Association between Exposure to Ambient Air Particulates and Metabolic Syndrome Components in a Saudi Arabian Population
Abstract
:1. Introduction
2. Materials and Methods
2.1. Air Quality Measurements in Jeddah
2.2. Source Identification
2.3. Study Population Recruitment, Exposure Assessment, and Health Measurements
2.4. Statistical Analyses
3. Results and Discussion
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Alghamdi, M.; Alam, M.; Yin, J.; Stark, C.; Jang, E.; Harrison, R.; Shamy, M.; Khoder, K.; Shabbaj, I. Receptor modeling study of polycyclic aromatic hydrocarbons in Jeddah, Saudi Arabia. Sci. Total Environ. 2015, 506–507, 401–408. [Google Scholar] [CrossRef]
- Brook, R.; Rajagopalan, S.; Pope, C.; Brook, J.; Bhatnagar, A.; Diez-Roux, A.; Holguin, F.; Hong, Y.; Luepker, R.; Mittleman, M.; et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 2010, 121, 2331–2378. [Google Scholar] [CrossRef] [PubMed]
- Park, S.; Auchincloss, A.; O’neill, M.; Prineas, R.; Correa, J.; Keeler, J.; Roux, A. Particulate air pollution, metabolic syndrome, and heart rate variability: The Multi-Ethnic Study of Atherosclerosis (MESA). Environ. Health Perspect. 2010, 118, 1406–1411. [Google Scholar] [CrossRef]
- National Cholesterol Education Program (NCEP). Executive Summary of the Third Report of the National Cholesterol Education Program. Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 2001, 285, 2486–2497. [Google Scholar]
- Pope, C.; Turner, M.; Burnett, R.; Jerrett, M.; Gapstur, S.; Diver, W.; Krewski, D.; Brook, R. Relationships between fine particulate air pollution, cardiometabolic disorders, and cardiovascular mortality. Circ. Res. 2015, 116, 108–115. [Google Scholar] [CrossRef]
- Alzahrani, A.; Karawagh, A.; Alshahrani, F.; Naser, T.; Ahmed, A.; Alsharef, E. Prevalence and predictors of metabolic syndrome among healthy Saudi Adults. Br. J. Diabetes Vasc. Dis. 2012, 12, 78–80. [Google Scholar] [CrossRef]
- Pearson, J.; Bachireddy, C.; Shyamprasad, S.; Goldfine, A.; Brownstein, J. Association between fine particulate matter and diabetes prevalence in the U.S. Diabetes Care 2010, 33, 2196–2201. [Google Scholar] [CrossRef]
- Chen, H.; Burnett, R.; Kwong, J.; Villeneuve, P.; Goldberg, M.; Brook, R.; Van Donkelaat, A.; Jerrett, M.; Martin, R.; Brook, J.; et al. Risk of incident diabetes in relation to long-term exposure to fine particulate matter in Ontario, Canada. Environ. Health Perspect. 2013, 121, 804–810. [Google Scholar] [CrossRef]
- Park, S.; Wang, W. Ambient air pollution and type 2 diabetes mellitus: A systematic review of epidemiologic research. Curr. Environ. Health Rep. 2014, 1, 275–286. [Google Scholar] [CrossRef] [PubMed]
- Yitshak Sade, M.; Kloog, I.; Liberty, I.; Schwartz, J.; Novack, V. The association between air pollution exposure and glucose and lipids levels. J. Clin. Endocrinol. Metab. 2016, 101, 2460–2467. [Google Scholar] [CrossRef] [PubMed]
- Auchincloss, A.; Diez Roux, A.; Dvonch, J.; Brown, P.; Barr, R.G.; Daviglus, M.; Goff, D.; Kaufman, G.; O’neill, M. Associations between recent exposure to ambient fine particulate matter and blood pressure in the multi-ethnic study of atherosclerosis (MESA). Environ. Health Perspect. 2008, 116, 486–491. [Google Scholar] [CrossRef] [PubMed]
- Wu, S.; Deng, F.; Huang, J.; Wang, H.; Shima, M.; Wang, X.; Qin, Y.; Zheng, C.; Wei, H.; Hao, Y.; et al. Blood pressure changes and chemical constituents of particulate air pollution: Results from the Healthy Volunteer Natural Relocation (HVNR) Study. Environ. Health Perspect. 2013, 121, 66–72. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Burnett, R.; Kwong, J.; Villeneuve, P.; Goldberg, M.; Brook, R.; Van Donkelaar, A.; Jerrett, M.; Martin, R.; Kopp, A.; et al. Spatial association between ambient fine particulate matter and incident hypertension. Circulation 2014, 129, 562–569. [Google Scholar] [CrossRef] [PubMed]
