Addressing lipid treatment targets beyond cholesterol: a role for prescription omega-3 fatty acid therapy.

Decreasing very high triglyceride (TG) levels (>or=500 mg/dL) is recommended to prevent pancreatitis. Decreasing low-density lipoprotein cholesterol (LDL-C) is the primary lipid treatment target to reduce the risk of atherosclerotic coronary heart disease. A secondary lipid treatment target for patients at LDL-C goal, but with persistent TG elevations, includes achievement of non-high density lipoprotein cholesterol goals (non-HDL-C). Statins are the mainstay of therapy to lower LDL-C, but statin monotherapy may not achieve all lipid treatment goals. Thus, in patients with multiple lipid abnormalities, combination lipid-altering therapy is often necessary. Drugs such as niacin and fibrates provide lipid benefits beyond LDL-C when used in combination with a statin. Prescription omega-3-fatty acids combined with statin therapy also provide improvements in lipid parameters beyond cholesterol alone.

Discordance between apolipoprotein B and low-density lipoprotein particle number is associated with insulin resistance in clinical practice.

BACKGROUND: Discordance between measures of atherogenic lipoprotein particle number (apolipoprotein B [ApoB] and low-density lipoprotein [LDL] particle number by nuclear magnetic resonance spectroscopy [LDL-PNMR]) is not well understood. Appropriate treatment considerations in such cases are unclear. OBJECTIVES: To assess discordance between apoB determined by immunoassay and LDL-PNMR in routine clinical practice, and to characterize biomarker profiles and other clinical characteristics of patients identified as discordant. METHODS: Two retrospective cohorts were evaluated. First, 412,013 patients with laboratory testing performed by Health Diagnostic Laboratory, Inc., as part of routine care; and second, 1411 consecutive patients presenting for risk assessment/reduction at 6 US outpatient clinics. Discordance was quantified as a percentile difference (LDL-PNMR percentile - apoB percentile) and attainment of percentile cutpoints (LDL-PNMR ≥ 1073 nmol/L or apoB ≥ 69 mg/dL). A wide range of cardiovascular risk factors were compared. RESULTS: ApoB and LDL-PNMR values were highly correlated (R(2) = 0.79), although substantial discordance was observed. Similar numbers of patients were identified as at-risk by LDL-PNMR when apoB levels were < 69 mg/dL (5%-6%) and by apoB values when LDL-PNMR was < 1073 nmol/L (6%-7%). Discordance (LDL-PNMR > apoB) was associated with insulin resistance, smaller LDL particle size, increased systemic inflammation, and low circulating levels of "traditional" lipids, whereas discordance (apoB > LDL-PNMR) was associated with larger LDL particle size, and elevated levels of lipoprotein(a) and lipoprotein-associated phospholipase A2 (Lp-PLA2). CONCLUSION: Discordance between apoB and LDL-PNMR in routine clinical practice is more widespread than currently recognized and may be associated with insulin resistance.

Comparison of cardiometabolic risk biomarkers from a national clinical laboratory with the US adult population.

BACKGROUND: Clinical laboratory patient databases are an untapped source of valuable diagnostic and prognostic information. However, the lack of associated clinical and/or demographic information and questionable generalizability to nonpatient populations often limit utility of these data. OBJECTIVES: This study compared levels of cardiometabolic biomarkers between a national clinical laboratory patient cohort (Health Diagnostic Laboratory [HD Lab]) and the US population as inferred from the National Health and Nutrition Examination Survey (NHANES, 2011-2012). METHODS: Sample sizes for HD Lab ranged from 199,000 to 739,000 and for NHANES from 2200 to 5300. The latter were weighted to represent the adult US population (∼220 million). Descriptive statistics were compared for body mass index, 5 lipid biomarkers, and 3 glycemic biomarkers. RESULTS: Using age- and sex-matched data, mean biomarker values (mg/dL unless noted) and percent differences (%) for HD Lab vs NHANES were body mass index (kg/m(2)), 29.1 vs 28.6 (1.7%); total cholesterol, 185 vs 193 (-4.1%); apolipoprotein B, 92 vs 90 (2.2%); low-density lipoprotein cholesterol, 107 vs 115 (-7%); high-density lipoprotein cholesterol, 53 vs 53 (0%); triglycerides, 128 vs 127 (0.8%); glucose, 99 vs 108 (-8.3%); insulin (uU/mL), 13.7 vs 13.4 (2.2%); and hemoglobin A1c (%), 5.6 vs 5.8 (-3.4%). Although all differences were statistically significant, only low-density lipoprotein cholesterol and glucose differed by more than 5%. These may reflect a greater use of medications among HD Lab patients and/or preanalytical factors. CONCLUSIONS: Cardiometabolic risk markers from a national clinical laboratory were broadly similar to those of the US population; thus, with certain caveats, data from the former may be generalizable to the latter.

Comprehensive biomarker testing of glycemia, insulin resistance, and beta cell function has greater sensitivity to detect diabetes risk than fasting glucose and HbA1c and is associated with improved glycemic control in clinical practice.

