Spouses, social networks and other upstream determinants of type 2 diabetes mellitus

Spouses, social networks and other upstream determinants of type 2 diabetes mellitus Diabetes risk factors outside the individual are receiving increasing attention. In this issue of Diabetologia, Nielsen et al (DOI: https://doi.org/10.1007/s00125-018-4587-1) demonstrate that an individual’s obesity level is associated with incident type 2 diabetes in their spouse. This is in line with studies providing evidence for spousal and peer similarities in lifestyle behaviours and obesity. Non-random mating and convergence over time are two explanations for this phenomenon, but shared exposure to more upstream drivers of diabetes may also play a role. From a systems-science perspective, these mechanisms are likely to occur simultaneously and interactively as part of a complex system. In this commentary, we provide an overview of the wider system- level factors that contribute to type 2 diabetes. . . . . . . . Keywords Diabetes Environmental drivers Lifestyle behaviours Obesity Prevention Social networks Systems science Upstream determinants Abbreviations Upstream determinants of type 2 diabetes IRR Incidence rate ratio SEP Socioeconomic position There is increasing recognition that the conditions in which SSB Sugar-sweetened beverages individuals are born, grow up, live, work and age are impor- tant for understanding the aetiology of type 2 diabetes. There is a growing evidence base for such upstream determinants of health [7]; for example, adults living in rural areas have a lower risk of type 2 diabetes. Also, more walkable areas and Introduction more greenspace are associated with a lower risk of type 2 diabetes, probably owing to a higher level of physical activity Despite major investment in research and treatment options, type in these areas [8, 9]. Moreover, availability, accessibility and 2 diabetes mellitus remains a pressing public health issue that is affordability of food is associated with dietary intake and may, approaching epidemic proportions globally [1, 2]. Excess weight therefore, be associated with type 2 diabetes [10], whilst noise is an established risk factor for type 2 diabetes and the global pollution may increase the risk of type 2 diabetes via disrupted epidemic of obesity largely explains the major increase in its sleep patterns [11]. Although individual-level factors, such as prevalence in recent decades. Behavioural risk factors for type genetic, biological and psychological factors, remain of im- 2 diabetes include a poor diet, physical inactivity, stress and poor portance, they are unlikely to fully explain the enormous in- sleep quality [3]. Type 2 diabetes [4], obesity [5] and behavioural crease in type 2 diabetes incidence over the past decades. risk factors [6] are socioeconomically patterned, with individuals Hence, the research focus is shifting to more upstream deter- at lowest socioeconomic position (SEP) being at the highest risk. minants of health, which may be of importance for the early identification of individuals at high risk of type 2 diabetes and * Joreintje D. Mackenbach the development of subsequent initiatives to intervene in high- j.mackenbach@vumc.nl risk populations. In this issue of Diabetologia, Nielsen et al [12] contribute to this field of research by investigating the Department of Epidemiology and Biostatistics, Amsterdam Public influence of spousal diabetes status and cardiometabolic risk Health Research Institute, VU University Medical Center, De factors for an individual’sdiabetesrisk. Boelelaan 1089a, 1081HV Amsterdam, the Netherlands 1518 Diabetologia (2018) 61:1517–1521 Spousal diabetes concordance and stronger social networks were less likely to be obese than individuals living in neighbourhoods with lower levels of so- The study by Nielsen and colleagues [12], using data from cial cohesion and weaker social networks [19]. 3649 men and 3478 women included in the English Following the reasoning above, health behaviours and Longitudinal Study of Ageing (ELSA), demonstrated that chronic conditions may not just ‘spread’ via spouses, friends obesity levels in one spouse were associated with incident and siblings, but even across entire families, neighbourhoods type 2 diabetes in the other spouse. Interestingly, having an or cities. If Nielsen et al had had data on cardiometabolic risk obese spouse increased the risk of type 2 diabetes in men over factors of other family members, friends or neighbours, they and above the effect of their own obesity level, while this was may have found that, not only spousal factors, but wider social not the case for women. In addition, having a spouse with environmental factors were associated with risk of developing diabetes was associated with an increased risk of type 2 dia- type 2 diabetes. In turn, these similarities in type 2 diabetes betes in women (incidence rate ratio [IRR] 1.40 [95% CI 0.95, risk across a social network may be explained by lifestyle 2.08]) but not in men (IRR 1.02 [95% CI 0.64, 1.65]) [12]. behaviours, socioeconomic conditions across the lifespan, or This association in women was not statistically significant but, exposure to food and physical activity environments. Indeed, given the low number of cases of diabetes in this study and the a third explanation for behavioural and health similarities relatively large effect size, this may be regarded as