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Effects of fasting intervention regulating anthropometric and metabolic parameters in subjects with overweight or obesity: a systematic review and meta-analysis

Shoumeng Yan a, Changcong Wang a, Hantong Zhao a, Yingan Pan a, Han Wang a, Yinpei Guo a, Nan Yao a, Bo Li *a and Weiwei Cui *b
aDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, 130021, P. R. China. E-mail: li_bo@jlu.edu.cn; Tel: +86 431 85619451
bDepartment of Nutrition and Food Hygiene, School of Public Health, Jilin University, Changchun, 130021, P. R. China. E-mail: cuiweiwei@jlu.edu.cn; Tel: +86 431 85619455

Received 2nd February 2020 , Accepted 16th April 2020

First published on 27th April 2020


Background: Previous studies have shown that fasting produces a potential effect in the prevention and treatment of many diseases. However, the role of fasting in people with overweight or obesity remains controversial. The aim of this study was to assess the intervention of fasting in the regulation of anthropometric and metabolic parameters of subjects with overweight or obesity. Methods: The PubMed, Cochrane library, Web of science and EMBASE databases were searched from the inception dates to October 2019, identifying published literature evaluating the effect of fasting intervention on the people with overweight or obesity. Results: Twenty-five studies with 1358 participants with overweight or obesity were included in the meta-analysis. Fasting was associated with the significant reduction of body weight, body mass index (BMI), fat free mass (FEM), fat mass (FM), waist circumference (WC), low density lipoprotein cholesterol (LDL-C), triglycerides (TG), systolic blood pressure (SBP) and diastolic blood pressure (DBP). However, there was no significant difference in the variations in the total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), blood glucose and insulin concentrations. Conclusion: Our meta-analysis found that fasting was associated with a significant effect on the regulation of anthropometric (body weight, BMI, FEM, FM and WC) and metabolic parameters (LDL-C, TG, SBP and DBP) in people with overweight or obesity. Considering some limitations found in this study, additional data from large clinical trials are needed.


1. Introduction

Obesity is a pathological state in which excess abdominal weight is accumulated due to the imbalance between energy intake and consumption.1 The prevalence of obesity has doubled in more than 70 countries in the past 30 years and has constantly increased in most other countries.2 As excess weight gain increases the risk of cardiovascular disease, diabetes, cancer and other diseases; the increasing prevalence of obesity is a worldwide health problem and causes a large financial burden in all countries.3,4 By 2030, a 5% decrease in body mass index (BMI) parameter is expected to lead to a reduction of €495 million in obesity related direct health care expenses over 20 years.5

Fasting is regarded as ingesting no or minimal energy for a period of time, which including periodic fasting (PF), intermittent fasting (IF), very low calorie diet (VLCD) and other fasting.6,7 Among them, PF consists of fasting only 1 or 2 days per week with consuming food ad libitum on 5 to 6 days per week and VLCD is regarded as an energy intake of 800 kcal or less per day.7–9 IF includes limiting or no food consumption on 1–3 day per week and eating freely on the no restriction days, which comprises complete alternate-day fasting (CADF), alternate day modified fasting (ADMF), time-restricted feeding (TRF) and others.10,11 The main difference between CADF and ADMF is the energy intake on fasting day (CADF: zero calorie intake; ADMF: 20–25% of energy needs).11,12 Specially, TRF is an eating pattern in which daily food consumption is limited to 8 hours or less.13 Previous studies have indicated that fasting produces a potential effect in the prevention and treatment of many diseases. Several studies have found that fasting was profitable for symptoms and inflammatory parameters in patients with rheumatic diseases.14,15 Meanwhile, the risk of hypertension, cardiovascular and metabolic disease was reduced through fasting intervention.7 In addition, fasting may protect from cancer via reducing the harm to cells and DNA, and increasing the death of precancerous cells.16 Simultaneously, fasting could also debase the capacity of cancer cells to adapt and survive, thereby improving the effects of cancer therapies.17

Some studies have reported that IF and VLCD may improve the body composition, reduce cardiovascular risk factors, and positively affect glucose control in people with overweight or obesity.18,19 However, recent research has also shown that the advantages of IF for weight loss, weight management, or cardio-protection in subjects with overweight or obesity were not obvious.20 The role of fasting in people with overweight or obesity remains controversial. Therefore, a meta-analysis of all related randomized control trials (RCTs) was conducted focusing on the intervention of fasting in the regulation of anthropometric and metabolic parameters of people with overweight or obesity.

2. Materials and methods

2.1. Sources and methods of data retrieval

We searched the PubMed, Cochrane library, Web of science and EMBASE databases from the inception dates to October 2019, using the keywords fasting, very low calorie diet, intermittent fasting, alternate day fasting, periodic fasting, modified fasting regimen, time-restricted feeding, overweight, obesity and obese to identify published literature evaluating the effect of fasting intervention on regulating anthropometric and metabolic parameters in people with overweight or obesity. The literature search was limited to English language and human subjects. The detailed search strategy is shown in Table S1.

2.2. Inclusion criteria

The inclusion criteria were (1) RCTs comparing fasting intervention and a normal diet or energy restriction group; (2) overweight or obesity was defined based on a local criterion; (3) the outcomes were quantitative data that could be extracted or calculated. Exclusion criteria were as follows: (1) randomized studies were single-arm studies or without a placebo; (2) fasting due to religious habits; and (3) non-human studies, reviews and conference literature. Two researchers independently reviewed the literature and collected all eligible studies. Once disagreements existed, the nutritionist was involved to discuss and solve it (Fig. 1).
image file: d0fo00287a-f1.tif
Fig. 1 Flow diagram of the literature search and selection.

2.3. Data abstraction

Data collected from all included literature were as follows: (1) first author, nationality, publication year, numbers, mean age and gender of fasting intervention subjects and the control group; (2) the fasting type, subject type, fasting time and dose, diabetes or not, whether measured immediately after fasting or not (outcome type); and (3) the variations in the body weight, BMI, fat free mass (FEM), fat mass (FM), waist circumference (WC), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglycerides (TG), systolic blood pressure (SBP), diastolic blood pressure (DBP), blood glucose and insulin parameters in the fasting intervention and control subjects.

2.4. Risk of bias within individual studies

The methodological quality for the selected literature was evaluated independently using the Cochrane Collaboration (RevMan Version 5.3) software by two investigators according to Cochrane risk-of-bias criteria,21 which included seven items (randomization sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other bias) to estimate bias in each trial. Each quality item was graded as low risk, unclear risk, or high risk. Simultaneously, the GRADE system was used to classify the quality of evidence. The included trials were graded as high quality, moderate quality, low quality, or very low quality according to the risk of bias, inconsistency, indirectness, imprecision and other considerations.22

2.5. Statistical analysis

Statistical analysis was performed using the statistical software Review Manager 5.3 and Stata 12.0. The mean change (standard deviation) in anthropometric and metabolic parameters from the baseline was used to evaluate the differences between the fasting intervention and control subjects. When it could not obtained via the original literature, we calculated the standard deviation in the mean change in the anthropometric and metabolic parameters using the formula in the Cochrane handbook.23 The correlation coefficient of the equation was estimated using the data from the included literature providing the baseline and endpoint values and the variations, simultaneously.
image file: d0fo00287a-t1.tif

The random effects model was used to compute the summary standard mean difference (SMD) and 95% confidence interval (CI) and to evaluate the differences in the variations in the anthropometric and metabolic parameters between the fasting intervention group and the controls.

