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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: whhowell@ag.arizona.edu.
Received March 21, 1996.
Accepted for publication December 17, 1996.
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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)
BLOOD LIPID RESPONSES TO DIET I 753
‘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
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= 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,
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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
Location of subjects
Dietary cholesterol
. Confined Inst meals Free
Location of subjects
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%
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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
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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
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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
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BLOOD LIPID RESPONSES TO DIET 1759
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