Multi-level Determinants of Type 2 Diabetes Risk Among Migrant Populations in High Income Settings: A Systematic Review

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Falobi, Sarah Olatunji, Opeolu O. Ojo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9453308/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Type 2 diabetes is a major global public health challenge, with increasing evidence of disproportionate burden among migrant populations. Migrants often experience higher prevalence and earlier onset of disease compared with host populations, reflecting complex interactions between biological, behavioural, and structural determinants. However, there is limited synthesis of how these multi-level factors jointly contribute to risk. This systematic review aimed to identify and synthesise determinants of type 2 diabetes among migrant populations across diverse settings. Methods We conducted a systematic review of observational studies published between January 2011 and December 2024. Electronic databases were searched using predefined terms related to migration and type 2 diabetes. Studies were included if they examined determinants of type 2 diabetes among adult migrant populations. Data were extracted using a standardised form and synthesised using a thematic approach. Determinants were grouped into four domains: biological and early-life factors, behavioural and acculturation-related factors, socioeconomic and environmental conditions, and health system influences. Results Twenty-six studies were included, encompassing diverse migrant populations across Europe, Africa, Asia, and North America. Evidence consistently showed that type 2 diabetes risk among migrants is shaped by interacting determinants across the life course. Pre-migration factors, including genetic susceptibility and early-life exposures, contributed to baseline risk. Post-migration behavioural changes, particularly dietary transitions and reduced physical activity, were associated with increased risk but were strongly influenced by socioeconomic and environmental conditions. Structural determinants, including income, education, and living conditions, were consistently linked to disparities in risk and outcomes. Health system factors, including access to care, health literacy, and limitations in diagnostic tools, further contributed to delayed diagnosis and suboptimal management. Conclusions Type 2 diabetes risk among migrant populations is driven by multi-level, interacting determinants that extend beyond individual behaviours. Effective prevention and control strategies require a shift towards equity-oriented approaches that address structural and social determinants, alongside culturally tailored interventions and migrant-responsive health systems. Registration This systematic review was not registered in PROSPERO or any other review registry. Type 2 diabetes migrant populations health inequalities social determinants of health acculturation life-course approach Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Type 2 diabetes represents a major global public health challenge, affecting an estimated 537 million adults worldwide and contributing substantially to morbidity, mortality, and healthcare expenditure [ 1 ]. The distribution of the disease is markedly unequal, with migrant and ethnic minority populations in high-income countries experiencing a disproportionately higher burden, including earlier onset and more rapid disease progression [ 2 ]. In Europe, increasing population diversity driven by migration has intensified concerns about health inequalities, particularly in relation to chronic non-communicable diseases such as type 2 diabetes [ 3 ]. Migration is a complex and multidimensional determinant of health that shapes disease risk through a range of interrelated biological, behavioural, and structural pathways. Individuals who migrate are often exposed to significant changes in living conditions, socioeconomic status, and healthcare environments [ 4 ]. Evidence suggests that migrants frequently encounter adverse social conditions, including economic insecurity, suboptimal housing, and restricted access to healthcare services, particularly during the early stages of settlement. These challenges may be compounded by policy-related barriers and inequities in healthcare entitlement, which can delay access to preventive services and appropriate treatment [ 5 ]. Collectively, these factors contribute to increased vulnerability to chronic diseases, including type 2 diabetes. The elevated risk of type 2 diabetes among migrant populations is also influenced by pre-migration characteristics. Genetic susceptibility, early-life exposures, and baseline health status prior to migration have been identified as important contributors to disease risk. Studies have consistently shown that individuals of South Asian and African origin are more likely to develop type 2 diabetes at younger ages and at lower body mass index thresholds compared with European populations, suggesting differences in metabolic risk profiles [ 6 ]. These intrinsic susceptibilities interact with environmental exposures across the life course, underscoring the importance of adopting a comprehensive perspective that integrates both biological and contextual determinants. Following migration, acculturation processes play a significant role in shaping health behaviours and risk profiles. Adaptation to the host environment often involves changes in dietary patterns, physical activity levels, and broader lifestyle behaviours, which may increase exposure to obesogenic environments and sedentary living [ 7 ]. Such changes are frequently mediated by structural constraints, including limited access to healthy and culturally appropriate foods, financial barriers, and environmental conditions that discourage physical activity [ 8 ]. These behavioural transitions have been strongly associated with increased risk of obesity, insulin resistance, and subsequent development of type 2 diabetes. Structural and systemic determinants further contribute to disparities in diabetes risk among migrants. Barriers to healthcare access, including language differences, lack of culturally appropriate services, and challenges navigating health systems, can lead to delayed diagnosis and suboptimal disease management [ 9 ]. In addition, emerging evidence suggests that commonly used diagnostic thresholds and risk assessment tools may not adequately reflect the risk profiles of diverse populations, potentially resulting in underestimation of disease burden among migrant groups [ 10 ]. These limitations highlight the need for more inclusive and context-sensitive approaches to diabetes prevention and care. Despite a growing body of research, the evidence base on type 2 diabetes among migrants remains fragmented. Many studies focus on individual risk factors in isolation, with limited consideration of the complex interactions between biological, behavioural, and structural determinants. Furthermore, while research has been conducted across multiple European settings, there is a lack of comprehensive synthesis that integrates findings across populations and contexts. This systematic review therefore aims to synthesise existing evidence on the biological, behavioural, social, and structural determinants of type 2 diabetes among migrant populations, with a primary focus on studies conducted in Europe. By adopting a multi-level analytical framework, this review seeks to enhance understanding of how these determinants interact to shape disease risk and to inform the development of targeted public health strategies aimed at reducing health inequalities. METHODS Study Design This study was conducted as a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review synthesised evidence on determinants of type 2 diabetes among migrant populations, with a primary focus on studies conducted in Europe and comparable high-income settings. Data Sources and Search Strategy A comprehensive and systematic search of the literature was undertaken in PubMed, MEDLINE, PsycINFO, and CINAHL (Cumulative Index to Nursing and Allied Health Literature). Search strategies were developed using a Population, Exposure, Outcome (PEO/PICO-informed) framework, incorporating controlled vocabulary (MeSH terms) and free-text keywords related to migration , ethnicity , and type 2 diabetes . The search included studies published within the preceding 15 years and was conducted up to Dec 31, 2024. No restrictions were applied on geographic setting at the search stage to ensure comprehensive capture of relevant evidence. All identified records were imported into a reference management system, and duplicates were removed prior to screening. Title and abstract screening were conducted, followed by full-text review of potentially eligible studies. The study selection process was undertaken in accordance with PRISMA guidelines, and a PRISMA flow diagram was used to document the screening process. Eligibility Criteria Inclusion criteria Studies were eligible for inclusion if they met the following prespecified criteria: Population: Migrant populations, defined as individuals residing outside their country of birth; studies predominantly included participants from Black, Asian, and other minority ethnic groups Exposure: Factors associated with the development or increased risk of type 2 diabetes, including biological, behavioural, social, and structural determinants Outcome: Type 2 diabetes or related metabolic indicators (e.g. impaired glucose metabolism, insulin resistance, obesity, HbA1c levels) Study design: Observational studies (cross-sectional, cohort, case-control) and qualitative studies Setting: Studies conducted in Europe and other high-income or comparable settings Timeframe: Studies published between Jan 1, 2010, and Dec 31, 2024 Language: English Given the aetiological focus of the review, interventions and comparators were not prespecified. However, included studies commonly incorporated internal comparisons (e.g., migrants vs non-migrants, or between ethnic groups), which were considered during synthesis. Exclusion criteria Studies were excluded if they met any of the following criteria: Non-peer-reviewed publications (such as reports, editorials, conference abstracts without full text) Previous systematic reviews or narrative reviews Studies not focused on type 2 diabetes or its determinants among migrant populations Studies not published in English Studies published outside the specified timeframe During screening, studies were also excluded if they did not meet relevance criteria based on title, abstract, or full-text assessment. Study Selection Study selection was conducted independently by two reviewers. Titles and abstracts were screened for eligibility, followed by full-text review of potentially relevant studies. Disagreements between reviewers were resolved through discussion and, where necessary, consultation with a third reviewer to reach consensus. Data Extraction Data extraction was conducted independently by two reviewers using a standardised data extraction form. Extracted variables included: Author and year of publication Study design and setting Population characteristics (including migrant group and country of origin) Sample size Exposure variables (risk factors) Outcome measures related to type 2 diabetes Key findings and reported associations Data extraction was based on summary data reported in published articles. Individual patient-level data were not requested from study authors. Quality Assessment The methodological quality of included studies was assessed using Joanna Briggs Institute (JBI) critical appraisal tools, with checklists selected according to study design (cross-sectional, cohort, case-control, and qualitative studies). Quality appraisal was conducted independently by two reviewers, and discrepancies were resolved through discussion. Studies were not excluded based on quality assessment alone; however, appraisal findings were considered during interpretation of results. Outcomes The primary outcome of this review was the identification and synthesis of determinants associated with increased risk of type 2 diabetes among migrant populations. Data Synthesis Given the heterogeneity in study designs, populations, exposures, and outcome measures, meta-analysis was not undertaken. Instead, a narrative and thematic synthesis approach was used. Extracted findings were systematically coded and grouped into thematic domains representing key categories of determinants. These included: Pre-migration biological and early-life factors Behavioural and lifestyle factors (including acculturation-related changes) Socioeconomic and environmental determinants Structural and healthcare system-related factors This approach enabled the identification of patterns and interactions across studies while preserving contextual differences. Adverse Events Adverse events were not assessed, as this review focused on determinants of disease risk rather than clinical interventions or treatment outcomes. RESULTS Study Selection The database search identified 1416 records (PubMed n = 312, MEDLINE n = 414, PsycINFO n = 298, and CINAHL n = 392). After removal of 1045 duplicates, 371 unique records remained for title and abstract screening. Of these, 345 records were excluded based on relevance, leaving 26 studies that met the inclusion criteria and were included in the final synthesis. The study selection process is summarised in a PRISMA flow diagram (Fig. 1 ). Study Characteristics The 26 included studies were published between 2011 and 2024 and represented a wide range of geographic