- Eze, I.; Schaffner, E.; Foraster, M.; Imboden, M.; Von Eckardstein, A.; Gerbase, M.; Rothe, T.; Rochat, T.; Kunzli, N.; Schindler, C.; et al. Long-term exposure to ambient air pollution and metabolic syndrome in adults. PLoS ONE 2015, 10, e0130337. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sun, Q.; Yue, P.; Deiuliis, J.; Lumeng, C.; Kampfrath, T.; Mikolaj, M.; Caj, Y.; Ostrowsky, M.; Parthasarathy, S.; Brook, R.; et al. Ambient air pollution exaggerates adipose inflammation and insulin resistance in a mouse model of diet-induced obesity. Circulation 2009, 119, 538–546. [Google Scholar] [CrossRef] [PubMed]
- Liu, C.; Bai, Y.; Xu, X.; Sun, L.; Wang, A.; Wang, T.; Maurya, S.; Periasamy, M.; Morishita, M.; Harkema, J.; et al. Exaggerated effects of particulate matter air pollution in genetic type II diabetes mellitus. Part. Fibre Toxicol. 2014, 11, 27. [Google Scholar] [CrossRef] [PubMed]
- Lindmark, S.; Wiklund, U.; Bjerle, P.; Eriksson, J. Does the autonomic nervous system play a role in the development of insulin resistance? A study on heart rate variability in first-degree relatives of type 2 diabetes patients and control subjects. Diabet. Med. 2003, 20, 399–405. [Google Scholar] [CrossRef] [PubMed]
- Tsai, D.; Riediker, M.; Wuerzner, G.; Maillard, M.; Marques-Vidal, P.; Paccaud, F.; Vollenweider, P.; Burnier, M.; Bochud, M. Short-term increase in particulate matter blunts nocturnal blood pressure dipping and daytime urinary sodium excretion. Hypertension 2012, 60, 1061–1069. [Google Scholar] [CrossRef] [PubMed]
- National Research Council (NRC). Research Priorities for Airborne Particulate Matter: Continuing Research Progress; National Academies Press: Washington, DC, USA, 2004. [Google Scholar]
- Ostro, B.; Roth, L.; Malig, B.; Marty, M. The effects of fine particle components on respiratory hospital admissions in children. Environ. Health Perspect. 2009, 117, 475–480. [Google Scholar] [CrossRef] [PubMed]
- Bell, M.; Ebisu, K.; Leaderer, B.; Gent, J.; Lee, H.; Koutrakis, P.; Wang, Y.; Dominici, F.; Peng, R. Associations between PM2.5 constituents and sources with hospital admissions: Analysis of four counties in Connecticut and Massachusetts (USA) for persons >65 years of age. Environ. Health Perspect. 2014, 122, 138–144. [Google Scholar] [PubMed]
- Thurston, G.; Burnett, R.; Turner, M.; Shi, Y.; Krewski, D.; Lall, R.; Ito, K.; Jerrett, M.; Gapstur, S.; Diver, W.; et al. Ischemic heart disease mortality and long-term exposure to source-related components of U.S. fine particle air pollution. Environ. Health Perspect. 2016, 124, 785–794. [Google Scholar] [CrossRef] [PubMed]
- Khodeir, M.; Shamy, M.; Alghamdi, M.; Zhong, M.; Sun, H.; Costa, M.; Chen, L.; Maciejczyk, P. Source apportionment and elemental composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia. Atmos. Pollut. Res. 2012, 3, 331–340. [Google Scholar] [CrossRef] [PubMed]
- Turner, W.; Olson, B.; Allen, G. Calibration of sharp cut impactors for indoor and outdoor particle sampling. J. Air Waste Manag. Assoc. 2000, 50, 484–487. [Google Scholar] [CrossRef] [PubMed]
- Thurston, G.; Spengler, J. A quantitative assessment of source contributions to inhalable particulate matter in metropolitan Boston, Massachusetts. Atmos. Environ. 1985, 19, 9–21. [Google Scholar] [CrossRef]
- Bahijri, S.; Al Raddadi, R.; Jambi, H.; Alaamer, M.; Ferns, G. The prevalence of metabolic syndrome in an apparently healthy, normotensive and non-diabetic population in Saudi Arabia by two definitions: Implications for local practice. Open J. Endocr. Metab. Dis. 2013, 3, 18–24. [Google Scholar] [CrossRef]
- WHO. World Health Organization. Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide. Global Update. Summary of Risk Assessment 2006. Available online: http://apps.who.int/iris/bitstream/10665/69477/1/WHO_SDE_PHE_OEH_06.02_eng.pdf (accessed on 13 July 2016).
