History Data Research Articles

Sexual Orientation Differences in Age of First Treatment for a Mental Health Diagnosis: A Population-Based Study of Childhood and Adolescence

The aim of the current study was to identify the age at which sexual orientation disparity in mental health diagnoses can be first identified, as well as gender and sexual identity subgroup differences in such treatment, in a population-based sample. Young people aged 16 to 25 (N= 10,365) participating in the probability-based Swedish National Public Health Survey in 2018 were included. This sample was linked to physician-assessed mental health care treatment history data starting when all participants were 8 years old using national health care registries. Participants reporting a sexual minority identity in adolescence and young adulthood were more than 3 times as likely to have been treated for an internalizing disorder diagnosis (eg, depression, anxiety) and more than twice as likely to have been treated for a neurodevelopmental disorder diagnosis during childhood than participants reporting a heterosexual identity. Sexual minority participants overall and women in particular were more likely to have been treated for an internalizing disorder diagnosis at an early age compared with heterosexual participants, with this disparity starting at age 13. The sexual orientation disparity in likelihood of treatment for a neurodevelopmental disorder diagnosis was particularly elevated among bisexual/pansexual women with this disparity starting in early/middle adolescence. This population-based study linked to physician-assessed mental health diagnoses during childhood and adolescence identifies the age at which sexual orientation differences in treatment for common mental disorders emerge. The early emergence of this disparity suggests a potential benefit of interventions that facilitate social belonging for all youth.

Optimization of typical structural components of composite materials based on fatigue life

Abstract Optimizing the fatigue life of reinforced composite panels in hypersonic aircraft is crucial for enhancing performance and reliability. Employing the rainflow counting method and a linear cumulative damage model, we process stress-time history data under random loading. The optimization model, with structural fatigue life as the objective function and parameters as constraints, is tackled using genetic algorithms. This efficient approach, exemplified with needle-punched C/SiC composite reinforced panels, significantly improves fatigue life, offering substantial benefits to hypersonic aircraft.

Incremental value of multidomain risk factors for dementia prediction: A machine learning approach

ObjectiveThe current evidence regarding how different predictor domains contribute to predicting incident dementia remains unclear. This study aims to assess the incremental value of five predictor domains when added to a simple dementia risk prediction model (DRPM) for predicting incident dementia in older adults. DesignPopulation-based, prospective cohort study SettingUK Biobank study ParticipantsIndividuals aged 60 or older without dementia. Measurements55 dementia-related predictors were gathered and categorized into clinical and medical history, questionnaire, cognition, polygenetic risk, and neuroimaging domains. Incident dementia (all-cause) and the subtypes, Alzheimer's disease (AD) and vascular dementia (VaD), were determined through hospital and death registries. Ensemble machine learning (ML) DRPMs were employed for prediction. The incremental values of risk predictors were assessed using the percent change in Area Under the Curve (∆AUC%) and the net reclassification index (NRI). ResultsThe simple DRPM which included age, body mass index, sex, education, diabetes, hyperlipidaemia, hypertension, depression, smoking, and alcohol consumption yielded an AUC of 0.711 (± 0.008 SD). The five predictor domains exhibited varying levels of incremental value over the basic model when predicting all-cause dementia and the two subtypes. Neuroimaging markers provided the highest incremental value in predicting all-cause dementia (∆AUC% +9.6%) and AD (∆AUC% +16.5%) while clinical and medical history data performed the best at predicting VaD (∆AUC% +12.2%). Combining clinical and medical history, and questionnaire data synergistically enhanced ML DRPM performance. ConclusionCombining predictors from different domains generally results in better predictive performance. Selecting predictors entails trade-offs, and while neuroimaging markers can significantly enhance predictive accuracy, they may pose challenges in terms of cost or accessibility.

Research on personal credit scoring model based on deep learning

The increasing integration of internet technology and the financial industry has led to the gradual replacement of traditional credit evaluation models with those based on deep learning, which have demonstrated excellent accuracy. This has become a prominent area of research. Nevertheless, the credit scoring model based on a deep neural network encounters significant challenges in terms of its applicability in the field of credit scoring, largely due to the opaque nature of its learning and decision-making processes. The application of deep learning to personal credit scoring has been shown to enhance the accuracy of the resulting scores by leveraging large amounts of data. The model employs a deep neural network (DNN) architecture that integrates multiple input features, including the user's transaction history, social behaviour and other relevant data. The model is trained using supervised learning, with a large amount of labelled data used to optimise its prediction performance. Experimental results demonstrate that the deep learning-based model exhibits a notable improvement in accuracy and robustness compared to traditional credit scoring models.

