Restricted accessResearch articleFirst published online 2025-11
Development and validation of a predictive nomogram for mild cognitive impairment in middle-aged and elderly populations: A multi-module analysis integrating air quality and sociodemographic factors
Growing evidence links air pollution to mild cognitive impairment (MCI), yet existing models often overlook environmental exposures. We developed a novel MCI nomogram integrating air pollution, sociodemographic, and clinical predictors for Chinese adults ≥ 45 years.
Objective
To develop and validate a personalized MCI risk assessment tool incorporating sociodemographic, clinical, and environmental factors.
Methods
Using the 2015 CHARLS cohort (n = 7702), we built two MCI prediction models with city- and county-level air pollution exposures. Model performance was assessed via discrimination (C-index, ROC), calibration, clinical utility (decision curves), predictive performance (net reclassification improvement, NRI and integrated discrimination improvement, IDI).
Results
This study analyzed 7702 participants, randomly split into training (n = 5391) and validation (n = 2311) groups. Uni-variate analysis identified MCI risk in age ≥75 years, private medical insurance, married individuals and males. Multivariate analysis in Model 2 identified 13 key factors associated with MCI, among them age ≥75 years (OR = 5.437, 95%CI: 3.524–8.388, p < 0.001), private medical insurance (OR = 4.994, 95%CI: 2.340–11.337, p = 0.002), being mental disorders (OR = 2.210, 95%CI: 1.217–4.014, p < 0.001), males (OR = 0.638, 95%CI: 0.493–0.824, p = 0.04), PM10 (OR = 1.059, 95%CI: 1.051–1.067, p < 0.001) and PM2.5 (OR = 1.025, 95%CI: 1.013 −1.038, p < 0.001) as strongest predictors. Model 2 outperformed Model 1 with higher discrimination, better calibration, greater clinical utility and improved accuracy.
Conclusions
The validated nomogram enables individualized MCI risk stratification, supporting targeted community prevention strategies.
LinSChenM. Gender-specific impact of cognitive impairment on all-cause mortality in older persons: a meta-analysis. Exp Gerontol2022; 10: 111–118.
4.
YuanZJGuoLYZhangY, et al.A systematic review of the prevalence and influencing factors of mild cognitive impairment in Chinese elderly population. J Jining Med Univ2022; 45: 183–188.
5.
WangXZhouSYeN, et al.Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal. BMC Geriatr2024; 24: 531–542.
6.
ValdiglesiasVSánchez-FloresMMasedaA, et al.Immune biomarkers in older adults: role of physical activity. J Toxicol Environ Health A2017; 80: 605–620.
7.
RizzoMRDi MeoIPolitoR, et al.Cognitive impairment and type 2 diabetes mellitus: focus of SGLT2 inhibitors treatment. Pharmacol Res2022; 176: 106–121.
8.
SrikanthVSinclairAJHill-BriggsF, et al.Type 2 diabetes and cognitive dysfunction-towards effective management of both comorbidities. Lancet Diabetes Endocrinol2020; 8: 535–545.
9.
NagarajanMMaadurshniGBManivannanJ, et al.Systems toxicology approach explores target-pathway relationship and adverse health impacts of ubiquitous environmental pollutant bisphenol A. J Toxicol Environ Health A2021; 85: 217–229.
10.
WangCXuJYangL, et al.Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China pulmonary health [CPH] study): a national cross-sectional study. Lancet2018; 391: 1706–1717.
11.
FatmiZCoggonD. Coronary heart disease and household air pollution from use of solid fuel: a systematic review. Br Med Bull2016; 118: 91–109.
12.
ChenHXuYRappoldA, et al.Effects of ambient ozone exposure on circulating extracellular vehicle microRNA levels in coronary artery disease patients. J Toxicol Environ Health A2020; 83: 351–362.
13.
SantistebanMMIadecolaCCarnevaleD, et al.Hypertension, neurovascular dysfunction, and cognitive impairment. Hypertension2023; 80: 22–34.
14.
WangYYeCKongL, et al.Independent associations of education, intelligence, and cognition with hypertension and the mediating effects of cardiometabolic risk factors: a Mendelian randomization study. Hypertension2021; 80: 192–203.
15.
Sala-VilaAFlemingJKris-EthertonP, et al.Impact of α-linolenic acid, the vegetable ω-3 fatty acid, on cardiovascular disease and cognition. Adv Nutr2022; 13: 1584–1602.
