http://orcid.org/0000-0002-7462-0144
Correction
14 Nov 2019: Park KM, Sung JM, Kim WJ, An SK, Namkoong K, et al. (2019) Correction: Population-based dementia prediction model using Korean public health examination data: A cohort study. PLOS ONE 14(11): e0225553. https://doi.org/10.1371/journal.pone.0225553 View correction
Figures
Abstract
The early identification and prevention of dementia is important for reducing its worldwide burden and increasing individuals’ quality of life. Although several dementia prediction models have been developed, there remains a need for a practical and precise model targeted to middle-aged and Asian populations. Here, we used national Korean health examination data from adults (331,126 individuals, 40–69 years of age, mean age: 52 years) from 2002–2003 to predict the incidence of dementia after 10 years. We divided the dataset into two cohorts to develop and validate of our prediction model. Cox proportional hazards models were used to construct dementia prediction models for the total group and sex-specific subgroups. Receiver operating characteristics curves, C-statistics, calibration plots, and cumulative hazards were used to validate model performance. Discriminative accuracy as measured by C-statistics was 0.81 in the total group (95% confidence interval (CI) = 0.81 to 0.82), 0.81 in the male subgroup (CI = 0.80 to 0.82), and 0.81 in the female subgroup (CI = 0.80 to 0.82). Significant risk factors for dementia in the total group were age; female sex; underweight; current hypertension; comorbid psychiatric or neurological disorder; past medical history of cardiovascular disease, diabetes mellitus, or hypertension; current smoking; and no exercise. All identified risk factors were statistically significant in the sex-specific subgroups except for low body weight and current hypertension in the female subgroup. These results suggest that public health examination data can be effectively used to predict dementia and facilitate the early identification of dementia within a middle-aged Asian population.
Citation: Park KM, Sung JM, Kim WJ, An SK, Namkoong K, Lee E, et al. (2019) Population-based dementia prediction model using Korean public health examination data: A cohort study. PLoS ONE 14(2): e0211957. https://doi.org/10.1371/journal.pone.0211957
Editor: Perla Werner, University of Haifa, ISRAEL
Received: September 11, 2018; Accepted: January 24, 2019; Published: February 12, 2019
Copyright: © 2019 Park et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Korean legal restrictions prohibit the authors from making the data publicly available, and the authority implemented the restrictions are the NHIS (National Health Insurance Service), one of the government agencies of the Republic of Korea. NHIS provides limited portions of anonymized data to researchers for the purpose of public interest. However, they exclusively provide data to researchers with direct contact and agreement of policy of NHIS. Redistribution of the data is not permitted for the interested researchers. We requested cohort data of dementia population for the purpose of public interest which is providing information about primary and secondary prevention of dementia by the dementia prediction model (No. NHIS-2017-2-279). We made our request to NHIS by stating our purpose of research and agreement of their policies. All interested researchers can request NHIS data by accessing the following URL: https://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do. Requests for data access may also be sent to Haeryoung Park, Information analysis department, Big data operation room, NHIS. Tel: +82-33-736-2430. E-mail: lumen77@nhis.or.kr.
Funding: This research was supported by the Leading Foreign Research Institute Recruitment Program (No. 2012027176 to H. Chang) and the Basic Science Research Program (No. 2017R1A2B3008214 to E. Lee) through the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning, and the Korean Mental Health Technology R& Project, Ministry of Health & Welfare, Republic of Korea [HM15C0995 to E. Lee]. The funders plays no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Dementia is a major public health concern within the older population. In 2012, the World Health Organization (WHO) estimated that 36 million people were living with dementia. With demographic changes and longer life expectancies, more than 115 million people are expected to have dementia by 2050. At this rate, the total cost of the disease will rise from €515 billion to €16 trillion per year [1]. At the individual level, dementia shortens life expectancy and decreases quality of life. Dementia is estimated to contribute 11.2% of years lived with disability among individuals >60 years of age compared with 9.5% for stroke and 5.0% for cardiovascular disease [2].
