Abstract
Type 2 diabetes (T2D) is a growing epidemic worldwide, and implementation of diabetes prevention programs 1 and initiatives such as DE-PLAN (Diabetes in Europe—Prevention Using Lifestyle, Physical Activity, and Nutritional Intervention) 2 are therefore important. Lifestyle counseling aiming at weight loss and improvement in glucose tolerance has been effective in the prevention of T2D in randomized controlled trials (RCTs)3-7 and implementation studies.8-13 In RCTs, being outside of the labor force, 14 low education, 15 high age, 14 high initial weight, 16 high body mass index (BMI), 17 high physical activity, 18 consumption of meal replacements, 18 and high attendance at intervention sessions16,18 have been related to weight loss. Furthermore, 2-hour glucose levels in the low range, 3 increasing age, and high level of Finnish Diabetes Risk Scores resulted in lower diabetes incidence rates. 19
The question is whether predictors of the success of lifestyle intervention in RCTs are similar in “real-life settings.” This information is important to optimize resource allocation. 20 The aim of this study is to examine predictors of the success of lifestyle intervention in terms of weight loss and improvement in glucose tolerance in individuals at high risk for T2D or with screen-detected T2D in the Finnish National Diabetes Prevention Program (FIN-D2D)21,22 after 1-year follow-up.
Material and Methods
All together, 400 primary health care centers or occupational health care clinics were involved in the FIN-D2D. 23 High-risk individuals for T2D were identified using modified Finnish Diabetes Risk Scores, 24 including questions on family history of diabetes 25 (scoring ≥ 15) or history of impaired fasting glucose, impaired glucose tolerance, cardiovascular events, or gestational diabetes. 21 After identification, individuals visited voluntarily health checkups as part of normal routine in the primary health care units. Therefore, informed consent was not used, but individuals received written information on the FIN-D2D. The Ministry of Social Affairs and Health gave the National Public Health Institute permission to collect data from the participating health care units for research and evaluation purposes.
In total, 10 149 individuals (3379 men and 6770 women) participated in the baseline examination (January 17, 2004–August 28, 2007); 8353 of them underwent an oral glucose tolerance test. Follow-up information was available for 5523 individuals, but this study concerns the 3880 (1339 men and 2541 women) who participated in the 1-year follow-up (visits that occurred 9-18 months after baseline visits, January 17, 2005–June 12, 2008). 25 In sum, 2464 of them had information on fasting glucose and 2455 on 2-hour glucose when blood sampling and analytical methods were similar at both baseline and 1-year examinations.
Local health care nurses measured weight (kg), height (cm), and waist circumference (cm); calculated BMI (kg/m2); and gave brief counseling for lifestyle changes.22,23 The change in weight was dichotomized to weight loss < or ≥ 5%. Oral glucose tolerance test was performed with a 75-g glucose load. Fasting and 2-hour samples were collected from venous or capillary blood samples. Glucose tolerance status was classified as normal and impaired fasting glucose, impaired glucose tolerance, or screen-detected T2D (ie, abnormal glucose tolerance [AGT]) according to World Health Organization 1999 criteria. 26 Improvement in glucose tolerance was classified with arbitrary criteria (ie, change of ≥ 1 standard deviation) in improvement in fasting and/or 2-hour glucose for individuals with information from both baseline and 1-year examinations. The ∆s for fasting glucose were ≤ –1.1 mmol/L for capillary plasma, −0.6 mmol/L for venous plasma, −1.2 mmol/L for capillary serum, −0.9 mmol/L for venous serum, and −0.3 mmol/L for venous whole blood improvement. The corresponding figures for 2-hour glucose were −2.2 mmol/L, −2.1 mmol/L, −0.9 mmol/L, −2.1 mmol/L, and −0.8 mmol/L.
Background factors were inquired with a questionnaire. Marital status was dichotomized to married/cohabiting and unmarried. Education was classified into low, intermediate, and high classes. 27 Working life included the following options: (1) agriculture, animal husbandry, forestry; (2) factory, mine, construction, or corresponding work; (3) clerical, service, or intellectual work; (4) studying; (5) housewife/husband; (6) retired; and (7) unemployed. 27 Classes 1-4 were combined into employed and 5-7 into not employed. Smoking was dichotomized to nonsmokers and smokers. Alcohol intake was assessed by the use of alcoholic beverages on average per week. Leisure time physical activity included the following options: (1) read, watch television, and do things that do not require physical activity; (2) walk, ride a bicycle, or move in other ways requiring physical activity ≥ 4 hours a week; (3) engage in physical activities to maintain my condition (eg, jogging, cross-country skiing, aerobics, swimming, or ball games ≥ 3 hours a week); and (4) practice regularly for competitions in running, cross-country skiing, orienteering, ball games, or other physically demanding sports several times a week. 28 Classes 2-4 were combined as active. Family history of diabetes was positive if at least one first-degree relative had diabetes. 29 Diseases was assessed by the sum index, including (1) hypertension/elevated blood pressure, (2) chronic heart failure, (3) angina pectoris, (4) coronary artery disease, (5) history of myocardial infarction, (6) cardiac bypass surgery/angioplasty, (7) stroke/cerebrovascular accident, (8) intermittent claudication, (9) dyslipidemia, (10) depression/mental health problem, (11) illness restricting physical activity, or (12) other serious illness. Depression was assessed with current use of antidepressive drugs.
