Skip to main content
Download PDF

Association of socioeconomic status with diabetic microvascular complications: a UK Biobank prospective cohort study

Diabetology & Metabolic Syndrome volume 17, Article number: 24 (2025) Cite this article

Abstract

Background

Prior studies on the link between socioeconomic status (SES) and diabetic microvascular complications have been inconclusive. This study aimed to explore whether SES is associated with the risk of diabetic retinopathy (DR), nephropathy (DN) and diabetic peripheral neuropathy (DPN) using large prospective cohort.

Methods

SES was evaluated using education attainment (individual level), household income (household level), and Townsend deprivation index (TDI, neighborhood level). This study included 28,339 participants without DR, 29,951 without DN and 29,762 without DPN at baseline from the UK Biobank. Weighted Cox proportional hazard models were used to investigate the relationship between SES and the risk of diabetic microvascular complications.

Results

The median follow-ups of the DR, DN and DPN cohorts were 12.95, 12.89 and 13.02 years, respectively. In total, 3,177 (11.2%) participants developed DR, 4,418 (14.8%) developed DN and 1,604 (5.4%) developed DPN. After adjusting for confounders, higher education levels (DN: hazard ratios [HR] = 0.85; 95% CI, 0.82–0.89; P < 0.001; DPN: HR = 0.93; 95% CI, 0.87-1.00; P = 0.040), higher household income (DN: HR = 0.80; 95% CI, 0.75–0.85; P < 0.001; DPN: HR = 0.80; 95% CI, 0.73–0.89; P < 0.001), and lower TDI (DN: HR = 1.19; 95% CI, 1.14–1.23; P < 0.001; DPN: HR = 1.27; 95% CI, 1.19–1.36; P < 0.001) were associated with a lower risk of DN and DPN. In contrast, a lower risk of DR was only related to higher household income (HR = 0.92; 95% CI, 0.87–0.97; P = 0.004) and lower TDI (HR = 1.08; 95% CI, 1.02–1.13; P = 0.004).

Conclusions

Low SES increases the risk of diabetic microvascular complications, emphasizing the need for equitable medical resource allocation to reduce diabetes-related inequity.

Background

Diabetes is becoming a defining disease of the 21st century [1]. The global age-standardized prevalence of diabetes was estimated to have increased from 3.2% in 1990 to 6.1% in 2021 and is expected to reach 9.8% by 2050 [2]. More worryingly, 80% of diabetes cases occur in low-income and middle-income countries [3], and people with lower socioeconomic status (SES) exhibit a higher risk of diabetes [4]. SES refers to an individual’s or group’s position within the socioeconomic hierarchy, which is determined by a combination of factors such as wealth, prestige, and place of residence [5]. The increasing disparity in diabetes burden due to socioeconomic inequalities has been increasingly concerning [6]. However, the association between SES and the risk of diabetic microvascular complications remains controversial.

Diabetic microvascular complications mainly comprise diabetic retinopathy (DR), nephropathy (DN) and peripheral neuropathy (DPN), which are considered to be the main causes of blindness, end-stage renal disease and lower limb amputation in developed countries [7,8,9]. Epidemiological studies show that at least one out of every five to ten patients with diabetes will develop DR, DN or DPN, which poses an enormous burden on society and the family [10,11,12]. Thus, exploring the relationship between SES and diabetic microvascular complications can assist in the allocation of medical resources and reduce diabetes inequity. Despite several studies, the relationship between SES and diabetic microvascular complications is not fully understood and lacks consistency. While many studies suggest that low SES increases the risk of these complications [13,14,15,16,17,18], others report no correlation [19]. Very few studies adjusted their analyses according to glycated hemoglobin level (HbA1c) [20]. In addition, most previous studies assessed SES using a single indicator, such as education [18], which does not reflect the comprehensive SES. Moreover, most of the previous studies were limited by small sample sizes and cross-sectional designs, which may be biased by residual confounding and reverse causality [13, 15].

In this study, SES was evaluated at the individual, household, and neighborhood levels using education attainment, household income, and Townsend deprivation index (TDI) [21]. The present study aimed to investigate the association between SES and the risk of diabetic microvascular complications based on a prospective cohort study of 30,541 individuals with diabetes from the UK Biobank.

