizing a diverse array of neurotransmitters, including dopamine and norepinephrine, among others \u003csup\u003e[45]\u003c/sup\u003e. The presence of dopaminergic neurons and dopamine transporter proteins within the intestinal lamina propria potentially plays a crucial role in facilitating dopamine transport and activating dopaminergic neurons\u003csup\u003e\u0026nbsp;[46]\u003c/sup\u003e. These findings suggest that specific Lactobacillus microorganisms can modulate the physiological activities of organisms through the secretion of neurotransmitters, including dopamine. Meanwhile, Lactobacillus casei has been demonstrated to mitigate depressive-like behaviors in rats and to affect the plasma levels of dopamine (DA), norepinephrine (NE), and serotonin (5-HT), highlighting the intricate relationship between Lactobacillus microorganisms and neurotransmitters dynamics within the brain \u003csup\u003e[47]\u003c/sup\u003e. In summary, low environmental temperature may reduce the biodiversity of the intestinal flora, intervene in the accumulation of peripheral neurotransmitters such as dopamine, alter gene expression in tissues such as the pancreas, and disturb the biological rhythms of tissues/organs.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn summary, this study has revealed that the synergistic effects of low-temperature environments and circadian dietary rhythms contribute to increased food intake, promote excessive fat deposition, exacerbate glucose and lipid metabolic disorders, and precipitate the early onset of metabolic diseases, including type 2 diabetes. Furthermore, exposure to low environmental temperature significantly diminishes the biodiversity of the intestinal microbiota, disrupts bile acid metabolism and the synthesis of peripheral neurotransmitters (such as dopamine), and disturbs the biological rhythms of tissues and organs. Consequently, the strategic regulation of environmental temperature and the implementation of time-restricted dietary interventions are anticipated to emerge as innovative and effective strategies for the management of type 2 diabetes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eYinglan Shi and Zhaoxia Huang: Investigation, Methodology, Software, Writing-original draft, Writing-review \u0026amp; editing, Validation. Xiaofang Tang: Investigation, Methodology, Validation. Yongqin Zhang: Visualization, Data curation, Investigation. Die Shi: Conceptualization, Supervision. Jing Chen: Software. Qingxue Wang: Conceptualization, Methodology, Supervision, Writing-review \u0026amp; editing, Project administration. Jun Li: Resources, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (NO.82060163 and 82160167), the scientific and technological research project of traditional Chinese medicine and ethnic medicine of Guizhou Provincial Administration of traditional Chinese medicine (QZYY-2022-004)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe Committee on the Ethics of Animal Experiments of Guizhou University of Traditional Chinese Medicine approved all the animal experiments (Permission number: 20210188).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eAll the data generated or analyzed throughout this study are encompassed in the text and supplementary files. They can also be obtained from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRuze R, Liu T, Zou X, Song J, Chen Y, Xu R, Yin X, Xu Q. Obesity and type 2 diabetes mellitus: connections in epidemiology, pathogenesis, and treatments. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e. \u003cstrong\u003e2023\u003c/strong\u003e Apr 21;14:1161521.[PubMed]\u003c/li\u003e\n\u003cli\u003eGeng T, Zhu K, Lu Q, Wan Z, Chen X, Liu L, Pan A, Liu G. 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The antidepressant potential of lactobacillus casei in the postpartum depression rat model mediated by the microbiota-gut-brain axis. \u003cem\u003eNeurosci Lett\u003c/em\u003e. \u003cstrong\u003e2022\u003c/strong\u003e Mar 23;774:136474.[PubMed]\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Low-Temperature Stress, Circadian Rhythm Disruption, Time-Restricted Feeding, Intestinal Microbiota, Glycolipid Metabolism","lastPublishedDoi":"10.21203/rs.3.rs-6258553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6258553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e Diabetes has emerged as a prominent public health issue significantly impacting human health, evidenced by a consistent annual increase in patient cases. Winter plays a pivotal role in either initiating or exacerbating diabetes, although the precise mechanisms underlying this phenomenon remain unclear. Our previous work was consistent with the findings, indicating an intriguing potential link between glycolipid metabolism and the circadian clock, such as ambient temperature and dietary rhythms. So in this research, we endeavor to delve into the intricate relationship among ambient temperature, diurnal dietary rhythms, and glycolipid metabolism via animal experiments, ultimately aiming to shed light on the potential mechanisms through which the circadian clock may initiate or exacerbate diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Thirty-six healthy rats were randomly assigned to four groups(N=9), with each group exposed to a unique combination of temperature (25°C or 16°C) and time-restricted feeding schedules (8:00 ~ 20:00 or 20:00 ~ 8:00). After a 10-day experimental period, we assayed the levels of fasting insulin (FINS), adiponectin, cortisol, leptin, and other homeostatic energy substances in serum. Furthermore, we investigated the neurotransmitter content in serum, blood metabolic profiles, and alterations in gut microbiota.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Notably, exposure to low temperatures elevated the food consumption and body mass of the rats, whereas nocturnal eating syndromes contributed to hyperinsulinemia and insulin resistance, subsequently improving microbial imbalances. In the experiment, the low-temperature nocturnal eating group rats showed a notable decrease in the relative abundances of Bacteroidetes and Actinobacteria (P \u0026lt; 0.05). Serum metabolite analysis revealed that both ambient temperature and dietary rhythm affect glucose, lipids, and amino acid metabolism. Neurotransmitters and blood lipid profile changes can cause an intestinal flora imbalance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study indicates that glycolipid metabolism disorders are caused by low temperatures and nocturnal eating, possibly due to changes in gut microbiota and neurotransmitter levels. Increasing ambient temperature and managing gut microbiota in winter may help prevent and treat diabetes.\u003c/p\u003e","manuscriptTitle":"Joint Effects of Low Ambient Temperature and Nocturnal Eating Behavior Contributing to Glycolipid Metabolism Disorder by Changing Gut Microbiota Structure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:22:52","doi":"10.21203/rs.3.rs-6258553/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13ae39f9-3833-47cf-8926-11dd50ac462f","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57175559,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Metabolic syndrome"},{"id":57175560,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2025-11-10T16:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 05:22:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6258553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6258553","identity":"rs-6258553","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}