The association of glucose metabolism measures and diabetes status with Alzheimer's disease biomarkers of amyloid and tau: A systematic review and meta-analysis

Conflicting evidence exists on the relationship between diabetes mellitus (DM) and Alzheimer's disease (AD) biomarkers. Therefore, we conducted a random-effects meta-analysis to evaluate the correlation of glucose metabolism measures (glycated hemoglobin, fasting blood glucose, insulin resistance indices) and DM status with AD biomarkers of amyloid-β and tau measured by positron emission tomography or cerebrospinal fluid. We selected 37 studies from PubMed and Embase, including 11,694 individuals. More impaired glucose metabolism and DM status were associated with higher tau biomarkers (r=0.11[0.03-0.18], p=0.008; I2=68%), but were not associated with amyloid-β biomarkers (r=-0.06[-0.13-0.01], p=0.08; I2=81%). Meta-regression revealed that glucose metabolism and DM were specifically associated with tau biomarkers in population settings (p=0.001). Furthermore, more impaired glucose metabolism and DM status were associated with lower amyloid-β biomarkers in memory clinic settings (p=0.004), and in studies with a higher prevalence of dementia (p<0.001) or lower cognitive scores (p=0.04). These findings indicate that DM is associated with biomarkers of tau but not with amyloid-β. This knowledge is valuable for improving dementia and DM diagnostics and treatment

Genetic, clinical and socio-demographic factors associated with stimulant-treatment outcomes in ADHD

Objective: Stimulant medications are effective for treating attention deficit hyperactivity disorder (ADHD), yet discontinuation and switch to nonstimulant ADHD medications are common. This study aimed to identify genetic, clinical, and sociodemographic factors influencing stimulant treatment initiation, discontinuation, and switch to nonstimulants in individuals with ADHD. Methods: The authors obtained genetic and national register data for 9,133 individuals with ADHD from the Danish iPSYCH2012 sample and defined stimulant treatment initiation, discontinuation, and switch from prescriptions. For each stimulant treatment outcome, they examined associations with polygenic risk scores (PRSs) for psychiatric disorders and clinical and sociodemographic factors using survival analyses, and conducted genome-wide association studies (GWASs) and estimated single-nucleotide polymorphism heritability (h2SNP). Results: Eighty-one percent of the sample initiated stimulant treatment. Within 2 years, 45% discontinued stimulants and 15% switched to nonstimulants. Bipolar disorder PRS (hazard ratio=1.05, 95% CI=1.02, 1.09) and schizophrenia PRS (hazard ratio=1.07, 95% CI=1.03, 1.11) were associated with discontinuation. Depression, bipolar disorder, and schizophrenia PRSs were marginally but not significantly associated with switch (hazard ratio range, 1.05–1.07). No associations were observed for ADHD and autism PRSs. Individuals diagnosed with ADHD at age 13 or older had higher rates of stimulant initiation, discontinuation, and switch (hazard ratio range, 1.27–2.01). Psychiatric comorbidities generally reduced rates of initiation (hazard ratio range, 0.84–0.88) and increased rates of discontinuation (hazard ratio range, 1.19–1.45) and switch (hazard ratio range, 1.40–2.08). h2SNP estimates were not significantly different from zero. No GWAS hits were identified for stimulant initiation or discontinuation. A locus on chromosome 16q23.3 reached genome-wide significance for switch. Conclusions: The study findings suggest that individuals with ADHD with higher polygenic liability for mood and/or psychotic disorders, delayed ADHD diagnosis, and psychiatric comorbidities have a higher risk for stimulant treatment discontinuation and switch to nonstimulants. Despite the study’s limited sample size, one putative GWAS hit for switch was identified, illustrating the potential of utilizing genomics linked to prescription databases to advance ADHD pharmacogenomics

Stata as a numerical tool for scientific thought experiments: A tutorial with worked examples

Thought experiments based on simulation can be used to explain the impact of the chosen study design, statistical analysis strategy, or the sensitivity of results to fellow researchers. In this article, we demonstrate with two examples how to implement quantitative thought experiments in Stata. The first example uses a large-sample approach to study the impact on the estimated effect size of dichotomizing an exposure variable at different values. The second example uses simulations of datasets of realistic size to illustrate the necessity of using sampling fractions as inverse probability weights in statistical analysis for protection against bias in a complex sampling design. We also give a brief outline of the general steps needed for implementing quantitative thought experiments in Stata. We demonstrate how Stata provides programming facilities for conveniently implementing such thought experiments, with the advantage of saving researchers time, speculation, and debate as well as improving communication in interdisciplinary research groups

High polygenic predisposition for ADHD and a greater risk of all-cause mortality:a large population-based longitudinal study

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a highly heritable, neurodevelopmental disorder known to associate with more than double the risk of death compared with people without ADHD. Because most research on ADHD has focused on children and adolescents, among whom death rates are relatively low, the impact of a high polygenic predisposition to ADHD on accelerating mortality risk in older adults is unknown. Thus, the aim of the study was to investigate if a high polygenetic predisposition to ADHD exacerbates the risk of all-cause mortality in older adults from the general population in the UK. METHODS: Utilising data from the English Longitudinal Study of Ageing, which is an ongoing multidisciplinary study of the English population aged ≥ 50 years, polygenetic scores for ADHD were calculated using summary statistics for (1) ADHD (PGS-ADHD(single)) and (2) chronic obstructive pulmonary disease and younger age of giving first birth, which were shown to have a strong genetic correlation with ADHD using the multi-trait analysis of genome-wide association summary statistics; this polygenic score was referred to as PGS-ADHD(multi-trait). All-cause mortality was ascertained from the National Health Service central register that captures all deaths occurring in the UK. RESULTS: The sample comprised 7133 participants with a mean age of 64.7 years (SD = 9.5, range = 50–101); of these, 1778 (24.9%) died during a period of 11.2 years. PGS-ADHD(single) was associated with a greater risk of all-cause mortality (hazard ratio [HR] = 1.06, 95% CI = 1.02–1.12, p = 0.010); further analyses showed this relationship was significant in men (HR = 1.07, 95% CI = 1.00–1.14, p = 0.043). Risk of all-cause mortality increased by an approximate 11% for one standard deviation increase in PGS-ADHD(multi-trait) (HR = 1.11, 95% CI = 1.06–1.16, p < 0.001). When the model was run separately for men and women, the association between PGS-ADHD(multi-trait) and an increased risk of all-cause mortality was significant in men (HR = 1.10, 95% CI = 1.03–1.18, p = 0.003) and women (HR = 1.11, 95% CI = 1.04–1.19, p = 0.003). CONCLUSIONS: A high polygenetic predisposition to ADHD is a risk factor for all-cause mortality in older adults. This risk is better captured when incorporating genetic information from correlated traits. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02279-3