We use the I2 statistic to estimate statistical heterogeneity. The potential publication bias was evaluated via the Egger test, where the trim-and-fill method (sensitivity analysis) was used to correct outcomes and evaluate the impact of bias on the outcomes. Subgroup analyses were conducted based on the fasting type (ADMF, CADF, TRF, VLCD), subject type (Adult(women + men), Adult(women), Adult(men)), Region(Oceania, America, Europe, Asia), fasting time (<12weeks, ≥12 weeks), diabetes or not, and outcome type (follow-up, non follow-up). Because only one literature was for PF, the subgroup analysis was not conducted in view of PF.

3. Results

A total of 25 studies met inclusion criteria, involving 1358 samples, with 690 interventions and 668 controls.12,24–47 The variations in the body weight, BMI, FEM, FM, WC, TC, LDL-C, HDL-C, TG, SBP, DBP, blood glucose and insulin parameters were evaluated in twenty-four,12,24–27,29–47 thirteen,12,25,26,30,32–37,41,44,45 nine,12,24,25,27,28,34,36,40,41 ten,12,24–29,31,36,41 eight,25,27,30,31,37,41,44,45 thirteen,12,24–28,30,35–38,44,47 ten,12,24–28,30,34,38,47 twelve,12,24–28,30,36,38,44,45,47 thirteen,12,24–28,30,36–38,44,45,47 eleven,12,24,25,27,30,35–38,44,45 eleven,12,24,25,27,30,35–38,44,45 twelve12,25–28,30,35,36,38,44,45,47 and ten12,25–28,30,38,44,45,47 studies, respectively. The detailed outcomes are performed in Table 1 and Table S2. Almost all studies12,24–36,38–47 (n = 24) were randomized, and nearly half of the studies12,24,25,28,30,31,33,39,41,42,44,45 (n = 12) described allocation concealment methods. Specially, as fasting cannot blind between the intervention group and controls, double-blind setup were lacking in all studies. However, five studies stated that outcome assessments were blinded.12,28,31,33,41 Four studies were considered to have attrition bias as the reason for the drop out was not explained.37,38,43,46 Only six trials had clinical trial registration codes, where reporting bias might have existed.12,27,31,33,41,44 After evaluation comprehensively, other biases existed in ten studies.26,27,32,34–38,43,46 The risk of bias within individual studies detailed is performed in Fig. 2. In addition, the GRADE system was performed to determine the quality of evidence for different results. We considered that the grades of evidence were moderate quality in all outcomes except for the FEM parameter (low quality) (Table 2).
image file: d0fo00287a-f2.tif
Fig. 2 Risk of within-study bias.
Table 1 Characteristics of studies examining the effects of fasting intervention on regulating anthropometric and metabolic parameters
Author Region Year Fasting type Subjects Outcome type Fasting time Fasting dose Diabetes n Gender(M/F) Age
IG CG IG CG IG CG
Bowen J. et al.a Australia 2018 ADMF A(W + M) Non follow-up 16 weeks 573.6 kcal ND 67 68 15/67 16/65 40.0 ± 8.3 40.6 ± 8.8
Varady K. A. et al. US 2013 ADMF A(W + M) Non follow-up 12 weeks 400–600 kcal ND 15 15 5/10 3/12 47 ± 3 48 ± 2
Bhutani S. et al.a US 2013 ADMF A(W + M) Non follow-up 12 weeks 450 kcal ND 16 16 1/24 1/15 42 ± 2 49 ± 2
Catenacci V. A. et al. US 2016 CADF A(W + M) Non follow-up 8 weeks 0 kcal ND 13 12 3/10 3/9 39.6 ± 9.5 42.7 ± 7.9
Hutchison A. T. et al.(1) Australia 2019 CADF A(W) Non follow-up 8 weeks 0 kcal ND 22 11 0/22 0/11 51 ± 2 49 ± 3
Hutchison A. T. et al.(2) Australia 2019 CADF A(W) Non follow-up 8 weeks 0 kcal ND 22 11 0/22 0/11 49 ± 2 49 ± 3
Moro T. et al. Italy 2016 TRF A(M) Non follow-up 8 weeks 2735 kcal ND 17 17 17/0 17/0 29.9 ± 4.1 28.5 ± 3.5
Tinsley G. M. et al. US 2017 TRF A(M) Non follow-up 8 weeks 1674 kcal ND 10 8 10/0 8/0 22.9 ± 4.1 22.0 ± 2.4
Li C. et al.a Germany 2017 VLCD A(W + M) Follow-up 1 week 300 kcal D 16 16 64.7 ± 7.0 65.4 ± 5.7
Haywood C. J. et al.(1) Australia 2017 VLCD A(W + M) Non follow-up 12 weeks 400–600 kcal ND 41 36 16/25 13/23
Haywood C. J. et al.(2) Australia 2017 VLCD A(W + M) Non follow-up 12 weeks 400–600 kcal ND 41 40 16/25 16/24
Hussin N. M. et al. Malaysia 2013 VLCD A(M) Non follow-up 12 weeks 300–500 kcal ND 16 15 16/0 15/0 59.7 ± 6.6 59.7 ± 6.2
Burnand K M. et al. UK 2016 VLCD A(W + M) Follow-up 2 weeks 800 kcal ND 21 25 0/21 4/21 43.5 ± 31.1 48 ± 35.5
Teng N. I. et al. Malaysia 2011 VLCD A(M) Non follow-up 12 weeks 300–500 kcal ND 12 13 12/0 13/0 59.3 ± 3.4 58.3 ± 6.3
Arai K. et al. Japan 1992 VLCD A(W + M) Non follow-up 8 weeks 400 kcal ND 20 25 31.6 ± 13.1 35.3 ± 11.7
Teng N. I. et al. Malaysia 2013 VLCD A(M) Non follow-up 12 weeks 300–500 kcal ND 28 28 28/0 28/0 59.6 ± 5.4 59.1 ± 6.2
Paisey R. B. et al. UK 1995 VLCD A(W + M) Non follow-up 12 weeks 400–670 kcal D 14 14 7/7 3/11 53.9 ± 5.7 55.4 ± 7.3
Wing R. R. et al. US 1994 VLCD A(W + M) Follow-up 12 weeks 400–500 kcal D 45 48 15/30 18/30 52.3 ± 10.7 51.3 ± 8.7
Torgerson J. S. et al. Sweden 1997 VLCD A(W + M) Non follow-up 12 weeks 456–608 kcal ND 58 55 22/36 17/38 47.3 ± 6.7 46.9 ± 5.8
Wadden T. A. et al.a US 1994 VLCD A(W) Non follow-up 16 weeks 420 kcal ND 26 17 0/28 0/21 36.8 ± 8.9 42.9 ± 10.1
Purcell K. et al.a Australia 2014 VLCD A(W + M) Non follow-up 12 weeks 450–800 kcal ND 76 51 26/71 25/78 49.6 ± 10.9 50.1 ± 11.1
Stenius-Aarniala B. et al. Finland 2000 VLCD A(W + M) Follow-up 8 weeks 420 kcal ND 19 19
Wadden T. A. et al. US 1986 VLCD A(W + M) Non follow-up 8 weeks 400–500 kcal ND 15 16 2/13 3/13 44.3 ± 8.7 44.3 ± 8.6
Tuomilehto H. et al. Finland 2010 VLCD A(W + M) Follow-up 12 weeks 600–800 kcal ND 35 36 26/9 27/9 51.8 ± 9.0 51.7 ± 8.8
Tuomilehto H. P. et al. Finland 2009 VLCD A(W + M) Follow-up 12 weeks 600–800 kcal ND 35 37 26/9 27/10 51.8 ± 9.0 50.9 ± 8.6
Wadden T. A. et al. US 1988 VLCD A(W + M) Follow-up 8 weeks 400–500 kcal ND 15 16 2/13 3/13 44.3 ± 8.7 44.3 ± 8.6
Williams K. V. et al.a US 1998 PF A(W + M) Follow-up 20 weeks 400–600 kcal D 16 14 9/9 7/11 51.4 ± 7.9 54.1 ± 7.0