Methods

Study design

This large prospective cohort study investigated the link between SES, DR, DN and DPN using data from the UK Biobank (Fig. 1). SES was evaluated based on education attainment (individual level), household income (household level), and TDI (neighborhood level). The UK Biobank study (RRID: SCR_012815) was approved by the North West Multicenter Research Ethical Committee (REF: 11/NW/03820), and all participants provided informed consent.

Fig. 1
figure 1

Overview of the study design. This flow chart presents the inclusion and exclusion criteria for the UK Biobank cohort study. ICD: International Classification of Diseases, DM: diabetes mellitus, DR: diabetic retinopathy, DN: diabetic nephropathy, DPN: diabetic peripheral neuropathy, HbA1c: glycated hemoglobin

Full size image

Study populations

The UK Biobank is a population-based prospective cohort study that recruited over 500,000 participants aged 37 to 73 years from 22 medical centers throughout the United Kingdom from 2006 to 2010 [22]. The baseline information of all participants was obtained through self-reported touchscreen questionnaires, physical measurements, and biospecimen collection. The present study included 30,541 participants with confirmed baseline diabetes mellitus (DM) (including type 1 and type 2 diabetes). Subsequently, individuals without DR, DN or DPN at baseline were separated to yield three cohorts for the primary analysis. The inclusion and exclusion criteria for the study populations are shown in Fig. 1.

Exposure and outcome in the cohort study

The highest education qualifications of participants reported in the UK biobank were converted to the International Standard Classification for Education (ISCED) coding for years of education [23] (Supplementary Table 1). Household income was defined using a five-point scale corresponding to the total household income before tax, which was obtained through questionnaires. Each category was assigned a midpoint to allow for continuous analysis [24]. TDI was available as an area-level SES variable retrieved from national census data according to postcodes of residence, which considered factors including unemployment, family overcrowding, and not owning a house or car [25]. Higher TDIs indicate lower area-level SES.

The outcomes of the current study were incident DR, DN and DPN during follow-up. The follow-up period extended from the time participants enrolled in the UK Biobank until July 19, 2022, when the data were collected. Outcome events were defined using the following sources: hospital inpatient records of diagnosis (Field ID: 41270) and operation (Field ID: 41272), and self-reported information of diagnosis (Field ID: 20002) and operation (Field ID: 20004) [26,27,28]. Details of the disease-definition codes are provided in Supplementary Table 2. For patients with different sources of records, the earliest diagnosis was taken.

Covariates in the cohort study

The covariates included in the study were age, sex, race (Caucasian/other race), smoking status (yes or no), alcohol consumption status (heavy or not), mean arterial pressure (MAP, mmHg), HbA1c (%), triglyceride (TG, mmol/L), high-density lipoprotein cholesterol (HDL, mmol/L), low-density lipoprotein cholesterol (LDL, mmol/L), duration of DM (years), body mass index (BMI), oral hypoglycemic drug use (yes or no), and insulin treatment (yes or no). Blood pressure was measured with an Omron digital sphygmomanometer. Blood samples were obtained for measurement of HbA1c, TG, HDL, and LDL levels. Diabetes duration was calculated based on the disease onset to the recruitment time. In addition, BMI was calculated as body weight (kilogram) divided by the square of height (meters). All the remaining covariates were derived from participants’ self-reported information at baseline.

Statistical analysis

Continuous and categorical variables were represented by the median (interquartile ranges, IQR) and frequency (percentages, %), respectively. Subsequently, medians and proportions were compared using the Mann‒Whitney U test and chi-square test, respectively. Kaplan-Meier cumulative incidence plots were generated and statistically analyzed using the log-rank test. Weighted Cox proportional hazard models were used to estimate hazard ratios (HR) and their corresponding 95% confidence intervals (CI), both with and without adjustments for covariates. This model assigns relative importance to the HRs at different times based on the number of people at risk during those periods, creating a weighting function that accurately reflects each time point’s contribution to the partial likelihood. This approach ultimately provides an unbiased estimate of the average HR without depending on the proportional-hazard (PH) assumption [29]. Simulation studies have shown that when the PH assumption is violated, the weighted Cox model better summarizes the average effect of the exposure. Conversely, when the PH assumption holds true, the estimates produced by the weighted Cox model are similar to those generated by the standard Cox model [29, 30]. Model multicollinearity was measured using the variance inflation factor (VIF), with values greater than 10 indicating severe collinearity. Several sensitivity analyses were performed to test the robustness of our findings: (1) Excluding participants with less than 2 years of follow-up. (2) Using multiple imputations for missing data through chained Eq. 3) Converting continuous measures of exposure into categorical formats. 4) Adding further adjustments for covariates, which encompassed other medications (specifically lipid-lowering and antihypertensive drugs), key comorbidities (including hypertension, dyslipidemia, coronary heart disease, and stroke), diabetes type, family history of diabetes, physical activity, and heavy physical labor work. Subgroup analyses were conducted based on gender and diabetes type, with likelihood ratio tests employed to evaluate multiplicative scale interactions. Restricted cubic spline (RCS) curves were plotted to visualize the relationships between TDI and outcome (DR, DN and DPN). All statistical tests were two-sided, and P < 0.05 was considered statistically significant. All the above analyses were performed using R software (version 4.2.1), and the weighted Cox models were fitted using the “coxphw” R package (version 4.0.3) and RCS curves were plotted using the “rcssci” R package (version 0.3.0).

