Dementia is a major public health challenge, yet existing prediction models often overlook sleep-related symptoms, despite their known links to cognitive decline.
Objective
To develop and validate a four-year Dementia Risk Score (DRS) incorporating self-reported sleep-related symptoms with demographic and clinical factors to predict all-cause dementia, including Alzheimer's disease.
Methods
Data from 3082 Korean adults aged 60–79 years were analyzed. Predictors were selected using LASSO regression and included in a multivariate logistic regression model. A point-based scoring system, the DRS, was constructed from the model coefficients. Internal validation was conducted using bootstrapping and a separate dataset.
Results
The DRS achieved robust predictive performance, with AUC values of 0.824 in the training set and 0.826 in the validation set. Key predictors included sleep disturbance, use of sleep medications, daytime dysfunction, leg discomfort, and urge to move legs.
Conclusions
The DRS provides a practical, scalable tool for predicting dementia risk, supporting community-based screening and early intervention. External validation is needed to confirm its broader applicability.
NicholsESteinmetzJDVollsetSE, et al.Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health2022; 7: e105–e125.
2.
World Health Organization. Dementia: a public health priority. Geneva, Switzerland: World Health Organization, 2012.
3.
BubuOMBrannickMMortimerJ, et al.Sleep, cognitive impairment, and Alzheimer’s disease: a systematic review and meta-analysis. Sleep2017; 40: zsw032.
4.
LinC-CChouC-HFanY-M, et al.Increased risk of dementia among sleep-related movement disorders: a population-based longitudinal study in Taiwan. Medicine (Baltimore)2015; 94: e2331.
5.
KimKYKimEHLeeM, et al.Restless leg syndrome and risk of all-cause dementia: a nationwide retrospective cohort study. Alzheimers Res Ther2023; 15: 46.
6.
XuWTanC-CZouJ-J, et al.Sleep problems and risk of all-cause cognitive decline or dementia: an updated systematic review and meta-analysis. J Neurol Neurosurg Psychiatry2020; 91: 236–244.
7.
WestwoodAJBeiserAJainN, et al.Prolonged sleep duration as a marker of early neurodegeneration predicting incident dementia. Neurology2017; 88: 1172–1179.
8.
SuhSWHanJWLeeJR, et al.Sleep and cognitive decline: a prospective nondemented elderly cohort study. Ann Neurol2018; 83: 472–482.
9.
IrwinMRVitielloMV. Implications of sleep disturbance and inflammation for Alzheimer’s disease dementia. Lancet Neurol2019; 18: 296–306.
10.
SpiraAPGonzalezCEVenkatramanVK, et al.Sleep duration and subsequent cortical thinning in cognitively normal older adults. Sleep2016; 39: 1121–1128.
11.
LoJCLohKKZhengH, et al.Sleep duration and age-related changes in brain structure and cognitive performance. Sleep2014; 37: 821.
12.
TononiGCirelliC. Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron2014; 81: 12–34.
13.
KivipeltoMNganduTLaatikainenT, et al.Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol2006; 5: 735–741.
14.
RawtaerIFengLYuenVHK, et al.A risk score for the prediction of neurocognitive disorders among community-dwelling Chinese older adults. Dement Geriatr Cogn Disord2016; 41: 348–358.
15.
AnsteyKJCherbuinNHerathPM, et al.A self-report risk index to predict occurrence of dementia in three independent cohorts of older adults: the ANU-ADRI. PLoS One2014; 9: e86141.
16.
WaltersKHardoonSPetersenI, et al.Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data. BMC Med2016; 14: 1–12.
17.
HanJWKimTHKwakKP, et al.Overview of the Korean longitudinal study on cognitive aging and dementia. Psychiatry Investig2018; 15: 767–774.
18.
BomboisSDeramburePPasquierF, et al.Sleep disorders in aging and dementia. J Nutr Health Aging2010; 14: 212–217.
19.