Author Body weight (kg) BMI (kg m−2) FEM (kg) FM (kg) WC (cm) TC (mmol L−1) LDL-C (mmol L−1)
IG CG IG CG IG CG IG CG IG CG IG CG IG CG
Bowen J. et al.a −10.0 ± 3.9 −12.5 ± 3.4 −3.8 ± 1.2 −4.4 ± 1.1 −1.4 ± 1.8 −1.9 ± 1.6 −8.4 ± 2.6 −10.3 ± 2.4 −0.5 ± 0.2 −0.6 ± 0.2 −0.4 ± 0.2 −0.4 ± 0.3
Varady K. A. et al. −5.1 ± 1.0 −0.6 ± 1.0 −3.6 ± 0.5 −0.3 ± 0.5 −1.6 ± 0.5 −0.2 ± 0.5 −0.7 ± 0.2 −0.2 ± 0.1 −0.5 ± 0.2 −0.2 ± 0.1
Bhutani S. et al.a −3.0 ± 1.0 0.0 ± 0.1 −1.0 ± 1.0 0.0 ± 1.0 −1.0 ± 1.0 −1.0 ± 1.0 −2.0 ± 1.0 0.0 ± 1.0 −5.0 ± 1.0 −1.0 ± 1.0 0.3 ± 0.1 0.1 ± 0.1 0.0 ± 0.1 0.1 ± 0.1
Catenacci V. A. et al. −8.2 ± 0.9 −7.1 ± 1.0 −3.2 ± 0.3 −2.4 ± 0.3 −3.7 ± 0.5 −3.7 ± 0.5 −0.8 ± 0.2 −0.6 ± 0.2 −0.6 ± 0.1 −0.4 ± 0.1
Hutchison A. T. et al.(1) −2.7 ± 0.5 0.4 ± 0.4 −0.5 ± 0.3 −0.4 ± 0.4 −2.3 ± 0.4 −0.2 ± 0.5 −4.3 ± 1.0 −1.4 ± 1.7 −0.4 ± 0.2 −0.3 ± 0.2 −0.2 ± 0.1 −0.2 ± 0.1
Hutchison A. T. et al.(2) −5.4 ± 0.5 0.4 ± 0.4 −1.4 ± 0.3 −0.4 ± 0.4 −3.9 ± 0.4 −0.2 ± 0.5 −7.6 ± 1.2 −1.4 ± 1.7 −0.6 ± 0.1 −0.3 ± 0.2 −0.4 ± 0.1 −0.2 ± 0.1
Moro T. et al. 0.6 ± 0.9 0.5 ± 0.8 −1.6 ± 1.2 −0.3 ± 0.9 −0.1 ± 0.1 0.0 ± 0.2 −0.1 ± 0.1 0.0 ± 0.1
Tinsley G. M. et al. −1.0 ± 4.0 3.0 ± 3.5 −0.6 ± 1.7 0.8 ± 1.1
Li C. et al.a −3.5 ± 4.5 −2.0 ± 4.8 −1.2 ± 1.7 −0.6 ± 2.6 −4.4 ± 4.3 −0.3 ± 2.0 0.0 ± 0.7 −0.4 ± 0.7 −0.1 ± 0.7 −0.2 ± 0.4
Haywood C. J. et al.(1) −11.6 ± 1.5 −4.0 ± 1.2 −7.8 ± 1.5 −2.8 ± 1.2 −10.8 ± 1.8 −3.9 ± 1.1
Haywood C. J. et al.(2) −11.6 ± 1.5 −5.4 ± 1.2 −7.8 ± 1.5 −3.1 ± 0.9 −10.8 ± 1.8 −5.6 ± 1.6
Hussin N. M. et al. −2.8 ± 1.6 −0.6 ± 1.5 −1.0 ± 0.4 −0.3 ± 0.5
Burnand K. M. et al. −3.5 ± 2.0 −1.0 ± 1.7 −1.3 ± 0.7 −0.4 ± 0.6
Teng N. I. et al. −2.3 ± 1.2 0.8 ± 1.7 −0.7 ± 0.6 0.3 ± 0.4 −0.5 ± 0.8 0.2 ± 1.0
Arai K. et al. −9.0 ± 5.2 −5.2 ± 3.0 −4.1 ± 1.2 −2.1 ± 1.0 −0.7 ± 0.2 −0.2 ± 0.2
Teng N. I. et al. −2.5 ± 1.4 0.0 ± 1.6 −0.9 ± 0.4 0.0 ± 0.4 0.2 ± 1.0 −0.9 ± 3.1 −1.5 ± 0.7 0.3 ± 1.4 −0.5 ± 0.2 0.1 ± 0.2 −0.3 ± 0.2 0.1 ± 0.2
Paisey R. B. et al. −14.0 ± 7.0 −2.0 ± 4.6 −5.0 ± 2.4 −1.0 ± 2.4 −15.0 ± 7.4 0.0 ± 6.7 −0.6 ± 1.0 −0.1 ± 0.6
Wing R. R. et al. −16.9 ± 0.6 −14.4 ± 0.9 −0.3 ± 0.3 −0.6 ± 0.2 −0.1 ± 0.3 −0.3 ± 0.2
Torgerson J. S. et al. −15.8 ± 7.5 −6.4 ± 4.5
Wadden T. A. et al.a −22.6 ± 6.0 −10.1 ± 6.2 −6.6 ± 3.5 −1.5 ± 2.9
Purcell K. et al.a −14.6 ± 2.7 −14.3 ± 2.9 −5.3 ± 0.4 −5.2 ± 0.7 1.1 ± 4.4 0.5 ± 5.1 −12.7 ± 4.9 −14.1 ± 5.5 −15.5 ± 5.3 −16.2 ± 4.4
Stenius-Aarniala B. et al. −14.2 ± 10.7 −0.3 ± 0.3
Wadden T. A. et al. −13.2 ± 3.9 −9.6 ± 4.4
Tuomilehto H. et al. −7.3 ± 6.5 −2.9 ± 7.5 −2.4 ± 2.1 −1.0 ± 2.6 −7.7 ± 6.7 −3.5 ± 7.3 0.1 ± 0.8 −0.1 ± 0.8
Tuomilehto H. P. et al. −10.7 ± 6.5 −2.4 ± 5.6 −3.5 ± 2.1 −0.8 ± 2.0 −11.6 ± 6.6 −3.0 ± 6.0
Wadden T. A. et al. −14.1 ± 5.1 −14.3 ± 6.7
Williams K. V. et al.a −9.6 ± 1.4 −6.2 ± 1.4 −0.3 ± 0.4 −0.3 ± 0.2 −0.2 ± 0.3 −0.2 ± 0.3

