<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Publications on Dönertas Group</title><link>https://donertas-group.github.io/categories/publications/</link><description>Recent content in Publications on Dönertas Group</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Fri, 26 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://donertas-group.github.io/categories/publications/index.xml" rel="self" type="application/rss+xml"/><item><title>New review paper published in FEBS Letters!</title><link>https://donertas-group.github.io/news/2026-06-26-febs-review/</link><pubDate>Fri, 26 Jun 2026 00:00:00 +0000</pubDate><guid>https://donertas-group.github.io/news/2026-06-26-febs-review/</guid><description>&lt;p&gt;We are excited to share our new review article, &amp;ldquo;Design and analysis strategies for robust microbiome ageing research&amp;rdquo;. The gut microbiome is one of the promising biomarkers and intervention targets in ageing biology. Yet the field faces a reproducibility problem, and methodology is at its core.&lt;/p&gt;
&lt;p&gt;In this review, we present an integrated framework organised around five methodological challenges:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Confounding &amp;amp; collinearity&lt;/strong&gt; — Chronological age cannot be varied independently of diet, polypharmacy, or lifestyle. We discuss DAGs, mediation analysis, and staggered cohort designs to disentangle these effects.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Selection bias&lt;/strong&gt; — Ageing cohorts are systematically enriched for healthy survivors. Centenarian microbiome profiles may partly reflect selective survival rather than typical ageing. Deliberate inclusion of frail and multimorbid individuals is essential.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Biological heterogeneity&lt;/strong&gt; — Microbiomes fluctuate on short timescales and diverge across decades. Single time points capture transient states rather than representative baselines. Mixed-effects models and dispersion testing are necessary to partition these variance components.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Machine learning &amp;amp; ageing clocks&lt;/strong&gt; — Batch effects can enable a model to function as a cohort detector rather than a genuine biological clock. Batch-aware, leave-one-cohort-out validation is the minimum standard for demonstrating generalisability.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Mendelian randomisation&lt;/strong&gt; — MR offers causal leverage, yet low taxon heritability, compositionality, and age-dependent genetic architecture each pose specific challenges requiring transparent reporting and triangulation with longitudinal evidence.&lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;We conclude with a practical checklist (Box 1) and visual workflow (Fig. 1) spanning study design through to reporting.&lt;/p&gt;</description></item></channel></rss>