Comprehensive immunoprofiling of neurodevelopmental disorders suggests three distinct classes based on increased neurogenesis, Th-1 polarization or IL-1 signaling.

Neurodevelopmental disorders (NDDs) are a spectrum of conditions with commonalities as well as differences in terms of phenome, symptomatome, neuropathology, risk factors and underlying mechanisms. Immune dysregulation has surfaced as a major pathway in NDDs. However, it is not known if neurodevelopmental disorders share a common immunopathogenetic mechanism. In this study, we explored the possibility of a shared immune etiology in three early-onset NDDs, namely Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and Intellectual Disability Disorder (IDD). A panel of 48 immune pathway-related markers was assayed in 135 children with NDDs, represented by 45 children with ASD, ADHD and IDD in each group, along with 35 typically developing children. The plasma levels of 48 immune markers were analyzed on the Multiplex Suspension Assay platform using Pro Human cytokine 48-plex kits. Based on the cytokine/chemokine/growth factor levels, different immune profiles were computed. The primary characteristics of NDDs are depletion of the compensatory immune-regulatory system (CIRS) (z composite of IL-4, IL-10, sIL-1RA, and sIL-2R), increased interleukin (IL)-1 signaling associated with elevated IL-1α and decreased IL-1-receptor antagonist levels, increased neurogenesis, M1/M2 macrophage polarization and increased IL-4 as well as C-C Motif Chemokine Ligand 2 (CCL2) levels. With a cross-validated sensitivity of 81.8% and specificity of 94.4%, these aberrations seem specific for NDDs. Many immunological abnormalities are shared by ASD, ADHD and IDD, which are distinguished by minor differences in IL-9, IL-17 and CCL12. In contrast, machine learning reveals that NDD group consists of three immunologically distinct clusters, with enhanced neurogenesis, Th-1 polarization, or IL-1 signaling as the defining features. NDD is characterized by immune abnormalities that have functional implications for neurogenesis, neurotoxicity, and neurodevelopment. Using machine learning, NDD patients could be classified into subgroups with qualitatively distinct immune disorders that may serve as novel drug targets for the treatment of NDDs.

Toward a generalized developmental model of psychopathological liabilities and psychiatric disorders.

BACKGROUND: Most psychiatric disorders are associated with several risk factors, but a few underlying psychopathological dimensions account for the common co-occurrence of disorders. If these underlying psychopathological dimensions mediate associations of the risk factors with psychiatric disorders, it would support a trans-diagnostic orientation to etiological research and treatment development. METHOD: An analysis was performed of the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions III (NESARC-III), a US nationally representative sample of non-institutionalized civilian adults, focusing on respondents who were aged ⩾21 (n = 34 712). Structural equation modeling was used to identify the psychopathological dimensions underlying psychiatric disorders; to examine associations between risk factors, psychopathological dimensions and individual disorders; and to test whether associations of risk factors occurring earlier in life were mediated by risk factors occurring later in life. RESULTS: A bifactor model of 13 axis I disorders provided a good fit (CFI = 0.987, TLI = 0.982, and RMSEA = 0.011) including an overall psychopathology factor as measured by all 13 disorders and 2 specific factors, one for externalizing disorders and one for fear-related disorders. A substantial proportion of the total effects of the risk factors occurring early in life were indirectly mediated through factors occurring later in life. All risk factors showed a significant total effect on the general psychopathology, externalizing and fear-related factors. Only 23 of 325 direct associations of risk factors with psychiatric disorders achieved statistical significance. CONCLUSION: Most risk factors for psychiatric disorders are mediated through broad psychopathological dimensions. The central role of these dimensions supports trans-diagnostic etiological and intervention research.

The term CAKUT has outlived its usefulness: the case for the defense.

Congenital anomalies of the kidney and urinary tract form a spectrum of congenital structural disorders that are generally known under the term CAKUT. The term CAKUT was introduced 20 years ago and has been used extensively in literature since. Prof. Woolf has made a plea for abandoning this term in his "case for the prosecution." Here, I advocate for the continued use of CAKUT as an umbrella term for these related congenital kidney and urinary tract abnormalities. I explain why the term CAKUT accurately and usefully defines this group of related structural disorders with prenatal origin and why it makes sense to continue grouping these disorders given accumulating evidence for shared etiology of CAKUT phenotypes and the importance of grouping CAKUT phenotypes in genetic counseling.

