Abstracts

Introduction

Globally, 537 million (10.5%) adults (20-79 years) live with diabetes. Projections indicate that 783 million people will have diabetes by 2045 (12.2%). In Brazil, the number of adults with diabetes will increase from an estimated 15.7 million by 2021 to 23.2 million by 2045 ¹1. International Diabetes Federation. IDF diabetes atlas. 10th Ed. Brussels: International Diabetes Federation; 2021..

Some studies found differences between males and females in risk factors, clinical manifestations, and sequelae of type 2 diabetes, and that prevention, detection, and treatment can differently affect them ²2. Kautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocr Rev 2016; 37:278-316.^,33. Peters SAE, Woodward M. Sex differences in the burden and complications of diabetes. Curr Diab Rep 2018; 18:33.^,44. Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia 2020; 63:453-61..

Studies found a change in diabetes prevalence pattern from female predominance to equality or even male preponderance with the country’s development ¹1. International Diabetes Federation. IDF diabetes atlas. 10th Ed. Brussels: International Diabetes Federation; 2021.^,55. Gale EA, Gillespie KM. Diabetes and gender. Diabetologia 2001; 44:3-15.^,66. Lipscombe LL, Hux JE. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995-2005: a population-based study. Lancet 2007; 369:750-6.^,77. Chang H-Y, Hsu C-C, Pan W-H, Liu W-L, Cheng JY-C, Tseng C-H, et al. Gender differences in trends in diabetes prevalence from 1993 to 2008 in Taiwan. Diabetes Res Clin Pract 2010; 90:358-64.. In Brazil, although this change in the prevalence pattern remaining unclear, the national diabetes mortality statistics verified this inversion. In other words, there was a shift from female preponderance to equality or male predominance ⁸8. Malhão TA, Brito AS, Pinheiro RS, Cabral CS, Camargo TM, Coeli CM. Sex differences in diabetes mellitus mortality trends in Brazil, 1980-2012. PLoS One 2016; 11:e0155996..

A meta-analysis by Agardh et al. ⁹9. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol 2011; 40:804-18. found an association between low levels of education, income, and occupation with a 30% to 40% higher risk of type 2 diabetes compared with higher levels of these measures. The associations were more consistent among high-income countries, demonstrating the need for more studies in low- and middle-income countries. In addition to the results that suggest that socioeconomic disadvantages favor type 2 diabetes, these associations were more prominent among females than males.

Most studies assessing socioeconomic status and diabetes use traditional measures such as income, education, and occupation. Although education defines potential occupations, which influence income levels, these measures of socioeconomic position should not be used indistinctly, as they represent different causal paths and processes ⁹9. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol 2011; 40:804-18.^,1010. Hill-Briggs F, Adler NE, Berkowitz SA, Chin MH, Gary-Webb TL, Navas-Acien A, et al. Social determinants of health and diabetes: a scientific review. Diabetes Care 2020; 44:258-79.. Thus, these attributes must be considered together, since these relationships are constructed and reconstructed in tandem with and crossed by others ¹¹11. Fonseca RMGS. Gênero como categoria para a compreensão e a intervenção no processo saúde-doença no âmbito da Saúde do Adulto. In: Kalinowski CE, editor. Proenf - Programa de Atualização em Enfermagem: saúde do adulto. Porto Alegre: Artmed Panamericana; 2008. p. 9-39.. For this reason, the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), in cooperation with economists from the Center for Development and Regional Planning, Federal University of Minas Gerais (CEDEPLAR/UFMG), created a new measure of socioeconomic position, named occupational social class. It is a summary measure created by deriving scores for a set of occupations, considering education and income that represents social class ¹²12. Machado AF, Oliveira AMHC, Antigo MF, Rabelo A, Aburachid V. Tipologias ocupacionais aplicadas à análise socioeconômica da amostra ELSA (1ª onda). Relatório técnico Projeto ELSA-Brasil. Belo Horizonte: Centro de Desenvolvimento e Planejamento Regional, Universidade Federal de Minas Gerais; 2013..

