BMJ Open · the mean value of two supine readings (Korotkoff 1–5) with a cuff of appropriate size and after at least 5 minutes of rest. LDL, HDL and total cholesterol were measured

For peer review only

Impact of Ethnicity on Progress of Glycaemic Control: a Study of 131,935 Newly Diagnosed Patients with Type 2

Diabetes

Journal: BMJ Open

Manuscript ID: bmjopen-2015-007599

Article Type: Research

Date Submitted by the Author: 07-Jan-2015

Complete List of Authors: Rawshani, Araz; Institute of Medicine, Department of Molecular and Clinical Medicine Svensson, Ann-Marie; Centre of Registers in Region Västra Götaland,

Rosengren, Annika; University of Gothenburg, Department of Medicine Zethelius, Bjorn.; Uppsala University, Public Health/Geriatrics Eliasson, Björn; University of Gothenburg, Department of Medicine Gudbjörnsdottir, Soffia; University of Gothenburg, Department of Medicine

<b>Primary Subject Heading</b>:

Diabetes and endocrinology

Secondary Subject Heading: Health policy, Epidemiology, Global health

Keywords:

DIABETES & ENDOCRINOLOGY, Diabetic nephropathy & vascular disease < DIABETES & ENDOCRINOLOGY, General diabetes < DIABETES & ENDOCRINOLOGY, EPIDEMIOLOGY, Diabetes & endocrinology < INTERNAL MEDICINE, Adult nephrology < NEPHROLOGY

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Impact of Ethnicity on Progress of Glycaemic Control: a Study of

131,935 Newly Diagnosed Patients with Type 2 Diabetes

Report by the Swedish National Diabetes Register

Araz Rawshani1, 2

Ann-Marie Svensson1, 2, 3

Annika Rosengren1, 2

Björn Zethelius4, 5

Björn Eliasson1, 2, 3

Soffia Gudbjornsdottir1, 2, 3

1 Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden

2 Sahlgrenska University Hospital, Gothenburg, Sweden

3 National Diabetes Register, Centre of Registers, Gothenburg, Sweden

4 Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala,

Sweden

5 Medical Products Agency, Epidemiology, Uppsala, Sweden

Corresponding author

Araz Rawshani

[email protected]

National Diabetes Register, Centre of Registers

Västra Götaland Region, 41345 Gothenburg, Sweden

ABSTRACT WORD COUNT 270

MAIN TEXT WORD COUNT 2708

TABLES 2

FIGURES 3

SUPPLEMENTARY MATERIAL 2 Figures

REFERENCES 38

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Abstract

Objectives: Studies on ethnic disparities in glycaemic control have been contradictory and

compromised by excessively broad categories of ethnicity and inadequate adjustment for

socioeconomic differences. We aimed to study the effect of ethnicity on glycaemic control in a

large cohort of patients with type 2 diabetes.

Setting: We used nationwide data (mainly from primary care) from the Swedish National Diabetes

Register (2002-2011) to identify patients with newly diagnosed (within 12 months) type 2 diabetes.

Participants: We included 131,935 patients (with 713,495 appointments), representing 10 ethnic groups,

who were followed up to 10 years.

Primary and secondary outcome measures: Progress of HbA1c for up to 10 years was examined.

Mixed models were used to correlate ethnicity with HbA1c (mmol/mol). The effect of glycaemic

disparities was examined by assessing the risk of developing albuminuria. The impact of ethnicity

was compared to that of income, education and physical activity.

Results: Immigrants, particularly those of non-Western origin, received glucose-lowering therapy

earlier, had 30% more appointments but displayed poorer glycaemic control (2–5 mmol/mol higher

HbA1c than native Swedes). Probability of therapy failure was 28–111% higher for non-Western

groups than for native Swedes. High-income Western groups remained below the target-level of

HbA1c for 4–5 years, whereas non-Western populations never reached the target level. These

disparities translated into 51–92% higher risk of developing albuminuria. The impact of ethnicity

was greater than the effect of income and education, and equal to the effect of physical activity.

Conclusions: Despite earlier pharmacological treatment and more frequent appointments,

immigrants of non-Western origin display poorer glycaemic control and this is mirrored in a higher

risk of developing albuminuria.

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Strengths and limitations of this study

• To our knowledge, this is the largest study on ethnic differences in progress of glycaemic control.

We included 131,935 patients (with 713,495 appointments) who were followed up to 10 years.

• All major ethnic groups in the world were represented in this study cohort and they had fully equal

access to, and use of, health care, regardless of ethnic and socioeconomic background. Indeed, we

report that immigrants had higher consumption of health care. This contrasts against previous

studies, which have been hampered by inappropriately broad categories of ethnicity, cross-sectional

design, small samples, short follow-up and unequal access to or consumption of health care.

• The longitudinal design, with at least annually updated information on all time-varying variables,

allow for reliable estimates of the effect of ethnicity. We could control adequately for

socioeconomic, demographic and health-related confounders.

• The main findings (immigrants, particularly those of non-Western origin, display poor glycaemic

control and have a considerably higher risk of developing albuminuria) indicate that much can be

done to improve diabetes care for a large proportion of the diabetic population in Western countries.

• When extrapolating our results, it need be taken into account that these ethnic differences could be

expected in an equitable health care system. This should be taken into consideration in countries

were access to health care is not equitable.

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Introduction

Tight glucose control in type 2 diabetes has shown long-term beneficial effects on microvascular

complications, cardiovascular disease and mortality [1-5]. Studies of ethnic disparities in glycaemic

control have been contradictory [6-8]. They have, however, been hampered by inappropriately

broad categories of ethnicity, cross-sectional design, small samples, short follow-up and unequal

access to—or consumption of—health care. There are no reliable estimates of the true effect of

ethnicity on glycaemic control.

This issue is important in Western societies that are becoming more ethnically diverse due to

accelerated migration from other areas of the world [9]. The ethnic admixture of Western societies

is currently far more diverse than the risk estimate tools of clinicians are prepared to handle.

Immigrants may be at particular risk due to genetic susceptibility to insulin resistance [10,11],

difficult transitional phases, and rapid changes in diet and lifestyle [12], as well as linguistic,

cultural and financial barriers to obtaining proper health care [13,14].

Sweden is an ethnically heterogeneous country in which all inhabitants enjoy access to every level

of health care at a minimal cost [15]. Immigrants are targeted in an effort to promote healthy

lifestyles and consumption of health care [16,17]. The great majority of Swedes with type 2 diabetes

are included in the Swedish National Diabetes Register, which we used to analyse the impact of

ethnicity on the progress of glycaemic control and on albuminuria as a marker for diabetic

complications.

Methods

Data sources

Swedish authorities manage several nationwide registers, which may be linked through the unique

personal identification number assigned to every Swede. The National Diabetes Register (NDR) has

been described previously [18]. It was launched in 1996 as a care giver tool for local quality assurance

purposes and as a feed-back tool in diabetes care [19]. Data provided by nurses and physicians trained in

register procedures are obtained at visits in outpatient clinics of hospitals and primary care clinics.

Clinical information and various measurements are updated at least once a year.

Patients with at least one entry in the NDR from 1 January 2002 to 31 December 2011 were

included if they had been reported within 12 months of the date of diagnosis. Ninety-six per cent of

the subjects had been diagnosed with type 2 diabetes on the basis of a clinical assessment. The

remainder were included on the basis of the following definition: age 40 or older at the time of

diagnosis and treated either with diet only, diet combined with oral hypoglycaemic agents (OHA),

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or a combination of OHA combined and insulin. This definition has been validated and used

previously [20-22].

Measures

Data on annual income in Swedish kronor, highest educational level regardless of country of

domicile and country of birth (used as proxy for ethnicity/race) were obtained from the

Longitudinal Integration Database for Health Insurance and Labour Market Studies, which is an

official database administered by the Swedish National Board of Health and Welfare. Educational

level was stratified into lower (9 years or less—the length of compulsory education in Sweden),

intermediate (10-12 years—upper secondary) and higher (college or university). Income was

stratified into quintiles (Q1 to Q5), with the highest (Q5) being the reference. Ethnic categories

were based on an appraisal of ancestry and geography, [23] with the exception of the fact that

Nordic countries were classified separately in the present study for the purpose of examining

whether immigrants from neighbouring countries also exhibited differences. Native Swedish

patients served as the reference group. Sixty-two individuals were excluded because information

about their country of birth was unavailable.

Glycaemic control was measured as HbA1c. Analyses were quality assured nationwide by regular

calibration with the HPLC Mono-S method, and then converted to mmol/mol [International

Federation of Clinical Chemistry (IFCC)] [24]. Albuminuria was defined as urine albumin excretion

>20µg/min at two out of three consecutive tests (microalbuminuria or macroalbuminuria). BMI was

calculated as weight in kilograms divided by height in metres squared. Systolic blood pressure was

the mean value of two supine readings (Korotkoff 1–5) with a cuff of appropriate size and after at

least 5 minutes of rest. LDL, HDL and total cholesterol were measured in mmol/L. Physical activity

was rated from 1 (never) to 5 (daily). Smoking was coded as present if the patient currently smoked.

Use of lipid lowering medications was dichotomized. Glucose-lowering treatment was categorized

as diet and lifestyle modifications, oral hypoglycaemic agents (OHAs), insulin, or insulin and

OHAs. A history of cardiovascular disease (CVD) was defined as previous hospitalization due to

acute myocardial infarction or stroke (ICD-10, I21, I60–I69; ICD-9, 410, 430–434, 436–438)

according to the Swedish National Discharge Register, which is a reliable validated method [25,26].

All time-varying variables are updated in the NDR following each appointment.

Analyses and statistics

Baseline

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The first observation was used to present characteristics at time of diagnosis (within 12 months).

Continuous variables are reported as means, percentages and quintiles. T tests were used for

continuous variables with native Swedes as the reference group. Chi-square tests were used for

categorical variables. A p-value <0.01 was considered statistically significant but multiple

testing should be considered when interpreting the results.

Glycaemic control

Progress of glycaemic control was calculated as an unadjusted annual mean in relation to

ethnicity. Adjusted figures were calculated using linear regression, to estimate differences in

mmol/mol. Logistic regression was used to estimate the probability of achieving glycaemic

control (i.e. reaching target-level HbA1c <52 mmol/mol) during the second year after diagnosis.

To examine whether hypothesized differences in glycaemia is reflected on the risk of

complications, we calculated the probability of developing albuminuria during the second year

after diagnosis. Mixed effect models were used to account for repeated measurements on the

same unit [27-29]. HbA1c was centered around the mean.

Kaplan-Meier calculations were used to examine whether time to pharmacological treatment

differed among ethnic groups. This was done to examine whether differences in HbA1c could be

explained by disparities in use of medications.

Ethics

The study was approved by the regional ethical review board at the University of Gothenburg,

Gothenburg, Sweden. All patients give informed consent before being included in the NDR.

Results We included 131,935 individuals with newly diagnosed type 2 diabetes. A total of 713,495

appointments were registered. All major ethnic groups were represented in the study (Figure A,

supplementary material). A total of 82.7% of the study population consisted of native Swedes.

Immigrants had more appointments per year. Non-Western immigrants in particular had almost

30% more appointments (Table I).

