Plasma lipid and lipoprotein responses to dietary fat and ...

Am J C/in Nutr 1997:65:1747-64. Printed in USA. © 1997 American Society for Clinical Nutrition I 747

Plasma lipid and lipoprotein responses to dietary fat andcholesterol: a meta-analysis13

Wanda H Howe/i, Dona/d J McNamara, Mark A Tosca, Bruce T Smith, and John A Gaines

ABSTRACT Quantitative relations between dietary fat and

cholesterol and plasma lipid concentrations have been the subject

of much study and some controversy during the past 40 y. Previous

meta-analyses have focused on the most tightly controlled,

highest-quality experiments. To test whether the findings of these

investigations are generalizable to broader experimental settings

and to the design of practical dietary education interventions, data

from 224 published studies on 8143 subjects in 366 independent

groups including 878 diet-blood lipid comparisons were subjected

to weighted multiple-regression analysis. Inclusion criteria speci-

fled intervention studies published in English between 1966 and

1994 reporting quantitative data on changes in dietary cholesterol

and fat and corresponding changes in serum cholesterol, triacyl-

glycerol, and lipoprotein cholesterol concentrations. Regression

models are reported for serum total cholesterol, triacylglycerol,

and low-density- high-density-, and very-low-density-lipoprotein

cholesterol, with multiple correlations of 0.74, 0.65, 0.41, 0.14,

and 0.34, respectively. Interactions of dietary factors, initial di-

etary intakes and serum concentrations, and study and subject

characteristics had little effect on these models. Predictions mdi-cated that compliance with current dietary recommendations (30%

of energy from fat, < 10% from saturated fat, and < 300 mg

cholesterolld) will reduce plasma total and low-density-lipopro-

tein-cholesterol concentrations by �5% compared with amounts

associated with the average American diet. Am J C/in Nutr

l997;65: 1747-64.

KEY WORDS Meta-analysis, plasma lipids, low-density

lipoprotein, LDL, high-density lipoprotein, HDL, dietary fat

energy, saturation of dietary fat, dietary cholesterol, human

nutrition

INTRODUCTION

The prediction of blood lipid responses to multiple-compo-

nent dietary changes have been investigated extensively by

Keys et al (1-3) and Hegsted et al (4, 5). Although their early

prediction equations were based on narrow sets of data inclu-

sion criteria, these equations have formed, in part, the founda-

tion for the current National Cholesterol Education Program

(NCEP) dietary guidelines for the prevention of coronary artery

disease (6). More recent comparisons of these predictive mod-

els by their authors, however, have resulted in an ongoing

controversy regarding the extent of serum cholesterol response

to dietary cholesterol (7, 8). Although the effects of saturated

fatty acids (SFAs) and polyunsaturated fatty acids (PUFAs) are

similar in the two equations, the Hegsted model predicts a

substantially greater effect of dietary cholesterol on serum

cholesterol than does that of Keys. This difference was cx-

tended recently in Hegsted’s latest overview of the effects of

dietary change, which considered not only changes in serum

total cholesterol but also changes in concentrations of plasma

low-density-lipoprotein (LDL) and high-density-lipoprotein

(HDL) cholesterol (5).

In addition to these efforts, Hopkins (9), Mensink and Katan

(10), and McNamara (I 1) reported prediction equations gener-

ated from meta-analytic investigations that also had narrow

inclusion criteria. Hopkins combined baseline dietary choles-

terol with added dietary cholesterol as the two predictors of

change in total serum cholesterol. Mensink and Katan incor-

porated the effects of changes in carbohydrate and fatty acid

(but not dietary cholesterol) intakes on serum total cholesterol,

LDL cholesterol, and HDL cholesterol. McNamara pooled data

summarizing the effects of changes in dietary cholesterol on

serum total cholesterol concentrations.

None of these previously reported quantitative reviews in-

cluded sensitivity analyses to assess the potential effects of

numerous study and subject characteristics on lipid responses

to dietary change. This investigation assessed these potential

effects with a comprehensive database and rigorous meta-

analytic procedures. Our objectives were to determine whether

different study and subject conditions significantly influenced

reported responses to diet manipulation and to explore similar-

ities and differences in published prediction models. A second-

ary aim was to use a wide range of data sets to develop models

to predict the effects of dietary change on concentrations of

very-low-density-lipoprotein (VLDL) cholesterol and triacyl-

glycerol in addition to serum total cholesterol, LDL choles-

terol, and HDL cholesterol. Other published reports have not

included models to predict VLDL cholesterol and triacylglyc-

erol responses.

C From the Department of Nutritional Sciences. The University of An-

zona, Tucson, and the Biomedical Information Communication Center,

Oregon Health Sciences Center, University of Oregon. Portland.

2 Supported by a grant-in-aid from the American Egg Board adminis-tered through the Egg Nutrition Center and by funds from The University

of Arizona Agricultural Experiment Station.3 Address reprint requests to WH Howell, Department of Nutritional

Sciences, The University of Arizona, P0 Box 210038, Tucson, AZ 8572 1-

0038. E-mail: [email protected].

Received March 21, 1996.

Accepted for publication December 17, 1996.

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ic.oup.com/ajcn/article/65/6/1747/4655489 by guest on 02 February 2022

1748 HOWELL ET AL

The rather narrow scope of all previously reported quantita-

tive reviews indicated the need for a comprehensive, system-

atic approach to integrating the diverse scientific literature.

This meta-analytic investigation was designed to determine the

extent to which study and subject characteristics, initial serum

lipid concentrations, interactions of dietary manipulations, and

the duration of treatment influenced the predictive models. Our

intent was to develop a more broadly applicable model, span-

ning a diversity of study designs and types of subjects, to

predict more appropriately the extent to which meeting the

NCEP Steps 1 and 2 national dietary guidelines could be

expected to affect changes in blood lipid concentrations of the

American population.

METHODS

Study retrieval and selection

The data set consisted of published studies meeting the

following inclusion criteria: 1) dietary interventions on adults

(> 18 y of age) published in English between January 1966

and February 1994; 2) studies reporting single-group or

multiple-group repeated-measures comparisons; 3) studies re-

porting quantitative measures of manipulated dietary compo-

nents including one or more of the following: cholesterol, total

fat (% of energy), SFAs, PUFAs, and monounsaturated fatty

acids (MUFAs) (% of energy); 4) studies reporting group

means ± SDs or SEMs for quantitative measures of response

variables including any or all of the following: serum total

cholesterol, LDL cholesterol, HDL cholesterol, VLDL choles-

terol, and serum triacylglycerol.

