Methodological Considerations in TG/HDL Ratio and Cognitive Function in Older Adults

We have read with great interest the recently published article titled “Relationship of the TG/HDL Ratio With Cognitive Function in Older Adults (≥ 60 Years): A Cross-Sectional Analysis of NHANES 2011–2014” [1] by “Li et. al.” We want to commend the authors for highlighting the metabolic underpinnings of cognitive impairment in older adults by enrolling a large, nationally representative data set and validating evidence that links it to insulin resistance and dyslipidemia. Their findings focus on the nonlinear relationships between TG/HDL and cognition, offering key insights into the role of the it's as a surrogate for insulin resistance by and its association with cognition, that are of value to metabolic neurology. However, despite these useful contributions, several methodological concerns warrant further discussion. Firstly, although the TG/HDL ratio is a well-validated surrogate marker, the study relies solely on it as a proxy for insulin resistance without validating it against direct measures like the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) or hyperinsulinemia-euglycemic clamp. However evidence suggests that TG/HDL's association with IR is versatile varying across different populations, ethnicities, ages, and metabolic profiles, with it's accuracy varying between nonobese and obese individuals, and HOMA-IR seen to be strongly associated with cardiovascular risk. Without the use of concurrent gold standard measures, the observed cognitive associations could be attributed to other pathways linked to dyslipidemia, potentially introducing a misclassification bias in the findings [2]. Future analyses should be done to strengthen the claim of using insulin-specific mechanisms by using NHANES that include insulin levels calculating HOMA-IR, and include both a covariate and a mediator in statistical models. Secondly, the study uses CERAD, DSST, and AFT as cognitive tests, which, although sensitive to cognitive decline, do not specify if the cognitive impairment stems from Alzheimer's disease or some other pathology. The findings by Mattsson-Carlgren et. al., who directly investigated the relationship between plasma biomarkers (including lipids) and PET-based amyloid and tau deposition, reported that distinct lipid profiles are associated with amyloid positivity and highlighted that use of plasma p-tau217 accurately identified most Aβ-positive individuals [3] Hence, for formal clarification of IR linkage with AD, blood biomarkers should be integrated with neuroimaging (Amyloid-PET, Tau-PET, MRI) or CSF biomarkers (Aβ42, p-tau) in cohorts like the Alzheimer's Disease Neuroimaging Initiative (ADNI). Thirdly, the use of a composite score based on a limited test battery (CERAD, DSST, AFT) may dilute specific domain-level effects such as visuospatial or social cognition. This warrants the use of additional clinically adjudicated diagnoses of MCI by using informant reports and clinical judgment, and additional use of broader neuropsychological batteries as an outcome to enhance the clinical translatability of the findings [4]. Furthermore, the study does not take into consideration key lifestyle confounders such as diet, physical activity, and depression, which are important determinants of lipid profiles and cognitive health. Therefore, using NHANES data on dietary recall as covariates is essential to better isolate the independent effects of the TG/HDL ratio [5]. In conclusion, while the study by Li et al. makes a valuable contribution to metabolic neurology by linking the TG/HDL ratio to cognitive performance in the older population, the pathways linking dyslipidemia to brain health are often complex and likely extend beyond insulin resistance alone. Hence the aforementioned limitations should be kept in mind in future research by incorporation of direct measures of IR, AD biomarkers, more subtle cognitive diagnoses, and key lifestyle confounders. This is essential for clarification of the clinical utility of TG/HDL as a biomarker of cognitive risk and for advancement of more effective, mechanism-based interventions. Maheen Rizwan: conceptualization, writing – review and editing. The author has nothing to report. The author affirms having read and approved the final version of the manuscript and takes full complete responsibility for the integrity of the data and the accuracy of the analysis, and received no financial support for the research, authorship, or publication of this study. No Artificial Intelligence tools (e.g., ChatGPT or other large language models) were used in the writing, editing, or analysis of this manuscript. The letter does not involve human participants, animals, and patient's data, and therefore ethical approval is not required. It does not reproduce any previously published figures, tables or images. The author declares no conflicts of interest. The author have read and approved the final version of the manuscript and took complete responsibility for the data's integrity and the data analysis's accuracy. Data sharing does not apply to this article as no datasets were generated during the current study; all data were sourced from published literature.