Athyros VG, Ganotakis ES, Elisaf M, Mikhailidis DP. The prevalence of the metabolic syndrome using the National cholesterol educational program and international diabetes federation definitions. Curr Med Res Opin. 2005;21:1157–9.
Google Scholar
Engin A. The definition and prevalence of obesity and metabolic syndrome. Obes Lipotoxicity, 2017:1–17.
Moore JX, Chaudhary N, Akinyemiju T. Metabolic syndrome prevalence by race/ethnicity and sex in the united states, National health and nutrition examination survey, 1988–2012. Prev Chronic Dis. 2017;14:E24.
Google Scholar
Miccoli R, Bianchi C, Odoguardi L, Penno G, Caricato F, Giovannitti MG, Pucci L, Del Prato S. Prevalence of the metabolic syndrome among Italian adults according to ATP III definition. Nutr Metab Cardiovasc Dis. 2005;15(4):250–4.
Google Scholar
Haverinen E, Paalanen L, Palmieri L, Padron-Monedero A, Noguer-Zambrano I, Sarmiento Suárez R, Tolonen H. Joint action on health information (InfAct). Comparison of metabolic syndrome prevalence using four different definitions – a population-based study in Finland. Arch Public Health. 2021;79(1):231.
Google Scholar
Yao F, Bo Y, Zhao L, Li Y, Ju L, Fang H, Piao W, Yu D, Lao X. Prevalence and influencing factors of metabolic syndrome among adults in China from 2015 to 2017. Nutrients. 2021;13(12):4475.
Google Scholar
Liu J, Liu Q, Li Z, Du J, Wang C, Gao Y, Wei Z, Wang J, Shi Y, Su J, et al. Prevalence of metabolic syndrome and risk factors among Chinese adults: results from a Population-Based Study – Beijing, china, 2017–2018. China CDC Wkly. 2022;4(29):640–5.
Google Scholar
Neeland IJ, Lim S, Tchernof A, Gastaldelli A, Rangaswami J, Ndumele CE, Powell-Wiley TM, Després JP. Metabolic syndrome. Nat Rev Dis Primers. 2024;10(1):77.
Google Scholar
Lim S, Shin H, Song JH, Kwak SH, Kang SM, Won Yoon J, Choi SH, Cho SI, Park KS, Lee HK, et al. Increasing prevalence of metabolic syndrome in korea: the Korean National health and nutrition examination survey for 1998–2007. Diabetes Care. 2011;34(6):1323–8.
Google Scholar
Aguilar M, Bhuket T, Torres S, Liu B, Wong RJ. Prevalence of the metabolic syndrome in the united states, 2003–2012. JAMA. 2015;313:1973–4.
Google Scholar
Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Bonadonna RC, Muggeo M. Bruneck study. Carotid atherosclerosis and coronary heart disease in the metabolic syndrome: prospective data from the Bruneck study. Diabetes Care. 2003;26(4):1251–7.
Google Scholar
Hunt KJ, Resendez RG, Williams K, Haffner SM, Stern MP. San Antonio heart study. National cholesterol education program versus world health organization metabolic syndrome in relation to all-cause and cardiovascular mortality in the San Antonio heart study. Circulation. 2004;110(10):1251–7.
Google Scholar
Malik S, Wong ND, Franklin SS, Kamath TV, L’Italien GJ, Pio JR, Williams GR. Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in united States adults. Circulation. 2004;110(10):1245–50.
Google Scholar
Holguin F. The metabolic syndrome as a risk factor for lung function decline. Am J Respir Crit Care Med. 2012;185(4):352–3.
Google Scholar
Molina-Luque R, Molina-Recio G, de-Pedro-Jiménez D, Álvarez Fernández C, García-Rodríguez M, Romero-Saldaña M. The impact of metabolic syndrome risk factors on lung function impairment: Cross-Sectional study. JMIR Public Health Surveill. 2023;9:e43737.
Google Scholar
Lee YY, Tsao YC, Yang CK, Chuang CH, Yu W, Chen JC, Li WC. Association between risk factors of metabolic syndrome with lung function. Eur J Clin Nutr. 2020;74(5):811–7.
Google Scholar
Baffi CW, Wood L, Winnica D, Strollo PJ Jr, Gladwin MT, Que LG, Holguin F. Metabolic syndrome and the lung. Chest. 2016;149(6):1525–34.