settings, including multiple European countries, the United Kingdom, Australia, New Zealand, Peru, and the United States (Table 1 ). Most studies focused on migrant populations from African, South Asian, and other minority ethnic backgrounds, with some studies including multi-ethnic cohorts. The majority of studies were cross-sectional (n = 17), followed by cohort studies (n = 6), one case-control study, and one qualitative study. Sample sizes varied substantially, ranging from 76 participants to 500,000 (Fig. 2 ), reflecting considerable heterogeneity in study scale and design. Table 1 Characteristics of studies selected for review Article Type of study Data collection method Participants Migrants’ country of origin Sample size Major findings/Identified risk factors Determinant category Gray et al[ 11 ] Cross-sectional study Screening questionnaire plus the collection of data on blood glucose levels, blood pressure, and HBA1C Adults living in Leicester, United Kingdom South Asia and Europeans 6749 The study found that South Asian populations exhibit significant cardiovascular and obesity-related risks at lower BMI and waist circumference thresholds compared with Europeans, reinforcing evidence of elevated cardiometabolic risk in this group. A Miranda et al[ 12 ] Cross-sectional study Survey questionnaire administration Adults living in rural and urban Peru metropolis and rural-urban migrants Peru 989 The study showed that migrants have a higher overall risk of cardiovascular disease and diabetes, although this risk varies across factors during rural-to-urban transition, and is significantly influenced by age at migration, socioeconomic status, and gender. A, C Renzaho et al[ 13 ] Cross-sectional survey Research questionnaire, medical assessment and fasting blood glucose measurement Migrants and refugees, aged > 20 years Africans 21,720 The study found that low vitamin D levels among migrants and refugees were associated with impaired glucose and lipid metabolism, indicating increased risk of type 2 diabetes, and suggested vitamin D–targeted interventions. A Abouzeid et al[ 14 ] Cross-sectional study The study used population data collected using questionnaires Adults Multi-ethnic 186,279 The study confirmed that socioeconomic disparities contribute to higher prevalence of type 2 diabetes among migrant groups and highlighted the need for health service planning that addresses these inequalities. C Tillin et al[ 3 ] Population-based tri-ethnic cohort study Lifestyle questionnaire administration, glucose measurement and collection of anthropometric data Adults (aged 40–69 years) without diabetes Asian, African Caribbeans and Europeans 2528 The study reported that Indian and African women have a 2-fold increased risk of developing obesity and type 2 diabetes. However, factors responsible for these are unclear. A Jowitt et al[ 15 ] Cross-sectional study Lifestyle questionnaire administration, blood glucose and blood pressure measurement and collection of anthropometric data Adult aged 20–75 years living in New Zealand Asian 175 The study found that Asian migrants exhibit dyslipidaemia and impaired glucose metabolism at lower anthropometric thresholds than White populations, indicating the need for lower cut-offs and validation of screening tools for these groups. D Darko et al[ 16 ] Case-control study design Lifestyle questionnaire administration, glucose measurement and collection of anthropometric data Adults aged 25–70 years living in rural and urban area Africans 189 The study observed that pro-inflammatory biomarker levels were higher in rural populations than in urban migrants. No association between the level of these biomarkers and the risk of diabetes was reported A Dhillon et al[ 17 ] Cross sectional study Food Frequency Questionnaire administration, semi-structured interviews and the collection of anthropometric data Adults living in four urban regions of India (rural-urban migrants). Men and women (aged 15–76 years) Indians 6367 The study found that legume consumption was associated with reduced risk of type 2 diabetes in individuals with high BMI, but did not modify migration-related risk, with similar glycaemic control, insulin resistance, and diabetes prevalence observed regardless of migration status B Chambre et al[ 18 ] Cohort study Migration history and lifestyle research questionnaire administration, collection of anthropometric data and HBA1c measurement. Patients with type 2 diabetes Multi-ethnic 76 The study found that migrants have poorer diabetes management than non-migrants, with no association between duration of stay and disease control, and identified social support deficits, economic constraints, and acculturation challenges as contributing factors. B, C Meeks et al[ 19 ] Multi-centre cross-sectional study Lifestyle questionnaire administration, glucose measurement and collection of anthropometric data Adults (18–96 years) from Africa living across Europe compared with those living in Ghana Africans 6385 The study found that geographical variation in type 2 diabetes risk among African migrants is primarily driven by insulin resistance rather than beta cell dysfunction, with obesity (high BMI and waist circumference) playing a key role, linked to lifestyle and dietary changes following migration B Boateng et al[ 20 ] Multi-centre cross sectional survey Questionnaires administration and collection of anthropometric data Africans living in rural Africa and across Europe. Adults (40–70 years old) Sub-Saharan Africans 3586 The study reported that Framingham laboratory and non-laboratory risk assessment methods have limitations in migrant populations, potentially leading to inaccurate estimation of diabetes and cardiovascular risk and contributing to increased disease burden. D Danquah et al[ 21 ] Multi-centre cross-sectional study Lifestyle questionnaire administration, glucose measurement and collection of anthropometric data Adult Ghanaians living across Europe Africans 3810 The study found that diet diversification, particularly increased intake of protein-rich foods, is associated with reduced risk of type 2 diabetes and improved glucose metabolism among African migrants in Europe, supporting dietary interventions for prevention. B Skogberg et al[ 22 ] Cross-sectional study Administration of a standardised Diabetes Knowledge Test Adult (age 30–64 years) migrants living in Finland Russian, Somali, Kurdish and Finnish 1804 The study found that foreign-born participants had poorer diabetes knowledge than native-born individuals, with knowledge levels associated with country of birth, marital status, and employment, and particularly lower among those from the Middle East, indicating a need for targeted educational interventions. C, D Pettersson et al[ 23 ] Cross- sectional study Questionnaires administration and collection of anthropometric data Adults born in Sweden or abroad (refugees) Swedish 138 The study found that waist circumference and waist-to-hip ratio are useful for detecting obesity and type 2 diabetes, but show reduced accuracy among Somali and Kurdish populations, highlighting the need to validate anthropometric risk assessment tools for non-Western ethnic groups. D De-Graft Aikins et al[ 24 ] Qualitative study involving focus group discussions Focus group discussion Adult (25–70 years) living in Ghana and across Europe Africans 6530 The study found that migrants generally understand diabetes as a chronic condition and are aware of basic biomedical management, but have limited knowledge of complications, with psychosocial and supernatural beliefs influencing reliance on herbal and faith-based practices, highlighting the role of culture in diabetes risk and management. B, D Boateng et al[ 25 ] Cross sectional study Administration of Food Frequency Questionnaire Africans living in rural Africa and across Europe. Adults (40–70 years old) Africans 2976 The study found that dietary patterns are significantly associated with 10-year risk of cardiovascular and metabolic diseases, with mixed dietary patterns linked to lower risk, while some starchy African diets showed an inverse association with type 2 diabetes, supporting the role of dietary interventions. B Chilunga et al[ 26 ] Prospective cohort study Questionnaire administration. Adults (25–70 years) living in Ghana and across Europe Ghana 5898 The study reported that African migrants have elevated risk of type 2 diabetes irrespective of location, suggesting a strong genetic contribution to disease susceptibility. A Danquah et al[ 27 ] Multi-centre cross sectional survey Questionnaire administration and collection of anthropometric data Africans living in rural Africa and across Europe Africans 5,575 The study found a significant association between childhood low socioeconomic status, early-life malnutrition, and the development of obesity, suggesting that early-life interventions could reduce the risk of type 2 diabetes in adulthood across both men and women. A, C Skogberg et al[ 28 ] Cross-sectional study Lifestyle questionnaire administration, glucose and HbA1C measurement and collection of anthropometric data Adult migrants aged 30–64 years living in Finland Multi-ethnic (Russian, Somali and Kurdish) 1405 The study found significant ethnic differences in the relationship between anthropometric measures and glycaemic indices, indicating that standard diagnostic tools should be used with caution and require ethnic validation. D van der Linden et al[ 29 ] Multi-centre cross-sectional study Lifestyle questionnaire administration, glucose measurement and collection of anthropometric data Africans living in Europe and Ghana African 5659 The study reported a high prevalence of metabolic syndrome among African migrants across Europe, which may contribute to increased risk of type 2 diabetes, and highlighted the need to understand underlying mechanisms to inform prevention strategies. A Fiorini et al[ 30 ] Cross sectional study Assessment by a doctor and questionnaire administration Adults living with diabetes Italians and migrants of mixed origin 838 The study found significant differences in diabetes phenotypes between migrants and Caucasians, along with variations in pharmacological management (excluding metformin), which may contribute to disparities in disease prevalence across ethnic groups. D Gujral et al[ 31 ] Longitudinal cohort study Lifestyle questionnaire administration, glucose measurement and collection of anthropometric data Adult aged 40 and over from San Francisco Bay and greater Chicago areas. South Asians in the United States 1164 The study identified that differences in biological make-up, including pathophysiology, epigenesis, and body composition, alongside behavioural factors such as diet, physical activity, and sleep, as well as social, cultural, and economic influences, are strongly associated with the prevalence of type 2 diabetes. A, B, C Modesti et al[ 32 ] Cohort study Lifestyle questionnaire administration, glucose and blood pressure measurement and collection of anthropometric data Adult under and over 45 years of age living in Italy Chinese 2589 The study found a strong association between acculturation and increased risk of type 2 diabetes and hypertension among Chinese migrants, with gender differences observed, and higher risk among those residing in Italy for over 20 years, likely linked to dietary changes and sedentary lifestyles. B Farmaki et al[ 33 ] Population-based cohort study Administration of self-reporting questionnaire., collection of anthropometric data, and HbA1c measurement. Adults (40–69 years) with or without type 2 diabetes South Asia, Africa and Caribbean 500,000. The study reported significantly higher prevalence of type 2 diabetes among ethnic minority groups compared with Europeans, associated with environmental risks such as obesity, low socioeconomic status, and persistent social disadvantage. C Abdiha et al[ 34 ] Cross-sectional study Lifestyle questionnaire administration, glucose measurement and collection of anthropometric data Adults living across UK Africans 713 The study found that epigenetic changes are associated with lifestyle patterns that increase the risk of type 2 diabetes, suggesting biological influences on diabetes-predisposing behaviours among migrants, although further research is needed to address these mechanisms A, B Determinant Category: A = Pre-migration biological and early-life factors, B = Behavioural and lifestyle factors (including acculturation-related changes), C = Socioeconomic and environmental determinants and D = Structural and healthcare system-related factors Participants were predominantly adult populations, with only one study including individuals younger than 18 years. Several studies included wide age ranges, extending into older adult populations (up to 96 years), allowing examination of age-related risk patterns. Data collection methods across studies commonly included questionnaires, anthropometric measurements (e.g. body mass index, waist circumference), and biochemical assessments (e.g. blood glucose, HbA1c, lipid profiles). Quality Assessment Overall, the methodological quality of included studies was judged to be acceptable (Supplementary Table 1–3). Most studies clearly defined inclusion criteria described study populations and settings in detail and used valid and reliable methods for measuring exposures and outcomes. However, identification and control of confounding factors were inconsistent across studies, particularly among cross-sectional designs. While some studies (n = 6) explicitly accounted for confounders, others did not clearly identify cofounder ( n = 11) or describe strategies for addressing them (n = 12). Despite this limitation, all studies employed appropriate statistical analyses, and findings were considered sufficiently robust for inclusion in the synthesis. Synthesis of Findings The summary of findings from selected articles are presented in Table 1 . Thematic analysis of included studies identified four major domains of determinants associated with increased risk of type 2 diabetes among migrant populations. Pre-migration biological and early-life determinants Evidence from included studies indicates that pre-migration characteristics play a substantial role in shaping the risk of type 2 diabetes among migrant populations. Tillin et al.