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TABLE I - CHARACTERISTICS OF 131'935 INDIVIDUALS WITH NEWLY DIAGNOSED TYPE 2 DIABETES BY ETHNICITY/RACE

All

Sw

eden

Nord

ic

cou

ntr

ies

Euro

pe

(hig

h

inco

me)

,

Nort

h

Am

eric

a an

d

Oce

ania

Med

iter

ranea

n B

asi

n

Euro

pe

(low

inco

me)

,

Russ

ia a

nd

Cen

tral

Asi

a

Mid

dle

Eas

t

and N

ort

h

Afr

ica

Su

b-S

ahar

an

Afr

ica

So

uth

Asi

a

Eas

t A

sia

Lat

in

Am

eric

a an

d

Car

ibbea

n

n (%) 131935 109058

(82.66)

6760 (5.12) 3306 (2.51) 511 (0.39) 3870 (2.93) 5031 (3.81) 1127 (0.85) 610 (0.46) 802 (0.61) 860 (0.65)

Males 57 (75083) 57 (62526) 51 (3479) 53 (1751) 70 (360) 54 (2075) 62 (3142) 59 (663)α 56 (344)

α 37 (298) 52 (445)

Age (years)

62.8 (12.5) 63.7 (12.2)

63.8

(10.5)α 64.4 (11.8) 62.5 (10.6)

α 57.6 (11.7) 52.3 (11.2) 47.3 (11.2) 46.0 (11.3) 50.0 (12.8) 54.5 (12.0)

Annual visits 1.73 (2.14) 1.67 (2.0) 1.80 (2.22) 1.96 (2.70) 2.20 (3.24) 2.05 (2.70) 2.30 (3.34) 2.22 (3.42) 2.32 (3.40) 1.91 (2.70) 2.50 (3.20)

BMI (kg/m2) 30.4 (5.5) 30.3 (5.5) 31.2 (5.6) 30.5 (5.3)α 30.1 (4.8)

α 31.3 (5.3) 30.9 (5.2) 28.8 (5.4) 28.6 (5.2) 27.3 (4.3) 31.4 (5.5)

HbA1c (mmol/mol)

52.2 (15.4) 51.9 (15.1)

52.3

(15.2)α 52.2 (14.7)

α 52.8 (15.2)

α 54.3 (16.8) 54.9 (17.2) 58.0 (19.8) 55.8 (16.7) 55.9 (17.1) 56.7 (19.8)

Systolic BP (mmHg) 137.5

(17.2)

138.1 (17.1) 139.5

(17.4)

138.2 (17.4)α 134.1 (16.2) 135.2 (18.2) 129.3

(16.4)

126.9

(16.4)

124.8

(14.4)

126.7 (15.9) 130.8 (15.4)

Chol/HDL ratio 4.5 (1.5) 4.4 (1.5) 4.3 (1.5) 4.5 (1.6)α 4.6 (1.4)

α 4.9 (1.7) 4.8 (1.5) 4.6 (1.5) 4.8 (1.4) 4.4 (1.3)

α 4.7 (1.5)

LDL/HDL ratio 2.6 (1.1) 2.6 (1.1) 2.5 (1.1) 2.6 (1.1)α 2.8 (1.1) 2.9 (1.2) 2.9 (1.1) 2.9 (1.1) 2.9 (1.0) 2.6 (1.0)

α 2.7 (1.1)

eGFR (mL/min)

83.4 (25.2) 81.8 (24.0) 83.0 (22.3) 81.2 (21.8)α 87.2 (25.0) 90.4 (37.4) 99.9 (24.9)

124.9

(39.1)

100.0

(23.9) 100.9 (28.0) 96.2 (26.6)

Smoker 15 (19657) 14 (15173) 20 (1341) 17 (556)α 19 (99) 23 (888) 22 (1098) 13 (149)

α 13 (77)

α 15 (122)

α 18 (154)

No physical activity 12 (15973) 12 (12935) 13 (907)α 12 (390)

α 15 (76)

α 13 (497)

α 16 (803) 11 (128)

α 13 (82)

α 8 (66) 10 (89)

α

Daily physical activity 26 (33983) 26 (28207) 24 (1651)α 25 (842)

α 27 (139)

α 31 (1186)

α 22 (1116) 23 (260)

α 23 (142)

α 29 (233)

α 24 (207)

α

INCOME & EDUCATION

Income Q1 (lowest) 20 (24925) 16 (16872) 23 (1439) 27 (852) 23 (113) 41 (1538) 55 (2745) 47 (526) 35 (212) 42 (333) 35 (295)

Income Q5 (highest) 20 (24928) 22 (22068) 15 (928) 14 (452) 14 (69) 11 (398) 10 (485) 15 (165) 17 (101) 14 (113) 18 (149)

<9 years education 39 (51890) 40 (43495) 44 (3005) 25 (814) 41 (211) 35 (1366)α 37 (1883) 33 (376) 24 (148) 42 (335)

α 30 (257)

10-12 years education 43 (56146) 43 (47370) 40 (2734)α 48 (1593) 42 (216) 39 (1516)

α 29 (1444) 35 (399) 44 (267)

α 30 (243) 42 (364)

α

University/college

education 17 (22140) 16 (17811) 12 (790) 24 (793) 13 (68) 15 (576)α 25 (1275) 22 (243) 29 (176) 24 (193) 25 (215)

GLUCOSE-LOWERING TREATMENT

Diet 53 (70445) 54 (59371) 53 (3602) 53 (1763)α 49 (251)

α 48 (1854)

α 44 (2225) 38 (429) 42 (255) 40 (320) 44 (375)

OHA 37 (49261) 36 (39662) 38 (2599)α 38 (1240)

α 43 (219) 43 (1653) 47 (2352) 48 (537) 47 (288) 45 (364) 40 (347)

α

Insulin 5 (6199) 5 (5249) 4 (251)α 4 (148)

α 4 (21)

α 4 (143)

α 3 (164) 7 (84) 5 (30)

α 7 (59) 6 (50)

α

Insulin + OHA 4 (5457) 4 (4318) 4 (284) 4 (136)α 4 (18)

α 5 (202)

α 5 (260) 6 (69) 6 (35)

α 7 (55) 9 (80)

COMPLICATIONS

Albuminuria 14 (17988) 14 (14845) 14 (920)α 14 (453)

α 17 (88)

α 13 (502)

α 14 (701)

α 14 (157)

α 12 (75)

α 13 (106)

α 16 (141)

Previous CVD 20 (26166) 21 (22373) 23 (1555) 23 (759) 19 (98)α 16 (610)

α 11 (547) 4 (46) 8 (47) 6 (48) 10 (83)

Sweden is the reference group for ethnicity. α

No statistically significant difference, at the 0.01 level.

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Characteristics at the time of diagnosis

Table 1 presents characteristics at the time of diagnosis. The proportion of males varied from

37% (East Asia) to 70% (Mediterranean Basin). Patients from high-income Western countries

were as much as 18 years older at the time of diagnosis. Particularly low age at the time of

diagnosis was found for South Asia (46.0), East Asia (50.0) and Sub-Saharan Africa (47.3). BMI

was 2–3 kg/m2 lower among non-Western groups. Native Swedish patients had the lowest mean

HbA1c (51.9 mmol/mol) of all groups. Low-income and non-Western groups had HbA1c

ranging from 54 to 58 mmol/mol. Systolic blood pressure was lower among non-Western

groups. Lipid profiles were less favourable among non-Western groups. There were no major

ethnic differences in terms of physical activity. Smoking rates were higher among patients from

the Middle East, North Africa and low-income Europe. A history of CVD was more common

among Western groups, but non-Western groups were 10–12 years younger at the onset of CVD.

The overall prevalence of albuminuria was approximately 14%, and there were no noteworthy

ethnic differences. College or university education was more common among non-Western

populations, but they were generally in lower income quintiles.

GLYCAEMIC CONTROL – CRUDE FIGURES

HbA1c declined during the first 1–3 years of follow-up and then increased for all ethnic groups

(Figure I). However, there were conspicuous and consistent ethnic differences in HbA1c levels

throughout the study. Swedish patients (the reference category) had the lowest mean HbA1c at

every point in time while patients from high-income Western countries had only slightly higher

levels. Patients from low-income Europe, Russia and Central Asia, as well as all non-Western

populations, had substantially higher HbA1c levels throughout the study. On average, Swedish

and other Nordic populations remained below the target level until the fifth year. Mediterranean

Basin, high-income European, North American and Oceanic populations remained below the

target level until the fourth year, whereas low-income European, Russian and Central Asian

populations remained below only until the second year. The remaining five ethnic groups did not

reach the target level at any point.

GLYCAEMIC CONTROL – ADJUSTED FIGURES

Because there was an interaction (p <0.0001) between ethnicity and glucose-lowering treatment,

we stratified the analysis by type of treatment. Table II presents β coefficients (i.e predicted

difference in mmol/mol HbA1c) by ethnicity. To convey a sense of the impact of ethnicity as

compared to other factors, coefficients for physical activity are also presented.

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Being from Sub-Saharan Africa, South Asia, East Asia, the Middle East and North Africa, Latin

America or low-income Europe, Russia and Central Asia predicted substantially higher HbA1c.

East Asian ethnicity predicted 1.8–3.8 mmol/mol higher HbA1c, depending on the stratum.

Similarly, low-income Europe, Russia and Central Asia predicted 1.2–1.9 mmol/mol higher

HbA1c. Latin America and the Caribbean predicted 1.9–4.8 mmol/mol higher HbA1c. South

Asian origin predicted 1.9–4.2 mmol/mol higher HbA1c. Sub-Saharan Africa predicted 1.6–3.6

mmol/mol higher HbA1c. In comparison, no physical activity predicted 0.8–1.3 mmol/mol

higher HbA1c, as compared to daily physical activity.

TABLE II: PREDICTION OF HBA1C (MMOL/MOL) BY TYPE OF GLUCOSE-LOWERING

TREATMENT

DIET & LIFESTYLE OHA INSULIN (±OHA)

ETHNICITY/RACE

Sweden reference reference reference

East Asia 3.79 (2.59, 5) 1.84 (0.7, 2.97) 2.13 (-0.36, 4.62)

Europe (high-income), North America &

Oceania

0.14 (-0.37, 0.65) 0.19 (-0.41, 0.79) 1.86 (0.42, 3.3)

Europe (low-income), Russia and Central

Asia

1.58 (1.05, 2.11) 1.2 (0.66, 1.74) 1.89 (0.63, 3.16)

Latin America & The Caribbean 1.89 (0.76, 3.02) 2.42 (1.3, 3.54) 4.84 (2.6, 7.07)

Mediterranean Basin 0.57 (-0.74, 1.88) 0.61 (-0.84, 2.05) 1.17 (-2.7, 5.04)

Middle East & North Africa 1.85 (1.37, 2.34) 0.93 (0.46, 1.41) 2.79 (1.58, 4.01)

Nordic countries -0.01 (-0.36, 0.35) 0.19 (-0.22, 0.6) 0.81 (-0.19, 1.8)

South Asia 4.21 (2.85, 5.56) 1.93 (0.6, 3.25) 1.91 (-1.12, 4.94)

Sub-Saharan Africa 3.26 (2.22, 4.3) 3.61 (2.57, 4.64) 1.58 (-0.47, 3.63)

PHYSICAL ACTIVITY

Daily physical activity reference reference reference

3-5 times/week 0.07 (0.01, 0.14) 0.11 (0.01, 0.22) 0.19 (-0.06, 0.45)

1-2 times/week 0.26 (0.19, 0.34) 0.51 (0.4, 0.62) 0.74 (0.46, 1.01)

Less than once / week 0.48 (0.39, 0.57) 1.03 (0.9, 1.17) 1.38 (1.06, 1.7)

No physical activity 0.76 (0.66, 0.86) 1.20 (1.06, 1.35) 1.32 (1.00, 1.65)

Figures are β coefficients (95% CI) that predict the change in HbA1c (mmol/mol).

The effect of physical activity is presented for comparison.

Example of interpretation: after accounting for included covariates, East Asian ethnicity predicts 3.79 mmol/mol higher HbA1c

among persons on diet and lifestyle modifications.

Model adjustments: age, sex, age at onset of diabetes, duration of diabetes, quadratic effect of duration of diabetes, BMI, smoking

status, history of cardiovascular disease, physical activity, income, education, lipid lowering medication and eGFR.

PROBABILITY OF FAILURE TO ACHIEVE THE TARGET LEVEL DURING THE

SECOND YEAR

Low-income Europe, Middle East and North Africa, East Asia, Sub-Saharan Africa and South

Asia displayed substantially higher odds of not achieving the target-level for HbA1c (Figure II).

Odds ratios (95% CI) for East Asia, Sub-Saharan Africa and South Asia were 1.76 (1.22, 2.54),

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1.78 (1.29, 2.45) and 2.11 (1.35, 3.29), respectively. The effects of income and education were

significant, although less pronounced. Not being physically active was associated with 57%

higher odds of failure, as compared to daily physical activity.

PROBABILITY OF HAVING ALBUMINURIA DURING THE SECOND YEAR

Immigrants had 6% to 92% higher odds of having albuminuria (Figure III). The risk of

albuminuria was particularly high (51% to 92% higher risk) in Non-Western and low-income

groups.