Google Scholar
Lin WY, Yao CA, Wang HC, Huang KC. Impaired lung function is associated with obesity and metabolic syndrome in adults. Obes (Silver Spring). 2006;14:1654–61.
Google Scholar
Leone N, Courbon D, Thomas F, Bean K, Jégo B, Leynaert B, Guize L, Zureik M. Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. Am J Respir Crit Care Med. 2009;179(6):509–16.
Google Scholar
Naveed B, Weiden MD, Kwon S, Gracely EJ, Comfort AL, Ferrier N, Kasturiarachchi KJ, Cohen HW, Aldrich TK, Rom WN, et al. Metabolic syndrome biomarkers predict lung function impairment: a nested case-control study. Am J Respir Crit Care Med. 2012;185(4):392–9.
Google Scholar
Hole DJ, Watt GC, Davey-Smith G, Hart CL, Gillis CR, Hawthorne VM. Impaired lung function and mortality risk in men and women: findings from the Renfrew and Paisley prospective population study. BMJ. 1996;313(7059):711–5. discussion 715-6.
Google Scholar
Sin DD, Wu L, Man SFP. The relationship between reduced lung function and cardiovascular mortality: a population-based study and a systematic review of the literature. Chest. 2005;127:1952–9.
Google Scholar
Silvestre OM, Nadruz W Jr, Roca Q, Claggett G, Solomon B, Mirabelli SD, London MC, Loehr SJ, Shah LR. Declining lung function and cardiovascular risk: the ARIC study. J Am Coll Cardiol. 2018;72(10):1109–22.
Google Scholar
Sinclair AJ, Conroy SP, Bayer AJ. Impact of diabetes on physical function in older people. Diabetes Care. 2008;31(2):233–5.
Google Scholar
Miele CH, Grigsby MR, Siddharthan T, Gilman RH, Miranda JJ, Bernabe-Ortiz A, Wise RA, Checkley W, CRONICAS Cohort Study Group. Environmental exposures and systemic hypertension are risk factors for decline in lung function. Thorax. 2018;73(12):1120–7.
Google Scholar
Xie Q, Xu S, Wan Q, Tong N. Metabolic syndrome, small airway dysfunction and the mediating role of inflammation. Sci Rep. 2025;15(1):12555.
Google Scholar
Pedersen KM, Çolak Y, Ellervik C, Hasselbalch HC, Bojesen SE, Nordestgaard BG. Loss-of-function polymorphism in IL6R reduces risk of JAK2V617F somatic mutation and myeloproliferative neoplasm: A Mendelian randomization study. EClinicalMedicine. 2020;21:100280.
Google Scholar
Conigrave KM, Ali RL, Armstrong R, Chikritzhs TN, d’Abbs P, Harris MF, Hewlett N, Livingston M, Lubman DI, McKenzie A, et al. Revision of the Australian guidelines to reduce health risks from drinking alcohol. Med J Aust. 2021;215(11):518–24.
Google Scholar
Smith GD, Ebrahim S. Data dredging, bias, or confounding. BMJ. 2002;325(7378):1437–8.
Google Scholar
Gage SH, Munafò MR, Davey Smith G. Causal inference in developmental origins of health and disease (DOHaD) research. Annu Rev Psychol. 2016;67:567–85.
Google Scholar
Hernán MA. Methods of public health Research – Strengthening causal inference from observational data. N Engl J Med. 2021;385(15):1345–8.
Google Scholar
Sanchez P, Voisey JP, Xia T, Watson HI, O’Neil AQ, Tsaftaris SA. Causal machine learning for healthcare and precision medicine. R Soc Open Sci. 2022;9(8):220638.
Google Scholar
Gong J, Wang G, Wang Y, Chen X, Chen Y, Meng Q, Yang P, Yao Y, Zhao Y. Nowcasting and forecasting the care needs of the older population in china: analysis of data from the China health and retirement longitudinal study (CHARLS). Lancet Public Health. 2022;7(12):e1005–13.
Google Scholar
Chinese Diabetes Society. China guideline for type 2 diabetes (2013). Chin J Diabetes Mellitus. 2014;6(7):447–98.
Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, Kaminsky DA, McCarthy K, McCormack MC, et al. Standardization of spirometry 2019 update. An official American thoracic society and European respiratory society technical statement. Am J Respir Crit Care Med. 2019;200(8):e70–88.
