Dementia Risk Score for a Population in Southern Europe Calculated Using Competing Risk Models

INTRODUCTION

Dementia has reached epidemic proportions in developed countries [1]. The number of patients is expected to double, or even triple, over the next 20 years, unless effective interventions are implemented [2, 3]. Until now, symptomatic and etiopathogenic pharmacological interventions for dementia have been the focus of research. Unfortunately, approved dementia drugs have small effect sizes, and disease-modifying treatments have so far failed to demonstrate efficacy [4]. Evidence that the neuropathological changes in Alzheimer’s disease (AD) begin decades before the onset of symptoms has prompted clinical trials of anti-amyloid therapies in asymptomatic individuals with abnormal levels of AD biomarkers [5]. Nonetheless, even if this approach proves successful and an effective drug is found, its application would be expensive and faces challenges such as target population identification and screening procedures [6, 7]. On the other hand, mathematical models have shown that dementia does not appear in two-thirds of individuals with some degree of amyloid-β deposition due to the existence of the competing risk of death from other causes [6]. Dementia management has evolved from the treatment of symptoms to focus on various primary prevention strategies in healthy individuals and secondary prevention in people with mild cognitive impairment (MCI) [8–10].

Predictive models have been developed to predict the risk of events associated with diseases such as complications or death [11, 12]. The translation of such models into risk scores by selecting only a few easily measurable factors enables the implementation of preventive measures in people at higher risk of a disease. Although the absolute risk of an event may differ between populations, the classification of individuals’ risk through the use of scores is consistent and enables prevention to be addressed in a stratified manner [13]. The evidence that modifiable cardiovascular risk factors such as hypertension, hypercholesterolemia, obesity, and diabetes are directly associated with the risk of dementia opens the door to the use of interventions tackling these factors to reduce the prevalence of dementia [3, 15]. Adding sociodemographic and genetic variables, scores have been proposed for assessing the risk of developing dementia in mid-life or after 65 years of age [14, 16]. These predictive models make it possible to identify asymptomatic individuals at high risk of dementia to be targeted by multimodal interventions to modify risk factors [5]. On the other hand, the external validation of these scores has not been satisfactory for several reasons [17, 18]. Among them, we note that the models have not included competing risks and their predictive performance is limited by the small number of events recorded when the exposure is measured at middle age [19, 20]. On the other hand, the studies have been based on populations from northern Europe and the United States, which hinders their application to other populations such as those in southern Europe [14, 21]. The objective of this study was to develop competing risk models to predict the late risk of dementia based on risk factors at middle age using data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Spain cohort.

METHODS

RESULTS

The initial data sample included 25,015 individuals. After cleansing records missing data in some variables (smoking, BMI, level of education, and/or rMed), we deleted 508 records and kept a database containing a total of 24,507 for analysis. Within these records, eleven variables were found to have some missing data, the one with the most missing data lacking values for 150 records and that with the least for just 3 records. Using multiple imputation for these cases, we processed the sample of 24,507 individuals. Their characteristics are summarized in Table 1. The 755 cases of dementia represented an incidence of 3.1% for the entire cohort. The incidence increased with age at recruitment, reaching 11.7% in those over 60 years of age. The mean follow-up was 21.5 years, with a mean age at recruitment of 49 years. The differences in risk of dementia associated with level of education stand out, ranging from 1.2% in individuals with higher education to 5.1% in the group without studies. The cases of dementia in over-50-year-olds represented 91.9% (694) of the total, 6.0% of individuals in this group being diagnosed with dementia compared to 0.5% of those in the younger group. Physical activity was associated with a lower probability of developing dementia (2.4% among those classified as active versus 3.2% among inactive participants).

The Fine and Gray models for the entire sample and the specific version for people≥55 years of age established total scores, from summing the scores for all the factors included, of 148 and 79 points respectively (Table 2). Unlike predictive models based on logistic regression, their calibration and discrimination indicators vary according to the length of follow-up. The calibration curves that compare the observed and predicted cases by quintile at 15, 20, and 25 years are shown in Fig. 1. A model is said to be calibrated if the number of expected cases is not significantly different from the number of observed cases.

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Fig. 1

Calibration plot for the entire population and those≥55 years of age by years of follow-up comparing observed and predicted cases at 15, 20 and 25 years of follow-up. Time, years after prediction.

Figure 2 and Supplementary Table 1 report the AUC and the Brier score throughout the fortnightly follow-up (5, 10, 15, 20, and 25 years). The AUC of the model for people over-55-year-olds was much higher (80.8%) than the overall AUC (68.5%) in the first 15 years of follow-up and remained that way in the subsequent follow-up. The Brier score is close to 0 in both models, although it rises slightly at 25 years of follow-up.

