Article:Effect of lifestyle interventions on cardiovascular risk factors among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis (5426619)

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This page is the ScienceSource HTML version of the scholarly article described at Its title is Effect of lifestyle interventions on cardiovascular risk factors among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis and the publication date was 2017-05-11. The initial author is Xuanping Zhang.

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Journal Information

Title: PLoS ONE

Effect of lifestyle interventions on cardiovascular risk factors among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis

Alternative Title: Lifestyle interventions and cardiovascular risk reduction

  • Xuanping Zhang
  • Heather M. Devlin
  • Bryce Smith
  • Giuseppina Imperatore
  • William Thomas
  • Felipe Lobelo
  • Mohammed K. Ali
  • Keri Norris
  • Stephanie Gruss
  • Barbara Bardenheier
  • Pyone Cho
  • Isabel Garcia de Quevedo
  • Uma Mudaliar
  • Christopher D. Jones
  • Jeffrey M. Durthaler
  • Jinan Saaddine
  • Linda S. Geiss
  • Edward W. Gregg

[1]Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America

[2]Office of Public Health Scientific Services, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America

[3]Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America

[4]Health Policy and Administration, Fulton-DeKalb Hospital Authority, Atlanta, Georgia, United States of America

[5]Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America

[6]Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America

  • Noel Christopher Barengo (Editor)

Florida International University Herbert Wertheim College of Medicine, UNITED STATES

Publication date (epub): 5/2017

Publication date (collection): /2017


Structured lifestyle interventions can reduce diabetes incidence and cardiovascular disease (CVD) risk among persons with impaired glucose tolerance (IGT), but it is unclear whether they should be implemented among persons without IGT. We conducted a systematic review and meta-analyses to assess the effectiveness of lifestyle interventions on CVD risk among adults without IGT or diabetes. We systematically searched MEDLINE, EMBASE, CINAHL, Web of Science, the Cochrane Library, and PsychInfo databases, from inception to May 4, 2016. We selected randomized controlled trials of lifestyle interventions, involving physical activity (PA), dietary (D), or combined strategies (PA+D) with follow-up duration ≥12 months. We excluded all studies that included individuals with IGT, confirmed by 2-hours oral glucose tolerance test (75g), but included all other studies recruiting populations with different glycemic levels. We stratified studies by baseline glycemic levels: (1) low-range group with mean fasting plasma glucose (FPG) <5.5mmol/L or glycated hemoglobin (A1C) <5.5%, and (2) high-range group with FPG ≥5.5mmol/L or A1C ≥5.5%, and synthesized data using random-effects models. Primary outcomes in this review included systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Totally 79 studies met inclusion criteria. Compared to usual care (UC), lifestyle interventions achieved significant improvements in SBP (-2.16mmHg[95%CI, -2.93, -1.39]), DBP (-1.83mmHg[-2.34, -1.31]), TC (-0.10mmol/L[-0.15, -0.05]), LDL-C (-0.09mmol/L[-0.13, -0.04]), HDL-C (0.03mmol/L[0.01, 0.04]), and TG (-0.08mmol/L[-0.14, -0.03]). Similar effects were observed among both low-and high-range study groups except for TC and TG. Similar effects also appeared in SBP and DBP categories regardless of follow-up duration. PA+D interventions had larger improvement effects on CVD risk factors than PA alone interventions. In adults without IGT or diabetes, lifestyle interventions resulted in significant improvements in SBP, DBP, TC, LDL-C, HDL-C, and TG, and might further reduce CVD risk.



Cardiovascular disease (CVD) is the number one killer globally.[[1]] CVD is also the major cause of morbidity and mortality among persons with diabetes, and the largest contributor to health care costs associated with diabetes.[[2],[3]] On the other hand, CVD and diabetes share similar risk factors such as unhealthy diet, physical inactivity, and obesity.[[2][4]] Previous studies have demonstrated that structured lifestyle interventions incorporating physical activity, diet, and behavior change strategies could prevent or delay type 2 diabetes incidence and reduce CVD risk factors.[[5][7]] However, these major prevention trials focused on populations with impaired glucose tolerance (IGT).[[5][7]] Although individuals with IGT are the priority target population because they lie at the higher end of the diabetes risk spectrum, populations without IGT but with other CVD risk factors may outnumber those with high diabetes risk and have the same urgent needs for risk reduction, as many RCT studies have indicated.[[8][14]] According to the American Diabetes Association’s (ADA) definitions of pre-diabetes (which includes impaired fasting glucose (IFG): 100-125mg/dL), about 60% of US individuals with pre-diabetes do not have IGT,[[15]] and according to the World Health Organization’s (WHO) definition of intermediate hyperglycemia (measured by fasting plasma glucose (FPG): 110-139mg/dL), about 70% of individuals with this condition do not have IGT.[[16]] Whether lifestyle interventions should be applied more broadly to the population at lower risk (i.e. those below the IGT threshold) to reduce CVD risk needs to be examined.

According to an American Heart Association (AHA) Special Report,[[17]] cardiovascular health is defined by 7 metrics, including health behaviors and health indicators as follows: smoking status, body mass index (BMI), physical activity (PA) levels, healthy diet scores, total cholesterol (TC), blood pressure (BP) level, and fasting plasma glucose level. To achieve the AHA ideal cardiovascular health promotion goal, each indicator must fall into certain ranges (e.g., FPG<100 mg/dL). This definition of cardiovascular health addresses health behaviors and health indicators related to both CVD and diabetes, and thus offer guidance for how to achieve improvements in preventing both CVD and diabetes at the same time.

Evidence regarding the effects of lifestyle intervention on CVD risk reduction has previously been systematically synthesized by examining 6 of the 7 CVD health indicators mentioned above, especially by examining the different stratum of BMI (e.g., moderate weight loss will reduce both diabetes and CVD risk among overweight or obese populations[[5][7]]), as indicated by the 2013 AHA/ACC Guideline on Lifestyle Management to Reduce Cardiovascular Risk.[[18]] However, how this evidence is aligned with the stratification of different glucose levels is still unclear. Lack of this information may prevent public health practitioners from fully understanding the role lifestyle interventions can play in reducing both diabetes and CVD risk among populations with varying risk levels. In contrast, a synthesis of evidence on the impact of lifestyle interventions among populations with different risk levels may help to inform decisions regarding the allocation of finite public health resources.

We conducted a systematic review to assess the aggregated impact of lifestyle interventions on glucose regulation and CVD risk factors among adults (age≥18 years) without IGT or diabetes. By conducting this review, we intend to answer the following research question: can lifestyle interventions similar to those found efficacious among populations with IGT achieve the same magnitude of improvement in CVD risk reduction among populations with lower diabetes risk? We also aimed to examine whether lifestyle interventions focused on diet, PA or their combination have varying impact on CVD risk reduction. To understand how to reach the comprehensive goal of preventing both CVD and diabetes, we also examined how the lifestyle interventional effect on CVD risk reduction is related to the effect sizes of glucose improvement and weight loss.

Materials and methods

Search strategy and selection criteria

We followed Cochrane Collaboration standards for a meta-analysis of randomized control trial (RCT) studies to develop our protocol.[[19]] We systemically searched MEDLINE, EMBASE, CINAHL, Web of Science, the Cochrane Library, and PsychInfo databases, from inception to May 4, 2016. Medical Subject Headings, text words, and search strategies are presented in our online-only supplements (S1 File). We examined reference lists of all included studies and relevant reviews for additional studies. We directly contacted authors to clarify data as needed.

We selected RCTs published in any language that examined lifestyle strategies involving PA and/or dietary (D) interventions, among adults (≥18 years) and with glycemic indicators and CVD risk factors reported as intervention outcomes (e.g., systolic blood pressure (SBP), diastolic blood pressure (DBP), TC, low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), or triglycerides (TG)). Included studies investigated persons without IGT or diabetes. We excluded all studies that included individuals with IGT, confirmed by 2-hours oral glucose tolerance test (75g), but included all other studies recruiting populations with different glycemic levels. However, to examine whether there was heterogeneity of effect by baseline glycemia, we grouped all studies as: (1) low range glycemia group with mean fasting plasma glucose (FPG)<5.5mmol/L or mean glycated hemoglobin (A1C)<5.5% and (2) high range group with mean FPG≥5.5mmol/L or mean A1C≥5.5%. Data from the low and high range glycemic groups were analyzed separately. We only included interventions with a follow-up interval of at least 12 months.

Study selection and data extraction

Two reviewers independently reviewed each article title and abstract for inclusion. If any disagreement occurred between two reviewers, a third reviewed the item and consensus was reached through discussions.

We extracted data regarding demographic and intervention characteristics. Primary outcomes included SBP, DBP, TC, LDL-C, HDL-C, and TG. In our review, all interventions were classified as PA alone, D alone, or combined interventions (PA+D). PA interventions included any strategy used to promote physical activity levels using counseling, exercise prescription, and/or a supervised or unsupervised exercise program. D interventions included any strategy used to reduce or control calorie intake, e.g., very low-calorie diet (<800 kcal/d) or low-calorie diet (800 to 1500 kcal/d). Studies using combined PA and D strategies usually also employed behavioral modification strategies, including counseling, education, cognitive-behavioral therapy, or social support, as an intervention component.

Statistical analysis and quality assessment

We assessed study quality by examining potential selection, attrition, and detection bias.[[19]] We did not exclude any study that was considered poor quality (e.g., studies with attrition ≥30%). However, we conducted a sensitivity analysis to compare pooled effects between studies with potentially significant bias and those without. For example, for those studies with attrition ≥30%, their data were not used in our primary meta-analyses, but were used in our sensitivity analyses.

Among studies with similar intervention and comparison groups reporting a similar outcome of interest, we conducted meta-analyses to determine pooled effects. We calculated the mean difference between baseline and follow-up measures for the intervention (I) and comparison (C) groups (delta I and delta C) and the standard error of each difference. We used three strategies to estimate pooled effects: (1) stratified by baseline glucose levels (low range vs. high range); (2) stratified by the length of follow-up (12months vs. 13–23 months vs. ≥24 months); and (3) stratified by type of interventions (PA vs. D vs. PA+D).

We used DerSimonian and Laird random-effects models[[20]] to determine pooled effects. Effect size was defined by the mean difference between delta I and delta C divided by the standard deviation of the mean. We used meta-regression to determine whether various study-level characteristics (mean age, follow-up interval, duration of the intervention, number of intervention contacts, attrition, and year of publication) affected the between-group differences in SBP, DBP, TC, LDL-C, HDL-C, and TG, and we examined interaction terms for all models. We also used meta-regression analyses to examine the relationship between interventional effects on CVD risk reduction and interventional effects on diabetes risk reduction measured by the effect sizes of glucose improvement and weight loss. The meta-regression was conducted using SPSS (version 20.0, Armonk, NY: IBM Corp.). We used the chi-squared test to examine heterogeneity, and we used Cochrane Review Manager software (version 5.1; Copenhagen, Denmark) to calculate pooled effects.

If a comparison group in a study used a similar approach as the intervention group did, but only differed in dose, intensity, or frequency (e.g., diet plan A vs. diet plan B; or swimming vs. walking), we analyzed the effects of treatment in a single arm model to determine within-group changes (between post-intervention and pre-intervention in one arm) for both intervention and comparison group. These effects were also estimated by using the DerSimonian and Laird random-effect model. We did not, however, conduct any sensitivity analysis for these studies. Because this paper focused on the net lifestyle intervention effect (any lifestyle intervention vs. no intervention [e.g., usual care (UC)]), pooled effects from our single arm model are not reported in our results section, but are presented as an online supplementary table (Table C in S1 File).


Seventy-nine studies[[10],[11],[13],[14], [21][95]] and 30 companion publications[[9],[96][124]] encompassing 15618 participants (Table 1: range, 20 to 1089) fulfilled the inclusion criteria (Fig 1). Follow-up time ranged from 12 to 54 months. The mean age of the participants was 50.6 years (range, 30.2 to 70.4 year), and mean BMI was 30.5 kg/m2 (range, 23.3 to 38.7 kg/m2). Mean baseline SBP, DBP, TC, LDL-C, HDL-C, and TG were 127.5 mmHg, 79.2 mmHg, 5.4 mmol/L, 3.3 mmol/L. 1.3 mmol/L, and 1.5 mmol/L, respectively. More studies took place in community settings than in clinics (58 vs. 21). Sampling methods varied, but most participants were recruited through screening programs. Attrition ranged from 0% to 60%, and in 16 studies,[[21],[34][36],[45],[60],[62],[66],[69],[74],[76],[78],[81],[82],[86],[94]] attrition was 30% or more; longer follow-up resulted in higher attrition. Thirty-nine studies with mean FPG <5.5mmol/L or mean A1C <5.5% were classified as low range group, and 40 studies with mean FPG ≥5.5mmol/L or mean A1C ≥5.5% were classified as high range group.

10.1371/journal.pone.0176436.g001Fig 1

Study flow diagram.

CINAHL, Cumulative Index to Nursing and Allied Health Literature EMBASE, Excerpta Medica Database MEDLINE, Medical Literature Analysis and Retrieval System Online PsycInfo, Psychological Information Database WOS, Web of Science.

10.1371/journal.pone.0176436.t001Table 1

Characteristics of study participants.

