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. 2026 Apr 28;61(5):595–605. doi: 10.1002/lipd.70061

Hepatic Lipids as Molecular Targets in Obesity‐Associated Insulin Resistance in Rodent Models: A Systematic Review and Meta‐Analysis

Rogelio I Servin‐Uribe 1,✉, Jara Pérez‐Jiménez 2,3, Eduardo Castaño Tostado 1, Diego Hernández‐Saavedra 4,5, Rosalía Reynoso Camacho 1,✉
PMCID: PMC13568993  PMID: 42050989

ABSTRACT

Obesity induced by a high‐calorie diet (HCD) results in the accumulation of ectopic fat in tissues not typically associated with lipid storage. This accumulation can cause lipotoxicity, a type of cellular stress triggered by the accumulation of lipid intermediates such as diacylglycerols (DAG) and ceramides (Cer) in nonadipose tissues like the liver. The accumulation of these bioactive lipids has been directly linked to the development of insulin resistance induced by a HCD. However, the specific molecular species of lipids associated with such metabolic abnormalities remain poorly defined. In this study, we conducted a systematic review and meta‐analysis to consolidate current evidence on how HCDs alter the liver lipidome in rodents. A search was performed in PubMed to identify lipidomic studies on the livers of mice (C57Bl/6) and rats (Wistar and Sprague–Dawley) fed high‐calorie diets. We assessed the effect size by calculating the standardized mean difference (SMD) using Hedges' g with a 95% confidence interval. A total of 18 studies were identified. The HCD had a significant effect on insulin resistance (HOMA‐IR, p < 0.01) and significantly increased (p < 0.05) hepatic levels of Cer(18:0_18:0), Cer(18:1_18:0), Cer(18:1_20:0), Cer(18:2_20:0), and DAG(16:0_18:1) (p < 0.05). Furthermore, the HCD negatively affected three specific phosphatidylcholine (PC) species and four phosphatidylethanolamine (PE) species containing monounsaturated and polyunsaturated fatty acid chains. These findings may provide valuable insights for targeting the molecular mechanisms underlying tissue‐specific insulin resistance, a significant condition in the context of obesity.

Keywords: high‐calorie diet, insulin resistance, lipidomics, obesity

Abbreviations

ALT

alanine aminotransferase

AST

aspartate aminotransferase

Cer

ceramides

DAG

diacylglycerol(s)

HCD

high‐calorie diet

HOMA‐IR

Homeostatic Model Assessment for Insulin Resistance

IR

insulin resistance

PC

phosphatidylcholine

PE

phosphatidylethanolamine

TAG

triacylglycerol(s)

1. Introduction

Obesity induced by a HCD leads to the accumulation of ectopic fat in tissues not typically involved in lipid storage, potentially leading to lipotoxicity (Longo et al. 2019). Lipotoxicity is defined as a form of cellular stress induced by the accumulation of lipids and their derivatives, and it has been associated with the development of insulin resistance (IR) in the liver (da Silva Rosa et al. 2020). Given the global rise in obesity and related metabolic diseases, understanding the mechanisms by which lipid accumulation drives IR is critical for identifying new therapeutic targets.

Particularly, certain lipids and lipid derivatives, such as diacylglycerols and ceramides, have been implicated in lipotoxicity. DAG are lipid intermediates involved in triacylglycerol (TAG) synthesis and may arise either through de novo synthesis or via the hydrolysis of membrane phospholipids (Yang et al. 2018). In the liver, DAGs have been shown to activate protein kinase C (PKC) isoforms, which in turn modify the phosphorylation of insulin signaling molecules (da Silva Rosa et al. 2020). On the other hand, ceramides are a family of lipid molecules synthesized from intracellular palmitic acid through the de novo pathway. Additionally, ceramides can also be produced by the hydrolysis of sphingomyelins (SM) through the action of sphingomyelinases (SMases), providing a rapid and transient mechanism for ceramide production (Insausti‐Urkia et al. 2020). Ceramides can activate protein kinase C (PKC) isoforms, which phosphorylate the pleckstrin homology (PH) domain of protein kinase B (PKB/Akt) on threonine residues. This phosphorylation inhibits the binding of phosphatidylinositol (3,4,5)‐trisphosphate (PIP3) to this site, thereby reducing the insulin response. Furthermore, PKC activation has been associated with increased CD36‐mediated fatty acid uptake in the liver (Ahmed et al. 2021).

In addition, clinical studies have reported that changes in the fatty acid composition of membrane phospholipids may be linked to alterations in insulin sensitivity in insulin‐dependent tissues (Chang et al. 2019). Glycerophospholipids, the primary components of cell membranes, consist of two fatty acids and a phosphate group. Within this lipid class, phosphatidylcholine (PC) is the most abundant cellular phospholipid, followed by phosphatidylethanolamine (PE) (Yang et al. 2018). In individuals with nonalcoholic fatty liver disease (NAFLD), a condition associated with IR, most species of PC and several species of PE have been found to be reduced in both liver and plasma. Additionally, PC and PE species containing saturated and monounsaturated fatty acids have been positively correlated with fatty liver disease, whereas those containing polyunsaturated and odd‐chain fatty acids have shown a negative correlation (Ooi et al. 2021).