[ 3 ] reported that individuals of South Asian and African Caribbean origin have approximately two-fold higher risk of developing type 2 diabetes compared with European populations, with differences attributed to insulin resistance and central adiposity. This finding is supported by Meeks et al. [ 19 ], in a multi-centre study involving over 6000 participants across Europe and Africa, demonstrated that insulin resistance is the principal determinant of impaired fasting glucose among African populations, rather than β-cell dysfunction. Chilunga et al.[ 26 ], analysing data from approximately 5900 participants, showed that the prevalence of type 2 diabetes remained elevated among African migrants regardless of geographic location, suggesting a strong underlying biological predisposition. Similarly, Gujral et al.[ 31 ], in a longitudinal cohort of over 1100 South Asian participants, reported disproportionately high diabetes prevalence relative to body mass index, highlighting differences in metabolic susceptibility and fat distribution. Early-life exposures were also quantitatively associated with risk. Danquah et al.[ 25 ], in a multi-centre study of more than 5500 participants, found that markers of childhood socioeconomic disadvantage and early-life malnutrition were significantly associated with increased waist circumference and higher odds of type 2 diabetes in adulthood. Complementing this, van der Linden et al.[ 29 ] reported a high prevalence of metabolic syndrome exceeding 30% among African migrants in Europe, indicating elevated baseline cardiometabolic risk prior to or independent of migration. At the molecular level, Abdiha et al.[ 34 ] identified statistically significant associations between lifestyle-related epigenetic markers and type 2 diabetes risk in a cohort of over 700 participants, suggesting that gene–environment interactions may contribute to disease development. Behavioural and acculturation-related determinants Migration was consistently associated with behavioural changes linked to acculturation, particularly in relation to diet and physical activity. Danquah et al.[ 21 ], in a cross-sectional analysis of approximately 3800 participants, reported that greater dietary diversity was associated with lower prevalence of type 2 diabetes and improved metabolic profiles, particularly among individuals consuming protein-rich diets. In contrast, Boateng et al.[ 20 ], analysing dietary patterns in nearly 3000 participants, demonstrated that adherence to certain mixed dietary patterns was associated with higher predicted 10-year cardiovascular and metabolic risk, indicating that not all dietary transitions are beneficial. Meeks et al.[ 19 ] quantified the impact of behavioural factors by linking increased insulin resistance to lifestyle changes associated with migration, including reduced physical activity and dietary shifts. Modesti et al.[ 32 ], in a cohort of over 2500 Chinese migrants, reported that individuals residing in host countries for more than 20 years exhibited significantly higher prevalence of type 2 diabetes and hypertension, suggesting cumulative exposure to adverse behavioural risk factors. Dhillon et al.[ 17 ] found that while legume consumption was associated with improved glycaemic control among individuals with high body mass index, it did not significantly modify migration-related diabetes risk, indicating that overall dietary patterns and lifestyle factors are more influential than single dietary components. Socioeconomic, environmental, and structural determinants A consistent finding across studies was the influence of socioeconomic and structural factors on diabetes risk among migrant populations. Abouzeid et al.[ 14 ], analysing data from over 186,000 participants, demonstrated that lower socioeconomic status was strongly associated with higher prevalence of type 2 diabetes, with substantial variation observed both within and between migrant groups. Similarly, Farmaki et al.[ 33 ], in a large population-based cohort of approximately 500,000 individuals in the United Kingdom, reported significantly higher prevalence of type 2 diabetes among ethnic minority populations compared with European populations, with disparities largely explained by socioeconomic disadvantage, obesity, and environmental factors. Miranda et al .[ 12 ] further showed that migration from rural to urban environments is associated with increased risk of type 2 diabetes and cardiovascular disease, with risk influenced by age at migration, gender, and socioeconomic factors. Chambre et al .[ 18 ] reported that limited social support and economic constraints negatively affect diabetes management among migrants, reflecting broader structural challenges that extend beyond disease onset and influence long-term outcomes. Health system factors and knowledge-related determinants The final domain relates to health system factors, diagnostic limitations, and knowledge of diabetes. Pettersson et al.[ 23 ], in a study of 138 participants, reported that migrants scored significantly lower on standardised diabetes knowledge assessments compared with native-born populations, with disparities associated with country of origin and socioeconomic factors. De-Graft Aikins et al.[ 24 ], drawing on qualitative data from more than 6500 participants across multiple settings, identified substantial gaps in knowledge of diabetes complications and the influence of culturally mediated beliefs on disease understanding and management. Studies also highlighted quantitative limitations in risk assessment tools. Skogberg et al.[ 22 , 28 ], in analyses involving approximately 1800 participants, demonstrated that standard anthropometric thresholds failed to accurately identify diabetes risk in certain ethnic groups, with some individuals developing diabetes at lower body mass index and waist circumference levels than established cut-offs. Boateng et al.[ 25 ], in a cohort of over 3500 participants, reported poor agreement between commonly used cardiovascular risk prediction models, suggesting systematic misclassification of risk among migrant populations. Fiorini et al.[ 30 ] extended these findings by showing that differences in diabetes phenotype and treatment response exist between migrant and non-migrant populations, suggesting that standardised clinical approaches may not adequately address the needs of ethnically diverse groups. DISCUSSION This review demonstrates that disparities in type 2 diabetes risk among migrant populations are best understood as the product of interacting biological, behavioural, and structural determinants operating across the life course, rather than isolated risk factors. The findings suggest that migration functions as a critical transition point at which pre-existing vulnerabilities are reconfigured within new social and environmental contexts, amplifying underlying risk trajectories [ 1 , 2 ]. The conceptual framework developed in this review (Fig. 3 ) provides a structured interpretation of these relationships by situating diabetes risk within a multi-level system of determinants spanning pre-migration, behavioural, socioeconomic, and structural domains. The framework emphasises that these determinants are not independent but interact dynamically, with bidirectional relationships between behavioural and socioeconomic factors and cumulative effects across the life course. This perspective highlights how early-life exposures and biological predisposition are modified by post-migration environments, reinforcing the need to conceptualise migrant health within a systems-based and life-course approach [ 35 ]. A key implication is that biological susceptibility alone does not adequately explain observed disparities. While differences in insulin resistance, adiposity, and metabolic function contribute to elevated risk in certain populations, these factors are strongly shaped by early-life conditions and subsequently modified by environmental exposures [3.4]. The persistence of elevated risk across diverse geographic settings indicates that migration does not generate risk de novo but rather accelerates disease progression through interaction with host environments. This interpretation aligns with life-course epidemiological frameworks that emphasise cumulative exposure to disadvantage as a determinant of chronic disease [ 32 ]. Behavioural explanations, although important, are insufficient in isolation. Changes in diet and physical activity following migration are frequently identified as drivers of increased risk; however, these behaviours are structured by broader socioeconomic and environmental conditions. Migrant populations are disproportionately exposed to environments characterised by limited access to healthy food, insecure employment, and constrained opportunities for physical activity [ 6 , 7 ]. These conditions systematically shape behavioural patterns, reinforcing the need to move beyond individual-level explanations towards contextual and structural interpretations of risk. The role of socioeconomic and structural determinants is particularly prominent. Income inequality, educational disadvantage, and suboptimal living conditions are consistently associated with increased diabetes risk, with migrants disproportionately represented in these contexts [ 8 ]. Structural barriers—including restricted access to healthcare, policy-related exclusions, and social marginalisation—further compound these risks by limiting opportunities for prevention, early diagnosis, and effective management [ 9 ]. These findings are consistent with evidence demonstrating that disparities in non-communicable diseases are fundamentally driven by social and economic inequalities [ 10 , 36 ]. The findings also reveal important limitations in current health system approaches. Standard diagnostic thresholds and risk prediction tools, often derived from homogeneous populations, may underestimate risk in diverse groups [ 3 ]. This has implications for both disease detection and prevention, as individuals at high risk may not be identified using existing criteria. In addition, barriers related to language, cultural differences, and health literacy reduce engagement with healthcare services, contributing to delayed diagnosis and poorer outcomes [ 11 , 37 ]. These findings have direct implications for public health policy. First, they underscore the need to shift from behaviour-focused interventions towards structural approaches that address the social determinants of health. Policies aimed at improving housing, employment conditions, and access to healthy environments are likely to have greater and more sustained impact than those targeting individual behaviours alone [ 38 ]. Second, there is a need to develop migrant-responsive health systems. This includes adapting screening thresholds, improving cultural competence in healthcare delivery, and reducing administrative and financial barriers to care. Without such adaptations, health systems risk perpetuating existing inequalities in both disease detection and management [ 39 ]. Third, interventions should be context-specific and community-engaged. Community-based programmes that incorporate cultural norms and local contexts have been shown to improve effectiveness, particularly when co-designed with migrant populations [ 40 ]. Such approaches recognise that health behaviours are embedded within social and cultural systems and cannot be effectively modified through standardised interventions alone. This review has several strengths, including the integration of evidence across diverse populations and study designs, enabling a comprehensive understanding of multi-level determinants of diabetes risk. However, limitations should be acknowledged. The heterogeneity of included studies limited quantitative synthesis, and the predominance of cross-sectional designs restricts causal inference. Variation in the identification and control of confounding factors may also affect findings, and restriction to English-language publications may have excluded relevant evidence. Future research should prioritise longitudinal and multi-level approaches to better understand causal pathways and the interaction between biological and structural determinants. There is also a need for research evaluating the effectiveness of structural and policy interventions, as well as greater attention to underrepresented migrant populations and the role of migration policies in shaping health outcomes. CONCLUSION This review demonstrates that type 2 diabetes risk among migrant populations is shaped by a complex interplay of determinants across the life course, with structural and social factors playing a central role. Migration does not act as a singular causal factor but instead modifies pre-existing biological and developmental vulnerabilities through exposure to new socioeconomic and environmental conditions. These interactions produce a cumulative pattern of risk that contributes to persistent disparities in disease prevalence and outcomes. The findings highlight the limitations of current public health strategies that prioritise individual behaviour change without addressing underlying structural determinants. Interventions focused solely on diet and physical activity are unlikely to achieve sustained impact in the absence of broader changes to the social and environmental conditions that shape these behaviours. Effective responses require a shift towards multi-level, equity-oriented strategies that address the root causes of disease. This includes reducing socioeconomic inequalities, improving access to healthcare, and ensuring that health systems are responsive to population diversity. Adaptation of screening tools, enhancement of culturally competent care, and removal of structural barriers are critical components of this approach. Without such systemic changes, efforts to reduce the burden of type 2 diabetes among migrant populations are unlikely to succeed, and existing health inequalities will persist. Declarations Ethics approval and consent to participate: Not applicable Consent for publication : Not applicable Competing interests: The authors declare no competing interests Funding: OOO and AAF were funded by the UK Department of Health and Social Care through the Global Health Workforce Programme delivered by Global Heath Partnerships (Grant No = GHWP 2_LG.10). Author Contribution OOO conceptualised the idea for the manuscript. AAF and SO conducted literature search. AAF and SO produced the first draft of the manuscript. OOO provided feedback on the manuscript. AAF and SO revised the first draft to incorporate feedback from authors. All authors reviewed and approved the manuscript for submission for publication. Acknowledgements: Not applicable. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References International Diabetes Federation. IDF Diabetes Atlas, 10th edn. Brussels: IDF. 2021. Available from: https://diabetesatlas.org/ Meeks KA, Freitas-Da-Silva D, Adeyemo A, Beune EJ, Modesti PA, Stronks K, et al. Disparities in type 2 diabetes prevalence among ethnic minority groups resident in Europe: a systematic review and meta-analysis. Intern Emerg Med. 2016;11(3). https://doi.org/10.1016/S2213-8587(16)00088-6 . ,327 – 40. World Health Organization. Report on the health of refugees and migrants in the WHO European Region. 2018. Available from https://iris.who.int/server/api/core/bitstreams/a0ba38d6-632c-4870-b962-edc5abe324c5/content Rechel B, Mladovsky P, Ingleby D, Mackenbach JP, McKee M. Migration and health in an increasingly diverse Europe. Lancet. 2013;381(9873):1235–45. https://doi.org/10.1016/S0140-6736(12)62086-8 . Hannigan A, O’donnell P, O’Keeffe M, MacFarlane A. How do variations in definitions of migrant and their application influence the access of migrants to health care services? World Health Organization 2016. Regional Office for Europe. Available from: https://iris.who.int/server/api/core/bitstreams/461993b7-dd76-4189-b6d2-4cb2e4497d46/content Tillin T, Hughes AD, Mayet J, Whincup P, Sattar N, Forouhi NG, et al. The relationship between metabolic risk factors and incident cardiovascular disease in Europeans, South Asians, and African Caribbeans: SABRE (Southall and Brent Revisited)—a prospective population-based study. J Am Coll Cardiol. 2013;61(17):1777–86. https://doi.org/10.1016/j.jacc.2012.12.046 . Osei-Kwasi HA, Nicolaou M, Powell K, Terragni L, Maes L, Stronks K, et al. Systematic mapping review of the factors influencing dietary behaviour in ethnic minority groups living in Europe: a DEDIPAC study. Int J Behav Nutr Phys Act. 2016;13(1):85. https://doi.org/10.1017/S0954422416000178 . Public Health England. Health matters: preventing type 2 diabetes. 2018. Available from: https://www.gov.uk/government/publications/health-matters-preventing-type-2-diabetes Chiarenza A, Dauvrin M, Chiesa V, Baatout S, Verrept H. Supporting access to healthcare for refugees and migrants in European countries under particular migratory pressure. BMC Health Serv Res. 2019;19(1):513. https://doi.org/10.1186/s12913-019-4353-1 . Tillin T, Hughes AD, Whincup P, Mayet J, Sattar N, McKeigue PM et al. Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a UK tri-ethnic prospective cohort study (SABRE—Southall And Brent REvisited). Heart, 2014;100(1);60 – 7. Gray LJ, Yates T, Davies MJ, Brady E, Webb DR, Sattar N, Khunti K. Defining obesity cut-off points for migrant South Asians. PLoS ONE. 2011;6(10):e26464. Miranda JJ, Gilman RH, Smeeth L. Differences in cardiovascular risk factors in rural, urban and rural-to-urban migrants in Peru. Heart. 2011;97(10):787–96. Renzaho AM, Nowson C, Kaur A, Halliday JA, Fong D, DeSilva J. Prevalence of vitamin D insufficiency and risk factors for type 2 diabetes and cardiovascular disease among African migrant and refugee adults in Melbourne. Asia Pac J Clin Nutr. 2011;20(3):397–403. Abouzeid M, Philpot B, Janus ED, Coates MJ, Dunbar JA. Type 2 diabetes prevalence varies by socio-economic status within and between migrant groups: analysis and implications for Australia. BMC Public Health. 2013;13:1–9. Jowitt LM, Lu LW, Rush EC. Migrant Asian Indians in New Zealand; prediction of metabolic syndrome using body weights and measures. Asia Pac J Clin Nutr. 2014;23(3):385–93. Darko SN, Yar DD, Owusu-Dabo E, Awuah AAA, Dapaah W, Addofoh N, et al. Variations in levels of IL-6 and TNF-α in type 2 diabetes mellitus between rural and urban Ashanti Region of Ghana. BMC Endocr Disord. 2015;15:1–7. Dhillon PK, Bowen L, Kinra S, Bharathi AV, Agrawal S, Prabhakaran D, et al. Legume consumption and its association with fasting glucose, insulin resistance and type 2 diabetes in the Indian Migration Study. Public Health Nutr. 2016;19(16):3017–26. Chambre C, Gbedo C, Kouacou N, Fysekidis M, Reach G, Le Clesiau H, et al. Migrant adults with diabetes in France: Influence of family migration. J Clin Transl Endocrinol. 2017;7:28–32. Meeks KA, Stronks K, Adeyemo A, Addo J, Bahendeka S, Beune E et al. 2017. Peripheral insulin resistance rather than beta cell dysfunction accounts for geographical differences in impaired fasting blood glucose among sub-Saharan African individuals: findings from the RODAM study. Diabetologia. 2017;60:854 – 64. Boateng D, Agyemang C, Beune E, Meeks K, Smeeth L, Schulze MB, et al. Cardiovascular disease risk prediction in sub-Saharan African populations—Comparative analysis of risk algorithms in the RODAM study. Int J Cardiol. 2018;254:310–5. Danquah I, Galbete C, Meeks K, Nicolaou M, Klipstein-Grobusch K, Addo J, et al. Food variety, dietary diversity, and type 2 diabetes in a multi-center cross-sectional study among Ghanaian migrants in Europe and their compatriots in Ghana: the RODAM study. Eur J Nutr. 2018;57:2723–33. Skogberg N, Laatikainen T, Lundqvist A, Lilja E, Härkänen T, Koponen P. Which anthropometric measures best indicate type 2 diabetes among Russian, Somali and Kurdish origin migrants in Finland? A cross-sectional study. BMJ Open. 2018;8(5):e019166. Pettersson S, Hadziabdic E, Marklund H, Hjelm K. Lower knowledge about diabetes among foreign-born compared to Swedish‐born persons with diabetes—A descriptive study. Nurs Open. 2019;6(2):367–76. de-Graft Aikins A, Dodoo F, Awuah RB, Owusu-Dabo E, Addo J, Nicolaou M, et al. Knowledge and perceptions of type 2 diabetes among Ghanaian migrants in three European countries and Ghanaians in rural and urban Ghana: The RODAM qualitative study. PLoS ONE. 2019;14(4):e0214501. Boateng D, Galbete C, Nicolaou M, Meeks K, Beune E, Smeeth L, et al. Dietary patterns are associated with predicted 10-year risk of cardiovascular disease among Ghanaian populations: the research on obesity and diabetes in African migrants (RODAM) study. J Nutr. 2019;149(5):755–69. Chilunga FP, Henneman P, Meeks KA, Beune E, Requena-Méndez A, Smeeth L, et al. Prevalence and determinants of type 2 diabetes among lean African migrants and non-migrants: the RODAM study. J Glob Health. 2019;9(2):020426. Danquah I, Addo J, Boateng D, Klipstein-Grobusch K, Meeks K, Galbete C, et al. Early-life factors are associated with waist circumference and type 2 diabetes among Ghanaian adults: The RODAM Study. Sci Rep. 2019;9(1):10848. Skogberg N, Laatikainen T, Lilja E, Lundqvist A, Härkänen T, Koponen P. The association between anthropometric measures and glycated haemoglobin (HbA1c) is different in Russian, Somali and Kurdish origin migrants compared with the general population in Finland: a cross-sectional population-based study. BMC Public Health. 2019;19:1–12. van Der Linden EL, Meeks K, Beune E, de-Graft Aikins A, Addo J, Owusu-Dabo E, anquah I, Schulze MB, Spranger J et al. 2019. The prevalence of metabolic syndrome among Ghanaian migrants and their homeland counterparts: the Research on Obesity and type 2 Diabetes among African Migrants (RODAM) study. Eur J Public Health. 2019;29(5):906 – 13. Fiorini G, Cortinovis I, Corrao G, Franchi M, Pincelli AI, Perotti M, et al. Current pharmacological treatment of type 2 diabetes mellitus in undocumented migrants: Is it appropriate for the phenotype of the disease? Int J Environ Res Public Health. 2020;17(21):8169. Gujral UP, Kanaya AM. Epidemiology of diabetes among South Asians in the United States: lessons from the MASALA study. Ann N Y Acad Sci. 2021;1495(1):24–39. Modesti PA, Marzotti I, Calabrese M, Stefani L, Toncelli L, Modesti A, et al. Gender differences in acculturation and cardiovascular disease risk-factor changes among Chinese immigrants in Italy: Evidence from a large population-based cohort. Int J Cardiol Cardiovasc Risk Prev. 2021;11:200112. Farmaki AE, Garfield V, Eastwood SV, Farmer RE, Mathur R, Giannakopoulou O, et al. Type 2 diabetes risks and determinants in second-generation migrants and mixed ethnicity people of South Asian and African Caribbean descent in the UK. Diabetologia. 2022;65:113–27. Abidha CA, Meeks KAC, Chilunga FP, Venema A, Schindlmayr R, Hayfron-Benjamin C, et al. A comprehensive lifestyle index and its associations with DNA methylation and type 2 diabetes among Ghanaian adults: the rodam study. Clin Epigenetics. 2024;16(1):143. Kuh D, Shlomo YB, editors. Eds. A life course approach to chronic disease epidemiology (No. 2). Oxford University Press; 2004. Solar O, Irwin A. A conceptual framework for action on the social determinants of health. WHO Document Production Services 2010. Available from: https://www.who.int/publications/i/item/9789241500852 Abubakar I, Aldridge RW, Devakumar D, Orcutt M, Burns R, Barreto ML, et al. The UCL–Lancet Commission on Migration and Health: the health of a world on the move. Lancet. 2018;392(10164):2606–54. World Health Organization. Social determinants of health. Available from: https://www.who.int/health-topics/social-determinants-of-health Schilling T, Rauscher S, Menzel C, Reichenauer S, Müller-Schilling M, Schmid S, et al. Migrants and refugees in Europe: challenges, experiences and contributions. Visc Med. 2017;33(4):295–300. Riza E, Kalkman S, Coritsidis A, Koubardas S, Vassiliu S, Lazarou D, et al. Community-based healthcare for migrants and refugees: a scoping literature review of best practices. Healthc. 2020;8(2):115. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9453308","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":626961648,"identity":"34295088-5052-4404-ad79-8c1dcf84f7bc","order_by":0,"name":"Ayodele A. Falobi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACZgiVACY/ADEbO2EtjA0wLYwzQFqYCduD0MLMg2QvTiDfznv8cUVFXZ7B7cMPH9v82ibPB7T3w8cc3FoMDvMlNp45c7jY4FyasXFu323DNmYGZsmZ2/BoYeYxbGxsO5C44QyDmXRuz21GoBY2Zl48WuSbQVr+1QG1sH+Ttuy5bU9QC8NhkJYGZqAWHjNphh+3EwlqMQBqmdlw7HCx5BmeYsPehtvJbcyMzXj9It9/xuBjQ01dHt8Z9o0Pfvy5bTu/vfngh4/4HIYCGNvAZAOx6kHgDymKR8EoGAWjYKQAAEp8UAP85320AAAAAElFTkSuQmCC","orcid":"","institution":"University of Burao","correspondingAuthor":true,"prefix":"","firstName":"Ayodele","middleName":"A.","lastName":"Falobi","suffix":""},{"id":626961649,"identity":"a1b739b7-8bf6-4b67-97df-ad3260978071","order_by":1,"name":"Sarah Olatunji","email":"","orcid":"","institution":"University of Wolverhampton","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Olatunji","suffix":""},{"id":626961650,"identity":"16e05480-a0aa-462b-878a-f3dd3092bfad","order_by":2,"name":"Opeolu O. Ojo","email":"","orcid":"","institution":"University of Burao","correspondingAuthor":false,"prefix":"","firstName":"Opeolu","middleName":"O.","lastName":"Ojo","suffix":""}],"badges":[],"createdAt":"2026-04-18 01:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9453308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9453308/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107492914,"identity":"1750fcd0-4d90-4e77-8fb8-5f0896defece","added_by":"auto","created_at":"2026-04-22 03:25:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA Flowchart of Article Selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9453308/v1/60bc21a0a00571dd4a5377d6.png"},{"id":107492915,"identity":"161b68d2-33ed-430b-b80c-278ee88833c3","added_by":"auto","created_at":"2026-04-22 03:25:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of articles reviewed by sample size\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9453308/v1/35d3994c545a10cae5092331.png"},{"id":107705755,"identity":"937557f1-de6c-43b5-86e9-18ea9971704b","added_by":"auto","created_at":"2026-04-24 09:15:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework of multi-level determinants of type 2 diabetes risk among migrant populations. \u003c/strong\u003eThe framework shows how pre-migration, behavioural, socioeconomic, and structural determinants interact to shape type 2 diabetes risk among migrant populations. Arrows indicate their dynamic, interdependent relationships. The model emphasises cumulative effects across the life course and the role of migration in modifying exposure to risk.