Citation Sample size Length offollow-up(month) Age at BL(years)[mean (SD)] Sex(% female) Setting;Race/ethnicity BMI at BL(kg/m2)[mean (SD)] SBP/DBPat BL(mmHg)[mean (SD)] TC at BL(mmol/L)[mean (SD)] LDL/HDLat BL(mmol/L)[mean (SD)] TG at BL(mmol/L)[mean (SD)] Inclusion criteria Sampling method Attrition(%)
Ackermann et al. 2008 92 12 58.3 (10.1) 55.4 CommunityIndianapolisIN81.5% white,12.0% black 31.4 (4.9) 132.5 (16.6)/81.5 (9.1) 4.9 (1.0) NR/1.2(0.4) NR People with ADA risk score≥10 and casual capillary blood glucose (CCBG) of 110–199 mg/dl Recruited from YMCA by a community-based screening 32.6
Almeida et al. 2011 53 12 Range:20–29: 12%30–39: 26%≥40: 52% 18.9 ClinicSao PauloBrazil 23.3 (2.7) 111.1 (11.6)/75.2 (7.3) 4.8 (1.0) 2.8 (0.8)/1.2 (0.3) 1.5 (0.8) Aged: 20-59yrs; without hyperlipidemia, hypertrygliceridemia, hyperglycemia, obesity, cancer, anabolic, or corticosteroid drugs use, or pregnancy Recruited from a reference HIV clinic 20.8
Anderson et al. 2014Craigie et al. 2011 329 12 63.6 (6.8) 26.0 CommunityScotlandUK99.0% white 30.7 (4.2) 142.5 (17.8)/84 (10.0) 5.1 (1.2) 3.0 (1.1)/1.4 (0.4) 1.7 (1.1) Aged: 50-74yrs; BMI>25kg/m2; with polypectomy for adenoma, without pregnancy, DM Recruited from a bowel screening program 7.3
Anderssen et al. 1996 & 1998Jacobs et al. 2009The ODES Investigators 1993Torjesen et al. 1997 219 12 44.9 (2.5) 9.6 CommunityOsloNorway 28.8 (3.4) 131.5 (12.4)/90.1 (8.1) 6.3 (0.8) NR/1.0 (0.2) 2.3 (1.1) BMI>24 kg/m2DBP: 86–99 mmHgTC: 5.20–7.74 mmol/LHDL-C<1.2 mmol/LTG>1.4 mmol/L Recruited from a continuously ongoing screening program in Oslo 4.6
Arguin et al. 2012 25 12 60.5 (6.0) 100.0 CommunitySherbrookeQuebecCanada Weight (SD)79.6 (10.7) NR/NR 5.8 (0.7) 3.5 (0.6)/1.5 (0.3) 1.8 (0.9) Sedentary obese postmenopausal women without:(1) abnormal fasting lipid profile(2) CVD(3) DM Using a computer-generated randomization list 12.0
Bazzano et al. 2014 148 12 46.8 (10.1) 88.5 CommunityNew OrleansLA45.3% white51.4% black2% Hispanic 35.4 (4.2) 122.6 (13.3)/78.4 (8.7) 5.2 (1.1) 3.2 (1.0)/1.4 (0.3) 1.3 (0.8) Obese people (BMI: 30–45 kg/m2) without DM and CVD Recruited from community screenings and TV ads 17.8
Bo et al. 2007&2009 375 48 55.7 (5.7) 58.2 CommunityAstiItaly 29.7 (4.4) 142.1 (14.7)/88.0 (9.2) 5.9 (1.1) NR/1.4 (0.3) 1.9 (0.9) People with MetS defined by FPG>110 mg/dL, without DM and CVD Recruited from a metabolic screening 10.7
Bouchonville et al. 2014Villareal et al. 2011 107 12 69.7 (4.0) 62.6 CommunitySt. LouisMO 37.2 (5.0) 134.7 (18.8)/73.0 (10.1) NR NR/1.4 (0.4) 1.6 (0.7) Old (≥65yrs) and obese (≥30 kg/m2) people without DM Recruited from ads 13.0
Brinkworth et al. 2004 58 12 50.2 (NR) 77.6 CommunityAdelaideAustralia 34.0 (NR) 132.0 (13.9)/75.1 (10.7) 5.6 (0.9) 3.8 (0.9)/1.0 (0.3) 1.9 (0.7) Obese, hyperinsulinemic persons aged between 20 and 65yrs, insulin > 12 mu/l without DM NR 25.9
Broekhuizen et al. 2012 340 12 45.3 (12.9) 56.7 CommunityAmsterdamThe Netherland 26.5 (5.0) 124.5 (15.0)/NR 5.2 (1.3) 3.6 (1.3)/1.2 (0.4) 1.2 (0.6) Aged: 18-70yrs, with familial hypercholesterolemia, a LDL-C level>75th percentile Recruited from the national cascade screening program 7.4
Burke V, et al. 2007 & 2008 241 36 56.2 (7.3) 55.6 CommunityPerthAustralia 30.1 (2.7) 126.5 (9.5)/76.5 (7.5) 5.1 (0.9) NR/1.3 (0.3) 1.3 (0.7) Overweight, age>40yrs persons using 1 or 2 drugs to treat HT >3 Months without DM, chronic renal failure, CVD Recruited by media advertising 16.2
Burtscher et al. 2009&2012 36 12 57.5 (6.9) 55.6 ClinicInnsbruckAustria 29.0 (3.9) 191 0 (25.9)/91.6 (11.0) 5.8 (1.0) NR/1.4 (0.4) NR Patients with IFG (FPG:100–125 mg/dl), aged: 40-65yrs; BMI>25 kg/m2, and without DM Recruited from family physicians through screening 0.0
Chirinos 2016 120 12 51.7 (8.4) 55.8 ClinicsCoral GablesFL84.0% Hispanic10.9% black NR 125.2 (16.8)/79.3 (9.5) NR NR/1.0 (0.2) 2.4 (1.1) Aged: 30-70yrs, obese adults with WC≥102 cm for males, 88 cm for females, TG≥ 150 mg/dl, HDL-C< 40 mg/dl for males, <50mg/dl for females, IFG≥100 mg/dl. Recruited from low-income community clinics 22.5
Choo et al. 2014 110 12 43.1 (9.0) 100.0 CommunitySeoulSouth Korea 28.5 (3.8) 116.5 (13.1)/NR 5.5 (1.0) 3.3 (0.9)/1.4 (0.3) 1.5 (0.9) Age: 18-65yrs; elevated waist circumference (≥85cm), abdominal obesity without DM and CVD Recruited via poster, leaflet, telephone, and ads 55.5
Clifton et al. 2008 119 12 49.0 (9.0) 100.0 CommunityAdelaideAustralia 32.8 (3.5) NR/NR 5.8 (1.1) 3.9 (0.9)/1.3 (0.3) 1.4 (0.6) Women, aged: 20-65yrs, BMI:27-40kg/m2, without DM, or renal or liver disease Recruited from public ads and screened 33.6
Cole et al. 2013 94 12 58.3 (9.6) 46.0 CommunitySan AntonioTX64.0% white,17.0% black,19.0% Hispanic 30.8 (4.9) 143.0 (17.0)/83.0 (10.0) 5.0 (1.0) 2.9 (0.9)/1.4 (0.4) 1.8 (1.4) Aged:18+yrs; without DM, but with pre-DM, by ADA defined IFG (100–125 mg/dL) Recruited from a pre-DM education class 31.0
Coon et al. 1989 20 12 59.5 (7.5) 0.0 CommunityBaltimoreMD 29.0 (3.0) NR/NR 4.6 (0.7) 3.1 (0.7)/0.8 (0.2) 1.5 (0.4) Aged 45+yrs, healthy persons without DM Recruited by ads 0.0
Cox et al 2006 & 2008 & 2010 116 12 55.5 (4.7) 100.0 CommunityBerthWesternAustralia 26.4 (3.3) NR/NR 5.2 (0.7) 3.2 (0.7)/1.5 (0.3) 1.1 (0.5) Aged: 50-70yrs; BMI<34 kg/m2; non-smoker, with sedentary lifestyle, without DM Recruited by ads. 25.9
Ditschuneit et al. 1999 & 2001 100 24 45.7 (10.6) 79.0 ClinicsUlmGermany 33.4 (3.6) 139 5 (14.5)/82.5 (6.0) 5.9 (1.0) NR/1.3 (0.4) 2.2 (1.3) Age>18yrs, BMI between 25 and 40 kg/m2 without endocrine disorders Recruited by referring to the obesity clinics 27.0
Donnelly et al. 2000 22 18 51.5 (8.5) 100.0 CommunityKearneyNE 31.2 (4.0) 133.0 (16.1)/80.5 (9.2) 4.9 (1.1) NR/1.1 (0.3) NR BMI>25 kg/m2, low aerobic capacity, at risk for continued weight gain NR 0.0
Esposito et al. 2003 120 24 34.6 (5.0) 100.0 ClinicNaplesItaly 34.9 (2.4) 123.5 (8.2)/85.0 (4.8) 5.1 (0.6) NR/1.2 (0.3) 1.6 (0.6) Obese premenopausal women, aged: 20-46yrs; without DM, IGT (140–200 mg/dl), CAD, pregnancy. OGTT confirmed Recruited from an outpatient dept. 6.7
Esposito et al. 2004a 110 24 43.3 (5.0) 0.0 ClinicNaplesItaly 36.7 (2.4) 127.5 (7.6)/85.5 (3.9) 5.5 (0.8) NR/1.0 (0.3) 1.9 (0.6) Obese men with erectile dysfunction, aged:35-55yrs; without DM and IGT, OGTT confirmed Recruited from an outpatient department list 5.5
Esposito et al. 2004b (JAMA v.292) & 2009 180 24 43.9 (6.2) 45.0 ClinicNaplesItaly 28.0 (3.3) 135.0 (9.5)/85.5 (6.5) 5.1 (0.9) NR/1.1 (0.2) 1.9 (0.6) Sedentary people with MetS, FPG≥110 mg/dL, Recruited from a screening program 8.9
Fatouros et al. 2005 50 12 70.4 (3.8) 0.0 CommunityAlexandroupolisGreece 29.5 (3.3) NR/NR NR NR/NR NR Inactive old men, nonsmoker, without DM, FPG≤7 mmol/L Recruited from a volunteer database in local community 0.0
Fernandez et al. 2012 40 12 40.9 (13.5) 67.5 CommunityLeonSpain 31.8 (2.4) 124.8 (17.6)/78.5 (12.6) 5.2 (0.9) 3.1 (0.7)/1.4 (0.5) 1.7 (1.0) Aged: 18-70yrs; BMI: 28–35 kg/m2; without DM and pregnancy Recruited from a clinic trial 60.0
Ferrara et al. 2012 188 24 56.4 (9.5) 47.9 ClinicNaplesItaly 29.2 (4.5) 134.1 (16.0)/84.4 (10.6) 5.1 (0.9) 3.2 (0.9)/1.3 (0.3) 1.5 (1.0) People with HT Recruited from an outpatient clinic 0.0
Fischer et al. 2016 163 12 46.4 (11.5) 75.8 ClinicsDenverCO NR 118.8 (14.1)/NR NR NR/NR NR Patients aged 18+yrs, with A1C: 5.7–6.4%; BMI: 25–50 kg/m2; without DM Recruited from health centers 5.7
Fisher et al. 2012 97 12 Range:21–46 100.0 CommunityBirminghamAL53.6% black;46.4% white 28.0 (1.0) NR/NR NR NR/NR NR Aged: 21-46yrs; BMI: 27–30 kg/m2; non-smoker, with sedentary lifestyle premenopausal women Recruited from a previous parent study 0.0
Fogelholm et al. 2000 82 24 Range:30–45 100.0 CommunityTampereFinland 34.0 (3.6) 119.0 (10.0)/78.0 (7.0) 5.0(0.9) NR/1.2 (0.2) 1.3 (0.5) Aged: 30-45yrs, BMI: 30–45 kg/m2, physical inactive Recruited by ads 9.8
Fonolla et al. 2009 297 12 46.0 (8.4) 15.5 CommunityGranadaSpain 28.8 (5.0) 122.1 (15.2)/79.5 (9.0) 5.6 (1.0) 3.7 (1.0)/1.1 (0.3) 1.6 (1.2) People with moderate risk of CVD, without DM and pregnancy Recruited from a screening program 14.8
Frank et al. 2005 173 12 60.7 (6.7) 100.0 CommunitySeattleWashington 30.4 (3.9) NR/NR NR NR/NR 1.4 (0.6) Postmenopausal women, aged: 50-75yrs, sedentary at baseline BMI≥25 kg/m2 without DM, nonsmoker Recruited through a combination of mailings and media placements 1.7
Groeneveld et al. 2008 & 2010 816 12 46.6 (9.0) 0.0 CommunityAmsterdamThe Netherlands 28.5 (3.5) 142.9 (15.3)/88.8 (9.6) NR NR/1.1 (0.2) NR Male construction workers with elevated risk of CVD Recruited from Periodical Health Screening 27.6
Heshka et al. 2003 423 24 44.5 (10.0) 84.6 ClinicsNY, Medison, Baton Rouge, Boulder, Davis, Durham, Woodbury 33.7 (3.6) 122.0 (13.0)/79.0 (8.5) 5.5 (1.0) NR/1.3 (0.3) 1.7 (1.0) Aged: 18-65yrs; BMI: 27–40 kg/m2; with FPG<7.8 mmol/L, Recruited from existing clinic records, or by ads 27.0
Imayama et al. 2013Foster-Schubert et al. 2012Mason et al. 2011&2013 439 12 58.0 (5.0) 100.0 CommunitySeattleWA85.0% white 30.9 (4.1) NR/NR NR NR/NR NR Aged: 50-75yrs; BMI: ≥25 kg/m2; <100 min/w PA; postmenopausal; without DM; FPG<126 mg/dL Recruited from mass mailing ads 9.1
Juul et al. 2016 127 12 NR 68.6 CommunityHolstebroDenmark NR 133.0 (14.1)/82.5 (8.5) 5.3 (1.1) 3.2 (0.9)/1.3 (0.3) NR Aged<70yrs, FPG: 6.1–6.9 mmol/l; A1C: 6.0-<6.5% Recruited from a referral 15.0
Kanaya et al. 2012Delgadillo et al. 2010 238 12 56.5 (16.5) 73.5 CommunityBerkeley,Oakland, etcCA22.5% white,23.0% black,37.0% Hispanic 30.0 (5.7) 127.2 (20.0)/NR NR 3.0 (1.1)/1.4 (0.4) 1.6 (1.2) Aged: 25+yrs; a capillary blood glucose:106–160 mg/dL, without DM Recruited from a community-based education outreach 12.2
Kanaya et al. 2014 180 12 55.0 (7.0) 72.0 ClinicsSan Francisco,San DiegoCA65% white 34.3 (6.7) 124.0 (14.0)/72.5 (9.0) 5.3 (1.0) 3.2 (0.9)/1.3 (0.3) 1.8 (0.8) Aged: 21-65yrs; with MetS (FPG:100–125 mg/dL), HT, and underactive lifestyle (<150min/w of moderate intensity activity), without DM Recruited by ads and flyers in community and clinical settings 21.1
Katula et al. 2010&2011&2013 301 24 57.9 (9.5) 57.5 CommunityWinston-SalemNC73.8%white,24.6%black 32.7 (4.0) NR/NR NR NR/NR NR Patients with pre-DM defined by FPG of 95–125 mg/dl and BMI of 25–39 kg/m2 and without DM and CVD Recruited from mass mailing, community health fair or referrals 12.6
Kawano et al. 2009 217 17 60.9 (13.8) 66.5 CommunitySaga CityJapan 23.7 (4.4) 127.5 (17.8)/72.3 (8.9) 5.3 (0.9) 3.1 (0.7)/1.5 (0.4) 1.4 (0.8) People with FPG: 100–140 mg/dL, or A1C: 5.5–6.0% Recruited from health checkup 27.2
