Nicklas TA, O'Neil CE, Fulgoni, VL, J Nutr. 2015 Jan;145(1):170S-6S. doi: 10.3945/jn.114.194068. Epub 2014 Dec 3. http://www.ncbi.nlm.nih.gov/pubmed/25527676
Background: Associations between food patterns and adiposity are poorly understood.
Objective: Two statistical approaches were used to examine the potential association between egg consumption and adiposity.
Methods: Participants (n = 18,987) aged$19 y were fromthe 2001–2008 NHANES who provided 24-h diet recall data, body mass
index (BMI) andwaist circumference (WC)–determined adipositymeasures, and blood pressure, circulating insulin, glucose, and lipid
concentrationswere considered cardiovascular risk factors (CVRFs). Covariate-adjusted least-squaresmeans6 SEs were generated.
Results: The first statistical approach categorized participants into egg consumers or nonconsumers. Consumers had higher
mean BMI (in kg/m2; 28.7 6 0.19; P = 0.006) andWC (98.26 0.43 cm; P = 0.002) than did nonconsumers (28.26 0.10 and 96.9
6 0.23 cm, respectively). Second, cluster analysis identified 8 distinct egg consumption patterns (explaining 39.5% of the variance
in percentage of energy within the food categories).Only 2 egg patterns [egg/meat, poultry, fish (MPF)/grains/vegetables and egg/
MPF/grains], consumed by #2% of the population, drove the association (compared with the no-egg pattern) between egg
consumption and BMI and WC. Another analysis controlled for the standard covariates and the other food groups consumed with
eggs in those 2 egg patterns. Only the egg/MPF/other-grains pattern remained associated with BMI and WC (both P # 0.0063).
The pattern analyses identified associations between an egg pattern (egg/MPF/other grains/potatoes/other beverages) and
diastolic blood pressure (DBP) and serumLDL cholesterol (both P# 0.0063). A final analysiswas conducted by adding percentage
of energy fromfast foods andmedication use for diabetes to the covariates. The association between the egg/MPF/grains pattern
and BMI and the egg/MPF/potatoes/other beverages and DBP and LDL cholesterol disappeared.
Conclusions: Care needs to be taken with data interpretation of diet and health risk factors and the choice of statistical
analyses and covariates used in the analyses because these studies are typically used to generate hypotheses. Additional
studies are needed to better understand these relations.