Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trial.
Free full text in Europe PMC
Abstract
Background
Intensive treatment of multiple cardiovascular risk factors can halve mortality among people with established type 2 diabetes. We investigated the effect of early multifactorial treatment after diagnosis by screening.Methods
In a pragmatic, cluster-randomised, parallel-group trial done in Denmark, the Netherlands, and the UK, 343 general practices were randomly assigned screening of registered patients aged 40-69 years without known diabetes followed by routine care of diabetes or screening followed by intensive treatment of multiple risk factors. The primary endpoint was first cardiovascular event, including cardiovascular mortality and morbidity, revascularisation, and non-traumatic amputation within 5 years. Patients and staff assessing outcomes were unaware of the practice's study group assignment. Analysis was done by intention to treat. This study is registered with ClinicalTrials.gov, number NCT00237549.Findings
Primary endpoint data were available for 3055 (99·9%) of 3057 screen-detected patients. The mean age was 60·3 (SD 6·9) years and the mean duration of follow-up was 5·3 (SD 1·6) years. Improvements in cardiovascular risk factors (HbA(1c) and cholesterol concentrations and blood pressure) were slightly but significantly better in the intensive treatment group. The incidence of first cardiovascular event was 7·2% (13·5 per 1000 person-years) in the intensive treatment group and 8·5% (15·9 per 1000 person-years) in the routine care group (hazard ratio 0·83, 95% CI 0·65-1·05), and of all-cause mortality 6·2% (11·6 per 1000 person-years) and 6·7% (12·5 per 1000 person-years; 0·91, 0·69-1·21), respectively.Interpretation
An intervention to promote early intensive management of patients with type 2 diabetes was associated with a small, non-significant reduction in the incidence of cardiovascular events and death.Funding
National Health Service Denmark, Danish Council for Strategic Research, Danish Research Foundation for General Practice, Danish Centre for Evaluation and Health Technology Assessment, Danish National Board of Health, Danish Medical Research Council, Aarhus University Research Foundation, Wellcome Trust, UK Medical Research Council, UK NIHR Health Technology Assessment Programme, UK National Health Service R&D, UK National Institute for Health Research, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Novo Nordisk, Astra, Pfizer, GlaxoSmithKline, Servier, HemoCue, Merck.Free full text
Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trial
Summary
Background
Intensive treatment of multiple cardiovascular risk factors can halve mortality among people with established type 2 diabetes. We investigated the effect of early multifactorial treatment after diagnosis by screening.
Methods
In a pragmatic, cluster-randomised, parallel-group trial done in Denmark, the Netherlands, and the UK, 343 general practices were randomly assigned screening of registered patients aged 40–69 years without known diabetes followed by routine care of diabetes or screening followed by intensive treatment of multiple risk factors. The primary endpoint was first cardiovascular event, including cardiovascular mortality and morbidity, revascularisation, and non-traumatic amputation within 5 years. Patients and staff assessing outcomes were unaware of the practice's study group assignment. Analysis was done by intention to treat. This study is registered with ClinicalTrials.gov, number NCT00237549.
Findings
Primary endpoint data were available for 3055 (99·9%) of 3057 screen-detected patients. The mean age was 60·3 (SD 6·9) years and the mean duration of follow-up was 5·3 (SD 1·6) years. Improvements in cardiovascular risk factors (HbA1c and cholesterol concentrations and blood pressure) were slightly but significantly better in the intensive treatment group. The incidence of first cardiovascular event was 7·2% (13·5 per 1000 person-years) in the intensive treatment group and 8·5% (15·9 per 1000 person-years) in the routine care group (hazard ratio 0·83, 95% CI 0·65–1·05), and of all-cause mortality 6·2% (11·6 per 1000 person-years) and 6·7% (12·5 per 1000 person-years; 0·91, 0·69–1·21), respectively.
Interpretation
An intervention to promote early intensive management of patients with type 2 diabetes was associated with a small, non-significant reduction in the incidence of cardiovascular events and death.
Funding
National Health Service Denmark, Danish Council for Strategic Research, Danish Research Foundation for General Practice, Danish Centre for Evaluation and Health Technology Assessment, Danish National Board of Health, Danish Medical Research Council, Aarhus University Research Foundation, Wellcome Trust, UK Medical Research Council, UK NIHR Health Technology Assessment Programme, UK National Health Service R&D, UK National Institute for Health Research, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Novo Nordisk, Astra, Pfizer, GlaxoSmithKline, Servier, HemoCue, Merck.
Introduction
Type 2 diabetes mellitus is common, expensive to manage, and associated with a substantial burden of morbidity and mortality, particularly owing to cardiovascular complications.1 Risk of cardiovascular events and death can be halved among patients with longstanding diabetes and microalbuminuria by intensive multifactorial treatment.2,3 Treatment of individual risk factors, such as blood pressure,4,5 cholesterol,6 and glucose,7 is effective. Outcomes might be improved if this approach were used early in the course of the disease.8 The effect of starting multifactorial treatment from the time of diagnosis is unknown.
Type 2 diabetes is detectable well before it is clinically diagnosed9 and many patients already have evidence of diabetic complications and potentially modifiable cardiovascular risk factors at the time of diagnosis.10 Early detection by screening is not associated with harmful psychological effects11 and, therefore, diabetes meets many suitability criteria for screening.12 Modelling studies have indicated that screening would be an efficient use of resources,13 but there are several critical uncertainties that have prevented its routine widespread implementation.14 No evidence from trials is available to show whether early intensive multifactorial treatment improves outcomes when started between detection by screening and clinical diagnosis. We did the multicentre Anglo-Danish-Dutch Study of Intensive Treatment In People with Screen Detected Diabetes in Primary Care (ADDITION-Europe) to investigate this issue.
Methods
Design
The study design and rationale have been reported.15–18 Briefly, ADDITION-Europe consisted of two phases: a screening phase and a pragmatic, cluster-randomised, parallel-group trial in Denmark, the Netherlands, and the UK (in Cambridge and Leicester). The study was approved by the ethics committee local to each study centre. All participating patients provided informed consent.
General practices in the four study areas within a maximum of 100 miles of the study centres were invited to participate according to previously reported inclusion criteria.17–20 Between April, 2001, and December, 2006, eligible practices undertook population-based, stepwise screening of registered patients aged 40–69 years (50–69 years in the Netherlands) without known diabetes.17–20 Screening involved calculation of a risk score from information in general practice medical records in Cambridge or calculation of a risk score from self-completed questionnaires in Denmark and the Netherlands, followed by capillary glucose testing, or by invitation to attend for an oral glucose tolerance test without previous risk assessment in Leicester.
Individuals at risk were assessed in general practice, and those diagnosed as having type 2 diabetes according to WHO criteria,21 including the requirement for confirmatory testing on a separate occasion, were included in the study. Exclusion criteria for patients were assessed by family physicians. They were illness with a life expectancy of less than 12 months or psychological or psychiatric disorders that might invalidate informed consent, or being housebound or pregnant, or lactation.
Randomisation
The practices were randomly assigned to provide routine diabetes care or intensive multifactorial treatment in a 1:1 ratio, by statisticians in each centre, according to a computer-generated list, independent of measurement teams.