Keogh et al. 2007 36 12 48.6 (5.2) 68.0 CommunityAdelaideAustralia 32.9 (4.5) 122.0 (10.8)/75.0 (3.6) 5.5 (1.4) 3.6 (1.4)/1.3 (0.4) 1.6 (0.6) Overweight or obese people, aged: 20-65yrs; BMI: 27–40 kg/m2; without DM, with FPG≤7.0mmol/L. Recruited from newspaper ads 30.6
Lawton et al. 2009 1089 24 58.9 (6.9) 100.0 ClinicsWellingtonNew Zealand 29.2 (6.0) 123.1 (17.5)/74.3 (9.3) 6.1 (1.2) NR/1.6 (0.5) NR Physically inactive women, aged: 40-74yrs without medical condition Recruited by invitation letters or practice register 7.4
Lim et al. 2010 113 12 47.0 (10.0) 82.3 CommunityAdelaideAustralia 32.0 (6.0) 127.0 (12.6)/76.3 (10.2) 5.6 (1.0) 2.9 (1.7)/1.3 (0.3) 1.6 (0.8) Aged: 20-65yrs, BMI: 28–40 kg/m2, with at least one CVD risk factor, without DM Recruited by ads 38.9
Lombard et al. 2010 250 12 40.4 (4.8) 100.0 CommunityMelbourneAustralia 27.8 (5.4) NR/NR 4.9 (0.9) 2.6 (0.8)/1.7 (0.4) 1.0 (0.7) Women with a child in schools without pregnancy and serious medical conditions Recruited through an invitation attached to school newsletter 14.0
Ma et al. 2009&2013 241 15 52.9 (10.6) 47.0 ClinicSan FranciscoCA78% white,17% Asian 32.0 (5.4) 118.8 (11.7)/73.6 (8.3) 4.9 (0.9) 2.8 (0.8)/1.2 (0.3) 1.9 (0.8) Patients aged≥18yrs, BMI≥25 kg/m2, with pre-DM defined by FPG of 100–125 mg/dl, or MetS Recruited from a single primary care clinic 8.3
Marrero et al. 2016 225 12 52.0 (11.0) 84.4 CommunityIndianapolisIN64.5% white25.3% black 36.8 (7.2) 130.2 (14.0)/81.4 (8.5) 4.9 (0.9) NR/1.2 (0.4) NR Aged 18+yrs, BMI>24 kg/m2 (>/ = 23 kg/m2 for Asian); ADA risk score≥5; A1C: 5.7–6.5% Recruited from a screening 22.2
Marsh et al. 2010 96 12 30.2 (5.2) 100.0 ClinicSydneyAustralia 34.5 (4.2) NR/NR 4.8 (0.7) 2.8 (0.7)/1.4 (0.7) 1.3 (0.7) Women, aged: 18-40yrs; BMI<25 kg/m2, with polycystic ovary syndrome, without pregnancy and DM Recruited from a screening program 49.0
Mason et al. 2016 194 12 47.0 (12.7) 78.0 CommunitySan FranciscoCA58.8% white12.9% black11.9% Hispanic 35.5 (3.6) NR/NR NR NR/NR NR Obese adults aged 18+yrs, with BMI: 30–45.9 kg/m2; WC>102 cm for males, >88 cm for females, without DM, confirmed by FPG<126 mg/dl Recruited from community by newspaper ads. 23.2
McAuley et al. 2005&2006 93 12 Range:30–70 100.0 CommunityDunedinNew Zealand 35.7 (5.0) 126.8 (13.0)/81.9 (10.0) 5.8 (1.0) 3.8 (0.8)/1.2 (0.3) 1.9 (0.7) Overweight women, aged: 30-70yrs; BMI:>27 kg/m2; without pregnancy Recruited by local ads 18.3
Mellberg et al. 2014 70 24 59.9 (5.7) 100.0 CommunityUmeaSweden 32.7 (3.5) 139.5 (13.0)/83.0 (8.3) 5.7 (1.1) 3.8 (1.0)/1.4 (0.4) 1.2 (0.6) Postmenopausal non-smoking women, BMI≥27 kg/m2, without DM, FPG<7 mmol/L Recruited by newspapers ads 30.0
Muto et al. 2001 326 18 42.5 (3.7) 0.0 CommunityTokyoJapan 24.7 (3.0) 123.2 (15.6)/78.4 (12.1) 5.5 (0.9) NR/1.3 (0.4) 2.3 (1.4) Male workers with at least one abnormality, including FPG>100 mg/dL Recruited from a building maintenance company 7.4
Narayan et al. 1998 95 12 Range:25–50 75.8 CommunityPimaAZ Range:20.2–59.9 Range:90.0-176/48.0–98.0 Range:2.1–6.1 NR/NR Range:0.3–3.6 Overweight/obese people, aged: 25-54yrs; BMI>25kg/m2, without DM, OGTT<7.8mmol/L Recruited from an epidemiological study 2.0
Nilsson et al. 1992 94 12 55.0 (7.2) NR CommunityDalbySweden Weight (kg):81.4 (11.6) 145.0 (18.0)/84.3 (7.6) 5.6 (0.8) 3.9 (0.7)/0.9 (0.2) 1.6 (0.7) Patients with or without HT, but no DM Recruited from a cross-sectional study 8.5
Nilsson et al 2001 113 18 49.7 (6.2) 60.9 CommunityHelsingborgSweden 27.8 (5.6) 132.5 (18.0)/77.4 (9.7) 5.8 (0.9) 3.9 (0.9)/1.2 (0.3) 1.3 (0.7) Aged: 40-50yrs; with a cardiovascular risk score sum of ≥9 Recruited from a screening program 18.6
Ockene et al. 2012Merriam et al. 2009 312 12 52.0 (11.2) 74.4 CommunityLawrenceMA 33.9 (5.6) 128.7 (12.4)/NR NR NR/1.2 (0.3) NR Age>25+yrs, BMI>24kg/m2, with risk for DM, but without DM Recruited from the Greater Lawrence Family Health Center 7.4
Poston et al. 2006 250 12 41.0 (8.5) 92.4 CommunityHustonTX 36.1 (3.1) 121.5 (12.0)/72.3 (8.6) 5.2 (1.0) 3.1 (0.8)/1.4 (0.3) 1.5 (0.8) Overweight/obese people, aged: 25-55yrs; BMI: 27–40 kg/m2; without DM or pregnancy, FPG<7mmol/L, confirmed by OGTT Recruited from a screening program 45.6
Potteiger et al. 2003 & 2002 66 16 NR 57.6 CommunityDenverCO Range:25–34.9 NR/NR NR NR/NR NR Sedentary people without DM and heart disease Recruited from the Midwest Exercise Trial 10.1
Reid et al. 2014 426 12 51.5 (11.6) 61.3 ClinicOttawaCanada95.3% white 29.4 (5.7) 121.1 (16.1)/76.5 (9.5) 5.2 (1.0) 3.3 (0.9)/1.3 (0.4) 1.3 (0.8) Obese people with coronary risk, without DM, pregnancy, FPG<7 mmol/L Recruited from a care cardiac center by ads and flyers 25.8
Rossner et al. 1997 93 12 41.0 (NR) 67.7 ClinicsStockhlomSweden 38.7 (4.5) 136.3 (16.9)/86.5 (12.2) 5.7 (0.9) NR/NR 1.9 (1.0) Obese people with BMI> 30 kg/m2, without DM Recruited from hospital waiting list 38.7
Ryttig et al. 1997 81 28 42.5 (10) 54.3 ClinicsStockhlomSweden 37.7 (4.6) 136.2 (17.3)/85.3 (9.9) 5.7 (1.0) NR/1.1 (0.2) 2.0 (1.2) Obese people, aged: 21-64yrs; BMI:>30 kg/m2; without DM and pregnancy Recruited from hospital waiting list 4.9
Sartorelli et al. 2005 104 12 45.5 (9.1) 79.8 CommunitySao PauloBrazil 28.7 (2.5) 116.6 (17.6)/77.5 (18.3) 5.3 (1.2) 3.6 (1.1)/1.2 (0.4) 1.6 (0.9) Overweight or obese people, aged: 30-65yrs; BMI: 24–35 kg/m2; without DM, or pregnancy Recruited from a screening of high-risk group for DM 31.7
Sattin et al. 2016 604 12 46.5 (10.9) 83.0 CommunityAugustaGA 35.7 (7.3) 130.5 (16.6)/82.6 (9.7) NR NR/NR NR African Americans aged: 20-64yrs; BMI≥25 kg/m2; without DM, confirmed by FPG<126 mg/dl Recruited from church 0.0
Simkin-Silverman et al. 1995 & 1998 & 2003Kuller et al. 2001 & 2006&2012 535 54 47.0 (1.0) 100.0 CommunityAlleghenyPN92.0% white 25.1 (3.3) 110.0 (12.8)/68.0 (8.2) 4.9 (0.6) 3.0 (0.6)/1.5 (0.3) 0.9 (0.5) Premenopausal women, aged: 44-50yrs; BMI: 20–34 kg/m2; FPG<7.8mmol/L Recruited from the Women's Healthy Lifestyle Project 2.8
Siu et al. 2015 182 12 56.0 (9.1) 74.2 CommunityHong KongChina NR 133.8 (16.8)/82.4 (9.8) NR NR/1.2 (0.3) 2.2 (1.8) Aged: 18-94yrs; with MetS by 1) WC: 90 cm for males, 80 cm for females; 2) SBP>130 mmHg, DBP>85 mmHg; 3) FPG>/ = 5.5 mmol/l; 4) TG>1.7 mmol/l; 5) HDL-C<40 mmol/l for males, 50 mmol/l for females Recruited from a screening 35.7
Staten et al. 2004 361 12 57.2 (4.8) 100.0 CommunityTucsonAZ100% Hispanics 29.5 (5.3) 124.8 (16.7)/74.1 (9.6) 5.6 (1.3) NR/NR NR Uninsured Hispanic women, aged≥50yrs, Recruited from clinic registration 33.4
Stefanick et al. 1998 377 12 52.1 (7.3) 47.7 CommunityPalo AltoCA 26.7 (3.0) 115.5 (12.8)/73.2 (7.4) 6.2 (0.6) 4.2 (0.5)/1.2 (0.2) 1.8 (0.8) Postmenopausal women, aged: 45-64yrs; men aged:30-64yrs; without DM, FPG<7.8mmol/L, OGTT confirmed Recruited from the Diet and Exercise for Elevated Risk Trial 27.0
Tapsell et al. 2014 120 12 48.9 (9.3) 75.0 CommunityWollongongAustralia 30.0 (2.7) NR/NR 5.2 (0.9) 3.2 (0.8)/NR NR Healthy adults aged 18-65yrs, BMI: 25–35 kg/m2, without DM Recruited by ads in the local media 22.5
ter Bogt et al. 2009 457 12 56.1 (7.8) 57.9 CommunityBilthovenThe Netherlands 29.6 (3.4) 145.5 (17.0)/86.5 (8.9) 5.6 (1.0) 3.5 (0.9)/1.4 (0.4) NR Overweight or obese people, aged: 40-70yrs; BMI: 25–40 kg/m2; with HT or dyslipidemia, without DM Recruited from a screening program 9.0
Thompson et al. 2005 90 12 41.4 (8.9) 85.6 ClinicKnoxvilleTN 34.8 (3.1) NR/NR 5.0 (0.9) 3.1 (0.9)/1.1 (0.3) 1.8 (1.2) Obese people, aged: 25-70yrs; BMI: 30–40 kg/m2; without DM or pregnancy Recruited from ad posters 13.3
Tsai et al. 2010 50 12 49.4 (11.9) 88.0 ClinicPhiladelphiaPA81% black;19% white 36.5 (6.0) 129.4 (12.2)/80.7 (8.2) 4.9 (0.9) 3.0 (0.9)/1.4 (0.3) 1.1 (0.7) Overweight or obese people with BMI: 27–50 kg/m2, without serious psychiatric illness Recruited from flyers, and referrals from PCPs 6.0
Vainionpaa et al. 2007 120 12 Range:35–40 100.0 CommunityOuluFinland 25.3 (4.6) NR/NR 5.3 (0.9) 3.2 (0.8)/1.7 (0.4) 1.0 (0.5) Women with age: 35-40yrs, without chronic disease Recruited from the National Population Register of Finland 33.3
Vetter et al. 2013Wadden et al. 2011 390 24 51.5 (11.5) 79.7 ClinicPhiladelphiaPA59% white,38.5%black 38.5 (4.7) 121.4 (16.3)/76.2 (10.4) 4.6 (1.0) 2.9 (0.8)/1.1 (0.3) 1.3 (0.7) Aged: 21+yrs; BMI: 30–50 kg/m2; with MetS (FPG≥110mg/dL); without cardiovascular events Recruited from primary care practices 13.8
von Thiele Schwarz et al. 2008 195 12 46.6 (10.8) 100.0 CommunityStockholmSweden NR 114.0 (16.9)/79.1 (11.6) 5.2 (1.0) 2.9 (0.8)/1.8 (0.4) 1.0 (0.6) Working age women without DM and pregnancy Recruited from a public dental health care organization 9.2
Watanabe et al. 2003 173 12 55.1 (7.1) 0.0 CommunityTokyoJapan 24.4 (2.9) 121.7 (14.4)/76.9 (10.5) 5.2 (0.9) NR/1.4 (0.4) 1.4 (0.8) Male workers with risk for DM, aged:35-70yrs; OGTT confirmed Recruited from annual check-up list 9.8
Weinstock et al. 1998 45 23 43.3 (7.4) 100.0 CommunitySyracuseNY 35.9 (6.0) NR/NR NR NR/NR NR Women without DM, CAD, and pregnancy Recruited from a cohort study 0.0
Weiss et al. 2006 48 12 56.8 (3.0) 63.2 CommunitySt. LouisMO 27.3 (2.1) NR/NR NR NR/NR NR Sedentary people, aged: 50-60yrs; BMI:23.5–29.9kg/m2; non-smoker without DM. FPG<7mmol/L, OGTT confirmed Recruited from a screening program 4.2
Wing et al. 1995 202 18 37.4 (5.3) 48.1 CommunityPittsburghPA 30.9 (2.1) 111.7 (10.7)/71.8 (8.1) 5.0 (0.8) NR/1.2 (0.2) 1.2 (0.7) Aged: 25-45yrs; 13.6–31.8 kg above ideal body weight, without serious disease Recruited from newspaper or radio ads 21.3
Wing et al. 1998 154 24 45.7 (4.4) 79.0 CommunityPittsburghPA 35.9 (4.3) 116.7 (14.9)/74.8 (10.1) 5.0 (0.8) 3.1 (0.8)/1.2 (0.3) NR Overweight people, aged:40-55yrs; with diabetic parents Recruited from newspaper ads 22.0
Wycherley et al. 2012 123 12 50.8 (9.3) 0.0 ClinicAdelaideAustralia 33.0 (3.9) 135.1 (12.5)/84.0 (10.7) 5.2 (0.9) 3.2 (0.8)/1.3 (0.4) 1.7 (0.7) Overweight or obese males, aged: 20-65yrs; BMI: 27–40 kg/m2, without DM Recruited by a screening program 44.7
Yeh et al. 2016 60 12 58.9 (10.9) 56.7 CommunityNew York100% Asian 26.1 (2.4) 126.9 (16.1)/78.4 (9.6) 4.8 (1.0) 2.8 (0.9)/1.4 (0.3) 1.4 (0.7) Patients with pre-DM defined by A1C: 5.7–6.4% and BMI>/ = 24kg/m2 Recruited from hospital record 3.3
Mean (SD)     50.6 (8.7)     30.5 (4.6) 127.5 (15.2)/79.2 (9.3) 5.4 (1.0) 3.3 (0.9)/1.3 (0.3) 1.5 (0.9)      
TotalRange 1561820–1089 12–54   0–100   23.3–38.7             0–60.0

