Comment on “Multiethnic community cardio-metabolic risk factor screening in Northeast Florida: The VIDASANA Project”

Dear Editor,

We read with great interest the study by Velarde et al. [1], examining cardiometabolic risk disparities across multiethnic communities in Northeast Florida.

1. Population selection and comparator validity

Although the recruitment strategy was methodologically sound for community engagement, it produced a sample whose ethnic composition diverged sharply from the region it purported to represent [2]. Non-Hispanic White participants constituted only 9.6% of the final cohort against an estimated 49% regional share, whereas Southeast Asian participants were overrepresented threefold relative to their regional proportion. Because health fair attendance is inherently self-selected, the resulting non-Hispanic White reference group likely reflects individuals motivated to seek screening rather than a demographically typical low-risk population [3]. This distortion carries direct clinical weight: odds ratios anchored to a comparator group of uncertain representativeness risk overstating the relative burden attributed to minoritized groups, a distinction that matters when such estimates inform resource allocation for targeted screening programs in the future. Reporting the expected prevalence of metabolic syndrome under regionally weighted sampling, alongside the observed value, would allow readers to judge how much of the sevenfold odds difference reflects the true disparity versus comparator selection.

2. Screening classification and threshold stability

Metabolic syndrome status was determined using capillary point-of-care lipid and glucose values rather than venous laboratory analysis. Point-of-care platforms have wider measurement variability near diagnostic cutoffs, and because the Adult Treatment Panel III classification depends on a binary threshold crossing rather than a continuous risk gradient, small assay deviations can shift individual participants across the three-criterion boundary [4]. This raises a consequential question for community screening programs: participants near the triglyceride, high-density lipoprotein, or glucose cutoffs may be misclassified in either direction, directly altering referral decisions at the point of care, rather than only affecting prevalence estimates in aggregate.

3. From detection to treatment: an actionable gap

Beyond prevalence, the reported treatment rates reveal a translatable target that is not fully developed in the discussion. Among participants with documented hyperlipidemia, treatment coverage lagged prevalence by approximately 15–18 percentage points in Hispanic, Black, and Southeast Asian participants, compared with a ten-point gap among non-Hispanic White participants. Given that non-Hispanic Black participants had the highest median Framingham risk despite intermediate metabolic syndrome prevalence, closing this treatment gap through statin initiation and lipid management follow-up represents a more immediately actionable lever than further prevalence characterization. To strengthen clinical interpretation, future iterations of this screening model should pair point-of-care detection with an onsite or expedited referral pathway specifically targeting diagnosed but untreated dyslipidemia, rather than relying on downstream primary care engagement that these populations may access inconsistently. A pre-specified analysis linking treatment gap magnitude to subsequent Framingham risk within each ethnic group would convert this cross-sectional observation into a testable target for community intervention design.

4. Conclusion

The VIDASANA project documents cardiometabolic disparities with clear public health implications. Its next contribution will come from clarifying how comparator selection shapes the reported effect sizes and from converting the treatment gap identified within its own data into a structured intervention target, extending the study's value beyond descriptive epidemiology toward measurable risk reduction in the communities it serves.

CRediT authorship contribution statement

S. Gokulnath: Writing – review & editing, Writing – original draft, Visualization. Harish Narayanan: Writing – review & editing, Writing – original draft, Visualization. P.M. Aishwarya: Conceptualization, Data curation, Formal analysis.

Ethical approval

Not applicable.

Declaration of Generative AI and AI-assisted technologies in the writing process

Generative artificial intelligence was used only to improve language clarity and manuscript organization. The scientific interpretation, critical appraisal, and conclusions were independently developed, verified, and approved by the authors. The authors accept full responsibility for the content of this manuscript.

Funding

No funding was received for this work.

Declaration of competing interest

The authors declare that they have no competing interests.

Data availability

No datasets were generated or analyzed during the preparation of this correspondence.