Based on the assumption that behavioral characteristics and metabolic alterations of males and females may vary according to social class, potentially resulting in different prevalence ratios for type 2 diabetes, this study aimed to evaluate the presence of effect modification of the occupational social class on the association between sex and type 2 diabetes prevalence in the additive and multiplicative scales.

Methods

Study sample

This is a cross-sectional study using data from the ELSA-Brasil baseline, collected from 2008 to 2010. ELSA-Brasil is a multi-center cohort study of 15,105 active or retired public servants (support staff, administrative staff, and professors) of public teaching and research institutions from six Brazilian state capitals, aged from 35 to 74 years ¹³13. Aquino EM, Barreto SM, Benseñor IM, Carvalho MS, Chor D, Duncan BB, et al. Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): objectives and design. Am J Epidemiol 2012; 175:315-24..

In this study, only participants diagnosed type 2 diabetes according to the criteria chosen by ELSA-Brasil and those who self-declared race/skin color as white, mixed-race, black, or yellow (Figure 1) were included. To minimize the inclusion of type 1 diabetes, patients who had records of diabetes before the age of 30 and used insulin as the first medication were excluded. Records with missing data for the variables used in the statistical model were also excluded. Thus, the final sample was 14,156 individuals.

Measures

The outcome variable was type 2 diabetes, classified according to the criteria chosen by ELSA-Brasil, which considers both cases with self-referred previous diagnoses and individuals with no previous diagnosis who were diagnosed based on plasma glucose levels (fasting plasma glucose ≥ 126mg/dL or plasma glucose after 2-hour oral glucose tolerance test ≥ 200mg/dL or glycated hemoglobin ≥ 6.5%) ¹⁴14. Schmidt MI, Hoffmann JF, Diniz MFS, Lotufo PA, Griep RH, Benseñor IM, et al. High prevalence of diabetes and intermediate hyperglycemia: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Diabetol Metab Syndr 2014; 6:123..

The exposure variable retrieved by the ELSA-Brasil questionnaire was biological sex. However, this study aimed to understand relations between the sexes as social relations. Thus, the variable sex was considered as proxy for gender, since most of the population is cisgender ¹⁵15. Spizzirri G, Eufrásio R, Lima MCP, Nunes HRC, Kreukels BPC, Steensma TD, et al. Proportion of people identified as transgender and non-binary gender in Brazil. Sci Rep 2021; 11:2240.. Notably, social relations are sexed. In this way, the study also aimed to avoid a polarization between the biological and the social dimensions to understand the social processes involved in the association between type 2 diabetes, occupational social class, and gender. The potential effect modifier variable was occupational social class. This variable is a summary measure calculated by deriving scores for a set of occupations while also considering educational level, social status, and income as socioeconomic position ¹²12. Machado AF, Oliveira AMHC, Antigo MF, Rabelo A, Aburachid V. Tipologias ocupacionais aplicadas à análise socioeconômica da amostra ELSA (1ª onda). Relatório técnico Projeto ELSA-Brasil. Belo Horizonte: Centro de Desenvolvimento e Planejamento Regional, Universidade Federal de Minas Gerais; 2013.. This study worked only with three occupational social class strata: high (high-upper and high-lower), middle (middle-upper, middle-middle, and middle-lower), and low (low-upper and low-lower). Educational level was evaluated as a potential effect modifier in the sensitivity analysis.

Based on the theoretical model used as a reference in the analysis, the following variables were considered as potential confounders: age, maternal education, and self-declared race/skin-color. These factors are predictive of type 2 diabetes and might influence how males and females perceive their role in society and, consequently, which behaviors are related to the roles they perform.

Statistical analysis

To simplify, in the characterization of the study population (Table 1), age in four groups.

Crude and age-adjusted prevalence and their 95% confidence intervals (95%CI) were calculated (Table 2) using generalized linear models with binomial distribution and logarithm link function ¹⁶16. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 2003; 3:21.. Sex, occupational social class, an interaction term between these two variables, and age (in years old) were included, depending on the model. As the model included age, the age at 51.9 years (median) was fixed to calculate the adjusted prevalence for each sex and occupational social class levels.

