Original Study| Volume 21, ISSUE 6, P864-871.e6, June 2020

Development and Validation of a Nomogram for Predicting the 6-Year Risk of Cognitive Impairment Among Chinese Older Adults



      Although some people with mild cognitive impairment may not suffer from dementia lifelong, about 5% of them will progress to dementia within 1 year in community settings. However, a general tool for predicting the risk of cognitive impairment was not adequately studied among older adults.


      Prospective cohort study.


      Community-living, older adults from 22 provinces in China.


      We included 10,066 older adults aged 65 years and above (mean age, 83.2 ± 11.1 years), with normal cognition at baseline in the 2002–2008 cohort and 9354 older adults (mean age, 83.5 ± 10.8 years) in the 2008–2014 cohort of the Chinese Longitudinal Healthy Longevity Survey.


      We measured cognitive function using the Chinese version of the Mini-Mental State Examination. Demographic, medical, and lifestyle information was used to develop the nomogram via a Lasso selection procedure using a Cox proportional hazards regression model. We validated the nomogram internally with 2000 bootstrap resamples and externally in a later cohort. The predictive accuracy and discriminative ability of the nomogram were measured by area-under-the-curves and calibration curves, respectively.


      Eight factors were identified with which to construct the nomogram: age, baseline of the Mini-Mental State Examination, activities of daily living and instrumental activities of daily living score, chewing ability, visual function, history of stroke, watching TV or listening to the radio, and growing flowers or raising pets. The area-under-the-curves for internal and external validation were 0.891 and 0.867, respectively, for predicting incident cognitive impairment. The calibration curves showed good consistency between nomogram-based predictions and observations.

      Conclusions and Implications

      The nomogram-based prediction yielded consistent results in 2 separate large cohorts. This feasible prognostic nomogram constructed using readily ascertained information may assist public health practitioners or physicians to provide preventive interventions of cognitive impairment.


