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Are social determinants of health associated with the development of early complications among young adults with type 2 diabetes? A population based study using linked databases

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    Emma A. Nieuwenhuijse
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    Correspondence to: Department of Public Health and Primary Care, Health-Campus The Hague, Leiden University Medical Centre, the Netherlands.
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    1 both authors equally contributed to the article
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    Department of Public Health and Primary Care / Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands
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    Teske B. van Hof
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    Department of Public Health and Primary Care / Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands
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  • Mattijs.E. Numans
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    Department of Public Health and Primary Care / Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands
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  • Jeroen N. Struijs
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    Department of Public Health and Primary Care / Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands

    National Institute for Public Health and the Environment, Bilthoven, the Netherlands
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    3 0000-0003-1074-6255
    Rimke C. Vos
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    Department of Public Health and Primary Care / Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands
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    2 0000-0003-3277-4003
    3 0000-0003-1074-6255
Open AccessPublished:January 18, 2023DOI:https://doi.org/10.1016/j.pcd.2023.01.002

      Highlights

      • Social disadvantage may be present in diabetes complications in young adults.
      • Young men with non-Western origin were more likely to develop a complication.
      • Income was not associated with diabetes complication development.
      • Young adults with uncontrolled HbA1c were more likely to develop a complication.
      • Proactive treatment should focus on HbA1c regulation in all at-risk populations.

      Abstract

      Aims

      To quantify the impact of social determinants of health (SDOH) on top of medical determinants on the development of diabetes-related complications in young adults with type 2 diabetes.

      Methods

      In this observational population-based study, SDOH (income and origin) were linked to routine primary care data. Young adults (18–45 years) with incident type 2 diabetes between 2007 and 2013 were included. The main outcome, the development of the first micro- or macrovascular complication, was analyzed by multivariate Cox regression. Medical determinants included antidiabetic treatment, HbA1c in the year after diagnosis, body mass index, comorbidity and smoking.

      Results

      Of 761 young adults (median age: 39 years (IQR 33–42), men: 49%, Western origin: 36%, low income: 48%), 154 developed at least one complication (median follow-up 99 months (IQR 73–123)). Young men of non-Western origin were more likely to develop a complication (HR 1.98 (1.19–3.30)), as were young adults with HbA1c > 7% (>53 mmol/mol) (HR: 1.72 95% CI: 1.15–2.57). No associations were found with income. Being women was protective.

      Conclusion

      In this multi-ethnic population, non-Western origin was associated with the development of complications, but only in men. Low income was not associated with developing complications. The importance of adequate HbA1c regulation was re-emphasized by this study.

      Keywords

      1. Introduction

      The prevalence of early-onset type 2 diabetes is increasing [
      • Pinhas-Hamiel O.
      • Zeitler P.
      The global spread of type 2 diabetes mellitus in children and adolescents.
      ] and well-known microvascular and macrovascular diabetes complications are also appearing at younger ages in this population. Early-onset type 2 diabetes results in higher morbidity and mortality compared to people diagnosed after 45 years of age [
      • Arslanian S.
      • Bacha F.
      • Grey M.
      • Marcus M.D.
      • White N.H.
      • Zeitler P.
      Evaluation and management of youth-onset type 2 diabetes: a position statement by the American Diabetes Association.
      ,
      • Dart A.B.
      • Martens P.J.
      • Rigatto C.
      • Brownell M.D.
      • Dean H.J.
      • Sellers E.A.
      Earlier onset of complications in youth with type 2 diabetes.
      ,
      • Nanayakkara N.
      • Curtis A.J.
      • Heritier S.
      • Gadowski A.M.
      • Pavkov M.E.
      • Kenealy T.
      • et al.
      Impact of age at type 2 diabetes mellitus diagnosis on mortality and vascular complications: systematic review and meta-analyses.
      ]. It has been argued that current high morbidity and mortality rates are the result of underscreening, underdiagnosis and possibly undertreatment of young people with type 2 diabetes. Other explanations are the impact of a longer exposure to obesity [
      • Wright A.K.
      • Welsh P.
      • Gill J.M.R.
      • Kontopantelis E.
      • Emsley R.
      • Buchan I.
      • et al.
      Age-, sex- and ethnicity-related differences in body weight, blood pressure, HbA(1c) and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes.
      ], poor glycemic control [
      • Malik M.O.
      • Govan L.
      • Petrie J.R.
      • Ghouri N.
      • Leese G.
      • Fischbacher C.
      • et al.
      Ethnicity and risk of cardiovascular disease (CVD): 4.8 year follow-up of patients with type 2 diabetes living in Scotland.
      ,

      Tatulashvili S., Fagherazzi G., Dow C., Cohen R., Fosse S., Bihan H. Socioeconomic inequalities and type 2 diabetes complications: A systematic review. Diabetes & metabolism. 2019.

      ,
      • Struijs J.N.
      • Drewes H.W.
      • Heijink R.
      • Baan C.A.
      How to evaluate population management? Transforming the care continuum alliance population health guide toward a broadly applicable analytical framework.
      ,
      • Pouwels K.B.
      • Voorham J.
      • Hak E.
      • Denig P.
      Identification of major cardiovascular events in patients with diabetes using primary care data.
      ,
      Extramural Academic Network of the Leiden University Medical Centre Data Warehouse [Available from: https://www.lumc.nl/org/campusdenhaag/projecten/gezond-en-gelukkig-den-haag/
      ], dyslipidemia, hypertension and chronic infection in young adults with type 2 diabetes compared to older adults with type 2 diabetes or young adults with type 1 diabetes [
      • Dabelea D.
      • Stafford J.M.
      • Mayer-Davis E.J.
      • D'Agostino R Jr
      • Dolan L.
      • Imperatore G.
      • et al.
      Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood.
      ]. Next to these medical factors, diabetes type 2 prevalence in young adults has a suggested association with lower socio-economic status (SES) [
      • van Bruggen S.
      • Kasteleyn M.J.
      • Bonten T.N.
      • Chavannes N.H.
      • Numans M.E.
      • Rauh S.P.
      Socioeconomic status is not associated with the delivery of care in people with diabetes but does modify HbA1c levels: an observational cohort study (Elzha-cohort 1).
      ,

      N.H.G. Diabetes mellitus type 2 (Vierde herziening) [Available from: https://www.nhg.org/standaarden/volledig/nhg-standaard-diabetes-mellitus-type-2.

