To Örebro University

There is a significant knowledge gap in research on Attention-Deficit/Hyperactivity Disorder (ADHD) in older adults. Via a systematic review and meta-analysis, we aimed to investigate the prevalence of ADHD in older adults, considering different assessment methods. We searched five electronic databases up to June 26, 2020. We identified 20 relevant studies with 32 datasets providing a total sample size of 20,999,871 individuals (41,420 individuals with ADHD). The pooled prevalence estimates differed significantly across assessment methods: 2.18 % (95 % CI = 1.51, 3.16) based on research diagnosis via validated scales, 0.23 % (0.12, 0.43) relying on clinical ADHD diagnosis, and 0.09 % (0.06, 0.15) based on ADHD treatment rates. Heterogeneity was significant across studies for all assessment methods. There is a considerable number of older adults with elevated levels of ADHD symptoms as determined via validated scales, and the prevalence of treated ADHD is less than half of the prevalence of clinically diagnosed ADHD. This highlights the need for increased awareness of ADHD clinical diagnosis and treatment in older adults.

Background: Previous research has indicated that attention-deficit/hyperactivity disorder (ADHD) is associated with an increased risk for dementia, but studies are scarce and inconclusive. We aimed to investigate the association between ADHD, and dementia and mild cognitive impairment (MCI). Additionally, we aimed to investigate the impact of comorbid conditions, educational attainment, head injuries, other developmental disorders, and sex on the association.

Methods: The study population consisted of 3,591,689 individuals born between 1932 and 1963, identified from Swedish population-based registers. Cases of ADHD, dementia and MCI were defined according to ICD diagnostic codes and ATC codes for medication prescriptions. A Cox proportional hazards model was used to test the associations between ADHD, and dementia and MCI.

Results: Individuals with ADHD had an increased risk for dementia and MCI. After adjusting for sex and birth year, a hazard ratio (HR) was 2.92 (95% confidence interval 2.40-3.57) for dementia, and 6.21 (5.25-7.35) for MCI. Additional adjustment for psychiatric disorders (depression, anxiety, substance use disorder, and bipolar disorder) substantially attenuated the associations, HR = 1.62 (1.32-1.98) for dementia, and 2.54 (2.14-3.01) for MCI. Common metabolic disorders (hypertension, type 2 diabetes, and obesity), sleep disorders, head injuries, educational attainment, and other developmental disorders, had a limited impact on the association. The association between ADHD and dementia was stronger in men.

Conclusions: ADHD is a potential risk factor for dementia and MCI, although the risk significantly attenuates after controlling for psychiatric disorders. Further research is needed to confirm these findings and to explore underlying mechanisms of the associations.

Background: Available prediction models ofcardiovascular diseases (CVDs) may not accuratelypredict outcomes among individuals initiatingpharmacological treatment for attention-deficit/hyperactivity disorder (ADHD).

Objective: To improve the predictive accuracyof traditional CVD risk factors for adults initiatingpharmacological treatment of ADHD, by consideringnovel CVD risk factors associated with ADHD (comorbidpsychiatric disorders, sociodemographic factors andpsychotropic medication).

Methods: The cohort composed of 24 186 adultsresiding in Sweden without previous CVDs, born between1932 and 1990, who started pharmacological treatmentof ADHD between 2008 and 2011, and were followedfor up to 2 years. CVDs were identified using diagnosesaccording to the International Classification of Diseases,and dispended medication prescriptions from Swedishnational registers. Cox proportional hazards regressionwas employed to derive the prediction model.

Findings: The developed model included eighttraditional and four novel CVD risk factors. Themodel showed acceptable overall discrimination (Cindex=0.72, 95% CI 0.70 to 0.74) and calibration(Brier score=0.008). The Integrated DiscriminationImprovement index showed a significant improvementafter adding novel risk factors (0.003 (95% CI 0.001 to0.007), p<0.001).

Conclusions: The inclusion of the novel CVD riskfactors may provide a better prediction of CVDs in thispopulation compared with traditional CVD predictorsonly, when the model is used with a continuous riskscore. External validation studies and studies assessingclinical impact of the model are warranted.

Clinical implications: Individuals initiatingpharmacological treatment of ADHD at higher risk ofdeveloping CVDs should be more closely monitored.