Effect of Lifestyle Interventions on Glucose Regulation among Adults without Impaired Glucose Tolerance or Diabetes: A Systematic Review and Meta-Analysis

Xuanping Zhang; Giuseppina Imperatore; William Thomas; Yiling J Cheng; Felipe Lobelo; Keri Norris; Heather M Devlin; Mohammed K Ali; Stephanie Gruss; Barbara Bardenheier; Pyone Cho; Isabel Garcia de Quevedo; Uma Mudaliar; Jinan Saaddine; Linda S Geiss; Edward W Gregg

doi:10.1016/j.diabres.2016.11.020

. Author manuscript; available in PMC: 2019 Jul 12.

Published in final edited form as: Diabetes Res Clin Pract. 2016 Dec 3;123:149–164. doi: 10.1016/j.diabres.2016.11.020

Effect of Lifestyle Interventions on Glucose Regulation among Adults without Impaired Glucose Tolerance or Diabetes: A Systematic Review and Meta-Analysis

Xuanping Zhang ¹, Giuseppina Imperatore ¹, William Thomas ², Yiling J Cheng ¹, Felipe Lobelo ³, Keri Norris ⁴, Heather M Devlin ¹, Mohammed K Ali ³, Stephanie Gruss ¹, Barbara Bardenheier ¹, Pyone Cho ¹, Isabel Garcia de Quevedo ⁵, Uma Mudaliar ³, Jinan Saaddine ¹, Linda S Geiss ¹, Edward W Gregg ¹

¹Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

²Office of Public Health Scientific Services, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

³Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA

⁴Health Policy and Administration, Fulton-DeKalb Hospital Authority, Atlanta, Georgia, USA

⁵Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

Xuanping Zhang, PhD, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, xbz2@cdc.gov

Giuseppina Imperatore, MD, PhD, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, gai5@cdc.gov

William Thomas, MLIS, Office of Public Health Scientific Services, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, bxt2@cdc.gov

Yiling J. Cheng, PhD, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, ycc1@cdc.gov

Felipe Lobelo, MD, PhD, FAHA, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA, felipelobelo@emory.edu

Keri Norris, PhD, Health Policy and Administration, Fulton-DeKalb Hospital Authority, Atlanta, Georgia, USA, dr.kerinorris@gmail.com

Heather M. Devlin, MA, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, jji3@cdc.gov

Mohammed K. Ali, MBChB, MSc, MBA, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA, mkali@emory.edu

Stephanie Gruss, PhD, MSW, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, inf6@cdc.gov

Barbara Bardenheier, PhD, MPH, MA, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, bfb7@cdc.gov

Pyone Cho, MBBS, MPH, MA, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, igz1@cdc.gov

Isabel Garcia de Quevedo, MSPH, Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, vpr5@cdc.gov

Uma Mudaliar, MD, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA, umamudaliar@gmail.com

Jinan Saaddine, MD, MPH, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, zna2@cdc.gov

Linda S. Geiss, MA, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, lsg2@cdc.gov

Edward W. Gregg, PhD, Division of Diabetes Translation, National Centers for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, edg7@cdc.gov

Author Contributors:

XZ designed the study, wrote the protocol and manuscript, and conducted all statistical analyses. WT contributed to the acquisition of data. XZ GI WT YJC FL KN HMD MKA SG BB PC IGQ UM JS LSG EWG contributed to abstract screening, data abstraction, and manuscript revision. XZ is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

^✉

Corresponding Author: Xuanping Zhang, Address: 4770 Buford Hwy, NE. MS: F-75, Atlanta, Georgia 30341, USA, xbz2@cdc.gov, Tel: +0117704885037 Fax: +0117704888550

PMCID: PMC6625761 NIHMSID: NIHMS1030299 PMID: 28024276

Summary

This study systematically assessed the effectiveness of lifestyle interventions on glycemic indicators among adults (≥18years) without IGT or diabetes. Randomized controlled trials using physical activity (PA), diet (D), or their combined strategies (PA+D) with follow-up ≥12 months were systematically searched from multiple electronic-databases between inception and April 17, 2015. Outcome measures included fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), fasting insulin (FI), homeostasis model assessment-estimated insulin resistance (HOMA-IR), and bodyweight. Included studies were divided into low-range (FPG <5.5mmol/L or HbA1c <5.5%) and high-range (FPG ≥5.5mmol/L or HbA1c ≥5.5%) groups according to baseline glycemic levels. Seventy-one studies met inclusion criteria. Random-effect models demonstrated that compared with usual care, lifestyle interventions achieved significant reductions in FPG (−0.14mmol/L[95%CI, −0.18, −0.09]), HbA1c (−0.05%[−0.08, −0.03]), FI (%change: −15.18%[− 20.01–10.35]), HOMA-IR (%change: −22.66%[−29.19, −16.14]), and bodyweight (%change: −4.00%[−4.73, −3.26]). The same effect sizes in FPG reduction (0.08) appeared among both low-range and high-range groups. Similar effects were observed among all groups regardless of lengths of follow-up. D and PA+D interventions had larger effects on glucose reduction than PA alone. Lifestyle interventions significantly improved FPG, HbA1c, FI, HOMA-IR, and bodyweight among adults without IGT or diabetes, and might reduce progression of hyperglycemia to type 2 diabetes mellitus.

Keywords: Lifestyle intervention, glucose regulation, systematic review, meta-analysis

1. Introduction

Diabetes imposes a large burden on human health, society, and the economy due to its wide-ranging complications and extensive treatment costs [1]. Physical inactivity, unhealthy diet, and obesity are well-established risk factors for type 2 diabetes mellitus, and structured lifestyle interventions incorporating behavior change, dietary modifications, and regular moderate-intensity physical activity resulting in modest weight reduction have been shown to reduce type 2 diabetes mellitus incidence [2–4]. However, for practical reasons of statistical power and study cost, the major diabetes prevention trials have focused on the subset of individuals with impaired glucose tolerance (IGT) rather than these other risk factors [2–4]. This has raised considerable debate about whether structured lifestyle interventions should be limited to people with IGT or could be applied more broadly to the population that includes individuals without IGT.

In studies among persons with normal glucose levels, researchers need large sample sizes and long follow-up periods for exploring the effect of lifestyle interventions on reducing the incidence of diabetes, making these studies costly and difficult to conduct [5]. However, a systematic review and meta-analysis of aggregate data from studies of lifestyle interventions among people without IGT may provide evidence of the impact of such interventions on risk factors for diabetes or on the potential to prevent type 2 diabetes mellitus.