In this context, several studies involving both animals and humans have linked the development of obesity‐associate IR to specific lipid molecular species (Yang et al. 2018). Increased Cer (16:0) in the liver has been correlated with IR (Lair et al. 2020). Similarly, the 1,2‐DAG has been associated with the onset of IR. These DAG species are commonly enriched in saturated (e.g., palmitic acid, 16:0) and monounsaturated fatty acids (e.g., oleic acid, 18:1) (Perreault et al. 2018). Furthermore, reductions in the ratio of phosphatidylcholine to phosphatidylethanolamine have been related to increased ectopic lipid accumulation in the liver and altered insulin sensitivity (van der Veen et al. 2017).

However, beyond specific modifications, a broader understanding of these alterations can be achieved through a metabolomics approach. Metabolomics examines global changes, as well as the identification and quantification of metabolites present in cells, fluids, tissues, and whole organisms. A key branch of this field is lipidomics, which focuses on the complete set of lipids, known as the lipidome, within a cell or biological system. The isolation and identification of this extensive array of lipids have given rise to novel strategies in both research and clinical diagnostics. These techniques have enabled a more thorough exploration of lipid metabolism and its association with metabolic diseases (Wu et al. 2020).

Therefore, a lipidomic approach may provide a comprehensive overview of lipid species linked to the development of IR associated with obesity. This is highly biologically relevant given the close association between obesity‐induced IR and lipotoxicity, as well as the connection of this condition with the onset of other diseases such as diabetes and hepatic steatosis. Thus, identifying lipid species as potential molecular targets is crucial for a better understanding of these pathologies. In light of this, the objective of the present study was to perform a systematic review and meta‐analysis to identify liver lipid species as molecular targets related to IR induced by a HCD, a highly relevant factor in the pathophysiology of obesity.

2. Methods

2.1. Search Strategy and Selection Criteria

The systematic review and meta‐analysis were conducted according to the criteria established by PRISMA (Page et al. 2021). For the search in the indexed databases using query terms, the RISmed (version 2.3.0) and easyPubMed (version 2.13) packages were utilized within the RStudio 2022.12.0.r environment. Citations were extracted from the articles using the rcrossref (version 1.2.0) package. The search was focused on publications from 2013 to January 2024 and was carried out by a single researcher. The following keywords and their combinations were utilized in the search: metabolomics, lipidomics, high‐fat diet, high‐fat/high‐sucrose diet, high‐fat/high‐fructose diet, high‐sucrose diet, and high‐fructose diet. A comprehensive search strategy is provided in Figure 1.

FIGURE 1.

FIGURE 1

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Flow diagram of the literature search and study selection: The flow diagram represents the number and reasons for elimination of studies at every step based on the selection criteria.

2.2. Inclusion Criteria

The selection of studies included the following criteria for inclusion:

  • Insulin resistance induced by a HCD.

  • Studies conducted on Sprague–Dawley or Wistar rats.

  • Studies were conducted on mice of the C57BL/6J strain.

  • Studies involving a control group (fed a standard diet).

  • Studies aimed at lipidomic analysis in the liver.

  • Identification Level 2: Molecular species level (acyl and/or alk(en)yl chains identified).

  • Lipidomics is performed by LC–MS methodologies.

2.3. Exclusion Criteria

  • IR induced by factors other than diet.

  • Study focused on other diseases, e.g., cancer, endocrine disorders, renal conditions, and neurological disorders, as well as the evaluation of surgical procedures.

  • Studies in knockout animal models.

  • Studies on lactation and pregnancy models.

  • Studies involving nonrodent animals, such as dogs, birds, rabbits, and pigs.

2.4. Data Extraction and Analysis

The data obtained from each study included the name of the first author, year of publication, strain of the animal under investigation, sex, age of the animal at the start of the study, type of hypercaloric diet, type of control diet, and duration of dietary intervention. Among the biochemical parameters obtained were the levels of glucose, insulin, TAG, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) in serum, as well as HOMA‐IR, body weight, and the lipidomic profile in liver tissue. From the quantitative data, the sample size, mean of the samples, and standard deviation were calculated. Data analysis was conducted following the protocol described by Harrer et al. (2021) in the Rstudio 2022.12.0.r environment.

For data analysis, the meta package in the R platform was used. Meta‐analysis was performed when the extracted data values were available from two or more studies. To evaluate the effect size, the standardized mean difference (SMD) was calculated; this was determined using Hedges'g with a 95% confidence interval (CI) for the effect size. In addition, publication bias was performed using Egger's linear regression test, where the asymmetry of the funnel plot was evaluated and was performed when 10 or more studies were included. Using the Q statistic and I 2 values, the existence of variability was tested and the proportion of total variability due to heterogeneity was evaluated, respectively (Q‐value p < 0.1 indicates heterogeneity). Heterogeneity was considered low, moderate, and high when I 2 values of approximately 20%, 50%, and 80%, respectively, were presented. The total amount of heterogeneity was estimated using the heterogeneity variance (τ2) (Harrer et al. 2021).

