Triglyceride-Rich Lipoprotein Cholesterol and Risk of Cardiovascular Events Among Patients Receiving Statin Therapy in the TNT Trial.

BACKGROUND: Mendelian randomization data suggest that the genetic determinants of lifetime higher triglyceride-rich lipoprotein-cholesterol (TRL-C) are causally related to cardiovascular disease and therefore a potential therapeutic target. The relevance of TRL-C among patients receiving statins is unknown. We assessed the relationship between TRL-C and cardiovascular risk, and whether this risk was modifiable among patients receiving statins in the TNT trial (Treating to New Targets). METHODS: Patients with coronary heart disease and low-density lipoprotein cholesterol (LDL-C) 130 to 250 mg/dL entered an 8-week run-in phase with atorvastatin 10 mg/d (ATV10). After this period, participants with LDL-C <130 mg/dL entered the randomized phase with ATV10 (n=5006) versus atorvastatin 80 mg/d (ATV80, n=4995). The primary end point was coronary heart disease death, nonfatal myocardial infarction, resuscitated cardiac arrest, or stroke (major adverse cardiovascular events [MACE]). TRL-C was calculated as total cholesterol minus high-density lipoprotein cholesterol minus LDL-C. The effect of atorvastatin on TRL-C was assessed during the run-in phase (ATV10) and randomized phase (ATV80 versus ATV10). The risk of MACE was assessed across quintiles (Q) of baseline TRL-C (and, for comparison, by baseline triglycerides and non-high-density lipoprotein cholesterol) during the randomized period. Last, the association between TRL-C changes with atorvastatin and cardiovascular risk was assessed by multivariate Cox regression. RESULTS: ATV10 reduced TRL-C 10.7% from an initial TRL-C of 33.9±16.6 mg/dL. ATV80 led to an additional 15.4% reduction. Cardiovascular risk factors positively correlated with TRL-C. Among patients receiving ATV10, higher TRL-C was associated with higher 5-year MACE rates (Q1=9.7%, Q5=13.8%; hazard ratio Q5-versus-Q1, 1.48; 95% confidence interval, 1.15-1.92; P-trend<0.0001). ATV80 (versus ATV10) did not significantly alter the risk of MACE in Q1-Q2, but significantly reduced risk in Q3-Q5 (relative risk reduction, 29%-41%; all P<0.0250), with evidence of effect modification ( P-homogeneity=0.0053); results were consistent for triglycerides ( P-homogeneity=0.0101) and directionally similar for non-high-density lipoprotein cholesterol ( P-homogeneity=0.1387). Last, in adjusted analyses, a 1 SD percentage reduction in TRL-C with atorvastatin resulted in a significant lower risk of MACE (hazard ratio, 0.93; 95% confidence interval, 0.86-1.00; P=0.0482) independent of the reduction in LDL-C and of similar magnitude to that per 1 SD lowering in LDL-C (hazard ratio, 0.89; 95% confidence interval, 0.83-0.95; P=0.0008). CONCLUSIONS: The present post hoc analysis from TNT shows that increased TRL-C levels are associated with an increased cardiovascular risk and provides evidence for the cardiovascular benefit of lipid lowering with statins among patients who have coronary heart disease with high TRL-C. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifier: NCT00327691.

Bridging the "Know-Do" Gaps in Five Non-Communicable Diseases Using a Common Framework Driven by Implementation Science.

According to the United Nations High-Level Meeting 2018, five non-communicable diseases (NCDs) including cardiovascular diseases, chronic respiratory diseases, diabetes mellitus, cancer, and mental health conditions accounted for two-thirds of global deaths. These five NCDs share five common risk factors including tobacco use, unhealthy diets, physical inactivity, alcohol use, and air pollution. Low- and middle-income countries (LMICs) face larger burden of NCDs than high-income countries (HICs), due to differences in ecological, technological, socioeconomic and health system development. Based on high-level evidence albeit mainly from HICs, the burden caused by NCDs can be reduced by affordable medicines and best practices. However, "know-do" gaps, ie, gaps between what we know in science and what we do in practice, has limited the impact of these strategies, especially in LMICs. Implementation science advocates the use of robust methodologies to evaluate sustainable solutions in health, education and social care aimed at informing practice and policies. In this article, physician researchers with expertise in NCDs reviewed the common challenges shared by these five NCDs with different clinical courses. They explained the principles of implementation science and advocated the use of an evidence-based framework to implement solutions focusing on early detection, prevention and empowerment, supplemented by best practices in HICs and LMICs. These successful stories can be used to motivate policymakers, payors, providers, patients and public to co-design frameworks and implement context-relevant, multi-component, evidence-based practices. In pursuit of this goal, we propose partnership, leadership, and access to continuing care as the three pillars in developing roadmaps for addressing the multiple needs during the journey of a person with or at risk of these five NCDs. By transforming the ecosystem, raising awareness and aligning context-relevant practices and policies with ongoing evaluation, it is possible to make healthcare accessible, affordable and sustainable to reduce the burden of these five NCDs.

TRS2P and LDL-C alone or in combination for predicting absolute benefits from additional LDL-C lowering: Analysis from the TNT trial.

BACKGROUND AND AIMS: Despite trial evidence, high intensity statins are underutilized in routine clinical practice. This study sought to assess the individual and joint contributions of the TRS2P score as a measure of residual risk and LDL-C levels to benefits from further LDL-C lowering in the TNT trial. METHODS: A total of 9980 patients were divided into 4 groups based on TRS2P and LDL-C at baseline: median (7.2%, group 4), NNT 78 vs. 14 (p-interaction <0.0001). CONCLUSIONS: Measures of residual risk as well LDL-C identify patients who remain at high risk despite statins with the combination identifying those who derive the greatest benefits from even modest additional LDL-C lowering. Attention to residual risk as well as LDL-C may further help to optimize guideline implementation.