We conducted a systematic review to assess the aggregated impact of lifestyle interventions on glycemic indicators among adults (≥18 years) without IGT or diabetes.

2. Methods

2.1. Data source and searches

We developed a study protocol following Cochrane Collaboration standards [6]. We systemically searched MEDLINE, EMBASE, CINAHL, Web of Science, Cochrane Library, and Psychlnfo databases, from inception to April 17, 2015. Medical Subject Headings, text words, and search strategies are available from the authors. We examined reference lists of all studies and relevant reviews for potential additional studies. We directly contacted authors to clarify unclear data.

2.2. Study selection

We selected randomized controlled trials (RCTs) published in any language that examined lifestyle strategies involving physical activity (PA), diet (D), or their combination (PA+D) among adults (≥18 years), and with at least 1 glycemic indicator reported as the intervention outcome (e.g., fasting plasma glucose [FPG], glycated hemoglobin A1c [HbAlc], fasting insulin [FI], and homeostasis model assessment-estimated insulin resistance [HOMA-IR]) with a follow-up interval ≥12 months. Included studies investigated individuals without IGT or diabetes. We excluded all studies among individuals with IGT confirmed by 2-hour oral glucose tolerance test (OGTT) (75g). We included studies regardless of baseline glucose levels. However, we classified studies with mean baseline FPG <5.5mmol/L or HbA1c <5.5% as the low-range group, and with mean baseline FPG ≥5.5mmol/L or HbA1c ≥5.5% as the high-range group, and analyzed data as a whole and by glycemic level groups.

2.3. Data extraction and quality assessment

One primary reviewer and a secondary reviewer independently assessed the manuscript titles and abstracts for inclusion. If any disagreement occurred between the 2 reviewers, a third reviewer reviewed the item, and consensus was reached by a discussion. The study team extracted data regarding demographic and intervention characteristics. Primary outcomes in this review included FPG, A1C, FI, and HOMA-IR. In our synthesis models, we used percent changes in FI and HOMA-IR, rather than raw values, to account for non-standardized insulin measurements. We also examined percent body weight change from baseline.

In our review, all interventions were classified into 3 categories: PA, D, and their combination. PA interventions included any strategy to increase physical activity levels using counseling, exercise prescription, or a supervised or unsupervised exercise program. The D interventions included any strategy used to reduce or control calorie intake, such as very low-calorie diet (<800 kcal/d) or low-calorie diet (800 to 1500 kcal/d). PA+D interventions usually also employed a behavioral modification component, such as counseling, education, cognitive-behavioral therapy, or social support.

We assessed study quality by examining potential selection, attrition, and detection bias [6]. We excluded from our main analyses studies of 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%, data were not used in our primary meta-analyses, but were used in our sensitivity analyses.

2.4. Data Synthesis and Analysis

If 2 or more studies with similar intervention and comparison groups reported a similar outcome of interest, we conducted a meta-analysis to determine pooled effects. We calculated the mean difference between baseline and follow-up measures for the intervention and comparison groups (delta I and delta C) and the standard error of each difference. We used 3 data synthesis strategies to estimate pooled effects: stratification by glycemic levels, stratification by follow-up duration, and stratification by type of intervention.

We used the DerSimonian and Laird random-effects model [7] to determine pooled effects. We used meta-regression to determine whether various study-level characteristics (mean age, follow-up duration, duration of the intervention, number of intervention contacts, attrition, and year of publication) affected the between-group change in FPG, and we examined interaction terms for all models. We also used meta-regression to test if there is an association between the magnitude of body weight change and the magnitude of FPG change. The meta-regression was conducted using SPSS (version 20.0, Armonk, NY: IBM Corp.). We used the chi-squared test to examine heterogeneity, and Cochrane Review Manager software (version 5.1; Copenhagen, Denmark) to calculate pooled effects. Effect size was defined by the mean difference between delta I and delta C divided by the standard deviation of the mean.

If a comparison group in a study used a similar approach as the intervention group, 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 1 arm) for both intervention and comparison group. These effects were also estimated by using the DerSimonian and Laird random-effects model. We did not conduct sensitivity analysis for these studies.

3. Results

Seventy-one studies [8–78] (plus 30 additional publications based on those studies [79–108]) encompassing 13943 participants (Table 1: range, 20 to 1089) fulfilled the inclusion criteria (Figure 1). Follow-up duration ranged from 12 to 54 months. The weighted mean age of the participants was 50.9 years (range, 30.2 to 70.4 year), and mean body mass index (BMI) was 30.1 kg/m² (range, 23.3 to 38.7 kg/m²). Mean baseline FPG, HbAlc, FI, and HOMA-IR were 5.3 mmol/L, 5.4%, 13.7 μU/ml, and 3.9, respectively. More studies took place in a community setting vs a clinic setting (52 vs 19). Sampling methods varied, but most participants were recruited through screening programs. Attrition ranged from 0% to 60%, and was 30% or more in 15 studies [8,20–22,31,44,46,49,51,57,60,62,64,70,78]; higher attrition rates were associated with longer follow-up duration. Thirty-eight studies with FPG <5.5mmol/L or HbA1c <5.5 % were classified as low-range glycemic level studies, and 33 studies with FPG ≥5.5mmol/L or HbA1c ≥5.5% were classified as high-range glycemic level studies (Table 1).

Table 1:

Characteristics of Study Participants

Citation	Sample size	Length of follow- up (month)	Age at baseline (years) mean (SD)	Sex (% female)	Setting; Race/ethnicity	BMI at baseline (kg/m²) mean (SD)	FPG at baseline (mmol/L) mean (SD)	HbAlc at baseline (%) mean (SD)	Insulin at baseline (μU/ml) mean (SD)	Inclusion criteria	Sampling method	Attrition (%)

