Obesity risk in young adults from the Jerusalem Perinatal Study (JPS): the contribution of polygenic risk and early life exposure.

BACKGROUND/OBJECTIVES: The effects of early life exposures on offspring life-course health are well established. This study assessed whether adding early socio-demographic and perinatal variables to a model based on polygenic risk score (PRS) improves prediction of obesity risk. METHODS: We used the Jerusalem Perinatal study (JPS) with data at birth and body mass index (BMI) and waist circumference (WC) measured at age 32. The PRS was constructed using over 2.1M common SNPs identified in genome-wide association study (GWAS) for BMI. Linear and logistic models were applied in a stepwise approach. We first examined the associations between genetic variables and obesity-related phenotypes (e.g., BMI and WC). Secondly, socio-demographic variables were added and finally perinatal exposures, such as maternal pre-pregnancy BMI (mppBMI) and gestational weight gain (GWG) were added to the model. Improvement in prediction of each step was assessed using measures of model discrimination (area under the curve, AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: One standard deviation (SD) change in PRS was associated with a significant increase in BMI (ß = 1.40) and WC (ß = 2.45). These associations were slightly attenuated (13.7-14.2%) with the addition of early life exposures to the model. Also, higher mppBMI was associated with increased offspring BMI (ß = 0.39) and WC (ß = 0.79) (p < 0.001). For obesity (BMI ≥ 30) prediction, the addition of early socio-demographic and perinatal exposures to the PRS model significantly increased AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early socio-demographic and perinatal exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225). CONCLUSIONS: Inclusion of early life exposures, such as mppBMI and maternal smoking, to a model based on PRS improves obesity risk prediction in an Israeli population-sample.

The Input-Output Relation of Primary Nociceptive Neurons is Determined by the Morphology of the Peripheral Nociceptive Terminals.

The output from the peripheral terminals of primary nociceptive neurons, which detect and encode the information regarding noxious stimuli, is crucial in determining pain sensation. The nociceptive terminal endings are morphologically complex structures assembled from multiple branches of different geometry, which converge in a variety of forms to create the terminal tree. The output of a single terminal is defined by the properties of the transducer channels producing the generation potentials and voltage-gated channels, translating the generation potentials into action potential (AP) firing. However, in the majority of cases, noxious stimuli activate multiple terminals; thus, the output of the nociceptive neuron is defined by the integration and computation of the inputs of the individual terminals. Here, we used a computational model of nociceptive terminal tree to study how the architecture of the terminal tree affects the input-output relation of the primary nociceptive neurons. We show that the input-output properties of the nociceptive neurons depend on the length, the axial resistance (Ra), and location of individual terminals. Moreover, we show that activation of multiple terminals by a capsaicin-like current allows summation of the responses from individual terminals, thus leading to increased nociceptive output. Stimulation of the terminals in simulated models of inflammatory or neuropathic hyperexcitability led to a change in the temporal pattern of AP firing, emphasizing the role of temporal code in conveying key information about changes in nociceptive output in pathologic conditions, leading to pain hypersensitivity.SIGNIFICANCE STATEMENT Noxious stimuli are detected by terminal endings of primary nociceptive neurons, which are organized into morphologically complex terminal trees. The information from multiple terminals is integrated along the terminal tree, computing the neuronal output, which propagates toward the CNS, thus shaping the pain sensation. Here, we revealed that the structure of the nociceptive terminal tree determines the output of nociceptive neurons. We show that the integration of noxious information depends on the morphology of the terminal trees and how this integration and, consequently, the neuronal output change under pathologic conditions. Our findings help to predict how nociceptive neurons encode noxious stimuli and how this encoding changes in pathologic conditions, leading to pain.

Machine learning vs. classic statistics for the prediction of IVF outcomes.

PURPOSE: To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes. METHODS: The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data. RESULTS: Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models. CONCLUSIONS: Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists' counselling and their patients in adjusting the appropriate treatment strategy.

Obesity Prediction in Young Adults from the Jerusalem Perinatal Study: Contribution of Polygenic Risk and Early Life Exposures.

We assessed whether adding early life exposures to a model based on polygenic risk score (PRS) improves prediction of obesity risk. We used a birth cohort with data at birth and BMI and waist circumference (WC) measured at age 32. The PRS was composed of SNPs identified in GWAS for BMI. Linear and logistic models were used to explore associations with obesity-related phenotypes. Improvement in prediction was assessed using measures of model discrimination (AUC), and net reclassification improvement (NRI). One SD change in PRS was associated with a significant increase in BMI and WC. These associations were slightly attenuated (13.7%-14.2%) with the addition of early life exposures to the model. Also, higher maternal pre-pregnancy BMI was associated with increase in offspring BMI and WC (p<0.001). For prediction obesity (BMI ≥ 30), the addition of early life exposures to the PRS model significantly increase the AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early life exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225). We conclude that inclusion of early life exposures to a model based on PRS improves obesity risk prediction in an Israeli population-sample.

Encoding of inflammatory hyperalgesia in mouse spinal cord.

ABSTRACT: Inflammation modifies the input-output properties of peripheral nociceptive neurons such that the same stimulus produces enhanced nociceptive firing. This increased nociceptive output enters the superficial dorsal spinal cord (SDH), an intricate neuronal network composed largely of excitatory and inhibitory interneurons and a small percentage of projection neurons. The SDH network comprises the first central nervous system network integrating noxious information. Using in vivo calcium imaging and a computational approach, we characterized the responsiveness of the SDH network in mice to noxious stimuli in normal conditions and investigated the changes in SDH response patterns after acute burn injury-induced inflammation. We show that the application of noxious heat stimuli to the hind paw of naïve mice results in an overall increase in SDH network activity. Single-cell response analysis reveals that 70% of recorded neurons increase or suppress their activity, while ∼30% of neurons remain nonresponsive. After acute burn injury and the development of inflammatory hyperalgesia, application of the same noxious heat stimuli leads to the activation of previously nonresponding neurons and desuppression of suppressed neurons. We further demonstrate that an increase in afferent activity mimics the response of the SDH network to noxious heat stimuli under inflammatory conditions. Using a computational model of the SDH network, we predict that the changes in SDH network activity result in overall increased activity of excitatory neurons, amplifying the output from SDH to higher brain centers. We suggest that during acute local peripheral inflammation, the SDH network undergoes dynamic changes promoting hyperalgesia.