Research on dementia treatment, including that for Alzheimer’s dementia (AD), has made remarkable progress, but treatment efficacy is still limited [3]. Thus, the early identification of individuals at a high risk of dementia and correction of modifiable risk factors are crucial. About one-third of cases of AD, which is the most frequent type of dementia, is attributable to modifiable risk factors [4]. It is assumed that a 10–25% reduction in modifiable risk factors could prevent 3 million cases of AD, and delaying the onset of dementia by 1 year could lower the incidence of dementia by more than 9 million over the next 40 years [5]. There is also a close relationship between AD and vascular dementia (VD), which is the second most common type of dementia [6,7]. Epidemiological studies show that AD and VD share common risk factors such as hypertension, diabetes mellitus, smoking, hypercholesterolemia, and age [8–10]. Pathological studies also suggest overlap between AD and vascular pathology in older adults with dementia [11,12]. Therefore, the development of a prediction model for all-cause dementia, including both AD and VD, could have several practical benefits.
Some existing dementia prediction models use clinic-based criteria to detect mild cognitive impairment, whereas others use more comprehensively constructed algorithms including information such as demographic (e.g., age, sex, education) and health-related (e.g., comorbid diseases, laboratory data, lifestyle information) characteristics [13]. Such models can be broadly categorized into demographic-only models, cognitive-based models, health risk models, risk models using genetic information, and multi-variable models [14]. Although prediction models using genetic or neuroimaging data can be particularly informative [15,16], they are too expensive for primary prevention.
Also, most existing dementia prediction models are designed for an older population >65 years of age. Around 60–70% of aging-related dementia is usually diagnosed after the age of 65 [17], but the disease process may start much earlier. To allow early identification of high-risk individuals and modification of risk factors, dementia prediction models must be developed for a mid-life population. Furthermore, few existing prediction models target Asian populations, especially Koreans. Therefore, we sought to develop a practical and precise dementia prediction model for a Korean middle-aged population.
In South Korea, the National Health Insurance Service (NHIS) conducts a health examination for all citizens >40 years of age every 2 years. The NHIS stores all health examination data and national records of medical utilization and prescriptions for billing purposes. For research purposes, the NHIS constructs the Health Screening Cohort (NHIS-HEALS) dataset through anonymization [18]. More than 500,000 randomly sampled individuals are included in NHIS-HEALS, representing 10% of the entire Korean population. The objective of this study was to develop a dementia prediction model using health examination data from NHIS-HEALS for middle-aged Koreans.
Materials and methods
Study population and sample selection
We used baseline NHIS-HEALS data from the years 2002 and 2003. As our primary outcome was the incidence of all-cause dementia, we identified the following medical diagnostic codes according to the International Classification of Diseases 10th edition (ICD-10): dementia in Alzheimer’s disease (F00), vascular dementia (F01), dementia in Pick’s disease (F02), unspecified dementia (F03), and Alzheimer’s disease (G30). We selected these codes as our target variable and excluded cases with codes for dementia at baseline.
A total of 473,036 individuals (259,259 males and 213,777 females) between the ages of 40 and 69 years with no history of any dementia at baseline were included. We included the following variables in our analysis: age, sex, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose, past medical history (i.e., cardiovascular disease, diabetes, or hypertension), current comorbid psychiatric or neurologic disorders, current smoking, and exercise. After reviewing previous studies on risk factors for dementia, these variables were chosen by expert psychiatrists (SK An, K Namkoong, and E Lee). Explanatory variables, including height, weight, SBP, and DBP, were obtained directly from the health examination. Other variables, including age, medical history, smoking, and exercise, were obtained from questionnaires administered during the health examination. BMI was calculated as weight in kilograms divided by the square of height in meters. In addition, we obtained information on comorbid psychiatric or neurologic diseases in NHIS-HEALS at baseline. We excluded individuals with a diagnosis of dementia at baseline or who died within the first 2 years of observation.
For construction of a dementia prediction model, we divided the baseline data into two datasets: a development (70%) and a validation (30%) dataset. The development dataset was used to fit the parameters of classifiers to establish a prediction model. Validation datasets were used to assess the accuracy of the final model. We used simple random sampling to divide the two datasets regardless of the occurrence of dementia (Fig 1). For missing data, the mean imputation was used for the whole study sample at every time they gathered by using SAS MI (multiple imputation) procedure [19]. This study was approved by the Institutional Review Board of the National Cancer Center, Korea (IRB No. 4-2016-0383). The need for participant consent was waived by the ethics committee because this study involved routinely collected medical data that were anonymously managed at all stages, including data cleaning and statistical analysis.
