The market for microbiome analysis is booming. For around 100 to 300 euros, providers promise deep insights into your own immune system and individual nutritional recommendations. By sending in a stool sample , alleged imbalances in the “intestinal flora” can be detected and corrected in a targeted manner. But while the colorful evaluations of many providers suggest clarity and science, a crucial question arises: How meaningful are intestinal flora home tests really?

In order to understand their results – and their limitations – one must first consider the underlying technology.

How do microbiome analyses work?

Genetic material is isolated and analyzed directly from a stool sample. Most intestinal bacteria cannot simply be cultivated in the laboratory. It also takes far too long and is costly. This is why modern microbiome research uses DNA sequencing. This involves analyzing all the genetic material that can be found in a stool sample, regardless of whether it is DNA from bacteria, your food or yourself.

Two methods currently dominate:

1. 16S rRNA gene sequencing
2. Shotgun metagenomics

Both methods differ significantly in terms of informative value, costs and resolution.

16S sequencing – the “fingerprint”

16S rRNA sequencing examines a specific gene that occurs in all bacteria: the 16S rRNA gene.

This gene contains fixed and variable regions that differ between different bacteria. By sequencing these regions, it is possible to determine which bacterial groups are present in a sample. This is relatively inexpensive, technically robust and well suited for comprehensive overview studies.

Disadvantage: The resolution is limited. Bacteria can often only be identified at the genus level, not at the exact species or strain level (Johnson et al., 2019).

16S sequencing is by no means an “outdated method”. It remains a crucial tool in microbiome research – albeit primarily for population-based analyses and ecological questions, not for precise diagnoses of individual people.

Shotgun metagenomics – the “mugshot”

In shotgun metagenomics, on the other hand, the entire DNA of a sample is randomly fragmented and sequenced. The resulting sequences are then analyzed bioinformatically. This allows microorganisms – not just bacteria! – can often be identified down to the species level, sometimes even down to the strain level, as well as metabolic genes and functions.

This method provides significantly more detailed information about the microbiome (Quince et al., 2017).

Disadvantage: significantly more expensive, more data-intensive, more complex evaluation

Microbiome analysis in practice

Preanalytics: The way to the lab

An important factor is sample storage and shipping. Home tests and tests in the consultant’s office are sent by post. Transportation time and temperature can influence the microbial composition. Although many kits today contain DNA stabilizers, storage conditions and transport time can still influence the result.

Why 16S tests can distort frequencies

One technical detail often leads to misinterpretations: the number of copies of the 16S gene. Many bacteria have several copies of this gene. The number can vary between 1 and over 10 copies per genome (Vetrovsky & Baldrian, 2013). This can distort the measured frequency.

Here is a thought experiment:

• 100 bacteria species A → 1 gene copy
• 100 bacteria species B → 5 gene copies

A 16S test therefore measures 100 sequences of species A and 500 sequences of species B.

As a result, it appears as if species B is present five times more frequently – although both species occur equally often. In home tests, this can sometimes lead to certain bacteria “occurring” en masse and others almost disappearing. But only apparently. Shotgun analyses are less susceptible to this effect, as the entire DNA is analyzed here.

What a stool test actually measures

Another important point: home tests analyze stool samples, not the entire gut microbiome directly. The human digestive tract consists of different microbial habitats:

• Small intestine
• Large intestine
• Mucosa (mucous membrane)
• Intestinal contents

Stool samples mainly reflect microorganisms from the last section of the large intestine. The microbiome of other sections of the intestine can differ significantly (Falony et al., 2016). A home test therefore only provides a vague section of the entire system.

The problem of “normal values”

Many home tests use the 16S methodology and rate individual bacteria as “too high” or “too low”. We have already seen that this does not work at all. But there is another fundamental problem: There are currently hardly any clinically validated reference values for individual intestinal bacteria.

The reason: the human microbiome is extremely variable.

Even healthy people can have completely different bacterial compositions (Human Microbiome Project Consortium, 2012).

The microbiome is influenced by:

• Nutrition
• Medication (especially antibiotics)
• Age
• genetic factors
• Geographical origin
• Lifestyle

Even short-term dietary changes (vacation, new cook in the canteen…) can measurably change the microbiome (David et al., 2014).