- Mielke, H.; Laidlaw, M.; Gonzales, C. Lead (Pb) legacy from vehicle traffic in eight California urbanized areas: Continuing influence of lead dust on children’s health. Sci. Total Environ. 2010, 408, 3965–3975. [Google Scholar] [CrossRef] [PubMed]
- ATSDR. Agency for Toxic Substances and Disease Registry. Regulations and Guidelines Applicable. Available online: http://www.atsdr.cdc.gov/toxprofiles/tp11-c8.pdf (accessed on 13 July 2016).
- Peng, R.; Dominici, F.; Pastor-Barriuso, R.; Zeger, S.; Samet, S. Seasonal analyses of air pollution and mortality in 100 US cities. Am. J. Epidemiol. 2005, 161, 585–594. [Google Scholar] [CrossRef] [PubMed]
- Dominici, F.; Peng, R.; Zeger, S.; White, R.; Samet, J. Particulate air pollution and mortality in the United States: Did the risks change from 1987 to 2000? Am. J. Epidemiol. 2007, 166, 880–888. [Google Scholar] [CrossRef] [PubMed]
- Pun, V.; Yu, I.; Qiu, H.; Ho, K.; Sun, Z.; Louie, P.; Wong, T.; Tian, L. Short-term associations of cause-specific emergency hospitalizations and particulate matter chemical components in Hong Kong. Am. J. Epidemiol. 2014, 179, 1086–1095. [Google Scholar] [CrossRef] [PubMed]
- Brocato, J.; Hernandez, M.; Laulicht, F.; Sun, H.; Shamy, M.; Alghamdi, M.; Khoder, M.; Kluz, T.; Chen, L.; Costa, M. In vivo exposures to particulate matter collected from Saudi Arabia or nickel chloride display similar dysregulation of metabolic syndrome genes. J. Toxicol. Environ. Health A 2015, 78, 1421–1436. [Google Scholar] [CrossRef] [PubMed]
- Brocato, J.; Sun, H.; Shamy, M.; Kluz, T.; Alghamdi, M.; Khoder, M.; Chen, L.; Costa, M. Particulate matter from Saudi Arabia induces genes involved in inflammation, metabolic syndrome and atherosclerosis. J. Toxicol. Environ. Health A 2014, 77, 751–766. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Z.; Rong, Y.; Cui, X.; Shen, Y.; Zhou, Y.; Xiao, L.; Chen, W. Oxidative stress and mitochondrion-related cell apoptosis in human bronchial epithelial 16HBE cells induced by silica dust. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2015, 33, 801–805. [Google Scholar] [PubMed]
- Schmidt, P.; Escobar, A.; Torres, J.; Martinez, C.; Rizzetti, D.; Kunz, S.; Vassallo, D.; Alonso, M.; Peçanha, F.; Wiggers, G. Aluminum exposure for one hour decreases vascular reactivity in conductance and resistance arteries in rats. Toxicol. Appl. Pharmacol. 2016, 15, 109–118. [Google Scholar] [CrossRef] [PubMed]
- Srikanth, K.; Trindade, T.; Duarte, A.; Pereira, E. Cytotoxicity and oxidative stress responses of silica-coated iron oxide nanoparticles in CHSE-214 cells. Environ. Sci. Pollut. Res. Int. 2017, 24, 2055–2064. [Google Scholar] [CrossRef] [PubMed]
