An increasingly aging population means a higher incidence of aging-related disorders (Klimova and Maresova, 2017). Cognitive impairment is a part of the aging process (Rabin et al., 2018), which is characterized by a gradual deterioration of cognitive abilities in multiple domains, including memory and at least one additional area, such as learning, orientation, language, comprehension, and judgment (Daviglus et al., 2010). Though cognitive impairment is insufficiently severe to be diagnosed as dementia, it is still different from normal aging, which interferes with daily life. As an intermediate state between dementia and normal cognition, cognitive impairment preserves functional abilities (Hugo and Ganguli, 2014).

In the last decade, much effort has been made toward the early identification of cognitive impairment. Previous researchers have found associations among cognitive health, level of education (Yuan et al., 2018), and physical activity (Brown et al., 2021). Considering that cognitive impairment may be the prodrome of dementia, early identification and intervention may help slow down the process of cognitive decline (Lebedeva et al., 2015). Thus, developing a cognitive impairment prediction model to help the older people identify their own risk of developing cognitive impairment not only would lighten the patients’ burden but also improve their quality of life.

The NHANES is a series of cross-sectional surveys conducted by the Centers for Disease Control and Prevention (CDC) on a nationally representative population to provide health and nutrition data. The study participants were interviewed in their homes for the collection of demographic information. Subsequently, they visited a mobile examination center (MEC) for the collection of other data, including cognition tests. So far, many researchers (Li et al., 2022; Yang C. et al., 2022; Yang H. et al., 2022) have used the NHANES database to establish nomogram prediction models and achieved positive results. Therefore, we accessed it to conduct this research.

Previous studies identified the common risk factors. Diabetes mellitus can significantly increase the incidence of mild cognitive impairment as well as dementia (Biessels and Despa, 2018). Physical activity benefits cognition, especially executive functioning and memory (Nuzum et al., 2020). Males have a higher risk of cognitive impairment (Fu and Liu, 2022). A poorer education level is an independent risk factor for cognitive impairment (Wang et al., 2020). Hence, this study attempts to establish a predictive nomogram for cognitive impairment according to the social demographic characteristics, medical history, education level, and physical activity of the people aged above 60 years.

Though previous efforts have been made to develop models for cognitive impairment, some of them are complex and involve genetic sequencing (You et al., 2022) or are based on single-center retrospective studies (Zhou et al., 2021), requiring further validation of their clinical effectiveness. The purpose of our study is to develop a highly feasible predictive model for cognitive impairment using large-scale data from NHANES. Through this study, a nomogram was developed to predict the incidence of cognitive impairment in people over 60 years of age. It can help in the early detection of the risk of cognitive impairment in the older people and allow them to undergo further examination to adopt early intervention and even reduce the incidence of dementia.

This study developed and validated a prognostic nomogram based on a cross-sectional study from NHANES (2011–2014) to predict the probability of cognitive impairment in people above 60 years of age. The nomogram included five variables, each of which can be easily acquired from its’ user.

Our study findings are consistent with evidence from a previous study (Lövdén et al., 2020). Among the five independent predictors, education level contributed the most to predicting the outcome. It had been widely accepted that education plays an important role in the decline of cognitive functions. People with a higher education level are less likely to experience a decline in cognitive function. They also experience a slower rate of cognitive decline regardless of neurodegenerative or vascular pathologies (Members et al., 2010). Therefore, improving the education conditions during the initial decades of life and prolonging the educational years are crucial to reducing the cognitive impairment of the older people (Lövdén et al., 2020).

Table 2 shows the significant difference between the two groups in terms of moderate-intensity physical activity. Although there was no statistical difference in sedentary time between the two groups, there was a significant difference in cognitive function due to the difference in moderate activity time. Notably, physical activity was a direct and feasible variable among the five variables and had been proven as being highly related to better cognitive function in old age (Reas et al., 2019). In an umbrella review conducted by the 2018 Health and Human Services Physical Activity Guidelines for Americans Advisory Committee, Erickson et al. analyzed large amounts of data from randomized controlled trials to prove that moderate-intensity physical activity is associated with cognitive improvement (Erickson et al., 2019). They also found strong evidence proving that higher physical activity is associated with a reduced risk of developing cognitive impairment, including Alzheimer’s disease. Thus, increasing moderate-intensity physical activity may be an effective and quick way to improve cognitive function among the older people, and many experts agree with this (Bangsbo et al., 2019).

Taste plays a crucial role in individual assessment of the nutritional value, safety, and quality of food. Although both olfaction and taste tend to decline with age, research (Ogawa et al., 2017) has shown that older adults who experience taste problems or a decline in taste sensitivity often exhibit earlier cognitive function decline.

Although a study (Hu et al., 2019) also using the NHANES database to discuss cognitive impairment showed that moderate to severe depressive symptoms are associated with poorer cognitive function in the older people and more so in the case of women than men. This is contrary to our study wherein being male proved to be a risk factor for cognitive impairment. The explanation for the controversy is that Hu’s study focuses on the association between depression, cognitive function, and gender, rather than exploring any potential factors related to cognitive decline in the general elderly population, as done in our study. Due to the population restriction of Hu’s study to the elderly depression group, it’s not appropriate to compare the variable” gender” at the same level of influencing factors between the two articles. Additionally, many studies explored the relationship between gender and cognitive impairment and eventually obtained different answers. Consistent with our conclusion, Petersen et al. found that the prevalence of mild cognitive impairment is higher in men (Petersen et al., 2010). The relationship between gender and cognitive impairment is still inconclusive. Conducting more studies in this regard may yield clear conclusions.

As a non-intervention factor, age plays an important role in cognitive impairment. Cognitive impairment is increasingly common in the process of aging (Plassman et al., 2008), and the prevalence of mild cognitive impairment increases with age (Li et al., 2020). Hence, the older population experiences a higher incidence of cognitive impairment. That is why we focused on studying and building a nomogram for cognitive impairment for the older people.

Our nomogram integrated different prognostic variables and can generate an individual probability of cognitive impairment among the older people. It included age, sex, education level, taste problem and moderate activity. Each of these is easy to access from the user. Moreover, our nomogram showed good performance in the cohort, regardless of the discriminatory and calibration capacity. It is very convenient to use. For example, a 76-year-old male with no history of diabetes and no regular physical exercise and who graduated from high school when he was young, received a total score of 135 (0 for diabetes, 20 for moderate activity, 55 for age, and 39 for sex), indicating the predicted risk of cognitive impairment of about 69%. This case shows that the older people can easily complete the risk assessment and seek help or intervention on time. A larger cohort study is needed to further explore the five results proposed by our study. Other variables not included in the final regression model but which have the predictive value of statistical differences within the studied population should also be explored.

By conducting a thorough analysis of NHANES data from 2 years cycles, this study provides compelling evidence to validate the significant association between cognitive impairment in the older people and five key factors, namely sex, age, educational level, engagement in moderate-intensity physical activities in daily life, and the presence of taste problems. Our findings emphasize the importance of considering these factors for achieving accurate predictions of cognitive impairment among the older population.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

SS, ML, and JY came up with the idea and designed the study. XM and WH conducted statistical analysis and scientific writing. LL, HL, and JD wrote the manuscript. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted without any commercial of financial relationships that could be constructed as a potential conflict of interest.

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