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Learn more: PMC Disclaimer | PMC Copyright Notice J Diabetes Metab Disord . 2025 Apr 10;24(1):102. doi: 10.1007/s40200-025-01615-7 Search in PMC Search in PubMed View in NLM Catalog Add to search Noncommunicable disease syndemic among the general population in Iran: a cross-sectional study Zahra Torabi Zahra Torabi 1 Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Find articles by Zahra Torabi 1, ✉ , Farshad Farzadfar Farshad Farzadfar 2 Noncommunicable Diseases Research Centre, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran Find articles by Farshad Farzadfar 2 , Negar Rezaei Negar Rezaei 2 Noncommunicable Diseases Research Centre, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran Find articles by Negar Rezaei 2 , Merrill Singer Merrill Singer 3 Department of Anthropology, University of Connecticut, 354 Mansfield Road, Storrs, Connecticut, 06269 USA Find articles by Merrill Singer 3 , Shahin Roshani Shahin Roshani 4 The Netherlands Cancer Institute (NKI), Amsterdam, Netherlands Find articles by Shahin Roshani 4 , Maryam Tajvar Maryam Tajvar 5 Department of Health Management, Policy and Economic, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Find articles by Maryam Tajvar 5 , Elham Shakibazadeh Elham Shakibazadeh 1 Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Find articles by Elham Shakibazadeh 1 Author information Article notes Copyright and License information 1 Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 2 Noncommunicable Diseases Research Centre, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran 3 Department of Anthropology, University of Connecticut, 354 Mansfield Road, Storrs, Connecticut, 06269 USA 4 The Netherlands Cancer Institute (NKI), Amsterdam, Netherlands 5 Department of Health Management, Policy and Economic, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ✉ Corresponding author. Received 2024 Nov 30; Accepted 2025 Mar 24; Collection date 2025 Jun. © The Author(s), under exclusive licence to Tehran University of Medical Sciences 2025. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. PMC Copyright notice PMCID: PMC11985718 PMID: 40224526 Abstract Objectives This study aims to investigate the status of the NCD syndemic among individuals over 25 in all provinces of Iran. Methods This study was based on a national survey. Using a systematic cluster random sampling framework, 26,707 participants were selected from all 31 Iranian provinces. The data were analyzed for descriptive statistics by gender and age, followed by principal component analysis and logistic regression, using R software for the statistical analysis. Results There was an interaction between diabetes mellitus and cardiovascular diseases, in their association with healthy life lost due to disability in the Iranian adult population. Additionally, there was a clustering of diabetes mellitus and cardiovascular diseases, partly due to the shared specific causes such as obesity, hypertension, shared baseline features, and poverty. Conclusions The syndemic analysis showed that social inequalities in diabetes and cardiovascular disease arise not only from exposure levels but also from varying vulnerabilities and disease outcomes. Iran’s health policy should prioritize reducing these health inequalities. Keywords: Syndemic, Noncommunicable diseases, NCDs, Poverty, Diabetes mellitus, CVDs Introduction Non-communicable diseases (NCDs) commonly share risk factors that lead to increased comorbidities, especially in low-income and marginalized populations [ 1 – 6 ]. Individuals with lower incomes lack access to diets that are rich in nutritious foods such as fresh fruits and vegetables, lean meats, fish, and legumes, which are linked to the onset of obesity. Poor families often reside in disadvantaged neighborhoods where it is difficult to find healthy foods. Rather, they often have greater access to and consume higher quantities of sugars, fats, and highly processed or ultra-processed foods. Ultra-processed products are characterized by their high energy density, long shelf lives, convenience in being ready-to-eat, and affordability. Furthermore, economic insecurity often leads to stress, causing individuals to turn to high-fat, high-carbohydrate foods as a way of coping [ 7 , 8 ]. The intricate interplay of these health conditions and socioeconomic factors underscores the importance of comprehensive approaches to address and mitigate the impact of comorbidities on individual and population health outcomes [ 9 – 12 ]. Social and health issues are frequently neglected in epidemiology, highlighting the need to address these connections for effective public health strategies. The overlap of health issues within socio-cultural contexts underscores the need for frameworks that improve our understanding of risks and the effectiveness of prevention programs