By using the same type of statistical modeling, the strength of the association between sex and type 2 diabetes through prevalence ratios and their 95%CI (Table 3) were evaluated. Sex, occupational social class, and an interaction term between both were included, adjusting for age group (evaluated in four age groups), race/skin-color, and maternal education.

The effect modification of occupational social class on the association between sex and type 2 diabetes prevalence in the multiplicative and additive scales was measured ¹⁷17. Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol 2012; 41:514-20.. In the multiplicative scale, the prevalence ratio and the respective Wald 95%CI was estimated based on the exponents of the B parameters of the regression model. To calculate the measures on the additive scale, and their 95%CI - estimated by the delta method - the analysis was restricted to two specific occupational social class strata. That is, even though there were originally three possible strata (high, middle, and low), two databases were created: the first included only records from participants with high and middle occupational social classes, and the other, only records from participants with high and low occupational social classes. After running the model again, the Excel (https://products.office.com/) “product term” spreadsheet developed by Knol & VanderWeele ¹⁷17. Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol 2012; 41:514-20. was used, which considers the estimates of the coefficients and the covariance matrix, generating three measures that evaluate the departure from additivity of the studied factors effects, namely: (1) relative excess risk due to interaction (RERI), which expresses the part of the total effect due to the interaction ¹⁸18. MacMahon B, Pugh TF. Epidemiology, principles, and methods. Boston: Little, Brown & Company; 1970.; (2) attributable proportion (AP), which expresses the proportion of the combined effect attributable to the interaction ¹⁹19. Walker AM. Proportion of disease attributable to the combined effect of two factors. Int J Epidemiol 1981; 10:81-5.; (3) synergy index (S), defined as the ratio of the combined effects and individual effects ²⁰20. Rothman KJ. Synergy and antagonism in cause-effect relationships. Am J Epidemiol 1974; 99:385-8.. When RERI and AP = 0 and S = 1 no effect modification was found. Synergy and antagonism occur, respectively, when RERI and AP > 0 and S > 1 and RERI and AP < 0 and S < 1 ²¹21. Rothman KJ. Interacciones entre causas. In: Rothman KJ, editor. Epidemiología moderna. Madrid: Ediciones Diaz de Santos; 1987. p. 347-64..

A sensitivity analysis was conducted, substituting occupational social class with educational level. Moreover, the same analysis was carried out without excluding records with missing data for the maternal education and race/skin color variables and without adjusting for age, race/skin color, and maternal education to evaluate changes in the direction of the association.

The residuals were analyzed to verify the models goodness of fit. A good fit was considered when the diagnostic graphs for verifying influential and leverage points did not present values close to one and when the deviance residuals ranged from -3 to 3 ²²22. McCullagh P, Nelder JA. Generalized linear models. 2nd Ed. London: Chapman and Hall; 1989.. To evaluate model quality, the goodness of fit test was used, with chi-squared distribution, under the null hypothesis that each model is well-adjusted at the 5% level.

Except for the additive and multiplicative interaction calculations, all analyses were carried out using SPSS, version 18.0 (https://www.ibm.com/).

Since ELSA-Brasil is a multi-center study, the ethics review committees of each participating institution and the National Ethics Research Committee approved the study research protocol. All participants signed an informed consent form before data collection. The Ethics Review Committee of the Institute of Collective Health Studies, Federal University of Rio de Janeiro approved this study (CAAE: 57801616.4.0000.5286).

Results

Table 1 shows the characteristics of the study population by sex and occupational social class, according to potentially confounding variables. We found a higher proportion of participants whose mothers had up to incomplete primary education in all groups. However, this proportion increased as the occupational social class decreased. Most participants in the high occupational social class were white, while in the other strata black and mixed-race participants predominated, among males and females. The age distribution differed considerably between the groups. We observed a higher proportion of older participants in the high occupational social class, especially among males, followed by the low occupational social class, but with a predominance of females. In the middle occupational social class, females and males populations were younger when compared with the high and low occupational social class.

Table 2 shows that males had higher crude and age-adjusted type 2 diabetes prevalence in all strata of the occupational social class. As occupational social class increases, diabetes prevalence decreases in males and females.