In Denmark, randomisation included stratification by county and number of full-time family physicians. In the Netherlands, practices were stratified by single-handed or group status. In the UK, randomisation included minimisation for the local district hospital, the number of patients per practice with diabetes in Cambridge, and for practice demographics, deprivation status, and prevalence of type 2 diabetes in Leicester. Patients were unaware of their general practice's group assignment throughout the study.
Intervention
The characteristics of the intensive treatment intervention, which involved the addition of several features to existing diabetes care, have been described previously,15–18 and the methods used to educate and support staff in providing intensive treatment are summarised in table 1 and on the study website. Target-driven and guideline-driven management of hyperglycaemia, blood pressure, and cholesterol levels by medical treatment and promotion of healthy lifestyles, based on the stepwise regimen used in the Steno-2 study2 and other trial data obtained from people with type 2 diabetes,4–6,26 was added to routine care (table 2). The same approach was used across all centres, although final decisions about prescriptions, including choice of individual drugs, were made by family physicians and patients.
Table 1
Characteristics of screening programmes, intervention delivery, and outcome ascertainment, by study centre
| Screening programme | Intervention delivery | Outcome ascertainment | |
|---|---|---|---|
| Cambridge, UK | Electronic medical records of patients aged 40–69 years searched for routinely collected information to enable calculation of Cambridge diabetes risk scores22 Individuals with scores ≥0·17 invited by their family physcian to participate in a stepwise screening programme, including capillary RBG, FBG, and HbA1c tests | Practice-based educational meetings held with family physicians and nurses to discuss treatment targets, algorithms, and lifestyle advice Audit and feedback via follow-up practice-based meetings up to twice per year Practice staff provided with educational materials for patients Small financial incentives given to family physicians* | Participants tagged for mortality with the Office for National Statistics Sensitive electronic searches of general practice records undertaken between March, 2009, and February, 2010, in which possible events highlighted and copies made of medical records Additional information obtained from hospital medical records and coroners' offices, as required |
| Denmark | Patients aged 40–69 years sent letters that included questions from the Danish Diabetes Risk Score Questionnaire23 and advised recipients with scores ≥5 (high risk) to arrange an appointment with family physician for assessment, including RBG, FBG, and HbA1c tests, or patients attending family physician practice asked to complete risk score questionnaire and those with scores ≥5 underwent blood glucose tests | Small group or practice-based educational meetings with family physicians and nurses to discuss treatment targets, algorithms, and lifestyle advice Audit and feedback included in follow-up group meetings up to twice per year or coordinated by post Practice staff provided with educational materials for patients Small financial incentives given to family physicians* Patients sent reminders if annual assessments overdue | National patient register searched on Dec 31, 2009, for deaths, ICD-10 codes for cardiovascular events (I08–77), and surgical amputations and revascularisations. For possible events information obtained from hospital medical records and coroners' offices, as required |
| Leicester, UK | All patients aged 40–69 years invited directly for an OGTT in a local testing facility | Patients referred to the DESMOND structured education programme24 Patients offered appointments every 2 months with a diabetes nurse or family physician, in a community clinic, for 1 year, and every 4 months thereafter Clinic staff prompted to contact patients who missed appointments Small financial incentives given to family physicians* | Participants tagged for mortality with the Office for National Statistics Sensitive electronic searches of general practice records undertaken between March, 2009, and February, 2010, in which possible events highlighted and copies made of medical records Additional information obtained from hospital medical records and coroners' offices, as required |
| Netherlands | Patients aged 50–69 years sent letters from family physicians that included the Hoorn study Symptom Risk Questionnaire25 and advised recipients with scores ≥4 (41 practices closest to study centre) or ≥6 (38 practices furthest from study centre) to arrange an appointment with their family physician for assessment Attending patients assessed with RGB, FBG, and OGTT (41 practices closest to study centre) or a FBG and OGTT (38 practices furthest from study centre) | Small group or practice-based educational meetings with family physicians and nurses to discuss treatment targets, algorithms, and lifestyle advice Audit and feedback included in follow-up meetings up to twice per year or coordinated by post Patients seen by diabetes nurses authorised to prescribe medication and adjust doses under supervision by family physicians Patients sent reminders if annual assessments overdue Small financial incentives given to family physicians* | General practice records searched manually and extracted endpoint and vital status information recorded on standard forms For patients who had moved practice, endpoint data obtained by telephone interview with current family physician |
RBG=random blood glucose. FBG=fasting blood glucose. HbA1c=glycated haemoglobin A1c. ICD-10=International Classification of Diseases, version ten. OGTT=oral glucose tolerance test. DESMOND=diabetes education and self management for ongoing and newly diagnosed programme.
Table 2
Treatment recommendations and targets in the intensive treatment group
| Treatment target | Treatment threshold | Approach at baseline if threshold passed | Action at review | |||
|---|---|---|---|---|---|---|
| Review 1 | Review 2 | Review 3 | ||||
| HbA1c | <7·0% | >6·5% | Diet | Value >threshold, prescribe metformin | Value >threshold, increase metformin dose or add a PGR, sulphonylurea, or TZD | Value >threshold, add second or third medication (PGR or sulphonurea, or TZD) and consider adding insulin |
| Blood pressure | ≤135/85 mm Hg* | ≥120/80 mm Hg | If CVD+, prescribe an ACE inhibitor titrated to maximum dose | Value >target, add thiazide diuretic or calcium-channel blocker | Value >target, add calcium-channel blocker or thiazide diuretic | Value >target, add β blocker or α blocker |
| Total cholesterol level without IHD | <5·0 mmol/L | ≥3·5 mmol/L | Prescribe a statin | Value >target, increase statin dose up to maximum | Value >target, increase statin dose up to maximum | Value >target, consider adding a fibrate |
| Cholesterol level with IHD | <4·5 mmol/L | ≥3·5 mmol/L | Prescribe a statin | Value >target, increase statin up to maximum dose | Value >target, continue statin titration if maximum dose not reached | Value >target, consider adding a fibrate |
| Aspirin† | None | None | 75–80 mg daily | 75–80 mg daily | 75–80 mg daily | 75–80 mg daily |
HbA1c=glycated haemoglobin A1c. PGR=prandial glucose regulator. TZD=thiazolidinedione. ACE=angiotensin-converting enzyme. IHD ischaemic heart disease, CVD+=cardiovascular event or presence of cardiovascular risk factor other than diabetes. BP=blood pressure.
Intensive treatment was promoted to the participating general practices by small group or practice-based educational meetings attended by family physicians and nurses to discuss the treatment targets and algorithms and lifestyle advice and provide supporting evidence. Audit and feedback were done in follow-up meetings held up to twice per year or were coordinated by post. All participating practices received additional funding to support the delivery of care (up to the equivalent of three 10 min consultations with a family physician and three 15 min consultations with a nurse, per patient, per year for 3 years).