Statistical analysis and prediction model development
The primary outcome of this study was the incidence of all-cause dementia including dementia in Alzheimer’s disease (F00), vascular dementia (F01), dementia in Pick’s disease (F02), unspecified dementia (F03), or Alzheimer’s disease (G30). We defined this event as occurring when an individual was first diagnosed with dementia corresponding to an ICD-10 code. We followed individuals from the first health examination date during the baseline period (2002–2003) until December 31, 2013. The time to event was defined as the time from the first examination date to the diagnosis date. If an individual was not diagnosed with dementia during the follow-up period, the time to event was defined as the time from the first examination date to the most recent date among the last health examination date, the last hospital visit date, and the date of death. Cases of loss to follow up or death without a dementia diagnosis were considered as cases without a dementia event, and were censored.
Cox proportional hazards models were used to construct the total and sex-specific subgroup models using baseline data. The time to event was 10.24 ± 1.73 years (mean ± standard deviation) in the total group, 10.26 ± 1.78 years in the male subgroup, and 10.21 ± 1.66 years in the female subgroup in the development cohort and 10.23 ± 1.73 years in the total group, 10.26 ± 1.79 years in the male subgroup, and 10.20 ± 1.67 years in the female subgroup in the validation cohort.
Explanatory variables included age (5 years), sex (male or female), BMI (underweight: BMI < 18.5, normal: 18.5 ≤ BMI < 23, overweight: 23 ≤ BMI < 25, obese: BMI ≥ 25 hypertension (normotensive: SBP < 120mmHg and DBP < 80mmHg, prehypertensive: 120mmHg ≤ SBP < 140mmHg and 80mmHg ≤ DBP < 90mmHg, hypertensive I: 140mmHg ≤ SBP < 160 mmHg and 90 mmHg ≤ DBP < 100 mmHg, hypertensive II: SBP ≥ 160 mmHg and DBP ≥ 100 mmHg), current smoking status (currently smoking or has smoked >100 cigarettes in their life), regular exercise (>30 minutes a week), past medical history (cardiovascular disease, diabetes mellitus, or hypertension), and comorbid psychiatric or neurologic disorders. Comorbid psychiatric disorders included the following diagnoses: organic amnesic syndrome not induced by alcohol or other psychoactive substances (F04), delirium not induced by alcohol or other psychoactive substances (F05); other mental disorders due to brain damage and dysfunction or to physical disease (F06); personality and behavioral disorders due to brain disease, damage, or dysfunction (F07); unspecified organic or symptomatic mental disorders (F09); schizophrenia, schizotypal, or delusional disorders (F2x); mood (affective) disorders (F3x); mental retardation (F7x); and disorders of psychological development (F8x). Neurologic disorders included the following diagnoses: inflammatory diseases of the central nervous system (G0x), systemic atrophies primarily affecting the central nervous system (G1x), extrapyramidal and movement disorders (G2x), other degenerative diseases of the nervous system not elsewhere classified (G31), other degenerative disorders of the nervous system in diseases classified elsewhere (G32), multiple sclerosis (G35), other acute disseminated demyelination (G36), other demyelinating diseases of the central nervous system (G37), and episodic and paroxysmal disorders (G4x).
Cases with missing variables were included in analysis using the fully conditional specification (FCS) method, a multiple imputation algorithm, to compensate for the missing values. Because the FCS approach imputes data on a variable-by-variable basis by specifying an imputation model per variable, this allows tremendous flexibility in creating multivariate models.
To develop the dementia prediction model, Cox proportional hazards regression was performed to analyze the onset of dementia. The probability of dementia (P) at t years for an individual with K risk factors was estimated as follows:
where
is the survival rate at the mean value of the risk factor at 10 years,
are the regression coefficients,
represent an individual’s risk factors at baseline, and
are the mean values of the risk factors in NHIS-HEALS.
