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Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study

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Abstract

Introduction

Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist.

Aim

Investigating the association between CVDs and anthropometric measurements among Iranian adults.

Methods

For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models.

Results

During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%).

Conclusion

BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction.

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Author notes

  1. Amin Mansoori and Zeinab Sadat Hosseini: Equally first author.

Authors and Affiliations

  1. International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, 99199-91766, Iran

    Amin Mansoori, Rana Kolahi Ahari & Majid Ghayour Mobarhan

  2. Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran

    Amin Mansoori & Habibollah Esmaily

  3. Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran

    Zeinab Sadat Hosseini

  4. Student Research committee, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran

    Mohadeseh Poudineh

  5. Student Research Committee, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran

    Elias Sadooghi Rad

  6. Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Mostafa Mahmoudi Zo, Faezeh Salmani Izadi, Mahdieh Hoseinpour, Amirreza Miralizadeh & Omolbanin Hajhoseini

  7. Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Yalda Alizadeh Mashhadi, Maryam Hormozi & Mohadeseh Taj Firoozeh

  8. Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Omolbanin Hajhoseini

  9. Division of Medical Education, Brighton and Sussex Medical School, Brighton, United Kingdom

    Gordon Ferns

  10. Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

    Habibollah Esmaily

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  1. Amin Mansoori
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Corresponding authors

Correspondence to Habibollah Esmaily or Majid Ghayour Mobarhan.

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Author contributions

Conceptualization, ZSH and HE; data curation, AM; formal analysis, AM; investigation, YM; project administration, HE and MG-M; software, AM; Writing—original draft, ZSH, MP, ESR, MMZ, MH, MH, MTF and OHH; writing—review and editing, RKA, MP, FI and GAF.

Statement of ethics

This study protocol was reviewed and approved by the Ethics Committee of MUMS, approval number IR.MUMS.REC.1386.250.

Conflict of interest statement

The Authors declare that there is no conflict of interest.

Funding sources

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

Data sharing is not applicable to this article as no new data were created in this study. Further enquiries can be directed to the corresponding author.

Appendix

Appendix

By applying the results obtained in Table 2 (Estimate column), the regression formula for predicting CVDs based on significant factors for male and female can be designed as follows:

$$P\left(CVD\right)=\frac{exp\left(f\right) }{1+exp\left(f\right) },$$

where f is obtained from Table 2 as:

$${f}_{male}= \, 0.03\left(BMI\right)+ 0.31\left(BRI\right)-0.05\left(BAI\right)+0.07\left(age\right)-0.03\left(\mathrm{Demispan}\right)-2.11$$
$${f}_{female}=0.05\left(BMI\right) -0.03\left(BAI\right)+0.13\left(age\right)-0.02\left(\mathrm{WC}\right)-9.07$$

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Mansoori, A., Hosseini, Z.S., Ahari, R.K. et al. Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study. High Blood Press Cardiovasc Prev 30, 243–253 (2023). https://doi.org/10.1007/s40292-023-00577-2

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