Multimorbidity pattern and risk of dementia in later life: an 11-year follow-up study using a large community cohort and linked electronic health records

Background: Several long-term chronic illnesses are known to be associated with an increased risk of dementia independently, but little is known how combinations or clusters of potentially interacting chronic conditions may influence the risk of developing dementia. Methods: 447,888 dementia free participants of the UK Biobank cohort at baseline (2006 – 2010) were followed-up until 31 May 2020 with a median follow-up duration of 11.3 years to identify incident cases of dementia. Latent Class Analysis (LCA) was used to identify multimorbidity patterns at baseline and covariate adjusted Cox regression was used to investigate their predictive effects on the risk of developing dementia. Potential effect moderations by C-reactive protein (CRP) and APOE genotype were assessed via statistical interaction. Results: LCA identified four multimorbidity clusters representing Mental health, Cardiometabolic, Inflammatory/autoimmune and Cancer related pathophysiology respectively. Estimated hazard ratios (HR) suggests that multimorbidity clusters dominated by Mental health (HR=2.12, p<0.001, 95%CI: 1.88 to 2.39), and Cardiometabolic conditions (2.02, p<0.001, 1.87 to 2.19) have the highest risk of developing dementia. Risk level for the Inflammatory/autoimmune cluster was intermediate (1.56, p<0.001, 1.37 to 1.78) and that for the Cancer cluster was least pronounced (1.36, p<0.001, 1.17 to 1.57). Contrary to expectation, neither C-reactive protein (CRP) nor APOE genotype was found to moderate the effects of multimorbidity clusters on the risk of dementia. Conclusions: Early identification of older adults at higher risk of accumulating multimorbidity of specific pathophysiology, and tailored interventions to prevent or delay the onset of such multimorbidity may help prevention of dementia

Exploiting electronic health records for research on atrial fibrillation: risk factors, subtypes, and outcomes

BACKGROUND: Electronic health records (EHRs), collected on large populations in routine clinical care, may hold novel insights into the heart rhythm disorder atrial fibrillation (AF). AIM: To exploit EHRs to investigate, validate and extend evidence for AF risk factors, subtypes, and outcomes. METHODS: The CALIBER dataset (1997–2010) linking primary care, secondary care, and mortality records for a representative subset of the UK population was used (i) to model associations between cardiovascular disease (CVD) risk factors and incident AF, including AF with (AF+) and AF without (AF–) intercurrent CVD, (ii) to create EHR definitions for eight AF subtypes (structural, focal, polygenic, postoperative, valvular, monogenic, respiratory and AF in athletes) and (iii) to investigate stroke outcomes by CHA2DS2-VASc, sex, and warfarin use. RESULTS: Among 1,949,052 individuals, 50,097 developed incident AF: 12,652 (25.3%) with AF+ and 37,445 (74.7%) with AF–. Smoking (HR [95%CI] for AF+ vs. AF–: 1.66 [1.56,1.77] vs. 1.21 [1.16,1.25]), hypertension (2.19 [2.11,2.27] vs. 1.65 [1.62,1.69]), and diabetes (2.03 [1.94,2.12] vs. 1.45 [1.41,1.49]) showed consistent direct associations with AF+ and AF–, while heavy drinking (1.17 [0.81,1.67] vs. 1.99 [1.68,2.34]) and total cholesterol levels (0.99 [0.96,1.02] vs. 0.85 [0.84,0.87]) showed inconsistent associations with AF+ and AF–. EHR definitions for AF subtypes were created by combining 2813 diagnosis, medication, and procedure codes. There were 12,751 individuals with AF and valvular heart disease. Prosthetic replacements, mitral stenosis and aortic stenosis showed higher HR [95%CI] for stroke, thromboembolism and mortality (1.13 [1.02,1.24], 1.20 [1.05,1.36], and 1.27 [1.19,1.37] respectively). The net-clinical benefit (NCB [95%CI] per 100 person-years) of warfarin was shown from CHA2DS2-VASc≥2 in men (0.5 [0.1,0.9]) and CHA2DS2-VASc≥3 in women (1.5 [1.1,1.9]). CONCLUSION: AF is a heterogeneous condition associated with diverse disease mechanisms. EHRs can help refine understanding of risk factors, subtypes, and outcomes with relevance for clinical practice