The association between rare genetic disorders, hereditary fructose intolerance (HFI) or alpha-1 antitrypsin deficiency (A1AT), and type 2 diabetes (T2D) has not yet been investigated. Therefore, the objective of this undertaking was to evaluate the association between both genetic disorders and T2D using four large observational databases and adjust for ascertainment bias. Patients with a HFI diagnosis (ICD-9: 271.2) or A1AT diagnosis (ICD-9: 273.4) and T2D diagnosis (ICD-9: 250.x0 or 250.x2) were identified in the Truven MarketScan Claims Database (2007-2012), Optum Claims Database (2002-2012), Humedica Electronic Health Records (EHR) Database (2007-2012), and GE Centricity EHR Database (1995-2012). The association between both genetic disorders and T2D was compared to the association between T2D and seven negative control chronic diseases with no established relationship with T2D. The unadjusted association between both genetic disorders and T2D was positive and heterogeneous (p<0.001) in all four databases. The unadjusted pooled odds ratio (OR) calculated using a random-effects model meta-analysis was 3.48 (95% CI: 2.21-5.46) for HFI and 2.71 for A1AT (95% CI: 1.75-4.20). After pooling all patients and adjusting for the negative controls using a random-effects model meta-analysis, it was found that HFI patients have a 73% increased odds of T2D (ratio of odds ratios [ROR]=1.73, 95% CI: 1.08-2.75) compared to patients with negative control diseases; the association was stronger when utilizing a fixed-effects model meta-analysis (ROR=2.19, 95% CI: 2.07-2.31). The adjusted association between A1AT and T2D was statistically significant in the fixed-effects (ROR=1.33, 95% CI: 1.27-1.40) model meta-analysis but not the random-effects model meta-analysis (ROR=1.35, 95% CI: 0.86-2.12). HFI and T2D were positively associated after adjustment for negative control chronic diseases in both meta-analysis models. Rare disease researchers using observational data to conduct comorbidity analyses can utilize negative controls and multiple datasets to account for ascertainment bias and database heterogeneity, respectively.