Fat facts instead of BMI

How Lipotype is revolutionizing personalized medicine with cutting-edge lipid analysis

The Body Mass Index (BMI) is not enough to assess overweight and metabolic health. Lipid analysis is far more precise – offering deep insights into human metabolism. Dresden-based Lipotype GmbH develops high-resolution analyses that open up new avenues in research, diagnostics, and therapy. Supported by leap:up in its international growth and technological development, the company is setting new standards for personalized medicine – far beyond BMI.

BMI – An inadequate health indicator

According to an international study involving the WHO, one in eight adults worldwide is affected by obesity. Because obesity increases the risk of numerous diseases such as diabetes, cardiovascular conditions, and cancer, it is one of the greatest health threats of our time. In everyday medical practice, obesity is usually determined by the Body Mass Index (BMI), which relates body weight to height. But the method has major weaknesses: it cannot distinguish between muscle and fat mass and ignores individual metabolic profiles. As a result, some patients with a seemingly normal BMI are still at high risk of obesity-related diseases, while overweight but metabolically healthy individuals may be misclassified. This is where a more modern and precise method comes in: lipid analysis.

Lipid analysis – A closer look at metabolism

The lipid composition of human blood plasma – the so-called plasma lipidome – provides far more detailed information about health than BMI. With the help of artificial intelligence, individual lipid molecules can be identified that serve as precise indicators of obesity and other metabolic disorders. The plasma lipidome comprises hundreds of different lipids that reflect metabolic health like a unique fingerprint. This allows obesity to be assessed on a molecular level – far more accurately than with conventional methods. Risks can be detected early and addressed in a targeted way – before serious diseases develop.

Lipotype – A pioneer in lipid analysis

Dresden-based Lipotype GmbH is a global leader in lipidomics. Using state-of-the-art mass spectrometry, the company precisely analyzes the lipid composition of a wide range of biological samples. Lipotype’s platform can identify more than 4,200 different lipid molecules across more than 100 lipid classes, giving researchers in biotechnology, pharmaceuticals, nutrition, cosmetics, and academia deep insights into the role of lipids in health and disease.

With these high-resolution analyses, Lipotype makes a major contribution to identifying new disease biomarkers, improving diagnostic procedures, and developing new therapeutic approaches. The success of its technology is evident in advances in diabetes treatment, neurodegenerative disease research, and obesity prevention. Lipotype Lipidomics is robust and reproducible, combining strict quality control with advanced data visualization and statistical analysis. The company translates complex datasets into actionable results – within just two weeks.

“We are currently developing a lipidomics test kit based on dried blood spot technology, which will support decentralized clinical studies and reduce patient sampling to a simple finger prick. Market launch is planned for 2025. We serve customers worldwide, with more than 50% of our revenue generated outside the EU,” says Oliver Uecke, COO of Lipotype.

Since its founding in 2012 as a spin-off from the Max Planck Institute of Molecular Cell Biology and Genetics, Lipotype has helped shape the field of lipidomics. Key milestones include: market entry in 2014, launch of an online shop in 2016, introduction of Lipotype Skin Lipidomics in 2017, doubling of analytical capacity in 2020, founding of the US subsidiary in Cambridge, Massachusetts in 2022, and expansion of international sample transfer in 2024. For 2025, the company is pursuing ISO 17025 accreditation, which will provide customers with even greater support in the analysis and interpretation of clinical study results.