Computer-assisted literature searches were undertaken by

using MEDLINE (National Library of Medicine, Bethesda,

MD) and the new OVID 3.0 system (National Library of

Medicine) to locate potentially appropriate studies. The key-

words diet, dietary cho/estero/, dietaryfat, andfatty acid were

intersected with the words serum (p/asma) cho/estero/, serum

(plasma) lipoproteins, LDL, HDL, VLDL, triacv/g/vcero/, hu-

man, and English. To locate candidate studies not included in

the computer-assisted literature searches, manual techniques

were used, including a reference scan of primary studies and

published narrative reviews and a search of indexes of review

publications such as Nutrition Abstracts and Reviews, Nutrition

Reviews, and World Review of Nutrition and Dietetics.

These search procedures resulted in 1 2 520 citations. A scan

of their titles led to the elimination of all but 896 (7%) of them.

Abstracts of the remaining citations (and, if necessary, the

papers themselves) were examined for compliance with the

meta-analytic inclusion criteria listed earlier. Discarded cita-

tions included letters to editors, reviews of related literature,

reports of epidemiologic studies, reports lacking sufficient de-

tail on dietary manipulation, reports of studies using nonex-

perimental (“one-shot”) single- or comparison-group designs,

and reports of other descriptive, nonintervention studies. Large

clinical trials involving multiple interventions were not in-

cluded. In addition, studies reporting data on weight-reduction

diets, fish oils, trans fatty acids, and hydrogenated fats were

excluded. Of the 896 articles passing the title scan, 224 (26%)

met the inclusion criteria (12-235).

Data extraction

One of the objectives of this investigation was to consider

the extent to which subject and study design characteristics

may influence the effect of dietary manipulations on blood

lipid concentrations. To address this objective, a coding instru-

ment was designed to facilitate the collection of data relevant

to study and subject characteristics. Both substantive and meth-

odologic issues were incorporated into the coding form. Sub-

ject group characteristics included location during dietary in-

tervention, sample size, sex, age, recruitment procedure, and

inclusion and exclusion criteria.

Design issues were type of design, group assignment proce-

dure, blinding, attrition, and control of selected confounding

variables. Location of subjects was classified as free-living,

confined to a hospital or metabolic ward, or free-living with

institutional meals provided. Internal validity was assessed as

high, medium, or low. To be rated as having high internal

validity, studies had to use a control group or random assign-

ment of subjects to groups or use repeated-measures designs

with multiple observations and treatments and control for con-

founding variables. Medium ratings were assigned to studies

that used matching or pretreatment equation procedures or

repeated measures at a limited number of time points with

limited control for confounding variables. Studies judged as

having low internal validity used cross-sectional single group,

pre- and postintervention designs with limited control for con-

founding variables.

Treatment characteristics included type of dietary manipula-

tion, quantitative levels of dietary components, duration of

treatment, compliance with treatment, and method of assessing

dietary compliance. Two items were added to the coding form

after initial pilot testing: the method used to provide dietary

treatment and indication that multiple posttreatment measures

were taken and averaged for each reported time point. Before

the study data were coded, a list of coding instructions was

developed and independent coders were trained with periodic

tests to maximize intercoder agreement. The average agree-

ment was 94%.

Data organization

The data extracted during this protocol were organized into

three databases. The study-level database included the authors’

credentials and affiliations, design type, and internal validity.

Each study involved one or more groups of subjects. A second

database contained 366 records storing characteristics of those

individual groups, such as size (n), percentage of male subjects,

mean age, attrition rates, and subject compliance. The third, or

observation-level, database included quantitative blood lipid

data recorded for a group at a particular time as well as

quantitative measures of the subjects’ diet in the preceding

period and the duration of that treatment.

To identify appropriate comparisons for each group, a sche-

matic diagram of each study design was drawn. For groups that

received a single dietary manipulation, each subsequent obser-

vation was compared with the initial (baseline) observation. In

crossover designs, the last observation before a new diet reg-

imen began became the new baseline for subsequent observa-

(ions until the next dietary change. In either case, n observa-

(ions yielded n - 1 comparisons. On the basis of these

definitions, a final comparison-level database was constructed

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When a person’s diet is changed, the effect of that change

would not be immediate but would be expected to emerge over

time. One factor recorded for each comparison in these studieswas the time that each treatment had been in effect when the

serum lipid data were obtained, permitting exploration of the

rates at which diet-serum lipid equilibria are reestablished.

Although most of these studies were designed to allow suffi-cient time for the effects of dietary manipulations to express

themselves, there is enough variation in treatment durations

(ranging from 1 d to 6 y) to warrant exploration of this issue.

This was done by modifying a linear prediction model, such as

{z�Y = B - z�.X), into a nonlinear one of the form {L�Y = BL

(1 _1t�) � where t is treatment duration and k specifies the

rate at which the influence of AX on �Y exponentially ap-

proaches a limit, BL, from an expected value of 0 at t = 0. The

half-life of the process is given by ln(2)/k. Nonlinear least-

squares estimates of k significantly different from 0 were taken

as indicative of a discernible treatment duration effect.

Study and subject characteristics

Data from 224 published studies of 8143 subjects in 366

independent groups including 878 diet-blood lipid comparisons

were presented for the weighted least-squares-regression anal-yses. An average of 70% of the subjects in the independent

groups were men. The combined sex group mean age was 37 y,

ranging from 18 to 69 y.

Coding of several other characteristics of the subjects andstudies allowed for further descriptive analysis of the database.

The descriptive data for some of the coded characteristics are

provided in Table 1. Most of the data represent healthy, free-living individuals with reasonably good compliance with di-

etary intervention.

that included 878 records, each containing an initial and a final

set of dietary and serum measures to be compared.

Weighting of comparisons

The study groups differed considerably in the number ofsubjects and in the number of times that each group was

observed. Larger groups provide more statistically reliableinformation, but multiple observations of a single group are

clearly not independent. To allow proportionally greater influ-

ence from larger studies and to control for nonindependence of

data, study groups were weighted proportionally to their size

and inversely to the number of times observed. The final

weighting used for each comparison can be described by usingthe following formula:

n 1G�-� (1)

where G is the total number of groups in the meta-analysis, n1

is the group size, and C, is the number of comparisons for each

group.