Lifestyle interventions were individual (weight, meal frequency, fat [intake, quality], use of salt, fiber intake, exercise, alcohol, smoking) or group sessions (weight maintenance/exercise groups, lectures of lifestyle changes and diabetes) 22 based on the Finnish Diabetes Prevention Study 30 applying different stages of change if possible. A form was filled during the visits. More information is available in Project Plan. 22 The frequency of intervention visits was based on the resources and needs of the local health care centers. 25 Number of interventions was classified as 0, 1, 2, and 3 or more.
Statistical analyses included cross-tabulation, χ2 test, and independent sample t test. Age,14,19 education, 15 working status, 14 smoking, 31 family history of diabetes, 29 glucose tolerance status,3,12,32 BMI, 17 number of intervention visits,16,18 and health care provider were selected on the basis of literature/analysis as predictors with sex to logistic regression models (forced entry). Analyses for improvement in glucose tolerance were performed only for individuals who had AGT (n = 1854) at baseline. SAS 9.2 for Windows was used for statistical analyses.
Results
Baseline characteristics are presented in Table 1. Over half (58.8%) were obese (BMI > 30 kg/m2) and only 7.3% had BMI < 25 kg/m2.
Baseline Characteristics of Individuals at High Risk for Type 2 Diabetes or Screen-Detected Type 2 Diabetes in the Finnish National Diabetes Prevention Program
Furthermore, 19.3% of the individuals lost ≥ 5% weight (mean 8.5 ± 5.8 kg). The average weight change for those who lost < 5% was 0.3 ± 3.3 kg. A higher proportion of individuals who had intermediate education, were not working, had AGT, and participated in primary health care had successful weight loss compared to those in lower educational groups, who were employed, had normal glucose tolerance, and participated in occupational health care clinics. High attendance at lifestyle intervention sessions and a high initial body weight, BMI, and waist circumference were also associated with favorable outcome (Table 2). Finally, individuals who were not working, had impaired glucose tolerance or screen-detected T2D, high BMI, and a high number of intervention visits were more likely to achieve successful weight loss, after full adjustment (Table 3).
Baseline Characteristics and Their Association With Successful Weight Loss and Improvement in Glucose Tolerance During the 1-Year Follow-up in Individuals at High Risk for Type 2 Diabetes or Screen-Detected Type 2 Diabetes in the Finnish National Diabetes Prevention Program a
aImprovement in glucose tolerance: ≥ 1 standard deviation decrease of fasting and/or 2-h glucose values. Cutoff points for fasting glucose: −1.1 mmol/L for capillary plasma, −0.6 mmol/L for venous plasma, −1.2 mmol/L for capillary serum, −0.9 mmol/L for venous serum, and −0.3 mmol/L for venous whole blood. Cutoff points for 2-hour glucose: −2.2 mmol/L, −2.1 mmol/L, −0.9 mmol/L, −2.1 mmol/L, and −0.8 mmol/L, respectively.
Weight loss, n = 3654; glucose tolerance, n = 2372.
Weight loss, n = 3879; glucose tolerance, n = 2453.
Weight loss, n = 3870; glucose tolerance, n = 2451.
Weight loss, n = 3726; glucose tolerance, n = 2388.
Logistic Regression Analyses for Successful Weight Loss and Improvement in Glucose Tolerance in individuals at High Risk for Type 2 Diabetes or Screen-Detected Type 2 Diabetes in the Finnish National Diabetes Prevention Program
Successful weight loss: ≥ 5%, n = 2967. Improvement in glucose tolerance: ≥ 1 standard deviation decrease of fasting and/or 2-h glucose values, n = 1267. Cutoff points for fasting glucose: −1.1 mmol/L for capillary plasma, −0.6 mmol/L for venous plasma, −1.2 mmol/L for capillary serum, −0.9 mmol/L for venous serum, and −0.3 mmol/L for venous whole blood. Cutoff points for 2-hour glucose: −2.2 mmol/L, −2.1 mmol/L, −0.9 mmol/L, −2.1 mmol/L, and −0.8 mmol/L, respectively. Variables were entered into the model simultaneously. Analyses for improvement in glucose tolerance done only for individuals with abnormal glucose tolerance at the baseline.
NGT, normal glucose tolerance; in the model concerning successful weight loss.
IFG, impaired fasting glucose; in the model concerning improvement in glucose tolerance.
