Once fat was fat, and simply that
In the days of yore, carrying a few extra pounds was symbolic of affluence and prosperity in an individual, and being physically fat was implored. Towards the latter stage of the 19th century and the early 20th century, however, insurance industries were noting increased mortalities in individuals that were of a larger weight. The ties between obesity and associated health complications such as cardiovascular disease were becoming increasingly recognized.
Consequently, epidemiological research gained traction in defining what an individual's body weight should be. Queue Adolphe Quetelet, a Belgian mathematician. Quetelet was interested in human growth and conducted several cross-sectional studies that led him to conclude that, aside from the surges in growth occurring after birth and throughout puberty, the "weight increases as the square of the height" – also known as the Quetelet Index.1
The birth of the BMI
If you're thinking that this mathematical formula sounds all too familiar, it might be because the Quetelet Index would later become known as the Body Mass Index (BMI) in 1973, and, despite significant controversy, has been the clinical "gold standard" approach for determining a healthy body weight ever since.
In 2013, Nick Trefethen, a Professor of numerical analysis at the University of Oxford, wrote a letter to The Economist in which he proclaimed:
"SIR - The body-mass index that you (and the National Health Service) count on to assess obesity is a bizarre measure. We live in a three-dimensional world, yet the BMI is defined as weight divided by height squared. It was invented in the 1840s, before calculators, when a formula had to be very simple to be usable. As a consequence of this ill-founded definition, millions of short people think they are thinner than they are, and millions of tall people think they are fatter."
Trefethen's reservations towards the BMI calculation as an indication of healthy bodyweight are shared by scientists across the globe.
Video credit: Mayo Clinic
Athletes with a high muscle mass, such as rugby players, are a pertinent example of where the BMI calculation falls short; many rugby players have been deemed "obese" by the formula, despite being at optimal fitness levels.
Are lipid measures the solution?
The world is facing a global obesity epidemic - one in six adults are obese. It's evident, therefore, that a novel, valid and reliable clinical marker of healthy body weight and obesity is crucial.
A new study published today in PLOS Biology sees an international collaboration of academia and industry experts introduce a novel "revolutionary" approach towards personalized and precision biomedicine.
The researchers, including scientists from TU Dresden, Lipotype GmbH (a spin-off company from the Max Planck Institute for Molecular Cell Biology and Genetics), Lund University in Sweden and the National Institute for Health and Welfare in Finland, have utilized artificial intelligence tools to develop an algorithm that analyzes the human blood plasma lipidome to provide significantly more information on bodyweight and metabolism than the BMI.
The plasma lipidome is composed of an array of lipid molecules. "Together, they are valuable indicators to explore the state of metabolism health of an individual - like a health fingerprint,” says Professor Mathias Gerl, Lipotype. "We used a machine learning algorithm to predict a set of obesity measures (e.g. body mass index (BMI), waist-hip ratio (WHR), and body fat percentage (BFP)) from 1000 plasma lipidomes," he adds.
The study investigated the BMI of 1,000 patients. Using the lipidomic data produced by the novel algorithm, a new "molecular lipidomics BMI" was calculated, which revealed that the molecular BMI was, in several cases, significantly higher than that of the traditional BMI.
In one out of seven patients, the lipidomic BMI improved the "classic" morphometric BMI, providing more data on obesity, such as the levels of visceral fat – a harmful fat deposit. "The models were trained to predict the obesity estimate of patients based on the plasma lipidome. However, they only did this with a certain error. This error is not random but indicates that the lipidome contains additional information about the metabolism of the subject. For example, we see worse clinical characteristics in subjects that are predicted to have a larger BMI than he or she actually had," Gerl told Technology Networks.
A look to the future: replacing BMI with a lipidomic marker
The next steps for the collaboration are to develop lipidomic signatures as a health measure for "multiple areas of medicine". Olle Melander, Lund University, concludes: "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."
1. Eknoyan (2007) Adolphe Quetelet (1796–1874)—the average man and indices of obesity. Nephrology Dialysis Transplantation. https://doi.org/10.1093/ndt/gfm517.
2. Gerl et al. (2019) Machine learning of human plasma lipidomes for obesity estimation in a large population cohort. PLOS Biology. DOI: 10.1371/journal.pbio.3000443.
Mathias Gerl, Head of Data and Statistical Analysis, Lipotype, was speaking with Molly Campbell, Science Writer, Technology Networks.