Family physicians were advised to consider prescribing angiotensin-converting-enzyme inhibitors for patients who had blood pressure of 120/80 mm Hg or higher, a cardiovascular risk factor other than diabetes, or who had had a previous cardiovascular event,5 and 75 mg aspirin daily for patients who had no specific contraindications. After publication of the Heart Protection Study27 we altered the treatment algorithm to recommend prescription of a statin to all patients with a cholesterol concentration of 3·5 mmol/L or higher at any time.
For the routine care group, family physicians were only provided with diagnostic test results. Patients received standard diabetes care, according to the recommendations applicable in each centre.28–30
Outcomes
The primary endpoint was a composite of first cardiovascular event, including cardiovascular mortality, cardiovascular morbidity (non-fatal myocardial infarction and non-fatal stroke), revascularisation, and non-traumatic amputation. The secondary outcomes were the individual components of the primary endpoint and all-cause mortality. Centrally trained staff assessed patients' health at baseline and after 5 years by collection of data on biochemical and anthropometric features and use of questionnaires to assess activities, including use of medication, according to standard operating procedures (see webappendix p 1). Additionally, information on prescribed medication, and data potentially linked to the endpoints were obtained from general practice records or national registers. All staff collecting data from records and undertaking health assessments were unaware of patients' treatment group allocations. For each endpoint possibly met, the relevant clinical information (eg, death certificates, post-mortem reports, medical records, hospital discharge summaries, electrocardiographs, laboratory results, etc) were sent to two members of the expert committee within the country for independent adjudication according to an agreed protocol. Committee members were unaware of treatment allocations. Outcomes were recorded on standard case report forms. Committee members met to reach consensus over discrepancies.
The date of completion of follow-up for the primary endpoint was deemed to be the date of the first primary endpoint event, the 5-year assessment if no event occurred, or the date that the endpoint search was undertaken if the participant did not experience an event or attend follow-up.
Statistical analysis
The analysis plan was finalised before preparation of the endpoint dataset, and analysis was by intention to treat. We summarised baseline characteristics of individuals and general practices within each randomised group, by centre, and overall. We plotted the cumulative probability of the primary endpoint. To assess intervention effects we used Cox's regression to estimate hazard ratios (HRs) and 95% CI within each centre. In Leicester there were few participants and events and, therefore, we combined the endpoint data with those for Cambridge. In view of randomisation being at the practice level, we calculated robust SE that took into account the two-level structure of the data and any potential correlation between the individuals within practices. We calculated the correlation coefficients within practices for the primary endpoint. Centre-specific log HRs and SE were combined with a fixed effects meta-analysis, and we calculated the I2 statistic to represent the proportion of variability (in log HRs) between centres owing to heterogeneity. We tested the proportional hazards assumption by including a parameter for treatment by time interaction in each centre-specific Cox's regression model.
Continuous intermediate endpoints were analysed within each centre with normal errors regression, with adjustment for the endpoint baseline values; individuals who died or who were lost to follow-up were excluded from this analysis. We calculated robust SE. We combined the estimated differences in mean change from baseline across centres with fixed effects meta-analysis. In the regression models, we included individuals with missing outcome values at baseline, according to the missing indicator method,31 and variables with a skewed distribution were log transformed. We estimated the effect of the intervention on prescribing endpoints within each centre by use of logistic regression, and combined the estimated odds ratios across centres with fixed effects meta-analysis. We undertook sensitivity analyses by excluding follow-up clinical data obtained from general practice records, and did prespecified subgroup analyses for the primary endpoint by including interaction terms between intervention group, patient's age, and self-reported history of cardiovascular disease, which were then pooled across centres. The cumulative incidence of the composite cardiovascular endpoint was calculated with the method for competing risks described by Gooley and colleagues,32 the competing events here being the primary endpoint and death from non-cardiovascular causes. For total mortality, Kaplan-Meier estimates of cumulative incidence were calculated. Analyses were done with STATA (version 11).
We calculated that a patient-level randomised trial would have required enrolment of 2700 individuals (1350 per treatment group) to detect a 30% reduction in the risk of the primary endpoint at a 5% significance level, and with 90% power. This calculation allowed for 10% loss to follow-up and assumed an event rate in the routine care group of 3% per year, on the basis of the results of the UK Prospective Diabetes Study Group (UKPDS).26 We expected a minimum effect of clustering within general practice, with the estimated within-cluster correlation coefficient being 0·01. We assumed that the average number of participants per general practice would be 10 and, therefore, the design effect was 1·09. Thus, we inflated the estimated sample size for this cluster trial to 3000 patients in total.
Role of the funding sources
The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Results
Of 1312 general practices invited, 379 (29%) agreed to participate in the study. 36 (9%) of these were excluded and 343 (91%) were randomised to provide routine diabetes care (n=176) or intensive multifactorial treatment (n=167; figure 1), of which 327 (routine care n=165, intensive treatment n=162) completed screening and 318 (routine care n=157, intensive treatment n=161) included eligible patients. Participating practices in the UK and the Netherlands have been described.17,18,20 The mean patient list size was 7378 in the routine care group and 7160 in the intensive treatment group. The median prevalence of known diabetes was 3·5%. Data for the prevalence of diabetes in the Denmark practices were not available.
