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OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites. DESIGN: Case-cohort design within a prospective cohort study. SETTING: Cambridge, UK. POPULATION OR SAMPLE: A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB. METHODS: An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB. MAIN OUTCOME MEASURES: sPTB and sETB. RESULTS: We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860). CONCLUSIONS: We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.
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OBJECTIVE: Common to many countries in the Middle East, Lebanon has an increasing cancer burden; however, national screening programs are limited to breast cancer. The literature on cancer screening practices and beliefs is scarce. This cross-sectional study investigates the knowledge, beliefs, and practices related to the prevention and screening for breast, cervical, colon, lung, and skin cancers among Lebanese residents, recruited through social media advertisements and community outreach activities. METHODS: Participants filled an anonymous questionnaire either via a web-based interface or using tablets distributed at primary health clinics. The characteristics of the two cohorts were compared with chi-square and t-tests. We performed descriptive analysis, followed by multivariate logistic regression for predictors of cancer screening. RESULTS: A total of 407 participants completed the survey online, and 262 filled the study in tablets available at primary care clinics. The two samples were significantly different in terms of age, education, and perceived socioeconomic status. Online participants demonstrated higher knowledge and higher participation in screening practices than their counterparts recruited through community outreach. Mammography (44.7% online and 39.9% in-person), and cervical cancer screening (44.5% online and 36.7% community) had the highest participation rates. In both samples, participants who were older and more educated were more likely to report engagement with cancer screening practices. CONCLUSIONS: Our study revealed significant knowledge gaps in cancer prevention and screening. Different sampling techniques accessed diverse populations, highlighting the need for educational messages and targeted screening programs to be inclusive of socio-economically disadvantaged communities with low education and health literacy.