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Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers

Obesity Surgery volume 30, pages 714–724 (2020)Cite this article

Abstract

Purpose

The focus of bariatric surgery is reduction of weight, reflected in body mass index (BMI). However, the resolution of comorbidity is a second important outcome indicator. The degree of comorbidity is hard to quantify objectively as comorbidities develop gradually and are interdependent. Multiple scoring systems quantifying comorbidity exist but they lack continuity and objectivity. In analogy with BMI as index for weight, the Metabolic Health Index (MHI) is developed as objective quantification of metabolic health status. Laboratory data were used as comorbidities affect biomarkers. Conversely, laboratory data can be used as objectively obtained variables to describe comorbidity.

Methods

Laboratory data were collected and crosschecked by national quality registry entries. Machine learning was applied to develop an ordinal logistic regression model, using 4 clinical and 32 laboratory input variables. The output was mathematically transformed into a continuous score for intuitive interpretation, ranging from 1 to 6 (MHI).

Results

In total, 4778 data records of 1595 patients were used. The degree of comorbidity is best described by age at phlebotomy, estimated Glomerular Filtration Rate (eGFR), and concentrations of glycated hemoglobin (HbA1c), triglycerides, and potassium. The model is independent of day of sampling and type of surgery. Mean MHI was significantly different between patient subgroups with increasing number of comorbidities.

Conclusion

The MHI reflects severity of comorbidity, enabling objective assessment of a bariatric patient’s metabolic health state, regardless day of sampling and surgery type. Next to weight-focused outcome measures like %TWL, the MHI can serve as outcome measure for metabolic health.

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Acknowledgements

We thank Edwin R. van den Heuvel (Prof) from the Eindhoven University of Technology for providing his insight and expertise that greatly assisted the research. In addition, we thank Carmen Gensen for her assistance in the data analysis process.

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Authors and Affiliations

  1. Department of Clinical Chemistry, Catharina Hospital, P.O. Box 1350, 5602, ZA, Eindhoven, The Netherlands

    Saskia L. M. van Loon, Ruben Deneer, Volkher Scharnhorst & Arjen-Kars Boer

  2. Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands

    Saskia L. M. van Loon, Anna Wilbik & Uzay Kaymak

  3. Expert Center Clinical Chemistry, Eindhoven, The Netherlands

    Saskia L. M. van Loon, Ruben Deneer, Volkher Scharnhorst & Arjen-Kars Boer

  4. Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Ruben Deneer, Natal van Riel, Volkher Scharnhorst & Arjen-Kars Boer

  5. Department of Surgery, Catharina Hospital, Eindhoven, The Netherlands

    Simon W. Nienhuijs

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  1. Saskia L. M. van Loon
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Correspondence to Saskia L. M. van Loon.

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van Loon, S.L.M., Deneer, R., Nienhuijs, S.W. et al. Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers. OBES SURG 30, 714–724 (2020). https://doi.org/10.1007/s11695-019-04244-1

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  • DOI: https://doi.org/10.1007/s11695-019-04244-1

Keywords

  • Bariatric surgery
  • METABOLIC SURGERY
  • Hypertension
  • Diabetes
  • Dyslipidemia
  • Metabolic syndrome
  • Machine learning
  • Biomarkers
  • Value based health care
  • Outcome measure