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Record W4405450041 · doi:10.1017/s0029665124005536

Differential Effects of Milk, Yogurt, and Cheese on Insulin Sensitivity, Hepatic Function, and Gut Microbiota in Diet-Induced Obese Mice

2024· article· en· W4405450041 on OpenAlexaff
Emad Yuzbashian, CB. Chan, Siegfried Ussar

Bibliographic record

VenueProceedings of The Nutrition Society · 2024
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInsulin resistanceGut floraBiologyInsulinFood scienceLipogenesisObesityDigestion (alchemy)SteatosisEndocrinologyInternal medicineLipid metabolismChemistryMedicineBiochemistry

Abstract

fetched live from OpenAlex

The prevalence of obesity and associated metabolic disorders, such as insulin resistance (IR) and fatty liver disease (steatosis), is often linked to a high-fat Western diet. Recent meta-analyses indicate that dairy consumption may mitigate these effects, particularly favoring low-fat dairy products. However, the underlying molecular mechanisms remain elusive. Dairy products vary in nutrient composition (calcium, fat) and structure, impacting digestion and nutrient absorption. Fermentation processes in cheese and yogurt introduce bacterial cultures with potentially positive metabolic effects. This study investigates the distinct effects of different dairy subgroups on insulin sensitivity, liver function, and gut microbiota in diet-induced obese mice, exploring potential mechanisms for mitigating Western diet-induced metabolic dysfunction. C57BL/6 mice (n = 16/group) were fed a high-fat diet (HFD, 45% fat) mimicking a Western diet rich in saturated fatty acids (SFA), or HFD supplemented with: fat-free milk (MILK), fat-free yogurt (YOG), or reduced-fat cheese (CHE, 19% fat) for 8 weeks. These supplements were administered daily at 10% of total caloric intake, reflecting typical U.S. dietary patterns. We assessed insulin resistance using the insulin tolerance test (ITT) and the homeostasis model assessment (HOMAIR). Weekly monitoring of food and water intake and body weight was conducted. After euthanasia, liver tissue was collected for Western blot analysis of key proteins in insulin signalling, gluconeogenesis, lipogenesis, lipid oxidation, uptake, and release pathways. Comprehensive lipidomics profiling of the liver and serum was performed using LC-MS, and fecal samples were analyzed for microbial diversity via 16S rRNA sequencing. Statistical analyses included QIIME2DEseq2, MetaboAnalyst 5.0, and MicrobiomeAnalyst. One-way and two-way ANOVA with Tukey’s post-hoc test were performed. The MILK and YOG interventions significantly reduced body weight, fat mass, and epididymal fat pad weight (p < 0.05) compared to HFD alone. Enhancements in glucose clearance and insulin sensitivity were notable in the MILK and YOG groups, with associated upregulations in glycogen synthase (GS), phosphoenolpyruvate carboxykinase (PEPCK), and protein kinase B (AKT) signaling. Regarding hepatic steatosis, MILK and YOG increased protein levels involved in fatty acid oxidation (ACSL, CPT1) and lowered PPARγ, suggesting reduced fat storage. Additionally, higher AMPK, PPARα, ATGL, and HSL protein abundances supported enhanced fatty acid breakdown. Gut microbiota analyses revealed a higher bacterial species richness in the MILK and YOG groups, with distinct fecal microbiota compositions across all dairy interventions. Lipidomics identified unique and shared metabolic markers among the groups, implicating glycerolipid, glycerophospholipid, sphingomyelin metabolism, and inflammatory pathways. This study demonstrates that different dairy products uniquely modulate metabolic health markers in diet-induced obese mice. Milk and yogurt particularly promote improved insulin sensitivity and hepatic functions, potentially mediated through alterations in metabolic pathways and gut microbiota composition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.236
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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