Will lowering population levels of serum cholesterol affect total mortality? Expectations from the honolulu heart program

J Clio Epidemiol Vol. 45, No. 4, pp. 333-346, 1992 Printed in Great Britain. All rights reserved

WILL LOWERING SERUM CHOLESTEROL EXPECTATIONS

08954356/92$5.00+ 0.00 Copyright 0 1992Pergamon Press plc

POPULATION LEVELS OF AFFECT TOTAL MORTALITY?

FROM THE HONOLULU

HEART PROGRAM*

JOHN W. FRANK,’ DWAYNE M. REED,‘? JOHN S. GROVB*.~.~

and RICHARD BENFANTE~.~ ‘Departments of Preventive Medicine and Biostatistics, Family and Community Medicine, University of Toronto and Canadian Institute of Advanced Research: Population Health Group, Toronto, Canada, ‘Honolulu Heart Program, Honolulu, HI 96817 and National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20014, U.S.A., ‘School of Public Health, University of Hawaii, Honolulu, HI 96822 and 4Kuakini Medical Center, Honolulu, HI 96822. U.S.A. (Received in revised form 9 December

1991)

Abstract-Major campaigns now underway to reduce the serum cholesterol levels of entire national populations have not given serious consideration to the high rates of noncardiovascular disease and death associated with low cholesterol levels (< 190 mg/dl). To explore this problem, the relationships between serum cholesterol levels, measured in 1965-1968 in 7478 Japanese American men in Hawaii, and subsequent total and cause-specific mortality through 198.5, were analyzed by multivariate Cox regression to control for potential confounders. Total mortality rates for 1648 deaths showed a Ushaped curve by baseline cholesterol level, with significant inverse trends (p < 0.03) for deaths due to hemorrhagic stroke, all cancer, benign liver disease, chronic obstructive lung disease and “unknown cause”. Only the inverse trends for cancer and benign liver disease showed flattening when 227 deaths in the first 5 years of follow-up were deleted from the analysis. Simulation models using three different strategies of cholesterol reduction in this cohort revealed that none of these approaches had any substantial impact on predicted total mortality over 15 years. However, the population-based approach might theoretically increase mortality for 60% of the cohort with baseline cholesterol levels less than 225 mg/dl. Cholesterol Coronary heart Cancer Hemorrhagic stroke

disease

INTRODUCTION

Evidence from many different types of studies over the last half-century shows that serum cholesterol levels above 150-180 mg/dl play a *Preliminary versions of portions of this paper were presented at the Annual Meeting of the American Epidemiological Society, Baltimore, 23 March 1990; the NHLBI Conference on Low Blood Cholesterol: Disease Associations, Bethesda, 12 October 1990; and the Society for Epidemiological Research, Buffalo, 12 June 1991. ?A11 correspondence should be addressed to: Dr J. W. Frank, Ontario Workers’ Compensation Institute, 250 Bloor St. East, Toronto, Ontario, Canada M4W lE6. CE 45/&B

(prevention)

Mortality

(all

cause)

causal role in the development of coronary heart disease [l-9]. Experimental evidence strongly suggests that this risk is reversible if cholesterol levels are lowered by diet and lifestyle changes, with medical treatment where necessary [lo-161. Furthermore, although serum cholesterol levels have been slowly falling in the U.S.A. and elsewhere, the most recent large surveys suggest that the mean level in adult Americans in the late 1970s was still about 210-215 mg/dl, with at least 25% of adults at levels deemed “high”, i.e. over 240 mg/dl [9, 171. There are thus sound

333

reasons to expect that lowering cholesterol levels across whole Western populations would be followed by reductions in coronary heart disease of public health importance. Both national and local campaigns are currently underway to reduce serum cholesterol levels in North American populations [6-91. Some authorities have argued primarily for a “high risk” approach, involving the identification of individuals with elevated levels, followed by medical treatment with prescribed dietary and drug regimens [l g-201. Others have called for greater emphasis on a “population-based” strategy, whereby an entire population’s cholesterol distribution would be shifted downward [2,21-231. These strategies have been hotly debated, largely in the context of their relative effectiveness and efficiency for controlling coronary heart disease [24-281. With few exceptions [25, 26, 29, 301, these debates have not reflected concern for possible adverse impacts of population-based cholesterol shifts in persons without clearcut hypercholesterolemia. Indeed, with one exception [30], no published models of the effects of various cholesterol reduction strategies have included projected effects on total mortality [31-341. In contrast, a number of cohort studies in a variety of settings have demonstrated that all-cause mortality has a J- or U-shaped relationship to serum cholesterol, with minimum death rates usually lying between 170 and 220 mg/dl[35-451. This U-shaped pattern raises the possibility that population-based strategies to lower serum cholesterol might actually increase overall mortality rates among persons with low to intermediate levels. In this paper we have examined the relationship of total and cause-specific mortality to serum cholesterol levels in the Honolulu Heart Program (HHP) cohort. We have then utilized these relationships to statistically model the effects of lowering this cohort’s original cholesterol levels on both coronary heart disease incidence and all-cause mortality, using both the “population-based” and “high-risk” strategies. MATERIALS