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Fig. 2

Area under the receiver operating characteristic curve throughout the follow-up in the model with the entire population and with people≥55 years of age. AUC, area under the curve

The score made it possible to differentiate three levels of risk of developing dementia in the model with the entire population (low < 8, moderate 8–11, and high > 11) and for those≥55 years (low < 12, moderate 12–16, and high > 16). Having classified the population, we obtained Fig. 3, which plots the cumulative incidence of dementia by the category of the score according to the years of follow-up (5, 10, 15, 20, or 25). In contrast, Fig. 4 presents separately both the probability of death and that of dementia for the two populations analyzed, disaggregated by risk group. This allowed us to see that the weight of the competing risk of death was greater than that of dementia, especially when the entire population is included. In the model with the entire population, the intermediate-risk group behaved like the low-risk group. In contrast, when selecting those≥55 years of age, the intermediate-risk group has a similar risk of death to the high-risk group and an intermediate risk of dementia.

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Fig. 3

Incidence of dementia for the different levels of scores by length of follow-up (5, 10, 15, 20, 25 years) in the whole population and in people≥55 years of age. t5, t10, t15, t20, t25 : 5, 10, 15, 20, 25 years of follow-up.

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Fig. 4

Probability of cumulative incidence of death and dementia in the whole population and people≥55 years of age according to level of risk. Probability, probability of having the event (dementia or death).

DISCUSSION

This study presents the first dementia risk score calculated in a southern European population in mid-life and followed up for 20 years. Its use allows early identification of individuals who could be a target population for the prevention of dementia based on the intensive control of risk factors. We are unable to compare our models with most risk prediction models since they address populations over 65 years of age [17, 21], while our score, like the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, analyzes a younger population [14]. It is difficult to establish which populations should be the focus of dementia prevention policies primarily due to the need to compromise between the optimal window for intervention and the ability to assess effectiveness [39, 40]. As Livingston et al. say, in the prevention of dementia, it is never too early or too late [41]. Generally, models have considered stratifying the risk from 65 years of age [17, 21]. They have the advantage that the older the age of the sample, the easier it is to measure the effectiveness of an intervention and the better the performance (predictive accuracy) of the model since it has more events for statistical analysis [19]. On the other hand, at older ages, it is biologically less viable to reverse or delay the neuropathological processes associated with dementia. That is, whether the pathological substrate is amyloid or vascular, the earlier the preventive intervention, the more effective it will be in delaying the progression to dementia. In practice, both early models (45–65 years) aimed at primary prevention in the entire population and later models (> 65 years) aimed at secondary prevention in individuals at higher risk will be necessary. As Sexton and Yaffe point out, preventive policies will differ depending on whether they try to slow down the transition from a healthy status to MCI or from the MCI stage to that of dementia [16].

When comparing our score with the CAIDE Dementia Risk Score, it is found that the protective and risk factors are similar, although the weight of each of them differs. Comparison with other dementia risk functions is more difficult because they are aimed at later stages. The external validation of these functions according to the literature has been disappointing when age is removed [17, 18]. As in our models, how age is included in the model is a key determinant of its predictive performance. Therefore, it is also suitable for use in the selection of target populations for multidomain preventive interventions such as the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) [5].

Our model achieved good predictive performance with AUCs of 80.8% at 15 years and at 20 years, which was based on its great discriminatory power when separating the population≥55 years of age into three risk groups for both dementia and death. Further, the calibration curves and the Brier score close to zero confirmed the good fit of the model by comparing its results with those observed. Licher et al. have highlighted the lack of dementia prediction models that take into account the competing risk of death and the relevance of their use to avoid overestimating the incidence of dementia [17, 42]. A strength of the Fine and Gray models is that they allowed us to analyze the performance of stratification into three groups separately in both competing risks (death and dementia). The two extreme risk groups (high and low) behaved consistently in the two models for both the incidence of dementia and death. On the other hand, in the model for people≥55 years of age, the incidence of dementia in the intermediate-risk group lay between the other two curves while it overlapped with the high-risk group for death.

Given the epidemiological differences between countries, dementia prevention policies must be adapted to local characteristics, which implies developing specific risk scores for the selection of target populations in each country [16]. Analyzing the differences in predictive performance between the two models that we have developed, we concluded that the score that makes the most sense to apply in our context is that of the population≥55 years of age. The general model weights age in such a way that it dilutes the weight of the rest of the risk factors. It indicates that below 55 years of age we do not need predictive risk stratification models since what matters is to closely monitor people who at that early age are smokers or have diabetes, hypertension, or hypercholesterolemia, while promoting physical, social, and intellectual activity in the whole population. On the contrary, the model in those≥55 years of age takes into account the weight of the other factors and makes it possible to identify higher risk profiles.

Most of the variables included are cardiovascular factors and their relationship with dementia is in line with that with heart health, confirming the well-known adage that “What is good for your heart is good for your brain” [43]. Long-term cohort analysis has shown a reduction in the incidence of dementia in recent years, associated with an improvement in cardiovascular factors [44]. In relation to this, a question that arises is whether these predictive models are taking into account a pattern of variables related to the vascular pathophysiology of dementia, but not to the amyloid cascade. As in all scores, years in education are also a protective factor in our model [14, 18]. Unlike the variables that measure cardiovascular risk, the protective effect of education is related to the improvement in cognitive reserve through a different pathophysiological mechanism [41, 45].