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Looking at multiple markers at the same time allows for more nuanced insights than looking at one marker alone. Especially in the case of mRNAs, it makes sense to look at the relative expression values of several markers, as certain mRNAs are closely interwoven in their biological activity. Only by looking at a panel of mRNA can the dynamics and complexity of the pathological condition of a patient’s adipose tissue be captured.

There are significant advantages to looking at relative gene expressions. On the one hand, the expressions measured in a qRT-PCR are measured as deviations from the mean value, so that they can be better compared. On the other hand, the expressions are normalized on the basis of the housekeeping gene. This complex representation requires the use of AI-based statistical methods for analysis.

These four mRNAs represent the central „drivers of pathology“ of adipose tissue in the context of metabolic diseases. Their analysis is crucial for understanding the dynamics of adipose tissue in metabolic diseases. Other mRNAs play only a subordinate role in this context, if they do, and offer little further benefit for diagnostics.

In the human body, a myriad of biological functions have to be fulfilled. A very large number of these are done by genetic activities in the cells, which in turn are controlled by mRNAs. This means that individual mRNAs usually have several functions to fulfill, this also applies to the mRNAs in the lipocyte panel. With regard to the relevant metabolic activities of lipid metabolism, there is therefore no redundancy here, i.e. the activities investigated here are only and exclusively controlled by the mRNA panel patented by Lipocyte BioMed.

The samples of subcutaneous fat tissue are relatively stable. In our own experiments, samples stored at room temperature also delivered valid evaluation results after 14 days. Ideally, however, they should be refrigerated.

The mRNAs studied by Lipocyte BioMed do not show significant differences in different ethnic cohorts.

Interpersonal and intrapersonal differences either do not exist or are so small that they can be neglected.

Interpersonal differences exist only to a very small extent as a random variable in the patient cohort between 19 and 64 years of age.: There are no systematic differences between the sexes, nor between ethnicities (see also 3.1.).

Intrapersonal differences exist to a very small extent depending on the fat deposit from which the samples were taken. However, even the deviations in relative gene expression between samples from the most diverse fat deposits in the human body, visceral fat and subcutaneous fat, are less than 3.0% (own calculations with Bland-Altmann plot). Other intrapersonal deviations, such as circadian differences, are not found in the relatively slow-reacting and stable adipose tissue, in contrast to the blood.

Lipocyte BioMed systematically uses samples from the abdominal subcutaneous fat deposit.

Due to the lack of redundancy (see 1.5), a work-around, i.e. replacement of the technology of the Lipocyte BioMed for the questions selected here, is not possible at the mRNA level.


Switching to the protein level would be extremely challenging, as the number of proteins to be considered is significantly larger and their distribution is much more fluctuating. In any case, protein concentrations in adipose tissue would have to be measured in order to determine the dynamics of adipose tissue. This is then considerably more time-consuming and complicated than the measurement of mRNAs. Measuring the proteins in the blood would be incorrect, since the relevant proteins for activities in adipose tissue are only randomly distributed in the blood.

Other methods can potentially be developed, but they would then be much further away from controlling the metabolic activity of adipose tissue than the analysis of the mRNAs of the lipocyte panel and would thus provide fuzzier information. According to the current state of research, alternatives to the technology of the lipocyte BioMed are likely to provide much less precise and significantly more limited results. In addition, these procedures are likely to be considerably more expensive.


In principle, the values for specificity and significance of the Lipocyte BioMed procedures are very high. The AUCs are above 0.95, and even above 0.98 for some questions.

These high values can be inherently explained as follows. mRNAs are used as biomarkers in the Lipocyte BioMed tests. At the same time, however, they are the regulators of the metabolic process („drivers of pathology“). Thus, they are more closely and precisely linked to the biological processes studied than is the case with other biomarkers, which are often only by-products of a biological process.

The results for the AUC values have been corroborated in independent multicenter studies. The values are stable over multi-year measurement series and over different analytical methods (qRT, PCR and RNAseq).

In principle, a change in the AUC values is to be expected – simply due to increasing dispersion of the results – if the database is further expanded. However, control calculations already show that this will not be significant. It is therefore assumed that very high AUC values above 95% will continue to exist.

Lipocyte BioMed is not based on outdated methods of medical statistics, in which markers are considered on the basis of specific values. In these procedures, the result measured on the patient is compared with the threshold value and the deviation above or below is pathologically classified, depending on the case. in such a method is completely unsuitable for complex relationships in which the interaction of several mRNAs is analyzed.

Lipocyte BioMed investigates the multitude of possible combinations of the four relative gene expressions and compares them to the patient’s gene activity (as measured by the hose-keeping gene). The aim here is to identify patterns that characterize subgroups that are as homogeneous as possible. This analysis is highly complex and cannot be implemented with classical statistics.


Wir erschließen das Potenzial des Fettgewebes

Unterhautfettgewebe (UFG)

Das Unterhautfettgewebe (UFG) spielt eine wichtige Rolle bei der Entstehung und Modulation von Stoffwechselerkrankungen. Es bietet vollkommen neue Ansätze für eine personalisierte Medizin.

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mRNAs sind ein Schlüssel zur personalisierten Medizin

mRNAs (messenger RNAs)

mRNAs (Messenger-RNAs) übermitteln genetische Informationen im Körper. Sie sind maßgeblich an der Modulation von Stoffwechselprozessen beteiligt, insbesondere an Prozessen des Energiestoffwechsels. Bei Stoffwechselerkrankungen können sie als „drivers of pathology“ angesehen werden.

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Wir gehen neue Wege in der Diagnostik, der Therapie und der Prävention


Die mRNA-Analyse des UFG bietet völlig neue Einblicke in den Energiestoffwechsel. Insbesondere bei Stoffwechselerkrankungen spielen die aktuelle Dynamik des Körpers und Umwelteinflüsse eine weitaus größere Rolle als die genetische Disposition des Patienten.

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Der Schlüssel zur erfolgreichen Behandlung von Stoffwechselerkrankungen liegt im Eingriff in die der Krankheit zugrundeliegenden biologischen Prozesse. Da die mRNA des UFG in zentraler Weise in die Pathologie dieser Erkrankungen involviert sind, bieten sie einen starken Hebel für die Therapie.

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Ein großer Vorteil der mRNA-Analyse des Unterhautfettgewebes liegt in der sehr frühen Erkennung von Fehlentwicklungen des Stoffwechsels, lange bevor in der Blutanalyse erste Anzeichen erkennbar werden oder körperliche Symptome auftreten. Sie ist daher ein wichtiger Baustein in der präventiven personalisierten Medizin.

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