scholarly journals Integration of Machine-Learning Algorithm to Identify Early Life Risk Factors for Future Overweight or Obesity Among Preterm Infants: A Prospective Birth Cohort

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 988-988
Author(s):  
Yuanqing Fu ◽  
Wanglong Gou ◽  
Wensheng Hu ◽  
Yingying Mao ◽  
Yuhong Guan ◽  
...  

Abstract Objectives To identify early life risk factors for childhood overweight/obesity among preterm infants and to determine feeding practices that could modify the identified risk factors. Methods Jiaxing Birth Cohort is a prospective cohort involving 338,413 mother-child pairs who were enrolled in between 1999 and 2013, of whom 2125 singleton preterm born children with adequate information documented were included in the analyses. Infant and maternal variables were summarized into 25 features. The LightGBM model based on a gradient-boosting framework was used to link input features with future overweight/obesity and a novel unified framework, SHAP (Shapley Additive exPlanations), was used to interpret predictions and identify predictive factors from the summarized features. Poisson regression model was used to examine the association between feeding practices and the identified leading predictive factor. Results Of the eligible 2125 preterm infants, 274 (12.9%) developed overweight/obesity at age 4–7 years. Using an interpretable machine learning-based analytic framework, we identified two most important features as predictors of Childhood overweight/obesity: trajectory of infant BMI Z-score change during the first year of corrected age and maternal BMI at enrollment. The identified features in the model showed similar predictive capacity compared with all features. According to the impacts of different BMI Z-score trajectories on model outputs, we classified this feature into favored and unfavored trajectory. Compared with early introduction of solid foods (≤3 months of corrected age), introducing solid foods after 6 months of corrected age was significantly associated with 11% lower risk (risk ratio, 0.89; 95% CI, 0.82 to 0.97, P < 0.01) of being in the unfavored trajectory. Conclusions Our results suggest that the trajectory of BMI Z-score change within the first year of life is the most important predictor for childhood overweight/obesity among preterm infants. Introducing solid foods after 6 months of corrected age is a recommended feeding practice for mitigating the risk of unfavored trajectories of BMI Z-score change early in life. Funding Sources This study was funded by the Open Project Program of China-Canada Joint Lab of Food Nutrition and Health, Beijing Technology and Business University (BTBU) (KFKT-ZJ-201,801).

Author(s):  
Yaqoot Fatima ◽  
Alice Cairns ◽  
Isabelle Skinner ◽  
Suhail A.R. Doi ◽  
Abdullah Al Mamun

Abstract Purpose This study aims to identify the prenatal and early life predictors of adolescence sleep problems. Methods Sleep data (n = 5081) from the 14-year (13.92 ± 0.34 years) follow-up of a birth cohort were analyzed to explore the predictors of adolescence trouble sleeping, nightmares, snoring and sleep talking/walking. Data from the antenatal period till adolescence were explored for identifying predictors of adolescence sleep problems. Modified Poisson regression with a robust error variance was used to identify significant predictors. Results Our results suggest that about a quarter of adolescents in our study sample had sleep maintenance problems (nightmares: 27.88%, snoring: 23.20%, sleepwalking/talking 27.72%). The prevalence rate of sleep initiation problems was even higher (trouble sleeping: 40.61%). Our results suggest that antenatal and early-life factors, e.g. maternal smoking, anxiety, sleep problems in childhood, attention deficit hyperactivity disorder (ADHD) symptoms, and poor health are significant predictors of adolescence sleep problems. Conclusions This study demonstrates the predictive role of prenatal and early life risk factors in adolescence sleep problems. It seems that exposure to prenatal and early life risk factors increase the vulnerability for sleep problems later in life, which is further supported by poor health and lifestyle choices in adolescence. Therefore, close observation and mitigation of factors associated with early life risk factors could be a potential strategy for preventing sleep problems later in life.


2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Costanza Pizzi ◽  
Chiara Moccia ◽  
Giovenale Moirano ◽  
Antonio d'Errico ◽  
Milena Maule ◽  
...  

Abstract Background Early exposure to unhealthy lifestyles and environmental risk factors is known to affect health throughout the life-course. There is also evidence that the exposure patterns are influenced by the socioeconomic position (SEP). Methods We use the data of the Turin participants of the Italian NINFEA birth cohort (n∼2500) to study how family SEP drives the early life exposome. SEP at birth is measured through the EHII (Equivalized Household Income Indicator), while the exposome includes urban environment, diet and lifestyle exposures measured during infancy. We use standard regression models to evaluate the effect of EHII on each exposome variable accounting for multiple comparison and potential confounders (Drivers-Exposome Wide Association Study – DExWAS) and the hierarchical clustering on the principal components approach to identify groups with similar exposure pattern. Results The DExWAS show that low EHII is associated with lower consumption of fruit and vegetables, lower levels of NO2, building and facilities densities, traffic, walkability and connectivity index, higher land-use diversity index, and higher exposure to pets. The hierarchical cluster analysis identifies three groups, with subjects belonging to the cluster characterized by higher level of urban environment risk factors and a healthier diet having a higher mean EHII. Conclusions These SEP-early life exposome analyses will be replicated in several European birth cohorts participating in the H2020 ATHLETE and LifeCycle projects. Key messages In the Italian city of Turin children from low SEP family are exposed to higher levels of environmental risk factors and unhealthy lifestyles during infancy.


Author(s):  
Carlos Alberto Feldens ◽  
Márcia Regina Vítolo ◽  
Renata Rocha Maciel ◽  
Paola Seffrin Baratto ◽  
Priscila Humbert Rodrigues ◽  
...  

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Wilfried Karmaus ◽  
Nandini Mukherjee ◽  
Vimala Devi Janjanam ◽  
Su Chen ◽  
Hongmei Zhang ◽  
...  

2015 ◽  
Vol 41 (1) ◽  
Author(s):  
Marcello Lanari ◽  
◽  
Federica Prinelli ◽  
Fulvio Adorni ◽  
Simona Di Santo ◽  
...  

2001 ◽  
Vol 108 (5) ◽  
pp. 720-725 ◽  
Author(s):  
Helen L. Rhodes ◽  
Richard Sporik ◽  
Peter Thomas ◽  
Stephen T. Holgate ◽  
Jeremy J. Cogswell

Author(s):  
Linda S. B. Johnson ◽  
Minna Salonen ◽  
Eero Kajantie ◽  
David Conen ◽  
Jeff S. Healey ◽  
...  

2021 ◽  
pp. cebp.1442.2020
Author(s):  
Anniina Tastula ◽  
Arja Jukkola ◽  
Anni-Emilia Alakokkare ◽  
Tanja Nordström ◽  
Sanna Eteläinen ◽  
...  

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