AND

METHODS

The Honolulu Heart Program is a populationbased cohort study of 8006 men of Japanese ancestry, born between 1900 and 1919, who were living on Oahu in 1965. Between 1965 and 1968, a comprehensive baseline examination of these men was completed, which both ascertained

prevalent major disease and measured potential risk factors, including total serum cholesterol. Details of the cohort’s recruitment and risk factor measurement, as well as outcome ascertainment methods, are reported elsewhere [46-481. In all the analyses to be presented here, 528 subjects with prevalent heart disease, stroke or cancer at their baseline examinations have been excluded, as is usual in attempts to model the effects of primary prevention strategies to reduce coronary heart disease. Through the end of 1985, a mean follow-up period of 17.2 years, there were 1648 deaths among 7478 eligible individuals (22.0%). Mortality and major cardiovascular events were identified by continuous surveillance of hospital discharges/deaths and obituaries in local newspapers and statewide death certificates. Cause of death was determined by a panel of study physicians on the basis of all available information from surveillance procedures, autopsy reports, and the personal physicians and families of subjects. Observational analyses

Mortality data have been analyzed for patterns of total and cause-specific mortality rates across quantiles of serum cholesterol at the initial examination. These relationships were first explored by examining crude and ageadjusted mortality rates across cholesterol levels, for total mortality and 11 individual diseasegroupings. [In this cohort, there was near-zero correlation between age and cholesterol level at baseline.] Where nominally significant causespecific mortality trends across cholesterol quintiles were observed (chi square for trend, p < O.OS), further analysis was undertaken with multivariate proportional hazards regression [49] to control for age and potential confounders measured at the baseline exam. Potential confounders included: systolic blood pressure (mmHg), total pack-years of smoking, body mass index: weight (kg)/height* (m*), reported alcohol consumption (oz/month) and serum glucose (mg/dl) measured 1 hour after a 50-g oral glucose load. For causes of death in which the covariateadjused inverse trend in risk against serum cholesterol was statistically significant (p < O.OS), the analyses were repeated deleting all 227 deaths during the first 5 years of follow-up, in order to test for any “pre-clinical disease effect” [50]. By this is meant the secondary lowering of

Effects of Lowering

serum cholesterol levels by undetected disease which is associated with excess risk of death, particularly within a short time after follow-up begins. In previous studies this phenomenon has been sought out exclusively in conjunction with cancer, although it could presumably occur with other disease processes. Simulations

There were four scenarios for which we simulated the total mortality and definite coronary heart disease incidence over 15 years of followup: (1) no change in cholesterol levels (allowing a comparison of predicted to observed event counts); (2) the population-based approach to cholesterol-lowering, involving a downward shift of 20 mg/dl for each member of the cohort; (3) and (4) two versions of the high-risk approach whereby only persons with initial levels above widely recognized cutoffs, 220 and 240 mg/dl respectively, would have their levels reduced by 20 mg/dl. The first strategy is based in part on the guidelines of the U.S. National Cholesterol Education Program which cite levels below 200 mg/dl as desirable for all Americans [6]. [The effects of fully implementing these complex guidelines are potentially much greater. They utilize various LDL cholesterol cutpoints for treatment indications and targets, according to risk factor status, which would tend, on average, to bring about much larger reductions in serum cholesterol than 20mg%. On the other hand, as demonstrated by Browner [31], the overall effectiveness of cholesterol-lowering programs in actual practice may be far less impressive than expected, once coverage, provider- and patient-compliance are taken into account.] The second strategy modelled conforms to the more conservative view that formal medical treatment of asymptomatic persons should be largely reserved for those with “high” cholesterol levels [51]. Modelling the effects of serum cholesterol shifts required life table predictions. For each subject, time since his baseline exam until death (or 15 years of follow-up, whichever came first) was divided into 12-month intervals; if a subject was alive at the beginning of the interval, a record was created with information on baseline measurements, age at the start of the interval, and whether he survived the year. The probability of surviving 1 year, conditional on being alive at the beginning of the year, age, and baseline measurements, was estimated by “yearwise” logistic regression. Prevalent cases of

Serum Cholesterol

335

cancer, coronary heart disease, and stroke were deleted from the dataset, as well as all 85 subjects who died from cancer or benign liver disease in the first 5 years of follow-up, based on the Cox regression results. The covariates used in the yearwise logistic regression were those in Cox regression (cf. above) plus age at the beginning of each 12-month interval and a variable indicating whether death occurred in the first 5 years of follow-up. A similar model was created for 529 incident cases of definite coronary heart disease over 15 years of follow-up (i.e. fatal and non-fatal myocardial infarction, as well as sudden death within 1 hour of being well). For simulating the effects of various cholesterol-reduction strategies, the probability of a subject surviving 1 year, conditional on being alive at the start of the year, was computed from the logistic regression equation for total mortality. The subject’s age was increased by one and the probabilty of surviving 1 year conditional on having survived the previous year was recomputed. This calculation was performed for 15 years. The cumulative probability of surviving 15 years, given the subject’s initial age and risk factors with appropriate cholesterol modification, was calculated as the product of the 15 conditional probabilities. The average over all subjects gives the proportion of the cohort expected to survive for 15 years after the baseline examination. This follow-up period would bring the average age of the cohort to about 70 years, implying that most of the allcause mortality modelled would, by any modern definition, be “premature”. An analogous procedure was followed to calculate the proportion of the cohort expected to remain free of definite coronary heart disease for 15 years, using the logistic regression equation developed for that outcome. RESULTS

Total mortality by cholesterol level

The pattern of age-adjusted all-cause mortality by initial cholesterol vicentiles (twentieths) shows an almost symmetrical U-shaped relationship (Fig. 1). There is a zone of minimum mortality lying approximately between 188 and 225 mg/dl, which encompasses the second and third quintiles of the cohort’s cholesterol distribution (mean 218 mg/dl). The height of the “sides” of the U is clearly of clinical and public health significance, with elevated death rates half-again as high as those at mid-levels of cholesterol.

