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Risk factor thresholds: their existence under scrutinyhttp://bmj.com/cgi/content/full/324/7353/1570
Education and debateRisk factor thresholds: their existence under scrutinyM R Law Wolfson Institute of Preventive Medicine, Department of Environmental and Preventive Medicine, Barts and The London, Queen Mary's School of Medicine and Dentistry, London EC1M 6BQ Correspondence to: M R Law m.r.law@qmul.ac.uk Interventions to lower blood pressure, serum cholesterol, and other risk factors reduce the risk of cardiovascular disease regardless of initial levels. It follows, say Malcolm Law and Nicholas Wald, that the goal is not to "normalise" risk factors but to reduce them as much as possible. This means targeting everyone at high risk, as determined by age or known cardiovascular disease rather than by the level of the risk factors Physiological variables such as blood pressure, serum cholesterol, body mass index, and bone mineral density are important in the aetiology of common diseases. They are not direct environmental causes of disease, like smoking, but they may be seen as biochemical or biophysical variables, under partial genetic control, that are intermediates between environmental factors and disease itself. It is known that risk can be reduced by lowering high levels of these variables by drug treatment or lifestyle change. But there is a view that changing the average values of these physiological variables is not worth while, a view that implies the presence of thresholds in the dose-response relations between the level of the variable and the risk of disease. This view is reinforced by terminology that regards extreme values as indicating a disease state (such as hypertension, hypercholesterolaemia, osteoporosis, and obesity) and average values as being "normal" (normotensive, normocholesterolaemia). Clinical guidelines specify risk factor thresholds1-5; these have been set at successively lower levels over time and redefined as "action levels" but they still deny treatment below specified values. We examine seven important dose-response relations to determine whether it is
useful to impose risk factor thresholds or whether there are better
ways to identify patients who should be treated.
We examined seven dose-response relations between physiological variables and
risk of disease that are medically important and for which a large
body of data was available: (i-iii) ischaemic heart disease and blood
pressure, serum cholesterol, and body mass index; (iv) stroke and
blood pressure; (v) diabetes and body mass index; (vi) neural tube
defects and maternal plasma folate; and (vii) hip fracture and bone
mineral density. For each relation we used a published meta-analysis
of cohort studies if one was available,6
otherwise the largest single cohort study.7-11
Dividing each cohort into subgroups (usually fifths) defined by
ranked values of the physiological variables, we plotted the risk of
disease (vertical axis) against the level of the risk factor
(horizontal axis), using the subgroups and measure of risk (incidence,
mortality, or relative risk) adopted in the original papers. Randomised
trials of risk factor modification were available for four of
these seven dose-response relations12-24; we
examined these to assess whether they supported the conclusions from
the observational studies.
Figures 1 and 2 show the seven dose-response relations. The data are from cohort studies because cohort studies best show dose-response relations; unlike randomised trials they show disease incidence across the entire range of values of a risk factor in the population. On the left hand side of the two figures, disease incidence is plotted by using an arithmetical scale, so that in figure 1 rates of 0, 1, 2, and 3 are evenly spaced on the vertical axis. These plots are curved. On the right hand side, incidence is plotted by using a proportional or logarithmic scale, so that in figure 1 rates of 0.25, 0.5, 1, and 2 are evenly spaced on the vertical axis; a given space on the vertical axis indicates a constant proportional change in incidence (such as a doubling). These plots yield reasonably straight lines, and this is so whether the level of the risk factor on the horizontal axis is plotted using an arithmetical or proportional scale. With an arithmetical scale the straight line fit is marginally better, or no worse, in every case but one (maternal plasma folate and neural tube defects). The plots on the right hand side in figures 1 and 2 therefore follow this "semi-logarithmic" approach in all the examples except this one, where a logarithmic scale is used on both axes.
In figures 1 and 2 the 95% confidence intervals about the risk estimates exclude a threshold within the population range of values, so that the lower the risk factor the lower the risk: no part of the dose-response relation would fit a horizontal line. This is clear from the plots using logarithmic scales, but the curved plots with an arithmetical scale on the vertical axes may falsely suggest a threshold. Identifying a straight line dose-response relation has an important advantage: the slope is constant, so a single number can summarise the dose-response relation. With a curved line there is no single summary of effect, so simplicity and generalisability are lost. The implications of a straight line dose-response relation according to whether arithmetical or logarithmic scales are used on the vertical and horizontal axes are set out in figure 3, together with the corresponding mathematical equations. Simple linear models (arithmetical scales on both axes) rarely fit risk factor-disease incidence relations, but the use of logarithmic vertical axes to plot incidence commonly yields a reasonable straight line fit, as is shown in figures 1 and 2. A straight line relation with a logarithmic (proportional) vertical axis scale indicates a constant proportional change in risk for a given change in the risk factor from any starting level.
