Using Machine Learning Techniques to Predict Factors Contributing to the Incidence of Metabolic Syndrome in Tehran: Cohort Study

被引:7
作者
Hosseini-Esfahani, Firoozeh [1 ]
Alafchi, Behnaz [2 ]
Cheraghi, Zahra [3 ,4 ]
Doosti-Irani, Amin [4 ,5 ]
Mirmiran, Parvin [1 ]
Khalili, Davood [6 ,7 ]
Azizi, Fereidoun [6 ]
机构
[1] Shahid Beheshti Univ Med Sci, Fac Nutr Sci & Food Technol, Natl Nutr & Food Technol Res Inst, Dept Clin Nutr & Dietet, Tehran, Iran
[2] Hamadan Univ Med Sci, Sch Publ Hlth, Dept Biostat, Hamadan, Iran
[3] Hamadan Univ Med Sci, Modeling Noncommunicable Dis Res Ctr, Shahid Fahmideh Ave, Hamadan 65157835129, Iran
[4] Hamadan Univ Med Sci, Sch Publ Hlth, Dept Epidemiol, Hamadan, Iran
[5] Hamadan Univ Med Sci, Hlth & Res Ctr Hlth Sci, Hamadan, Iran
[6] Shahid Beheshti Univ Med Sci, Prevent Metab Disorders Res Ctr, Res Inst Endocrine Sci, Tehran, Iran
[7] Shahid Beheshti Univ Med Sci, Res Inst Endocrine Sci, Dept Biostat & Epidemiol, Tehran, Iran
来源
JMIR PUBLIC HEALTH AND SURVEILLANCE | 2021年 / 7卷 / 09期
关键词
metabolic syndrome; Tehran Lipid and Glucose Study; data mining; 3RD NATIONAL-HEALTH; RELATIVE VALIDITY; PREVALENCE; RELIABILITY; POPULATION; DEFINITION; GLUCOSE; ADULTS; RISK;
D O I
10.2196/27304
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Metabolic syndrome (MetS), a major contributor to cardiovascular disease and diabetes, is considered to be among the most common public health problems worldwide. Objective: We aimed to identify and rank the most important nutritional and nonnutritional factors contributing to the development of MetS using a data-mining method. Methods: This prospective study was performed on 3048 adults (aged >= 20 years) who participated in the fifth follow-up examination of the Tehran Lipid and Glucose Study, who were followed for 3 years. MetS was defined according to the modified definition of the National Cholesterol Education Program/Adult Treatment Panel III. The importance of variables was obtained by the training set using the random forest model for determining factors with the greatest contribution to developing MetS. Results: Among the 3048 participants, 701 (22.9%) developed MetS during the study period. The mean age of the participants was 44.3 years (SD 11.8). The total incidence rate of MetS was 229.9 (95% CI 278.6-322.9) per 1000 person-years and the mean follow-up time was 40.5 months (SD 7.3). The incidence of MetS was significantly (P<.001) higher in men than in women (27% vs 20%). Those affected by MetS were older, married, had diabetes, with lower levels of education, and had a higher BMI (P<.001). The percentage of hospitalized patients was higher among those with MetS than among healthy people, although this difference was only statistically significant in women (P=.02). Based on the variable importance and multiple logistic regression analyses, the most important determinants of MetS were identified as history of diabetes (odds ratio [OR] 6.3, 95% CI 3.9-10.2, P<.001), BMI (OR 1.2, 95% CI 1.0-1.2, P<.001), age (OR 1.0, 95% CI 1.0-1.03, P<.001), female gender (OR 0.5, 95% CI 0.38-0.63, P<.001), and dietary monounsaturated fatty acid (OR 0.97, 95% CI 0.94-0.99, P=.04). Conclusions: Based on our findings, the incidence rate of MetS was significantly higher in men than in women in Tehran. The most important determinants of MetS were history of diabetes, high BMI, older age, male gender, and low dietary monounsaturated fatty acid intake.
引用
收藏
页数:11
相关论文
共 39 条
  • [1] Metabolic syndrome - a new world-wide definition. A consensus statement from the international diabetes federation
    Alberti, KGMM
    Zimmet, P
    Shaw, J
    [J]. DIABETIC MEDICINE, 2006, 23 (05) : 469 - 480
  • [2] Breast cancer diagnosis using GA feature selection and Rotation Forest
    Alickovic, Emina
    Subasi, Abdulhamit
    [J]. NEURAL COMPUTING & APPLICATIONS, 2017, 28 (04) : 753 - 763
  • [3] [Anonymous], 2013, Int J Comput Sci Inf Technol
  • [4] Cardiovascular risk factors in an Iranian urban population: Tehran Lipid and Glucose Study (Phase 1)
    Azizi, F
    Rahmani, M
    Emami, H
    Mirmiran, P
    Hajipour, R
    Madjid, M
    Ghanbili, J
    Ghanbarian, A
    Mehrabi, Y
    Saadat, N
    Salehi, R
    Mortazavi, N
    Heydarian, P
    Sarbazi, N
    Allahverdian, S
    Saadati, N
    Ainy, E
    Moeini, S
    [J]. SOZIAL-UND PRAVENTIVMEDIZIN, 2002, 47 (06): : 408 - 426
  • [5] Azizi F, 2010, ARCH IRAN MED, V13, P426, DOI 010135/AIM.0011
  • [6] Metabolic syndrome and insulin resistance: Perioperative considerations
    Baty, Hema S.
    Raghavendran, Sreekrishna
    Carli, Franco
    [J]. ANESTHESIOLOGY, 2008, 108 (03) : 506 - 523
  • [7] Metabolic Syndrome, Aging and Involvement of Oxidative Stress
    Bonomini, Francesca
    Rodella, Luigi Fabrizio
    Rezzani, Rita
    [J]. AGING AND DISEASE, 2015, 6 (02): : 109 - 120
  • [8] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [9] Statistical modeling: The two cultures
    Breiman, L
    [J]. STATISTICAL SCIENCE, 2001, 16 (03) : 199 - 215
  • [10] Effects of food items and related nutrients on metabolic syndrome using Bayesian multilevel modelling using the Tehran Lipid and Glucose Study (TLGS): a cohort study
    Cheraghi, Zahra
    Nedjat, Saharnaz
    Mirmiran, Parvin
    Moslehi, Nazanin
    Mansournia, Nasrin
    Etminan, Mahyar
    Mansournia, Mohammad Ali
    McCandless, Lawrence C.
    [J]. BMJ OPEN, 2018, 8 (12):