2025, Number 1
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Rev Biomed 2025; 36 (1)
Spatial analysis of overweight, obesity, and cardiometabolic risk factors in San Luis Potosi, Mexico
Santana-Arias R, Vega-Cárdenas M, Vidal-Batres M, Vargas-Morales JM, Terán-García M, Aradillas-García C, Cossío-Torres PE
Language: Spanish
References: 35
Page: 13-22
PDF size: 771.87 Kb.
ABSTRACT
Introduction. Obesogenic environment significantly contributes to
concentration of obesity prevalence in specific regions. However, the spatial
distribution of overweight, obesity and other cardiometabolic risk factors
(CMR) in young adults has not been fully elucidated.
Objective. To study the first and second order spatial properties of points of
overweight, obesity and other CMR factors in the Metropolitan Zone of San
Luis Potosí (ZMSLP), Mexico.
Material and Methods. Analytical cross-sectional study with 13,985
participants aged 18 to 24 years. The spatial distribution of points and density
mapping in the ZMSLP were characterized including overweight, obesity
and other CMR factors such as altered systolic and diastolic blood pressure
(BP), prediabetes, increased total cholesterol and triglycerides.
Results. There is considerable spatial heterogeneity in overweight and
obesity rates, which depend more spatially for men than for women. The
spatial pattern of other CMR factors such as altered levels of systolic and
diastolic BP, glucose, total cholesterol and triglycerides is random.
Conclusion. Spatial analysis allows us to understand the behavior of obesity
and other CMR factors from a regional perspective, identifying areas that
require prioritizing actions.
REFERENCES
Valenzuela PL, Carrera-Bastos P, Castillo-García A,Lieberman DE, Santos-Lozano A, Lucia A. Obesity andthe risk of cardiometabolic diseases. Nat Rev Cardiol. 2023;20:475–494. doi: 10.1038/s41569-023-00847-5.
Popkin BM, Du S, Green WD, Beck MA, Algaith T,Herbst CH, Alsukait RF, Alluhidan M, Alazemi N,Shekar M. Individuals with obesity and COVID‐19: Aglobal perspective on the epidemiology and biologicalrelationships. Obesity Reviews. 2020;21. doi: 10.1111/obr.13128.
Barquera S, Hernández-Barrera L, Trejo B, Shamah T,Campos-Nonato I, Rivera-Dommarco J. Obesidad enMéxico, prevalencia y tendencias en adultos. Ensanut2018-19. Salud Pública Mex. 2020;62:682–692. doi:10.21149/11630.
Drozdz D, Alvarez-Pitti J, Wójcik M, Borghi C,Gabbianelli R, Mazur A, Herceg-Čavrak V, Lopez-Valcarcel BG, Brzeziński M, Lurbe E, et al. Obesityand Cardiometabolic Risk Factors: From Childhoodto Adulthood. Nutrients. 2021;13:4176. doi: 10.3390/nu13114176.
Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ,Cleeman JI, Donato KA, Fruchart J-C, James WPT,Loria CM, Smith SC. Harmonizing the MetabolicSyndrome. Circulation. 2009;120:1640–1645. doi:10.1161/CIRCULATIONAHA.109.192644.
Ortiz-Rodríguez MA, Bautista-Ortiz LF, Villa AR,Antúnez-Bautista PK, Aldaz-Rodríguez MV, Estrada-Luna D, Denova-Gutiérrez E, Camacho-Díaz BH,Martínez-Salazar MF. Prevalence of MetabolicSyndrome Among Mexican Adults. Metab Syndr RelatDisord. 2022;20:264–272. doi: 10.1089/met.2021.0115.
Rojas-Martínez R, Aguilar-Salinas CA, Romero-Martínez M, Castro-Porras L, Gómez-Velasco D, MehtaR. Trends in the prevalence of metabolic syndrome andits components in Mexican adults, 2006-2018. SaludPublica Mex. 2021;63:713–724. doi: 10.21149/12835.
Zgodic A, Eberth JM, Breneman CB, Wende ME,Kaczynski AT, Liese AD, McLain AC. Estimates ofChildhood Overweight and Obesity at the Region, State,and County Levels: A Multilevel Small-Area EstimationApproach. Am J Epidemiol. 2021;190:2618–2629. doi:10.1093/aje/kwab176.
Sun Y, Hu X, Huang Y, On Chan T. Spatial Patternsof Childhood Obesity Prevalence in Relation toSocioeconomic Factors across England. ISPRS Int JGeoinf. 2020;9:599. doi: 10.3390/ijgi9100599.
Kundu S, Sharma P, Singh S, Kumar P. District-levelheterogeneity in overweight or obesity among womenof reproductive age: A geo-spatial analysis in India.PLoS One. 2023;18:e0290020. doi: 10.1371/journal.pone.0290020.
Wang P, Li K, Xu C, Fan Z, Wang Z. Spatial analysisof overweight prevalence in China: exploring theassociation with air pollution. BMC Public Health.2023;23:1595. doi: 10.1186/s12889-023-16518-6.
Wong KYY, Moy FM, Shafie A, Rampal S. Identifyingobesogenic environment through spatial clusteringof body mass index among adults. Int J Health Geogr.2024;23:16. doi: 10.1186/s12942-024-00376-5.
Olaya V. Sistemas de información geográfica. 2014.
Lloyd C. Spatial data analysis: an introduction for GISusers. USA: Oxford University Press; 2010.
Diggle PJ. Statistical analysis of spatial and spatiotemporalpoint patterns. CRC press.; 2013.
