Investigations at the Intersection of Diabetes and Cardiovascular Disease
Bibliographic record
Abstract
Cardiovascular disease and diabetes are increasingly prevalent and projected only to increase in numbers in the coming years. As such, more individuals will be dealing with multimorbidity which may synergize to increase vascular and glycemic risks. Type 2 diabetes (T2D) is commonly comorbid with lipid and inflammatory disorders like cardiovascular disease and metabolic dysfunction-associated steatotic liver disease (MASLD), whereas type 1 diabetes (T1D) is an autoimmune disease and is frequently comorbid with other inflammatory autoimmune diseases. Dietary and pharmaceutical interventions are employed to manage cardiometabolic diseases or reduce the risk factors for them. However, management options for one disease may impact the development or management of another. For example, the ketogenic diet (KD) is used for diabetes but likely impacts cardiovascular risk through hypercholesterolemia. Similarly, statins, used to manage cardiovascular risk, potentially increase the risk of T2D. In this thesis, I use mouse models and self-reported human data to assess dietary, autoimmune, and pharmaceutical intersections of cardiovascular disease and diabetes. In the first article, we characterized the impacts of a high saturated fat, high cholesterol KD in obese, atherosclerotic, and control mouse models. Across models and sexes, the KD conferred benefit in either liver lipid accumulation or markers of hepatic inflammation. KD feeding also significantly increased glucagon-like peptide-1 (GLP-1), but insulin secretion was unchanged relative to a chow or western-style high fat diet (HFD). In PCSK9 overexpressing, male atherosclerotic mice, the KD lowered hypercholesterolemia and accordingly slowed the progression of atherosclerosis. However, circulating cholesterol was similar among diet groups in mice with intact LDL receptor signalling. In the second article, we identified that 36.3% of individuals on a Canadian registry of T1D live with at least one additional autoimmune disease. Additional autoimmune diseases were associated with greater vascular, glycemic, and mental health complications. In the third article, we investigated the role of overexpressing miR-192, identified as a potential marker of new-onset diabetes after statin use, on insulin secretion, glycemic control, and Glp1r expression. We found that overexpression of miR-192 in islets in vitro increased Glp1r expression and insulin response to GLP-1 at high glucose. In mice, delivery of miR-192 AAV8 only trended toward improved glucose tolerance but significantly increased GLP-1 amplified insulin secretion in islets isolated from the mice. Together, these data advance our understanding of some of the burdens of multimorbidity and the modulation of comorbidity risk with dietary and pharmaceutical management of diabetes and cardiovascular diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".