A biweekly overview of recent studies published by Institute investigators and their collaborators spans a wide variety of topics, including:

Lifestyle interventions for the prevention of gestational diabetes mellitus; safety assessment of the recombinant herpes zoster vaccine; sociodemographic patterns in aircraft noise exposure; menstrual cycle length and adverse pregnancy outcomes; electronic health records for comparative effectiveness research; unmeasured confounding on the MR Steiger approach; and distributed Cox proportional hazards regression.

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Benefits of precision medicine in GDM prevention during pregnancy
Gestational diabetes mellitus (GDM) is the most prevalent pregnancy-related endocrinopathy, affecting up to 25% of pregnancies worldwide. Pregnant individuals who develop GDM have an increased risk of complications during pregnancy and birth, as well as future development of type 2 diabetes mellitus and cardiovascular disease. This increased risk is subsequently passed along to the offspring, perpetuating a cycle of metabolic dysfunction across generations. A review published in Diabetologia summarizes literature examining the efficacy of lifestyle interventions for the prevention of GDM and aims to identify specific individual-level characteristics that may influence this outcome. Investigators including Nidhi Ghildayal and Marie-France Hivert determined that future trials should be designed to understand if initiation of lifestyle intervention in the preconception period is more effective to reduce GDM. Additionally, trials initiated during pregnancy should be developed through the lens of precision prevention by tailoring intervention approaches based on the presence of modifiable and non-modifiable risk factors.

Institute Investigator(s): Nidhi Ghildayal, Marie-France Hivert
 

Recombinant herpes zoster vaccine – a broad safety assessment
Approved in the U.S. in 2017, the recombinant herpes zoster vaccine (RZV) has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome (GBS) after vaccination was identified post-approval and questions remain about other possible adverse events. A team of investigators led by Katherine Yih and Judith Maro conducted a data-mining study to assess RZV safety in the U.S. by scanning data on thousands of diagnoses to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. Findings, published in the American Journal of Epidemiology, identified statistically significant clustering for conditions and events consistent with the known RZV safety profile, and notably did not detect any cluster of GBS, though this may be attributed to insufficient sample size. This signal-detection method has now been applied to 5 vaccines with consistently plausible results, indicating it could be a promising addition to vaccine safety evaluation methods.

Institute Investigator(s): Judith Maro, Katherine Yih
 

Who is affected by aircraft noise? A sociodemographic study
Communities with lower socioeconomic status and higher prevalence of racial/ethnic minority populations are often more exposed to environmental pollutants. Although studies have shown associations between aircraft noise and property values and various health outcomes, little is known about how aircraft noise exposures are sociodemographically patterned. A new study, co-authored by Peter James and published in Environmental Health Perspectives, described characteristics of populations exposed to aviation noise by race/ethnicity, education, and income in the United States. The team found that areas with a higher Hispanic population had higher odds of being exposed to aircraft noise, suggesting there is indication of sociodemographic disparities in noise exposures across U.S. airports.

Institute Investigator(s): Peter James
 

Menstrual cycle length and adverse pregnancy outcomes among women in Project Viva
Retrospective studies suggest that menstrual cycle length may be a risk marker of adverse pregnancy outcomes, but this evidence is susceptible to recall bias. A new study co-authored by Marie-France Hivert and senior author Emily Oken, evaluated the prospective association between menstrual cycle length and the risk of adverse pregnancy outcomes. Through a secondary analysis of 2,046 women enrolled in Project Viva at ~10 weeks of gestation and followed through delivery, investigators examined outcomes including gestational glucose tolerance, hypertensive disorders of pregnancy, gestational weight gain, birthweight-for-gestational age, preterm birth and birth outcome. Findings, published in Paediatric and Perinatal Epidemiology, indicated that variation in menstrual cycle length may be a risk marker of gestational diabetes/impaired glucose tolerance, lower birth size, and preterm birth. Consequently, variation in menstrual cycle length could flag women who may benefit from targeted monitoring and care before and during pregnancy.

Institute Investigator(s): Marie-France Hivert, Emily Oken
 

Opportunities and trade-offs of using electronic health records for comparative effectiveness research in COVID-19
The struggle to manage the ongoing pandemic reveals an immense need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19. Randomized controlled trials underrepresent the frail and complex patients seen in routine care and don’t provide data on long-term treatment effects. While electronic health records (EHRs) are increasingly available for clinical research and can offer timely real-world evidence reflective of routine care for optimal management of COVID-19, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. A recent article published in Clinical Pharmacology and Therapeutics describes the opportunities and challenges in EHR-based CER for COVID-19-related questions and introduces best practices in pharmacoepidemiology to minimize potential biases. Authors, including Xiaojuan Li, provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic.

Institute Investigator(s): Xiaojuan Li
 

The influence of unmeasured confounding on the MR Steiger approach
The Mendelian Randomization (MR) Steiger approach is used to determine the direction of a possible causal effect between two phenotypes. While the original MR Steiger paper shows that unmeasured confounding of the two phenotypes affects the validity of the approach, it does not elucidate how this occurs. A recent letter published in Genetic Epidemiology aims to show that this is because unmeasured confounding may rescale the magnitude of a non-zero association and thereby distort the comparison of the correlation between the single nucleotide polymorphism (SNP) variable and the two phenotypes in question. A team of researchers, led by Sharon Lutz and including Ann Wu, call for caution in using the MR Steiger method to infer causal directions, especially when unmeasured confounding is expected to induce a bias in a direction opposite to the effect between both phenotypes. Other major concerns about the MR Steiger method come from the assumption that the SNP has no direct effect on one of the phenotypes. Due to its reliance on strong mathematical convenience assumptions, the authors recommend sensitivity analyses to study the effects of confounding by unmeasured variables and pleiotropy on the MR Steiger approach.

Institute Investigator(s): Sharon Lutz, Ann Chen Wu
 

Distributed Cox proportional hazards regression using summary-level information
Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. A recent article published in Biostatistics proposes a distributed approach to estimate and make inferences about the parameters in Cox PH models based only on summary-level information. The team of researchers led by Dongdong Li, with Darren Toh and senior author Rui Wang, report that this approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. However, the proposed method and meta-analysis require that each data-contributing site has enough events to produce a reliable effect estimate. The proposed method is privacy-protecting in the sense that it does not require individual-level data, but future work will investigate privacy-protecting methods for extended Cox models and settings where covariates may be missing.

Institute Investigator(s): Dongdong Li, Darren Toh, Rui Wang