Different databases

Another problem lies in the reference databases. The assignment of gene sequences to specific bacteria depends heavily on the database and analysis pipeline used. Different laboratories can therefore sometimes calculate different taxonomic profiles from the same sample (Knight et al., 2018). In plain language: Some laboratories identify the same bacteria as different species!

What large microbiome studies really show

In research, microbiomes are usually compared in large groups.

For example, a meta-analysis of over 6300 metagenomes from 36 studies (shotgun method) identified several hundred bacterial species that are statistically associated with disease (Wirbel et al., 2024). However, these results show statistical patterns at population level, not diagnostic thresholds for individuals. Nevertheless, such studies are often cited by consultants as proof that the test method they use (16S rRNA) is useful for individualized therapy.

The example of Alistipes

An illustrative example is the bacterial genus Alistipes. Some studies link certain species to diseases such as depression or intestinal inflammation. Other studies show possible protective effects, for example in connection with immune reactions or metabolic processes (Parker et al., 2020). This highlights a fundamental problem: a single bacterium can rarely be clearly classified as “good” or “bad”.

“But the test helped me.”

Proponents of microbiome testing often refer to positive experiences in practice. Such testimonials are valuable, but can be influenced by several known effects.

Regression to the center

Many symptoms fluctuate over time. People often have tests carried out when their symptoms are particularly severe. A later improvement can therefore also occur without intervention.

Confirmation bias

People remember observations that confirm their expectations more strongly, while contradictory cases remain less present.

Accompanying measures

After a test, many people change their lifestyle at the same time:

• more fiber
• less ultra-processed food
• more movement

These measures have demonstrably positive effects – regardless of the test result.

Is an intestinal flora test worthwhile?

Intestinal flora home tests are based on real scientific methods. Nevertheless, their findings differ significantly from what is possible in scientific studies.

Useful as

• rough orientation on diversity at genus level – don’t panic if certain bacteria are underrepresented

Critical as

• sole basis for food supplements
• Diagnostic tool for diseases
• Individual therapy recommendations

Many of the test recommendations – such as more fiber or less ultra-processed food – make sense. However, they also work without prior microbiome analysis.

I wouldn’t spend money on such tests because I wouldn’t gain enough knowledge from them.

FAQ on microbiome tests

Literature

David, L. A., Maurice, C. F., Carmody, R. N., et al. (2014). Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505, 559-563. https://doi.org/10.1038/nature12820

Falony, G., Joossens, M., Vieira-Silva, S., et al. (2016). Population-level analysis of gut microbiome variation. Science, 352(6285), 560-564.

Human Microbiome Project Consortium (2012). Structure, function and diversity of the healthy human microbiome. Nature, 486(7402), 207-214. https://doi.org/10.1038/nature11234

Johnson, J.S., Spakowicz, D.J., Hong, BY. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun 10, 5029 (2019). https://doi.org/10.1038/s41467-019-13036-1

Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L. I., McDonald, D., Melnik, A. V., Morton, J. T., Navas, J., Quinn, R. A., Sanders, J. G., Swafford, A. D., Thompson, L. R., Tripathi, A., Xu, Z. Z., Zaneveld, J. R., … Dorrestein, P. C. (2018). Best practices for analyzing microbiomes. Nature reviews. Microbiology , 16(7), 410-422. https://doi.org/10.1038/s41579-018-0029-9

Parker BJ, Wearsch PA, Veloo ACM and Rodriguez-Palacios A (2020) The Genus Alistipes: Gut Bacteria With Emerging Implications to Inflammation, Cancer, and Mental Health. Front. Immunol. 11:906. doi: 10.3389/fimmu.2020.00906

Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017). Shotgun metagenomics, from sampling to analysis. Nature biotechnology, 35(9), 833-844. https://doi.org/10.1038/nbt.3935

Vetrovsky, T., & Baldrian, P. (2013). The variability of the 16S rRNA gene in bacterial genomes. PLOS ONE, 8, e57923.

Wirbel, J., Pyl, P. T., Kartal, E., et al. (2024). Shared microbial signatures across diseases revealed by large-scale metagenomic meta-analysis. npj Biofilms and Microbiomes. https://doi.org/10.1038/s41522-024-00567-9