Abbreviations: BG: blood glucose; BL: baseline; BMI: body mass index; CAD: coronary Artery Disease; CVD: cardiovascular disease; DBP: diastolic blood pressure; DM: diabetes mellitus; FBG: fasting blood glucose; FPG: fasting plasma glucose; HDL-C: high density cholesterol; HT: hypertension; IGT: impaired glucose tolerance; LDL-C: low density cholesterol; MetS: metabolic syndrome; min/w: minutes/week; NR: not reported; OGTT: oral glucose tolerance test; PG: plasma glucose; SD: standard deviation; TC: total cholesterol; TG: triglycerides.

We observed considerable heterogeneity in the treatments provided to both intervention and comparison groups (Tables A&B in S1 File). In 29 studies, a similar approach was used in both intervention and control groups: data from these studies were synthesized by a single arm model, and are presented in Table C in S1 File as an online supplement. In the other 50 studies, UC was used in the control group. In the 50 studies that compared an intervention to UC, 38 had two arms, 5 studies[[49],[64],[87],[88],[91]] had 3 arms, and 7 studies[[13],[24],[28],[44],[54],[62],[93]] had 4 arms (e.g., PA, D, PA+D and control arm). The randomization procedure was described in 48 studies (Table B in S1 File). In 29 studies, allocation concealment was adequately reported. Meta-regression analyses indicated that there was no significant interaction between the between-group change in FPG and all study-level characteristics, such as mean age, publication date, the length of F/U, number of contacts, attrition, and their interaction terms. An Egger’s plot demonstrated a symmetrical shape distribution (except for two outliers) which is consistent with no publication bias.

Changes in CVD risk factors

In 57 studies or study arms comparing interventions to UC with attrition <30%, the pooled effect estimate from all studies demonstrated that compared to UC, all lifestyle interventions, including PA, D, or PA+D interventions, achieved significant improvements in SBP (-2.05mmHg [95%CI, -2.81, -1.28]), DBP (-1.65mmHg [-2.16, -1.14]), TC (-0.09mmol/L [-0.14, -0.04]), LDL-C (-0.08mmol/L [-0.13, -0.03]), HDL-C (0.03mmol/L [0.01, 0.04]), and TG (-0.08mmol/L [-0.14, -0.03]) (Table 2). When including the 15 studies with attrition ≥30% in the sensitivity analysis, we observed similar effects. The remaining results are limited to studies with attrition <30%.

10.1371/journal.pone.0176436.t002Table 2

Lifestyle interventional effect: Meta-analyses results.

  SBP (mmHg)   DBP (mmHg)   TC (mmol/L)   LDL-C (mmol/L)   HDL-C (mmol/L)   TG (mmol/L)  
  Studies(samplesize) Pooled effectmean (effect size)(95% CI) Hetero-Geneityp value Studies(samplesize) Pooled effectmean (effect size)(95% CI) Hetero-Geneityp value Studies(samplesize) Pooled effectmean (effect size)(95% CI) Hetero-Geneityp value Studies(samplesize) Pooled effectmean (effect size)(95% CI) Hetero-Geneityp value Studies(samplesize) Pooled effectmean (effect size)(95% CI) Hetero-Geneityp value Studies(samplesize) Pooled effectmean (effect size)(95% CI) Hetero-Geneityp value
LI vs UC(all studies*) 42(8331) -2.05 (0.06)(-2.81, -1.28) <0.01 39(7631) -1.65 (0.07)(-2.16, -1.14) <0.01 36(6925) -0.09 (0.04)(-0.14, -0.04) <0.01 27(4563) -0.08 (0.05)(-0.13, -0.03) <0.01 43(8414) 0.03 (0.03)(0.01, 0.04) <0.01 38(5926) -0.08 (0.03)(-0.14, -0.03) <0.01
LI vs UC(all studiesƚ) 50(9053) -2.13 (0.04)(-2.88, -1.38) <0.01 46(8261) -1.57 (0.06)(-2.07, -1.07) <0.01 44(7541) -0.11 (0.05)(-0.16, -0.06) <0.01 34(5087) -0.09 (0.04)(-0.15, -0.04) <0.01 52(9212) 0.03 (0.03)(0.01, 0.04) <0.01 46(6632) -0.08 (0.04)(-0.13, -0.03) <0.01
LI vs UC(Group 1ⱡ) 17(3492) -0.95 (0.04)(-1.75, -0.15) 0.02 15(2949) -1.40 (0.06)(-2.24, -0.56) <0.01 16(2904) -0.06 (0.03)(-0.13, 0.01) <0.01 15(3065) -0.08 (0.05)(-0.14, -0.02) <0.01 19(3770) 0.01 (0.03)(0.00, 0.03) 0.06 19(3240) -0.04 (0.02)(-0.10, 0.02) 0.19
LI vs UC(Group 2§) 25(4839) -2.89 (0.08)(-3.95, -1.83) <0.01 24(4682) -1.83 (0.08)(-2.50, -1.17) <0.01 20(4021) -0.12 (0.06)(-0.18, -0.05) <0.01 12(1498) -0.10 (0.06)(-0.18, -0.01) 0.02 24(4644) 0.04 (0.06)(0.02, 0.06) <0.01 20(2686) -0.12 (0.05)(-0.21, -0.04) <0.01
LI vs UC(F/U = 12m) 34(6616) -2.07 (0.05)(-3.19, -0.95) <0.01 31(5916) -1.62 (0.06)(-2.29, -0.95) <0.01 29(5813) -0.06 (0.04)(-0.10, -0.01) <0.01 23(3643) -0.08 (0.05)(-0.13, -0.02) <0.01 33(6782) 0.02 (0.05)(0.01, 0.03) <0.01 27(3959) -0.08 (0.04)(-0.14, -0.03) <0.01
LI vs UC(F/U = 13-23m) 6(1418) -1.73 (0.08)(-2.80, -0.65) 0.98 6(1436) -1.25 (0.08)(-2.02, -0.48) 0.60 6(974) -0.19 (0.17)(-0.26, -0.11) 0.46 5(1033) -0.12 (0.10)(-0.19, -0.05) 0.36 7(1494) 0.00 (0.0)(-0.03, 0.03) 0.37 7(1494) -0.08 (0.03)(-0.21, 0.05) <0.01
LI vs UC(F/U≥24m) 14(3123) -1.58 (0.05)(-2.71, -0.45) <0.01 14(3122) -1.36 (0.05)(-2.30, -0.41) <0.01 13(2788) -0.07 (0.03)(-0.17, 0.03) <0.01 5(543) 0.06 (0.04)(-0.07, 0.20) 0.39 14(3122) 0.05 (0.06)(0.02, 0.08) <0.01 13(2034) -0.08 (0.03)(-0.19, 0.03) <0.01
PA vs UC 7(1466) -0.72 (0.03)(-1.89, 0.44) 0.22 7(1465) -1.12 (0.05)(-2.34, 0.10) 0.22 6(1429) -0.02 (0.01)(-0.09, 0.06) 0.76 3(256) -0.03 (0.02)(-0.18, 0.12) 0.91 7(1463) 0.01 (0.02)(-0.02, 0.04) 0.10 6(375) -0.10 (0.08)(-0.22, 0.02) 0.48
D vs UC 4(263) -1.45 (0.07)(-3.83, 0.94) 0.23 4(263) -2.28 (0.16)(-4.07, -0.49) 0.74 3(228) -0.17 (0.13)(-0.34, -0.01) 0.89 3(228) -0.14 (0.11)(-0.30, 0.02) 0.99 4(263) 0.00 (0.00)(-0.04, 0.04) 0.78 4(263) -0.15 (0.07)(-0.41, 0.10) 0.14
PA+D vs UC 31(6602) -2.29 (0.06)(-3.19, -1.40) <0.01 28(5903) -1.66 (0.07)(-2.24, -1.09) <0.01 27(5268) -0.10 (0.05)(-0.16, -0.05) <0.01 21(4079) -0.08 (0.04)(-0.14, -0.02) <0.01 32(6688) 0.03 (0.07)(0.02, 0.05) <0.01 29(5288) -0.07 (0.03)(-0.13, -0.01) 0.02

Abbreviations: D: dietary; DBP: diastolic blood pressure; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; LI: lifestyle intervention; m: month; NA: not applicable; PA: physical activity; SBP: systolic blood pressure; TC: total cholesterol; TG: triglycerides; UC: usual care; vs: versus

* All studies with attrition <30%.

ƚ All studies with attrition <30% plus studies with attrition ≥30%.

ǂ All studies with attrition <30% and participants with FPG<5.5 mmol/L or A1C <5.5%.

§ All studies with attrition <30% and participants with FPG≥5.5 mmol/L or A1C≥5.5%.

Comparison according to participant baseline glycemic level

In the 39 studies among persons with low range glycemic level, lifestyle interventions were associated with significantly improved SBP (-0.95mmHg [-1.75, -0.15]), DBP (-1.40mmHg [-2.24, -0.56]), LDL-C (-0.08mmol/L [-0.14, -0.02]), and HDL-C (0.01mmol/L [0.00, 0.03])), except for TC (-0.06mmol/L [-0.13, 0.01]) and TG (-0.04mmol/L [-0.10, 0.02). In the 40 studies among persons with high range glycemic level, lifestyle interventions significantly improved most CVD risk indicators, and the improvements were more substantial: SBP (-2.89mmHg [-3.95, -1.83]), DBP (-1.83mmHg [-2.50, -1.17]), TC (-0.12mmol/L [-0.18, -0.05]), LDL-C (-0.10mmol/L [-0.18, -0.01]), HDL-C (0.04mmol/L [0.02, 0.06]), and TG (-0.12mmol/L [-0.21, -0.04]).

Comparison according to intervention modality

Analyses stratified by intervention types showed that PA+D vs UC achieved the best incremental improvements in SBP (-2.29mmHg [-3.19, -1.40]), DBP (-1.66mmHg [-2.24, -1.09]), TC (-0.10mmol/L [-0.16, -0.05]), LDL-C (-0.08mmol/L [-0.14, -0.02]), HDL-C (0.03mmol/L [0.02, 0.05]), and TG (-0.07mmol/L [-0.13, -0.01]). D vs UC showed significant improvements in two categories: DBP (-2.28mmHg [-4.07, -0.49]), TC (-0.17mmol/L[-0.34, -0.01]); improvements in other measures did not reach statistical significance. Improvements with PA vs UC did not reach statistical significance in any category: SBP (-0.72mmHg [-1.89, 0.44]), DBP (-1.12mmHg [-2.34, 0.10]), TC (-0.02mmol/L [-0.09, 0.06]), LDL-C (-0.03mmol/L [-0.18, 0.12]), HDL-C (0.01mmol/L [-0.02, 0.04]), and TG (-0.10mmol/L [-0.22, 0.02]). Pooled effects of CVD risk reduction are presented in Figs 2–7.

