Triangulation of epidemiological evidence and risk of bias evaluation: A proposed framework and applied example using formaldehyde exposure and risk of myeloid leukemias.

Evidence triangulation may help identify the impact of study design elements on study findings and to tease out biased results when evaluating potential causal relationships; however, methods for triangulating epidemiologic evidence are evolving and have not been standardized. Building upon key principles of epidemiologic evidence triangulation and risk of bias assessment, and responding to the National Academies of Sciences, Engineering, and Medicine (NASEM) call for applied triangulation examples, the objective of this manuscript is to propose a triangulation framework and to apply it as an illustrative example to epidemiologic studies examining the possible relationship between occupational formaldehyde exposure and risk of myeloid leukemias (ML) including acute (AML) and chronic (CML) types. A nine-component triangulation framework for epidemiological evidence was developed incorporating study quality and ROB guidance from various federal health agencies (i.e., US EPA TSCA and NTP OHAT). Several components of the triangulation framework also drew from widely used epidemiological analytic tools such as stratified meta-analysis and sensitivity analysis. Regarding the applied example, fourteen studies were identified and assessed using the following primary study quality domains to explore potential key sources of bias: 1) study design and analysis; 2) study participation; 3) exposure assessment; 4) outcome assessment; and 5) potential confounding. Across studies, methodological limitations possibly contributing to biased results were observed within most domains. Interestingly, results from one study - often providing the largest and least-precise relative risk estimates, likely reflecting study biases, deviated from most primary study findings indicating no such associations. Triangulation of epidemiological evidence appears to be helpful in exploring inconsistent results for the identification of study results possibly reflecting various biases. Nonetheless, triangulation methodologies require additional development and application to real-world examples to enhance objectivity and reproducibility.

Establishing short-term occupational exposure limits (STELs) for sensory irritants using predictive and in silico respiratory rate depression (RD50) models.

Sensory irritation is a health endpoint that serves as the critical effect basis for many occupational exposure limits (OELs). Schaper 1993 described a significant relationship with high correlation between the measured exposure concentration producing a 50% respiratory rate decrease (RD50) in a standard rodent assay and the American Conference of Governmental Industrial Hygienists (ACGIH®) Threshold Limit Values (TLVs®) as time-weighted averages (TWAs) for airborne chemical irritants. The results demonstrated the potential use of the RD50 values for deriving full-shift TWA OELs protective of irritant responses. However, there remains a need to develop a similar predictive model for deriving workplace short-term exposure limits (STELs) for sensory irritants. The aim of our study was to establish a model capable of correlating the relationship between RD50 values and published STELs to prospectively derive short-term exposure OELs for sensory irritants. A National Toxicology Program (NTP) database that included chemicals with both an RD50 and established STELs was used to fit several linear regression models. A strong correlation between RD50s and STELs was identified, with a predictive equation of ln (STEL) (ppm) = 0.86 * ln (RD50) (ppm) - 2.42 and an R2 value of 0.75. This model supports the use of RD50s to derive STELs for chemicals without existing exposure recommendations. Further, for data-poor sensory irritants, predicted RD50 values from in silico quantitative structure activity relationship (QSAR) models can be used to derive STELs. Hence, in silico methods and statistical modeling can present a path forward for establishing reliable OELs and improving worker safety and health.

Systematic review of the scientific evidence on ethylene oxide as a human carcinogen.

Ethylene oxide is a highly reactive chemical primarily used as an intermediate in chemical production and as a sterilant of medical equipment and food products; it also is produced endogenously as a result of physiological processes. We conducted a systematic review of the potential carcinogenicity of inhaled ethylene oxide in humans using methods that adhere to PRIMSA guidelines and that incorporate aspects from the Institute of Medicine (IOM) (now the National Academy of Medicine) as well as several US Environmental Protection Agency (EPA) frameworks for systematic reviews. After a comprehensive literature search and selection process, study quality was evaluated following a method adapted from the EPA Toxic Substances Control Act (TSCA) framework. The literature screening and selection process identified 24 primary studies in animals or humans and more than 50 mechanistic studies. Integrating epidemiological, animal, and mechanistic literature on ethylene oxide and cancer according to the IOM framework yielded classifications of suggestive evidence of no association between ethylene oxide and stomach cancer, breast cancer and lymphohematopoietic malignancies at human relevant exposures. However, we acknowledge that there is additional uncertainty in the classification for lymphohematopoietic malignancies owing to a paucity of evidence for specific types of these tumors, each of which is a distinct disease entity of possibly unique etiology.

Should patients with type 2 diabetes and raised liver enzymes be referred for further evaluation of liver disease?

Raised liver enzymes are common in type 2 diabetes (T2DM) but often considered benign. Non-alcoholic fatty liver was the cause in 65% of cases but other causes included alcoholic liver disease and viral hepatitis. Cirrhosis was identified in 11 patients. There is a significant burden of advanced liver diseases from a variety of aetiologies in patients with T2DM.

Protein sequence threading: Averaging over structures.

Multiple sequence alignments are a routine tool in protein fold recognition, but multiple structure alignments are computationally less cooperative. This work describes a method for protein sequence threading and sequence-to-structure alignments that uses multiple aligned structures, the aim being to improve models from protein threading calculations. Sequences are aligned into a field due to corresponding sites in homologous proteins. On the basis of a test set of more than 570 protein pairs, the procedure does improve alignment quality, although no more than averaging over sequences. For the force field tested, the benefit of structure averaging is smaller than that of adding sequence similarity terms or a contribution from secondary structure predictions. Although there is a significant improvement in the quality of sequence-to-structure alignments, this does not directly translate to an immediate improvement in fold recognition capability.