Author HDL-C (mmol L−1) TG (mmol L−1) SBP( mmHg) DBP (mmHg) Blood glucose (mmol L−1) Insulin (mIU L−1)
IG CG IG CG IG CG IG CG IG CG IG CG
IG, intervention group; CG, control group; ADMF, alternate day modified fasting; CADF, complete alternate-day fasting; TRF, time-restricted feeding; VLCD, very low calorie diet; PF, periodic fasting; A(W + M), Adult(women + men); A(W), Adult(women); A(M), Adult(men); ND, non-diabetes; D, diabetes; Gender(M/F), Gender(male/female); BMI, body mass index; FEM, fat free mass; FM, fat mass; WC, waist circumference; TC, total cholesterol; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; TG, triglycerides; SBP, systolic blood pressure; DBP, diastolic blood pressure.a The variable of gender and age includes dropout.
Bowen J. et al.a −0.1 ± 0.1 0.0 ± 0.1 −0.2 ± 0.1 −0.4 ± 0.5 −7.2 ± 3.8 −8.4 ± 3.8 −4.1 ± 2.3 −3.6 ± 1.8 −0.1 ± 0.1 −0.2 ± 0.2 −4.3 ± 4.1 −4.4 ± 3.3
Varady K. A. et al. −0.1 ± 0.1 0.0 ± 0.1 −0.2 ± 0.1 0.1 ± 0.1 −7.0 ± 2.0 1.0 ± 3.0 −6.0 ± 2.0 2.0 ± 6.0
Bhutani S. et al.a 0.0 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 0.1 ± 0.0 −4.0 ± 0.6 −2.0 ± 1.5 −2.0 ± 0.4 −2.0 ± 2.1 −0.2 ± 0.1 0.1 ± 0.1 −2.0 ± 1.6 −4.0 ± 0.8
Catenacci V. A. et al. −0.1 ± 0.0 −0.1 ± 0.0 −0.3 ± 0.1 0.0 ± 0.1 0.3 ± 0.1 0.2 ± 0.1 3.0 ± 2.3 −0.2 ± 2.4
Hutchison A. T. et al.(1) −0.1 ± 0.1 0.0 ± 0.1 −0.3 ± 0.1 −0.3 ± 0.1 −5.6 ± 3.4 1.5 ± 1.7 −1.5 ± 2.0 −0.4 ± 1.0 0.1 ± 0.1 0.0 ± 0.1 2.9 ± 1.4 −0.4 ± 1.9
Hutchison A. T. et al.(2) −0.1 ± 0.0 0.0 ± 0.1 −0.2 ± 0.1 −0.3 ± 0.1 −0.6 ± 3.2 1.5 ± 1.7 −2.5 ± 1.4 −0.4 ± 1.0 −0.2 ± 0.1 0.0 ± 0.1 −3.6 ± 1.0 −0.4 ± 1.9
Moro T. et al. 0.1 ± 0.0 0.0 ± 0.1 −0.1 ± 0.0 0.0 ± 0.0 −0.6 ± 0.1 0.0 ± 1.2 −1.0 ± 0.3 −0.3 ± 0.1
Tinsley G. M. et al.
Li C. et al.a 0.2 ± 0.6 −0.1 ± 0.2 −0.3 ± 1.0 0.0 ± 0.9 −13.9 ± 15.3 0.4 ± 15.8 −9.0 ± 12.3 3.2 ± 11.9 −0.6 ± 1.7 −2.1 ± 2.6 −3.5 ± 9.3 −0.2 ± 5.4
Haywood C. J. et al.(1)
Haywood C. J. et al.(2)
Hussin N. M. et al.
Burnand K. M. et al.
Teng N. I. et al.
Arai K. et al. −15.6 ± 11.6 3.9 ± 7.5 −9.7 ± 7.1 −5.6 ± 10.9 −0.5 ± 0.9 −0.5 ± 0.4
Teng N. I. et al. 0.0 ± 0.0 0.0 ± 0.1 −0.2 ± 0.4 −0.1 ± 0.3 −6.5 ± 4.5 3.1 ± 3.4 −2.2 ± 2.1 2.1 ± 1.6 0.2 ± 0.1 0.5 ± 0.7
Paisey R. B. et al. −1.4 ± 1.4 0.3 ± 1.0 0.0 ± 25.9 0.0 ± 22.2 −6.0 ± 18.5 −11.0 ± 16.3
Wing R. R. et al. 0.1 ± 0.0 0.1 ± 0.0 −0.8 ± 0.7 −1.0 ± 1.1 −9.0 ± 3.0 −6.0 ± 3.8 −6.0 ± 1.8 −3.0 ± 3.1 −3.6 ± 1.1 −3.2 ± 0.6 −12.3 ± 6.9 −9.3 ± 7.3
Torgerson J. S. et al.
Wadden T. A. et al.a
Purcell K. et al.a
Stenius-Aarniala B. et al.
Wadden T. A. et al.
Tuomilehto H. et al. 0.1 ± 0.2 0.1 ± 0.2 −0.2 ± 0.9 −0.1 ± 0.8 0.6 ± 8.5 −3.0 ± 11.3 0.0 ± 5.4 −1.7 ± 6.9 −0.4 ± 2.1 −0.3 ± 1.3 −2.2 ± 8.4 0.0 ± 4.6
Tuomilehto H. P. et al. 0.1 ± 0.2 0.1 ± 0.2 −0.5 ± 1.1 −0.1 ± 0.7 −1.7 ± 14.7 −1.1 ± 19.6 −1.9 ± 10.6 −0.4 ± 12.6 −0.6 ± 2.3 −0.4 ± 1.4 −5.9 ± 7.0 −1.2 ± 3.4
Wadden T. A. et al.
Williams K. V. et al.a 0.0 ± 0.0 −0.2 ± 0.1 −1.2 ± 0.2 −0.7 ± 0.8 −1.9 ± 0.6 −1.8 ± 0.6 −5.6 ± 1.0 −4.7 ± 3.1


Table 2 The summary of findings (SoF) with the GRADE system
Fasting intervention compared to no fasting intervention for regulating anthropometric and metabolic parameters
Population: subjects with overweight or obesity
Settings: four studies were conducted in Asia, eight studies were conducted in Europe, nine studies were conducted in America and four studies were conducted in Oceania
Intervention: fasting
Comparison: no fasting intervention

Outcomesa SMD(95%CI)b No of participants (studies) Quality of the evidence comments (GRADE)
Body weight −2.16(−2.76, −1.56) kg 1324 (24RCTs) ⊕⊕⊕○ MODERATEc
BMI −1.18(−1.72, −0.65) kg m−2 725 (13RCTs) ⊕⊕⊕○MODERATEc
FEM −0.82(−1.49, −0.15) kg 537 (9RCTs) ⊕⊕○ ○ LOWc,d
FM −2.20(−3.29, −1.11) kg 629 (10RCTs) ⊕⊕⊕○ MODERATEc
WC −2.31(−3.32, −1.30) cm 534 (8RCTs) ⊕⊕⊕○MODERATEc
TC −0.62(−1.36, 0.12) mmol L−1 666 (13RCTs) ⊕⊕⊕○ MODERATEc
LDL-C −0.87(−1.57, −0.17) mmol L−1 522 (10RCTs) ⊕⊕⊕○ MODERATEc
HDL-C −0.12(−0.75, 0.52) mmol L−1 665 (12RCTs) ⊕⊕⊕○ MODERATEc
TG −0.61(−1.04, −0.18) mmol L−1 693 (13RCTs) ⊕⊕⊕○ MODERATEc
SBP −1.08(−1.69, −0.47) mmHg 649 (11RCTs) ⊕⊕⊕○ MODERATEc
DBP −0.70(−1.13, −0.26) mmHg 649 (11RCTs) ⊕⊕⊕○ MODERATEc
Blood glucose −0.28(−0.75, 0.20) mmol L−1 680 (12RCTs) ⊕⊕⊕○ MODERATEc
Insulin −0.21(−0.82, 0.40) mIU L−1 579 (10RCTs) ⊕⊕⊕○ MODERATEc

a All subjects were followed up from 1 week to 5 months. b Results for variations of treatments compared with controls. SMD: standard mean deviation; CI: confidence interval; RCT: randomized controlled trial; BMI: body mass index; FEM: fat free mass; FM: fat mass; WC: waist circumference; TC: total cholesterol; LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprotein cholesterol; TG: triglycerides; SBP: systolic blood pressure; DBP: diastolic blood pressure.c Bias risk: Downgraded by one level as most of the included literature did not use the blind method (the main reason is that the intervention method cannot be blind).d Inconsistency: Downgraded by one level as a high heterogeneity existed and its source was not completely clear.
GRADE working group grades of evidence
High quality: We are very confident that the true effect lies close to that of the estimate of the effect
Moderate quality: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different
Low quality: Our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect
Very low quality: We have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect


The meta-analysis revealed that fasting intervention led to a significantly larger weight loss (SMD = −2.16 kg, 95%CI = −2.76, −1.56 kg; Fig. 3) compared with the control in subjects with overweight or obesity. Fasting intervention was associated with significantly larger reduction in BMI (SMD = −1.18 kg m−2, 95% CI = −1.72, −0.65 kg m−2; Fig. 4), FEM (SMD = −0.82 kg, 95% CI = −1.49, −0.15 kg; Fig. 5), FM (SMD = −2.20 kg, 95% CI = −3.29, −1.11 kg; Fig. 6), and WC (SMD = −2.31cm, 95% CI = −3.32, −1.30 cm; Fig. 7). Meanwhile, statistically significant differences in the variations in the LDL-C (SMD = −0.87 mmol L−1, 95% CI = −1.57, −0.17 mmol L−1; Fig. 8), TG (SMD = −0.61 mmol L−1,95% CI = −1.04, −0.18 mmol L−1; Fig. 9), SBP (SMD = −1.08 mmHg, 95% CI = −1.69, −0.47 mmHg; Fig. 10), and DBP (SMD = −0.70 mmHg, 95% CI = −1.13, −0.26 mmHg; Fig. 11) parameters were noticed between the intervention groups and the controls. However, the data obtained from the included literature did not indicate any significant effect of fasting on the TC (SMD = −0.62 mmol L−1, 95% CI = −1.36, 0.12 mmol L−1; Fig. 12), HDL-C (SMD = −0.12 mmol L−1, 95% CI = −0.75,0.52 mmol L−1; Fig. 13), blood glucose (SMD = −0.28 mmol L−1, 95% CI = −0.75, 0.20 mmol L−1; Fig. 14) and insulin (SMD = −0.21 mIU L−1, 95% CI = −0.82, 0.40 mIU L−1; Fig. 15) concentrations.


image file: d0fo00287a-f3.tif
Fig. 3 Meta-analysis results of fasting intervention for the body weight in subjects with overweight or obesity.

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Fig. 4 Meta-analysis results of fasting intervention for the body mass index (BMI) in subjects with overweight or obesity.

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Fig. 5 Meta-analysis results of fasting intervention for the fat free mass (FEM) in subjects with overweight or obesity.

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Fig. 6 Meta-analysis results of fasting intervention for the fat mass (FM) in subjects with overweight or obesity.

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Fig. 7 Meta-analysis results of fasting intervention for the waist circumference (WC) in subjects with overweight or obesity.

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Fig. 8 Meta-analysis results of fasting intervention for the low density lipoprotein cholesterol (LDL-C) in subjects with overweight or obesity.

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Fig. 9 Meta-analysis results of fasting intervention for the triglycerides (TG) in subjects with overweight or obesity.

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Fig. 10 Meta-analysis results of fasting intervention for the systolic blood pressure (SBP) in subjects with overweight or obesity.

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Fig. 11 Meta-analysis results of fasting intervention for the diastolic blood pressure (DBP) in subjects with overweight or obesity.

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Fig. 12 Meta-analysis results of fasting intervention for the total cholesterol (TC) in subjects with overweight or obesity.

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Fig. 13 Meta-analysis results of fasting intervention for the high density lipoprotein cholesterol (HDL-C) in subjects with overweight or obesity.

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Fig. 14 Meta-analysis results of fasting intervention for the blood glucose in subjects with overweight or obesity.

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Fig. 15 Meta-analysis results of fasting intervention for the insulin in subjects with overweight or obesity.

The outcomes of subgroup analyses for fasting intervention and the change of anthropometric and metabolic parameters in the people with overweight or obesity are summarized in Table 3. Region may be the sources of heterogeneity for body weight, BMI, TC and SBP in the associated studies. Subject type may be the sources of heterogeneity for FM and LDL-C and outcome type may be the sources of heterogeneity for TG, blood glucose, WC and insulin. In addition, fasting type may be the sources of heterogeneity for HDL-C and fasting time may be the sources of heterogeneity for DBP.

Table 3 Subgroup analyses were performed for anthropometric and metabolic parameters
Factors Number of studies
BW BMI FEM FM WC TC LDL-C HDL-C TG SBP DBP BG Insulin
Fasting type
 ADMF 3 2 3 3 3 3 3 3 3 3 2 2
 CADF 2 1 2 1 2 2 2 2 1 1 2 2
 TRF 2
 VLCD 17 10 4 3 6 6 3 5 6 7 7 6 4
Subjects
 Adult(W + M) 18 10 4 6 7 10 7 9 10 9 9 9 8
 Adult(W) 2 2 1 1 1 1 1 1 1 1 1 1
 Adult(M) 4 3 3 3 2 2 2 2 2
Outcome type
 NF 16 9 5 9 7 7 8 7 7 7 5
 F 8 4 3 4 3 5 5 4 4 5 5
Fasting time
 <12 weeks 9 4 2 4 2 5 4 4 4 3 3 5 4
 ≥12 weeks 15 9 7 6 6 8 6 8 9 8 8 7 6
Region
 Oceania 4 2 3 4 3 2 2 2 2 2 2 2 2
 America 10 2 3 4 5 5 5 5 3 3 4 4
 Europe 7 5 4 4 2 4 5 4 4 4 4
 Asia 4 4 2 2 2 2 2
Diabetes
 No 20 11 6 9 7 9 9 8 8 9 7
 Yes 4 2 2 4 3 3 4 3 3 3 3