relevant for between connected individuals is shared exposure to common public health. In general, the nationally representative sample, environmental factors. the long follow-up period and thorough analyses provide con- fidence in the findings. The implications of the results for clinical practice may, however, be limited since high-risk cou- Shared environmental factors and type 2 ples may be concordant in their non-attendance for screening; diabetes this should be subject to future investigations. The results of this study are in line with a previous meta- Although Nielsen et al adjusted for SEP, all the relevant analysis that demonstrated evidence for spousal diabetes con- socioeconomic variation in type 2 diabetes risk may not have cordance [13] and is in line with studies providing evidence been captured. They used the highest reported employment for spousal similarities in lifestyle behaviours and obesity [14, rate at the couple level to indicate SEP, while socioeconomic 15]. As Nielsen et al state [12], two commonly used explana- condition across the life span, including childhood SEP and tions for spousal similarities in behaviour and health are non- parental SEP, and area deprivation, may also explain spousal random mating and convergence over time [16]: individuals similarities [20, 21]. For example, a Swedish study on the are more likely to select a partner with similar phenotypes and effects of neighbourhood deprivation showed that refugees preferences, and over the course of a relationship, spouses assigned to high deprivation areas had increased risk of type converse in their behaviours because of social contagion. 2 diabetes, regardless of individual SEP, with neighbourhood effects growing over time [22]. Unfortunately, Nielsen and colleagues did not have data Impact of social networks on health available on other shared environmental factors and, thus, the authors could not investigate whether such factors may These effects outlined above may not be limited to spouses; explain spousal similarities and differences in type 2 diabetes Christakis and Fowler [15] showed that pairs of friends and risk. They did, however, touch upon the role of the food en- siblings of the same sex appeared to have more influence on vironment for spousal concordance in type 2 diabetes. They the weight gain of each other, than pairs of friends and siblings found that a wife’s obesity status was a stronger risk factor for of the opposite sex. The importance of social networks (e.g., incident type 2 diabetes in her husband than vice versa; they social structures composed of interdependent individuals, such speculate that this may be explained by the fact that women as spouses, relatives, colleagues, neighbours and friends) for are more likely to be responsible for planning, preparing and health has been recognised for decades [17]. Social contacts shopping for food. Indeed, in couples with a more traditional may shape norms about the acceptability of being overweight division of roles, a woman’s unhealthy dietary practices may or preferences for an active lifestyle or may provide support for influence both her own and her husband’s risk of type 2 dia- behaviour change. Not surprisingly, social influences are an betes, while a man’s unhealthy dietary practices (likely origi- important element of the behaviour change technique taxono- nating from the out-of-home food environment) may not in- my of Michie et al [18], which is used in many type 2 diabetes fluence his wife’s risk of type 2 diabetes. This is, however, prevention strategies. In addition, both risk factors and protec- discordant with the finding that triacylglycerol levels in men tive factors may spread through social networks. For example, (which are influenced by diet [23]) can impact upon type 2 our recent study in European adults showed that individuals diabetes risk in the wife [12]. Before any conclusive state- living in neighbourhoods with higher levels of social cohesion ments can be made about spousal or wider social network Diabetologia (2018) 61:1517–1521 1519 influences on type 2 diabetes, the effects of common exposure generate a larger preventative population effect, but is politi- to shared environments should be explored. Follow-up studies cally more difficult. Public, political and media discourse could investigate whether lifestyle behaviours, socioeconomic around obesity and diabetes has been dominated by a persistent conditions, wider social influences and exposure to food and skew towards individual-level choices as the primary determi- physical activity environments could explain similarities in nant, and this is then reflected in policies and interventions that type 2 diabetes risk. focus on individual-level behaviour change [25]. These down- stream endeavours should not be regarded as negative, as dia- betes treatment saves lives and secondary prevention helps to prevent people from developing complications. However, Clinical relevance healthcare professionals also have a role to play in raising awareness about primary prevention and could be a major part While the short-term clinical relevance of taking into account of the movement towards looking upstream [26]. such upstream factors in the early detection of type 2 diabetes may be limited, it may still be important to take a step back to see the larger picture. Trying to identify individuals at risk of type 2 diabetes by looking