Mood disorders in familial epilepsy: A test of shared etiology.

OBJECTIVE: Mood disorders are the most common comorbid conditions in epilepsy, but the cause remains unclear. One possible explanation is a shared genetic susceptibility to epilepsy and mood disorders. We tested this hypothesis by evaluating lifetime prevalence of mood disorders in relatives with and without epilepsy in families containing multiple individuals with epilepsy, and comparing the findings with rates from a general population sample. METHODS: The Composite International Diagnostic Interview was administered to 192 individuals from 60 families, including 110 participants with epilepsy of unknown cause (50 focal epilepsy [FE], 42 generalized epilepsy [GE], 6 FE and GE, 12 unclassifiable) and 82 relatives without epilepsy (RWOE). Odds ratios (ORs) for lifetime prevalence of mood disorders in participants with versus without epilepsy were computed through logistic regression, using generalized estimation equations to account for familial clustering. Standardized prevalence ratios (SPRs) were used to compare prevalence in family members with general population rates. RESULTS: Compared with RWOE, ORs for mood disorders were significantly increased in participants with FE (OR = 2.4, 95% confidence interval [CI] = 1.1-5.2) but not in those with GE (OR = 1.0, 95% CI = 0.4-2.2). In addition, prevalence of mood disorders was increased in individuals with epilepsy who had ≥1 relative with FE. Compared with general population rates, mood disorders were significantly increased in individuals with FE but not in those with GE. Rates were also increased in RWOE, but not significantly so (SPR = 1.4, P = .14). SIGNIFICANCE: These findings are consistent with the hypothesis of shared genetic susceptibility to epilepsy and mood disorders, but suggest (1) the effect may be restricted to FE, and (2) the shared genetic effect on risk of mood disorders and epilepsy may be restricted to individuals with epilepsy, that is, to those in whom the genetic risk for epilepsy is "penetrant."

Applications of Polygenic Risk Scores in Psychiatric Genetics.

Polygenic risk score (PRS) is a powerful tool for studying the genetic architecture of complex diseases, including psychiatric disorders. This review highlights the use of PRS in psychiatric genetics, including its application in identifying high-risk individuals, estimating heritability, assessing shared etiology between phenotypes, and personalizing treatment plans. It also explains the methodology for calculating PRS, the challenges associated with their use in clinical settings, and future research directions. The main limitation of PRS is that the current models only account for a small fraction of the heritability of psychiatric disorders. Despite this limitation, PRS represents a valuable tool that has already yielded important insights into the genetic architecture of psychiatric disorders.

Assessment of surveillance versus etiologic factors in the reciprocal association between papillary thyroid cancer and breast cancer.

BACKGROUND: Mutually increased risks for thyroid and breast cancer have been reported, but the contribution of etiologic factors versus increased medical surveillance to these associations is unknown. METHODS: Leveraging large-scale US population-based cancer registry data, we used standardized incidence ratios (SIRs) to investigate the reciprocal risks of thyroid and breast cancers among adult females diagnosed with a first primary invasive, non-metastatic breast cancer (N = 652,627) or papillary thyroid cancer (PTC) (N = 92,318) during 2000-2017 who survived ≥1-year. RESULTS: PTC risk was increased 1.3-fold [N = 1434; SIR = 1.32; 95 % confidence interval (CI) = 1.25-1.39] after breast cancer compared to the general population. PTC risk declined significantly with time since breast cancer (Poisson regression = Ptrend <0.001) and was evident only for tumors ≤2 cm in size. The SIRs for PTC were higher after hormone-receptor (HR)+ (versus HR-) and stage II or III (versus stage 0-I) breast tumors. Breast cancer risk was increased 1.2-fold (N = 2038; SIR = 1.21; CI = 1.16-1.26) after PTC and was constant over time since PTC but was only increased for stage 0-II and HR + breast cancers. CONCLUSION: Although some of the patterns by latency, stage and size are consistent with heightened surveillance contributing to the breast-thyroid association, we cannot exclude a role of shared etiology or treatment effects.