When we analyzed the male/female ratio of type 2 diabetes prevalence within the occupational social class strata, we verified that males were associated with a 66% (PR = 1.66; 95%CI: 1.44; 1.90), 39% (PR = 1.39; 95%CI: 1.02; 1.89), and 28% (PR = 1.28; 95%CI: 0.94; 1.75) prevalence ratio, higher in the high, middle, and low occupational social class, respectively (Table 3). However, this result was not statistically significant at a 5% level in low occupational social class. At the same time, we verified a negative effect modification of occupational social class on the association between sex and type 2 diabetes, which was only statistically significant on the multiplicative scale.

When we substituted occupational social class with educational level, we found similar results while evaluating the effect modification on the multiplicative and additive scales (Table 4). Furthermore, when we carried out the analyses without excluding records with missing data for maternal education and race/skin color variables and without adjusting for age group, race/skin color, and maternal education (Supplementary Material. Table S1: http://cadernos.ensp.fiocruz.br/static//arquivo/supl-e00150322_4483.pdf), we found no changes in the direction of the association. Regarding the residuals analysis, we found satisfactory results for all measures of socioeconomic position (data not shown).

Discussion

We found a greater type 2 diabetes prevalence among males in all occupational social class strata. However, the occupational social class had an effect modification on the association between sex and type 2 diabetes on the multiplicative scale, with a reduction in the male/female prevalence ratio from the high occupational social class to the low occupational social class.

Other studies ²³23. Nordström A, Hadrévi J, Olsson T, Franks PW, Nordström P. Higher prevalence of type 2 diabetes in men than in women is associated with differences in visceral fat mass. J Clin Endocrinol Metab 2016; 101:3740-6.^,2424. Dwyer-Lindgren L, Mackenbach JP, van Lenthe FJ, Flaxman AD, Mokdad AH. Diagnosed and undiagnosed diabetes prevalence by county in the U.S., 1999-2012. Diabetes Care 2016; 39:1556-62.^,2525. Wändell PE, Carlsson AC. Gender differences and time trends in incidence and prevalence of type 2 diabetes in Sweden: a model explaining the diabetes epidemic worldwide today? Diabetes Res Clin Pract 2014; 106:e90-2. also found a pattern of male preponderance in diabetes prevalence. However, study outcomes vary in Brazil; for example, a national multi-center investigation in 1986 and 1987 - including plasma glucose triage - found similar prevalence among males and females ²⁶26. Malerbi DA, Franco LJ. Multicenter study of the prevalence of diabetes mellitus and impaired glucose tolerance in the urban Brazilian population aged 30-69 yr. The Brazilian Cooperative Group on the Study of Diabetes Prevalence. Diabetes Care 1992; 15:1509-16.. The household survey of Risk Behaviors and Referred Morbidity from Non-Communicable Diseases, carried out from 2002 to 2005 in 18 capitals of Federative Units, verified a similar result when analyzing the prevalence of self-referred diabetes ²⁷27. Costa LC, Thuler LCS. Fatores associados ao risco para doenças não transmissíveis em adultos brasileiros: estudo transversal de base populacional. Rev Bras Estud Popul 2012; 29:133-45.. However, most national studies based on self-referred diagnoses found a higher prevalence among females ²⁸28. Freitas LRS, Garcia LP. Evolução da prevalência do diabetes e deste associado à hipertensão arterial no Brasil: análise da Pesquisa Nacional por Amostra de Domicílios, 1998, 2003 e 2008. Rev Epidemiol Serv Saúde 2012; 21:7-19.^,2929. Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional de Saúde: 2019: percepção do estado de saúde, estilos de vida, doenças crônicas e saúde bucal: Brasil e grandes regiões. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2020.^,3030. Departamento de Análise em Saúde e Vigilância de Doenças Não Transmissíveis, Secretaria de Vigilância em Saúde, Ministério da Saúde. Vigitel Brasil 2021: Vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2021. Brasília: Ministério da Saúde; 2021.. Since mortality statistics can shed light on sex differences in diabetes prevalence, we highlight a study that evaluated the Brazilian pattern of diabetes mortality from 1980 to 2012 for both sexes. This study verified a shift from female preponderance to equality or male predominance ⁸8. Malhão TA, Brito AS, Pinheiro RS, Cabral CS, Camargo TM, Coeli CM. Sex differences in diabetes mellitus mortality trends in Brazil, 1980-2012. PLoS One 2016; 11:e0155996..