Data analysis

For each comparison, differences between the final and

initial values of dietary cholesterol (mg/d) and total fat, PUFA,

MUFA, and SFA (% of energy) were computed to create thedietary change variables �cholesterol, Mat, L�PUFA, �MUFA,and �SFA, respectively. Corresponding differences for serumtotal cholesterol, VLDL cholesterol, LDL cholesterol, HDL

cholesterol, and triacylglycerol were derived to create the se-

rum lipid response variables z�serum total cholesterol, L�VLDL,L�LDL, I�HDL, and �triacylglycerol.

Bivariate Pearson correlations were used to explore the re-

lations among the dietary variables and between these variablesand the response variables. Stepwise-multiple-regression anal-

ysis was used to identify the best linear prediction equations for

each of the response measures, evaluating the combined andindependent contributions of specified dietary variables. Each

of the dependent serum variables (i�serum total cholesterol,LWLDL, Z�LDL, �HDL, and �triacylglycerol) in turn was

regressed on the dietary change variables (�cholesterol, Mat,�PUFA, �MUFA, and �SFA) by using all the comparisonshaving complete data for all variables for that regression.

Forward stepwise variable selection allowed exploration of all

data relations and controlled for problems of linear dependence

(eg, Mat = �PUFA + �MUFA + z�SFA). That is, the step-

wise addition stops before all four variables enter, although any

three of them could. Each model identified by the stepwise

procedure was then reestimated, including only the selectedpredictor variables; this made maximum use of the availabledata. SEs of the unstandardized regression coefficients wereused to compute CIs.

Effects of other factors (interactions among dietary compo-

nents, initial values of serum lipid and dietary variables, studyand subject traits, etc) on the basic models were evaluated

by constructing appropriate interaction terms, such as�cholesterol - Mat (a dietary interaction), serum total

cholesterol1 . z�cholesterol (an interaction with an initial serumconcentration), and age - �cholesterol (a subject-effect interac-tion), and reestimating the models with these interaction termsavailable as predictors.

Percentage of studies

%

81

19

61

14

25

67

23

10

54

106

30

49

30

3

18

BLOOD LIPID RESPONSES TO DIET I 749

‘ n = 224 studies.

RESULTS

TABLE 1Characteristics of studies and subjects’

Subject selection criteria

Healthy

Coronary artery disease, or at risk

Subject locationFree-living

Free-living with institutional meals provided

Confined to a hospital or metabolic wardMethod of diet intervention

Total diet providedManipulated diet component providedInstructions to modify

Subject compliance with diet

GoodFair

Poor

No assessment

Method used to assess complianceWeighed or measured intake

Subject-reported intake records

Subject recallMethod not reported

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1750 HOWELL ET AL

All experiments used some type of repeated-measures study

design: 38% used single-group designs with two or more

treatments, 22% used a crossover treatment design with two or

more groups, 14% used multiple groups with one or more

treatments, and 26% used a single-group, pre- and posttreat-ment design. Of the experiments with multiple-group designs,

65% randomly assigned subjects to groups, 21% used match-ing, and 14% used a pretreatment equation. Only 10% ofstudies were blinded: 7% double- and 3% single-blinded. Con-

trol for changes in body weight of subjects during treatmentphases was attempted by adjusting energy intake during 83% of

the investigations. Subjects were requested to maintain their

usual physical activity during participation in 35% of theexperiments.

Judgments regarding the level of internal validity of studies

were made according to the predetermined criteria outlined

earlier. Only 9% of these studies were rated as having highinternal validity, 49% had medium internal validity, and the

remaining 42% had low internal validity.

Bivariate relations between variables

Correlations between changes in dietary variables are pre-

sented in Table 2. All relations, except that between Mat andL�PUFA and that between L�PUFA and �MUFA, were signif-icantly different from zero. Correlations between Mat and

�MUFA (r = 0.57) and I�SFA (r 0.63) were moderatelyhigh, which is to be expected because PUFA, MUFA, and SFA

are constituents of fat. The level of correlation between these

independent variables was sufficient to require multiple regres-

sions to isolate their independent effects. However, none of the

correlations between dietary variables were large enough to

induce multicollinearity problems.

The correlation matrix in Table 3 shows significant relations

between changes in all dietary variables and correspondingchanges in serum total cholesterol, LDL cholesterol, and HDL

cholesterol. When the strengths of these correlations for each

TABLE 2Correlations between changes in dietary variables’

z�Fat �XPUFA L�MUFA �SFA

�Cholestenol (mg/d)

r�

F’

0.353

300

-0.211

185

0.306

156

0.336

206

P < 0.0005 0.004 < 0.0005 < 0.0005

�Fat (% of energy)4r �. 106 0.568 0.634

F’

222 194 243

P 0.1 16 < 0.0005 < 0.0005

�PUFA (% of energy)

r 0.051 -0.438

0 192 216

P 0.481 < 0.0005

�MUFA (% of energy)r 0.150

n

191

P 0.038

I PUFA, polyunsaturated fatty acids; MUFA, monounsaturated fatty

acids; SFA, saturated fatty acids.

2 Pearson correlation coefficient.

3 Number of independent groups.

4 % of energy from total fat.

response variable were compared, it appeared that the change

in serum total cholesterol was most strongly associated with the

change in SFA (r = 0.80) and PUFA (r -0.62). Changes in

both LDL cholesterol and HDL cholesterol were most highly

associated with the change in SFA (r = 0.79 and r = 0.60,

respectively), but the strength of the association between

changes in LDL and changes in PUFA was greater than that

between changes in HDL and PUFA (r = -0.48 and r =

-0.24, respectively). Changes in both LDL and HDL were

moderately correlated with changes in total dietary fat (r =

0.52 and r = 0.58, respectively). The only significant relation

between changes in VLDL cholesterol or plasma triacylglyc-erol concentrations and changes in dietary variables was for

PUFA (r = -0.37 and r -0.40, respectively).

