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. 2025 Feb 21;48(5):3252–3263. doi: 10.1007/s10753-025-02263-y

High-Resolution Untargeted Metabolomics Reveals Alternate-Day Fasting May Attenuate Diabetic Kidney Disease Progression in BTBR ob/ob Mice by Affecting the HCA, IPA and Reducing Inflammation

Huiqing Yu 1,2, Liping Yan 1,2, Jiaqing Ma 1, Xinduo Zhang 1,2, Hongman Wu 1, Yahui Yan 1, Hong Shen 3, Zhiguo Li 1,✉
PMCID: PMC12596302  PMID: 39982673

Abstract

Diabetic kidney disease (DKD) is one of the most severe complications of diabetes mellitus, with limited effective therapeutic interventions. Alternate-day fasting (ADF) shows potential in treating DKD, though its mechanisms are not fully understood. In this study, BTBR ob/ob mice underwent 12 weeks of ADF, and high-resolution untargeted metabolomics were performed to uncover the underlying mechanisms. After 12 weeks of ADF, the BTBR ob/ob mice exhibited weight loss, lower blood glucose and LDL-C levels, reduced 24-h urinary protein excretion, and decreased renal collagen deposition. A total of 44 metabolites were differentially expressed, with 25 up-regulated and 19 down-regulated. Notably, hyocholic acid (HCA) and indole-3-propionic acid (IPA), both products of intestinal bacteria, can modulating inflammation were differentially expressed. Furthermore, the kidneys of BTBR ob/ob mice showed significantly lower NF-κB pathway activity and reduced inflammation after 12 weeks of ADF. This study indicates that ADF may alleviate DKD progression by modulating HCA, IPA, and decreasing inflammation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10753-025-02263-y.

Keywords: Diabetic kidney disease, Alternate day fasting, Kidney untargeted metabolomics, Inflammation

Introduction

Diabetic Kidney Disease (DKD) is a severe complication of diabetes mellitus and a leading cause of end-stage renal disease (ESRD) [1]. The pathogenesis of DKD is intricate, involving multiple pathways and mediators such as oxidative stress, inflammation, and autophagy, which collectively worsen kidney damage [2, 3]. Current treatments for DKD focus on controlling blood glucose and blood pressure, and include medications like renin–angiotensin–aldosterone system (RAS) inhibitors and sodium-glucose cotransporter 2 (SGLT2) inhibitors [4, 5]. However, these therapies only slow disease progression rather than addressing its root cause [6]. Consequently, many DKD patients progress to ESRD, necessitating dialysis or kidney transplantation, which imposes a significant financial burden on patients and their families [7]. Therefore, there is an urgent need for more effective treatment strategies to improve the prognosis of DKD.

Recent studies have shown that alternate-day fasting (ADF), a form of intermittent fasting (IF) involving unrestricted food intake on one day and strict calorie restriction on the next, holds promise for treating DKD [8]. A 12-week ADF intervention in the db/db mouse model significantly lowered blood glucose and cholesterol levels, reduced body weight, and improved renal function and pathology, indicating ADF’s potential as a treatment for DKD [9]. Additionally, studies in a streptozotocin-induced diabetic rat model have demonstrated that IF not only lowers blood glucose levels and reduces histopathological kidney damage but also decreases mRNA expression levels of TNF-α, NLRP-3, TGF-β1, and VCAM-1 [10]. IF has been shown to improve blood glucose levels, insulin resistance, weight, and lipid profiles, all of which are crucial factors in the development of DKD [11, 12]. However, the precise mechanisms by which ADF benefits DKD remain unclear. Further research into these mechanisms could enhance its efficacy and potentially identify new drug targets.

Metabolomics, which emerged after genomics, is an advanced technique used to explore unknown mechanisms by monitoring hundreds or even thousands of metabolites, providing comprehensive insights into complex biochemical processes [13, 14]. In a diabetic model of db/db mice, metabolomic analyses showed that intermittent fasting (IF) effectively prevented diabetic retinopathy by increasing the level of taurocholic acid synthesized by the Thicket phylum [15]. In a mouse model of type 1 diabetes mellitus, metabolomic analyses revealed that IF recalibrated the metabolic profile of the prefrontal cortex, activated the aspartate and glutamate metabolic pathways, reversed the accumulation of glycerophospholipids and sphingomyelins, significantly improved memory functions related to the prefrontal cortex, and reduced neuronal cell loss [16].

However, studies on changes in renal metabolic profiles in DKD with ADF are still relatively limited. Ultra-performance liquid chromatography (UPLC) coupled with mass spectrometry (MS) is one of the most frequently used technologies for monitoring a vast array of metabolites. Various strategies can enhance this capacity further. For instance, a two-step extraction process involving ‘aqueous’ and ‘organic’ phases for polar and nonpolar metabolites can significantly increase the number of metabolites monitored [17]. Recently, ion mobility spectrometry (IMS) has been introduced into UPLC-MS, enhancing its capabilities even further [18]. IMS can separate gaseous ions based on size, shape, mass, and charge, improving detection sensitivity, peak capacity, and the signal-to-noise ratio [19]. Additionally, IMS provides collision cross-section (CCS) data, a new physicochemical property that offers additional confidence in substance identification beyond the mass-to-charge ratio (m/z) [20, 21]. Combining these technologies could greatly enhance the ability of metabolomics to analyze complex samples.

BTBR ob/ob mice were used in this study due to their presentation of typical type 2 diabetes mellitus (T2DM) and nearly all human diabetic kidney disease (DKD) pathologies [22]. These mice are considered one of the best models for understanding and treating human DKD [22]. The mice were subjected to alternate-day fasting (ADF) for 12 weeks to assess its effects on DKD. High-resolution kidney metabolomics were employed to explore the potential mechanisms of ADF.