3. Results

3.1. Study Selection and Characteristics of Selected Studies

The selection process for the studies included in the meta‐analysis is illustrated in Figure 1. The search and selection of studies was divided into three phases: identification, evaluation, and eligibility, and included. In the first phase, over 3100 studies were identified. After removing duplicates and unsuitable studies, 2470 studies were screened based on their titles and abstracts. Of these, 129 eligible studies underwent a final evaluation. Ultimately, 18 studies were included in the review and meta‐analysis (Figure 1). The general characteristics of the selected 18 studies are shown in Table 1. The most commonly used strain was C57BL/6, followed by Wistar and Sprague–Dawley. The HCDs varied in macronutrient composition, with fat content primarily ranging from 40% to 60% of total energy intake. This range is consistent with the fat percentages recommended by Research Diets Inc. for diet‐induced obesity (DIO). Additionally, the duration of dietary interventions in the studies selected for the meta‐analysis ranged from 5 to 52 weeks.

TABLE 1.

Characteristics of studies included in the meta‐analysis.

Author Animal strain Age (weeks) Tissue Diet Sample size Experimental time (weeks) Methodology for lipidomics analysis Type of mass analyzer
Babiy et al. (2023) C57BL/6J mice 6 Liver SD: 72.4% Kcal C, 19.3% Kcal P, 8.4% Kcal G. HCD: 40% Kcal C, 20% Kcal P, 40% Kcal F. beverage high sucrose (23.1 g/L D‐fructose, 18.9 g/L D‐glucose)

SD: 7

HCD: 16

 22 LC–MS/MS Híbrido TIL‐QqQ
Liu et al. (2023) C57BL/6 mice 6 Liver SD: 15.8% Kcal F. HCD: 60% Kcal F

SD: 6

HCD: 6

 8 UHPLC–MS ND
Mráziková et al. (2023) Wistar Kyoto rat 8 Liver SD: 58% C, 33% P, 9% F; HCD: 20% C, 20% P, 60% F.

SD: 8

HCD: 24

 52 LC–MS Orbitrap
Xie et al. (2023) C57BL/6 mice ND Liver SD: 9.8% C, 13.7% P, 76.5% F; HCD: 20% C, 20% P, 60% F ND 12 LC–ESI–MS/MS Q‐Tof
Sun et al. (2023) C57BL/6J mice 6 Liver HCD: 60% Kcal F

SD: 10

HCD: 10

 18 RPLC–MS (PD)

Híbrido

Q‐Orbitrap
Xia et al. (2022) C57BL/6N mice 5 Liver HCD: 60% Kcal F

SD: 7

HCD: 6

 8 UHPLC–MS/MS

Híbrido

Q‐Orbitrap
Kim et al. (2022) C57BL/6J mice 5 Liver SD: 61% Kcal C, 24.7% Kcal P, 13.2% Kcal F. HCD: 20% Kcal C, 20% Kcal P, 60% Kcal F

SD: 6

HCD: 6

 12 LC–ESI–MS/MS (PD)

Híbrido

Q‐Orbitrap
Sarkar et al. (2021) C57BL/6 mice 8–12 Liver SD: 65% kcal C, 24% kcal P, 11% kcal F. DCH: 19% kcal fructose, 60% kcal F

SD: 8

HCD: 8

 12 UPLC–ESI–MS (PD) Orbitrap
Yao et al. (2021) miceC57BL/6 5–6 Suerum SD: 13.5% Kcal F, HCD: 60% Kcal F ND 20 UHPLC–MS QTRAP
Wang et al. (2021) C57BL/6J mice ND Liver SD: 4.3% de F, HCD: 24% de F

SD: 6

HCD: 6

 12 LC–ESI–MS/MS (PD) QTRAP
Cong et al. (2021) Wistar rat 6 Liver SD: 80% C, 15% P, 5% F. HCD: 20% C, 20% P, 60% F

SD: 8

HCD: 8

 12 LC–ESI–MS (PD)

Híbrido

Q‐Orbitrap
Feng et al. (2020) Sprague–Dawley rat 6 Liver SD: 68% C, 11% P, 11% F, 3.4 kcal/g; HCD: 46% C, 17% P, 37% F, 4.4 kcal/g

SD: 8

HCD: 8

 42 días LC–ESI–MS/MS QTRAP
Taltavull et al. (2020) Wistar Kyoto rat 8–10 Liver SD: 60.5% kcal C, 21.3% kcal P, 18.2% kcal F, 3.1 kcal/g; HCD: 37.2% kcal C, 17.9% kcal P, 44.9% kcal F, 4.8 kcal/g

