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Comparison of prevalence of diabetes complications in Brazilian and Mexican adults: a cross-sectional study

Abstract

Background

Type 2 diabetes is more frequent in Latin American people than in non-Hispanic whites due to a combination of genetic and lifestyle risk factors. Brazil and Mexico are the most populous countries in Latin America. The present study aimed to compare the results of the National Health Survey “PNS” in Brazil and the National Survey Health and Nutrition “ENSANUT” in Mexico regarding the prevalence, complications and healthcare issues of diabetes in both countries.

Methods

A cross-sectional study was conducted with data from the National Health Survey (PNS) of 2013 in Brazil and the National Survey of Health and Nutrition (ENSANUT) of 2018 in Mexico. The prevalence of diabetes, complications and risk factors related to developing diabetes were considered.

Results

The respondents included 3636 individuals in Brazil and 4555 individuals in Mexico. There were significant differences in age and time living with diabetes between the two countries. Mexican people had twice as likely as Brazilian people to have a complication (p < 0.0001). The principal risk factor (OR 2.47; p ≤ 0.0001) for developing any diabetic complication was living with diabetes for more than 15 years. Visual impairment was the most frequent complication in both countries, but it was more prevalent in Mexico (p ≤ 0.001).

Conclusions

Diabetes complications are important health problems in Brazil and Mexico. Visual impairment was the principal complication in both countries. Several factors, such as access to and type of health system, living in a rural area, treatment, BMI and performing preventive actions, affected the risk of developing a complication. However, living with diabetes for more than 15 years was the principal risk factor. National health surveys have added significant information on the impact of diabetes in these Latin American populations. This comparison of data could provide valuable information to guide national policies and program decisions in both countries.

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Background

Diabetes is a growing global problem. The last report of the International Diabetes Federation (IDF) in 2017 estimated the global prevalence of diabetes at 424.9 million (8.8%), indicating that 1 in 11 people had diabetes [1]. Type 2 diabetes is more frequent in Latin American people than non-Hispanic whites [2] due to a combination of genetic and lifestyle risk factors [3]. Brazil and Mexico are the most populous countries in Latin America [4]. The age-adjusted prevalence of diabetes in Brazil is 8.7%, compared to 14.7% in Mexico. This estimation classifies Brazil as the country with the 3rd highest number of undiagnosed diabetics and the 5th highest number of diabetic older-adults, accounting for 4.9 million people. This number is expected to increase to 11.9 million by 2045. The same report ranks Mexico as the 8th country for undiagnosed diabetics and the 9th country for diabetic older-adults, at 4.5 million; this is expected to increase to 7.6 million people in 2045 [1].

The cost generated by diabetes in Mexico is near USD 19 billion, and it reaches USD 24 billion in Brazil. Those costs correspond to a per-patient expense of $1583 and 1920 in Mexico and Brazil, respectively [1, 5, 6].

Although laws in the constitutions of both Mexico and Brazil ensure access to health, they emphasize the issue of diabetes in both populations affected by this disease from different perspectives [7,8,9,10,11]. In response to this health problem, both countries performed national surveys of health in order to undertake a situational diagnostic of the population regarding their health problems, including non-communicable diseases (NCDs), such as diabetes, hypertension, dyslipidemia, and obesity, among others [12, 13]. Although the national surveys were developed independently by each country, many of their questions were similar, allowing us to compare different aspects of the prevalence and healthcare of the diseases. Although the two surveys were conducted in different years, these are the most important recent epidemiological reports in their respective countries.

The aim of the present study was to compare the results of the National Health Survey “PNS” in Brazil and the National Survey Health and Nutrition “ENSANUT” in Mexico regarding the prevalence, complications and healthcare issues of diabetes in both countries.

Methods

A cross-sectional study was conducted with data from the National Health Survey (PNS) of 2013 in Brazil and the National Survey of Health and Nutrition (ENSANUT) of 2018 in Mexico. These are national surveys based on household sampling conducted by the Brazilian Institute of Geography and Statistics with the Ministry of Health/Brazil and the National Institute of Public Health/Mexico, respectively. We decided to compare these surveys from different years because both provide the most recent results from each country. These surveys are not conducted annually, and these reports contain the most important epidemiological information.

We included all adult (18 years and older) participants from both surveys who reported receiving a diagnosis of diabetes by a medical doctor. The present analysis excluded those participants reporting diabetes only during pregnancy.