Materials and Methods

Animal Husbandry and Experimental Grouping

BTBR ob/ob mice were obtained from Jackson Laboratory (USA) and housed in the SPF-grade Animal Experimentation Centre at North China University of Science and Technology, Tangshan, China (Approval No. 2022-SY-044). The mice were maintained on a standard diet with water, in an environment controlled at 22–24 °C, with a 12-h light/dark cycle and 50% humidity. Breeding followed Jackson Laboratory’s instructions: after one week of acclimatization, heterozygous male and female mice were bred together. Newborn pups were reared for three weeks post-birth and then genotyped. BTBR ob/ob and BTBR wild-type (WT) mice were used in this study.

Entry at 7 to 9 weeks of age, the mice were randomly assigned to four experimental groups: normal control group (WT) with 11 males; control group (ADF-WT) with 15 mice (10 males, 5 females); DKD group (ob/ob) with 14 mice (5 males, 9 females); and DKD group (ADF-ob/ob) with 15 mice (5 males, 10 females). The WT and ob/ob groups had ad libitum access to food, while the ADF-WT and ADF-ob/ob groups were fasted from 8:00 a.m. on the first day until 8:00 a.m. the following day Fig. 1.

Fig. 1.

Fig. 1

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Alternate day fasting pattern. In the latter two fasting groups, mice were subjected to a 24h food deprivation every other day, followed by ad libitum access to food for the subsequent 24 h

At 20 weeks of age, 24-h urine samples were collected using metabolic cages. The mice were then anesthetized with tribromoethanol, and blood was drawn before they were sacrificed. Kidney tissues were dissected, weighed, segmented, and either stored at −80 °C or fixed in paraformaldehyde for future analysis.

Measurement of Mice Weight Blood Glucose and Food Intake

The weight of each mouse was recorded at regular weekly intervals. Fasting blood glucose was measured every four weeks, with each measurement taken after more than 10 h of fasting or alternate-day fasting (ADF). Fasting blood glucose levels were measured using the Contour Plus Glucometer (manufactured by Bayer HealthCare, LLC). Food intake was monitored daily. Each cage of mice was given a pre-weighed amount of food, and the remaining food was weighed at the end of each day to calculate the daily food intake in grams (g). This data was used to monitor any differences in food consumption between the groups.

Detection of Biochemical Parameters

Blood samples were collected in EDTA K2 anticoagulation tubes via cardiac puncture. The plasma was obtained through a two-step centrifugation process at 1,300 g and 2,300 g to remove cells and platelets. The plasma was then stored at −80 °C until measurement. Levels of low-density lipoprotein (LDL), high-density lipoprotein (HDL), creatinine, blood urea nitrogen (BUN), triglycerides, and total cholesterol were measured using commercially available kits, following the manufacturer’s instructions.

Histopathological Examination of the Kidney

Kidney tissues were fixed in 4% paraformaldehyde for at least 24 h, then dehydrated, embedded in paraffin, and cooled to solidify. The tissues were sectioned into 4-micron thick slices, heated at 60 °C for 2 h to remove wax, and stained with Sirius Red following standard procedures. Pathological images of the renal tissues were captured using the DMSCAN V1.1.10.0310 Slide Scanning Imaging System Software (Shenzhen Shengqiang Technology Co., Ltd.). For each sample, images were taken from five different fields of view, and the average of these observations was calculated using ImageJ.

Renal Metabolomics

To monitor a broader range of metabolites, a two-step extraction process for polar and nonpolar metabolites [23], combined with ion mobility spectrometry (IMS), was used in this metabolomics study.

Fifty milligrams of kidney tissue were placed in a 2 ml centrifuge tube and homogenized with pre-cooled methanol/water (1:1) on ice for 2 min. The tissue was disrupted using an ultrasonic processor (Fisher Science, USA) at 30 kHz. The mixture was centrifuged at 16,000 g for 10 min at 4 °C, and the supernatant was collected and vacuum-dried to a powder (4 h, 45 °C) to obtain the aqueous phase extract. The precipitate was then thoroughly extracted by adding pre-cooled dichloromethane/methanol (3:1) and homogenized on ice for 2 min. The mixture was centrifuged at 12,000 g for 10 min at 4 °C, and the supernatant was collected and vacuum-dried to a powder (7 h, 45 °C) to obtain the organic phase extract.

Prior to metabolomics analysis, the tissue powders were placed on ice and pre-cooled methanol:water (1:1) was added, then disrupted by ultrasound to re-dissolve the organic phase. The mixture was centrifuged at 12,000 g for 10 min at 4 °C, the supernatant was collected, centrifuged again, and finally the supernatant was loaded into a sample vial with an inner tube. The re-dissolution of the dried aqueous phase powder was performed in the same way as for the organic phase. For quality control (QC), 5 μL of supernatant from each extract was pooled to minimize residual effects and monitor experimental stability. To ensure data quality for metabolic profiling, all samples were analyzed in a random sequence, and a QC sample was analyzed every five samples.

