Obesity risk quantification: Lipidomic BMI better than traditional BMI

Obesity is a prime threat to human health. In daily healthcare practice, the go-to indicator of overweight and obesity is the body mass index (BMI). Now, an international team of scientists led by Lipotype introduces a revolutionary A.I. BMI approach towards personalized and precision medicine.


A joint effort of academy and industry
When academy meets industry significant jumps towards the future are possible. Researchers from TU Dresden and Lipotype GmbH, a spin-off of the Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, with the international participation of scientists from Lund University (Sweden) and National Institute for Health and Welfare (Finland) teamed up to critically investigate the BMI of more than 1000 patients. The international research team applied advanced artificial intelligence tools to develop an algorithm which makes use of the human blood plasma lipid composition, the plasma lipidome.


The lipidomic BMI
The plasma lipidome contains hundreds of distinct lipids. “Together, they are valuable indicators to explore the state of metabolism health of an individual – like a health fingerprint”, explains Mathias Gerl from Lipotype. This lipidomic data was used for training the algorithm to predict the BMI of each patient.

In comparison to the ‘household measures’-based BMI, the lipidomic data provided the new algorithm with the power to propose a new ‘molecular lipidomic BMI’. The lipidomic BMI calculation revealed that the molecular BMI was in a number of cases significantly higher than the traditional BMI. In approximately 1 out of 7 patients, the lipidomic BMI improved the classic ‘morphometric BMI’, and provided more information about obesity compared to the traditional BMI measurement, e.g. about the amount of visceral fat, a harmful kind of fat deposit.


The future of BMI
“Long-time consequences can occur when a patient in need for a weight reducing therapy to combat the risk for obesity-associated disease is sent home without remedy”, states Olle Melander from Lund University. “These patients may suddenly suffer from a heart attack at age 40 leaving their doctors puzzled”, comments Carlo Vittorio Cannistraci from the Biotechnology Center (BIOTEC) at the TU Dresden and adds: “We should overcome the obsolete logic that a single marker can help to assess risk in complex systems such as humans. Computational biomedicine adopts artificial intelligence to design multidimensional markers composed of many variables that increase precision of diagnosis. Hence, we hope that the traditional BMI will be replaced with a lipidomic marker to outpace the misclassification of 14% of patients.”


Resources

1 – Publication: Machine learning of human plasma lipidomes for obesity estimation in a large population cohort
2 – Press Release: Obesity risk quantification, a jump towards the future
3 – Pressemitteilung: Adipositas-Risikobestimmung, ein Sprung in die Zukunft
4 – TV news: Blutanalyse soll bei Erkennung von Adipositas helfen (only available until October 28)


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