Understanding of Diabetic Dyslipidemia by Using the Anion-Exchange High Performance Liquid Chromatography Data
Notice bibliographique
Résumé
Type 2 diabetes and dyslipidemia are cardiovascular risk factors which should be managed [1]. However, the precise lipoprotein profiles and the underlying mechanisms for diabetic dyslipidemia remain largely unknown. We previously developed an anion-exchange liquid chromatographic method (AEX-HPLC) which can measure cholesterol levels of triglyceride (TG)-rich lipoproteins such as very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL) and chylomicron (CM) in addition to low-density lipoprotein (LDL) and high-density lipoprotein (HDL) [2]. Here we compared lipoprotein profiles obtained by our previous studies using AEX-HPLC in young lean men [3], subjects with low Framingham risk score (FRS) [4, 5], type 2 diabetic patients without obesity and type 2 diabetic patients with obesity [6, 7]. The mean ± SD values of age, body mass index, HbA1c in in young lean men (n = 7) [3], low FRS subjects (n = 304) [4], type 2 diabetic patients without obesity (n = 194) [6], and type 2 diabetic patients with obesity (n = 5) [7] were 24 ± 2, 51 ± 8, 63 ± 13, and 60 ± 9 years old, 20.8 ± 2.2, 23.8 ± 3.0, 23.1 ± 2.0, and 29.5 ± 7.0 kg/m2, 5.0 ± 0.2, 5.4 ± 0.5, 6.3 ± 1.0, and 9.1±2.1%, respectively. HDL-cholesterol (HDL-C) in type 2 diabetes (49.9 ± 16.6 mg/dL (1.29 ± 0.43 mmol/L)), especially in type 2 diabetic patients with obesity (36.4 ± 5.3 mg/dL (0.94 ± 0.14 mmol/L)) was lower than young lean men (59.4 ± 10.1 mg/dL (1.54 ± 0.26 mmol/L)) and low FRS subjects (57.6 ± 14.7 mg/dL (1.49 ± 0.38 mmol/L)). IDL-C in type 2 diabetes was higher than other two groups, and IDL-C was higher in the order of type 2 diabetic patients with obesity (9.8 ± 3.0 mg/dL (0.25 ± 0.08 mmol/L)), type 2 diabetic patients without obesity (9.3 ± 4.6 mg/dL (0.24 ± 0.12 mmol/L)), low FRS subjects (7.3 ± 3.1 mg/ dL (0.19 ± 0.08 mmol/L)), young lean men (4.3 ± 2.2 mg/dL (0.11 ± 0.06 mmol/L)). VLDL-C clearly showed higher values in the order of type 2 diabetic patients with obesity (27.3 ± 22.7 mg/dL (0.71 ± 0.59 mmol/L)), type 2 diabetic patients without obesity (20.1 ± 16.2 mg/dL (0.52 ± 0.42 mmol/L)), low FRS subjects (16.6 ± 12.8 mg/dL (0.43 ± 0.33 mmol/L)), and young lean men (4.0 ± 4.6 mg/dL (0.10 ± 0.12 mmol/L)). LDL-C and CM-C did not show the characteristic profile for diabetes. According to accumulation of our previous AEX-HPLC data [3, 4, 6, 7], the characteristics in diabetic dyslipidemia is reduced HDL-C, and increased IDL-C and VLDL-C, which is further deteriorated by complication with obesity. Relative insulin deficiency due to insulin resistance increases activity and expression of hormone-sensitive lipase (HSL) in adipose tissue, which catalyzes the breakdown of TG, releasing free fatty acids (FFA) (Fig. 1) [8]. Insulin promotes apoB100 degradation, and hepatic insulin resistance causes reduction of apoB100 degradation [9]. Insulin resistance also induces an enhanced expression of microsomal TG transfer protein (MTP), a key enzyme involved in VLDL assembly [10]. In type 2 diabetes, increased FFA entry to liver, reduced degradation of apoB100 and enhanced expression of MTP may elevate hepatic production of VLDL. Relative insulin deficiency also decreases the activity of lipoprotein lipase (LPL), the rate-limiting enzyme of the catabolism of TG-rich lipoproteins such as CM, VLDL and IDL [11]. The formation of HDL is related with the catabolism of TG-rich lipoproteins by LPL [12]. Therefore, reduced LPL activity increases IDL and VLDL, and reduces HDL. The activity of hepatic TG lipase (HTGL), the enzyme that facilitates the catabolism of HDL, is correlated with insulin requirement [13]. In type 2 diabetes, low serum HDL-C may be partially due to an increased rate of clearance by HTGL [13]. LDL size and buoyancy are inversely proportional to HTGL activity [14], and patients with high HTGL have smaller, denser LDL particles, as compared with subjects with low HTGL activity [15]. Increased HTGL activity due Manuscript accepted for publication March 03, 2016
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».