Add Health Wave IV Documentation: Candidate Genes

During Wave IV, Add Health collected biological specimens from a large, nationally representative sample of young adults. Given the size of the Wave IV sample, its geographic distribution, and in-home setting of the respondent interviews, biological specimen collection involved practical, relatively non-invasive, cost-efficient and innovative methods. These methods included collection of saliva by trained and certified field interviewers, salivary buccal cell lysis and DNA stabilization in the field, then shipment to a central lab for DNA extraction, genotyping, and archiving. The collection of saliva followed the interview and collection of cardiovascular and anthropometric measures (Entzel et al. 2009). It preceded the collection of capillary whole blood (Whitsel et al. 2012) and data on respondent use of prescription and select over-the-counter medications (Tabor et al. 2010). Further details on the design of Add Health Waves I-IV are available elsewhere (Harris 2012; Harris et al. in press)

Add Health Wave IV Documentation: Measures of Inflammation and Immune Function

During Wave IV, Add Health collected biological specimens from a large, nationally representative sample of young adults. Given the size of the Wave IV sample, its geographic distribution, and in-home setting of the respondent interviews, biological specimen collection involved practical, relatively non-invasive, cost-efficient and innovative methods. These methods included collection of capillary whole blood via finger prick by trained and certified field interviewers, its in situ desiccation, then shipment, assay and archival of dried blood spots. The collection of capillary whole blood followed the collection of cardiovascular and anthropometric measures (Entzel et al, 2009) and saliva (Smolen et al, 2012). It preceded the collection of data on respondent use of prescription and select over-the-counter medications (Tabor et al, 2010). Further details on the design of Add Health Waves I-IV, are available elsewhere (Harris, 2011). Included in the Add Health Wave IV data are two measures of inflammation and immune function based on assay of the dried blood spots: • High Sensitivity C-Reactive Protein (hsCRP, mg/L) and • Epstein Barr Viral Capsid Antigen IgG (EBV, AU/ml) To facilitate analysis and interpretation of hsCRP and EBV, the restricted-use Add Health Wave IV data also include two data quality flags and 11constructed measures: • CRP_FLAG • EBV_FLAG • Classification of hsCRP (Pearson et al, 2003) • Count of Common Subclinical Symptoms (Vaidya et al, 2006) • Count of Common Infectious or Inflammatory Diseases • NSAID/Salicylate Medication Use in the Past 24 Hours • NSAID/Salicylate Medication Use in the Past 4 Weeks • Cox-2 Inhibitor Medication Use in the Past 4 Weeks • Inhaled Corticosteroid Medication Use in the Past 4 Weeks • Corticotropin/Glucocorticoid Medication Use in the Past 4 Weeks • Anti-rheumatic/Anti-psoriatic Medication Use in the Past 4 Weeks • Immunosuppressive Medication Use in the Past 4 Weeks • Anti-inflammatory Medication Use. This document summarizes the rationale, equipment, protocol, assay, internal quality control, data cleaning, external quality control, and classification procedures for each measure listed above. Measures of glucose homeostasis and candidate genes are documented elsewhere 3 (Whitsel et al, 2012; Smolen et al, 2012). Documentation of lipids will be provided in a separate report