SD: 7

HCD: 7

 24 UHPLC–MS/MS Q‐Tof
Yue et al. (2020) Sprague–Dawley rat 8 Suerum SD: 66% C, 22% P, 12% F; HCD: 45.5% C, 17.5% P, 37% F

SD: 8

HCD: 8

 12 UHPLC/MS QTRAP
Taltavull et al. (2016) Wistar Kyoto rat 5–6 Liver SD: 69.4% Kcal C, 16.5% Kcal P, 14.1% Kcal F. HCD: 37.7% Kcal C, 16.5% Kcal P, 45.8% Kcal F

SD: 7

HCD: 7

 26 UPLC–MS/MS

Q‐Tof DAG analysis

QqQ Cer analysis
Montgomery et al. (2016) C57BL/6J mice 8 Liver SD: 71% Kcal C, 21% Kcal P, 8% Kcal F. HCD: 35% Kcal C, 20% Kcal P, 45% Kcal F.

SD: 4

HCD: 4

 8 LC–MS (PD) QTRAP
Matsakas et al. (2015) C57BL/6 mice 16–20 Muscle HCD: 35% C, 20% P, 45% F

SD: 4

HCD: 4

 10 LC–MS/MS ND
Goto‐Inoue et al. (2013) Wistar rat 3 Muscle HCD: 60% Kcal F 6–7 SD and HCD 12 TLC–Blot–MALDI–IMS Q‐tof
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Abbreviations: C, carbohydrates; F, fat; HCD, high‐caloric diet; ND, not data; P, protein; PD, published data (metabolomic data are available for analysis); Q‐orbitrap, quadrupole‐orbitrap; Q‐Tof, quadrupole‐time of flight; QqQ, triple quadrupole; QTRAP, linear‐triple quadrupole ion trap; SD, standard diet; TIL, linear ion trap.

3.2. Effect of Diet on Body Weight and Biochemical Parameters Related to Insulin Resistance

To determine the effects of diet on body weight and blood biochemical parameters, a subset of 13 studies (Table 1) was included: Goto‐Inoue et al. (2013), Matsakas et al. (2015), Montgomery et al. (2016), Taltavull et al. (2016, 2020), Feng et al. (2020), Yue et al. (2020), Cong et al. (2021), Yao et al. (2021), Babiy et al. (2023), Liu et al. (2023), Mráziková et al. (2023), and Xie et al. (2023) (Table 2). Some exceptions included are the studies by Goto‐Inoue et al. (2013) and Matsakas et al. (2015), which performed muscle lipidomics, and the studies by Yue et al. (2020) and Yao et al. (2021) based on serum lipidomics. These studies meet the majority of the inclusion criteria and were included in the analysis of biochemical and zoometric parameters (Table S1).

TABLE 2.

Results of meta‐analysis of body weight and biochemical parameters associated with insulin resistance and obesity in rodent models fed with high‐calorie diet.

Parameters SMD [95% CI] p I 2 (%) [95% CI] Q statistic (DF, p) Egger's test Z‐statistic (p)
Body weight 2.71 [1.15, 4.27] 0.0045 77.5 [55.4, 88.6] 31.06 (7, p < 0.0001) NA
Glucose 1.99 [0.88, 3.90] 0.0023 76.1 [58.1, 86.3] 45.95 (11, p < 0.0001) 4.88 (p = 0.004)
Insulin 2.33 [1.47, 3.19] 0.0006 41.4 [0.075.4] 10.24 (6, p = 0.115) NA
HOMA‐IR 2.40 [1.54, 3.25] 0.0008 28.8 [0.0, 70.8] 7.02 (5, p = 0.219) NA
Triglycerides 0.398 [−0.97, 1.77] 0.5154 82.2 [66.1, 90.6] 39.27 (7, p < 0.0001) NA
ALT 1.37 [−1.34, 4.08] 0.2326 82.8 [60.7, 92.5] 23.26 (4, p = 0.0001) NA
AST 1.75 [−2.25, 5.76] 0.258 76.7 [36.1, 91.5] 12.85 (3, p < 0.005) NA
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Note: Egger's test was conducted for parameters that had more than 10 studies. Data are presented as standardized mean differences (SMD) with a 95% confidence interval (CI). The p‐value is derived from the random effects model, with p < 0.05 indicating statistical significance. Heterogeneity among studies was assessed using Cochran's Q, with significance set at p < 0.10, and quantified by I 2.

Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; DF, degrees of freedom; HOMA‐IR, insulin resistance index; NA, not applicable.

