Sustained postprandial decrease in plasma levels of LDL cholesterol in patients with type‐2 diabetes mellitus

Lund, Søren S; Petersen, MB; Frandsen, M.; Smidt, Ulla M; Parving, Hans‐Henrik; Vaag, Allan; Jensen, Tonny

doi:10.1080/00365510801995736

Cited by 11 publications

(16 citation statements)

References 56 publications

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“…org/content/vol57/issue2). We previously reported part of the LDL-C results measured by the MBQ and part of the results for non-LDL lipids for all 74 patients (29 ). Unless otherwise noted, here we report data for the 66 patients in whom LDL-C could be determined with all 3 methods.…”

Section: Methodsmentioning

confidence: 93%

“…Participants were insulin-naive white T2DM patients with a body mass index (BMI) Յ27 kg/m 2 (29,42 ). At the time of investigation, median age was 61.7 years; median duration of diabetes was 5 years; 17% were women; median BMI was 24.6 kg/m 2 ; median hemoglobin A 1c (Hb A 1c ) was 7.7%.…”

Section: Patient Characteristicsmentioning

confidence: 99%

“…Most of the study procedures have been published (29,43 ). They are also described in detail in the online Supplemental Data and summarized below.…”

Section: Patient Characteristicsmentioning

confidence: 99%

“…Nonfasting samples do not meet these assumptions (19,29 ). Chylomicrons are present, and they are more triglyceride-rich than VLDLs, resulting in a rising ratio of total triglyceride to TRL cholesterol.…”

Section: Specificity Interferences and Patient Groupsmentioning

confidence: 99%

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Agreement Between Fasting and Postprandial LDL Cholesterol Measured with 3 Methods in Patients with Type 2 Diabetes Mellitus

et al. 2011

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BACKGROUND LDL cholesterol (LDL-C) is a modifiable cardiovascular disease risk factor. We used 3 LDL-C methods to study the agreement between fasting and postprandial LDL-C in type 2 diabetes (T2DM) patients. METHODS We served 74 T2DM patients a standardized meal and sampled blood at fasting and 1.5, 3.0, 4.5, and 6.0 h postprandially. We measured LDL-C by use of modified β quantification (MBQ), the Friedewald equation (FE), and a direct homogeneous assay (DA). We evaluated agreement using 95% limits of agreement (LOA) within ±0.20 mmol/L (±7.7 mg/dL). RESULTS LDL-C concentrations at all postprandial times disagreed with those at fasting for all methods. In 66 patients who had complete measurements with all LDL-C methods, maximum mean differences (95% LOA) in postprandial vs fasting LDL-C were −0.16 mmol/L (−0.51; 0.19) [−6.2 mg/dL (−19.7; 7.3)] with MBQ at 3 h; −0.36 mmol/L (−0.89; 0.17) [−13.9 mg/dL (−34; 6.6)] with FE at 4.5 h; and −0.24 mmol/L (−0.62; 0.05) [−9.3 mg/dL (−24; 1.9)] with DA at 6.0 h. In postprandial samples, FE misclassified 38% of patients (two-thirds of statin users) into lower Adult Treatment Panel III (ATP III) risk categories. Greater disagreement between fasting and postprandial LDL-C was observed in individuals with postprandial triglyceride concentrations >2.08 mmol/L (>184 mg/dL) and in women (interactions: P ≤ 0.038). CONCLUSIONS Differences up to 0.89 mmol/L (34 mg/dL) between fasting and postprandial LDL-C concentrations, with postprandial LDL-C concentrations usually being lower, were found in T2DM by 3 different LDL-C methods. Such differences are potentially relevant clinically and suggest that, irrespective of measurement method, postprandial LDL-C concentrations should not be used to assess cardiovascular disease risk.

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Section: Methodsmentioning

confidence: 93%

Section: Patient Characteristicsmentioning

confidence: 99%

“…Most of the study procedures have been published (29,43 ). They are also described in detail in the online Supplemental Data and summarized below.…”

Section: Patient Characteristicsmentioning

confidence: 99%

Section: Specificity Interferences and Patient Groupsmentioning

confidence: 99%

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Agreement Between Fasting and Postprandial LDL Cholesterol Measured with 3 Methods in Patients with Type 2 Diabetes Mellitus

et al. 2011

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“…3,4 Although the mechanism underlying this phenomenon is not fully understood, it most likely involves a postprandial increase in triglycerides that stimulates the cholesterylester transfer protein to promote transfer of cholesterol from LDL particles into very low-density lipoprotein (VLDL) particles. Also, an increase in nonfasting triglycerides has been associated with increased CVD risk (probably because it increases nonfasting VLDL cholesterol).…”

mentioning

confidence: 99%

Letter by Lund et al Regarding Article, “Fasting Compared With Nonfasting Lipids and Apolipoproteins for Predicting Incident Cardiovascular Events”