Bivariate regression coefficients corresponding to the signif-icant correlations in Table 3 are presented in Table 4. Because

of the significant and often substantial correlations between thedietary variables, these bivariate regressions only serve as a

basis for detailed analysis via multiple regression to isolate the

independent effects of dietary manipulations.

Prediction equations

The multiple-regression analyses for this data set are com-plicated by a substantial amount of missing data, as can be seen

in the variations among the n values reported in Tables 2 and

3. We first estimated stepwise-multiple-regression models for

each of the serum lipid response variables on all the dietarychange variables with listwise deletion. The models were then

reestimated by using only the predictors selected in the firstpass to make maximum use of available data. The results of

these analyses are presented in Table 5.As indicated in Table 5, the best-fitting model for change in

serum total cholesterol included changes in SFA (% of totalenergy), PUFA (% of total energy), and dietary cholesterol(mg). This prediction model explained 74% of the variance inchange in serum total cholesterol. These relations indicate that

a 1% alteration in total energy from SFA will result in a

49.l-p.molIL (l.9-mg/dL) change in serum total cholesterol.Likewise, a change in PUFA of 1% of total energy will produce

a 23.3-prnol/L (0.90-mg/dL) change (in the opposite direction)

in serum total cholesterol. Finally, a change of 1 mg/d indietary cholesterol will produce a change of 0.57 �.tmol/L

(0.022 mg/dL) in serum total cholesterol. The full linear pre-

diction equation can be expressed as follows:

�Serum total cholesterol (.tmoLIL) = 49.599 . I�SFA

- 23.274 - �PUFA + 0.5741 - z�cholesterol (2)

z�Serum total cholesterol (mg/dL) = 1 .9 1 8 - �SFA

- 0.900 - L�PUFA + 0.0222 - &holesterol (3)

Note that even though all five dietary factors were significantly

related to z�serum total cholesterol at the bivariate level (Tables3 and 4), neither Mat nor L�MUFA added significant predictive

power to this model.

Although the bivariate regression of LWLDL on �PUFA was

significant, no other dietary factors entered the �VLDL pre-

diction model in our multivariate analysis. This predictionequation accounted for only 14% of the variance in plasmaVLDL.

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BLOOD LIPID RESPONSES TO DIET I751

TABLE 3Correlations between changes in dietary variables and corresponding changes in blood lipid response variables’

�Serum total cholesterol �VLDL �LDL �HDL �Triacylglycerol

(�tmoWL) (�.tmol/L) (�tmol/L) (�tmol/L) (�.tmolfL)

�Cholesterol (mg/d)�2 0.502 0.050 0.463 0.358 -0.013

n3 307 61 185 225 218

P < 0.0005 0.704 < 0.0005 < 0.0005 0.852

i�Fat (% of energy)4

r 0.423 -0.201 0.523 0.579 -0.091

n 345 65 194 237 233

P < 0.0005 0.109 < 0.0005 < 0.0005 0.166

�PUFA (% of energy)

r -0.624 -0.371 -0.479 -0.237 -0.401

n 221 39 117 146 135

P < 0.0005 0.020 < 0.0005 0.004 < 0.0005

�MUFA (% of energy)

r 0.177 -0.060 0.120 0.251 -0.116

n 191 32 107 127 123

P 0.014 0.747 0.219 0.004 0.203

�SFA (% of energy)

r 0.803 0.051 0.790 0.604 -0.020

n 244 40 129 169 155

P < 0.0005 0.755 < 0.0005 < 0.0005 0.807

‘ PUFA, polyunsaturated fatty acids; MUFA, monounsaturated fatty acids; SFA, saturated fatty acids.2 Pearson correlation coefficient.

3 Number of independent groups.

4 % of energy from total fat.

Changes in �SFA and L�PUFA were the best predictors of �LDL (�molIL) = 46.755 - �SFA - 12.801 - �PUFA (4)change in LDL cholesterol and explained a combined total of65% of its variance. The unstandardized regression coefficients L�LDL (mg/dL) = 1.808 - �SFA - 0.495 - L�PUFA (5)

indicated that for every 1% change in SFA (% of total energy),

a change in LDL cholesterol of 46.5 mmollL (1.8 mg/dL) was Again, not all the significant bivariate predictors of �LDLpredicted and an increase of 1% in PUFA (% of total energy) entered the equation. In particular, �cholesterol was almostwas expected to decrease LDL cholesterol by 12.93 mmolIL significant enough to enter (P 0.067). This suggests that

(0.50 mg/dL). The combined linear prediction equation for even though L�cholesterol had a significant bivariate relation tochange in LDL cholesterol is as follows: L�LDL, it was its joint relation with �SFA and �PUFA that

TABLE 4Bivaniate regressions of changes in blood lipid response variables to changes in dietary variables’

�Serum total cholesterol �VLDL L�LDL �HDL z�Triacylglycerol

(�mol/L) (�tmol/L) (Mmol/L) (�tmol/L) (�imol/L)

�Cholesterol (mg/d)�2 0.7318 0.0548 0.1267

r23 0.252 0.214 0.128

i�Fat (% of energy)/3 29.170 26.998 8.586�2 0.179 0.274 0.336

�PUFA (% of energy);3 -61.831 -7.034 -41.531 -5.715 -13.232

r2 0.389 0.137 0.230 0.056 0.161

�MUFA (% of energy)

f3 21.748 6.284�2 0.03 1 0.063

�SFA (% of energy)

/3 70.210 51.875 11.740

r2 0.645 0.623 0.365‘ PUFA, polyunsaturated fatty acids; MUFA, monounsaturated fatty acids; SFA, saturated fatty acids.