Results

A total of 28,339, 29,951 and 29,762 eligible participants were included in the DR, DN and DPN cohorts, respectively. The baseline characteristics of the participants and missing data information are shown in Table 1. Among the DR cohort, the median follow-up was 12.95 years, and 3,177 (11.2%) participants suffered from DR (median age 62.00 years, 61.4% male). Among the DN cohort, the median follow-up was 12.89 years, and 4,418 (14.8%) participants suffered from DN (median age 64.00 years, 62.6% male). Among the DPN cohort, the median follow-up was 13.02 years, and 1,604 (5.4%) participants suffered from DPN (median age 61.00 years, 65.9% male). Compared with subjects without diabetic microvascular complications, those with DR, DN or DPN were more likely to have lower household income and higher TDI. They were also more prone to have higher HbA1c levels, longer duration of diabetes, and use of diabetes medication. According to the Kaplan-Meier analyses, higher educational attainment, higher household income and lower TDI were associated with a reduced risk of diabetic microvascular complications (log-rank test P-value < 0.001, except for education and DR; Fig. 2).

Table 1 Baseline characteristics of participants stratified by Incident Diabetic Microvascular complications
Full size table
Fig. 2
figure 2

Kaplan–Meier estimates of the cumulative incidence of diabetic microvascular complications according to SES. Kaplan-Meier cumulative incidence plots were generated according to education, income, and TDI levels to assess the associations between SES and the incidence of DR, DN, and DPN, and statistically analyzed using the log-rank test. SES: socioeconomic status, TDI: Townsend deprivation index, DR: diabetic retinopathy, DN: diabetic nephropathy, DPN: diabetic peripheral neuropathy

Full size image

The association between SES and incident diabetic microvascular complications is displayed in Table 2. After adjusting for confounders, every SD increment of education attainment was found to be associated with a 15% and 7% lower risk of DN (95% CI, 0.82–0.89; P < 0.001) and DPN (95% CI, 0.87-1.00; P = 0.040), but had no protective effect on DR (HR = 1.01; 95% CI, 0.97–1.06; P = 0.581). Moreover, each SD increase in household income was associated with an 8% decreased risk of DR (95% CI, 0.87–0.97; P = 0.004), a 20% reduced risk of DN (95% CI, 0.75–0.85; P < 0.001) and DPN (95% CI, 0.73–0.89; P < 0.001). Furthermore, each SD increase in TDI showed an 8% increased risk of DR (95% CI, 1.02–1.13; P = 0.004), a 19% increased risk of DN (95% CI, 1.14–1.23; P < 0.001) and a 27% increased risk of DPN (95% CI, 1.19–1.36; P < 0.001). These results did not change significantly after excluding participants with less than 2 years of follow-up (Supplementary Table 3), multiple imputations for missing data (Supplementary Table 4), converting continuous measures of exposure to categorical forms (Supplementary Table 5), or further adjusting for covariates (Supplementary Table 6). Furthermore, the subgroup analyses indicated no interaction, highlighting that the effect of SES on diabetic microvascular complications was unaffected by either gender or diabetes type (Supplementary Tables 7–8). RCS curves were plotted to further evaluate the relationships between TDI and incident diabetic microvascular complications (Fig. 3).

