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Genetics

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes – Nature.com

This study comprised two primary analyses: a genetic-centric analysis (Analysis 1; detailed in Fig.1 and the Methods section) and a genetic-imaging integrative analysis (Analysis 2; detailed in Fig.2 and the Methods section). Data used in the two analyses are summarized (Supplementary TableS1). A total of 68,911 participants from the TWB were included in the analysis (Fig. S1).

The dataset containing information from 60,747 individuals after data quality control (QC) was divided into several subsets: (i) The genome-wide association study (GWAS) samples (Dataset 1, N=35,688), training samples (Dataset 2, N=12,236; Dataset 4, N=40,787), and validation samples (Dataset 3, N=3060; Dataset 5, N=10,197). For classification analysis, testing samples comprised Dataset 6 (N=8827) and Dataset 7 (N=936), while for prediction analysis, they were represented as Datasets 6 (N=8827) and Dataset 7 (N=936); B Sample size. Total sample size, along with the number of cases and the number of controls, are shown for each of the four phenotype definitions in Datasets 1 7; C Phenotype definition criteria. The definition and sample size for the four Type 2 Diabetes (T2D) phenotype definitions is shown. D Analysis flowchart. The analysis flow comprises three steps, starting with selecting T2D-associated single nucleotide polymorphisms (SNPs) and polygenic risk score (PRS), then selecting demographic and environmental covariates, and the best XGBoost model was established using the selected features. As to the first step, SNPs can be chosen from A our own GWAS with an adjustment for age, sex, and top ten principal components (PCs), B published studies based on single ethnic populations, and C published studies based on multiple ethnic populations. Source data are provided as a Source Data file.

Phenotype Definition IV was used as an example to illustrate the process. The data containing information from 7,786 individuals were divided into four subsets: a training dataset (N=4689), a validation dataset (N=1175), and two independent testing datasets (N=1469 for the first dataset and N=444 for the second independent dataset). Subsequently, the best XGBoost model was established. B Flowchart of PRS construction. The Polygenic Risk Score (PRS) was constructed using PRS-CSx, utilizing genome-wide association study (GWAS) summary statistics from the European (EUR), East Asian (EAS), and South Asian (SAS) populations obtained from the analysis of the DIAGRAM Project. Source data are provided as a Source Data file.

We evaluated the prediction performance under different scenarios hierarchically (the best scenario at a previous variable was given for a discussion of the next variable) in the following order: the sources and significance levels of T2D-associated SNPs (Fig.3A and Fig. S2), T2D phenotype definitions (Fig.3B), family history variable combinations (Fig.3C and Fig. S3), demographic variable combinations (Fig.3D), demographic and genetic variable combinations (Fig.3E), and SNP and PRS combinations (Figs.3F and 3G). The findings are summarized as follows: First, using T2D-associated SNPs from the previous large-sample-size GWAS11 as predictors had the highest AUC of 0.557, but its AUC was not significantly higher than that used the SNPs identified by our smaller-sample-size GWAS under different thresholds of statistical significance (Fig.3A), although our GWASs did identify some T2D-associated SNPs (Fig. S4). Second, the phenotype defined by self-reported T2D with HbA1C6.5% or fasting glucose 126mg/dL (i.e., T2D Definition IV) had the highest AUC of 0.640. Its AUC was significantly higher than the AUCs of the other three T2D definitions (Fig.3B). Third, sibs disease history had a significantly higher AUC of 0.732 than parents disease history with an AUC of 0.670 (p=0.009). Moreover, additive parent-and-sib disease history had the highest AUC of 0.758. Its AUC was significantly higher than parent-only (p<0.001) (Fig.3C). Fourth, a joint effect of age, sex, and additive parent-sib disease history had the highest AUC of 0.884. Its AUC was significantly higher than other demographic variable combinations, except for the combination of age and additive parent-sib disease history (Fig.3D). Fifth, whatever SNPs were included or not, demographic and PRS combinations outperformed the models without incorporation of PRS (Fig.3E), although genetic factors only improved up to 3% of AUC conditional on demographic characteristics (age, sex, and family history of T2D). Finally, given T2D-associated SNPs, AUC significantly increased if PRS was included (Fig.3F); T2D-associated SNPs provided a limited additional effect if PRS was already included (Fig.3G).

A bar chart displays AUC. The two-sided DeLong test examined the difference between AUCs. Bonferronis correction was applied to control for a family-wise error rate in multiple comparisons. Symbols *, **, and *** indicate p-values<0.05, 0.01, and 0.001, respectively. A SNP selection. Model predictors were SNPs selected from published studies or our GWAS under different p-value thresholds, where our GWAS association test is a two-sided Wald test for the slope coefficient in a logistic regression. The average AUCs of prediction models for four phenotype definitions were compared. B T2D Phenotype Definition. In addition to including the selected variables in Fig.3A, the AUCs of four phenotype definitions were compared. C Family history of T2D. In addition to including the selected variables in Fig.3A, B, the AUCs of the four types of T2D family history (i.e., (i): parents (binary factor), (ii) sibs (binary factor), (iii) either parents or sibs (binary factors), and (iv) both parents and sibs (ordinal factor)) were compared. D Demographic variables. In addition to including the selected variables in Fig.3AC, the AUCs of different combinations of demographic factors, including age, sex, and family history of T2D, are compared. E PRS and demographic variables. In addition to including the selected variables in Fig.3AD, the AUCs of different combinations of genetic variables, including SNPs, PRS-CS, and PRS-CSx, and demographic variables, including age, sex, and family history of T2D, are compared. F Impact of including PRS after SNPs. The AUCs of the models that consider SNPs, SNPs+PRS-CS, and SNPs+PRS-CSx as predictors are compared. G Impact of including additional SNPs after PRS. The additional 137 SNPs were collected from published studies (Supplemental Text2). The AUCs of the models that consider additional SNPs given PRS in the model are compared. Source data are provided as a Source Data file.

Among different prediction models, the model with predictors PRS-CSx, age, sex, and family history of T2D had the highest AUC 0.915 (Fig.4A) for Type VI definition of T2D based on the first testing dataset (i.e., Dataset 6 in Fig.1). The optimal threshold, determined by the Youden index, for the fitted value that used to predict T2D or non-T2D in the XGboost model was 0.16. The Accuracy, Sensitivity, Specificity, and F1 indices were 0.843, 0.844, 0.843, and 0.672, respectively. Furthermore, the model was tested in the second independent testing dataset (i.e., Dataset 7 in Fig.1), and a promising result similar to the first testing dataset was found: AUC=0.905, Accuracy = 0.843, Sensitivity = 0.846, Specificity = 0.842, and F1=0.644. AUCs are also provided for the other three T2D definitions (Fig. S5).