      ,
      • Collier A.
      • Ghosh S.
      • Hair M.
      • Waugh N.
      Impact of socioeconomic status and gender on glycaemic control, cardiovascular risk factors and diabetes complications in type 1 and 2 diabetes: a population based analysis from a Scottish region.
      ,
      • Hill-Briggs F.
      • Adler N.E.
      • Berkowitz S.A.
      • Chin M.H.
      • Gary-Webb T.L.
      • Navas-Acien A.
      • et al.
      Social determinants of health and diabetes: a scientific review.
      ]. SES is a multifactorial concept which includes i.a. status of income, occupation and ethnic minority (social determinants of health) (SDOH) [
      World Health Organisation About Social Determinants of Health [Available from: http://www.who.int/social_determinants/en.
      ]. Although the medical determinants of the disease in young adults itself are clear, the relative impact of SDOH on the development of complications in young adults with type 2 diabetes is still not well understood.
      Like many Western cities The Hague is a large urban area with large ethnic diversity. In older adults, it has been shown that ethnicity may be a risk factor as well as protective for progression to complications [
      • Malik M.O.
      • Govan L.
      • Petrie J.R.
      • Ghouri N.
      • Leese G.
      • Fischbacher C.
      • et al.
      Ethnicity and risk of cardiovascular disease (CVD): 4.8 year follow-up of patients with type 2 diabetes living in Scotland.
      ]. Non-Western migrants often have lower SES and type 2 diabetes is more prevalent in people of South-Asian and African origin compared to people of Western origin [
      • Arslanian S.
      • Bacha F.
      • Grey M.
      • Marcus M.D.
      • White N.H.
      • Zeitler P.
      Evaluation and management of youth-onset type 2 diabetes: a position statement by the American Diabetes Association.
      ,
      • Wright A.K.
      • Welsh P.
      • Gill J.M.R.
      • Kontopantelis E.
      • Emsley R.
      • Buchan I.
      • et al.
      Age-, sex- and ethnicity-related differences in body weight, blood pressure, HbA(1c) and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes.
      ,

      N.H.G. Diabetes mellitus type 2 (Vierde herziening) [Available from: https://www.nhg.org/standaarden/volledig/nhg-standaard-diabetes-mellitus-type-2.

      ,
      World Health Organisation About Social Determinants of Health [Available from: http://www.who.int/social_determinants/en.
      ,

      Tatulashvili S., Fagherazzi G., Dow C., Cohen R., Fosse S., Bihan H. Socioeconomic inequalities and type 2 diabetes complications: A systematic review. Diabetes & metabolism. 2019.

      ]. In a disease in which both prevalence and risk factors are closely related to a patient’s SES, the identification of people at risk for specific outcomes [
      • Struijs J.N.
      • Drewes H.W.
      • Heijink R.
      • Baan C.A.
      How to evaluate population management? Transforming the care continuum alliance population health guide toward a broadly applicable analytical framework.
      ] should, next to medical factors, include SDOH at the individual level. However, SDOH are frequently missing from routine care databases and earlier studies were often based on self-reported data, neighborhood deprivation scores [
      • Pouwels K.B.
      • Voorham J.
      • Hak E.
      • Denig P.
      Identification of major cardiovascular events in patients with diabetes using primary care data.
      ], or were not corrected for HbA1c-levels [

      Tatulashvili S., Fagherazzi G., Dow C., Cohen R., Fosse S., Bihan H. Socioeconomic inequalities and type 2 diabetes complications: A systematic review. Diabetes & metabolism. 2019.

      ].
      The aim of this study was therefore to quantify the additional impact of ethnic origin and income next to medical determinants on the risk of developing diabetes complications in young adults with type 2 diabetes in a socio-economically diverse region of the Netherlands.

      2. Materials and methods

      2.1 Design and data sources

      In this observational population-based cohort study we used routine care data from primary care practice (PCP)-practices in The Hague from the database ELAN (Extramural Academic Network of the Leiden University Medical Centre) [
      Extramural Academic Network of the Leiden University Medical Centre Data Warehouse [Available from: https://www.lumc.nl/org/campusdenhaag/projecten/gezond-en-gelukkig-den-haag/
      ]. Data derived from the PCP registries included diagnoses (International Classification of Primary Care, ICPC codes), medication prescriptions (7-digit Anatomic, Therapeutic, Chemical Classification System (ATC) codes) and diagnostic measurements (Dutch registration codes) for approximately 180,000 citizens in The Hague and vicinity. These data were linked to SDOH within the Social Statistical Datasets (SSD) from Statistics Netherlands (SN). SSD covers longitudinal microdata on several domains, including SDOH details [
      • Bakker B.F.M.
      • van Rooijen J.
      • van Toor L.
      The System of social statistical datasets of Statistics Netherlands: an integral approach to the production of register-based social statistics.
      ]) for all governmental registered citizens of the Netherlands.
      Currently, 80–85% of type 2 diabetes care in the Netherlands takes place in primary care, and is covered by obliged basic health insurance. Only the most complex cases are referred to secondary care [
      • Baan C.A.
      • van Baal P.H.
      Diabetes mellitus in the Netherlands: estimate of the current disease burden and prognosis for 2025.
      ,
      • Mohnen S.M.
      • Molema C.C.
      • Steenbeek W.
      • van den Berg M.J.
      • de Bruin S.R.
      • Baan C.A.
      • et al.
      Cost variation in diabetes care across Dutch care groups?.
      ]. As the adequate registration of diagnosis and diagnostic measurements is encouraged via bundled payments reimbursement schemes, all patients with a diabetes diagnosis can be recognized within the ELAN database [
      • Mohnen S.M.
      • Molema C.C.
      • Steenbeek W.
      • van den Berg M.J.
      • de Bruin S.R.
      • Baan C.A.
      • et al.
      Cost variation in diabetes care across Dutch care groups?.
      ].