Ultra-performance liquid chromatography-IMS-quadrupole time-of-flight mass spectrometry (UPLC-IMS-QToF) assays were performed on a Waters system (Vion IMS QToF). The aqueous phase extraction was separated by a UPLC® HSS T3 column (1.8 µm particle size, 2.1 mm diameter, 100 mm length; Waters, Ireland). Mobile phase A consisted of 0.1% formic acid in water, and mobile phase B consisted of 0.1% formic acid in methanol. The flow rate was 0.4 mL/minute, the injection volume was 5 μL, and the sample temperature was set at 4 °C. The organic phase extraction was separated by an ACQUITY UPLC® BEH C8 column (1.7 µm particle size, 2.1 mm diameter, 100 mm length; Waters, Ireland). Mobile phase A consisted of 0.1% formic acid in water, and mobile phase B consisted of 85% methanol, 15% isopropanol, and 0.1% formic acid. The flow rate was 0.4 mL/minute, the injection volume was 5 μL, and the sample temperature was set at 4 °C. MSE scan mode was performed in positive and negative ion modes with capillary voltages of 3.2 kV (ESI +) and 2.4 kV (ESI-), respectively. The mass range was set to 50–1000 m/z. The source temperature was maintained at 120 °C, and the desolvation airflow was set to 900 L/h at 350 °C. The cone airflow was set to 25 L/h. Locked mass calibration was performed using a leucine-enkephalin reference spray with masses of 556.2766 m/z ( +) and 554.2620 m/z (-) at 0.5-min intervals, with a sampling time of 0.5 min, a collision energy (CE) of 6 eV, and a flow rate of 10 μL/min.

Raw data for this study were acquired using Waters UNIFI 1.8.1. The raw data were imported into Progenesis QI 3.0 software (Waters, Nonlinear Dynamics, Newcastle upon Tyne, UK) and processed for peak alignment, design of experiments, peak extraction, and inverse convolution normalization. The normalized data were then exported from QI and analyzed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) using SIMCA 14.1 (Umetrics AB, Sweden). Metabolite identification was based on conditions of VIP > 1, p < 0.05, and FC > 2, which were considered differentially expressed. The ChemSpider plug-in in QI, which includes the Human Metabolome Database (HMDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the PubChem database, was used for identification. The main database used was HMDB, with criteria for identification set at a mass tolerance of 5 ppm, a fragment mass tolerance of 5 ppm, isotopic similarity greater than 80%, and a predicted collision cross section (CCS) of 5%. The identified differentially expressed metabolites were further analyzed using MetaboAnalyst (http://www.metaboanalyst.ca/MetaboAnalyst/).

Western Blotting

Kidney tissues were ground to a powder in liquid nitrogen using RIPA lysis buffer (50 mM Tris–HCl (pH 8.0), 150 mM NaCl, 0.02% sodium azide, 1% Triton X-100 with a cocktail inhibitor (ROCH)). The samples were transferred to centrifuge tubes and allowed to thaw on ice. They were then centrifuged at 12,000 xg for 30 min to remove any debris. Protein concentration was measured using the Bradford method. Fifteen micrograms of protein were denatured, loaded, and separated by SDS-PAGE gel electrophoresis. The proteins were then transferred to a nitrocellulose (NC) membrane using semi-dry transfer. The NC membrane was blocked with 5% skim milk in TBST for 1 h at room temperature, followed by incubation with primary antibodies at 4 °C overnight. After washing the membrane with TBST, it was incubated with secondary antibodies for 1 h at room temperature and visualized using chemiluminescence autoradiography.

The primary antibodies used in this study were β-actin (1:2000, Biolong, China), IL-6 (1:1000, Biolong, China), MCP-1 (1:1000, Biolong, China), and NF-κB p65 (1:1000, Biolong, China).

Statistical Analysis

The experimental data were statistically analyzed using GraphPad Prism version 8.0.1. All results were expressed as mean ± standard deviation (SD). Two-way and one-way analyses of variance (ANOVA) were used to compare the means of more than two groups, followed by the Tukey test. An independent samples t-test was used to compare the means of two groups. A p-value of < 0.05 was considered statistically significant.

Results

ADF Intervention Ameliorates Diabetes in BTBR ob/ob Mice

To compare the effects of alternate-day fasting on type 2 diabetes, mice in the WT and ob/ob groups were fed a normal diet, whereas mice in the ADF-WT and ADF-ob/ob groups were fed a 24-h alternate feeding method. Final metabolic analyses were performed when the mice were 20 weeks old. No adverse effects of fasting were observed in the ADF-WT group compared to the WT group. The fed BTBR ob/ob mice developed a diabetic phenotype with significant weight gain, significantly elevated blood glucose at 8 weeks, increased intake, and dyslipidemia (Fig. 2A-G). Compared with ob/ob mice, ADF-ob/ob mice had significantly lower body weight (Fig. 2A), blood glucose levels at 8, 10, and 18 weeks (Fig. 2B), food intake (Fig. 2C), and LDL-C levels (Fig. 2F). However, HDL-C, triglyceride, and total cholesterol levels did not show statistically significant differences (Fig. 2A-F). Interestingly, the ADF intervention improved body weight, blood glucose at 8, 10, and 18 weeks, intake, and LDL-C levels, whereas no statistically significant changes were observed in triglyceride, total cholesterol, and HDL-C levels.

Fig. 2.

Fig. 2

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Alternate-day fasting alters diabetes in BTBR OB/OB mice. (A) Body weight; (B) fasting blood glucose; (C) Food intake; (D) Triglyceride; (E) Total cholesterol; (F) LDL-C; (G) HDL-C; n = 11–15 mice/group.*p < 0.05;**p < 0 .01;***p < 0 .001; ns p > 0.05;#p < 0.05;###p < 0 .001.#:WT vs ob/ob; *: ob/ob vs ADF-ob/ob. Statistical analysis was performed using two-way and one-way ANOVA in Prism 10

ADF Intervention Attenuates DKD in BTBR ob/ob Mice

To investigate the effect of alternate day fasting (ADF) on the progression of DKD, we measured 24-h urine protein, total kidney weight, plasma creatinine and collagen area. There were no adverse effects in the ADF-WT group compared to the WT group, whereas the obese/obese group had elevated 24-h urine protein levels and Sirius red staining showed increased collagen fibre deposition in the glomerular and tubular interstitium., the ob/ob group exhibited increased 24-h urinary protein levels, and Sirius Red staining revealed high deposition of collagen fibers in the glomerular and tubular interstitium. However, creatinine levels and total kidney weight were not statistically different, indicating that the ob/ob group developed DKD by 20 weeks (Fig. 3A-E).