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9453308/v1/4453a8bd1e5675fa5161fe3a.png"},{"id":107708952,"identity":"71dd1209-fc3a-4c7d-b33e-f11330e11f32","added_by":"auto","created_at":"2026-04-24 09:33:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":513874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9453308/v1/e716454d-b386-455e-874c-4aae72740a24.pdf"},{"id":107492917,"identity":"c0f54802-5949-4be3-85f3-282cf2190c4b","added_by":"auto","created_at":"2026-04-22 03:25:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25644,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLE.docx","url":"https://assets-eu.researchsquare.com/files/rs-9453308/v1/055ce12a9167d33cf8af52b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-level Determinants of Type 2 Diabetes Risk Among Migrant Populations in High Income Settings: A Systematic Review","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eType 2 diabetes represents a major global public health challenge, affecting an estimated 537\u0026nbsp;million adults worldwide and contributing substantially to morbidity, mortality, and healthcare expenditure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The distribution of the disease is markedly unequal, with migrant and ethnic minority populations in high-income countries experiencing a disproportionately higher burden, including earlier onset and more rapid disease progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Europe, increasing population diversity driven by migration has intensified concerns about health inequalities, particularly in relation to chronic non-communicable diseases such as type 2 diabetes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMigration is a complex and multidimensional determinant of health that shapes disease risk through a range of interrelated biological, behavioural, and structural pathways. Individuals who migrate are often exposed to significant changes in living conditions, socioeconomic status, and healthcare environments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Evidence suggests that migrants frequently encounter adverse social conditions, including economic insecurity, suboptimal housing, and restricted access to healthcare services, particularly during the early stages of settlement. These challenges may be compounded by policy-related barriers and inequities in healthcare entitlement, which can delay access to preventive services and appropriate treatment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Collectively, these factors contribute to increased vulnerability to chronic diseases, including type 2 diabetes.\u003c/p\u003e \u003cp\u003eThe elevated risk of type 2 diabetes among migrant populations is also influenced by pre-migration characteristics. Genetic susceptibility, early-life exposures, and baseline health status prior to migration have been identified as important contributors to disease risk. Studies have consistently shown that individuals of South Asian and African origin are more likely to develop type 2 diabetes at younger ages and at lower body mass index thresholds compared with European populations, suggesting differences in metabolic risk profiles [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These intrinsic susceptibilities interact with environmental exposures across the life course, underscoring the importance of adopting a comprehensive perspective that integrates both biological and contextual determinants.\u003c/p\u003e \u003cp\u003eFollowing migration, acculturation processes play a significant role in shaping health behaviours and risk profiles. Adaptation to the host environment often involves changes in dietary patterns, physical activity levels, and broader lifestyle behaviours, which may increase exposure to obesogenic environments and sedentary living [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Such changes are frequently mediated by structural constraints, including limited access to healthy and culturally appropriate foods, financial barriers, and environmental conditions that discourage physical activity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These behavioural transitions have been strongly associated with increased risk of obesity, insulin resistance, and subsequent development of type 2 diabetes.\u003c/p\u003e \u003cp\u003eStructural and systemic determinants further contribute to disparities in diabetes risk among migrants. Barriers to healthcare access, including language differences, lack of culturally appropriate services, and challenges navigating health systems, can lead to delayed diagnosis and suboptimal disease management [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, emerging evidence suggests that commonly used diagnostic thresholds and risk assessment tools may not adequately reflect the risk profiles of diverse populations, potentially resulting in underestimation of disease burden among migrant groups [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These limitations highlight the need for more inclusive and context-sensitive approaches to diabetes prevention and care.\u003c/p\u003e \u003cp\u003eDespite a growing body of research, the evidence base on type 2 diabetes among migrants remains fragmented. Many studies focus on individual risk factors in isolation, with limited consideration of the complex interactions between biological, behavioural, and structural determinants. Furthermore, while research has been conducted across multiple European settings, there is a lack of comprehensive synthesis that integrates findings across populations and contexts.\u003c/p\u003e \u003cp\u003e This systematic review therefore aims to synthesise existing evidence on the biological, behavioural, social, and structural determinants of type 2 diabetes among migrant populations, with a primary focus on studies conducted in Europe. By adopting a multi-level analytical framework, this review seeks to enhance understanding of how these determinants interact to shape disease risk and to inform the development of targeted public health strategies aimed at reducing health inequalities.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003e This study was conducted as a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review synthesised evidence on determinants of type 2 diabetes among migrant populations, with a primary focus on studies conducted in Europe and comparable high-income settings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources and Search Strategy\u003c/h3\u003e\n\u003cp\u003eA comprehensive and systematic search of the literature was undertaken in PubMed, MEDLINE, PsycINFO, and CINAHL (Cumulative Index to Nursing and Allied Health Literature). Search strategies were developed using a Population, Exposure, Outcome (PEO/PICO-informed) framework, incorporating controlled vocabulary (MeSH terms) and free-text keywords related to \u003cem\u003emigration\u003c/em\u003e, \u003cem\u003eethnicity\u003c/em\u003e, and \u003cem\u003etype 2 diabetes\u003c/em\u003e. The search included studies published within the preceding 15 years and was conducted up to Dec 31, 2024. No restrictions were applied on geographic setting at the search stage to ensure comprehensive capture of relevant evidence.\u003c/p\u003e \u003cp\u003eAll identified records were imported into a reference management system, and duplicates were removed prior to screening. Title and abstract screening were conducted, followed by full-text review of potentially eligible studies. The study selection process was undertaken in accordance with PRISMA guidelines, and a PRISMA flow diagram was used to document the screening process.\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eInclusion criteria\u003c/h2\u003e \u003cp\u003eStudies were eligible for inclusion if they met the following prespecified criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePopulation: Migrant populations, defined as individuals residing outside their country of birth; studies predominantly included participants from Black, Asian, and other minority ethnic groups\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExposure: Factors associated with the development or increased risk of type 2 diabetes, including biological, behavioural, social, and structural determinants\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOutcome: Type 2 diabetes or related metabolic indicators (e.g. impaired glucose metabolism, insulin resistance, obesity, HbA1c levels)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudy design: Observational studies (cross-sectional, cohort, case-control) and qualitative studies\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSetting: Studies conducted in Europe and other high-income or comparable settings\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTimeframe: Studies published between Jan 1, 2010, and Dec 31, 2024\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLanguage: English\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eGiven the aetiological focus of the review, interventions and comparators were not prespecified. However, included studies commonly incorporated internal comparisons (e.g., migrants vs non-migrants, or between ethnic groups), which were considered during synthesis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eStudies were excluded if they met any of the following criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNon-peer-reviewed publications (such as reports, editorials, conference abstracts without full text)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrevious systematic reviews or narrative reviews\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudies not focused on type 2 diabetes or its determinants among migrant populations\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudies not published in English\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudies published outside the specified timeframe\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eDuring screening, studies were also excluded if they did not meet relevance criteria based on title, abstract, or full-text assessment.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection\u003c/h2\u003e \u003cp\u003eStudy selection was conducted independently by two reviewers. Titles and abstracts were screened for eligibility, followed by full-text review of potentially relevant studies. Disagreements between reviewers were resolved through discussion and, where necessary, consultation with a third reviewer to reach consensus.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Extraction\u003c/h3\u003e\n\u003cp\u003eData extraction was conducted independently by two reviewers using a standardised data extraction form. Extracted variables included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAuthor and year of publication\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudy design and setting\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePopulation characteristics (including migrant group and country of origin)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExposure variables (risk factors)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOutcome measures related to type 2 diabetes\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eKey findings and reported associations\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eData extraction was based on summary data reported in published articles. Individual patient-level data were not requested from study authors.\u003c/p\u003e\n\u003ch3\u003eQuality Assessment\u003c/h3\u003e\n\u003cp\u003eThe methodological quality of included studies was assessed using Joanna Briggs Institute (JBI) critical appraisal tools, with checklists selected according to study design (cross-sectional, cohort, case-control, and qualitative studies). Quality appraisal was conducted independently by two reviewers, and discrepancies were resolved through discussion. Studies were not excluded based on quality assessment alone; however, appraisal findings were considered during interpretation of results.