10.1371/journal.pone.0176436.g002Fig 2

changes in systolic blood pressure in the intervention versus usual care groups (mmHg).

Group 1: low-range glycemic group (FPG<5.5mmol/L or A1C <5.5%). Group 2: high-range glycemic group (FPG ≥5.5mmol/L or A1C ≥5.5%). D, diet, PA, physical activity, UC, usual care, vs, versus.

10.1371/journal.pone.0176436.g003Fig 3

changes in diastolic blood pressure in the intervention versus usual care groups (mmHg).

Group 1: low-range glycemic group (FPG<5.5mmol/L or A1C <5.5%). Group 2: high-range glycemic group (FPG ≥5.5mmol/L or A1C ≥5.5%). D, diet, PA, physical activity, UC, usual care, vs, versus.

10.1371/journal.pone.0176436.g004Fig 4

changes in total cholesterol in the intervention versus usual care groups (mmol/L).

Group 1: low-range glycemic group (FPG<5.5mmol/L or A1C <5.5%). Group 2: high-range glycemic group (FPG ≥5.5mmol/L or A1C ≥5.5%). D, diet, PA, physical activity, UC, usual care, vs, versus.

10.1371/journal.pone.0176436.g005Fig 5

changes in low density lipoprotein cholesterol in the intervention versus usual care groups (mmol/L).

Group 1: low-range glycemic group (FPG<5.5mmol/L or A1C <5.5%). Group 2: high-range glycemic group (FPG ≥5.5mmol/L or A1C ≥5.5%). D, diet, PA, physical activity, UC, usual care, vs, versus.

10.1371/journal.pone.0176436.g006Fig 6

changes in high density lipoprotein cholesterol in the intervention versus usual care groups (mmol/L).

Group 1: low-range glycemic group (FPG<5.5mmol/L or A1C <5.5%). Group 2: high-range glycemic group (FPG ≥5.5mmol/L or A1C ≥5.5%). D, diet, PA, physical activity. UC, usual care, vs, versus.

10.1371/journal.pone.0176436.g007Fig 7

Changes in triglycerides in the intervention versus usual care groups (mmol/L).

Group 1: low-range glycemic group (FPG<5.5mmol/L or A1C <5.5%). Group 2: high-range glycemic group (FPG ≥5.5mmol/L or A1C ≥5.5%). D, diet, PA, physical activity, UC, usual care, vs, versus.

Comparison according to length of follow-up

In 34 studies or study arms with 12 months of follow-up, lifestyle interventions significantly improved all CVD risk factors: SBP (-2.07mmHg [-3.19, -0.95]), DBP (-1.62mmHg [-2.29, -0.95]), TC (-0.06mmol/L [-0.10, -0.01]), LDL-C (-0.08mmol/L [-0.13, -0.02]), HDL-C (0.02mmol/L [0.01, 0.03]), and TG (-0.08mmol/L [-0.14, -0.03]). For 7 studies or study arms with 13–23 months of follow-up, significant improvements were observed in four CVD risk factors: SBP (-1.73mmHg [-2.80, -0.65]), DBP (-1.25mmHg [-2.02, -0.48]), TC (-0.19mmol/L [-0.26, -0.11]), and LDL-C (-0.12mmol/L [-0.19, -0.05]). When the follow-up was ≥24 months (n = 14), significant improvements remained visible only for: SBP (-1.58mmHg [-2.71, -0.45]), DBP (-1.36mmHg [-2.30, -0.41]), and HDL-C (0.05mmol/L [0.02, 0.08]).

Correlation between interventional effects on CVD risk reduction and glucose change and weight loss effect sizes

Findings from meta-regression analyses demonstrated that except for LDL-C category, Pearson’s correlation, r between CVD risk reduction effect sizes and glucose effect sizes ranged from 0.73 to 0.83 in SBP, DBP, TC, HDL-C, and TG, but r between CVD risk reduction effect sizes and baseline FPG were very low, only ranging from 0.26 to 0.44 in SBP, DBP, TC, HDL-C, and TG. The r between CVD risk reduction effect sizes and weight followed the same patterns: except for LDL-C category, r between CVD risk reduction effect sizes and weight loss effect sizes ranged from 0.51 to 0.75 in SBP, DBP, TC, HDL-C, and TG, but r between CVD risk reduction effect sizes and baseline weight were very low, only ranging from 0.02 to 0.30 in SBP, DBP, TC, HDL-C, and TG. Compared to weight loss, glucose response is a better indicator of the CVD risk factor response because the glucose response has a stronger correlation with the CVD risk factor response as r ranges showed above (Table 3).

10.1371/journal.pone.0176436.t003Table 3

Correlation between CVD Risk Reduction and FPG and Weight.

CVD risk reduction R
Effect size Baseline FPG FPG effect size Baseline weight Weight loss effect size
SBP 0.32 0.752 0.068 0.506
DPB 0.259 0.728 0.023 0.58
TC 0.301 0.827 0.127 0.75
LDL-C 0.186 0.117 0.196 0.18
HDL-C 0.437 0.82 0.301 0.708
TG 0.38 0.82 0.172 0.707

Abbreviations: CVD: cardiovascular disease; DBP: diastolic blood pressure; FPG: fasting plasma glucose; HDL-C: high density cholesterol; LDL-C: low density cholesterol; SBP: systolic blood pressure; TC: total cholesterol; TG: triglycerides


In this review of the effectiveness of lifestyle interventions on the reduction of CVD risk factors among adults with low glycemic levels (below the IGT threshold), we found that lifestyle interventions, including physical activity, diet, and behavioral modification, can significantly improve CVD risk profiles, including SBP, DBP, TC, LDL-C, HDL-C, and TG. When stratified by glycemic levels, we found similar intervention effects between studies of participants with low vs high-range glycemic levels, except for TC and TG. Greater improvements were observed among studies with 12 months of follow-up than those with longer follow up, such that only SBP, DBP, and HDL-C improvements were sustained after 24 months. Studies that used a combined strategy of PA and D had the strongest effect on improving CVD profiles, followed by studies using D interventions only; studies only using a PA intervention strategy had the weakest effect. We have previously reported that multi-faceted interventions combining PA and D are effective in improving glucose regulation in populations with average low-range and high-range glucose levels.[[125]] The results of the present analyses suggest the effect of such interventions also applies to traditional biologic CVD risk factors.

Lifestyle interventional effects on CVD risk reduction observed in our studies among people without IGT or diabetes are consistent with those from the main trials of diabetes prevention among persons with IGT. For example, the US Diabetes Prevention Program (DPP) Study among people with IGT reported improvements in CVD profiles for all categories as measured by the mean differences between lifestyle intervention and placebo groups. The magnitude of improvements in CVD profiles in the DPP[[126]] in 1-year follow-up are consistent with those from our review (DPP vs this review: SBP, -2.50 vs -2.07 mmHg; DBP, -2.71 vs -1.62 mmol/L; TC, -0.06 vs -0.06 mmol/L; LDL-C, -0.02 vs -0.08 mmol/L; HDL-C, 0.01 vs 0.02 mmol/L; TG, -0.18 vs. -0.08 mmol/L, respectively). This comparison is also true for other major diabetes prevention trials (e.g., Finish Diabetes Prevention Study).[[127]]

Our findings may have important implications for decision makers in the areas of both diabetes and CVD primary prevention. Our meta-regression analyses indicated that the magnitude of improvements in CVD risk profiles is less correlated with baseline glucose level, but highly correlated with the effect sizes of glucose improvement. Meanwhile, the meta-regression analyses also indicated that the magnitude of improvements in CVD risk profiles is less correlated with baseline body weight, but highly correlated with the effect sizes of weight loss. We thus conclude that lifestyle interventions may provide important benefits across the full distribution of glycemic levels and body weight, including populations with glycemic levels below the IGT threshold, for both the low and high ranges of baseline FPG, and for populations with normal weight but with CVD risk factors. However, economic factors as well as the effectiveness of interventions influence decisions regarding the types of interventions provided to individuals with glycemic levels below the IGT threshold.[[128],[129]] The cost-effectiveness of lifestyle interventions that can simultaneously reduce diabetes and CVD risk among individuals with glycemic levels below the IGT threshold should be examined.

Our findings demonstrate that lifestyle interventions, compared to UC, achieved improvement in both diabetes prevention and CVD risk reduction, and these improvements were not only statistically significant, but also have clinical relevance. Previous studies indicated that each 0.03 mmol/L increase in HDL-C is associated with the reduction of coronary heart disease risk by 2–3%,[[130]] and each 5 mmHg reduction in SBP and 2 mmHg reduction in DBP reduce stroke risk by 13% and 11.5%, respectively.[[131]] According to an epidemiology study, a 1% decrease in total cholesterol leads to a decrease in the incidence of coronary events by 2%.[[132]] One study also indicated that weight loss improved CVD profiles because each kilogram change in body weight was related to the change in the risk of coronary heart disease by 3.1%.[[133]]

Given that lifestyle intervention program participants in our reviewed studies usually achieved improvements in CVD across a full spectrum of outcomes simultaneously, the overall combined benefits brought by lifestyle interventions could be amplified. An estimation of overall effect on CVD risk would be helpful for our understanding the importance of interventional impact. Unfortunately, although there are several models available for CVD risk calculation (e.g., Framingham Risk Score,[[134]] and the ACC/AHA CVD risk calculator[[135]]), we are not aware of any available estimation model by which we can calculate the overall combined effect of changes of different individual risk factor. Further research and validation test, therefore, maybe needed for creating this model. If this kind model is available in the future, we can apply this model to our meta-analytic findings to estimate the overall combined effect of changes of different individual risk factor. For example, if a population, through lifestyle and behavior changes, achieved CVD risk reductions as much as showed in our meta-analyses, we can estimate the overall health benefits (e.g., how many CVD events can be prevented in the future). Despite this unavailability, the improvement in glucose regulation[[125]] coupled with our findings regarding the improvement in CVD risk reduction suggested that lifestyle interventions can achieve a comprehensive improvement goal as stated in AHA Special Report[[17]] of preventing CVD and diabetes simultaneously among persons with lower diabetes risk.

Strong evidence shows that PA programs have important independent effects on non-insulin-mediated glucose transport, markers of inflammation, insulin resistance, blood pressure, lipid profile, fitness, and improved lean-to-fat mass ratio.[[136]] Our findings suggest that these effects were more likely observed in studies using multi-component interventions, including PA, calorie restriction, and behavioral support but less so for PA-only interventions. This finding may be related to methodological shortcomings in exercise-only interventions such as low adherence, insufficient exercise volume or length of intervention. Previous studies suggest that it may take up to 2 years for a previously sedentary obese individual to attain enough volume of exercise to effectively reduce CVD risk factors, and individuals in unverified, out-patient interventions are less likely to engage in the prescribed amount of exercise.[[137],[138]] However, we previously reported that exercise-only interventions in our included studies significantly reduced FPG and body weight[[125]] which in turn further prevented diabetes. Since PA-related improvements in glucose regulation and weight loss can lead to reductions in CVD risk profiles, potential indirect benefits should be taken into account when interpreting our findings.

Unhealthy lifestyle factors are related to the atherosclerotic process and these long-term exposures lead to the clinical manifestations of cardiovascular events.[[139]] A previous study also indicated that lifestyle changes, only in the long-term, are likely to lead to CVD risk factor reduction.[[30]] Our findings demonstrate that the effects of lifestyle changes on the reduction in CVD risk factors reached their highest point at 12 months of follow-up, then gradually decreased over time. This may reflect the fact that the longer-term intervention may be more effective on reducing CVD risks only if participates remain highly adherent to the intended interventions, which is seldom observed. It could be also true that using CVD mortality, rather than CVD risk reduction alone, to measure the long-term effect of lifestyle changes on CVD is more appropriate as the extended legacy findings of the Chinese Da Qing Study indicated.[[140]]

Because we used a comprehensive search strategy including all major medical databases, we found a large number of eligible studies. Pooled effects based on a large sample size provide more robust findings than those from any single study. Our review has some limitations as well. First, lifestyle interventions were used in heterogeneous settings, among different populations of varying ages, health status, and race/ethnicity background. While the main components of the lifestyle interventions were generally PA and D, each of the strategies had its own requirements in type, dose, intensity, and frequency. UC also had varying definitions among different comparison groups. Heterogeneity across studies was also reflected in the length of intervention, duration and follow-up, and number of sessions. However, our meta-regression analyses found no interactions between the between-group change in glycemic indicators and study-level characteristics. We also stratified our data syntheses by glycemic level, length of follow-up, and type of interventions, taking the heterogeneity among included studies into account. Second, although we stratified by level of glycemic risk at the study level, there was considerable heterogeneity within studies, and the nature of aggregated data prevented individual level classification by glucose level. As a result, there was likely considerable overlap in participant characteristics between low range and high range glycemic groups in our study, which may introduce some misclassification bias. Misclassification bias could be also introduced by usage of both FPG and A1C in our review to identify population with low glycemic risks. Although a previous study indicated that the agreement between FPG and A1C is high,[[141]] they are not equal with each other.[[142]] Because of this misclassification bias, some individuals identified as with low glycemic risks could actually have glucose metabolism abnormalities. Audiences need to be cautious when interpreting our findings.


Our review is the first comprehensive examination of the impact of lifestyle interventions on risk for progression of dysglycemia and CVD risk reduction among persons below the IGT threshold. This systematic review suggests that lifestyle change is critical to both CVD risk reduction and diabetes prevention across the full spectrum of risk, complementing the major trials of diabetes prevention that focused on persons with IGT. This review also provides supportive evidences for designing strategies aimed at reducing CVD burden as delineated in the AHA Strategic Impact Goal through 2020 and Beyond.[[17]] Our findings demonstrated that among adults without IGT or diabetes, PA and D interventions, especially combined can significantly improve SBP, DBP, TC, LHL-C, HDL-C, and TG, in addition to glucose regulation and weight loss, and that these risk reductions may further prevent CVD events.