JOHNW. FRANK et al.

336

Chdesterol (mg/dl) Fig. 1. Age-adjusted death rate per 1000 person-years by vicentiles of baseline serum alcohol in the Honolulu Heart Program, 19651985.

A least-squares quadratic regression for this curve gave a good fit, with both linear and quadratic terms highly statistically significant (t = 4.9, p < 0.001 for the linear and quadratic terms; r* = 0.59). The fitted curve predicted a relative risk of 1.44 for mortality at the first vicentile (mean cholesterol = 146 mg/dl) vs that at the thirteenth vicentile (the nadir of the fitted curve, mean cholesterol = 227 mg/dl). The results of modelling total mortality risk by proportional hazards regression are shown in Table 1, showing fitted beta-coefficients for cholesterol and cholesterol-squared terms for a basic model (No. l), controlling only for age at baseline, as well as the full model (No. 2), controlling for other potential confounders. The addition of the covariate terms to model 2 did not substantially alter either the size or the statistical significance of the linear or quadratic cholesterol terms, both of which retained very high chi-square values (p < 0.0001). The quantity (- b/2c), where b and c are respectively the linear and squared terms’ coefficients in an overall fitted quadratic curve of risk against cholesterol level (x), of the form (a + bx + KC’), estimates the cholesterol level at which the minimum mortality occurs-the nadir of the “U-shaped curve”. It changed very little across the models we fitted, remaining very close to the sixtieth percentile of our cohort’s cholesterol distribution (225 mg/dl), no matter how many

of the covariates were added. Furthermore, the relative height of the sides of the fitted curves did not change substantially as confounders were added to the basic model (No. 1, Table 1). The relative risk of death at a cholesterol level two standard deviations (76mg/ml) from the nadir of the fitted curve, compared to that at the nadir, was 1.2 in both covariate-adjusted and unadjusted models. These results suggest that both the “position” and the steepness of the U-shaped curve relating total mortality to cholesterol level is relatively unaffected by controlling for potential confounders. In addition, both linear and quadratic terms for cholesterol remained significant in the full model (No. 2, Table 1) after deleting all deaths that occurred within the first 5 years of follow-up (p < 0.002 for both terms), and even the first 10 years (p < 0.02 for both terms.) Cause-specific mortality patterns

Much of the excess mortality at high cholesterol levels in this cohort (i.e. in the righthand arm of the “U”) was predictably due to strong positive trends for fatal coronary disease and, to a much smaller extent, non-hemorrhagic stroke (Table 2). No other major causes of death in this cohort had risks directly associated with serum cholesterol. There were inverse risk-gradients for fatal hemorrhagic stroke, cancer, chronic obstructive lung disease, “benign liver disease” (virtually all cirrhosis), as well as deaths of “unknown cause” (Table 2). A number of other major causes of death showed no evident risk-gradient across cholesterol levels. Table 3 shows the results of multivariate Cox regression for these five causes of death for which inverse trends were observed against cholesterol quintiles in univariate analysis. The potential confounders included in the final model for each outcome were chosen on the basis of previous studies or exploratory Cox regression results in this dataset. To assist in the

Table 1. Results from proportional hazards regressions of total mortality on cholesterol

Cc)

-b/*

p chol.

/I cho?

2c

-0.01513*+ -0.01298**

0.00003274** 0.00002874**

231 mg/dl 226 mgldl

@I Model 1. AGE, cholesterol (mg/dl) [cholesterol (mg/d1)12 2. Model 1 plus SBP, BMI, BMI’, CIG, GLU, ALC

*Cholesterol level with minimum mortality rate. **p < O.OOQl. Key to symbols for covariates (all measured at baseline): AGE = age at baseline exam in years; CIG = pack-years of cigarette smoking; SBP = systolic blood pressure (mmHg); ALC = ounces per month of alcohol consumed; GLU = serum glucose (mg/dl) 1 hr after a 50-g oral load; BMI = body mass index = (weight (kg)/height (m)*); BM12.