To validate the dementia prediction model, we evaluated model performance by measuring its ability to discriminate between event and non-event cases using receiver operating characteristic (ROC) curves, C-statistics, calibration plots, and cumulative hazards. ROC curves and C-statistics were used to evaluate the ability of the model to predict a binary outcome [20]. We performed discrimination analysis of the model to assess its performance. We also used bootstrap method for the calculation of area under the cover (AUC) to estimate corresponding confidence intervals. Calibration plot is a method about the estimated probabilities and meant to present how well the classifier is calibrated [21]. In this study, it was used to determine the similarity of the actual target variable to the predicted variable [22]. Hosmer-Lemeshow (H-L) tests were used for calibration, which was performed by splitting the observations into five equal-sized risk groups and comparing the observed proportions of event outcomes to the mean predicted proportions of events within each group [23]. Lastly, we calculated cumulative hazards for the five risk groups divided by their prediction probabilities. All statistical analyses were performed using SAS 9.4 (SAS Institute, North Carolina, USA) and R version 3.4 (The R Foundation for Statistical Computing, Vienna, Austria).
Results
Table 1 shows baseline characteristics of the development cohort from NHIS-HEALS (40–69 years of age), including the total group and sex-specific subgroups. Of the total 331,126 individuals (181,500 males and 149,626 females) in the development cohort, 15,501 (6,664 males and 8,837 females) were diagnosed with dementia, with an event rate of 4.7% (3.7% for males and 6.0% for females) during 10 years of follow up (S1 Table). Baseline characteristics of the validation cohort, including the total group and sex-specific subgroups, are shown in S2 Table. All characteristics exhibited significant sex differences in both the development and validation cohorts.
We calculated hazard ratios for dementia for the total group (Table 2) and sex-specific subgroups (S3 Table) to construct prediction models using Cox proportional hazards regression analysis. ROC curves for the total group and sex-specific subgroups are presented in Fig 2. Discriminative accuracy measured by C-statistics was 0.81 for the total group (95% confidence interval (CI) = 0.81 to 0.82), 0.81 for the male subgroup (CI = 0.80 to 0.82), and 0.81 in the female subgroup (CI = 0.80 to 0.82). Further discrimination analyses can be seen in S4 Table. Concordance measured by AUC of 1000 times of bootstrap-estimated value was 0.81 for the total group (CI = 0.81 to 0.81), 0.81 for the male subgroup (CI = 0.81 to 0.81), 0.81 for the female subgroup (CI = 0.81 to 0.81). H-L tests for the calibration plots were performed for the total group and sex-specific subgroups (S1 and S2 Figs). We also calculated cumulative hazard ratios for five risk groups (S3 Fig).
Our prediction model identified the following risk factors for dementia in the total group: age; female sex; underweight (BMI < 18.5); current hypertension (SBP ≥ 120 mmHg and DBP ≥ 80 mmHg); any comorbid psychiatric or neurological disorder; any past medical history of cardiovascular disease, diabetes mellitus, or hypertension; current smoking; and no exercise (Fig 3). In the male subgroup, all identified risk factors in the total group analysis were statistically significant (S4 Fig). Although the hazard ratios in the female subgroup were similar to those in the total and male subgroups, underweight and current hypertension did not reach statistical significance (S4 Fig).
Discussion
We used national health examination data from Korean individuals aged 40–69 years to predict the development of dementia over 10 years. Using a development cohort, we successfully constructed a dementia prediction model with a good discrimination index comparable to that in some previous cohort studies [24–26]. In addition, with a validation cohort, we validated our prediction model using ROC curves, C-statistics, calibration plots, and cumulative hazards, the results of which demonstrate good performance of the model.
The discriminative accuracy of our model was constructed from health examination data obtained by simple laboratory tests, self-report questionnaires, and brief interviews. This good discriminative accuracy of our model is comparable to or better than that of other multi-variable or demographic data-only dementia prediction models that have discriminative accuracies ranging from 0.49 to 0.89 [14]. Therefore, in terms of balancing discriminative accuracy and efficiency of data collection [27], the present model may be desirable for the early detection of high-risk individuals. The finding of this study can be used for patient education about future dementia risk, especially middle-aged individuals who have several risk factors which found related to dementia occurrence.
Our cohort was consisted of middle-aged individuals from South Korea, who were expected to undergo a health examination every 2 years. Thus, this cohort represents the characteristics of the total population of middle-aged Koreans. Population-based studies have several advantages [28]. In addition to providing prevalence rates of relevant variables in the reference population, they allow comparisons with future cross-sectional studies. They are also ideal for carrying out unbiased evaluations of relationships between variables. Therefore, our model allows the expansion and application of follow-up studies of populations in Korea or other countries.