Changes in the Language of the Reformed Church in the Second Half of the 20th Century: The Convergence of Oral History and Big Data

The author’s journey begins with a deep immersion in his traditional Protestant faith, where he was taught to thoroughly know both the Old and the New Testament. As he reflects on his faith, the concept of being a “stranger and sojourner” comes to the forefront, highlighting the feeling of alienation within the secular world. The narrative takes an interesting turn when the author discusses the impact of changing political language on his perspective. The introduction of new political ideas and discourse challenges the previously straightforward worldview, leading to a sense of uncertainty in decision-making. Believers find themselves in a world with a multiplicity of perspectives that require a re-evaluation of what it means to “deny” or to make choices. In a changing social and political context, the narrative continually explores the intricate connection between faith and language. The author’s experiences and reflections provide a compelling insight into the complexities of navigating a shifting spiritual landscape while grappling with the ever-evolving meaning of words and beliefs. The second half of the article presents the results of a computer analysis of the vocabulary of two Bible translations that defined the language usage of the Reformed Church in the second half of the 20th century: the 1908 Károli and the 1975 New Translation. The analysis focuses on words that present difficulties in understanding due to their archaic or religious nature. As a point of comparison, it also showcases the vocabulary of the Gospel of John, where a mere 199 word roots constitute 80% of word occurrences. Keywords: language of the church, Bible translation, vocabulary of the Bible, letter and soul, protestant language use

The first record of ants as potential pollinators of a Neotropical tree species (Lomatia hirsuta Diels ex J.F.Macbr., Proteaceae)

AbstractWe provide evidence that Camponotus chilensis (Spinola) visit flowers of the Radal tree (Lomatia hirsuta Diels ex J.F.Macbr.), possibly acting as pollinators of this important tree of the Proteaceae family in the Andean Patagonian forests of Argentina. We found several ant workers transporting pollen from different trees during the blooming season. Pollen transported by workers varied in amount, with some individuals observed fully coated with pollen grains. This note constitutes the first observation of ant activity on the flowers of this plant species and also the first potential example of a tree pollinated by ants in the Neotropics. This note highlights the importance of natural history data in the emergent study field of ant‐pollination.

A sensitive fluorescence assay of serum dipeptidyl peptidase IV activity to predict the suitability of its inhibitors in patients with type 2 diabetes mellitus

DPP-IV inhibitors, which are close to the natural hypoglycemic pathway of human physiology and have few side effects, have been extensively employed in the management of type 2 diabetes mellitus (T2DM). However, there are currently no specific blood indicators that can indicate or predict a patient's suitability for DPP-IV inhibitors. In this study, based on the self-developed high-specificity fluorescent substrate glycyl-prolyl-N-butyl-4-amino-1, 8-naphthimide (GP-BAN), a detection method of human serum DPP-IV activity was established and optimized. The method demonstrates a favorable lower limit of detection (LOD) at 0.32 ng/mL and a satisfactory lower limit of quantification (LOQ) of 1.12 ng/mL, and can be used for the detection of DPP-IV activity in trace serum (2 μL). In addition, Vitalliptin and Sitagliptin showed similar IC50 values when human recombinant DPP-IV and human serum were used as enzyme sources, and the intra-day and inter-day precision obtained by the microplate analyzer were less than 15 %. These results indicate that the microplate reader based detection technique has good accuracy, repeatability and reproducibility. A total of 700 volunteers were recruited, and 646 serum samples were tested for DPP-IV activity. The results showed that serum DPP-IV activity was higher in patients with T2DM than in controls (P < 0.01). However, the statistical data of family history of diabetes, gender and age of diabetic patients showed no statistical significance, and there was no contrast difference. The DPP-IV activity of serum in T2DM patients ranged from 2.4 μmol/min/L to 78.6 μmol/min/L, with a huge difference of up to 32-fold. These results suggest that it is necessary to test DPP-IV activity in patients with T2DM when taking DPP-IV inhibitors to determine the applicability of DPP-IV inhibitors in T2DM patients. These results suggest that it is necessary to detect the activity of DPP-IV in blood before taking DPP-IV inhibitors in patients with T2DM to judge the applicability of DPP-IV inhibitors in patients with T2DM.

