Combined effect of body mass index and atherogenic index of plasma on risk of stroke among individuals with abnormal glucose metabolism: Evidence from the China health and retirement longitudinal study

Dear Editor,

I commend the authors for their valuable contribution to understanding the combined effects of body mass index (BMI) and atherogenic index of plasma (AIP) on stroke risk in individuals with abnormal glucose metabolism. ¹ Their study, using the China Health and Retirement Longitudinal Study (CHARLS), provides crucial insights into metabolic risk factors for stroke. ¹ However, while the study presents essential findings, several limitations need to be addressed to improve the reliability and generalizability of the conclusions. These limitations can be divided into two categories: essential considerations for this study and additional factors that could enhance future research.

Essential considerations for this study

Role of medications

While the study adjusts for the use of antihypertensives and lipid-lowering drugs, it does not fully consider the influence of other medicines, such as newer antidiabetic treatments or statins, which may impact stroke risk. Research has shown that statins, for example, not only lower cholesterol but also reduce inflammation, which plays a key role in stroke risk. Boehme et al. (2017) found that statins, when used alongside lifestyle modifications, can significantly reduce stroke risk by up to 30% in high-risk individuals with metabolic abnormalities. ² The failure to account for these medications may have skewed study findings, overestimating the effect of BMI and AIP without considering the role of modern pharmacological treatments.

Adequate matching of groups

Another limitation is the inadequate matching of groups. The study divides participants into categories based on BMI and AIP levels. Still, there is no clear discussion of how participants were matched for other key characteristics, such as comorbidities and socioeconomic factors. This can lead to confounding bias, as differences in baseline health factors (e.g., hypertension, age, and family history of stroke) could influence both BMI and AIP. Liu et al. (2018) highlighted the importance of careful matching in observational studies to ensure that comparisons between groups are valid and that observed effects are not due to confounding factors. ³ A more detailed matching procedure would enhance the internal validity of the findings.

Stroke subtypes

The study does not differentiate between stroke subtypes, which could be crucial in understanding how BMI and AIP influence stroke risk. Research has shown that ischemic and hemorrhagic strokes have different underlying mechanisms, and the role of metabolic factors may differ between these subtypes. Boehme et al. (2023) highlighted that obesity and dyslipidemia are more strongly associated with ischemic strokes, whereas other factors, like hypertension, may be more relevant for hemorrhagic strokes. ² This distinction would have added specificity to the study’s findings.

Comorbidities

The limited scope of comorbidities considered in the study is a significant limitation. While the study adjusts for common comorbidities like hypertension and diabetes, it fails to account for other essential conditions, such as chronic kidney disease or depression, which can exacerbate metabolic dysfunction and influence stroke risk. Zhang et al. (2023) found that chronic kidney disease interacts with BMI and lipid profiles to increase stroke risk, particularly in individuals with diabetes. ⁴ By not including a broader range of comorbidities, the study misses an opportunity to capture the full complexity of factors that contribute to stroke risk in individuals with abnormal glucose metabolism.

Temporal variability in metabolic factors

Additionally, the study fails to account for temporal variability in metabolic factors. By using a single-time-point measurement of BMI and AIP, the study overlooks fluctuations in these markers over time. As Zheng et al. (2023) emphasized, long-term tracking of BMI and AIP is crucial for understanding their cumulative impact on stroke risk, as fluctuations in these markers are associated with a significantly higher risk of cardiovascular events, including stroke. ⁵ A single-time-point measurement provides an incomplete view of the dynamic nature of metabolic disturbances and their long-term consequences.

Additional factors for future studies

Role of microbiota

The study also overlooks the role of microbiota and inflammatory markers in the relationship between BMI, AIP, and stroke. Recent research has shown that systemic inflammation, measured by biomarkers like high-sensitivity C-reactive protein (hs-CRP), and changes in gut microbiota can significantly affect metabolic health and stroke risk. Sun et al. (2025) demonstrated that chronic inflammation exacerbates the effects of high BMI and AIP on stroke risk, and the gut microbiome plays a pivotal role in regulating lipid metabolism and systemic inflammation. ⁶ Including these factors would offer a more holistic view of the underlying mechanisms through which metabolic disturbances lead to stroke, although it is appreciated that this will require dedicated studies.

Genetic factors

Another vital oversight is the lack of exploration of genetic factors. Genetic predisposition plays a significant role in both stroke and metabolic disorders, including BMI and lipid metabolism. For instance, variations in the APOE gene are known to increase susceptibility to both stroke and dyslipidemia. Liu et al. (2018) found that genetic factors could modify the impact of metabolic markers like AIP on stroke risk. ³ By not considering genetic data, the study misses an opportunity to explore how genetic predispositions might interact with BMI and AIP to increase stroke risk.

While the study provides valuable insights into the combined effects of BMI and AIP on stroke risk, addressing the limitations discussed above would strengthen the reliability and generalizability of the findings. We recommend that future research consider more robust methods for participant matching, adjust for additional medications, explore the role of stroke subtypes, and account for a broader range of comorbidities. Furthermore, incorporating microbiota analysis and genetic factors would provide a more comprehensive understanding of the pathways linking metabolic dysfunction to stroke.