Ackermann et al. 2008	92	12	58.3 (10.1)	55.4	Community (YMCA) Indianapolis Indiana 81.5% white 12.0% black	31.4 (4.9)	NR	5.6 (0.5)	NR	People with ADA risk score>10 and casual capillary blood glucose (CCBG) of 6.1–11.0 mmol/L	Recruited from YMCA by a community- based screening	32.6%
Almeida et al. 2011	53	12	range: 20–29: 12 30–39: 26 >40:15	18.9	Clinic Sao Paulo Brazil	23.3 (2.7)	4.7 (0.6)	NR	NR	Aged 20–59yrs; without hyperlipidemia, hypertriglyceridemia, hyperglycemia, obesity, cancer, anabolic, or corticosteroid drugs use, or pregnancy	Recruited from a reference HIV clinic	20.8%
Anderson et al. 2014 Craigie etal. 2011	329	12	63.6 (6.8)	26.0	Community Scotland UK 99.0% white 1.0% others	30.7 (4.2)	6.1(2.0)	6.0(1.1)	10.6(8.6) HOMA-IR: 3.0 (2.9)	Aged 50–74yrs; BMI>25kg/m²; with polypectomy for adenoma, without pregnancy, DM	Recruited from a bowel screening program	7.3%
Anderssen et al.& 1998 Jacobs et al. 2009 The ODES Investigators 1993 Torjesen et al.1997	219	12	44.9 (2.5)	9.6	Community Oslo Norway	28.8(3.4)	5.6 (0.7)	NR	23.2(13.3)	BMI>24 kg/m²DBP: 86–99 mmHg TC: 5.20–7.74 mmol/L HDL-C<1.2 mmol/L Fasting TG>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	Community Sherbrooke Quebec Canada	NR (body weight mean (SD) 79.6 (10.7)	5.0 (0.4)	NR	NR	Sedentary obese postmenopausal women without: (1) abnormal fasting lipid profile; (2) CVD; and (3) DM	Using a computergenerated randomization list	12.0%
Bazzano et al. 2014	148	12	46.8(10.1)	88.5	Community New Orleans LA 45.3% white 51.4% black 2.0% Hispanic	35.4 (4.2)	5.2 (0.6)	NR	17.3 (10.0)	Obese people (BMI: 30–45 kg/m²) without DM and CVD	Recruited from community screening and TV ads	17.8%
Bo et al. 2007&2009	375	48.0	55.7(5.7)	58.2	Community Asti Italy	29.7 (4.4)	5.8 (0.8)	NR	median (interquartile range) 20.4 (24.0) for IG; 21.3 (31.2) for CG	People with MetS defined by FPG>6.1 mmol/L, without DM and CVD	Recruited from a metabolic screening program	10.7%
Bouchonville et al. 2014 Villareal et al. 2011	107	12.0	69.7 (4.0)	62.6	Community St. Louis MO	37.2 (5.0)	5.5 (0.6)	NR	16.6(10.6) HOMA-IR: 4.2 (3.0)	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	Community Adelaide Australia	34.0 (NR)	5.4 (0.2)	NR	16.0(1.3) HOMA-IR: 3.8 (0.4)	Obese, hyperinsulinemic persons aged 20–65yrs, insulin >12 mu/1 without DM,	NR	25.9%
Broekhuizen et al. 2012	340	12	45.3 (12.9)	56.7	Community Amsterdam The Netherland	26.5 (5.0)	4.9 (0.9)	NR	NR	Aged 18–70 yrs, with familial hypercholesterolemia, a LDL-C level>75th percentile	Recruited from the national cascade screening program	7.4%
Burke et al. 2007 & 2008	241	36	56.2(7.3)	55.6	Community Perth Australia	30.1 (2.7)	5.0 (0.9)	NR	1.8 (0.9) HOMA-IR: 2.4 (1.7)	Overweight, age>40yrs using 1 or 2 drugs to treat HT >3 months without DM, chronic renal failure, CVD	Recruited bymediaadvertising	16.2%
Burtscher et al. 2009&2012	36	12	57.5 (6.9)	55.6	Clinic Innsbruck Austria	29.0 (3.9)	6.0 (0.4)	NR	NR	Patients with IFG (FPG: 5.6– 6.9 mmol/L), aged 40–65yrs; BMI>25 kg/m², and without DM	Recruited byphysiciansthroughmemberscreening	0.0%
Choo et al. 2014	110	12	43.1 (9.0)	100.0	Community Seoul South Korea	28.5 (3.8)	5.0 (0.8)	NR	NR	Aged 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	Community Adelaide Australia	32.8(3.5)	6.1 (0.6)	NR	9.9 (4.7)	Women, aged 20–65yrs, BMI:27–40kg/m², without DM, or renal or liver disease	Recruited from public ads and screened by questionnaires	33.6%
Cole et al. 2013	94	12	58.3 (9.6	46.0	Community San Antonio Texas 64.0% white 17.0% black 19.0% Flispanic	30.8 (4.9)	6.1 (0.5)	5.9 (0.5)	NR	Aged 18yrs+; without DM, but with pre-DM, by ADA IFG (5.6–6.9 mmol/L)	Recruited from a pre-DM education class	31.0%
Coon et al. 1989	20	12	59.5 (7.5)	0.0	Community Baltimore MD	29.0 (3.0)	5.4 (0.5)	NR	13.0(6.0)	Aged 45yrs+, healthy persons without DM	Recruited by ads	0.0%
Cox et al 2006 & 2008 & 2010	116	12	55.5 (4.7)	100.0	Community Berth Australia	26.4 (3.3)	5.1 (0.4)	NR	6.2 (3.9)	Aged 50–70 yrs; 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	Clinic sUlm Germany	33.4 (3.6)	5.0 (0.6)	NR	21.5 (8.1)	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	Community Kearney NE	31.2 (4.0)	5.5 (0.8)	NR	14.0 (9.8)	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	Clinic Naples Italy	34.9 (2.4)	5.9 (0.8)	NR	14.0 (4.0) HOMA-IR: 3.7 (0.5)	Obese premenopausal women, aged 20–46yrs; without DM, IGT (7.8–11.0 mmol/L), CAD, pregnancy. OGTT confirmed	Recruited from a outpatient department	6.7%
Esposito et al. 2004a	110	24	43.3 (5.0)	0.0	Clinic Naples Italy	36.7 (2.4)	5.8 (0.6)	NR	20.0 (7.5)	Obese men with erectile dysfunction, aged 35–55yrs; without DM and IGT, OGTT confirmed	Recruited from a outpatient department list	5.5%
Esposito et al. 2004b (JAMA v.292) & 2009	180	24	43.9 (6.2)	45.0	Clinic Naples Italy	28.0 (3.3)	6.3 (0.6)	NR	15.5 (6.5) HOMA: 3.8 (0.7)	Sedentary people with MetS defined by FPG>6.1 mmol/L,	Recruited from a screening program	8.9%