Regarding biological mechanisms that may explain the higher prevalence among males, we emphasize that males develop type 2 diabetes at a lower body mass index (BMI) and present higher amounts of visceral and hepatic adipose tissue and greater insulin resistance, even after adjusting for BMI ²⁵25. Wändell PE, Carlsson AC. Gender differences and time trends in incidence and prevalence of type 2 diabetes in Sweden: a model explaining the diabetes epidemic worldwide today? Diabetes Res Clin Pract 2014; 106:e90-2.^,3131. Logue J, Walker JJ, Colhoun HM, Leese GP, Lindsay RS, McKnight JA, et al. Do men develop type 2 diabetes at lower body mass indices than wo? Diabetologia 2011; 54:3003-6.^,3232. Sattar N. Gender aspects in type 2 diabetes mellitus and cardiometabolic risk. Best Pract Res Clin Endocrinol Metab 2013; 27:501-7.. Males also have higher fasting plasma glucose levels from normoglycemia, prediabetes, and type 2 diabetes diagnosis ³¹31. Logue J, Walker JJ, Colhoun HM, Leese GP, Lindsay RS, McKnight JA, et al. Do men develop type 2 diabetes at lower body mass indices than wo? Diabetologia 2011; 54:3003-6.^,3333. Vistisen D, Witte DR, Tabák AG, Brunner EJ, Kivimäki M, Færch K. Sex differences in glucose and insulin trajectories prior to diabetes diagnosis: the Whitehall II study. Acta Diabetol 2014; 51:315-9.. The underlying mechanism responsible for the higher fasting plasma glucose levels among males may be the result of anthropometric differences. While BMI is a better predictor among females, waist circumference seems to be a better predictor for increased levels of fasting plasma glucose in males. Moreover, during diagnosis, males have greater waist circumferences. High estrogen levels in women may also play a role in their lower levels of fasting plasma glucose since estrogen concentrations are related to an improvement in sensitivity to insulin and reduction of liver glucose production. Another possibility is that glucose detection in the liver may be better in females than in males. Under normal conditions, a self-regulatory liver mechanism operates on the level of the glucose-6-phosphate reserves resulting in glucose production suppression in the liver. Thus, differences between males and females in the activity of the liver enzyme glucokinase, which catalyzes the phosphorylation of glucose into glucose-6-phosphate, or differences in the expression of genes involved in detecting glucose may explain part of the differences in levels of fasting plasma glucose among males and females ³³33. Vistisen D, Witte DR, Tabák AG, Brunner EJ, Kivimäki M, Færch K. Sex differences in glucose and insulin trajectories prior to diabetes diagnosis: the Whitehall II study. Acta Diabetol 2014; 51:315-9..

Similarly to our study, a nationwide prospective study of about 500,000 Chinese adults also revealed that the associations between socioeconomic position and diabetes prevalence differ between males and females. Among males, the Chinese study found a positive association between diabetes prevalence and educational level (adjusted OR = 1.21; 95%CI: 1.09; 1.35) and household income (adjusted OR = 1.45; 95%CI: 1.34; 1.56). Among females, it found an inverse association between educational level and diabetes risk (adjusted OR = 0.69; 95%CI: 0.63; 0.76), while for household income, the positive associations with diabetes prevalence were weaker than in males (adjusted OR = 1.26; 95%CI: 1.19; 1.34) ³⁴34. Wu H, Bragg F, Yang L, Du H, Guo Y, Jackson CA, et al. Sex differences in the association between socioeconomic status and diabetes prevalence and incidence in China: cross-sectional and prospective studies of 0.5 million adults. Diabetologia 2019; 62:1420-9..

