Aging is inevitable, accompanied by reduced intellectual ability, decline in sensory functions, and reduced emotional responses [1]. The decline in intellectual ability begins with memory issues, and impairments of judgment and comprehension [2]. In most parts of the world, the prevalence of dementia in adults aged 60 and up is around 5%-7% [3].

In 2018, it was estimated that 50 million people globally had dementia, with the number expected to triple by 2050 [4]. Due to the fact that dementia is a prominent cause of mortality and is accompanied by expensive healthcare costs [5], preventative and interventional measures that reduce cognitive decline in older adults are essential.

Several sociodemographic characteristics and health habits can affect cognitive decline. These included older age, lower income [6], not cohabiting with a spouse [7], poorer education [8], rural residence [9], poor self-rated health,10 limitations on the activities of daily living [6], and chronic illness [11]. Current smokers [12] exhibited low cognitive functioning but normal alcohol consumers [13] and those who exercised [14] frequently maintained a healthy weight [6] and participated in social activities [15], exhibited high-level functioning [16]. In this regard, decline of cognitive function affects people’s general health as well as their mental health. Therefore, identifying factors that affect cognitive function decline is essential for the creation of effective interventions.

The trajectory analysis is intended to account for individual variability with respect to the average population trend. The trajectory is necessary in order to provide an intervention suitable for the group because the demographic characteristics or risk factors will be different for each group grouped with similar characteristics over time [17].

To implement the trajectory, a group-based trajectory model (GBTM) is used. GBTM methods identify the individual characteristics of each study subject. And it is a method to show the progress pattern of how a specific variable changes with time and to classify it appropriately [18].

Among the studies related to mental illness, there are not many studies that have analyzed the trajectory of cognitive function, but there are many studies related to the trajectory of depression. In some psychiatric studies, there are studies that confirm the multiple change patterns of depression while reflecting the heterogeneity and dynamics of the group. Depression change does not appear as a single trajectory, but multiple trajectories exist. In addition, various patterns of change were found to be diverse, such as a pattern of maintaining a low level of depression or a high level of depression, a pattern of decreasing the level of depression, and a pattern of increasing it [19-21].

Other previous study on the trajectory of depression, there was a high-risk group particularly vulnerable to depression among the elderly with disabilities, and risk factors affecting this group included social relationships and daily life performance. In that paper, it was revealed that the results can be used as evidence to prepare alternatives such as increasing opportunities to socialize with people and supporting assistive devices to improve daily life performance to prevent depression in the elderly [22]. As such, it is possible to devise an intervention method that can select and prevent high-risk groups through trajectory analysis.

Similarly, cognitive function may also appear in various ways, such as a pattern in which cognitive function is maintained high, a pattern in which the cognitive function is maintained at a moderate level, and a pattern in which cognitive function is rapidly decreased, depending on the person. Finding influencing factors for enhancing or sustaining cognitive function may be done by looking at the categorization of individuals’ cognitive function trajectory. And ultimately, we investigated effective strategies for improving mental health in older adults.

The aims of our study were 1) to identify distinct trajectories of cognitive function using the GBTM method and 2) to find out what risk factors exist for each identified group, such as demographics, lifestyle, comorbidities, and economic conditions.

The purpose of this study were to look into various changes in cognitive function trajectories over time and to investigate what factors contributed to these changes by using GBTM. We found four distinct trajectories of change in cognitive function in adults over 40 years of age: high, medium, low, and rapid decline. Although cognitive decline is a natural phenomenon with age, the pattern of cognitive decline is different for each individual. Some people have a gradual decline in cognitive function, while others have a sharp decline in cognitive function.

Following the confirmation of the cognitive function trajectories, the relevant factors that influenced membership in each trajectory were identified. Older age, lower education level, bad dietary habits, comorbidities, a low household income, and technical workers were more likely to indicate group of the low and rapid cognitive function decline. Such variables should be modified to prevent the risk of rapid cognitive function decline. Similar to the results of previous studies on Alzheimer’s disease and dementia, our study found that cognitive function declines with age, with lower education, and with lower household income [6,8]. Life competencies such as resilience, self-esteem, and productive efficiency to risky and stressful circumstances are all acquired through higher education [33]. Individuals with higher life competences are better able to avoid risk factors that may impair cognitive function and, on the other hand, engage in more cognitively demanding activities, which also help to maintain cognitive function in later life [34]. Furthermore, education enhances socioeconomic competencies, allowing access to additional resources such as increased earnings.