The HCD intervention revealed a significant effect of the diet on rodent body weight (SMD: 2.71 [95% CI: 1.15, 4.27]; p = 0.0045), but showed a high degree of heterogeneity among the studies (I 2 = 77.5%) [95% CI: 55.4, 88.6]. Additionally, the effect of HCD on blood biochemical parameters associated with IR were also evaluated (Table 2). Meta‐analysis showed a significant effect of HCD on fasting blood glucose, insulin levels, and HOMA‐IR. Heterogeneity was high for glucose (I 2 = 76.1% [95% CI: 58.1, 86.3]), moderate for insulin (I 2 = 41.4% [95% CI: 0.0, 75.4]), and low for HOMA‐IR (I 2 = 28.8% [95% CI: 0.0, 70.8]). Conversely, no significant effect of HCD on serum TAG, ALT, and AST levels was observed (Table 2).

3.3. Effect of Diet on the Levels of Lipid Molecular Species in the Liver

Table 1 presents the total number of studies included in the meta‐analysis, of which only seven provided their liver lipidomic datasets (published data—PD) (Montgomery et al. 2016; Cong et al. 2021; Sun et al. 2023; Xia et al. 2022; Sarkar et al. 2021; Wang et al. 2021; Kim et al. 2022). Among these studies, some of them provided less than 200 lipids (for instance, Xia et al. (2022) provided data for only 12 ceramides) and, in the cases of Montgomery et al. (2016) and Sun et al. (2023), only the mean and standard error for each variable (lipid) were available. Therefore, data from these three studies were not filtered according to any criteria and were fully incorporated into the databases generated for this study. All lipid species included in the meta‐analysis performed in this study are detailed in Table S2 (ceramides), Table S3 (diacylglycerols), Table S4 (phosphatidylcholines), and Table S5 (phosphatidylethanolamines).

On the other hand, Cong et al. (2021) reported lipid values as abundance, while Sarkar et al. (2021), Wang et al. (2021), and Kim et al. (2022) reported values as normalized intensity and provided their complete lipidomic datasets. For these three studies, the p‐value for each lipid species was calculated by comparing the HCD and standard diet (SD) groups using a Student's t‐test with Benjamini and Hochberg correction. Additionally, the fold change (FC), defined as the mean value of each variable in the HCD group divided by that in the SD group, was also calculated. Finally, the variable importance in projection (VIP) value was determined. Regarding the data published by Cong et al. (2021), it included all the statistical analyses mentioned above. Lipids that exhibited a significant change (HCD vs. SD groups) were selected based on the criteria of FC ≥ 2 or ≤ 0.5 and VIP > 1. Subsequently, each selected lipid was then cross‐referenced across all databases, and the corresponding mean abundance, standard deviation, and sample size for both SD and HCD groups were compiled into a unified dataset for further analysis (Tables S2–S5).

As shown in Table 3, the meta‐analysis of the effects of HCD on specific lipid species revealed a positive effect on four ceramide species: Cer(18:0_18:0), Cer(18:1_18:0), Cer(18:1_20:0), and Cer(18:2_20:0). These ceramides showed low levels of heterogeneity, with percentages of 0.0%, 26.5%, 25.5%, and 0.0%, respectively. Additionally, a monounsaturated diacylglycerol species, DAG(16:0_18:1), was also found to be significantly increased in response to HCD. Conversely, a negative effect of HCD was observed on three phosphatidylcholine (PC) species and four phosphatidylethanolamine (PE) species.

TABLE 3.

Results of meta‐analysis of specific liver lipid species associated with IR and obesity in rodent models fed with high‐caloric diet.

Lipids Lipid species SMD [95% CI] p I 2 (%) [95% CI] Q statistic (DF, p) Studies
Ceramides Cer(18:0_18:0) 3.07 [1.35, 4.80] 0.016 0.0 [0.0, 89.6] 1.19 (2, p = 0.55) Table S2
Cer(18:1_18:0) 3.15 [1.70, 4.61] 0.003 26.5 [0.0, 70.8] 5.44 (4, p = 0.24)
Cer(18:1_20:0) 3.13 [1.35, 4.91] 0.011 25.5 [0.0, 71.5] 4.03 (3, p = 0.25)
Cer(18:2_20:0) 3.65 [2.51, 4.79] 0.015 0.0 0.02 (1, p = 0.89)
Diacylglycerols DAG(16:0_18:1) 1.55 [0.03, 3.07] 0.046 78.2 [54.9, 89.5] 27.55 (6, p = 0.0001) Table S3
Phosphatidylcholines PC(16:0_16:1) −2.68 [−4.44, −0.92] 0.013 63.7 [4.3, 86.2] 11.02 (4, p = 0.0263) Table S4
PC(16:1_18:2) −1.81 [−3.11, −0.51] 0.013 73.4 [45.8, 86.9] 26.29 (7, p = 0.0004)
PC(16:1_22:6) −1.20 [−1.89, −0.51] 0.008 0.0 [0.0, 79.2] 2.68 (4, p = 0.6122)
PC(17:0_22:6) 1.46 [1.31, 1.61] 0.005 0.0 0.0 (1, p = 0.9782)
Phosphatidylethanolamines PE(15:0_20:4) −2.88 [−3.97, −1.78] 0.019 0.0 0.02 (1, p = 0.8836) Table S5
PE(16:0_18:2) −2.55 [−4.50, −0.61] 0.018 78.5 [55.6, 89.6] 27.89 (6, p < 0.0001)
PE(16:0_20:4) −1.86 [−2.90, −0.82] 0.007 27.7 [0.0, 71.6] 5.53 (4, p = 0.2369)
PE(18:1_18:2) −2.17 [−4.15, −0.18] 0.038 75.4 [39.4, 90.0] 16.24 (4, p = 0.0027)
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Note: Egger's test was conducted for parameters that had more than 10 studies. Data are presented as standardized mean differences (SMD) with a 95% confidence interval (CI). The p‐value is derived from the random effects model, with p < 0.05 indicating statistical significance. Heterogeneity among studies was assessed using Cochran's Q, with significance set at p < 0.10, and quantified by I 2.