TIME TO START OF PHARMACOLOGICAL TREATMENT

Median time to start of pharmacological treatment was shorter for non-Western populations

compared with Western populations (Figure B, supplementary material).

Discussion Our study provides firm evidence that ethnicity is a strong predictor of glycaemic control, on a

par with physical activity. We also show ethnic differences in glycaemia for all major ethnic

groups and how these disparities are mirrored in another important riskmarker, i.e albuminuria.

We believe that our results call for a more individualized management and increased efforts to

eliminate ethnic inequalities.

Glucose control is a cornerstone of diabetes care. Previous studies on ethnic differences in

glycaemic control might be compromised in several ways; unequal access to—or use of—health

care, inappropriately broad categories of ethnicity, cross-sectional design, small samples and

short follow-up are frequent flaws, which might explain contradictory results [6-8].

We examined 131,935 newly diagnosed patients with type 2 diabetes, including 713,495

observations during a decade of monitoring. Our cohort contains the majority of all new cases of

type 2 diabetes in Sweden during the study period. All major ethnic groups were adequately

represented. All participants had full access to every level of health care at a minimal cost. The

Swedish social welfare system fully covers all necessary health care for individuals who do not

have an income. Thus, neither access to nor consumption of health care are likely to have

confounded our results. Swedish authorities frequently target immigrant and disadvantaged

groups in ways to increase their use of health care and promote health behaviours. This was

reflected in our study by the fact that immigrants had 30% more appointments at their clinic.

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We noted marked ethnic differences in HbA1c both at the time of diagnosis and during follow-

up. Immigrants consistently exhibited poorer glycaemic control. High-income Western groups

remained below the target-level of HbA1c for 4–5 years after diagnosis, whereas low-income

Europe, Russia and Central Asian patients maintained the target level for an average of only 3

years. Non-Western populations had substantially higher HbA1c throughout the study and never

reached the guideline target level. Adjusted figures showed 2–5 mmol/mol higher HbA1c among

non-Western groups. These disparities translated into a 28–111% higher risk of not achieving

target level of HbA1c and a 51–92% higher risk of developing albuminuria among non-Western

groups compared with native Swedes. After the end of follow-up, 40–45% of individuals from

high-income Western countries were in glycaemic control, compared to 5%, 25% and 30% for

Sub-Saharan Africa, South Asia and the Middle East and North Africa, respectively. These

differences could not be explained by disparities in instituting glucose-lowering medications, use

or access to health care.

The risk of albuminuria was assessed in order to determine whether ethnic differences in

glycaemic control were reflected in the development of diabetes-related complications. Poor

glycaemic control is a main cause of albuminuria and renal lesions in diabetes, making

albuminuria a suitable marker for complications [30,31]. Studies have shown that African

Americans and Hispanics have a higher prevalence of albuminuria compared with Caucasians

[32]. Jolly et al reported that this was also true for Asians [33]. Our study describes the adjusted

risk of albuminuria in all major ethnic groups; immigrants, particularly those of non-Western

origin, have a substantially higher risk of developing albuminuria. This predicts a high future

risk of developing cardiovascular disease. It also suggests that ethnic differences in HbA1c

reflect actual differences in glucose-levels. Above all it underlines the need for ethnic-specific

screening and management.

The groups with the poorest glycaemic control and greatest risk of albuminuria in our study were

Asia, Sub-Saharan Africa, the Middle East & North Africa, low-income Europe, Russia and

Central Asia. They represent a large and growing proportion of the population in high-income

areas such as North American and the EU. We believe our results can be generalized to

economically developed Western countries. Clinicians and health care planners should be aware

of the challenges posed by immigrants and adjust the management accordingly. Effective

strategies to reduce these health disparities remain elusive and need to be addressed. The

problem might be further complicated by a potential interaction between ethnicity and the

effectiveness of glucose-lowering medications. Although our study was not designed to explore

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these associations, we show that there was an effect-modification of ethnicity on glucose-

lowering therapy. A previous studies revealed ethnic differences in the efficacy of insulin, [34]

but there are considerable gaps in the knowledge that is currently available on this topic.

LIMITATIONS

It is important to consider the implications of the use of HbA1c when making ethnic

comparisons. HbA1c is not a direct measure of glycaemia and might be influenced by factors

that are independent of blood glucose levels. Studies have shown that African Americans

average approximately 0.20 percentage points higher HbA1c than Caucasian Americans [35,36].

However, the design of these studies differs from ours. Furthermore, African Americans descend

largely from West Africa. Africans exhibit the greatest genetic diversity of all human

populations [37]. A recent study examined two African populations and showed that their HbA1c

differed; lower levels were found for the East African population [38]. The African population in

our study descends largely from East Africa. Thus, we believe that our results for Sub-Saharan

Africans and the remaining ethnicities should be interpreted as reflecting differences in

glycaemic control. This is underscored by the fact that Sub-Saharan Africa, as well as the

remaining non-Western populations, in our study had considerably higher risk of developing the

glycaemia-related complication albuminuria.

CONCLUSIONS

Despite earlier start of glucose-lowering therapies, full access to health care at a minimal cost

and more appointments, immigrants—particularly those of non-Western origin—with type 2

diabetes have substantially higher HbA1c, greater risk of therapy failure and higher probability

of developing albuminuria than native Swedes. The impact of ethnicity on glycaemic control is

greater than the effect of income and educational level, and on a par with the effect of physical

activity. Thus, ethnicity is integral glycaemic control and needs to be carefully considered if

diabetes care is to improve.

Acknowledgements

We would like to thank the various regional NDR coordinators, as well as contributing nurses,

physicians, and patients. The Swedish Diabetes Association, and the Swedish Society of

Diabetology support the NDR. We would also like to thank George Lappas (University of

Gothenburg) and Szilard Nemes (University of Gothenburg) for their valuable statistical guidance.

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Funding

This study was funded by the Swedish National Diabetes Register. The Swedish Association of

Local Authorities and Regions funds the NDR. Additional support: the Swedish Heart and Lung

Foundation, and the Swedish Research Council (SIMSAM) (grants number 2013-5187 and 2013-

4236).

Contributors ARa, AMS, ARo, BZ, BE and SG contributed to the conception and design of the study. ARa

performed statistical analyses and wrote the first draft of the article. ARa, AMS, ARo, BZ, BE and

SG contributed to the interpretation of data. ARa, AMS, ARo, BZ, BE and SG contributed to

critical revision of the article for important intellectual content.

Disclaimer The results and views of the study represent those of the authors only and not necessarily the

official position of the Swedish Medical Products Agency, at which BZ is employed.

Conflicts of interest There are no conflicts of interest to be reported (ARa, AMS, ARo, BZ, BE, SG).

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REFERENCES

1. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HAW (2008) 10-year follow-up of intensive

glucose control in type 2 diabetes. The New England journal of medicine 359: 1577–1589.

doi:10.1056/NEJMoa0806470.

2. ADVANCE Collaborative Group, Patel A, MacMahon S, Chalmers J, Neal B, et al. (2008) Intensive

blood glucose control and vascular outcomes in patients with type 2 diabetes. The New England

journal of medicine 358: 2560–2572. doi:10.1056/NEJMoa0802987.

3. Emerging Risk Factors C, Sarwar N, Gao P, Seshasai SR, Gobin R, et al. (2010) Diabetes mellitus,

fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102

prospective studies. 375: 2215–2222. Available: http://www.ncbi.nlm.nih.gov/pubmed/20609967.

4. Emerging Risk Factors C, Seshasai SR, Kaptoge S, Thompson A, Di Angelantonio E, et al. (2011)

Diabetes mellitus, fasting glucose, and risk of cause-specific death. 364: 829–841. Available:

http://www.ncbi.nlm.nih.gov/pubmed/21366474.

5. Lind M, Bounias I, Olsson M, Gudbjörnsdottir S, Svensson A-M, et al. (2011) Glycaemic control and

incidence of heart failure in 20,985 patients with type 1 diabetes: an observational study. Lancet 378:

140–146. doi:10.1016/S0140-6736(11)60471-6.

6. Fan T, Koro CE, Fedder DO, Bowlin SJ (2006) Ethnic disparities and trends in glycemic control

among adults with type 2 diabetes in the U.S. from 1988 to 2002. Diabetes care 29: 1924–1925.

doi:10.2337/dc05-2238.

7. Harris MI, Eastman RC, Cowie CC, Flegal KM, Eberhardt MS (1999) Racial and ethnic differences

in glycemic control of adults with type 2 diabetes. 22: 403–408. Available:


8. Davis TM, Cull CA, Holman RR, U.K. Prospective Diabetes Study (UKPDS) Group (2001)

Relationship between ethnicity and glycemic control, lipid profiles, and blood pressure during the

first 9 years of type 2 diabetes: U.K. Prospective Diabetes Study (UKPDS 55). Diabetes care 24: 1167–1174.

9. Information UDOP (2013) International migration and development. United Nations Press Release.

10. Ramachandran A, Ma RCW, Snehalatha C (2010) Diabetes in Asia. Lancet 375: 408–418.

doi:10.1016/S0140-6736(09)60937-5.

11. Shai I, Jiang R, Manson JE, Stampfer MJ, Willett WC, et al. (2006) Ethnicity, obesity, and risk of

type 2 diabetes in women: a 20-year follow-up study. 29: 1585–1590. Available:


12. Richard Sicree JSPZ (2013) The Global Burden of Diabetes and Impaired Glucose Tolerance.

International Diabetes Federation.

13. Lutsey PL, Pereira MA, Bertoni AG, Kandula NR, Jacobs DRJ (2010) Interactions between race/ethnicity and anthropometry in risk of incident diabetes: the multi-ethnic study of

atherosclerosis. 172: 197–204. Available: http://www.ncbi.nlm.nih.gov/pubmed/20570825.

14. Narayan KM, Boyle JP, Thompson TJ, Sorensen SW, Williamson DF (2003) Lifetime risk for

diabetes mellitus in the United States. 290: 1884–1890. Available:


15. Davis K, Ballreich J (2014) Equitable access to care--how the United States ranks internationally. The New England journal of medicine 371: 1567–1570. doi:10.1056/NEJMp1406707.

Page 14 of 20


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/B

MJ O


ownloaded from



16. Billing A (n.d.) Health assessment of newly immigrated individuals. Available:

http://www.folkhalsomyndigheten.se/pagefiles/12920/halsoundersokningar-nyanlanda-

invandrare.pdf.

17. Haglind P, editor (2012) Tobacco prevention among immigrants. 2nd ed. Institute of Population Health. Available: http://www.folkhalsomyndigheten.se/documents/livsvillkor-

levnadsvanor/andts/tobak/uppdrag/nationella-tobaksuppdraget/samverkansprojektet/slutrapport-

tobaksprevention-invandrargrupper.pdf.

18. Lind M, Svensson A-M, Kosiborod M, Gudbjörnsdottir S, Pivodic A, et al. (2014) Glycemic Control

and Excess Mortality in Type 1 Diabetes. The New England journal of medicine 371: 1972–1982.

doi:10.1056/NEJMoa1408214.

19. Gudbjornsdottir S, Cederholm J, Nilsson PM, Eliasson B, Steering Committee of the Swedish

National Diabetes R (2003) The National Diabetes Register in Sweden: an implementation of the St.

Vincent Declaration for Quality Improvement in Diabetes Care. 26: 1270–1276. Available:


20. Ekstrom N, Schioler L, Svensson AM, Eeg-Olofsson K, Miao Jonasson J, et al. (2012) Effectiveness

and safety of metformin in 51 675 patients with type 2 diabetes and different levels of renal function:

a cohort study from the Swedish National Diabetes Register. 2. Available: http://www.ncbi.nlm.nih.gov/pubmed/22798258.

21. Cederholm J, Gudbjörnsdottir S, Eliasson B, Zethelius B, Eeg-Olofsson K, et al. (2012) Blood

pressure and risk of cardiovascular diseases in type 2 diabetes: further findings from the Swedish

National Diabetes Register (NDR-BP II). J Hypertens 30: 2020–2030. Available:


22. Lind M, Olsson M, Rosengren A, Svensson AM, Bounias I, et al. (2012) The relationship between

glycaemic control and heart failure in 83,021 patients with type 2 diabetes. Diabetologia 55: 2946–

2953. Available: http://www.ncbi.nlm.nih.gov/pubmed/22895807.

23. Rotimi CN, Jorde LB (2010) Ancestry and disease in the age of genomic medicine. The New England journal of medicine 363: 1551–1558. doi:10.1056/NEJMra0911564.