To our knowledge, we are the first to develop a dementia prediction model for Koreans. Also, this is the first model developed using simple health examination data from Asians, although two other dementia prediction models already exist for Asians [29,30]. One model using data from Japanese adults aged 60–79 years reports risk factors similar to those in our study [30]. However, the primary goal of this Japanese study was to examine the effect of the APOE allele on dementia onset, which had a very different focus than that of our study. The other model, which was modified from the one developed for non-Asian people, was found to work well for Chinese older adults [29]. However, there is still a lack of studies examining whether dementia prediction models for certain ethnic groups are applicable to other ethnic groups. In addition, no studies have examined the effect of ethnicity on dementia risk factors.
Another strength of our model is that it targets a middle-aged population. To allow the early identification and modification of risk factors, it would be optimal to develop a dementia prediction model for middle-aged people [31]. There are a few comparable multi-variable prediction models targeting middle-aged populations [24,26,31]. All the models were constructed using data from non-Asian people and had discriminative accuracies between 0.7 and 0.8. One was a model constructed using level of education, occupational attainment, and APOE status [31]. The other two were CAIDE risk score models constructed using simple demographic data and health-related features [24,26]. One CAIDE risk score model identifies age, low education, hypertension, hypercholesterolemia, and obesity as predictive of AD [26]. Although some of our risk factors, such as age and hypertension, are similar to those in CAIDE risk score models for middle-aged cohorts, the predictive value of BMI differs among models [24,26]. In the present study, a BMI < 18.5 was a risk factor, whereas in the original CAIDE study, a BMI > 30 was predictive of dementia [26]. Furthermore, in the model that predicted dementia using CAIDE risk scores in a United States middle-aged cohort, obesity was not a pronounced risk factor [24]. Among dementia prediction studies among older adults (i.e., >65 years of age), some report obesity as a risk factor [32,33], whereas others report a low BMI as a risk factor [34]. According to a 2016 WHO report [35], average BMI differs between Asia and the United States or Europe. In the United States, United Kingdom, and Australia, the average BMI for adults >18 years of age is 28.9 (28.5–29.3), 27.1 (26.6–27.6), and 27.1 (26.8–27.5), respectively, whereas the average BMI for Korean adults is 23.8 (23.5–24.0). Moreover, the Asia-Pacific classification of BMI has lower cutoffs for overweight and obese categories compared with the WHO classification [36]. Thus, the low obesity rate in our cohort may have contributed to no association between obesity and dementia onset. Furthermore, meta-analyses suggest a possibility of a U-shaped relationship between BMI and dementia in middle-aged populations [37,38]. A recent prospective study conducted in a French cohort shows that rapid weight loss in people >65 years of age can contribute to the development of dementia as a preclinical change or a result of malnutrition [39]. BMI is considered a modifiable risk factor along with smoking and alcohol drinking. However, further studies are needed to determine the optimal modification of BMI and to compare the association of BMI with dementia between different countries. The effects of risk factor modification on the development of dementia should also be investigated prospectively.
This study has some limitations. First, we did not include measurements of cognitive function. Although we intended to predict dementia without any measurement of cognitive function, gathering this information could have allowed us to compare the performance of our model with that of previous models and to further validate its predictability. Second, we targeted all-cause dementia, not specifically AD or VD. Thus, our prediction model using health examination data is not appropriate for identifying or predicting risk factors that could contribute to the occurrence of specific dementias. However, AD and VD share several risk factors, and most cases of dementia involve brain damage with a neurodegenerative or vascular insult [40]. Therefore, our model could be used for predicting overall dementia without specifying its type. Third, the follow-up period was only 10 years, which is relatively short considering that our targeted population was middle-aged people. We will continue to follow up this population and conduct further studies to overcome this limitation.
Conclusions
Our results suggest that the dementia prediction model can be constructed using information from simple, routine health examinations of middle-aged people and can be used to detect individuals with a high risk of developing dementia and administer early interventions via lifestyle modifications.
