Methodological reflections on a nutritional status-based nomogram for predicting cognitive impairment in elderly hypertensive patients

Abstract

This letter critiques the article by Xu et al in World Journal of Psychiatry, which developed a nomogram to predict cognitive impairment in elderly hypertensive patients using nutritional and biochemical parameters. While the model’s use of variables like body mass index, albumin, hemoglobin, alkaline phosphatase, and mini-nutritional assessment scores is promising, we raise concerns about the small validation cohort size, potential reverse causality in cross-sectional data, insufficiently discussed mechanisms for alkaline phosphatase as a risk factor, omission of key cognitive predictors, and possible overfitting given high area under the curve values. We suggest external validation, longitudinal studies, and calibration metrics to enhance the model’s robustness and clinical utility.

Key Words: Cognitive impairment; Hypertension; Elderly patients; Nomogram; Biochemical markers; Reverse causality

Core Tip: This critique highlights key methodological limitations in Xu et al’s predictive model for cognitive impairment in elderly hypertensive patients. Chief concerns include the small validation sample, potential reverse causality due to the cross-sectional design, insufficient discussion of alkaline phosphatase as a biomarker, omission of key cognitive risk factors, and possible overfitting. We recommend external validation, longitudinal analysis, incorporation of psychosocial and genetic variables, and rigorous calibration to improve reliability and clinical applicability.

TO THE EDITOR

We read with great interest the recent article by Xu et al[1], published in the World Journal of Psychiatry. The authors’ use of a nomogram incorporating accessible clinical indicators body mass index (BMI), albumin (ALB), hemoglobin (Hb), alkaline phosphatase (ALP), and mini-nutritional assessment scores represents a commendable step toward practical, individualized risk stratification in a vulnerable population. However, we wish to raise several methodological and interpretative concerns that warrant further clarification and investigation.

While the derivation cohort comprised 200 patients, the external validation was performed on a relatively small cohort of 60 individuals, including only 14 cases of cognitive impairment. This limited sample size undermines statistical power and raises concerns about the model’s robustness and generalizability. External validation in larger, multi-center cohorts with ethnically and geographically diverse populations is essential to assess true clinical utility.

The study identifies ALP as a risk factor, while BMI, ALB, and Hb are noted as protective. However, given the cross-sectional design, causality cannot be established. Nutritional and biochemical alterations may be consequences not cause of cognitive impairment. For instance, patients with existing cognitive decline may experience reduced dietary intake, weight loss, or decreased mobility, which in turn may lead to declining ALB, BMI, and Hb[2,3]. Longitudinal studies are needed to clarify the directionality of these associations.

ALP is reported as an independent predictor of cognitive impairment, but its mechanistic link to cognitive decline remains insufficiently discussed. ALP levels are influenced by hepatic function, bone turnover, and systemic inflammation, which may confound their relationship with cognitive outcomes[4]. Elevated ALP has been associated with vascular calcification neuroinflammation, and blood-brain barrier dysfunction, all of which are plausible pathways for cognitive impairment[5-9]. For example, vascular calcification may impair cerebral blood flow, while neuroinflammation could exacerbate neuronal damage[10]. Further exploration of these mechanisms could enhance the interpretability and translational potential of this finding.

The model focuses narrowly on nutritional and biochemical markers but excludes well-recognized predictors of cognitive decline, such as educational attainment, depressive symptoms, and APOE genotype. These variables are integral to cognitive risk profiling, as demonstrated in models like cardiovascular risk factors, aging, and incidence of dementia and National Institute of Aging-Alzheimer’s Association frameworks[6,7]. Their exclusion may limit the model’s comprehensiveness and predictive accuracy. Future iterations could benefit from a more holistic approach, incorporating psychosocial and genetic variables.

The reported area under the curve (AUC) values-0.921 in the derivation cohort and 0.980 in the validation cohort are unusually high and raise the possibility of overfitting, particularly in a small validation dataset. High AUCs in small samples may indicate model instability, as cautioned by Harrell’s guidelines on predictive modeling[8]. Additionally, the model’s calibration, such as the Brier score or calibration plots, is not reported, which is critical for assessing the alignment between predicted and observed outcomes. Bootstrapping or cross-validation techniques could further clarify the model’s stability and generalizability. While the visual nomogram is an appealing tool, its real-world integration into routine clinical practice has not yet been demonstrated in clinical practice, particularly in resource-limited primary care settings. Embedding such tools into electronic health records or mobile platforms, as demonstrated in other clinical decision support systems[11,12], could enhance accessibility and adoption.

In conclusion, Xu et al[1] contribute meaningfully to understanding cognitive decline in elderly hypertensive patients, particularly by emphasizing nutritional status, a modifiable domain. However, several methodological limitations must be addressed, particularly concerning validation, confounding, and model scope, before this model can be confidently applied in clinical practice. We encourage the authors to pursue external validation in broader populations, incorporate additional cognitive and psychosocial risk markers, and engage in collaborative efforts to refine predictive models. Such academic dialogue will advance methodological rigor and ultimately improve clinical tools for cognitive risk prediction.