Ibrahim AM, Singh DKA, Ludin AFM, Sakian NIM, Rivan NFM, Shahar S. Cardiovascular risk factors among older persons with cognitive frailty in middle income country. World J Clin Cases 2024; 12(17): 3076-3085 [PMID: 38898873 DOI: 10.12998/wjcc.v12.i17.3076]
Corresponding Author of This Article
Suzana Shahar, PhD, Dean, Centre for Healthy Ageing and Wellness, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Wilayah Persekutuan Kuala Lumpur 50300, Malaysia. suzana.shahar@ukm.edu.my
Research Domain of This Article
Geriatrics & Gerontology
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Azianah Mohamad Ibrahim, Devinder Kaur Ajit Singh, Arimi Fitri Mat Ludin, Nurul Fatin Malek Rivan, Suzana Shahar, Centre for Healthy Ageing and Wellness, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Wilayah Persekutuan Kuala Lumpur 50300, Malaysia
Noor Ibrahim Mohamed Sakian, Basic Medical Sciences, University of Cyberjaya, Selangor Darul Ehsan, Malaysia 63000, Malaysia
Author contributions: Ibrahim AM and Shahar S contributed to the first draft of the manuscript was written; and all authors commented on previous versions of the manuscript; all authors read and approved the final manuscript; all authors contributed to the study’s conception and design.
Supported byLong-term Research Grant Scheme provided by Ministry of Education Malaysia, No. LRGS/1/2019/UM-UKM/1/4; and Grand Challenge Grant Project 1 and Project 2, No. DCP-2017-002/1 and No. DCP-2017-002/2.
Institutional review board statement: The studies were approved by the Medical Research and Ethics Committee of the Universiti Kebangsaan Malaysia for LRGS TUA (UKM1.21.3/244/NN-2018=145) and AGELESS (UKM PPI/111/8/ JEP- 2020-347).
Informed consent statement: Written informed consent was obtained from all participants at baseline.
Conflict-of-interest statement: All authors declare no competing interests.
Data sharing statement: The dataset supporting the conclusions of this article is available upon request from the corresponding author.
STROBE statement: The authors have read the STROBE Statement – checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Suzana Shahar, PhD, Dean, Centre for Healthy Ageing and Wellness, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Wilayah Persekutuan Kuala Lumpur 50300, Malaysia. suzana.shahar@ukm.edu.my
Received: January 11, 2024 Revised: February 11, 2024 Accepted: April 15, 2024 Published online: June 16, 2024 Processing time: 145 Days and 0.6 Hours
Abstract
BACKGROUND
Cognitive frailty, characterized by the coexistence of cognitive impairment and physical frailty, represents a multifaceted challenge in the aging population. The role of cardiovascular risk factors in this complex interplay is not yet fully understood.
AIM
To investigate the relationships between cardiovascular risk factors and older persons with cognitive frailty by pooling data from two cohorts of studies in Malaysia.
METHODS
A comprehensive approach was employed, with a total of 512 community-dwelling older persons aged 60 years and above, involving two cohorts of older persons from previous studies. Datasets related to cardiovascular risks, namely sociodemographic factors, and cardiovascular risk factors, including hypertension, diabetes, hypercholesterolemia, anthropometric characteristics and biochemical profiles, were pooled for analysis. Cognitive frailty was defined based on the Clinical Dementia Rating scale and Fried frailty score. Cardiovascular risk was determined using Framingham risk score. Statistical analyses were conducted using SPSS version 21.
RESULTS
Of the study participants, 46.3% exhibited cognitive frailty. Cardiovascular risk factors including hypertension (OR:1.60; 95%CI: 1.12-2.30), low fat-free mass (OR:0.96; 95%CI: 0.94-0.98), high percentage body fat (OR:1.04; 95%CI: 1.02-1.06), high waist circumference (OR:1.02; 95%CI: 1.01-1.04), high fasting blood glucose (OR:1.64; 95%CI: 1.11-2.43), high Framingham risk score (OR:1.65; 95%CI: 1.17-2.31), together with sociodemographic factors, i.e., being single (OR 3.38; 95%CI: 2.26-5.05) and low household income (OR 2.18; 95%CI: 1.44-3.30) were found to be associated with cognitive frailty.