Trial profile
Screening identified 3233 patients with type 2 diabetes, of whom 3057 agreed to take part (Denmark, n=1533; Cambridge, UK, n=867; Netherlands, n=498; and Leicester, UK, n=159). The characteristics of these patients did not differ significantly from those of the 176 patients who were eligible but chose not to participate. Baseline demographic, clinical, biochemical, and treatment characteristics of patients in the two treatment groups were well matched overall (table 3). In Denmark, however, more patients were identified in practices assigned intensive treatment than in those assigned routine care (910 vs 623), and more patients in the former group had a history of ischaemic heart disease (International Classification of Diseases [version 10; ICD-10] codes I20–25, 102 [11·2%] vs 53 [8·5%]) or other cardiac diagnosis (ICD10 codes I30–52, 76 [8·4%] vs 28 [4·5%]). As previously described, patients overall exhibited high levels of untreated cardiovascular risk factors at diagnosis.19
Table 3
Clinical, biochemical, and treatment characteristics of patients at baseline and mean follow-up of 5·3 years
| Routine care | Intensive treatment | Change from baseline to follow-up (β/OR [95% CI]) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Baseline (n=1379) | Follow-up (n=1285) | Baseline (n=1678) | Follow up (n=1574) | |||||||
| Total with data available (%) | Value | Total with data available (%) | Value | Total with data available (%) | Value | Total with data available (%) | Value | |||
| Demographic variables | ||||||||||
| Male sex | 1379 (100%) | 790 (57·3%) | NA | NA | 1678 (100%) | 981 (58·5%) | NA | NA | NA | |
| Mean (SD) age at diagnosis (years) | 1379 (100%) | 60·2 (6·8) | NA | NA | 1678 (100%) | 60·3 (6·9) | NA | NA | NA | |
| White ethnic origin | 1334 (96·7%) | 1246 (93·4%) | NA | NA | 1607 (95·8%) | 1539 (95·8%) | NA | NA | NA | |
| Employed | 1013 (73·5%) | 425 (42·0%) | NA | NA | 1197 (71·3%) | 482 (40·3%) | NA | NA | NA | |
| Clinical variables | ||||||||||
| History of myocardial infarction | 1286 (93·3%) | 79 (6·1%) | NA | NA | 1593 (94·9%) | 109 (6·8%) | NA | NA | .. | |
| History of stroke | 1270 (92·1%) | 24 (1·9%) | NA | NA | 1558 (92·8%) | 45 (2·9%) | NA | NA | .. | |
| Current smoker | 1347 (97·7%) | 375 (27·8%) | 1014 (78·9%) | 187 (18·4%) | 1649 (98·3%) | 444 (26·9%) | 1293 (82·1%) | 261 (20·2%) | 1·06 (0·77 to 1·45) | |
| Median (IQR) units of alcohol per week | 1183 (85·8%) | 4 (1–13) | 1010 (78·6%) | 3 (0–10) | 1492 (88·9%) | 4 (1–13) | 1280 (81·3%) | 3 (0–10) | −0·24 (−0·77 to 0·29) | |
| Mean (SD) BMI (kg/m2) | 1342 (97·3%) | 31·6 (5·6) | 1112 (86·5%) | 31·0 (5·6) | 1615 (96·2%) | 31·6 (5·6) | 1405 (89·3%) | 31·1 (5·7) | 0·03 (−0·17 to 0·22) | |
| Mean (SD) weight (kg) | 1344 (97·5%) | 90·3 (17·6) | 1192 (92·8%) | 88·4 (17·8) | 1615 (96·2%) | 90·9 (17·5) | 1490 (94·7%) | 89·1 (18·2) | −0·02 (−0·58 to 0·55) | |
| Mean (SD) waist circumference (cm) | 1346 (97·6%) | 106·8 (13·5) | 1064 (82·8%) | 105·3 (13·6) | 1612 (96·1%) | 107·1 (13·5) | 1363 (86·6%) | 105·4 (13·6) | −0·30 (−1·01 to 0·42) | |
| Median (IQR) HbA1c (%) | 1298 (94·1%) | 6·6 (6·1–7·3) | 1226 (95·4%) | 6·5 (6·1–7·1) | 1591 (94·8%) | 6·5 (6·1–7·3) | 1513 (96·1%) | 6·4 (6·0–6·9) | −0·08 (−0·14 to −0·02) | |
| Mean (SD) HbA1c (%) | 1298 (94·1%) | 7·0 (1·5) | 1226 (95·4%) | 6·7 (0·95) | 1591 (94·8%) | 7·0 (1·6) | 1513 (96·1%) | 6·6 (0·95) | −0·08 (−0·14 to −0·02) | |
| Mean (SD) systolic blood pressure (mm Hg) | 1346 (97·6%) | 149·8 (21·3) | 1205 (93·8%) | 138·1 (17·6) | 1617 (96·4%) | 148·5 (22·1) | 1517 (96·4%) | 134·8 (16·8) | −2·86 (−4·51 to −1·20) | |
| Mean (SD) diastolic blood pressure (mm Hg) | 1346 (97·6%) | 86·5 (11·3) | 1203 (93·6%) | 80·7 (10·8) | 1618 (96·4%) | 86·1 (11·1) | 1517 (96·4%) | 79·5 (10·7) | −1·44 (−2·30 to −0·58) | |
| Mean (SD) total cholesterol (mmol/L) | 1300 (96·3%) | 5·6 (1·2) | 1226 (95·4%) | 4·4 (0·9) | 1593 (94·9%) | 5·5 (1·1) | 1523 (96·8%) | 4·2 (0·9) | −0·27 (−0·34 to −0·19) | |
| Median (IQR) HDL cholesterol (mmol/L) | 1289 (93·5%) | 1·2 (1·0–1·5) | 1200 (93·3%) | 1·3 (1·1–1·6) | 1568 (93·4%) | 1·2 (1·0–1·5) | 1517 (96·4%) | 1·2 (1·0–1·5) | 0 (−0·03 to 0·02) | |
| Mean (SD) LDL cholesterol (mmol/L) | 1238 (89·8%) | 3·5 (1·0) | 1173 (91·3%) | 2·3 (0·8) | 1511 (90·0%) | 3·4 (1·0) | 1477 (93·8%) | 2·1 (0·8) | −0·20 (−0·26 to −0·13) | |
| Median (IQR) triglycerides (mmol/L) | 1295 (93·9%) | 1·7 (1·2–2·4) | 1213 (94·4%) | 1·6 (1·1–2·3) | 1579 (94·1%) | 1·6 (1·2–2·3) | 1512 (96·1%) | 1·5 (1·0–2·1) | −0·05 (−0·12 to 0·01) | |
| Mean (SD) creatinine (μmol/L) | 1266 (91·8%) | 84·9 (18·6) | 1208 (94·0%) | 79·8 (29·9) | 1565 (93·3%) | 83·4 (17·1) | 1513 (95·5%) | 81·0 (30·5) | 1·81 (0·10 to 3·53) | |
| Self-reported drug use | ||||||||||
| Any glucose-lowering drug | 1340 (97·2%) | 7 (0·5%) | 1208 (94·0%) | 681 (56·4%) | 1609 (95·9%) | 8 (0·5%) | 1524 (96·8%) | 990 (65·0%) | 1·53 (1·25 to 1·89) | |
| Median (IQR) total number of glucose-lowering drugs | 1340 (97·2%) | 0 (0–0) | 1208 (94·0%) | 1 (0–1) | 1609 (95·9%) | 0 (0–0) | 1524 (96·8%) | 1 (0–1) | .. | |
| Metformin | 1340 (97·2%) | 5 (0·4%) | 1208 (94·0%) | 583 (48·3%) | 1609 (95·9%) | 6 (0·4%) | 1524 (96·8%) | 835 (54·8%) | .. | |
| Sulphonylurea | 1340 (97·2%) | 2 (0·1%) | 1208 (94·0%) | 215 (17·8%) | 1609 (95·9%) | 2 (0·1%) | 1524 (96·8%) | 291 (19·1%) | .. | |
| Thiazolidinedione | 1340 (97·2%) | 0 | 1208 (94·0) | 50 (4·1%) | 1609 (95·9%) | 0 | 1524 (96·8%) | 69 (4·5%) | .. | |
| Insulin | 1340 (97·2%) | 0 | 1208 (94·0%) | 43 (3·6%) | 1609 (95·9%) | 0 | 1524 (96·8%) | 96 (6·3%) | .. | |
| Other | 1340 (97·2%) | 0 | 1208 (94·0%) | 31 (2·6%) | 1609 (95·9%) | 0 | 1524 (96·8%) | 81 (5·3%) | .. | |
| Any antihypertensive drugs | 1340 (97·2%) | 585 (43·7%) | 1208 (94·0%) | 911 (75·4%) | 1609 (95·9%) | 752 (46·7%) | 1524 (96·8%) | 1274 (83·6%) | 1·61 (1·27 to 2·04) | |
| Median (IQR) total number of antihypertensive drugs | 1340 (97·2%) | 0 (0–1) | 1208 (94·0%) | 2 (1–3) | 1609 (95·9%) | 0 (0–2) | 1524 (96·8%) | 2 (1–3) | .. | |
| ACE inhibitor or ARB | 1340 (97·2%) | 248 (18·5%) | 1208 (94·0%) | 721 (59·7%) | 1609 (95·9%) | 345 (21·4%) | 1524 (96·8%) | 1126 (73·9%) | 1·84 (1·52 to 2·22) | |
| β-blocker | 1340 (97·2%) | 252 (18·8%) | 1208 (94·0%) | 285 (23·6%) | 1609 (95·9%) | 366 (22·7%) | 1524 (96·8%) | 462 (30·3%) | .. | |
| Calcium-channel blocker | 1340 (97·2%) | 166 (12·4%) | 1208 (94·0%) | 326 (27·0%) | 1609 (95·9%) | 202 (12·6%) | 1524 (96·8%) | 446 (29·3%) | .. | |
| Diuretic | 1340 (97·2%) | 330 (24·6%) | 1208 (94·0%) | 529 (43·8%) | 1609 (95·9%) | 415 (25·8%) | 1524 (96·8%) | 767 (50·3%) | .. | |