at their ‘nearest neighbour’ (e.g., Type 2 diabetes as the result of a complex spouse, sibling or friend) may be regarded as fighting a running system battle, given that there are more distal drivers that cause these spouses, siblings and friends to develop type 2 diabetes in the It is important to realise that type 2 diabetes (like obesity) is first place. As an example, Fig. 1 displays a framework for likely to be the result of a complex, adaptive system [27]. obesity as proposed by Swinburn et al [24]. Environmental There are multiple factors that exert an influence on the de- factors may be viewed as moderators that have an attenuating velopment and progression of type 2 diabetes and these factors effect on lifestyle interventions, as such being of relevance to are likely to interact in a dynamic way. Complex systems are clinicians. Indeed, trying to adhere to dietary recommendations defined by several properties, such as emergence, feedback in an obesogenic environment may feel like swimming against and adaptation [28]. Emergence refers to the development of the stream: individuals may be able to cope for a while, but an outcome (e.g., type 2 diabetes) that cannot be explained then they get tired and give in. Focusing on the environmental sufficiently by the individual elements in a system, because it and systemic drivers of type 2 diabetes is, therefore, likely to is the result of more than the sum of parts. Feedback loops Fig. 1 A framework to categorise obesity determinants and solutions. target the systemic drivers might have larger effects, but their political The more distal drivers are to the left and the environmental moderators implementation is more difficult than health promotion programmes and that have an attenuating or accentuating effect are shown, along with medical services. Although this figure relates to obesity, it is likely that the some examples. The usual interventions for environmental change are environmental and systemic drivers shown are also likely to influence policy based, whereas health promotion programmes can affect environ- type 2 diabetes risk. Reprinted from The Lancet [24], with permission ments and behaviours. Drugs and surgery operate at the physiological from Elsevier level. The framework shows that the more upstream interventions that 1520 Diabetologia (2018) 61:1517–1521 approved of the version to be published. NRB and JWJB made substantial describe the situation in which a change in the system leads to contributions to the conception of this commentary, critically revised it further change; for example, a fast food ban around schools and approved of the version to be published. leads to reduced social acceptability of the consumption of fast Open Access This article is distributed under the terms of the Creative foods, which leads to a reduced demand for fast food, Commons Attribution 4.0 International License (http:// resulting in reduced supply of fast food. Adaptation refers to creativecommons.org/licenses/by/4.0/), which permits unrestricted use, adjustments in behaviour in response to changes in the system, distribution, and reproduction in any medium, provided you give appro- for example, a change in the formulation of sugar-sweetened priate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. beverages (SSB) in response to an announced SSB-tax. If we can agree that type 2 diabetes is the result of a complex set of interacting factors from within and outside the medical sector, we can see that the problem cannot necessarily be solved with simple, short-term and isolated initiatives. It will likely take References actions in multiple areas of the system to bring about a sus- tainable shift in type 2 diabetes. This encompasses actions that 1. NCD Risk Factor Collaboration (NCD-RisC) (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 move beyond the direct effects on individuals and focus on population-based studies with 4.4 million participants. Lancet reshaping the system itself [28]. A biomedical approach will 387:1513–1530 remain important for type 2 diabetes but, alone, it is unlikely 2. Danaei G, Finucane MM, Lu Y et al (2011) National, regional, and to result in a significant decrease in the prevalence of type 2 global trends in fasting plasma glucose and diabetes prevalence diabetes. Hence, healthcare professionals should move be- since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million yond a static, clinical view and look at other factors in the participants. Lancet 378:31–40 patient’s life that may affect disease trajectories, such as spou- 3. Wu Y, Ding Y, Tanaka Y, Zhang W (2014) Risk factors contributing sal risk factors, taking into account that these factors are to type 2 diabetes and recent advances in the treatment and preven- dynamic, and may interact with and impact on each other over tion. Int J Med Sci 11:1185–1200 time [29]. 4. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A (2011) Type 2 diabetes incidence and socio-economic position: a system- atic review and meta-analysis. Int J Epidemiol 40:804–818 5. Newton S, Braithwaite D, Akinyemiju TF (2017) Socio-economic status over the life course and obesity: systematic review and meta- Conclusions analysis. PLoS One 12:e0177151 6. Lynch JW, Kaplan GA, Salonen JT (1997) Why do poor people In conclusion, Nielsen et al made an important contribution to behave poorly? Variation in adult health behaviours and psychologi- the field by explaining the relevance of taking factors external cal characteristics by stages of the socioeconomic lifecourse. Soc Sci Med 44:809–819 to the individual into account when assessing risk of type 2 7. Lakerveld J, Mackenbach JD (2017) The upstream determinants of diabetes. Indeed, early detection of diabetes risk and subse- adult obesity. Obes Facts 10:216–222 quent interventions may be improved by using a couple- 8. den Braver NR, Lakerveld J, Rutters F, Schoonmade LJ, Brug J, based, rather than an individual-based, approach. Moreover, Beulens JWJ (2018) Built environmental characteristics and diabe- healthcare professionals, researchers and policy makers tes: a systematic review and meta-analysis. BMC Med 16:12 should take into account the wider systemic drivers of the type 9. Dendup T, Feng X, Clinga S, Astell-Burt T (2018) Environmental risk factors for developing type 2 diabetes mellitus: a systematic 2 diabetes epidemic and realise that the effect of downstream review. Int J Environ Res Public Health 15:78 interventions may be attenuated by upstream drivers. This 10. Caspi CE, Sorense G, Subramanian SV, Kawachi I (2012) The local implies that a systems response may be necessary to bring food environment and diet: a systematic review. Health Place 18: about the desired reduction in type 2 diabetes risk. To enable 1172–1187 further research into this, broader data collection is required, 11. Dzhambov AM (2015) Long-term noise exposure and the risk for type 2 diabetes: a meta-analysis. Noise Health 17:23–33 not only on the influence of spouses, friends and siblings, but 12. Nielsen J, Hulman A, Witte DR (2018) Spousal cardiometabolic also neighbours, other family members and employers, the risk factors and incidence of type 2 diabetes: a prospective analysis recreation, transport and food environment, and policy and from the English Longitudinal Study of Ageing. Diabetologia economic systems. https://doi.org/10.1007/s00125-018-4587-1 13. Leong A, Rahme E, Dasgupta K (2014) Spousal diabetes as diabetes risk factor: a systematic review and meta-analysis. BMC Med 12:12 Funding JDM is funded by an NWO VENI grant on ‘Making the healthy choice easier – role of the local food environment’ (grant number 451-17-032). 14. Jackson SE, Steptoe A, Wardle J (2015) The influence of partner’s behaviour on health behaviour change. The English Longitudinal Duality of interest The authors declare that there is no duality of interest Study of Ageing. JAMA Intern Med 17:385–392 associated with this manuscript. 15. Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. N Engl J Med 357:370–379 Contribution statement JDM made substantial contributions to the con- 16. Ask H, Rognmo K, Ask Torvik F, Roysamb E, Tambs K (2012) Non-random mating and convergence over time for alcohol ception of this commentary, drafted the article, critically revised it and Diabetologia (2018) 61:1517–1521 1521 consumption, smoking and exercise: the Nord-Trondelag Health 23. Anderson JW, Randles KM, Kendall CW, Jenkins DJ (2004) Carbohydrate and fiber recommendations for individuals with dia- Study. Behav Genet 42:354–365 17. Bot SD, Mackenbach JD, Nijpels G, Lakerveld J (2016) betes: a quantitative assessment and meta-analysis of the evidence. Association between social network characteristics and lifestyle JAm CollNutr 23:5–17 behaviours in adults at risk of diabetes and cardiovascular disease. 24. Swinburn BA, Sacks G, Hall KD et al (2011) The global obesity PLoS One 11:e0165041 pandemic: shaped by global drivers and local environments. Lancet 18. Michie S, Wood CE, Johnston M, Abraham C, Francis JJ, Hardeman 378:804–814 W (2015) Behaviour change techniques: the development and eval- 25. Rutter H (2017) The complex systems challenge of obesity. Clin uation of a taxonomic method for reporting and describing behaviour Chem 64:1 change interventions (a suite of five studies involving consensus 26. Cypress M (2004) Looking upstream. Diabetes Spectr 17:249–253 methods, randomized controlled trials and analysis of qualitative da- 27. Government Office for Science. Foresight. Tacking obesities: future ta). Health Technol Assess 19:1–187 nd choices—project report, 2 Edition. 2007. Available from: www. 19. Mackenbach JD, Lakerveld J, van Lenthe FJ et al (2016) gov.uk/government/uploads/system/uploads/attachment_data/file/ Neighbourhood social capital: measurement issues and associa- 287937/07-1184x-tackling-obesities-future-choices-report.pdf. tions with health outcomes. Obes Rev 17:96–107 Accessed 18 Mar 2018 20. Power C, Matthews S (1997) Origins of health inequalities in a 28. Rutter H, Savona N, Glonti K et al (2017) The need for a com- national population sample. Lancet 350:1584–1589 plex systems model of evidence for public health. Lancet 390: 21. Kuh DJL, Wadsworth MEJ (1993) Physical health status at 36 years 2602–2604 in a British national birth cohort. Soc Sci Med 37:905–916 29. Lounsbury DW, Hirsch GB, Vega C, Schwartz CW (2014) 22. White JS, Hamad R, Li X et al (2016) Long-term effects of Understanding social forces involved in diabetes outcomes: a sys- neighbourhood deprivation on diabetes risk: quasi-experimental tems science approach to quality-of-life research. Qual Life Res 23: evidence from a refugee dispersal policy in Sweden. Lancet 959–969 Diabetes Endocrinol 4:517–524 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Diabetologia Springer Journals

Spouses, social networks and other upstream determinants of type 2 diabetes mellitus

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Abstract

Diabetes risk factors outside the individual are receiving increasing attention. In this issue of Diabetologia, Nielsen et al (DOI: https://doi.org/10.1007/s00125-018-4587-1) demonstrate that an individual’s obesity level is associated with incident type 2 diabetes in their spouse. This is in line with studies providing evidence for spousal and peer similarities in lifestyle behaviours and obesity. Non-random mating and convergence over time are two explanations for this phenomenon, but shared exposure to more upstream drivers of diabetes may also play a role. From a systems-science perspective, these mechanisms are likely to occur simultaneously and interactively as part of a complex system. In this commentary, we provide an overview of the wider system- level factors that contribute to type 2 diabetes. . . . . . . . Keywords Diabetes Environmental drivers Lifestyle behaviours Obesity Prevention Social networks Systems science Upstream determinants Abbreviations Upstream determinants of type 2 diabetes IRR Incidence rate ratio SEP Socioeconomic position There is increasing recognition that the conditions in which SSB Sugar-sweetened beverages individuals are born, grow up, live, work and age are impor- tant for understanding the aetiology of type 2 diabetes. There is a growing evidence base for such upstream determinants of health [7]; for example, adults living in rural areas have a lower risk of type 2 diabetes. Also, more walkable areas and Introduction more greenspace are associated with a lower risk of type 2 diabetes, probably owing to a higher level of physical activity Despite major investment in research and treatment options, type in these areas [8, 9]. Moreover, availability, accessibility and 2 diabetes mellitus remains a pressing public health issue that is affordability of food is associated with dietary intake and may, approaching epidemic proportions globally [1, 2]. Excess weight therefore, be associated with type 2 diabetes [10], whilst noise is an established risk factor for type 2 diabetes and the global pollution may increase the risk of type 2 diabetes via disrupted epidemic of obesity largely explains the major increase in its sleep patterns [11]. Although individual-level factors, such as prevalence in recent decades. Behavioural risk factors for type genetic, biological and psychological factors, remain of im- 2 diabetes include a poor diet, physical inactivity, stress and poor portance, they are unlikely to fully explain the enormous in- sleep quality [3]. Type 2 diabetes [4], obesity [5] and behavioural crease in type 2 diabetes incidence over the past decades. risk factors [6] are socioeconomically patterned, with individuals Hence, the research focus is shifting to more upstream deter- at lowest socioeconomic position (SEP) being at the highest risk. minants of health, which may be of importance for the early identification of individuals at high risk of type 2 diabetes and * Joreintje D. Mackenbach the development of subsequent initiatives to intervene in high- j.mackenbach@vumc.nl risk populations. In this issue of Diabetologia, Nielsen et al [12] contribute to this field of research by investigating the Department of Epidemiology and Biostatistics, Amsterdam Public influence of spousal diabetes status and cardiometabolic risk Health Research Institute, VU University Medical Center, De factors for an individual’sdiabetesrisk. Boelelaan 1089a, 1081HV Amsterdam, the Netherlands 1518 Diabetologia (2018) 61:1517–1521 Spousal diabetes concordance and stronger social networks were less likely to be obese than individuals living in neighbourhoods with lower levels of so- The study by Nielsen and colleagues [12], using data from cial cohesion and weaker social networks [19]. 3649 men and 3478 women included in the English Following the reasoning above, health behaviours and Longitudinal Study of Ageing (ELSA), demonstrated that chronic conditions may not just ‘spread’ via spouses, friends obesity levels in one spouse were associated with incident and siblings, but even across entire families, neighbourhoods type 2 diabetes in the other spouse. Interestingly, having an or cities. If Nielsen et al had had data on cardiometabolic risk obese spouse increased the risk of type 2 diabetes in men over factors of other family members, friends or neighbours, they and above the effect of their own obesity level, while this was may have found that, not only spousal factors, but wider social not the case for women. In addition, having a spouse with environmental factors were associated with risk of developing diabetes was associated with an increased risk of type 2 dia- type 2 diabetes. In turn, these similarities in type 2 diabetes betes in women (incidence rate ratio [IRR] 1.40 [95% CI 0.95, risk across a social network may be explained by lifestyle 2.08]) but not in men (IRR 1.02 [95% CI 0.64, 1.65]) [12]. behaviours, socioeconomic conditions across the lifespan, or This association in women was not statistically significant but, exposure to food and physical activity environments. Indeed, given the low number of cases of diabetes in this study and the a third explanation for behavioural and health similarities relatively large effect size, this may be regarded as relevant for between connected individuals is shared exposure to common public health. In general, the nationally representative sample, environmental factors. the long follow-up period and thorough analyses provide con- fidence in the findings. The implications of the results for clinical practice may, however, be limited since high-risk cou- Shared environmental factors and type 2 ples may be concordant in their non-attendance for screening; diabetes this should be subject to future investigations. The results of this study are in line with a previous meta- Although Nielsen et al adjusted for SEP, all the relevant analysis that demonstrated evidence for spousal diabetes con- socioeconomic variation in type 2 diabetes risk may not have cordance [13] and is in line with studies providing evidence been captured. They used the highest reported employment for spousal similarities in lifestyle behaviours and obesity [14, rate at the couple level to indicate SEP, while socioeconomic 15]. As Nielsen et al state [12], two commonly used explana- condition across the life span, including childhood SEP and tions for spousal similarities in behaviour and health are non- parental SEP, and area deprivation, may also explain spousal random mating and convergence over time [16]: individuals similarities [20, 21]. For example, a Swedish study on the are more likely to select a partner with similar phenotypes and effects of neighbourhood deprivation showed that refugees preferences, and over the course of a relationship, spouses assigned to high deprivation areas had increased risk of type converse in their behaviours because of social contagion. 2 diabetes, regardless of individual SEP, with neighbourhood effects growing over time [22]. Unfortunately, Nielsen and colleagues did not have data Impact of social networks on health available on other shared environmental factors and, thus, the authors could not investigate whether such factors may These effects outlined above may not be limited to spouses; explain spousal similarities and differences in type 2 diabetes Christakis and Fowler [15] showed that pairs of friends and risk. They did, however, touch upon the role of the food en- siblings of the same sex appeared to have more influence on vironment for spousal concordance in type 2 diabetes. They the weight gain of each other, than pairs of friends and siblings found that a wife’s obesity status was a stronger risk factor for of the opposite sex. The importance of social networks (e.g., incident type 2 diabetes in her husband than vice versa; they social structures composed of interdependent individuals, such speculate that this may be explained by the fact that women as spouses, relatives, colleagues, neighbours and friends) for are more likely to be responsible for planning, preparing and health has been recognised for decades [17]. Social contacts shopping for food. Indeed, in couples with a more traditional may shape norms about the acceptability of being overweight division of roles, a woman’s unhealthy dietary practices may or preferences for an active lifestyle or may provide support for influence both her own and her husband’s risk of type 2 dia- behaviour change. Not surprisingly, social influences are an betes, while a man’s unhealthy dietary practices (likely origi- important element of the behaviour change technique taxono- nating from the out-of-home food environment) may not in- my of Michie et al [18], which is used in many type 2 diabetes fluence his wife’s risk of type 2 diabetes. This is, however, prevention strategies. In addition, both risk factors and protec- discordant with the finding that triacylglycerol levels in men tive factors may spread through social networks. For example, (which are influenced by diet [23]) can impact upon type 2 our recent study in European adults showed that individuals diabetes risk in the wife [12]. Before any conclusive state- living in neighbourhoods with higher levels of social cohesion ments can be made about spousal or wider social network Diabetologia (2018) 61:1517–1521 1519 influences on type 2 diabetes, the effects of common exposure generate a larger preventative population effect, but is politi- to shared environments should be explored. Follow-up studies cally more difficult. Public, political and media discourse could investigate whether lifestyle behaviours, socioeconomic around obesity and diabetes has been dominated by a persistent conditions, wider social influences and exposure to food and skew towards individual-level choices as the primary determi- physical activity environments could explain similarities in nant, and this is then reflected in policies and interventions that type 2 diabetes risk. focus on individual-level behaviour change [25]. These down- stream endeavours should not be regarded as negative, as dia- betes treatment saves lives and secondary prevention helps to prevent people from developing complications. However, Clinical relevance healthcare professionals also have a role to play in raising awareness about primary prevention and could be a major part While the short-term clinical relevance of taking into account of the movement towards looking upstream [26]. such upstream factors in the early detection of type 2 diabetes may be limited, it may still be important to take a step back to see the larger picture. Trying to identify individuals at risk of type 2 diabetes by looking at their ‘nearest neighbour’ (e.g., Type 2 diabetes as the result of a complex spouse, sibling or friend) may be regarded as fighting a running system battle, given that there are more distal drivers that cause these spouses, siblings and friends to develop type 2 diabetes in the It is important to realise that type 2 diabetes (like obesity) is first place. As an example, Fig. 1 displays a framework for likely to be the result of a complex, adaptive system [27]. obesity as proposed by Swinburn et al [24]. Environmental There are multiple factors that exert an influence on the de- factors may be viewed as moderators that have an attenuating velopment and progression of type 2 diabetes and these factors effect on lifestyle interventions, as such being of relevance to are likely to interact in a dynamic way. Complex systems are clinicians. Indeed, trying to adhere to dietary recommendations defined by several properties, such as emergence, feedback in an obesogenic environment may feel like swimming against and adaptation [28]. Emergence refers to the development of the stream: individuals may be able to cope for a while, but an outcome (e.g., type 2 diabetes) that cannot be explained then they get tired and give in. Focusing on the environmental sufficiently by the individual elements in a system, because it and systemic drivers of type 2 diabetes is, therefore, likely to is the result of more than the sum of parts. Feedback loops Fig. 1 A framework to categorise obesity determinants and solutions. target the systemic drivers might have larger effects, but their political The more distal drivers are to the left and the environmental moderators implementation is more difficult than health promotion programmes and that have an attenuating or accentuating effect are shown, along with medical services. Although this figure relates to obesity, it is likely that the some examples. The usual interventions for environmental change are environmental and systemic drivers shown are also likely to influence policy based, whereas health promotion programmes can affect environ- type 2 diabetes risk. Reprinted from The Lancet [24], with permission ments and behaviours. Drugs and surgery operate at the physiological from Elsevier level. The framework shows that the more upstream interventions that 1520 Diabetologia (2018) 61:1517–1521 approved of the version to be published. NRB and JWJB made substantial describe the situation in which a change in the system leads to contributions to the conception of this commentary, critically revised it further change; for example, a fast food ban around schools and approved of the version to be published. leads to reduced social acceptability of the consumption of fast Open Access This article is distributed under the terms of the Creative foods, which leads to a reduced demand for fast food, Commons Attribution 4.0 International License (http:// resulting in reduced supply of fast food. Adaptation refers to creativecommons.org/licenses/by/4.0/), which permits unrestricted use, adjustments in behaviour in response to changes in the system, distribution, and reproduction in any medium, provided you give appro- for example, a change in the formulation of sugar-sweetened priate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. beverages (SSB) in response to an announced SSB-tax. If we can agree that type 2 diabetes is the result of a complex set of interacting factors from within and outside the medical sector, we can see that the problem cannot necessarily be solved with simple, short-term and isolated initiatives. It will likely take References actions in multiple areas of the system to bring about a sus- tainable shift in type 2 diabetes. This encompasses actions that 1. NCD Risk Factor Collaboration (NCD-RisC) (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 move beyond the direct effects on individuals and focus on population-based studies with 4.4 million participants. Lancet reshaping the system itself [28]. A biomedical approach will 387:1513–1530 remain important for type 2 diabetes but, alone, it is unlikely 2. Danaei G, Finucane MM, Lu Y et al (2011) National, regional, and to result in a significant decrease in the prevalence of type 2 global trends in fasting plasma glucose and diabetes prevalence diabetes. Hence, healthcare professionals should move be- since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million yond a static, clinical view and look at other factors in the participants. Lancet 378:31–40 patient’s life that may affect disease trajectories, such as spou- 3. Wu Y, Ding Y, Tanaka Y, Zhang W (2014) Risk factors contributing sal risk factors, taking into account that these factors are to type 2 diabetes and recent advances in the treatment and preven- dynamic, and may interact with and impact on each other over tion. Int J Med Sci 11:1185–1200 time [29]. 4. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A (2011) Type 2 diabetes incidence and socio-economic position: a system- atic review and meta-analysis. Int J Epidemiol 40:804–818 5. Newton S, Braithwaite D, Akinyemiju TF (2017) Socio-economic status over the life course and obesity: systematic review and meta- Conclusions analysis. PLoS One 12:e0177151 6. Lynch JW, Kaplan GA, Salonen JT (1997) Why do poor people In conclusion, Nielsen et al made an important contribution to behave poorly? Variation in adult health behaviours and psychologi- the field by explaining the relevance of taking factors external cal characteristics by stages of the socioeconomic lifecourse. Soc Sci Med 44:809–819 to the individual into account when assessing risk of type 2 7. Lakerveld J, Mackenbach JD (2017) The upstream determinants of diabetes. Indeed, early detection of diabetes risk and subse- adult obesity. Obes Facts 10:216–222 quent interventions may be improved by using a couple- 8. den Braver NR, Lakerveld J, Rutters F, Schoonmade LJ, Brug J, based, rather than an individual-based, approach. Moreover, Beulens JWJ (2018) Built environmental characteristics and diabe- healthcare professionals, researchers and policy makers tes: a systematic review and meta-analysis. BMC Med 16:12 should take into account the wider systemic drivers of the type 9. Dendup T, Feng X, Clinga S, Astell-Burt T (2018) Environmental risk factors for developing type 2 diabetes mellitus: a systematic 2 diabetes epidemic and realise that the effect of downstream review. Int J Environ Res Public Health 15:78 interventions may be attenuated by upstream drivers. This 10. Caspi CE, Sorense G, Subramanian SV, Kawachi I (2012) The local implies that a systems response may be necessary to bring food environment and diet: a systematic review. Health Place 18: about the desired reduction in type 2 diabetes risk. To enable 1172–1187 further research into this, broader data collection is required, 11. Dzhambov AM (2015) Long-term noise exposure and the risk for type 2 diabetes: a meta-analysis. Noise Health 17:23–33 not only on the influence of spouses, friends and siblings, but 12. Nielsen J, Hulman A, Witte DR (2018) Spousal cardiometabolic also neighbours, other family members and employers, the risk factors and incidence of type 2 diabetes: a prospective analysis recreation, transport and food environment, and policy and from the English Longitudinal Study of Ageing. Diabetologia economic systems. https://doi.org/10.1007/s00125-018-4587-1 13. Leong A, Rahme E, Dasgupta K (2014) Spousal diabetes as diabetes risk factor: a systematic review and meta-analysis. BMC Med 12:12 Funding JDM is funded by an NWO VENI grant on ‘Making the healthy choice easier – role of the local food environment’ (grant number 451-17-032). 14. Jackson SE, Steptoe A, Wardle J (2015) The influence of partner’s behaviour on health behaviour change. The English Longitudinal Duality of interest The authors declare that there is no duality of interest Study of Ageing. JAMA Intern Med 17:385–392 associated with this manuscript. 15. Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. N Engl J Med 357:370–379 Contribution statement JDM made substantial contributions to the con- 16. Ask H, Rognmo K, Ask Torvik F, Roysamb E, Tambs K (2012) Non-random mating and convergence over time for alcohol ception of this commentary, drafted the article, critically revised it and Diabetologia (2018) 61:1517–1521 1521 consumption, smoking and exercise: the Nord-Trondelag Health 23. Anderson JW, Randles KM, Kendall CW, Jenkins DJ (2004) Carbohydrate and fiber recommendations for individuals with dia- Study. Behav Genet 42:354–365 17. Bot SD, Mackenbach JD, Nijpels G, Lakerveld J (2016) betes: a quantitative assessment and meta-analysis of the evidence. Association between social network characteristics and lifestyle JAm CollNutr 23:5–17 behaviours in adults at risk of diabetes and cardiovascular disease. 24. Swinburn BA, Sacks G, Hall KD et al (2011) The global obesity PLoS One 11:e0165041 pandemic: shaped by global drivers and local environments. Lancet 18. Michie S, Wood CE, Johnston M, Abraham C, Francis JJ, Hardeman 378:804–814 W (2015) Behaviour change techniques: the development and eval- 25. Rutter H (2017) The complex systems challenge of obesity. Clin uation of a taxonomic method for reporting and describing behaviour Chem 64:1 change interventions (a suite of five studies involving consensus 26. Cypress M (2004) Looking upstream. Diabetes Spectr 17:249–253 methods, randomized controlled trials and analysis of qualitative da- 27. Government Office for Science. Foresight. Tacking obesities: future ta). Health Technol Assess 19:1–187 nd choices—project report, 2 Edition. 2007. Available from: www. 19. Mackenbach JD, Lakerveld J, van Lenthe FJ et al (2016) gov.uk/government/uploads/system/uploads/attachment_data/file/ Neighbourhood social capital: measurement issues and associa- 287937/07-1184x-tackling-obesities-future-choices-report.pdf. tions with health outcomes. Obes Rev 17:96–107 Accessed 18 Mar 2018 20. Power C, Matthews S (1997) Origins of health inequalities in a 28. Rutter H, Savona N, Glonti K et al (2017) The need for a com- national population sample. Lancet 350:1584–1589 plex systems model of evidence for public health. Lancet 390: 21. Kuh DJL, Wadsworth MEJ (1993) Physical health status at 36 years 2602–2604 in a British national birth cohort. Soc Sci Med 37:905–916 29. Lounsbury DW, Hirsch GB, Vega C, Schwartz CW (2014) 22. White JS, Hamad R, Li X et al (2016) Long-term effects of Understanding social forces involved in diabetes outcomes: a sys- neighbourhood deprivation on diabetes risk: quasi-experimental tems science approach to quality-of-life research. Qual Life Res 23: evidence from a refugee dispersal policy in Sweden. Lancet 959–969 Diabetes Endocrinol 4:517–524

Journal

DiabetologiaSpringer Journals

Published: Apr 13, 2018

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