24. Hoelzel W, Weykamp C, Jeppsson J-O, Miedema K, Barr JR, et al. (2004) IFCC reference system for

measurement of hemoglobin A1c in human blood and the national standardization schemes in the United States, Japan, and Sweden: a method-comparison study. Clin Chem 50: 166–174.

doi:10.1373/clinchem.2003.024802.

25. Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim J-L, et al. (2011) External review and

validation of the Swedish national inpatient register. BMC Public Health 11: 450. doi:10.1186/1471-

2458-11-450.

26. Ingelsson E, Arnlöv J, Sundström J, Lind L (2005) The validity of a diagnosis of heart failure in a

hospital discharge register. Eur J Heart Fail 7: 787–791. doi:10.1016/j.ejheart.2004.12.007.

27. Diggle PJ HPLK-YZS (2002) Analysis of longitudinal data. Oxford University Press 2nd ed.

28. Egede LE, Gebregziabher M, Hunt KJ, Axon RN, Echols C, et al. (2011) Regional, geographic, and racial/ethnic variation in glycemic control in a national sample of veterans with diabetes. Diabetes

care 34: 938–943. doi:10.2337/dc10-1504.

29. Weinstock RS, Teresi JA, Goland R, Izquierdo R, Palmas W, et al. (2011) Glycemic control and health disparities in older ethnically diverse underserved adults with diabetes: five-year results from

the Informatics for Diabetes Education and Telemedicine (IDEATel) study. Diabetes care 34: 274–

279. doi:10.2337/dc10-1346.

Page 15 of 20


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/B

MJ O


ownloaded from



30. Mogensen CE, Poulsen PL (2004) Microalbuminuria, glycemic control, and blood pressure predicting

outcome in diabetes type 1 and type 2. Kidney Int Suppl 66: S40–S41. doi:10.1111/j.1523-

1755.2004.09210.x.

31. Levin SR, Coburn JW, Abraira C, Henderson WG, Colwell JA, et al. (2000) Effect of intensive glycemic control on microalbuminuria in type 2 diabetes. Veterans Affairs Cooperative Study on

Glycemic Control and Complications in Type 2 Diabetes Feasibility Trial Investigators. Diabetes care

23: 1478–1485.

32. Sinha SK, Shaheen M, Rajavashisth TB, Pan D, Norris KC, et al. (2014) Association of

race/ethnicity, inflammation, and albuminuria in patients with diabetes and early chronic kidney

disease. Diabetes care 37: 1060–1068. doi:10.2337/dc13-0013.

33. Jolly SE, Burrows NR, Chen S-C, Li S, Jurkovitz CT, et al. (2010) Racial and ethnic differences in

albuminuria in individuals with estimated GFR greater than 60 mL/min/1.73 m(2): results from the

Kidney Early Evaluation Program (KEEP). Am J Kidney Dis 55: S15–S22.

doi:10.1053/j.ajkd.2009.09.034.

34. Davidson JA, Wolffenbuttel BH, Arakaki RF, Caballero AE, Jiang HH, et al. (2013) Impact of

race/ethnicity on efficacy and safety of two starter insulin regimens in patients with type 2 diabetes: a

posthoc analysis of the DURABLE trial. Ethn Dis 23: 393–400.

35. Ziemer DC, Kolm P, Weintraub WS, Vaccarino V, Rhee MK, et al. (2010) Glucose-independent,

black-white differences in hemoglobin A1c levels: a cross-sectional analysis of 2 studies. Ann Intern

Med 152: 770–777. doi:10.7326/0003-4819-152-12-201006150-00004.

36. Herman WH, Ma Y, Uwaifo G, Haffner S, Kahn SE, et al. (2007) Differences in A1C by race and

ethnicity among patients with impaired glucose tolerance in the Diabetes Prevention Program.

Diabetes care 30: 2453–2457. doi:10.2337/dc06-2003.

37. Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A, et al. (2009) The genetic structure and

history of Africans and African Americans. Science 324: 1035–1044. doi:10.1126/science.1172257.

38. Hare MJL, Magliano DJ, Zimmet PZ, Söderberg S, Joonas N, et al. (2013) Glucose-independent ethnic differences in HbA1c in people without known diabetes. Diabetes care 36: 1534–1540.

doi:10.2337/dc12-1210.

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FIGURE I: PROGRESS OF GLYCEMIC CONTROL FROM TIME OF DIAGNOSIS BY

ETHNICITY/RACE

CAPTION: Annual mean HbA1c from time of diagnosis by ethnicity/race. The red horizontal line in the background

depicts the national target level (52 mmol/mol) set for type 2 diabetes.

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FIGURE II: PROBABILITY OF FAILURE TO ACHIEVE THE TARGET LEVEL OF HBA1C

DURING 2ND YEAR AFTER DIAGNOSIS

CAPTION Probability (odds ratio) of achieving glycemic control (<52 mmol/mol) during the second year after

diagnosis. Adjusted for ethnicity, sex, age, BMI, income, education, smoking status, physical activity and type of glucose

lowering treatment.

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FIGURE III: PROBABILITY OF HAVING ALBUMINURIA DURING THE 2ND YEAR AFTER

DIAGNOSIS

CAPTION: Probability (odds ratio) of having albuminuria during the second year after diagnosis. Adjusted for age, sex,

systolic blood pressure and eGFR.

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Figure A: World map with depicted ethnic categories.

FIGURE B: TIME TO INITIATION OF ANY PHARMACOLOGIC TREATMENT

CAPTION Kaplan-Meier plots showing time to initiation of any pharmacologic therapy by ethnicity/race. Patients

initially on diet & lifestyle modifications where included in the calculation.

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Impact of Ethnicity on Progress of Glycaemic Control in 131,935 Newly Diagnosed Patients with Type 2 Diabetes: a

Nationwide Observational Study from the Swedish National Diabetes Register

Journal: BMJ Open

Manuscript ID: bmjopen-2015-007599.R1

Article Type: Research

Date Submitted by the Author: 31-Mar-2015

Complete List of Authors: Rawshani, Araz; Institute of Medicine, Department of Molecular and Clinical Medicine Svensson, Ann-Marie; Centre of Registers in Region Västra Götaland, Rosengren, Annika; University of Gothenburg, Department of Medicine Zethelius, Bjorn.; Uppsala University, Public Health/Geriatrics Eliasson, Björn; University of Gothenburg, Department of Medicine Gudbjörnsdottir, Soffia; University of Gothenburg, Department of Medicine

<b>Primary Subject Heading</b>:

Diabetes and endocrinology

Secondary Subject Heading: Health policy, Epidemiology, Global health

Keywords:

DIABETES & ENDOCRINOLOGY, Diabetic nephropathy & vascular disease < DIABETES & ENDOCRINOLOGY, General diabetes < DIABETES & ENDOCRINOLOGY, EPIDEMIOLOGY, Diabetes & endocrinology < INTERNAL MEDICINE, Adult nephrology < NEPHROLOGY


BMJ Open on 25 M

arch 2019 by guest. Protected by copyright.

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1

Impact of Ethnicity on Progress of Glycaemic Control in 131,935

Newly Diagnosed Patients with Type 2 Diabetes: a Nationwide

Observational Study from the Swedish National Diabetes Register.

Araz Rawshani1, 2

Ann-Marie Svensson1, 2, 3

Annika Rosengren1, 2

Björn Zethelius4, 5

Björn Eliasson1, 2, 3

Soffia Gudbjornsdottir1, 2, 3

1 Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden

2 Sahlgrenska University Hospital, Gothenburg, Sweden

3 National Diabetes Register, Centre of Registers, Gothenburg, Sweden

4 Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala,

Sweden

5 Medical Products Agency, Epidemiology, Uppsala, Sweden

Corresponding author

Araz Rawshani

[email protected]

National Diabetes Register, Centre of Registers

Västra Götaland Region, 41345 Gothenburg, Sweden

ABSTRACT WORD COUNT 270

MAIN TEXT WORD COUNT 3092

TABLES 2

FIGURES 3

SUPPLEMENTARY MATERIAL 2 Figures

REFERENCES 53

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2

Abstract

Objectives: Studies on ethnic disparities in glycaemic control have been contradictory and

compromised by excessively broad categories of ethnicity and inadequate adjustment for

socioeconomic differences. We aimed to study the effect of ethnicity on glycaemic control in a

large cohort of patients with type 2 diabetes.

Setting: We used nationwide data (mainly from primary care) from the Swedish National Diabetes

Register (2002-2011) to identify patients with newly diagnosed (within 12 months) type 2 diabetes.

Participants: We included 131,935 patients (with 713,495 appointments), representing 10 ethnic groups,

who were followed up to 10 years.

Primary and secondary outcome measures: Progress of HbA1c for up to 10 years was examined.

Mixed models were used to correlate ethnicity with HbA1c (mmol/mol). The effect of glycaemic

disparities was examined by assessing the risk of developing albuminuria. The impact of ethnicity

was compared to that of income, education and physical activity.

Results: Immigrants, particularly those of non-Western origin, received glucose-lowering therapy

earlier, had 30% more appointments but displayed poorer glycaemic control (2–5 mmol/mol higher

HbA1c than native Swedes). Probability of therapy failure was 28–111% higher for non-Western

groups than for native Swedes. High-income Western groups remained below the target-level of

HbA1c for 4–5 years, whereas non-Western populations never reached the target level. These

disparities translated into 51–92% higher risk of developing albuminuria. The impact of ethnicity

was greater than the effect of income and education, and equal to the effect of physical activity.

Conclusions: Despite earlier pharmacological treatment and more frequent appointments,

immigrants of non-Western origin display poorer glycaemic control and this is mirrored in a higher

risk of developing albuminuria.

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3

Strengths and limitations of this study

• To our knowledge, this is the largest study on ethnic differences in progress of glycaemic control.

We included 131,935 patients (with 713,495 appointments) who were followed up to 10 years.

• All major ethnic groups in the world were represented in this study cohort and they had fully equal

access to, and use of, health care, regardless of ethnic and socioeconomic background. Indeed, we

report that immigrants had higher consumption of health care. This contrasts against previous

studies, which have been hampered by inappropriately broad categories of ethnicity, cross-sectional

design, small samples, short follow-up and unequal access to or consumption of health care.

• The longitudinal design, with at least annually updated information on all time-varying variables,

allow for reliable estimates of the effect of ethnicity. We could control adequately for

socioeconomic, demographic and health-related confounders.

• The main findings (immigrants, particularly those of non-Western origin, display poor glycaemic

control and have a considerably higher risk of developing albuminuria) indicate that much can be

done to improve diabetes care for a large proportion of the diabetic population in Western countries.

• When extrapolating our results, it need be taken into account that these ethnic differences could be

expected in an equitable health care system. This should be taken into consideration in countries

were access to health care is not equitable.

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4

Introduction

Tight glucose control in type 2 diabetes has shown long-term beneficial effects on microvascular

complications, cardiovascular disease and mortality [1-5]. Studies of ethnic disparities in glycaemic

control have been contradictory [6-9]. They have, however, been hampered by inappropriately

broad categories of ethnicity, cross-sectional design, small samples, short follow-up and unequal

access to—or consumption of—health care. There are no reliable estimates of the true effect of

ethnicity on glycaemic control.

This issue is important in Western societies that are becoming more ethnically diverse due to

accelerated migration from other areas of the world [10]. The ethnic admixture of Western societies

is currently far more diverse than the risk estimate tools of clinicians are prepared to handle.

Immigrants may be at particular risk due to genetic susceptibility to insulin resistance [11,12],

difficult transitional phases, and rapid changes in diet and lifestyle [13], as well as linguistic,

cultural and financial barriers to obtaining proper health care [14,15].

Sweden is an ethnically heterogeneous country in which all inhabitants enjoy access to every level

of health care at a minimal cost [16]. Immigrants are targeted in an effort to promote healthy

lifestyles and consumption of health care [17,18]. The great majority of Swedes with type 2 diabetes

are included in the Swedish National Diabetes Register, which we used to analyse the impact of

ethnicity on the progress of glycaemic control and on albuminuria as a marker for diabetic

complications.