      2.2 Study population

      All individuals with type 2 diabetes, aged 18–45 years in the year of diagnosis (index year), were included for the period January 1, 2007 to December 31, 2013. A diagnosis of diabetes was based on ICPC code T90.02/T90.00 (type 2 diabetes /diabetes unspecified) and/or the first prescription of medication in ATC group A10B (oral blood glucose-lowering drugs, excluding insulins) in the ELAN dataset [
      • de Boer A.W.
      • Blom J.W.
      • de Waal M.W.M.
      • Rippe R.C.A.
      • de Koning E.J.P.
      • Jazet I.M.
      • et al.
      Coded diagnoses from general practice electronic health records are a feasible and valid alternative to self-report to define diabetes cases in research.
      ]. Individuals were excluded when an outcome event occurred before diagnosis, or if a reference to Latent Autoimmune Diabetes in Adults (LADA) or Maturity-Onset Diabetes of the Young (MODY) was mentioned in the description of the ICPC code. People were censored on the date of their first complication, date of death, or if they left the practice, with a follow-up until 31 December 2019.

      2.3 Primary outcome

      The primary outcome was the development of either a first microvascular or a first macrovascular complication. Microvascular complications included: nephropathy (U99.01), retinopathy (F83), and neuropathy (N94.02). Macrovascular complications included: angina pectoris (K74), myocardial infarction (K75), other ischemic diseases (K76), transient ischemic attack (K89), cerebral infarction (K90), and/or peripheral artery disease (K92.01). In addition, macrovascular complications included cardiovascular- or diabetes-related death as identified in the SSD (national death registries with linked ICD-10 codes).

      2.4 Medical determinants

      Medical determinants included glucose-lowering treatment, comorbidity and anthropometric measurements (Supplementary material A). Three treatment categories were defined based on Dutch guidelines: 1) lifestyle advice only (no glucose-lowering medication), 2) oral blood glucose-lowering medication (A10B medication prescribed but no A10A medication), and 3) both oral blood glucose-lowering medication and insulin (both A10B and A10A).
      Comorbidity (yes/no) was defined as the presence of one or more chronic diseases as defined by Oostrom et al. [
      • van Oostrom S.H.
      • Picavet H.S.
      • van Gelder B.M.
      • Lemmens L.C.
      • Hoeymans N.
      • van Dijk C.E.
      • et al.
      Multimorbidity and comorbidity in the Dutch population - data from general practices.
      ] plus hypertension (ICPC codes K85, K86, K87) and adiposity (A82) prevalent in the index year. Smoking was defined as ‘non- smoker’, ‘previous smoker’ or ‘current smoker’ in all observation years, as lower cardiovascular disease risk is only achieved 10 years after quitting smoking. Mean body mass index (BMI (kg/m2)) was calculated from all registered or calculated BMIs in a 3-year range around the diabetes diagnosis. Mean HbA1c (%, mmol/mol) was calculated from registered HbA1c 6–18 months after diagnosis, eliminating measurements from the initial phase of the treatment [
      • Rathmann W.
      • Schwandt A.
      • Hermann J.M.
      • Kuss O.
      • Roden M.
      • Laubner K.
      • et al.
      Distinct trajectories of HbA1c in newly diagnosed Type 2 diabetes from the DPV registry using a longitudinal group-based modelling approach.
      ] and divided into two categories as a relevant clinical cut-off point: > 7% (>53 mmol/mol) or ≤ 7% (≤53 mmol/mol) [

      N.H.G. Diabetes mellitus type 2 (Vierde herziening) [Available from: https://www.nhg.org/standaarden/volledig/nhg-standaard-diabetes-mellitus-type-2.

      ].

      2.5 SDOH

      SDOH included information on sex, age, ethnic origin and income (see Supplementary material A for a detailed description) [

      Solar O. IA. A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2 (Policy and Practice). World Health Organization. 2010.

      ]. Ethnic origin was based on migration background as registered by SN and was clustered into two groups: ‘Western’ (Dutch and other Western origin) and ‘non-Western’ (Turkish, Moroccan, South-Asian, and other non-Western). Migration generation was specified as first or second migration generation. Income during the index year was derived from the SN-calculated measure ‘standardized disposable household income’, which represents the net amount a household can spend on an annual basis, adjusted for any differences in household size and composition, divided into percentiles on a population-wide national level. Income was clustered into 3 categories: 0–33 percentile, 33–66 percentile, and 67–100 percentile.