Abbreviations: Cer, ceramides; DAG, diacylglycerol(s); DF, degrees of freedom; PC, phosphatidylcholines, PE, phosphatidylethanolamines.

4. Discussion

The objective of this study was to evaluate the effect of a HCD on the hepatic lipid profile of rodents through a systematic review and meta‐analysis, due to the relevance of lipotoxicity in the context of IR and obesity. According to the general characteristics of the included studies, the most commonly used strains in HCD‐induced models of obesity and metabolic alterations were C57BL/6, Wistar, and Sprague–Dawley. These strains exhibit a strong predisposition to obesity, diabetes, and atherosclerosis when exposed to an HCD (Buettner et al. 2007). We found that the studies examined diets containing 40%–60% of total energy from fat, exceeding the 30% threshold previously shown to trigger diet‐induced metabolic changes (Buettner et al. 2007). Furthermore, most studies assessed the effects of HCD over durations ranging from 5 to 52 weeks. de Moura et al. (2021) indicate that the duration required to induce obesity with a high‐fat diet varies significantly, ranging from 8 days to 27 weeks, with interventions exceeding 3 weeks generally producing better results in inducing obesity and associated metabolic changes.

Moreover, these dietary interventions may also influence the progression of IR. For instance, Do et al. (2011) found that C57BL/6J mice fed a high‐fat diet (HFD) containing 21% fat and 1% cholesterol for 24 weeks exhibited alterations in parameters associated with systemic IR, including elevated fasting blood glucose and insulin levels, impaired glucose tolerance, and increased HOMA‐IR, starting at week 16 in the HFD group. In contrast, C57BL/6J mice fed an HFD comprising 42%–45% fat for 16 weeks showed early signs of hepatic IR from the first week, characterized by reduced suppression of hepatic glucose production and impaired glucose uptake in adipose tissue (Turner et al. 2013). In addition, Turner et al. (2013) suggested that the development of muscle‐IR may be a crucial factor driving hyperinsulinemia, as fasting insulin levels remained unchanged until Week 3 of the HFD, which coincided with the onset of muscle IR. In line with this, the studies selected for this meta‐analysis demonstrate a wide range of dietary fat contents and intervention durations, all of which are associated with the onset of metabolic dysfunctions, including IR and lipotoxicity.

Regarding the meta‐analysis results, it should be first mentioned that the limited number of studies providing lipidomic databases restricted the assessment of publication bias for each parameter. Specifically, the number of included articles did not meet the recommended criterion for calculating bias (n ≥ 10) (Harrer et al. 2021), representing a methodological limitation of the present study.

As regards to the biochemical parameters analyzed in this meta‐analysis, some exhibited high heterogeneity, which reflects the degree of variability among individual study results and serving as a useful indicator for identifying contexts in which the effects may differ in magnitude (Harrer et al. 2021). In this analysis, the elevated heterogeneity observed for body weight may be primarily attributed to intrinsic strain‐specific variability. For instance, Wistar rats are more susceptible to diet‐induced obesity by a HFD, due to their higher food intake compared to Sprague–Dawley rats, as well as greater fatty acid absorption and enhanced lipogenesis (Miranda et al. 2018). Similarly, Toop and Gentili (2016), in a meta‐analysis evaluating the metabolic effects of fructose beverage consumption (~10%, w/v) across different rat strains, reported a significant effect on body weight independent of intervention duration, which also exhibited a high degree of heterogeneity (I 2 = 75.9%).

Blood glucose and insulin responses also displayed high to moderate heterogeneity, which may again stem from genetic variability. In this regard, Stöckli et al. (2017) evaluated the metabolic response of three inbred strains of ILSXISS mice to a high‐fat, high‐sucrose diet and found that genetic diversity among the strains resulted in significant heterogeneity in insulin sensitivity. On the other hand, the HOMA‐IR in our study exhibited low heterogeneity. It has been suggested that in C57BL/6J mice exposed to a high‐fat and high‐fructose diet for 24 weeks, this parameter does not exhibit a cumulative or progressive effect. Instead, HOMA‐IR increases early after the onset of dietary intervention and remains relatively stable over time and is not affected by the progressive accumulation of triglycerides in the muscle and liver (Burchfield et al. 2018). Consequently, this may explain why differences in experimental durations do not substantially influence HOMA‐IR outcomes across studies.

