Censored data considerations and analytical approaches for salivary bioscience data

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay’s measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data

Oral microbial communities in children, caregivers, and associations with salivary biomeasures and environmental tobacco smoke exposure

Human oral microbial communities are diverse, with implications for oral and systemic health. Oral microbial communities change over time; thus, it is important to understand how healthy versus dysbiotic oral microbiomes differ, especially within and between families. There is also a need to understand how the oral microbiome composition is changed within an individual including by factors such as environmental tobacco smoke (ETS) exposure, metabolic regulation, inflammation, and antioxidant potential. Using archived saliva samples collected from caregivers and children during a 90-month follow-up assessment in a longitudinal study of child development in the context of rural poverty, we used 16S rRNA gene sequencing to determine the salivary microbiome. A total of 724 saliva samples were available, 448 of which were from caregiver/child dyads, an additional 70 from children and 206 from adults. We compared children’s and caregivers’ oral microbiomes, performed “stomatotype” analyses, and examined microbial relations with concentrations of salivary markers associated with ETS exposure, metabolic regulation, inflammation, and antioxidant potential (i.e., salivary cotinine, adiponectin, C-reactive protein, and uric acid) assayed from the same biospecimens. Our results indicate that children and caregivers share much of their oral microbiome diversity, but there are distinct differences. Microbiomes from intrafamily individuals are more similar than microbiomes from nonfamily individuals, with child/caregiver dyad explaining 52% of overall microbial variation. Notably, children harbor fewer potential pathogens than caregivers, and participants’ microbiomes clustered into two groups, with major differences being driven by Streptococcus spp. Differences in salivary microbiome composition associated with ETS exposure, and taxa associated with salivary analytes representing potential associations between antioxidant potential, metabolic regulation, and the oral microbiome

Intestinal-derived FGF15 protects against deleterious effects of vertical sleeve gastrectomy in mice

Bariatric surgeries such as the Vertical Sleeve Gastrectomy (VSG) are invasive but provide the most effective improvements in obesity and Type 2 diabetes. We hypothesized a potential role for the gut hormone Fibroblast-Growth Factor 15/19 which is increased after VSG and pharmacologically can improve energy homeostasis and glucose handling. We generated intestinal-specific FGF15 knockout (FGF15INT-KO) mice which were maintained on high-fat diet. FGF15INT-KO mice lost more weight after VSG as a result of increased lean tissue loss. FGF15INT-KO mice also lost more bone density and bone marrow adipose tissue after VSG. The effect of VSG to improve glucose tolerance was also absent in FGF15INT-KO. VSG resulted in increased plasma bile acid levels but were considerably higher in VSG-FGF15INT-KO mice. These data point to an important role after VSG for intestinal FGF15 to protect the organism from deleterious effects of VSG potentially by limiting the increase in circulating bile acids.http://deepblue.lib.umich.edu/bitstream/2027.42/169579/2/s41467-021-24914-y.pdfAccepted versio

Science of interdisciplinary salivary bioscience: history and future directions.

Salivary bioscience is noteworthy in its history, as well as in the breadth and scope of its impact. The minimally invasive nature of sampling oral fluid allows for evaluation of individual and intra-individual change in biological processes in ways and settings not possible with traditional biospecimens. The range of measurements is expansive (e.g., DNA, hormones, cytokines, antibodies) and modern technologies enable simultaneous multisystem assessment from a singlet specimen. Used in combination with modern multivariate analytical models, the capacity to repeatedly assess multisystem and level measurements collected from the same individual over time enable operationalization, testing and refinement of complex biobehavioral models. This review describes the emerging narrative of salivary bioscience, and aims to inform and reveal opportunity for innovation and discovery

Antibodies to sclerostin or G-CSF receptor partially eliminate bone or marrow adipocyte loss, respectively, following vertical sleeve gastrectomy

Vertical sleeve gastrectomy (VSG), the most utilized bariatric procedure in clinical practice, greatly reduces body weight and improves a variety of metabolic disorders. However, one of its long-term complications is bone loss and increased risk of fracture. Elevated circulating sclerostin (SOST) and granulocyte-colony stimulating factor (G-CSF) concentrations have been considered as potential contributors to VSG-associated bone loss. To test these possibilities, we administrated antibodies to SOST or G-CSF receptor and investigated alterations to bone and marrow niche following VSG. Neutralizing either SOST or G-CSF receptor did not alter beneficial effects of VSG on adiposity and hepatic steatosis, and anti-SOST treatment provided a further improvement to glucose tolerance. SOST antibodies partially reduced trabecular and cortical bone loss following VSG by increasing bone formation, whereas G-CSF receptor antibodies had no effects on bone mass. The expansion in myeloid cellularity and reductions in bone marrow adiposity seen with VSG were partially eliminated by treatment with Anti-G-CSF receptor. Taken together, these experiments demonstrate that antibodies to SOST or G-CSF receptor may act through independent mechanisms to partially block effects of VSG on bone loss or marrow niche cells, respectively

Oral microbial communities in children, caregivers, and associations with salivary biomeasures and environmental tobacco smoke exposure.