Fig. 3.

Fig. 3

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Alternate-day fasting alters diabetic nephropathy in BTBR OB/OB mice. (A) Serum creatinine; (B) Total kidney weight; (C) 24-h proteinuria; (D). Collagen area proportion. (E) Sirius red staining(× 20). shows high deposition of collagen fibres in the glomerular and tubular interstitium (arrowhead); n = 11–15 mice/group;*p < 0.05, **p < 0.01 ***p < 0 .001; ns p > 0.05. Statistical analysis was performed using one-way ANOVA in Prism 10

Following ADF intervention, the ADF-ob/ob group showed significant improvement in 24-h urinary protein levels compared to the ob/ob group (Fig. 3A-C). Sirius Red staining also demonstrated a significant reduction in collagen fibers in the ADF-ob/ob mice (Fig. 3D-E). Overall, alternate-day fasting significantly improved 24-h urine protein levels and collagen area. Notably, renal function and total kidney weight were not statistically different in the BTBR ob/ob group at 20 weeks of age (Fig. 3A-B), but further changes in renal function and kidney weight may occur with increasing age.

Altered Renal Metabolic Profiles in BTBR ob/ob Mice Due to ADF Intervention

Comparable Distribution of Renal Metabolites in WT, ADF-WT, ob/ob, and ADF-ob/ob Groups

Non-targeted metabolomics analyses of kidney tissues from WT, ADF-WT, ob/ob, and ADF-ob/ob mice were conducted. As shown in Fig. 4, the two-dimensional ion intensity plot demonstrates mouse kidney metabolites by retention time and mass-to-charge ratio (m/z). Retention times gradually increase from top to bottom, and m/z values increase from left to right. Darker areas indicate an increase in ion peak intensity in the mass spectrometry (MS) signal, while black dots represent metabolic ions detected by QTOF. The comparable ion distributions observed among the WT, ADF-WT, ob/ob, and ADF-ob/ob groups indicate that this metabolomics approach is high-resolution, capable of detecting a large number of compounds, and highly repeatable.

Fig. 4.

Fig. 4

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Under positive and negative ion detection modes, respectively, two-dimensional ion intensity maps depicting the relationship between retention time and mass-to-charge ratio (m/z) are plotted for the four sets of samples: WT, ADF-WT, ob/ob, and ADF-ob/ob

Multivariate Statistical Analysis of Renal Metabolites in Mice

Unsupervised PCA was performed on all samples in positive and negative ion modes to analyze the differences in metabolomics between groups. The PCA results (Fig. 5A-D) showed distinct clustering of the WT, ADF-WT, ob/ob, and ADF-ob/ob groups, with significant clustering of the QC samples, indicating good reproducibility and stability of the instrument. To further investigate the metabolite differences between the WT, ADF-WT, ob/ob, and ADF-ob/ob groups, supervised OPLS-DA analysis was used. The results (Fig. 6, positive ion mode A, B, C, G, H, I; negative ion mode D, E, F, G, K, L) showed significant separation between ob/ob and ADF-ob/ob kidney tissue samples (C8 positive ion R2Y = 0.945, Q2 = 0.569; C8 negative ion R2Y = 0.99, Q2 = 0.769; HSST3 positive ion R2Y = 0.977, Q2 = 0.332; HSST3 negative ion R2Y = 0.999, Q2 = 0.429). Similarly, significant separation was observed between WT and ob/ob samples (Fig. 6, positive ion mode M, N, O, S, T, U; negative ion mode P, Q, R, V, W, X) (C8 positive ion R2Y = 0.964, Q2 = 0.839; C8 negative ion R2Y = 0.992, Q2 = 0.933; HSST3 positive ion R2Y = 0.979, Q2 = 0.936; HSST3 negative ion R2Y = 0.998, Q2 = 0.952). No overfitting was detected using 200 cross-ranking experiments, indicating that all models were reliable. These results showed significant differences between the control and model groups, as well as between the model and alternate-day fasting DKD groups.

Fig. 5.

Fig. 5

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PCA analysis of UPLC-IMS-QTOF data in nephridial tissue of Kidney model rats. (A) PCA scores plot, C8(ESI +). (B) PCA scores plot, C8(ESI-). (C) PCA scores plot, HSST3(ESI +). (D) PCA scores plot, HSST3(ESI-)

Fig. 6.