A AUCs of all models based on Phenotype Definition IV. A heatmap summarizes the AUCs of all models based on Phenotype Definition IV (i.e., T2D was defined by self-reported T2D, HbA1c, and fasting glucose). The genetic variables are shown on the X-axis, and the demographic variables are shown on the Y-axis. B Positive correlation between PRS and T2D odds ratio. In each decile of PRS based on PRS-CSx, the odds ratio of T2D risk and its 95% confidence interval were calculated based on an unadjusted model (blue line) and an adjusted model considering age, sex, and T2D family history (red line). The reference group was the PRS group in the 4060% decile. The horizontal bars are presented as the odds ratio estimates (square symbol) +/ its 95% confidence intervals (left and right ends) at a PRS decile. C High-risk group. In the chart, the figures from the inner to the outer represent (i) the case-to-control ratio, (ii) the number of cases, and (iii) the number of controls in the PRS decile subgroups. D Association of age, sex, T2D family history, and PRS with T2D. In the univariate analysis, the p-values for age, sex, family history, and PRS were 4.17 1020, 7.08 107, 9.41 1013, and 2.06 1013, respectively. In the multivariate analysis, the p-values for age, sex, family history, and PRS were 2.00 1016, 5.56 105, 1.43 1010, and 5.49 1013, respectively. E Risk factors for T2D. Kaplan-Meier curves reveal that Age (older individuals), sex (males), T2D family history (the larger number of parents and siblings who had T2D), and PRS (high decile PRS group) are risk factors (high-risk level) for T2D risk. F Median event time of T2D. Examples of the median event time for developing T2D are provided based on a multivariate Cox regression model, both without and with incorporating PRS. NA indicates not assessable. Source data are provided as a Source Data file.

The importance of each predictor was evaluated through a backward elimination procedure of variables. The optimal model incorporating age, sex, family history of T2D, and PRS achieved an AUC of 0.915. The AUC reductions upon removing individual variables are as follows: (a) Omitting the age variable resulted in an AUC of 0.839, representing a reduction of 0.076. (b) Excluding the sex variable resulted in an AUC of 0.905, with a decrease of 0.01. (c) Removing the family history of the T2D variable yielded an AUC of 0.881, with a reduction of 0.034. (d) Eliminating the PRS variable resulted in an AUC of 0.884, decreasing to 0.031. Based on the decrease in AUC, the impact size appears to be in the order of age > family history > PRS > sex. Additionally, we evaluated feature importance (see the Methods section), and the order of feature importance is family history > age > PRS > sex. Our findings consistently highlight age and family history as the most crucial risk factors for T2D.

Family history encompasses genetics and environment. We delved into the connection between the family history of T2D treated as a graded scale (0, 1, 2, 3, and 4) and the genetic component represented by the PRS. Through ordinal logistic regression, we observed a beta coefficient of 0.808 and an associated odds ratio (OR) of 2.24 (p=1.65 10296). The remarkably small p-value emphasizes the robust statistical significance, signaling a substantial association between the PRS and familial T2D status. For each incremental unit rise in an individuals PRS, their odds of belonging to a higher family history category for T2D increase by 2.24 times. This implies a tangible shift in the likelihood of different family history classifications as the PRS changes. The findings underscore a strong statistical link between genetic predisposition, as captured by the PRS, and the gradation of family history of T2D.

Furthermore, we calculated the Population Attributable Risk (PAR) by dichotomizing PRS into a high-risk group (PRS tercile >80%) and a non-high-risk group (PRS tercile <80%). Among the 59,811 participants, the breakdown was as follows: high PRS with family history (N=5473), high PRS without family history (N=6489), non-high PRS with family history (N=16,054), and non-high PRS without family history (N=31,795). The PAR estimate was 10.17%, indicating that 10.17% of the family history of T2D is attributed to genetic heritability. If considering a broader definition of the high-risk group (PRS tercile >60%) and non-high-risk group (PRS tercile <60%), the PAR estimate increased to 18.41%.

Further consideration of environmental factors, including education level, drinking experience, exercise habits, the number of exercise types, and SNP-SNP interactions with and without SNPs main effect, did not improve T2D prediction (Supplementary TableS2). Considering model parsimony, the final model did not include these environmental factors and SNP-SNP interactions. In addition to prediction models, classification models were also established. The AUCs in classification models (Fig. S6) were generally slightly higher than those in prediction models (Fig. S5).

The positive association between PRS and T2D risk is shown (Fig.4B). Compared to the participants in the 4060% PRS decile group, those in the top 10% decile group had a 4.738-fold risk of developing T2D (95% confidence interval: 3.1477.132, p<0.001) and a 4.660-fold risk (95% confidence interval: 2.6828.097, p<0.001) after adjusting for age, sex, and family history. In addition, we performed a stratified analysis across various combinations of age subgroups, sex subgroups, and family history subgroups to identify high-risk subgroups, where age was stratified into four subgroups based on quartiles: 025%, 2550%, 5075%, and 75100%, corresponding to age subgroups of 43, 4352, 5259, and >59 years of age, respectively (Fig. S7). We identified a high-risk subgroup of women who were older than 59 and had a family history of T2D. The ratio of case vs. control sample size was as high as 7.313.0-fold in the 80100% decile groups (Fig.4C). The ratio was much higher than a 1.6-fold that did not consider PRS (i.e., PRS at 0100%) (Fig.4C). Due to ambiguity or instability in the evidence for other combinations, we chose not to report them.

Among 8347 non-T2D participants at baseline in the first testing dataset of 8827 participants, 220 reported T2D in the follow-up. The Cox regression analyses considered two types of time scales and three types of sex variable treatment and obtained a consistent result (Supplementary TableS3). Using the analysis in which we considered time-on-study as the time-scale with age at baseline, sex, family history of T2D, and PRS as covariates for illustration, age, sex, family history of T2D, and PRS were all significantly associated with T2D (p<0.001) (Fig.4D). Increased age, higher PRS, and stronger T2D family history had a higher T2D risk. The elderly male, with a strong family history and high PRS, had a severe T2D risk (Fig.4E for multivariate Cox regression and Fig. S8 for univariate Cox regression). We also provided the predicted time-to-event (week) (Fig.4F). For example, a 50-year-old man with one of his family members had T2D will achieve median T2D-free time after 460 weeks (95% CI, 384NA). The median time to develop T2D was shortened to 419 weeks (95% CI, 384NA) after considering a standardized PRS of 0.66 (equivalent to a PRS risk subgroup in the top 25% of the population).