The study protocols were approved by the ethics committee of the Centro Universitario de Tonalá of the University of Guadalajara in Mexico and the Pontifical Catholic University of Rio Grande do Sul, Brazil and were conducted according to Good Clinical Practice and the principles of the Declaration of Helsinki. The report considered the STROBE statement.

Materials and variables

The variables used in PNS were taken from modules P (lifestyles) and Q (chronic diseases). In the case of ENSANUT, the variables of section III (diabetes mellitus) and XIII (risk factors) were considered. After comparison with a X2 test, all variables that were significant (p < 0.05) were included in the logistic regression analysis. These variables were sex (female/male); age (years); area of residence (rural/urban); use of alcohol (yes/no); diagnosis of diabetes made by a physician (yes/no); time living with diabetes (years); type of diabetes treatment (none/oral medications/insulin/both); if they engaged in physical activity (yes/no); BMI based on silhouette images; diagnosis of cardiovascular disease (yes/no) or dyslipidemia (yes/no); access to medical care (yes/no); type of medical care (public/private/do not receive/other); indicators of glycemic control, such as measurements of venous glucose (yes/no) and HbA1C (yes/no) and use of glucose strips (yes/no); preventive measures, such as urinalysis (yes/no) and foot exam (yes/no); and complications due to diabetes (amputation/coma/renal impairment/leg ulcers/myocardial infarction/visual impairment). All the data were obtained through a direct question of whether the participant had been diagnosed with any of these complications during the last year.

For the purposes of statistical analysis, some variables had their responses recategorized. The duration of living with diabetes was categorized as < 5 years, 5 to < 10 years, 10 to < 15 years, or 15 years or more. Obesity status was collected in different ways across the surveys. In Brazil, self-reported height and weight were recorded, and the body mass index was calculated in kg/m2. Mexico used the Stunkard scale, consisting of 9 silhouette figures that gradually increased in size from very thin (a value of 1) to very obese (a value of 9). Those results were classified as underweight (Figs. 1 and 2 and BMI, < 18.5 kg/m2), normal weight (Figs. 3 and 4 and BMI between 18.5 and 25 kg/m2), overweight (Figs. 5 through 7 and BMI between 25 and 30 kg/m2), and obese (Figs. 8 and 9 and BMI ≥ 30 kg/m2) following the classification of Bhuiyan et al. [14] The frequency of alcohol consumption was reclassified for both countries: never drinks, drinks less than once a month, and drinks once or more per month. All of the complications reported were questioned if these were present within the past year.

Statistical analysis

The frequency distribution of sociodemographic and healthcare characteristics of diabetes and the diabetes complications for each country were computed, and their associations were tested by Χ2, except for age, where mean and standard deviation were calculated for each country, and the difference was tested by an unpaired Student’s t-test. The odds ratio for having any diabetes complication was calculated for all variables that were significant (p < 0.05) in the descriptive analyses in the two logistic regression models: simple models, with a regression performed with each variable separately, and an adjusted model, with all variables selected. All statistical analyses were conducted using SPSS software, version 25 (IBM Corporation, Armonk, NY, USA).

Results

Characteristics of the subjects

Diabetes was reported by 3636 Brazilians (6.8% of all n = 60,203 participants) and 4555 Mexicans (10.5% of n = 43,071). Table 1 shows the demographic characteristics of both populations and some variables used in the logistic regression. Most of the population with diabetes lived in urban areas in both countries (p ≤ 0.0001). In Mexico, more people overall live in rural areas than in the rural areas of Brazil (p = 0.001). Most people that lived with diabetes reported having less than 5 years from diagnosis in both countries (p ≤ 0.0001). However, a quarter of the interviewed population reported more than 15 years of living with the disease. Physical activity is a problem in both countries because most people mentioned they do not engage in physical activity (p ≤ 0.0001). Regarding body weight, more than half of the participants selected silhouettes related to overweight and obesity (p ≤ 0.0001). Both categories were more prevalent in Mexicans than Brazilian people (p ≤ 0.0001). More than three quarters of the people that participated in both surveys answered that they do not drink alcohol (p = 0.0001). Although most people indicated that they did not have cardiovascular diseases or dyslipidemia, these questions were likely misinterpreted.