Supporting information
S1 Table. Dementia event rate in the development and validation cohorts (7:3).
https://doi.org/10.1371/journal.pone.0211957.s001
(DOCX)
S2 Table. Baseline characteristics of the validation cohort.
https://doi.org/10.1371/journal.pone.0211957.s002
(DOCX)
S3 Table. Hazard ratios for baseline variables in the development cohort by sex.
https://doi.org/10.1371/journal.pone.0211957.s003
(DOCX)
S4 Table. Model performance of dementia prediction.
https://doi.org/10.1371/journal.pone.0211957.s004
(DOCX)
S2 Fig. Calibration plots for sex-specific subgroups.
https://doi.org/10.1371/journal.pone.0211957.s006
(TIF)
S4 Fig. Hazard ratios for risk factors in sex-specific subgroups.
https://doi.org/10.1371/journal.pone.0211957.s008
(TIF)
References
- 1. Ritchie CW, Molinuevo JL, Truyen L, Satlin A, Van der Geyten S, Lovestone S, et al. Development of interventions for the secondary prevention of Alzheimer's dementia: the European Prevention of Alzheimer's Dementia (EPAD) project. Lancet Psychiatry. 2016;3: 179–186. pmid:26683239
- 2. Qiu C, Kivipelto M, von Strauss E. Epidemiology of Alzheimer's disease: occurrence, determinants, and strategies toward intervention. Dialogues Clin Neurosci. 2009;11: 111–128. pmid:19585947
- 3. Szeto JY, Lewis SJ. Current Treatment Options for Alzheimer's Disease and Parkinson's Disease Dementia. Curr Neuropharmacol. 2016;14: 326–338. pmid:26644155
- 4. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer's disease: an analysis of population-based data. Lancet Neurol. 2014;13: 788–794. pmid:25030513
- 5. Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. 2011;10: 819–828. pmid:21775213
- 6. Roman GC, Nash DT, Fillit H. Translating current knowledge into dementia prevention. Alzheimer Dis Assoc Disord. 2012;26: 295–299. pmid:22710935
- 7. Attems J, Jellinger KA. The overlap between vascular disease and Alzheimer's disease—lessons from pathology. BMC Med. 2014;12: 206. pmid:25385447
- 8. Casserly I, Topol E. Convergence of atherosclerosis and Alzheimer's disease: inflammation, cholesterol, and misfolded proteins. Lancet. 2004;363: 1139–1146. pmid:15064035
- 9. Gorelick PB, Scuteri A, Black SE, Decarli C, Greenberg SM, Iadecola C, et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the american heart association/american stroke association. Stroke. 2011;42: 2672–2713. pmid:21778438
- 10. Meng XF, Yu JT, Wang HF, Tan MS, Wang C, Tan CC, et al. Midlife vascular risk factors and the risk of Alzheimer's disease: a systematic review and meta-analysis. J Alzheimers Dis. 2014;42: 1295–1310. pmid:25024338
- 11. Launer LJ, Hughes TM, White LR. Microinfarcts, brain atrophy, and cognitive function: the Honolulu Asia Aging Study Autopsy Study. Ann Neurol. 2011;70: 774–780. pmid:22162060
- 12. Schneider JA, Bennett DA. Where vascular meets neurodegenerative disease. Stroke. 2010;41: S144–146. pmid:20876491
- 13. Stephan BC, Kurth T, Matthews FE, Brayne C, Dufouil C. Dementia risk prediction in the population: are screening models accurate? Nat Rev Neurol. 2010;6: 318–326. pmid:20498679
- 14. Tang EY, Harrison SL, Errington L, Gordon MF, Visser PJ, Novak G, et al. Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review. PLoS One. 2015;10: e0136181. pmid:26334524
- 15. Barnes DE, Covinsky KE, Whitmer RA, Kuller LH, Lopez OL, Yaffe K. Predicting risk of dementia in older adults: The late-life dementia risk index. Neurology. 2009;73: 173–179. pmid:19439724
- 16. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA. 2010;303: 1832–1840. pmid:20460622
- 17. Jack CR Jr., Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9: 119–128. pmid:20083042
- 18. Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, et al. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open. 2017;7: e016640. pmid:28947447
- 19.
SAS Institute Inc. SAS/STAT® 14.1 User’s Guide: The MI Procedure [cited 26 Dec 2018]. Database: User's Guide [Internet]. Available from: https://support.sas.com/documentation/onlinedoc/stat/141/mi.pdf.