2 Unstandardized regression coefficient.

3 Coefficient of determination. Insignificant regressions not shown.

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I 752 HOWELL ET AL

- 8.422 - Mat + 0.892 - L�PUFA - Mat (10)

TABLESPrediction models for changes in serum total and lipoprotein cholesterol and triacylglycerol’

Serum lipid (�molIL) and

dietary factorB SE 95% CI P

�Serum total cholesterol

(Ft 177. R2 0.736)

�SFA 49.599 3.646 42.824, 56.892 < 0.00005

�PUFA -23.274 4.215 -31.704, - 14.844 < 0.00005

�Cholestenol 0.5741 0.0957 0.3776, 0.7603 < 0.00005

�LDL (r, I 15, R2 0.649)

�SFA 46.755 4.034 38.687, 54.823 < 0.00005

�PUFA -12.801 5.405 -23.610, -1.991 0.0197

�HDL (n 167, R2 0.410)

�SFA 7.422 1.681 4.060, 10.784 < 0.00005

�Fat 4.965 1.396 2.172,7.758 0.0004�Triacylglycerol, (n 124, R2 0.337)

�PUFA - 12.035 2.540 - 17.1 16, -6.955 < 0.00005

�Fat - 10.376 1.976 - 14.327, -6.424 < 0.00005

�Cholesterol 0.1626 0.0756 0.01 13, 0.3139 0.0340

I ,,, number of independent study groups: R2, percentage of response variance accounted for; B, unstandardized regression coefficient; SE, standard error

of B: 95% CI. confidence interval of B: P. level of significance testing the hypothesis that B = 0; SFA, saturated fatty acids; PUFA, polyunsaturated fatty

acids.

affected L�LDL rather than any independent effect it might havehad.

The best-fitting model for change in HDL cholesterol in-

cluded changes in SFA and total fat. The predictive model for

HDL cholesterol explained 41 % of the variance associated with

dietary change. The regression coefficients indicated that for

every 1% change in total energy from SFA, a change in HDLcholesterol of 7.4 �mol/L (0.3 mg/dL) was predicted. A 1%

change in energy from total fat will produce a 5.0-p.mol/L(0.2-mg/dL) change in HDL cholesterol. The combined linearprediction model for change in HDL cholesterol was asfollows:

L�HDL (�tmolIL) = 7.422 . L�SFA + 4.965 - Mat (6)

L�HDL (mg/dL) 0.287 - L�SFA + 0. 192 - Mat (7)

The prediction model for triacylglycerol presented in Table

5 accounts for 36% of the variance. The full equation is as

follows:

�Triacylglycerol (�.tmolIL) = 0. 1626 - &holesterol

- 12.035 - �PUFA - 10.376 - Mat (8)

�Triacylglycerol (mg/dL) = 0.0144 - �cholesterol

- 1 .066 - �PUFA - 0.9 19 . �fat (9)

In this model, Mat enters with a negative coefficient. That is,a decrease in total fat in the diet will lead to an increase intriacylglycerol, other things being equal. This seemingly anom-

alous finding may be due to the substitution of simple carbo-hydrate energy for fat energy in isoenergetic diets.

Observed compared with expected changes for individualcomparisons from each of the four prediction models are

shown in Figure 1. The relative degrees of scatter reflect

variations in R2 among the four models. No outliers of undue

influence are apparent. Note that in each case there are many

comparisons in which the predicted and observed values differ

in sign.

Effects of other factors

After these prediction models were identified, additionalregressions (sensitivity analyses) were undertaken to explore

the effects of other factors, such as dietary interactions, initial

dietary and plasma lipid values, subject and study characteris-tics, and treatment duration. These analyses do not assess the

independent effects of these factors but rather their influence

on the effects of dietary changes on serum lipid responses.The first factors considered were interactions among the

dietary change variables. The models described earlier as-

sumed that the effects of dietary changes are additive, ie, that

if more than one dietary component is changed at once, their

combined effect will be the simple sum of their independenteffects as specified in the model. This need not be the case

because simultaneous changes in multiple dietary componentscould potentiate or inhibit each other’s effects. To explorethese issues, product terms were computed for each pair ofdietary variables included in each model. For example, for

�serum total cholesterol they were z�cholesterol - �PUFA,z�cholesterol - L�SFA, and �PUFA - �SFA. The model equa-

tions were then reestimated with these new product termsavailable for stepwise inclusion.

Only one dietary interaction term entered any of the four

equations. The modified model for z�triacylglycerol became

�Ttiacylglycerol (�tmo1IL)

= 0. 1400 ‘ &holesterol - 9.698 - L�PUFA

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Serum total cholesterol LDL cholesterol

1500

1000

500

‘05)

5) 0Cl)

.00

-500

-1000

HDL cholesterol Triacyiglycerol

_________ 1500

0

Expected

2000

1000

0

.1000

-2000

50S

401

300

200

100

0

.100

.500

-1000 -500 0 500 1000 1500

Expected

‘04’

a)t12

.00

.200

.300

4’vi

‘a0

1000

500

0

-500

-1000

�0.

.500 .400 .300 .200 .100 I 100 200

Expected

300 400 500

Expected

+ (-8.422 + 0.892 . �PUFA) - Mat (12)


‘05)

5)Cl)

‘a0

9

-H.9

j%

�;

-1�o -500 500 1000 1500

FIGURE 1. Observed compared with expected values of changes in serum total cholesterol (R2 = 0.736), LDL cholesterol (R2 = 0.649. HDL

cholesterol (R2 - 0.410), and triacylglycerol (R2 = 0.337). Individual comparisons are plotted, up to 16 per independent group.

�Triacylglycerol (mg/dL)

= 0.0124 - �cholesterol - 0.859 - �PUFA

- 0.746 . �fat + 0.079 - L�PUFA - Mat (11)

This modification increased the R2 for the model by 5%. from0.34 to 0.39. Interpretation of this model can be facilitated by

factoring the model equation to give

�Triacylglycerol (�tmolIL)

= 0. 1400 - &holesterol - 9.698 - L�PUFA

�Ttiacylglycerol (mg/dL)

= 0.0124 - �cholesterol - 0.859 - �PUFA

+ (-0.746 + 0.079 - �PUFA) . �fat (13)

Here, the effect of Mat on changes in triacylglycerol concen-

trations is seen as a linear function of �PUFA. A decrease in

total fat energy will increase triacylglycerol only if �PUFA is

< 9.4% (8.422/0.892) of total energy. For positive changes in

�PUFA > 9.4% of total energy, a decrease in fat will result in

a reduction in triacylglycerol.

