Diabetes is a chronic (long-lasting) disease and its incidence is quickly growing, in both developing and developed nations. Diabetes patients have more risk of developing multiple conditions than the ones without diabetes. In clinical literature, this phenomenon in general is known as comorbidity. The current majority works have made great progress in extracting comorbidities patterns, but these works still have a few limitations: first, little is known about comorbidities patterns; second, identifying top interesting comorbidities patterns. The purpose of this research is to identify top-k diabetes-specific disease comorbidity patterns from large clinical datasets. We have used the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to recognize patients diagnosed with diabetes between 2001 and 2019 in Medical Information Mart for Intensive Care (MIMIC) datasets. We have extracted diabetes-specific disease comorbidity patterns using Association Rule Mining (ARM) techniques, namely Apriori, Frequent Pattern Growth (FP-Growth) and Frequent Pattern Maximal (FP-Max) to address the first limitation.We have proposed an effective or a novel approach to identify top-k association patterns of diabetes-specific disease comorbidities to address the second limitation. We have also continued our investigation to find differences in gender-specific comorbidities patterns and major diabetes subtypes-specific comorbidities patterns. Chronic kidney disease was the top most common comorbidity (Support: 13.20%; Confidence: 85.51%, Lift: 3.766), followed by Atrial fibrillation (Support: 13.72%; Confidence: 59.87%, Lift: 1.778). Also, we found that the diabetes-specific disease comorbidities patterns are gender-specific and major diabetes subtypes-specific. Finally, identified top-k comorbidities patterns are validated with medical literature. Notably, our results uncover novel interesting and clinically meaningful comorbidities patterns and thereby assist clinical practitioners to prescribe an optimal care for diabetic patients.