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

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

Effect of placental growth factor in models of experimental pre‐eclampsia and trophoblast invasion

Placental growth factor (PlGF) is decreased in early gestation of pregnant women who subsequently develop pre-eclampsia. In this study, pre-emptive treatment with PlGF to prevent pre-eclampsia was evaluated in an in vivo rodent model of experimental pre-eclampsia (EPE) induced by TNF-α and in an in vitro model of human first-trimester trophoblast invasion. Pregnant C57/BL6 mice were treated with recombinant mouse placental growth factor-2 (rmPlGF-2) 100 μg/kg/day IP from gestational day (gd) 10. Animals had EPE induced by continuous TNF-α infusion on gd 13 and were subject to either continuous blood pressure monitoring by radiotelemetry throughout pregnancy or live placenta T2-weighted magnetic resonance imaging (MRI) to demonstrate placental function on gd 17. There was no difference in BP (P > .99), proteinuria (P =.9) or T2 values on MRI (P =.9) between control and rmPlGF-2-treated animals. On gd 13, animals treated with rmPlGF-2 demonstrated increased placenta PlGF (P =.01) and Toll-like receptor-3 (P =.03) mRNA expression as compared with controls. Fluorescent-labelled human uterine microvascular endothelial cells and HTR8/SVNeo cells were co-cultured on Matrigel™ and treated with recombinant human PlGF (rhPlGF) (10 ng/mL) and/or TNF-α (0.5 ng/mL). Trophoblast integration into endothelial networks was reduced by added TNF-α (P =.006), as was rhPlGF concentration in conditioned media (P .9). Although TNF-α-induced EPE was not reversed with pre-emptive rmPlGF-2, a further trial of pre-emptive rhPlGF in vivo is required to determine whether the absence of effect of rhPlGF demonstrated in vitro precludes PlGF as a preventative therapy for pre-eclampsia