High blood pressure in dementia: How low can we go?
Yuda TuranaJeslyn TengkawanYook-Chin ChiaBoon Wee TeoJong Shin WooGuru Prasad SogunuruArieska Ann SoenartaHuynh Van MinhPeera BuranakitjaroenChen-Huan ChenJennifer NailesKazuomi KarioSungha ParkSaulat SiddiqueJorge SisonApichard SukonthasarnJam Chin TayTzung-Dau WangNarsingh VermaYu-Qing ZhangJi-Gwang WangKazuomi KarioPublished in: Journal of clinical hypertension (Greenwich, Conn.) (2019)
Hypertension is an important public health concern. The prevalence keeps increasing, and it is a risk factor for several adverse health outcomes including a decline in cognitive function. Recent data also show that the prevalence of hypertension and age-related dementia is rising in Asian countries, including in the oldest old group. This study aims to discuss possible treatments for high blood pressure in the elderly and propose an optimal target for BP relative to cognitive outcomes. This review discusses several studies on related blood pressure treatments that remain controversial and the consequences if the treatment target is too low or aggressive. Longitudinal, cross-sectional, and RCT studies were included in this review. An optimum systolic blood pressure of 120-130 mm Hg is recommended, especially in nondiabetic hypertensive patients with significant risk factors. In the oldest old group of patients, hypertension might have a protective effect. The use of calcium channel blockers (CCB) and angiotensin receptor blocker (ARB) is independently associated with a decreased risk of dementia in older people. However, personalized care for patients with hypertension, especially for patients who are frail or very old, is encouraged.
Keyphrases
- blood pressure
- hypertensive patients
- risk factors
- heart rate
- end stage renal disease
- public health
- cross sectional
- mild cognitive impairment
- ejection fraction
- newly diagnosed
- chronic kidney disease
- healthcare
- peritoneal dialysis
- emergency department
- cognitive impairment
- angiotensin converting enzyme
- palliative care
- heart failure
- machine learning
- metabolic syndrome
- type diabetes
- patient reported outcomes
- skeletal muscle
- artificial intelligence
- pain management
- health insurance
- global health