- 20. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39: 561–577. pmid:8472349
- 21. Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clinical chemistry. 2008;54: 17–23. pmid:18024533
- 22. Vuk M, Curk Tz. ROC Curve, Lift Chart and Calibration Plot. Metodoloˇski zvezki. 2006;3: 89–108.
- 23. Fenlon C, O'Grady L, Doherty ML, Dunnion J. A discussion of calibration techniques for evaluating binary and categorical predictive models. Prev Vet Med. 2018;149: 107–114. pmid:29290291
- 24. Exalto LG, Quesenberry CP, Barnes D, Kivipelto M, Biessels GJ, Whitmer RA. Midlife risk score for the prediction of dementia four decades later. Alzheimers Dement. 2014;10: 562–570. pmid:24035147
- 25. Exalto LG, Biessels GJ, Karter AJ, Huang ES, Katon WJ, Minkoff JR, et al. Risk score for prediction of 10 year dementia risk in individuals with type 2 diabetes: a cohort study. Lancet Diabetes Endocrinol. 2013;1: 183–190. pmid:24622366
- 26. Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol. 2006;5: 735–741. pmid:16914401
- 27. Hou XH, Feng L, Zhang C, Cao XP, Tan L, Yu JT. Models for predicting risk of dementia: a systematic review. J Neurol Neurosurg Psychiatry. 2018. 2018/06/30. doi: pmid:29954871
- 28. Szklo M. Population-based cohort studies. Epidemiol Rev. 1998;20: 81–90. pmid:9762511
- 29. Rawtaer I, Feng L, Yuen VH, Li J, Chong MS, Lim WS, et al. A Risk Score for the Prediction of Neurocognitive Disorders among Community-Dwelling Chinese Older Adults. Dement Geriatr Cogn Disord. 2016;41: 348–358. pmid:27433801
- 30. Ohara T, Ninomiya T, Kubo M, Hirakawa Y, Doi Y, Hata J, et al. Apolipoprotein genotype for prediction of Alzheimer's disease in older Japanese: the Hisayama Study. J Am Geriatr Soc. 2011;59: 1074–1079. pmid:21649613
- 31. Vuoksimaa E, Rinne JO, Lindgren N, Heikkila K, Koskenvuo M, Kaprio J. Middle age self-report risk score predicts cognitive functioning and dementia in 20–40 years. Alzheimers Dement (Amst). 2016;4: 118–125.
- 32. Anstey KJ, Cherbuin N, Herath PM. Development of a new method for assessing global risk of Alzheimer's disease for use in population health approaches to prevention. Prev Sci. 2013;14: 411–421. pmid:23319292
- 33. Hayden KM, Zandi PP, Lyketsos CG, Khachaturian AS, Bastian LA, Charoonruk G, et al. Vascular risk factors for incident Alzheimer disease and vascular dementia: the Cache County study. Alzheimer Dis Assoc Disord. 2006;20: 93–100. pmid:16772744
- 34. Barnes DE, Beiser AS, Lee A, Langa KM, Koyama A, Preis SR, et al. Development and validation of a brief dementia screening indicator for primary care. Alzheimers Dement. 2014;10: 656–665 e651. pmid:24491321
- 35.
Mean body mass index trends among adults, age-standardized (kg/m²): Estimates by country. World Health Organization; 2016.
- 36. Pan WH, Yeh WT. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pac J Clin Nutr. 2008;17: 370–374. pmid:18818155
- 37. Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obes Rev. 2011;12: e426–437. pmid:21348917
- 38. Albanese E, Launer LJ, Egger M, Prince MJ, Giannakopoulos P, Wolters FJ, et al. Body mass index in midlife and dementia: Systematic review and meta-regression analysis of 589,649 men and women followed in longitudinal studies. Alzheimers Dement (Amst). 2017;8: 165–178.
- 39. Wagner M, Helmer C, Tzourio C, Berr C, Proust-Lima C, Samieri C. Evaluation of the Concurrent Trajectories of Cardiometabolic Risk Factors in the 14 Years Before Dementia. JAMA Psychiatry. 2018. 2018/07/26. doi: pmid:30043038
- 40. Mangialasche F, Kivipelto M, Solomon A, Fratiglioni L. Dementia prevention: current epidemiological evidence and future perspective. Alzheimers Res Ther. 2012;4: 6. pmid:22339927