Table 2 The association of SES with incident diabetic microvascular complications
Full size table
Fig. 3
figure 3

Associations between TDI and incident DR (A), DN (B) and DPN (C) were evaluated by RCS curves. The HR (solid red line) and 95% CI (light red area) are from a multivariate adjusted Cox regression model using RCS curves with four knots (5th, 35th, 65th, and 95th percentiles). The reference value (red dot) of TDI was set to the median. The histogram shows the distribution of TDI. TDI: Townsend deprivation index, RCS: restricted cubic spline, DR: diabetic retinopathy, DN: diabetic nephropathy, DPN: diabetic peripheral neuropathy, CI: confidence interval, HR: hazard ratio

Full size image

Discussion

The present study revealed that higher education attainment, higher household income, and lower TDI were associated with a lower risk of DN and DPN in a population-based cohort, whereas the lower risk of DR was only related to higher household income and lower TDI (Fig. 4). With large cohort data from the UK Biobank, we present a clearer picture of the impact of SES on diabetic microvascular complications.

Fig. 4
figure 4

Schematic illustration. Low socioeconomic status increases the risk of incident diabetic microvascular complications based on a large prospective cohort. TDI: Townsend deprivation index, HR: hazard ratio, CI: confidence intervals

Full size image

SES is broadly conceptualized as an individual’s or group’s position in the socioeconomic hierarchy, which has been significantly associated with multiple diseases. In a population-based cohort study, Zhang et al. reported that low SES was significantly related to higher risks of mortality and incidence of cardiovascular diseases [25]. A recent study by Conrad et al. revealed a socioeconomic gradient across several autoimmune diseases, including pernicious anemia, rheumatoid arthritis, Graves’ disease, and systemic lupus erythematosus [31]. In addition, the association between socioeconomic inequality as a risk factor and diabetes is well established [6]. However, the association between SES and diabetic microvascular complications remains controversial and high-quality evidence is lacking.

The results of the present study are in accordance with most previous observational studies and are well integrated with the role of SES on the three main diabetic microvascular complications, suggesting that patients with low SES are at increased risk of DR, DN and DPN. In a study with a mean follow-up of 6.2 years, Low et al. found that both person-level and area-level SES were associated with DR incidence, progression, and associated vision loss in Asians [16]. In addition, Emoto et al. revealed that lower educational attainment is a strong risk factor for DR in patients with difficult-to-control type 2 DM [32]. Partially consistent with these studies, the current study found that people with low household income and high TDI were associated with a higher risk of DR, but longer education attainment had no protective effect on DR. This may be due to residual confounding factors or mediators between SES and DR, such as lifestyle factors [25]. Further studies are needed to examine the possibility of other factors mediating the associations of SES with incident DR and the extent of the interactions. Winitzki et al. found that patients with low educational attainment had a higher risk for mortality, kidney failure, and DN in the German Chronic Kidney Disease Cohort study [17]. A cross-sectional study conducted by Wolf et al. revealed that lower SES was associated with DN [33]. Two other cross-sectional studies indicated that diabetic neuropathy is more prevalent in patients with type 1 and type 2 DM who have lower SES, emphasizing the necessity for longitudinal studies [13, 34]. Consistent with these studies, the current prospective study found that people with low SES (low education attainment, low household income, and high TDI) were exposed to a higher risk of DN and DPN.

The mechanism underlying the associations between low SES and diabetic microvascular complications is likely multifactorial. First, people with low SES may be more susceptible to malnutrition. In a prospective study, Du et al. found that higher fruit consumption was associated with lower risks of microvascular complications [35]. In addition, early screening and referral can effectively reduce the visual loss caused by DR [36], but patients with low SES may not have access to adequate healthcare services, such as screening. Further studies are needed to elucidate the underlying mechanism.

The main strengths of this study are the prospective design, large sample size, long-term follow-up, and adequate covariate adjustment to minimise the effect of confounding factors on the results. In addition, SES was comprehensively evaluated from the individual, household, and neighborhood levels using education attainment, household income, and TDI. Furthermore, a series of sensitivity analyses were conducted to demonstrate the robustness of the findings. Nevertheless, the limitations of this study should be acknowledged. First, due to the observational nature, residual and unmeasured confounders or mediators cannot be completely excluded in the cohort study. Second, the present study lacks an overall SES variable comprising different aspects of SES. Third, the reduced sample size after grouping may have limited the statistical power of the subgroup analyses, leading to inconsistent results with the overall trend. Consequently, larger sample sizes will be warranted in the future to confirm the findings of the subgroup analyses. Finally, as the enrolled patients were mainly European, the relationship between SES and diabetic microvascular complications in other populations needs further investigations.