Fig. 6

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Multivariate analysis of nephridial tissue UPLC-IMS-QTOF data. (A-C) OPLS-DA, S-plot and permutation score plots of the ob/ob and ADF-ob/ob, C8(ESI +). (D-F) OPLS-DA, S-plot and permutation score plots of the ob/ob and ADF-ob/ob, C8(ESI-). (G-I) OPLS-DA, S-plot and permutation score plots of the ob/ob and ADF-ob/ob, HSST3(ESI +). (J-L) OPLS-DA, S-plot and permutation score plots of the ob/ob and ADF-ob/ob,HSST3(ESI-). (M–O) OPLS-DA, S-plot and permutation score plots of theWT and ob/ob, C8(ESI +). (P-R) OPLS-DA, S-plot and permutation score plots of the WT and ob/ob, C8(ESI-). (S-U) OPLS-DA, S-plot and permutation score plots of the WT and ob/ob, HSST3(ESI +). (V-X) OPLS-DA, S-plot and permutation score plots of theWT and ob/ob,HSST3 (ESI-)

ADF Intervention Leads to Significant Differences in Renal Metabolism in ADF-ob/ob Mice

In this study, we identified a total of 77 differential ions in the kidneys of ADF-ob/ob mice. Using the HMDB database via the ChemSpider search engine, 62 metabolites were identified. We then compared the measured CCS values with those stored in HMDB, retaining only metabolites with CCS deviations of less than ± 10%. This rigorous screening process ultimately identified 44 metabolites with high confidence (Table S1). The heatmap (Fig. 7A) shows 25 up-regulated and 19 down-regulated metabolites in the ADF-ob/ob group compared to the ob/ob group. These differential metabolites were classified as glycerophospholipids (23%), carboxylic acids and their derivatives (16%), Organooxygen compounds (14%), Benzene and substituted derivatives (11%), Steroids and steroid derivatives (9%), Fatty Acyls (7%), Prenol lipids(4%) and other compounds including indoles and their derivatives (16%) (Fig. 7B).

Fig. 7.

Fig. 7

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Potential differential metabolites of alternate-day fasting. (A) Heat map of potential metabolites between the WT、ADF-WT、ob/ob and ADF-ob/ob. (B) Classification of differential metabolites in the ADF-ob/ob group

ADF Intervention Results in Significant Improvement in Renal Inflammation in BTBR ob/ob Mice

After ADF intervention, Hyocholic acid(HCA) and Indole-3-acetic acid (IPA) were differentially expressed in the kidneys of the ADF-ob/ob group (Fig. 8A-B). Considering that HCA and IPA can affect disease development by modulating the inflammatory pathway, we hypothesize that ADF intervention may alleviate DKD inflammation through these metabolites. We further analyzed the HCA and IPA-associated inflammatory pathways. Western blot analysis showed that, compared to the WT group, the expression levels of NF-κB p65, IL-6, and CCL2 were significantly upregulated in the ob/ob group (Fig. 9A-D). After ADF intervention, NF-κB p65, IL-6, and MCP-1 expression levels were significantly downregulated (Fig. 9A-D).

Fig. 8.

Fig. 8

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Schematic diagram of an alternate day fasting intervention affecting differential metabolites and thus inflammatory responses to ameliorate DKD. (A) Content change of IPA; (B) Content change of HCA. n = 11–15 mice/group; *p < 0.05, **p < 0 .01; ***p < 0 .001; ns p > 0.05. Statistical analysis was performed using t-test and one-way ANOVA in Prism 10

Fig. 9.

Fig. 9

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Alternate-day fasting intervention improved kidney inflammatory response in BTBR OB/OB mice. (A) Western blot results showed that the expressions of NF-κB P65, IL-6, and MCP-1 were significantly reduced after alternate-day fasting intervention; (B-D) Statistical analysis revealed that alternate-day fasting intervention significantly decreased the expressions of NF-κB P65, IL-6, and MCP-1. n = 11–15 mice/group; *p < 0.05, **p < 0 .01; ***p < 0 .001; ns p > 0.05. Statistical analysis was performed using one-way ANOVA in Prism 10

Discussion

ADF shows great potential in treating diabetes and DKD. Compared to traditional calorie restriction, ADF is more easily accepted. Studies have demonstrated that ADF has significant therapeutic effects on diabetes, including lowering food intake, lowering fasting blood glucose, total cholesterol, intake and LDL-C levels, improving glucose tolerance, and reducing insulin resistance [24, 25]. Similar effects have been observed in humans. In individuals with prediabetes, 3 to 24 weeks of ADF can lower fasting blood glucose and serum insulin levels, and increase insulin sensitivity [26].In a Canadian case report, three patients with type 2 diabetes followed an ADF diet until the end of the follow-up period. Their HbA1c levels significantly decreased, and they were able to discontinue insulin therapy, with two patients even stopping their oral hypoglycemic medications [26]. Recently, ADF has also been found to have beneficial effects on DKD, including lowering urinary protein excretion and improving DKD pathology [9]. In our research, ADF showed similar effects in the BTBR ob/ob mouse model. ADF significantly improved diabetes and DKD in BTBR ob/ob mice. ADF can reduce body weight, blood glucose, intake and LDL-C levels. ADF had no adverse effect on normal mice. Additionally, ADF reduced 24-h urinary protein excretion and renal collagen deposition. It is noteworthy that ADF has minimal impact on normal WT control mice, with only body weight reduction observed. No significant changes were seen in food intake, fasting blood glucose, total cholesterol, triglycerides, or LDL-C levels. This suggests that ADF is safe for WT mice. These findings underscore ADF's potential as a therapeutic strategy for DKD. Further investigation into the underlying mechanisms could enhance its effectiveness in treating DKD and potentially identify new drug targets for the disease.

We performed high resolution untargeted metabolomics by using a two-step extraction process for polar and nonpolar metabolites [23] combined with IMS. As mentioned previously, IMS is an efficient analytical tool that not only add new separation dimensions, but also can obtain CCS values [27], which will get more reliable identification [28]. In this research, 62 metabolites were identified after using HMDB database via the ChemSpider search engine. There are 18 metabolites with CCS deviations of more than ± 10% indicating they are wrongly identified. Finally, 44 different metabolites associated with ADF intervention on ob/ob is identified with high confidential. Among the differentially expressed metabolites hyocholic acid (HCA) and Indole-3-Propionic Acid (IPA) were notified. they both can influence inflammatory response through the NF-κB pathway [29, 30]. we all know inflammation and NF-κB pathway play an important role in the development of DKD [31].