16.
MayedaERWhitmerRAYaffeK. Diabetes and cognition. Clin Geriatr Med2015; 31: 101–115.
17.
TampiRR. Diabetes, cognition, and mortality. Am J Geriatr Psychiatry2023; 31: 583–585.
18.
Navarro-PradoSSchmidt-RioValleJMontero-AlonsoMA, et al.Stricter adherence to dietary approaches to stop hypertension (DASH) and its association with lower blood pressure, visceral fat, and waist circumference in university students. Nutrients2020; 12: 740–756.
19.
Fernández-GómezEMartín-SalvadorALuque-VaraT, et al.Content validation through expert judgement of an instrument on the nutritional knowledge, beliefs, and habits of pregnant women. Nutrients2020; 12: 1136–1151.
20.
ZhouYLiuCZhangZ, et al.Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies. Comput Biol Med2023; 159: 106–119.
21.
BuFDengXHZhanNN, et al.Development and validation of a risk prediction model for frailty in patients with diabetes. BMC Geriatr2023; 23: 172–187.
22.
DengXHouHWangX, et al.Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China. eLife2021; 10: 45–72.
23.
ZhouNXieZPLiuQ, et al.The dietary inflammatory index and its association with the prevalence of hypertension: a cross-sectional study. Front Immunol2022; 13: 109–122.
24.
WenXZhangD. Can education delay cognitive aging? Evidence from China's education revolution. World Econ Papers2016; 1: 1–20.
25.
GanLLiuTWangS, et al.Clinical application progress of Chinese version of Mini-mental state examination and Montreal cognitive assessment. Chin J Rehabilit Med2017; 32: 842–845.
26.
GilLde SánchezCRGilF, et al.Validation of the Montreal cognitive assessment (MoCA) in spanish as a screening tool for mild cognitive impairment and mild dementia in patients over 65 years old in Bogotá, Colombia. Int J Geriatr Psychiatry2015; 30: 655–662.
27.
ChenXZhangRXiaoY, et al.Reliability and validity of the Beijing version of the Montreal cognitive assessment in the evaluation of cognitive function of adult patients with OSAHS. PLoS One2015; 10: 132–137.
28.
KimHShulmanLMShakyaS, et al.The effects of medical comorbidity, cognition, and age on patient-reported outcomes in Parkinson's disease. Parkinsonism Relat Disord2023; 116: 105–119.
29.
FletcherJToppingMZhengF, et al.The effects of education on cognition in older age: evidence from genotyped siblings. Soc Sci Med2021; 280: 3083–3094.
30.
WangXYeT. Alzheimer’s disease neuroimaging initiative, et al. Sex-specific association of depressive symptom trajectories with cognitive decline and clinical progression in mild cognitive impairment. Alzheimers Dement2025; 21: e70548.
31.
ChenGZhaoMYangK, et al.Education exerts different effects on cognition in individuals with subjective cognitive decline and cognitive impairment: a population-based study. J Alzheimers Dis2021; 79: 653–661.
32.
NelsonMEJesterDJPetkusAJ, et al.Cognitive reserve, Alzheimer’s neuropathology, and risk of dementia: a systematic review and meta-analysis. Neuropsychol Rev2021; 31: 233–250.
33.
SternY. Cognitive reserve in ageing and Alzheimer's disease. Lancet Neurol2022; 11: 1006–1012.
34.
SaenzJLDownerBGarciaMA, et al.Cognition and context: rural-urban differences in cognitive aging among older Mexican adults. J Aging Health2018; 30: 965–986.
35.
WoodsMJLundgrenSWPoonCYM, et al.Pre-left ventricular assist device cognition: a comparison of rural and urban implant recipients. ASAIO J2022; 68: 369–373.
36.
LövdénMFratiglioniLGlymouM, et al.Education and cognitive functioning across the life span. Psychol Sci Public Interest2020; 21: 6–41.
37.
SantiniZIJosePEKoyanagiA, et al.The moderating role of social network size in the temporal association between formal social participation and mental health: a longitudinal analysis using two consecutive waves of the survey of health, ageing and retirement in Europe (SHARE). Soc Psychiatry Psychiatr Epidemiol2020; 56: 417–428.
38.
FratiglioniLWangHXEricssonK, et al.Influence of social network on occurrence of dementia: a community-based longitudinal study. Lancet2021; 355: 1315–1319.
