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Diabetic dyslipidaemia: from basic research to clinical practice*
Open accessAbstract
The recognition that the increase of plasma triglyceride rich lipoproteins (TRLs) is associated with multiple alterations of other lipoproteins species that are potentially atherogenic has expanded the picture of diabetic dyslipidaemia. The discovery of heterogeneity within major lipoprotein classes VLDL, LDL and HDL opened new avenues to reveal the specific pertubations of diabetic dyslipidaemia. The increase of large VLDL 1 particles in Type 2 diabetes initiates a sequence of events that generates atherogenic remnants, small dense LDL and small dense HDL particles. Together these components comprise the atherogenic lipid triad. Notably the malignant nature of diabetic dyslipidaemia is not completely shown by the lipid measures used in clinical practice. The key question is what are the mechanisms behind the increase of VLDL 1 particles in diabetic dyslipidaemia? Despite the advances of recent years, our understanding of VLDL assembly and secretion is still surprisingly incomplete. To date it is still unclear how the liver is able to regulate the amount of triglycerides incorporated into VLDL particles to produce either VLDL 1 or VLDL 2 particles. The current evidence suggests that the machinery driving VLDL assembly in the liver includes (i) low insulin signalling via PI-3 kinase pathway that enhances lipid accumulation into "nascent " VLDL particles (ii) up-regulation of SREBP-1C that stimulates de novo lipogenesis and (iii) excess availability of "polar molecules" in hepatocytes that stabilizes apo B 100. Recent data suggest that all these steps could be fundamentally altered in Type 2 diabetes explaining the overproduction of VLDL apo B as well as the ability of insulin to suppress VLDL 1 apo B production in Type 2 diabetes. Recent discoveries have established the transcription factors including PPARs, SREBP-1 and LXRs as the key regulators of lipid assembly in the liver. These observations suggest these factors as a new target to tailor more efficient drugs to treat diabetic dyslipidaemia.
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“Multiple disturbances in lipoprotein metabolism in individuals with prediabetes and T2D result from the combined effects of insulin deficiency, insulin resistance, and hyperglycemia.”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [305]
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Metadata
- Authors
- Taskinen, M.-R.
- Affiliations
- University of Helsinki (FI)
- Corresponding author
- Taskinen, M.-R.
- ORCID
- Taskinen, M.-R.
- Venue
- Diabetologia
- Published
- 2003-06-01
- Volume
- 46
- Issue
- 6
- Pages
- 733-749
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2020659754
- Cited by
- 888
- References
- 183
- Citations by year
- 2026: 27 · 2025: 37 · 2024: 32 · 2023: 36 · 2022: 48 · 2021: 41 · 2020: 39 · 2019: 27 · 2018: 32 · 2017: 28 · 2016: 29 · 2015: 47 · 2014: 41 · 2013: 46 · 2012: 42
- FWCI
- 6.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Helsingin ja Uudenmaan Sairaanhoitopiiri
- Academy of Finland
- Diabetesliitto
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipid metabolism and disorders
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Keywords
- diabetic dyslipidemia, type 2 diabetes, small dense LDL, triglyceride-rich lipoproteins, VLDL assembly, hepatic lipid metabolism, insulin signaling, PI3-kinase, SREBP-1c, de novo lipogenesis, apolipoprotein B-100, peroxisome proliferator-activated receptors, SREBP1, liver X receptor
- MeSH terms
- Diabetes Mellitus, Diabetes Mellitus, Type 2, Humans, Hyperlipidemias, Lipoproteins, Lipoproteins, VLDL, Diabetes Complications, Lipid Metabolism
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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