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1.
Nat Med ; 30(5): 1416-1423, 2024 May.
Article in English | MEDLINE | ID: mdl-38589601

ABSTRACT

Previous studies report an association between maternal diabetes mellitus (MDM) and attention-deficit/hyperactivity disorder (ADHD), often overlooking unmeasured confounders such as shared genetics and environmental factors. We therefore conducted a multinational cohort study with linked mother-child pairs data in Hong Kong, New Zealand, Taiwan, Finland, Iceland, Norway and Sweden to evaluate associations between different MDM (any MDM, gestational diabetes mellitus (GDM) and pregestational diabetes mellitus (PGDM)) and ADHD using Cox proportional hazards regression. We included over 3.6 million mother-child pairs between 2001 and 2014 with follow-up until 2020. Children who were born to mothers with any type of diabetes during pregnancy had a higher risk of ADHD than unexposed children (pooled hazard ratio (HR) = 1.16, 95% confidence interval (CI) = 1.08-1.24). Higher risks of ADHD were also observed for both GDM (pooled HR = 1.10, 95% CI = 1.04-1.17) and PGDM (pooled HR = 1.39, 95% CI = 1.25-1.55). However, siblings with discordant exposure to GDM in pregnancy had similar risks of ADHD (pooled HR = 1.05, 95% CI = 0.94-1.17), suggesting potential confounding by unmeasured, shared familial factors. Our findings indicate that there is a small-to-moderate association between MDM and ADHD, whereas the association between GDM and ADHD is unlikely to be causal. This finding contrast with previous studies, which reported substantially higher risk estimates, and underscores the need to reevaluate the precise roles of hyperglycemia and genetic factors in the relationship between MDM and ADHD.


Subject(s)
Attention Deficit Disorder with Hyperactivity , Diabetes, Gestational , Prenatal Exposure Delayed Effects , Humans , Attention Deficit Disorder with Hyperactivity/epidemiology , Attention Deficit Disorder with Hyperactivity/genetics , Female , Pregnancy , Diabetes, Gestational/epidemiology , Child , Male , Prenatal Exposure Delayed Effects/epidemiology , Cohort Studies , Adult , Risk Factors , Mothers , Proportional Hazards Models , Taiwan/epidemiology , New Zealand/epidemiology , Hong Kong/epidemiology
2.
J Forensic Sci ; 66(2): 758-765, 2021 Mar.
Article in English | MEDLINE | ID: mdl-33275295

ABSTRACT

The opioid crisis has continued to progress in the United States and the rest of the world. As this crisis continues, there is a pressing need for a rapid and cost-effective method for detecting fentanyl. Recent studies have suggested that lateral flow immunoassays (LFIs) could fill this technology gap. These qualitative paper-based assays contain antibodies designed to react with fentanyl and provide positive or negative results within a matter of minutes. In this study, two different LFI configurations for the detection of fentanyl were examined (dipsticks and cassettes) for effectiveness of detection using seized drug samples and postmortem urine samples. In the current study, 44 seized drug samples (32 fentanyl-positive, 12 fentanyl-negative) and 14 postmortem urine samples (10 fentanyl-positive, 4 fentanyl-negative) were analyzed. All 32 fentanyl-containing seized drug samples and 10 postmortem fentanyl-positive urine samples displayed positive LFI results with both LFI configurations. The fentanyl dipsticks displayed a sensitivity of 100%, a specificity of 75%, and an efficiency of 93.2% for seized drug samples and a sensitivity, specificity, and efficiency of 100% for postmortem urine. Analysis of the fentanyl cassettes displayed a sensitivity, specificity, and efficiency of 100% for seized drug samples and a sensitivity of 100%, a specificity of 75%, and an efficiency of 92.9% for postmortem urine samples. These data point to the utility of LFIs as a quick and low resource-dependent option for presumptive detection of fentanyl in real-world situations.


Subject(s)
Fentanyl/analysis , Illicit Drugs/analysis , Immunoassay/methods , Opioid-Related Disorders/urine , Gas Chromatography-Mass Spectrometry , Humans , Sensitivity and Specificity , Substance Abuse Detection
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