Laura Jelliffe-Pawlowski

Faculty

Jelliffe-Pawlowski Headsot

Laura Jelliffe-Pawlowski

MS PhD

1 212 998 9020

433 First Ave
New York, NY 10010
United States

Laura Jelliffe-Pawlowski, PhD, MS, is a Professor. Prof. Jelliffe-Pawlowski’s research interests focus on understanding and addressing the drivers and consequences of adverse pregnancy outcomes with a special emphasis on preterm birth and associated racial/ethnic and socioeconomic inequities. Her work is highly transdisciplinary and looks at the interplay of biomolecular, social, and policy factors in observed patterns and outcomes. Her teaching and mentorship activities reflect this transdisciplinary approach with an emphasis on motivating the translation of research findings into action.

 

Prof. Jelliffe-Pawlowski leads a number of statewide, national, and international research efforts funded by the National Institutes of Health, the Bill and Melinda Gates Foundation, the March of Dimes, the State of California, and other entities. These includes, notably, the “Healthy Outcomes of Pregnancy for Everyone (HOPE)” consortium and study which focuses on understanding the experience of pregnant people and their infants pre- and post-COVID 19 pandemic. HOPE examines how biomolecular, social, and community factors affect the well-being and outcomes of mothers and infants and includes enrollment during pregnancy with outcome follow-up to 18-months after birth. Other ongoing projects include, for example, the NIH funded “Prediction Of Maturity, Morbidity, and Mortality in PreTerm Infants (PROMPT)”, study which focuses on examining the metabolic profiles of newborns with early preterm birth and associated outcomes, the “Transforming Health and Reducing PerInatal Anxiety through Virtual Engagement (THRIVE)”, randomized control trial (RCT), funded by the State of California which examines whether digital cognitive behavior therapy delivered by mobile app can assist in reducing anxiety symptoms in pregnant people and also examines participant acceptability of the application. Ongoing efforts also include leading the “California Prediction of Poor Outcomes of Pregnancy (CPPOP)” cohort study which focuses on investigating multi-omic drivers of preterm birth. The study interrogates biomolecular signals associated with preterm birth and includes full genome sequencing and mid-pregnancy biomolecular signaling related to metabolic, immune, stress, and placental function in hundreds of pregnancies with and without preterm birth. 

 

Prior to her joining NYU Meyers, Prof. Jelliffe-Pawlowski was a Professor of Epidemiology & Biostatistics, Chief of the Division of Lifecourse Epidemiology, a Professor in the Institute of Global Health Sciences, and Director of Discovery and Precision Health for the UCSF California Preterm Birth Initiative in the University of California San Francisco (UCSF) School of Medicine. She has a lifetime appointment as an Emeritus Professor of Epidemiology & Biostatistics in the UCSF School of Medicine and continues to work closely with the new Center for Birth Equity at UCSF. Prior to her appointment at UCSF, she was a leader at the Genetic Disease Screening Program within the California Department of Public Health. 

 

Prof. Jelliffe-Pawlowski efforts have been highlighted in numerous academic and lay articles including in the New York Times, in WIRED Magazine, in the Atlantic, on CNN, and on MSNBC. In 2023, she was recognized by Forbes Magazine as one of the top 50 over 50 Innovators in the United States. She is also a Phase I and Phase II Bill and Melinda Gates Foundation Grand Challenges awardee for her work in the United States and Uganda which focused on the development and validation of newborn metabolic profile as a novel measure of gestational age in infants.

BA, Psychology, University of California Los Angeles
MS, Child Development, University of California Davis
PhD, Human Development, University of California Davis

Preterm Birth

Forbes 50 over 50 awardee in Innovation (2023)
Delegate, African Academy of Sciences (2016)
Governor Brown Appointee for the California Department of Public Health, Interagency Coordinating Council on Early Intervention
Awardee, Bill and Melinda Bates Foundation, Gates Grand Challenges Phase I and II

Towards precision quantification of contamination in metagenomic sequencing experiments