Google Scholar
Zhong NS, Zhang YG, Yu MJ, Zhang JL, Qiao ZP, Liu LW, Luo DF, Guo XC. Normal values of peak expiratory flow and its application in bronchial asthma. Chin J Tubere Respir Dis. 1985;8(3):138–41.
Google Scholar
Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.
Google Scholar
Tukey JW. Exploratory data analysis[M]. Reading, MA: Addison-wesley; 1977.
Miot HA. Anomalous values and missing data in clinical and experimental studies. J Vasc Bras. 2019;18:e20190004.
Google Scholar
Shete S, Beasley TM, Etzel CJ, Fernández JR, Chen J, Allison DB, Amos CI. Effect of winsorization on power and type 1 error of variance components and related methods of QTL detection. Behav Genet. 2004;34(2):153–9.
Google Scholar
Kwak SK, Kim JH. Statistical data preparation: management of missing values and outliers. Korean J Anesthesiol. 2017;70(4):407–11.
Google Scholar
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects[J]. Biometrika. 1983;70(1):41–55.
Google Scholar
Thomas LE, Li F, Pencina MJ. Overlap weighting: a propensity score method that mimics attributes of a randomized clinical trial. JAMA. 2020;323(23):2417–8.
Google Scholar
Li F, Morgan KL, Zaslavsky AM. Balancing covariates via propensity score weighting. J Am Stat Assoc. 2018;113(521):390–400.
Google Scholar
Nie X, Wager S. Quasi-oracle Estimation of heterogeneous treatment effects[J]. Biometrika. 2021;108(2):299–319.
Google Scholar
Athey S, Wager S. Estimating treatment effects with causal forests: an application[J]. Observational Stud. 2019;5(2):37–51.
Google Scholar
Athey S, Tibshirani J, Wager S. Generalized random forests[J]. 2019.
Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests[J]. J Am Stat Assoc. 2018;113(523):1228–42.
Google Scholar
Imbens GW, Rubin DB. Causal inference in statistics, social, and biomedical sciences[M]. Cambridge University Press; 2015.
Lundberg SM, Lee SI. A unified approach to interpreting model predictions[J]. Adv Neural Inf Process Syst, 2017, 30.
Gubela RM, Lessmann S. Uplift modeling with value-driven evaluation metrics[J]. Decis Support Syst. 2021;150:113648.
Google Scholar
Yadlowsky S, Fleming S, Shah N, Brunskill E, Wager S. Evaluating treatment prioritization rules via rank-weighted average treatment effects. arXiv[J]. arXiv preprint arXiv:2111.07966, 2021.
Chernozhukov V, Demirer M, Duflo E, Fernández-Val I. Fisher-Schultz lecture: generic machine learning inference on heterogenous treatment effects in randomized experiments, with an application to immunization in India[J]. arXiv preprint arXiv:1712.04802, 2017.
Radcliffe N. Using control groups to target on predicted lift: Building and assessing uplift model[J]. Direct Mark Analytics J, 2007: 14–21.
Eggers AC, Tuñón G, Dafoe A. Placebo tests for causal inference[J]. Am J Polit Sci. 2024;68(3):1106–21.
Google Scholar
Wyss R, Ellis AR, Brookhart MA, Girman CJ, Jonsson Funk M, LoCasale R, Stürmer T. The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score. Am J Epidemiol. 2014;180(6):645–55.
Google Scholar
Sverdrup E, Wu H, Athey S, Wager S. Qini curves for multi-armed treatment rules[J]. J Comput Graphical Stat, 2024: 1–13.
Yadlowsky S, Fleming S, Shah N, Brunskill E, Wager S. Evaluating treatment prioritization rules via rank-weighted average treatment effects[J]. J Am Stat Assoc, 2024: 1–14.
Brumpton BM, Camargo CA Jr, Romundstad PR, Langhammer A, Chen Y, Mai XM. Metabolic syndrome and incidence of asthma in adults: the HUNT study. Eur Respir J. 2013;42(6):1495–502.
Google Scholar
Marott JL, Ingebrigtsen TS, Çolak Y, Kankaanranta H, Bakke PS, Vestbo J, Nordestgaard BG, Lange P. Impact of the metabolic syndrome on cardiopulmonary morbidity and mortality in individuals with lung function impairment: a prospective cohort study of the Danish general population. Lancet Reg Health Eur. 2023;35:100759.