Among the differentially expressed metabolites, hyocholic acid (HCA) and indole-3-propionic acid (IPA) were particularly notable. Both metabolites can modulate the inflammatory response through the NF-κB pathway, which plays a critical role in DKD development. HCA and IPA are products of intestinal bacteria, suggesting that long-term ADF may impact intestinal bacterial metabolism. These bacterial metabolites could help reduce inflammation and potentially mitigate DKD progression.

HCA, a bile acid synthesized by gut bacterial biotransformation, has multiple bioactivities [32]. It can stimulate glucagon-like peptide-1 (GLP-1), which plays a significant role in maintaining blood glucose levels in humans [32–34]. This suggests that HCA could benefit DKD by regulating blood glucose. Interestingly, HCA can inhibit the activity of bile acid receptor farnesoid X receptor (FXR), which influences the NF-κB signaling pathway [29]. Activation of NF-κB may release inflammatory mediators such as MCP-1 and IL-6. These inflammatory factors can cause glomerular inflammation, which in turn can mediate endothelial injury, GBM thickening, mesangial cell proliferation and hypertrophy, mesangial matrix expansion, mesangiolysis, and tubulointerstitial inflammation. Such inflammatory responses trigger tubulointerstitial injury during DKD, ultimately leading to fibrosis and functional loss [35]. Controlling inflammation duration can significantly prevent DKD progression. FXR expression and its target genes are highly present in the kidney. FXR deletion accelerates DKD progression in STZ-induced mouse models, increasing fibrosis and plasma lipid levels [36]. The FXR agonist GW4064 reduces inflammation and fibrosis in DKD mouse models [37]. In this study, BTBR ob/ob mice exhibited higher levels of HCA, NF-κB, IL-6, and MCP-1 compared to wild-type mice. After 12 weeks of ADF, the ADF-ob/ob mice showed a significant decrease in HCA levels, as well as reductions in NF-κB, IL-6, and MCP-1 levels. In contrast, ADF-WT mice did not show obvious changes in HCA, NF-κB, IL-6, and MCP-1 levels. This suggests that ADF may specifically reduce the production of HCA by certain intestinal bacteria in the context of DKD. This reduction in HCA levels may lead to decreased FXR inhibition, which subsequently inhibits the NF-κB signaling pathway and reduces IL-6 and MCP-1 expression. As a result, inflammation is decreased, and DKD progression is attenuated (Fig. 9).

IPA, a tryptophan metabolite produced by intestinal bacteria, is believed to protect against DKD [38, 39]. IPA activates its receptor, aryl hydrocarbon receptor (AhR), inhibiting the NF-κB [38, 40] signaling pathway and reducing IL-6 and MCP-1 expression. In this study, BTBR ob/ob mice exhibited lower levels of IPA and higher levels of NF-κB, IL-6, and MCP-1 compared to wild-type (WT) mice. After 12 weeks of ADF, IPA levels in ADF-ob/ob mice increased dramatically, while NF-κB, IL-6, and MCP-1 levels decreased. In contrast, there were no obvious changes in IPA, NF-κB, IL-6, and MCP-1 levels in the ADF-WT mice. This suggests that ADF may specifically enhance certain intestinal bacteria's IPA production in the context of DKD, which in turn inhibits the NF-κB signaling pathway and reduces IL-6 and MCP-1 expression. As a result, inflammation decreases, thereby attenuating DKD progression (see Fig. 9).

Conclusion

ADF significantly slowed the progression of DKD in BTBR ob/ob mice. After 12 weeks of ADF treatment, 44 metabolites were differentially expressed in the kidneys. Among these, HCA and IPA, both products of intestinal bacteria, were noteworthy. These metabolites may inhibit inflammatory factors such as NF-κB p65, IL-6, and MCP-1, suggesting that ADF may mitigate DKD progression. HCA and IPA, therefore, may play a crucial role in the beneficial effects of ADF on DKD progression. These differentially expressed metabolites not only provide insights into the pathogenesis of DKD but also highlight potential therapeutic targets for its treatment.

Limitation

This study has several limitations. First, the gender imbalance between the WT and WT-ADF groups could influence the results, as the WT group contained only male mice. Although we carefully controlled for gender in the ob/ob groups, the lack of gender balance in the WT group warrants caution when interpreting the data for WT mice. Second, while our results suggest that ADF can mitigate DKD progression, the underlying mechanisms remain speculative and would benefit from further investigation. Immunohistological examination of immune cell infiltration in the renal tissue, for example, may provide more direct evidence of inflammation reduction. Additionally, the impact of ADF on gut microbiota and its potential interaction with specific metabolites like HCA and IPA requires further exploration to confirm their roles in DKD pathogenesis. Finally, while the study used the BTBR ob/ob mouse model, which is well-characterized for DKD, the applicability of these findings to other models or human populations needs further validation.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 15 kb) (16KB, xlsx)

Acknowledgements

Thanks Dr. Fuyuan, Cao for his invaluable technical support in the care and breeding of experimental animals.

Author contributions

(H.Y.): investigation, Data Organization,data analysis and manuscript writing. (L.Y.): Data Organization and investigation. (J.M.): investigation. (X.Z.): investigation. (H.W): investigation. (Y.Y): investigation. (H.S): bioinformatics and correlated analysis. (Z.L): Conceptualization and project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the China Central Government Guides Local Science and Technology Development Foundation (236Z7712G), Natural Science Foundation of Hebei Province (H202020924).