Add Health Wave IV Documentation: Measures of Glucose Homeostasis

During Wave IV, Add Health collected biological specimens from a large, nationally representative sample of young adults. Given the size of the Wave IV sample, its geographic distribution, and in-home setting of the respondent interviews, biological specimen collection involved practical, relatively non-invasive, cost-efficient and innovative methods. These methods included collection of capillary whole blood via finger prick by trained and certified field interviewers, its in situ desiccation, then shipment, assay and archival of dried blood spots. The collection of capillary whole blood followed the collection of cardiovascular and anthropometric measures (Entzel et al., 2009) and saliva (in preparation). It preceded the collection of data on respondent use of prescription and select over-the-counter medications (Tabor et al., 2010). Further details on the design of Add Health Waves I-IV, are available elsewhere (Harris, 2011). Included in the Add Health Wave IV data are two measures of glucose homeostasis based on assay of the dried blood spots: • Glucose (mg/dl) and • Hemoglobin A1c (HbA1c, %). To facilitate analysis and interpretation of HbA1c, the restricted-use Add Health Wave IV data also include a trichotomous flag distinguishing the original (0) from two types (1,2) of inter-converted assay results (see Section 4.2.3.3): • Convert (0,1,2) Moreover, the restricted-use Add Health Wave IV data include six constructed measures: • Fasting duration (h) • Classification of fasting glucose (ADA, 2011) • Classification of non-fasting glucose (ADA, 2011) • Classification of HbA1c (ADA, 2011) • Anti-diabetic medication use • Joint classification of glucose, HbA1c, self-reported history of diabetes, and anti-diabetic medication use. This document summarizes the rationale, equipment, protocol, assay, internal quality control, data cleaning, external quality control, and classification procedures for each measure listed above. Documentation of other (metabolic; inflammatory; immune; genetic) measures based on assay of the dried blood spots and genotyping of DNA extracted from salivary buccal cells will be provided in separate reports

Add Health Wave IV Documentation: Lipids

During Wave IV, Add Health collected biological specimens from a large, nationally representative sample of young adults. Given the size of the Wave IV sample, its geographic distribution, and in-home setting of the respondent interviews, biological specimen collection involved practical, relatively non-invasive, cost-efficient and innovative methods. These methods included collection of capillary whole blood via finger prick by trained and certified field interviewers, its in situ desiccation, then shipment, assay and archival of dried blood spots. The collection of capillary whole blood followed the collection of cardiovascular and anthropometric measures (Entzel et al. 2009) and saliva (Smolen et al. 2013). It preceded the collection of data on respondent use of prescription and select over-the-counter medications (Tabor et al. 2010). Further details on the design of Add Health Waves I-IV, are available elsewhere (Harris 2012; Harris et al. in press). Included in the Add Health Wave IV restricted use and public use data are thirteen constructed measures designed to facilitate analysis and interpretation of lipids results: • Total cholesterol decile • High-density lipoprotein cholesterol decile • Triglycerides decile • Total cholesterol measurement method • High-density lipoprotein cholesterol measurement method • Triglycerides measurement method • Low-density lipoprotein cholesterol decile • Non-high-density lipoprotein cholesterol decile • Total to high-density lipoprotein cholesterol ratio decile • Fasting duration • Fasted for nine hours or more • Antihyperlipidemic medication use • Hyperlipidemia. This document summarizes the rationale, equipment, protocol, assay, internal quality control, data cleaning, external quality control, and classification procedures for each measure listed above. Measures of glucose homeostasis, inflammation, immune function, and candidate genes are documented elsewhere (Whitsel et al. 2012a, 2012b; Smolen et al. 2013)

Add Health Wave IV Documentation: Cardiovascular Measures Appendix I: Baroreflex Sensitivity and Hemodynamic Recovery

This is an appendix to Add Health Wave IV Documentation: Cardiovascular and Anthropometric Measures (Entzel et al., 2009). Please refer to that user guide for complete descriptions of the cardiovascular data collection procedures and measures disseminated by the study at that time. In addition to the measures described there, this appendix introduces three more constructed measures that are included in the Add Health Wave IV public use data: Baroreflex sensitivity Pulse rate recovery Systolic blood pressure recovery. The rationale for their estimation and description of their quality control are provided below

Blood spot–based measures of glucose homeostasis and diabetes prevalence in a nationally representative population of young US adults

We investigated under-studied, biomarker-based diabetes among young U.S. adults, traditionally characterized by low cardiovascular disease risk

Analysis of the common genetic component of large-vessel vasculitides through a meta- Immunochip strategy