Factors Standard mean difference (95% CI), P
BW BMI FEM FM
Fasting type
 ADMF −2.67(−6.67,1.34), 0.192 −0.20(−1.69,1.29), 0.790 −1.78(−3.93,0.37), 0.105 −1.28(−3.67,1.11), 0.295
 CADF −6.53(−12.33,−0.73), 0.027 −1.61(−4.24,1.02), 0.229 −4.36(−9.13,0.42), 0.074
 TRF −1.14(−1.73,−0.55), <0.001
 VLCD −1.73(−2.31,−1.15), <0.001 −1.25(−1.73,−0.76), <0.001 −0.39(−1.21,0.44), 0.360 −2.20(−4.31,−0.08), 0.042
Subjects
 Adult(W + M) −1.85(−2.53,−1.17), <0.001 −0.97(−1.54,−0.40), 0.001 −0.94(−2.04,0.17), 0.097 −1.58(−3.00,−0.15), 0.030
 Adult(W) −6.80(−11.97,−1.62), 0.010 −1.58(−2.97,−0.18), 0.027 −6.56(−10.16,−2.96), <0.001
 Adult(M) −1.56(−1.95,−1.16), <0.001 −1.92(−2.37,−1.46), <0.001 0.03(−0.64,0.70), 0.926 −1.36(−1.78,−0.94), <0.001
Outcome type
 NF −2.58(−3.42,−1.75), <0.001 −1.34(−2.11,−0.56), 0.001
 F −1.40(−2.15,−0.65), <0.001 −0.90(−1.41,−0.39), 0.001
Fasting time
 <12 weeks −1.94(−2.85,−1.04), <0.001 −1.48(−2.37,−0.59), 0.001 −0.99(−2.65,0.68), 0.245 −2.88(−4.90,−0.86), 0.005
 ≥12 weeks −2.26(−3.06,−1.45), <0.001 −1.05(−1.68,−0.42), 0.001 −0.75(−1.52,0.02), 0.057 −1.80(−3.21,−0.39), 0.012
Region
 Oceania −4.39(−6.64,−2.15), <0.001 0.18(−0.51,0.86), 0.614 −0.57(−1.46,0.31), 0.205 −3.12(−5.07,−1.16), 0.002
 America −2.13(−3.09,−1.17), <0.001 −1.78(−3.42,−0.15), 0.032 −2.52(−5.02,−0.03), 0.047 −1.39(−2.59,−0.20), 0.022
 Europe −1.26(−1.68,−0.84), <0.001 −1.02(−1.50,−0.54), <0.001
 Asia −1.44(−1.89,−0.99), <0.001 −1.89(−2.27,−1.51), <0.001 −0.09(−1.28,1.11), 0.880
Diabetes
 No −2.19(−2.84,−1.53), <0.001 −1.23(−1.82,−0.63), <0.001
 Yes −2.03(−3.47,−0.60), 0.006 −0.94(−2.30,0.42), 0.174

Factors Standard mean difference (95% CI), P
WC TC LDL-C HDL-C
Fasting type
 ADMF 0.10(−2.47,2.68), 0.937 −1.30(−2.86,0.27), 0.104 −2.11(−3.59,−0.64), 0.005
 CADF −3.33(−5.49,−1.18), 0.002 −1.53(−2.79,−0.26), 0.018 −1.16(−2.49,0.18), 0.091 −0.70(−1.53,0.13), 0.098
 TRF
 VLCD −1.82(−2.95,−0.69), 0.002 −0.63(−1.86,0.60), 0.313 −0.23(−1.95,1.49), 0.790 0.19(−0.02,0.41), 0.082
Subjects
 Adult(W + M) −2.07(−3.17,−0.96), <0.001 −0.22(−1.01,0.56), 0.579 −0.50(−1.27,0.27), 0.200 −0.15(−0.94,0.64), 0.717
 Adult(W) −3.33(−5.49,−1.18), 0.002 −1.55(−3.74,0.63), 0.163 −1.16(−3.46,1.15), 0.325 −1.05(−1.89,−0.20), 0.015
 Adult(M) −1.67(−3.95,0.61), 0.152 −1.87(−2.37,−1.37), <0.001 0.94(−0.96,2.85), 0.331
Outcome type
 NF −2.90(−4.56,−1.24), 0.001 −1.06(−2.09,−0.04), 0.042 −1.38(−2.15,−0.61), <0.001 −0.80(−1.66,0.07), 0.071
 F −1.04(−1.56,−0.52), <0.001 0.47(−0.07,1.00), 0.087 0.50(−0.06,1.06), 0.081 0.85(0.07,1.62), 0.032
Fasting time
 <12 weeks −2.59(−4.26,−0.91), 0.002 −1.15(−2.15,−0.16), 0.023 −1.00(−1.99,−0.02), 0.046 0.06(−1.01,1.14), 0.907
 ≥12 weeks −2.20(−3.44,−0.95), 0.001 −0.22(−1.18,0.74), 0.654 −0.77(−1.76,0.22), 0.129 −0.23(−1.06,0.60), 0.590
Region
 Oceania −2.85(−4.97,−0.73), 0.008 −0.84(−2.49,0.82), 0.321 −0.72(−1.96,0.51), 0.251 −1.09(−1.45,−0.72), <0.001
 America −0.15(−1.79,1.49), 0.858 −0.79(−2.09,0.50), 0.228 −0.27(−1.97,1.43), 0.756
 Europe −1.25(−1.83,−0.67), <0.001 −0.03(−0.55,0.49), 0.914 −0.79(−2.82,1.23), 0.443 0.71(0.10,1.32), 0.024
 Asia −2.65(−3.19,−2.11), <0.001
Diabetes
 No −2.48(−3.71,−1.26), <0.001 −0.98(−1.96,0.01), 0.052 −1.38(−2.15,−0.61), <0.001 −0.55(−1.24,0.15), 0.125
 Yes −1.63(−2.51,−0.75), <0.001 0.26(−0.51,1.03), 0.506 0.50(−0.06,1.06), 0.081 1.48(−0.32,3.29), 0.108

Factors Standard mean difference (95% CI), P
TG SBP DBP BG
Fasting type
 ADMF −0.89(−2.68,0.90), 0.331 −1.48(−3.58,0.62), 0.166 −0.62(−1.49,0.26), 0.168 −1.51(−5.72,2.69), 0.481
 CADF −0.67(−1.83,0.49), 0.255 −1.55(−3.16,0.07), 0.060 −1.11(−2.10,−0.13), 0.026 0.02(−1.97,2.01), 0.986
 TRF
 VLCD −0.33(−0.69,0.04), 0.079 −0.83(−1.55,−0.10), 0.027 −0.63(−1.26,0.01), 0.054 −0.14(−0.45,0.17), 0.384
Subjects
 Adult(W + M) −0.66(−1.21,−0.12), 0.017 −0.82(−1.46,−0.19), 0.011 −0.44(−0.84,−0.04), 0.033 −0.13(−0.66,0.41), 0.644
 Adult(W) −0.07(−0.58,0.44), 0.791 −1.55(−3.16,0.07), 0.060 −1.11(−2.10,−0.13), 0.026 −0.59(−3.53,2.35), 0.694
 Adult(M) −0.93(−2.05,0.20), 0.107 −0.65(−1.07,−0.22), 0.003
Outcome type
 NF −0.86(−1.57,−0.14), 0.019 −1.49(−2.47,−0.51), 0.003 −0.81(−1.42,−0.21), 0.008 −0.49(−1.32,0.35), 0.252
 F −0.24(−0.58,0.11), 0.178 −0.35(−0.98,0.28), 0.280 −0.49(−1.20,0.22), 0.174 −0.08(−0.43,0.26), 0.641
Fasting time
 <12 weeks −0.76(−1.51,−0.00), 0.049 −1.50(−2.28,−0.72), 0.001 −0.88(−1.38,−0.38), 0.001 0.00(−0.88,0.89), 0.995
 ≥12 weeks −0.53(−1.05,−0.01), 0.046 −0.87(−1.61,−0.13), 0.021 −0.61(−1.18,−0.03), 0.039 −0.49(−1.10,0.11), 0.108
Region
 Oceania 0.22(−0.27,0.71), 0.384 −0.89(−2.40,0.61), 0.245 −0.77(−1.56,0.02), 0.056 −0.16(−1.68,1.35), 0.832
 America −1.14(−2.28,−0.00), 0.049 −1.85(−3.08,−0.61), 0.003 −0.97(−1.88,−0.06), 0.037 −0.72(−2.13,0.69), 0.317
 Europe −0.69(−1.22,−0.17), 0.010 −0.10(−0.58,0.39), 0.692 −0.11(−0.62,0.40), 0.664 −0.06(−0.55,0.42), 0.793
 Asia −2.23(−2.74,−1.73), <0.001 −1.34(−3.13,0.45), 0.141 −0.30(−0.86,0.26), 0.294
Diabetes
 No −0.65(−1.21,−0.10), 0.021 −1.26(−2.09,−0.43), 0.003 −0.71(−1.24,−0.19), 0.007 −0.38(−0.99,0.23), 0.221
 Yes −0.53(−1.25,0.20), 0.156 −0.64(−1.18,−0.11), 0.018 −0.66(−1.52,0.20), 0.134 −0.02(−0.73,0.68), 0.947