      2.6 Validation sample

      To determine the representativeness of the sample of the ELAN-HHTH sample compared to all young adults with type 2 diabetes in The Hague in terms of SDOH, an validation cohort was created based on the medication data files registered in the SSD (Supplementary material A). Incident type 2 diabetes in citizens aged 18–45 years was identified based on reimbursement for diabetes medication (A10B group) during the years of inclusion (2007–2013) and a lack of reimbursement for diabetes medication in the previous year. The SDOH were merged with this sample.

      2.7 Statistical analysis

      Descriptive statistics were summarized using numbers (%), mean ( ± standard deviation (SD)) and median (+interquartile range (IQR)). The study cohort was compared with the validation sample, taking into account that the validation sample underestimates real incidence as not all newly diagnosed patients are prescribed medication. Groups were compared with chi-square tests, independent sample t-tests or Mann-Whitney U-tests, as appropriate.
      The main analysis was a multivariate Cox model with a composite score of first complication as outcome (model A). The model was adjusted for age-of-onset, sex, BMI, treatment category, HbA1c groups [
      • Patel A.
      • MacMahon S.
      • Chalmers J.
      • Neal B.
      • Billot L.
      • Woodward M.
      • et al.
      Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes.
      ], co-morbidity [

      N.H.G. Diabetes mellitus type 2 (Vierde herziening) [Available from: https://www.nhg.org/standaarden/volledig/nhg-standaard-diabetes-mellitus-type-2.

      ], and smoking status [

      N.H.G. Diabetes mellitus type 2 (Vierde herziening) [Available from: https://www.nhg.org/standaarden/volledig/nhg-standaard-diabetes-mellitus-type-2.

      ,
      • Collier A.
      • Ghosh S.
      • Hair M.
      • Waugh N.
      Impact of socioeconomic status and gender on glycaemic control, cardiovascular risk factors and diabetes complications in type 1 and 2 diabetes: a population based analysis from a Scottish region.
      ] as medical determinants, and non-Western origin (yes/no) and standardized household income as SDOH. To assess effect-modification with 1) sex and 2) HbA1c group with 1) ethnicity and 2) income, interaction terms were added to the fully adjusted model. The assumptions of the proportional hazards model were checked for each variable using a time-dependent covariate and Kaplan-Meier curves and were met.
      Two sub-analyses were performed. First, we repeated the analysis with solely microvascular complications (model B) and with solely macrovascular events (model C). The most significant medical determinants were selected using backward elimination, and SDOH were subsequently added [
      • Collier A.
      • Ghosh S.
      • Hair M.
      • Waugh N.
      Impact of socioeconomic status and gender on glycaemic control, cardiovascular risk factors and diabetes complications in type 1 and 2 diabetes: a population based analysis from a Scottish region.
      ,

      Tatulashvili S., Fagherazzi G., Dow C., Cohen R., Fosse S., Bihan H. Socioeconomic inequalities and type 2 diabetes complications: A systematic review. Diabetes & metabolism. 2019.

      ,
      • Funakoshi M.
      • Azami Y.
      • Matsumoto H.
      • Ikota A.
      • Ito K.
      • Okimoto H.
      • et al.
      Socioeconomic status and type 2 diabetes complications among young adult patients in Japan.
      ,
      • Walker J.J.
      • Livingstone S.J.
      • Colhoun H.M.
      • Lindsay R.S.
      • McKnight J.A.
      • Morris A.D.
      • et al.
      Effect of socioeconomic status on mortality among people with type 2 diabetes: a study from the Scottish Diabetes Research Network Epidemiology Group.
      ]. To investigate the role of country of origin and migration generation within persons with a non-Western origin, a sub-analysis identical to the main analysis was performed within this subgroup (Supplementary material C).
      Missing data were handled using multiple imputation (MI) (Supplementary material D). The MI procedure of SPSS version 25.0 was used, with the fully conditional specification setting using predictive mean modeling for continuous data, and binary logistic and poly-logistic modeling for categorical data, with 50 iterations and the creation of 10 imputed datasets. Results of the imputed datasets were pooled using Rubin’s Rules.
      The performance of models A, B and C were assessed in the imputed datasets using discrimination and calibration, with the bootstrapping technique for internal validation [
      • Steyerberg E.W.
      • Vergouwe Y.
      Towards better clinical prediction models: seven steps for development and an ABCD for validation.
      ,
      • Harrell Jr., F.E.
      • Lee K.L.
      • Mark D.B.
      Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.
      ]. The discriminative ability – the ability to distinguish between patients with and without complications – of the model was evaluated on the basis of the Concordance-index (c-index). A c-index of more than 0.7 indicates good model discrimination, 0.5–0.7 moderate discrimination. Model calibration was assessed in each imputed dataset based on the intercept and slope of the calibration curves, plotting predicted probability of complications against the observed complications [
      • Wahl S.
      • Boulesteix A.L.
      • Zierer A.
      • Thorand B.
      • van de Wiel M.A.
      Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation.
      ,
      • Schomaker M.
      • Heumann C.
      Bootstrap inference when using multiple imputation.
      ]. An ideal slope would be close to one and intercept would be zero.
      Analyses were performed using SPSS version 25 and R studio software version 3.6.2, with the MICE, Hmisc and rms packages.