The effect modification of the occupational social class - which we have identified - reinforces the theory of resource substitution, suggesting that education has a stronger moderating effect on females since they have fewer socioeconomic resources, which plausibly compensate for genetic risk, when compared to males. Thus, a higher educational level is necessary for females to be able to reach better socioeconomic conditions ³⁵35. Kim SR, Han K, Choi JY, Ersek J, Liu J, Jo SJ, et al. Age- and sex-specific relationships between household income, education, and diabetes mellitus in Korean adults: the Korea National Health and Nutrition Examination Survey, 2008-2010. PLoS One 2015; 10:e0117034., consume healthier diets, and have a greater interest in and access to information and sources that may improve their health ³⁶36. Wajchenberg BL, Cohen RV. Adipose tissue and type 2 diabetes mellitus. In: Fantuzzi G, Braunschweig C, editors. Adipose tissue and adipokines in health and disease. New York: Humana Press; 2014. p. 235-48. and nutritional status. Furthermore, education also has a direct effect on income ³⁷37. Balassiano M, Seabra AA, Lemos AH. Escolaridade, salários e empregabilidade: tem razão a teoria do capital humano? Revista de Administração Contemporânea 2005; 9:31-52., especially because it enables upward social mobility and occupational insertion ³⁸38. Galobardes B, Lynch J, Smith GD. Measuring socioeconomic position in health research. Br Med Bull 2007; 81-82:21-37.^,3939. Camelo LV. Posição socioeconômica no curso de vida, inflamação crônica e aterosclerose subclínica no Estudo Longitudinal de Saúde no Adulto (ELSA-Brasil) [Doctoral Dissertation]. Belo Horizonte: Universidade Federal de Minas Gerais; 2014..

These results can be interpreted not only according to the underlying biological mechanisms, but according to the social dimension that crosses the bodies of males and females and which also imprints distinct forms of illness, suffering, and care. Thus, we sought to incorporate another possible interpretation to these results, bringing the gender perspective to the heart of the discussion. We believe that this theoretical framework can be fruitful when seeking to understand the differences we have found in the patterns of male preponderance of type 2 diabetes prevalence.

Commonly, the literature indicates that males have a greater susceptibility to adopt risk behaviors, such as alcoholism, smoking ¹¹11. Fonseca RMGS. Gênero como categoria para a compreensão e a intervenção no processo saúde-doença no âmbito da Saúde do Adulto. In: Kalinowski CE, editor. Proenf - Programa de Atualização em Enfermagem: saúde do adulto. Porto Alegre: Artmed Panamericana; 2008. p. 9-39. and unhealthy diets ⁴⁰40. Read JG, Gorman BK. Gender and health inequality. Annu Rev Sociol 2010; 36:371-86.; denying pain or suffering to reinforce the image of male strength. The expectation that men adopt risk behavior is part of the gender norms. Similarly, gender expectations strongly associate care, whether for oneself or others, with females ⁴¹41. Mol A. The logic of care: health and the problem of patient choice. London: Routlegde; 2008.^,4242. Boltanski L. As classes sociais e o corpo. 3rd Ed. Rio de Janeiro: Edições Graal; 1989.. These factors are strongly associated with gender differences in socialization, according to which there is an expectation that males present themselves as strong, fearless, and invulnerable providers ⁴³43. Machin R, Couto MT, Silva GS, Schraiber LB, Gomes R, Santos Figueiredo W, et al. Concepções de gênero, masculinidade e cuidados em saúde: estudo com profissionais de saúde da atenção primária. Ciênc Saúde Colet 2011; 16:4503-12., and females, as more vigilant and responsible for the care, in general.