Supporting information

S1 File

Appendix A. Protocol-Study Protocol with Search Strategy.

Appendix B. PRISMA Checklist- Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist.

Table A. Intervention Characteristics.

Table B. Quality Assessment.

Table C. Lifestyle Interventional Effect: Meta-analyses Results in A Single Arm Model.

Table D. Intervention effect on FPG and percent weight: meta-analyses results.


Click here for additional data file.


This study was supported by the Centers for Disease Control and Prevention. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.


  1. World Health Organization. Cardiovascular Disease (CVDs). available from
  2. American Diabetes Association. Cardiovascular disease and risk management. Diabetes Care. 2015;38: S49–S57. doi: 10.2337/dc15-S01125537708
  3. AD Shah, C Langenberg, E Rapsomaniki, S Denaxas, M Pujades-Rodriguez, CP Gale, et alType 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1.9 million people. Lancet Diabetes Endocrinol. 2015;3: 105–113. pii: S2213-8587(14)70219-0. doi: 10.1016/S2213-8587(14)70219-025466521
  4. American Diabetes Association. Standards of medical care in diabetes-2014. Diabetes Care. 2014;37: S14–S80. doi: 10.2337/dc14-S01424357209
  5. WC Knowler, E Barrett-Connor, SE Fowler, RF Hamman, JM Lachin, EA Walker, et alDiabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346: 393–403. doi: 10.1056/NEJMoa01251211832527
  6. XR Pan, GW Li, YH Hu, JX Wang, WY Yang, ZX An, et alEffects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20: 537–544. 9096977
  7. J Tuomilehto, J Lindstrom, JG Eriksson, TT Valle, H Hamalainen, P Ilanne-Parikka, et alFinnish Diabetes Prevention Study Group. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344: 1343–1350. doi: 10.1056/NEJM20010503344180111333990
  8. LJ Appel, TJ Moore, E Obarzanek, WM Vollmer, LP Svetkey, FM Sacks, et alA clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group. N Engl J Med1997;336: 1117–1124. doi: 10.1056/NEJM1997041733616019099655
  9. V Burke, J Mansour, LJ Beilin, TA Mori. Long-term follow-up of participants in a health promotion program for treated hypertensives (ADAPT). Nutr Metab Cardiovasc Dis. 2008;18: 198–206. doi: 10.1016/j.numecd.2006.10.00417327140
  10. PM Nilsson, EB Klasson, P Nyberg. Life-style intervention at the worksite—Reduction of cardiovascular risk factors in a randomized study. Scandinavian Journal of Work, Environment and Health. 2001;27: 57–62. 11266148
  11. RD Reid, LA McDonnell, DL Riley, AE Mark, L Mosca, L Beaton, et alEffect of an intervention to improve the cardiovascular health of family members of patients with coronary artery disease: a randomized trial. CMAJ. 2014;186: 23–30. Pii: cmaj.130550. doi: 10.1503/cmaj.13055024246588
  12. L Simkinsilverman, RR Wing, DH Hansen, ML Klem, A Pasagianmacaulay, EN Meilahn, et alPrevention of cardiovascular risk factor elevations in healthy premenopausal women. Prev Med. 1995;24: 509–517. 8524727
  13. ML Stefanick, S Mackey, M Sheehan, N Ellsworth, WL Haskell, PD Wood. Effects of diet and exercise in men and postmenopausal women with low levels of HDL cholesterol and high levels of LDL cholesterol. N Engl J Med. 1998;339: 12–20. doi: 10.1056/NEJM1998070233901039647874
  14. NC ter Bogt, WJ Bemelmans, FW Beltman, J Broer, AJ Smit, K van der Meer. Preventing weight gain: One-year results of a randomized lifestyle intervention. Am J Prev Med. 2009;37: 270–277. doi: 10.1016/j.amepre.2009.06.01119765497
  15. CC Cowie, KF Rust, ES Ford, MS Eberhardt, DD Byrd-Holt, C Li, et alFull accounting of diabetes and pre-diabetes in the U.S. population in 1988–1994 and 2005–2006. Diabetes Care. 2009;32: 287–294. pii: dc08-1296. doi: 10.2337/dc08-129619017771
  16. The DECODE study group. Is fasting glucose sufficient to define diabetes? Epidemiological data from 20 European studies. Diabetologia. 1999;42: 647–654. 10382583
  17. DM Lloyd-Jones, Y Hong, D Labarthe, D Mozaffarian, LJ Appel, HL van, et alDefining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation. 2010;121: 586–613. pii: CIRCULATIONAHA.109.192703. doi: 10.1161/CIRCULATIONAHA.109.19270320089546
  18. RH Eckel, JM Jakicic, JD Ard, JM de Jesus, MN Houston, VS Hubbard, et al2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129: S76–S99. pii: 01.cir.0000437740.48606.d1. doi: 10.1161/01.cir.0000437740.48606.d124222015
  19. Higgins JPT Green SE. Cochrane Handbook for Systematic Reviews of Interventions. [Version 5.1.0 [updated March 2011]]. 2011. The Cochrane Collaboration. 2012.
  20. R DerSimonian, N Laird. Meta-analysis in clinical trials. Control Clin Trials. 1954;7: 177–188.
  21. RT Ackermann, EA Finch, E Brizendine, H Zhou, DG Marrero. Translating the Diabetes Prevention Program into the community. The DEPLOY Pilot Study. Am J Prev Med. 2008;35: 357–363. doi: 10.1016/j.amepre.2008.06.03518779029
  22. LB Almeida, AC Segurado, ACF Duran, PC Jaime. Impact of a nutritional counseling program on prevention of HAART-related metabolic and morphologic abnormalities. AIDS Care—Psychological and Socio-Medical Aspects of AIDS/HIV. 2011;23: 755–763.
  23. AS Anderson, AM Craigie, S Caswell, S Treweek, M Stead, M Macleod, et alThe impact of a bodyweight and physical activity intervention (BeWEL) initiated through a national colorectal cancer screening programme: randomised controlled trial. BMJ. 2014;348: 1–13.
  24. SA Anderssen, I Hjermann, P Urdal, PA Torjesen, I Holme. Improved carbohydrate metabolism after physical training and dietary intervention in individuals with the "atherothrombogenic syndrome'. Oslo Diet and Exercise Study (ODES). A randomized trial. J Intern Med. 1996;240: 203–209. 8918511
  25. H Arguin. Short- and long-term effects of continuous versus intermittent restrictive diet approaches on body composition and the metabolic profile in overweight and obese postmenopausal women: a pilot study. Menopause. 2012;19: 870–876. doi: 10.1097/gme.0b013e318250a28722735163
  26. LA Bazzano, T Hu, K Reynolds, L Yao, C Bunol, Y Liu, et alEffects of low-carbohydrate and low-fat diets: a randomized trial. Ann Intern Med. 2014;161: 309–318. pii: 1900694. doi: 10.7326/M14-018025178568
  27. S Bo, G Ciccone, C Baldi, L Benini, F Dusio, G Forastiere, et alEffectiveness of a lifestyle intervention on metabolic syndrome. A randomized controlled trial. J Gen Intern Med. 2007;22: 1695–1703. doi: 10.1007/s11606-007-0399-617922167
  28. M Bouchonville, R Armamento-Villareal, K Shah, N Napoli, DR Sinacore, C Qualls, et alWeight loss, exercise or both and cardiometabolic risk factors in obese older adults: results of a randomized controlled trial. Int J Obes (Lond). 2014;38: 423–431. pii: ijo2013122.23823329
  29. GD Brinkworth, M Noakes, JB Keogh, ND Luscombe, GA Wittert, PM Clifton. Long-term effects of a high-protein, low-carbohydrate diet on weight control and cardiovascular risk markers in obese hyperinsulinemic subjects. Int J Obes Relat Metab Disord. 2004;28: 661–670. doi: 10.1038/sj.ijo.080261715007396
  30. K Broekhuizen, MN van Poppel, LL Koppes, I Kindt, J Brug, W van Mechelen. No significant improvement of cardiovascular disease risk indicators by a lifestyle intervention in people with familial hypercholesterolemia compared to usual care: results of a randomised controlled trial. BMC Res Notes. 2012;5: 181–189. doi: 10.1186/1756-0500-5-18122490761
  31. V Burke, LJ Beilin, HE Cutt, J Mansour, A Williams, TA Mori. A lifestyle program for treated hypertensives improved health-related behaviors and cardiovascular risk factors, a randomized controlled trial. J Clin Epidemiol. 2007;60: 133–141. doi: 10.1016/j.jclinepi.2006.05.01217208119
  32. M Burtscher, H Gatterer, H Kunczicky, E Brandstatter, H Ulmer. Supervised exercise in patients with impaired fasting glucose: impact on exercise capacity. Clin J Sport Med. 2009;19: 394–398. doi: 10.1097/JSM.0b013e3181b8b6dc19741312
  33. DA Chirinos, RB Goldberg, MM Llabre, M Gellman, M Gutt, J McCalla, et alLifestyle modification and weight reduction among low-income patients with the metabolic syndrome: the CHARMS randomized controlled trial. J Behav Med. 2016;39: 483–492. pii: 10.1007/s10865-016-9721-2. doi: 10.1007/s10865-016-9721-226846133
  34. J Choo, J Lee, JH Cho, LE Burke, A Sekikawa, SY Jae. Effects of weight management by exercise modes on markers of subclinical atherosclerosis and cardiometabolic profile among women with abdominal obesity: a randomized controlled trial. BMC Cardiovasc Disord. 2014;14: 82 pii: 1471-2261-14-82. doi: 10.1186/1471-2261-14-8225011384
  35. PM Clifton, JB Keogh, M Noakes. Long-term effects of a high-protein weight-loss diet. Am J Clin Nutr. 2008;87: 23–29. 18175733
  36. RE Cole, KM Boyer, SM Spanbauer, D Sprague, M Bingham. Effectiveness of prediabetes nutrition shared medical appointments: prevention of diabetes. Diabetes Educ. 2013;39: 344–353. pii: 0145721713484812. doi: 10.1177/014572171348481223589326
  37. PJ Coon, ER Bleecker, DT Drinkwater, DA Meyers, AP Goldberg. Effects of body composition and exercise capacity on glucose tolerance, insulin, and lipoprotein lipids in healthy older men: a cross-sectional and longitudinal intervention study. Metabolism. 1989;38: 1201–1209. 2687639
  38. KL Cox, V Burke, LJ Beilin, IB Puddey. A comparison of the effects of swimming and walking on body weight, fat distribution, lipids, glucose, and insulin in older women-the Sedentary Women Exercise Adherence Trial 2. Metabolism: Clinical and Experimental. 2010;59: 1562–1573.20197194
  39. HH Ditschuneit, M Flechtner-Mors, TD Johnson, G Adler. Metabolic and weight-loss effects of a long-term dietary intervention in obese patients. Am J Clin Nutr. 1999;69: 198–204. 9989680
  40. JE Donnelly, DJ Jacobsen, KS Heelan, R Seip, S Smith. The effects of 18 months of intermittent vs. continuous exercise on aerobic capacity, body weight and composition, and metabolic fitness in previously sedentary, moderately obese females. Int J Obes Relat Metab Disord. 2000;24: 566–572. 10849577
  41. K Esposito, R Marfella, M Ciotola, C Palo, F Giugliano, G Giugliano, et alEffect of a mediterranean-style diet on endothelial dysfunction and markers of vascular inflammation in the metabolic syndrome: a randomized trial. JAMA. 2004;292: 1440–1446. doi: 10.1001/jama.292.12.144015383514
  42. K Esposito, F Giugliano, PC Di, G Giugliano, R Marfella, F D'Andrea, et alEffect of lifestyle changes on erectile dysfunction in obese men: a randomized controlled trial. JAMA. 2004;291: 2978–2984. pii: 291/24/2978. doi: 10.1001/jama.291.24.297815213209
  43. K Esposito, A Pontillo, C Di Palo, G Giugliano, M Masella, R Marfella, et alEffect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: A randomized trial. JAMA. 2003;289: 1799–1804. doi: 10.1001/jama.289.14.179912684358
  44. IG Fatouros, S Tournis, D Leontsini, AZ Jamurtas, M Sxina, P Thomakos, et alLeptin and adiponectin responses in overweight inactive elderly following resistance training and detraining are intensity related. J Clin Endocrinol Metab. 2005;90: 5970–5977. doi: 10.1210/jc.2005-026116091494
  45. AC Fernandez, AV Casariego, IC Rodriguez, MDB Pomar. One-year effectiveness of two hypocaloric diets with different protein/carbohydrate ratios in weight loss and insulin resistance. Nutr Hosp. 2012;27: 2093–2101. doi: 10.3305/nh.2012.27.6.613323588462
  46. AL Ferrara, D Pacioni, V Di Fronzo, BF Russo, L Staiano, E Speranza, et alLifestyle Educational Program Strongly Increases Compliance to Nonpharmacologic Intervention in Hypertensive Patients: A 2-Year Follow-Up Study. J Clin Hypertens (Greenwich). 2012;14: 767–772.23126348
  47. HH Fischer, IP Fischer, RI Pereira, AL Furniss, JM Rozwadowski, SL Moore, et alText Message Support for Weight Loss in Patients With Prediabetes: A Randomized Clinical Trial. Diabetes Care. 2016;39: 1364–1370. pii: dc15-2137. doi: 10.2337/dc15-213726861922
  48. G Fisher, GR Hunter, BA Gower. Aerobic exercise training conserves insulin sensitivity for 1 yr following weight loss in overweight women. J Appl Physiol. 2012;112: 688–693. doi: 10.1152/japplphysiol.00843.201122174391