Table 1 Demographic characteristics of diabetic participants in Brazil and Mexico

Table 2 shows the distribution of the characteristics of access to health systems and actions for the control and prevention of diabetes and its complications in Brazil and Mexico. In Mexico, more patients have access to medical care than in Brazil (p ≤ 0.0001). In both countries, most of the participants received health care in a public system (p ≤ 0.0001); however, in Brazil, people have more access to private health care than in Mexico (p ≤ 0.001). During the national surveys, the interviewers asked about the kind of treatment used for their diabetes control. Oral medications as exclusive treatment were most frequently mentioned in both countries (p ≤ 0.0001). There was a significant difference between Mexico and Brazil related to the use of oral antidiabetics, being used more frequently by Mexicans (p ≤ 0.001). Compared to Mexico, more participants in Brazil mentioned not using any medication for glucose control, whether that be insulin, pills or both.

Table 2 Healthcare characteristics of diabetes participants in Brazil and Mexico surveys

Relating to indicators of glycemic control, more participants in Brazil reported measuring venous glucose and HbA1C and using a capillary glucose (glucometer). As a preventive action, more people in Brazil performed urinalysis (p ≤ 0.001) and foot exams (p ≤ 0.001).

Principal diabetic complications

The most prevalent complication was visual impairment in both countries (p ≤ 0.0001). More participants reported visual alterations due to diabetes in Mexico than in Brazil (p ≤ 0.001). Leg ulcers were the second most common complication associated with diabetes mentioned by people in both countries. The remaining complications evaluated did not reveal differences between the interviewed populations. Importantly, nearly 50% of participants developed one or more complication.

Logistic regression

After the selection of variables, the total participants with and without complications were 4198 and 3993, respectively. In the simple logistic models, each variable with significant differences between the countries was tested for its association with diabetic complications (Table 3). In these models, Mexico had twice the probability of having a complication (p ≤ 0.0001). Comparing those participants living in metropolitan areas with those in interior and rural homes, rural dwellers had higher odds of complications (11, and 21%, respectively). Sex and HbA1C were not significant in either the simple or adjusted model. However, we observed a statistical trend (p = 0.07) in sex in the adjusted model, where women presented as having lower odds (10%) than men for having a diabetic complication. When adjusting for other variables (area of residence, time living with diabetes, type of medical care, type of diabetes treatment, measure of venous glucose, use of glucose strips, urinalysis, foot exam, diagnosis of cardiovascular disease and dyslipidemia), participants from Mexico were three times more likely to present complications from diabetes. The time with diabetes was a significant predictor of complications. Participants with a history of diabetes between 5 and less than 10 years had a 39% greater chance of having a complication than those with less than 5 years. The odds for those between 10 and 15 years were 94%, and the odds for those with 15 years or more were 147%.

Table 3 Regression analysis and multiple logistic predictions for diabetic complications in adults

Participants that had private health care; were using any type of medication; who performed blood strip testing, urinalysis, serum blood glucose measurements, and foot exams; were underweight; or had a history of cardiovascular and dyslipidemia had a significantly greater chance of having diabetes complications even in the adjusted model.

Discussion

This article aimed to compare the prevalence of diabetes complications in Brazil and Mexico using two official national surveys. The data collected in both the National Health Survey (PNS) of Brazil and the National Survey of Health and Nutrition (ENSANUT) in Mexico had several similarities. Both are public health instruments with the objective of collecting information about the population in different regions and represent the most important epidemiological information available about the population’s health.

The prevalence of diabetes complications was higher in Mexico than in Brazil. This result could be explained from different perspectives; however, the most important factor was the time living with diabetes after diagnosis. More than 15 years living with diabetes conferred a two times greater risk of developing a complication, most commonly retinopathy. In our study in Mexico, more people reported more than 15 years since diagnosis than in Brazil. Another important aspect was the genetic variants in both populations [15, 16]. In the Mexican population, some specific genotypes conferred a higher risk of developing diabetes and its complications [17]. Although these two Latin American countries have different social structures, Mexico has a greater degree of miscegenation based on autochthonous indigenous populations [18], and Brazil has a higher proportion of Afro-descendants and a lower indigenous component [19]; these genetic factors affect the risks of developing metabolic diseases [20, 21].

Older age was associated with a higher odds of complications in the simple model but lost its significance when adjusting for length of diabetes. This finding may indicate that the duration of the disease is a major risk factor for diabetes complications, more so than age. In the study by Al-Saeed et al., Australian patients with an earlier onset of diabetes had an increased risk of renal and peripheral nerve complications and higher standardized mortality than those whose onset was middle age and older-adult stage [22]. The frequency of diabetes complications was similar in both sexes and countries. In a recent publication, Gedebjerg et al. [23] observed no sex differences in microvascular complications of diabetes. However, they did find a higher frequency of macrovascular complications of diabetes in men. The evidence about this item is inconclusive, but other authors have mentioned that the principal factors in the development of complications are BMI and age rather than inherent sex-related factors. In our study, BMI and age were similar in both sexes [24].