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003e The primary outcome of this review was the identification and synthesis of determinants associated with increased risk of type 2 diabetes among migrant populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Synthesis\u003c/h2\u003e \u003cp\u003eGiven the heterogeneity in study designs, populations, exposures, and outcome measures, meta-analysis was not undertaken. Instead, a narrative and thematic synthesis approach was used. Extracted findings were systematically coded and grouped into thematic domains representing key categories of determinants. These included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePre-migration biological and early-life factors\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBehavioural and lifestyle factors (including acculturation-related changes)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSocioeconomic and environmental determinants\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStructural and healthcare system-related factors\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis approach enabled the identification of patterns and interactions across studies while preserving contextual differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAdverse Events\u003c/h2\u003e \u003cp\u003eAdverse events were not assessed, as this review focused on determinants of disease risk rather than clinical interventions or treatment outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection\u003c/h2\u003e \u003cp\u003eThe database search identified 1416 records (PubMed n\u0026thinsp;=\u0026thinsp;312, MEDLINE n\u0026thinsp;=\u0026thinsp;414, PsycINFO n\u0026thinsp;=\u0026thinsp;298, and CINAHL n\u0026thinsp;=\u0026thinsp;392). After removal of 1045 duplicates, 371 unique records remained for title and abstract screening. Of these, 345 records were excluded based on relevance, leaving 26 studies that met the inclusion criteria and were included in the final synthesis. The study selection process is summarised in a PRISMA flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStudy Characteristics\u003c/h2\u003e \u003cp\u003eThe 26 included studies were published between 2011 and 2024 and represented a wide range of geographic settings, including multiple European countries, the United Kingdom, Australia, New Zealand, Peru, and the United States (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most studies focused on migrant populations from African, South Asian, and other minority ethnic backgrounds, with some studies including multi-ethnic cohorts. The majority of studies were cross-sectional (n\u0026thinsp;=\u0026thinsp;17), followed by cohort studies (n\u0026thinsp;=\u0026thinsp;6), one case-control study, and one qualitative study. Sample sizes varied substantially, ranging from 76 participants to 500,000 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), reflecting considerable heterogeneity in study scale and design.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of studies selected for review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData collection method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMigrants\u0026rsquo; country of origin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMajor findings/Identified risk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDeterminant category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGray et al[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScreening questionnaire plus the collection of data on blood glucose levels, blood pressure, and HBA1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults living in Leicester, United Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouth Asia and Europeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that South Asian populations exhibit significant cardiovascular and obesity-related risks at lower BMI and waist circumference thresholds compared with Europeans, reinforcing evidence of elevated cardiometabolic risk in this group.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiranda et al[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvey questionnaire administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults living in rural and urban Peru metropolis and rural-urban migrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study showed that migrants have a higher overall risk of cardiovascular disease and diabetes, although this risk varies across factors during rural-to-urban transition, and is significantly influenced by age at migration, socioeconomic status, and gender.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA, C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenzaho et al[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResearch questionnaire, medical assessment and fasting blood glucose measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMigrants and refugees, aged\u0026thinsp;\u0026gt;\u0026thinsp;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that low vitamin D levels among migrants and refugees were associated with impaired glucose and lipid metabolism, indicating increased risk of type 2 diabetes, and suggested vitamin D\u0026ndash;targeted interventions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbouzeid et al[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe study used population data collected using questionnaires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMulti-ethnic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study confirmed that socioeconomic disparities contribute to higher prevalence of type 2 diabetes among migrant groups and highlighted the need for health service planning that addresses these inequalities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTillin et al[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation-based tri-ethnic cohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults (aged 40\u0026ndash;69 years) without diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAsian, African Caribbeans and Europeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study reported that Indian and African women have a 2-fold increased risk of developing obesity and type 2 diabetes. However, factors responsible for these are unclear.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJowitt et al[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, blood glucose and blood pressure measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdult aged 20\u0026ndash;75 years living in New Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that Asian migrants exhibit dyslipidaemia and impaired glucose metabolism at lower anthropometric thresholds than White populations, indicating the need for lower cut-offs and validation of screening tools for these groups.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDarko et al[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-control study design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults aged 25\u0026ndash;70 years living in rural and urban area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study observed that pro-inflammatory biomarker levels were higher in rural populations than in urban migrants. No association between the level of these biomarkers and the risk of diabetes was reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhillon et al[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFood Frequency Questionnaire administration, semi-structured interviews and the collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults living in four urban regions of India (rural-urban migrants). Men and women (aged 15\u0026ndash;76 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that legume consumption was associated with reduced risk of type 2 diabetes in individuals with high BMI, but did not modify migration-related risk, with similar glycaemic control, insulin resistance, and diabetes prevalence observed regardless of migration status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChambre et al[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMigration history and lifestyle research questionnaire administration, collection of anthropometric data and HBA1c measurement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients with type 2 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMulti-ethnic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that migrants have poorer diabetes management than non-migrants, with no association between duration of stay and disease control, and identified social support deficits, economic constraints, and acculturation challenges as contributing factors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB, C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeks et al[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-centre cross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults (18\u0026ndash;96 years) from Africa living across Europe compared with those living in Ghana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that geographical variation in type 2 diabetes risk among African migrants is primarily driven by insulin resistance rather than beta cell dysfunction, with obesity (high BMI and waist circumference) playing a key role, linked to lifestyle and dietary changes following migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoateng et al[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-centre cross sectional survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestionnaires administration and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfricans living in rural Africa and across Europe. Adults (40\u0026ndash;70 years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSub-Saharan Africans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study reported that Framingham laboratory and non-laboratory risk assessment methods have limitations in migrant populations, potentially leading to inaccurate estimation of diabetes and cardiovascular risk and contributing to increased disease burden.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDanquah et al[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-centre cross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdult Ghanaians living across Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that diet diversification, particularly increased intake of protein-rich foods, is associated with reduced risk of type 2 diabetes and improved glucose metabolism among African migrants in Europe, supporting dietary interventions for prevention.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkogberg et al[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdministration of a standardised Diabetes Knowledge Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdult (age 30\u0026ndash;64 years) migrants living in Finland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRussian, Somali, Kurdish and Finnish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that foreign-born participants had poorer diabetes knowledge than native-born individuals, with knowledge levels associated with country of birth, marital status, and employment, and particularly lower among those from the Middle East, indicating a need for targeted educational interventions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC, D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePettersson et al[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross- sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestionnaires administration and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults born in Sweden or abroad (refugees)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSwedish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that waist circumference and waist-to-hip ratio are useful for detecting obesity and type 2 diabetes, but show reduced accuracy among Somali and Kurdish populations, highlighting the need to validate anthropometric risk assessment tools for non-Western ethnic groups.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe-Graft Aikins et al[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQualitative study involving focus group discussions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFocus group discussion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdult (25\u0026ndash;70 years) living in Ghana and across Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that migrants generally understand diabetes as a chronic condition and are aware of basic biomedical management, but have limited knowledge of complications, with psychosocial and supernatural beliefs influencing reliance on herbal and faith-based practices, highlighting the role of culture in diabetes risk and management.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB, D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoateng et al[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdministration of Food Frequency Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfricans living in rural Africa and across Europe. Adults (40\u0026ndash;70 years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that dietary patterns are significantly associated with 10-year risk of cardiovascular and metabolic diseases, with mixed dietary patterns linked to lower risk, while some starchy African diets showed an inverse association with type 2 diabetes, supporting the role of dietary interventions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChilunga et al[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProspective cohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestionnaire administration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults (25\u0026ndash;70 years) living in Ghana and across Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study reported that African migrants have elevated risk of type 2 diabetes irrespective of location, suggesting a strong genetic contribution to disease susceptibility.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDanquah et al[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-centre cross sectional survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestionnaire administration and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfricans living in rural Africa and across Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found a significant association between childhood low socioeconomic status, early-life malnutrition, and the development of obesity, suggesting that early-life interventions could reduce the risk of type 2 diabetes in adulthood across both men and women.