Zinter, M. S., Mayday, M. Y., Ryckman, K. K., Jelliffe-Pawlowski, L. L., & Derisi, J. L. (2019). Microbiome, 7(1). 10.1186/s40168-019-0678-6
Abstract
Abstract
Metagenomic next-generation sequencing (mNGS) experiments involving small amounts of nucleic acid input are highly susceptible to erroneous conclusions resulting from unintentional sequencing of occult contaminants, especially those derived from molecular biology reagents. Recent work suggests that, for any given microbe detected by mNGS, an inverse linear relationship between microbial sequencing reads and sample mass implicates that microbe as a contaminant. By associating sequencing read output with the mass of a spike-in control, we demonstrate that contaminant nucleic acid can be quantified in order to identify the mass contributions of each constituent. In an experiment using a high-resolution (n = 96) dilution series of HeLa RNA spanning 3-logs of RNA mass input, we identified a complex set of contaminants totaling 9.1 ± 2.0 attograms. Given the competition between contamination and the true microbiome in ultra-low biomass samples such as respiratory fluid, quantification of the contamination within a given batch of biological samples can be used to determine a minimum mass input below which sequencing results may be distorted. Rather than completely censoring contaminant taxa from downstream analyses, we propose here a statistical approach that allows separation of the true microbial components from the actual contribution due to contamination. We demonstrate this approach using a batch of n = 97 human serum samples and note that despite E. coli contamination throughout the dataset, we are able to identify a patient sample with significantly more E. coli than expected from contamination alone. Importantly, our method assumes no prior understanding of possible contaminants, does not rely on any prior collection of environmental or reagent-only sequencing samples, and does not censor potentially clinically relevant taxa, thus making it a generalized approach to any kind of metagenomic sequencing, for any purpose, clinical or otherwise.

Using Index of Concentration at the Extremes as Indicators of Structural Racism to Evaluate the Association with Preterm Birth and Infant Mortality—California, 2011–2012

Chambers, B. D., Baer, R. J., McLemore, M. R., & Jelliffe-Pawlowski, L. L. (2019). Journal of Urban Health, 96(2), 159-170. 10.1007/s11524-018-0272-4
Abstract
Abstract
Disparities in adverse birth outcomes for Black women continue. Research suggests that societal factors such as structural racism explain more variation in adverse birth outcomes than individual-level factors and societal poverty alone. The Index of Concentration at the Extremes (ICE) measures spatial social polarization by quantifying extremes of deprived and privileged social groups using a single metric and has been shown to partially explain racial disparities in black carbon exposures, mortality, fatal and non-fatal assaults, and adverse birth outcomes such as preterm birth and infant mortality. The objective of this analysis was to assess if local measures of racial and economic segregation as proxies for structural racism are associated and preterm birth and infant mortality experienced by Black women residing in California. California birth cohort files were merged with the American Community Survey by zip code (2011–2012). The ICE was used to quantify privileged and deprived groups (i.e., Black vs. White; high income vs. low income; Black low income vs. White high income) by zip code. ICE scores range from − 1 (deprived) to 1 (privileged). ICE scores were categorized into five quintiles based on sample distributions of these measures: quintile 1 (least privileged)–quintile 5 (most privileged). Generalized linear mixed models were used to test the likelihood that ICE measures were associated with preterm birth or with infant mortality experienced by Black women residing in California. Black women were most likely to reside in zip codes with greater extreme income concentrations, and moderate extreme race and race + income concentrations. Bivariate analysis revealed that greater extreme income, race, and race + income concentrations increased the odds of preterm birth and infant mortality. For example, women residing in least privileged zip codes (quintile 1) were significantly more likely to experience preterm birth (race + income ICE OR = 1.31, 95% CI = 1.72–1.46) and infant mortality (race + income ICE OR = 1.70, 95% CI = 1.17–2.47) compared to women living in the most privileged zip codes (quintile 5). Adjusting for maternal characteristics, income, race, and race + income concentrations remained negatively associated with preterm birth. However, only race and race + income concentrations remained associated with infant mortality. Findings support that ICE is a promising measure of structural racism that can be used to address racial disparities in preterm birth and infant mortality experienced by Black women in California.

Altered metabolites in newborns with persistent pulmonary hypertension

Steurer, M. A., Oltman, S., Baer, R. J., Feuer, S., Liang, L., Paynter, R. A., Rand, L., Ryckman, K. K., Keller, R. L., & Jelliffe-Pawlowski, L. L. (2018). Pediatric Research, 84(2), 272-278. 10.1038/s41390-018-0023-y
Abstract
Abstract
Background: There is an emerging evidence that pulmonary hypertension is associated with amino acid, carnitine, and thyroid hormone aberrations. We aimed to characterize metabolic profiles measured by the newborn screen (NBS) in infants with persistent pulmonary hypertension of the newborn (PPHN) Methods: Nested case–control study from population-based database. Cases were infants with ICD-9 code for PPHN receiving mechanical ventilation. Controls receiving mechanical ventilation were matched 2:1 for gestational age, sex, birth weight, parenteral nutrition administration, and age at NBS collection. Infants were divided into derivation and validation datasets. A multivariable logistic regression model was derived from candidate metabolites, and the area under the receiver operator characteristic curve (AUROC) was generated from the validation dataset. Results: We identified 1076 cases and 2152 controls. Four metabolites remained in the final model. Ornithine (OR 0.32, CI 0.26–0.41), tyrosine (OR 0.48, CI 0.40–0.58), and TSH 0.50 (0.45–0.55) were associated with decreased odds of PPHN; phenylalanine was associated with increased odds of PPHN (OR 4.74, CI 3.25–6.90). The AUROC was 0.772 (CI 0.737–0.807). Conclusions: In a large, population-based dataset, infants with PPHN have distinct, early metabolic profiles. These data provide insight into the pathophysiology of PPHN, identifying potential therapeutic targets and novel biomarkers to assess the response.