Bos et al. [25] observed that the socioeconomic and health conditions in Brazilian rural older-adults were associated with poor glucose control relative to those living in an urban environment. Our results showed that the odds of having diabetes complications were higher in those participants living in rural areas, independently of the country of the origin, age, physical activity, health access and medical treatment. However, more people live in rural areas in Mexico than in Brazil. Some factors that have been associated with this result in rural areas are a low income, low education level, migration, and health habits, such as the use of sugar and salt [26]. In this context, we observed a negative relationship between living in urban areas and physical exercise (data not shown). Lack of exercise is a known risk factor for developing diabetes and other NCDs. Moreover, it is linked to poor glucose control, which is a major risk factor for developing diabetic complications [27,28,29].

Compared to Brazilians, Mexicans exercise less. Physical activity, aerobic exercise specifically, has different benefits in patients with diabetes. It can improve insulin resistance and diminish hyperglycemia, therefore delaying the onset of complications [27]. Moreover, aerobic exercise can modify inflammatory and metabolic pathways that participate in macro and microvascular diabetic complications [28, 29]. This confirms the results of the present study, which found physical exercise to be a protective factor against developing diabetic complications.

In the present study, we observed that the participants who were underweight had a greater risk of diabetes complications. In Mexico, more people were underweight than in Brazil. A cohort study conducted by Sairenchi et al. [30] in Japan found that a lower BMI in older-adults 60 to 79 years old was associated with a higher risk of developing diabetic complications. Some authors have tried to explain this phenomenon by the fact that lean patients with diabetes have an accelerated loss of pancreatic beta-cells and poorer glycemic control [31]. However, this pathophysiological paradigm continues to be unanswered.

Access to a health care system plays an important role in the development of complications. In Brazil, more people have access to private medical care than in Mexico. However, in Mexico, people have more access to medical care in general. Some authors have reported a higher risk of developing retinopathy and nephropathy in people that use public healthcare systems. This relation is sustained by socioeconomic disparities because most people that use public services have a low income and therefore have less access to medicines and novel therapies. Moreover, low education level and limited access to diagnostic tools are other contributing factors [30,31,32].

Our findings showed a significant increase in the risk of complications according to treatment. However, the use of insulin or oral antidiabetics per se did not have a direct relationship with the presence of complications [33]. In Mexico and Brazil, certain drugs are not available in the public health system due to their high cost, despite some medications being subsidized by the government [34,35,36,37]. In our study, this was a limitation because neither national health survey distinguished between groups of medications. Another factor that could contribute to this relationship is adherence to treatment. Only 37% of the patients who used insulin in combination with an oral antidiabetic agent had high compliance [36, 37]. However, several other factors, such as visual impairments, education level, and polypharmacy, affect adherence to treatment [38,39,40].

In the present study, the consumption of alcohol was higher in Mexicans than in Brazilians, and its consumption at least monthly was protective against diabetic complications. The scientific literature indeed has shown that moderate alcohol consumption has been consistently associated with a decreased risk of type 2 diabetes compared to abstention or excessive consumption [41,42,43]. However, we could not distinguish between the kinds of alcohol (e.g., beer or red wine) or other potential confounders that might have affected our results.

Finally, visual impairment was the most frequent complication in both countries, being more prevalent in Mexico. However, because the results came from patient interviews, it was difficult to determine the exact diagnosis; the questions about visual alterations were not specific in either survey. Nonetheless, visual impairment in people with diabetes is generally (64% of the time) a consequence of diabetic retinopathy [44].

A limitation of the present study was a lack of clarity over whether the patients questioned were indigenous or Afro-descendants because the risk of developing diabetes and its complications are different in these two groups. Moreover, our study was unable to show exact information related to diagnosis, glucose control, treatment, how often people undertook prevention measures and differences between the populations. However, this first approach allowed us to observe similarities and differences between these two countries with the largest populations in Latin America.