Fatouros et al. 2005	50	12	70.4(3.8)	0.0	Community Alexandroupolis Greece	29.5 (3.3)	5.9 (0.7)	NR	14.2(3.1) HOMA-IR: 3.7 (2.8)	Inactive old men, nonsmoker, without DM, FPG<7 mmol/L	Recruited from a volunteer database in localcommunity	0.0%
Fernandez et al. 2012	40	12	40.9(13.5)	67.5	Community Leon Spain	31.8(2.4)	4.6 (0.9)	NR	21.2(27.4)	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	Clinic Naples Italy	29.2 (4.5)	5.6 (1.5)	NR	NR	People with HT	Recruited from a outpatient clinic	0.0%
Fisher et al. 2012	97	12	age range: 21–46 y	100.0	Community Birmingham AL 46.4% white 53.6% black	28.0(1.0)	4.8 (0.4)	NR	11.4(3.6)	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	age range: 30–45y	100.0	Community Tampere Finland	34.0 (3.6)	5.1 (0.5)	NR	12.7(5.2)	Aged 30–45yrs, BMI: 30–45 kg/m², physically inactive	Recruited by ads	9.8%
Fonolla et al. 2009	297	12	46.0 (8.4)	15.5	Community Granada Spain	28.8(5.0)	5.6 (1.9)	NR	NR	People with moderate risk of CVD, without DM, or pregnancy	Recruited from a screening program	14.8%
Frank et al. 2005	173	12	60.7 (6.7)	100.0	Community Seattle Washington	30.4 (3.9)	5.4 (0.5)	NR	17.9(8.3) HOMA: 4.4 (2.2)	Postmenopausal women, aged 50–75yrs, sedentary at baseline BMI>25 kg/m² without DM, nonsmoker	Recruited through mailings and media ads	1.7%
Groeneveld et al. 2008 & 2010	816	12	46.6 (9.0)	0.0	Community Amsterdam The Netherlands	28.5 (3.5)	NR	5.7 (0.4)	NR	Male construction workers with elevated risk of CVD	Recruited from periodical health screening	27.6%
Fleshka et al. 2003	423	24	44.5 (10.0)	84.6	Clinics NY, Medison, Baton Rouge, Boulder, Davis, Durham, Woodbury	33.7 (3.6)	5.0 (0.7)	NR	18.0 (9.5) (IU/L)	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. 2012 Mason et al. 2011&2013	439	12	58.0(5.0)	100.0	Community Seattle WA 85.0% white	30.9 (4.1)	5.4 (0.5)	NR	12.9(8.1) HOMA-IR: 3.1 (2.1)	Aged 50–75yrs; BMI: >25 kg/m²; <100 min/w PA; postmenopausal; without DM; FPG<7.0 mmol/I,	Recruited from mass mailing ads	9.1%
Kanaya et al. 2012 Delgadillo et al. 2010	238	12	56.5 (16.5)	73.5	Community Berkeley, Oakland, etc CA22.5% white 23.0% black 37.0% Hispanic	30.0 (5.7)	5.2 (0.7)	NR	NR	Aged 25yrs+; a capillary blood glucose:5.9–8.9 mmol/L, without DM	Recruited from a community- based education outreach	12.2%
Kanaya et al. 2014	180	12	55.0 (7.0)	72.0	Clinics San Francisco, San Diego CA65.0% white	34.3 (6.7)	5.8 (0.7)	5.9 (0.4)	27.5 (17.6) HOMA-IR: 7.2 (5.1)	Aged 21–65yrs; with MetS (FPG:5.6–6.9 mmol/L), 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	Community Winston-Salem NC 73.8% white 24.6% black	32.7 (4.0)	5.9 (0.6)	NR	16.7(9.8) HOMA-IR: 4.4 (2.9)	Patients with pre-DM defined by FPG of 5.3–6.9 mmol/L 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	Community Saga City Japan	23.7 (4.4)	5.1 (0.5)	5.1 (0.3)	NR	People with FPG: 5.6–7.8 mmol/L, or HbAlc: 5.5–6.0%	Recruited from health checkup	27.2%
Keogh et al. 2007	36	12	48.6 (5.2)	68.0	Community Adelaide Australia	32.9 (4.5)	5.9 (0.5)	NR	14.8(11.0)	Overweight or obese people, aged 20–65yrs; BMI: 27–40 kg/m²; without DM, with FPG<7.0 mmol/L	Recruited from newspaper ads	30.6%
Lawton et al. 2009	1089	24	58.9(6.9)	100.0	Clinics Wellington New Zealand	29.2 (6.0)	5.0 (0.6)	5.5 (0.6)	6.9 (4.7)	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	Community Adelaide Australia	32.0(6.0)	5.4 (0.6)	NR	9.1 (4.6)	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	Community Melbourne Australia	27.8(5.4)	4.6 (0.4)	NR	NR	Women with a child in schools without pregnancy and serious medical conditions	Recruited by invitation in school newsletter	14.0%
Ma et al. 2009&2013	241	15	52.9(10.6)	47.0	Clinic San Francisco CA78.0% white 17.0% Asian	32.0 (5.4)	5.6 (0.5)	NR	NR	Patients with age>18 yrs, BMI>25 kg/m², with pre-DM defined by FPG of 5.6–6.9 mmol/L, or MetS	Recruited from a single primary care clinic	8.3%
Marsh etal. 2010	96	12	30.2(5.2)	100.0	Clinic Sydney Australia	34.5 (4.2)	4.8 (0.7)	NR	15.6(10.8)	Women, aged 18–40yrs; BMI<25 kg/m², with polycystic ovary syndrome, without pregnancy and DM	Recruited from a screening program	49.0%
McAuley et al. 2005&2006	93	12	Range: 30– 70y	100.0	Community Dunedin New Zealand	35.7 (5.0)	5.1 (0.6)	NR	13.9(6.9)	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	Community Umea Sweden	32.7 (3.5)	5.2 (1.1)	NR	8.7 (4.4)	Postmenopausal non-smoking women, BMI>27 kg/m², without DM, FPG<7.0 mmol/L	Recruited by newspapers ads	30.0%
Muto et al. 2001	326	18	42.5 (3.7)	0.0	Community Tokyo Japan	24.7 (3.0)	5.6 (1.3)	NR	NR	Male workers with at least one abnormality, including FPG>5.6 mmol/L	Recruited from a building maintenance company	7.4%
Narayan et al. 1998	95	12	Range: 25– 50y	75.8	Community Pima AZ	Range:20.2–59.9	Range: 4.2–6.5	Range: 4.5–6.3	Range: 24–137 (pM)	Overweight or obese people, aged 25–54yrs; BMI>25kg/m², without DM, OGTT<7.8 mmol/L	Recruited from an ongoing epidemiological study	2.0%