Copy number variants in hypoplastic right heart syndrome

Giannakou, A., Sicko, R. J., Kay, D. M., Zhang, W., Romitti, P. A., Caggana, M., Shaw, G. M., Jelliffe-Pawlowski, L. L., & Mills, J. L. (2018). American Journal of Medical Genetics, Part A, 176(12), 2760-2767. 10.1002/ajmg.a.40527
Abstract
Abstract
Hypoplastic right heart syndrome (HRHS) is a rare congenital defect characterized by underdeveloped and malformed structures of the right heart. Familial recurrence of HRHS indicates genetic factors contribute to its etiology. Our study investigates the presence of copy number variants (CNVs) in HRHS cases. We genotyped 42 HRHS cases identified from live births throughout California (2003–2010) using the Illumina HumanOmni2.5-8 array. We identified 14 candidate CNVs in 14 HRHS cases (33%) based on the genes included in the CNVs and their functions. Duplications overlapping part of ERBB4 were identified in two unrelated cases. ERBB4 is a neuregulin receptor with a pivotal role in cardiomyocyte differentiation and heart development. We also described a 7.5 Mb duplication at 16q11-12. Multiple genes in the duplicated region have previously been linked to heart defects and cardiac development, including RPGRIP1L, RBL2, SALL1, and MYLK3. Of the 14 validated CNVs, we identified four CNVs in close proximity to genes linked to the Wnt signaling pathway. This study expands on our previous work supporting the role of genetics in HRHS. We identified CNVs affecting crucial genes and signaling pathways involved in right heart development. ERBB4 and duplication of the 16q11-12 region are important areas for future investigation.

Effect of fetal growth on 1-year mortality in neonates with critical congenital heart disease

Steurer, M. A., Baer, R. J., Burke, E., Peyvandi, S., Oltman, S., Chambers, C. D., Norton, M. E., Rand, L., Rajagopal, S., Ryckman, K. K., Feuer, S. K., Liang, L., Paynter, R. A., McCarthy, M., Moon-Grady, A. J., Keller, R. L., & Jelliffe-Pawlowski, L. L. (2018). Journal of the American Heart Association, 7(17). 10.1161/JAHA.118.009693
Abstract
Abstract
Background—Infants with critical congenital heart disease (CCHD) are more likely to be small for gestational age (GA). It is unclear how this affects mortality. The authors investigated the effect of birth weight Z score on 1-year mortality separately in preterm (GA <37 weeks), early-term (GA 37–38 weeks), and full-term (GA 39–42 weeks) infants with CCHD. Methods and Results—Live-born infants with CCHD and GA 22 to 42 weeks born in California 2007–2012 were included in the analysis. The primary predictor was Z score for birth weight and the primary outcome was 1-year mortality. Multivariable logistic regression was used. Results are presented as adjusted odds ratios and 95% confidence intervals (CIs). The authors identified 6903 infants with CCHD. For preterm and full-term infants, only a Z score for birth weight <−2 was associated with increased mortality compared with the reference group (Z score 0–0.5, adjusted odds ratio, 2.15 [95% CI, 1.1–4.21] and adjusted odds ratio, 3.93 [95% CI, 2.32–6.68], respectively). In contrast, in early-term infants, the adjusted odds ratios for Z scores <−2, −2 to −1, and −1 to −0.5 were 3.42 (95% CI, 1.93–6.04), 1.78 (95% CI, 1.12–2.83), and 2.03 (95% CI, 1.27–3.23), respectively, versus the reference group. Conclusions—GA seems to modify the effect of birth weight Z score on mortality in infants with CCHD. In preterm and full-term infants, only the most severe small-for-GA infants (Z score <−2) were at increased risk for mortality, while, in early-term infants, the risk extended to mild to moderate small-for-GA infants (Z score <−0.5). This information helps to identify high-risk infants and is useful for surgical planning.