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        • Schrag A.
        • Siddiqui U.F.
        • Anastasiou Z.
        • et al.
        Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: A cohort study.
        Lancet Neurol. 2017; 16: 66-75
        • Espeland M.A.
        • Carmichael O.
        • Yasar S.
        • et al.
        Sex-related differences in the prevalence of cognitive impairment among overweight and obese adults with type 2 diabetes.
        Alzheimers Dement. 2018; 14: 1184-1192
        • Salthouse T.A.
        Trajectories of normal cognitive aging.
        Psychol Aging. 2019; 34: 17-24
        • National Bureau of Statistics of China
        Statistical Communique of the People's Republic of China on the 2019 National Economic and Social Development [online].
        (Available at:)
        • Vauzour D.
        • Camprubi-Robles M.
        • Miquel-Kergoat S.
        • et al.
        Nutrition for the ageing brain: Towards evidence for an optimal diet.
        Ageing Res Rev. 2017; 35: 222-240
        • Cummings J.L.
        • Morstorf T.
        • Zhong K.
        Alzheimer's disease drug-development pipeline: Few candidates, frequent failures.
        Alzheimers Res Ther. 2014; 6: 37
        • Livingston G.
        • Sommerlad A.
        • Orgeta V.
        • et al.
        Dementia prevention, intervention, and care.
        Lancet. 2017; 390: 2673-2734
        • Locke D.E.C.
        • Ivnik R.J.
        • Cha R.H.
        • et al.
        Age, family history, and memory and future risk for cognitive impairment.
        J Clin Exp Neuropsyc. 2008; 31: 111-116
        • Song X.
        • Mitnitski A.
        • Rockwood K.
        Nontraditional risk factors combine to predict Alzheimer disease and dementia.
        Neurology. 2011; 77: 227-234
        • Exalto L.G.
        • Biessels G.J.
        • Karter A.J.
        • et al.
        Risk score for prediction of 10 year dementia risk in individuals with type 2 diabetes: A cohort study.
        Lancet Diabetes Endo. 2013; 1: 183-190
        • Li J.
        • Ogrodnik M.
        • Devine S.
        • et al.
        Practical risk score for 5-, 10-, and 20-year prediction of dementia in elderly persons: Framingham Heart Study.
        Alzheimers Dement. 2018; 14: 35-42
        • Licher S.
        • Leening M.J.G.
        • Yilmaz P.
        • et al.
        Development and validation of a dementia risk prediction model in the general population: An analysis of three longitudinal studies.
        Am J Psychiatry. 2019; 176: 543-551
        • Downer B.
        • Kumar A.
        • Veeranki S.P.
        • et al.
        Mexican-American dementia nomogram: Development of a dementia risk index for Mexican-American older adults.
        J Am Geriatr Soc. 2016; 64: e265-e269
        • Li C.I.
        • Li T.C.
        • Liu C.S.
        • et al.
        Risk score prediction model for dementia in patients with type 2 diabetes.
        Eur J Neurol. 2018; 25: 976-983
        • Yin Z.
        • Shi X.
        • Kraus V.B.
        • et al.
        Gender-dependent association of body mass index and waist circumference with disability in the Chinese oldest old.
        Obesity. 2014; 22: 1918-1925
        • Zeng Y.
        Toward deeper research and better policy for healthy aging-using the unique data of Chinese Longitudinal Healthy Longevity Survey.
        China Economic J. 2012; 5: 131-149
        • Tombaugh T.N.
        • McIntyre N.J.
        The Mini-Mental State Examination: A comprehensive review.
        J Am Geriatr Soc. 1992; 40: 922-935
        • Katzman R.
        • Zhang M.Y.
        • Ouang-Ya-Qu
        • et al.
        A Chinese version of the Mini-Mental State Examination: Impact of illiteracy in a Shanghai dementia survey.
        J Clin Epidemiol. 1988; 41: 971-978
        • Pezzotti P.
        • Scalmana S.
        • Mastromattei A.
        • et al.
        The accuracy of the MMSE in detecting cognitive impairment when administered by general practitioners: A prospective observational study.
        BMC Fam Pract. 2008; 9: 29
        • Cui G.H.
        • Yao Y.H.
        • Xu R.F.
        • et al.
        Cognitive impairment using education-based cutoff points for CMMSE scores in elderly Chinese people of agricultural and rural Shanghai China.
        Acta Neurol Scand. 2011; 124: 361-367
        • Zhang M.Y.
        • Katzman R.
        • Salmon D.
        • et al.
        The prevalence of dementia and Alzheimer's disease in Shanghai, China: Impact of age, gender, and education.
        Ann Neurol. 1990; 27: 428-437
        • Yang M.
        • Ding X.
        • Dong B.
        The measurement of disability in the elderly: A systematic review of self-reported questionnaires.
        J Am Med Dir Assoc. 2014; 15: 150-151
        • Naka O.
        • Anastassiadou V.
        • Pissiotis A.
        Association between functional tooth units and chewing ability in older adults: A systematic review.
        Gerodontology. 2014; 31: 166-177
        • Tibshirani R.
        Regression shrinkage and selection via the lasso: A retrospective.
        J R Stat Soc B. 2011; 73: 273-282
        • Pavlou M.
        • Ambler G.
        • Seaman S.R.
        • et al.
        How to develop a more accurate risk prediction model when there are few events.
        BMJ. 2015; 351: h3868
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement.
        BMC Med. 2015; 13: 1
        • Kamarudin A.N.
        • Cox T.
        • Kolamunnage-Dona R.
        Time-dependent ROC curve analysis in medical research: Current methods and applications.
        BMC Med Res Methodol. 2017; 17: 53
        • Fitzgerald M.
        • Saville B.R.
        • Lewis R.J.
        Decision curve analysis.
        JAMA. 2015; 313: 409-410
        • Hippisley-Cox J.
        • Coupland C.
        Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: Cohort study.
        BMJ. 2017; 358: j4208
        • Verhaeghen P.
        • Salthouse T.A.
        Meta-analyses of age-cognition relations in adulthood: Estimates of linear and nonlinear age effects and structural models.
        Psychol Bull. 1997; 122: 231-249
        • Marrie R.A.
        • Dawson N.V.
        • Garland A.
        Quantile regression and restricted cubic splines are useful for exploring relationships between continuous variables.
        J Clin Epidemiol. 2009; 62: 511-517
        • Iasonos A.
        • Schrag D.
        • Raj G.V.
        • Panageas K.S.
        How to build and interpret a nomogram for cancer prognosis.
        J Clin Oncol. 2008; 26: 1364-1370
        • Jessen F.
        • Wiese B.
        • Bickel H.
        • et al.
        Prediction of dementia in primary care patients.
        Plos One. 2011; 6: e16852
        • Seshadri S.
        • Beiser A.
        • Au R.
        • et al.
        Operationalizing diagnostic criteria for Alzheimer's disease and other age-related cognitive impairment—Part 2.
        Alzheimers Dement. 2011; 7: 35-52
        • Depp C.
        • Vahia I.V.
        • Jeste D.
        Successful aging: Focus on cognitive and emotional health.
        Annu Rev Clin Psychol. 2010; 6: 527-550
        • van Boxtel M.P.J.
        • Speckens A.E.
        Mindfulness, cognitive function and ‘successful ageing'.
        Tijdschr Gerontol Geriatr. 2014; 45: 137-143
        • Kuo H.
        • Leveille S.G.
        • Yu Y.
        • Milberg W.P.
        Cognitive function, habitual gait speed, and late-life disability in the National Health and Nutrition Examination Survey (NHANES) 1999–2002.
        Gerontology. 2007; 53: 102-110
        • Di Carlo A.
        • Baldereschi M.
        • Lamassa M.
        • et al.
        Daily function as predictor of dementia in cognitive impairment, no dementia (CIND) and mild cognitive impairment (MCI): An 8-year follow-up in the ILSA Study.
        J Alzheimers Dis. 2016; 53: 505-515
        • Lexomboon D.
        • Trulsson M.
        • Wårdh I.
        • Parker M.G.
        Chewing ability and tooth loss: Association with cognitive impairment in an elderly population study.
        J Am Geriatr Soc. 2012; 60: 1951-1956
        • Zhu X.
        • Qiu C.
        • Zeng Y.
        • Li J.
        Leisure activities, education, and cognitive impairment in Chinese older adults: A population-based longitudinal study.
        Int Psychogeriatr. 2017; 29: 727-739