Conclusion

Diabetes complications are important health problems in Brazil and Mexico. Visual impairment was the principal complication in both countries, being more prevalent in Mexico. Several factors, such as access to and type of health system, living in rural areas, treatment, BMI and performing preventive actions, increased the risk of developing a complication. However, having lived with diabetes for more than 15 years was the principal risk factor. National health surveys add significant information about the impact of diabetes on these Latin American populations. This comparison of data could provide valuable information to guide national policies and program decisions in both countries.

Availability of data and materials

The data that support the findings of this study are free available from at the Brazilian Institute of Geography and Statistics with the Ministry of Health/Brazil (https://biblioteca.ibge.gov.br/visualizacao/livros/liv91110.pdf.) and the National Institute of Public Health/Mexico (https://ensanut.insp.mx/encuestas/ensanut2018/descargas.php) respectively.

All data generated or analyzed during this study are included in this published article.

Abbreviations

BMI:

Body Mass Index

ENSANUT:

National Survey of Health and Nutrition (Mexico)

IDF:

International Diabetes Federation

NCDs:

Non-communicable Diseases

OR:

Odds ratio

PNS:

National Health Survey (Brazil)

References

  1. International Diabetes Federation. IDF Diabetes Atlas. 8th ed. Brussels: International Diabetes Federation; 2017.

    Google Scholar 

  2. Centers for Disease Control and Prevention. National Diabetes Fact Sheet: National Estimates and General Information on Diabetes and Prediabetes in the United States. Atlanta: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2011.

    Google Scholar 

  3. Mercader JM, Florez JC. The genetic basis of type 2 diabetes in Hispanics and Latin Americans: challenges and opportunities. Front Public Health. 2017;5:329. https://0-doi-org.brum.beds.ac.uk/10.3389/fpubh.2017.00329.

    Article  PubMed  PubMed Central  Google Scholar 

  4. United Nations. Department of economic and social affairs, population division. In: World population prospects: the 2017 revision, Volume I: Comprehensive tables; 2017.

    Google Scholar 

  5. American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033–46.

    Article  Google Scholar 

  6. Barcelo A, Arredondo A, Gordillo-Tobar A, Segovia J, Qiang A. The cost of diabetes in Latin America and the Caribbean in 2015: evidence for the use and policy makers. JOGH. 2017;7(2):020410. https://0-doi-org.brum.beds.ac.uk/10.7189/jogh.07.020410.

    Article  PubMed  Google Scholar 

  7. World Health Organization. Diabetes. 2018. [online] Available at: http://www.who.int/es/news-room/fact-sheets/detail/diabetes. (Accessed 4 Jul 2018).

  8. The World Health Organization. Assessing National Capacity for the Prevention and Control of Non–communicable Diseases. Washington D.C: Report of the Americas Region; 2013. December 2013

    Google Scholar 

  9. Secretaría de Salud. Estrategia Nacional para la Prevención y el Control del Sobrepeso, la Obesidad y la Diabetes. Primera Edición. México; 2013. http://www.cenaprece.salud.gob.mx/descargas/pdf/EstrategiaNacionalSobrepeso.pdf. Accessed 10 Jan 2020.

  10. Norma Oficial Mexicana. NOM-015-SSA2-2010, Para la prevención, tratamiento y control de la diabetes mellitus. 2010.

    Google Scholar 

  11. Brazil. Law No. 11.347 of September 27, 2006. Provides for the free distribution of drugs and materials necessary for its application and monitoring of capillary glycemia for diabetic patients enrolled in diabetic education programs. Official Journal of the Union, Brasília. Available: http://www.planalto.gov.br/ccivil_03/_ato2004-2006/2006/lei/l11347.htm. Access 13 Jul 2018.

  12. Encuesta Nacional de Salud y Nutrición. Resultados nacionales. 2018. Available: https://ensanut.insp.mx/encuestas/ensanut2018/doctos/informes/ensanut_2018_presentacion_resultados.pdf.

    Google Scholar 

  13. Brazilian Institute of Geography and Statistics (IBGE). National Health Survey 2013: perception of health status, lifestyles and chronic diseases. Rio de Janeiro: Brazilian Institute of Geography and Statistics; 2014. Available: https://biblioteca.ibge.gov.br/visualizacao/livros/liv91110.pdf. Access 13 Jul 2018

    Google Scholar 

  14. Bhuiyan AR, Gustat J, Srinivasan SR, Berenson GS. Differences in body shape representations among young adults from a biracial (black-white), semirural community: the Bogalusa heart study. Am J Epidemiol. 2003;158(8):792–7. https://0-doi-org.brum.beds.ac.uk/10.1093/aje/kwg218.