Next, effects of initial dietary values were considered, al-

lowing for the possibility that, for example, the effect of

Dow



= 48.539 - L�SFA 31.549 - �PUFA

+ 0.5456 - &holesterol + 0.879 - PUFA1 - LWUFA (14)

�Serum total cholesterol (mg/dL)

= 1.877 �SFA - l.220-z�PUFA + 0.0211

. z�cholesterol + 0.034 - PUFA� - Z�PUFA (15)

This factors to

�XSerum total cholesterol (p�molIL)

= 48.539 - �SFA + (-31.549 + 0.879 - PUFA1)

. �PUFA + 0.5456 ‘ &holesterol (16)

z�Serum total cholesterol (mg/dL)

= 1.877 - L�SFA + (- 1.220 + 0.034 - PUFA1)

I 754 HOWELL ET AL

changes in dietary cholesterol might depend on the amount of

dietary cholesterol at the beginning of treatment. Product vari-

ables of the form cholesterol, ‘ &holesterol were constructedand the models were reestimated. Again, only one such termentered any of the regressions, yielding

z�Serum total cholesterol (�mol/L)

- �PUFA + 0.021 1 - �choIesterol (17)

The effect of L�PUFA here is a linear function of the initial

amount of PUFA in the diet. Increasing L�PUFA will reduce

serum cholesterol only if PUFA� is < 36% of total energy

(31.549/0.879); the lower the initial amount of PUFA in

the diet, the greater the effect of a given increase in it. Forinitial concentrations > 36% of energy, increasing �PUFA

will increase serum cholesterol concentrations. The multiple

R2 increases by 0.01 to 0.76. It is questionable whether

any of these modified regression equations improve the

predictive accuracy relative to the increase in model

complexity.Given that the effects of dietary changes on serum lipid

concentrations might differ if the subjects already had unusu-

ally high or low serum lipid amounts, interactions of initial

lipid concentrations with dietary changes were considered.

Two of these entered the models: HDL1 - Mat for Z�HDL,which increased the R2 by 0.03 to 0.44, and triacyl-

glycerol1 - �PUFA for �triacylglycerol, which increased the R2

by 0.04 to 0.38.

Next, effects of subject mean age and percentage of male

subjects were considered. The only significant interaction to

enter any of the models was %male - Mat for �triacylglycerol,

increasing the R2 by 0.02 to 0.36.

Design characteristics, such as assessed internal validity, the

location of subjects, the use of eggs or liquid diets, and the

average of multiple observations were considered next. Only

the location of subjects had significant interactions with the

dietary change variables. A modified equation for &erum totalcholesterol was identified:

z�Serum total cholesterol (�tmol/L)

= 49.134 - �SFA - 28.317 - �PUFA + 0.5586

&holesterol + 28.653 - Conf- LWUFA (18)

z�Serum total cholesterol (mg/dL)

= l.900�SFA- l.095-�PUFA+0.0216

. �cholesterol + I . 108 - Conf . L�PUFA (19)

where Conf is a dummy (0/1) variable identifying groups ofsubjects confined in hospitals or metabolic wards. This mdi-

cated that the effect of L�PUFA for confined subjects wassignificantly less than that for free-living subjects. The coeffi-

cients for confined, free-living, and free-living but institution-

ally fed subjects are plotted in Figure 2. Note that the plotted

limits of the CI for confined subjects does not overlap that forfree-living subjects.

Finally, treatment duration effects were considered. These

were treated differently, modifying the model equations to

include exponential terms in treatment duration, t, and by usingnonlinear regression to estimate the effects. Only one of these

gave a significant result: �SFA for i�serum total cholesterol.

The resultant equation was as follows:

�Serum total cholesterol (�.tmol/L)

= 51.849 . [1 - exp(-4.888 - t)] . �SFA - 27.256

. L�PUFA + 0.57 1 5 - &holesterol (20)

�Serum total cholesterol (mg/dL)

= 2.005 - [1 -exp(-0.l89 . t)] - �SFA - 1.054

L�PUFA + 0.0221 - &holesterol (21)

The corresponding half-life is 3.7 d, indicating 90% of

maximal effect in =12 d. This effect was barely significantat P = 0.05, with a t ratio of 1.97 and an increase of < 0.01in explained variance. The finding that treatment duration

effects were not readily discernible in this data set was notsurprising because most studies were designed to allow

sufficient time for the effects of dietary manipulations to

establish themselves.

DISCUSSION

Comparison of prediction models

Over the years, several researchers have published modelsfor predicting changes in serum lipids, particularly plasma totalcholesterol, from changes in dietary lipid composition. Boththe diversity and consistency of these models is remarkable.Several prediction models are given in Table 6. The first six

equations listed in Table 6 are multivariate models predicting a

change in serum total cholesterol (i�serum total cholesterol)

from changes in dietary fat and cholesterol. All models include

terms for L�SFA and �PUFA and their coefficients are quite

similar. Four of the models also include linear terms for �cho-

lesterol, which are somewhat more variable. These coefficients

are plotted in Figure 3. Particularly for L�SFA and L�PUFA,

Dow



Saturated fat

01a

.�

a0).�E

4)a

a0)

‘#{248}E

0)a

I

a0)

-CE

-r-

110

100

90

80

70

�: �

ConfIned Inst meals Free All

Location of subjects

Polyunsaturated fat

50

40

30

20

10

1.4

1.2

1.0

0.8

0.6

0.4

0.2

BLOOD LIPID RESPONSES TO DIET 1755

Confined Inst meals Free


Dietary cholesterol

. Confined Inst meals Free


FIGURE 2. Regression coefficients of �saturated fatty acids, �poly-

unsaturated fatty acids, and &holesterol for studies of subjects free-living(free), confined to a hospital or metabolic ward (confined), and free-living

with institutional meals provided (Inst meals). 95% CIs are calculated as

± 1.96 X SEs where possible.

these plots display the similarity among the coefficients in the

various models, with all falling within the range of the over-

-C-. lapping CIs. In contrast, Hegsted et al’s 1965 data (4) yield a

comparatively high coefficient for dietary cholesterol, whereasthe cholesterol coefficient estimated for the present data setfalls between those estimated in Hegsted’s 93M (metabolic

ward) and 93F (free-living) models. The latter observation was

expected because this meta-analysis combined data from both

settings.

