Periodontitis and brain magnetic resonance imaging markers of Alzheimer's disease and cognitive aging.

INTRODUCTION: We examined the association of clinical, microbiological, and host response features of periodontitis with MRI markers of atrophy/cerebrovascular disease in the Washington Heights Inwood Columbia Aging Project (WHICAP) Ancillary Study of Oral Health. METHODS: We analyzed 468 participants with clinical periodontal data, microbial plaque and serum samples, and brain MRIs. We tested the association of periodontitis features with MRI features, after adjusting for multiple risk factors for Alzheimer's disease/Alzheimer's disease-related dementia (AD/ADRD). RESULTS: In fully adjusted models, having more teeth was associated with lower odds for infarcts, lower white matter hyperintensity (WMH) volume, higher entorhinal cortex volume, and higher cortical thickness. Higher extent of periodontitis was associated with lower entorhinal cortex volume and lower cortical thickness. Differential associations emerged between colonization by specific bacteria/serum antibacterial IgG responses and MRI outcomes. DISCUSSION: In an elderly cohort, clinical, microbiological, and serological features of periodontitis were associated with MRI findings related to ADRD risk. Further investigation of causal associations is warranted.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models.

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

Alterations in the sarcopenia index are associated with inflammation, gut, and oral microbiota among heart failure, left ventricular assist device, and heart transplant patients.

BACKGROUND: Sarcopenia, characterized by loss of muscle mass and function, is prevalent in heart failure (HF) and predicts poor outcomes. We investigated alterations in sarcopenia index (SI), a surrogate for skeletal muscle mass, in HF, left ventricular assist device (LVAD), and heart transplant (HT), and assessed its relationship with inflammation and digestive tract (gut and oral) microbiota. METHODS: We enrolled 460 HF, LVAD, and HT patients. Repeated measures pre/post-procedures were obtained prospectively in a subset of LVAD and HT patients. SI (serum creatinine/cystatin C) and inflammatory biomarkers (C-reactive protein, interleukin-6, tumor necrosis factor-alpha) were measured in 271 and 622 blood samples, respectively. Gut and saliva microbiota were assessed via 16S ribosomal ribonucleic acid sequencing among 335 stool and 341 saliva samples. Multivariable regression assessed the relationship between SI and (1) New York Heart Association class; (2) pre- versus post-LVAD or HT; and (3) biomarkers of inflammation and microbial diversity. RESULTS: Median (interquartile range) natural logarithm (ln)-SI was -0.13 (-0.32, 0.05). Ln-SI decreased across worsening HF class, further declined at 1 month after LVAD and HT, and rebounded over time. Ln-SI was correlated with inflammation (r = -0.28, p < 0.01), gut (r = 0.28, p < 0.01), and oral microbial diversity (r = 0.24, p < 0.01). These associations remained significant after multivariable adjustment in the combined cohort but not for all individual cohorts. The presence of the gut taxa Roseburia inulinivorans was associated with increased SI. CONCLUSIONS: SI levels decreased in symptomatic HF and remained decreased long-term after LVAD and HT. In the combined cohort, SI levels covaried with inflammation in a similar fashion and were significantly related to overall microbial (gut and oral) diversity, including specific taxa compositional changes.