<p>We read with great interest the study by Sarabhai et al., which provides valuable insights into the temporal evolution of cardiovascular events in newly diagnosed type 2 diabetes (T2D) patients in Germany over a 17-year period [<span>1</span>]. Although the reduction in the 5-year incidence of coronary heart disease (CHD) and transient ischemic attack (TIA) is encouraging, we believe several methodological and conceptual limitations deserve further discussion to contextualize the findings.</p><p>First, the study's exclusion of laboratory parameters such as glycated hemoglobin (HbA1c), lipid panels, and renal function significantly constrains the capacity to adjust for the quality of glycemic and metabolic control—central determinants of cardiovascular outcomes in T2D [<span>2, 3</span>]. Reliance solely on ICD-10 codes, though validated for primary diagnoses, does not differentiate between stable and unstable angina, nor does it account for silent myocardial infarctions, which are prevalent in diabetic populations [<span>4</span>]. This coding limitation may partially explain the paradoxically unchanged incidence of myocardial infarction (MI) despite improvements in CHD.</p><p>Second, the use of chronic obstructive pulmonary disease (COPD) as a proxy for smoking status introduces exposure misclassification. Smoking is a potent modifiable risk factor for both MI and ischemic stroke (IS) [<span>5</span>], and its secular decline in Germany over this period likely contributed to cardiovascular risk reduction. Without direct smoking data, the differential attribution of risk to diabetes-specific interventions versus population-wide trends remains unresolved.</p><p>Third, the apparent stability in MI and IS incidence may also be an artifact of cohort composition rather than a true epidemiological plateau. Although the authors matched for age and sex, no stratification by socioeconomic status, regional healthcare access, or medication use—particularly statins and antihypertensives—was undertaken. These variables influence the uptake and effectiveness of cardioprotective interventions [<span>6</span>]. Furthermore, the analysis does not disaggregate event timing within the five-year follow-up, thereby overlooking early versus late event clustering, which may offer clues about legacy effects from undiagnosed prediabetes.</p><p>Fourth, the demographic distribution masks evolving baseline risk. Although mean age and sex proportions remained unchanged, the increased prevalence of hypertension and obesity in the later cohort signals rising baseline cardiovascular risk. Paradoxically, these upward shifts might dilute the observable impact of improved care. Consequently, the unchanged IS and MI rates may reflect counterbalancing effects between therapeutic progress and population-level risk escalation.</p><p>Finally, the study's conclusion that CHD and TIA reduction reflects successful diabetes management may overstate causality. CHD includes chronic, often
{"title":"Comment on “Time Trends in Cardiovascular Event Incidence in New-Onset Type 2 Diabetes: A Population-Based Cohort Study From Germany”","authors":"Saraswati Sah, Rachana Mehta, Ranjana Sah","doi":"10.1111/1753-0407.70130","DOIUrl":"https://doi.org/10.1111/1753-0407.70130","url":null,"abstract":"<p>We read with great interest the study by Sarabhai et al., which provides valuable insights into the temporal evolution of cardiovascular events in newly diagnosed type 2 diabetes (T2D) patients in Germany over a 17-year period [<span>1</span>]. Although the reduction in the 5-year incidence of coronary heart disease (CHD) and transient ischemic attack (TIA) is encouraging, we believe several methodological and conceptual limitations deserve further discussion to contextualize the findings.</p><p>First, the study's exclusion of laboratory parameters such as glycated hemoglobin (HbA1c), lipid panels, and renal function significantly constrains the capacity to adjust for the quality of glycemic and metabolic control—central determinants of cardiovascular outcomes in T2D [<span>2, 3</span>]. Reliance solely on ICD-10 codes, though validated for primary diagnoses, does not differentiate between stable and unstable angina, nor does it account for silent myocardial infarctions, which are prevalent in diabetic populations [<span>4</span>]. This coding limitation may partially explain the paradoxically unchanged incidence of myocardial infarction (MI) despite improvements in CHD.</p><p>Second, the use of chronic obstructive pulmonary disease (COPD) as a proxy for smoking status introduces exposure misclassification. Smoking is a potent modifiable risk factor for both MI and ischemic stroke (IS) [<span>5</span>], and its secular decline in Germany over this period likely contributed to cardiovascular risk reduction. Without direct smoking data, the differential attribution of risk to diabetes-specific interventions versus population-wide trends remains unresolved.</p><p>Third, the apparent stability in MI and IS incidence may also be an artifact of cohort composition rather than a true epidemiological plateau. Although the authors matched for age and sex, no stratification by socioeconomic status, regional healthcare access, or medication use—particularly statins and antihypertensives—was undertaken. These variables influence the uptake and effectiveness of cardioprotective interventions [<span>6</span>]. Furthermore, the analysis does not disaggregate event timing within the five-year follow-up, thereby overlooking early versus late event clustering, which may offer clues about legacy effects from undiagnosed prediabetes.</p><p>Fourth, the demographic distribution masks evolving baseline risk. Although mean age and sex proportions remained unchanged, the increased prevalence of hypertension and obesity in the later cohort signals rising baseline cardiovascular risk. Paradoxically, these upward shifts might dilute the observable impact of improved care. Consequently, the unchanged IS and MI rates may reflect counterbalancing effects between therapeutic progress and population-level risk escalation.</p><p>Finally, the study's conclusion that CHD and TIA reduction reflects successful diabetes management may overstate causality. CHD includes chronic, often","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>We sincerely thank the authors of the comment and appreciate the opportunity to respond to clarify specific aspects of our study.</p><p>First of all, we agree that laboratory parameters are key indicators of metabolic control and cardiovascular risk. Although the Disease Analyzer database includes laboratory data from a subset of practices, laboratory values were not consistently available over time and across patients in our cohort. Therefore, we opted not to include them in our analyses. To address this limitation, we adjusted for chronic comorbidities known to be associated with poor metabolic control, such as hypertension, dyslipidemia, and obesity (coded diagnoses). Previous studies demonstrated the validity of the Disease Analyzer database especially for case–control studies focusing on diabetes mellitus [<span>1</span>]. Furthermore, by focusing on first cardiovascular events in a well-defined incident T2D cohort without prior CVD, we aimed to reduce confounding and improve internal validity despite the absence of uniformly available laboratory markers.</p><p>We acknowledge the potential limitations of relying solely on ICD-10 codes, as we stated in the manuscript. However, the Disease Analyzer database has been extensively validated and has demonstrated a high positive predictive value for major cardiovascular diagnoses, including MI [<span>1-3</span>]. We appreciate the reference by Tsai et al. [<span>4</span>], which highlights the strong validity of ICD-10-CM codes for identifying AMI subtypes. Although their work confirms excellent performance metrics, our interest was not the subtype but rather the trend in overall MI incidence.</p><p>Indeed, smoking is a critical risk factor. As stated, individual smoking status is not recorded in the Disease Analyzer database. We therefore followed established practice in using COPD as a proxy, recognizing its limitations. As noted in the literature, around 90% of patients with COPD are current or former smokers [<span>5</span>]. However, not all smokers develop COPD, so COPD cannot fully replace smoking status, but provides an approximate indicator. We clearly acknowledged this limitation in the manuscript. Importantly, smoking prevalence in Germany has declined over the study period, which may have contributed to overall cardiovascular improvements. However, as these trends would influence both diabetic and nondiabetic populations alike, our interpretation focused on diabetes-specific outcomes in a matched T2D cohort.</p><p>We agree that socioeconomic status and medication use are important factors. Although these data were not available in sufficient detail in our database, our large, matched cohorts and adjustment for key comorbidities offer valuable insights into temporal patterns. The unchanged MI and IS incidence, despite improvements in CHD and TIA, likely reflects a balance between earlier vascular damage and therapeutic progress. Although we did not stratify events by time since diagno
{"title":"Author Response to the Comment on “Time Trends in Cardiovascular Event Incidence in New-Onset Type 2 Diabetes: A Population-Based Cohort Study From Germany”","authors":"Theresia Sarabhai, Karel Kostev","doi":"10.1111/1753-0407.70128","DOIUrl":"https://doi.org/10.1111/1753-0407.70128","url":null,"abstract":"<p>We sincerely thank the authors of the comment and appreciate the opportunity to respond to clarify specific aspects of our study.</p><p>First of all, we agree that laboratory parameters are key indicators of metabolic control and cardiovascular risk. Although the Disease Analyzer database includes laboratory data from a subset of practices, laboratory values were not consistently available over time and across patients in our cohort. Therefore, we opted not to include them in our analyses. To address this limitation, we adjusted for chronic comorbidities known to be associated with poor metabolic control, such as hypertension, dyslipidemia, and obesity (coded diagnoses). Previous studies demonstrated the validity of the Disease Analyzer database especially for case–control studies focusing on diabetes mellitus [<span>1</span>]. Furthermore, by focusing on first cardiovascular events in a well-defined incident T2D cohort without prior CVD, we aimed to reduce confounding and improve internal validity despite the absence of uniformly available laboratory markers.</p><p>We acknowledge the potential limitations of relying solely on ICD-10 codes, as we stated in the manuscript. However, the Disease Analyzer database has been extensively validated and has demonstrated a high positive predictive value for major cardiovascular diagnoses, including MI [<span>1-3</span>]. We appreciate the reference by Tsai et al. [<span>4</span>], which highlights the strong validity of ICD-10-CM codes for identifying AMI subtypes. Although their work confirms excellent performance metrics, our interest was not the subtype but rather the trend in overall MI incidence.</p><p>Indeed, smoking is a critical risk factor. As stated, individual smoking status is not recorded in the Disease Analyzer database. We therefore followed established practice in using COPD as a proxy, recognizing its limitations. As noted in the literature, around 90% of patients with COPD are current or former smokers [<span>5</span>]. However, not all smokers develop COPD, so COPD cannot fully replace smoking status, but provides an approximate indicator. We clearly acknowledged this limitation in the manuscript. Importantly, smoking prevalence in Germany has declined over the study period, which may have contributed to overall cardiovascular improvements. However, as these trends would influence both diabetic and nondiabetic populations alike, our interpretation focused on diabetes-specific outcomes in a matched T2D cohort.</p><p>We agree that socioeconomic status and medication use are important factors. Although these data were not available in sufficient detail in our database, our large, matched cohorts and adjustment for key comorbidities offer valuable insights into temporal patterns. The unchanged MI and IS incidence, despite improvements in CHD and TIA, likely reflects a balance between earlier vascular damage and therapeutic progress. Although we did not stratify events by time since diagno","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}