Comprehensive periodic health evaluations of 454 Norwegian Paralympic and Olympic athletes over 8 years: what did we learn?

ObjectiveA periodic health evaluation (PHE) is a comprehensive and multidisciplinary investigation of athlete health widely used in elite sport, but its contents and benefits can be questioned. This study aimed...

Medication Adherence and Treatment Satisfaction With Lipid-Lowering Drugs Among Patients With Diabetes and Dyslipidemia.

Poor adherence to lipid-lowering drugs in diabetic patients with dyslipidemia increases has been linked with an increased cardiovascular risk. A better understanding of the determinants of adherence to lipid-lowering drugs and treatment satisfaction among people with diabetes and dyslipidemia is crucial. We aimed to assess the prevalence of adherence to lipid-lowering drugs, identify its determinant factors, and evaluate treatment satisfaction among users of lipid-lowering drugs who have diabetes and dyslipidemia. We surveyed 398 adult patients with diabetes and dyslipidemia, using a validated medication adherence survey (Adherence to Refills and Medications Scale) and a validated treatment satisfaction survey (Treatment Satisfaction Questionnaire for Medication, TSQM). Sociodemographic and medical history data were collected through questionnaires. The prevalence of poor medication adherence was 36%. Factors associated with poor adherence included adverse reactions to medications, lack of medication availability, and lack of family support. Adherent patients reported lower low-density lipoprotein-cholesterol (LDL-C) and total cholesterol levels, higher treatment satisfaction, and a higher prevalence of cardiovascular disease and comorbidities. Having a family history of dyslipidemia was negatively associated with adherence, while the number of comorbidities positively influenced it. The scores of TSQM components such as effectiveness, global satisfaction, and convenience were significantly higher in people who were adherent or achieved the LDL-C target. Our findings highlight the need for interventions targeting several factors impacting adherence to lipid-lowering drugs in patients with diabetes and dyslipidemia. Managing adverse effects, leveraging family support, and ensuring medication access represent crucial aspects of improving adherence and potentially mitigating cardiovascular risks in this high-risk population.

Hypothermia in preterm infants admitted to low-resource neonatal units in northern Nigeria: an observational study of occurrence and risk factors

BackgroundHypothermia is an important cause of morbidity and mortality among preterm and low-birth-weight neonates. In resource-constrained settings, limited referral infrastructure and technologies for temperature control potentiate preterm hypothermia. While there is some documentation on point-of-admission hypothermia from single center studies, there are limited multicenter studies on the occurrence of hypothermia among preterm infants in resource-limited-settings. Therefore, we conducted a multicenter study to determine the prevalence and risk factors for hypothermia at the time of admission and during the first 72 h after admission in northern Nigeria.MethodWe carried out a prospective cohort study on preterm infants admitted to four referral hospitals in northern Nigerian between August 2020 and July 2021. We documented temperature measurements at admission and the lowest and highest temperatures in the first 72 h after admission. We also collected individual baby-level data on sociodemographic and perinatal history data. We used the World Health Organization classification of hypothermia to classify the babies’ temperatures into mild, moderate, and severe hypothermia. Poisson regression analysis was used to identify risk factors for moderate-severe hypothermia.ResultsOf the 933 preterm infants enrolled, 682 (72.9%) had hypothermia at admission although the prevalence of hypothermia varied across the four hospitals. During the first 24 h after admission, 7 out of every 10 babies developed hypothermia. By 72 h after admission, between 10 and 40% of preterm infants across the 4 hospitals had at least one episode of moderate hypothermia. Gestational age (OR = 0.86; CI = 0.82–0.91), birth weight (OR = 8.11; CI = 2.87–22.91), presence of a skilled birth attendant at delivery (OR = 0.53; CI = 0.29–0.95), place of delivery (OR = 1.94 CI = 1.13–3.33) and resuscitation at birth (OR = 1.79; CI = 1.27–2.53) were significant risk factors associated with hypothermia.ConclusionThe prevalence of admission hypothermia in preterm infants is high and hypothermia is associated with low-birth-weight, place of delivery and presence of skilled birth attendant. The prevalence of hypothermia while in care is also high and this has important implications for patient safety and quality of patient care. Referral services for preterm infants need to be developed while hospitals need to be better equipped to maintain the temperatures of admitted small and sick newborns.

