BACKGROUND: Spatial elemental analysis of deciduous tooth dentin combined with odontochronological estimates can provide an early life (in utero to ~2 years of age) history of inorganic element exposure and status.

OBJECTIVE: To demonstrate the importance of data normalization to a certified reference material to enable between-study comparisons, using populations with assumed contrasting elemental exposures.

METHODS: We used laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of dentin to derive a history of elemental composition from three distinct cohort studies: a present day rural cohort, (the New Hampshire Birth Cohort Study (NHBCS; N = 154)), an historical cohort from an urban area (1958-1970), (the St. Louis Baby Tooth Study (SLBT; N = 78)), and a present-day Nigerian cohort established to study maternal HIV transmission (Dental caries and its association with Oral Microbiomes and HIV in young children-Nigeria (DOMHaIN; N = 31)).

RESULTS: We report Li, Al, Mn, Cu, Zn, Sr, Ba and Pb concentrations (µg/g) and qualitatively examine As, Cd and Hg across all three cohorts. Rates of detection were highest, both overall and for each cohort individually, for Zn, Sr, Ba and Li. Zinc was detected in 100% of samples and was stably present in teeth at a concentration range of 64 - 86 µg/g. Mercury, As and Cd detection rates were the lowest, and had high variability within individual ablated spots. We found the highest concentrations of Pb in the pre- and postnatal dentin of the SLBT cohort, consistent with the prevalent use of Pb as an additive to gasoline prior to 1975. The characteristic decline in Mn after the second trimester was observed in all cohorts.

IMPACT: Spatially resolved elemental analysis of deciduous teeth combined with methods for estimating crown formation times can be used to reconstruct an early-life history of elemental exposure inaccessible via other biomarkers. Quantification of data into absolute values using an external standard reference material has not been conducted since 2012, preventing comparison between studies, a common and highly informative component of epidemiology. We demonstrate, with three contrasting populations, that absolute quantification produces data with the lowest variability, compares well with available data and recommends that future tooth biomarker studies report data in this way.

Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen dioxide (NO2) exposure and birth weight, we demonstrate that bias amplification and variance inflation can manifest under certain combinations of DLM estimation approaches and time-trend adjustment methods when using low-spatial-resolution exposures with extended lags. Our simulations showed that when using high-spatial-resolution exposure data, any time-trend adjustment method produced low bias and nominal coverage for the distributed lag estimator. When using either low- or no-spatial-resolution exposures, bias due to time trends was amplified for all adjustment methods. Variance inflation was higher in low- or no-spatial-resolution DLMs when using a long-term spline to adjust for seasonality and long-term trends due to concurvity between a distributed lag function and secular function of time. NO2-birth weight analyses in a Massachusetts-based cohort showed that associations were negative for exposures experienced in gestational weeks 15-30 when using high-spatial-resolution DLMs; however, associations were null and positive for DLMs with low- and no-spatial-resolution exposures, respectively, which is likely due to bias amplification. DLM analyses should jointly consider the spatial resolution of exposure data and the parameterizations of the time trend adjustment and lag constraints.

BACKGROUND: Aging-related cognitive decline is an early symptom of Alzheimer's disease and other dementias, and on its own can have substantial consequences on an individual's ability to perform important everyday functions. Despite increasing interest in the potential roles of extracellular microRNAs (miRNAs) in central nervous system (CNS) pathologies, there has been little research on extracellular miRNAs in early stages of cognitive decline. We leverage the longitudinal Normative Aging Study (NAS) cohort to investigate associations between plasma miRNAs and cognitive function among cognitively normal men.

METHODS: This study includes data from up to 530 NAS participants (median age: 71.0 years) collected from 1996 to 2013, with a total of 1,331 person-visits (equal to 2,471 years of follow up). Global cognitive function was assessed using the Mini-Mental State Examination (MMSE). Plasma miRNAs were profiled using small RNA sequencing. Associations of expression of 381 miRNAs with current cognitive function and rate of change in cognitive function were assessed using linear regression ( = 457) and linear mixed models ( = 530), respectively.

RESULTS: In adjusted models, levels of 2 plasma miRNAs were associated with higher MMSE scores ( < 0.05). Expression of 33 plasma miRNAs was associated with rate of change in MMSE scores over time ( < 0.05). Enriched KEGG pathways for miRNAs associated with concurrent MMSE and MMSE trajectory included Hippo signaling and extracellular matrix-receptor interactions. Gene targets of miRNAs associated with MMSE trajectory were additionally associated with prion diseases and fatty acid biosynthesis.

CONCLUSIONS: Circulating miRNAs were associated with both cross-sectional cognitive function and rate of change in cognitive function among cognitively normal men. Further research is needed to elucidate the potential functions of these miRNAs in the CNS and investigate relationships with other neurological outcomes.