Use of Multivariable Mendelian Randomization to Address Biases Due to Competing Risk Before Recruitment.

Background: Mendelian randomization (MR) provides unconfounded estimates. MR is open to selection bias when the underlying sample is selected on surviving to recruitment on the genetically instrumented exposure and competing risk of the outcome. Few methods to address this bias exist. Methods: We show that this selection bias can sometimes be addressed by adjusting for common causes of survival and outcome. We use multivariable MR to obtain a corrected MR estimate for statins on stroke. Statins affect survival, and stroke typically occurs later in life than ischemic heart disease (IHD), making estimates for stroke open to bias from competing risk. Results: In univariable MR in the UK Biobank, genetically instrumented statins did not protect against stroke [odds ratio (OR) 1.33, 95% confidence interval (CI) 0.80-2.20] but did in multivariable MR (OR 0.81, 95% CI 0.68-0.98) adjusted for major causes of survival and stroke [blood pressure, body mass index (BMI), and smoking initiation] with a multivariable Q-statistic indicating absence of selection bias. However, the MR estimate for statins on stroke using MEGASTROKE remained positive and the Q statistic indicated pleiotropy. Conclusion: MR studies of harmful exposures on late-onset diseases with shared etiology need to be conceptualized within a mechanistic understanding so as to identify any potential bias due to survival to recruitment on both genetically instrumented exposure and competing risk of the outcome, which may then be investigated using multivariable MR or estimated analytically and results interpreted accordingly.

Genome-Wide Assessment for Resting Heart Rate and Shared Genetics With Cardiometabolic Traits and Type 2 Diabetes.

BACKGROUND: High resting heart rate (RHR) occurs in parallel with type 2 diabetes (T2D) and metabolic disorders, implying shared etiology between them. However, it is unknown if they are causally related, and no study has been conducted to investigate the shared mechanisms underlying these associations. OBJECTIVES: The objective of this study was to understand the genetic basis of the association between resting heart rate and cardiometabolic disorders/T2D. METHODS: This study examined the genetic correlation, causality, and shared genetics between RHR and T2D using LD Score regression, generalized summary data-based Mendelian randomization, and transcriptome wide association scan (TWAS) in UK Biobank data (n = 428,250) and summary-level data for T2D (74,124 cases and 824,006 control subjects) and 8 cardiometabolic traits (sample size ranges from 51,750 to 236,231). RESULTS: Significant genetic correlation between RHR and T2D (rg = 0.22; 95% confidence interval: 0.18 to 0.26; p = 1.99 × 10-22), and 6 cardiometabolic traits (fasting insulin, fasting glucose, waist-hip ratio, triglycerides, high-density lipoprotein, and body mass index; rg range -0.12 to 0.24; all p < 0.05) were observed. RHR has significant estimated causal effect on T2D (odds ratio: 1.12 per 10-beats/min increment; p = 7.79 × 10-11) and weaker causal estimates from T2D to RHR (0.32 beats/min per doubling increment in T2D prevalence; p = 6.14 × 10-54). Sensitivity analysis by controlling for the included cardiometabolic traits did not modify the relationship between RHR and T2D. TWAS found locus chr2q23.3 (rs1260326) was highly pleiotropic among RHR, cardiometabolic traits, and T2D, and identified 7 genes (SMARCAD1, RP11-53O19.3, CTC-498M16.4, PDE8B, AKTIP, KDM4B, and TSHZ3) that were statistically independent and shared between RHR and T2D in tissues from the nervous and cardiovascular systems. These shared genes suggested the involvement of epigenetic regulation of energy and glucose metabolism, and AKT activation-related telomere dysfunction and vascular endothelial aging in the shared etiologies between RHR and T2D. Finally, FADS1 was found to be shared among RHR, fasting glucose, high-density lipoprotein, and triglycerides. CONCLUSIONS: These findings provide evidence of significant genetic correlations and causation between RHR and T2D/cardiometabolic traits, advance our understanding of RHR, and provide insight into shared etiology for high RHR and T2D.