      3. Results

      3.1 Baseline characteristics

      In total, 761 young adults with incident type 2 diabetes aged between 18 and 45 years were identified (Fig. 1). Overall, 154 (20.2%) persons developed a complication during follow-up. Median follow-up was 99 months (IQR 73–123), while median time to complication was 59 months (IQR 29–86).
      Median age was 39 years (IQR 33–42), 49.3% were men, a third of the population was of Western origin (35.8%), and half (47.8%) of the population had a low income (percentiles 0–33) (Table 1). Most (58.7%) used only oral blood glucose-lowering drugs and more than one third (39%) had a chronic comorbidity in the index year. In the first year after diagnosis, 36.0% of patients had uncontrolled (HbA1c >7% (>53 mmol/mol)). Analysis of SDOH in the regulated and unregulated HbA1c groups showed similar HbA1c means in the index year for the three income groups and for the Western versus non-Western origin groups (supplementary material B).
      Table 1Pooled baseline characteristics of 10 imputed datasets index year.
      First micro- or macrovascular complication
      Total

      (N = 761)
      With complication

      (n = 154)
      Without complication (n = 607)
      Social determinants of health
      Age in years39 (33–42)40 (35–43)39 (33–42)
      Men,375 (49.3)97.8 (63.5)277.2 (45.7)
      Standardized household income

      Percentile

      0–33

      33–66

      66–100
      363.4 (47.8)

      230.5 (30.3)

      167.1 (22.0)
      74.9 (48.6)

      55.2 (35.8)

      24 (15.6)
      288.5 (47.5)

      175.3 (28.9)

      143.1 (23.6)
      Ethnicity group

      Dutch or Western origin

      Non-Western origin

      Origin

      Dutch or other Western

      Turkey

      Morocco

      South-Asian

      Other non-Western
      272.6 (37.4)

      488.4 (64.2)

      272.6 (35.8)

      76 (10.0)

      92.2 (12.1)

      165.6 (21.8)

      154.8(20.3)
      45.4 (29.5)

      108.6 (70.5)

      45.4 (29.5)

      21 (13.6)

      15 (9.7)

      38.8 (25.2)

      33.9 (22.0)
      227.2 (37.4)

      379,8 (62,6)

      227.2 (37.4)

      55 (9.1)

      77.2 (12.7)

      126.8 (20.9)

      120.9 (19.9)
      Medical determinants
      Diabetes treatment class

      Lifestyle

      Oral blood glucose-lowering drugs

      Insulin and oral blood glucose-lowering drugs
      200 (26.3)

      447 (58.7)

      114 (15.0)
      33 (21.4)

      97.5 (63.3)

      23.6 (15.3)
      167 (27.5)

      349.5 (57.6)

      90.4 (14.9)
      Chronic comorbidity at diagnosis

      Of which:

      Hypertension

      Adiposity

      Mental illness

      Chronic neck and back pain

      Arthrosis

      Alcohol abuse
      296.5 (39.0)

      104 (13.7)

      71.7 (9.4)

      79.1 (10.4)

      45.6 (6.0)

      11 (1.5)

      12 (1.6)
      60.9 (39.5)

      25(16.2)

      14.1 (9.1)

      10.1 (6.6)

      < 10 (< 6.5)

      < 10 (< 6.5)

      < 10 (< 6.5)
      235.6 (38.8)

      78.9(13.0)

      57.6(9.5)

      69 (11.4)

      < 10 (<1.6)

      < 10 (<1.6)

      < 10 (<1.6)
      Complications diagnosis-specified

      Retinopathy

      Nephropathy

      Polyneuropathy

      Cerebrovascular incident/TIA

      Ischemic heart disease

      Peripheral artery disease

      Cardiovascular or diabetes-related death
      70 (9.2)

      18 (2.4)

      15 (2.0)

      24 (3.2)

      41 (5.4)

      < 10 (<1.5)

      < 10 (<1.5)
      70 (45.4)

      18 (11.7)

      15 (9.7)

      24 (15.6)

      41 (26.5)

      < 10 (<6.5)

      < 10 (<6.5)
      n/a
      HbA1c

      mmol/mol

      %
      48.62 (43.20 −57.02)

      6.6 (6.1–7.4)
      52.12 (45.24–64.38)

      6.9 (6.3–8.0)
      47.55 (43.03–55.65)

      6.5 (6.1–7.2)
      Poorly controlled HbA1c274 (36.0)74.7 (48.5)199.3 (32.8)
      BMI kg/m231.93 (6.18)31.32 (5.13)32.10 (6.43)
      Smoking

      Never

      Former

      Current
      286.2 (37.6)

      95.7 (12.6)

      379.1 (49.8)
      42.8 (27.8)

      18.7 (12.1)

      92.6 (60.1)
      243.4 (40.1)

      77 (12.7)

      286.5 (47.2)
      Results are displayed n (%),mean ±SD or median (IQR)
      Except for smoking (25.8%), BMI (41.9%) and HbA1c (41.9%), proportion of missing data was less than 5%.

      3.2 Validation sample

      The validation sample was comparable to our study population and characteristics were similar for all included years (data for 2010 are shown in Table 2; for 2007–2013 see Supplementary material E).
      Table 2Baseline characteristics validation sample.
      Study cohort

      2010 (N = 127)
      Validation sample

      2010 (N = 622)
      Age (median ± IQR)38(33–42)39(33–42)
      men n (%)60(47.2)299(48.1)
      Diabetes treatment class n (%)

      Lifestyle

      Oral blood glucose-lowering drugs

      Insulin and oral blood glucose-lowering drugs
      36 (28.4)

      76 (59.8)*

      15(11.8)
      N/A

      550(88.4)*

      72(11.6)
      Standardized household income n (%)

      0–33 percentile

      33–66 percentile

      66–100 percentile

      Missing
      58 (45.7)

      34 (26.8)

      28 (22.0)

      7 (5.5)
      283 (45.5)

      195 (31.4)

      133 (21.4)

      12 (1.9)
      Ethnicity group n (%)

      Non-Western origin

      Origin

      Dutch or other Western

      Turkey

      Morocco

      South-Asian

      Other non-Western
      79 (62.2)

      48 (37.8)