| Other | 1340 (97·2%) | 23 (1·7%) | 1208 (94·0%) | 49 (4·1%) | 1609 (95·9%) | 32 (2·0%) | 1524 (96·8%) | 70 (4·6%) | .. | |
| Any cholesterol-lowering drugs | 1340 (97·2%) | 206 (15·4%) | 1208 (94·0%) | 889 (73·6%) | 1609 (95·9%) | 274 (17·0%) | 1524 (96·8%) | 1241 (81·4%) | .. | |
| Statins | 1340 (97·2%) | 200 (14·9%) | 1208 (93·9%) | 864 (71·5%) | 1609 (95·9%) | 271 (16·8%) | 1524 (96·8%) | 1217 (79·9%) | 1·46 (1·20 to 1·78) | |
| Aspirin | 1340 (97·2%) | 169 (12·6%) | 1208 (93·9%) | 504 (41·7%) | 1609 (95·9%) | 249 (15·5%) | 1524 (96·8%) | 1078 (70·7%) | .. | |
The denominators at follow-up exclude 92 patients in the routine care group and 104 in the intensive treatment group who died between baseline and follow-up, and two others in the routine care group who withdrew from the study and for whom primary endpoint data were not available. The denominators used to calculate the percentages of individuals with a particular characteristic are the number of individuals with values for that characteristic. OR=odds ratio. NA=not applicable. BMI=body-mass index. HbA1c=glycated haemoglobin A1c. ACE-angiotensin-converting enzyme. ARB=angiotensin-receptor blocker.
Screening programmes varied by centre (table 1). The median number of follow-up meetings for audit and feedback was four (range two to ten). Primary endpoint data were available for 3055 (99·9%) of 3057 participants. The mean follow-up period was 5·3 (SD 1·6) years, during which 238 first cardiovascular events occurred, with similar numbers and risk in each group (table 4). The cumulative probability plot for the primary endpoint seemed to diverge after 4 years of follow-up (figure 2). In the predefined subgroup analyses, no interactions were seen between the intervention and age or previous cardiovascular event (p>0·1). Estimated HRs, however, were 1·12 (95% CI 0·70–1·79) in patients younger than 60 years and 0·70 (95% CI 0·52–0·95) in those aged 60 years and older. HRs for individual components of the composite endpoint all favoured the intensive treatment group (table 4), although none achieved significance (figure 2). No patient underwent amputation as a first event. The correlation coefficient within clusters for the primary endpoint was 0·002 (Denmark: 0·014, UK [combined] 0·0000016, and the Netherlands 0·025), which suggests that the cluster design had little effect on study power.
Cumulative incidence and relative risk of composite cardiovascular endpoint
(A) Cumulative incidence curves by treatment group. The p value was calculated with Cox's regression and fixed-effects meta-analysis. (B) HRs of development of cardiovascular death, non-fatal myocardial infarction, non-fatal stroke, and revascularisation as a first event (secondary endpoints), and these cardiovascular events combined (primary endpoint), by country and overall. HR=hazard ratio. CVD=cardiovascular disease.
Table 4
Cardiovascular events and all-cause mortality in the two treatment groups
| Routine care (n=1377) | Intensive treatment (n=1678) | Hazard ratio(95% CI) | I2 (%) | p for routine care vs intensive treatment | |
|---|---|---|---|---|---|
| Primary endpoint (n [%]) | |||||
| Composite cardiovascular events* | 117 (8·5%) | 121 (7·2%) | 0·83 (0·65–1·05) | 0% | 0·12 |
| Secondary endpoints (n [%]) | |||||
| Cardiovascular death† | 22 (1·6%) | 26 (1·5%) | 0·88 (0·51–1·51) | 52% | .. |
| Myocardial infarction† | 32 (2·3%) | 29 (1·7%) | 0·7 (0·41–1·21) | 0% | .. |
| Stroke† | 19 (1·4%) | 22 (1·3%) | 0·98 (0·57–1·71) | 0% | .. |
| Revascularisation† | 44 (3·2%) | 44 (2·6%) | 0·79 (0·53–1·18) | 0% | .. |
| Amputation† | 0 | 0 | .. | .. | .. |
| Total mortality | 92 (6·7%) | 104 (6·2%) | 0·91 (0·69–1·21) | 55% | .. |
Hazard ratios were first estimated within each country with Cox's regression and Huber-White adjustment of SE for clustering within practice, then combined across countries with fixed-effects meta-analysis. The I2 statistic estimates heterogeneity between countries. A p value was calculated for primary endpoint only.
196 patients died overall (60 [30·6%] cardiovascular deaths, including six patients with first events classified as myocardial infarction, two as stroke, and four as revascularisation, 97 [49·5%] cancer deaths, and 39 [19·9%] from other causes; table 4). The combined HR for death in the intensive treatment group compared with the routine care group was 0·91 (95% CI 0·69–1·21). Heterogeneity of results between countries was not significant (figure 3). In the UK, significantly fewer patients died in the intensive treatment group than in the routine care group, but in Denmark the risk in the routine care group was lower, although not significantly so. All results were unchanged after sensitivity analysis that excluded participants in the two practices for which endpoint data were obtained while aware of treatment allocation (webappendix p 2).
Cumulative incidence and relative risk of all-cause mortality
(A) Kaplan-Meier survival estimates by treatment group. (B) HRs of all-cause mortality, by country and overall. HR=hazard ratio. CVD=cardiovascular disease.
Of the 2859 patients still alive at 5 years, 2400 (84%) returned to a clinical research facility for follow-up health assessments. Clinical and biochemical outcomes could be obtained from general practice records for a further 328 (11·5%) participants. Compared with patients for whom follow-up data were available, the 131 with missing data were more likely to be from an ethnic minority group (10·2% vs 5·7%, p=0·04) and have higher baseline total cholesterol (5·9 mmol/L vs 5·6 mmol/L, p=0·004) and LDL cholesterol values (3·7 mmol/L vs 3·4 mmol/L, p=0·009). Changes in clinical and biochemical values and prescribed medications in the two groups are shown in table 3. By 5 years of follow-up improvements were seen for cardiovascular risk factors in both groups. Small but significant differences between groups were seen in change from baseline for glycated haemoglobin A1c (HbA1c), systolic and diastolic blood pressure and total and LDL cholesterol, in favour of the intensive treatment group. Prescription of glucose-lowering, antihypertensive, and lipid-lowering drugs increased in both groups. At follow-up more patients in the intensive treatment group were prescribed aspirin, angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers, glucose-lowering drugs, antihypertensive drugs, and lipid-lowering drugs than were those in the routine care group (table 3).