CONCLUSION
Cardiovascular-risk specific risk factors and sociodemographic factors were associated with risk of cognitive frailty, a prodromal stage of dementia. Early identification and management of cardiovascular risk factors, particularly among specific group of the population might mitigate the risk of cognitive frailty, hence preventing dementia.
Core Tip: Cognitive frailty is a concept describing a condition in older adults characterized by the simultaneous presence of physical frailty and cognitive impairment. This prodromal stage of dementia refers to the early phase where subtle cognitive changes are evident, but not yet meeting the criteria for a full diagnosis. Cardiovascular risk factors, e.g., hypertension, diabetes and high cholesterol, play a significant role in this stage. They contribute to vascular damage and compromise blood flow to the brain, increasing the risk of cognitive decline. Therefore, this study aimed to investigate the relationships between cardiovascular risk factors and older persons with cognitive frailty.
Citation: Ibrahim AM, Singh DKA, Ludin AFM, Sakian NIM, Rivan NFM, Shahar S. Cardiovascular risk factors among older persons with cognitive frailty in middle income country. World J Clin Cases 2024; 12(17): 3076-3085
Cognitive impairment and physical frailty represent two of the most pressing challenges in the realm of cognitive health and aging, leading to dementia. As the global population continue to age, including Malaysia, 15% of its population will be occupied by older person 65 years and above by 2050[1]. The prevalence and incidence of cognitive frailty are on the rise, incurring a substantial burden on healthcare systems and societies worldwide. The United States has reported that 6.7 million Americans age of 65 and older are living with dementia and expected to double in 2060 whereas Malaysia has reported 8.5% or roughly 260000 older persons with dementia[2,3]. Dementia, characterized by progressive cognitive decline and functional impairment, affects an estimated 50 million individuals globally, with a new case diagnosed every three seconds[4]. Cognitive frailty (CF), a reversible pre-demented state, a concept that encompasses cognitive impairment and physical frailty, further compounds the challenges faced by older persons, with a prevalence ranging from 6% to 16% globally[5], and 37.4% pre-CF and 2.2% CF in Malaysia[6]. The burden of dementia and cognitive frailty extends beyond the affected individuals, impacting families, caregivers, and the broader healthcare infrastructure[7]. The economic and emotional costs are staggering, making dementia a healthcare and societal priority[8]. Understanding the risk factors associated with dementia and cognitive frailty is paramount for effective prevention and management. Emerging evidence highlights a complex interplay of genetics, lifestyle, and comorbidities in the pathogenesis of dementia. Notably, the significance of cardiovascular risk factors, such as hypertension, diabetes and dyslipidemia as one third of modifiable risk factors, has gained prominence, offering a potential avenue for interventions preventing dementia[9]. Numerous literature reported contribution of mid-life and late-life cardiovascular risk factors on the incidence of dementia[10-13]. Evidences also showed that cardiovascular risk factors were associated with frailty[14-17] or cognitive impairment[18,19]. Cardiovascular risk factors have been implicated in the pathogenesis of dementia, particularly Alzheimer’s disease (AD) and vascular dementia[20]. Compared to low cardiovascular health metrics, moderate to high cardiovascular health metrics is associated with lower risk of dementia[20]. Chronic exposure to cardiovascular risk factors leads to gross chronic cerebral infarctions, cerebral atherosclerosis and global AD pathology[21]. These vascular changes compromise cerebral blood flow, contributing to the development of small vessel disease and microvascular pathology. Additionally, systemic inflammation and oxidative stress associated with cardiovascular risk factors may further exacerbate neurodegenerative processes[22]. However, the association between cardiovascular risk factors with cognitive frailty among a multiethnic older persons’ population from a middle-income country, such as Malaysia has yet to be investigated. Thus, this study aimed to determine multifaceted risk factors associated with cognitive frailty, with a particular focus on the role of cardiovascular risk factors.