Conclusions

In summary, low SES was found to significantly increase the risk of incident DR, DN and DPN, which highlights the importance of allocating medical resources to reduce diabetes inequity. The mechanisms of the association between SES and diabetic microvascular complications should be studied further.

Data availability

The datasets generated and/or analyzed during the current study are available in the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). This research was conducted using the UK Biobank Resource under Application Number 88982.

Abbreviations

BMI:

Body mass index

CI:

Confidence interval

DM:

Diabetes mellitus

DN:

Diabetic nephropathy

DR:

Diabetic retinopathy

HbA1c:

Glycated hemoglobin

HDL:

High-density lipoprotein cholesterol

HR:

Hazard ratio

ISCED:

International Standard Classification for Education

LDL:

Low-density lipoprotein cholesterol

MAP:

Mean arterial pressure

PH:

Proportional-hazard

RCS:

Restricted cubic spline

SD:

Standard deviation

SES:

Socioeconomic status

TDI:

Townsend deprivation index

TG:

Triglyceride

References

  1. The L. Diabetes: a defining disease of the 21st century. Lancet. 2023;401(10394):2087.

    Article  Google Scholar 

  2. Collaborators GD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of Disease Study 2021. Lancet; 2023.

  3. Chan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P, Lau ESH, Eliasson B, Kong APS, Ezzati M, et al. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives. Lancet. 2021;396(10267):2019–82.

    Article  PubMed  Google Scholar 

  4. Liu C, He L, Li Y, Yang A, Zhang K, Luo B. Diabetes risk among US adults with different socioeconomic status and behavioral lifestyles: evidence from the National Health and Nutrition Examination Survey. Front Public Health. 2023;11:1197947.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Villalba CMH. Socioeconomic Status (SES). In: Encyclopedia of Quality of Life and Well-Being Research. edn. Edited by Michalos AC. Dordrecht: Springer Netherlands; 2014: 6210–6212.

  6. Walker AF, Graham S, Maple-Brown L, Egede LE, Campbell JA, Walker RJ, Wade AN, Mbanya JC, Long JA, Yajnik C, et al. Interventions to address global inequity in diabetes: international progress. Lancet. 2023;402(10397):250–64.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Fong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, Ferris FL 3rd, Klein R. Diabetic retinopathy. Diabetes Care. 2003;26(Suppl 1):S99–102.

  8. Molitch ME, DeFronzo RA, Franz MJ, Keane WF, Mogensen CE, Parving HH. Diabetic nephropathy. Diabetes Care. 2003;26(Suppl 1):S94–98.

    PubMed  Google Scholar 

  9. Pop-Busui R, Boulton AJ, Feldman EL, Bril V, Freeman R, Malik RA, Sosenko JM, Ziegler D. Diabetic Neuropathy: A position Statement by the American Diabetes Association. Diabetes Care. 2017;40(1):136–54.

    Article  CAS  PubMed  Google Scholar 

  10. Teo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, Bikbov MM, Wang YX, Tang Y, Lu Y, et al. Global prevalence of Diabetic Retinopathy and Projection of Burden through 2045: systematic review and Meta-analysis. Ophthalmology. 2021;128(11):1580–91.

    Article  PubMed  Google Scholar 

  11. Selby NM, Taal MW. An updated overview of diabetic nephropathy: diagnosis, prognosis, treatment goals and latest guidelines. Diabetes Obes Metab. 2020;22:1.

    Article  Google Scholar 

  12. Andersen ST, Witte DR, Dalsgaard EM, Andersen H, Nawroth P, Fleming T, Jensen TM, Finnerup NB, Jensen TS, Lauritzen T, et al. Risk factors for Incident Diabetic Polyneuropathy in a Cohort with screen-detected type 2 diabetes followed for 13 years: ADDITION-Denmark. Diabetes Care. 2018;41(5):1068–75.