for comorbidities [ 13 ]. Syndemic theory The concept of “syndemics” was developed by Merrill Singer in the 1990s to provide a new approach to public health and clinical practice. It emphasizes the interaction of multiple health issues within specific communities, taking into account biological, behavioral, and social factors that impact the occurrence and effects of diseases [ 14 ]. A syndemic refers to a complex and interconnected set of epidemics that interact and exacerbate each other at a biopsychosocial level, leading to the development and persistence of health issues in a community or population due to detrimental social circumstances and harmful social relationships [ 15 ]. Recent evaluations have pointed out that numerous research studies aiming to demonstrate connections between diseases and conditions have employed a “sum-score” method, which may indicate that effects are accumulating but do not assess whether they interact in a manner in which one condition’s impact is influenced by the presence of another [ 16 ]. For many NCDs, the biological interactions may be difficult to identify, but most of the studies applying the concept of syndemics have focused on comorbidities, in which biological interaction is more apparent [ 16 – 18 ]. Recent research shows that in Iran, years lived with disability from cardiovascular diseases now exceed years of life lost and the morbidity and mortality associated with diabetes have risen significantly [ 19 ]. Conventional approaches to controlling NCDs are necessary but insufficient. By utilizing syndemic theory, actions can be leveraged at multiple levels to address the underlying causes of poor health and inform more effective strategies for improving health outcomes and reducing health disparities [ 20 ]. This study aims to investigate the status of the NCD syndemic among individuals over 25 in all provinces of Iran. Materials and methods Study design This study is based on a cross-sectional survey followed by the STEP-wise approach to surveillance (STEPs), the National and Subnational Burden of Diseases, injuries, and risk factors in Iran (NASBOD), and the Global burden of disease (GBD) studies. Setting The STEPs program consists of three assessment levels: the first gathers baseline information, epidemiological data, and risk-related behaviors; the second collects physical measurements; and the third includes laboratory tests on a nationally representative sample of Iranian adults. Surveys were conducted in urban and rural areas across all 31 provinces of Iran in 2016 [ 21 ]. Participants/ inclusion criteria This study examined data from 26,707 individuals aged 25 and older from the STEPs national survey, which offers insights at the national level across urban and rural areas and various socioeconomic statuses. Both genders who participated in laboratory tests as part of a nationally representative sample of Iranian adults met the inclusion criteria. Variable/ data sources/ measurement We used the data from the STEPs questionnaire to identify desirable variables, including baseline characteristics, BMI (BMI ≥ 30), DM (FBG ≥ 126, self-reported, or based on a positive answer to whether a healthcare professional diagnosed them with hyperglycemia or DM in the past12 months), HTN (average 2nd and 3rd systolic blood pressure greater than 140 and diastolic greater than 90 or taking HTN medication), and CVD (based on a positive answer to whether a healthcare professional diagnosed them with a heart attack or stroke in the past 12 months) [ 25 ]. The wealth index was calculated using the principal component analysis method, considering factors like location, car type and usage, availability of facilities and equipment, and fuel consumption for various purposes. The lowest socioeconomic level was represented by the first quartile of the wealth index, while the highest life years with disabilities (YLD) rate was represented by the fourth quartile, as determined by NASBOD and GBD mortality data and prevalence data from the STEPs study for CVD and DM. Multiple pathways of interaction may exist, but four potential mechanisms may explain the clustering of DM and CVD: DM could influence the occurrence of CVD through bidirectional causality, or DM and CVD may share a specific underlying cause such as obesity and HTN, or they may share an individual or contextual fundamental cause, such as socioeconomic status and baseline features, which could mediate their effect through specific causes. For instance, the impact of obesity on DM could be more significant among individuals facing economic stress due to poverty. This study presents an epidemiological analysis of a significant sample of the Iranian population. It is important to examine this syndemic in a low- and middle-income country (LMIC) setting, where substantial social disparities and elevated poverty levels play a role in the high rates of obesity, HTN, DM, and CVD. We examined the syndemic’s four potential mechanisms, illustrated in Fig. 1 . The interaction between the two disorders (DM and CVDs) in their association with YLD and to what extent the consequences of diseases are modified by the fundamental causes [ 1 ]. The clustering of DM and CVDs [ 2 ], to some extent, is generated by specific causes such as obesity and HTN [ 3 ], and more fundamental causes here exemplified by socioeconomic status [ 4 ]. Fig. 1. Open in a new tab A model of the main causal relationships of syndemic [ 1 ] Study size In the STEPs program to achieve proportional-to-size sampling, a systematic cluster random sampling framework was developed, and 31,050 participants (3105 clusters) were chosen from urban and rural areas across all 31 provinces in Iran. This study analyzed data from 26,707 individuals over 25 who took part in the laboratory phase. Bias/ statistical methods The data was first utilized to present descriptive statistics of key variables by gender and age groups at the national level. Analytical statistics, including principal component analysis for the wealth index, were conducted. Logistic regression assessed the association between DM, CVD, HTN, and obesity, adjusting for confounding factors such as socioeconomic status and other variables [ 21 ]. Statistical analyses were performed using R software. Results Disease prevalence rates and the number of survey participants are presented in Table 1 . In the sample, 10.7% reported diabetes diagnosis, while 15.2% had CVD. Additionally, 28.8% had hypertension, 24.2% were obese, 24.3% had a low wealth index, and 27.2% had a high YLD. The first empirical question was the extent to which obesity, DM, HTN, CVD, and poverty cluster together. A total of 867 individuals reported both DM and CVD. Table 1 shows that females experienced a higher occurrence of disorders and had a lower wealth index, highlighting gender disparities, socioeconomic challenges, and limited income among females. Table 1. Study population: prevalence of variables, proportions in % weighted, Ns unweighted, steps 2016 With diabetes With CVD Total % N % N % N Female 11.49 1136 15.52 2147 51.86 13,886 Age 50+ 21.11 1491 32.17 3240 37.68 10,108 Low wealth index 9.21 451 12.83 868 24.39 6677 Obese 16.50 750 21.97 1362 24.22 6235 Hypertensive 20.64 1166 31.71 2427 28.81 7696 Diabetic - - 43.60 867 10.77 1944 With CVD 29.64 867 - - 15.21 4053 High YLD 17.94 701 25.56 1711 27.27 6710 Total 10.77 1944 15.21 4053 100 26,707 Open in a new tab Table 2 indicates that after adjusting for age, gender, and other diseases, individuals living in poverty had a 20% higher likelihood of obesity, 10% higher likelihood of diabetes, 30% higher likelihood of cardiovascular disease, and 40% higher likelihood of disability. Interestingly, poverty appeared to have a protective effect against high blood pressure. Table 2 also indicates that after adjusting for age, gender, and other diseases, Individuals with DM were 2.7 times CI95%:(2.41 to 3.03) more likely to develop CVD, while those with CVD were 2.8 times CI95%:(2.5 to 3.13) more likely to develop DM. Table 2. Correlation between study variables wealth index, obesity, HTN, DM, CVD and YLD, Iran STEPS 2016 (CI95%) OR a OR b (CI95%) Low wealth index Obesity 1.2 (1.24 to 1.33) 1.24 (1.20 to 1.27) HTN 0.89 (0.86 to 0.92) 0.95 (0.92 to 0.97) DM 1.13 (1.08 to 1.19) 1.22 (1.16 to 1.28) CVD 1.3 (1.24 to 1.36) 1.3 (1.27 to 1.37) YLD high 1.4 (1.4 to 1.5) 1.57 (1.52 to 1.62) Obesity HTN 1.88 (1.73 to 2.04) 2.09 (1.95 to 2.24) DM 1.56 (1.4 to 1.7) 1.88 (1.7 to 2.09) CVD 1.31 (1.19 to 1.45) 1.65 (1.52 to 1.69) YLD high 1.1 (1 to 1.2) 1.1 (1.03 to 1.18) HTN Obesity 1.92 (1.78 to 2.09) 2.14 (2 to 2.29) DM 1.83 (1.64 to 2.04) 2.2 (1.98 to 2.44) CVD 1.82(1.65 to 2) 2.18 (2.02 to 2.36) YLD high 0.93 (0.85 to 1.02) 0.89 (0.83 to 0.95) DM Obesity 1.7 (1.53 to 1.9) 2.09 (1.88 to 2.32) HTN 1.92 (1.72 to 2.15) 2.3 (2.7 to 2.57) CVD 2.7 (2.41 to 3.03) 3.24 (2.9 to 3.61) YLD high 1.24 (1.11 to 1.4) 1.36 (1.22 to 1.52) CVD Obesity 1.49 (1.34 to 1.64) 1.9 (1.75 to 2.07) HTN 1.92 (1.74 to 2.11) 2.3 (2.13 to 2.49) DM 2.8 (2.5 to 3.13) 3.3 (3.01 to 3.74) YLD high 1.06 (0.95 to 1.17) 1.17 (1.08 to 1.26) Open in a new tab a, Adjusted for Age + Sex + Obesity + HTN + DM + CVD b, Adjusted for for Age + Sex We have detailed the findings of the analysis on how the two disorders interact with each other in Table 3 A crucial criterion for determining the existence of a syndemic was the interaction between DM and CVD with disease consequences, such as YLD. Unsurprisingly, DM and CVD were associated with YLD. Among females, the effect of DM on YLD without CVD was 9.96 times CI%95: (7.13 to 12.8), while for CVD without DM, it was 11.72 times CI%95: (9.64 to 13.8). The joint effect was 16.8 times CI%95: (13.43 to 20.18) among individuals with both disorders, which