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Competing Interests

The authors declare no competing interests.

Ethics Statement

This animal study was reviewed and approved by the Animal Ethics Committee of North China University of Science and Technology, with Approval No. 2022-SY-044.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Pelle, M.C., M. Provenzano, M. Busutti. et al. 2022. Up-date on diabetic nephropathy [J]. Life (Basel) 12(8). [DOI] [PMC free article] [PubMed]
  • 2.Pérez-Morales, R.E., M.D.D. Pino, J.M. Valdivielso. et al. 2019. Inflammation in diabetic kidney disease [J] 143 (1): 12-6. [DOI] [PubMed]
  • 3.Rico-Fontalvo, J., G. Aroca, J. Cabrales. et al. 2022. Molecular mechanisms of diabetic kidney disease [J]. International Journal of Molecular Sciences 23(15). [DOI] [PMC free article] [PubMed]
  • 4.Kim, Y.K., X. Ning, K.M. Munir. et al. 2022. Emerging drugs for the treatment of diabetic nephropathy [J]. 27 (4): 417-30 [DOI] [PubMed]
  • 5.Sawaf, H., G. Thomas, J.J. Taliercio. et al. 2022. Therapeutic advances in diabetic nephropathy [J]. Journal of Clinical Medicine 11(2). [DOI] [PMC free article] [PubMed]
  • 6.Wang, J., H. Xiang, Y. Lu, et al. 2021. New progress in drugs treatment of diabetic kidney disease [J]. Biomedicine & Pharmacotherapy 141:111918. [DOI] [PubMed] [Google Scholar]
  • 7.Vanorio-Vega, I., P. Constantinou, P. Tuppin. et al. 2021. Additional cost of end-stage kidney disease in diabetic patients according to renal replacement therapy modality: a systematic review [J]. Renal Replacement Therapy 7(1).
  • 8.Vasim, I., C.N. Majeed, M.D. Deboer. 2022. Intermittent fasting and metabolic health [J]. Nutrients 14(3). [DOI] [PMC free article] [PubMed]
  • 9.Bilen, A., I. Calik, M. Yayla, et al. 2021. Does daily fasting shielding kidney on hyperglycemia-related inflammatory cytokine via TNF-alpha, NLRP3, TGF-beta1 and VCAM-1 mRNA expression [J]. International Journal of Biological Macromolecules 190:911–918. [DOI] [PubMed] [Google Scholar]
  • 10.Bilen, A., I. Calik, M. Yayla. et al. 2021. Does daily fasting shielding kidney on hyperglycemia-related inflammatory cytokine via TNF-α, NLRP3, TGF-β1 and VCAM-1 mRNA expression [J] 190:911–8. [DOI] [PubMed]
  • 11.Joaquim, L., A. Faria, H. Loureiro, et al. 2022. Benefits, mechanisms, and risks of intermittent fasting in metabolic syndrome and type 2 diabetes [J]. Journal of Physiology and Biochemistry 78 (2): 295–305. [DOI] [PubMed] [Google Scholar]
  • 12.Khan, M.N., S.I. Khan, M.I. Rana, et al. 2022. Intermittent fasting positively modulates human gut microbial diversity and ameliorates blood lipid profile [J]. Frontiers in Microbiology 13:922727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Macel, M., N.M. van Dam, and J.J. Keurentjes. 2010. Metabolomics: The chemistry between ecology and genetics [J]. Molecular Ecology Resources 10 (4): 583–593. [DOI] [PubMed] [Google Scholar]
  • 14.Trifonova, O.P., D.L. Maslov, E.E. Balashova, et al. 2021. Mass spectrometry-based metabolomics diagnostics - myth or reality? [J]. Expert Review of Proteomics 18 (1): 7–12. [DOI] [PubMed] [Google Scholar]
  • 15.Beli, E., Y. Yan, L. Moldovan, et al. 2018. Restructuring of the Gut microbiome by Intermittent fasting prevents retinopathy and prolongs survival in db/db Mice [J]. Diabetes 67 (9): 1867–1879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Xiong, F., K. Jiang, Y. Wu, et al. 2023. Intermittent fasting alleviates type 1 diabetes-induced cognitive dysfunction by improving the frontal cortical metabolic disorder [J]. Biochimica et Biophysica Acta, Molecular Basis of Disease 1869 (7): 166725. [DOI] [PubMed] [Google Scholar]
  • 17.Want, E.J., P. Masson, F. Michopoulos, et al. 2013. Global metabolic profiling of animal and human tissues via UPLC-MS [J]. Nature Protocols 8 (1): 17–32. [DOI] [PubMed] [Google Scholar]
  • 18.Lacalle-Bergeron, L., T. Portolés, F.J. López. et al. 2020. Ultra-performance liquid chromatography-ion mobility separation-quadruple time-of-flight MS (UHPLC-IMS-QTOF MS) metabolomics for short-term biomarker discovery of orange intake: A randomized, controlled crossover study [J] 12(7). [DOI] [PMC free article] [PubMed]
  • 19.Lacalle-Bergeron, L., T. Portolés, F.J. López. et al. 2020. Ultra-Performance Liquid Chromatography-Ion Mobility Separation-Quadruple Time-of-Flight MS (UHPLC-IMS-QTOF MS) Metabolomics for Short-Term Biomarker Discovery of Orange Intake: A Randomized, Controlled Crossover Study [J]. Nutrients 12(7). [DOI] [PMC free article] [PubMed]