A linear regression analysis was performed to assess the impact of exercise on HbA1c. Multiple testing for 110 analyses was corrected using Bonferroni correction, and the significance level was set as 4.5 10-4. It was observed that individuals engaging in regular exercise experienced a significant reduction in HbA1c by an average of 0.09% mg/dL (p<0.001) compared to those who did not engage in regular exercise. Moreover, individuals with a high PRS who engaged in exercise demonstrated a greater reduction in HbA1c (0.13% mg/dL) than those with a low PRS (0.08% mg/dL). The results also suggested that the T2D patients who regularly engaged in exercise can have a noteworthy improvement of 0.32% mg/dL in HbA1c than those T2D patients who did not exercise regularly. In addition, among the various types of exercise, walking for fitness exhibited the most robust reduction in HbA1c for all samples, including high and low-risk subgroups and both T2D and non-T2D groups (Fig. S9). On average, participants engaged in walking for fitness 18.30 times a month (standard deviation = 8.64) for approximately 48.13minutes per session (standard deviation = 22.92).

To investigate the early detection capability of our model for T2D, we performed an analysis focusing on 550 women participants older than 59 years, all of whom had a family history of T2D. We identified them as at high risk if they possessed a high PRS, even though they were initially reported as non-T2D at baseline. Thirty-six were changed to T2D, and 514 were still non-T2D at follow-up. We predicted their T2D status. G1 G4 are the groups of participants in true positive, false negative, false positive, and true negative, respectively (Fig.5A). We evaluated that G3 was indeed misclassified by our prediction model or our prediction had corrected the problem in the self-reported T2D by further investigating: (1) their follow-up time and current risk in the Cox regression model; (2) HbA1c and fasting glucose; (3) the accuracy of self-reported disease status.

A Four subgroups (N=550). B Survival rate (N=550). C Median survival time (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 0.092, 0.0014 (**), and 2.22 1016 (***), respectively. D Follow-up time (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 0.056, 0.32, and 0.14, respectively. E T2D risk (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 0.018 (*), 0.073, and 0.0039 (**), respectively. F HbA1c (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 2.21 1014, 0.0039, and 3.00 105; respectively; in the follow-up, p-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 1.50 10-13, 6.01 104, and 4.55 10-6, respectively. G Fasting glucose (N=550). In the baseline, p-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 2.06 10-12, 6.66 104, and 1.63 102; respectively; in the follow-up, p-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 8.30 108, 1.38 103, and 1.84 102, respectively. H Phenotype definition in G3 (N=395). Many individuals in G3 cannot satisfy the T2D Phenotype Definition IV. In Fig.5CG, two-sided Wilcoxon rank-sum tests were applied to compare group differences. The box plots center lines indicate the medians, the lower and upper boundaries of the boxes represent the first and third quartiles, and the whiskers extend to cover a range of 1.5 interquartile distances from the edges. The violin plots upper and lower bounds depict the minimum and maximum values. Source data are provided as a Source Data file.

The Kaplan-Meier curve for each subgroup is depicted (Fig.5B). The distributions of median survival time for each subgroup are illustrated (Fig.5C). The distributions of the time period from baseline to follow-up for each subgroup are presented (Fig.5D). The distributions of Type 2 diabetes (T2D) risk at follow-up for each subgroup are shown (Fig.5E). The distributions of HbA1c levels at baseline and follow-up for each subgroup are displayed (Fig.5F). The distributions of fasting glucose levels at baseline and follow-up are demonstrated (Fig.5G).

First, compared to G4 (true negative), G3 had a significantly lower T2D-free probability (Fig.5B), shorter median survival time (Fig.5C), higher T2D-risk under similar follow-up time (Fig.5D and 5E), higher HbA1c (Fig.5F), and higher fasting glucose (Fig.5G). Second, compared to G1 (true positive), G3 had a comparable survival rate (Fig.5B), median survival time (Fig.5C), and T2D-risk under similar follow-up time (Fig.5D and5E) but lower HbA1c (Fig.5F) and fasting glucose (Fig.5G). We didnt compare G2 and G3 because of the small sample size in G2. Finally, among the 395 participants in G3, 80.76% of them were removed from our previous analysis because their baseline HbA1c and fasting glucose violated the criteria for the phenotype definition (Fig.1C); 339 participants were removed because of their follow-up HbA1c and fasting glucose violated the formal T2D criteria; only 34 self-reported non-T2D were really non-T2D participants who had HbA1C<6.5% and fasting glucose <126mg/dL (Fig.5H). Overall, the results consistently indicate that G3 represents individuals in a pre-T2D stage, which our model can detect early.

The model that combined four types of image features performed best. Moreover, the model based on BMD image features exhibited a higher AUC, accuracy, specificity, and F1 than the models based on any other three types of images (Fig.6A). The models based on image features had an AUC of 0.898, higher than the ones of genetic information (AUC=0.677) and demographic factors (AUC=0.843). Integrating image features, genetic information, and demographic factors increased AUC to 0.949 in the first testing data (Fig.6B); the results for each of the four images are also provided (Fig. S10). The accuracy, sensitivity, specificity, and F1 of the model in the first testing data were 0.871, 0.878, 0.870, and 0.663, respectively, based on a classification threshold of 0.03. The model also performed reasonably well in the second testing dataset with AUC=0.929, Accuracy = 0.854, Sensitivity = 0.789, Specificity = 0.862, and F1=0.558. The results of a prediction model using tuned parameters are also provided (Supplementary TableS4). As no significant improvement was observed, this paper discusses the default model. According to the estimated feature importance in the best XGBoost model, all genetic factors (PRS), four types of medical images, and demographic variables provided informative features for risk assessment, such as PRS (genetics), family history and age (demographic factors), fatty liver (ABD images), end-diastolic velocity in the right common carotid artery (CAU images), RR interval (ECG images), and spine thickness (BMD images). Of the 152 medical imaging features, 125 were selected in the final model. (Fig.6C).

A Performance comparison of medical imaging data analysis. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPEC), and F1 score are compared for the integrative analysis of four types of medical images (All) and individual medical image analyses, including BMD, ECG, CAU, and ABD. B The model that combines four types of medical imaging, PRS, and demographic variables shows the highest AUC of 0.949. ROC plots and the corresponding AUC for the models considering medical image features (I), genetic PRS (G), and demographic variables, including age, sex, T2D family history (D), and their combinations. C An optimal model combining medical imaging, PRS, and demographic variables. The best models top 20 features with a high feature impact include the medical image, genetic, and demographic features. D Positive correlation between MRS and T2D odds ratio. In each decile of MRS based on four types of medical images, the odds ratio of T2D risk and its 95% confidence interval were calculated based on an unadjusted model (blue line) and an adjusted model considering age, sex, and T2D family history (red line), with the MRS group in the 4060% decile serving as the reference group. The horizontal bars are presented as the odds ratio estimates (square symbol) +/ its 95% confidence intervals (left and right ends). E High-risk group. The figures from the inner to the outer in the chart display (i) the case-to-control ratio, (ii) the number of cases, and (iii) the number of controls in the MRS decile subgroups. F Input page of the online T2D prediction website. Personal information, including age, sex, family history of T2D, PRS, and MRS, is input to calculate T2D risk. PRS and MRS are optional, and a reference distribution is provided. G Output page of the online T2D prediction website. Source data are provided as a Source Data file.