Nilsson et al. 1992	94	12	55.0 (7.2)	NR	Community Dalby Sweden	Weight(kg):81.4(11.6)	5.0 (0.5)	NR	17.6 (8.9)	Patients with or without FIT, but no DM	Recruited from a cross- sectional study	8.5%
Nilsson et al 2001	113	18	49.7 (6.2)	60.9	Community Flelsingborg Sweden	27.8(5.6)	4.9 (1.2)	NR	8.7 (5.7)	Aged 40–50yrs; with a cardiovascular risk score sum of >9	Recruited from a screening program	18.6%
Ockene et al. 2012 Merriam et al. 2009	312	12	52.0(11.2)	74.4	Community Lawrence MA	33.9 (5.6)	5.8 (0.7)	NR	20.0(13.6) HOMA-IR: 5.2 (3.8)	Age>25yrs+, BMI>24kg/m², with risk for DM, but without DM	Recruited from the Greater Lawrence Family Flealth Center patient panel	7.4%
Poston et al. 2006	250	12	41.0(8.5)	92.4	Community Huston TX	36.1 (3.1)	4.5 (0.7)	NR	NR	Overweight or obese people, aged 25–55yrs; BMI: 27–40 kg/m²; without DM or pregnancy for women, FPG<7.0 mmol/L, confirmed byOGTT	Recruited from a screening program	45.6%
Potteiger et al. 2003 & 2002	66	16	NR	57.6	Community Denver CO	Range: 25– 34.9	5.5 (0.4)	NR	11.4(4.4)	Sedentary people without DM and heart disease	Recruited from part of the Midwest Exercise Trial	10.1%
Reid etal. 2014	426	12	51.5 (11.6)	61.3	Clinic Ottawa Canada 95.3% white	29.4 (5.7)	5.1 (0.6)	NR	NR	Obese people with coronary risk, without DM, pregnancy, FPG<7.0 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	Clinics Stockhlom Sweden	38.7 (4.5)	5.2 (1.3)	NR	NR	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	Clinics Stockhlom Sweden	37.7 (4.6)	5.5 (1.2)	NR	NR	Obese people, aged 21–64yrs; BMI:>30 kg/m²; without DM or pregnancy	Recruited from hospital waiting list	4.9%
Sartorelli et al. 2005	104	12	45.5 (9.1)	79.8	Community Sao Paulo Brazil	28.7 (2.5)	5.2 (0.5)	NR	NR	Overweight or obese people, aged 30–65yrs; BMI: 24–35 kg/m²; without DM, or pregnancy	Recruited from a screening program of high-risk group for DM, or from ads	31.7%
Simkin-Silverman et al. 1995 & 1998 & 2003Kuller et al. 2001 &2006&2012	535	54	47.0(1.0)	100.0	Community Allegheny PN 92.0% white	25.1 (3.3)	5.4 (0.8)	NR	NR	Premenopausal women, aged 44–50yrs; BMI: 20–34 kg/m²; FPG<7.8 mmol/L	Recruited from the Women’s Healthy Lifestyle Project	2.8%
Staten et al. 2004	361	12	57.2 (4.8)	100.0	Community Tucson AZ 100% Hispanics	29.5 (5.3)	5.9 (2.5)	NR	NR	Uninsured Hispanic women, age>50yrs,	Recruited from clinicregistration	33.4%
Stefanick et al. 1998	377	12	52.1 (7.3)	47.7	Community Palo Alto CA	26.7 (3.0)	5.3 (0.5)	NR	NR	Postmenopausal women, aged 45–64yrs; men aged 30–64yrs; without DM, FPG<7.8 mmol/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	Community Wollongong Australia	30.0(2.7)	5.3 (0.5)	NR	Median (IQR): 10.7 (7.5–13.7) for IG 11.4(8.3– 15.1) for CG	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	Community Bilthoven The Netherlands	29.6 (3.4)	5.2 (0.6)	NR	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	Clinic Knoxville TN	34.8(3.1)	5.2 (0.5)	NR	11.0(6.5)	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	Clinic Philadelphia PN 19.0% white 81.0% black	36.5 (6.0)	5.5 (1.5)	NR	NR	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– 40y	100.0	Community Oulu Finland	25.3 (4.6)	4.8 (0.5)	NR	4.9 (3.3)	Women aged 35–40yrs, without chronic disease	Recruited from the National Population Register of Finland	33.3%
Vetter et al. 2013 Wadden et al. 2011	390	24	51.5 (11.5)	79.7	Clinic Philadelphia PA 59.0% white 38.5%black	38.5 (4.7)	5.8 (1.8)	NR	13.5 (10.0) HOMA-IR: 3.4 (2.9)	Aged 21yrs+; BMI: 30–50 kg/m²; with MetS (FPG> 6.1 mmol/L); without cardiovascular events	Recruited from primary care practices	13.8%
von Thiele Schwarz et al. 2008	195	12	46.6(10.8)	100.0	Community Stockholm Sweden	NR	5.0 (0.5)	4.4 (0.3)	NR	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	Community Tokyo Japan	24.4 (2.9)	5.8 (0.6)	NR	NR	Male workers with risk for DM, aged 35–70yrs; OGTT confirmed	Recruited from annual checkup list	9.8%
Weinstock et al. 1998	45	23	43.3 (7.4)	100.0	Community Syracuse NY	35.9 (6.0)	5.1 (0.6)	NR	15.4 (6.9)	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	Community St. Louis MO	27 .3 (2 . 1)	5.3 (0.4)	NR	7.8 (5.1)	Sedentary people, aged 50– 60yrs; BMI:23.5–29.9kg/m²; non-smoker without DM. FPG<7.0 mmol/L, OGTT confirmed	Recruited from a screening program	4.2%
Wing et al. 1995	202	18	37.4 (5.3)	48.1	Community Pittsburgh PA	30 .9 (2 . 1 )	5.5 (0.7)	NR	27.1 (15.6)	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	Community Pittsburgh PA	3 5 . 9 ( 4.3)	5.9 (0.6)	7.2 (0.5)	15.9 (13.4)	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	Clinic Adelaide Australia	33 . 0 (3.9)	5.8 (0.7)	NR	10.0 (6.7)	Overweight or obese males, aged 20–65yrs; BMI: 27–40 kg/m², without DM	Recruited by a screening program	44.7%

