Hypothetical blood-pressure-lowering interventions and risk of stroke and dementia.
Liliana Paloma Rojas-SauneroSaima HilalEleanor J MurrayRoger W LoganMohammad Arfan IkramSonja A SwansonPublished in: European journal of epidemiology (2020)
We aimed to study the effects of hypothetical interventions on systolic blood pressure (SBP) and smoking on risk of stroke and dementia using data from 15 years of follow-up in the Rotterdam Study. We used data from 4930 individuals, aged 55-80 years, with no prior history of stroke, dementia or cognitive impairment, followed for 15 years within the Rotterdam Study, a population-based cohort. We defined the following sustained interventions on SBP: (1) maintaining SBP below 120 mmHg, (2) maintaining SBP below 140 mmHg, (3) reducing SBP by 10% if above 140 mmHg, (4) reducing SBP by 20% if above 140 mmHg, and a combined intervention of quitting smoking with each of these SBP-lowering strategies. We considered incident stroke and incident dementia diagnoses as outcomes. We applied the parametric g-formula to adjust for baseline and time-varying confounding. The observed 15-year risk for stroke was 10.7%. Compared to no specified intervention (i.e., the "natural course"), all interventions that involved reducing SBP were associated with a stroke risk reduction of about 10% (e.g., reducing SBP by 20% if above 140 mmHg risk ratio: 0.89; 95% CI 0.76, 1). Jointly intervening on SBP and smoking status further decreased the risk of stroke (e.g., risk ratio: 0.83; 95% CI 0.71, 0.94). None of the specified interventions were associated with a substantive change in dementia risk. Our study suggests that a joint intervention on SBP and smoking cessation during later life may reduce stroke risk, while the potential for reducing dementia risk were not observed.
Keyphrases
- cognitive impairment
- atrial fibrillation
- smoking cessation
- blood pressure
- mild cognitive impairment
- randomized controlled trial
- physical activity
- cardiovascular disease
- heart failure
- type diabetes
- risk assessment
- metabolic syndrome
- electronic health record
- cerebral ischemia
- big data
- skeletal muscle
- heart rate
- hypertensive patients
- deep learning
- replacement therapy
- breast cancer risk
- human health