Factors Standard mean difference (95% CI), P Heterogeneity I2(%), P
Insulin BW BMI FEM FM WC
Fasting type
 ADMF 0.76(−0.76,2.28), 0.329 98.0, <0.001 92.6, <0.001 96.0, <0.001 97.0, <0.001
 CADF 0.37(−2.27,3.01), 0.784 97.0, <0.001 94.2, <0.001 97.3, <0.001 85.9, 0.008
 TRF 0.0,0.667
 VLCD −0.51(−0.75,−0.26), <0.001 93.5, <0.001 83.6, <0.001 88.3, <0.001 98.1, <0.001 96.2, <0.001
Subjects
 Adult(W + M) 0.00(−0.47,0.47), 0.997 95.6, <0.001 90.3, <0.001 94.1, <0.001 97.6, <0.001 96.1, <0.001
 Adult(W) −0.13(−4.49,4.23), 0.953 96.4, <0.001 88.8, <0.001 86.6, 0.006 85.9, <0.001
 Adult(M) 0.0, 0.509 0.0, 0.595 67.2, 0.047 0.0, 0.522
Outcome type
 NF 0.02(−1.31,1.35), 0.974 96.1, <0.001 92.9, <0.001 96.9, <0.001
 F −0.50(−0.73,−0.26), <0.001 90.8, <0.001 68.2, 0.024 60.0, 0.082
Fasting time
 <12 weeks −0.39(−2.15,1.37), 0.665 91.4, <0.001 81.9, 0.001 92.4, <0.001 94.7, <0.001 88.7, <0.001
 ≥12 weeks −0.12(−0.61,0.37), 0.628 96.3, <0.001 91.5, <0.001 92.2, <0.001 97.7, <0.001 96.6, <0.001
Region
 Oceania −0.07(−2.02,1.88), 0.941 98.5, <0.001 86.9, 0.006 91.6, <0.001 98.4, <0.001 97.7, <0.001
 America 0.50(−0.56,1.55), 0.355 91.2, <0.001 84.0, 0.012 95.5, <0.001 85.9, <0.001
 Europe −0.99(−1.77,−0.20), 0.014 70.4, 0.002 66.8, 0.017 69.9, 0.019
 Asia 35.5, 0.199 0.0, 0.778 83.5, 0.014
Diabetes
 No −0.13(−1.01,0.74), 0.764 95.3, <0.001 91.6, <0.001 96.6, <0.001
 Yes −0.42(−0.74,−0.10), 0.010 92.5, <0.001 83.3, 0.014 53.7, 0.142

Factors Heterogeneity I2(%), P
TC LDL-C HDL-C TG SBP DBP BG Insulin
ADMF, alternate day modified fasting; CADF, complete alternate-day fasting; TRF, time-restricted feeding; VLCD, very low calorie diet; Adult(W + M), Adult(women + men); Adult(W), Adult(women); Adult(M), Adult(men); NF, non follow-up; F, follow-up; BW, body weight; BMI, body mass index; FEM, fat free mass; FM, fat mass; WC, waist circumference; TC, total cholesterol; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; TG, triglycerides; SBP, systolic blood pressure; DBP, diastolic blood pressure; BG, blood glucose.
Fasting type
 ADMF 96.8, <0.001 93.6, <0.001 91.2, <0.001 95.3, <0.001 96.1, <0.001 83.6, 0.002 97.9, <0.001 91.9, 0.001
 CADF 84.4, 0.002 87.2, <0.001 70.9, 0.032 84.2, 0.002 86.2, 0.007 68.0, 0.077 94.2, <0.001 96.2, <0.001
 TRF
 VLCD 95.9, <0.001 96.2, <0.001 0.0, 0.550 63.7, 0.017 91.1, <0.001 88.8, <0.001 54.2, 0.053 0.0, 0.411
Subjects
 Adult(W + M) 93.8, <0.001 91.1, <0.001 93.8, <0.001 88.5, <0.001 91.3, <0.001 79.5, <0.001 88.2, <0.001 83.5, <0.001
 Adult(W) 92.1, <0.001 93.5, <0.001 57.8, 0.124 0.0, 0.445 86.2, 0.007 68.0, 0.077 96.0, <0.001 97.8, <0.001
 Adult(M) 95.1, <0.001 0.0, 0.911 93.4, <0.001 82.9, 0.016 0.0, 0.645
Outcome type
 NF 95.0, <0.001 90.0, <0.001 92.3, <0.001 90.3, <0.001 93.7, <0.001 85.9, <0.001 92.6, <0.001 95.0, <0.001
 F 72.3, 0.013 60.9, 0.078 89.2, <0.001 51.9, 0.081 83.9, <0.001 87.2, <0.001 52.1, 0.079 0.0, 0.563
Fasting time
 <12 weeks 89.6, <0.001 87.1, <0.001 89.8, <0.001 79.3, 0.001 74.8, 0.008 48.1, 0.123 88.4, <0.001 95.3, <0.001
 ≥12 weeks 95.3, <0.001 94.4, <0.001 94.4, <0.001 87.6, <0.001 93.2, <0.001 89.2, <0.001 89.8, <0.001 83.1, <0.001
Region
 Oceania 94.7, <0.001 91.0, <0.001 18.8, 0.292 53.1, 0.119 93.7, <0.001 79.0, 0.009 93.9, <0.001 95.6, <0.001
 America 95.7, <0.001 93.9, <0.001 95.6, <0.001 91.9, <0.001 87.4, <0.001 83.1, 0.003 93.4, <0.001 89.8, <0.001
 Europe 61.3, 0.051 93.1, <0.001 76.8, 0.005 72.2, 0.006 64.0, 0.040 67.2, 0.027 65.0, 0.036 84.9, <0.001
 Asia 0.0, 0.474 0.0, 0.485 93.7, <0.001 49.7, 0.159
Diabetes
 No 95.3, <0.001 90.0, <0.001 92.2, <0.001 88.2, <0.001 94.0, <0.001 86.5, <0.001 90.4, <0.001 93.8, <0.001
 Yes 82.8, 0.001 60.9, 0.078 94.6, <0.001 79.9, 0.002 54.9, 0.109 82.0, 0.004 75.4, 0.017 0.0, 0.995


The results of publication bias for included studies are given in Table 4. Publication biases were observed in the body weight, BMI, FEM, FM, WC, TC, LDL-C and SBP (P < 0.05). However, there was no significant difference between the SMD and that before the trim and fill. Therefore, the effect of publication bias was considered slight and the results were stable (Table 4).