Prior traumatic brain injury is a risk factor for in-hospital mortality in moderate to severe traumatic brain injury: a TRACK-TBI cohort study

ObjectivesAn estimated 14–23% of patients with traumatic brain injury (TBI) incur multiple lifetime TBIs. The relationship between prior TBI and outcomes in patients with moderate to severe TBI (msTBI) is...

Crosstalk between skeletal muscle ratio and cholesterol metabolism disorders: a cross-section study

BackgroundDysfunction of cholesterol metabolism may be associated with low skeletal muscle mass. This study aimed to explore the relationship between skeletal muscle mass and cholesterol metabolic disorders in adults.MethodsThe data of a total of 5949 people with complete medical history data, biochemical data and body composition analysis were recruited. According to the serum cholesterol, low density lipoprotein (LDL), high density lipoprotein (HDL) and nonHDL, the population was divided into a disorder group and a normal group. Independent sample t tests, chi-square tests, Pearson's correlation analyses and binary logistic regression analyses were used to study the effect of body composition on abnormal cholesterol metabolism. According to BMI and sex, the population was divided into different subgroups, and binary logistic regression analysis was used to study the effect of the skeletal mass ratio on cholesteral metabolic disorders in different subgroups.ResultsThere were significant differences in sex, alcohol consumption, body weight, BMI, skeletal muscle mass index (SMI) [total skeletal muscle mass (kg)/height 2 (m2)] and skeletal muscle mass ratio (SMR) [total skeletal muscle mass (kg)/weight (kg) *100] between the disorder group (hypercholesterolemia, hyper-LDL, lower-HDL and hyper-nonHDL) and the normal group. Pearson correlation analysis revealed that the SMR was negatively correlated, while the SMI was positively correlated with cholesterol metabolic disorders in both sexes. The overweight group was older and had a greater SMI, abnormal cholesteral metabolism ratio and lower SMR than the normal-weight group. In the normal-weight group, the SMR was an independent protective factor against different kinds of cholesteral metabolic disorders in both sexes, while the SMI was a risk factor. In the overweight subgroup, the protective effect on HDL and nonHDL metabolism remained in the male subgroup but disappeared in the female subgroup. However, the SMI was an independent risk factor for different kinds of cholesteral metabolic disorders in both sexes.ConclusionsSMR was an independent protective factor against cholesterol metabolic disorders in both males and females, especially in the normal weight group. SMI was an independent risk factor, especially in the overweight group.

Retinal Sensitivity in Macular Subfields and Their Association with Contrast Sensitivity in Early and Intermediate Age-Related Macular Degeneration.

The objective of this study was to evaluate retinal sensitivity in subfields and its association with the novel quantitative contrast sensitivity function (qCSF) in patients with early age-related macular degeneration (eAMD), in patients with intermediate AMD (iAMD), and in healthy controls. In this prospective longitudinal study, retinal sensitivity of a customized 24-point grid was assessed by microperimetry Macular Integrity Assessment (MAIA, CenterVue, Padova, Italy) and divided into different subfields. The Multiple Contrast Vision Meter (Adaptive Sensory Technology, San Diego, CA, USA) was used for qCSF testing. Linear models were used to test the association of functional metrics with variables of interest. 92 study eyes from 92 participants were analyzed (13 eAMD, 31 iAMD, and 48 controls). Microperimetry subfield comparison showed significant differences (p &lt; 0.0001) in the control group between superior and inferior hemifield as well as between central and peripheral subfields. For eAMD, significant differences were found between central and peripheral subfields (p &lt; 0.001) and specific subfields (p &lt; 0.05) and finally for iAMD between specific quadrants (p &lt; 0.05) and specific squares (p &lt; 0.05). Significant associations of retinal sensitivity with qCSF metrics were found for the area underneath the logarithmic contrast sensitivity function, contrast acuity and for the contrast sensitivity at specific spatial frequencies. This study showed significant differences in the evaluated retinal sensitivity subfields, providing localized natural history data for retinal sensitivity in healthy controls and patients with eAMD and iAMD.