  • 20.Dwivedi, P., A.J. Schultz, H.H.H.J.I.J.O.M. Spectrometry. 2010. Metabolic Profiling of Human Blood by High Resolution Ion Mobility Mass Spectrometry (IM-MS) [J] 298 (1–3): 78–90. [DOI] [PMC free article] [PubMed]
  • 21.Li, A., K.M. Hines, L.X.J.M.I.M. Biology. 2020. Lipidomics by HILIC-Ion Mobility-Mass Spectrometry [J] 2084:119-32. [DOI] [PMC free article] [PubMed]
  • 22.Alpers, C.E., K.L.H.J.K. Research, C. Practice. 2018. Pathology identifies glomerular treatment targets in diabetic nephropathy [J] 37 (2): 106-11. [DOI] [PMC free article] [PubMed]
  • 23.Want, E.J., P. Masson, F. Michopoulos, et al. 2012. Global metabolic profiling of animal and human tissues via UPLC-MS [J]. Nature Protocols 8 (1): 17–32. [DOI] [PubMed] [Google Scholar]
  • 24.Kim, Y.H., J.H. Lee, J.L-H. Yeung. et al. 2019. Thermogenesis-independent metabolic benefits conferred by isocaloric intermittent fasting in ob/ob mice [J] 9 (1): 2479. [DOI] [PMC free article] [PubMed]
  • 25.Swoap, S ., M.J. Bingaman, E.M. Hult. et al. 2019. Alternate-day feeding leads to improved glucose regulation on fasting days without significant weight loss in genetically obese mice [J] 317 (3): R461-R9. [DOI] [PubMed]
  • 26.Barnosky, A.R., K.K. Hoddy, T.G. Unterman et al. 2014. Intermittent fasting vs daily calorie restriction for type 2 diabetes prevention: a review of human findings [J]. 164 (4): 302–11. [DOI] [PubMed]
  • 27.Spectrometry J A D J J O M, LAB A I T C. 2022. Lipid analysis by ion mobility spectrometry combined with mass spectrometry: A brief update with a perspective on applications in the clinical laboratory [J] 23:7–13. [DOI] [PMC free article] [PubMed]
  • 28.H.C. Köfeler, T.O. Eichmann, R. Ahrends et al. 2021. Quality control requirements for the correct annotation of lipidomics data [J] 12 (1): 4771. [DOI] [PMC free article] [PubMed]
  • 29.Zhang, Z., B. Zhang, X. Jiang et al. 2023. Hyocholic acid retards renal fibrosis by regulating lipid metabolism and inflammatory response in a sheep model [J] 122:110670. [DOI] [PubMed]
  • 30.Zhao, Z-H., F-Z. Xin, Y. Xue et al. 2019. Indole-3-propionic acid inhibits gut dysbiosis and endotoxin leakage to attenuate steatohepatitis in rats [J] 51 (9): 1–14. [DOI] [PMC free article] [PubMed]
  • 31.Ke, G., X. Chen, R. Liao et al. 2021. Receptor activator of NF-κB mediates podocyte injury in diabetic nephropathy [J] 100 (2): 377–90. [DOI] [PubMed]
  • 32.Zheng, X., T. Chen, R. Jiang et al. 2021. Hyocholic acid species improve glucose homeostasis through a distinct TGR5 and FXR signaling mechanism [J] 33 (4): 791–803.e7. [DOI] [PubMed]
  • 33.Xu, J., Zhang, K., Qiu, B. et al. 2022. Decreased hyocholic acid and lysophosphatidylcholine induce elevated blood glucose in a transgenic porcine model of metabolic disease [J]. 12(12). [DOI] [PMC free article] [PubMed]
  • 34.J-H. Yu, J-B. Zheng, J. Qi et al. 2019. Bile acids promote gastric intestinal metaplasia by upregulating CDX2 and MUC2 expression via the FXR/NF-κB signalling pathway [J] 54 (3): 879–92. [DOI] [PMC free article] [PubMed]
  • 35.Chatterjee, A., J. Tumarin, S. Prabhakar. 2024. Role of inflammation in the progression of diabetic kidney disease [J]. 8 (1): 28
  • 36.Fang, Y., M. Qin, Q. Zheng et al. 2024. Role of bile acid receptors in the development and function of diabetic nephropathy [J]. 10;9 (11): 3116–3133. [DOI] [PMC free article] [PubMed]
  • 37.Jiang, T., X.X. Wang, P. Scherzer et al. 2007. Farnesoid X receptor modulates renal lipid metabolism, fibrosis, and diabetic nephropathy [J] 56 (10): 2485–93. [DOI] [PubMed]
  • 38.H. Zhuang, X. Ren, F. Jiang et al. 2023. Indole-3-propionic acid alleviates chondrocytes inflammation and osteoarthritis via the AhR/NF-κB axis [J] 29 (1): 17. [DOI] [PMC free article] [PubMed]
  • 39.Zhang, Y., S. Li, X. Fan et al. 2024. Pretreatment with Indole-3-Propionic acid attenuates lipopolysaccharide-induced cardiac dysfunction and inflammation through the AhR/NF-κB/NLRP3 Pathway [J] 17:5293–309. [DOI] [PMC free article] [PubMed]
  • 40.Huang, Z-B., Z. Hu, C-X. Lu et al. 2022. Gut microbiota-derived indole 3-propionic acid partially activates aryl hydrocarbon receptor to promote macrophage phagocytosis and attenuate septic injury [J] 12:1015386. [DOI] [PMC free article] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (XLSX 15 kb) (16KB, xlsx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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