No significant effects of diet were found on serum triglycerides, ALT, or AST. While IR is often linked to dyslipidemia, and triglycerides have been explored as predictors of IR (Ma et al. 2020), current evidence indicates triglycerides are relatively inert, with lipid intermediates being more directly implicated in IR (Preuss et al. 2019). Thus, our meta‐analysis focused on diverse lipid classes to identify potential biomarkers tied to IR and obesity.

The liver is one of the primary organs responsible for producing sphingolipids, particularly sphingomyelins and ceramides (Insausti‐Urkia et al. 2020). Among these, ceramides especially long‐chain ceramides (C < 22) have been strongly associated with the development of IR (Longo et al. 2019; Montgomery et al. 2016). In the present meta‐analysis, HCD exposure significantly affected four ceramides species in the liver: Cer(18:0_18:0), Cer(18:1_18:0), Cer(18:1_20:0), and Cer(18:2_20:0). This aligns with rodent studies showing that genetic inhibition of enzymes involved in the synthesis of very long‐chain ceramides (C > 22) has been associated with hepatic accumulation of long‐chain ceramides (C < 22), which correlates with the development of IR (Raichur et al. 2014; Turpin et al. 2014). Norheim et al. (2018) examined ceramides profile in the livers of five different mouse strains (A/J, BALB/cJ, C3H/HeJ, C57BL/6J, and DBA/2J) fed a high‐fat, high‐fructose diet for 8 weeks and found that Cer(d18:1_16:0) and Cer(d18:1_20:0) levels were positively correlated with both IR and hepatic steatosis. Additionally, other studies involving rats fed a high‐fat, high‐carbohydrate diet also demonstrated increased hepatic levels of Cer(d18:1_18:0) and Cer(d18:1_16:0) compared to a standard diet (SD), with Cer(d18:1_18:0) in particular being associated with obesity‐related IR (Taltavull et al. 2016, 2020). Taken together, the findings of this meta‐analysis reveal a pattern of ceramide molecular species, particularly those containing C18:0 and C20:0 acyl chains that may serve as relevant biochemical markers for these metabolic alterations.

On the other hand, in a targeted lipidomic analysis, Zheng et al. (2023) reported that C57BL/6J mice fed HCD for 24 weeks exhibited increased hepatic levels of sn‐1,2‐DAG, including C10:0_C10:0, C14:0_C14:0, C16:0_C18:1, C16:0_C16:0, C18:1_C18:1, C18:0_C18:0, and C12:0_C12:0 compared to the DS control. In our meta‐analysis, only one monounsaturated DAG molecular species (C16:0_C18:1) showed a significant increase in response to HCD, although it exhibited high heterogeneity. This variability suggests that factors such as mouse strain, age, and the duration of dietary intervention may influence the accumulation of specific DAG species. Indeed, previous studies have shown that hepatic levels of DAG containing oleic acid (18:1 n‐9) and palmitic acid (16:0) increase markedly in aged mice following HCD feeding (Ishizuka et al. 2020). However, these studies did not monitor temporal changes in DAG concentrations. Therefore, further research is needed to determine whether the accumulation of these lipid species is causally linked to the development of obesity or whether it is more strongly driven by aging. Furthermore, in the context of hepatic IR, it is essential to elucidate the specificity of these DAG molecular species for PKCε activation.

Phospholipid disruptions also emerged from our meta‐analysis. PCs are essential for VLDL assembly; reduced hepatic PC impairs lipid export, fostering ectopic lipid accumulation and IR (Meikle and Summers 2017). In this context, the present meta‐analysis demonstrated that HCD negatively affected three PC molecular species containing monounsaturated and polyunsaturated acyl chains: PC ((16:0_18:1), (16:1_18:2), and (16:1_22:6)). In ob/ob (OK) mice, a decreased PC/PE ratio has been associated with hepatic steatosis (Li et al. 2006). Similarly, C57BL/6J mice fed an HCD for 8 weeks exhibited glucose intolerance accompanied by a decreased PC/PE ratio, primarily due to reduced PC levels rather than increased PE levels (Jordy et al. 2015). Furthermore, Ma et al. (2016) reported that levels of arachidonic acid (C20:4) containing PEs were significantly lower in individuals with obesity‐associated nonalcoholic fatty liver disease (NAFLD) compared to those with simple steatosis and healthy individuals. Consistently, our meta‐analysis revealed that HCD negatively impacted two PE species containing C20:4 acyl chains (Table 3).

Therefore, although alterations in the PC/PE ratio are already recognized as a hallmark of hepatic steatosis contributing to the development of IR‐obesity, the present meta‐analysis provides evidence on specific phospholipid species that may represent molecular targets in the progression of metabolic alterations, such as hepatic lipotoxicity.