In both groups, more patients had values below target thresholds for HbA1c concentrations, blood pressure, and cholesterol concentrations at follow-up than at baseline (figure 4). The proportion of patients meeting the targets was higher in the intensive treatment group than in the routine care group. The proportions of individuals reporting hypoglycaemia, as assessed by the diabetes treatment satisfaction questionnaire, did not differ (χ2 4·44, p=0·62).33 All results were unchanged after sensitivity analysis was done that excluded participants with follow-up clinical data obtained from general practice records.
Proportion of patients for whom cardiovascular risk factor values were below the intensive treatment intervention target thresholds at baseline and after 5 years of follow-up
1 SE are shown. CVD=cardiovascular disease. HbA1c=glycated haemoglobin A1c.
Discussion
An intervention to promote target-driven, intensive management of patients with type 2 diabetes detected by screening was associated with slightly, but significantly, increased prescription of treatments and improvements in cardiovascular risk factors, and with a non-significant relative reduction in the incidence of cardiovascular events at 5 years. Differences between study groups for all components of the primary endpoint favoured the intensive treatment group. Differences were greatest for myocardial infarction and smallest for stroke.
We cannot rule out the possibility that these findings were due to chance. Rates of cardiovascular events seemed to diverge after 4 years of follow-up, although the risk of adverse outcomes, including death or self-reported hypoglycaemia, was not increased. The incidence of cardiovascular events in the routine care group (8·5%) was lower than expected compared with that in newly diagnosed patients in the UKPDS (12·1%).34 Mortality in this group (6·7%) was lower than that in patients with type 2 diabetes detected by screening in Hoorn35 (25% over 10 years) and newly diagnosed patients in a study in Denmark (33% over 7·4 years).36 In our study, mortality was similar to that reported for people of the same age without diabetes in the general population of Denmark in 1995–2006.37 This lack of difference is likely to be due to the quality of care delivered to patients early in the course of their disease in both groups.
This trial shows that screening for type 2 diabetes and early intensive multifactorial treatment of the detected patients are feasible in general practice. In both study groups, cardiovascular risk factors, such as blood pressure and cholesterol concentrations, improved notably after diagnosis and glycaemia and weight did not increase. Absolute values for risk factors at follow-up and changes from baseline compare favourably with those in other trials in which patients were recruited at clinical diagnosis and followed up for 6 years.34,35 Clinically important differences in risk factors between groups at 1 year,18 however, were not maintained and were considerably smaller than those achieved in similar studies.2,34
Adherence to treatment algorithms might have been suboptimum in this pragmatic trial. In three centres the screening programme used risk scores, including treatment for hypertension as one of the factors to predict the risk of type 2 diabetes, which could have limited the achievable differences between groups in blood pressure. Furthermore, the trial was undertaken against a background of improvements in the delivery of diabetes care in general practice, such as that associated with the introduction of the Quality and Outcomes Framework for primary care in the UK,38 and evidence-based guidelines in Denmark39 and the Netherlands,40 which might also have lowered the achievable differences in treatment between groups.
Participants were drawn from a large, representative, population-based sample in three different European countries. They were identified by a range of different screening programmes and all were diagnosed according to WHO criteria.21 The intensive treatment intervention incorporated various methods that encouraged changes in practitioner behaviour, and treatment algorithms and targets that were based on robust trial data.2,4,6,26 Because the intervention was delivered mainly via family physicians and nurses, we randomised general practices rather than individuals to minimise the risk of contamination. The analyses were appropriate to the cluster design. We achieved high participant retention and independently adjudicated endpoint ascertainment in both trial groups. We assessed clinically important outcomes with standard equipment and protocols by especially trained staff members who were unaware of study group allocation. Patients with and without follow-up data differed little.
The generalisability of our findings to other settings should be considered in light of the non-random recruitment of general practices. We did, however, cover a large geographical area in each country, and the 26% of invited practices that were randomised were nationally representative for key descriptive characteristics.17,18,20 Assessment of hypoglycaemia was imprecise, as it was limited to one question, but we believe this factor is unlikely to be biased because the questionnaire was administered in the same way to participants in the two study groups by staff unaware of study group allocations. We feel that the size of the study means that even with a potentially imprecise instrument the power was sufficient to quantify the effects of the intervention on the frequency of hypoglycaemic events. In two centres (Denmark and the Netherlands), assays used to assess biochemical factors differed slightly between baseline and follow-up, but not between groups (webappendix p 1). By necessity cluster randomisation was done before screening and recruitment of patients, a design feature that can introduce differences between groups in relation to participant characteristics. Overall, patients in the two study groups were well matched, although the intervention could have influenced the number and type of patients recruited to the intensive treatment group. This effect might have been particularly relevant in Denmark, where staff in the intensive treatment group might have been especially alert to the potential benefits of early detection and treatment and, therefore, have been more active testing in individuals at high cardiovascular risk, which could have led to the observed differences in baseline characteristics between study groups in this centre. These differences between groups might have lessened the apparent effects of the intervention and, therefore, could explain, at least partly, differences between centres, which seemed to be present but were not necessarily significant owing to the limited power of heterogeneity test.
Methods of screening, outcome assessment, and laboratory testing were standard across patient groups within centres but did differ between centres. These differences might have contributed to heterogeneity, even though interlaboratory validation suggested that methods were consistent between centres and all potential primary endpoints were adjudicated in the same way. Other sources of heterogeneity include the characteristics of patients, which varied by centre, presumably because of differences in the screening programmes and underlying populations, the delivery of the intensive treatment intervention (table 1), and other unmeasured factors.
The lower-than-expected event rate suggests that 5 years of follow-up is insufficient, and the apparent divergence from 4 years onwards suggests that further follow-up is justified to test whether early intensive multifactorial treatment reduces cardiovascular risk in the long term, as seen in the UKPDS.8 Finally, the complex nature of the intensive treatment intervention and the pragmatic design of the trial make it difficult to say whether any components are individually associated with a reduction in cardiovascular risk.
The observed absolute and relative risks for cardiovascular events and mortality among participants with a mean HbA1c concentration of around 6·5% 5 years after diagnosis, should allay concerns about early intensive treatment of hyperglycaemia.
We saw no interaction between intensive treatment and age or history of cardiovascular events, although benefits associated with this intervention seemed to be greatest in patients aged 60 years or older at diagnosis. Multifactorial treatment of screen-detected patients in both study groups was associated with improvements in cardiovascular risk factors and lower than expected rates of cardiovascular events and mortality. The small differences in treatment between groups at 5 years were associated with a non-significant 17% reduction in the incidence of cardiovascular events, with no obvious adverse consequences. We are undertaking analyses of trial data on microvascular endpoints, quality of life, functional status, and health service costs. Although there is no evidence of harm associated with screening11 and intensive therapy,41 the extent to which the complications of diabetes can be reduced by earlier detection and treatment remains uncertain (panel).