To address the challenges of practical clinical implementation in the best XGBoost model, we have proposed an alternative model that requires a limited number of features. We systematically calculated each features incremental area under the AUC by sequentially including those with the highest feature importance. We selected the top features showing a positive AUC increment. The analysis revealed that a sub-model incorporating only the following eight crucial variables: family history (from the questionnaire), age (from the questionnaire), fatty liver (from ABD images), spine thickness (from BMD images), PRS (from genetic data), end-diastolic velocity in the right common carotid artery (R_CCA_EDV) (from CAU images), RR interval (from ECG images), and end-diastolic velocity in the left common carotid artery (L_CCA_EDV) (from CAU images), maintains a commendable AUC of 0.939 (Fig. S11). This streamlined model significantly reduces the number of risk predictors while preserving high prediction accuracy, demonstrating promising potential for practical application in clinical settings. Moreover, the reduced number of risk predictors in the streamlined model alleviates concerns about model overfitting.

Each participants multi-image risk score (MRS) was calculated as the likelihood of being predicted as a T2D case using XGBoost, which analyzed the medical imaging features for T2D prediction. The odds ratio and its confidence interval for the association between MRS and T2D are shown by percentiles of MRS (Fig.6D). Compared to the participants in the 4060% MRS decile group, the risk of T2D increased with MRS. Of importance, we further identified that, for the men older than 54 years old with a family history of T2D, the case vs. control ratio of sample size was 9.3 in the 90100% MRS decile group, much higher than 1.3, which MRS was not considered (Fig.6E).

We have established a website where users can calculate their T2D risk online. To obtain the risk assessment, users are required to provide age, sex, and family history of T2D, and they can optionally provide PRS and MRS (Fig.6F). PRS and MRS can be entered manually or uploaded as a file (Supplemental Text1). Additionally, we have provided PRS and MRS risk percentages based on the study population as a reference. The online risk assessment offers information, including the risk of developing T2D over 3, 5, and 7 years, T2D-free probability, and T2D risk with and without considering PRS (Fig.6G). The assessment takes into account both PRS and MRS (Fig.6G). For example, consider a 50-year-old male with a family history of T2D and PRS 1.5 and MRS 1.5. Without considering PRS, the risk (probability) of developing T2D after a 7-year follow-up is 0.23. However, when PRS is considered, the risk increases to 0.37. Furthermore, considering MRS further increases the risk to 0.81. The online tool provides these valuable insights to users based on their input data.

Link:
AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes - Nature.com

Genetics

World-first AI algorithm developed at CHEO leads to rare disease diagnosis for families – CHEO

Harnessing the power of artificial intelligence (AI), CHEO researchers have developed a groundbreaking search algorithm that identifies children and youth who may have an undiagnosed rare genetic disease and refers them for genetic testing putting an end to their diagnostic odyssey.

The ThinkRare algorithm is incredibly exciting and promising because it means we can help families find answers and get the care and support they need sooner, said Dr. Kym Boycott, Senior Scientist at the CHEO Research Institute and Chief of Genetics at CHEO. This algorithm is a game changer. Using AI to scour CHEOs electronic health record based on set criteria, ThinkRare can accurately identify kids who may have an undiagnosed rare genetic disease and refer them to our clinic something that may have never happened without it.

Ten-year-old Antony Wistaff and hisfamily have spentcountlesshours at CHEO, callingit a second home. Antony wasbornprematurely in October 2013 and a few dayslaterunderwent emergency surgery at CHEO to place a shunt for hydrocephalus. But thatwasonly the beginning of whatwouldbecome a decade-long diagnostic journeyconsisting of more than 100 outpatientappointmentsacross six differentspecialtyclinics at CHEO, and 30 trips to the emergency department for variousreasons.

That was until recently, when the ThinkRare algorithm identified Antony as potentially having an undiagnosed rare genetic disease and flagged him for a referral to receive genome-wide sequencing testing a test that simultaneously analyzes the more than 5,000 genes that have been associated with rare disease and is now available clinically in Ontario.

The results of the genetictestingdiscoveredthat Antony has Chung-Jansen Syndrome a rare disorderresultingfrom a pathogenic variant in the PHIP gene. At present, the syndrome has been diagnosed in only about 400 people worldwide and itexplainedmany of Antonyshealth and behavioural challenges, includinghisdevelopmentaldelays, learningdifficulties, and large head size.

When we found out that Antony was diagnosed with Chung-Jansen Syndrome, it answered so many questions for our family, said Georges Wistaff, Antonys dad. This research brought a kind of peace to our house. Hadweknownthissooner, itwould have meantlessquestioning as parents, less stress, and more support becausewewould have had a cleardiagnosis for Antony. A little bit of blood and a simple test, answeredsomany questions.

To date, Think Rare, whichiscurrently operating as a researchprojectapproved by the CHEO ResearchEthicsBoard, isthree for three meaning the first three patients identified by ThinkRare and referred to genetics have received test results and been diagnosedwith a rare disease. Genetictestingisunderway for manyotherfamiliesidentified by ThinkRare.

Our goal is to flip the diagnostic care journey on itshead and start withgenetictestingearlier on the care pathway. By incorporating the ThinkRarealgorithmintoclinical care, wewillbe able to support CHEO clinicians and frontlineworkerswith the power of machine learning to find the needle in the haystack, added Dr. Boycott, whois a Tier 1 Canada Research Chair in Rare DiseasePrecisionHealth and Professor of Pediatrics at the University of Ottawa.

Work iscurrentlyunderway at CHEO to transition the ThinkRareprojectfrom researchintoclinical practice, with all the necessary patient privacymechanisms in place.

CHEO isuniquelypositioned to develop an impactfulalgorithmsuch as ThinkRarebecause of CHEOsinvestment in a robustelectronichealth record system, ourcommitment to innovation, our close collaboration betweenclinical and researchteams, and becausewe are the only pediatric healthcare centre in Eastern Ontario serving a widegeographic area. At CHEO, we have broughttogether all the necessaryelementswhenitcomes to making AI advancements in healthcare, said Dr. Jason Berman, CEO and Scientific Director, CHEO Research Institute, and Vice-PresidentResearch, CHEO.

The ThinkRareprojectwas made possible withfundingfrom the CHEO Foundation, the CHAMO Innovation Fund, and Ontario Genomics.

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Media contact:

Jennifer Ruff Director of Communications CHEO Research Institute (613) 261-3979 jruff@cheo.on.ca

About the CHEO Research Institute

The CHEO Research Institute is a global centre of excellence in pediatric research that connects talent and technology in pursuit of life-changing research for every child, youth and family in the CHEO community and beyond. The CHEO Research Institute coordinates the researchactivities of CHEO and isaffiliatedwith the University of Ottawa. At the CHEO Research Institute, discoveries inspire the best life for everychild and youth. For more information, visitcheoresearch.ca.