Effects of Lowering Serum Cholesterol

331

Table 2. Cause-smcific Datterns of mortality across serum cholesterol suintiles Cause of death

Pattern

Number (%) of deaths

Direct association

Coronary heart disease*** Non-hemorrhagic stroke*

313 85

(19.0) (5.2)

Inverse association

Cancer (all sites)* Hemorrhagic stroke* COPD*t Benign liver disease* Unknown cause*

639 70 49 40 116

(38.8) (4.2) (3.0) (2.4) (7.0)

No association

Trauma Other circulatory Infections Miscellaneous known cause

92 76 58 110 1648

(5.6) (4.6) (3.5) ~ (6.7) (100)

*chi square for trend 0.01 < p < 0.03; ***chi square for trend p < 0.000001. TCOPD: chronic obstructive pulmonary disease.

biological interpretation of the effect-sizes for these trends, regression results for cholesterol linear terms are presented for a one-standarddeviation (38 mg/dl) difference in cholesterol levels. Comparison of covariate-adjusted results (Table 3, middle columns) to those obtained in univariate analysis (Table 2) shows that the inverse trend of risk for five causes of death against cholesterol level remained statistically significant (p < 0.05) after adjusting for potential confounders, albeit with a small effect-size and a borderline p-value for cancer. The right-hand columns of Table 3 give the results of the same Cox regression analyses when all deaths during the first 5 years of follow-up were deleted from the dataset. Because of the inevitable loss of power caused by reducing the

numbers of deaths, the extent of any attenuation of cholesterol effect is best examined by comparing the relative risk estimates from “undeleted” and “deleted” analyses, rather than the p-values. There was no diminution of the previously observed inverse associations between mortality risk and serum cholesterol for chronic obstructive pulmonary disease, “unknown cause”, and hemorrhagic stroke when early deaths were deleted. However, for deaths from benign liver disease and cancer, there was an approximate halving of the fitted beta-coefficients for cholesterol after deletion of early mortality, which is reflected in the relative risks’ attenuation for these outcomes. Because of the controversial nature of preclinical disease effects in the case of cancer, and the borderline nature of the above results for

Table 3. Multivariate Cox regression results for causes of death with risks inversely related to serum chloesterol Analysis 1: all deaths included

Analysis 2: first 5 year’s deaths deleted

Covariates in best model

Number of deaths*

RRt (95% CI)

Hemorrhagic stroke

AGE, CIG, SBP, ALC, GLU

69

0.73 (0.574.94)

0.014

51

0.75 (0.56-1.01)

0.057

Cancer (all sites)

AGE, CIG, ALC

632

0.92 (0.85-1.00)

0.051

557

0.96 (0.88-1.04)

0.335

COPD

AGE, CIG

49

0.71 (0.524.96)

0.026

44

0.72 (0.52-0.99)

0.042

Benign liver disease

AGE, ALC, GLU

39

0.70 (0.50-0.97)

0.032

29

0.81 (0.55-1.19)

0.28 1

Unknown

AGE, CIG, SBP, GLU

116

0.79 (0.65-0.95)

0.014

106

0.80 (0.66-0.98)

0.033

Cause of death

p-Value

_ Number of deaths

RRt (95% CI)

p-Value

*Numbers of deaths not necessarily equal to those given in Table 1 due to the omission, in Cox regression, of cases with missing covariate values. tRelative risk of cause-specific death associated with an increase in serum cholesterol of 38 mg/dl (1 SD), calculated as [exponent (38&l where B = Cox regression coefficient for cholesterol linear term. Key to symbols for covariates (all measured at baseline exam): AGE = age in years; CIG = pack-years of cigarette smoking; SBP = systolic blood pressure (mmHg); ALC = ounces per month of alcohol consumed; GLU = serum glucose (mg/dl) 1 hr after a 50-g oral load.

338

JOHN

W.

FRANK

et al.

Non-cancer,non-CVD

~:~ ~~:

B

-2./***......__

‘,
ZoL’