Although males, when compared with females, present biological and behavioral factors that increase the risk of developing type 2 diabetes, this is not always reflected in type 2 diabetes prevalence. In this study, we observed a reduction in the male/female ratio of type 2 diabetes prevalence, from high to low occupational social class strata, with no significant differences between males and females in the low occupational social class. Thus, the low occupational social class led to a loss of the advantage presented by females in the other socioeconomic strata. However, we may assume another aspect related to this dimension, considering differences in eating behavior patterns according to social class. Currently, the literature has widely discussed the obesity epidemic in Brazil and globally, suggesting the sedentary lifestyles and ultra-processed foods and beverages consumption as relevant determinants ⁴⁴44. Pan American Health Organization. Ultra-processed food and drink products in Latin America: trends, impact on obesity, policy implications. Washington DC: Pan American Health Organization; 2015.. A recent document showed that individuals with the lowest educational and income levels have an eating pattern that prioritizes these high-energy foods with low-nutritional values ⁴⁵45. Organización de las Naciones Unidas para la Alimentación y la Agricultura; Fondo Internacional de Desarrollo Agrícola; Organización Panamericana de la Salud; Programa Mundial de Alimentos; Fondo de las Naciones Unidas para la Infancia. Panorama de la seguridad alimentaria y nutrición en América Latina y el Caribe 2020. Santiago de Chile: Organización de las Naciones Unidas para la Alimentación y la Agricultura/Organización Panamericana de la Salud; 2020.. It certainly contributes to the reduction of the advantage initially observed among females.

Our discussion has been based on the social uses of the body, gender differences in socialization, different appropriations of the perspective of care, gender differences in educational attainment, and in obtaining work positions that are privileged in the social hierarchy, eating behaviors that are shaped by social class and gender, among others. Without ignoring the “biological predisposition”, according to sex, to metabolizing certain substances, all the dimensions listed above intersect and complexify the challenge of understanding the experiences of vulnerability, of both males and females, when facing specific health issues. Intersectionality theory ⁴⁶46. Crenshaw K. Demarginalizing the intersection of race and sex: a black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Univ Chic Leg Forum 1989; 140:139-67.^,4747. Bauer GR. Incorporating intersectionality theory into population health research methodology: challenges and the potential to advance health equity. Soc Sci Med 2014; 110:10-7. may help us understand that complex phenomena, such as those found in the health field, are not only the result of distinct units that are added together, but rather of intersecting lines over multiple dimensions of subjects’ social existence. In other words, an intersecting factors combination reinforces one another and passes through bodies, producing diverse effects from multiple interactions ⁴¹41. Mol A. The logic of care: health and the problem of patient choice. London: Routlegde; 2008.. To acknowledge this complexity, which crosses and surpasses the individual-biological dimension, that we seek to raise some hypotheses to understand the phenomenon we presented in this study.

The study’s limitations are (1) lack of population representativity because the sample consists of university and research institute workers and (2) selective survival biases due to its cross-sectional design. In the first limitation, since people who experienced extreme social adversity or who are at the top of the social hierarchy are not well-represented, we might observe an underestimation of the magnitude of the association between occupational social class and type 2 diabetes. The selective survival bias may contribute to the lower male/female ratio in the type 2 diabetes prevalence that we observed in the lower occupational social class.

The study strengths are the use of a new measure of socioeconomic position; the large sample size, which allowed effect modification testing; the fact that the type 2 diabetes diagnosis considered both self-referred, previously-diagnosed cases, and previously-undiagnosed cases diagnosed by plasma glucose evaluation; the sensitivity analysis carried out with a different measure of socioeconomic position; and the exploration of the effect modification in the additive and multiplicative scales with an emphasis on sex, unlike other studies.

Conclusion

We observed a male preponderance in the type 2 diabetes prevalence in all occupational social class strata, as well as the effect modification of this measure of socioeconomic position on the association between sex and type 2 diabetes. The last result indicates that health inequalities between males and females unevenly affect all occupational social class strata.

The definitive reasons for these differences remain unclear and require further research. However, to prevent type 2 diabetes, we need policies and actions focused on reducing gender asymmetries, drawing attention to the fact that the historically constructed sociocultural relations between males and females are not biologically determined and may be changed.

Acknowledgments

The authors thank the staff and participants of the ELSA-Brasil study for their important contributions. We also thank the Brazilian Ministry of Health (Department of Science and Technology), the Brazilian Ministry of Science, Technology and Innovation (FINEP, CNPq), the Brazilian Ministry of Education (CAPES), and the Rio de Janeiro State Research Foundation (FAPERJ) for their financial support.

References

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