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World-first AI algorithm developed at CHEO leads to rare disease diagnosis for families - CHEO

Genetics

Enhancing Chickpea Crop Improvement With Wild Chickpea Genes – Technology Networks

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The studyCicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea,published in Nature Genetics, provides insights into the evolutionary history and divergence time of the Cicer genus, sequencing the genomes of eight wild Cicer species and comparing them with two cultivated chickpea varieties.

The study also constructs a graph-based super-pangenome that can help identify and transfer valuable genes from wild species to cultivated ones.

Director of Murdoch UniversitysCentre for Crop and Food InnovationProfessor Rajeev Varshney, who coined the term super-pangenome in 2019 inTrends in Plant Science, said the findings in the new study could accelerate crop improvement globally.

Subscribe to Technology Networks daily newsletter, delivering breaking science news straight to your inbox every day.

The Cicer super-pangenome offers a powerful way to study chickpea genes to perform association analyses and determine the most important traits for our farming industry.

Our study found that the wild species have more genetic diversity and variations that could be useful for improving chickpea traits such as disease resistance, flowering time, and stress tolerance.

Traditional and modern breeding efforts have improved chickpea productivity, but more exhaustive steps have been needed to meet the growing worldwide demand.

Chickpeas are highly nutritious, economically significant and important contributors to soil fertility, fixing atmospheric nitrogen but chickpea production currently faces several biotic and abiotic constraints.

They are widely grown, with an annual global production exceeding 17 million tonnes.

In the context of Australia, chickpea production reached more than 2 million tonnes in 2017, but at present it is only 500,000 tonnes, so there is huge scope for enhancing local production to contribute to both environmental sustainability and growers profitability.

Reference:Khan AW, Garg V, Sun S, et al. Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea. Nature Genetics. 2024. doi:10.1038/s41588-024-01760-4

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Enhancing Chickpea Crop Improvement With Wild Chickpea Genes - Technology Networks

Genetics

What links our Siberian ancestors to a heightened risk of developing multiple sclerosis? – The Conversation

The genetic predisposition to suffering from multiple sclerosis (MS) is closely linked to historical migration patterns, and to our ancestors lifestyles. Specifically, MS is linked to the genetic contributions made by nomadic populations who reached Western Europe 5000 years ago from the Siberian Steppe.

Human genomes vary by 0.1%, and this difference is often the result of responses to environmental pressures. When faced with epidemic diseases, for instance, natural selection means the genetic variants that provide individual resistance to pathogens are the ones that survive. These markers are, in terms of population genetics, positively selected.

However, variants that are beneficial in one situation can be counterproductive in another. Immune systems the first line of defence against harmful viruses and bacteria tend to be involved in such a mismatch between genetics and environment.

In some cases, immune systems are unable to distinguish between the bodys own cells and external ones, leading them to destroy tissues. This is what causes autoimmune diseases such as rheumatoid arthritis, lupus or MS.

In the case of MS, the immune system attacks the insulating covers of nerve fibres in the brain and spinal cord. Until recently, the cause of MS was unknown, as were the causes of different distributions among the worlds population. However, new hypotheses have been proposed that may shine a light on these mysteries.

Two sources of information can help us understand when, where and how MS originated. The first is the diseases prevalence across continental Europe: there are higher concentrations of MS in the north and lower ones in the south. The second comes from palaeogenomics, the study of DNA recovered from ancient remains. Research in this field suggests that the European gene pool is made up of three major lineages.

Read more: Early humans reached northwest Europe 45,000 years ago, new research shows

The base of the European genome is linked to the first European settlers: hunter gatherers who arrived in Western Europe around 45,000 years ago. Later, between 8000 and 6000 years ago, Neolithic populations from Anatolia migrated into the region, mixing with the hunter gatherer genome. These groups were linked to the domestication of plants and animals.

To these two previous genomes we can add the Yamnaya migration. The Yamnaya was made up of pastoralist groups from the Siberian Pontic Steppes who dispersed across Eurasia in the Bronze Age around 5000 years ago.

Europeans are therefore a complex mixture in varying proportions of these three gene pools.

A recent study based on analysis of ancient DNA has found a direct relationship between the genetic risk of developing MS and an individuals proportion of Yamnaya ancestry. Like the incidence of MS, the Yamnaya genome is more prevalent in northern than southern Europe.

The contribution of these nomadic pastoralists to the European cultural and genetic ancestry had been overlooked by archaeologists until palaeogeneticists detected traces of them in Bronze Age populations.

They were hierarchical, patrilineal and patriarchal groups who introduced, among other innovations, the domestication of horses, the use of carriages, and Indo-European languages to Europe.

Read more: Indo-European languages: new study reconciles two dominant hypotheses about their origin

Their arrival in Western Europe also brought about the contribution of new genetic variants that had been selected to suit lifestyles based on pastoralism and animal husbandry.

Coexistence with cattle meant access to milk, an exceptional dietary source of energy. This in turn led to the selection of genetic variants that allowed adults to properly digest lactose.

Another interesting finding is the presence of certain pathogens such as the bacteria Yersina pestis, which causes the plague among the remains recovered from Siberian pastoral groups.

We can therefore explain the relationship between Yamnaya ancestry and MS, as contact with the pathogens carried by livestock caused the Yamnayas immune systems to adapt. They became hypersensitive to infections, which sometimes led their immune systems to confuse their own cells with those of others, resulting in the development of autoimmune diseases.

It is perhaps surprising to learn that some characteristics of modern day humans such as the ability to digest lactose as adults, resistance to infectious diseases, or the development of autoimmune diseases are inherited from a remote past that developed in the Pontic steppes. This discovery also has potential benefits in the field of medicine, such as in allocating healthcare resources to regions with a higher genetic predisposition to developing MS. This practical application would, however, require further, more focused research.

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What links our Siberian ancestors to a heightened risk of developing multiple sclerosis? - The Conversation

Pediatrics

Dr. Robert Wilmott, pediatrics chair who gave parents advice as ‘Dr. Bob,’ dies at 75 – St. Louis Post-Dispatch

Dr. Robert Wilmott, long-time chair of pediatrics at Cardinal Glennon Childrens Hospital who wrote advice columns for area parents as Dr. Bob, died Sunday.

Dr. Robert W. Wilmott, who served as chair of the pediatrics department at SSM Health Cardinal Glennon Childrens Hospital for 17 years and provided advice to parents across the St. Louis region through his Dr. Bob columns for the hospital and St. Louis Post-Dispatch, died Sunday from bile duct cancer. He was 75.

