Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers
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.
KeywordsBariatric 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 was obtained from all individual participants included in the study.
For this type of study formal consent is not required.
- 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
- 6.Kaur J. A comprehensive review on metabolic syndrome. Cardiol Res Pract. 2014;2014:1–21.Google Scholar
- 23.Harrell FE. Regression modeling strategies. 2nd ed. Switzerland: Springer International Publishing; 2015.Google Scholar
- 24.R Core. Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical. Computing. 2019Google Scholar
- 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.Steyerberg E. Clinical prediction models - a practical approach to development. 1st ed. New York: Springer-Verlag; 2009.Google Scholar