2009

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We are delighted about the report from Mora et al showing a lack of predictive value for future cardiovascular disease (CVD) of nonfasting low-density lipoprotein cholesterol (LDL-C) contrasting with the well-known predictive value of fasting LDL-C. 1 In our opinion, the applied methodology has some pitfalls:1. Measurement by a direct assay (Genzyme or Roche) in frozen samples might not produce reliable LDL-C estimates compared with fresh samples. 2. The evidence for using direct LDL-C assays in nonfasting samples (fresh or frozen) with respect to the agreement with the method recommended by the Centers for Disease Control and Prevention for measurement of LDL-C (ie, the ␤ quantification) is sparse. 3. In daily clinical practice, when LDL-C is "measured" in nonfasting samples, in our experience this is rarely made by direct assays. Instead, probably because of more favorable costs, estimation of LDL-C in nonfasting samples is usually made using the Friedewald equation (LDL-Cϭtotal cholesterolϪHDL cholesterolϪ triglycerides/2.2). 2 This seems to be a widespread practice, despite the (well-known) limited validity of the Friedewald equation in nonfasting samples (ie, it cannot take into account the magnitude of postprandial change in triglycerides). 2 However, despite such technical controversies, the Friedewald formula might still produce LDL-C estimates that predict future CVD in fasting as well as nonfasting samples. Thus, because of the widespread use of the formula, the degree to which calculated nonfasting LDL-C predicts future CVD remains an important clinical question. Therefore, to improve the clinical utility and, thereby, potentially the future guidelines for measurement of LDL-C, we encourage the authors to report from their data the predictive value for future CVD of calculated LDL-C by the Friedewald formula in fasting and nonfasting samples.As previously demonstrated and noted by Mora et al, LDL-C is lower in the nonfasting than in the fasting state (also, when measured by ␤ quantification). 3,4 Although the mechanism underlying this phenomenon is not fully understood, it most likely involves a postprandial increase in triglycerides that stimulates the cholesterylester transfer protein to promote transfer of cholesterol from LDL particles into very low-density lipoprotein (VLDL) particles. Also, an increase in nonfasting triglycerides has been associated with increased CVD risk (probably because it increases nonfasting VLDL cholesterol). 5 If this is correct, the postprandial decrease in LDL-C could be a marker of increased nonfasting (proatherogenic) VLDL cholesterol. Such an effect would weaken any positive association between nonfasting LDL-C and future CVD and could potentially explain the lack of association with future CVD of nonfasting LDL-C observed by Mora et al. Moreover, our group recently used ␤ quantification to demonstrate that in patients with type 2 diabetes mellitus, postprandial LDL-C decreases to a greater extent in women than in men. 4 Hence, if postprandial LDL-C decrease is a ma...

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Meal frequency differentially alters postprandial triacylglycerol and insulin concentrations in obese women

Heden

Liu

Sims

et al. 2013

Obesity

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The aim of this study was to compare postprandial lipemia, oxidative stress, antioxidant activity, and insulinemia between a three and six isocaloric high carbohydrate meal frequency pattern in obese women. In a counterbalanced order eight obese women completed two, 12 h conditions in which they consumed 1500 calories (14% protein, 21% fat, and 65% carbohydrate) either as three 500 calorie liquid meals every 4 h or six 250 calorie liquid meals every 2 h. Blood samples were taken every 30 min and analyzed for triacylglycerol (TAG), total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, oxidized low-density lipoprotein cholesterol, myeloperoxidase, paraoxonase-1 activity, and insulin. The TAG incremental area under the curve (iAUC) during the three meal condition (321 ± 129 mg/dL·12 h) was significantly lower (P = 0.04) compared to the six meal condition (481 ± 155 mg/dL·12 h). The insulin iAUC during the three meal condition (5,549 ± 1,007 pmol/L·12 h) was significantly higher (P = 0.05) compared to the six meal condition (4,230 ± 757 pmol/L·12 h). Meal frequency had no influence on the other biochemical variables. Collectively, a three and six isocaloric high carbohydrate meal frequency pattern differentially alters postprandial TAG and insulin concentrations but has no effect on postprandial cholesterol, oxidative stress, or antioxidant activity in obese women.

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Sustained postprandial decrease in plasma levels of LDL cholesterol in patients with type‐2 diabetes mellitus

Cited by 11 publications

References 56 publications

Agreement Between Fasting and Postprandial LDL Cholesterol Measured with 3 Methods in Patients with Type 2 Diabetes Mellitus

Agreement Between Fasting and Postprandial LDL Cholesterol Measured with 3 Methods in Patients with Type 2 Diabetes Mellitus

Letter by Lund et al Regarding Article, “Fasting Compared With Nonfasting Lipids and Apolipoproteins for Predicting Incident Cardiovascular Events”

Meal frequency differentially alters postprandial triacylglycerol and insulin concentrations in obese women

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