Methods

Data sources

Swedish authorities manage several nationwide registers, which may be linked through the unique

personal identification number assigned to every Swede. The National Diabetes Register (NDR) has

been described previously [19]. It was launched in 1996 as a care giver tool for local quality assurance

purposes and as a feed-back tool in diabetes care [20]. Data provided by nurses and physicians trained in

register procedures are obtained at visits in outpatient clinics of hospitals and primary care clinics.

Clinical information and various measurements are updated at least once a year.

Patients with at least one entry in the NDR from 1 January 2002 to 31 December 2011 were

included if they had been reported within 12 months of the date of diagnosis. Ninety-six per cent of

the subjects had been diagnosed with type 2 diabetes on the basis of a clinical assessment. The

remainder were included on the basis of the following definition: age 40 or older at the time of

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diagnosis and treated either with diet only, diet combined with oral hypoglycaemic agents (OHA),

or a combination of OHA combined and insulin. This definition has been validated and used

previously [21-23].

Measures

Data on annual income in Swedish kronor, highest educational level regardless of country of

domicile and country of birth (used as proxy for ethnicity/race) were obtained from the

Longitudinal Integration Database for Health Insurance and Labour Market Studies, which is an

official database administered by the Swedish National Board of Health and Welfare. Educational

level was stratified into lower (9 years or less—the length of compulsory education in Sweden),

intermediate (10-12 years—upper secondary) and higher (college or university). Income was

stratified into quintiles (Q1 to Q5), with the highest (Q5) being the reference. Ethnic categories

were based on an appraisal of ancestry and geography, [24] with the exception of the fact that

Nordic countries were classified separately in the present study for the purpose of examining

whether immigrants from neighbouring countries also exhibited differences. Native Swedish

patients served as the reference group. Sixty-two individuals were excluded because information

about their country of birth was unavailable.

Glycaemic control was measured as HbA1c. Analyses were quality assured nationwide by regular

calibration with the HPLC Mono-S method, and then converted to mmol/mol [International

Federation of Clinical Chemistry (IFCC)] [25]. Microalbuminuria was defined as two positive

results for three urine samples obtained within 1 year, with positivity defined as an

albumin:creatinine ratio of 3 to 30 mg per millimole (approximately 30 to 300 mg per gram) or a

urinary albumin clearance of 20 to 200 µg per minute (20 to 300 mg per liter). Macroalbuminuria

was defined as an albumin:creatinine ratio of more than 30 mg per millimole (close to 300 or more

mg per gram) or a urinary albumin clearance of more than 200 µg per minute (>300 mg per liter).

BMI was calculated as weight in kilograms divided by height in metres squared. Systolic blood

pressure was the mean value of two supine readings (Korotkoff 1–5) with a cuff of appropriate size

and after at least 5 minutes of rest. LDL, HDL and total cholesterol were measured in mmol/L.

Physical activity was rated from 1 (never) to 5 (daily). Smoking was coded as present if the patient

currently smoked. Use of lipid lowering medications was dichotomized. Glucose-lowering

treatment was categorized as diet and lifestyle modifications, oral hypoglycaemic agents (OHAs),

insulin, or insulin and OHAs. Antihypertensive medication was dichotomized. Estimated

glomerular filtration rate (eGFR) was calculated by the MDRD equation [26]. A history of

cardiovascular disease (CVD) was defined as previous hospitalization due to acute myocardial

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infarction or stroke (ICD-10, I21, I60–I69; ICD-9, 410, 430–434, 436–438) according to the

Swedish National Discharge Register, which is a reliable validated method [27,28]. All time-

varying variables are updated in the NDR following each appointment.

Analyses and statistics

Baseline

The first observation was used to present characteristics at time of diagnosis (within 12 months).

Continuous variables are reported as means, percentages and quintiles. T tests were used for

continuous variables with native Swedes as the reference group. Chi-square tests were used for

categorical variables. A p-value <0.01 was considered statistically significant but multiple

testing should be considered when interpreting the results.

Glycaemic control

Progress of glycaemic control was calculated as an unadjusted annual mean in relation to

ethnicity. Adjusted figures were calculated using linear regression, to estimate differences in

mmol/mol. Logistic regression was used to estimate the probability of achieving glycaemic

control (i.e. reaching target-level HbA1c <52 mmol/mol) during the second year after diagnosis.

To examine whether hypothesized differences in glycaemia is reflected on the risk of

complications, we calculated the probability of developing albuminuria during the second year

after diagnosis.

Mixed effect models were used to account for repeated measurements on the same unit [29-31].

HbA1c was centered around the mean.

Kaplan-Meier calculations were used to examine whether time to pharmacological treatment

differed among ethnic groups. This was done to examine whether differences in HbA1c could be

explained by disparities in use of medications.

Analyses were performed with SAS version 9.4 (SAS Institute, USA) and R (R Foundation for

Statistical Computing).

Ethics

The study was approved by the regional ethical review board at the University of Gothenburg,

Gothenburg, Sweden. All patients give informed consent before being included in the NDR.

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Results We included 131,935 individuals with newly diagnosed type 2 diabetes. A total of 713,495

appointments were registered. All major ethnic groups were represented in the study (Figure A,

supplementary material). A total of 82.7% of the study population consisted of native Swedes.

Immigrants had more appointments per year. Non-Western immigrants in particular had almost

30% more appointments (Table I).

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TABLE I - CHARACTERISTICS OF 131'935 INDIVIDUALS WITH NEWLY DIAGNOSED TYPE 2 DIABETES BY ETHNICITY/RACE

All

Sw

eden

Nord

ic

cou

ntr

ies

Euro

pe

(hig

h

inco

me)

,

Nort

h

Am

eric

a an

d

Oce

ania

Med

iter

ranea

n B

asi

n

Euro

pe

(low

inco

me)

,

Russ

ia a

nd

Cen

tral

Asi

a

Mid

dle

Eas

t

and N

ort

h

Afr

ica

Su

b-S

ahar

an

Afr

ica

So

uth

Asi

a

Eas

t A

sia

Lat

in

Am

eric

a an

d

Car

ibbea

n

n (%) 131935 109058

(82.66)

6760 (5.12) 3306 (2.51) 511 (0.39) 3870 (2.93) 5031 (3.81) 1127 (0.85) 610 (0.46) 802 (0.61) 860 (0.65)

Males 57 (75083) 57 (62526) 51 (3479) 53 (1751) 70 (360) 54 (2075) 62 (3142) 59 (663)α 56 (344)

α 37 (298) 52 (445)

Age (years)

62.8 (12.5) 63.7 (12.2)

63.8

(10.5)α 64.4 (11.8) 62.5 (10.6)

α 57.6 (11.7) 52.3 (11.2) 47.3 (11.2) 46.0 (11.3) 50.0 (12.8) 54.5 (12.0)

Annual visits 1.73 (2.14) 1.67 (2.0) 1.80 (2.22) 1.96 (2.70) 2.20 (3.24) 2.05 (2.70) 2.30 (3.34) 2.22 (3.42) 2.32 (3.40) 1.91 (2.70) 2.50 (3.20)

BMI (kg/m2) 30.4 (5.5) 30.3 (5.5) 31.2 (5.6) 30.5 (5.3)α 30.1 (4.8)

α 31.3 (5.3) 30.9 (5.2) 28.8 (5.4) 28.6 (5.2) 27.3 (4.3) 31.4 (5.5)

Waist circumference

(cm)

104.7

(13.4) 105.2 (13.5)

106.3

(13.5) 104.2 (12.9) 104.4 (11.8) 105.0 (11.9)

103.6

(11.5)

100.2

(11.4) 97.8 (11.8) 92.4 (11.2) 104.3 (11.7)

HbA1c (mmol/mol)

52.2 (15.4) 51.9 (15.1)

52.3

(15.2)α 52.2 (14.7)

α 52.8 (15.2)

α 54.3 (16.8) 54.9 (17.2) 58.0 (19.8) 55.8 (16.7) 55.9 (17.1) 56.7 (19.8)

Systolic BP (mmHg) 137.5 (17.2)

138.1 (17.1) 139.5 (17.4)

138.2 (17.4)α 134.1 (16.2) 135.2 (18.2) 129.3

(16.4) 126.9 (16.4)

124.8 (14.4)

126.7 (15.9) 130.8 (15.4)

Chol/HDL ratio 4.5 (1.5) 4.4 (1.5) 4.3 (1.5) 4.5 (1.6)α 4.6 (1.4)

α 4.9 (1.7) 4.8 (1.5) 4.6 (1.5) 4.8 (1.4) 4.4 (1.3)

α 4.7 (1.5)

LDL/HDL ratio 2.6 (1.1) 2.6 (1.1) 2.5 (1.1) 2.6 (1.1)α 2.8 (1.1) 2.9 (1.2) 2.9 (1.1) 2.9 (1.1) 2.9 (1.0) 2.6 (1.0)

α 2.7 (1.1)

eGFR (mL/min)

83.4 (25.2) 81.8 (24.0) 83.0 (22.3) 81.2 (21.8)α 87.2 (25.0) 90.4 (37.4) 99.9 (24.9)

124.9

(39.1)

100.0

(23.9) 100.9 (28.0) 96.2 (26.6)

Smoker 15 (19657) 14 (15173) 20 (1341) 17 (556)α 19 (99) 23 (888) 22 (1098) 13 (149)

α 13 (77)

α 15 (122)

α 18 (154)

No physical activity 12 (15973) 12 (12935) 13 (907)α 12 (390)

α 15 (76)

α 13 (497)

α 16 (803) 11 (128)

α 13 (82)

α 8 (66) 10 (89)

α

Daily physical activity 26 (33983) 26 (28207) 24 (1651)α 25 (842)

α 27 (139)

α 31 (1186)

α 22 (1116) 23 (260)

α 23 (142)

α 29 (233)

α 24 (207)

α

INCOME & EDUCATION

Income Q1 (lowest) 20 (24925) 16 (16872) 23 (1439) 27 (852) 23 (113) 41 (1538) 55 (2745) 47 (526) 35 (212) 42 (333) 35 (295)

Income Q5 (highest) 20 (24928) 22 (22068) 15 (928) 14 (452) 14 (69) 11 (398) 10 (485) 15 (165) 17 (101) 14 (113) 18 (149) <9 years education 39 (51890) 40 (43495) 44 (3005) 25 (814) 41 (211) 35 (1366)

α 37 (1883) 33 (376) 24 (148) 42 (335)

α 30 (257)

10-12 years education 43 (56146) 43 (47370) 40 (2734)α 48 (1593) 42 (216) 39 (1516)

α 29 (1444) 35 (399) 44 (267)

α 30 (243) 42 (364)

α

University/college

education 17 (22140) 16 (17811) 12 (790) 24 (793) 13 (68) 15 (576)α 25 (1275) 22 (243) 29 (176) 24 (193) 25 (215)

GLUCOSE-LOWERING TREATMENT

Diet 53 (70445) 54 (59371) 53 (3602) 53 (1763)α 49 (251)

α 48 (1854)

α 44 (2225) 38 (429) 42 (255) 40 (320) 44 (375)

OHA 37 (49261) 36 (39662) 38 (2599)α 38 (1240)

α 43 (219) 43 (1653) 47 (2352) 48 (537) 47 (288) 45 (364) 40 (347)

α

Insulin 5 (6199) 5 (5249) 4 (251)α 4 (148)

α 4 (21)

α 4 (143)

α 3 (164) 7 (84) 5 (30)

α 7 (59) 6 (50)

α

Insulin + OHA 4 (5457) 4 (4318) 4 (284) 4 (136)α 4 (18)

α 5 (202)

α 5 (260) 6 (69) 6 (35)

α 7 (55) 9 (80)

COMPLICATIONS

Albuminuria 14 (17988) 14 (14845) 14 (920)α 14 (453)

α 17 (88)

α 13 (502)

α 14 (701)

α 14 (157)

α 12 (75)

α 13 (106)

α 16 (141)

Previous CVD 20 (26166) 21 (22373) 23 (1555) 23 (759) 19 (98)α 16 (610)

α 11 (547) 4 (46) 8 (47) 6 (48) 10 (83)

Sweden is the reference group for ethnicity. α

No statistically significant difference, at the 0.01 level.

