Food patterns measured by factor analysis and anthropometric
changes in adults1–3
PK Newby, Denis Muller, Judith Hallfrisch, Reubin Andres, and Katherine L Tucker
Background: Sixty-five percent of US adults are overweight, and
31% of these adults are obese. Obesity results from weight gains
over time; however, dietary determinants of weight gain remain
Objective: Our objective was to examine whether food patterns
ric changes. We hypothesized that we would derive a healthy food
(BMI; in kg/m2) and waist circumference (in cm) than would other
Design: The subjects were 459 healthy men and women participat-
by using 7-d dietary records, from which 40 food groups were
formed and entered into a factor analysis.
products, fruit, and fiber) was most strongly associated with fiber
(r ? 0.39) and loaded heavily on reduced-fat dairy products, cereal,
and fruit and loaded moderately on fruit juice, nonwhite bread, nuts
and seeds, whole grains, and beans and legumes. In a multivariate-
adjusted model in which the highest and lowest quintiles were com-
pared, factor 1 was inversely associated with annual change in BMI
(? ? ?0.51; 95% CI: ?0.82, ?0.20; P ? 0.05; P for trend ? 0.01)
in women and inversely associated with annual change in waist
circumference (? ? ?1.06 cm; 95% CI: ?1.88, ?0.24 cm; P ?
0.05; P for trend ? 0.04) in both sexes.
Conclusion: Our results suggest that a pattern rich in reduced-fat
in women and smaller gains in waist circumference in both women
Am J Clin Nutr 2004;80:504–13.
principal components, obesity, body composition, diet assessment,
body mass index, BMI, waist circumference
mass index (BMI; in kg/m2) ? 25], and 31% of these adults are
obese (BMI ?30) (1). Obesity results from weight gains over
time; however, dietary determinants of weight gain remain con-
troversial (2, 3). Inconsistent findings may be explained in part
by limitations in the single-nutrient approach, which is com-
monly used in nutritional epidemiologic research (4, 5). Cluster
and factor analyses have emerged as methods to empirically
derive dietary (or food) patterns and possibly explain nutrient-
cluster analysis were significantly related to longitudinal
changes in anthropometric measures (6). Specifically, the
healthy dietary pattern—which was high in fruit, reduced-fat
in both BMI and waist circumference than did the other dietary
patterns (white bread, sweets, alcohol, and meat and potatoes).
In the current study, we continued to examine prospectively
whether eating patterns are related to anthropometric changes in
women and men participating in the Baltimore Longitudinal
method—factor analysis. Specifically, we used principal com-
ponents to extract factors, in which a given set of variables is
analysis is statistically quite different from cluster analysis.
Whereas cluster analysis separates persons into mutually exclu-
between foods (factors), and persons receive a factor score for
each of the derived factors. With the use of the factor analysis
procedure, a person’s dietary pattern would be best represented
by looking at his or her factor scores for each of the derived
vectors when examining relations rather than the total diet for a
patterns and not as dietary patterns.
Other studies that have used factor or principal components
(8–11). None of these studies, nor other studies using factor
analysis (12–14), examined whether food patterns derived by
1From the Jean Mayer US Department of Agriculture Human Nutrition
Research Center on Aging at Tufts University, Boston (PKN and KLT), and
the National Institute on Aging, National Institutes of Health, Baltimore
(DM, JH, and RA).
2Supported in part by the US Department of Agriculture, Agricultural
Research Service, contract number 53-3K06-01; the National Institutes of
Health, National Institute on Aging Intramural Program; and the General
Mills Bell Institute of Health and Nutrition.
3Reprints not available. Address correspondence to PK Newby, Jean
Mayer USDA Human Nutrition Research Center on Aging at Tufts Univer-
sity, 711 Washington Street, 9th Floor, Boston, MA 02111. E-mail:
Received August 19, 2003.
Accepted for publication January 9, 2004.