Time Series Analysis for Tractor Sales using SARIMAX and Deep Learning Models

Time series forecasting is known for playing vital role in many industries to make important decisions and strategies. This study concentrates on providing accurate insights that can help manufactures and stakeholders of agriculture machinery industry on future sales of tractors by applying both traditional and deep learning models like SARIMAX which is extension of SARIMA and deep learning models. Research starts by observing history data which include years of tractor sales then preprocess the data to find its quality and stationarity further applying SARIMAX model to find trends and seasons and cycles in the data and this model is evaluated by famous metrics like Root Mean Squared Error (RMSE).Deep learning models like Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, CNN LSTM Encoder Decoder, Convolutional Neural networks (CNN). They can help in enhancing the forecasting accuracy by handling all the non-linear relationships and their dependencies in the timeseries and this study will provide comparative analysis of deep learning models and SARIMAX model. Where SARIMAX outperformed the deep learning models with RMSE score 0.01 and provide forecast of next year’s tractor sales using SARIMAX model from the study and use q-q plot, residual plots and ACF and PACF graphs to make sure forecast was done accurately.

Mapping the evidence about the natural history of acute infections commonly seen in primary care and managed with antibiotics: a scoping review

BackgroundKnowing the natural history of acute infections in primary care, defined as the course of a disease over time in the absence of specific therapy or treatment, can inform clinicians’ and patients’ expectations about illness recovery, but this evidence is fragmented across the literature. This scoping review aimed to map existing research and research gaps relevant to the natural history of acute infections.MethodsWe searched MEDLINE, Embase and CENTRAL using a 2-phase hierarchical search approach. In Phase A, we focused on identifying systematic reviews synthesising natural history data for eligible infections (acute respiratory, urinary, and skin and soft tissue) and systematic reviews of treatment effectiveness (of RCTs with placebo or no treatment arm, or cohort studies). For infections without existing reviews, in Phase B, we searched for primary studies (placebo-controlled RCTs or cohort studies). Two reviewers independently screened and extracted the data (study characteristics, outcome data - e.g., symptom duration, proportion with resolution at various time points).ResultsWe identified 40 systematic reviews, reporting on 45 infections, most commonly (90%) respiratory tract infections. Six (15%) of these aimed to synthesise natural history information. Most reviews reported the proportion of participants with symptom resolution at various time point/s, with 58% providing data on mean symptom duration. Recovery data show the spontaneous resolution of some infections in some people. We found no eligible studies for cellulitis, ecthyma, carbuncle, and erysipelas.ConclusionsOur review has shown that natural history evidence exists for many common acute infections. It can be utilised by clinicians in implementing patient-centred antibiotic stewardship strategies in primary care. Future research should focus on generating natural history evidence for skin and soft tissue infections and urinary tract infections.

Methanogenic patterns in the gut microbiome are associated with survival in a population of feral horses

Gut microbiomes are widely hypothesised to influence host fitness and have been experimentally shown to affect host health and phenotypes under laboratory conditions. However, the extent to which they do so in free-living animal populations and the proximate mechanisms involved remain open questions. In this study, using long-term, individual-based life history and shallow shotgun metagenomic sequencing data (2394 fecal samples from 794 individuals collected between 2013–2019), we quantify relationships between gut microbiome variation and survival in a feral population of horses under natural food limitation (Sable Island, Canada), and test metagenome-derived predictions using short-chain fatty acid data. We report detailed evidence that variation in the gut microbiome is associated with a host fitness proxy in nature and outline hypotheses of pathogenesis and methanogenesis as key causal mechanisms which may underlie such patterns in feral horses, and perhaps, wild herbivores more generally.

Prediction of the mechanism of suicide among Minnesota residents using data from the Minnesota violent death reporting system (MNVDRS)

BackgroundSuicide remains a major public health problem, and firearms are used in approximately half of all such incidents. This study sought to predict the occurrence of suicide specifically by firearm,...

Integrated Applied Clinical Pharmacology in the Advancement of Rare and Ultra-Rare Disease Therapeutics.