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Characteristics at the time of diagnosis

Table 1 presents characteristics at the time of diagnosis. The proportion of males varied from

37% (East Asia) to 70% (Mediterranean Basin). Patients from high-income Western countries

were as much as 18 years older at the time of diagnosis. Particularly low age at the time of

diagnosis was found for South Asia (46.0), East Asia (50.0) and Sub-Saharan Africa (47.3). BMI

was 2–3 kg/m2 lower among non-Western groups. Native Swedish patients had the lowest mean

HbA1c (51.9 mmol/mol) of all groups. Low-income and non-Western groups had HbA1c

ranging from 54 to 58 mmol/mol. Systolic blood pressure was lower among non-Western

groups. Lipid profiles were less favourable among non-Western groups. There were no major

ethnic differences in terms of physical activity. Smoking rates were higher among patients from

the Middle East, North Africa and low-income Europe. A history of CVD was more common

among Western groups, but non-Western groups were 10–12 years younger at the onset of CVD.

The overall prevalence of albuminuria was approximately 14%, and there were no noteworthy

ethnic differences. College or university education was more common among non-Western

populations, but they were generally in lower income quintiles.

GLYCAEMIC CONTROL – CRUDE FIGURES

HbA1c declined during the first 1–3 years of follow-up and then increased for all ethnic groups

(Figure I). However, there were conspicuous and consistent ethnic differences in HbA1c levels

throughout the study. Swedish patients (the reference category) had the lowest mean HbA1c at

every point in time while patients from high-income Western countries had only slightly higher

levels. Patients from low-income Europe, Russia and Central Asia, as well as all non-Western

populations, had substantially higher HbA1c levels throughout the study. On average, Swedish

and other Nordic populations remained below the target level until the fifth year. Mediterranean

Basin, high-income European, North American and Oceanic populations remained below the

target level until the fourth year, whereas low-income European, Russian and Central Asian

populations remained below only until the second year. The remaining five ethnic groups did not

reach the target level at any point.

GLYCAEMIC CONTROL – ADJUSTED FIGURES

Because there was an interaction (p <0.0001) between ethnicity and glucose-lowering treatment,

we stratified the analysis by type of treatment. Table II presents β coefficients (i.e predicted

difference in mmol/mol HbA1c) by ethnicity. To convey a sense of the impact of ethnicity as

compared to other factors, coefficients for physical activity are also presented.

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Being from Sub-Saharan Africa, South Asia, East Asia, the Middle East and North Africa, Latin

America or low-income Europe, Russia and Central Asia predicted substantially higher HbA1c.

East Asian ethnicity predicted 1.8–3.8 mmol/mol higher HbA1c, depending on the stratum.

Similarly, low-income Europe, Russia and Central Asia predicted 1.2–1.9 mmol/mol higher

HbA1c. Latin America and the Caribbean predicted 1.9–4.8 mmol/mol higher HbA1c. South

Asian origin predicted 1.9–4.2 mmol/mol higher HbA1c. Sub-Saharan Africa predicted 1.6–3.6

mmol/mol higher HbA1c. In comparison, no physical activity predicted 0.8–1.3 mmol/mol

higher HbA1c, as compared to daily physical activity.

TABLE II: PREDICTION OF HBA1C (MMOL/MOL) BY TYPE OF GLUCOSE-LOWERING

TREATMENT

DIET & LIFESTYLE OHA INSULIN (±OHA)

ETHNICITY/RACE

Sweden reference reference reference

East Asia 3.79 (2.59, 5) 1.84 (0.7, 2.97) 2.13 (-0.36, 4.62)

Europe (high-income), North America &

Oceania

0.14 (-0.37, 0.65) 0.19 (-0.41, 0.79) 1.86 (0.42, 3.3)

Europe (low-income), Russia and Central

Asia

1.58 (1.05, 2.11) 1.2 (0.66, 1.74) 1.89 (0.63, 3.16)

Latin America & The Caribbean 1.89 (0.76, 3.02) 2.42 (1.3, 3.54) 4.84 (2.6, 7.07)

Mediterranean Basin 0.57 (-0.74, 1.88) 0.61 (-0.84, 2.05) 1.17 (-2.7, 5.04)

Middle East & North Africa 1.85 (1.37, 2.34) 0.93 (0.46, 1.41) 2.79 (1.58, 4.01)

Nordic countries -0.01 (-0.36, 0.35) 0.19 (-0.22, 0.6) 0.81 (-0.19, 1.8)

South Asia 4.21 (2.85, 5.56) 1.93 (0.6, 3.25) 1.91 (-1.12, 4.94)

Sub-Saharan Africa 3.26 (2.22, 4.3) 3.61 (2.57, 4.64) 1.58 (-0.47, 3.63)

PHYSICAL ACTIVITY

Daily physical activity reference reference reference

3-5 times/week 0.07 (0.01, 0.14) 0.11 (0.01, 0.22) 0.19 (-0.06, 0.45)

1-2 times/week 0.26 (0.19, 0.34) 0.51 (0.4, 0.62) 0.74 (0.46, 1.01)

Less than once / week 0.48 (0.39, 0.57) 1.03 (0.9, 1.17) 1.38 (1.06, 1.7)

No physical activity 0.76 (0.66, 0.86) 1.20 (1.06, 1.35) 1.32 (1.00, 1.65)

Figures are β coefficients (95% CI) that predict the change in HbA1c (mmol/mol).

The effect of physical activity is presented for comparison.

Example of interpretation: after accounting for included covariates, East Asian ethnicity predicts 3.79 mmol/mol higher HbA1c

among persons on diet and lifestyle modifications.

Model adjustments: age, sex, age at onset of diabetes, duration of diabetes, quadratic effect of duration of diabetes, BMI, smoking

status, history of cardiovascular disease, physical activity, income, education, lipid lowering medication and eGFR.

PROBABILITY OF FAILURE TO ACHIEVE THE TARGET LEVEL DURING THE

SECOND YEAR

Low-income Europe, Middle East and North Africa, East Asia, Sub-Saharan Africa and South

Asia displayed substantially higher odds of not achieving the target-level for HbA1c (Figure II).

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Odds ratios (95% CI) for East Asia, Sub-Saharan Africa and South Asia were 1.76 (1.22, 2.54),

1.78 (1.29, 2.45) and 2.11 (1.35, 3.29), respectively. The effects of income and education were

significant, although less pronounced. Not being physically active was associated with 57%

higher odds of failure, as compared to daily physical activity.

PROBABILITY OF HAVING ALBUMINURIA DURING THE SECOND YEAR

Immigrants had 6% to 92% higher odds of having albuminuria (Figure III). The risk of

albuminuria was particularly high (51% to 92% higher risk) in Non-Western and low-income

groups.

TIME TO START OF PHARMACOLOGICAL TREATMENT

Median time to start of pharmacological treatment was shorter for non-Western populations

compared with Western populations (Figure B, supplementary material).

Discussion Our study provides firm evidence that ethnicity is a strong predictor of glycaemic control, on a

par with physical activity. We also show ethnic differences in glycaemia for all major ethnic

groups and how these disparities are mirrored in another important riskmarker, i.e albuminuria.

We believe that our results call for a more individualized management and increased efforts to

eliminate ethnic inequalities.

Glucose control is a cornerstone of diabetes care. Previous studies on ethnic differences in

glycaemic control might be compromised in several ways; unequal access to—or use of—health

care, inappropriately broad categories of ethnicity, cross-sectional design, small samples and

short follow-up are frequent flaws, which might explain contradictory results [6-8].

We examined 131,935 newly diagnosed patients with type 2 diabetes, including 713,495

observations during a decade of monitoring. Our cohort contains the majority of all new cases of

type 2 diabetes in Sweden during the study period. All major ethnic groups were adequately

represented. All participants had full access to every level of health care at a minimal cost. The

Swedish social welfare system fully covers all necessary health care for individuals who do not

have an income. Thus, neither access to nor consumption of health care are likely to have

confounded our results. Swedish authorities frequently target immigrant and disadvantaged

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groups in ways to increase their use of health care and promote health behaviours. This was

reflected in our study by the fact that immigrants had 30% more appointments at their clinic.

We noted marked ethnic differences in HbA1c both at the time of diagnosis and during follow-

up. Immigrants consistently exhibited poorer glycaemic control. High-income Western groups

remained below the target-level of HbA1c for 4–5 years after diagnosis, whereas low-income

Europe, Russia and Central Asian patients maintained the target level for an average of only 3

years. Non-Western populations had substantially higher HbA1c throughout the study and never

reached the guideline target level. Adjusted figures showed 2–5 mmol/mol higher HbA1c among

non-Western groups. These disparities translated into a 28–111% higher risk of not achieving

target level of HbA1c and a 51–92% higher risk of developing albuminuria among non-Western

groups compared with native Swedes. After the end of follow-up, 40–45% of individuals from

high-income Western countries were in glycaemic control, compared to 5%, 25% and 30% for

Sub-Saharan Africa, South Asia and the Middle East and North Africa, respectively. These

differences could not be explained by disparities in instituting glucose-lowering medications, use

or access to health care.

A recent Scottish study compared glycaemic control in Pakistanis, Indians, Chinese and African-

Caribbeans to White Scottish individuals. Unadjusted figures revealed that, compared to the White

Scottish group, all other groups had significantly greater proportions of people with suboptimal

glycaemic control (defined as HbA1c >58 mmol/mol). Adjusted odds ratios for suboptimal

glycaemic control were significantly higher among Pakistanis (odds ratio 1.85 [95% CI 1.68, 2.04])

and Indians (1.62 [95% CI 1.38, 1.89]) [9]. This is in line with our results and we provide additional

details and ethnic groups to this comparison.

The risk of albuminuria was assessed in order to determine whether ethnic differences in

glycaemic control were reflected in the development of diabetes-related complications. Poor

glycaemic control is a main cause of albuminuria and renal lesions in diabetes, making

albuminuria a suitable marker for complications [32,33].

Studies have shown that African Americans and Hispanics have a higher prevalence of

albuminuria compared with Caucasians [34]. Jolly et al reported that this was also true for Asians

[35]. Our study describes the adjusted risk of albuminuria in all major ethnic groups; immigrants,

particularly those of non-Western origin, have a substantially higher risk of developing

albuminuria. This predicts a high future risk of developing cardiovascular disease. It also

suggests that ethnic differences in HbA1c reflect actual differences in glucose-levels. Above all

it underlines the need for ethnic-specific screening and management.

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This study shows that South Asians have the greatest risk of developing albuminuria. Previous

studies have shown that South Asians exhibit poor risk factor control and have an exceptionally

high waist-hip-ratio; [36,37] they are at greater risk of developing diabetes; [38,39] they develop

diabetes earlier in life, [40,41] and they exhibit poor glycaemic control [9,42]. A recent study

demonstrated that the cut-offs for overweight and obesity in South Asians are substantially lower

than cut-offs for white Europeans [43]. Clinicians should be aware of this vulnerability and

intensify risk factor control in South Asians.

The groups with the poorest glycaemic control and greatest risk of albuminuria in our study were

Asia, Sub-Saharan Africa, the Middle East & North Africa, low-income Europe, Russia and

Central Asia. They represent a large and growing proportion of the population in high-income

areas such as North American and the EU. We believe our results can be generalized to

economically developed Western countries. Clinicians and health care planners should be aware

of the challenges posed by immigrants and adjust the management accordingly. Effective

strategies to reduce these health disparities remain elusive and need to be addressed. The

problem might be further complicated by a potential interaction between ethnicity and the

effectiveness of glucose-lowering medications. Although our study was not designed to explore

these associations, we show that there was an effect-modification of ethnicity on glucose-

lowering therapy. A previous studies revealed ethnic differences in the efficacy of insulin, [44]

but there are considerable gaps in the knowledge that is currently available on this topic.

LIMITATIONS

It is important to consider the implications of the use of HbA1c when making ethnic

comparisons. HbA1c is not a direct measure of glycaemia and might be influenced by factors

that are independent of blood glucose levels. Studies have shown that African Americans

average approximately 0.20 percentage points higher HbA1c than Caucasian Americans [45,46].

However, the design of these studies differs from ours. Furthermore, African Americans descend

largely from West Africa. Africans exhibit the greatest genetic diversity of all human

populations [47]. A recent study examined two African populations and showed that their HbA1c

differed; lower levels were found for the East African population [48]. The African population in

our study descends largely from East Africa. Thus, we believe that our results for Sub-Saharan

Africans and the remaining ethnicities should be interpreted as reflecting differences in

glycaemic control. This is underscored by the fact that Sub-Saharan Africa, as well as the

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remaining non-Western populations, in our study had considerably higher risk of developing the

glycaemia-related complication albuminuria.

We created ethnic categories by grouping geographically adjacent countries. One could argue that

this does not measure ethnicity per se. Regrettably there is no consensus regarding criteria for

defining an ethnic group. Many populations, both within and between countries, share

characteristics. Moreover, ethnicity is a changeable concept; neighbouring populations tend to adapt

and adopt each other, which blurs differences. Some individuals identify themselves with several

ethnicities [49]. We defined rather large ethnic categories by assessing each countries ethnical

composition, economic development, history and religion [50,51]. This ought to encircle ethnically

homogenous populations.

CONCLUSIONS

Despite earlier start of glucose-lowering therapies, full access to health care at a minimal cost

and more appointments, immigrants—particularly those of non-Western origin—with type 2

diabetes have substantially higher HbA1c, greater risk of therapy failure and higher probability

of developing albuminuria than native Swedes. The impact of ethnicity on glycaemic control is

greater than the effect of income and educational level, and on a par with the effect of physical

activity. Thus, ethnicity is integral glycaemic control and needs to be carefully considered if

diabetes care is to improve.

Acknowledgements

We would like to thank the various regional NDR coordinators, as well as contributing nurses,

physicians, and patients. The Swedish Diabetes Association, and the Swedish Society of

Diabetology support the NDR. We would also like to thank George Lappas (University of

Gothenburg) and Szilard Nemes (University of Gothenburg) for their valuable statistical guidance.

Funding

This study was funded by the Swedish National Diabetes Register. The Swedish Association of

Local Authorities and Regions funds the NDR. Additional support: the Swedish Heart and Lung

Foundation, and the Swedish Research Council (SIMSAM) (grants number 2013-5187 and 2013-

4236).

Contributors ARa, AMS, ARo, BZ, BE and SG contributed to the conception and design of the study. ARa

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performed statistical analyses and wrote the first draft of the article. ARa, AMS, ARo, BZ, BE and

SG contributed to the interpretation of data. ARa, AMS, ARo, BZ, BE and SG contributed to

critical revision of the article for important intellectual content.

Data sharing The Swedish Data Protection Authority regulates access to the NDR, and any other public database,

through the Personal Data Act. Access to data requires approval from The Regional Ethical Review

Board, the National Board of Health and Welfare, Statistics Sweden and the Swedish National

Diabetes Register. The authors are happy to provide detailed information regarding this.

Competing interests There are no conflicts of interest to be reported (ARa, AMS, ARo, BZ, BE, SG).

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REFERENCES

1 Holman RR, Paul SK, Bethel MA, et al. 10-year follow-up of intensive glucose control in type 2

diabetes. The New England journal of medicine 2008;359:1577–89. doi:10.1056/NEJMoa0806470

2 ADVANCE Collaborative Group, Patel A, MacMahon S, et al. Intensive blood glucose control and

vascular outcomes in patients with type 2 diabetes. The New England journal of medicine

2008;358:2560–72. doi:10.1056/NEJMoa0802987

3 Emerging Risk Factors C, Sarwar N, Gao P, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies.

Lancet 2010;375:2215–22. doi:10.1016/S0140-6736(10)60484-9

4 Emerging Risk Factors C, Seshasai SR, Kaptoge S, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. 2011;364:829–41. doi:10.1056/NEJMoa1008862

5 Lind M, Bounias I, Olsson M, et al. Glycaemic control and incidence of heart failure in 20,985 patients

with type 1 diabetes: an observational study. Lancet 2011;378:140–6. doi:10.1016/S0140-

6736(11)60471-6

6 Fan T, Koro CE, Fedder DO, et al. Ethnic disparities and trends in glycemic control among adults with

type 2 diabetes in the U.S. from 1988 to 2002. Diabetes care 2006;29:1924–5. doi:10.2337/dc05-2238

7 Harris MI, Eastman RC, Cowie CC, et al. Racial and ethnic differences in glycemic control of adults

with type 2 diabetes. 1999;22:403–8.http://www.ncbi.nlm.nih.gov/pubmed/10097918

8 Davis TM, Cull CA, Holman RR, et al. Relationship between ethnicity and glycemic control, lipid profiles, and blood pressure during the first 9 years of type 2 diabetes: U.K. Prospective Diabetes Study

(UKPDS 55). Diabetes care 2001;24:1167–74.

9 Negandhi PH, Ghouri N, Colhoun HM, et al. Ethnic differences in glycaemic control in people with type 2 diabetes mellitus living in Scotland. PLoS ONE 2013;8:e83292.

doi:10.1371/journal.pone.0083292

10 Information UDOP. International migration and development. United Nations Press Release 2013.

11 Ramachandran A, Ma RCW, Snehalatha C. Diabetes in Asia. Lancet 2010;375:408–18.

doi:10.1016/S0140-6736(09)60937-5

12 Shai I, Jiang R, Manson JE, et al. Ethnicity, obesity, and risk of type 2 diabetes in women: a 20-year

follow-up study. Diabetes care 2006;29:1585–90. doi:10.2337/dc06-0057

13 Richard Sicree JSPZ. The Global Burden of Diabetes and Impaired Glucose Tolerance. International

Diabetes Federation 2013.

14 Lutsey PL, Pereira MA, Bertoni AG, et al. Interactions between race/ethnicity and anthropometry in

risk of incident diabetes: the multi-ethnic study of atherosclerosis. 2010;172:197–204.

doi:10.1093/aje/kwq100

15 Narayan KM, Boyle JP, Thompson TJ, et al. Lifetime risk for diabetes mellitus in the United States. 2003;290:1884–90. doi:10.1001/jama.290.14.1884

16 Davis K, Ballreich J. Equitable access to care--how the United States ranks internationally. The New

England journal of medicine 2014;371:1567–70. doi:10.1056/NEJMp1406707

17 Billing A. Health assessment of newly immigrated individuals.

http://www.folkhalsomyndigheten.se/pagefiles/12920/halsoundersokningar-nyanlanda-invandrare.pdf

Page 16 of 29


BMJ Open

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/B

MJ O


ownloaded from



17

18 Haglind P, editor. Tobacco prevention among immigrants. 2nd ed. Institute of Population Health 2012.

http://www.folkhalsomyndigheten.se/documents/livsvillkor-

levnadsvanor/andts/tobak/uppdrag/nationella-tobaksuppdraget/samverkansprojektet/slutrapport-

tobaksprevention-invandrargrupper.pdf

19 Lind M, Svensson A-M, Kosiborod M, et al. Glycemic Control and Excess Mortality in Type 1

Diabetes. The New England journal of medicine 2014;371:1972–82. doi:10.1056/NEJMoa1408214

20 Gudbjornsdottir S, Cederholm J, Nilsson PM, et al. The National Diabetes Register in Sweden: an implementation of the St. Vincent Declaration for Quality Improvement in Diabetes Care.

2003;26:1270–6.http://www.ncbi.nlm.nih.gov/pubmed/12663609

21 Ekström N, Schiöler L, Svensson A-M, et al. Effectiveness and safety of metformin in 51 675 patients

with type 2 diabetes and different levels of renal function: a cohort study from the Swedish National

Diabetes Register. BMJ Open 2012;2:e001076–6. doi:10.1136/bmjopen-2012-001076

22 Cederholm J, Gudbjörnsdottir S, Eliasson B, et al. Blood pressure and risk of cardiovascular diseases in

type 2 diabetes: further findings from the Swedish National Diabetes Register (NDR-BP II). J

Hypertens 2012;30:2020–30. doi:10.1097/HJH.0b013e3283577bdf

23 Lind M, Olsson M, Rosengren A, et al. The relationship between glycaemic control and heart failure in

83,021 patients with type 2 diabetes. Diabetologia 2012;55:2946–53. doi:10.1007/s00125-012-2681-3

24 Rotimi CN, Jorde LB. Ancestry and disease in the age of genomic medicine. The New England journal

of medicine 2010;363:1551–8. doi:10.1056/NEJMra0911564

25 Hoelzel W, Weykamp C, Jeppsson J-O, et al. IFCC reference system for measurement of hemoglobin

A1c in human blood and the national standardization schemes in the United States, Japan, and Sweden:

a method-comparison study. Clin Chem 2004;50:166–74. doi:10.1373/clinchem.2003.024802

26 Levey AS, Bosch JP, Lewis JB, et al. A more accurate method to estimate glomerular filtration rate

from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

Ann Intern Med 1999;130:461–70.

27 Ludvigsson JF, Andersson E, Ekbom A, et al. External review and validation of the Swedish national inpatient register. BMC Public Health 2011;11:450. doi:10.1186/1471-2458-11-450

28 Ingelsson E, Arnlöv J, Sundström J, et al. The validity of a diagnosis of heart failure in a hospital

discharge register. Eur J Heart Fail 2005;7:787–91. doi:10.1016/j.ejheart.2004.12.007

29 Diggle PJ HPLK-YZS. Analysis of longitudinal data. Oxford University Press 2002;2nd ed.

30 Egede LE, Gebregziabher M, Hunt KJ, et al. Regional, geographic, and racial/ethnic variation in

glycemic control in a national sample of veterans with diabetes. Diabetes care 2011;34:938–43.

doi:10.2337/dc10-1504

31 Weinstock RS, Teresi JA, Goland R, et al. Glycemic control and health disparities in older ethnically

diverse underserved adults with diabetes: five-year results from the Informatics for Diabetes Education

and Telemedicine (IDEATel) study. Diabetes care 2011;34:274–9. doi:10.2337/dc10-1346

32 Mogensen CE, Poulsen PL. Microalbuminuria, glycemic control, and blood pressure predicting

outcome in diabetes type 1 and type 2. Kidney Int Suppl 2004;66:S40–1. doi:10.1111/j.1523-1755.2004.09210.x

33 Levin SR, Coburn JW, Abraira C, et al. Effect of intensive glycemic control on microalbuminuria in

type 2 diabetes. Veterans Affairs Cooperative Study on Glycemic Control and Complications in Type 2 Diabetes Feasibility Trial Investigators. Diabetes care 2000;23:1478–85.

Page 17 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



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/B

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ownloaded from



18

34 Sinha SK, Shaheen M, Rajavashisth TB, et al. Association of race/ethnicity, inflammation, and

albuminuria in patients with diabetes and early chronic kidney disease. Diabetes care 2014;37:1060–8.

doi:10.2337/dc13-0013

35 Jolly SE, Burrows NR, Chen S-C, et al. Racial and ethnic differences in albuminuria in individuals with estimated GFR greater than 60 mL/min/1.73 m(2): results from the Kidney Early Evaluation Program

(KEEP). Am J Kidney Dis 2010;55:S15–22. doi:10.1053/j.ajkd.2009.09.034

36 Lean ME, Han TS, Bush H, et al. Ethnic differences in anthropometric and lifestyle measures related to coronary heart disease risk between South Asian, Italian and general-population British women living

in the west of Scotland. Int J Obes Relat Metab Disord 2001;25:1800–5. doi:10.1038/sj.ijo.0801823

37 Forouhi NG, Sattar N, Tillin T, et al. Do known risk factors explain the higher coronary heart disease

mortality in South Asian compared with European men? Prospective follow-up of the Southall and

Brent studies, UK. Diabetologia 2006;49:2580–8. doi:10.1007/s00125-006-0393-2

38 Danaei G, Finucane MM, Lu Y, et al. National, regional, and global trends in fasting plasma glucose

and diabetes prevalence since 1980: systematic analysis of health examination surveys and

epidemiological studies with 370 country-years and 2·7 million participants. Lancet 2011;378:31–40.

doi:10.1016/S0140-6736(11)60679-X

39 Bellary S, O'Hare JP, Raymond NT, et al. Premature cardiovascular events and mortality in south Asians with type 2 diabetes in the United Kingdom Asian Diabetes Study - effect of ethnicity on risk.