MATERIALS AND METHODS
Two cohorts comprising of multiethnic older persons with cognitive frailty, i.e., AGELESS Trial and LRGS TUA, a Longitudinal Study on Neuroprotective Model for Healthy Longevity, were included in this analysis. Matching variables from both datasets pertaining to cardiovascular risks were identified, such as sociodemographic factors, and cardiovascular risk factors, including medical history, anthropometric measurements and biochemical profiles. These datasets were pooled for subsequent analysis for identifying the cardiovascular risk factors among older persons with cognitive frailty. The studies were approved by the Medical Research and Ethics Committee of the Universiti Kebangsaan Malaysia for LRGS TUA (UKM1.21.3/244/NN-2018-145) and AGELESS (UKM PPI/111/8/JEP-2020-347). The studies were performed in accordance with the ethical standards confirming to the Declaration of Helsinki. Written informed consent was obtained from all participants at baseline.
Study design and population
AGELESS Trial is an ongoing, multidomain intervention for reversal of cognitive frailty[23]. For LRGS TUA, the wave 3 data was used for this study[24]. The inclusion criteria were community-dwelling individuals aged 60 years and above with no severe physical disabilities, major psychiatric illnesses or mental disorders. Sixty-years-old was used as cut off point as Malaysia adopted United Nations definition for older persons[25]. Participants with a Mini-Mental State Examination score of 14 and below (moderately severe or severe cognitive impairment) and those conditions that affected engagement in the interventions, such as major depression, dementia, other major psychiatric disorders, severe cognitive impairment, and malignant diseases were excluded from this study. The details of both the AGELESS and LRGS TUA studies are available in the literature[23,24]. Baseline data of AGELESS trial (n = 106), consisting of CF participants, and follow up assessment wave 3 of LRGS TUA data (n = 406), with CF and non-CF participants were used to allow merging of cognitive frailty data as similar assessment tool were used.
Study parameters
Parameters used in this analysis included socio-demography, health and lifestyle, body composition, blood pressure, cognitive function, and frailty status, and biochemical profile (Table 1).
Gender, education level, marital status, total household income, smoking status, medical history based on self-report during the interview
Body composition
Waist circumference measured using Lufkin tape; weight, fat free mass and percentage body fat measured using Bio-electrical Impedance Analysis Inbody 270 (Biospace, Seoul, Korea) and height was measured using Leicester Height Measure (CMS Weighing Equipment, United Kingdom)
Blood pressure
Blood pressure was measured using Omron blood pressure monitor (Kyoto, Japan) and average values were recorded
Cognitive function & Frailty status
Clinical Dementia Rating Scale, FRIED criteria
Biochemical profile
A total of 20 mL fasting venous blood sample was taken and analyzed for fasting blood glucose, HbA1c and lipid profile using butterfly syringe by phlebotomist
BMI cut-off point developed by Malaysian Dietary Guidelines for Older Persons was used due to higher risk of type 2 diabetes and cardiovascular disease among Asian people even with lower cut-off point[26,27]. Cut-off point for fasting blood glucose of 6.1 mmol/L and HbA1c of 5.6% was based on the Malaysian Clinical Practice Guideline (CPG) on Management of type 2 diabetes mellitus (2020)[28], and cut-off point for lipid profile was based on the Malaysian CPG on management of dyslipidaemia (2020)[29]. Framingham risk score (FRS)[30] was calculated based on sex, age, high density lipid cholesterol, total cholesterol, medication for hypertension, systolic blood pressure, diabetes, and smoking for each participant at assessment. Gender-based score obtained was then categorized as high for FRS ≥ 20%, intermediate for FRS 10%-19% dan low for FRS < 10%[31]. Cognitive frailty was identified by the International Academy on Nutrition and Aging and the International Association of Gerontology and Geriatrics as the main precursor of dementia[32,33]. Cognitive frailty was defined as a score of 0.5 for Clinical Dementia Rating Scale and Fried criteria for frailty whereby a score of one or two was pre-frail and ≥ three was frail[32-34]. Frailty was defined as: (1) Shrinking, subjective report of unintentional loss of weight around 5 kg in prior year which is not by effort, such as diet; (2) self-report of exhaustion and poor endurance and energy; (3) low physical activity determined based on the lowest scores of the Physical Activity Scale for Elderly (PASE); (4) slowness defined using five-meter gait speed test with the cut-off points stated in the original reference based on gender and height specific; and lastly; and (5) weakness defined using hand grip strength test with the cut-off points stated in the original reference based on gender and body mass index specific. Participants scoring one and above for frailty only, or 0.5 for Clinical Dementia Rating Scale only, or less than one for frailty and/or 0 or more than 0.5 for Clinical Dementia Rating Scale were categorized as non-CF.
