Abstract
The postmortem microbiome plays an important functional role in host decomposition after death. Postmortem microbiome community successional patterns are specific to body site, with a significant shift in composition 48 h after death. While the postmortem microbiome has important forensic applications for postmortem interval estimation, it also has the potential to aid in manner of death (MOD) and cause of death (COD) determination as a reflection of antemortem health status. To further explore this association, we tested beta-dispersion, or the variability of microbiomes within the context of the "Anna Karenina Principle" (AKP). The foundational principle of AKP is that stressors affect microbiomes in unpredictable ways, which increases community beta-dispersion. We hypothesized that cases with identified M/CODs would have differential community beta-dispersion that reflected antemortem conditions, specifically that cardiovascular disease and/or natural deaths would have higher beta-dispersion compared to other deaths (e.g., accidents, drug-related deaths). Using a published microbiome data set of 188 postmortem cases (five body sites per case) collected during routine autopsy in Wayne County (Detroit), MI, we modeled beta-dispersion to test for M/COD associations a priori. Logistic regression models of beta-dispersion and case demographic data were used to classify M/COD. We demonstrated that beta-dispersion, along with case demographic data, could distinguish among M/COD - especially cardiovascular disease and drug related deaths, which were correctly classified in 79% of cases. Binary logistic regression models had higher correct classifications than multinomial logistic regression models, but changing the defined microbial community (e.g., full vs. non-core communities) used to calculate beta-dispersion overall did not improve model classification or M/COD. Furthermore, we tested our analytic approach on a case study that predicted suicides from other deaths, as well as distinguishing MOD (e.g., homicides vs. suicides) within COD (e.g., gunshot wound). We propose an analytical workflow that combines postmortem microbiome indicator taxa, beta-dispersion, and case demographic data for predicting MOD and COD classifications. Overall, we provide further evidence the postmortem microbiome is linked to the host's antemortem health condition(s), while also demonstrating the potential utility of including beta-dispersion (a non-taxon dependent approach) coupled with case demographic data for death determination.