159

..*-

__.........

~~~~~~~

. . . . .::.‘:r.:,:

.*-

”

o-

,I

<140

160- 180-ZOO-220-240-260-280-m 179 199 219 239 259 279 299

WO159

. . . . . . . . . . . . . *....._

. ....

160-180-20&220-24O-260-280-H 179 199 219 239 259 279 299

Cholesterol (mg/dl )

Cholesterol (mg/dl)

Fig. 2. Death rates per 100,000 person-years from all cardiovascular causes (coronary and other heart disease, and all types of stroke) by baseline serum cholesterol in the Honolulu Heart Program, 1965-1987, with and without deletion of deaths in the first 5 and 10 years of follow-up“late” and “very late” deaths respectively.

Fig. 4. Age-adjusted death rates per 100,000 person-years from all other (non-cancer, non-cardiovascular) causes by baseline serum cholesterol in the Honolulu Heart Program, 1965-1987, with and without deletion of deaths in the first 5 and 10 years of follow-up-“late ” and “very late” deaths respectively.

that outcome, we subsequently performed additional analyses to test for the effects of deleting the first 10 years of deaths on three broad categories of mortality rates: cardiovascular, cancer, and “other”. In order to maximize power with so many deaths to be deleted, we made use of two extra years of follow-up data on mortality, through 1987. Figures 2 through

4 show the three category-specific mortality rates per 100,000 person-years of follow-up (adjusted for age or confounding by other risk factors) by 20-mg/dl-wide bands of serum cholesterol, with both the first 5 and 10 years of deaths deleted (“late” and “very late” deaths, respectively.) These curves demonstrate that this cohort’s excess mortality rates, at low cholesterol levels, due to cancer, some cardiovascular diseases (hemorrhagic stroke) and “other causes”, like its excess risk for cardiovascular deaths at high cholesterol levels, are not confined to the first decade of follow-up. Furthermore, multivariate Cox regression results analogous to those in Table 3 but with deaths in the first 10 years deleted (not shown), showed no attenuation of excess risks at low cholesterol levels for any of the three major categories of cause-of-death, including cancer, after controlling for potential confounders. These findings, taken together with the results presented earlier, suggest that pre-clinical disease effects are not the entire explanation for higher death rates in this cohort at low cholesterol levels.

““‘Very

200'

Cl40

late death IN=5761

14C- 160- 180- 200- 220- 240- 260-280-&%X 159 179 199 219 239 259 279 299

,I

Cholesterol (mg/dl)

Fig. 3. Death rates per 100,000 person-years from cancer (all sites) by baseline serum cholesterol in the Honolulu Heart Program, 1965-1987, with and without inclusion of deaths in the first 5 and 10 years of follow-up“late” and “very late” deaths respectively.

Table 4. Observed and expected numbers of total deaths and incident cases of coronary heart disease (CHD) from multivariate models* of lowering serum chlolesterol by 20 mg/dl Total deaths (%) Observed events: Expected events: Test of model with baseline risk factor levels Total population intervention model High risk intervention model (> 22Omg/dl) High risk intervention model (> 24Omg/dl)

Incident CHD cases (%)

993

(100.0)

529

(94.6)

992.6 1007.8 98 I .4 981.2

(100) (101.5) (98.9) (98.9)

559.1 (100) 479.9 (85.8) 517.2 (92.5) 533.6 (95.4)

*Covariates included in models of both outcomes (measured at baseline): age2 in years at beginning of each interval; age at baseline exam; follow-up at start of interval less than or equal to 5 years; cholesterol and cholesterol2 (mg/dl); pack-years of cigarette smoking; systolic blood pressure (mmHg); ALC = ounces per month of alcohol consumed; serum glucose (mg/dl) lhr after a 50-g oral load; BMI (weight (kg)/height (m)‘); BMF.

Effects of Lowering

Simulation models of cholesterol reduction

Table 4 shows the results of modelling three strategies for lowering serum cholesterol levels in this cohort at its inception. In an internal test of its predictive accuracy, the simulation performed well for total mortality, giving a predicted death-count based on actual baseline risk-factor levels within 0.05% of the observed figure. The simulation for the cumulative incidence of coronary heart disease over 15 years overpredicted by about 5.7%, as is usual for projections from life-table-based probabilities, conditional upon no deaths due to any other cause, during lengthy periods of follow-up [52]. To avoid overestimating any modelled increases in overall mortality as a result of cholesterol shifts, we therefore compared simulated event-counts to those on the second line of the table (the predicted baseline burden) rather than the observed event-count. Simulation of a “population-based” shift in cholesterol of 20 mg/dl for the entire cohort predicted a 14% reduction in coronary heart disease, but a 1.5% increase in all-cause mortality. The last two lines of Table 4 show the results of simulating the two different “high risk” strategies of cholesterol reduction, shifting downward by 20 mg/dl the cholesterol levels of persons with an initial level above 220 and 240 mg/dl respectively. Neither approach makes an appreciable impact on all-cause mortality. Predictably, these high-risk strategies have much less impact on total coronary heart disease incidence than the population-based approach, with only 7.5 and 4.6% reductions respectively. This is due to their failure to affect the bulk of the population-attributable-risk for coronary heart disease in the middle of the cholesterol distribution.

DISCUSSION

Patterns of mortality by cholesterol level

The main observational findings of this study in a population-based cohort of JapaneseAmerican men, initially examined in mid-life and followed for over 17 years, are as follows: (1) There is a U-shaped curve of all-cause mortality risk across serum cholesterol levels, which remains after controlling for confounding and probable preclinical disease effects, and which has biological and public health significance.

Serum Cholesterol

339

