After the samples were received and sequenced, the relative abundance of the 16 VMBs was compared using Lactobacillus as a reference in each cohort. To differentiate the VMB profile, the relative level of microorganism abundance was applied for each cohort. The significant personal factors and microbiome were expressed as a number for categorical variables and mean ± SD for continuous variables. Analysis of variance (ANOVA) was used to compare the demographic factors. A p-value Fig 1. Flowchart for identifying biomarkers between vaginal microbiota and HPV status.
In the pilot stage, we focused on 17 VMBs (vaginal microbiomes), including nine community state types (CSTs) and eight gynecological diseases from the literature [29–31] through metagenomic sequencing. Overall, metagenomic sequencing identified 17 species in 9 clades (Fig 2A and 2B and S2 Text). Lactobacillus genus microorganisms were predominant in the VMB of the three cohorts, composing over 80% in most samples, agreeing closely with the patterns in previous vaginal samples . In particular, Lactobacillus iners was identified as the predominant species among all three cohorts in this study (Fig 2A & 2B), with Lactobacillus crispatus being the second most abundant.
Legend: (A) Pie charts show the relative microorganism abundance between the three cohorts. Proportion was calculated from the average value of abundance for each group by CST type. (B) Bar charts showing the proportion of dominant species in each sample. Selected microorganism level was selected from the CST type to show the relative abundance and characterization. (C) Bar charts showing the proportion of pathogenic microorganism species as indicated in the key.
Pathogenic Gardnerella spp had a higher presence in HPV current or past infections. However, atopobium was only substantially observed in HPV-positive samples. On the other hand, all of the nine previously were identified as pathogenic gynecological infection-causing pathogens on Trachoma chlamydia, Neisseria gonorrheae, Microureaplasma, Mycoplasma hominis, Candida albicans, Prevotella bivia, Diallisteria, Streptococcus agalactiae and Timona prevotella. They were presented in only minor proportions at 0.40% ± 2.45% among the three cohorts (Fig 2C).
Translational eHealth platform
The eHealth application is used to interact with the participants to manage HPV test results and to collect three personal character groups and two participant-reported outcome (PRO) groups related to HPV infections (Fig 3). Participants were requested to answer a list of questions related to several factors, including simple biometrics (age, body mass index (BMI), demographic state (education, occupation, salary and marital status), medical history (six factors), substance abuse (six factors), lifestyle (six factors) and sexual history and behavior (six factors), which may affect the risk of being infected with HPV or other pathogenic microorganisms (Fig 3).
Legend: https://besthookupwebsites.org/fruzo-review/ IPAQ = international physical activity questionnaires. DBI = diet balance index. PSQI = Pittsburgh sleep quality index. PHQ-9 = Patient Depression Questionnaire-9. GAD 7 = Generalized Anxiety Disorder 7.
When the participants were positive for any of the HPV serotypes covered by the HPV test, they were prompted to update the eHealth questionnaires every three months. Then, the program prompts the participant to provide updates on PROs, seroconversion period, additional confirmatory diagnostic test results, and updates on their medical history, immunization (HPV vaccination) history, lifestyle changes such as starting new sports activities, changes to their usual diet, quality of sleep and psychological status, substance abuse smoking, alcohol, sexual history and behavior.
Personal risk factors: Precision medicine
The results of the 32 PROs and their statistical association with the three types of HPV status are shown in Fig 4 (S3 Table). Demographic factors were significantly correlated with HPV status on age and salary, while education, career, BMI and matrial status were weakly correlated (Fig 4A). Fig 4B shows the medical history, including history of disease or current infection. Both reproductive tract infection (RTI) and a history of consanguineous hereditary or nonhereditary cancer seem potentially related to HPV-positive cases. Fig 4C shows the behavior factors and their association with HPV status. Malnourishment was a pseudosignificant factor for HPV infection. However, the number of days smoking and daily cigarette consumption were not correlated with HPV status. This suggests that smoking and alcohol consumption may ultimately be indirect demographic factors (Fig 4E).