  49. M Fogelholm, K Kukkonen-Harjula, A Nenonen, M Pasanen. Effects of walking training on weight maintenance after a very-low-energy diet in premenopausal obese women: a randomized controlled trial. Arch Intern Med. 2000;160: 2177–2184. 10904461
  50. J Fonollá, E López-Huertas, FJ Machado, D Molina, I Alvarez, E Mármol, et alMilk enriched with "healthy fatty acids" improves cardiovascular risk markers and nutritional status in human volunteers. Nutrition (Burbank, Los Angeles County, Calif). 2009;25: 408–414.
  51. LL Frank, BE Sorensen, Y Yasui, SS Tworoger, RS Schwartz, CM Ulrich, et alEffects of exercise on metabolic risk variables in overweight postmenopausal women: a randomized clinical trial. Obes Res. 2005;13: 615–625. doi: 10.1038/oby.2005.6615833948
  52. IF Groeneveld, KI Proper, AJ van der Beek, W van Mechelen. Sustained body weight reduction by an individual-based lifestyle intervention for workers in the construction industry at risk for cardiovascular disease: results of a randomized controlled trial. Prev Med. 2010;51: 240–246. doi: 10.1016/j.ypmed.2010.07.02120692282
  53. S Heshka, JW Anderson, RL Atkinson, FL Greenway, JO Hill, SD Phinney, et alWeight Loss with Self-help Compared with a Structured Commercial Program: A Randomized Trial. JAMA. 2003;289: 1792–1798. doi: 10.1001/jama.289.14.179212684357
  54. I Imayama, CM Alfano, C Mason, C Wang, C Duggan, KL Campbell, et alWeight and metabolic effects of dietary weight loss and exercise interventions in postmenopausal antidepressant medication users and non-users: a randomized controlled trial. Prev Med. 200357: 525–532. pii: S0091-7435(13)00230-2.
  55. L Juul, VJ Andersen, J Arnoldsen, HT Maindal. Effectiveness of a brief theory-based health promotion intervention among adults at high risk of type 2 diabetes: One-year results from a randomised trial in a community setting. Prim Care Diabetes. 2016;10: 111–120. pii: S1751-9918(15)00099-6. doi: 10.1016/j.pcd.2015.07.00226259517
  56. AM Kanaya, J Santoyo-Olsson, S Gregorich, M Grossman, T Moore, AL Stewart. The Live Well, Be Well study: a community-based, translational lifestyle program to lower diabetes risk factors in ethnic minority and lower-socioeconomic status adults. Am J Public Health. 2012;102: 1551–1558. doi: 10.2105/AJPH.2011.30045622698027
  57. AM Kanaya, MR Araneta, SB Pawlowsky, E Barrett-Connor, D Grady, E Vittinghoff, et alRestorative yoga and metabolic risk factors: the Practicing Restorative Yoga vs. Stretching for the Metabolic Syndrome (PRYSMS) randomized trial. J Diabetes Complications. 2014;28: 406–412. pii: S1056-8727(13)00327-9. doi: 10.1016/j.jdiacomp.2013.12.00124418351
  58. JA Katula, MZ Vitolins, TM Morgan, MS Lawlor, CS Blackwell, SP Isom, et alThe Healthy Living Partnerships to Prevent Diabetes study: 2-year outcomes of a randomized controlled trial. Am J Prev Med. 2013;44: S324–S332. pii: S0749-3797(13)00023-8. doi: 10.1016/j.amepre.2012.12.01523498294
  59. M Kawano, N Shono, T Yoshimura, M Yamaguchi, T Hirano, A Hisatomi. Improved cardio-respiratory fitness correlates with changes in the number and size of small dense LDL: Randomized controlled trial with exercise training and dietary instruction. Intern Med. 2009;48: 25–32. 19122353
  60. JB Keogh, GD Brinkworth, PM Clifton. Effects of weight loss on a low-carbohydrate diet on flow-mediated dilatation, adhesion molecules and adiponectin. Br J Nutr. 2007;98: 852–859. doi: 10.1017/S000711450774781517490508
  61. BA Lawton, SB Rose, C Raina Elley, AC Dowell, A Fenton, SA Moyes. Exercise on prescription for women aged 40–74 recruited through primary care: two year randomised controlled trial. BJSM online. 2009;43: 120–126.
  62. SS Lim, M Noakes, JB Keogh, PM Clifton. Long-term effects of a low carbohydrate, low fat or high unsaturated fat diet compared to a no-intervention control. Nutr Metab Cardiovasc Dis. 2010;20: 599–607. doi: 10.1016/j.numecd.2009.05.00319692216
  63. C Lombard, A Deeks, D Jolley, K Ball, H Teede. A low intensity, community based lifestyle programme to prevent weight gain in women with young children: cluster randomised controlled trial. BMJ. 2010;41: 1–12.
  64. J Ma, AC King, SR Wilson, L Xiao, RS Stafford. Evaluation of lifestyle interventions to treat elevated cardiometabolic risk in primary care (E-LITE): a randomized controlled trial. BMC Fam Pract. 2009;10: 71–82. doi: 10.1186/1471-2296-10-7119909549
  65. DG Marrero, KN Palmer, EO Phillips, K Miller-Kovach, GD Foster, CK Saha. Comparison of Commercial and Self-Initiated Weight Loss Programs in People With Prediabetes: A Randomized Control Trial. Am J Public Health. 2016;106: 949–956. doi: 10.2105/AJPH.2015.30303526890171
  66. KA Marsh, KS Steinbeck, FS Atkinson, P Petocz, JC Brand-Miller. Effect of a low glycemic index compared with a conventional healthy diet on polycystic ovary syndrome. Am J Clin Nutr. 2010;92: 83–92. doi: 10.3945/ajcn.2010.2926120484445
  67. AE Mason, ES Epel, J Kristeller, PJ Moran, M Dallman, RH Lustig, et alEffects of a mindfulness-based intervention on mindful eating, sweets consumption, and fasting glucose levels in obese adults: data from the SHINE randomized controlled trial. J Behav Med. 2016;39: 201–213. pii: 10.1007/s10865-015-9692-8. doi: 10.1007/s10865-015-9692-826563148
  68. KA McAuley, KJ Smith, RW Taylor, RT McLay, SM Williams, JI Mann. Long-term effects of popular dietary approaches on weight loss and features of insulin resistance. International journal of obesity. 2006;30: 342–349. doi: 10.1038/sj.ijo.080307516158081
  69. C Mellberg, S Sandberg, M Ryberg, M Eriksson, S Brage, C Larsson, et alLong-term effects of a Palaeolithic-type diet in obese postmenopausal women: a 2-year randomized trial. Eur J Clin Nutr. 2014;68: 350–357. pii: ejcn2013290. doi: 10.1038/ejcn.2013.29024473459
  70. T Muto, K Yamauchi. Evaluation of a multicomponent workplace health promotion program conducted in Japan for improving employees' cardiovascular disease risk factors. Prev Med. 2001;33: 571–577. doi: 10.1006/pmed.2001.092311716652
  71. KM Narayan, M Hoskin, D Kozak, AM Kriska, RL Hanson, DJ Pettitt, et alRandomized clinical trial of lifestyle interventions in Pima Indians: a pilot study. Diabet Med. 1998;15: 66–72. doi: 10.1002/(SICI)1096-9136(199801)15:1<66::AID-DIA515>3.0.CO;2-A9472866
  72. PM Nilsson, LH Lindholm, BF Schersten. Life style changes improve insulin resistance in hyperinsulinaemic subjects: a one-year intervention study of hypertensives and normotensives in Dalby. J Hypertens. 1992;10: 1071–1078. 1328367
  73. IS Ockene, TL Tellez, MC Rosal, GW Reed, J Mordes, PA Merriam, et alOutcomes of a Latino community-based intervention for the prevention of diabetes: the Lawrence Latino Diabetes Prevention Project. Am J Public Health. 2012;102: 336–342. doi: 10.2105/AJPH.2011.30035722390448
  74. WSC Poston, CK Haddock, MM Pinkston, P Pace, RS Reeves, N Karkoc, et alEvaluation of a primary care-oriented brief counselling intervention for obesity with and without orlistat. Journal of Intern Med. 2006;260: 388–398.16961676
  75. JA Potteiger, DJ Jacobsen, JE Donnelly, JO Hill, T Midwest Exercise. Glucose and insulin responses following 16 months of exercise training in overweight adults: the Midwest Exercise Trial. Metabolism. 2003;52: 1175–1181. 14506624
  76. S Rossner, H Flaten. VLCD versus LCD in long-term treatment of obesity. Int J Obes Relat Metab Disord. 1997;21: 22–26. 9023596
  77. KR Ryttig, H Flaten, S Rossner. Long-term effects of a very low calorie diet (Nutrilett) in obesity treatment. A prospective, randomized, comparison between VLCD and a hypocaloric diet+behavior modification and their combination. Int J Obes Relat Metab Disord. 1997;21: 574–579. 9226488
  78. DS Sartorelli, EC Sciarra, LJ Franco, MA Cardoso. Beneficial effects of short-term nutritional counselling at the primary health-care level among Brazilian adults. Public Health Nutr. 2005;8: 820–825. 16277797
  79. RW Sattin, LB Williams, J Dias, JT Garvin, L Marion, TV Joshua, et alCommunity Trial of a Faith-Based Lifestyle Intervention to Prevent Diabetes Among African-Americans. J Community Health. 2016;41: 87–96. pii: 10.1007/s10900-015-0071-8. doi: 10.1007/s10900-015-0071-826215167
  80. LR Simkin-Silverman, RR Wing, MA Boraz, LH Kuller. Lifestyle intervention can prevent weight gain during menopause: results from a 5-year randomized clinical trial. Ann Behav Med. 2003;26: 212–220. 14644697
  81. P Siu, A Yu, I Benzie, J Woo. Effects of 1-year yoga on cardiovascular risk factors in middle-aged and older adults with metabolic syndrome: a randomized trial. Diabetology & Metabolic Syndrome. 2015;7: 1–12.25810781
  82. LK Staten, KY Gregory-Mercado, J Ranger-Moore, JC Will, AR Giuliano, ES Ford, et alProvider counseling, health education, and community health workers: The Arizona WISEWOMAN project. Journal of Womens Health. 2004;13: 547–556.
  83. LC Tapsell, MJ Batterham, RL Thorne, JE O'Shea, SJ Grafenauer, YC Probst. Weight loss effects from vegetable intake: a 12-month randomised controlled trial. Eur J Clin Nutr. 2014;68: 778–785. pii: ejcn201439. doi: 10.1038/ejcn.2014.3924667750
  84. WG Thompson, N Rostad Holdman, DJ Janzow, JM Slezak, KL Morris, MB Zemel. Effect of energy-reduced diets high in dairy products and fiber on weight loss in obese adults. Obes Res. 2005;13: 1344–1353. doi: 10.1038/oby.2005.16316129716
  85. AG Tsai, TA Wadden, MA Rogers, SC Day, RH Moore, BJ Islam. A primary care intervention for weight loss: Results of a randomized controlled pilot study. Obesity (Silver Spring). 2010;18: 1614–1618.20019680
  86. A Vainionpaa, R Korpelainen, H Kaikkonen, M Knip, J Leppaluoto, T Jamsa. Effect of impact exercise on physical performance and cardiovascular risk factors. Med Sci Sports Exerc. 2007;39: 756–763. doi: 10.1249/mss.0b013e318031c03917468572
  87. ML Vetter, TA Wadden, J Chittams, LK Diewald, E Panigrahi, S Volger, et alEffect of lifestyle intervention on cardiometabolic risk factors: results of the POWER-UP trial. Int J Obes (Lond). 2013;37(Suppl 1): S19–S24. pii: ijo201392.23921777
  88. U von Thiele Schwarz, P Lindfors, U Lundberg. Health-related effects of worksite interventions involving physical exercise and reduced workhours. Scandinavian Journal of Work, Environment and Health. 2008;34: 179–188. 18728907
  89. M Watanabe, K Yamaoka, M Yokotsuka, T Tango. Randomized controlled trial of a new dietary education program to prevent type 2 diabetes in a high-risk group of Japanese male workers. Diabetes Care. 2003;26: 3209–3214. 14633803
  90. RS Weinstock, H Dai, TA Wadden. Diet and exercise in the treatment of obesity: effects of 3 interventions on insulin resistance. Arch Intern Med. 1998;158: 2477–2483. 9855386
  91. EP Weiss, SB Racette, DT Villareal, L Fontana, K Steger-May, KB Schechtman, et alImprovements in glucose tolerance and insulin action induced by increasing energy expenditure or decreasing energy intake: a randomized controlled trial. Am J Clin Nutr. 2006;84: 1033–1042. 17093155
  92. RR Wing, RW Jeffery. Effect of modest weight loss on changes in cardiovascular risk factors: Are there differences between men and women or between weight loss and maintenance?Int J Obes (Lond). 1995;19: 67–73.
  93. RR Wing, E Venditti, JM Jakicic, BA Polley, W Lang. Lifestyle intervention in overweight individuals with a family history of diabetes. Diabetes Care. 1998;21: 350–359. 9540015
  94. TP Wycherley, GD Brinkworth, PM Clifton, M Noakes. Comparison of the effects of 52 weeks weight loss with either a high-protein or high-carbohydrate diet on body composition and cardiometabolic risk factors in overweight and obese males. Nutr Diabetes. 2012;2: e40–e47. pii: nutd201211. doi: 10.1038/nutd.2012.1123448804
  95. MC Yeh, M Heo, S Suchday, A Wong, E Poon, G Liu, et alTranslation of the Diabetes Prevention Program for diabetes risk reduction in Chinese immigrants in New York City. Diabet Med. 2016;33: 547–551. doi: 10.1111/dme.1284826179569
  96. SA Anderssen, I Holme, P Urdal, I Hjermann. Associations between central obesity and indexes of hemostatic, carbohydrate and lipid metabolism. Results of a 1-year intervention from the Oslo Diet and Exercise Study. Scand J Med Sci Sports. 1998;8: 109–115. 9564716
