“Global burden, trends and forecast analysis of early-onset type 2 diabetes and diabetes-related chronic kidney disease: a global burden of disease 2021 analysis” [Letter]
Year: 2026
Type: article
Abstract: Dear Editor, We have recently read with great interest the article by Zhang et al[1], entitled “Global burden, trends and forecast analysis of early-onset type 2 diabetes and diabetes-related chronic kidney disease: a global burden of disease 2021 analysis.” This study provides important insights regarding the epidemiological trends, regional differences, and current research status of early-onset type 2 diabetes (T2DM) and diabetes-related chronic kidney disease (CKD-T2DM). Nevertheless, we aim to elaborate on the following aspects for further clarification. This commentary complies with the 2025 TITAN Guideline[2] on the disclosure of artificial intelligence (AI) applications in academic communications. Details of AI usage: Doubao (ByteDance; no specific version) was only utilized for linguistic processing, including draft translation and expression refinement to improve clarity. All scientific content is the sole responsibility of the authors. Usage date: 12 January 2026. The tool was accessed via ByteDance’s cloud API, with no on-premises deployment or external integration. First, the study claims: “T2DM and CKD-T2DM in the 15–39 age group were divided into seven subgroups for further assessment: 15–19, 20–24, 25–29, 30–34 and 35–39 years.” Yet, merely five subgroups are presented instead of the stated seven. A similar inconsistency exists in the decomposition analysis, where it is noted that “aiy represents the population proportion of one of the four age categories.” Additionally, the methods section contains an inaccuracy in describing decomposition analysis: the authors stated, “By analyzing these trends, we gained deeper insights into the underlying drivers of global ASCVD burden changes.” The term “ASCVD” is not clearly defined – does it refer to atherosclerotic cardiovascular disease? Given the study’s focus, the relevant outcomes should relate to CKD-T2DM and T2DM. Second, Figure 6 presents an age-period-cohort (APC) analysis, but the x-axis of the bottom-left subgraph is labeled “Date of diagnosis,” which excludes the period dimension, a core element of the APC model. “Date of diagnosis” refers to the time of individual confirmation, while “period” in the APC framework denotes population-level observation or statistical years. Replacing “period” with “Date of diagnosis” might confuse “individual diagnosis time” with “population observation period,” deviating from the standard analytical framework of the APC model. Third, the authors’ joinpoint regression results indicate that in 1998, the global age-standardized disability-adjusted life year rate (ASDR) of CKD-T2DM shifted from a declining to an ascending trend. This inflection point was also observed in regions with high, high-middle, and middle socio-demographic index (SDI), whereas ASDR continued to decrease in low-middle- and low-SDI regions. This inflection point is of great significance for global disease prevention and control, and its occurrence is closely associated with the cumulative disease course of early-onset T2DM. The incidence of early-onset T2DM rose in the 1990s; after 1998, this population entered the stage of cumulative disease progression – early-onset T2DM patients develop diabetic nephropathy an average of 4.3 years after diagnosis[3]. Meanwhile, advances in medical care have prolonged patient survival, exposing the kidneys to persistent damage caused by hyperglycemia and thus exacerbating this adverse trend. In contrast, the increase in early-onset T2DM is delayed in low-middle- and low-SDI regions. These areas have no access to renoprotective drugs (e.g., ACEI/ARB) and standardized dialysis services[4], leading most patients to rapidly progress to end-stage renal disease and die at an early age. As a result, no such inflection point is observed in these regions. Lastly, the authors’ APC analysis notes: “Furthermore, the APC model highlights that later birth cohorts face elevated T2DM risk due to sedentary behaviors.” While the original text attributes the cohort effect to “increased overall population risk,” it ignores the impact of periodic advances in medical testing technology on T2DM detection. Since the 1990s, fingertip blood glucose meters and HbA1c testing have been widely adopted in high- and middle-SDI regions[5,6]. In 1990, T2DM was mainly diagnosed in patients with typical symptoms (e.g., polydipsia, polyuria). After 2010, “asymptomatic screening” (e.g., blood glucose testing during routine physical examinations) markedly enhanced the detection rate of early-onset T2DM. This may lead to the observation of “increased late-period T2DM risk” in the APC model; however, part of this apparent increase is attributed to improved detection capabilities, especially among asymptomatic individuals aged 15–24 years. In summary, Zhang et al’s work lays a solid foundation for addressing the growing burden of early-onset T2DM and its complications. We believe that supplementing the aforementioned perspectives will further improve the article’s practical value in promoting global health equity. Ethical approval Not applicable. Consent Not applicable.
Source: International Journal of Surgery
Authors: Yujun He, Zhaoyang Liu
Language: English
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
OpenAlex ID: W7138204283