Blood-based biomarker testing of insulin resistance (IR) and beta cell dysfunction may identify diabetes risk earlier than current glycemia-based approaches. This retrospective cohort study assessed 1,687 US patients at risk for cardiovascular disease (CVD) under routine clinical care with a comprehensive panel of 19 biomarkers and derived factors related to IR, beta cell function, and glycemic control. The mean age was 53 ± 15, 42 % were male, and 25 % had glycemic indicators consistent with prediabetes. An additional 45 % of the patients who had normal glycemic indicators were identified with IR or beta cell abnormalities. After 5.3 months of median follow-up, significantly more patients had improved than worsened their glycemic status in the prediabetic category (35 vs. 9 %; P < 0.0001) and in the "high normal" category (HbA1c values of 5.5-5.6; 56 vs. 18 %, p < 0.0001). Biomarker testing can identify IR early, enable and inform treatment, and improve glycemic control in a high proportion of patients.

Serum α-hydroxybutyrate (α-HB) predicts elevated 1†h glucose levels and early-phase ß-cell dysfunction during OGTT.

OBJECTIVE: Serum α-hydroxybutyrate (α-HB) is elevated in insulin resistance and diabetes. We tested the hypothesis that the α-HB level predicts abnormal 1 h glucose levels and ß-cell dysfunction inferred from plasma insulin kinetics during a 75 g oral glucose tolerance test (OGTT). RESEARCH DESIGN AND METHODS: This cross-sectional study included 217 patients at increased risk for diabetes. 75 g OGTTs were performed with multiple postload glucose and insulin measurements over a 30-120 min period. OGTT responses were analyzed by repeated measures analysis of variance (ANOVA). Multivariable logistic regression was used to predict 1 h glucose ≥155 mg/dL with α-HB added to traditional risk factors. RESULTS: Mean±SD age was 51±15 years (44% male, 25% with impaired glucose tolerance). Fasting glucose and insulin levels, but not age or body mass index (BMI), were significantly higher in the second/third α-HB tertiles (>3.9 µg/mL) than in the first tertile. Patients in the second/third α-HB tertiles exhibited a higher glucose area under the receiver operating characteristics curve (AUC) and reduced initial slope of insulin response during OGTT. The AUC for predicting 1 h glucose ≥155 mg/dL was 0.82 for a base model that included age, gender, BMI, fasting glucose, glycated hemoglobin (HbA1c), and insulin, and increased to 0.86 with α-HB added (p=0.015), with a net reclassification index of 52% (p<0.0001). CONCLUSIONS: Fasting serum α-HB levels predicted elevated 1 h glucose during OGTT, potentially due to impaired insulin secretion kinetics. This association persisted even in patients with an otherwise normal insulin-glucose homeostasis. Measuring serum α-HB could thus provide a rapid, inexpensive screening tool for detecting early subclinical hyperglycemia, ß-cell dysfunction, and increased risk for diabetes.

Opportunities for using lipoprotein subclass profile by nuclear magnetic resonance spectroscopy in assessing insulin resistance and diabetes prediction.

The incidence of type 2 diabetes mellitus (T2DM) has reached epidemic levels, and current trends indicate that its prevalence will continue to rise. The development of T2DM can be delayed by several years, and may even be prevented, by identifying individuals at risk for T2DM and treating them with lifestyle modification and/or pharmacological therapies. There are a number of methods available for assessing the insulin resistance (IR) that characterizes, and is the precursor to, T2DM. However, current clinical methods for assessing IR, based on measures of plasma glucose and/or insulin are either laborious and time-consuming or show a low specificity. IR manifests its earliest measurable abnormalities through changes in lipoproteins, and thus we propose that by examining lipoprotein subclass profile, it may be possible to alert physicians and patients to a heightened risk of developing diabetes. This will allow us to institute appropriate lifestyle changes and treatment potentially to delay the onset or possibly prevent the progression to diabetes.

Effects of an office-based carotid ultrasound screening intervention.

BACKGROUND: Carotid ultrasound screening (CUS) has been recommended for cardiovascular disease risk prediction, but its effectiveness in clinical practice is unknown. The purpose of this study was to prospectively determine the effects of office-based CUS on physician decision making and patient health-related behaviors. METHODS: Physicians from five nonacademic, community practices recruited patients aged ≥40 years with ≥1 cardiovascular disease risk factor. Abnormal results on CUS (AbnlCUS) were defined as carotid intima-media thickness >75th percentile or carotid plaque presence. Subjects completed questionnaires before and immediately after CUS and then 30 days later to determine self-reported behavioral changes. Odds ratios (ORs) for changes in physician management and patient health-related behaviors were determined from multivariate hierarchical logistic regression models. RESULTS: There were 355 subjects (mean age, 53.6 ± 7.9 years; mean number of risk factors, 2.3 ± 0.9; 58% women); 266 (74.9%) had AbnlCUS. The presence of AbnlCUS altered physicians' prescription of aspirin (P < .001) and cholesterol medications (P < .001). Immediately after CUS, subjects reported increased ability to change health-related behaviors (P = .002), regardless of their test results. Subjects with AbnlCUS reported increased cardiovascular disease risk perception (OR, 4.14; P < .001) and intentions to exercise (OR, 2.28; P = .008), make dietary changes (OR, 2.95; P < .001), and quit smoking (OR, 4.98; P = .022). After 30 days, 34% increased exercise frequency and 37% reported weight loss, but these changes were not predicted by the CUS results. AbnlCUS modestly predicted reduced dietary sodium (OR, 1.45; P = .002) and increased fiber (OR, 1.55; P = .022) intake. CONCLUSIONS: Finding abnormal results on CUS had major effects on physician but not patient behaviors.