A total of 20.7% of individuals had an improvement in glucose tolerance. If individuals with normal glucose tolerance at the baseline were excluded, the corresponding number was 32.6%. Over half the individuals with screen-detected T2D had an improvement in glucose tolerance. Individuals with a high number of intervention visits and high initial body weight, BMI, and waist circumference were more successful in having a favorable outcome (Table 2). Final logistic regression analyses were only performed for individuals with AGT at baseline. High education and AGT (impaired glucose tolerance, screen-detected T2D) emerged as predictors of improvement in glucose tolerance after adjustment (Table 3). Finally, 11.0% of individuals with AGT at baseline were successful in both weight loss and improvement in glucose tolerance.
Discussion
We examined the predictors of success of lifestyle intervention 1 year after lifestyle intervention in the FIN-D2D program which has affected DE-PLAN2 and IMAGE33 initiatives. Over 19% of individuals were successful in weight loss, and almost 33% with AGT at baseline showed an improvement in glucose tolerance. The strongest predictor of success was AGT at baseline. An earlier implementation study showed that individuals with AGT lost more weight. 12 Furthermore, AGT predicted nondeterioration of glucose tolerance in a lifestyle intervention study 32 contrary to results of an RCT that showed that lifestyle intervention was more effective in persons with lower baseline 2-hour glucose concentrations in terms of diabetes incidence. 3 We suggest that AGT may have increased individuals’ awareness of T2D risk and perhaps also its long-term consequences and thus increased motivation to lifestyle changes. Individuals are more likely to change their health behavior when they are in immediate threat of the condition. 34
High number of intervention visits was a strong predictor of success of weight loss, which underscores the importance of attending lifestyle intervention to lose weight. This finding is in line with RCTs.16,18 Our results indicate that at least 3 intervention visits are needed to gain benefit from interventions. It is therefore important to motivate individuals to participate in and commit to lifestyle intervention visits.
Furthermore, being outside of labor force and high initial BMI predicted success in weight loss, as in earlier RCTs.14,17 The result concerning unemployed individuals is important because they usually have poorer health. 35 Yet, they may also have more time than employed individuals to engage in lifestyle interventions. 8 Initial BMI may help in setting realistic goals for weight loss. 16 Interestingly, highly educated individuals were more successful in improvement in glucose tolerance. This may be due to their having more knowledge of the principles of healthy diet 36 and having accomplished a healthier lifestyle in general than individuals with lower education. 35 It is noteworthy that selected factors in final models differed in their properties; for example, education could be seen as a moderator but smoking as a mediator. 37
The success in weight loss was defined as ≥ 5% loss of initial weight 38 ; 19.3% succeeded in this. In other implementation studies, after 1 year, 12% of individuals lost > 5% weight in a Finnish study, 8 and all together, 34% lost ≥ 5% in a study carried out in the United States, 13 but this variation may be due to different study populations and intervention programs applied. Some individuals in our study did not even aim at weight loss. Because classification of improvement in glucose tolerance was arbitrary, it is difficult to compare our results with those of earlier implementation studies regarding glucose tolerance.10-12
It would be reasonable to target effective interventions to carefully selected participants who are deemed to respond successfully. 20 Our results apply to a high-risk strategy and highlight the possible barriers for success in lifestyle intervention. We did not measure the motivation of the individuals directly, but motivation might be one of the key factors contributing to the long-term success of lifestyle intervention. For individuals who were not so successful, alternative lifestyle interventions, such as Internet and/or e-mail counseling, might be provided. 39 Furthermore, we suggest that motivation and possibilities to participate in lifestyle intervention should be evaluated before any intensive programs are offered to people at increased risk for T2D.
We examined a multitude of factors as predictors of the success of a lifestyle intervention, and glucose tolerance was tested with the oral glucose tolerance test. 26 However, only 38.2% of the individuals had 1-year follow-up information, 25 and 32.0% did not participate in intervention visits; the reasons for these nonparticipations are not available. However, it could be that the more motivated individuals participated in these visits, thus affecting the results of this study. Furthermore, we cannot describe our intervention program in detail due to local variations (resources and experience).
The predictors of success in RCTs, such as high BMI at the baseline, being outside the workforce, and a high number of intervention visits seemed to be similar in “real-life settings,” but differences exist according to baseline glucose tolerance. In terms of weight loss, success was characterized by AGT, high BMI, being outside the workforce, and a high number of intervention visits, whereas improvement in glucose tolerance was characterized by AGT and high education. These individual characteristics should be taken into account in targeting lifestyle interventions for individuals at high risk for T2D.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: FIN-D2D was supported by financing from the hospital districts of Pirkanmaa, Southern Ostrobothnia, North Ostrobothnia, Central Finland, and Northern Savo; the Finnish National Public Health Institute; the Finnish Diabetes Association; the Ministry of Social Affairs and Health in Finland; Finland’s Slottery Machine Association; the Academy of Finland (grant No. 129293); and the Commission of the European Communities, Directorate C-Public Health (grant agreement No. 2004310), in cooperation with the FIN-D2D Study Group and the Steering Committee: Huttunen J, Kesäniemi A, Kiuru S, Niskanen L, Oksa H, Pihlajamäki J, Puolakka J, Puska P, Saaristo T, Vanhala M, and Uusitupa M.