Scientists have redefined obesity by discovering two main subtypes

Newswise – Grand Rapids, Michigan (September 12, 2022) – Team led Van Andel Institute Scientists have identified two distinct types of obesity with physiological and molecular differences that may have lifelong consequences for health, disease and response to medication.

the findings, Published today in the magazine nature metabolismprovides a more accurate understanding of obesity than current definitions and may one day offer more accurate ways to diagnose and treat obesity and its associated metabolic disorders.

The study also reveals new details about the role of epigenetics and chance in health and provides insight into the relationship between insulin and obesity.

“Approximately two billion people worldwide are overweight and there are more than 600 million people who are obese, yet we don’t have a framework for dividing individuals according to their most precise aetiology,” he said. Andrew Pospisilic, Ph.D.President of the Van Andel Institute Epigenetics department and the corresponding author of the study. “Using a purely data-driven approach, we are seeing for the first time that there are at least two different subtypes of metabolic obesity, each with its own physiological and molecular features that affect health. Translating these findings into a clinically usable test could help clinicians provide more accurate patient care. “.

Currently, obesity is diagnosed using body mass index (BMI), an indicator of body fat that results from comparing weight for height. It’s an imperfect measure, Pospisilic says, because it doesn’t take into account basic biological differences and can skew an individual’s health status.

Using a combination of in vitro studies in rat models and in-depth analysis of data from TwinsUK, a leading research source and a UK-developed study group, Pospisilik and his collaborators discovered four metabolic subtypes that affect individual body types: two prone to being thin and two prone to obesity.

One subtype of obesity is characterized by increased fat mass while the other is characterized by greater fat mass and lean muscle mass. Somewhat surprisingly, the team found that type 2 obesity is also associated with increased inflammation, which can increase the risk of some cancers and other diseases. Both subtypes have been observed across multiple study groups, including in children. These insights are an important step toward understanding how these different types affect disease risk and response to treatment.

After identifying the subtypes in the human data, the team verified the results in mouse models. This approach allowed the scientists to compare individual, genetically identical mice, which grew up in the same environment and fed the same amounts of food. The study revealed that the inflammatory subtype appears to be caused by genetic changes caused by pure chance. They also found that there did not appear to be a middle ground – the genetically identical sister mice either grew larger or stayed smaller, with no gradation between them. A similar pattern was seen in data from more than 150 human twin pairs, nearly all of whom were genetically homozygous.

“Our in vitro findings nearly carbon-replicated the human twin data. We again saw two distinct subtypes of obesity, one of which appeared to be epigenetically ‘inducible’, and was characterized by higher lean mass and lipids, higher inflammatory signals, and higher insulin levels, And a strong genetic imprint,” Pospisilic said.

Depending on the account and traits involved, only 30% to 50% of human trait results can be linked to genetics or environmental influences. This means that up to half of us are doomed to something else. This phenomenon is called unexplained apparent variance (UPV) and it presents a challenge and an untapped potential for scientists like Pospisilic and his collaborators.

The study suggests that the roots of UPV likely lie in epigenetics, the processes that govern when and to what extent instructions in DNA are used. Epigenetic mechanisms are the reason why individuals with the same genetic instruction manual, such as twins, may develop different traits, such as eye color and hair color. Epigenetics also presents puzzling targets for precision therapy.

“It’s hard to study this unexplained difference, but the payoff from a deeper understanding is enormous,” Pospisilic said. “Epigenetics can work like a light switch that turns genes ‘on’ or ‘off’, which can boost health or, when things go wrong, disease. There is no account for UPV in precision medicine at the moment, but it looks like it might be Half the puzzle. Today’s findings underscore the power of recognizing these subtle differences between people to guide more precise ways to treat disease.”

Pospisilic hopes the team’s findings will inform the development of future precision medicine strategies and lead to a version of their method that can be used in physicians’ offices to better understand patients’ health and deliver health care.


Chih-Hsiang Yang, Ph.D. , and Luca Fagnocchi, Ph.D. , of VAI are the study’s first co-authors. Other authors are Stefanos Apostle, MS, Vanessa Wegert, MA, Ilaria Panzeri, Ph.D. , Darrell P. Chandler, Ph.D., Di Lu, Ph.D. , Tao Yang, Ph.D. , Elizabeth Gibbons, Ph.D., Rita Guerrero, Ph.D., and Jose Bras, Ph.D. from VAI; Erez Dror, Ph.D., Stephen Heine, Ph.D., even Wörpel of the Max Planck Institute for Immunobiology and Epigenetics; Salvador Cassani Galdon, Ph.D. Bioinformatics at BioBam; Katherine Landgrave, Ph.D., University of Leipzig; Martin Thomassen, Louise J. Gronet, Ph.D., and Alan Fagg, MD, PhD, MD, of Rijschuspitalt; Lynn Gilberg, Ph.D., University of Copenhagen; Eileen Grundberg, Ph.D., of the Children’s Mercy Research Institute; Ana Conesa, Ph.D. from the Spanish National Research Council and the University of Florida; Antje Koerner, MD, University of Leipzig and Helmholtz Institute for Metabolic, Obesity, and Vascular Research; And the permute. The authors thank the facilities of MPI-IE, Van Andel Institute for Bioinformatics and Basic Biostatistics, Core Genomics, Optical Imaging Core, Pathology and Biorepository Core, and Vivarium Core. Access to the paired data was generously provided by UKTwins, without which this study would not have been possible.

The research presented in this publication was supported by the Van Andel Institute. Max Planck Gesellschaft; The European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska-Curie Grant Agreement No. 675610; Novo Nordisk Foundation and European Foundation for the Study of Diabetes; Danish Council for Independent Research; The National Human Genome Research Institute of the National Institutes of Health under Award No. R21HG011964 (Pospisilic); and the National Institutes of Health Joint Fund, through the Office of the Director of the National Institutes of Health (OD), and the National Human Genome Research Institute of the National Institutes of Health under Award No. R01HG012444 (Pospisilk and Nadio). The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other donor organizations. Approximately 5% ($50,000) of funding for this study came from federal sources; Approximately 95% ($950,000) is from non-US government sources.


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