Because cholesterol and dietary fats are measured in dif-

ferent units, it is difficult to assess their relative effects.However, there is a useful test case available that permits adirect comparison. Data from the second National Health

and Nutrition Examination Survey (NHANES II) show thatthe average American diet provides 385 mg cholesterol/d

and 37% of total energy from fat (236). The energy from fatwas distributed as 7% from PUFA, 17% from MUFA, and

13% from SFA. The NCEP Step I diet recommends reduc-

ing both cholesterol and total fat intakes, with a redistribu-

tion of types of fat to provide 300 mg cholesterol/d and 30%

of total energy from fat (10% from PUFA, 10% fromMUFA, and 10% from SFA). The Step 2 diet further reduces

cholesterol and SFA to provide 200 mg cholesterol/d and

30% of total energy from fat (10% from PUFA, 13% from

MUFA, and 7% from SFA). NCEP Step I and 2 diets

provide a standard set of dietary changes that can be used to

evaluate these multivariate models. Given specific changes

in these dietary components, the change in serum total

cholesterol each model predicts and how much of that

change is attributable to each dietary component can be

determined.

Predicted changes in serum cholesterol based on each of

.�.1... these multivariate models, resulting from a change from theAll average American Diet to the NCEP Steps 1 and 2 diets, along

with the proportions of the predicted responses that are a result

of each of the dietary factors are presented in Figure 4. [Note

that the Keys-57 (3) model included no cholesterol term.JThe last four models in Table 6 were all derived considering

changes in dietary cholesterol only, either by ignoring or con-

�1�� trolling dietary fat in the treatment designs. The multivariatemodels presented in the upper panel controlled for dietary fat

statistically. All models in Table 6 (except for Keys-57) gen-

erate predictions of the serum total cholesterol response tomanipulations of dietary cholesterol. These equations are evenmore difficult to compare directly because several different

functional forms are used. Plots of these models’ predictedreductions in serum total cholesterol for a range of reductions

in dietary cholesterol from an initial value of 385 mg/d are

shown in Figure 5. For a reduction of 100 mg/d, the predic-

tions of the reduction in serum total cholesterol range from35.43 p.mol/L (1 .37 mg/dL; Hop-92) to I 75.07 �molfL (6.77

:�:ii- mg/dL; Heg-65). Assuming an initial serum cholesterol of 6.21mmol/L (240 mg/dL), these predicted reductions range from

0.6% to 3.3%.On the basis of the predictive equation developed in this

analysis of the most comprehensive set of data, a shift from

the current diet to the Step 1 diet would reduce the average

plasma total cholesterol concentration by 263.8 p.mol/L(10.2 mg/dL) and a further reduction of 204.3 �tmol/L (7.9

mg/dL) with a shift from a Step 1 to a Step 2 diet. This

reduction of 465 �molfL (18 mg/dL) represents an 8.6%

Dow



1756 HOWELL ET AL

TABLE 6Prediction equations for diet-mediated changes in serum total cholesterol concentrations’

Source2 Mnemonic3 Equation4

Keys et al (3)2 Keys-57 70.86 . �SFA - 33.88 . �PUFA(4.65) (4.91)

Hegsted et al (4) Heg-65 55.86 . �SFA - 42.67 . �PUFA + I .7507 - z�cholestero1

(8.02) (9.31) (0.4008)

Keys and Panlin (2) Keys-66 67.2 - L�SFA - 33.6 - �PUFA + 24.57 - (cholesterol�’2 - cholesterol�2)

Hegsted et al (5)

Metabolic ward Heg-93M 54.3 1 - �SFA - 30.00 - �PUFA + 0.6930 - �cholesterol

(3.88) (3.88) (0.1086)

Free-living Heg-93F 71.12 . �SFA - 26.64 . L�PUFA + 0.4138 - �cholesterol

(7.50) (11.64) (0.1500)

Howell et al (1997) How-97 49.65 - L�SFA - 23.27 - �PUFA + 0.5741 � �cholesterol

(3.62) (4.14) (0.0957)

Hegsted (7)

Exponential Heg-86E 241 1.96 - 2406.79 . exp(-0.0l25l6 . z�cholesterol)

Linear Heg-86L 1 .009 ‘ �cholesterol

McNamara ( 1 1 ) McN-90 0.595 - �cholestenol

(0.052)

Hopkins (9) Hop-92 1220.6 - exp(-0.09930 - cholesterol,) - I 1 - exp(-0.035l7 . �cholesterol)]‘ SFA, saturated fatty acids; PUFA, polyunsaturated fatty acids.

2 When there were multiple models, the particular equation is identified.

3 Defines short-hand tags that will be used in subsequent tables and figures.

4 The coefficients in the equations have been rescaled for application to the measurement units used in this study (�molIL. for serum lipids, % of total

energy for dietary fats, and mg/d for dietary cholesterol). SEs for the coefficients are in parentheses.

decrease in the average plasma cholesterol concentration of

5.43 mmol/L (210 mg/dL); however, for those in the high-

risk classification with cholesterol concentrations > 6.21

mmol/L (240 mg/dL), the relative percentage reduction in

plasma cholesterol becomes less with higher initial (pre-

treatment) plasma cholesterol concentrations. It is this typeof predictive response that helps to explain observationssuch as those of Hunninghake et al (237): that initiation of

a Step 2 diet in a population had little effect on the averageplasma cholesterol concentrations of hypercholesterolemic

individuals. Although this study sample may have been

diet-insensitive (able to compensate for cholesterol intake

by reducing endogenous production of cholesterol), the rel-

atively reduced response of hypercholesterolemic subjects

can be predicted with the present model, which includes data

from both diet-sensitive and diet-insensitive individuals.

Plasma cholesterol response to dietary cholesterol

The extent of the plasma cholesterol response to changes

in dietary cholesterol has been debated for many years. Thereport of a 1988 National Institutes of Health conference on

the effect of dietary cholesterol on plasma cholesterol (238)

suggested that a 100-mg increase in dietary cholesterol per

4185 kJ/d increased plasma cholesterol by 259 �.tmolIL (10

mg/dL), which for a lO.5-MJ diet predicts an increase in

plasma cholesterol of 103 �mol/L (4 mg/dL) per lOO-mg/d

increase in cholesterol intake. In contrast, all prediction

equations published since 1990 presented in Table 6 and

Figure 5 suggest a rather narrow range of plasma cholesterol

reductions (35.43-69.30 .tmol/L, or 1.37-2.68 mg/dL) for a

lOO-mg/d reduction in dietary cholesterol. The predicted

plasma cholesterol response to a l00-mg/d decrease in di-

etary cholesterol based on the results of this meta-analysis is

a decrease of 56.9 �.tmol/L (2.2 mg/dL), or �l% in the

average population cholesterol concentration.