was higher than the combined effects of the two individual disorders. In males, the interaction effect was even stronger at 25.4 times CI%95: (21.09 to 29.71). Table 3 shows that the interaction between DM and a wealth index of 11.5 times CI%95: (7.06 to 15.93) and the interaction between CVD and a wealth index of 10.2 times CI%95: (6.93 to 13.48) were more pronounced among females compared to males. Table 3. The odds ratio of the interaction between DM and CVD in relation to high YLD, Iran steps 2016 Male Female 0 DM 0 CVD 0 (ref.) 0 (ref.) 1 DM 0 CVD 9.44 %95 CI: (6.04 to 12.85) 9.96 %95 CI: (7.13 to 12.8) 0 DM 1 CVD 9.45 %95 CI: (7.4 to 11.5) 11.72 %95 CI: (9.64 to 13.8) 1 DM 1 CVD 21.14 %95 CI: (16.75 to 25.53) 12.39 %95 CI: (8.97 to 15.81) Interaction: DM *CVD 25.4 %95 CI: (21.09 to 29.71) 16.8 %95 CI: (13.43 to 20.18) DM *Low wealth index 8.67 %95 CI: (2.39 to 14.95) 11.5 %95 CI: (7.06 to 15.93) CVD*Low wealth index 8.71 %95 CI: (4.37 to 13.06) 10.2 %95 CI: (6.93 to 13.48) Open in a new tab Discussion In this study, for the first time in the Middle East, we identified a NCD syndemic, among the general population over 25 years old in all provinces of Iran. If we go back to Fig. 1 and the four mechanisms outlined there, we conclude that there was an interaction between DM and CVDs and their effect on YLD in the Iranian adult population. Previous studies from other countries have found the clustering and interaction between DM and CVDs [ 22 – 27 ]. Some studies have pointed out that, the global burden of DM reached 66.3 million DALYs, reflecting an age-standardized rate of 801.5 DALYs per 100,000 population, a 27.6% increase since 1990 [ 28 ]. In the Iranian adult population, the clustering was stronger in males than in females, Such conclusions reflected the results of some studies that, the DALY rate of CVD was 1.5 times higher in men than in women [ 29 ]. Nevertheless, a prospective study reported relative risks (RR) for incident CVDs of 2.63 for females and 1.85 for males with DM [ 24 ]. The variation in study results may stem from differing socio-economic backgrounds and gender differences worldwide, as the socio-economic status in London creates vastly different conditions than those in Afghanistan. Another systematic review noted that females with DM face a 40% higher risk of developing CVDs than males with DM [ 30 ]. Additionally, there was a clustering of the DM and CVDs, partly due to the shared specific causes such as obesity, and HTN. Also, after adjusting for shared causes, a strong clustering was still evident, indicating a bidirectional causal relationship. Some early studies suggested that several potential mechanisms could explain an association between HTN and CVD, HTN leads to both electrophysiological and structural alterations in the left atrium, which may change its size and function, playing a crucial role in the development of atrial fibrillation [ 5 , 6 , 17 , 31 – 33 ]. Another systematic review noted that HTN is the most significant co-morbidity linked to DM. Individuals with HTN are eight times more likely to develop DM compared to those without. While the exact reasons for this association remain unclear, one hypothesis suggests that DM may increase plasma volume and vascular resistance by hardening the arteries, leading to elevated HTN [ 4 ]. Lifestyle changes, decreased physical activity, and the consumption of processed foods have contributed to normal weight obesity, where individuals have a normal BMI but high body fat (typically above 30%). Recent studies show a direct link between body fat percentage and cardiometabolic conditions [ 3 , 34 – 36 ]. Finally, there was a clustering of the DM and CVDs, partly due to the shared specific causes such as shared baseline features, and poverty. It appears that this variation could be attributed to improved access to healthcare services in developed countries and higher income levels [ 37 ]. In total, 24.3% of Iranian patients with DM and CVDs lived in poverty. The connection between NCDs and poverty has received high-level recognition. Individuals living in poverty are at a higher risk of NCDs due to factors such as lack of resources, psychological stress [ 38 ], engaging in risky coping behaviors, poor living conditions, inadequate healthcare access, and limited opportunities for prevention [ 11 , 13 ]. Both DM and CVD were strongly associated with poverty, particularly among females. Additionally, a study of 40 LMICs in the Asia-Pacific region revealed that girls in childhood and adolescence experience higher levels of gender inequality, leading to significant health consequences for them [ 39 ]. The interaction between DM and CVD was analyzed for YLD and is well-known, while nations with high/very high human development index (HDI) showed a significant decrease in CVD YLD slope trends compared to those with lower HDI levels [ 40 ]. Additionally, the global burden of DM YLD was highest in LMICs [ 41 ]. Some relationships might be through inverse causality influenced by the disease (for example, obesity might be both an effect and cause of DM); and controlling for obesity as a confounder might lead to an underestimation of the interaction between the diseases. A central issue of the syndemic concept is biopsychological pathways of interaction among diseases and between social conditions/diseases and even intersectional interactions among socioeconomic factors [ 42 ]. In this study, we have applied a formal interaction analysis that illustrates how the social context affects the clustering and interactions involved. The concept map illustrated in Fig. 1 . might look complicated but is still a crude simplification of what our data suggests is occurring. While lacking the depth of anthropological studies with the understanding generated by reference to a local context, we found clear tentative evidence of many of the criteria for an NCD syndemic in this population and showed the public health relevance of the concept. Strengths and limitations This study has several limitations. Firstly, the use of cross-sectional data restricts our ability to establish causal relationships between the dependent variables and their potential syndemic effects over time. Additionally, we could not determine the sequence of disease onset or the duration of exposure. Future longitudinal studies could provide further insights into these issues. One notable strength of this study is its comprehensive scope, as the findings pertain to the adult population aged 25 years and older in LMIC. Additionally, this research is the first joint analysis of DM and CVD using national survey data. Conclusions Socioeconomic inequalities in Iran create exposure to some health determinants and large social differences in exposure to others. The syndemic and interaction analyses illustrate that social inequalities in the burden of DM and CVDs are not only generated by these exposure levels but also by a differential vulnerability to the effects of these determinants and the differential consequences of the diseases. We recommend that adopting a syndemic framework as a means to identify, investigate, trial, assess, and deploy integrated health programs, particularly those focused on addressing multiple chronic conditions. Health policy in Iran should focus on reducing socioeconomic and health inequalities, when multiple conditions interact, the policy implication is that interventions should be comprehensive and address all interacting conditions. Conditional causation means that by solving one condition, some of the effects of the other condition (due to the interaction and dependent on the first) will also be eliminated. A syndemic approach will improve global health policy and outcomes for the most disadvantaged populations. Acknowledgements This study would not have been possible without the invaluable support of the NCDRC research assistants who hosted, facilitated, and assisted with this project. We would also like to express our sincere gratitude to all the participants as well as the scientific and executive partners of Tehran University of Medical Sciences who made this experience possible. Abbreviations LMICs Low-and middle-income countries YLD Healthy life lost due to disability NCDs Noncommunicable diseases HTN Hypertension DM Diabetes mellitus CVD Cardiovascular diseases HICs High-income countries WHO World Health Organization HIV Human immunodeficiency virus SDH Social determinants of health STEPs STEP wise approach to surveillance NASBAD National and Subnational Burden of Diseases, Injuries, and Risk Factors in Iran GBD Global burden of disease NCDRC Non-Communicable Diseases Research Center of Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences BMI Body mass index FBG Fasting blood glucose CI Confidence interval HDI Human Development Index Author contributions All authors read and approved the final manuscript. Funding The Tehran University of Medical Science has granted this project funding. Data availability Not applicable. Declarations Ethics approval and consent to participate In the STEPs study, participants provided informed written consent before enrollment. This study received approval from the Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.SPH.REC.1398.129) and followed national research ethics guidelines and the Declaration of Helsinki. Consent for publication Not applicable. Competing interests Not applicable. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Shariq OA, McKenzie TJ. Obesity-related hypertension: a review of pathophysiology, management, and the role of metabolic surgery. Gland Surg. 2020;9(1):80. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Jayedi A, Rashidy-Pour A, Khorshidi M, Shab‐Bidar S. 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