The results suggest that white coat hypertension or a large white coat effect is not an innocent phenomenon. It tends to co-exist with metabolic risk factors and predicts total and cardiovascular mortality during long-term follow-up.

The collection of fecal material and developments in sequencing technologies have enabled cost-efficient, standardized, and non-invasive gut microbiome profiling. As a result, microbiome composition data from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterized due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a large, well-phenotyped, and representative population cohort (n = 7211, FINRISK 2002; Finland). We report specific taxonomic and functional signatures related to the Enterobacteriaceae family in the human gut microbiome that predict mortality during a 15-year follow-up. These associations can be observed both in the Eastern and Western Finns who have differing genetic backgrounds, lifestyles, and mortality rates. Our results supplement previously reported cross-sectional associations, and help to establish a methodological and conceptual basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status. These findings could serve as a solid framework for microbiome profiling in clinical risk prediction, paving the way towards clinical applications of human microbiome sequencing aimed at prediction, prevention, and treatment of disease.

Observational studies have shown that elevated systolic blood pressure (SBP) is associated with future onset of type 2 diabetes, but whether this association is causal is not known. We applied the Mendelian randomization framework to evaluate the causal hypothesis that elevated SBP increases risk for type 2 diabetes. We used 28 genetic variants associated with SBP and evaluated their impact on type 2 diabetes using a European-centric meta-analysis comprising 37,293 case and 125,686 control subjects. We found that elevation of SBP levels by 1 mmHg due to our genetic score was associated with a 2% increase in risk of type 2 diabetes (odds ratio 1.02, 95% CI 1.01–1.03, P = 9.05 × 10−5). To limit confounding, we constructed a second score based on 13 variants exclusively associated with SBP and found a similar increase in type 2 diabetes risk per 1 mmHg of genetic elevation in SBP (odds ratio 1.02, 95% CI 1.01–1.03, P = 1.48 × 10−3). Sensitivity analyses using multiple, alternative causal inference measures and simulation studies demonstrated consistent association, suggesting robustness of our primary observation. In line with previous reports from observational studies, we found that genetically elevated SBP was associated with increased risk for type 2 diabetes. Further work will be required to elucidate the biological mechanism and translational implications.

We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies.