Hypertriglyceridemia and residual dyslipidemia in statin-treated, patients with diabetes at the highest risk for cardiovascular disease and achieving very-low low-density lipoprotein-cholesterol levels
As the result of the high prevalence of comorbidities and conventional risk factors among patients with type 2 diabetes (T2DM), most patients belong to the highest cardiovascular disease risk category, and have a target low-density lipoprotein cholesterol (LDL-C) of <70 mg/dL. Because substantial residual risk persists at LDL-C <70 mg/dL, a more comprehensive control of non-LDL-C and particles was recommended in the joint 2008 American Diabetes Association/American College of Cardiology Consensus.
To ascertain, in statin-treated T2DM patients belonging to this greatest-risk group, with on-statin LDL-C <70 mg/dL, (1) the proportion of patients meeting all three critical levels (LDL-C <70 mg/dL, non-high-density lipoprotein cholesterol [HDL-C] <100 mg/dL, apoB <80 mg/dL) and (2) the variables associated with target attainment versus nonattainment.
Among 675 unselected patients with T2DM, 367 were both at very high cardiometabolic risk and taking statins; 118 of these patient had LDL-C levels <70 mg/dL. Patients meeting all three criteria (LDL-C, non-HDL-C, and apoB; n = 79; all three at goal group) were compared with those only reaching LDL-C (n = 49; only LDL-C at goal group).
LDL-C was 54 (12) for the all three at goal group versus 57 (10) mg/dL for the only LDL-C at goal group (NS). The two groups were similar regarding age, gender, diabetes duration, body mass index, waist circumference, blood pressure, renal function and micro-/macroangiopathy prevalence. A statin plus fibrate was given to 16% of patients in the all three at goal group and 32% in the only LDL-C at goal group. The two groups did not differ in baseline (prestatin) LDL-C, HDL-C, and non-HDL-C, except for pre-/post-lipid-lowering drug(s) triglycerides (TG): 177 (95)/118 (56) for all three at goal versus 279 (134)/ 241 (103) mg/dL for only LDL-C at goal (P = .0230 and P = .0001). The only LDL-C at goal group had lower HDL-C (vs. all three at goal): 41 (12) vs. 47 (14) mg/dL (P = .0237), with atherogenic dyslipidemia [hypo-HDL-C + hyper-TG] prevalence of 35% in the all three at goal versus 56% in the only LDL-C at goal group (P < .0001). log(TG)/HDL-C was 0.049 (0.021) for all three at goal versus 0.063 (0.021) for only LDL-C at goal (P < .0001). The LDL-C/apoB ratio was 0.92 (0.24) for all three at goal vs. 0.67 (0.18) for only LDL-C at goal (P < .0001), suggestive of smaller/denser LDL.
The presence of atherogenic dyslipidemia was associated with a failure to meet all three critical modifiable targets for hypercholesterolemia, such a nonachievement being found in a large proportion (one-third) of very-high risk T2DM patients with very-low on-statin LDL-C. Attainment of all three targets will require (1) titration/permutation of statins, (2) lifestyle (re)inforcement; and/or (3) statin-fibrate bitherapy.
"Coronary and peripheral artery disease (CAD and PAD) were diagnosed as in , while stroke was defined according to UK Prospective Diabetes Study (UKPDS) criteria . Atherogenic dyslipidemia (AD) was defined according to [28-30]. "
[Show abstract][Hide abstract] ABSTRACT: Non-fasting triglyceride-rich lipoproteins cholesterol (TRL-C) contributes to cardiovascular risk, in that it includes remnant cholesterol (RC). TRL-C is computed as total C - [LDL-C + HDL-C]). Such calculation applies only if LDL-C is directly measured, or obtained from a non-Friedewald's formula, a method as yet never benchmarked against independent markers of TRL burden.
The Discriminant Ratio (DR) methodology was used in 120 type 2 diabetic patients in order: (i) to compute TRL-C from non-fasting lipids; (ii) to establish the performance of TRL-C and TRL-C/apoA-I (vs. TG-based markers) to grade TRLs and atherogenic dyslipidemia (AD); and (iii) to relate TRL-C with non-fasting TG.
Depending on apoB100 availability, TRL-C (mg/dL) can be derived from non-fasting lipids in two ways: (a) total cholesterol (TC) - [(0.0106 * TC - 0.0036 * TG + 0.017 * apoB100 - 0.27) * 38.6] - HDL-C; and (b) TC - [(0.0106 * TC - 0.0036 * TG + 0.017 * [0.65 * (TC - HDL-C) + 6.3] - 0.27) * 38.6] - HDL-C. Discrimination between log[TG] and TRL-C was similar (DR 0.94 and 0.84, respectively), whereas that of log[TG]/HDL-C was better than TRL-C/apoA-I (DR 1.01 vs. 0.65; p 0.0482). All Pearson's correlations between pairs reached unity, allowing formulation of two unbiased equivalence equations: (a) TRL-C = 97.8 * log[TG] - 181.9; and (b) TRL-C/apoA-I = 8.15 * (log[TG]/HDL-C) - 0.18.