- Peters, A. Ambient particulate matter and the risk for cardiovascular disease. Prog. Cardiovasc. Dis. 2011, 53, 327–333. [Google Scholar] [CrossRef] [PubMed]
- Brook, R.; Sun, Z.; Brook, J.; Zhao, X.; Yan, J.; Mukherjee, B.; Rao, X.; Duan, F.; Sun, L.; Liang, R.; et al. Extreme air pollution conditions adversely affect blood pressure and insulin resistance: The air pollution and cardiometabolic disease study. Hypertension 2016, 67, 77–85. [Google Scholar] [CrossRef] [PubMed]
- Mascarenhas, S.; Mutnuri, S.; Ganguly, A. Deleterious role of trace elements—Silica and lead in the development of chronic kidney disease. Chemosphere 2017, 177, 239–249. [Google Scholar] [CrossRef] [PubMed]
- Grandjean, P. Even low-dose lead exposure is hazardous. Lancet 2010, 376, 855–856. [Google Scholar] [CrossRef]
- Poręba, R.; Gać, P.; Poręba, M.; Andrzejak, R. Environmental and occupational exposure to lead as a potential risk factor for cardiovascular disease. Environ. Toxicol. Pharmacol. 2011, 31, 267–277. [Google Scholar] [CrossRef] [PubMed]
- Tyrell, J.; Hafida, S.; Stemmer, P.; Adhami, A.; Leff, T. Lead (Pb) exposures promotes diabetes in obese rodents. J. Trace Elem. Med. Biol. 2017, 39, 221–226. [Google Scholar] [CrossRef] [PubMed]
- Puett, R.; Hart, J.; Schwartz, J.; Hu, F.; Liese, A.; Laden, F. Are particulate matter exposures associated with risk of type 2 diabetes? Environ. Health Perspect. 2011, 119, 384–389. [Google Scholar] [CrossRef] [PubMed]
- Coogan, P.; White, L.; Jerrett, M.; Brook, R.; Su, J.; Seto, E.; Burnett, R.; Palmer, J.; Rosenberg, L. Air pollution and incidence of hypertension and diabetes mellitus in black women living in Los Angeles. Circulation 2012, 125, 767–772. [Google Scholar] [CrossRef] [PubMed]
- Song, C.; He, J.; Wu, L.; Jin, T.; Chen, X.; Li, R.; Ren, P.; Zhang, L.; Mao, H. Health burden attributable to ambient PM2.5 in China. Environ. Pollut. 2017, 223, 575–586. [Google Scholar] [CrossRef] [PubMed]
Total | Al-Nuzlah | Al-Alfiyya | Al-Rehab | Pitrumin | Al-Rughama | University | Al-Muhammadiyah | ||
---|---|---|---|---|---|---|---|---|---|
N = 2025 | N = 266 | N = 157 | N = 409 | N = 104 | N = 103 | N = 697 | N = 298 | ||
Age | Mean (SD) | 31.46 (10.69) | 29.43 (11.36) | 35.26 (11.73) | 32.28 (10.41) | 30.66 (10.80) | 28.38 (8.73) | 31.37 (10.98) | 31.75 (9.06) |
Sex | Male N (%) | 996 (49.2) | 185 (70.1) | 130 (86.1) | 175 (42.8) | 42 (40.4) | 61 (59.2) | 246 (35.3) | 157 (52.9) |
Female N (%) | 1029 (50.8) | 79 (29.9) | 21 (13.9) | 234 (57.2) | 62 (59.6) | 42 (40.8) | 451 (64.7) | 140 (47.1) | |