LiJVitielloMVGooneratneNS. Sleep in normal aging. Sleep Med Clin2022; 17: 161–171.
20.
JormAFJolleyD. The incidence of dementia: a meta-analysis. Neurology1998; 51: 728–733.
21.
DaviglusMLBellCCBerrettiniW, et al.NIH state-of-the-science conference statement: preventing Alzheimer’s disease and cognitive decline. NIH Consens State Sci Statements2010; 27: 1–30.
22.
CollinsGSReitsmaJBAltmanDG, et al.Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med2015; 162: 55–63.
23.
BuysseDJReynoldsCFIIIMonkTH, et al.The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res1989; 28: 193–213.
24.
SohnSIKimDHLeeMY, et al.The reliability and validity of the Korean version of the Pittsburgh Sleep Quality Index. Sleep Breath2012; 16: 803–812.
25.
Stiasny-KolsterKMayerGSchäferS, et al.The REM sleep behavior disorder screening questionnaire—a new diagnostic instrument. Mov Disord2007; 22: 2386–2393.
26.
YouSMoonH-JDoSY, et al.The REM sleep behavior disorder screening questionnaire: validation study of the Korean version (RBDQ-KR). J Clin Sleep Med2017; 13: 1429–1433.
27.
ChungFYegneswaranBLiaoP, et al.STOP Questionnaire: a tool to screen patients for obstructive sleep apnea. J Am Soc Anesthesiol2008; 108: 812–821.
28.
JeonHJBangYRYoonI-Y. A validation study on three screening questionnaires for obstructive sleep apnea in a Korean community sample. Sleep Breath2019; 23: 969–977.
29.
ChulKJoong-SaengCSung-WanK. Usefulness of Berlin and STOP questionnaires as a screening test for sleep apnea in Korea. Korean J Otolaryngol Head Neck Surg2010; 53: 768–772.
30.
AllenRPBurchellBJMacDonaldB, et al.Validation of the self-completed Cambridge-Hopkins questionnaire (CH-RLSq) for ascertainment of restless legs syndrome in a population survey. Sleep Med2009; 10: 1097–1100.
31.
KimT-JYoonJEParkJA, et al.Prevalence and characteristics of restless legs syndrome in Korean adults: a study in two independent samples of the general population. Neuroepidemiology2019; 52: 193–204.
32.
AllenRPBharmalMCallowayM. Prevalence and disease burden of primary restless legs syndrome: results of a general population survey in the United States. Mov Disord2011; 26: 114–120.
33.
KimJYParkJHLeeJJ, et al.Standardization of the Korean version of the Geriatric Depression Scale: reliability, validity, and factor structure. Psychiatry Investig2008; 5: 232.
34.
MillerMDParadisCFHouckPR, et al.Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res1992; 41: 237–248.
35.
LivingstonGHuntleyJSommerladA, et al.Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet2020; 396: 413–446.
36.
LeeJHLeeKULeeDY, et al.Development of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet (CERAD-K): clinical and neuropsychological assessment batteries. J Gerontol B Psychol Sci Soc Sci2002; 57: P47–P53.
37.
SheehanDVLecrubierYSheehanKH, et al.The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry1998; 59: 22–33; quiz 34–57.
38.
HanJYSeoEHYiD, et al.A normative study of total scores of the CERAD neuropsychological assessment battery in an educationally diverse elderly population. Int Psychogeriatr2014; 26: 1897–1904.
39.
SeoEHLeeDYLeeJH, et al.Total scores of the CERAD neuropsychological assessment battery: validation for mild cognitive impairment and dementia patients with diverse etiologies. Am J Geriatr Psychiatry2010; 18: 801–809.
40.
LeeDYLeeKULeeJH, et al.A normative study of the CERAD neuropsychological assessment battery in the Korean elderly. J Int Neuropsychol Soc2004; 10: 72–81.
41.
ChoiHJLeeDYSeoEH, et al.A normative study of the digit span in an educationally diverse elderly population. Psychiatry Investig2014; 11: 39.