Statistical analysis
The characteristics of the study population by cognitive frailty status were compared using independent t-test for continuous data and chi-square for categorical data. Chi-square/Fisher exact was used to find association between cognitive frailty and cardiovascular risk factors. All data was normally distributed. Unadjusted odds ratio (OR) with 95% confidence interval (CI) was estimated. Binomial logistic regression was used to find out independent determinant associated with cognitive frailty. The basic models were then adjusted for level of education. The level of statistical significance was set at a P value less than 0.05. All analyses were performed with SPSS version 21.
RESULTS
Profiles of participants were compared between non-CF and CF, as tabulated in Table 2. Their mean age was about the same for non-CF and CF, 71.6 ± 4.7 and 71.4 ± 6.3 years old, respectively. However, the older persons with CF group had more women (62.9% vs 51.3%), lower percentage with married status (56.1% vs 81.8%), lower years of education (5.8 ± 4.2 vs 7.8 ± 4.5 years), higher percentage of no formal education attained (18.6% vs 8.4%) and lower household income (80.9% vs 64.4%), with P value < 0.05.
Table 2 Sociodemographic, health, anthropometry, and biochemical characteristics of participants.
Comparing their medical history, older persons with CF had a higher percentage of hypertension (65% vs 53.8%) and diabetes (37.6% vs 28.7%) as compared to robust. Various anthropometry measure also indicated that older persons with CF had significantly poorer health status as compared to robust (P < 0.05 for all parameters); i.e., higher waist circumference (91.5 ± 12.8 cm vs 88.6 ± 11.3 cm), higher body mass index (26.5 ± 4.9 vs 25.5 ± 4.4), higher percentage body fat (36.8 ± 10.1% vs 32.6 ± 9.43%) and lower fat-free mass (39.3 ± 7.7 kg vs 44.1 ± 22.7 kg). With respect to biochemical parameters, older persons with CF had a higher value. However, significant difference was only observed for high fasting blood glucose (> 6.1 mmol/L) (34.9% vs 24.5%). Framingham risk score indicated that most older persons with CF had a higher 10-year risk of myocardial infarct than non-CF (55.7% vs 43.4%) (P < 0.05).
Heat map in Figure 1 demonstrates association between each cardiovascular risk factor. Being female (OR: 1.61; 95%CI: 1.13-2.30) and single (OR: 3.52; 95%CI: 2.36-5.25), and having no formal education (OR: 2.50; 95%CI: 1.46-4.28), lower household income (OR: 2.36; 95%CI: 1.57-3.55), hypertension (OR: 1.59; 95%CI: 1.11-2.28) and higher fasting blood glucose (OR: 1.65; 95%CI: 1.12-2.43) were more likely to be associated with CF.
Figure 1 Odds ratio of association between each cardiovascular risk factor.P < 0.05, bolded font.