(2) Much of the excess mortality at low cholesterol levels (first quintile: < 187 mg/dl) appears to be due to 5 categories of death showing inverse trends in risk, which again cannot be explained by confounding: hemorrhagic stroke, cancer, benign liver disease, chronic obstructive pulmonary disease and deaths of unknown cause. (3) Of these five causes of death more likely to occur at low cholesterol levels, preclinical disease effects appear to be a plausible explanation only for benign liver disease and perhaps some cancers, in that the inverse trend of mortality risk for these conditions is substantially attenuated when events in the first 5 years of follow-up are deleted from the analysis (Table 3). On the other hand, we could not confirm this observation for cancer deaths when we utilized two extra years of follow-up data, with the first 5 and 10 years of deaths removed. Although many studies (see below) have suggested that excess cancer deaths at low cholesterol levels may be due to preclinical disease effects, we are unaware of any published studies suggesting that benign liver disease (and/ or its main cause in this cohort, high alcohol consumption) shows such an epidemiological pattern. The results in Table 3 suggest that some of these fatalities (and perhaps some cancer deaths) may have been presaged by long preclinical disease processes which secondarily caused hypocholesterolemia. This possibility is made more credible by two biological facts, Alcoholic cirrhosis, the main sort of benign liver disease in this population, is often an indolent process which only contributes to death after many years. Also the liver is the major site of cholesterol synthesis, and secondary hypocholesterolemia can occur long before death in cirrhosis patients [53]. We consequently thought that it would be prudently conservative, modelling the effects of various cholesterol-shifting policies in this cohort, both to control for potential confounders by multivariate methods and to exclude all cancer and benign liver disease deaths in the first 5 years of follow-up, even though the results of our deletion analyses for cancer deaths were borderline and inconsistent. In this way we hoped to avoid overprediction of increased deaths due to these causes after cholesterol reductions in the cohort. To delete all deaths

340

JOHNW. FRANK et al

in the first 5 years of follow-up, as Rose and Shipley have done [30], would seem to unfairly discount all interventions’ beneficial effects on CHD mortality, which are likely to begin to accrue well before 5 years have elapsed [lo-161. The U-shaped curve of overall mortality by serum cholesterol levels in this study is consistent with the findings of several prospective studies, which have shown, particularly in males, L-, J- or U-shaped curves, which remain after control for potential confounding variables [35-45,541. The J- and U-shaped curves in these populations tend, like the HHP cohort, to have mortality excesses at high cholesterol levels which are due to high rates of fatal coronary heart disease and, to a lesser extent, thromboembolic stroke [55]. In one of the few cohort studies in middle-aged men large enough to define minimum overall mortality precisely, it lay in a range very similar to that found in this cohort, between 170 and 210 mg/dl [43]. There is also evidence from other settings of some of the specific disease associations with low cholesterol levels found in this study. These associations, like those in the present analysis, remain after controlling for potential confounders, though not always after deletion of early deaths. First, higher subsequent cancer incidence and mortality rates have been repeatedly observed, most consistently in men, at cholesterol levels below 170-l 90 mg/dl, and relate to a wide variety of cancer sites. In a number of well-conducted studies, the persistence of these excess death rates through several years following cholesterol measurement suggests that they are not necessarily a pre-clinical disease effect [35, 44,50,56-611. On the other hand, equally well-designed studies have shown that excess cancer risks at low cholesterol levels do “disappear” when events in the first 2-5 years of follow-up are deleted, in keeping with a pre-clinical disease effect [62-691. Secondly, clear-cut increases in hemorrhagic stroke rates at cholesterol levels below 160-190 mg/ml have been demonstrated in both Western and Japanese populations, with a much higher burden of this disease in the Orient [55,70-721. There is arguably now sufficient biological and epidemiological rationale [72-751 to warrant the inference that this association is causal. The relative risk of death from hemorrhagic stroke in the present study did not decline after removing the first 5 years of deaths (Table 3), which is consistent with a causal relationship between low cholesterol levels and subsequent disease.

Finally, in some studies there are hints of other, less consistent disease associations with low cholesterol levels, which show strong parallels with findings presented above for chronic obstructive lung disease, benign liver disease and deaths of unknown cause: “car pulmonale” and pulmonary tuberculosis [54]; the general category of “non-cardiovascular/ non-cancerous diseases” [41]; “alcohol-related diseases” in one small study [36]; and, among 30,000 men screened in Minnesota for the MRFIT trial, trauma, cirrhosis and other nonneoplastic digestive diseases (D. Jacobs, University of Minnesota: personal communication, 1990). Additional support for concern with deaths at low levels of serum cholesterol comes from controlled trials. Meta-analyses of all the cholesterol-lowering diet and drug trials to date [lo-161 demonstrate the very real benefits of such treatment in high-risk subjects, in terms of coronary heart disease risk reduction. The degree of risk reduction achieved in the trials of longer duration closely approaches that predicted in prospective studies, suggesting that cholesterol-associated cardiovascular risks are fully “reversible”. However these same metaanalyses also show a significant 10% increase in non-coronary heart disease deaths in treated groups compared to controls. This excess more or less cancels out observed reductions in coronary heart disease mortality, leaving all-cause death rates unchanged. Considerable controversy exists over whether these non-coronary heart disease effects are related to the lowering of cholesterol per se (which would seem unlikely at the very high initial lipid levels of the high-risk subjects in these trials) or the unintended sideeffects of dietary and especially drug treatments employed to reduce cholesterol [lo-i6, 25,26,29,76]. Simulation results

The biological rationale for the cholesterollowering strategies modelled here is sound, in terms of their potential to reduce coronary heart disease incidence. However, the amount of reduction in the “burden” of future coronary heart disease achieved by any given cholesterol shift depends on the extent to which that strategy encompasses the large majority of persons whose serum levels are only modestly elevated, but from whom the bulk of coronary heart disease cases arise [2,21-231. In short, the largest population-attributable-risk for

Effects of Lowering

