Association of Cardiovascular Health Metrics with Dementia in Rural Chinese Older Adults: A Population-Based Study.

Purpose: We explore the associations of individual and composite cardiovascular health metrics with all-cause dementia, Alzheimer's disease, and vascular dementia among rural-dwelling older adults and the potential age variations in their associations. Patients and
Methods: This community-based cross-sectional study included 4980 older adults (age ≥65 years; 57.23% women) from the baseline examination of MIND-China. In March-September 2018, data were collected via face-to-face interviews, clinical examinations, and laboratory test. We defined six cardiovascular health metrics according to the modified American Heart Association's recommendations. We diagnosed dementia and its subtypes following the international criteria. Data were analyzed using logistic regression models.
Results: Of all the participants, 250 were diagnosed with dementia, including 165 with Alzheimer's disease and 75 with vascular dementia. Ideal composite global cardiovascular health metrics (vs poor composite metrics) were associated with a multi-adjusted odds ratio (95% confidence interval) of 0.62 (0.42-0.93) for dementia, 0.88 (0.52-1.48) for Alzheimer's disease, and 0.31 (0.16-0.60) for vascular dementia. Moreover, ideal biological cardiovascular health metrics were associated with multi-adjusted odds ratio of 0.52 (0.28-0.95) for dementia and 0.21 (0.06-0.77) for vascular dementia in young-old adults (65-74 years), whereas ideal behavioral cardiovascular health metrics were associated with multi-adjusted odds ratio of 0.48 (0.26-0.89) for dementia and 0.16 (0.06-0.43) for vascular dementia in old-old adults (≥75 years).
Conclusion: Our results suggest that ideal cardiovascular health metrics are cross-sectionally associated with a low likelihood of dementia and vascular dementia among rural-dwelling older Chinese adults. The associations vary with age, components of cardiovascular health metrics, and dementia subtypes.

Introduction

Dementia has become a global epidemic driven primarily by population aging.1 Over 50 million people are living with dementia worldwide, and the number is projected to triple by 2050, unless effective preventive interventions are identified and implemented.2 In the past three decades, evidence has accumulated that modifiable cardiometabolic risk factors (eg, hypertension, dyslipidemia, diabetes, and obesity), especially occurring in midlife, and unhealthy lifestyles (eg, smoking and physical inactivity) are associated with an increased risk of dementia, especially vascular dementia (VaD).3,4 Cerebral micro- and macrovascular lesions are supposed to be the underlying mechanisms linking cardiovascular risk factors with cognitive impairment and dementia.3,5 In addition, there is a potential that dementia risk may be reduced or the onset of dementia may be delayed through maintaining cardiovascular health.6 Notably, most studies have so far investigated the association of individual cardiovascular risk factors with dementia. However, exploring the composite cardiovascular health (CVH) profiles in association with cognitive outcomes may be more meaningful than individual factors because lifestyle and cardiometabolic risk factors often coexist in older adults, and they may interact with each other to affect the risk of dementia.

In 2010, the American Heart Association (AHA) proposed composite CVH metrics that integrated seven lifestyle-related factors (Life’s Simple 7): four behavioral metrics (smoking, body mass index [BMI], diet, physical activity) and three biological metrics (blood pressure, blood glucose, and total cholesterol).7 Numerous population-based studies have consistently shown associations of ideal CVH metrics with reduced risk of coronary heart disease, stroke, and all-cause mortality,8–10 and a few studies from Europe and North America have also examined the associations of optimal CVH among middle-aged or young-old adults with dementia.11–14 The associations of composite CVH metrics with dementia and main subtypes of dementia amongolder Chinese adults have not yet been reported. This is important because CVH and cardiovascular risk profiles, as well as their association with health outcomes, may differ markedly across ethnic groups.15

In addition, evidence has shown that the associations of some cardiovascular risk factors with dementia may vary with age.16 For example, obesity and hypertension are reported to be risk factors for dementia when occurring during midlife or young-old adulthood but not necessarily in late life or old–old adults.17,18 This implies that age variations may be relevant to consider when examining the associations of composite CVH metrics with dementia.

Therefore, in this community-based cross-sectional study of rural-dwelling Chinese older adults, we sought to examine the associations of composite CVH metrics with all-cause dementia, Alzheimer’s disease (AD), and VaD while taking into account potential age variations.

Materials and Methods

Study Design and Participants

This community-based cross-sectional study used data from the baseline survey of the ongoing Multimodal Interventions to delay Dementia and disability in rural China (MIND-China), which is part of the World-Wide FINGERS Network.19,20 Baseline assessments of MIND-China targeted people who were aged ≥65 years by the end of 2017 and living in the 52 villages of Yanlou Town, Yanggu County, western Shandong Province, as previously reported.19,21 Briefly, in March–September 2018, 5246 participants (80.3% of all eligible people) were examined as the baseline survey for MIND-China. Of these, 266 were excluded due to missing data on CVH measurements (n = 220) or severe mental disease and major depressive disorders (n = 46), leaving 4980 persons for the current analysis ().