Giant cell arteritis (GCA) and Takayasu's arteritis (TAK) are major forms of large-vessel vasculitis (LVV) that share clinical features. To evaluate their genetic similarities, we analysed Immunochip genotyping data from 1,434 LVV patients and 3,814 unaffected controls. Genetic pleiotropy was also estimated. The HLA region harboured the main disease-specific associations. GCA was mostly associated with class II genes (HLA-DRB1/HLA-DQA1) whereas TAK was mostly associated with class I genes (HLA-B/MICA). Both the statistical significance and effect size of the HLA signals were considerably reduced in the cross-disease meta-analysis in comparison with the analysis of GCA and TAK separately. Consequently, no significant genetic correlation between these two diseases was observed when HLA variants were tested. Outside the HLA region, only one polymorphism located nearby the IL12B gene surpassed the study-wide significance threshold in the meta-analysis of the discovery datasets (rs755374, P?=?7.54E-07; ORGCA?=?1.19, ORTAK?=?1.50). This marker was confirmed as novel GCA risk factor using four additional cohorts (PGCA?=?5.52E-04, ORGCA?=?1.16). Taken together, our results provide evidence of strong genetic differences between GCA and TAK in the HLA. Outside this region, common susceptibility factors were suggested, especially within the IL12B locus

A genome-wide association study identifies risk alleles in plasminogen and P4HA2 associated with giant cell arteritis

Giant cell arteritis (GCA) is the most common form of vasculitis in individuals older than 50 years in Western countries. To shed light onto the genetic background influencing susceptibility for GCA, we performed a genome-wide association screening in a well-powered study cohort. After imputation, 1,844,133 genetic variants were analysed in 2,134 cases and 9,125 unaffected controls from ten independent populations of European ancestry. Our data confirmed HLA class II as the strongest associated region (independent signals: rs9268905, P = 1.94E-54, per-allele OR = 1.79; and rs9275592, P = 1.14E-40, OR = 2.08). Additionally, PLG and P4HA2 were identified as GCA risk genes at the genome-wide level of significance (rs4252134, P = 1.23E-10, OR = 1.28; and rs128738, P = 4.60E-09, OR = 1.32, respectively). Interestingly, we observed that the association peaks overlapped with different regulatory elements related to cell types and tissues involved in the pathophysiology of GCA. PLG and P4HA2 are involved in vascular remodelling and angiogenesis, suggesting a high relevance of these processes for the pathogenic mechanisms underlying this type of vasculitis

Significant benefits of AIP testing and clinical screening in familial isolated and young-onset pituitary tumors

Context Germline mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene are responsible for a subset of familial isolated pituitary adenoma (FIPA) cases and sporadic pituitary neuroendocrine tumors (PitNETs). Objective To compare prospectively diagnosed AIP mutation-positive (AIPmut) PitNET patients with clinically presenting patients and to compare the clinical characteristics of AIPmut and AIPneg PitNET patients. Design 12-year prospective, observational study. Participants & Setting We studied probands and family members of FIPA kindreds and sporadic patients with disease onset ≤18 years or macroadenomas with onset ≤30 years (n = 1477). This was a collaborative study conducted at referral centers for pituitary diseases. Interventions & Outcome AIP testing and clinical screening for pituitary disease. Comparison of characteristics of prospectively diagnosed (n = 22) vs clinically presenting AIPmut PitNET patients (n = 145), and AIPmut (n = 167) vs AIPneg PitNET patients (n = 1310). Results Prospectively diagnosed AIPmut PitNET patients had smaller lesions with less suprasellar extension or cavernous sinus invasion and required fewer treatments with fewer operations and no radiotherapy compared with clinically presenting cases; there were fewer cases with active disease and hypopituitarism at last follow-up. When comparing AIPmut and AIPneg cases, AIPmut patients were more often males, younger, more often had GH excess, pituitary apoplexy, suprasellar extension, and more patients required multimodal therapy, including radiotherapy. AIPmut patients (n = 136) with GH excess were taller than AIPneg counterparts (n = 650). Conclusions Prospectively diagnosed AIPmut patients show better outcomes than clinically presenting cases, demonstrating the benefits of genetic and clinical screening. AIP-related pituitary disease has a wide spectrum ranging from aggressively growing lesions to stable or indolent disease course