    Article  CAS  PubMed  Google Scholar 

  15. García-Chapa EG, Leal-Ugarte E, Peralta-Leal V, Durán-González J, Meza-Espinoza JP, et al. Biomed Res Int. 2017;ID 3937893:10.

    Google Scholar 

  16. Assmann TS, Duarte GCK, Rheinheimer J, Cruz LA, Canani LH, Crispim D. The TCF7L2 rs7903146 (C/T) polymorphism is associated with risk to type 2 diabetes mellitus in southern-Brazil. Arq Bras Endocrinol Metab. 2014;58(9):918–25. https://0-doi-org.brum.beds.ac.uk/10.1590/0004-2730000003510.

    Article  Google Scholar 

  17. Rusu V, Hoch E, Mercader JM, Tenen DE, Gymrek M, Hartigan CR, DeRan M, von Grotthuss M, Fontanillas P, Spooner A, Guzman G, Deik AA, Pierce KA, Dennis C, Clish CB, Carr SA, Wagner BK, Schenone M, Ng MCY, Chen BH, Centeno-Cruz F, Zerrweck C, Orozco L, Altshuler DM, Schreiber SL, Florez JC, Jacobs SBR, Lander ES, Ng MCY, Shriner D, Chen BH, Li J, Chen WM, Guo X, Liu J, Bielinski SJ, Yanek LR, Nalls MA, Comeau ME, Rasmussen-Torvik LJ, Jensen RA, Evans DS, Sun YV, An P, Patel SR, Lu Y, Long J, Armstrong LL, Wagenknecht L, Yang L, Snively BM, Palmer ND, Mudgal P, Langefeld CD, Keene KL, Freedman BI, Mychaleckyj JC, Nayak U, Raffel LJ, Goodarzi MO, Chen YDI, Taylor HA Jr, Correa A, Sims M, Couper D, Pankow JS, Boerwinkle E, Adeyemo A, Doumatey A, Chen G, Mathias RA, Vaidya D, Singleton AB, Zonderman AB, Igo RP Jr, Sedor JR, Kabagambe EK, Siscovick DS, McKnight B, Rice K, Liu Y, Hsueh WC, Zhao W, Bielak LF, Kraja A, Province MA, Bottinger EP, Gottesman O, Cai Q, Zheng W, Blot WJ, Lowe WL, Pacheco JA, Crawford DC, Grundberg E, Rich SS, Hayes MG, Shu XO, Loos RJF, Borecki IB, Peyser PA, Cummings SR, Psaty BM, Fornage M, Iyengar SK, Evans MK, Becker DM, Kao WHL, Wilson JG, Rotter JI, Sale MM, Liu S, Rotimi CN, Bowden DW, Mercader JM, Huerta-Chagoya A, García-Ortiz H, Moreno-Macías H, Manning A, Caulkins L, Burtt NP, Flannick J, Patterson N, Aguilar-Salinas CA, Tusié-Luna T, Altshuler D, Florez JC, Martínez-Hernández A, Centeno-Cruz F, Barajas-Olmos FM, Zerrweck C, Contreras-Cubas C, Mendoza-Caamal E, Revilla-Monsalve C, Islas-Andrade S, Córdova E, Soberón X, Orozco L, González-Villalpando C, González-Villalpando ME, Haiman CA, Wilkens L, le Marchand L, Monroe K, Kolonel L, Arellano-Campos O, Ordóñez-Sánchez ML, Rodríguez-Torres M, Segura-Kato Y, Rodríguez-Guillén R, Cruz-Bautista I, Muñoz-Hernandez LL, Sáenz T, Gómez D, Alvirde U, Almeda-Valdés P, Cortes ML. Type 2 diabetes variants disrupt function of SLC16A11 through two distinct mechanisms. Cell. 2017;170(1):199–212. https://0-doi-org.brum.beds.ac.uk/10.1016/j.cell.2017.06.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Baptista-Rosas RC, Mercado-Sesma A, Hernández Ortega L, Hernandez Gonzalez L, Vega-Avalos J, Arreola-Cruz AA. The utility of genomic public databases to mitochondrial haplotyping in contemporary mestizo population of Mexican origin. Mitochondrial DNA Part A. 2019;30(3):567–72. https://0-doi-org.brum.beds.ac.uk/10.1080/24701394.2019.1580271.