coronary heart disease, by cholesterol level, lies in the mid-zone of current Western populationdistubutions. Thus four quantitative analyses [3 l-341 have demonstrated that substantial shifts of virtually the entire U.S. population’s cholesterol levels would be required in order to make a major impact on coronary heart disease, a prediction confirmed by the simulations presented here. The “ideal” downward cholesterol shift for the U.S.A. as a whole has been envisaged as about 50 mg/dl, whereas that considered “feasible”, and therefore modelled in this study, is only about 20 mg/dl [21]. In the HHP cohort at baseline, this would have constituted almost exactly the 10% reduction now being proposed by the Population Panel of the National Cholesterol Education Program [9]. Most published discussions of the populationbased approach to cholesterol lowering do not seriously consider the potential for harm from such programs, particularly for persons with low to intermediate levels of cholesterol [2,9, 21-231. Given that no conclusive experimental evidence is likely to be gathered on the overall effects of various strategies to lower cholesterol levels in whole populations [77], it seemed worthwhile to attempt to model these effects from prospective data on a population-based cohort. The only other cohort-simulation results published to date for the effects of cholesterol changes on total mortality come from a U.K. civil service population with very high risk factor levels [30]. That study, which also projected changes in deaths by cause over 15 years of follow-up in a cohort of middle-aged men, appears not to have explicitly controlled for potential confounding by other frequentlymeasured risk factors for cardiovascular and overall mortality. Despite this methodological difference, and the U.K. study population’s much higher baseline cholesterol levels and coronary disease rates (overall observed PMR 39%, as compared to 19% in the Honolulu cohort), the Whitehall results are remarkably similar to those presented above. A 10% reduction in the cholesterol levels of the entire U.K. cohort was projected, after deletion of all deaths in the first 5 years of follow-up, to produce only a 3% decrease in overall mortality. A projected 12% reduction in coronary heart disease deaths was almost outweighed by a 0.6% increase in cancer deaths and a 6.1% increase in deaths due to “other causes”. The present attempt at modelling total mortality and the incidence of coronary heart

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disease after various interventions to lower serum cholesterol, in an Asian-American cohort of middle-aged men, suggests the following:

(1) There would be no decline in overall mortality in this population after a populationbased shift of 20 mg/dl in serum cholesterol levels. While some persons at high cholesterol levels would suffer less fatal coronary disease after such a shift, there would be a “compensatory” mortality increase borne by a larger number of persons with low to intermediate baseline cholesterol levels. (2) The two simulated high-risk approaches both lowered total mortality by about 1%. This modest result should perhaps temper one’s expectations from intervention trials which target high-risk groups, in terms of their likelihood of demonstrating substantial reductions in all-cause mortality-at least as a result of relatively small changes in cholesterol levels, such as that modelled here. (3) The more conservative high-risk policy, of treating only those with cholesterol levels above 240mg%, would have reduced the cohort’s burden of new coronary disease cases over the subsequent 15 years by less than 5% compared to about 14% for the population-based strategy. This high-risk policy would require widespread screening and lifelong medical treatment of 25% of the population. The more aggressive policy of targetting persons with cholesterol levels over 220 mg/dl would treat almost twice as many persons to achieve only a 7.5% subsequent decline in coronary heart disease. The rather discouraging results presented here for total mortality after a population-based cholesterol shift are not counter-intuitive. A graphic representation, dubbed “the bell and the bowl” (Fig. 5), shows that the bulk of the HHP cohort at inception had cholesterol levels in a range where the cohort’s curve of subsequent all-cause mortality risk is fairly flat. The “bell” of the cohort’s population cholesterol distribution sits down in a “bowl” of associated all-cause mortality risk. In this situation, there is a kind of inertia which resists populationbased efforts to obtain significant mortality benefits by relatively small shifts in cholesterol level. In addition, the nadir of the fitted Ushaped curve of all-cause mortality risk for the HHP cohort happens to lie at a cholesterol level only slightly (8 mg/dl) above the actual mean of

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Fig. 5. Age-adjusted total mortality per 1000 person-years and distribution of subjects by baseline serum cholesterol level in the Honolulu Heart Program, 1965-1985: “the bell and the bowl”.

the cohort’s symmetrically Gaussian cholesterol distribution, so that a substantial populationwide cholesterol shift downward can only increase the total mortality predicted by the model. In sum, modest longevity benefits, for the 40% of the cohort with cholesterol levels above this nadir, appear to be slightly outweighed by increases in mortality for the other 60%, whose cholesterol level already places them on the “left-hand side of the mortality bowl”. Before considering the broader implications of these results, there are several caveats to any more general conclusions one might draw from simulations of the type utilized here. Assumptions underlying the models. Several assumptions implicitly underlie this simulation exercise. First, we have assumed, as have other workers [30-341, that individuals whose cholesterol level is reduced obtain, without an important lag time, exactly the coronary disease risk of dzfirent persons with lower cholesterol levels to start with, who have been at those levels for an adult lifetime. With respect to our projections for coronary disease events, this is equivalent to saying that coronary heart disease risk is rapidly 100% reversible in all subjects, thus providing “ceiling values” for the coronary heart disease reduction effects of cholesterollowering-so-called best-case scenarios. Secondly, our approach assumes that all other risk factors, and especially those included in the models, remain unchanged. This has the advantage, for internal validity, of isolating the effects of cholesterol change from confounding. However it has the disadvantage, from the point of view of external validity, of not conforming to clinical practice and most public health programming to reduce coronary heart disease, which are usually multifactorial in scope. Thirdly, we have ignored entirely the effects of regression-to-the-mean for cholesterol

