The Kaiser Permanente Northern California research program on genes, environment, and health (RPGEH) pregnancy cohort: study design, methodology and baseline characteristics

Inflammatory Conditions During Pregnancy and Risk of Autism and Other Neurodevelopmental Disorders

Background

Maternal inflammation can result from immune dysregulation and metabolic perturbations during pregnancy. Whether conditions associated with inflammation during pregnancy increase the likelihood of autism spectrum disorder (ASD) or other neurodevelopmental disorders (DDs) is not well understood.

Methods

We conducted a case-control study among children born in California from 2011 to 2016 to investigate maternal immune-mediated and cardiometabolic conditions during pregnancy and risk of ASD (n = 311) and DDs (n = 1291) compared with children from the general population (n = 967). Data on maternal conditions and covariates were retrieved from electronic health records. Maternal genetic data were used to assess a causal relationship.

Results

Using multivariable logistic regression, we found that mothers with asthma were more likely to deliver infants later diagnosed with ASD (odds ratio [OR] = 1.62, 95% CI: 1.15-2.29) or DDs (OR = 1.30, 95% CI: 1.02-1.64). Maternal obesity was also associated with child ASD (OR = 1.51, 95% CI: 1.07-2.13). Mothers with both asthma and extreme obesity had the greatest odds of delivering an infant later diagnosed with ASD (OR = 16.9, 95% CI: 5.13-55.71). These increased ASD odds were observed among female children only. Polygenic risk scores for obesity, asthma, and their combination showed no association with ASD risk. Mendelian randomization did not support a causal relationship between maternal conditions and ASD.

Conclusions

Inflammatory conditions during pregnancy are associated with risk for neurodevelopmental disorders in children. These risks do not seem to be due to shared genetic risk; rather, inflammatory conditions may share nongenetic risk factors with neurodevelopmental disorders. Children whose mothers have both asthma and obesity during pregnancy may benefit from earlier screening and intervention.

Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.

Early Pregnancy Blood Pressure Patterns Identify Risk of Hypertensive Disorders of Pregnancy Among Racial and Ethnic Groups

BACKGROUND

Hypertensive disorders of pregnancy are a leading cause of severe maternal morbidity and mortality and confer 4-fold higher perinatal mortality in Black women. Early pregnancy blood pressure patterns may differentiate risk of hypertensive disorders of pregnancy.

METHODS

This study identified distinct blood pressure trajectories from 0 to 20 weeks' gestation to evaluate subsequent pregnancy-related hypertension in a retrospective cohort of 174 925 women with no prior hypertension or history of preeclampsia, prenatal care entry ≤14 weeks, and a stillborn or live singleton birth delivered at Kaiser Permanente Northern California hospitals in 2009 to 2019. We used electronic health records to obtain clinical outcomes, covariables, and longitudinal outpatient blood pressure measurements ≤20 weeks' gestation (mean 4.1 measurements). Latent class trajectory modeling identified 6 blood pressure groups: ultra-low-declining(referent), low-declining, moderate-fast-decline, low-increasing, moderate-stable, and elevated-stable. Multivariable logistic regression evaluated trajectory group-associations with the odds of preeclampsia/eclampsia and gestational hypertension' and effect modification by race-ethnicity and prepregnancy body size.

RESULTS

Compared with ultra-low-declining, adjusted odds ratios (95% confidence intervals [CIs]) for low-increasing, moderate-stable, and elevated-stable groups were 3.25 (2.7-3.9), 5.3 (4.5-6.3), and 9.2 (7.7-11.1) for preeclampsia/eclampsia' and 6.4 (4.9-8.3), 13.6 (10.5-17.7), and 30.2 (23.2-39.4) for gestational hypertension. Race/ethnicity, and prepregnancy obesity modified the trajectory-group associations with preeclampsia/eclampsia (interaction P<0.01), with highest risks for Black, then Hispanic and Asian women for all blood pressure trajectories, and with increasing obesity class.

CONCLUSIONS

Early pregnancy blood pressure patterns revealed racial and ethnic differences in associations with preeclampsia/eclampsia risk within equivalent levels and patterns. These blood pressure patterns may improve individual risk stratification permitting targeted surveillance and early mitigation strategies.

The Kaiser Permanente Research Bank Cancer Cohort: a collaborative resource to improve cancer care and survivorship

Background

The Kaiser Permanente Research Bank (KPRB) is collecting biospecimens and surveys linked to electronic health records (EHR) from approximately 400,000 adult KP members. Within the KPRB, we developed a Cancer Cohort to address issues related to cancer survival, and to understand how genetic, lifestyle and environmental factors impact cancer treatment, treatment sequelae, and prognosis. We describe the Cancer Cohort design and implementation, describe cohort characteristics after 5 years of enrollment, and discuss future directions.

Methods

Cancer cases are identified using rapid case ascertainment algorithms, linkage to regional or central tumor registries, and direct outreach to KP members with a history of cancer. Enrollment is primarily through email invitation. Participants complete a consent form, survey, and donate a blood or saliva sample. All cancer types are included.

Results

As of December 31, 2020, the cohort included 65,225 cases (56% female, 44% male) verified in tumor registries. The largest group was diagnosed between 60 and 69 years of age (31%) and are non-Hispanic White (83%); however, 10,076 (16%) were diagnosed at ages 18–49 years, 4208 (7%) are Hispanic, 3393 (5%) are Asian, and 2389 (4%) are Black. The median survival time is 14 years. Biospecimens are available on 98% of the cohort.

Conclusions

The KPRB Cancer Cohort is designed to improve our understanding of treatment efficacy and factors that contribute to long-term cancer survival. The cohort’s diversity - with respect to age, race/ethnicity and geographic location - will facilitate research on factors that contribute to cancer survival disparities.