Am J Clin Nutr 2004;80:504–13. Printed in USA. © 2004 American Society for Clinical Nutrition
by guest on May 23, 2011
changes. Our objective was to examine whether food patterns
derived from exploratory factor analysis, specifically, principal
components analysis, are related to changes in BMI and waist
circumference. We hypothesized that, using factor analysis, we
would derive a healthy food pattern and that this pattern would
lead to smaller changes in BMI and waist circumference than
would other dietary patterns.
SUBJECTS AND METHODS
and emotional effects of aging in healthy, active people; the
elsewhere (15). Briefly, initial study participants were white,
male, community-dwelling volunteers aged 22–88 y and living
in the Baltimore area. The study protocol expanded in 1978 to
include women and minorities. Enrollment in the BLSA is open
and participants return approximately every 24 mo (mean time
interval: 25 mo) for repeated measurements of height, weight,
body composition, diet, and a variety of other physiologic, psy-
chological, and behavioral measures. The Institutional Review
subjects gave written informed consent for their participation.
This analysis uses the same data set as described in our pre-
vious paper (6) to facilitate comparison of the factor and cluster
analyses in deriving food and dietary patterns, respectively. In
study on or after 1980 (n ? 921) to avoid bias from changing
as were subjects whose food group intake appeared implausible
(?6 SDs from the mean for each food group) (n ? 63). An
additional 134 subjects were omitted because they did not have
?2 measures of height or weight (at the time the dietary record
was collected and at follow-up). Finally, all subjects who re-
ceived a diagnosis of cancer, diabetes, stroke, or heart disease
either before or at baseline were omitted (n ? 190) to create a
for 449 subjects were used in this analysis.
the BLSA population were published previously (16–18). In
to complete 7-d food records. Food records were completed at
home by the participants and were sent back to the study center.
Before 1992, the subjects were given food models and a booklet
of food pictures to help them assess portion size. In 1994, the
subjects were given a portable scale to weigh food portions. The
participants were contacted by telephone with any questions
about the diet records.
Dietary records from 1984 to 1991 were originally coded and
entered into a nutrient database maintained by the BLSA,
whereas diet records completed since 1994 were coded and en-
tered into the Minnesota Nutrition Data System (Nutrition Co-
ordinating Center, University of Minnesota, Minneapolis) at
Tufts University; no dietary data were collected in 1992 and
1993. Dietary data from 1984 to 1991 were then reentered into
the Minnesota Nutrition Data System, and nutrient intakes were
back-adjusted to correct for changes in the food supply (eg,
to appropriate time intervals (19, 20).
Food pattern derivation
To perform a food pattern analysis with the use of principal
40 food groups, mainly according to macronutrient composition
and eggs) composed their own groups (Appendix A). Where
possible, foods were separated into full- and reduced-fat groups
(eg, high-fat dairy and reduced-fat dairy products).
Food groups may be entered into the principal components
analysis as absolute weight in grams, number of servings, or the
percentage of energy contributed by each of the 40 groups to
average intake of each food (group) over the 7-d dietary record.
The percentage contribution from each food group for each sub-
ject was then entered into the principal components analysis.
The PROC FACTOR procedure in SAS (version 8.2; SAS
Institute, Cary, NC) was used to perform the analysis. The pro-
cedure uses principal components analysis and orthogonal rota-
with other empirically derived pattern procedures, principal
components analysis requires preselection of the number of fac-
tors. To decide which number of factors to retain, we ran factor
solutions ranging from 2 to 15, including food-group compo-
nents with an eigenvalue ? 1 and examined both the scree plots
ingfully described distinct food patterns. From these analyses, a
6-factor solution was selected. Factor loadings were calculated
score was then calculated for each subject for each of the 6
factors, in which the standardized intakes of each of the 40 food
groups were weighted by their factor loadings and summed (7);
the sums were then standardized (x ? ? 0, SD ? 1). Food patterns
were named according to both the foods that loaded most posi-
tively on the factor and according to how the factors correlated
with nutrients (see Statistical analyses).
Some studies have found that factor solutions differ by sex
(22). We examined factor solutions separately for men and
women but chose to retain the factor solution that included both
an interaction between food pattern and sex in the regression
FOOD PATTERNS AND ANTHROPOMETRIC CHANGES
by guest on May 23, 2011