    Article  CAS  PubMed  Google Scholar 

  13. Tao X, Li J, Zhu X, Zhao B, Sun J, Ji L, Hu D, Pan C, Huang Y, Jiang S, et al. Association between socioeconomic status and metabolic control and diabetes complications: a cross-sectional nationwide study in Chinese adults with type 2 diabetes mellitus. Cardiovasc Diabetol. 2016;15:61.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Alvarez-Ramos P, Jimenez-Carmona S, Alemany-Marquez P, Cordoba-Doña JA, Aguilar-Diosdado M. Socioeconomic deprivation and development of diabetic retinopathy in patients with type 1 diabetes mellitus. BMJ Open Diabetes Res Care 2020, 8(2).

  15. Funakoshi M, Azami Y, Matsumoto H, Ikota A, Ito K, Okimoto H, Shimizu N, Tsujimura F, Fukuda H, Miyagi C, et al. Socioeconomic status and type 2 diabetes complications among young adult patients in Japan. PLoS ONE. 2017;12(4):e0176087.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Low JR, Gan ATL, Fenwick EK, Gupta P, Wong TY, Teo ZL, Thakur S, Tham YC, Sabanayagam C, Cheng CY, et al. Role of socio-economic factors in visual impairment and progression of diabetic retinopathy. Br J Ophthalmol. 2021;105(3):420–5.

    Article  PubMed  Google Scholar 

  17. Winitzki D, Zacharias HU, Nadal J, Baid-Agrawal S, Schaeffner E, Schmid M, Busch M, Bergmann MM, Schultheiss U, Kotsis F, et al. Educational Attainment is Associated with kidney and Cardiovascular outcomes in the German CKD (GCKD) cohort. Kidney Int Rep. 2022;7(5):1004–15.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Chaturvedi N, Stephenson JM, Fuller JH. The relationship between socioeconomic status and diabetes control and complications in the EURODIAB IDDM complications Study. Diabetes Care. 1996;19(5):423–30.

    Article  CAS  PubMed  Google Scholar 

  19. Litwin AS, Clover A, Hodgkins PR, Luff AJ. Affluence is not related to delay in diagnosis of type 2 diabetes as judged by the development of diabetic retinopathy. Diabet Medicine: J Br Diabet Association. 2002;19(10):843–6.

    Article  CAS  Google Scholar 

  20. Tatulashvili S, Fagherazzi G, Dow C, Cohen R, Fosse S, Bihan H. Socioeconomic inequalities and type 2 diabetes complications: a systematic review. Diabetes Metab. 2020;46(2):89–99.

    Article  CAS  PubMed  Google Scholar 

  21. Peng L, Wen WP. Socioeconomic status and asthma: a bidirectional mendelian randomization study. World Allergy Organ J. 2023;16(6):100790.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Carter AR, Gill D, Davies NM, Taylor AE, Tillmann T, Vaucher J, Wootton RE, Munafò MR, Hemani G, Malik R, et al. Understanding the consequences of education inequality on cardiovascular disease: mendelian randomisation study. BMJ. 2019;365:l1855.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Harrison S, Davies AR, Dickson M, Tyrrell J, Green MJ, Katikireddi SV, Campbell D, Munafò M, Dixon P, Jones HE, et al. The causal effects of health conditions and risk factors on social and socioeconomic outcomes: mendelian randomization in UK Biobank. Int J Epidemiol. 2020;49(5):1661–81.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhang YB, Chen C, Pan XF, Guo J, Li Y, Franco OH, Liu G, Pan A. Associations of healthy lifestyle and socioeconomic status with mortality and incident cardiovascular disease: two prospective cohort studies. BMJ. 2021;373:n604.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Huang Y, Zhang X, Li B, Zhu X, Li C, Zhou C, Gu C, Wang Y, Ma M, Fan Y et al. Association of BMI and Waist Circumference with Diabetic Microvascular complications: a prospective cohort study from the UK Biobank and mendelian randomization analysis. Diabetes Res Clin Pract 2023:110975.

  27. Kim J, Jensen A, Ko S, Raghavan S, Phillips LS, Hung A, Sun Y, Zhou H, Reaven P, Zhou JJ. Systematic heritability and Heritability Enrichment Analysis for Diabetes Complications in UK Biobank and ACCORD studies. Diabetes. 2022;71(5):1137–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li FR, Hukportie DN, Yang J, Yang HH, Chen GC, Wu XB. Microvascular burden and Incident Heart failure among Middle-aged and older adults with type 1 or type 2 diabetes. Diabetes Care 2022.