When compared with routine care, an intervention to promote target-driven, intensive management of patients with type 2 diabetes detected by screening was associated with small increases in the prescription of drugs and improvements in cardiovascular risk factors, but was not associated with significant reductions in the incidence of cardiovascular events or death over 5 years.
Acknowledgments
We thank all patients, practice nurses, and family physicians for their contributions to the ADDITION-Europe study. We thank Ronald P Stolk, and Bruce H R Wolffenbuttel, University Medical Centre Groningen, University of Groningen, Netherlands, for participation on the steering committee that designed the study. ADDITION-Denmark was supported by the National Health Services in the counties of Copenhagen, Aarhus, Ringkøbing, Ribe and South Jutland in Denmark, the Danish Council for Strategic Research, the Danish Research Foundation for General Practice, Novo Nordisk Foundation, the Danish Centre for Evaluation and Health Technology Assessment, the diabetes fund of the National Board of Health, the Danish Medical Research Council, and the Aarhus University Research Foundation. The trial has been given unrestricted grants from Novo Nordisk AS, Novo Nordisk Scandinavia AB, Novo Nordisk UK, ASTRA Denmark, Pfizer Denmark, GlaxoSmithKline Pharma Denmark, Servier Denmark, and HemoCue Denmark. Parts of the grants from Novo Nordisk Foundation, Danish Council for Strategic Research and Novo Nordisk were transferred to the other centres. We thank the Biochemistry Department at Steno Diabetes Centre for biochemical testing, and the following hospital departments for doing clinical assessments: Steno Diabetes Centre Research Laboratory, Esbjerg Hospital Diabetes Clinic, Holstebro Hospital Research department, Aabenraa Hospital Outpatient Clinic and Aarhus University Hospital Eye Department. ADDITION-Cambridge was supported by the Wellcome Trust (grant reference no: G061895), the Medical Research Council (grant reference no: G0001164), the NIHR Health Technology Assessment Programme (grant reference no: 08/116/300), National Health Service R&D support funding (including the Primary Care Research and Diabetes Research Networks), and the National Institute for Health Research under its Programme Grants for Applied Research scheme [RP-PG-0606-1259]. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the UK Department of Health. Bio-Rad provided equipment for haemoglobin A1c testing during the screening phase. We thank the Cambridge University Hospitals NHS Foundation Trust Department of Clinical Biochemistry and the NIHR Cambridge Biomedical Research Centre, Core Biochemical Assay Laboratory for carrying out the biochemical assays. ADDITION-Netherlands was supported by unrestricted grants from Novo Nordisk, Glaxo Smith Kline and Merck, and by the Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht. ADDITION-Leicester was supported by Department of Health and ad hoc Support Sciences, the NIHR Health Technology Assessment Programme (grant reference no: 08/116/300), National Health Service research and development support funding (including the Primary Care Research and Diabetes Research Network, and LNR CLAHRC) and the National Institute for Health Research. MJD and KK receive support from the Department of Health NIHR Programme Grant funding scheme [RP-PG-0606-1272]. We thank the University Hospitals of Leicester NHS Trust Department of Clinical Biochemistry for carrying out biochemical assays. Permission to use the DTSQ questionnaire was granted by Professor Clare Bradley, Health Psychology Research, Royal Holloway University of London, Surrey, UK. These results were presented at the European Association for the Study of Diabetes (EASD) Annual Scientific Meeting, Stockholm, Sweden, Sept 20–24, 2010.
Contributors
SJG acts as guarantor for this paper. TL is Chair and KB-J Vice-Chair of the steering committee, which also included the following people, who collectively designed the study: SJG, MJD, KK, GEHMR, AS, and NJW. SJG, KB-J, MJD, KK, GEHMR, AS, NJW, and TL are principal investigators for the trial who, along with RKS and MvdD, participated in the acquisition of the data. SJS did the statistical analyses. All authors participated in the analysis and interpretation of data. SJG and RKS drafted the report. All authors participated in the critical revision of the intellectual content of the report.
ADDITION-Denmark
Bendix Carstensen, Merete Frandsen (Steno Diabetes Centre, Gentofte); Else-Marie Dalsgaard, Ynna Nielsen, Søren Bech-Morsing, Mette Vinther Skriver, Helle Terkildsen, Morten Charles (School of Public Health, Aarhus); Toke Bek (Aarhus University Hospital, Aarhus); Henrik Lund Andersen (Glostrup Hospital, Glostrup).
ADDITION-Cambridge
Gisela Baker, Daniel Barnes, Mark Betts, Sandra Bovan, Parinya Chamnan, Sue Emms, Francis Finucane, Susie Hennings, Muriel Hood, Garry King, Christine May Hall, Joanna Mitchell, Emanuella De Lucia Rolfe, Liz White (MRC Epidemiology Unit, Cambridge); Amanda Adler, Sean Dinneen, Mark Evans (Cambridge University Hospitals, NHS Foundation Trust, Cambridge); Judith Argles, Rebecca Bale, Roslyn Barling, Sue Boase, Ryan Butler, Pesheya Doubleday, Tom Fanshawe, Philippa Gash, Julie Grant, Wendy Hardeman, Ann-Louise Kinmonth, Richard Parker, Nicola Popplewell, A Toby Prevost, Megan Smith, Stephen Sutton, Fiona Whittle, Kate Williams, Rebecca Abbott, Georgina Lewis, Lincoln Sargeant (Department of Public Health and Primary Care, University of Cambridge); Robert Henderson (Hinchingbrooke Hospital, Huntingdon).
ADDITION-Netherlands
Kees Gorter, Paul Janssen, Lidian Izeboud, Jacqueline Berends, Marlies Blijleven, Bart Thoolen, Denise de Ridder, Jozien Bensing, Mehmet Akarsubasi, Paula Koekkoek, Carla Ruis, Geert Jan Biessels, Jaap Kappelle, Michiel van der Linden (Utrecht University, Utrecht).
ADDITION-Leicester
Balasubramanian Thiagarajan Srinivasan, David Webb, Mary Quinn, Emma Wilmot, Samiul A Mostafa, Nitin Gholap, Hamid Mani, Winston Crasto, Steve Hiles, Joe Henson, Janet Jarvis, Sukhjit Sehmi, Fiona Ablett, Champa Merry, Emma Healey, Julia Stockman, Sandra Campbell, Janette Barnett, Nil Radia, Mo Radia, Jo Howe, Lesley Bryan, Jane Brela, Jayne Hill, Helen Bray, Rachel Plummer, Zubeir Essat, Francis Pullen, Susan Enright (University Hospitals of Leicester NHS Trust); Laura J Gray, Nick Taub (University of Leicester).