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA, C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkogberg et al[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose and HbA1C measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdult migrants aged 30\u0026ndash;64 years living in Finland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMulti-ethnic (Russian, Somali and Kurdish)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found significant ethnic differences in the relationship between anthropometric measures and glycaemic indices, indicating that standard diagnostic tools should be used with caution and require ethnic validation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evan der Linden et al[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-centre cross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfricans living in Europe and Ghana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study reported a high prevalence of metabolic syndrome among African migrants across Europe, which may contribute to increased risk of type 2 diabetes, and highlighted the need to understand underlying mechanisms to inform prevention strategies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiorini et al[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment by a doctor and questionnaire administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults living with diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItalians and migrants of mixed origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found significant differences in diabetes phenotypes between migrants and Caucasians, along with variations in pharmacological management (excluding metformin), which may contribute to disparities in disease prevalence across ethnic groups.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGujral et al[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLongitudinal cohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdult aged 40 and over from San Francisco Bay and greater Chicago areas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouth Asians in the United States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study identified that differences in biological make-up, including pathophysiology, epigenesis, and body composition, alongside behavioural factors such as diet, physical activity, and sleep, as well as social, cultural, and economic influences, are strongly associated with the prevalence of type 2 diabetes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA, B, C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModesti et al[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose and blood pressure measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdult under and over 45 years of age living in Italy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found a strong association between acculturation and increased risk of type 2 diabetes and hypertension among Chinese migrants, with gender differences observed, and higher risk among those residing in Italy for over 20 years, likely linked to dietary changes and sedentary lifestyles.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmaki et al[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation-based cohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdministration of self-reporting questionnaire., collection of anthropometric data, and HbA1c measurement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults (40\u0026ndash;69 years) with or without type 2 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouth Asia, Africa and Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e500,000.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study reported significantly higher prevalence of type 2 diabetes among ethnic minority groups compared with Europeans, associated with environmental risks such as obesity, low socioeconomic status, and persistent social disadvantage.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdiha et al[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLifestyle questionnaire administration, glucose measurement and collection of anthropometric data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults living across UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe study found that epigenetic changes are associated with lifestyle patterns that increase the risk of type 2 diabetes, suggesting biological influences on diabetes-predisposing behaviours among migrants, although further research is needed to address these mechanisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA, B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003eDeterminant Category: A = Pre-migration biological and early-life factors, B = Behavioural and lifestyle factors (including acculturation-related changes), C = Socioeconomic and environmental determinants and D = Structural and healthcare system-related factors \u003cp\u003e \u003c/p\u003e \u003cp\u003eParticipants were predominantly adult populations, with only one study including individuals younger than 18 years. Several studies included wide age ranges, extending into older adult populations (up to 96 years), allowing examination of age-related risk patterns. Data collection methods across studies commonly included questionnaires, anthropometric measurements (e.g. body mass index, waist circumference), and biochemical assessments (e.g. blood glucose, HbA1c, lipid profiles).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eQuality Assessment\u003c/h2\u003e \u003cp\u003eOverall, the methodological quality of included studies was judged to be acceptable (Supplementary Table\u0026nbsp;1\u0026ndash;3). Most studies clearly defined inclusion criteria described study populations and settings in detail and used valid and reliable methods for measuring exposures and outcomes. However, identification and control of confounding factors were inconsistent across studies, particularly among cross-sectional designs. While some studies (n\u0026thinsp;=\u0026thinsp;6) explicitly accounted for confounders, others did not clearly identify cofounder ( n\u0026thinsp;=\u0026thinsp;11) or describe strategies for addressing them (n\u0026thinsp;=\u0026thinsp;12). Despite this limitation, all studies employed appropriate statistical analyses, and findings were considered sufficiently robust for inclusion in the synthesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSynthesis of Findings\u003c/h2\u003e \u003cp\u003eThe summary of findings from selected articles are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Thematic analysis of included studies identified four major domains of determinants associated with increased risk of type 2 diabetes among migrant populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePre-migration biological and early-life determinants\u003c/h2\u003e \u003cp\u003eEvidence from included studies indicates that pre-migration characteristics play a substantial role in shaping the risk of type 2 diabetes among migrant populations. Tillin et al.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] reported that individuals of South Asian and African Caribbean origin have approximately two-fold higher risk of developing type 2 diabetes compared with European populations, with differences attributed to insulin resistance and central adiposity. This finding is supported by Meeks et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], in a multi-centre study involving over 6000 participants across Europe and Africa, demonstrated that insulin resistance is the principal determinant of impaired fasting glucose among African populations, rather than β-cell dysfunction.\u003c/p\u003e \u003cp\u003eChilunga et al.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], analysing data from approximately 5900 participants, showed that the prevalence of type 2 diabetes remained elevated among African migrants regardless of geographic location, suggesting a strong underlying biological predisposition. Similarly, Gujral et al.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], in a longitudinal cohort of over 1100 South Asian participants, reported disproportionately high diabetes prevalence relative to body mass index, highlighting differences in metabolic susceptibility and fat distribution.\u003c/p\u003e \u003cp\u003eEarly-life exposures were also quantitatively associated with risk. Danquah et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], in a multi-centre study of more than 5500 participants, found that markers of childhood socioeconomic disadvantage and early-life malnutrition were significantly associated with increased waist circumference and higher odds of type 2 diabetes in adulthood. Complementing this, van der Linden et al.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] reported a high prevalence of metabolic syndrome exceeding 30% among African migrants in Europe, indicating elevated baseline cardiometabolic risk prior to or independent of migration. At the molecular level, Abdiha et al.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] identified statistically significant associations between lifestyle-related epigenetic markers and type 2 diabetes risk in a cohort of over 700 participants, suggesting that gene\u0026ndash;environment interactions may contribute to disease development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBehavioural and acculturation-related determinants\u003c/h2\u003e \u003cp\u003eMigration was consistently associated with behavioural changes linked to acculturation, particularly in relation to diet and physical activity. Danquah et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], in a cross-sectional analysis of approximately 3800 participants, reported that greater dietary diversity was associated with lower prevalence of type 2 diabetes and improved metabolic profiles, particularly among individuals consuming protein-rich diets. In contrast, Boateng et al.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], analysing dietary patterns in nearly 3000 participants, demonstrated that adherence to certain mixed dietary patterns was associated with higher predicted 10-year cardiovascular and metabolic risk, indicating that not all dietary transitions are beneficial.\u003c/p\u003e \u003cp\u003eMeeks et al.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] quantified the impact of behavioural factors by linking increased insulin resistance to lifestyle changes associated with migration, including reduced physical activity and dietary shifts. Modesti et al.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], in a cohort of over 2500 Chinese migrants, reported that individuals residing in host countries for more than 20 years exhibited significantly higher prevalence of type 2 diabetes and hypertension, suggesting cumulative exposure to adverse behavioural risk factors. Dhillon et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] found that while legume consumption was associated with improved glycaemic control among individuals with high body mass index, it did not significantly modify migration-related diabetes risk, indicating that overall dietary patterns and lifestyle factors are more influential than single dietary components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic, environmental, and structural determinants\u003c/h2\u003e \u003cp\u003eA consistent finding across studies was the influence of socioeconomic and structural factors on diabetes risk among migrant populations. Abouzeid et al.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], analysing data from over 186,000 participants, demonstrated that lower socioeconomic status was strongly associated with higher prevalence of type 2 diabetes, with substantial variation observed both within and between migrant groups. Similarly, Farmaki et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], in a large population-based cohort of approximately 500,000 individuals in the United Kingdom, reported significantly higher prevalence of type 2 diabetes among ethnic minority populations compared with European populations, with disparities largely explained by socioeconomic disadvantage, obesity, and environmental factors.\u003c/p\u003e \u003cp\u003eMiranda \u003cem\u003eet al\u003c/em\u003e.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] further showed that migration from rural to urban environments is associated with increased risk of type 2 diabetes and cardiovascular disease, with risk influenced by age at migration, gender, and socioeconomic factors. Chambre \u003cem\u003eet al\u003c/em\u003e.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] reported that limited social support and economic constraints negatively affect diabetes management among migrants, reflecting broader structural challenges that extend beyond disease onset and influence long-term outcomes.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eHealth system factors and knowledge-related determinants\u003c/h2\u003e \u003cp\u003eThe final domain relates to health system factors, diagnostic limitations, and knowledge of diabetes. Pettersson et al.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], in a study of 138 participants, reported that migrants scored significantly lower on standardised diabetes knowledge assessments compared with native-born populations, with disparities associated with country of origin and socioeconomic factors. De-Graft Aikins et al.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], drawing on qualitative data from more than 6500 participants across multiple settings, identified substantial gaps in knowledge of diabetes complications and the influence of culturally mediated beliefs on disease understanding and management.