Human oral microbial communities are diverse, with implications for oral and systemic health. Oral microbial communities change over time; thus, it is important to understand how healthy versus dysbiotic oral microbiomes differ, especially within and between families. There is also a need to understand how the oral microbiome composition is changed within an individual including by factors such as environmental tobacco smoke (ETS) exposure, metabolic regulation, inflammation, and antioxidant potential. Using archived saliva samples collected from caregivers and children during a 90-month follow-up assessment in a longitudinal study of child development in the context of rural poverty, we used 16S rRNA gene sequencing to determine the salivary microbiome. A total of 724 saliva samples were available, 448 of which were from caregiver/child dyads, an additional 70 from children and 206 from adults. We compared childrens and caregivers oral microbiomes, performed stomatotype analyses, and examined microbial relations with concentrations of salivary markers associated with ETS exposure, metabolic regulation, inflammation, and antioxidant potential (i.e., salivary cotinine, adiponectin, C-reactive protein, and uric acid) assayed from the same biospecimens. Our results indicate that children and caregivers share much of their oral microbiome diversity, but there are distinct differences. Microbiomes from intrafamily individuals are more similar than microbiomes from nonfamily individuals, with child/caregiver dyad explaining 52% of overall microbial variation. Notably, children harbor fewer potential pathogens than caregivers, and participants microbiomes clustered into two groups, with major differences being driven by Streptococcus spp. Differences in salivary microbiome composition associated with ETS exposure, and taxa associated with salivary analytes representing potential associations between antioxidant potential, metabolic regulation, and the oral microbiome. IMPORTANCE The human oral cavity is a multi-environment habitat that harbors a diversity of microorganisms. This oral microbiome is often transmitted between cohabitating individuals, which may associate oral and systemic health within family members. Furthermore, family social ecology plays a significant role in childhood development, which may be associated with lifelong health outcomes. In this study, we collected saliva from children and their caregivers and used 16S rRNA gene sequencing to characterize their oral microbiomes. We also analyzed salivary biomeasures of environmental tobacco smoke exposure, metabolic regulation, inflammation, and antioxidant potential. We show there are differences in individuals oral microbiomes mainly due to Streptococcus spp. that family members share much of their microbes, and several bacterial taxa associate with the selected salivary biomeasures. Our results suggest there are large-scale oral microbiome patterns, and there are likely relationships between oral microbiomes and the social ecology of families

Censored data considerations and analytical approaches for salivary bioscience data.

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay's measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data

Censored data considerations and analytical approaches for salivary bioscience data.

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay's measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data

Best practice recommendations for the measurement and interpretation of salivary proinflammatory cytokines in biobehavioral research

Despite the integration of salivary inflammatory cytokines into research across the biobehavioral, psychological, clinical, and health-related disciplines, there is little guidance regarding the biospecimen collection, handling, and storage practices that maximize the quality and validity of salivary cytokine data. Furthermore, associations between salivary cytokines and measures related to oral health are rarely assessed and accounted for in studies outside the oral health fields. To address these gaps, we examine the sensitivity of salivary interleukin-1β (IL-1β), IL-6, IL-8, and tumor necrosis factor-α (TNF-α) to changes in saliva sample collection technique and cold chain management procedures. Using subsets of saliva samples collected from 150 healthy adults, we measure salivary IL-1β, IL-6, IL-8, TNF-α, and other oral health-related indices (i.e., blood contamination [transferrin], and salivary matrixmallotprotienase-8). In addition to examining changes in cytokine levels associated with sample collection technique and cold chain management procedures, we assess relations between cytokine concentrations and levels of other oral health-related measures. We found that IL-1β, IL-6, and IL-8 were more robust to changes in sample collection and cold chain management procedures than TNF-α, and all cytokines were positively associated with other oral health-related measures. Based on our findings, we recommend analyte-specific guidance for measuring and interpreting salivary cytokine concentrations

The case for the repeatability intra-class correlation as a metric of precision for salivary bioscience data: Justification, assessment, application, and implications.

Best practice standards for measuring analyte levels in saliva recommend that all biospecimens be tested in replicate with mean concentrations used in statistical analyses. This approach prioritizes minimizing laboratory-based measurement error but, in the process, expends considerable resources. We explore the possibility that, due to advances in salivary assay precision, the contribution of laboratory-based measurement error in salivary analyte data is very small relative to more important and meaningful variability in analyte levels across biological replicates (i.e., between different specimens). To evaluate this possibility, we examine the utility of the repeatability intra-class correlation (rICC) as an additional index of salivary analyte data precision. Using randomly selected subsamples (Ns=200 and 60) of salivary analyte data collected as part of a larger epidemiologic study, we compute the rICCs for seven commonly assayed salivary measures in biobehavioral research - cortisol, alpha-amylase, c-reactive protein, interlekin-6, uric acid, secretory immunoglobulin A, and testosterone. We assess the sensitivity of rICC estimates to assay type and the unique distributions of the underlying analyte data. We also use simulations to examine the bias, precision, and coverage probability of rICC estimates calculated for small to large sample sizes. For each analyte, the rICCs revealed that less than 5% of variation in analyte levels was attributable to laboratory-based measurement error. rICC estimates were similar across all analytes despite differences in analyte levels, average intra-assay coefficients of variation, and in the distributional properties of the data. Guidelines for calculating rICC are provided to enable investigators and laboratory staff to apply this metric and more accurately quantify, and communicate, the magnitude of laboratory-based measurement error in their data. By helping investigators scale measurement error relative to more scientifically meaningful variability between biological replicates, the application of the rICC has the potential to influence research strategies and tactics such that resources (e.g., finances, effort, number/volume of biospecimens) are allocated more efficiently and effectively