39.
MaYLiangLZhengF, et al.Association between sleep duration and cognitive decline. JAMA Netw Open2020; 3: 201–215.
40.
The Lancet Public Health. Making progress in the fight against tobacco. Lancet Public Health2019; 4: 581–592.
41.
ChenXGilesJYaoY, et al.The path to healthy ageing in China: a peking university-lancet commission. Lancet2022; 400: 1967–2006.
42.
YuFChenCLiuW, et al.Longevity humans have youthful erythrocyte function and metabolic signatures. Aging Cell2025; 24: 144–182.
43.
The Lancet Healthy Longevity. Care home staff and residents on the pandemic front line. Lancet Healthy Longev2020; 1: 48–56.
44.
AndersonSRLiWHanS, et al.Expressive suppression to pain in others reduces negative emotion but not vicarious pain in the observer. Cogn Affect Behav Neurosci2021; 21: 292–310.
45.
DaiYTengDZhangC, et al.Priorities in tackling noncommunicable diseases among the population aged 60 years and older in China, 1990–2021: a population-based study. Ageing Res Rev2024; 102: 102–147.
46.
LydonEANguyenLTNieQ, et al.An integrative framework to guide social engagement interventions and technology design for persons with mild cognitive impairment. Front Public Health2022; 9: 750–764.
47.
ZhuDAl MahmudALiuW. Social connections and participation among people with mild cognitive impairment: barriers and recommendations. Front Psychiatry2023; 14: 118–128.
48.
FangSLiWZhiS, et al.Promoting social participation in cognitive decline: a systematic review and meta-analysis of intervention effectiveness and behavior change mechanisms. Worldviews Evid Based Nurs2025; 22: 700–715.
49.
CarlsonMCEricksonKIKramerAF, et al.Evidence for neurocognitive plasticity in at-risk older adults: the experience corps program. J Gerontol A Biol Sci Med Sci2009; 64: 1275–1282.
50.
BellevilleSMellahSde BoyssonC, et al.The pattern and loci of training-induced brain changes in healthy older adults are predicted by the nature of the intervention. PLoS One2014; 9: 102–117.
51.
HikichiHKondoKTakedaT, et al.Social interaction and cognitive decline: results of a 7-year community intervention. Alzheimers Dement (NY)2016; 3: 23–32.
52.
PriyankaraSSenarathnaMJayaratneR, et al.Ambient PM(2.5) and PM(10) exposure and respiratory disease hospitalization in Kandy, Sri Lanka. Int J Environ Res Public Health2021; 18: 96–111.
53.
ZhengYChenSChenY, et al.Association between PM(2.5)-bound metals and pediatric respiratory health in Guangzhou: an ecological study investigating source, health risk, and effect. Front Public Health2023; 11: 117–129.
54.
PolichettiGCoccoSSpinaliA, et al.Effects of particulate matter (PM(10), PM(2.5) and PM(1)) on the cardiovascular system. Toxicology2009; 261: 1–8.
55.
TakyiSABasuNArko-MensahJ, et al.Micronutrient-rich dietary intake is associated with a reduction in the effects of particulate matter on blood pressure among electronic waste recyclers at Agbogbloshie, Ghana. BMC Public Health2016; 20: 1067–1082.
56.
DangMLiYZhaoL, et al.Causal association between particulate matter 2.5 and Alzheimer's disease: a Mendelian randomization study. Front Public Health2024; 12: 122–131.
57.
ZhangBLangaKMWeuveJ, et al.Hypertension and stroke as mediators of air pollution exposure and incident dementia. JAMA Netw Open2023; 6: 233–247.
58.
ThiankhawKChattipakornNChattipakornSC, et al.PM2.5 Exposure in association with AD-related neuropathology and cognitive outcomes. Environ Pollut2022; 292: 118–132.
59.
KimREShinCYHanSHK, et al.Astaxanthin suppresses PM2.5-induced neuroinflammation by regulating Akt phosphorylation in BV-2 microglial cells. Int J Mol Sci2020; 21: 722–727.
60.
WangBRShiJQGeNN, et al.PM2.5 Exposure aggravates oligomeric amyloid beta-induced neuronal injury and promotes NLRP3 inflammasome activation in an in vitro model of Alzheimer's disease. J Neuroinflammation2018; 15: 132–144.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