    Article  CAS  Google Scholar 

  19. Bernardo S, Hermida R, Desidério M, Silva DA, de Carvalho EF. MtDNA ancestry of Rio de Janeiro population, Brazil. Mol Biol Rep. 2014;41(4):1945–50. https://0-doi-org.brum.beds.ac.uk/10.1007/s11033-014-3041-9.

    Article  CAS  PubMed  Google Scholar 

  20. Schaan AP, Costa L, Santos D, Modesto A, Amador M, Lopes C, Rabenhorst SH, Montenegro R, Souza BDA, Lopes T, et al. mtDNA structure: the women who formed the Brazilian Northeast. BMC Evol Biol. 2017;17(1):185.

    Article  Google Scholar 

  21. Barbosa AB, da Silva LA, Azevedo DA, Balbino VQ, Mauricio-da-Silva L. Mitochondrial DNA control region polymorphism in the population of Alagoas state, North-Eastern Brazil. J Forensic Sci. 2008;53(1):142–6. https://0-doi-org.brum.beds.ac.uk/10.1111/j.1556-4029.2007.00619.x.

    Article  PubMed  Google Scholar 

  22. Al-Saeed AH, et al. An inverse relationship between age of type 2 diabetes onset and complication risk and mortality: the impact of youth-onset type 2 diabetes. Diabetes Care. 2016;39(5):823–9. https://0-doi-org.brum.beds.ac.uk/10.2337/dc15-0991.

    Article  CAS  PubMed  Google Scholar 

  23. Gedebjerg A, Almdal TP, Berencsi K, Rungby J, Nielsen JS, Witte DR, Friborg S, Brandslund I, Vaag A, Beck-Nielsen H, Sørensen HT, Thomsen RW. Prevalence of micro- and macrovascular diabetes complications at time of type 2 diabetes diagnosis and associated clinical characteristics: a cross-sectional baseline study of 6958 patients in the Danish DD2 cohort. J Diabetes Complicat. 2018;32(1):34–40. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jdiacomp.2017.09.010.

    Article  Google Scholar 

  24. Peters SAE, Woodward M. Sex differences in the burden and complications of diabetes. Curr Diab Rep. 2018;13:33.

    Article  Google Scholar 

  25. Bós AJG, Ianiski VB, Camacho NCA, Martins RB, Rigo IR, Grigol MC, Camargo LR, Rocha JP. Differences in the socioeconomic and health profiles of older adults in rural and urban environments: 2013 national health survey. Geriatr Gerontol Aging. 2018;12(3):148–53. https://0-doi-org.brum.beds.ac.uk/10.5327/Z2447-211520181800027.

    Article  Google Scholar 

  26. Price AJ, Crampin AC, Amberbir A, Kayuni-Chihana N, Musicha C, Tafatatha T, Branson K, Lawlor DA, Mwaiyeghele E, Nkhwazi L, Smeeth L, Pearce N, Munthali E, Mwagomba BM, Mwansambo C, Glynn JR, Jaffar S, Nyirenda M. Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi. Lancet Diabetes Endocrinol. 2018;6(3):208–22. https://0-doi-org.brum.beds.ac.uk/10.1016/S2213-8587(17)30432-1.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, Tate DF. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care. 2016;39(11):2065–79. https://0-doi-org.brum.beds.ac.uk/10.2337/dc16-1728.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mitranun W, Deerochanawong C, Tanaka H, Suksom D. Continuous vs interval training on glycemic control and macro-and microvascular reactivity in type 2 diabetic patients. Scand J Med Sci Sports. 2014;24(2):e69–76. https://0-doi-org.brum.beds.ac.uk/10.1111/sms.12112.

    Article  CAS  PubMed  Google Scholar 

  29. Yaribeygi H, Butler AE, Sahebkar A. Aerobic exercise can modulate the underlying mechanisms involved in the development of diabetic complications. J Cell Physiol. 2019;234(8):12508–15.

  30. Sairenchi T, Iso H, Irie F, Fukasawa N, Ota H, Muto T. Underweight as a predictor of diabetes in older adults. Diabetes Care. 2008;31(3):583–4. https://0-doi-org.brum.beds.ac.uk/10.2337/dc07-1390.