Noncommunicable diseases such as hypertension, diabetes mellitus, and obesity are associated with aging [35-37] and are caused by a lack of physical exercise [38,39]. Hypertension causes damage to the structure of cerebral blood vessels, increases atherosclerosis, and inhibits cerebrovascular regulating processes. These vascular abnormalities enhance the brain’s sensitivity to ischemia injury, particularly in sensitive white matter regions crucial for cognitive function, and may contribute to the development of cognitive decline [40]. Diabetes mellitus is a metabolic disorder in which the body does not produce enough insulin or does not operate normally. Insulin is thought to have neuromodulatory properties that help synapses become more flexible. Insulin signaling dysfunction, chronic inflammation, hyperglycemia, and increased oxidative stress all play important roles in the pathophysiology of Alzheimer’s disease [41-43].

We also found associations between dietary habits, obesity and cognitive function. When body fat increases, harmful substances in visceral fat interfere with the action of insulin. Then, the reaction that allows the sugar component in the blood to be converted into an energy source and enter the cells becomes dull, resulting in insulin resistance, which makes it difficult for insulin to function properly. To compensate for the impaired insulin function, the brain induces more insulin to be secreted, resulting in hyperinsulinemia. Beta-amyloid, a commonly known substance associated with cognitive decline, is a surface protein of brain cells. When this protein is made in excess and accumulates in the brain, it causes damage to brain cells. Insulin interferes with the decomposition of beta-amyloid and, as a result, reduces brain perfusion, leading to cognitive decline [44]. This pathophysiologically explains the relationship between obesity and cognitive decline. Dietary habits affect obesity. Irregular meals increase insulin secretion and fat synthesis by enhancing the activities of adipogenic enzymes [45]. Too many meals or difficulties in controlling intake enhance obesity. Skipping breakfast can trigger snacking and overeating at lunch or dinner. We noted frequent consumption of processed and fast foods; the higher these intakes, the higher the blood cholesterol and triglyceride levels, triggering cardiovascular disease and obesity [46]. According to these findings, it is possible that controlling one’s dietary habits and obesity is important factors in the prevention of cognitive function decline.

There are strengths in our research. Many previous studies used the Mini-Mental State Examination (MMSE) to evaluate cognitive function; the MMSE evaluates orientation, memory, calculation, recall, language, and spatial and temporal abilities over about 20 minutes [47]. The CERAD-K that we used evaluates verbal fluency, Boston Naming, MMSE, word list memory, construction praxis, word list recall, word list recognition, constructional recall, and trail making test A/B; the test requires over 1 hour [24]. CERAD-K has more questions and is more accurate than MMSE. The strengths of our study are that we tracked subjects for several years using the CERAD-K test, which is better than the MMSE.

The limitations are that it is difficult to generalize our results this was a single Healthcare system Gangnam Center of Seoul National University work. However, about 45,000 patients visit center annually; our results will be robust. Also, we did not assess social relationships such as meetings with close friends or social gatherings. In previous studies, meetings with close friends and family, and religious and club activities, improved mental health [48,49] because humans are constantly aware of the situation and keep thinking while understanding the other person’s point of view while talking to the other person and anticipating how their actions will affect their future relationships.

Despite these limitations, we identified trajectories of cognitive decline in adults aged over 40 years and identified risk factors. Our results can be used to prevent cognitive decline with aging. In the future, it is important more specific programs such as dietary improvement and weight management can be prepared based on the research results.

In conclusion, we identified four distinct trajectories of cognitive decline in those aged over 40 years; the declines were associated with different demographic, lifestyle, and economic factors, as well as comorbidities. A younger age, a higher educational level, a professional worker, good dietary habits, no diabetes mellitus, and no obesity improved cognitive function. The combination of these factors is a “cognitive reserve” that delays cognitive decline.

Our findings highlight the need to prevent such decline. Identification of those at high risk followed by interventions that prevent decline are essential.