A genome-wide association study identifies only two ancestry specific variants associated with spontaneous preterm birth

Rappoport, N., Toung, J., Hadley, D., Wong, R. J., Fujioka, K., Reuter, J., Abbott, C. W., Oh, S., Hu, D., Eng, C., Huntsman, S., Bodian, D. L., Niederhuber, J. E., Hong, X., Zhang, G., Sikora-Wohfeld, W., Gignoux, C. R., Wang, H., Oehlert, J., … Sirota, M. (2018). Scientific Reports, 8(1). 10.1038/s41598-017-18246-5
Abstract
Abstract
Preterm birth (PTB), or the delivery prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. Although twin studies estimate that maternal genetic contributions account for approximately 30% of the incidence of PTB, and other studies reported fetal gene polymorphism association, to date no consistent associations have been identified. In this study, we performed the largest reported genome-wide association study analysis on 1,349 cases of PTB and 12,595 ancestry-matched controls from the focusing on genomic fetal signals. We tested over 2 million single nucleotide polymorphisms (SNPs) for associations with PTB across five subpopulations: African (AFR), the Americas (AMR), European, South Asian, and East Asian. We identified only two intergenic loci associated with PTB at a genome-wide level of significance: rs17591250 (P = 4.55E-09) on chromosome 1 in the AFR population and rs1979081 (P = 3.72E-08) on chromosome 8 in the AMR group. We have queried several existing replication cohorts and found no support of these associations. We conclude that the fetal genetic contribution to PTB is unlikely due to single common genetic variant, but could be explained by interactions of multiple common variants, or of rare variants affected by environmental influences, all not detectable using a GWAS alone.

Initial Metabolic Profiles Are Associated with 7-Day Survival among Infants Born at 22-25 Weeks of Gestation

Oltman, S. P., Rogers, E. E., Baer, R. J., Anderson, J. G., Steurer, M. A., Pantell, M. S., Partridge, J. C., Rand, L., Ryckman, K. K., & Jelliffe-Pawlowski, L. L. (2018). Journal of Pediatrics, 198, 194-200.e3. 10.1016/j.jpeds.2018.03.032
Abstract
Abstract
Objective: To evaluate the association between early metabolic profiles combined with infant characteristics and survival past 7 days of age in infants born at 22-25 weeks of gestation. Study design: This nested case-control consisted of 465 singleton live births in California from 2005 to 2011 at 22-25 weeks of gestation. All infants had newborn metabolic screening data available. Data included linked birth certificate and mother and infant hospital discharge records. Mortality was derived from linked death certificates and death discharge information. Each death within 7 days was matched to 4 surviving controls by gestational age and birth weight z score category, leaving 93 cases and 372 controls. The association between explanatory variables and 7-day survival was modeled via stepwise logistic regression. Infant characteristics, 42 metabolites, and 12 metabolite ratios were considered for model inclusion. Model performance was assessed via area under the curve. Results: The final model included 1 characteristic and 11 metabolites. The model demonstrated a strong association between metabolic patterns and infant survival (area under the curve [AUC] 0.885, 95% CI 0.851-0.920). Furthermore, a model with just the selected metabolites performed better (AUC 0.879, 95% CI 0.841-0.916) than a model with multiple clinical characteristics (AUC 0.685, 95% CI 0.627-0.742). Conclusions: Use of metabolomics significantly strengthens the association with 7-day survival in infants born extremely premature. Physicians may be able to use metabolic profiles at birth to refine mortality risks and inform postnatal counseling for infants born at <26 weeks of gestation.