Mean (SD) Total Range	1394320-1089	12–54	50.9 (8.4)	0–100		30.1 (4.4) 23.3–38.7	5.3 (1.0)	5.4 (0.6)	13.7 (9.0) HOMA-IR: 3.9 (2.8)			0–60.0

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Abbreviations: BG: blood glucose; 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; HbAlc: glycated hemoglobin A1c; HDL: high density cholesterol; HT: hypertension; IGT: impaired glucose tolerance; LDL: low density cholesterol MetS: metabolic syndrome; NR: not reported; OGTT: oral glucose tolerance test; PCP: primary care physician; PG: plasma glucose; SD: standard deviation

Fig. 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 Infomration Database. WOS, Web of Science.

We observed considerable heterogeneity in the treatments provided to both intervention and comparison groups (Table S1 and Table S2, presented online as supplementary materials). In 27 studies, a similar approach was used in both intervention and control groups (data from these studies were synthesized by a single-arm model). In the other 44 studies, the control group received usual care (UC). In the 44 studies that compared an intervention to UC, 32 had 2 arms [8–10,14,17,18,22,27–29,32,38,40,42,43,45,47,52–59,62,67,69,70,72,76,104], 5 studies [34,48,71,72,75] had 3 arms, and 7 studies [11,15,30,39,46,65,77] had 4 arms (e.g., PA, D, PA+D and control arm). The randomization procedure was described in 43 studies (Table S2). Allocation concealment (i.e., blinding) was adequately reported in 26 studies. Meta-regression analyses indicated that there was no significant interaction between the between-group change in FPG and any study-level characteristic. An Egger’s plot demonstrated a symmetrical shape distribution (except for 2 outliers) which supports a hypothesis of no publication bias.

3.1. Changes in Glycemic Indicators

In 52 studies or study arms comparing interventions to UC with attrition <30%, the pooled effect estimate from all studies demonstrated that compared to UC, lifestyle interventions—including PA, D, or PA+D—achieved significant reductions in FPG (−0.14mmol/L [95% CI, −0.18, −0.09]), HbA1c (−0.05% [−0.08, −0.03]), FI (percent change: −15.18% [−20.01, −10.35]), HOMA-IR (percent change: −22.66% [−29.19, −16.14]), and body weight (percent change: −4.00%[−4.73, - 3.26]) (Table 2). Sensitivity analyses including studies with attrition ≥30% in the model produced similar results.

Table 2,

Lifestyle Interventional Effect: Meta-Analyses Results

	FPG (mmol/L)			HbA1c (%)			% Change in Fasting Insulin			% Change in HOMA-IR			Weight Loss (%)

	Studies (sample size)	pooled effect mean (Effect size) (95%CI)	Hetero- geneity p value	Studies (sample size)	pooled effect mean (Effect size) (95%CI)	Hetero- geneity p value	Studies (sample size)	pooled effect mean (Effect size) (95%CI)	Hetero- geneity p value	Studies (sample size)	pooled effect mean (Effect size) (95%CI)	Hetero- geneity p value	Studies (sample size)	pooled effect mean (Effect size) (95%CI)	Hetero- geneity p value

LI vs UC (all studies^a)	52 (8919)	−0.14 (0.07) (−0.18, −0.09)	<0.01	9 (2617)	−0.05 (0.06) (−0.08, −0.03)	0.54	33 (5308)	−15.18 (0.08) (−20.01, −10.35)	<0.01	19 (2846)	−22.66 (0.13) (−29.19, −16.14)	<0.01	45 (8167)	−4.00 (0.12) (−4.73, −3.26)	<0.01
LI vs UC (all studies^b)	59 (9446)	−0.12 (0.05) (−0.17, −0.08)	<0.01	10 (2709)	−0.05 (0.06) (−0.08, −0.02)	0.57	37 (5501)	−14.38 (0.08) (−19.04, −9.72)	<0.01	NA	53 (8786)	−3.80 (0.12) (−4.47, −3.13)	<0.01
LI vs UC (Group 1^c)	23 (4263)	−0.08 (0.08) (−0.11, −0.04)	0.05	2 (246)	−0.07 (0.12) (−0.14, −0.01)	0.29	12 (1551)	−11.69 (0.11) (−16.99, −6.38)	0.04	5 (837)	−13.11 (0.08) (−24.60, −1.61)	<0.01	18 (3165)	−3.71 (0.11) (−4.86, −2.56)	<0.01
LI vs UC (Group 2^d)	29 (4656)	−0.19 (0.08) (−0.26, −0.12)	<0.01	7 (2371)	−0.05 (0.04) (−0.08, −0.02)	0.52	21 (3747)	−16.56 (0.08) (−23.14, −9.98)	<0.01	14 (2009)	−28.05 (0.17) (−35.43, −20.67)	<0.01	27 (5002)	−4.19 (0.12) (−5.19, −3.18)	<0.01
LI vs UC (F/U=12 mo)	43 (7221)	−0.10 (0.06) (−0.14, −0.07)	<0.01	5 (2306)	−0.05 (0.07) (−0.08, −0.02)	0.41	25 (4521)	−15.45 (0.08) (−21.22, −9.69)	<0.01	15 (2147)	−24.39 (0.09) (−36.10, −12.69)	<0.01	37 (6627)	−3.66 (0.10) (−4.53, −2.80)	<0.01
LI vs UC (13–23 mo)	8 (1560)	−0.15 (0.12) (−0.21, −0.09)	0.91	1 (158)	−0.10 (0.19) (−0.18, −0.02)	NA	4 (496)	−11.04 (0.09) (−22.33, 0.25)	0.24	1 (158)	−14.63 (0.13) (−32.44, 3.18)	NA	6 (1289)	−3.28 (0.16) (−4.39, −2.17)	0.09
LI vs UC (≥24 mo)	15 (3423)	−0.12 (0.04) (−0.23, −0.001)	<0.01	4 (1242)	−0.03 (0.03) (−0.08, 0.01)	0.78	15 (3426)	−11.30 (0.05) (−18.68, −3.91)	<0.01	7 (1567)	−20.07 (0.13) (−27.73, −12.40)	<0.01	15 (3424)	−3.58 (0.09) (−4.98, −2.19)	<0.01
PA vs UC	14 (1813)	−0.07 (0.08) (−0.11, −0.03)	0.74	3 (1227)	−0.04 (0.06) (−0.08, 0.01)	0.94	9 (1555)	−7.61 (0,05) (−15.52, 0.30)	0.06	5 (233)	−7.25 (0.08) (−19.02, 4.51)	1.00	12 (1663)	−1.55 (0.08) (−2.53, −0.57)	0.38
D vs UC	7 (499)	−0.17 (0.15) (−0.27, −0.08)	0.46	1 (50)	0.00 (0.00) (−0.23, 0.23)	NA	5 (321)	−13.73 (0.10) (−28.64, 1.18)	0.06	2 (282)	−24.24 (0.22) (−37.21, −11.27)	0.94	6 (433)	−6.21 (0.24) (−8.63, −3.19)	<0.01
PA+D vs UC	31 (6607)	−0.15 (0.06) (−0.21, −0.09)	<0.01	5 (1340)	−0.07 (0.09) (−0.11, −0.02)	0.21	19 (3432)	−18.25 (0.10) (−24.18, −12.32)	<0.01	12 (2431)	−24.69 (0.13) (−32.15, −17.23)	<0.01	27 (6071)	−4.15 (0.12) (−5.02, −3.27)	<0.01