Smoking | Non-smoker N (%) | 1493 (73.7) | 219 (82.9) | 103 (68.2) | 296 (72.4) | 80 (76.9) | 59 (57.3) | 526 (75.5) | 210 (70.7) |
Smoker N (%) | 532 (26.3) | 45 (17.0) | 48 (31.8) | 113 (27.6) | 24 (23.1) | 44 (42.7) | 171 (24.5) | 87 (29.3) | |
Fruit and Vegetable Intake | ≤4 times/week N (%) | 959 (47.4) | 164 (62.1) | 88 (58.3) | 143 (35.0) | 46 (44.2) | 49 (476.) | 342 (49.1) | 127 (42.8) |
5–6 times/week N (%) | 787 (38.9) | 82 (31.1) | 55 (36.4) | 170 (41.6) | 49 (47.1) | 39 (37.9) | 276 (39.6) | 116 (39.1) | |
≥7 times/week N (%) | 279 (13.8) | 18 (6.8) | 9 (5.3) | 96 (23.5) | 9 (8.6) | 15 (14.6) | 79 (11.3) | 54 (18.2) | |
Residence | House N (%) | 681 (40.0) | 152 (57.6) | 25 (16.6) | 92 (22.5) | 74 (71.2) | 58 (56.3) | 221 (31.7) | 59 (19.9) |
Apartment N (%) | 1261 (68.9) | 108 (40.9) | 87 (57.6) | 301 (73.6) | 30 (28.8) | 44 (42.7) | 467 (67.0) | 224 (75.4) | |
Villa N (%) | 83 (3.1) | 4 (1.5) | 39 (25.8) | 16 (3.9) | 0 (0.0) | 1 (0.1) | 9 (1.3) | 14 (4.7) | |
Marietal Status | No N (%) | 971 (48.0) | 164 (62.1) | 44 (29.1) | 163 (39.9) | 57 (54.8) | 63 (61.2) | 351 (50.3) | 129 (43.4) |
Yes N (%) | 1054 (52.0) | 100 (37.9) | 107 (70.9) | 246 (60.1) | 47 (45.2) | 40 (38.8) | 346 (49.6) | 168 (56.6) | |
Type of Work | No Work N (%) | 523 (25.8) | 73 (27.7) | 30 (19.9) | 146 (35.7) | 32 (30.8) | 23 (22.3) | 129 (18.5) | 90 (30.3) |
Labor Work N (%) | 1339 (66.1) | 160 (60.6) | 76 (50.3) | 223 (54.5) | 69 (66.3) | 80 (77.7) | 562 (80.6) | 169 (56.9) | |
Desk Work N (%) | 163 (8.1) | 31 (11.7) | 45 (29.8) | 40 (9.8) | 3 (2.9) | 0 (0.0) | 6 (0.9) | 38 (12.8) | |
Education Level | Illiterate N (%) | 67 (3.3) | 17 (6.4) | 2 (1.3) | 15 (3.7) | 3 (2.9) | 2 (1.9) | 28 (4.0) | 0 (0.0) |
Can Read & Write N (%) | 59 (2.9) | 8 (3.0) | 2 (1.3) | 11 (2.7) | 3 (2.9) | 0 (0.0) | 26 (3.7) | 9 (3.0) | |
Primary School N (%) | 30 (1.5) | 9 (3.5) | 2 (1.3) | 3 (0.7) | 6 (5.8) | 0 (0.0) | 8 (1.1) | 2 (0.7) | |
Prepatory School N (%) | 116 (5.7) | 33 (12.5) | 1 (0.7) | 23 (5.6) | 10 (9.6) | 5 (4.9) | 31 (4.5) | 13 (4.4) | |
Secondary School N (%) | 758 (37.5) | 109 (41.3) | 52 (34.4) | 172 (42.1) | 43 (41.3) | 38 (36.9) | 230 (32.9) | 114 (38.4) | |
University Degree N (%) | 969 (47.9) | 85 (32.2) | 90 (59.6) | 174 (42.5) | 38 (36.5) | 57 (55.3) | 369 (52.9) | 156 (52.5) | |
Post Graduate N (%) | 26 (1.3) | 3 (1.1) | 2 (1.3) | 11 (2.7) | 1 (1.0) | 1 (1.0) | 5 (0.7) | 3 (1.0) | |
Physical Activity | No N (%) | 1315 (64.9) | 143 (64.2) | 37 (24.5) | 323 (78.9) | 56 (53.8) | 54 (52.5) | 496 (71.2) | 206 (69.4) |
