Dataset Information

An improved model of type 2 diabetes with effects on glucose tolerance, neuropathy and retinopathy with and without obesity.

ABSTRACT:

Rationale

Type 2 diabetes (T2D) costs billions of dollars annually, is also associated with pain (diabetic neuropathy), as well as retinopathy, lower urinary tract/urinary bladder dysfunction, depression, and systemic inflammation, affecting quality of life for patients. To that end, animal models are utilized to explore potential treatments, but may not reflect the complexity of the condition.

Objective

We aimed to test an improved model of T2D that more closely mimics the clinical mechanisms and symptoms in an outbred strain of mouse.

Findings

Male and female CD-1 mice (n = 72) were fed one of four diets: regular chow (REG), our Standard American Diet (SAD), a revised SAD (SAD2), or the commonly-used high-fat diet (HFD). Overall, HFD- and SAD-fed mice had significant weight gain and increased fat mass. Following injury, the SAD- and SAD2-fed mice showed protracted recovery, but the HFD-fed mice did not. Similarly, SAD- and SAD2-fed mice showed impaired retinal function compared to REG-fed mice, but the HFD-fed mice did not.

Conclusions

The SAD and SAD2 more closely model the problematic dietary intake and subsequent clinical symptoms associated with T2D.

Potential impact of study

The adjusted SAD2 may be a better representation of a human-translatable diet than the SAD and HFD, and may allow for increased advances in the investigation of T2D-related symptoms.

SUBMITTER: Wiggins AM

PROVIDER: S-EPMC10714886 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An improved model of type 2 diabetes with effects on glucose tolerance, neuropathy and retinopathy with and without obesity.

Wiggins Asia M AM Sorge Robert E RE

Physiology & behavior 20220212

<h4>Rationale</h4>Type 2 diabetes (T2D) costs billions of dollars annually, is also associated with pain (diabetic neuropathy), as well as retinopathy, lower urinary tract/urinary bladder dysfunction, depression, and systemic inflammation, affecting quality of life for patients. To that end, animal models are utilized to explore potential treatments, but may not reflect the complexity of the condition.<h4>Objective</h4>We aimed to test an improved model of T2D that more closely mimics the clinic ...[more]

PMID: 35167879

Similar Datasets

Project description:Aims/hypothesisDiabetic cardiomyopathy is a myocardial disease triggered by impaired insulin signalling, increased fatty acid uptake and diminished glucose utilisation. Liver X receptors (LXRs) are key transcriptional regulators of metabolic homeostasis. However, their effect in the diabetic heart is largely unknown.MethodsWe cloned murine Lxrα (also known as Nr1h3) behind the α-myosin heavy chain (αMhc; also known as Myh6) promoter to create transgenic (Lxrα-Tg) mice and transgene-negative littermates (wild-type [WT]). A mouse model of type 2 diabetes was induced by a high-fat diet (HFD, 60% energy from fat) over 16 weeks and compared with a low-fat diet (10% energy from fat). A mouse model of type 1 diabetes was induced via streptozotocin injection over 12 weeks.ResultsHFD manifested comparable increases in body weight, plasma triacylglycerol and insulin resistance per OGTT in Lxrα-Tg and WT mice. HFD significantly increased left ventricular weight by 21% in WT hearts, but only by 5% in Lxrα-Tg. To elucidate metabolic effects in the heart, microPET (positron emission tomography) imaging revealed that cardiac glucose uptake was increased by 1.4-fold in WT mice on an HFD, but further augmented by 1.7-fold in Lxrα-Tg hearts, in part through 5' adenosine monophosphate-activated protein kinase (AMPK) phosphorylation and restoration of glucose transporter 4 (GLUT4). By contrast, streptozotocin-induced ablation of insulin signalling diminished cardiac glucose uptake levels and caused cardiac dysfunction, indicating that insulin may be important in LXRα-mediated glucose uptake. Chromatin immunoprecipitation assays identified natriuretic peptides, atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP), as potential direct targets of cardiac LXRα overexpression.Conclusions/interpretationCardiac-specific LXRα overexpression ameliorates the progression of HFD-induced left ventricular hypertrophy in association with increased glucose reliance and natriuretic peptide signalling during the early phase of diabetic cardiomyopathy. These findings implicate a potential protective role for LXR in targeting metabolic disturbances underlying diabetes.

Project description:IntroductionImpaired glucose tolerance (IGT) is diagnosed by a standardized oral glucose tolerance test (OGTT). However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state.MethodsGiven a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA), proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1 year.ResultsThe most accurate prediction of IGT was reached with the recursive partitioning method (accuracy = 0.78). For all classifiers, mean accuracy was 0.73 ± 0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77.ConclusionMachine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a subsequent OGTT.

Dataset Information

An improved model of type 2 diabetes with effects on glucose tolerance, neuropathy and retinopathy with and without obesity.

Rationale

Objective

Findings

Conclusions

Potential impact of study

Publications

An improved model of type 2 diabetes with effects on glucose tolerance, neuropathy and retinopathy with and without obesity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets