Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Prevalence and correlates of diabetes and metabolic syndrome in a rural indigenous community in Baja California, Mexico

BMC Public Health volume 18, Article number: 1397 (2018) Cite this article

Abstract

Background

Diabetes is a leading cause of morbidity and mortality in Mexico and understudied among indigenous populations. This study aimed to determine the prevalence and identify correlates of Type 2 diabetes mellitus (Type 2 DM) and metabolic syndrome (MetS) in a rural, indigenous community in Northwestern Mexico.

Methods

A cross-sectional study was conducted in the community of San Quintin, Baja California, Mexico, among a sample of households. A total of 275 participants (≥18 years old) underwent a questionnaire, physical examination, and serologic test. Prevalence and adjusted odds ratio (AOR), using logistic regression modeling, were estimated with 95% confidence intervals (95% CI).

Results

The prevalence of Type 2 DM and MetS was 21.8 and 53.1%, respectively. Mean ± standard deviation (SD) age and body mass index of study participants was 35.8 ± 13.0 years and 28.7 ± 5.6 kg/m2, respectively. Participants were 75% female and 60.7% self-identified as indigenous. Thirty-seven percent of adults had high blood pressure. After controlling for age, higher educational attainment had a protective effect on Type 2 DM (AOR = 0.39; 95% CI 0.20, 0.77). Additionally, the presence of MetS was associated with being female (AOR = 2.27; 95% CI 1.23, 4.14) and having lower educational attainment (AOR = 0.62; 95% CI 0.37, 0.94).

Conclusions

The prevalence of Type 2 DM and MetS was high in this rural and indigenous population, and education was shown to play a critical role. These findings support the need for community-inclusive health-promoting interventions in rural communities.

Peer Review reports

Background

Type 2 diabetes mellitus (Type 2 DM) and metabolic syndrome (MetS) are global public health problems [1, 2]. Type 2 DM is a chronic disease with hyperglycemia as a hallmark [2]. MetS refers to a cluster of factors including central obesity, hyperglycemia, hypertriglyceridemia, low plasma high-density lipoprotein (HDL)-cholesterol and hypertension, where the presence of at least three of these factors indicates MetS [3]. Entities including the National Cholesterol Education Program Adult Treatment Panel III (ATP III), the World Health Organisation and the International Diabetes Federation (IDF), have developed MetS diagnosis guidelines [4,5,6]. MetS components underlie cardiometabolic diseases increasing the risk of developing diabetes and cardiovascular disease (CVD), and even mortality [7, 8]. It has been shown that there is a five-fold increased risk in Type 2 DM development among those with MetS [9].

The prevalence of these two conditions continues to increase in parallel. Diabetes prevalence has almost doubled in the last 35 years globally, and it is estimated that between 10 and 40% of the world’s adult population has MetS [9, 10]. In Latin America, there is a MetS prevalence (weighted mean) of 24.9% (range: 18.8–43.3%) [11]. According to data from the 2006 Mexican National Health and Nutrition Survey (ENSANUT 2006) the prevalence of MetS could be as high as 49.8% in the overall adult population [12]. Mexico has the fifth highest number of individuals with diabetes, after China, India, the United States and Brazil [9], with diabetes accounting for the leading cause of morbidity [13]. The ENSANUT-Medio Camino 2016, a mid-point national survey, determined the diabetes prevalence to be 9.4% nationally, and 9.6% in rural northern areas of Mexico [14].

There has been limited research on Type 2 DM and cardiovascular risk factors in Mexican indigenous populations, and even less emphasis on MetS. A 2007 cross-sectional study found a prevalence of Type 2 DM in an indigenous population in the central valley of Mexico of 4.4% [15]. Another study found a prevalence of Type 2 DM among indigenous groups in neighboring Southern Mexican states of 8.7% among Zapotec and 6.9% among Mixe groups [16]. Data from a 2008 study in a migrant Mixtec population in rural Northwestern Mexico, found the prevalence of MetS was 41.1%, while that of Type 2 DM was 26.2% [17].

The aim of this study was to determine the prevalence and correlates of Type 2 DM and MetS in adults in a rural, indigenous community in Baja California, Mexico, while addressing sex differences for burden of disease. The current study tackled some of the limitations of previous work in the same community, where the prevalence of Type 2 DM and MetS was determined using a convenience sample and medical records of self-identified Mixtec adults [17].

Participants and methods

Study design and protocol

This cross-sectional community-wide study was conducted in a rural community in San Quintin, Baja California, Mexico (2013–2014), led by Viaje Interinstitucional de Integración Docente, Asistencial y de Investigación (VIIDAI), a binational inter-institutional program involving two universities in the United States (University of California San Diego and San Diego State University [SDSU]) and one in Mexico (Universidad Autónoma de Baja California ([UABC]). VIIDAI faculty and students operate a weekend-long clinic twice a year, where primary care services are offered to underserved community residents. San Quintin is an agriculture-centered municipality located about 300 km south of the United States-Mexico border on the west coast of the Baja California Peninsula. Since agriculture plays a key role in the town’s economy, it attracts farmers from South Mexico, primarily Oaxaca, in search of year-round work; the majority self-identify as indigenous. According to the latest surveillance data from Mexico’s Indigenous Infrastructure Program, there were 3805 persons living in the rural community we studied, of which 51% were adults [18].

Formative research, six months prior to the study start date, involved geographical information system (GIS) mapping, supplemented by fieldwork to confirm all edifices, to produce a map from which to sample residences for the current study. The sampling frame consisted of an enumerated list of 779 residences successfully identified as occupied households, of which 340 sampling units (i.e., homes) were randomly pre-selected via simple random sample selection. This approach was taken to maximize study efficiency, using study personnel wisely while capturing a representative sample of homes from all sections of the community.

The study protocol consisted of visiting each pre-selected household and requesting an adult to be screened for eligibility. Only one adult per household was allowed to participate in the study therefore a die (highest number rolled) was used to select the participant. Eligibility criteria included residing in the neighborhood for ≥6 months, age ≥ 18 years old, and agreement to participate in the study. Before the comprehensive examination, written informed consent in Spanish was obtained from all participants. The examination took place in mobile units stationed within two blocks of participants’ residence and involved an interviewer-administered questionnaire, anthropometric measurements, and blood sample. Following the examination, participants attended a nutritional counseling session and received $100.00 pesos (Peso Mexicano) as supplementary nutritional support. The research team was trained on the study protocol and followed a culturally-sensitive approach.

The study procedures were approved by the Bioethics Committee of the Facultad de Medicina y Psicología of UABC and by the Institutional Review Board of SDSU’s Human Research Protection Program.

Socio-demographic and lifestyle questionnaire

Participants were interviewed in private by trained and native Spanish-speaking surveyors, following a standardised script and validated 45-item questionnaire to obtain socio-demographic and lifestyle data [19]. Socio-demographic questions included age, sex, occupation, years living in San Quintin, literacy, and number of school years completed. Lifestyle questions addressed physical activity, smoking habits, and alcohol consumption. Physical activity was assessed by a composite score of four variables, each graded from one (least activity) to four (most activity), for a maximum score of sixteen points. Variables ranked from least to most activity, included: (1) main mode of transportation: automobile, motorcycle, bicycle, walking; (2) physical activity intensity at work: sitting, standing and walking, carrying light load/climbing stairs, heavy work; (3) frequency of recreational activity (e.g. sport): none or rarely, 2–3 times/month, 1–2 times/week, and > 3 times/week); and (4) hours per week of watching television: > 13 h, 8–12 h, 4–7 h and < 3 h.

Anthropometric and clinical measurements

Height, weight, waist circumference, and blood pressure, were measured by trained research assistants following Mexican public health and clinical guidelines [20, 21]. Height and weight were determined to the nearest 0.1 cm and 0.1 kg, respectively, using a mobile combined scale and stadiometer (Healthteam, GA, USA). Waist circumference was measured to the nearest 0.1 cm using a standard, non-stretch measuring tape (Gulik, Lafayette Instrument Co, IN, USA). Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.

Blood pressure was obtained using a new digital blood pressure monitor (Omron 7 series Plus) from participants in a seated position after resting for 10 min, with arms relaxed and palms facing upward. Two readings were taken 5 min apart and the average of the two readings was recorded. If the difference between the first and the second reading was ≥10 mmHg for systolic blood pressure (SBP) and/or ≥ 6 mmHg for diastolic blood pressure (DBP), then a third measurement was done, and the average of all three measurements was calculated.

Anthropometry and blood pressure training was led by a study physician, certified dietitian-nutritionist and project researcher.

Biochemical assays

A non-fasting venous blood sample of 5 cc was obtained from each participant. Assays included triglycerides, total cholesterol, and both low-density lipoprotein (LDL)- and HDL-cholesterol. The CardioChek PA (Polymer Technology Systems Inc., IN, USA) determined the lipid panel with 40 μl of whole blood.

Glycated hemoglobin (HbA1c) was measured using the rapid diagnostic and point of care NycoCard HbA1c Reader II (Alere, MA, USA), utilising 5 μl of blood from a finger stick sample. Both devices were calibrated every 10 tests.

Outcomes measures

Type 2 DM was defined as HbA1c ≥6.5% according to American Diabetes Association (ADA) diagnosis criteria [22]. Participants that met the ADA’s diagnosis criteria for Type 2 DM were counseled by a physician and their test results were explained. A nutritional counseling session with a registered dietitian-nutritionist followed. Additionally, a sealed envelope that included a copy of their laboratory results and a clinical summary was provided to each participant, to take to their primary care physician for follow-up treatment and management.

MetS was defined using the IDF criteria [6]. This included participants with ethnic-specific waist circumference cut-offs of ≥90 cm in men and ≥ 80 cm in women. Additionally, participants had to meet at least two of the following to criteria: 1) elevated serum triglycerides (≥1.7 mmol/L); 2) low HDL-cholesterol levels (<1.03 mmol/L in men and <1.20 mmol/L in women); 3) elevated SBP ≥130 mmHg or DBP ≥85 mmHg; and 4) elevated fasting serum glucose levels, (≥5.6 mmol/L), or previously diagnosed diabetes.

Statistical analysis

Sample characteristics were expressed as mean ± standard deviation (SD) for continuous variables, and frequencies and percentages for categorical variables. Prevalence of Type 2 DM and MetS, and cardiovascular risk factors were stratified by sex. Student’s t tests, and Pearson’s Chi-square tests were used to assess the associations between each factor and binary outcome variables. For analyses, completed school years was dichotomised (<6 years vs ≥6 years) based on mean of five schooling years, and income was dichotomised (<$3200 vs ≥ $3200) based on median earnings.

Separate multivariable logistic regression analyses were performed to identify factors independently associated with Type 2 DM and MetS. Backward stepwise elimination was used to remove non-significant variables until only statistically significant variables remained in the final model. Unadjusted and adjusted odds ratios and corresponding 95% confidence intervals were computed using simple and multivariable logistic regression analysis. Goodness of fit for the models was determined with the Hesmer-Lemeshow test. A p < 0.05 denoted statistical significance. SAS version 9.2 (SAS Institute) was used for all statistical analyses.

Results

A total of 294 adults participated, corresponding to an 86% response rate. Ineligibility of participants and/or unresponsiveness explain the remaining 14%. Nineteen participants were excluded because of incomplete data, leaving 275 participants for statistical analysis.

Mean ± SD age was 35.8 ± 13.0 years, 75% of participants were women, close to two thirds of surveyed adults were born in the state of Oaxaca and, on average, had lived in San Quintin for almost two decades (Table 1). Number of completed school years was significantly lower among women (p = 0.004), and almost 82% of the study population was literate, with a significant sex difference (p = 0.016). Although the majority of women were homemakers, nearly 40% worked as field laborers. Sixty percent of study participants self-identified as having an indigenous ethnic background.

Table 1 Socio-demographic, lifestyle, clinical, and laboratory characteristics, stratified by sex, among adults in San Quintin, Baja California, Mexico – 2013-2014 (n = 275)
Full size table

Seven percent of the population reported consuming alcohol 2–3 times per month, with beer being the most frequently consumed alcoholic beverage. Only 8% of participants were current smokers, significantly higher in men (22.9%) compared to women (2.4%) (p < 0.001). The mean ± SD physical activity score was 9.0 ± 2.6 and did not differ by sex.

Mean ± SD BMI was 28.7 ± 5.6 kg/m2, however, women had a significantly higher BMI compared to men (p = 0.006). Obesity (BMI ≥30 kg/m2) was observed in 25.7% of men and 40.5% of women. There was no sex difference by total and LDL-cholesterol, and triglyceride levels, yet men had significantly lower HDL-cholesterol (p = 0.002). The mean ± SD HbA1c for the sample was 6.1 ± 1.4%, with no sex difference.

Prevalence of Type 2 DM and MetS was 21.8 and 53.1%, respectively (Table 2). Both conditions were higher in women; however, the difference by sex was only statistically significant for MetS (p = 0.011). Participants self-identified as having an indigenous background had a 19 and 53% prevalence of Type 2 DM and MetS, respectively; yet, there was no significant difference with those who identified as being mestizo. A large waist circumference was found in 80.7% of the population, significantly higher in women (p < 0.001), where more than 90% of adult women had a waist girth ≥80 cm. Eight percent of adults had hypertension and 28.7% had normal-high blood pressure, and more than half of adults displayed dyslipidemia.

Table 2 Type 2 Diabetes Mellitus, Metabolic Syndrome, and criteria diagnosis, stratified by sex, among adults in San Quintin, Baja California, Mexico – 2013-2014 (n = 275)
Full size table

In bivariate analyses, age, sex, schooling, and literacy were associated with Type 2 DM (Table 3); while age, sex, schooling, literacy and birth state were associated with MetS (Table 4).

Table 3 Socio-demographic and lifestyle characteristics by Type 2 Diabetes Mellitusa diagnosis among adults in San Quintin, Baja California, Mexico – 2013-2014 (n = 275)
Full size table
Table 4 Socio-demographic and lifestyle characteristics by Metabolic Syndromea diagnosis among adults in San Quintin, Baja California, Mexico – 2013-2014 (n = 275)
Full size table

After controlling for age, those who completed at least elementary school were almost 60% less likely to have Type 2 DM (adjusted OR [AOR] = 0.39, 95% CI 0.20, 0.77) (Table 5). Women had 2.27 (95% CI: 1.23, 4.14) higher odds of MetS compared to men, after adjusting for age and education. Furthermore, after controlling for sex and age, completion of at least elementary school was inversely associated with MetS (AOR = 0.62, 95% CI 0.37, 0.94).

Table 5 Multivariable logistic regressiona analyses of Type 2 Diabetes Mellitusb and Metabolic Syndromec in adults in San Quintin, Baja California, Mexico – 2013-2014 (n = 275)
Full size table

Discussion

This study found a Type 2 DM and MetS prevalence in adults in a rural indigenous community in San Quintin, Mexico, of 21.8 and 53.1%, respectively, with a higher prevalence of both conditions in women.

Prevalence of Type 2 DM in the community was high. The latest national survey results found a diabetes prevalence of 9.6% in northern rural areas of Mexico [23], much lower than our results. Escobedo et al. and Alvarado-Osuna et al., determined diabetes prevalence in indigenous groups in Southern Mexico, with our study population showing a much higher prevalence than that observed in Zapotec (8.7%), Mixe (6.9%) [16] and Otomi (4.4%) [15] groups. Zapotec and Mixe women had a higher diabetes prevalence, 13.3 and 7.1%, respectively, when compared to Zapotec and Mixe men, with a 6.2 and 5.7% prevalence, respectively. Our study shared the commonality of a higher proportion of women (22.9%) with Type 2 DM, in contrast to men (18.6%), yet recognize the larger prevalence of the condition in our sample of rural indigenous adults. Compared to the findings reported by Goodman et al., in the same community eight years prior (26.2%), the prevalence of Type 2 DM was reduced by 16.8% [17]. Goodman et al., defined Type 2 DM as having a HbA1c of ≥6.5% or a fasting glucose of ≥126 mg/dL; it was unclear of the proportion of those defined by either HbA1c or fasting glucose.

Similarly, we found a very high MetS prevalence. MetS was 29% higher than what was previously found in the same community in 2008 (41.1%) [17]. We are cautious when making direct comparisons because the previous study followed the ATP III guideline criteria, while we used the IDF definition, in which central obesity, using ethnic-specific waist circumference cut-offs, is a requirement. Our findings were similar to the ENSANUT 2006 MetS prevalence of 49.8% [12], and comparable to a recent Northeastern Mexico study that reported an overall MetS prevalence of 54.8% in the state of Nuevo Leon [24], with closer resemblance to the IDF definition. Although this Northeastern Mexico study included an older population (mean ± SD age 51.0 ± 19.0 and 50.7 ± 19.0 years, men and women, respectively), teenagers were also included. Considering the MetS prevalence in Latin America, our findings sit outside the estimated range of 18.8–43.4% [11].

A higher prevalence of MetS was observed in women when compared to men (56.7% vs 40.0%), analogous to the Northeastern Mexico study findings (60.4% in women vs 48.9% in men) [24]. This was further observed in our multivariable model results, when after adjusting for covariates, women had a 2.27 (95% CI 1.23, 4.14) greater odds of MetS compared to men. The 40% MetS prevalence in men was akin to the general prevalence reported by Ramirez-Vargas et al., in a sample of Oaxacan men living in rural and urban communities (40.0% in rural vs 41.2% in urban) [19]; yet our results were 31% higher than the prevalence of MetS among those Oaxacan men living in a rural area (27.6%). Regardless of the differences between studies, our results indicate that the prevalence of MetS was high in our study population and has increased since the last cross-sectional examination.

Schooling was independently associated with both Type 2 DM (AOR = 0.39, 95% CI 0.20, 0.77) and MetS (AOR = 0.62, 95% CI 0.37, 0.94). Recent Type 2 DM epidemiological surveillance data highlighted that among all identified cases for the 2016 year, almost 50% had less than elementary school education, with less cases having had at least elementary school [25]. The ENSANUT 2006 national results reported that the lower the education level, the higher prevalence of MetS [26]. Those adults with less than elementary school education had the highest prevalence of MetS, 60.1% (95% CI 56.9–63.3%) while those with more than high school attainment had 39.3% (95% CI 32.5–46.6%) prevalence of MetS in 2006. This evidence supports our findings, underscoring educational level as an important socio-demographic characteristic and possible protective factor for Type 2 DM and MetS in indigenous Mexican groups.

The population also exhibited altered cardiovascular characteristics. A large waist circumference was observed in a large proportion of the study population, indicating excess abdominal body fat and health consequences associated with central obesity. Intra-abdominal fat, MetS and Type 2 DM are inter-related [27], thus having an elevated intra-abdominal fat accumulation may have a direct function in the development of MetS [28] and Type 2 DM, because of its association with insulin resistance [27].

More than a third of participants were hypertensive or had normal-high blood pressure, worrisome since elevated blood pressure is a silent killer and major risk factor for CVD. The new hypertension guidelines acknowledge complications at lower levels of blood pressure, thus aggressive screening should be highlighted [29]. More than half of adults presented dyslipidemia, similar to adults in Northeastern Mexico, were two thirds had low HDL-cholesterol levels and almost half had elevated triglycerides [24].

The mean ± SD total activity score for the population was 9.0 ± 2.6, with no sex difference. This translates to an average level of total activity based on transportation, television watching time, occupation-related intensity and recreational physical activity. We understand the relationship between physical activity and Type 2 DM and MetS, yet we did not see this in the present study.

We acknowledge the cross-sectional nature of the study design and related shortcomings, yet our study aim consisted of determining Type 2 DM and MetS prevalence while describing potential disease correlates in this particular population, without a singular exposure-disease relationship. Thereby, study limitations include self-reported lifestyle measures and incomplete information on family history, introducing information bias. Dietary intake was not assessed, and likelihood of residual confounding is recognised. Generalisability is restricted to neighboring communities with a demographically similar population.

Study strengths include the use of GIS mapping and systematic random sampling of households, which allowed us to obtain a more representative sample of the community than prior studies. The application of a detailed protocol performed by a rigorously trained team in a rural and inaccessible area is a notable attainment, while determining health outcomes with standardised and validated objective measures, applied in a global public health setting. Diabetes in on the rise in Mexico, thereby we consider our findings to be relevant and timely. We are contributing to the study of Mexican rural and indigenous communities, whilst providing crucial evidence to help inform interventions and health policy.

Conclusions

Findings elucidate the elements of Type 2 DM and MetS in this rural population, while enhancing the literature on these two conditions and cardiometabolic risk factors in indigenous communities in Mexico. It is important to highlight that our study aimed to describe the magnitude of the burden of Type 2 DM and MetS, the latter serving as an indicator of the number of additional community members at risk of Type 2 DM. Comprehensive community-inclusionary interventions should be pursued to help prevent and mitigate potential health consequences related to these conditions, especially including women. Education must be considered as a key factor when addressing the prevention of Type 2 DM and MetS.

Abbreviations

95% CI:

95% confidence interval

AOR:

Adjusted odds ratio

ATP III:

Adult treatment panel III

BMI:

Body mass index

CVD:

Cardiovascular disease

DBP:

Diastolic blood pressure

ENSANUT:

Encuesta Nacional de Salud y Nutrición (Spanish), Mexican National Health & Nutrition Survey (English)

HbA1c:

Glycated hemoglobin

HDL-cholesterol:

High density lipoprotein cholesterol

IDF:

International diabetes federation

LDL-cholesterol:

Low density lipoprotein cholesterol

MetS:

Metabolic syndrome

SBP:

Systolic blood pressure

SD:

Standard deviation

SDSU:

San Diego State University

Type 2 DM:

Type 2 diabetes mellitus

UABC:

Universidad Autonoma de Baja California

VIIDAI:

Viaje Interinstitucional de Integración Docente, Asistencial y de Investigación (Spanish), Inter-institutional Field Experience for Integration, Teaching, Health Services and Research (English)

References

  1. Grundy SM. Metabolic syndrome update. Trends Cardiovasc Med. 2016;26(4):364–73.

    Article  Google Scholar 

  2. World Health Organization. Global report of diabetes [Internet]. Geneva: World Health Organization [cited 2018 May 2]. Available from: http://www.who.int/diabetes/global-report/en/.

  3. Grundy SM. Overnutrition, ectopic lipid and the metabolic syndrome. J Investig Med. 2016;64(6):1082–6.

    Article  Google Scholar 

  4. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97.

  5. Alberti KGMM, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53.

    Article  CAS  Google Scholar 

  6. Alberti KGMM, Zimmet P, Shaw J. Metabolic syndrome-a new world-wide definition. A consensus statement from the international diabetes federation. Diabet Med. 2006;23(5):469–80.

    Article  CAS  Google Scholar 

  7. Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK, et al. Metabolic syndrome and risk of incident cardiovascular events and death. J Am Coll Cardiol. 2007;49(4):403–14.

    Article  CAS  Google Scholar 

  8. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk: a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.

    Article  Google Scholar 

  9. Grundy SM. Metabolic syndrome pandemic. Arterioscler Thromb Vasc Biol. 2008;28(4):629–36.

    Article  CAS  Google Scholar 

  10. Diabetes Atlas Eight Edition Committee. IDF diabetes atlas: 2017 Atlas [Internet]. Brussels: International Diabetes Federation [cited 2018 May 2]. Available from: http://www.diabetesatlas.org/resources/2017-atlas.html

  11. Márquez-Sandoval F, Macedo-Ojeda G, Viramontes-Hörner D, Fernández Ballart J, Salas Salvadó J, Vizmanos B. The prevalence of metabolic syndrome in Latin America: a systematic review. Public Health Nutr. 2011;14(10):1702–13.

    Article  Google Scholar 

  12. Rojas R, Aguilar-Salinas CA, Jiménez-Corona A, Shamah-Levy T, Rauda J, Avila-Burgos L, et al. Metabolic syndrome in Mexican adults: results from the National Health and nutrition survey 2006. Salud Publica Mex. 2010;52(Suppl 1):S11–8.

    Article  Google Scholar 

  13. Pan American Health Organization. Health in the Americas +, 2017 Edition. Regional outlook and country profiles [Internet]. Washington, D.C: Pan American Health Organization [cited 2018 Feb 3]. Available from: http://iris.paho.org/xmlui/handle/123456789/34321.

  14. Hernández-Ávila M, Rivera-Dommarco J, Shamah-Levy T, Cuevas-Nasu L, Gómez-Acosta LM, Gaona-Pineda EB, et al. Encuesta Nacional de Salud y Nutrición de Medio Camino 2016. ENSANUT MC 2016: Informe final de resultados 2016 [Internet]. Cuernavaca: Instituto Nacional de Salud Pública [cited 2018 March 3]. Available from: https://www.insp.mx/images/stories/2017/Avisos/docs/180315_encuesta_nacional_de_salud_y_nutricion_de_medio_Ca.pdf.

  15. Alvarado-Osuna C, Milian-Suazo F, Valles-Sánchez V. Prevalencia de diabetes mellitus e hiperlipidemias en indígenas otomíes. Salud Publica Mex. 2001;43(5):459–63.

    Article  CAS  Google Scholar 

  16. Escobedo J, Chavira I, Martínez L, Velasco X, Escandón C, Cabral J. Diabetes and other glucose metabolism abnormalities in Mexican Zapotec and Mixe Indians. Diabet Med. 2010;27(4):412–6.

    Article  CAS  Google Scholar 

  17. Goodman D, Fraga MA, Brodine S, Ibarra ML, Garfein RS. Prevalence of diabetes and metabolic syndrome in a migrant Mixtec population, Baja California. Mexico J Immigr Minor Heal. 2013;15(1):93–100.

    Article  Google Scholar 

  18. Gobierno de Baja California. Programa de Infraestructura Básica para los Indígenas. Evaluación Específica de Desempeño 2013 [Internet]. Gobierno del Estado de Baja California [cited 2013 February 3]. Available from: http://indicadores.bajacalifornia.gob.mx/Uploads/Difusion%202013/INDIGENAS%20PIBAI%20Contenido%20Extenso.pdf.

  19. Ramírez-Vargas E, Arnaud-Viñas M, Del R, Delisle H. Prevalence of the metabolic syndrome and associated lifestyles in adult males from Oaxaca, Mexico. Salud Publica Mex. 2007;49(2):94–102.

    Article  Google Scholar 

  20. Centro de Investigación en Nutrición y Salud Instituto Nacional de Salud Pública. Manual de procedimientos para proyectos de nutrición [Internet]. Cuernavaca: Instituto Nacional de Salud Pública [cited 2013 March 24]. Available from: http://www.salud.gob.mx/unidades/cdi/documentos/proy_nutricion.pdf.

  21. Secretaría de Salud. Norma Oficial Mexicana NOM-030-SSA2–1999 para la prevención, tratamiento y control de la hipertensión arterial [Internet]. Mexico: Secretaría de Salud [cited 2013 May 15]. Available from: http://www.salud.gob.mx/unidades/cdi/nom/030ssa29.html.

  22. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2010; 33 Suppl 1: S62-S69.

  23. Gutiérrez JP, Rivera-Dommarco J, Shamah-Levy T, Villalpando-Hernández S, Franco A, Cuevas-Nasu L, et al. Encuesta Nacional de Salud y Nutrición 2012. ENSANUT 2012: Informe final de resultados 2012 [Internet]. Cuernavaca: Instituto Nacional de Salud Pública [cited 2018 April 28]. Available from: https://ensanut.insp.mx/informes/ENSANUT2012ResultadosNacionales.pdf.

  24. Salas R, Bibiloni M del M, Ramos E, Villarreal JZ, Pons A, Tur JA, et al. Metabolic syndrome prevalence among Northern Mexican adult population. PLoS One 2014; 9(8): e105581.

  25. Secretaría de Salud. Dirección General de Epidemiología. Sistema de vigilancia Hospitalaria de Diabetes Tipo 2. Boletín e cierre anual 2016 [Internet]. Mexico: Secretaría de Salud [cited 2018 April 17]. Available from: https://www.gob.mx/cms/uploads/attachment/file/266507/dmt2_cierre_2016.pdf.

  26. Olaiz-Fernández G, Rivera-Dommarco J, Shamah-Levy T, Rojas R, Villalpando-Hernández S, Hernández-Avila M, et al. Encuesta Nacional de Salud y Nutrición 2006. ENSANUT 2006: Informe final de resultados 2006 [Internet]. Cuernavaca: Instituto Nacional de Salud Pública [cited 2018 April 7]. Available from: https://ensanut.insp.mx/informes/ensanut2006.pdf.

  27. Han TS, Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Dis. 2016;5:1–13.

    Google Scholar 

  28. Han TS, Williams K, Sattar N, Hunt KJ, Lean MEJ, Haffner SM. Analysis of obesity and hyperinsulinemia in the development of metabolic syndrome: San Antonio heart study. Obes Res. 2002;10(9):923–31.

    Article  Google Scholar 

  29. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2018; 71(19): e127-e248.

Download references

Acknowledgements

Kind regards to the residents of the San Quintin community for their trust and participation in the study. Special recognition to Professor Javier Martínez, school principal of Escuela Primaria Federal Bilingüe, for his incessant support and important role as key informant and liaison. We also wish to acknowledge the Old Mission Rotary Club and students and faculty from San Diego State University Graduate School of Public Health, University of California San Diego School of Medicine and the Universidad Autónoma de Baja California Facultad de Medicina y Psicología for their commitment and zealous work ethic.

Funding

Funding for the study, including design, data collection, logistics, training, and field work activities considering anthropometric instruments, point of care devices, and biochemical assays, was provided by the Fred H. Bixby Foundation (Award C03093) and Universidad Autónoma de Baja California Facultad de Medicina y Psicología (Award 304/2/C/103/17), Tijuana, Baja California campus. Miss Pacheco is a trainee in the University of California San Diego Integrated Cardiovascular Epidemiology Fellowship (National Heart Lung and Blood Institute T32 HL079891–11). She was funded by the aforementioned traineeship as well as through the University of California San Diego School of Medicine Hispanic Center of Excellence (Health Resources and Services Administration D34HP31027).

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to the internal policies of UABC’ Facultad de Medicina and Psicología but are available from the corresponding author on reasonable request.

Author information

Authors and Affiliations

  1. Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, La Jolla, CA, USA

    Lorena S. Pacheco & Richard S. Garfein

  2. Facultad de Medicina y Psicología, Universidad Autónoma de Baja California, Tijuana, Baja California, Mexico

    David A. Hernández-Ontiveros & Miguel A. Fraga

  3. School of Public Health, San Diego State University, San Diego, CA, USA

    Lorena S. Pacheco, Esmeralda Iniguez-Stevens, Stephanie Brodine & Margarita Santibañez

  4. Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Richard S. Garfein

Authors
  1. Lorena S. Pacheco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David A. Hernández-Ontiveros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Esmeralda Iniguez-Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephanie Brodine
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Richard S. Garfein
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Margarita Santibañez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Miguel A. Fraga
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LSP led nutrition counseling, managed and analysed the collected data, interpreted results, and drafted the manuscript. DAHO assisted in field work logistics, contributed to power calculation, data collection, and statistical analysis in preparation for the manuscript. EIS helped develop the study design, led triage group coordination for field work data collection, and contributed to interpretation of results and discussion of manuscript. SB, San Diego State University team leader, helped conceive study and contributed to the interpretation of results and discussion, and acquired funding for project development. RSG, University of California San Diego team leader, participated in study design, interpretation of results and discussion. MS assisted in study logistics and triage group organization for data collection as well as manuscript discussion. MAF is the director of VIIDAI and Universidad Autónoma de Baja California team leader; he conceived, with SB, the study objective and design as well as implementation, led protocol design and research assistant trainings, contributed to discussion of manuscript, and acquired study funding. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Lorena S. Pacheco.

Ethics declarations

Ethics approval and consent to participate

This study and all associated procedures were approved by the Bioethics Committee of the Facultad de Medicina y Psicología of UABC (No. 991/2013–2) and by the Institutional Review Board of SDSU’s Human Research Protection Program (No. 1521089). In alignment with the tenets of the aforementioned institutional boards, written informed consent, in Spanish, was obtained from all study participants before study enrollment.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pacheco, L.S., Hernández-Ontiveros, D.A., Iniguez-Stevens, E. et al. Prevalence and correlates of diabetes and metabolic syndrome in a rural indigenous community in Baja California, Mexico. BMC Public Health 18, 1397 (2018). https://doi.org/10.1186/s12889-018-6276-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-018-6276-x

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us