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

  • Saskia L. M. van LoonEmail author
  • Ruben Deneer
  • Simon W. Nienhuijs
  • Anna Wilbik
  • Uzay Kaymak
  • Natal van Riel
  • Volkher Scharnhorst
  • Arjen-Kars Boer
Original Contributions



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.


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).


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.


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.


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



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.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Ethical Approval

For this type of study formal consent is not required.

Supplementary material

11695_2019_4244_MOESM1_ESM.pptx (239 kb)
ESM 1. (PPTX 238 kb).


  1. 1.
    Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet. 2014;384(9945):766–81.CrossRefGoogle Scholar
  2. 2.
    Buchwald H, Avidor Y, Braunwald E, et al. Bariatric surgery: a systematic review and meta-analysis. JAMA. 2004;292(14):1724–37.CrossRefGoogle Scholar
  3. 3.
    The GBD 2015 Obesity Collaborators, Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27.Google Scholar
  4. 4.
    Frühbeck G. Bariatric and metabolic surgery: a shift in eligibility and success criteria. Nat Rev Endocrinol. 2015;11(8):465–77.CrossRefGoogle Scholar
  5. 5.
    Sjöholm K, Anveden Å, Peltonen M, et al. Evaluation of current eligibility criteria for bariatric surgery: diabetes prevention and risk factor changes in the Swedish Obese Subjects (SOS) study. Diabetes Care. 2013;36(5):1335–40.CrossRefGoogle Scholar
  6. 6.
    Kaur J. A comprehensive review on metabolic syndrome. Cardiol Res Pract. 2014;2014:1–21.Google Scholar
  7. 7.
    Adams TD, Davidson LE, Litwin SE, et al. Weight and metabolic outcomes 12 years after gastric bypass. N Engl J Med. 2017;377(12):1143–55.CrossRefGoogle Scholar
  8. 8.
    Fried M, Yumuk V, Oppert JM, et al. Interdisciplinary European guidelines on metabolic and bariatric surgery. Obes Surg. 2014;24(1):42–55.CrossRefGoogle Scholar
  9. 9.
    Monteiro R, and Azevedo I. Chronic inflammation in obesity and the metabolic syndrome. Mediators Inflamm. vol. 2010, Article ID 289645, 10 pages, 2010. CrossRefGoogle Scholar
  10. 10.
    Han TS, and Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Dis. 2016;5 CrossRefGoogle Scholar
  11. 11.
    O’Neill S, Bohl M, Gregersen S, et al. Blood-based biomarkers for metabolic syndrome. Trends Endocrinol Metab. 2016;27(6):363–74.CrossRefGoogle Scholar
  12. 12.
    Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. The Lancet. 2005;365(9468):1415–28.CrossRefGoogle Scholar
  13. 13.
    Sikaris KA. The clinical biochemistry of obesity. Clin Biochem Rev. 2004;25(3):165–81.PubMedPubMedCentralGoogle Scholar
  14. 14.
    Alberti KGM, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition. The Lancet. 2005;366(9491):1059–62.CrossRefGoogle Scholar
  15. 15.
    Eckel RH, Alberti K, Grundy SM, et al. The metabolic syndrome. The Lancet. 2010;375(9710):181–3.CrossRefGoogle Scholar
  16. 16.
    Sharma AM, Kushner RF. A proposed clinical staging system for obesity. Int J Obes. 2009;33(3):289–95.CrossRefGoogle Scholar
  17. 17.
    Gill RS, Karmali S, Sharma AM. The potential role of the Edmonton obesity staging system in determining indications for bariatric surgery. Obes Surg. 2011;21(12):1947–9.CrossRefGoogle Scholar
  18. 18.
    DeBoer MD, Gurka MJ. Clinical utility of metabolic syndrome severity scores: considerations for practitioners. Diabetes Metab Syndr Obes Targets Ther. 2017;10:65–72.CrossRefGoogle Scholar
  19. 19.
    Cubeddu LX, Hoffmann IS. Metabolic syndrome: an all or none or a continuum load of risk? Metab Syndr Relat Disord. 2011;10(1):14–9.CrossRefGoogle Scholar
  20. 20.
    Wijndaele K, Beunen G, Duvigneaud N, et al. A continuous metabolic syndrome risk score. Diabetes Care. 2006;29(10):2329.CrossRefGoogle Scholar
  21. 21.
    Soldatovic I, Vukovic R, Culafic D, et al. siMS Score: simple method for quantifying metabolic syndrome. PLOS ONE. 2016;11(1):e0146143.CrossRefGoogle Scholar
  22. 22.
    Wiley JF, Carrington MJ. A metabolic syndrome severity score: a tool to quantify cardio-metabolic risk factors. Prev Med. 2016;88:189–95.CrossRefGoogle Scholar
  23. 23.
    Harrell FE. Regression modeling strategies. 2nd ed. Switzerland: Springer International Publishing; 2015.Google Scholar
  24. 24.
    R Core. Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical. Computing. 2019Google Scholar
  25. 25.
    Poelemeijer YQ, Liem RS, Nienhuijs SW. A Dutch nationwide bariatric quality registry: DATO. Obes Surg. 2018;28(6):1602-10.CrossRefGoogle Scholar
  26. 26.
    James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning - with applications in R. 1st ed. New York; Springer-Verlag; 2013 Google Scholar
  27. 27.
    Steyerberg E. Clinical prediction models - a practical approach to development. 1st ed. New York: Springer-Verlag; 2009.Google Scholar
  28. 28.
    Rothman MJ, Rothman SI, Beals J. Development and validation of a continuous measure of patient condition using the electronic medical record. J Biomed Inform. 2013;46(5):837–48.CrossRefGoogle Scholar
  29. 29.
    Cohen DD, Gómez-Arbeláez D, Camacho PA, et al. Low muscle strength is associated with metabolic risk factors in Colombian children: the ACFIES study. PLoS ONE. 2014;9(4):e93150CrossRefGoogle Scholar
  30. 30.
    Kim J, Tanabe K, Yokoyama N, et al. Association between physical activity and metabolic syndrome in middle-aged Japanese: a cross-sectional study. BMC Public Health. 2011;11:624.CrossRefGoogle Scholar
  31. 31.
    Ekblom Ö, Ekblom-Bak E, Rosengren A, et al. Cardiorespiratory fitness, sedentary behaviour and physical activity are independently associated with the metabolic syndrome, results from the SCAPIS pilot study. PLOS ONE. 2015;10(6):e0131586.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Clinical ChemistryCatharina HospitalEindhovenThe Netherlands
  2. 2.Department of Industrial Engineering and Innovation SciencesEindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Expert Center Clinical ChemistryEindhovenThe Netherlands
  4. 4.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  5. 5.Department of SurgeryCatharina HospitalEindhovenThe Netherlands

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