Table 3.

Multivariate Analysis of Gene Expression Levels

Predictor/ Outcome	GLUT4	HDAC1*	IRS2	LEPR	LPL	LRP1*	MMP2	PAI-1	TCF7L2	TXN*
Increased Birth Weight	1.60 p<0.001	−1.41 p<0.001	1.16 p=0.002	1.62 p<0.001	1.22 p=0.001	1.59 p<0.001	1.31 p<0.001	0.90 p=0.007	1.11 p=0.005	−0.86 p<0.001
Gravida	0.29 p=0.019	−0.29 p=0.016	0.35 p=0.010				0.26 p=0.054			−0.34 p=0.008
Part or All Bottle			−0.53 p=0.048		−0.68 p=0.018			−0.74 p=0.005		0.66 p=0.007
Glucose Tolerance Test			0.28 p=0.029					0.29 p=0.008
Gestational Age				−0.26 p=0.028				−0.27 p=0.018
Affirmed College Degree or Better					−0.62 p=0.032			−0.85 p=0.003
Gestational Weight Gain Over Recommended								0.62 p=0.008
Smoke During Pregnancy								0.63 p=0.018
Private Insurance								1.22 p=0.002
Full-Time Employment								−0.90 p=0.010
Day of Life Circumcision								−0.21 p=0.065
Ponderal Index										−0.26 p=0.030
R2	0.391	0.434	0.403	0.420	0.299	0.266	0.272	0.659	0.161	0.455

Each column represents a separate regression model. Variables actually selected for each model are those whose cell entries are filled in. Each regression coefficient is the estimated number of standard deviations by which the outcome is expected to increase when the predictor goes from no to yes (if the predictor is binary) or when the predictor increases by one standard deviation (if the predictor is continuous), while adjusting for all other predictors in the same model; these regression coefficients turned out to be greater than 0.50 in absolute value for all binary predictors and less than 0.50 in absolute value for all continuous predictors. Accompanying each regression coefficient is a p-value. Asterisks in column headings indicate a weighted least squares analysis. The final row contains R², the proportion of (weighted) variation in gene expression explained by the variables used to predict it.