      10 (7.9)

      14 (11.0)

      26 (20.5)

      29 (22.8)
      434 (69.7)

      188 (30.3)

      83 (13.3)

      55 (8.8)

      146 (23.5)

      150 (24.1)
      * indicates significantly different from validation sample 2010 p < 0.05. If no missing values are reported, number of missing values = 0

      3.3 Predictors of complications

      Men, but not women, with a non-Western origin were more likely to develop a complication in the follow-up period of 6–12 years (HR (CI95%) 1.98 (1.19–3.30)) (Table 4) Overall, income in the year of diagnosis was not associated with the development of complications (Table 3, Table 4). Young adults, in particular men, with a mean HbA1c > 7% (>53 mmol/mol) in the first year after diagnosis were more likely to develop a complication (HR (CI95%) 1.72 (1.15–2.57)) (Model A, Table 3). Women were less likely to develop a complication (HR (CI95%) 0.59 (0.41–0.84)). Crude estimates (only adjusted for medical determinants) were comparable to the fully adjusted HRs (Supplementary material F).
      Table 3Risk estimates of developing micro- or macrovascular complications in young adults with type 2 diabetes 6–12 years after diagnosis†.
      Main outcomeSub-analysis
      Model A: first micro OR macrovascular complicationModel B: first microvascular complicationModel C: first macrovascular complication
      Pooled data after MI (n = 761, complications = 154) Hazard ratio (95% CI)Complete case analysis (n = 574, complications 154) Hazard ratio (95% CI)Pooled data after MI (n = 761, complications = 98) Hazard ratio (95% CI)Pooled data after MI (n = 761, complications = 72) Hazard ratio (95% CI)
      SDOH
      Sex (women)0.59 (0.41–0.84)*0.51 (0.30 – 0.88) *0.50 (0.31 – 0.79)*0.83 (0.50 – 1.38)
      Age (years)1.03 (1.00–1.06)1.02 (0.97 – 1.07)0.99 (0.96 −1.03)1.09 (1.03 – 1.15)*
      Standardized household income (Reference: 0–33 percentile)

      33–66 percentile

      66–100 percentile
      1.24 (0.86 – 1.79)

      0.78 (0.47 – 1.28)
      1.18 (0.68 – 2.02)

      0.94 (0.49 – 1.80)
      1.29 (0.80.−2.06)

      1.12 (0.62 – 2.03)
      1.18 (0.70 – 1.97)

      0.50 (0.23 −1.10)
      Ethnicity group

      Non-Western (reference: Western)
      1.29 (0.87 – 1.90)1.79 (0.96 −3.35)2.23 (1.34 – 3.71)*0.61 (0.37 – 1.00)*
      Medical determinants index year
      Diabetes treatment class (reference: no medication)

      A10B medication

      Combination of A10B and A10A
      1.40 (0.93–2.11)

      1.25 (0.70 – 2.22)
      1.43 (0.74 – 2.76)

      0.72 (0.24 – 2.16)
      1.95 (1.08 – 3.52)*

      2.38 (1.12 – 5.06)*
      N/A

      N/A
      HbA1c> 7% (>53 mmol/mol)

      (reference HbA1c ≤7% (≤53 mmol/mol)
      1.72 (1.15 – 2.57)*2.67 (1.63 – 4.37)*1.76 (1.04 – 2.99)*N/A
      Mean BMI1.00 (0.97 – 1.03)0.99 (0.95 – 1.04)N/AN/A
      Co-morbidity (yes)1.10 (0.78 – 1.54)0.81 (0.50 – 1.34)N/AN/A
      Smoking (reference: never)

      Former

      Current
      1.17 (0.59 – 2.35)

      1.51 (0.98 – 2.31)
      1.53 (0.66 – 3.55)

      1.75 (0.99 – 3.12)
      1.45 (0.61 – 3.47)

      1.38 (0.79 – 2.39)
      1.04 (0.19 – 5.81)
      • 19.
        (1.11 – 4.32)*
      *indicates statistically significant compared to reference category p < 0.05

      † all displayed HR are adjusted for the effects of all other determinants in the model
      Internal validation
      C-index optimism-corrected (non-corrected)

      Calibration curve optimism-corrected ( non-corrected)

      Intercept

      Slope
      0.62 (0.64)

      0.24 (0.054)

      0.70 (0.95)
      0.66 (0.67)

      0.26 (0.39)

      0.73 (0.99)
      0.67 (0.68)

      0.12 (−0.02)

      0.86 (1.02)
      Table 4Risk estimates of developing micro-or macrovascular complications in young adults with type 2 diabetes 6–12 years after diagnosis, stratified by sex .
      Hazard ratio (95%) (confidence interval)
      Men

      N = 375

      n complications= 99
      p-valueWomen

      N = 386

      n complications= 58
      p-value
      Age (years)1.04 (1.00–1.08)0.0291.00 (0.96–1.05)0.945
      HbA1c > 7% (>53 mmol/mol)

      (reference HbA1c ≤7% (≤53 mmol/mol)
      2.02 (1.291–3.156)0.0021.59 (0.83–3.04)0.163
      Ethnicity group

      Non-Western (reference: Western)
      1.98 (1.194–3.295)0.0080.67 (0.38–1.18)0.164
      Standardized household income

      (reference: 0–33 percentile)

      33–66 percentile

      66–100 percentile
      1.08 (0.68–1.736)

      0.74 (0.40–1.36)
      0.737

      0.331
      1.54 (0.85–2.78)

      0.80 (0.35–1.83)
      0.153

      0.601
      adjusted for: age, HbA1c regulation, ethnicity, income Baseline characteristics and crude estimates are presented in supplementary file F
      No other effect modification than origin by sex was found (Supplementary material F).

      3.4 Sub-analyses

      The sub-analysis with first microvascular complication as outcome (model B) showed that young adults of non-Western origin (HR (CI95%) 2.23 (1.34–3.71)) were more likely to develop a complication, as were persons with a mean HbA1c > 7% (>53 mmol/mol) (HR: 1.76 95% CI: 1.04–2.99). Women (HR (CI95%): 0.50 (0.31–0.79)), and those treated with medication (HR (CI95%) A10B: HR: 1.95 (1.08–3.52) and: A10B+A10A: 2.38 (1.12–5.06)), were less likely to develop a complication.
      The sub-analysis with first macrovascular complications as outcome (model C) showed that older age (HR (CI95%) 1.09 (1.03–1.15)) and current smokers in the index year (HR (CI95%) 2.19 (1.11–4.32)) were more likely to develop a macrovascular complication, while non-Western origin (HR (CI95%) 0.61 (0.37–1.00)) appeared to be protective.
      Sub-analysis of young adults with non-Western origin showed no association between complications and country of origin or migration generation (Supplementary material C).
      Our model discriminated with a C-index of 0.64 and an optimism-corrected C-index of 0.62, indicating moderate to good discriminative ability. Pooled intercept and slope of the calibration curves of the model were 0.054 and 0.95, and 0.24 and 0.70 when optimism-corrected, respectively, indicating reasonably good calibration (Table 3).

      4. Discussion

      4.1 Summary of the main findings

      The results of this study suggest that in young adults with type 2 diabetes in an urban region, men with non-Western origin were more likely to develop diabetes complications. Being women was relatively protective. For microvascular complications only, a non-Western origin also emerged as a possible risk factor in both sexes. Income was not associated with developing diabetes complications in addition to the medical determinants. Overall, poor regulated HbA1c in the first year after diagnosis appeared to be the most important risk factor.
      Prevalence of diabetes is undeniably associated with socio-economic context [
      • Hill-Briggs F.
      • Adler N.E.
      • Berkowitz S.A.
      • Chin M.H.
      • Gary-Webb T.L.
      • Navas-Acien A.
      • et al.
      Social determinants of health and diabetes: a scientific review.
      ,
      • Walker J.J.
      • Livingstone S.J.
      • Colhoun H.M.
      • Lindsay R.S.
      • McKnight J.A.
      • Morris A.D.
      • et al.
      Effect of socioeconomic status on mortality among people with type 2 diabetes: a study from the Scottish Diabetes Research Network Epidemiology Group.
      ]. Independent from HbA1c-regulation, being of non-Western origin appeared to be a risk factor for the development of complications in young men, but not women. Country of origin or migration generation was not explanatory. The higher risk might be explained by higher diabetes prevalence in men [
      • Hill-Briggs F.
      • Adler N.E.
      • Berkowitz S.A.
      • Chin M.H.
      • Gary-Webb T.L.
      • Navas-Acien A.
      • et al.
      Social determinants of health and diabetes: a scientific review.
      ,
      • Dijkshoorn DU H.
      • Middelkoop B.J.C.
      Prevalentie van diabetes mellitus en hart- en vaatziekten onder Turkse, Marokkaanse en autochtone Nederlanders.
      ] or non-measured factors as genetic predisposition [
      • Wright A.K.
      • Welsh P.
      • Gill J.M.R.
      • Kontopantelis E.
      • Emsley R.
      • Buchan I.
      • et al.
      Age-, sex- and ethnicity-related differences in body weight, blood pressure, HbA(1c) and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes.
      ], adaptation to a Western (sedentary) lifestyle or other cultural factors [
      • Hill-Briggs F.
      • Adler N.E.
      • Berkowitz S.A.
      • Chin M.H.
      • Gary-Webb T.L.
      • Navas-Acien A.
      • et al.
      Social determinants of health and diabetes: a scientific review.
      ]. Correspondingly, the sub-analyses suggested that people of non-Western origin of both sexes were more likely to develop microvascular complications, while in contrast, for macrovascular complications non-Western origin appeared to be protective. We hypothesize that longer follow-up time and larger sample size may reveal more conclusive results to distinguish the risk for macrovascular- and microvascular complications separately.
      Sex differences may be explained by behavioral or biological factors[
      • Mauvais-Jarvis F.
      • Bairey Merz N.
      • Barnes P.J.
      • Brinton R.D.
      • Carrero J.-J.
      • DeMeo D.L.
      • et al.
      Sex and gender: modifiers of health, disease, and medicine.
      ], which were not quantified in this study. Another explanation is that in our study the number of events in women was too little to reveal the same risk factors in both sexes, in which poor recognition of cardiovascular disease in women may also play a role [
      • Mauvais-Jarvis F.
      • Bairey Merz N.
      • Barnes P.J.
      • Brinton R.D.
      • Carrero J.-J.
      • DeMeo D.L.
      • et al.
      Sex and gender: modifiers of health, disease, and medicine.
      ].
      Income was, in this population with a relatively large percentage of low income, not associated with the development of complications. Our results suggest that when HbA1c is regulated, biological or cultural factors are of greater importance than income for the development of complications within both native-Dutch as people with non-Western ethnicity.
      Irrespective of SDOH and sex, HbA1c regulation appeared to be an important predictor of the early development of diabetes-complications in young adults and proactive disease management should therefore primarily focus on HbA1c regulation in all at-risk populations. In agreement with our results, previous studies have highlighted the importance of the first year after diagnosis on the subsequent course of HbA1c-control [
      • Dart A.B.
      • Martens P.J.
      • Rigatto C.
      • Brownell M.D.
      • Dean H.J.
      • Sellers E.A.
      Earlier onset of complications in youth with type 2 diabetes.
      ,

      N.H.G. Diabetes mellitus type 2 (Vierde herziening) [Available from: https://www.nhg.org/standaarden/volledig/nhg-standaard-diabetes-mellitus-type-2.