The constant proportional relations in the right hand plots indicate that the absolute reduction in risk from changing the risk factor will be large in people who are at high risk for any reason (existing disease or older age, for example), regardless of the starting value of the risk factor. So, for example, halving risk by lowering serum cholesterol would be more important in a man who has an average serum cholesterol concentration but is at high risk because of a previous myocardial infarction than in a man with high serum cholesterol but no history of myocardial infarction. Table 1 gives the summary "dose-response" estimates for
each of the seven relations
Table 2, based on two large randomised trials of serum cholesterol reduction using statins, illustrates that the proportional reduction in risk is similar in groups at high and low risk. Coronary artery disease was recognised in all the participants in one of the trials (4S)22 and none in the other (WOSCOPS),23 but otherwise participants were similar (age, cholesterol, other coronary risk factors). The incidence of major coronary events in the placebo group was four times higher in the trial of people with existing disease (5.2% v 1.4% per year), but the proportional risk reduction in the treated groups was similar in the two trials. Consequently the absolute reduction in risk was greater in those with existing disease (and would have been even greater but for preventive treatment in the high risk group22). Trials of blood pressure lowering drugs show similar proportional reductions in risk in people with and without previous stroke or myocardial infarction.12-19 Another example is the randomised trial of folic acid supplementation in high risk women (with previous neural tube defect pregnancies), which showed the same proportional reduction in risk as that found in the general population with about a tenth of the background risk. 24 25 In general, the constant proportional effect model holds among men and women, people of different ages, and people with and without existing disease.
Our central conclusion is that the proportional relation between these
physiological variables and their associated diseases is constant
across all levels of the variables and all levels of risk. The lower
the risk factor the lower is the risk of disease, down to levels well
below average Western values. This raises the question as to whether
average Western values should be regarded as "normal." Today's
average levels are not typical of values throughout human evolution.
With the exception of bone density,27
the variables in our ancestors cannot be measured, but indirect
estimates are available from studies of isolated communities with a
hunter-gatherer lifestyle typical of the stone age. Table 3
contrasts typical values of the variables at age 60 in these communities
with present Western values. The rise in the variables with age
that is seen in Western populations 30
31 does not occur in hunter-gatherer
communities: throughout life blood pressure remains at about 110/70
mm Hg, serum cholesterol at 3.0-3.5 mmol/l, and body mass index at
about 22 kg/m2. The shift in the Western distributions makes the
current averages high (or low in the case of bone density and plasma
folate) in relation to the prehistoric values. Differences in
lifestyle (diet and habitual exercise) underlie the differences in
the physiological variables, 32
35 and relatively recent changes are likely to
have been responsible for the emergence of the associated diseases
(ischaemic heart disease, non-insulin dependent diabetes, hip
fracture). Present average values of certain key risk factors in
Western populations should not be regarded as "normal."
We focus mainly on the implications for preventing ischaemic heart disease and stroke. Treat anyone at high risk The major determinant of risk is existing disease. Without preventive treatment, mortality from heart disease in people who have had a myocardial infarction in the past is about 5% per year for the rest of their life.36 Mortality from stroke in people who have had a stroke is similar. 36 37 Both rates are much higher than in people with no history of cardiovascular disease; coronary mortality is 0.3% per year in men aged 60, for example, or about 0.5% per year in men with high cholesterol or blood pressure.7 In people with existing disease, however, the physiological risk factors do not predict recurrent events despite the fact that changing the risk factors alters risk. This is illustrated by data from the 4S trial (table 4): in the placebo group the incidence of recurrent events was similar in groups with high and low concentrations of low density lipoprotein cholesterol.23 This finding, though at first sight surprising, is not unexpected: once an event has occurred and disease is present there is little for a risk factor to predict. Table 4 also shows that the proportional reduction in the incidence of recurrent events in the treated group (about 35%) was, as expected, independent of the initial cholesterol level. Since the initial absolute risk and the proportional risk reduction were not materially related to the initial cholesterol level, neither was the absolute risk reduction of about 2% per year. Similarly, in people who have had a stroke the incidence of recurrent strokes37 and the proportional risk reduction with blood pressure lowering17-19 were not materially related to the initial blood pressure, so the absolute risk reduction will be substantial at any level of blood pressure. The conclusion is clear: anyone with existing disease (a previous myocardial infarction or stroke for example) should be treated irrespective of the level of the risk factors one seeks to modify.