Google Scholar
Singh S, Bodas M, Bhatraju NK, Pattnaik B, Gheware A, Parameswaran PK, Thompson M, Freeman M, Mabalirajan U, Gosens R, Prakash YS, Agrawal A, et al. Hyperinsulinemia adversely affects lung structure and function. Am J Physiol Lung Cell Mol Physiol. 2016;310(9):L837–45.
Google Scholar
Proskocil BJ, Calco GN, Nie Z. Insulin acutely increases agonist-induced airway smooth muscle contraction in humans and rats. Am J Physiol Lung Cell Mol Physiol. 2021;320(4):L545–56.
Google Scholar
Bueno M, Lai YC, Romero Y, Brands J, St Croix CM, Kamga C, Corey C, Herazo-Maya JD, Sembrat J, Lee JS, et al. PINK1 deficiency impairs mitochondrial homeostasis and promotes lung fibrosis. J Clin Invest. 2015;125(2):521–38.
Google Scholar
Angelidis I, Simon LM, Fernandez IE, Strunz M, Mayr CH, Greiffo FR, Tsitsiridis G, Ansari M, Graf E, Strom TM, et al. An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nat Commun. 2019;10(1):963.
Google Scholar
Carrascosa JM, Andrés A, Ros M, Bogónez E, Arribas C, Fernández-Agulló T, De Solís AJ, Gallardo N, Martínez C. Development of insulin resistance during aging: involvement of central processes and role of adipokines. Curr Protein Pept Sci. 2011;12(4):305–15.
Google Scholar
Redman LM, Smith SR, Burton JH, Martin CK, Il’yasova D, Ravussin E. Metabolic slowing and reduced oxidative damage with sustained caloric restriction support the rate of living and oxidative damage theories of aging. Cell Metab. 2018;27(4):805–e8154.
Google Scholar
Rochester DF, Enson Y. Current concepts in the pathogenesis of the obesity–hypoventilation syndrome: mechanical and circulatory factors. Am J Med. 1974;57:402–20.
Google Scholar
He S, Yang J, Li X, Gu H, Su Q, Qin L. Visceral adiposity index is associated with lung function impairment: a population-based study. Respir Res. 2021;22(1):2.
Google Scholar
Fontana L, Villareal DT, Das SK, Smith SR, Meydani SN, Pittas AG, Klein S, Bhapkar M, Rochon J, Ravussin E, et al. Effects of 2-year calorie restriction on Circulating levels of IGF-1, IGF-binding proteins and cortisol in Nonobese men and women: a randomized clinical trial. Aging Cell. 2016;15(1):22–7.
Google Scholar
Annema W, Dikkers A, de Boer JF, van Greevenbroek MMJ, van der Kallen CJH, Schalkwijk CG, Stehouwer CDA, Dullaart RPF, Tietge UJF. Impaired HDL cholesterol efflux in metabolic syndrome is unrelated to glucose tolerance status: the CODAM study. Sci Rep. 2016;6:27367.
Google Scholar
Lee C, Cha Y, Bae SH, Kim YS. Association between serum high-density lipoprotein cholesterol and lung function in adults: three cross-sectional studies from US and Korea National health and nutrition examination survey. BMJ Open Respir Res. 2023;10(1):e001792.
Google Scholar
Yoo B, Jung SH, Bae SH, Kim YS, Lee C. High-Density lipoprotein cholesterol trajectories and lung function decline: A prospective cohort study. Lung. 2025;203(1):54.
Google Scholar
Kennedy EH. Semiparametric doubly robust targeted double machine learning: a review[J]. Handbook of Statistical Methods for Precision Medicine. 2024:207–236.
Díaz I. Machine learning in the Estimation of causal effects: targeted minimum loss-based Estimation and double/debiased machine learning. Biostatistics. 2020;21(2):353–8.
Google Scholar
Moccia C, Moirano G, Popovic M, Pizzi C, Fariselli P, Richiardi L, Ekstrøm CT, Maule M. Machine learning in causal inference for epidemiology. Eur J Epidemiol. 2024;39(10):1097–108.
Google Scholar
Inoue K, Seeman TE, Horwich T, Budoff MJ, Watson KE. Heterogeneity in the association between the presence of coronary artery calcium and cardiovascular events: A Machine-Learning approach in the MESA study. Circulation. 2023;147(2):132–41.
Google Scholar
link