TRL-C and log[TG] are as effective and interchangeable for assessing remnant atherogenic particles. For grading TRL-AD, it is best to use log[TG]/HDL-C, inherently superior to TRL-C/apoA-I, while measuring the same underlying variable.
"On the other hand, some data suggest that hypertriglyceridemia , as the result of TG-rich lipoproteins overproduction and/or decreased catabolism, is a major factor associated with lack of goals attainment . Elevated TGs levels are considered an independent risk factor for CVD even when controlling for the other factors   , and treatment of elevated TGs in clinical trials has been shown to reduce CVD events, cardiac deaths, and total mortality   . "
[Show abstract][Hide abstract] ABSTRACT: This study intended to determine the impact of HDL-c and/or TGs levels on patients with average LDL-c concentration, focusing on lipidic, oxidative, inflammatory, and angiogenic profiles. Patients with cardiovascular risk factors (n = 169) were divided into 4 subgroups, combining normal and low HDL-c with normal and high TGs patients. The following data was analyzed: BP, BMI, waist circumference and serum glucose, Total-c, TGs, LDL-c, oxidized-LDL, total HDL-c and HDL subpopulations, paraoxonase-1 (PON1) activity, hsCRP, uric acid, TNF- α , adiponectin, VEGF, and iCAM1. The two populations with increased TGs levels, regardless of the normal or low HDL-c, presented obesity and higher waist circumference, Total-c, LDL-c, Ox-LDL, and uric acid. Adiponectin concentration was significantly lower and VEGF was higher in the population with cumulative low values of HDL-c and high values of TGs, while HDL quality was reduced in the populations with impaired values of HDL-c and/or TGs, viewed by reduced large and increased small HDL subfractions. In conclusion, in a population with cardiovascular risk factors, low HDL-c and/or high TGs concentrations seem to be associated with a poor cardiometabolic profile, despite average LDL-c levels. This condition, often called residual risk, is better evidenced by using both traditional and nontraditional CV biomarkers, including large and small HDL subfractions, Ox-LDL, adiponectin, VEGF, and uric acid.
The Scientific World Journal 11/2013; 2013(1):387849. DOI:10.1155/2013/387849 · 1.73 Impact Factor
"In the absence of consensual guidelines, the current recommendation for hypercholesterolemic patients at high cardiometabolic risk is to bring at target three key modifiable variables: (i) LDL-C; (ii) non-HDL-C; and (iii) apoB100[6,10]. In real life however, apoB100 determination is rarely performed alongside routine lipids, which precludes such comprehensive assessment of residual dyslipidemia. "
[Show abstract][Hide abstract] ABSTRACT: Apolipoprotein B100 (ApoB100) determination is superior to low-density lipoprotein cholesterol (LDL-C) to establish cardiovascular (CV) risk, and does not require prior fasting. ApoB100 is rarely measured alongside standard lipids, which precludes comprehensive assessment of dyslipidemia.
To evaluate two simple algorithms for apoB100 as regards their performance, equivalence and discrimination with reference apoB100 laboratory measurement.
Two apoB100-predicting equations were compared in 87 type 2 diabetes mellitus (T2DM) patients using the Discriminant ratio (DR). Equation 1: apoB100 = 0.65*non-high-density lipoprotein cholesterol + 6.3; and Equation 2: apoB100 = −33.12 + 0.675*LDL-C + 11.95*ln[triglycerides]. The underlying between-subject standard deviation (SDU) was defined as SDU = √ (SD2
B - SD2
W/2); the within-subject variance (Vw) was calculated for m (2) repeat tests as (Vw) = Σ(xj -xi)2/(m-1)), the within-subject SD (SDw) being its square root; the DR being the ratio SDU/SDW.
All SDu, SDw and DR’s values were nearly similar, and the observed differences in discriminatory power between all three determinations, i.e. measured and calculated apoB100 levels, did not reach statistical significance. Measured Pearson’s product-moment correlation coefficients between all apoB100 determinations were very high, respectively at 0.94 (measured vs. equation 1); 0.92 (measured vs. equation 2); and 0.97 (equation 1 vs. equation 2), each measurement reaching unity after adjustment for attenuation.
Both apoB100 algorithms showed biometrical equivalence, and were as effective in estimating apoB100 from routine lipids. Their use should contribute to better characterize residual cardiometabolic risk linked to the number of atherogenic particles, when direct apoB100 determination is not available.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.