Independent endpoint committee
Kristian Thygesen, Ole Færgeman (Aarhus University Hospital, Aarhus, Denmark); Hans Ibsen (Holbæk Hospital, Holbæk, Denmark); Birger Thorsteinsson (Hillerød Hospital, Hillerød, Denmark); Jane Armitage, Louise Bowman (University of Oxford, Oxford, UK); Cees Tack (Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands) and Jaap Kappelle (University Medical Centre, Utrecht, Netherlands)
Independent endpoint committee Cambridge
Nigel Stott (Chair; University of Cardiff, Cardiff, UK), John Weinman (King's College, London, London, UK), Richard Himsworth (University of Cambridge, Cambridge, UK), Paul Little (University of Southampton, Southampton, UK).
Independent data monitoring and safety committee
Christian Gluud, Per Winkel, Jørn Wetterslev (Copenhagen University, Copenhagen, Denmark).
Additional study support
Daniel Barnes (data cleaning, analysis, and presentation), Adam Dickinson (data management), Iain Morrison (information technology), Matt Sims (technical), Paul Roberts, Kim Mwanza, James Sylvester (field epidemiology; MRC Epidemiology Unit); Wim Rutten, Paulien van Hessen (collaboration), Mardy Eckhardt, Erna Erdtsieck, Leandra Boonman, Annelies van der Smissen (screening, health assessments, and biochemical assays; SHL Centre for Diagnostic Support in Primary Care, Etten-Leur, Netherlands). Nicole Boekema, Robert Veen (data management), Peter Zuithof (statistics), Lizeth Vendrig, Annette Bak (staff recruitment; Julius Centre, Utrecht, Netherlands).
References
Full text links
Read article at publisher's site: https://doi.org/10.1016/s0140-6736(11)60698-3
Read article for free, from open access legal sources, via Unpaywall:
https://europepmc.org/articles/pmc3136726
Citations & impact
Impact metrics
Citations of article over time
Alternative metrics
Discover the attention surrounding your research
https://www.altmetric.com/details/102366933
Smart citations by scite.ai
Explore citation contexts and check if this article has been
supported or disputed.
https://scite.ai/reports/10.1016/s0140-6736(11)60698-3
Article citations
Cost consequence analysis of adding semaglutide to treatment regimen for patients with Type II diabetes in Saudi Arabia.
Saudi Pharm J, 32(5):102057, 29 Mar 2024
Cited by: 0 articles | PMID: 38601976 | PMCID: PMC11004995
Feasibility of a Type 2 Diabetes Prevention Program at Nationwide Level in General Practice: A Pilot Study in Italy.
J Clin Med, 13(4):1127, 16 Feb 2024
Cited by: 0 articles | PMID: 38398440 | PMCID: PMC10888610
Causes of In-Hospital Death and Pharmaceutical Associations with Age of Death during a 10-Year Period (2011-2020) in Individuals with and without Diabetes at a Japanese Community General Hospital.
J Clin Med, 13(5):1283, 24 Feb 2024
Cited by: 0 articles | PMID: 38592103
Early use of oral semaglutide in the UK: A cost-effectiveness analysis versus continuing metformin and SGLT-2 inhibitor therapy.
BMJ Open, 13(9):e070473, 29 Sep 2023
Cited by: 0 articles | PMID: 37775297 | PMCID: PMC10546165
Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes.
Cochrane Database Syst Rev, 5:CD014513, 31 May 2023
Cited by: 2 articles | PMID: 37254718 | PMCID: PMC10233616
Review
Free full text in Europe PMC
Go to all (251) article citations
Data
Data behind the article
This data has been text mined from the article, or deposited into data resources.
BioStudies: supplemental material and supporting data
Clinical Trials
- (1 citation) ClinicalTrials.gov - NCT00237549
Similar Articles
To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation.
Long-term effects of intensive multifactorial therapy in individuals with screen-detected type 2 diabetes in primary care: 10-year follow-up of the ADDITION-Europe cluster-randomised trial.
Lancet Diabetes Endocrinol, 7(12):925-937, 01 Dec 2019
Cited by: 21 articles | PMID: 31748169
A randomised trial of the effect and cost-effectiveness of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with screen-detected type 2 diabetes: the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care (ADDITION-Europe) study.
Health Technol Assess, 20(64):1-86, 01 Aug 2016
Cited by: 7 articles | PMID: 27583404
Books & documents Free full text in Europe PMC
Effect of early intensive multifactorial therapy compared with routine care on self-reported health status, general well-being, diabetes-specific quality of life and treatment satisfaction in screen-detected type 2 diabetes mellitus patients (ADDITION-Europe): a cluster-randomised trial.
Diabetologia, 20 Aug 2013
Cited by: 11 articles | PMID: 23959571 | PMCID: PMC3824356
Effects of glucose-lowering and multifactorial interventions on cardiovascular and mortality outcomes: a meta-analysis of randomized control trials.
Diabet Med, 33(3):280-289, 08 Sep 2015
Cited by: 20 articles | PMID: 26282461
Review
Effect of intensive glucose lowering treatment on all cause mortality, cardiovascular death, and microvascular events in type 2 diabetes: meta-analysis of randomised controlled trials.
BMJ, 343:d4169, 26 Jul 2011
Cited by: 349 articles | PMID: 21791495 | PMCID: PMC3144314
Review
Free full text in Europe PMC
Funding
Funders who supported this work.
Department of Health and ad hoc Support Sciences, the NIHR Health Technology Assessment Programme (1)
Grant ID: 08/116/300
Medical Research Council (4)
Does adding facilitated behaviour change improve outcomes for people with type 2 diabetes detected by screening?
Professor Simon Griffin, University of Cambridge
Grant ID: G0001164
Aetiology of type 2 diabetes and obesity and related metabolic disorders
Professor Nicholas Wareham, MRC Epidemiology Unit
Grant ID: MC_U106179471
Epidemiological contribution to primary and secondary prevention of diabetes and obesity
Professor Simon Griffin, MRC Epidemiology Unit
Grant ID: MC_U106179474
Environmental and social influences on physical activity
Professor Simon Griffin, Medical Research Council
Grant ID: G0501294
National Health Service R&D support funding (1)
Grant ID: RP-PG-0606-1259
National Institute for Health Research (NIHR) (3)
A community based primary prevention programme for Type 2 Diabetes integrating identification, lifestyle intervention and community services for prevention.
Professor Melanie Davies, University Hospitals Of Leicester NHS Trust
Grant ID: RP-PG-0606-1272
Anglo-Danish-Dutch study of Intensive Treatment of people with Newly diagnosed diabetes in primary care (ADDITION)–five year follow-up
Professor Simon Griffin, University of Cambridge
Grant ID: 08/116/300
Reducing the burden of type 2 diabetes: translating epidemiology and behavioural science into preventive action
Professor Simon Griffin, Cambridge University Hospitals NHS Foundation Trust
Grant ID: RP-PG-0606-1259
Wellcome Trust (1)
Grant ID: G061895
the Department of Health NIHR Programme Grant funding scheme (1)
Grant ID: RP-PG-0606-1272
the Medical Research Council (1)
Grant ID: G0001164
the NIHR Health Technology Assessment Programme (1)
Grant ID: 08/116/300