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Abbreviations: CI: confidence interval; D: diet; FPG: fasting plasma glucose; HbA1c: glycated hemoglobin A1c; HOMA-IR: homeostasis model assessment of insulin resistance; LI: lifestyle intervention; mo: month; PA: physical activity; UC: usual care; vs: versus

All studies with attrition <30%

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

All studies with attrition <30% and participants with FPG<5.5 mmol/L or HbA1c <5.5%

All studies with attrition <30% and participants with FPG≥5.5 mmol/L or HbA1c≥5.5%

3.1.1. Comparison According to Participant Baseline Glycemic Status (Limited to studies with attrition <30% thereafter)

In studies among persons with low-range glycemic status, lifestyle interventions were associated with significantly reduced FPG (−0.08mmol/L [−0.11, −0.04]), HbA1c (−0.07% [−0.14, −0.01]), FI (percent change: −11.69% [−16.99, −6.38]), HOMA-IR (percent change: −13.11%[−24.60, −1.61]), and body weight (percent change: −3.71% [−4.86, −2.56]). In studies among persons with high-range glycemic status, lifestyle interventions decreased FPG (−0.19mmol/L [−0.26, −0.12]), HbA1c (−0.05% [−0.08, −0.02]), FI (percent change: −16.56% [−23.14, −9.98]), HOMA-IR (percent change: −28.05% [−35.43, −20.67]), and body weight (percent change: −4.19% [−5.19, − 3.18]). Notably, intervention effects on FPG differed in absolute values (−0.08mmol/L in low- range vs −0.19mmol/L in high-range groups), but the effect sizes were the same across groups (0.08).

3.1.2. Comparison According to Length of Follow-Up

Similar reductions in FPG and percent body weight were achieved in 12 months of follow-up (− 0.10mmol/L [−0.14, −0.07], and −3.66% [−4.53, −2.80]), 13–23 months (−0.15mmol/L[−0.21, - 0.09], and −3.28% [−4.39, −2.17]), and ≥24 months (−0.12mmol/L [−0.23, −0.001], and −3.58%[- 4.98, −2.19]). Meta-regression analyses demonstrated that the association between the magnitude of percent body weight change and the magnitude of FPG change was not significant (P = 0.183, and the R² for correlation between percent body weight change and FPG change was very low [0.037]).

3.1.3. Comparison According to Intervention Modality

Analyses stratified by intervention types showed that each type was effective, with D vs UC achieving the highest reduction in FPG (−0.17mmol/L [−0.27, −0.08]), followed by PA+D vs UC (−0.15mmol/L [−0.21, −0.09]), and then PA vs UC (−0.07mmol/L [−0.11, −0.03]). Reduction in body weight followed a similar pattern, with greater weight loss among comparisons of D vs UC (−6.21% [−8.63, −3.19]) and PA+D vs UC (−4.15% [−5.02, −3.27]) than for PA vs UC (−1.55% [2.53, −0.57]). Similar patterns were also observed for percent changes in FI and HOMA-IR with PA+D vs UC (FI: −18.25% [−24.18, −12.32], HOMA-IR: −24.69% [−32.15, −17.23]), followed by D vs UC (FI: −13.73% [−28.64, 1.18], HOMA-IR: −24.24% [−37.21, −11.27]), and PA vs UC (FI: - 7.61% [−15.52, 0.30], HOMA-IR: −7.25% [−19.02, 4.51]). Pooled effects of interventions on FPG are shown in Figure 2 and meta-analyses results in a single arm model are presented online as Table S3.

Fig. 2 -- — Changes in fasting plasma glucose in the intervention versus usual care groups (mmol/L). Group 1: low-range glycemic group (FPG <5.5mmol/L or HbA1c <5.5%). Group 2: high-range glycemic group (FPG ≥5.5 mmol/L or HbA1c ≥5.5%). D, diet. PA, physical activity. UC, usual care. vs versus.

4. Discussion

The target population for type 2 diabetes mellitus prevention has been a topic of debate since the completion of major diabetes prevention trials [109]. The difficulty stems from observations that diabetes prevalence has increased across all segments of society [110], yet the evidence for preventive interventions is mainly limited to persons with IGT [2–4,111,112]. This comprehensive review of the effects of structured lifestyle interventions yielded 3 main findings.

First, lifestyle interventions among individuals with lower risk than those with IGT and diabetes led to significant improvements in FPG, HbA1c, and FI among the full range of baseline risk, with no apparent differences measured by effect sizes between studies of persons with low-range vs high-range glycemic levels. The average magnitude of effect of 1-year change in FPG of about −0.3mmol/L was about 40% of the magnitude of effect seen among persons with IGT in the U.S. Diabetes Prevention Program and the Finnish Diabetes Prevention Study, wherein lifestyle interventions resulted in a −0.2mmol/L glucose change and a 58% reduced incidence of diabetes [2]. The findings from our meta-analyses imply that the reduction in glucose may bring about similar diabetes risk reductions among population without IGT.

Second, interventions that focused only on PA without a concomitant calorie restriction had weaker effects on glycemic indicators than studies that used PA and calorie restriction, or calorie restriction alone. Third, interventions were effective across a wide variation of follow-up durations, from 1 year in 43 studies, to more than 2 years in 15 studies. Taken as a whole, our findings suggest that multi-faceted interventions combining PA and D are likely to be effective in improving glucose regulation and reducing risk for diabetes in populations with average low- range and high-range glucose levels.

Several components of lifestyle interventions have been associated with improved insulinmediated glucose transport and therein reduce insulin resistance and progression to glucose intolerance. Our findings were generally supportive of the diabetes prevention trials, which suggest that multi-component interventions, including elements of calorie restriction, PA, and behavioral support are most effective in improving glucose tolerance. However, our study did not find a significant correlation between the magnitude of weight loss and magnitude of glucose benefit. Our study found slightly weaker effects for exercise-only interventions (e.g., PA alone resulted in only 1.55% weight loss, far lower than the 5% recommended by American Diabetes Association (ADA)) [113]. In addition to non-insulin mediated glucose transport in skeletal muscle, exercise programs have been shown to have important independent effects on insulinmediated glucose regulation, markers of inflammation, insulin resistance, blood pressure, lipid profile, fitness, and improved lean-to-fat mass ratio [114]. Given the fact that PA provides more benefits than just weight loss does, we need to take those extra benefits into account when we interpret our findings.

