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Development of the Anthropometric Grouping Index for the Eastern Caribbean Population Using the Eastern Caribbean Health Outcomes Research Network (ECHORN) Cohort Study Data

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  • Israel A. Almodóvar-Rivera

    (Department of Mathematical Sciences, University of Puerto Rico at Mayagüez, Mayagüez 00681, Puerto Rico)

  • Rosa V. Rosario-Rosado

    (Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico at Medical Sciences Campus, San Juan 00936, Puerto Rico)

  • Cruz M. Nazario

    (Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico at Medical Sciences Campus, San Juan 00936, Puerto Rico)

  • Johan Hernández-Santiago

    (Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico at Medical Sciences Campus, San Juan 00936, Puerto Rico)

  • Farah A. Ramírez-Marrero

    (Department of Exercise Physiology, University of Puerto Rico at Río Piedras, San Juan 00925, Puerto Rico)

  • Maxime Nunez

    (School of Nursing, University of the Virgin Islands, St. Thomas, VI 00802, USA)

  • Rohan Maharaj

    (Department of Paraclinical Sciences, University of the West Indies, Saint Augustine, Trinidad and Tobago)

  • Peter Adams

    (Department of Family Medicine, Faculty of Medical Sciences, University of the West Indies, Cave Hill BB11000, Barbados)

  • Josefa L. Martinez-Brockman

    (Equity Research and Innovation Center, Yale School of Medicine, New Haven, CT 06510, USA)

  • Baylah Tessier-Sherman

    (Equity Research and Innovation Center, Yale School of Medicine, New Haven, CT 06510, USA)

  • Marcella Nunez-Smith

    (Equity Research and Innovation Center, Yale School of Medicine, New Haven, CT 06510, USA
    Department of Medicine, Yale School of Medicine, New Haven, CT 06510, USA)

Abstract

Improving public health initiative requires an accurate anthropometric index that is better suited to a specific community. In this study, the anthropometric grouping index is proposed as a more efficient and discriminatory alternative to the popular BMI for the Eastern Caribbean population. A completely distribution-free cluster analysis was performed to obtain the 11 categories, leading to AGI-11. Further, we studied these groups using novel non-parametric clustering summaries. Finally, two generalized linear mixed models were fitted to assess the association between elevated blood sugar, AGI-11 and BMI. Our results showed that AGI-11 tends to be more sensitive in predicting levels of elevated blood sugar compared to BMI. For instance, individuals identified as obese III according to BMI are (POR: 2.57; 95% CI: (1.68, 3.74)) more likely to have elevated blood sugar levels, while, according to AGI, individuals with similar characteristics are (POR: 3.73; 95% CI: (2.02, 6.86)) more likely to have elevated blood sugar levels. In conclusion, the findings of the current study suggest that AGI-11 could be used as a predictor of high blood sugar levels in this population group. Overall, higher values of anthropometric measures correlated with a higher likelihood of high blood sugar levels after adjusting by sex, age, and family history of diabetes.

Suggested Citation

  • Israel A. Almodóvar-Rivera & Rosa V. Rosario-Rosado & Cruz M. Nazario & Johan Hernández-Santiago & Farah A. Ramírez-Marrero & Maxime Nunez & Rohan Maharaj & Peter Adams & Josefa L. Martinez-Brockman &, 2022. "Development of the Anthropometric Grouping Index for the Eastern Caribbean Population Using the Eastern Caribbean Health Outcomes Research Network (ECHORN) Cohort Study Data," IJERPH, MDPI, vol. 19(16), pages 1-9, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:10415-:d:893925
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    References listed on IDEAS

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    1. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
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