Fuentes-Santos I, Marey-Pérez MF, González-ManteigaW. Forest fire spatial pattern analysis in Galicia (NWSpain). J Environ Manage. 2013;128:30–42. doi:10.1016/j.jenvman.2013.04.020.
Mitchell A. The ESRI Guide to GIS Analysis: Spatialmeasurements & statistics, Volumen2. ESRI Guide toGIS analysis; 2005.
Thornton LE, Pearce JR, Kavanagh AM. UsingGeographic Information Systems (GIS) to assess therole of the built environment in influencing obesity: aglossary. International Journal of Behavioral Nutritionand Physical Activity. 2011;8:71. doi: 10.1186/1479-5868-8-71.
Rybnikova N, Stevens RG, Gregorio DI, SamociukH, Portnov BA. Kernel density analysis reveals a halopattern of breast cancer incidence in Connecticut. SpatSpatiotemporal Epidemiol. 2018;26:143–151. doi:10.1016/j.sste.2018.06.003.
Ray EL, Sakrejda K, Lauer SA, Johansson MA, ReichNG. Infectious disease prediction with kernel conditionaldensity estimation. Stat Med. 2017;36:4908–4929. doi:10.1002/sim.7488.
Santiago García M, Santiago LE. Análisis espacial delambiente alimentario no-saludable en municipios conalta prevalencia de obesidad en México. EconomíaSociedad y Territorio. 2023;723–751. doi: 10.22136/est20231923.
Vazquez-Vidal I, Voruganti VS, Hannon BA, AndradeFCD, Aradillas-García C, Nakamura MT, Terán-GarcíaM. Serum Lipid Concentrations and FADS GeneticVariants in Young Mexican College Students: The UPAMIGOSCohort Study. Lifestyle Genom. 2018;11:40–48. doi: 10.1159/000488085.
Robinson KN, Vazquez-Vidal I, Marques C, AndradeFCD, Aradillas-Garcia C, Teran-Garcia M. CirculatingTriglycerides and the Association of Triglycerides withDietary Intake Are Altered by Alpha-2-Heremans-Schmid Glycoprotein Polymorphisms. Lifestyle Genom.2017;10:75–83. doi: 10.1159/000478657.
Whelton PK, Carey RM, Aronow WS, Casey DE,Collins KJ, Dennison Himmelfarb C, DePalma SM,Gidding S, Jamerson KA, Jones DW, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection,Evaluation, and Management of High Blood Pressurein Adults: Executive Summary: A Report of theAmerican College of Cardiology/American HeartAssociation Task Force on Clinical Practice Guidelines.Hypertension. 2018;71:1269–1324. doi: 10.1161/HYP.0000000000000066.
2. Classification and Diagnosis of Diabetes: Standardsof Medical Care in Diabetes—2021. Diabetes Care.2021;44:S15–S33. doi: 10.2337/dc21-S002.
Jellinger PS, Handelsman Y, Rosenblit PD, BloomgardenZT, Fonseca VA, Garber AJ, Grunberger G, Guerin CK,Bell DSH, Mechanick JI, et al. American AssociationOf Clinical Endocrinologists And American CollegeOf Endocrinology Guidelines For Management OfDyslipidemia And Prevention Of CardiovascularDisease. Endocr Pract. 2017;23:1–87. doi: 10.4158/EP171764.APPGL.
Muñoz Robles CA, Santana Arias R. Puntos de calor enla Sierra Madre Oriental de San Luis Potosí: patronesespaciales y factores asociados. Madera y Bosques.2018;24. doi: 10.21829/myb.2018.2411565.
Hughey SM, Kaczynski AT, Porter DE, Hibbert J,Turner-McGrievy G, Liu J. Spatial clustering patternsof child weight status in a southeastern US county.Applied Geography. 2018;99:12–21. doi: 10.1016/j.apgeog.2018.07.016.
Qiu G, Liu X, Amiranti AY, Yasini M, Wu T, Amer S, Jia P.Geographic clustering and region-specific determinantsof obesity in the Netherlands. Geospat Health. 2020;15.doi: 10.4081/gh.2020.839.
Gartner DR, Taber DR, Hirsch JA, Robinson WR. Thespatial distribution of gender differences in obesityprevalence differs from overall obesity prevalenceamong US adults. Ann Epidemiol. 2016;26:293–298.doi: 10.1016/j.annepidem.2016.02.010.
Hajizadeh M, Campbell MK, Sarma S. A SpatialEconometric Analysis of Adult Obesity: Evidence fromCanada. Appl Spat Anal Policy. 2016;9:329–363. doi:10.1007/s12061-015-9151-5.
Yankey O, Amegbor PM, Essah M. The Effect ofSocioeconomic and Environmental Factors on Obesity.International Journal of Applied Geospatial Research.2021;12:58–74. doi: 10.4018/IJAGR.2021100104.
Fraser LK, Clarke GP, Cade JE, Edwards KL. Fast foodand obesity: a spatial analysis in a large United Kingdompopulation of children aged 13-15. Am J Prev Med.2012;42:e77-85. doi: 10.1016/j.amepre.2012.02.007.
Aradillas-García C, Palos-Lucio G, Padrón-SalasA. Socio-Urban Spatial Patterns Associated withDyslipidemia among Schoolchildren in the City ofSan Luis Potosi, Mexico. Journal of Urban Health.2016;93:53–72. doi: 10.1007/s11524-015-9997-5.
Ellaway A, Macdonald L, Lamb K, Thornton L, DayP, Pearce J. Do obesity-promoting food environmentscluster around socially disadvantaged schools inGlasgow, Scotland? Health Place. 2012;18:1335–1340.doi: 10.1016/j.healthplace.2012.06.001.