Yes N (%) | 710 (35.1) | 121 (45.8) | 114 (75.5) | 86 (21.0) | 48 (46.2) | 49 (47.5) | 201 (28.8) | 91 (30.6) | |
Metabolic Syndrome | No N (%) | 1656 (81.8) | 232 (87.9) | 133 (88.1) | 290 (70.9) | 86 (82.7) | 91 (88.3) | 607 (87.1) | 217 (73.1) |
Yes N (%) | 369 (18.2) | 32 (12.1) | 18 (11.9) | 119 (29.1) | 18 (17.3) | 12 (11.7) | 90 (12.9) | 80 (26.9) | |
Hyperglycemia | No N (%) | 941 (46.5) | 186 (70.5) | 122 (80.8) | 215 (52.6) | 77 (74.0) | 74 (71.8) | 491 (70.4) | 158 (53.2) |
Yes N (%) | 1084 (53.5) | 78 (29.5) | 29 (19.2) | 194 (47.4) | 27 (26.0) | 29 (28.2) | 206 (29.6) | 139 (46.8) | |
Hypertension | No N (%) | 1323 (65.3) | 132 (50.0) | 86 (56.9) | 147 (35.9) | 45 (43.3) | 45 (43.7) | 357 (51.2) | 129 (43.4) |
Yes N (%) | 702 (34.7) | 132 (50.0) | 65 (43.1) | 262 (64.1) | 59 (56.7) | 58 (56.3) | 340 (48.8) | 168 (56.6) | |
Obesity | No N (%) | 659 (32.5) | 112 (42.4) | 64 (42.4) | 129 (31.5) | 29 (27.9) | 41 (39.8) | 226 (32.4) | 58 (19.5) |
Yes N (%) | 1366 (67.5) | 152 (57.6) | 87 (57.6) | 280 (68.5) | 75 (72.1) | 62 (60.2) | 471 (67.6) | 239 (80.5) |
Total | Al-Nuzlah | Al-Alfiyya | Al-Rehab | |||||
PM2.5 | PM10 | PM2.5 | PM10 | PM2.5 | PM10 | PM2.5 | PM10 | |
PM | 29.07 (18.68) | 85.12 (30.30) | 29.10 (14.11) | 73.51 (10.13) | 24.51 (11.77) | 99.42 (43.94) | 18.04 (3.96) | 68.95 (20.48) |
Al | 0.89 (1.35) | 3.42 (2.05) | 0.28 (0.066) | 2.06 (0.46) | 0.58 (0.36) | 4.79 (2.50) | 0.28 (0.12) | 2.41 (0.86) |
Ca | 0.63 (0.77) | 4.18 (1.33) | 0.31 (0.037) | 3.55 (0.49) | 0.36 (0.18) | 4.48 (2.35) | 0.28 (0.18) | 3.48 (1.32) |
Cr | 0.0028 (0.0031) | 0.0093 (0.0058) | 0.0018 (0.00077) | 0.0066 (0.0011) | 0.0021 (0.00087) | 0.013 (0.0073) | 0.0014 (0.00099) | 0.0059 (0.0023) |
Cu | 0.0061 (0.0041) | 0.017 (0.0076) | 0.0024 (0.0023) | 0.012 (0.0048) | 0.013 (0.0097) | 0.022 (0.015) | 0.0018 (0.0014) | 0.013 (0.0038) |
Fe | 0.72 (1.18) | 3.32 (2.24) | 0.17 (0.027) | 1.84 (0.38) | 0.37 (0.21) | 4.25 (2.43) | 0.23 (0.15) | 2.29 (0.86) |
K | 0.21 (0.17) | 0.72 (0.27) | 0.15 (0.033) | 0.52 (0.083) | 0.013 (0.063) | 0.86 (0.51) | 0.11 (0.030) | 0.56 (0.19) |
Mg | 0.33 (0.43) | 1.44 (0.52) | 0.20 (0.079) | 1.17 (0.16) | 0.23 (0.15) | 1.93 (0.87) | 0.11 (0.057) | 1.21 (0.54) |
Mn | 0.022 (0.033) | 0.10 (0.063) | 0.0054 (0.0018) | 0.053 (0.012) | 0.012 (0.0057) | 0.12 (0.062) | 0.009 (0.0048) | 0.069 (0.028) |
Ni | 0.0071 (0.0028) | 0.011 (0.0039) | 0.0071 (0.0023) | 0.099 (0.0027) | 0.0064 (0.0033) | 0.012 (0.0067) | 0.0042 (0.00091) | 0.0073 (0.0025) |
Pb | 0.13 (0.14) | 0.16 (0.17) | 0.037 (0.049) | 0.039 (0.044) | 0.49 (0.44) | 0.47 (0.47) | 0.086 (0.14) | 0.11 (0.15) |
