More validation of Tubby Theory and Multiplier effect Update 2015

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More validation of Tubby Theory and Multiplier effect

From article below:

“Thus carotid intima-media thickness (CIMT)seems to work in younger individuals13

where calcified plaques are rare

whereas coronary artery calcium(CAC) identifies older high-risk subjects14 .”

Lifetime exposure and primordial prevention

Posted by Anthony Wierzbicki | Posted in From the Editors

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Atherosclerosis is a slow onset insidious disease. It is asymptomatic until its manifestations cause significant morbidity and mortality. There is no cheap easy way to detect it. As a result disease management strategies rely on epidemiologically-based risk calculators to identify individuals likely to have significant disease1 .

The greatest contribution to cardiovascular disease (CVD) risk is age rather than risk factors and some advocate treatment strategies simply based on this criterion to minimise health expenditure2 .

Everyone in cardiovascular prevention clinics is familiar with patients with isolated high cholesterol secondary to menopause or hepatic steatosis secondary to carbohydrate-rich diets where the diagnosis is often made by reference to a previous cholesterol result taken a few years ago.

What actually counts for all risk factors is not only their level but the years of exposure to the risk factor.

Epidemiological studies have repeatedly used smoking-pack-years3 and diabetes-years4 as prognostic indices that offer advantages over dichotomous distributions or single point levels.

This is also a feature for cholesterol and hypertension exposure and has been recorded as such in studies such as Framingham for surrogate outcomes such as carotid stenosis5 .

Cholesterol-years exposure neatly explains the epidemiology of CVD risk in familial hypercholesterolaemia6 .

A recent paper has brought this ancient insight back into focus by analysing the Framingham Offspring Cohort and showing that time-integrated cholesterol exposure is a strong predictor of future CVD events7 .

After15 years of follow-up coronary heart disease rates were 4.4% for those with no exposure prior to age 55 years, 8.1% for those with 1-10 years and 16.5% for those with 11-20 years exposure. Many of those with prolonged exposure would not have been identified by current CVD risk calculators.

The CVD risk calculator approach has its flaws and some have proposed lifetime risk as superior to 10-year risk as a better way of identifying high risk individuals in any age cohort8 9 .

Yet most lifetime risk calculation systems rely on single point measures from cross-sectional studies extrapolated to longitudinal conclusions10 . What is actually required is integrated risk exposure11 .

Electronic health record systems and systematic screening programmes are gathering the data to allow exposures to be adequately calculated. All the CVD risk factor calculation algorithms will require updating with this data, and more difficult, adequate validation. Alternatively a mechanism is required that integrates cholesterol exposure analogous to diabetes–years and HbA1c for glucose.

Modern imaging techniques can identify subsets of individuals who classical risk factors miss12 .

Thus carotid intima-media thickness (CIMT)seems to work in younger individuals13

where calcified plaques are rare

whereas coronary artery calcium(CAC) identifies older high-risk subjects14 .

   

This insight about cholesterol exposure suggests a number of approaches to intervention. Early intervention with small changes could translate into profound later benefits- the doctrine of primordial prevention17 . A 0.6mmol/L reduction in LDL-C at age 50 could reduce CVD events by 39%; while at age 70 a 1.4 mmol/L reduction is required to achieve similar benefit15 . The current debates about guidelines and their reduced intervention thresholds are in many ways arguments about which strategy is better in early middle age. What is required in populations over the long-term is a change of lifestyles to increase physical activity, reduce weight and banish smoking. That will take considerable time. In the interim medical intervention will be required in high risk groups if these can be satisfactorily and cost-effectively identified16 .

Contact details: Prof Anthony S. Wierzbicki DM, DPhil, FRCPath

Department of Chemical Pathology

St Thomas Hospital

Lambeth Palace Road

London SE1 7EH UK

E-mail: Anthony.Wierzbicki@kcl.ac.uk

ent TH. Predicting the risk of coronary heart disease I. The use of conventional risk markers. Atherosclerosis 2010; 213: 345-51.
2.	⇧	Wald NJ, Simmonds M, Morris JK. Screening for future cardiovascular disease using age alone compared with multiple risk factors and age. PLoSOne 2011; 6: e18742.
3.	⇧	Howard G, Wagenknecht LE, Burke GL et al. Cigarette smoking and progression of atherosclerosis: The Atherosclerosis Risk in Communities (ARIC) Study. JAMA 1998; 279: 119-24.
4.	⇧	Nathan DM, Lachin J, Cleary P et al. Intensive diabetes therapy and carotid intima-media thickness in type 1 diabetes mellitus. NEnglJ Med 2003; 348: 2294-303.
5.	⇧	Wilson PW, Hoeg JM, D’Agostino RB et al. Cumulative effects of high cholesterol levels, high blood pressure, and cigarette smoking on carotid stenosis. The New England journal of medicine 1997; 337: 516-22.
6.	⇧	Nordestgaard BG, Chapman MJ, Humphries SE et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J 2013; 34: 3478-90a.
7.	⇧	Navar-Boggan AM, Peterson ED, D’Agostino RB, Sr. et al. Hyperlipidemia in early adulthood increases long-term risk of coronary heart disease. Circulation 2015; 131: 451-8.
8.	⇧	Lloyd-Jones DM, Leip EP, Larson MG et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation 2006; 113: 791-8.
9, 10.	⇧	Hippisley-Cox J, Coupland C, Robson J, Brindle P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ 2010; 341: c6624.
11.	⇧	Martin SS, Michos ED. Mapping hyperlipidemia in young adulthood to coronary risk: importance of cumulative exposure and how to stay young. Circulation 2015; 131: 445-7.
12.	⇧	Erbel R, Budoff M. Improvement of cardiovascular risk prediction using coronary imaging: subclinical atherosclerosis: the memory of lifetime risk factor exposure. Eur Heart J 2012; 33: 1201-13.
13.	⇧	Ratchford EV, Carson KA, Jones SR, Ashen MD. Usefulness of coronary and carotid imaging rather than traditional atherosclerotic risk factors to identify firefighters at increased risk for cardiovascular disease. The American journal of cardiology 2014; 113: 1499-504.
14.	⇧	Nasir K, Rubin J, Blaha MJ et al. Interplay of coronary artery calcification and traditional risk factors for the prediction of all-cause mortality in asymptomatic individuals. CircCardiovascImaging 2012; 5: 467-73.] and convincingly adds to classical CVD risk factors in predicting disease[Kavousi M, Elias-Smale S, Rutten JH et al. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. AnnInternMed 2012; 156: 438-44.
15.	⇧	Law MR, Wald NJ, Rudnicka AR. Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: systematic review and meta-analysis. BMJ 2003; 326: 1423.
16.	⇧	Sniderman AD, Thanassoulis G, Lawler PR et al. Comparison of coronary calcium screening versus broad statin therapy for patients at intermediate cardiovascular risk. AmJ Cardiol 2012; 110: 530-3.
17.	⇧	Robinson JG, Gidding SS. Curing atherosclerosis should be the next major cardiovascular prevention goal. Journal of the American College of Cardiology 2014; 63: 2779-85.