Deep phenotypic analysis of psychiatric features in genetically defined cohorts: application to XYY syndrome.

BACKGROUND: Recurrent gene dosage disorders impart substantial risk for psychopathology. Yet, understanding that risk is hampered by complex presentations that challenge classical diagnostic systems. Here, we present a suite of generalizable analytic approaches for parsing this clinical complexity, which we illustrate through application to XYY syndrome. METHOD: We gathered high-dimensional measures of psychopathology in 64 XYY individuals and 60 XY controls, plus additional interviewer-based diagnostic data in the XYY group. We provide the first comprehensive diagnostic description of psychiatric morbidity in XYY syndrome and show how diagnostic morbidity relates to functioning, subthreshold symptoms, and ascertainment bias. We then map behavioral vulnerabilities and resilience across 67 behavioral dimensions before borrowing techniques from network science to resolve the mesoscale architecture of these dimensions and links to observable functional outcomes. RESULTS: Carriage of an extra Y-chromosome increases risk for diverse psychiatric diagnoses, with clinically impactful subthreshold symptomatology. Highest rates are seen for neurodevelopmental and affective disorders. A lower bound of < 25% of carriers are free of any diagnosis. Dimensional analysis of 67 scales details the profile of psychopathology in XYY, which survives control for ascertainment bias, specifies attentional and social domains as the most impacted, and refutes stigmatizing historical associations between XYY and violence. Network modeling compresses all measured symptom scales into 8 modules with dissociable links to cognitive ability, adaptive function, and caregiver strain. Hub modules offer efficient proxies for the full symptom network. CONCLUSIONS: This study parses the complex behavioral phenotype of XYY syndrome by applying new and generalizable analytic approaches for analysis of deep-phenotypic psychiatric data in neurogenetic disorders.

A systematic review and meta-analysis of the stability of peripheral immune markers in healthy adults.

Peripheral immune markers are widely used to predict risk for inflammatory disease. However, whether single assessments of inflammatory biomarkers represent stable individual differences remains unclear. We reviewed 50 studies (N = 48,674; 57 % male; mean age 54 (range 13-79) years) that assessed markers of inflammation on >1 occasion, with time between measures ranging from 24 h to 7+ years. Separate random effects meta-analyses were conducted for each inflammatory marker and time interval. Markers that had broad coverage across most time intervals included C-reactive protein (CRP; k = 37), interleukin (IL)-6 (k = 22), TNF-α (k = 10), and fibrinogen (Fg; k = 9). For CRP, IL-6, and TNF-α, stability estimates generally decreased with time, with strong to moderate stability over intervals <6 months (r's = 0.80-0.61), modest to moderate stability over 6 months - 3 years (r's = 0.60-0.51), and low stability for >3 years (r's = 0.39-0.30). Estimates were less reliable for Fg for time intervals ≤ 3 years although they generally followed the same pattern; more reliable findings suggested greater stability for Fg than other markers for intervals >3 years (r = 0.53). These findings suggest that single measures of inflammatory biomarkers may be an adequate index of stable individual differences in the short term (<6 months), with repeated measures of inflammatory biomarkers recommended over intervals ≥ 6 months to 3 years, and absolutely necessary over intervals >3 years to reliably identify stable individual differences in health risk. These findings are consistent with stability estimates and clinical recommendations for repeated measurement of other cardiovascular measures of risk (e.g., blood lipids, blood pressure).