      ,
      • Collier A.
      • Ghosh S.
      • Hair M.
      • Waugh N.
      Impact of socioeconomic status and gender on glycaemic control, cardiovascular risk factors and diabetes complications in type 1 and 2 diabetes: a population based analysis from a Scottish region.
      ,
      • Patel A.
      • MacMahon S.
      • Chalmers J.
      • Neal B.
      • Billot L.
      • Woodward M.
      • et al.
      Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes.
      ,
      Group UPDSU. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998;352(9131):837–53.
      ,
      Prevention of Cardiovascular Disease Guidelines for assessment and management of cardiovascular risk. World Health Organization. 2007.
      ]. HbA1c-regulation is more proximate in the causal pathway to diabetes complications than are SDOH, which may lead to a stronger association in the main analysis, especially with a small number of events. Macrovascular complications were only associated with older age and smoking, suggesting that diabetes regulation plays a less important role in the development of macrovascular complications. However, again considering the young adult population, a longer follow-up may reveal different results.

      4.2 Strengths and limitations

      When using routinely collected data, missing data and data accuracy are common problems [
      • Pouwels K.B.
      • Voorham J.
      • Hak E.
      • Denig P.
      Identification of major cardiovascular events in patients with diabetes using primary care data.
      ,
      • Wells B.J.
      • Chagin K.M.
      • Nowacki A.S.
      • Kattan M.W.
      Strategies for handling missing data in electronic health record derived data.
      ]. The use of MI for the handling of missing data could cause underestimation of the effects, as missing data could indicate either a no-show of the (non-adherent) patient or no clinical indication to measure or register [
      • Wells B.J.
      • Chagin K.M.
      • Nowacki A.S.
      • Kattan M.W.
      Strategies for handling missing data in electronic health record derived data.
      ]. This implies that the effects of ethnicity and income on the outcomes might have been underestimated in our study. However, our analysis showed a high accuracy and only low levels of missing data for the SDOH.
      To improve data accuracy a combination of diagnosis and medication registers was used to identify type 2 diabetes [
      • Pouwels K.B.
      • Voorham J.
      • Hak E.
      • Denig P.
      Identification of major cardiovascular events in patients with diabetes using primary care data.
      ]. This enlarged our cohort, although also introduced possible bias. As the prescription of medication is often initially delayed, year of incidence might be less precise in our study population. Furthermore, in some individual cases, metformin may occasionally be prescribed in other disease such as type 1 diabetes or polycystic ovary syndrome, although in such cases the metabolic syndrome is often present.
      The internal validation of our model can be considered a strength of our study [
      • Wright A.K.
      • Welsh P.
      • Gill J.M.R.
      • Kontopantelis E.
      • Emsley R.
      • Buchan I.
      • et al.
      Age-, sex- and ethnicity-related differences in body weight, blood pressure, HbA(1c) and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes.
      ,
      • Steyerberg E.W.
      • Vergouwe Y.
      Towards better clinical prediction models: seven steps for development and an ABCD for validation.
      ], as it indicates representativeness and accuracy [
      • Steyerberg E.W.
      • Vergouwe Y.
      Towards better clinical prediction models: seven steps for development and an ABCD for validation.
      ]. However, bootstrapping technique was used: drawing random samples from an imputed dataset might result in bootstrapping potentially non-realistic imputation data, resulting in overfitting [
      • Wahl S.
      • Boulesteix A.L.
      • Zierer A.
      • Thorand B.
      • van de Wiel M.A.
      Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation.
      ]. Therefore, the validation outcomes should be interpreted with care. Further steps to improve validation might be, 1) to enlarge the study sample and follow-up period, and 2) carry out an external validation to determine reproducibility.
      Lastly, in terms of generalizability, it should be noted that our study was purposely conducted in a socio-economically diverse urban area, and our results are only relevant to comparable healthcare systems and socio-economic compositions.
      In conclusion, the results of this study suggested that in young adults with type 2 diabetes in a urbanized population, men with Non-Western origin and those with poorly regulated HbA1c in the first year after diagnosis had the highest risk on developing diabetes-related complications. Being women was protective. This study re-emphasizes the importance of adequate HbA1c-regulation from the moment of diagnosis. Regarding sex and ethnicity differences, our results may guide to more sex- or cultural-specific (preventive) interventions in communities.

      Ethics approval

      This study involves human participants but an Ethics Committee exempted this study. Ethics Committee Leiden-The Hague -Delft ID 19-035 approval November 2020.

      Funding

      Funded by a European Foundation for the Study of Diabetes (EFSD) award supported by Servier: EFSD INTENSE Project 2018.

      Contributorship statement

      RV, TH, EN designed the study. JS conducted database management quality assurance. TH & EN carried out data management and data analysis; EN, TH, RV, JS interpreted the data. EN wrote the first draft of and revised the manuscript, incorporating contributions from co-authors. RV, JS, TH and MN critically revised the manuscript. All authors agreed on submission and are joint guarantors of this work.

      Conflict of interest statement

      The authors have no conflicts of interest to declare.

      Acknowledgments

      The authors thank all PCPs who provide their data for our research purposes.

      Data sharing statement

      As data contain personal health information and individual socio-economic information, data were strictly used under license for the current study and are therefore not publicly available.

      Appendix A. Supplementary material

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