New Studies Do Not Challenge the American College of Cardiology/American Heart Association Lipid Guidelines

Hofer, Timothy P.; Sussman, Jeremy B.; Hayward, Rodney A.

doi:10.7326/m15-2428

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…Although ∼100 risk models and scores for DM have been published (2), few have developed multivariable models to facilitate tailoring preventive interventions to individuals, and none has reported the impact of interventions on both progression to DM and regression to normal glucose regulation (NGR). Recently, Sussman et al (3) described the use of prediction models to estimate a person’s likelihood of benefit, describing this approach as “benefit-based tailored treatment.” They subsequently argued that treatment decisions should be based on the best estimate of absolute risk reduction (ARR) considering all of the patient and treatment factors that determine an individual patient’s chances of benefiting (4). An individual’s ARR can be calculated as the difference between an individual’s risk without treatment and the risk with treatment.…”

Section: Introductionmentioning

confidence: 99%

“…An individual’s ARR can be calculated as the difference between an individual’s risk without treatment and the risk with treatment. These risks may vary substantially among individuals, even in seemingly homogeneous study populations (4). Although Sussman et al (3) developed multivariable models to predict progression to DM for individuals in the DPP population with and without interventions, they assessed some variables not routinely assessed in clinical practice (such as waist circumference and waist-to-hip ratio [5]), did not account for treatment adherence, and did not assess the possibility of regression to NGR (3).…”

Section: Introductionmentioning

confidence: 99%

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Impact of Lifestyle and Metformin Interventions on the Risk of Progression to Diabetes and Regression to Normal Glucose Regulation in Overweight or Obese People With Impaired Glucose Regulation

et al. 2017

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OBJECTIVEBoth lifestyle and metformin interventions can delay or prevent progression to type 2 diabetes mellitus (DM) in people with impaired glucose regulation, but there is considerable interindividual variation in the likelihood of receiving benefit. Understanding an individual’s 3-year risk of progressing to DM and regressing to normal glucose regulation (NGR) might facilitate benefit-based tailored treatment.RESEARCH DESIGN AND METHODSWe used the values of 19 clinical variables measured at the Diabetes Prevention Program (DPP) baseline evaluation and Cox proportional hazards models to assess the 3-year risk of progression to DM and regression to NGR separately for DPP lifestyle, metformin, and placebo participants who were adherent to the interventions. Lifestyle participants who lost ≥5% of their initial body weight at 6 months and metformin and placebo participants who reported taking ≥80% of their prescribed medication at the 6-month follow-up were defined as adherent.RESULTSEleven of 19 clinical variables measured at baseline predicted progression to DM, and 6 of 19 predicted regression to NGR. Compared with adherent placebo participants at lowest risk of developing diabetes, participants at lowest risk of developing diabetes who adhered to a lifestyle intervention had an 8% absolute risk reduction (ARR) of developing diabetes and a 35% greater absolute likelihood of reverting to NGR. Participants at lowest risk of developing diabetes who adhered to a metformin intervention had no reduction in their risk of developing diabetes and a 17% greater absolute likelihood of reverting to NGR. Participants at highest risk of developing DM who adhered to a lifestyle intervention had a 39% ARR of developing diabetes and a 24% greater absolute likelihood of reverting to NGR, whereas those who adhered to the metformin intervention had a 25% ARR of developing diabetes and an 11% greater absolute likelihood of reverting to NGR.CONCLUSIONSUnlike our previous analyses that sought to explain population risk, these analyses evaluate individual risk. The models can be used by overweight and obese adults with fasting hyperglycemia and impaired glucose tolerance to facilitate personalized decision-making by allowing them to explicitly weigh the benefits and feasibility of the lifestyle and metformin interventions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Lifestyle and Metformin Interventions on the Risk of Progression to Diabetes and Regression to Normal Glucose Regulation in Overweight or Obese People With Impaired Glucose Regulation

et al. 2017

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“…Also, we did not simulate targeting of a specific LDL concentration for statin treatment, given current evidence favouring risk-based treatment rather than target-based treatment. 24 , 25 Future changes to statin therapy might switch back to a target-based approach that would require further analysis. Moreover, we have assumed in this study that most participants have type 2 diabetes given their age.…”

Section: Discussionmentioning

confidence: 99%

“…We did not simulate titration of statin treatment to a specific lipid biomarker concentration, given current evidence favouring risk-based treatment rather than target-based treatment. 24 , 25 We estimated the effect of reduced blood pressure, reduced glycaemia, or initiation of a statin on the risk reduction for each outcome for each individual on the basis of meta-analyses of randomised controlled trials ( appendix [pp 2–3] ).…”

Section: Methodsmentioning

confidence: 99%

Estimated effect of increased diagnosis, treatment, and control of diabetes and its associated cardiovascular risk factors among low-income and middle-income countries: a microsimulation model