The MIND-China study was approved by the Ethics Committee of Shandong Provincial Hospital in Jinan, Shandong. Written informed consent was obtained from all the participants, or in the case of cognitively impaired persons, from a proxy (usually a family member). The personal identity information in the MIND-China database had been removed before the data were released for analysis to ensure complete anonymity of all data. Research within the MIND-China study has been conducted in accordance with the Declaration of Helsinki. MIND-China was registered in the Chinese Clinical Trial Registry (registration no.: ChiCTR1800017758).

Data Collection and Assessments

The trained medical staff collected data via face-to-face interviews, clinical examinations, and laboratory tests following a structured questionnaire. Data included demographics, lifestyle factors (eg, smoking, alcohol consumption, and physical activity), medical history (eg, diabetes, coronary heart disease, and stroke), and use of medications. All medications were classified and coded according to the Anatomical Therapeutic Chemical system.22 Weight and height were measured with participants wearing light clothes and without shoes. BMI was calculated as weight (kg) divided by height squared (m2). After a 5-min rest, arterial blood pressure was measured on the right upper arm in a seated position using an electronic blood pressure monitor (Omron HEM-7127J; Omron Corporation, Kyoto, Japan). We assessed physical activity via questions of frequency (eg, daily, weekly, and monthly) and time (minutes) of walking, sports activities, and recreational activities. Physical activity was quantified as time (minutes) spent per week by multiplying participation frequency with average minutes spent per time. Then, we estimated the metabolic-equivalent of each kind of activity according to the 2011 compendium of physical activities.23 Peripheral blood samples were taken after an overnight fast, and blood samples were analyzed at the certified clinical laboratory of the local town health center. Fasting blood glucose (FBG) and total cholesterol were measured using an automatic biochemical analyzer (DIRUI CS-600B; DIRUI Corporation, Changchun, China). Apolipoprotein E (APOE) genotype was analyzed using polymerase chain reaction and dichotomized as carriers versus non-carriers of the ε4 allele.

Definition of Ideal Cardiovascular Health Metrics

We defined and categorized CVH metrics according to the AHA’s recommendations,7 with some modifications as previously reported and detailed in . Briefly, we categorized each of the 6 CVH metric components into poor (score = 0), intermediate (score = 1), and ideal (score = 2) level. The ideal levels of the 6 CVH metrics were defined as follows: (1) ideal smoking status: never or quit >5 years;24 (2) ideal BMI: <24 kg/m2, used for Chinese adults;25 (3) ideal physical activity: ≥150 min/week moderate intensity, ≥75 min/week vigorous intensity, or equivalent combination; (4) ideal total cholesterol: untreated and <5.26 mmol/L; (5) ideal blood pressure: untreated and <120/80 mm Hg; and (6) ideal fasting blood glucose: untreated and <5.60 mmol/L. The global CVH metric score was calculated as the sum of scores of the above six CVH metric components.26 In addition, smoking, physical activity, and BMI were classified as behavioral CVH metrics, and FBG, total cholesterol, and blood pressure as biological CVH metrics.26 The composite global CVH metric score was divided into 3 categories: poor (score ≤5, reference), intermediate (6–7), and ideal (≥8), and the composite behavioral CVH metric score and biological CVH metric score were grouped as poor (score ≤2), intermediate (3–4), and ideal (≥5) levels, respectively.26

Diagnosis of Dementia, AD, and VaD

Dementia was diagnosed following a 3-step procedure, as previously described.19,27 Briefly, in step 1, the trained staff performed the first interview, clinical examination, and testing, and recorded all information (eg, medical history, cognitive complaint, global cognitive function, and the Chinese version of activities of daily living). In step 2, the neurologists specialized in dementia treatment and care reviewed all the records to screen people who were suspected of having dementia or who had insufficient information (eg, lack of data on cognitive and physical functioning) for a judgement of dementia. Finally, the neurologists conducted the second interview with people who were selected in step 2 or with informants or both, and the diagnosis was made based on all the assessments. The Diagnostic and Statistical Manual of Mental Disorders, fourth edition, criteria were followed for the diagnosis of clinical dementia.28 AD was diagnosed according to the National Institute of Aging-Alzheimer’s Association criteria,29 for probable AD. The diagnosis of probable VaD was made according to the criteria of National Institute of Neurological Disorders and Stroke and Association Internationale pour la Recherch´e et l’Enseignement en Neurosciences,30 which was based essentially on a self-reported history of stroke and clinical or neuroimaging evidence of stroke identified via neurological examination, as well as the presence of a clear temporal relation between the history of stroke and the onset of dementia. Dementia cases who could not be classified as AD or VaD were considered to have other types of dementia.

Statistical Analysis

Characteristics of the study participants by age groups (65–74 and ≥75 years) were compared using the chi-square test for categorical variables and the t-test for normal distributed continuous variables. The individual CVH components and the composite CVH metric scores were considered as the primary independent variables of interest. We employed binary and multinomial logistic regression models to examine the odds ratio (OR) and 95% confidence interval (CI) of dementia, AD or VaD (as dependent variables) associated with individual and composite CVH metrics (as independent variables) in the total sample. To explore the possible variations in the CVH metrics–dementia association, we examined the statistical interaction of CVH metrics with age groups (65–74 and ≥75 years). If a statistical interaction (p for interaction <0.05) or a marginally statistical interaction (0.05