STUDY QUESTION: Is there an association between low-to-moderate levels of prenatal alcohol exposure (PAE) and children's facial shape? SUMMARY ANSWER: PAE before and during pregnancy, even at low level (<12 g of alcohol per week), was found associated with the facial shape of children, and these associations were found attenuated as children grow older. WHAT IS KNOWN ALREADY: High levels of PAE during pregnancy can have significant adverse associations with a child's health development resulting in recognizably abnormal facial development. STUDY DESIGN, SIZE, DURATION: This study was based on the Generation R Study, a prospective cohort from fetal life onwards with maternal and offspring data. We analyzed children 3-dimensional (3D) facial images taken at ages 9 (n = 3149) and 13 years (n = 2477) together with the data of maternal alcohol consumption. PARTICIPANTS/MATERIALS, SETTING, METHODS: We defined six levels of PAE based on the frequency and dose of alcohol consumption and defined three tiers based on the timing of alcohol exposure of the unborn child. For the image analysis, we used 3D graph convolutional networks for non-linear dimensionality reduction, which compressed the high-dimensional images into 200 traits representing facial morphology. These 200 traits were used for statistical analysis to search for associations with PAE. Finally, we generated heatmaps to display the facial phenotypes associated with PAE. MAIN RESULTS AND THE ROLE OF CHANCE: The results of the linear regression in the 9-year-old children survived correction for multiple testing with false discovery rate (FDR). In Tier 1 where we examined PAE only before pregnancy (exposed N = 278, unexposed N = 760), we found three traits survived FDR correction. The lowest FDR-P is 1.7e-05 (beta = 0.021, SE = 0.0040) in Trait #29; In Tier 2b where we examine any PAE during first trimester (exposed N = 756; unexposed N = 760), we found eight traits survived FDR correction. The lowest FDR-P is 9.0e-03 (beta = -0.013, SE = 0.0033) in Trait #139. Moreover, more statistically significant facial traits were found in higher levels of PAE. No FDR-significant results were found in the 13-year-old children. We map these significant traits back to the face, and found the most common detected facial phenotypes included turned-up nose tip, shortened nose, turned-out chin, and turned-in lower-eyelid-related regions. LIMITATIONS, REASONS FOR CAUTION: We had no data for alcohol consumption more than three months prior to pregnancy and thus do not know if maternal drinking had chronic effects. The self-reported questionnaire might not reflect accurate alcohol measurements because mothers may have denied their alcohol consumption. WIDER IMPLICATIONS OF THE FINDINGS: Our results imply that facial morphology, such as quantified by the approach we proposed here, can be used as a biomarker in further investigations. Furthermore, our study suggests that for women who are pregnant or want to become pregnant soon, should quit alcohol consumption several months before conception and completely during pregnancy to avoid adverse health outcomes in the offspring. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by Erasmus Medical Centre, Rotterdam, the Erasmus University Rotterdam, and the Netherlands Organization for Health Research. V.W.V.J. reports receipt of funding from the Netherlands Organization for Health Research (ZonMw 90700303). W.J.N. is a founder, a scientific lead, and a shareholder of Quantib BV. TRIAL REGISTRATION NUMBER: N/A.

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Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9–91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl.@en