Wilmott also served nearly three years as dean of the St. Louis University School of Medicine beginning in January 2019, a pivotal time as he strengthened the schools partnerships with SSM Health hospitals and SLUCare physician practices.

Despite his cancer diagnosis in 2020, Wilmotts leadership as dean was marked by the building of the new St. Louis University Hospital, the Center for Specialized Medicine, and the SLUCare Academic Pavilion, leaving a lasting impact on our growth and development, said Kevin Elledge, president of the SSM Health St. Louis Regional Medical Group.

Wilmott also shepherded the medical school through a difficult accreditation process and the first years of the COVID-19 pandemic.

As the chair of pediatrics at Cardinal Glennon, Wilmott was a thoughtful and engaging leader, said SSM Health St. Louis Regional President Jeremy Fotheringham. He was not only a gifted clinician, but a prolific researcher and mentor.

Wilmott was devoted to his patients, colleagues and work, Fotheringham said. His leadership was not just about maintaining the status quo, but about propelling us forward and reaching new heights.

Wilmott was born in London to Bill and Rose Wilmott on Sept. 12, 1948. He was the first member of his family to attend a university, earning his medical degree from University College London in 1973, according to family members. He received a research doctorate from the University of London, where he began specializing in treating children with cystic fibrosis.

During a fellowship at Londons Great Ormond Street Hospital in pediatric intensive care, he met his wife, Cathryn Clark, a nurse at the time. They married Dec. 12, 1981.

Wilmott first came to the United States in 1977 for a rotation at Childrens Hospital of Philadelphia, where he would return after his marriage in London. In 1986, he took an academic position at Wayne State University in Detroit; and from 1989 to 2001 he served as director of the pulmonary, allergy and immunology division at the Cincinnati Childrens Hospital Medical Center.

In 2001, Wilmott became the pediatrician-in-chief at Cardinal Glennon. He also served as an editor of the Journal of Pediatrics for 18 years and was a principal author of Disorders of the Respiratory Tract in Children, an authoritative textbook of pediatric pulmonology.

He was a frequent contributor to the former Healthy Kids advice column in the Post-Dispatch, writing dozens of columns between 2003 and 2015 on topics such as accidental poisonings, concussions, lice and screen time.

For years, parents could also submit questions about their childrens health under the Ask Dr. Bob section on Cardinal Glennons website.

Wilmott loved to ride horses, scuba dive, play the saxophone, ski and travel, his family said. He and his wife went on several medical mission trips together to Belize.

One of four daughters, Annabelle Wilmott, 31, of Sacramento Calif., said she observed her fathers kind and gentle approach with patients firsthand when she accompanied her parents to Belize. He would often maintain relationships with his patients long after caring for them as their doctor.

She also saw her dads compassion for the unhoused, getting to know many of their names and life stories.

Throughout my life, I witnessed the depth of my fathers care and empathy for others, she said.

Wilmott is survived by his wife of 42 years, Cathryn Wilmott; his sister, Rosemary Wilmott; his daughters, Jenny Wilmott, Francesca Wilmott, Gina Reed and Annabelle Wilmott; and five grandchildren.

A memorial service will be held at 1:30 p.m. Wednesday, May 29, at St. Francis Xavier College Church in St. Louis.

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Dr. Robert Wilmott, pediatrics chair who gave parents advice as 'Dr. Bob,' dies at 75 - St. Louis Post-Dispatch

Pediatrics

American Academy of Pediatrics launches ‘Rx4DC’ initiative to address gun violence – DC News Now | Washington, DC

WASHINGTON (DC News Now) The D.C. Chapter of the American Academy of Pediatrics (DCAAP) unveiled a new initiative Tuesday to address gun violence in the District.

Our prescription for the district is really a call to action for all people who interact with children who live, work and play, explained Nia Bodrick, a pediatrician and the president of the DCAAP.

Prescription for the District, or Rx4DC, urges local leaders and stakeholders to adopt preventative approaches to reducing violence.

Among the actions prescribed include an increase in community spaces, funding for out-of-school time, more support for school attendance, improving mental health access and prioritizing economic investment.

Its sort of like taking your fruits and vegetables to live a healthier life, said Bodrick. Prevention is key. What are all the things, all the assets in our communities that we can build upon to prevent some of these dangerous outcomes like violence in communities?

Bodrick said violence is a public health issue.

So far this year, seven juveniles have been killed by gun violence, including 3-year-old Tyah Settles.

The number of homicides overall is nearing 70.

I think violence affects everyone, said Bodrick. It affects those who are the victims, the perpetrators, the communities. It can have a lasting effect on the growth and development of children.

The DCAAP is calling on all stakeholders to work collaboratively towards solutions.

The initiative was presented at the organizations annual spring symposium.

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American Academy of Pediatrics launches 'Rx4DC' initiative to address gun violence - DC News Now | Washington, DC

Pediatrics

Utah Valley Pediatrics expands to Sanpete County by opening Ephraim location – Daily Herald

Utah Valley Pediatric nurse practitioner Viki Bailey will be working at the new clinic.

Children in Sanpete County now have access to pediatric care as Utah Valley Pediatrics recently opened its newest location in Ephraim. The new office, located off of North Main Street, will seek to maintain UVPs mission: Helping Children Be Healthy.

At Utah Valley Pediatrics, our providers receive extra training in the care of children, from infants to teenagers. That makes us more qualified to care for kids, said UVP administrator Kevin Moffitt. Our record of caring for children throughout Utah County is really unparalleled.

Were excited to expand, and we look forward to bringing the same quality of care children deserve to Ephraim and all of Sanpete County.

The new Utah Valley Pediatrics office is currently staffed by Viki Bailey, a family nurse practitioner with a Master of Science in Nursing degree from South University in Georgia. For as long as she can remember, Bailey wanted to be a nurse, but she says she found her true calling in 2018 once she began working in an urgent care that focused only on children.

Ive known I wanted to be a nurse since I was 4 years old, said Bailey, who speaks English, Spanish and Portuguese. As a mother of seven children, I completely empathize with parental concerns and worries as they relate to the health of their own children. My goal is to enthusiastically connect with the kids while addressing the concerns of parents as we face future health challenges together.

Bailey will be supported by the 31 full-time, board-certified pediatricians currently working in UVP offices throughout Utah County. These board-certified pediatricians specialize in childrens health and have an additional 30 months of training in child health beyond a family practice physician.

Utah Valley Pediatrics, Sanpete County Office is currently open 5-9 p.m., Mondays through Thursdays. To schedule an appointment, please call (435) 266-0500. Phone calls are answered 24/7.

Utah Valley Pediatrics, Sanpete County Office is located at 43 E. 450 North in Ephraim.

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Utah Valley Pediatrics expands to Sanpete County by opening Ephraim location - Daily Herald

Pediatrics

Pruitt named director of academic pediatrics division – The Source – Washington University in St. Louis

Cassandra M. Pruitt, MD, a professor of pediatrics, has been named director of theDivision of Academic Pediatricsin theDepartment of Pediatricsat Washington University School of Medicine in St. Louis. She had served as interim director since July 2022.