Our finding of no difference in effect by follow-up duration has potentially encouraging implications for the implementation of preventive interventions. Weight loss programs typically lead to a nadir of weight loss around 6 months followed by a gradual weight regain. Even programs with intensive attention to weight maintenance typically result in a 50% average weight regain over 3 to 4 years. Our findings that changes in glucose were as great in studies with longer (≥ 2 years) as shorter (12 months) follow-up duration suggest that the between-group improvement in FPG could persist [27–29,42,76]. These findings echo the extended benefits found in the Da Qing legacy study [115]. It is worth noting that most of these studies applied a PA+D strategy and included some behavioral modification components.

Our findings have important implications for the definitions of diabetes risk groups as well as clinical and public health strategies to reduce diabetes incidence. Despite strong evidence that lifestyle interventions can reduce diabetes incidence, the RCT evidence is limited to individuals with IGT. However, roughly 60% of individuals with ADA-defined pre-diabetes (measured by IFG: 5.6–6.9mmol/L) [116] and 70% of those with World Health Organization-defined intermediate hyperglycemia (measured by IFG: 6.1–7.7mmol/L) [117] do not have IGT. Individuals with isolated IFG are thought to be more affected by beta cell dysfunction than by insulin resistance and thus may be less likely to benefit from lifestyle interventions. This has fueled continued debate over who should be targeted for diabetes prevention programs. Our findings suggest that lifestyle interventions are likely to have important benefits across the full distribution of HbA1c and fasting glucose and insulin levels. However, the types of interventions that should be applied to individuals with low to moderate levels of glycemic risk are ultimately influenced by economic factors as well as the effectiveness of interventions. Economic analyses have shown that structured lifestyle interventions are considerably more cost-effective when applied to persons at the high end of the FPG and HbA1c distribution [118,119]. Comprehensive strategies to reduce incidence likely require graduated tiers of interventions. Population-wide approaches to improve nutrition and PA will likely provide benefit to the entire population, but the magnitude of that benefit is unknown. A comprehensive approach that includes both effective population-wide approaches along with structured lifestyle interventions proven to be effective should be the goal.

There are several limitations in our study. First, only 2 studies reported the number of cases of diabetes, thus precluding even pooled estimates of the effect of the interventions on diabetes incidence rates. This reflects the fact that an intervention trial of diabetes incidence conducted among persons in the low- to high-range glucose status (from normal glucose [<5.5mmol/L] - below the IGT threshold) would require large sample sizes (i.e. several thousand participants) over several years.

Second, the large number of studies evaluated lends itself to many sources of heterogeneity, including intervention type, dose, intensity, and frequency, as well as individual risk status and levels of adherence. Our analyses of heterogeneity were conducted at the study level rather than at the individual level, and thus may have lacked sensitivity to detect the impact of such factors on glycemic indicators. Although the study population was diverse in terms of age, race/ethnicity, and sex, we were unable to test whether intervention effectiveness varied across these factors. An advantage of such diversity in our review, however, is that the effect sizes may be more reflective of real-world variation than those observed in a single large RCT.

Third, although we attempted to quantify and stratify by level of glycemic risk, there was considerable heterogeneity within studies that prevented a clean classification. As a result, there was likely considerable overlap in participant characteristics between the low-range group and high-range group in our study. A precise determination of how intervention effectiveness varies by baseline levels of glycemia may require individual level data.

Despite these limitations, this is the first comprehensive compilation of the impact of lifestyle interventions on risk for progression of dysglycemia among individuals below the IGT threshold. This comprehensive systematic review suggests that lifestyle change is important for diabetes prevention across the full spectrum of risk, complementing the major trials of diabetes incidence that focused on individuals with IGT. Decisions about how to implement prevention in practice ultimately depend on a wider set of factors, including the cost of delivering different types of interventions and the disease incidence level in the target population. For example, structured lifestyle interventions have been found to be considerably more cost-effective among persons with higher levels of HbA1c or FPG because applying interventions to persons with a higher incidence of diabetes lead to greater reduction in costs of complications and health care utilization. Thus, multiple intervention tiers may be warranted for diabetes prevention, with intense structured programs delivered to persons at higher risk, and population-targeted policies aimed at making healthier food and physical activity choices easier for the lower end of the diabetes risk distribution.

Supplementary Material

Table S1

NIHMS1030299-supplement-Table_S1.pdf^{(547.6KB, pdf)}

Table S2

NIHMS1030299-supplement-Table_S2.pdf^{(873.3KB, pdf)}

Table S3

NIHMS1030299-supplement-Table_S3.pdf^{(216.6KB, pdf)}

Acknowledgement:

We thank Drs. Ann Albright, Elizabeth Luman, and Sharon Saydah for their very helpful comments on revising our manuscript.

No specific funding was received for this study. This study was supported by the Centers for Disease Control and Prevention. No funding bodies had any role in the study design, data collection, analysis, decision to publish or preparation of the manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC

Footnotes

Conflicts of interest disclosures:

No actual or potential conflicts of interest exist.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1

NIHMS1030299-supplement-Table_S1.pdf^{(547.6KB, pdf)}

Table S2

NIHMS1030299-supplement-Table_S2.pdf^{(873.3KB, pdf)}

Table S3

NIHMS1030299-supplement-Table_S3.pdf^{(216.6KB, pdf)}

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PERMALINK

Effect of Lifestyle Interventions on Glucose Regulation among Adults without Impaired Glucose Tolerance or Diabetes: A Systematic Review and Meta-Analysis

Xuanping Zhang

Giuseppina Imperatore

William Thomas

Yiling J Cheng

Felipe Lobelo

Keri Norris

Heather M Devlin

Mohammed K Ali

Stephanie Gruss

Barbara Bardenheier

Pyone Cho

Isabel Garcia de Quevedo

Uma Mudaliar

Jinan Saaddine

Linda S Geiss

Edward W Gregg

Summary

1. Introduction

2. Methods

2.1. Data source and searches

2.2. Study selection

2.3. Data extraction and quality assessment

2.4. Data Synthesis and Analysis

3. Results

Table 1:

Fig. 1 --.

3.1. Changes in Glycemic Indicators

Table 2,

3.1.1. Comparison According to Participant Baseline Glycemic Status (Limited to studies with attrition <30% thereafter)

3.1.2. Comparison According to Length of Follow-Up

3.1.3. Comparison According to Intervention Modality

Fig. 2 --.

4. Discussion

Supplementary Material

Acknowledgement:

Footnotes

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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