42.
KimTHHuhYChoeJY, et al.Korean Version of frontal assessment battery: psychometric properties and normative data. Dement Geriatr Cogn Disord2010; 29: 363–370.
43.
HughesCPBergLDanzigerW, et al.A new clinical scale for the staging of dementia. Br J Psychiatry1982; 140: 566–572.
44.
WinbladBPalmerKKivipeltoM, et al.Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med2004; 256: 240–246.
45.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Washington, DC: American Psychiatric Association, 1994.
46.
PavlouMAmblerGSeamanSR, et al.How to develop a more accurate risk prediction model when there are few events. Br Med J2015; 351: h3868.
47.
TibshiraniR. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol1996; 58: 267–288.
48.
EnwereKNdukaEOgokeU. Comparative analysis of Ridge, Bridge and Lasso regression models in the presence of multicollinearity. IPS Intelligentsia Multidiscip J2023; 3: 1–8.
49.
SteyerbergEWVickersAJCookNR, et al.Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology2010; 21: 128–138.
50.
TeamRC. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2016, http://www.R-project.org/.
51.
NyholmJGhaziANGhaziSN, et al.Prediction of dementia based on older adults’ sleep disturbances using machine learning. Comput Biol Med2024; 171: 108126.
52.
YingX. An overview of overfitting and its solutions. J Phys Conf Ser2019; 1168: 022022.
53.
AlkhawaldehIMAlbalkhiINaswhanAJ. Challenges and limitations of synthetic minority oversampling techniques in machine learning. World J Methodol2023; 13: 373.
54.
LiJOgrodnikMDevineS, et al.Practical risk score for 5-, 10-, and 20-year prediction of dementia in elderly persons: Framingham Heart Study. Alzheimers Dement2018; 14: 35–42.
55.
FriedLPBorhaniNOEnrightP, et al.The cardiovascular health study: design and rationale. Ann Epidemiol1991; 1: 263–276.
56.
DawberTRKannelWB. The Framingham Study an epidemiological approach to coronary heart disease. Circulation1966; 34: 553–555.
57.
FeinleibMKannelWBGarrisonRJ, et al.The Framingham offspring study. Design and preliminary data. Prev Med1975; 4: 518–525.
58.
KarpF. Growing older in America: The Health & Retirement Study. Bethesda, MD: National Institute on Aging, National Institutes of Health, US Department of Health and Human Services, 2007.
59.
HaanMNMungasDMGonzalezHM, et al.Prevalence of dementia in older Latinos: the influence of type 2 diabetes mellitus, stroke and genetic factors. J Am Geriatr Soc2003; 51: 169–177.
60.
SchubertCRCruickshanksKJDaltonDS, et al.Prevalence of sleep problems and quality of life in an older population. Sleep2002; 25: 48–52.
61.
CrowleyK. Sleep and sleep disorders in older adults. Neuropsychol Rev2011; 21: 41–53.
62.
SindiSKåreholtIJohanssonL, et al.Sleep disturbances and dementia risk: a multicenter study. Alzheimers Dement2018; 14: 1235–1242.
63.
ChenP-LLeeW-JSunW-Z, et al.Risk of dementia in patients with insomnia and long-term use of hypnotics: a population-based retrospective cohort study. PLoS One2012; 7: e49113.
64.
TsapanouAGuYManlyJ, et al.Daytime sleepiness and sleep inadequacy as risk factors for dementia. Dement Geriatr Cogn Disord Extra2015; 5: 286–295.
65.
FermanTJBoeveBFSmithGE, et al.Inclusion of RBD improves the diagnostic classification of dementia with Lewy bodies. Neurology2011; 77: 875–882.
66.
AlshaerHHummelRMendelsonM, et al.Objective relationship between sleep apnea and frequency of snoring assessed by machine learning. J Clin Sleep Med2019; 15: 463–470.
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