According to 2018 statistics, the prevalence of cognitive frailty among community-dwelling older people was approximately 1.0-12.1% [24]. The 2021 study found that the prevalence of cognitive frailty ranged from 2.5% ~ 50% [25]. The present study showed that the prevalence of cognitive frailty among elderly chronic pain patients in the community was 28.04%, which was similar to the Thai study (28.72%) [8]. It is evident that the incidence of cognitive frailty is increasing year by year, and timely screening identification and control of the disease needs to be enhanced to prevent evolution to dementia. Cognitive frailty is an important global public health problem. If cognitive frailty was not detected early and intervened in a timely manner, it may lead to adverse outcomes such as falls, injuries, disability, and even death in older adults, which can have serious implications in healthcare settings [26]. Therefore, it is particularly important to construct risk prediction models to identify and assess risks in a timely manner and to adopt necessary intervention programs. In this study, six risk factors of age, exercise habit, pain level, insomnia, malnutrition, and depression were screened to create nomogram and prediction tables with good visualization. These factors are common among older people, and the data are easily available with good convenience.
The results of this study found that age was a risk factor for cognitive frailty. Age 70–79 years and ≥ 80 years were 1.04 and 2.04 times more likely to be associated with cognitive frailty in older people with chronic pain aged 60–69 years. It had been shown that age was the most important risk factor for cognitive frailty in older people [27]. On the one hand, physical activity and memory decline with age, and the organism may deteriorate to different degrees, increasing the incidence of cognitive frailty. On the other hand, order people with chronic pain themselves were less mobile and long-term pain may also affect cognitive function [28]. As the elderly age, living in this state for a long time increased the severity of cognitive frailty and accelerates the transition to dementia. Regular screenings should be conducted for elderly with chronic pain to detect and intervene in time to avoid adverse health outcomes.
This study showed that older people without exercise habit were 1.23 times more likely to experience cognitive frailty than those with exercise habit. Less exercise accelerates the decline in muscle mass and strength in older people, leading to bone loss and deterioration of skeletal muscle function, resulting in physical frailty. Lack of exercise also negatively affects the hippocampus of the brain, reducing cognitive function [29]. Studies had found that exercise can improve physical frailty and cognitive function, as well as provide pain relief, thereby optimizing abilities during aging [30]. Therefore, encouraging older people to perform appropriate exercises according to their abilities, such as regular training in Ba Duan Jin (a traditional Chinese physical and mental exercise), can help improve physical function and cognitive ability [31].
The results of this study showed that the incidence of cognitive frailty increased with higher pain levels, with moderate and severe pain being 1.70 and 2.29 times more likely to cause cognitive frailty than mild pain. Some studies had confirmed that high pain intensity accelerates physical frailty and cognitive decline [32]. The incidence of cognitive decline and physical frailty increased with increasing pain intensity, and was much higher when combined with chronic pain [33, 34]. When high levels of pain are identified in older people with chronic pain, medical staff need to take the necessary interventions to relieve the patient's pain, such as medication, physical therapy, or changes in daily living habits such as exercise, which can effectively reduce pain levels and thereby slow the progression of cognitive frailty.
The present study found that the probability of cognitive frailty was 1.30-fold and 1.35-fold higher for suspected insomnia and insomnia than for well-sleeping older people with chronic pain, sleep was an influential factor in the development of cognitive frailty, which was consistent with the results of related study [12]. Sleep disruption may initially alter adipokines, increasing the risk of physical frailty through a direct effect on adipokines. Or sleep disruption may affect inflammatory pathways, which in turn can lead to physical frailty [35]. It had also been shown that sleep played an important role in consolidating memory, restoring brain function, and even regulating brain plasticity, sleep disorders can accelerate the development of neurodegenerative diseases such as Parkinson's disease and Lewy body dementia, significantly affecting cognitive function in the elderly, which in turn leaded to cognitive frailty [36]. Chronic pain patients themselves are prone to insomnia due to pain, so medical staff need to enhance screening and intervention for sleep problems in the elderly, and families should provide a quiet and comfortable environment for the elderly in their daily lives to ensure sleep quality.