  97. S Bo, R Gambino, G Ciccone, R Rosato, N Milanesio, P Villois, et alEffects of TCF7L2 polymorphisms on glucose values after a lifestyle intervention. Am J Clin Nutr. 2009;90: 1502–1508. doi: 10.3945/ajcn.2009.2837919864407
  98. M Burtscher, H Gatterer, T Dunnwald, D Pesta, M Faulhaber, N Netzer, et alEffects of supervised exercise on gamma-glutamyl transferase levels in patients with isolated impaired fasting glucose and those with impaired fasting glucose plus impaired glucose tolerance. Exp Clin Endocrinol Diabetes. 2012;120: 445–450. doi: 10.1055/s-0032-131164222639399
  99. KL Cox, V Burke, LJ Beilin, JR Grove, BA Blanksby, IB Puddey. Blood pressure rise with swimming versus walking in older women: the Sedentary Women Exercise Adherence Trial 2 (SWEAT 2). J Hypertens. 2006;24: 307–314. pii: 00004872-200602000-00017. doi: 10.1097/01.hjh.0000200514.25571.2016508577
  100. KL Cox, V Burke, LJ Beilin, AJ Derbyshire, JR Grove, BA Blanksby, et alShort and long-term adherence to swimming and walking programs in older women—the Sedentary Women Exercise Adherence Trial (SWEAT 2). Prev Med. 2008;46: 511–517. pii: S0091-7435(08)00048-0. doi: 10.1016/j.ypmed.2008.01.01018295324
  101. AM Craigie, S Caswell, C Paterson, S Treweek, JJ Belch, F Daly, et alStudy protocol for BeWEL: the impact of a BodyWEight and physicaL activity intervention on adults at risk of developing colorectal adenomas. BMC Public Health. 2011;1: 184–191. pii: 1471-2458-11-184.
  102. AT Delgadillo, M Grossman, J Santoyo-Olsson, E Gallegos-Jackson, AM Kanaya, AL Stewart. Description of an academic community partnership lifestyle program for lower income minority adults at risk for diabetes. Diabetes Educ. 2010;36: 640–650. pii: 0145721710374368. doi: 10.1177/014572171037436820576836
  103. HH Ditschuneit, M Flechtner-Mors. Value of structured meals for weight management: risk factors and long-term weight maintenance. Obes Res. 2001;9(Suppl 4): 284S–289S.11707555
  104. K Esposito, M Ciotola, F Giugliano, MI Maiorino, R Autorino, SM De, et alEffects of intensive lifestyle changes on erectile dysfunction in men. J Sex Med. 2009;6: 243–250. pii: JSM1030. doi: 10.1111/j.1743-6109.2008.01030.x19170853
  105. KE Foster-Schubert, CM Alfano, CR Duggan, L Xiao, KL Campbell, A Kong, et alEffect of diet and exercise, alone or combined, on weight and body composition in overweight-to-obese postmenopausal women. Obesity (Silver Spring). 2012;20: 1628–1638. pii: oby201176.21494229
  106. IF Groeneveld, KI Proper, AJ van der Beek, C van Duivenbooden, W van Mechelen. Design of a RCT evaluating the (cost-) effectiveness of a lifestyle intervention for male construction workers at risk for cardiovascular disease: the health under construction study. BMC Public Health. 2008;8: 1–12. doi: 10.1186/1471-2458-8-118173844
  107. DR Jacobs Jr., D Sluik, MH Rokling-Andersen, SA Anderssen, CA Drevon. Association of 1-y changes in diet pattern with cardiovascular disease risk factors and adipokines: results from the 1-y randomized Oslo Diet and Exercise Study. Am J Clin Nutr. 2009;89: 509–517. doi: 10.3945/ajcn.2008.2637119116328
  108. JA Katula, MZ Vitolins, EL Rosenberger, C Blackwell, MA Espeland, MS Lawlor, et alHealthy Living Partnerships to Prevent Diabetes (HELP PD): design and methods. Contemp Clin Trials. 2010;31: 71–81. doi: 10.1016/j.cct.2009.09.00219758580
  109. JA Katula, MZ Vitolins, EL Rosenberger, CS Blackwell, TM Morgan, MS Lawlor, et alOne-year results of a community-based translation of the Diabetes Prevention Program: Healthy-Living Partnerships to Prevent Diabetes (HELP PD) Project. Diabetes Care. 2011;34: 1451–1457. doi: 10.2337/dc10-211521593290
  110. LH Kuller, LR Simkin-Silverman, RR Wing, EN Meilahn, DG Ives. Women's Healthy Lifestyle Project: A randomized clinical trial: results at 54 months. Circulation. 2001;103: 32–37. 11136682
  111. LH Kuller, LS Kinzel, KK Pettee, AM Kriska, LR Simkin-Silverman, MB Conroy, et alLifestyle Intervention and Coronary Heart Disease Risk Factor Changes over 18 Months in Postmenopausal Women: The Women On the Move through Activity and Nutrition (WOMAN Study) Clinical Trial. J Womens Health (Larchmt). 2006;15: 962–974.17087620
  112. LH Kuller, KK Gabriel, LS Kinzel, DA Underwood, MB Conroy, Y Chang, et alThe Women on the Move Through Activity and Nutrition (WOMAN) Study: Final 48-month results. Obesity (Silver Spring). 2012;20.
  113. J Ma, V Yank, L Xiao, PW Lavori, SR Wilson, LG Rosas, et alTranslating the Diabetes Prevention Program lifestyle intervention for weight loss into primary care: a randomized trial. JAMA Intern Med. 2013;173: 113–121. Pii: 1485081. doi: 10.1001/2013.jamainternmed.98723229846
  114. C Mason, KE Foster-Schubert, I Imayama, A Kong, L Xiao, C Bain, et alDietary weight loss and exercise effects on insulin resistance in postmenopausal women. Am J Prev Med. 2011;41: 366–375. doi: 10.1016/j.amepre.2011.06.04221961463
  115. C Mason, RA Risques, L Xiao, CR Duggan, I Imayama, KL Campbell, et alIndependent and combined effects of dietary weight loss and exercise on leukocyte telomere length in postmenopausal women. Obesity (Silver Spring). 2013;21: E549–E554.23640743
  116. KA McAuley, CM Hopkins, KJ Smith, RT McLay, SM Williams, RW Taylor, et alComparison of high-fat and high-protein diets with a high-carbohydrate diet in insulin-resistant obese women. Diabetologia. 2005;48: 8–16. doi: 10.1007/s00125-004-1603-415616799
  117. PA Merriam, TL Tellez, MC Rosal, BC Olendzki, Y Ma, SL Pagoto, et alMethodology of a diabetes prevention translational research project utilizing a community-academic partnership for implementation in an underserved Latino community. BMC Med Res Methodol. 2009;9: 20–28. pii: 1471-2288-9-20. doi: 10.1186/1471-2288-9-2019284663
  118. JA Potteiger, DJ Jacobsen, JE Donnelly. A comparison of methods for analyzing glucose and insulin areas under the curve following nine months of exercise in overweight adults. Int J Obes Relat Metab Disord. 2002;26: 87–89. doi: 10.1038/sj.ijo.080183911791151
  119. WRBMMEKL Simkin-Silverman LR. Maintenance of cardiovascular risk factor changes among middle-aged women in a lifestyle intervention trial. Women's Health. 1998;4: 255–271. 9787651
  120. L Simkin-Silverman, RR Wing, DH Hansen, ML Klem, AP Pasagian-Macaulay, EN Meilahn, et alPrevention of cardiovascular risk factor elevations in healthy premenopausal women. Prev Med. 1995;24.
  121. The ODES investigators. The Oslo Diet and Exercise Study (ODES): design and objectives. Control Clin Trials. 1993;14: 229–243. 8339552
  122. PA Torjesen, KI Birkeland, SA Anderssen, I Hjermann, I Holme, P Urdal. Lifestyle changes may reverse development of the insulin resistance syndrome. The Oslo Diet and Exercise Study: a randomized trial. Diabetes Care. 1997;20: 26–31. 9028689
  123. DT Villareal, S Chode, N Parimi, DR Sinacore, T Hilton, R Armamento-Villareal, et alWeight loss, exercise, or both and physical function in obese older adults. N Engl J Med. 2011;364: 1218–1229. doi: 10.1056/NEJMoa100823421449785
  124. TA Wadden, S Volger, DB Sarwer, ML Vetter, AG Tsai, RI Berkowitz, et alA two-year randomized trial of obesity treatment in primary care practice. N Engl J Med. 2011;365: 1969–1979. doi: 10.1056/NEJMoa110922022082239
  125. X Zhang, G Imperatore, W Thomas, Y Cheng, F Lobelo, K Norris, et alEffect of lifestyle interventions on glucose regulation among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis. Diab Res Clin Pract. 2017;123:149–164.
  126. Diabetes Prevention Program Research Group. Impact of intensive lifestyle and Metformin therapy on cardiovascular disease risk factors in the Diabetes Prevention Program. Diabetes Care. 2005;28: 888–894. 15793191
  127. J Eriksson, J Lindstrom, T Valle, S Aunola, H Hamalainen, P Ilanne-Parikka, et alPrevention of Type II diabetes in subjects with impaired glucose tolerance: the Diabetes Prevention Study (DPS) in Finland. Study design and 1-year interim report on the feasibility of the lifestyle intervention programme. Diabetologia1999;42.
  128. X Zhuo, P Zhang, E Selvin, TJ Hoerger, RT Ackermann, R Li, et alAlternative HbA1c cutoffs to identify high-risk adults for diabetes prevention: a cost-effectiveness perspective. Am J Prev Med. 2012;42: 374–381. pii: S0749-3797(12)00026-8. doi: 10.1016/j.amepre.2012.01.00322424250
  129. X Zhuo, P Zhang, HS Kahn, EW Gregg. Cost-effectiveness of alternative thresholds of the fasting plasma glucose test to identify the target population for type 2 diabetes prevention in adults aged ≥45 years. Diabetes Care. 2013;36: 3992–3998. pii: dc13-0497. doi: 10.2337/dc13-049724135386
  130. PP Toth. High-density lipoprotein as a therapeutic target: clinical evidence and treatment strategies. Am J Cardiol. 2005;96: 50K–58K. pii: S0002-9149(05)01372-X. doi: 10.1016/j.amjcard.2005.08.00816291015
  131. G Reboldi, G Gentile, F Angeli, G Ambrosio, G Mancia, P Verdecchia. Effects of intensive blood pressure reduction on myocardial infarction and stroke in diabetes: a meta-analysis in 73,913 patients. J Hypertens. 2011;29: 1253–1269. doi: 10.1097/HJH.0b013e328346997621505352
  132. G Fager, O Wiklund. Cholesterol reduction and clinical benefit. Are there limits to our expectations?Arterioscler Thromb Vasc Biol. 1997;17: 3527–3533. 9437202
  133. WC Willett, JE Manson, MJ Stampfer, GA Colditz, B Rosner, FE Speizer, et alWeight, weight change, and coronary heart disease in women. Risk within the 'normal' weight range. JAMA. 1995;273: 461–465. 7654270
  134. TJ Wang, JM Massaro, D Levy, RS Vasan, PA Wolf, RB D’Agostino, et alA risk score for predicting stroke or death in individuals with new-onset atrial fibrillation in the community: The Framingham Heart Study. JAMA. 2003;290(8):1049–1056. doi: 10.1001/jama.290.8.104912941677
  135. DC Goff Jr, DM Lloyd-Jones, G Bennett, CJ O’Donnell, S Coady, J Robinson, et al2013 ACC/AHA guideline on the assessment of cardiovascular risk: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63: 2935–2959. doi: 10.1016/j.jacc.2013.11.00524239921
  136. C Fiuza-Luces, N Garatachea, NA Berger, A Lucia. Exercise is the real polypill. Physiology (Bethesda). 2013;28: 330–358. pii: 28/5/330.23997192
  137. BH Marcus, DM Williams, PM Dubbert, JF Sallis, AC King, AK Yancey, et alPhysical activity intervention studies: what we know and what we need to know: a scientific statement from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Physical Activity); Council on Cardiovascular Disease in the Young; and the Interdisciplinary Working Group on Quality of Care and Outcomes Research. Circulation. 2006;114: 2739–2752. pii: CIRCULATIONAHA.106.179683. doi: 10.1161/CIRCULATIONAHA.106.17968317145995
  138. PD Thompson. Exercise and physical activity in the prevention and treatment of atherosclerotic cardiovascular disease. Arterioscler Thromb Vasc Biol. 2003;23: 1319–1321. pii: 23/8/1319. doi: 10.1161/01.ATV.0000087143.33998.F212909570
  139. WL Haskell. Cardiovascular disease prevention and lifestyle interventions: effectiveness and efficacy. J Cardiovasc Nurs. 2003;18: 245–255. 14518600
  140. G Li, P Zhang, J Wang, Y An, Q Gong, EW Gregg, et alCardiovascular mortality, all-cause mortality, and diabetes incidence after lifestyle intervention for people with impaired glucose tolerance in the Da Qing Diabetes Prevention Study: a 23-year follow-up study. Lancet Diabetes Endocrinol. 2014;2: 474–480. pii: S2213-8587(14)70057-9. doi: 10.1016/S2213-8587(14)70057-924731674
  141. RW Mayega, D Guwatudde, FE Makumbi, FN Nakwagala, S Peterson, G Tomson, et alComparison of fasting plasma glucose and haemoglobin A1c point-of-care tests in screening for diabetes and abnormal glucose regulation in a rural low income setting. Diab Res Clin Pract. 2014;1 0 4:1 1 2–1 2 0.
  142. C Kim, W Herman, NW Cheung, E Gunderson, C Richardson. Comparison of hemoglobin A1c with fasting plasma glucose and 2-h postchallenge glucose for risk stratification among women with recent gestational diabetes mellitus. Diabetes Care2011;34:1949–1951. doi: 10.2337/dc11-026921750276
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