Basu

Flood

Geldsetzer

et al. 2021

The Lancet Global Health

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Background Given the increasing prevalence of diabetes in low-income and middle-income countries (LMICs), we aimed to estimate the health and cost implications of achieving different targets for diagnosis, treatment, and control of diabetes and its associated cardiovascular risk factors among LMICs. MethodsWe constructed a microsimulation model to estimate disability-adjusted life-years (DALYs) lost and healthcare costs of diagnosis, treatment, and control of blood pressure, dyslipidaemia, and glycaemia among people with diabetes in LMICs. We used individual participant data-specifically from the subset of people who were defined as having any type of diabetes by WHO standards-from nationally representative, cross-sectional surveys (2006-18) spanning 15 world regions to estimate the baseline 10-year risk of atherosclerotic cardiovascular disease (defined as fatal and non-fatal myocardial infarction and stroke), heart failure (ejection fraction of <40%, with New York Heart Association class III or IV functional limitations), end-stage renal disease (defined as an estimated glomerular filtration rate <15 mL/min per 1•73 m² or needing dialysis or transplant), retinopathy with severe vision loss (<20/200 visual acuity as measured by the Snellen chart), and neuropathy with pressure sensation loss (assessed by the Semmes-Weinstein 5•07/10 g monofilament exam). We then used data from meta-analyses of randomised controlled trials to estimate the reduction in risk and the WHO OneHealth tool to estimate costs in reaching either 60% or 80% of diagnosis, treatment initiation, and control targets for blood pressure, dyslipidaemia, and glycaemia recommended by WHO guidelines. Costs were updated to 2020 International Dollars, and both costs and DALYs were computed over a 10-year policy planning time horizon at a 3% annual discount rate.Findings We obtained data from 23 678 people with diabetes from 67 countries. The median estimated 10-year risk was 10•0% (IQR 4•0-18•0) for cardiovascular events, 7•8% (5•1-11•8) for neuropathy with pressure sensation loss, 7•2% (5•6-9•4) for end-stage renal disease, 6•0% (4•2-8•6) for retinopathy with severe vision loss, and 2•6% (1•2-5•3) for congestive heart failure. A target of 80% diagnosis, 80% treatment, and 80% control would be expected to reduce DALYs lost from diabetes complications from a median population-weighted loss to 1097 DALYs per 1000 population over 10 years (IQR 1051-1155), relative to a baseline of 1161 DALYs, primarily from reduced cardiovascular events (down from a median of 143 to 117 DALYs per 1000 population) due to blood pressure and statin treatment, with comparatively little effect from glycaemic control. The target of 80% diagnosis, 80% treatment, and 80% control would be expected to produce an overall incremental cost-effectiveness ratio of US$1362 per DALY averted (IQR 1304-1409), with the majority of decreased costs from reduced cardiovascular event management, counterbalanced by increased costs for blood pressure and statin treatment, producing an overall incre...

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“…Additionally, the individual variation in LDL-C is just one component of a patient’s total risk of first-time or recurrent CV events. As some have argued, focusing on just one factor of a patient’s risk is not sufficient for preventing major events, given that the numbers needed to treat (NNT) are relatively high for preventing one major event [40, 41]. These varied risk factors become even more prominent in the discussion on the use of statins for primary prevention, where the trade-offs in benefit and harms may not be as straightforward or as easily justified.…”

Section: Percent Reduction In Ldl-cmentioning

confidence: 99%

Targeting LDL Cholesterol: Beyond Absolute Goals Toward Personalized Risk

et al. 2017

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Purpose of Review The aim of this study was to review and assess the evidence for low-density lipoprotein cholesterol (LDL-C) treatment goals as presented in current guidelines for primary and secondary prevention of cardiovascular disease. Recent Findings Different sets of guidelines and clinical studies for secondary prevention have centered on lower absolute LDL-C targets [<70 mg/dL (<1.8 mmol/L)], greater percent reductions of LDL-C (≥50%), or more intense treatment to achieve greater reductions in cardiovascular risk. Population-based risk models serve as the basis for statin initiation in primary prevention. Reviews of current population risk models for primary prevention show moderate ability to discriminate [with c-statistics ranging from 0.67 to 0.77 (95% CIs from 0.62 to 0.83) for men and women] with poor calibration and overestimation of risk. Summary Individual clinical trial data are not compelling to support specific LDL-C targets and percent reductions in secondary prevention. Increasing utilization of electronic health records and data analytics will enable the development of individualized treatment goals in both primary and secondary prevention.

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New Studies Do Not Challenge the American College of Cardiology/American Heart Association Lipid Guidelines

Cited by 8 publications

References 9 publications

Impact of Lifestyle and Metformin Interventions on the Risk of Progression to Diabetes and Regression to Normal Glucose Regulation in Overweight or Obese People With Impaired Glucose Regulation

Impact of Lifestyle and Metformin Interventions on the Risk of Progression to Diabetes and Regression to Normal Glucose Regulation in Overweight or Obese People With Impaired Glucose Regulation

Estimated effect of increased diagnosis, treatment, and control of diabetes and its associated cardiovascular risk factors among low-income and middle-income countries: a microsimulation model

Targeting LDL Cholesterol: Beyond Absolute Goals Toward Personalized Risk

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