The introduction of safe and effective rare/ultra-rare disease treatments is a focus of many biotherapeutic enterprises. Despite this increased activity, a significant unmet need remains, and the responsibility to meet this need is augmented by enhanced genomic, biologic, medical, analytical, and informatic tools. It is recognized that the development of an effective and safe rare/ultra-rare disease therapeutic faces a number of challenges with an important role noted for clinical pharmacology. Clinical pharmacology is foundationally an integrative discipline which must be embedded in and is interdependent upon understanding the pathogenic biology, clinical presentation, disease progression, and end-point assessment of the disease under study. This manuscript presents an overview and two case examples of this integrative approach, the development of C5-targeted therapeutics for the treatment of generalized myasthenia gravis and asfotase alpha for the treatment of hypophosphatasia. The two presented case examples show the usefulness of understanding the biological drivers and clinical course of a rare disease, having relevant animal models, procuring informative natural history data, importing assessment tools from relevant alternative areas, and using integrated applied clinical pharmacology to inform target engagement, dose, and the cascade of pharmacodynamic and clinical effects that follow. Learnings from these programs include the importance of assuring cross-validation of assays throughout a development program and continued commitment to understanding the relationship among the array of Pd end points and clinical outcomes. Using an integrative approach, substantive work remains to be done to meet the unmet needs of patients with rare/ultra-rare disease.

Impact of 4D Seismic Data Assimilation in the Production Forecasting of a Heavy Oil Turbidite Sandstone Field Offshore Brazil

The incorporation of 4D seismic data can be done at model characterization and data assimilation steps, the latter applying the observed data to update the model properties. In this work, we evaluate the impact of 4DS data in the production forecasting results investigating the influence on these distinct steps. We employ a prior model set generated without 4DS data and another prior set with 4DS in the characterisation. We perform, then, the DA in three different ways: (1) using only well history data with a prior set without 4DS; (2) using well history and 4DS data with this same prior set; and (3) using well history and 4DS data with the prior set generated with 4DS data. We employ the Ensemble Smoother with Multiple Data Assimilation method for data assimilation and implement a procedure to adjust the well productivities and injectivities after DA. This work assesses the differences among the three DA cases in terms of field oil production forecasting, model attributes and model misfits, the latter evaluated in terms of the assimilated data set and of a different data set for validation purposes only. We apply the work to a real heavy oil turbiditic field located offshore Brazil. The case application demonstrates a successful data assimilation study on a real field integrating well history and 4DS data. The results show that the utilisation of 4DS in both model characterisation and DA steps brought value to the final uncertainty reduction results. The posterior model ensemble with 4DS data in model characterization and DA provided the lowest model misfits overall to the full data set, being the best choice for application in field production forecasting studies. The case without 4DS data provided significantly more pessimistic field oil production than the cases with 4DS, making very evident the importance of employing 4DS at uncertainty reduction workflows.

Polygenic scores for psychiatric traits mediate the impact of multigenerational history for depression on offspring psychopathology.

A family history of depression is a well-documented risk factor for offspring psychopathology. However, the genetic mechanisms underlying the intergenerational transmission of depression remain unclear. We used genetic, family history, and diagnostic data from 11,875 9-10 year-old children from the Adolescent Brain Cognitive Development study. We estimated and investigated the children's polygenic scores (PGSs) for 30 distinct traits and their association with a family history of depression (including grandparents and parents) and the children's overall psychopathology through logistic regression analyses. We assessed the role of polygenic risk for psychiatric disorders in mediating the transmission of depression from one generation to the next. Among 11,875 multi-ancestry children, 8,111 participants had matching phenotypic and genotypic data (3,832 female [47.2%]; mean (SD) age, 9.5 (0.5) years), including 6,151 [71.4%] of European ancestry). Greater PGSs for depression (estimate = 0.129, 95% CI = 0.070-0.187) and bipolar disorder (estimate = 0.109, 95% CI = 0.051-0.168) were significantly associated with higher family history of depression (Bonferroni-corrected P < .05). Depression PGS was the only PGS that significantly associated with both family risk and offspring's psychopathology, and robustly mediated the impact of family history of depression on several youth psychopathologies including anxiety disorders, suicidal ideation, and any psychiatric disorder (proportions mediated 1.39%-5.87% of the total effect on psychopathology; FDR-corrected P < .05). These findings suggest that increased polygenic risk for depression partially mediates the associations between family risk for depression and offspring psychopathology, showing a genetic basis for intergenerational transmission of depression. Future approaches that combine assessments of family risk with polygenic profiles may offer a more accurate method for identifying children at elevated risk.