Atorvastatin Has a Dose-Dependent Beneficial Effect on Kidney Function and Associated Cardiovascular Outcomes: Post Hoc Analysis of 6 Double-Blind Randomized Controlled Trials.

Background Kidney function decreases during the lifetime, and this decline is a powerful predictor of both kidney and cardiovascular outcomes. Statins lower cardiovascular risk, which may relate to beneficial effects on kidney function. We studied whether atorvastatin influences kidney function decline and assessed the association between individual kidney function slopes and cardiovascular outcome. Methods and Results Data were collected from 6 large atorvastatin cardiovascular outcome trials conducted in patients not selected for having kidney disease. Slopes of serum creatinine reciprocals representing measures of kidney function change ([mg/dL]-1/y), were analyzed in 30 621 patients. Based on treatment arms, patients were categorized into 3 groups: placebo (n=10 057), atorvastatin 10 mg daily (n=12 763), and 80 mg daily (n=7801). To assess slopes, mixed-model analyses were performed for each treatment separately, including time in years and adjustment for study. These slopes displayed linear improvement over time in all 3 groups. Slope estimates for patients randomized to placebo or atorvastatin 10 mg and 80 mg were 0.009 (0.0008), 0.011 (0.0006), and 0.014 (0.0006) (mg/dL)-1/y, respectively. A head-to-head comparison of atorvastatin 10 and 80 mg based on data from 1 study ( TNT [Treating to New Targets]; n=10 001) showed a statistically significant difference in slope between the 2 doses ( P=0.0009). From a Cox proportional hazards model using slope as a predictor, a significant ( P<0.0001) negative association between kidney function and cardiovascular outcomes was found. Conclusions In patients at risk of or with cardiovascular disease, atorvastatin improved kidney function over time in a dose-dependent manner. In the 3 treatment groups, kidney function improvement was strongly associated with lower cardiovascular risk. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifiers: NCT00327418; NCT00147602; NCT00327691.

Visit-to-visit variability of lipid measurements as predictors of cardiovascular events.

BACKGROUND: Higher visit-to-visit variability in risk factors such as blood pressure and low-density lipoprotein (LDL)-cholesterol are associated with an increase in cardiovascular (CV) events. OBJECTIVE: The purpose of this study was to determine whether variability in high-density lipoprotein cholesterol (HDL-C) and triglyceride levels predicted coronary and CV events in a clinical trial population with known coronary disease. METHODS: We assessed intraindividual variability in fasting high-density lipoprotein (HDL)-cholesterol, triglyceride, and LDL-cholesterol measurements among 9572 patients in the Treating to New Targets trial and correlated the results with coronary events over a median follow-up of 4.9 years. RESULTS: In the fully adjusted Cox model, 1 standard deviation of average successive variability, defined as the average absolute difference between successive values, was associated with an increased risk of a coronary event for HDL-cholesterol (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.11-1.21, P < .0001), for triglycerides (HR 1.09, 95% CI 1.04-1.15, P = .0005), and for LDL-cholesterol (HR 1.14, 95% CI 1.09-1.19, P < .0001). Similar results were found for the 3 other measures of variability, standard deviation, coefficient of variability, and variability independent of the mean. Similar results were seen for CV events, stroke, and nonfatal myocardial infarction. Higher variability in triglyceride and LDL-cholesterol, but not HDL-cholesterol, was predictive of incident diabetes. The correlation among the variability of the 3 lipid measurements was weak. CONCLUSION: Visit-to-visit variability in fasting measurements of HDL-cholesterol, triglycerides, and LDL-cholesterol are predictive of coronary events, CV events, and for triglyceride and low-density lipoprotein cholesterol variability, incident diabetes. The mechanisms accounting for these associations remain to be determined.

Relationship of High-Density Lipoprotein Cholesterol With Renal Function in Patients Treated With Atorvastatin.

BACKGROUND: It is not known whether the concentration of high-density lipoprotein (HDL) cholesterol is related to renal function in statin-treated patients. We therefore investigated whether HDL cholesterol levels predicted renal function in atorvastatin-treated patients in the TNT (Treating to New Targets) trial. METHODS AND RESULTS: A total of 9542 participants were included in this analysis. Renal function was assessed by estimated glomerular filtration rate (eGFR). HDL cholesterol levels at month 3 were used as this is the time point at which on-treatment HDL cholesterol levels became stable. Among 6319 participants with a normal eGFR (≥60 mL/min per 1.73 m2) at baseline, higher HDL cholesterol levels at month 3 were significantly associated with lower risk of decline in eGFR (ie, having eGFR <60 mL/min per 1.73 m2) during follow-up (HR of 1.04, 0.88, 0.85, and 0.77 for HDL cholesterol quintiles 2, 3, 4, and 5, respectively, relative to quintile 1, P for trend=0.006). Among 3223 participants with an eGFR (<60 mL/min per 1.73 m2) at baseline, higher HDL cholesterol levels at month 3 had less impact on eGFR during follow-up, with statistical significance observed only when analyzing HDL cholesterol levels as a continuous variable (P=0.043), but not as a categorical quintile variable (P for trend=0.27). CONCLUSIONS: In patients treated with atorvastatin, higher HDL cholesterol levels were associated with lower risk of eGFR decline in patients with normal eGFR at baseline. However, further study is needed to establish whether there is any causal relationship between HDLs and renal function. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00327691.