Tag Biomarker

When to eat fatty meals: nutrition researchers discover new “biological lipid metabolism clock”

Just in time of Christmas, scientists from the German Institute for Human Nutrition (DIfE) and Lipotype GmbH have published their results of their research on the influence of fatty breakfasts and dinners on lipid metabolism. Their newly discovered “biological lipid metabolism clock” fills a gap in nutritional medicine to activate nutrition for prevention and intervention, and to research how specific foods at specific times of the day can contribute to our health or disease.


Two groups of scientists, one goal
The research group of PD Dr. Olga Ramich at the German Institute for Human Nutrition (DIfE) and the scientists of Lipotype GmbH share one mission: combat the diseases which plague modern society. Together, they want to activate nutrition and diet as a tool for prevention and intervention of widespread diseases such as diabetes.

Four years ago, during a meetup in Berlin, both agreed that the lack of molecular data about the influence of the diet on lipid metabolism was a black box of scientific mysteries. “In nutrition research, we had come to grips with general recommendations like less sugar and less fat.”, remembers Dr. Christian Klose from Lipotype GmbH, “But the questions we as a group of scientists wanted to answer were: are there foods with measurable health benefits and what happens with us if we eat fatty in the morning or in the evening?” The DIfE research group specialized in nutritional medicine developed a setup for a clinical trial to answer these questions.


A clinical study to answer these questions
In a first step, the metabolism of the health study participants was calibrated through a strict diet plan. After this period, one group of the study participants ate a fatty meal for breakfast and a carbohydrate-rich meal for dinner. The second group received the reversed meal plan. During this last step, blood samples were drawn from all participants before and after each meal.

“We wanted to understand how the lipid metabolism and its hundreds of different lipids in blood plasma react to our diet program.”, explains PD Dr. Olga Ramich from DIfE, “And, we were interested in how these changes in blood plasma lipid levels are linked to insulin sensitivity, which can be a great indicator to identify patients who are prone to developing diabetes.” The crux: traditional lipid analysis was not detailed enough to answer these questions. Which is why the samples were sent to Lipotype for a shotgun lipidomics analysis, a detailed molecular analysis of hundreds of lipids at once.


Lipidomics discovers a new biological clock
The extracted blood plasma lipids were shot into a mass spectrometer. Bioinformatics solutions unveiled 14 lipid classes with a total of 672 different lipids from the mass spectrometer results, and bio-statistical methods converted these into lipidomics charts and graphs. “We discovered a daily lipid metabolism pattern – a biological lipid metabolism clock. This clock responded significantly differently to same meals in the morning than in the evening, and such time-dependent pattern  was found for both high-carb and high-fat meals.”, states Dr. Christian Klose. Next, the lipidomics results were plotted against insulin sensitivity measurements to discover a link between 7 of the 14 lipid classes and insulin sensitivity.

“These results are fundamental to activate nutrition and diet as a tool for prevention and intervention of widespread diseases. It’s the basis to research which specific foods at specific time of the day can help adjust insulin sensitivity to healthy levels and act against diabetes.”, comments PD Dr. Olga Ramich, “Discovering the lipid metabolism clock underlines what our nutritional medicine research group has been emphasizing for years: the concept of an internal clock applies to our metabolism too. Living against this clock is unhealthy and increases the risk for diabetes.”


Resources

1 – Publication: Shotgun lipidomics discovered diurnal regulation of lipid metabolism linked to insulin sensitivity in non-diabetic men
2 – Press Release: The Biological Lipid Metabolism Clock
3 – Pressemitteilung: Die Biologische Fettstoffwechsel-Uhr


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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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New White Paper “Unlocking the Power of Multiomics”

Multiomics approaches harness synergies and eliminate shortcomings to effectively deliver what one omics science alone cannot achieve. Lipotype’s new white paper “Unlocking the Power of Multiomics” highlights why combining genomics with lipidomics makes for a great omics pair.


Genomics for genes, lipidomics for lipids
Being major players in cardiovascular disease research, genomics and lipidomics are perfectly suited for a joint multiomics approach. It matches the capacity to identify genetic predestinations with one or multiple snapshots of the lipid metabolic status.

Linking genotype with phenotype will be critical when it comes to clinical diagnostics and to the goal of identifying the onsets of a disease.


From genotype to phenotype: interconnected disease risk factors
In this study, a large number of SNPs (single-nucleotide polymorphisms) from genomes was drawn. They were then correlated with corresponding lipid species levels from human blood plasma. The blood plasma has been analyzed with Lipotype Shotgun Lipidomics technology. The most important findings are the following.

First, the heritability of lipid species levels was quantified from the SNP correlations. Second, genetic predispositions influencing lipid species levels provide useful information for cardiovascular disease risk predictions. And third, lipidomics yields greater statistical power to identify SNPs with direct roles in lipid metabolism. Lipidomics outperforms traditional lipid panel measures used in clinical routine.


Resources

Lipotype White Paper: Unlocking the Power of Multiomics

Press release: Unlocking the Power of Multiomics – Lipotype releases new Multiomics White Paper


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Lipids in diabetes type 2 research

The Paul Langerhans Institute Dresden (PLID) tackles diabetes by applying lipidomics analysis form Lipotype.


Lipids and diabetes
The PLID frequently applies Lipotype Shotgun Lipidomics services for their molecular diabetes research to step by step reveal lipid-associated cellular processes. Their investigations lead to the definition of molecular lipid signatures that tell about the development on non-diabetic to diabetes type 2 and progression towards diabetes complications.

Furthermore, the PLID is a project partner of Lipotype within the RHAPSODY consortium, an IMI project combining new and existing data to refine diagnosis, promote prevention and support drug discovery for personalized management of diabetes.


About the PLID
In type 1 and type 2 diabetes, destroyed or impaired beta cells cause an elevated blood sugar level. PLID scientists are working on deciphering the mechanisms that lead to the destruction and/or functional impairment of beta cells and are also trying to develop new approaches to replace damaged or destroyed beta cells.

The PLID is a founding partner of the German Center for Diabetes Research (DZD).


Resources

1 – Multi-omics insights into functional alterations of the liver in insulin-deficient diabetes mellitus


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Release of White Paper: Big data lipidomics for lipid biomarker identification

Lipidomics is the large-scale investigation of lipids in biological systems. Venturing into the analysis of large dataset, with potential thousands of lipidomes, by advanced multiparametric statistical approaches is a challenging endeavour. We established algorithms and a full set of methods tailored to quantitative lipid data. This allows to perform statistical analysis and enrichment analysis in order to identify lipid biomarkers. In our recently published white paper a cohort of healthy subjects is compared with a cohort of diseased subjects. The result is a lipid signature that discriminates healthy from disease. Those could then potentially be of use for disease stratification or diagnosis by means of predictive modelling (machine learning). In this white paper, we will guide you through the data analysis process for lipid biomarkers.   Read more


Learn more about Big Data Lipidomics and get the White Paper.


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Lipotype and Lund University publish about lipid profiles in Diabetes and infarction

In the latest issue of the Journal of the American Heart Association, Lipotype and Lund University published a paper in which high-throughput lipidomic screening was successfully applied to reveal molecular lipid profiles in blood serum specific for Diabetes mellitus and myocardial infarction.

Read the whole publication here:

Identification of Shared and Unique Serum Lipid Profiles in Diabetes Mellitus and Myocardial Infarction, , 2016
Sanela Kjellqvist, Christian Klose, Michal A. Surma, George Hindy, Inês G. Mollet, Anna Johansson, Patrick Chavaux, Johan Gottfries, Kai Simons, Olle Melander and Céline Fernandez