In people without known cardiovascular disease, age is the most important
determinant of risk. Mortality from ischaemic heart disease and from
stroke doubles with about every eight years of increasing age.17
In England and Wales 95% of deaths from heart disease occur in the
23% of the population at oldest age (men
In people without cardiovascular disease, intervention to change risk factors could be introduced when a person's risk of a disease event over the next few years exceeds a specified value. Risk could be estimated from age alone, or age and sex. Values of the physiological variables might also be taken into account, but the limited additional discrimination may not justify the added complexity.
Change all relevant risk factors together
Aim for a large change to achieve a large benefit
Carry out only necessary randomised trials
Trials have low statistical power at low risk factor levels (because there are relatively few people at low risk and because the event rates are lower), so their results tend to be inconclusive. In three trials testing cholesterol reduction from low starting levels (<5.2 mmol/l) the confidence intervals were consistent both with no reduction in the event rate and with the expected reduction from the cohort studies.44-47 It is inappropriate to require randomised trials to exclude successively lower thresholds of effect; the trials are large, expensive, and unnecessary. It is inappropriate to restrict cholesterol lowering drugs to people with concentrations above 5 mmol/l or blood pressure lowering drugs to people with pressures above 140 mm Hg systolic or 90 mm Hg diastolic, as currently recommended, 4 5 when cohort studies have shown continuous relations down to cholesterol concentrations of 3.8 mmol/l26 and blood pressure of 118 systolic and 76 diastolic.7 In contrast, it is appropriately recommended that all women with a previous neural tube defect pregnancy take folic acid regardless of their serum folate (which is usually not even measured).
Thresholds have been excluded down to cholesterol levels (3.8 mmol/l)26
and blood pressure levels (118 systolic/76 diastolic)7
that are close to the prehistoric levels (table 3). The
condition of heterozygous familial hypobetalipoproteinaemia, in which
total serum cholesterol is as low as 2 mmol/l, provides an important
natural experiment: life expectancy is prolonged because coronary
artery disease is avoided, but no adverse effects from the low
cholesterol are recognised. 47
48 There must be lower limits to the physiological
variables we have considered, beyond which harm will arise: everyone
needs blood pressure, and cholesterol is essential for life. These
lower limits are, however, beyond Western values and not reached by
current dietary or drug interventions. They should not be invoked as
obstacles to offering effective preventive treatments.
Practical conclusions arise from the simple observations that certain key
dose-response relations have no threshold and yield straight lines
when risk of disease is plotted on a logarithmic scale. The most
important are that, irrespective of the level of the risk factor, a
given change in risk factor results in the same proportional
reduction in risk regardless of the initial risk; the selection of
individuals for preventive treatment should be based only on a
person's absolute level of risk; and individuals at high risk should
receive drug treatment to modify all important reversible risk
factors simultaneously.
We thank Joan Morris and Neville Young for statistical and computing help and Leo Kinlen, David Wald, George Miller, and John Garrow for their comments on drafts of the manuscript.
Funding: None.
Competing interests: The authors have an interest in a patent application for a medical formulation designed to simultaneously reduce four cardiovascular risk factors.
(Accepted 12 December 2001)
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 "A foolish faith in authority is the worst enemy of truth." -- Albert Einstein, letter to a friend, 1901 "I know of no safe depository of the ultimate powers of the society but the people themselves, and if we think them not enlightened enough to exercise control with a wholesome discretion, the remedy is not to take it from them, but to inform their discretion by education." -- Thomas Jefferson, letter to William C. Jarvis, September 28, 1820 "What's the point of vaccination if it doesn't protect you from the unvaccinated?" -- Sandy Gottstein "Who gets to decide what the greater good is and how many will be sacrificed to it?" -- Sandy Gottstein |
the
percentage change in risk for a specified change in the risk factor
from any starting level. The cohort study estimates are derived from
the slopes of the best fitting straight lines in the right hand plots
in figures 
55 and women
An
expanded version of figure 