The academic pediatrics division is home to the universitysComplex Care Clinic, which offers primary care to children with complex medical needs, and theGeneral Academic Pediatrics Clinic, which provides a range of services, including well-child visits, immunizations and same-day visits for illness and other concerns. The division also offers physicians who specialize in developmental and behavioral pediatrics, which includes medical and psychosocial aspects; and physicians who specialize in pediatric physical medicine and rehabilitation, including musculoskeletal and neurologic conditions.

Read more on the School of Medicine website.

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Pruitt named director of academic pediatrics division - The Source - Washington University in St. Louis

Pediatrics

Integrating behavioral health within primary care settings – Contemporary Pediatrics

Behavioral health within the primary care setting: pressmaster - stock.adobe.com

Virginia Hatch-Pigotts, MD, FAAP, LMSW, article, "Child welfare: Now that we know better. Lets do better," is a powerful read for all pediatric health care providers to think about and collectively consider meaningful, impactful policy changes for children living within the foster care system.

Hatch-Pigott highlights the trauma children experience before and often while living within the foster care system. She states, The real problem is the lack of timely appropriate mental health services for these [foster care] children (p. 14).1

Her experiences caring for children within the foster care system, advising foster care parents, as well as her analysis of foster care statistics led her to recommend changes for funding at the macro and micro child welfare levels as well as the importance of changing the immediate evaluation of the children to focus on trauma-informed therapies. I highly recommend reading Hatch-Pigotts article.

Early in my career as a pediatric nurse practitioner (PNP), I had the pleasure of working with children within the foster care system, their foster care parents, and meeting the biological parents who were receiving therapies to improve their own behavioral issues and parenting skills.

My role was embedded within a foster care agency that provided comprehensive services from psychiatrists, psychologists, and social workers including case workers for each child and family, nursing care, with medical care provided by PNPs and pediatricians. The overarching goals were family healing and returning the children safely to their biological parents. We understood the importance of integrating mental health and behavioral health services within the primary care visits to enable the children to emerge as healthy individuals from the trauma they experienced prior to admission.

Today, the integration of mental health within the primary care system is supported in the literature but how often it is operationalized, and what is the effectiveness of these systems? A literature search shows several models have been developed and implemented to support behavioral health integration into primary care systems.

A report of an 18-month pilot study in which a Developmental and Behavior Access Clinic (DBAC) was designed for pediatricians to be trained and initially mentored by developmental-behavioral pediatricians to provide developmental care to children revealed that the average wait time for children to receive the needed developmental behavioral (DB) care decreased from 218 days to 41 days. This pilot study supports opportunities to include behavioral health into primary care settings.2

A comprehensive study for the integration of behavioral health (BH) services included an educational program, Behavioral Health Learning Community (BHLC), that delivered 10 sessions (16 hours) over a 2-year period was reported for 13 pediatric practices enrolled in a statewide program that included 105 primary care providers who cared for approximately 114,000 patients.3 Study outcomes revealed increased access to quality behavioral health (BH) services, provider self-efficacy and professional satisfaction, without increasing health care costs.3

I recently published an editorial in the Journal of Pediatric Health Care discussing the integration of behavioral and mental health care in pediatric primary care populations.4 I discussed the role of Pediatric Primary Care Mental Health Specialists (PMHS) developed and offered by the Pediatric Nursing Certification Board.5 Individuals who hold the PMHS credential often practice in dual roles serving both primary health care and behavioral/mental health care needs of the pediatric populations. From my personal experiences, parents appreciate having access to pediatric and/or pediatric-focused family nurse practitioner providers who provide these comprehensive services within one practice setting.

If infants and young children living within the foster care system and all infants and young children could speak for themselves, what would they say to policy makers? Help me please, I need to be safe, cared for, and loved.School-age children and adolescents can inform their healthcare providers of their concerns while living in the foster care system, but do we, the professionals, speak with policymakers on their behalf? The mental health of the pediatric population is in crisis. As mentioned, Dr. Hatch-Pigott supports funding at the macro and micro levels within the child welfare system to improve the outcomes for children within the foster care system. Funding for mental health services for all children also needs to be a legislative priority. PNPs need to continue their advocacy efforts through collaboration with all pediatric providers, remain actively engaged in helping children and families by supporting timely and appropriate health policy initiatives, and through continued support for legislative initiatives offered by the National Association of Pediatric Nurse Practitioners (NAPNAP).

References

1. Hatch-Pigott, V. Child welfare: Now that we know better, lets do better. Contemporary Pediatrics. 2014;40(04):13-19. https://www.contemporarypediatrics.com/journals/contemporary-peds-journal/may-2024

2. Jeung J, Talgo J, Sparks A, Martin-Herz SP. Expanding developmental and behavioral health capacity in pediatric primary care. Clin Pediatr (Phila). 2023;62(8):919-925. doi:10.1177/00099228221147753

3. Walter HJ, Vernacchio L, Trudell EK, et al. Five-year outcomes of behavioral health integration in pediatric primary care. Pediatrics. 2019;144(1):e20183243. doi:10.1542/peds.2018-3243

4. Hallas D. Integrating Behavioral and Mental Health Care in Primary Care for Pediatric Populations. J Pediatr Health Care. 2024;38(3):293-294. doi:10.1016/j.pedhc.2024.01.004

5. Pediatric Nursing Certification Board. The Pediatric Primary Care Mental Health Specialist (PMHS) role, settings, and ethics. Accessed May 20, 2024. https://www.pncb.org/pmhs-role

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Integrating behavioral health within primary care settings - Contemporary Pediatrics

Pediatrics

Phones and kids: new pediatric guidelines, expert advice and info on new school rules – Kidsburgh

Photo above by Julia Coimbra via Unsplash.

The first iPhones and Androids hit the market when todays high schoolers were babies. Theyve never known life without smartphones. And today, the Surgeon Generals office estimates that 95 percent of kids ages 13-17 and nearly 40 percent of kids ages 8-12 use social media, connect to the internet and use a massive array of interactive apps through their phones.

Until recently, the advice was to limit kids screen time to two hours per day or less. That wasnt always easy and were now discovering that it wasnt enough to just focus on the number of minutes kids spent in the glow of their screens. It matters what theyre watching and reading, and how it affects a given child or teen.

Phones connect our kids with information and ideas, but they also appear to be causing increases in anxiety, depression, bullying and other distractions, especially in the classroom.

How do parents help their kids navigate our digitally connected world?

Last month the American Academy of Pediatrics (AAP) Center of Excellence on Social Media and Youth Mental Health unveiled its 5 Cs of Media Use a guideline for parents to better understand media influences and to strive for healthy screen time habits (we break down all the details on that below). And schools have begun testing new rules and grappling with the growing issue of phones in schools at all grade levels.