With regards to gender comparison, women were most likely to be single (OR: 11.69; 95%CI: 6.69-20.43), have a lower education level (OR: 8.01; 95%CI: 3.58-17.91) and have lower household income (OR: 1.73; 95%CI: 1.17-2.56) as compared to men. Single older adults were more likely to have better education (OR: 1.95; 95%CI: 1.16-3.31) and low household income (OR: 2.54; 95%CI: 1.57-4.11). Respondents with hypertension were more likely to have lower household income (OR: 0.65; 95%CI: 0.45-0.94), hypercholesterolemia (OR: 6.60; 95%CI: 4.46-9.76) and higher fasting blood glucose (OR: 2.33; 95%CI: 1.54-3.54) whereas less education was more likely to have lower household income (OR: 0.23; 95%CI: 0.11-0.48).
Logistic regression analysis indicated that being single (OR = 3.38, 95%CI: 2.26-5.05), and having low household income (OR = 2.18, 95%CI: 1.44-3.30), hypertension (OR = 1.60, 95%CI: 1.12-2.30), low fat-free mass (OR = 0.96, 95%CI: 0.94-0.98), high percentage body fat (OR = 1.04, 95%CI: 1.02-1.06), high waist circumference (OR: 1.02; 95%CI: 1.01-1.04), high fasting blood glucose (OR = 1.64, 95%CI: 1.11-2.43), and high Framingham risk score (OR = 1.65, 95%CI: 1.17-2.31) had a higher risk to have CF (Table 3).
This study has determined cardiovascular risk factors among older persons with cognitive frailty. This study which focused on prodromal stage of dementia, cognitive frailty, is the first of its kind in existing literature.
Cardiovascular risk factors are more prevalent in older persons with a predementia state, such as cognitive frailty. Cardiovascular risk factors may appear prior, at least 14 years, to dementia diagnosis[35]. Older persons with cognitive frailty in our study had significantly higher cardiovascular risk score (21.0 ± 8.4) compared to those without CF (18.1 ± 3.4). Similarly, moderate to high cardiovascular risk was significantly associated with older persons with cognitive impairment[36]. Numerous studies emphasize the role of vascular risk factors in the development and progression of dementia[20,21,37]. For example, the subsample of Rush Memory and Aging Project demonstrated a compelling association between vascular risk factors and incident dementia. However, challenges arise in disentangling causality as dementia itself may influence lifestyle factors and exacerbate vascular conditions[21].
Cardiovascular risk specific factors associated with cognitive frailty in this study included high fasting blood sugar and hypertension. High blood glucose levels, as seen in individuals with diabetes or prediabetes, may contribute to accumulation of amyloid plaques and tau tangles in the brain[38,39]. Furthermore, insulin resistance, often associated with elevated blood glucose levels, can impair the brain ability to utilize glucose for energy[40]. Meanwhile, hypertension can damage blood vessels, including those in the brain, leading to reduced cerebral blood flow which then can result in brain hypoxia and possibly the development of white matter lesions and vascular dementia[41]. These will then lead to reduced brain function and an increased risk of cognitive decline and dementia. Managing these risk factors through lifestyle modifications, and in some cases, medication, may play a crucial role in preventing or delaying the onset of dementia.
This study has also revealed the association of low fat-free mass, higher percentage of body fat and higher waist circumference with CF. Muscle mass is an indicator of physical fitness and may contribute to better mobility and overall well-being in older persons, potentially reducing the risk of cognitive decline[42]. Dysfunctional myokine secretion triggered by physical inactivity exacerbates inflammation and impairs glucose metabolism, consequently increasing tissue oxidative stress and affecting the transport of insulin across blood-brain barriers[43]. The loss of skeletal muscle mass with decreased number of mitochondria will also lead to decrease in tissue oxidative capacity. Furthermore, a higher percentage of body fat, particularly abdominal obesity, has been linked to metabolic disturbances and insulin resistance, which are associated with an increased risk of cognitive decline and dementia[43,44]. Elevated body fat percentage, especially when combined with low muscle mass, may contribute to systemic inflammation and oxidative stress, which can have detrimental effects on the brain[44].