measurements on the observed risks for both total mortality and coronary heart disease incidence. The general effect of measurement error or temporal fluctuations in cholesterol (the independent variable) is to underestimate the steepness of observed gradients in risk across cholesterol levels. The size of this “regression dilution error”, for studies of hypertension and coronary heart disease, has recently been shown to underestimate the slope of the curve of coronary heart disease risk against blood pressure by a factor of over 35% [78]. These authors suggest that a similar-sized error affects estimates of coronary heart disease risk by cholesterol level where, as here, only one measurement is used for prediction. This sort of problem may well have caused us to underestimate the reduction in the incident coronary heart disease case-burden resulting from various downward shifts in cholesterol. However, it is unlikly to have substantially affected our predictions for total mortality for populationbased cholesterol reductions, since both sides of the U-shaped curve would be affected almost equally. Finally, it might be objected that the overall effects of truly population-based cholesterollowering strategies should also include major beneficial effects of such programs on hypercholesterolemic persons who already have symptomatic coronary heart disease [15] and perhaps even stroke-i.e. we ought not to have deleted from the cohort at the outset all prevalent cases of coronary heart disease and stroke. However, we suggest that such patients will largely, in the present era, be treated with aggressive medical treatment anyway. Thus one is justified in modelling only the marginal primary preventive effects of cholesterol-lowering -surely the main aim of population-based programs. Deletion of prevalent cases of these major diseases in the present study also avoids the thorny question of how to control for the poorly-understood effects of clinically evident cardiovascular and neoplastic disease on measured risk factor levels at baseline. Undetected preclinical disease effects. The chief threat to the internal validity of the modelling presented here may be undetected “reverse causation”. Unsuspected chronic subclinical diseases, other than cancer and benign liver disease (largely alcoholic cirrhosis in this population) which were eventually diagnosed as the cause of death, may have lowered some subjects’ cholesterol levels at their baseline

Effects of Lowering Serum Cholesterol

exams and also predisposed them to excess mortality many years into the future. As a subclinical condition, alcoholic liver damage is probably many times more common as an undiagnosed contributing caue of death than is indicated by usual data on hospitalizations and deaths without autopsy, so that its potential impact on the relationship between total mortality and cholesterol may be underestimated in this study and others. It would seem worthwhile for other large cohort studies investigating the relationship of cholesterol to total mortality to explicitly analyze for pre-clinical disease effects involving diseases other than cancer, and especially those which may involve liver damage. Generalizability. How relevant are these results to the rest of North America and other Western populations today? First, it should be recalled that the original Ni-Hon-San studies, which included the Honolulu Heart Program, clearly demonstrated that Japanese genetic factors provided little protection against environmentally-influenced risk factors for coronary heart disease such as serum cholesterol, which so rapidly brought disease rates among JapaneseAmerican immigrants up to contemporary American levels [79, 801. Secondly, although the mean of the HHP cohort’s cholesterol distribution at baseline exam in the 196Os, at 2 18 mg/dl, was substantially lower than the contemporaneous mainland American mean for middle-aged males, it is not greatly different from the current mainland mean for this age and sex group [9, 171. Additionally, coronary heart disease mortality rates among men of Japanese ancestry living in Hawaii over the last two decades, although slowly declining, have generally been close to current (late- 1980s) rates for white U.S. mainland males of comparable age [81], so that our simulation is perhaps of greater relevance to present campaigns to lower cholesterol levels than might first appear. With respect to the generalizability of the relationship between total mortality and cholesterol observed in this cohort, the exact shape and position of such curves will obviously vary substantially across populations and historical time-periods, according to the relative proportionate-mortality contributions of various diseases strongly associated with serum cholesterol and especially coronary heart disease. For example, in a study from Finland in the early 197Os, there is a consistently positive trend in middle-aged males’ all-cause mortality risk by cholesterol level, presumably because the

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high range of these levels (mean 268 mg/dl) and coronary heart disease death rates (proportionate mortality 53.8%) in this population completely dominated the mortality picture [82]. At the other extreme, a Yugoslavian study shows a strong inverse trend for total mortality against cholesterol, which seems largely due to the very low proportionate mortality for coronary heart disease (less than 10%) in this only partly “Westernized” cohort-a pattern of mortality presumably attributable in turn primarily to low cholesterol levels (mean 198 mg/dl) [54]. In the Finnish situation, one might expect overall mortality reductions from a population-based cholesterol shift downwards. In the Yugoslavian population, on the other hand, such a program would seem unlikely to decrease all-cause mortality. The HHP cohort lies somewhere in betweeen. Thus ideally one should know the cholesterol distribution, the associated curve of total mortality, and the pattern of proportionate mortality by cholesterol level before attempting to estimate the mortality effects of major cholesterol shifts in a population. Broader implications of simulation results. The models presented suggest that no populationbased or high-risk interventions to lower serum cholesterol by 20 mg/dl will substantially improve total mortality in a population with a symmetrical U-shaped curve of mortality against current cholesterol levels, a pattern found in several but not all [54,82-871 settings investigated to date. Potentially more worrisome, but much less certain in this analysis, is the possibility that population-based interventions which include persons with low to intermediate cholesterol levels, implemented in populations with such symmetrical U-shaped curves of cholesterol-related mortality, might marginally increase those persons’ overall mortality risk. In the HHP cohort, this would appear to include the 60% of all subjects who had initial serum cholesterol values below 225 mg/dl. Any attempt to reduce such already-low cholesterol levels would appear to bring few health benefits and could well leave some persons worse off than before. In the interim, until we fully understand the degree to which such concerns can be generalized, we wonder if discretion is not the better part of valor-primum non nocere. Acknowledgements-The authors gratefully acknowledge the skilful programming assistance of Darryl Chiu in this research. This study was supported by National Heart, Lung and Blood Contract No. NO + -HC-02901. The first author was

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supported in part for this research by a Canadian Institute of Advanced Research (Population Health Program) Scholar award and a research leave grant from the University of Toronto during his sabbatical at the Honolulu Heart Program.

18.

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