\u003c/p\u003e \u003cp\u003eStudies also highlighted quantitative limitations in risk assessment tools. Skogberg et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], in analyses involving approximately 1800 participants, demonstrated that standard anthropometric thresholds failed to accurately identify diabetes risk in certain ethnic groups, with some individuals developing diabetes at lower body mass index and waist circumference levels than established cut-offs. Boateng et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], in a cohort of over 3500 participants, reported poor agreement between commonly used cardiovascular risk prediction models, suggesting systematic misclassification of risk among migrant populations. Fiorini et al.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] extended these findings by showing that differences in diabetes phenotype and treatment response exist between migrant and non-migrant populations, suggesting that standardised clinical approaches may not adequately address the needs of ethnically diverse groups.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis review demonstrates that disparities in type 2 diabetes risk among migrant populations are best understood as the product of interacting biological, behavioural, and structural determinants operating across the life course, rather than isolated risk factors. The findings suggest that migration functions as a critical transition point at which pre-existing vulnerabilities are reconfigured within new social and environmental contexts, amplifying underlying risk trajectories [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe conceptual framework developed in this review (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) provides a structured interpretation of these relationships by situating diabetes risk within a multi-level system of determinants spanning pre-migration, behavioural, socioeconomic, and structural domains. The framework emphasises that these determinants are not independent but interact dynamically, with bidirectional relationships between behavioural and socioeconomic factors and cumulative effects across the life course. This perspective highlights how early-life exposures and biological predisposition are modified by post-migration environments, reinforcing the need to conceptualise migrant health within a systems-based and life-course approach [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA key implication is that biological susceptibility alone does not adequately explain observed disparities. While differences in insulin resistance, adiposity, and metabolic function contribute to elevated risk in certain populations, these factors are strongly shaped by early-life conditions and subsequently modified by environmental exposures [3.4]. The persistence of elevated risk across diverse geographic settings indicates that migration does not generate risk de novo but rather accelerates disease progression through interaction with host environments. This interpretation aligns with life-course epidemiological frameworks that emphasise cumulative exposure to disadvantage as a determinant of chronic disease [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBehavioural explanations, although important, are insufficient in isolation. Changes in diet and physical activity following migration are frequently identified as drivers of increased risk; however, these behaviours are structured by broader socioeconomic and environmental conditions. Migrant populations are disproportionately exposed to environments characterised by limited access to healthy food, insecure employment, and constrained opportunities for physical activity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These conditions systematically shape behavioural patterns, reinforcing the need to move beyond individual-level explanations towards contextual and structural interpretations of risk.\u003c/p\u003e \u003cp\u003eThe role of socioeconomic and structural determinants is particularly prominent. Income inequality, educational disadvantage, and suboptimal living conditions are consistently associated with increased diabetes risk, with migrants disproportionately represented in these contexts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Structural barriers\u0026mdash;including restricted access to healthcare, policy-related exclusions, and social marginalisation\u0026mdash;further compound these risks by limiting opportunities for prevention, early diagnosis, and effective management [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings are consistent with evidence demonstrating that disparities in non-communicable diseases are fundamentally driven by social and economic inequalities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings also reveal important limitations in current health system approaches. Standard diagnostic thresholds and risk prediction tools, often derived from homogeneous populations, may underestimate risk in diverse groups [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This has implications for both disease detection and prevention, as individuals at high risk may not be identified using existing criteria. In addition, barriers related to language, cultural differences, and health literacy reduce engagement with healthcare services, contributing to delayed diagnosis and poorer outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings have direct implications for public health policy. First, they underscore the need to shift from behaviour-focused interventions towards structural approaches that address the social determinants of health. Policies aimed at improving housing, employment conditions, and access to healthy environments are likely to have greater and more sustained impact than those targeting individual behaviours alone [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, there is a need to develop migrant-responsive health systems. This includes adapting screening thresholds, improving cultural competence in healthcare delivery, and reducing administrative and financial barriers to care. Without such adaptations, health systems risk perpetuating existing inequalities in both disease detection and management [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, interventions should be context-specific and community-engaged. Community-based programmes that incorporate cultural norms and local contexts have been shown to improve effectiveness, particularly when co-designed with migrant populations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Such approaches recognise that health behaviours are embedded within social and cultural systems and cannot be effectively modified through standardised interventions alone.\u003c/p\u003e \u003cp\u003eThis review has several strengths, including the integration of evidence across diverse populations and study designs, enabling a comprehensive understanding of multi-level determinants of diabetes risk. However, limitations should be acknowledged. The heterogeneity of included studies limited quantitative synthesis, and the predominance of cross-sectional designs restricts causal inference. Variation in the identification and control of confounding factors may also affect findings, and restriction to English-language publications may have excluded relevant evidence.\u003c/p\u003e \u003cp\u003eFuture research should prioritise longitudinal and multi-level approaches to better understand causal pathways and the interaction between biological and structural determinants. There is also a need for research evaluating the effectiveness of structural and policy interventions, as well as greater attention to underrepresented migrant populations and the role of migration policies in shaping health outcomes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis review demonstrates that type 2 diabetes risk among migrant populations is shaped by a complex interplay of determinants across the life course, with structural and social factors playing a central role. Migration does not act as a singular causal factor but instead modifies pre-existing biological and developmental vulnerabilities through exposure to new socioeconomic and environmental conditions. These interactions produce a cumulative pattern of risk that contributes to persistent disparities in disease prevalence and outcomes.\u003c/p\u003e \u003cp\u003eThe findings highlight the limitations of current public health strategies that prioritise individual behaviour change without addressing underlying structural determinants. Interventions focused solely on diet and physical activity are unlikely to achieve sustained impact in the absence of broader changes to the social and environmental conditions that shape these behaviours.\u003c/p\u003e \u003cp\u003eEffective responses require a shift towards multi-level, equity-oriented strategies that address the root causes of disease. This includes reducing socioeconomic inequalities, improving access to healthcare, and ensuring that health systems are responsive to population diversity. Adaptation of screening tools, enhancement of culturally competent care, and removal of structural barriers are critical components of this approach. Without such systemic changes, efforts to reduce the burden of type 2 diabetes among migrant populations are unlikely to succeed, and existing health inequalities will persist.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent for publication\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003e OOO and AAF were funded by the UK Department of Health and Social Care through the Global Health Workforce Programme delivered by Global Heath Partnerships (Grant No\u0026thinsp;=\u0026thinsp;GHWP 2_LG.10).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eOOO conceptualised the idea for the manuscript. AAF and SO conducted literature search. AAF and SO produced the first draft of the manuscript. OOO provided feedback on the manuscript. AAF and SO revised the first draft to incorporate feedback from authors. All authors reviewed and approved the manuscript for submission for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Diabetes Federation. IDF Diabetes Atlas, 10th edn. Brussels: IDF. 2021. 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Healthc. 2020;8(2):115.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes, migrant populations, health inequalities, social determinants of health, acculturation, life-course approach","lastPublishedDoi":"10.21203/rs.3.rs-9453308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9453308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eType 2 diabetes is a major global public health challenge, with increasing evidence of disproportionate burden among migrant populations. Migrants often experience higher prevalence and earlier onset of disease compared with host populations, reflecting complex interactions between biological, behavioural, and structural determinants. However, there is limited synthesis of how these multi-level factors jointly contribute to risk. This systematic review aimed to identify and synthesise determinants of type 2 diabetes among migrant populations across diverse settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We conducted a systematic review of observational studies published between January 2011 and December 2024. Electronic databases were searched using predefined terms related to migration and type 2 diabetes. Studies were included if they examined determinants of type 2 diabetes among adult migrant populations. Data were extracted using a standardised form and synthesised using a thematic approach. Determinants were grouped into four domains: biological and early-life factors, behavioural and acculturation-related factors, socioeconomic and environmental conditions, and health system influences.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwenty-six studies were included, encompassing diverse migrant populations across Europe, Africa, Asia, and North America. Evidence consistently showed that type 2 diabetes risk among migrants is shaped by interacting determinants across the life course. Pre-migration factors, including genetic susceptibility and early-life exposures, contributed to baseline risk. Post-migration behavioural changes, particularly dietary transitions and reduced physical activity, were associated with increased risk but were strongly influenced by socioeconomic and environmental conditions. Structural determinants, including income, education, and living conditions, were consistently linked to disparities in risk and outcomes. Health system factors, including access to care, health literacy, and limitations in diagnostic tools, further contributed to delayed diagnosis and suboptimal management.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eType 2 diabetes risk among migrant populations is driven by multi-level, interacting determinants that extend beyond individual behaviours. Effective prevention and control strategies require a shift towards equity-oriented approaches that address structural and social determinants, alongside culturally tailored interventions and migrant-responsive health systems.\u003c/p\u003e\u003ch2\u003eRegistration\u003c/h2\u003e \u003cp\u003e This systematic review was not registered in PROSPERO or any other review registry.\u003c/p\u003e","manuscriptTitle":"Multi-level Determinants of Type 2 Diabetes Risk Among Migrant Populations in High Income Settings: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 03:25:48","doi":"10.21203/rs.3.rs-9453308/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be3d5eac-0654-4ac5-9b50-1d8601deed43","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T03:25:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 03:25:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9453308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9453308","identity":"rs-9453308","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}