In addition to cardiovascular risk factors, sociodemographic factors, including single marital status, was found to be associated with CF. Compared to single marital status in most of our CF participants, married status can be associated with social engagement and shared healthier lifestyle[45]. Married individuals may be more likely to engage in a healthier lifestyle, such as encouraging a balanced diet, regular exercise, and management of chronic health conditions, which are all associated with a reduced risk of dementia. Married individuals also show protective health benefits by increasing social support and stability of life, and reducing loneliness, all of which are associated with cognitive performance in older persons[46]. On the other hand, married status might involve a caretaker role for spouse which may also increase stress; therefore, it is a complex relationship[47].
Association of years of education with CF, as cited by various studies, can be explained via the cognitive reserve theory. Cognitive reserve is the brain property that allows a person to withstand age- or disease-related brain changes[48]. A study with three population-based studies in Northern Europe, with total sample of 3436 participants, found that education halved the risk of dementia despite being carrier of Apolipoprotein E gene ε4-allele[49]. In a recent study among 4051 twins aged 65 years and above, it was found that education moderated relationship between midlife cardiovascular risk factors and old age cognition[50]. Older persons with higher education background were able to cope deteriorative effects of midlife cardiovascular risk factors on cognition at old age better than those with lower education background.
As reported in other study from Korean national health and nutrition examination survey in 2017, lower household income was associated with higher risk of cardiovascular disease incident, cognitive impairment, and dementia[51]. Better income allows better quality of life attainment, including access to healthcare, healthier lifestyles, and improved living conditions. This can lead to better management of conditions, such as diabetes and hypertension that may decrease dementia risks. Other than that, higher household income is associated with healthier lifestyles, hence lowering risks of dementia[9]. Household income is also intertwined with various social determinants of health, including education and living conditions. These factors can influence cognitive reserve and resilience, which are believed to play a role in dementia risk.
The strength of this study lies in its comprehensive approach which delves into the multifaceted nature of cognitive frailty, incorporating both cognitive and physical assessments, and utilizing community dwelling older persons, to enhance the generalizability of our findings and ensure that the results resonate with the broader aging population. However, several limitations need to be pointed out. Firstly, several medical conditions were self-reported retrospectively, which could have led to measurement errors. Besides that, generalizing these findings to the younger population will need caution as the mean age of our population was 71.5 years. This study is a step forward in navigating the intricate landscape of cognitive frailty and cardiovascular risk factors, providing a foundation for future studies and intervention that will enhance the well-being of the aging population. Future studies should explore follow-up assessments, which will allow more comprehensive understanding of the trajectory of cognitive frailty and role of cardiovascular risk factors.
CONCLUSION
Cardiovascular risk factors (i.e., having hypertension, high fasting blood glucose, low fat-free mas, high percentage body fat, high waist circumference, and high composite index of Framingham risk score) and sociodemographic factors (i.e., being single, no formal education, low household income) were associated with CF. Early identification and management of cardiovascular risk factors, such as blood pressure, glucose level, fat profile and body mass index can help mitigate the risk of cognitive frailty, hence reducing risk of dementia.
ACKNOWLEDGEMENTS
The authors would like to thank all researchers from AGELESS Trial and LRGS TUA wave 3, and the staff of the Centre for Healthy Aging and Wellness (H-Care), Faculty of Health Sciences, Universiti Kebangsaan Malaysia. We would also like to convey our gratitude to all participants for their participation.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Geriatrics and gerontology
Country/Territory of origin: Malaysia
Peer-review report’s classification
Scientific Quality: Grade C, Grade C
Novelty: Grade B, Grade B
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade B, Grade C
P-Reviewer: Dziegielewska-Gesiak S, Poland S-Editor: Liu JH L-Editor: A P-Editor: Cai YX
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