The results of this study showed that malnutrition was an important risk factor for cognitive frailty, with the risk of malnutrition and malnutrition being 1.08 and 2.68 times higher than the occurrence of cognitive frailty with good nutrition, which was consistent with the results of previous studies [8]. Malnutrition in the elderly itself causes physical frailty, loss of body fat due to inadequate nutritional intake and consequent loss of physical strength, which in the long run is likely to cause physical frailty. In addition, malnutrition can lead to deficiencies in various vitamins, and deficiencies in Omega-3 and − 6 and vitamin supplements can accelerate neuronal degeneration and eventually cognitive decline [37]. Malnutrition is closely related to cognitive frailty, and families should pay attention to the nutritional balance of the elderly in their daily lives, and if malnutrition is more severe, they need to go to the hospital for timely intervention to avoid complications.
This study showed that depression was 1.18 times more likely to lead to cognitive frailty than in older people without depression. Depression, which is characterized by a reluctance to interact with others, isolation and low mobility, leads to an increase in inflammatory cytokines such as interleukin-6 and C-reactive protein. Inflammatory cytokines can penetrate the blood-brain barrier and act on skeletal muscle, causing physical frailty and cognitive decline and eventually appear cognitive frailty, thus depression was closely associated with cognitive frailty [38]. The elderly people were prone to psychological problems due to their physical condition, unaccompanied and aging, medical staff need to promptly assess the psychological condition of the elderly, provide targeted psychological counseling, moreover encourage the elderly to participate in more leisure and social activities which can be helpful in preventing cognitive frailty.
Recently, Chang et al. mapped nomogram of cognitive frailty in elderly patients with chronic kidney disease, also highlighting age and depression as independent risk factors for cognitive frailty [39]. In contrast, our study was to develop a risk prediction model for community-dwelling older people, and risk factors for cognitive frailty in older adults under different conditions changed with specific conditions. The presence of chronic pain in community-dwelling older people requires special attention as it is not a minority and is associated with both frailty and cognition function. To our knowledge, this is the first risk prediction model for cognitive frailty in community-dwelling older people with chronic pain that can fill a gap in screening for older people with chronic pain in the community. The nomogram can be used as an easy tool to identify risk factors and guide early intervention. For example, the presence of advanced age, insomnia, and depression in the elderly can be treated promptly in these areas, making the value of the risk prediction model truly valid and of good clinical utility. Promoting a rapid screening program for cognitive frailty in older people with chronic pain in the community is important for finding high-risk groups, implementing effective interventions, and reducing poor prognosis.
The model had been validated internally and externally to have good predictive ability, and can play an important role in graded health management based on the risk assessment results of the nomogram or prediction table. The prediction results can be divided into three levels of low, medium and high risk, for the low-risk elderly to achieve early detection and continuous monitoring, and to see the doctor when the disease has developed; for the medium-risk can start the next step of more accurate diagnosis and early intervention, targeted treatment for the risk problems; for the high-risk need to delay the development of the disease as the first priority, follow the doctor's instructions for the full range of treatment. For example, a community-based elderly patient with chronic pain, 72 years old (40 points), no exercise habit (45 points), moderate pain (50 points), good nutrition (0 points), suspected insomnia (27 points), depression (41 points), and a total score of 203 points, with a risk of cognitive frailty between 0.5 and 0.6, belongs to a medium-risk group, which requires further diagnosis and lifestyle habit modification interventions based on these areas. Therefore, it is clinically important to reduce the risk of cognitive frailty and improve the quality of life of the elderly through more targeted interventions based on risk assessment with the nomogram or prediction table.
There were some limitations in this study. First, although the researcher had completed the survey using a series of questionnaires, there were still some missing potential influences, and the next study should actively combine other relevant factors to complete a more comprehensive exploration. Second, the research design used in this study was a cross-sectional survey study, which prevented the researcher from exploring potential causal relationships among variables in depth. Although the current study had obtained risk factors for cognitive frailty and constructed a risk prediction model, the current findings should be enriched and extended by further longitudinal study designs in the future. Third, because the data mostly originated from participants' self-reports, some bias may exist during the study. For example, the participants in this study were all 60 years old and above, and had some hearing and memory loss, so there may be memory bias in answering the questionnaire. Future work needs to consider a combination of quantitative and qualitative studies to better understand cognitive frailty with minimal bias.
In conclusion, we found that cognitive frailty in elderly patients with chronic pain in the community had a high prevalence, thus should receive more attention. The predictive model with six variables: age, exercise habit, pain level, insomnia, malnutrition and depression were developed to effectively assess the risk of cognitive frailty, and the model was validated to have good predictive performance, providing a theoretical basis for early detection, diagnosis and treatment in cognitive frailty, a practical basis for early screening and intervention by clinical medical staff.