  29. Schemper M, Wakounig S, Heinze G. The estimation of average hazard ratios by weighted Cox regression. Stat Med. 2009;28(19):2473–89.

    Article  PubMed  Google Scholar 

  30. Dunkler D, Ploner M, Schemper M, Heinze G. Weighted Cox Regression using the R Package coxphw. J Stat Softw. 2018;84(2):1–26.

    Article  Google Scholar 

  31. Conrad N, Misra S, Verbakel JY, Verbeke G, Molenberghs G, Taylor PN, Mason J, Sattar N, McMurray JJV, McInnes IB, et al. Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK. Lancet. 2023;401(10391):1878–90.

    Article  PubMed  Google Scholar 

  32. Emoto N, Okajima F, Sugihara H, Goto R. A socioeconomic and behavioral survey of patients with difficult-to-control type 2 diabetes mellitus reveals an association between diabetic retinopathy and educational attainment. Patient Prefer Adherence. 2016;10:2151–62.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Wolf G, Busch M, Müller N, Müller UA. Association between socioeconomic status and renal function in a population of German patients with diabetic nephropathy treated at a tertiary centre. Nephrol Dial Transpl. 2011;26(12):4017–23.

    Article  Google Scholar 

  34. Talbo MK, Katz A, Dostie M, Legault L, Brazeau AS. Associations between Socioeconomic Status and Patient Experience with type 1 diabetes management and Complications: cross-sectional analysis of a Cohort from Québec, Canada. Can J Diabetes. 2022;46(6):569–77.

    Article  PubMed  Google Scholar 

  35. Du H, Li L, Bennett D, Guo Y, Turnbull I, Yang L, Bragg F, Bian Z, Chen Y, Chen J, et al. Fresh fruit consumption in relation to incident diabetes and diabetic vascular complications: a 7-y prospective study of 0.5 million Chinese adults. PLoS Med. 2017;14(4):e1002279.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wong TY, Sun J, Kawasaki R, Ruamviboonsuk P, Gupta N, Lansingh VC, Maia M, Mathenge W, Moreker S, Muqit MMK, et al. Guidelines on Diabetic Eye Care: the International Council of Ophthalmology Recommendations for Screening, Follow-up, Referral, and treatment based on resource settings. Ophthalmology. 2018;125(10):1608–22.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This research was conducted using the UK Biobank Resource under Application Number 88982. The authors would like to thank the study participants for volunteering for the study and thank the staff for their contributions.

Funding

This study was supported by grants from the National Natural Science Foundation of China (82271111). The founders had no role in the design of the study and collection, analysis, and interpretation of data and in writing of this manuscript.

Author information

Authors and Affiliations

  1. Department of Ophthalmology, Shengli Clinical College of Fujian Medical University; Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China

    Chufeng Gu

  2. Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine; National Clinical Research Center for Eye Diseases; Shanghai Clinical Research Center for Eye Diseases; Shanghai Key Clinical Specialty; Shanghai Key Laboratory of Ocular Fundus Diseases; Shanghai Engineering Center for Visual Science and Photomedicine; Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China

    Yikeng Huang & Zhi Zheng

  3. Department of Endocrinology and Metabolism, Shanghai 10th People’s Hospital, School of Medicine, Tongji University, Shanghai, China

    Haibing Chen

  4. Ningde Municipal Hospital, Ningde Normal University, Ningde, Fujian, China

    Zhi Zheng

  5. Fujian Medical University, Fuzhou, Fujian, China

    Zhi Zheng

Authors
  1. Yikeng Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhi Zheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Haibing Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Chufeng Gu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Y.H.: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization and Writing – original draft. Z.Z.: Funding acquisition, Supervision and Writing – review & editing. H.C.: Project administration, Resources, Supervision and Writing – review & editing. C.G.: Conceptualization, Data curation, Investigation, Writing – original draft and Writing – review & editing.

Corresponding authors

Correspondence to Haibing Chen or Chufeng Gu.

Ethics declarations

Ethics approval and consent to participate

The UK Biobank study was approved by the North West Multicenter Research Ethical Committee (REF: 11/NW/03820), and all participants provided informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Y., Zheng, Z., Chen, H. et al. Association of socioeconomic status with diabetic microvascular complications: a UK Biobank prospective cohort study. Diabetol Metab Syndr 17, 24 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01584-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01584-0

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us