S | 3.48 (0.75) | 3.39 (0.51) | 4.04 (0.45) | 3.76 (0.47) | 3.87 (2.34) | 3.47 (2.17) | 2.51 (0.35) | 2.69 (0.15) |
Si | 2.47 (3.99) | 11.19 (6.70) | 0.72 (0.23) | 7.08 (1.36) | 1.44 (0.89) | 15.29 (8.10) | 0.68 (0.43) | 7.69 (2.80) |
Sr | 0.0049 (0.0061) | 0.026 (0.0085) | 0.0021 (0.00066) | 0.022 (0.0034) | 0.003 (0.0019) | 0.029 (0.015) | 0.0018 (0.0020) | 0.023 (0.015) |
Ti | 0.070 (0.13) | 0.32 (0.25) | 0.011 (0.0027) | 0.15 (0.028) | 0.032 (0.021) | 0.42 (0.26) | 0.017 (0.014) | 0.21 (0.08) |
V | 0.025 (0.010) | 0.032 (0.012) | 0.029 (0.0098) | 0.33 (0.0096) | 0.023 (0.012) | 0.031 (0.015) | 0.015 (0.0038) | 0.021 (0.06) |
Zn | 0.038 (0.023) | 0.073 (0.044) | 0.017 (0.0069) | 0.039 (0.012) | 0.056 (0.064) | 0.13 (0.17) | 0.019 (0.0088) | 0.049 (0.011) |
Pitrumin | Al-Rughama | University | Al-Muhammadiyah | |||||
PM2.5 | PM10 | PM2.5 | PM10 | PM2.5 | PM10 | PM2.5 | PM10 | |
PM | 31.14 (5.85) | 107.13 (28.75) | 73.16 (65.08) | 141.27 (124.20) | 29.91 (11.67) | 85.67 (33.09) | 15.78 (3.05) | 47.01 (6.52) |
Al | 0.41 (0.22) | 3.51 (1.07) | 3.93 (4.07) | 7.17 (6.07) | 0.63 (1.02) | 3.07 (1.77) | 0.15 (0.43) | 0.91 (0.41) |
Ca | 0.53 (0.17) | 6.11 (1.42) | 2.36 (2.76) | 4.95 (2.94) | 0.39 (0.30) | 4.77 (2.18) | 0.19 (0.15) | 1.96 (0.61) |
Cr | 0.0015 (0.0010) | 0.0089 (0.0031) | 0.0097 (0.010) | 0.021 (0.020) | 0.0019 (0.0021) | 0.0083 (0.0053) | 0.00092 (0.00058) | 0.0022 (0.00081) |
Cu | 0.0054 (0.0053) | 0.029 (0.0096) | 0.0098 (0.0057) | 0.016 (0.011) | 0.0077 (0.011) | 0.019 (0.016) | 0.0029 (0.0037) | 0.0048 (0.0022) |
Fe | 0.36 (0.15) | 3.59 (1.16) | 3.37 (3.94) | 7.71 (7.73) | 0.39 (0.64) | 2.81 (1.59) | 0.12 (0.093) | 0.75 (0.22) |
K | 0.21 (0.091) | 0.91 (0.29) | 0.59 (0.62) | 1.14 (0.93) | 0.16 (0.13) | 0.69 (0.31) | 0.12 (0.024) | 0.35 (0.075) |
Mg | 0.19 (0.098) | 1.55 (0.47) | 1.29 (1.21) | 2.21 (1.51) | 0.25 (0.33) | 1.41 (0.60) | 0.058 (0.025) | 1.55 (0.16) |
Mn | 0.014 (0.0073) | 0.11 (0.035) | 0.097 (0.11) | 0.23 (0.21) | 0.015 (0.021) | 0.095 (0.052) | 0.0051 (0.0028) | 0.11 (0.0079) |
Ni | 0.012 (0.0048) | 0.018 (0.0044) | 0.0097 (0.0058) | 0.13 (0.0088) | 0.0058 (0.0022) | 0.011 (0.0044) | 0.0043 (0.00096) | 0.018 (0.0021) |
Pb | 0.031 (0.020) | 0.041 (0.022) | 0.17 (0.27) | 0.021 (0.34) | 0.21 (0.42) | 0.27 (0.52) | 0.0066 (0.0067) | 0.0093 (0.0063) |
S | 4.51 (0.97) | 4.06 (0.67) | 3.65 (1.32) | 2.82 (1.25) | 3.11 (1.19) | 3.71 (0.99) | 2.63 (0.47) | 3.19 (0.73) |
Si | 1.16 (0.62) | 12.18 (3.96) | 11.49 (12.57) | 23.35 (20.07) | 1.47 (2.46) | 10.07 (5.42) | 0.34 (0.21) | 2.64 (1.04) |
Sr | 0.0044 (0.0017) | 0.036 (0.021) | 0.019 (0.022) | 0.036 (0.025) | 0.0029 (0.0027) | 0.027 (0.014) | 0.0021 (0.00097) | 0.012 (0.003) |
