Table 1. Mendelian Randomization of Birth Weight and Risk of Type 2 Diabetes.

MR Estimatesa	Summary Data Ab		Summary Data Bb
	OR (95% CI)	P Value	OR (95% CI)	P Value
Simple median–based methodc	1.57(1.24 to 2.00)	2.0 × 10-4	1.24(1.09 to 1.41)	.001
Weighted median–based methodc	1.52(1.24 to 1.86)	1.1 × 10-4	1.29(1.13 to 1.47)	6.0 × 10-4
Inverse-variance–weighted methodc	1.69(1.12 to 2.55)	.045	1.36(1.14 to 1.62)	.001
MR-Egger methodc	2.79(1.90 to 4.20)	.02	1.96(1.07 to 3.60)	.03
MR-Egger regressiond	0.007 (−0.081 to 0.095)	.94	0.011 (−0.002 to 0.02)	.22

Abbreviations: MR, mendelian randomization; OR, odds ratio.

^aIn an MR framework, genetic variants for birth weight were assumed to influence type 2 diabetes only through birth weight, not through other pathways. In the present study, we used MR-Egger regression to assess for the presence of pleiotropy.¹⁶ This approach is based on Egger regression, which was used to assess publication bias in the meta-analysis.³⁴ Using the MR-Egger method, the β coefficient of the MR-Egger regression provides pleiotropy-corrected causal estimates and an intercept distinct from the origin provides evidence for pleiotropic effects.¹⁶

^bSample sizes of patients with type 2 diabetes and control individuals were 12 171 and 56 862 for both summary data A and summary data B. Number of single-nucleotide polymorphisms used of summary data A and summary data B are 7 and 43, respectively. Number of participants with birth weight in summary data A and summary data B are 69 308 and 153 781, respectively.

^cWe used simple median–based method, weighted median–based method, inverse-variance–weighted method, and MR-Egger method to provide consistent results for causal effect of birth weight on type 2 diabetes.

^dValues in this row are intercept (95% CI).