Curr Med Res Opin 2010;26:1873–9. doi:10.1185/03007995.2010.490468

40 Mukhopadhyay B, Forouhi NG, Fisher BM, et al. A comparison of glycaemic and metabolic control

over time among South Asian and European patients with Type 2 diabetes: results from follow-up in a

routine diabetes clinic. Diabetic medicine : a journal of the British Diabetic Association 2006;23:94–8.

doi:10.1111/j.1464-5491.2005.01735.x

41 Ethnicity and cardiovascular disease. The incidence of myocardial infarction in white, South Asian, and

Afro-Caribbean patients with type 2 diabetes (U.K. Prospective Diabetes Study 32). Diabetes care

1998;21:1271–7.

42 Fischbacher CM, Bhopal R, Steiner M, et al. Is there equity of service delivery and intermediate

outcomes in South Asians with type 2 diabetes? Analysis of DARTS database and summary of UK

publications. J Public Health (Oxf) 2009;31:239–49. doi:10.1093/pubmed/fdp003

43 Ntuk UE, Gill JMR, Mackay DF, et al. Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional

study of 490,288 UK biobank participants. Diabetes care 2014;37:2500–7. doi:10.2337/dc13-2966

44 Davidson JA, Wolffenbuttel BH, Arakaki RF, et al. Impact of race/ethnicity on efficacy and safety of

two starter insulin regimens in patients with type 2 diabetes: a posthoc analysis of the DURABLE trial.

Ethn Dis 2013;23:393–400.

45 Ziemer DC, Kolm P, Weintraub WS, et al. Glucose-independent, black-white differences in

hemoglobin A1c levels: a cross-sectional analysis of 2 studies. Ann Intern Med 2010;152:770–7.

doi:10.7326/0003-4819-152-12-201006150-00004

46 Herman WH, Ma Y, Uwaifo G, et al. Differences in A1C by race and ethnicity among patients with impaired glucose tolerance in the Diabetes Prevention Program. Diabetes care 2007;30:2453–7.

doi:10.2337/dc06-2003

47 Tishkoff SA, Reed FA, Friedlaender FR, et al. The genetic structure and history of Africans and African Americans. Science 2009;324:1035–44. doi:10.1126/science.1172257

48 Hare MJL, Magliano DJ, Zimmet PZ, et al. Glucose-independent ethnic differences in HbA1c in people

without known diabetes. Diabetes care 2013;36:1534–40. doi:10.2337/dc12-1210

Page 18 of 29


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19

49 Braun L, Fausto-Sterling A, Fullwiley D, et al. Racial categories in medical practice: how useful are

they? PLoS Med 2007;4:e271. doi:10.1371/journal.pmed.0040271

50 The Central Intelligence Agency. The World Fact Book. https://www.cia.gov/library/publications/the-

world-factbook/ (accessed 7 Mar2015).

51 Bank TW. Classification of Countries. http://www.worldbank.org/en/country (accessed 7 Mar2015).

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FIGURE I: PROGRESS OF GLYCEMIC CONTROL FROM TIME OF DIAGNOSIS BY

ETHNICITY/RACE

CAPTION: Annual mean HbA1c from time of diagnosis by ethnicity/race. The red horizontal line in the background depicts the national target level (52 mmol/mol) set for type 2 diabetes.

FIGURE II: PROBABILITY OF FAILURE TO ACHIEVE THE TARGET LEVEL OF

HBA1C DURING 2ND YEAR AFTER DIAGNOSIS

CAPTION Probability (odds ratio) of achieving glycemic control (<52 mmol/mol) during the second year

after diagnosis. Adjusted for ethnicity, sex, age, BMI, income, education, smoking status, physical activity

and type of glucose lowering treatment.

FIGURE III: PROBABILITY OF HAVING ALBUMINURIA DURING THE 2ND YEAR

AFTER DIAGNOSIS

CAPTION: Probability (odds ratio) of having albuminuria during the second year after diagnosis. Adjusted

for age, sex, systolic blood pressure and eGFR.

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Annual mean HbA1c from time of diagnosis by ethnicity/race. The red horizontal line in the background depicts the national target level (52 mmol/mol) set for type 2 diabetes.

162x127mm (300 x 300 DPI)

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Probability (odds ratio) of achieving glycemic control (<52 mmol/mol) during the second year after diagnosis. Adjusted for ethnicity, sex, age, BMI, income, education, smoking status, physical activity and

type of glucose lowering treatment. 176x132mm (300 x 300 DPI)

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Probability (odds ratio) of having albuminuria during the second year after diagnosis. Adjusted for age, sex, systolic blood pressure and eGFR.

94x37mm (300 x 300 DPI)

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Supplementary material

Impact of Ethnicity on Progress of Glycaemic Control in 131,935 Newly Diagnosed Patients with Type 2 Diabetes: a Nationwide Observational Study from the Swedish National Diabetes Register.

Araz Rawshani, Ann-Marie Svensson, Annika Rosengren, Björn Zethelius, Björn Eliasson, Soffia

Gudbjornsdottir

Figure A: World map with depicted ethnic categories.

Sweden (reference)

Europe (high income),North America & Oceania

Nordic countries Mediterranean Basin

Latin America & The Caribbean South Asia

Middle East & North Africa Sub-Saharan Africa

East Asia

Europe (low income),Russia and Central Asia

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FIGURE B: TIME TO INITIATION OF ANY PHARMACOLOGIC TREATMENT

CAPTION Kaplan-Meier plots showing time to initiation of any pharmacologic therapy by ethnicity/race. Patients initially on diet & lifestyle modifications where included in the calculation.

0,0

0,2

0,4

0,6

0,8

1,0

0 500 1000 1500 2000 2500 3000 3500

East AsiaEurope (High income), North America Europe (Low income), Russia and CenLatin America & CaribbeanMediterranean BasinMiddle East & North AfricaNordic countriesSouth AsiaSub-Saharan AfricaSweden

10361144940929110087213569959241331

869 -1062 -850 -736 -903 -792 -1254 -670 -746 -1309 -

11361265

1033106512809421434

111611411351

Log-Rank P < 0.0001

Prob

abili

ty b

eing

on

phar

mac

olog

ic tr

eatm

ent

Time (days) since diagnosis

Median 95% CI

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STROBE Statement—Checklist of items that should be included in reports of cohort studies

Manuscript:

Impact of Ethnicity on Progress of Glycaemic Control in 131,935 Newly Diagnosed Patients

with Type 2 Diabetes: a Nationwide Observational Study from the Swedish National Diabetes

Register

Item

No Recommendation

Reference to manuscript

Title and abstract 1 (a) Indicate the study’s design with a

commonly used term in the title or the

abstract

The title is:

“Impact of Ethnicity on Progress of

Glycaemic Control in 131,935 Newly

Diagnosed Patients with Type 2

Diabetes: a Nationwide Observational

Study from the Swedish National

Diabetes Register”

Refer to page 1 in manuscript.

(b) Provide in the abstract an informative

and balanced summary of what was done

and what was found

This has been carried out.

Refer to page 2 in the manuscript.

Introduction

Background/rationale 2 Explain the scientific background and

rationale for the investigation being

reported

This has been explained on page 4.

Objectives 3 State specific objectives, including any

prespecified hypotheses

This has been explained on pages 2

(Abstract) and 4 (Introduction).

Methods

Study design 4 Present key elements of study design

early in the paper

This has been described in the last

paragraph of “Abstract” and, in more

detail, in the four first paragraphs of

“Methods”. Refer to pages 4 and 5.

Setting 5 Describe the setting, locations, and

relevant dates, including periods of

recruitment, exposure, follow-up, and

data collection

This is described on pages 4 and 5.

Participants 6 (a) Give the eligibility criteria, and the

sources and methods of selection of

participants. Describe methods of

follow-up

Eligibility criteria are described in the

second paragraph of “Methods”.

(b) For matched studies, give matching

criteria and number of exposed and

unexposed

Not applicable/relevant.

Variables 7 Clearly define all outcomes, exposures,

predictors, potential confounders, and

effect modifiers. Give diagnostic criteria,

if applicable

Outcomes are described on page 6.

All confounders and covariates are

described on page 5 (“Methods” section,

the “Measures” paragraph).

Data sources/ 8* For each variable of interest, give The data sources are described in the two

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2

measurement sources of data and details of methods of

assessment (measurement). Describe

comparability of assessment methods if

there is more than one group

first paragraphs of “Methods”, under the

headline “ Data sources”. Refer to page 4

and 5.

Bias 9 Describe any efforts to address potential

sources of bias

Confounders and covariates are

addressed by use of multiple regression,

as explained in the “Methods” as well as

in the “Results”.

Study size 10 Explain how the study size was arrived

at

This is described in paragraph 1 and 2 of

“Methods”. The study includes over

100,000 individuals and should therefore

be adequately powered.

Quantitative variables 11 Explain how quantitative variables were

handled in the analyses. If applicable,

describe which groupings were chosen

and why

This is described on page 5.

Statistical methods 12 (a) Describe all statistical methods,

including those used to control for

confounding

This is described on page 6.

(b) Describe any methods used to

examine subgroups and interactions

We have described, in the ”Results”,

that the linear model had to be

stratified by type of diabetes

treatment. Refer to page 8, under the

headline: “GLYCAEMIC

CONTROL – ADJUSTED

FIGURES”, where this is explained.

(c) Explain how missing data were

addressed

We have explained that mixed models

were used to model the data; these

models account for missing data. Refer

to the headline “Glycaemic control” on

page 6.

(d) If applicable, explain how loss to

follow-up was addressed

Not applicable

(e) Describe any sensitivity analyses None performed

Results

Participants 13* (a) Report numbers of individuals at

each stage of study—eg numbers

potentially eligible, examined for

eligibility, confirmed eligible, included

in the study, completing follow-up, and

analysed

We have explained under the headline

“Data sources” (see “Methods” section)

that we included all individuals with

type 2 diabetes, provided that their time

of diagnosis was recorded. Everyone

completes follow-up since only 1

observation is required for inclusion and

analysis. This is described under the

same headline.

(b) Give reasons for non-participation at

each stage


(c) Consider use of a flow diagram Judged not to be necessary but will

certainly be provided if BMJ Open

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requires it.

Descriptive data 14* (a) Give characteristics of study

participants (eg demographic, clinical,

social) and information on exposures and

potential confounders

Table 1 page 7. First paragraph page 8.

(b) Indicate number of participants with

missing data for each variable of interest

Refer to 12 c above.

(c) Summarise follow-up time (eg,

average and total amount)

This cohort study did not follow time

until an event or endpoint. Follow-up is

conveyed by Figure 1 and Table 1

(variable: annual visits). A formal

follow-up variable will be added if BMJ

Open requires it.

Outcome data 15* Report numbers of outcome events or

summary measures over time


Main results 16 (a) Give unadjusted estimates and, if

applicable, confounder-adjusted

estimates and their precision (eg, 95%

confidence interval). Make clear which

confounders were adjusted for and why

they were included

This is described in Table 1, as well

as under the headline “GLYCAEMIC

CONTROL – CRUDE FIGURES”

on page 8.

(b) Report category boundaries when

continuous variables were categorized


(c) If relevant, consider translating

estimates of relative risk into absolute

risk for a meaningful time period


Other analyses 17 Report other analyses done—eg analyses

of subgroups and interactions, and

sensitivity analyses


Discussion

Key results 18 Summarise key results with reference to

study objectives

Key findings are presented in the first

paragraphs of the discussion: page 10.

Limitations 19 Discuss limitations of the study, taking

into account sources of potential bias or

imprecision. Discuss both direction and

magnitude of any potential bias

Refer to the headline “Limitations” on

page 11.

Interpretation 20 Give a cautious overall interpretation of

results considering objectives,

limitations, multiplicity of analyses,

results from similar studies, and other

relevant evidence

Refer to page 12, headline:

“Conclusions”.

Generalisability 21 Discuss the generalisability (external

validity) of the study results

Refer to the second paragraph on page

11.

Other information

Funding 22 Give the source of funding and the role

of the funders for the present study and,

if applicable, for the original study on

which the present article is based

Page 12.

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*Give information separately for exposed and unexposed groups.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and

published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely

available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is

available at http://www.strobe-statement.org.

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BMJ Open · the mean value of two supine readings (Korotkoff 1–5) with a cuff of appropriate size and after at least 5 minutes of rest. LDL, HDL and total cholesterol were measured