On the other hand it is important to consider the translational relevance and limitations of extrapolating the findings of this meta‐analysis to humans. Human studies have shown that hepatic ceramide content (C16:0, C18:0, and C22:0) increases in obese individuals with glucose intolerance and this increase has been associated with IR (Razak Hady et al. 2019). The findings of the present meta‐analysis align with these observations, demonstrating a significant increase in several ceramide species in rodents exposed to hypercaloric diets such as Cer(18:0_18:0), Cer(18:1_18:0), Cer(18:1_20:0), and Cer(18:2_20:0), all of which exhibit low heterogeneity across independent studies.

Furthermore, the DAG‐PKCε mechanism linking ectopic lipid accumulation to IR in the liver has been demonstrated in both human and rodent models (Lyu et al. 2020). In this context, the identification of DAG(16:0_18:1) as a consistently altered lipid molecule in our meta‐analysis supports and extends the established role of DAG‐mediated PKCε activation in IR. It also highlights the potential importance of fatty acid composition within DAG species in modulating their biological effects.

However, several limitations must be considered when extrapolating these results to humans. First, the dietary interventions in the studies included in our meta‐analysis involved diets providing 40%–60% of total energy from fat, with metabolic effects assessed over periods ranging from 5 to 52 weeks. These conditions differ from the heterogeneous dietary exposures observed in human populations and represent relatively short durations compared to the chronic progression of obesity and insulin resistance in humans (Kleinert et al. 2018).

Second, specific differences in lipid metabolism between rodents and humans must be carefully analyzed. Although PPARα is a key regulator of hepatic lipid metabolism in both rodents and humans, the specific genes regulated within lipid metabolic pathways differ between species (Rakhshandehroo et al. 2009), which may affect both the composition of accumulated lipids and the magnitude of the metabolic effects. Furthermore, methodological variability among lipidomic studies can contribute to heterogeneity and limit direct comparisons with human datasets. Therefore, future research should focus on validating these lipid signatures in humans using high‐resolution lipidomic profiling and integrating these data with standard insulin sensitivity measurements to determine whether modulation of ceramide, DAG, and phospholipid species can predict metabolic outcomes.

5. Conclusion

Despite the growing interest in lipotoxicity as a key mechanism underlying IR and obesity, a well‐defined list of lipid molecular species that should be explored in this context remains lacking. In order to progress in this knowledge, the results of the present meta‐analysis, based on murine studies, indicate that a HCD is associated with increased hepatic levels of specific ceramides (Cer(18:0_18:0), Cer(18:1_18:0), Cer(18:1_20:0), and Cer (18:2_20:0)), diacylglycerol (DAG(16:0_18:1)), phosphatidylcholines (PC(17:0_22:6)). Conversely, the levels of certain phosphatidylcholine species (PC(16:0_16:1), PC(16:1_18:2), and PC(16:1_22:6)) and phosphatidylethanolamines (PE(15:0_20:4), PE(16:0_18:2), PE(16:0_20:4), and PE(18:1_18:2)) were found to be decreased. Therefore, this lipidomic pattern may serve as a potential biomarker for assessing metabolic alterations in the liver. However, due to the inherent limitations of the present meta‐analysis, additional experimental studies are warranted to validate this lipid profile and determine its causal role in the development of insulin resistance.

Author Contributions

R.I.S.‐U. was responsible for study selection, data extraction, and data analysis. The draft of the manuscript was written by R.I.S.‐U., J.P.‐J., E.C.T., D.H.‐S., and R.R.C. provided supervision, guidance and critical revisions of the manuscript. All authors reviewed and approved the final version of the manuscript.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Studies included in the evaluation of the effect of a high‐calorie diet on body weight and biochemical parameters.

Table S2: Characteristics of specific ceramides species in the liver integrated into the meta‐analysis.

Table S3: Characteristics of specific diacylglycerols species in the liver integrated into the meta‐analysis.

Table S4: Characteristics of specific phosphatidylcholines species in the liver integrated into the meta‐analysis.

Table S5: Characteristics of specific phosphatidylethanolamines species in the liver integrated into the meta‐analysis.

LIPD-61-595-s001.pdf (3.6MB, pdf)

Contributor Information

Rogelio I. Servin‐Uribe, Email: kso_21@hotmail.com.

Rosalía Reynoso Camacho, Email: rrcamachomx@yahoo.com.mx.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

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

Supplementary Materials

Table S1: Studies included in the evaluation of the effect of a high‐calorie diet on body weight and biochemical parameters.

Table S2: Characteristics of specific ceramides species in the liver integrated into the meta‐analysis.

Table S3: Characteristics of specific diacylglycerols species in the liver integrated into the meta‐analysis.

Table S4: Characteristics of specific phosphatidylcholines species in the liver integrated into the meta‐analysis.

Table S5: Characteristics of specific phosphatidylethanolamines species in the liver integrated into the meta‐analysis.

LIPD-61-595-s001.pdf (3.6MB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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