The authors analyzed 1,292,264 participants over a median follow-up period of 8.4 years. The primary outcome was death, and the authors collected data on all-cause, system-specific, and disease-specific mortality for the enrolled participants. During the follow-up period, there were 257,356 death events. Overall, elderly individuals with DM were found to have an increased risk of mortality compared to their counterparts without DM, a finding that aligns with a recent study from Hong Kong [4]. When cause-specific mortality was analyzed, individuals with DM exhibited a higher hazard ratio of mortality across all system categories (circulatory, respiratory, genitourinary, neoplasm, and others). Furthermore, the risk of mortality progressively increased in a stepwise manner across the glucose spectrum for all-cause mortality and death, irrespective of underlying diseases such as chronic kidney disease, heart failure, and stroke. Malignancy consistently emerged as the most common cause of death across all glucose spectrum categories in this elderly population. These findings and trends are consistent with previous studies conducted on Korean patients with DM [5,6]. However, another Korean study reported that the first and second leading causes of death in type 2 diabetes mellitus (T2DM) were cardiovascular disease and infectious disease, respectively [7]. The discrepancy in causes of mortality may be attributed to differences in the study population and advancements in the management of risk factors associated with cardiovascular disease.

The mechanistic process potentially linking DM to cancer is not yet fully understood. However, several experimental observations have suggested that multiple risk factors, which involve proliferation and apoptosis pathways, may be shared between T2DM and cancer [8]. These major risk factors include hyperglycemia, hyperinsulinemia, increased bioactivity of insulinlike growth factor 1, oxidative stress, dysregulations of sex hormones, and chronic inflammation. The supraphysiological concentrations of insulin and glycemia to which body tissues are exposed act as a potent growth factor and energy source, respectively. These are essential for neoplastic transformation and cancer progression [9]. On a broader scale, between 8% and 18% of individuals with cancer also have DM. This association may be due to shared risk factors between T2DM and cancer, such as older age, obesity, physical inactivity, and smoking. A recent study using a large database suggested that the global increases in DM and body mass index (BMI) could lead to a significant increase in the burden of cancer in the coming decades [10]. Therefore, population-based strategies aimed at preventing DM and high BMI could have a substantial impact, given the overlapping risk factors, comorbidities, and shared sequelae, such as cancer, between these two diseases [11]. Consequently, as I have previously suggested in a review article, cancer should be included in routine diabetes assessments [12].

The strength of this study [3] is that it aimed to classify subjects along the diabetes spectrum using easily obtainable variables from a large participant pool. To the best of our knowledge, the participant count in this study surpasses that of other studies. Furthermore, this study accurately represents the general characteristics of the elderly South Korean population. This was achieved by utilizing the Korean National Health Insurance Service database, a single health insurance program that encompasses nearly all South Koreans.

DM is increasingly becoming a significant socioeconomic burden in Korea, particularly among the elderly population. A recent consensus [13] advocates for a comprehensive, multifactorial, person-centered approach to address the lifelong nature of T2DM. As such, it is crucial to classify the elderly population-based on their glucose spectrum and relative risk of mortality. This categorization is vital for the optimal allocation of limited public resources within society. Consequently, these data are invaluable in providing the evidence needed to support the health administration’s decisions to optimize healthcare expenses according to each individual’s risk category.