Ti | 0.025 (0.014) | 0.31 (0.095) | 0.36 (0.43) | 0.83 (0.90) | 0.034 (0.069) | 0.25 (0.17) | 0.012 (0.0094) | 0.07 (0.03) |
V | 0.045 (0.0099) | 0.055 (0.0072) | 0.028 (0.015) | 0.031 (0.014) | 0.019 (0.0069) | 0.029 (0.0099) | 0.016 (0.0037) | 0.021 (0.0078) |
Zn | 0.073 (0.13) | 0.13 (0.17) | 0.043 (0.027) | 0.061 (0.036) | 0.044 (0.0.76) | 0.079 (0.13) | 0.0099 (0.0044) | 0.022 (0.005) |
Metabolic Syndrome | Hyperglycemia | Hypertension | BMI | |
---|---|---|---|---|
PM2.5 | 1.11 (1.05–1.18) | 1.08 (1.03–1.14) | 1.09 (1.04–1.14) | 0.95 (0.91–1.00) |
Factor 1: Soil & Road Dust | 1.16 (1.08–1.25) | 1.12 (1.06–1.19) | 1.11 (1.05–1.18) | 0.95 (0.90–1.01) |
Factor 2: Residual Oil | 0.94 (0.88–1.00) | 0.88 (0.77–1.01) | 0.98 (0.96–1.01) | 1.00 (0.98–1.03) |
Factor 3: Incineration | 0.99 (0.92–1.07) | 0.98 (0.91–1.05) | 1.00 (0.96–1.04) | 0.85 (0.76–0.95) |
Factor 4: Traffic | 1.28 (0.94–1.76) | 1.33 (1.05–1.71) | 1.12 (0.89–1.12) | 0.74 (0.55–1.00) |
PM10 | 1.03 (0.95–1.13) | 0.98 (0.92–1.05) | 1.06 (1.00–1.13) | 0.95 (0.89–1.02) |
Factor 1: Soil & Road Dust | 1.03 (0.95–1.10) | 0.99 (0.95–1.05) | 1.05 (1.00–1.10) | 0.99 (0.89–1.10) |
Factor 2: Incineration | 1.02 (0.82–1.26) | 0.90 (0.76–1.06) | 1.08 (0.93–1.26) | 0.84 (0.61–1.17) |
Factor 3: Traffic | 1.01 (0.56–1.83) | 1.02 (0.67–1.57) | 0.77 (0.53–1.12) | 1.13 (0.90–1.40) |
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Shamy, M.; Alghamdi, M.; Khoder, M.I.; Mohorjy, A.M.; Alkhatim, A.A.; Alkhalaf, A.K.; Brocato, J.; Chen, L.C.; Thurston, G.D.; Lim, C.C.; et al. Association between Exposure to Ambient Air Particulates and Metabolic Syndrome Components in a Saudi Arabian Population. Int. J. Environ. Res. Public Health 2018, 15, 27. https://doi.org/10.3390/ijerph15010027
Shamy M, Alghamdi M, Khoder MI, Mohorjy AM, Alkhatim AA, Alkhalaf AK, Brocato J, Chen LC, Thurston GD, Lim CC, et al. Association between Exposure to Ambient Air Particulates and Metabolic Syndrome Components in a Saudi Arabian Population. International Journal of Environmental Research and Public Health. 2018; 15(1):27. https://doi.org/10.3390/ijerph15010027
Chicago/Turabian StyleShamy, Magdy, Mansour Alghamdi, Mamdouh I. Khoder, Abdullah M. Mohorjy, Alser A. Alkhatim, Abdulrahman K. Alkhalaf, Jason Brocato, Lung Chi Chen, George D. Thurston, Chris C. Lim, and et al. 2018. "Association between Exposure to Ambient Air Particulates and Metabolic Syndrome Components in a Saudi Arabian Population" International Journal of Environmental Research and Public Health 15, no. 1: 27. https://doi.org/10.3390/ijerph15010027