    Article  PubMed  Google Scholar 

  31. Zhang Y, Guo Y, Shen X, Zhao F, Yan S. Lower body mass index is not of more benefit for diabetic complications. J Diabetes Investig. 2019;10(5):1307–17. https://0-doi-org.brum.beds.ac.uk/10.1111/jdi.13003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Funakoshi M, Azami Y, Matsumoto H, Ikota A, Ito K, Okimoto H, Shimizu N, Tsujimura F, Fukuda H, Miyagi C, Osawa S, Osawa R, Miura J. Socieconomic status and type 2 diabetes complications among young adult patients in Japan. PLoS One. 2017;12(4):e0176087. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0176087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Coutinho WF, Silva Junior WS. Diabetes Care in Brazil. Ann Glob Health. 2015;81(6):735–41. https://0-doi-org.brum.beds.ac.uk/10.1016/j.aogh.2015.12.010.

    Article  PubMed  Google Scholar 

  34. Barquera S, Campos-Nonato I, Aguilar-Salinas C, López-Ridaura R, Arredondo A, Rivera-Dommarco J. Diabetes in Mexico: cost and management of diabetes and its complications and challenges for health policy. Glob Health. 2013;9(1):3. https://0-doi-org.brum.beds.ac.uk/10.1186/1744-8603-9-3.

    Article  Google Scholar 

  35. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998;352:837–53.

    Article  Google Scholar 

  36. Smith-Palmer J, Brändle M, Trevisan R, Orsini Federici M, Liabet S, Valentine W. Assessment of the association between glycemic variability and diabetes-related complications in type 1 and type 2 diabetes. Diabetes Res Clin Pract. 2014;105(3):273–84. https://0-doi-org.brum.beds.ac.uk/10.1016/j.diabres.2014.06.007.

    Article  CAS  PubMed  Google Scholar 

  37. American Diabetes Association. 9. Pharmacologic Approaches to glycemic treatment: Standards of medical care in diabetes. Diabetes Care. 2019;42(Suppl 1):S90–S102.

    Article  Google Scholar 

  38. Yurgin NR, Boye KS, Dilla T, Suriñach NL, Llach XB. Physician and patient management of type 2 diabetes and factors related to glycemic control in Spain. Patient Prefer Adherence. 2008;2:87–95.

  39. Luis-Emilio García-Pérez, María Álvarez, Tatiana Dilla, Vicente Gil-Guillén, Domingo Orozco-Beltrán, Adherence to Therapies in Patients with Type 2 Diabetes. Diabetes Therapy. 2013;4(2):175–94.

  40. The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus. New England J Med. 1993;329(14):977–86.

  41. Koppes LLJ, Dekker JM, Hendriks HFJ, Bouter LM, Heine RJ. Moderate Alcohol Consumption Lowers the Risk of Type 2 Diabetes: A metaanalysis of prospective observational studies. Diab Care. 2005;28(3):719–25.

  42. Baliunas DO, Taylor BJ, Irving H, Roerecke M, Patra J, Mohapatra S, Rehm J. Alcohol as a Risk Factor for Type 2 Diabetes: A systematic review and meta-analysis. Diabetes Care. 2009;32(11):2123–32.

  43. Hendriks HFJ. Moderate Alcohol Consumption and Insulin Sensitivity: Observations and Possible Mechanisms. Ann Epidemiol. 2007;17(5):S40–2.

  44. Takao T, Ide T, Yanagisawa H, Kikuchi M, Kawazu S, Matsuyama Y. The effects of fasting plasma glucose variability and time-dependent glycemic control on the long-term risk of retinopathy in type 2 diabetic patients. Diabetes Res Clin Pract. 2011;91(2):e40–2.

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Acknowledgments

We appreciate the comments and support of Ian Capstick and Naomi Twery for language assistance.

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RBM, AJGB and, ARMS participated in the design of the study. RBM, SAOB, SFH and, RCBR performed evaluation and data collection. RBM, SAOB, SFH and, RCBR participated in data interpretation. ARMS and AJGB performed the statistical analysis of the data. All authors helped to draft the manuscript and read and approved the final manuscript.

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Correspondence to Arieh Roldán Mercado-Sesma.

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The study protocol was approved by the Research and Ethical Committee of the Universitary Center of Tonala of University of Guadalajara in Mexico and Pontifical Catholic University of Rio Grande do Sul, Brazil. We do not require administrative permissions to access and download raw data or any kind for the analysis due to both national surveys are free available.

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Martins, R.B., Ordaz-Briseño, S.A., Flores-Hernández, S. et al. Comparison of prevalence of diabetes complications in Brazilian and Mexican adults: a cross-sectional study. BMC Endocr Disord 21, 48 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12902-021-00711-y

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