Maternal dyslipidemia and risk for preterm birth

Smith, C. J., Baer, R. J., Oltman, S. P., Breheny, P. J., Bao, W., Robinson, J. G., Dagle, J. M., Liang, L., Feuer, S. K., Chambers, C. D., Jelliffe-Pawlowski, L. L., & Ryckman, K. K. (2018). PloS One, 13(12). 10.1371/journal.pone.0209579
Abstract
Abstract
Maternal lipid profiles during pregnancy are associated with risk for preterm birth. This study investigates the association between maternal dyslipidemia and subsequent preterm birth among pregnant women in the state of California. Births were identified from California birth certificate and hospital discharge records from 2007–2012 (N = 2,865,987). Preterm birth was defined as <37 weeks completed gestation and dyslipidemia was defined by diagnostic codes. Subtypes of preterm birth were classified as preterm premature rupture of membranes (PPROM), spontaneous labor, and medically indicated, according to birth certificate data and diagnostic codes. The association between dyslipidemia and preterm birth was tested with logistic regression. Models were adjusted for maternal age at delivery, race/ethnicity, hypertension, pre-pregnancy body mass index, insurance type, and education. Maternal dyslipidemia was significantly associated with increased odds of preterm birth (adjusted OR: 1.49, 95%CI: 1.39, 1.59). This finding was consistent across all subtypes of preterm birth, including PPROM (adjusted OR: 1.54, 95%CI: 1.34, 1.76), spontaneous (adjusted OR: 1.51, 95%CI: 1.39, 1.65), and medically indicated (adjusted OR: 1.454, 95% CI: 1.282, 1.649). This study suggests that maternal dyslipidemia is associated with increased risk for all types of preterm birth.

Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth

Baer, R. J., McLemore, M. R., Adler, N., Oltman, S. P., Chambers, B. D., Kuppermann, M., Pantell, M. S., Rogers, E. E., Ryckman, K. K., Sirota, M., Rand, L., & Jelliffe-Pawlowski, L. L. (2018). European Journal of Obstetrics and Gynecology and Reproductive Biology, 231, 235-240. 10.1016/j.ejogrb.2018.11.004
Abstract
Abstract
Objective To develop a pre-pregnancy or first-trimester risk score to identify women at high risk of preterm birth. Study design In this retrospective cohort analysis, the sample was drawn from California singleton livebirths from 2007 to 2012 with linked birth certificate and hospital discharge records. The dataset was divided into a training (2/3 of sample) and a testing (1/3 of sample) set for discovery and validation. Predictive models for preterm birth using pre-pregnancy or first-trimester maternal factors were developed using backward stepwise logistic regression on a training dataset. A risk score for preterm birth was created for each pregnancy using beta-coefficients for each maternal factor remaining in the final multivariable model. Risk score utility was replicated in a testing dataset and by race/ethnicity and payer for prenatal care. Results The sample included 2,339,696 pregnancies divided into training and testing datasets. Twenty-three maternal risk factors were identified including several that were associated with a two or more increased odds of preterm birth (preexisting diabetes, preexisting hypertension, sickle cell anemia, and previous preterm birth). Approximately 40% of women with a risk score ≥ 3.0 in the training and testing samples delivered preterm (40.6% and 40.8%, respectively) compared to 3.1–3.3% of women with a risk score of 0.0 [odds ratio (OR) 13.0, 95% confidence interval (CI) 10.7–15.8, training; OR 12.2, 95% CI 9.4–15.9, testing). Additionally, over 18% of women with a risk score ≥ 3.0 had an adverse outcome other than preterm birth. Conclusion Maternal factors that are identifiable prior to pregnancy or during the first-trimester can be used create a cumulative risk score to identify women at the lowest and highest risk for preterm birth regardless of race/ethnicity or socioeconomic status. Further, we found that this cumulative risk score could also identify women at risk for other adverse outcomes who did not have a preterm birth. The risk score is not an effective screening test, but does identify women at very high risk of a preterm birth.

Prediction of preterm birth with and without preeclampsia using mid-pregnancy immune and growth-related molecular factors and maternal characteristics

Jelliffe-Pawlowski, L. L., Rand, L., Bedell, B., Baer, R. J., Oltman, S. P., Norton, M. E., Shaw, G. M., Stevenson, D. K., Murray, J. C., & Ryckman, K. K. (2018). Journal of Perinatology, 38(8), 963-972. 10.1038/s41372-018-0112-0
Abstract
Abstract
Objective:: To evaluate if mid-pregnancy immune and growth-related molecular factors predict preterm birth (PTB) with and without (±) preeclampsia. Study design:: Included were 400 women with singleton deliveries in California in 2009–2010 (200 PTB and 200 term) divided into training and testing samples at a 2:1 ratio. Sixty-three markers were tested in 15–20 serum samples using multiplex technology. Linear discriminate analysis was used to create a discriminate function. Model performance was assessed using area under the receiver operating characteristic curve (AUC). Results:: Twenty-five serum biomarkers along with maternal age <34 years and poverty status identified >80% of women with PTB ± preeclampsia with best performance in women with preterm preeclampsia (AUC = 0.889, 95% confidence interval (0.822–0.959) training; 0.883 (0.804–0.963) testing). Conclusion:: Together with maternal age and poverty status, mid-pregnancy immune and growth factors reliably identified most women who went on to have a PTB ± preeclampsia.