Weve got all that information, along with info on how starter phones can help:

SCHOOLS TAKE ACTION

To help control negative effects from cell phone overuse, schools are increasingly invoking strict rules to eliminate phones in classrooms. And earlier this month, PA state senator Ryan Aument (RLancaster) drafted a bill to lock up student phones due to the steep decline in mental health in children since the early 2010s, according to his website.

Data from Common Sense Media also found that 97 percent of students surveyed used a phone on average for 43 minutes during school hours, and 37 percent of that time was spent on social media.

Starting this year, Sto-Rox School District banned cell phones in classrooms in all grades.

Heres how it works: Over the course of about 10 minutes, nearly 600 students in grades 7 through 12 enter their school building, hand their phones to a staff member who places it in an envelope with the childs name on it, then its put in a bin to be locked in storage for the day. The students then pass through metal detectors and head to breakfast. Phones are returned by the students last period teachers during the days final five minutes.

The process was planned carefully and has been running smoothly. We are very good at it, says Sto-Rox superintendent Megan Marie Van Fossan. Were very strategic.

And the impact? At the start of the school year, the students werent happy about the new policy. But then positive changes began surfacing.

Van Fossan says kids have begun talking to each other again in the cafeteria. Back when phones were allowed, the cafeteria was a relatively quiet place where students were focused on their phones rather than one another.Mornings in the hallway are now the same: Rather than scrolling on their phones or texting, students are greeting each other as the day begins.

Rather than revolving around social media, these students days are full of in-person interaction and connection. No parents have complained about the policy, Van Fossan says, and the rule isnt difficult to enforce.

Other school districts in the region have been taking notice.

We get phone calls and emails (from other school districts), saying, We are looking at going to this policy. Tell us about your experience, Van Fossan says.

Why ban phones?

Phones were taken out of seventh and eighth grade classrooms last year and were never permitted in kindergarten. But the choice to start a district wide ban came because of increasing safety and security concerns.

Kids were texting one another to meet, fight someone in the bathroom, hurt someone after school, Van Fossan says. We dont need that going on during the school day.

Students were also paying less attention in class.

Beyond helping with focus, the new system also helps inspire kids to be on time: Late students must drop off and retrieve their phones at the schools office, potentially adding 20 minutes to the end of their school day.

In the Pittsburgh Public Schools 54 buildings, the electronic device policy generally prohibits students from using, displaying or turning on cell phones on school grounds. And in some PPS high school buildings, student phones are sealed in pouches at the start of the day.

But in many buildings, students have traditionally kept their phones with them.

A lot of our high schools are leaning (toward) collecting phones; not every high school does, says Carrie Woodard, director of school counseling for the district.

In recent years, PPS counselors have seen increases in cyber bullying in addition to anxiety and depression symptoms in students who arrive at school upset from social media postings made after school hours.

Its something I think weve been battling for over a decade now, Woodard says.

What can help besides banning?

To help win that battle, Woodard said its important for educators and school counselors to support the whole child, academically and personally by:

Some parents, Woodard says, are anxious about phones being taken away from students. They want to have instant communication with their child in the event of an emergency.

From the school level, we can always assure them, she says, that if there is an emergency there are systems in place where the educational team will get in touch with the parent.

What is a starter phone?

Starter phones are entry-level devices that allow kids to text, call and store photos. Some have limited access to the Internet or social media. They come in many shapes and sizes, and are usually budget-friendly. Here are some options parents may want to pursue:

The Bark Android phone has parental controls included. It sends alerts about your childs texts and searches and has location tracking. Approval to download apps is necessary. You can also install a Bark parental control app on any smartphone. Plans starting at $39/month at Bark.us

Also an Android phone, the Pinwheel has parental controls built in, and there is no web browser so it has no direct access to social media. There are several models. Note that you wont receive alerts about messages that will be a potential problem. The Plus 3 is $489 on Amazon.

The iPhone SE lets parents manage how much screen time a child spends in their browser. Through Apples Family Sharing, parents set screen time permission, approve what their child buys or downloads, and can disable apps and set limits from their own device. Like almost any iPhone, it can be set up with Apples parental controls. Costs starts at $429 from Apple.

The TCL Flip 2 flip phone allows calling and messaging, and it includes simple games and a limited web browser. $100 from Amazon.

The Nokia 2780 Flip phone is easy to use for texting and calling. $90 at Best Buy.

The Gabb Phone has no internet or social media and no app store. It does include a GPS tracker, and other basics like a camera, calculator, photo album. $75 at Gabb.com.

SCREEN TIME ADVICE FOR EVERYONE

Last month the American Academy of Pediatrics (AAP) Center of Excellence on Social Media and Youth Mental Health unveiled its 5 Cs of Media Use a guideline for parents to better understand media influences and to strive for healthy screen time habits.

The AAP is looking for a way to help parents and educators understand the issues cropping up with phones and other screens, and understand how they can help the children in their lives, says Pamela Schoemer, MD of UPMC Childrens Community Pediatrics. Schoemer tells Kidsburgh she has discussions about screen time effects in about half of her patient visits.

The 5 Cs stand for:

The calm element of the guideline, Dr. Schoemer notes, typically comes up when there are issues with falling asleep something that can spill over into the ability to focus or even stay awake throughout the next school day.

Kids need the ability to calm themselves and to deal with their emotions, she said. So often I see parents, especially with younger kidsputting something (a cell phone or tablet) in front of their child to calm them.

Instead of handing kids a digital device, she suggests:

Dr. Schoemer considers that final C, communication, to be the best resource for parents. Its helpful to have discussions about time limits with devices. But communication isnt just about how many minutes a child is looking at a screen. Its also important to know what your child is looking at it and explore its impact.

Its okay to ask what your child is looking at, she says, and it might even lead to a moment of shared laughter:A TikTok can be just as funny to us as it is to them.

Valuable screen time, like exploring interests, communication with extended family or for schoolwork, is great. Healthy screen habits at home can include educational videos that help deal with emotions or those that encourage an activity, like cooking or science experiments for younger children. Anything on PBS Kids (from Mister Rogerss Neighborhood and Daniel Tigers Neighborhood to the friendship-focused show City Island) is suitable for younger children over the age of 2 or 3.

All screen time isnt equal, and you have to assess it, Schoemer says. If that young person is following an influencer or playing video games with more violence or rudeness or language that you dont approve of or, unfortunately, is being bullied, those are bad screen times.

One last note: Kids are smart and may manage to work around parental controls. So parents should check devices, and also educate themselves by consulting friends, pediatricians and other resources like the AAP website or Common Sense Media.

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Phones and kids: new pediatric guidelines, expert advice and info on new school rules - Kidsburgh