Plasma lipoprotein response to dietary change

Although the majority of prediction equations address

changes in plasma total cholesterol with changes in dietary fat

and cholesterol, changes in specific plasma lipoproteins are ofgreater importance given their relative atherogenic and anti-

atherogenic properties. Data from this meta-analysis indicatethat plasma LDL-cholesterol concentrations are determined by

�SFA and LWUFA, whereas plasma HDL-cholesterol concen-

trations are determined by �SFA and Mat. Comparable find-

ings have been reported from epidemiologic studies (239) and

the meta-analysis conducted by Mensink and Katan (10).

Knuiman et al (239) found that in free-living populations,exchanging carbohydrate for fat energy in the diet significantly

reduced plasma HDL-cholesterol concentrations by 10.3

.tmol/L (0.4 mg/dL) for every 1 % of energy exchanged (239).

More specifically, the analysis by Mensink and Katan (10)

indicated that all fatty acids elevated HDL cholesterol when

substituted for carbohydrates and that the effect diminished

with increasing unsaturation of the fatty acids (10). These

results are comparable with our finding that the two major

dietary determinants of changes in plasma HDL cholesterol

were total fatty acids and SFA.

Plasma lipid response to specific fatty acids

Few studies in this meta-analysis included data on the effectsof specific fatty acids on plasma total or lipoprotein cholesterol

Dow



Saturated fat

f

Keys-57 Hegs-65 Keys-66 Hegs.93M Hegs-93F How.97

Source study

Polyunsaturated fat

90

80

70

60

50

40

30

20

10

0

70

60

50

40

30

20

10

I)aI

a4)

‘C

4)aI

a4)

‘CEC.)

4)aI

a4)‘C

C.)

H � � . � . � . .Keys.57 Hegs-65 Keys-66 Hegs.93M Hegs.93F How-97

Source study

Dietary cholesterol

3.0 �

2.5

2.0

1.5

E� . � . � . � . � .

Keys-57 Hegs-65 Keys-66 Hegs.93M Hep.93F How-97

Source study

FIGURE 3. Regression coefficients of �saturated fatty acids. �poly-

unsaturated fatty acids, and �cholesterol in selected multivariate models.

95% CIs are calculated as ± 1.96 X SEs where possible. See Table 6 formnemonic designations. The model in Keys-66 (2) is a nonlinear model in

which there is no coefficient for �cholesterol.

BLOOD LIPID RESPONSES TO DIET I757

concentrations. There is strong evidence, however, that the

cholesterol-raising SFAs are lauric, myristic, and palmitic ac-

ids, whereas stearic acid is not hypercholesterolemic (240). The

hypercholesterolemic SFAs in mixed natural diets are fairly

constant at 60-70% of the total saturates (10), indicating that

specific effects are probably not a major concern. Although it

has been suggested that the hypocholesterolemic response to

dietary linoleic acid reaches a threshold at 5-6% of total energy

(241), this study found no evidence for a threshold response to

PUFA intake, indicating that the response factor is therefore

linear.

Implications for clinical management

There is little debate regarding the advisability of lower-

ing an elevated LDL-cholesterol concentration to reduce the

associated coronary artery disease risk. The efficacy of

population-wide dietary modifications to achieve this end is,

however, controversial. For patients in the high-risk range

for LDL cholesterol (> 4.14 mmol/L, or 160 mg/dL), data

from this meta-analysis indicate that 4.5% and 7.7% average

reductions in this amount would be predicted from modify-

ing current average intakes of SFAs and PUFAs to NCEP

Steps 1 and 2 recommendations, respectively. There is,

however, a large degree of individual variability in re-

sponses to changes in both dietary fat quality and choles-

terol. The existence of precise feedback mechanisms bal-

ancing the input of exogenous dietary cholesterol and

endogenous synthesis of cholesterol in the majority of

individuals (139) is a primary reason why a reduction

in dietary cholesterol has a relatively small effect on

plasma cholesterol concentrations in most patients who are

diet-sensitive.

The variations among individuals in the plasma choles-

terol response to dietary changes ( 12 1, 1 39) also may be

related to factors such as ethnicity, adiposity, physical ac-

tivity, and hyperlipidemia. The effects of these variables on

the response to dietary interventions are not well defined. In

addition, support for a strong genetic influence on the ef-

fects of dietary factors on plasma cholesterol is growing and

provides evidence that both levels and response genes in-

fluence the efficacy of dietary interventions to lower cho-

lesterol concentrations and reduce cardiovascular disease

risk (242-244).

The data supporting a relation between dietary fat and

cholesterol intake and elevated plasma lipid concentrations

should be evaluated not as mean values but on the basis of

individual patient responses. Some individuals can lower

their plasma cholesterol concentrations by decreasing di-

etary SFA and cholesterol intakes; however, this meta-

analysis presents persuasive evidence that there are those for

whom dietary change within the range of the NCEP recom-

mendations will have a modest effect on heart disease risk.

Clinically, the problem is first to determine who needs

intervention, and second, to determine how effective the

intervention is for each patient. Effective screening, includ-

ing multiple plasma lipid determinations, and follow-up

evaluation to determine the efficacy of dietary intervention

are essential. U

Dow



1000

900

800

700

600

500

400

300

200

100

0

Keys-57 Keys-66 Heg-65 Heg-93M Heg-93F How-97 Keys-57 Keys.66 Heg-65 Heg-93M Heg-93F How-97

Study

700

600

500

400

300

200

100

0

Key-66Heg-65

----- Heg-86EHeg-86L

- - - - Hop-93- - - Heg-93M-�--- Heg-93F

How-97McN-90

Reduction in cholesterol (mg/d)

FIGURE 5. Predicted reductions in serum total cholesterol for given reductions in dietary cholesterol. See Table 6 for mnemonic designations. NCEP,

National Cholesterol Education Program.

1758 HOWELL ET AL

FIGURE 4. Reductions in serum total cholesterol, resulting from subjects changing to the National Cholesterol Education Program’s Steps 1 and 2 diets,

predicted by various models, with proportional contributions of the changes in saturated fatty acids, polyunsaturated fatty acids, and dietary cholesterol.

See Table 6 for mnemonic designations.

0 100 200 300 400

Dow




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