Table 4 Publication bias (Egger test) and sensitivity analysis (trim and fill method) performed for included studies
  Egger test (t, P) Number of trim and fill SMD(95%CI), Pa SMD(95%CI), Pb
BMI: body mass index; FEM: fat free mass; FM: fat mass; WC: waist circumference; TC: total cholesterol; LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprotein cholesterol; TG: triglycerides; SBP: systolic blood pressure; DBP: diastolic blood pressure.a Original variation.b Variation after trim and fill.
Body weight (kg) −5.72, <0.001 3 −2.16(−2.76,−1.56), <0.001 −2.58(−3.39,−1.77), <0.001
BMI (kg m−2) −5.03, <0.001 0 −1.18(−1.72,−0.65), <0.001 −1.18(−1.72,−0.65), <0.001
FEM (kg) −3.96, 0.004 1 −0.82(−1.49,−0.15), 0.016 −1.04(−1.79,−0.28), 0.007
FM (kg) −4.60, 0.001 0 −2.20(−3.29,−1.11), <0.001 −2.20(−3.29,−1.11), <0.001
WC (cm) −4.55, 0.002 0 −2.31(−3.32,−1.30), <0.001 −2.31(−3.32,−1.30), <0.001
TC (mmol L−1) −2.42, 0.032 0 −0.62(−1.36,0.12), 0.099 −0.62(−1.36,0.12), 0.099
LDL-C (mmol L−1) −2.88, 0.018 0 −0.87(−1.57,−0.17), 0.014 −0.87(−1.57,−0.17), 0.014
HDL-C (mmol L−1) 0.37, 0.717 −0.12(−0.75,0.52), 0.720
TG (mmol L−1) −5.29, <0.001 −0.61(−1.04,−0.18), 0.005 −0.61(−1.04,−0.18), 0.005
SBP (mmHg) −3.93, 0.003 0 −1.08(−1.69,−0.47), 0.001 −1.08(−1.69,−0.47), 0.001
DBP (mmHg) −1.44, 0.181 −0.70(−1.13,−0.26), 0.002
Blood glucose (mmol L−1) −1.47, 0.169 −0.28(−0.75,0.20), 0.253
Insulin (mIU L−1) 0.08, 0.941 −0.21(−0.82,0.40), 0.496


4. Discussion

According to the existing evidence of relevant animal and human studies, fasting has a beneficial effect on advancing health and reduces the risk of several chronic illnesses in adults, especially for overweight and sedentary people.6 Similarly, our study found that participants with overweight or obesity receiving fasting intervention had significantly larger reductions in body weight, BMI, FEM, FM, WC, LDL-C, TG, SBP and DBP parameters than the control subjects. One crucial mechanism resulting in these profitable influences seems to be “flipping” of the metabolic switch.8 When the hepatic glycogen is depleted due to fasting, some metabolic adaptations are observed in the liver, thus retaining systemic energy balance and supplying the major organs, tissues and cells with sufficient nutrients.48 The characteristics are increased numbers of circulating ketones, whereas circulating fatty acids, amino acids, glucose, and insulin are preserved at low concentrations.49 And it is owing to the fat mobilization and the oxidative decomposition of fatty acids.50,51 Ketones are metabolized to acetyl coenzyme A, which then enters the tricarboxylic acid (TCA) cycle to generate ATP, thus serving as an energy source to sustain the function of muscle and brain cells during fasting.51 In other words, the primary energy source for the body shifts from glucose to free fatty acids derived from adipose tissue lipolysis and ketones, which means “flipping” of the metabolic switch.8 For this reason, fasting may have a potential effect on the regulation of obesity and related metabolic parameters.

The existence of small LDL particles and higher post-prandial hyperlipemia are markers of myocardial infarction (MI) and ischemic heart disease (IHD) progression.52,53 Our study found that the fasting intervention group had significant decreases more in LDL-C, and TG parameters than the control group. Recent clinic trials have demonstrated that alternate day fasting could reduce the ratio of small LDL particles, thereby causing the reduction of LDL-C and TG parameters.25,54 Meanwhile, weight loss may also be an important factor. Previous literature indicated that only subjects with more weight loss had a significant blood lipid decrease, as with blood pressure.8 Similarly, in view of our outcomes of the subgroup meta-analysis, after fasting was stopped for a period of time, the amount of weight loss was smaller based on SMD values. Correspondingly, the variations in blood lipids and blood pressure between the fasting intervention subjects and controls change to no statistically significant differences.

Based on the outcomes of the subgroup meta-analysis, apart from HDL-C, blood glucose and insulin, the anthropometric and metabolic parameters had a larger reduction in the CADF group than the VLCD group in view of SMD values (especially at body weight, WC, TC and DBP parameters). Although the previous literature showed that the differences between intermittent and continuous energy restriction were not observed in boosting weight reduction and metabolic improvements, our study especially indicated that CADF is more effective in regulating anthropometric and metabolic parameters than VLCD for people with overweight or obesity.55 The possible mechanisms include improving autophagy via sirtuin-1 activity stimulation, changing cell apoptosis, increasing the expression of vascular endothelial growth factor (VEGF), and reducing glycosylation end products.56–59 Meanwhile, some studies indicated that subjects of IF usually did not take enough energy in the normal diet period to compensate for the fasting period, thus indicating that IF could decrease the total energy intake.8,60 In addition, the incidence of adverse events of short-term intermittent fasting was lower than that of long-term fasting.8 Similarly, the results of subgroup analysis indicated no differences in the influence of fasting time on regulating anthropometric and metabolic parameters. Therefore, short-term CADF may be a better choice for people with overweight or obesity. However, as previous studies have shown, due to differences in trial design, participant characteristics, and subject compliance, as well as limited sample size and studies, more clinic trials are needed to estimate the role of CADF in people with overweight or obesity.61

Although the differences between the fasting intervention and control subjects were not found in the variations in the insulin parameter, significant differences were noticed for the VLCD intervention in view of subgroup analysis. It is universally known that weight gain and obesity are critical risk factors in terms of insulin resistance.62 Simultaneously, insulin-resistant patients with obesity need more insulin to regulate blood glucose, leading to weight gain progressively, thus a vicious circle formation.63 Previous studies found that the VLCD could normalize insulin sensitivity and total insulin secretion via reducing the hepatic and pancreatic fat.64 Meanwhile, as an anabolic hormone, insulin could induce cell growth and advance the storage of fatty acids in adipose and muscle tissue, irritating muscle hypertrophy and restraining proteolysis.65,66 Therefore, the insulin secretion returns to normal and can reduce body weight and a virtuous circle is established. Correspondingly, fasting intervention can effectively improve patients with obesity and diabetes through the dual regulation of body weight and insulin levels in terms of the outcomes of subgroup analysis. Moreover, statistically significant differences in the variations in insulin parameter were noticed between the fasting intervention and control subjects after fasting was stopped for a period of time. This beneficial effect may result from a cellular stress response induced by the fasting state. However, the theory has been proved only in animal experiments, human trials are needed.67

This study has some limitations. The blind method is not applicable to all included studies due to the intervention contents and methods for participants and nutritionists could not be masked. Although the research staff of some studies evaluating the outcome were unaware of the apportion of the participants, true intervention effects could be biased by the restricted quality of the methodology.68 Meanwhile, the heterogeneity of most results is high. The reasons may be the differences of subjects and region, as well as the outcome type and other factors. In addition, due to the lack of control or changes before and after the intervention in many studies, there are few studies of IF meeting the inclusion criteria.

5. Conclusion

Our study found that fasting intervention had a significant effect on the regulation of anthropometric and metabolic parameters by significantly reducing the body weight, BMI, FEM, FM, WC, LDL-C, TG, SBP and DBP in people with overweight or obesity. Considering some limitations found in this study, more data from large clinical trials are needed to affirm the efficacy of fasting for the regulation of anthropometric and metabolic parameters in people with overweight or obesity.

Author contributions

BL, WC, and SY made the study design; SY, CW, and HZ conducted the study; SY, CW, and YP analyzed the data and wrote the manuscript; SY, HW, YG and NY attended the manuscript revision. All authors agreed with the final manuscript.

Conflicts of interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 81973129).

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Footnote

Electronic supplementary information (ESI) available. See DOI: 10.1039/d0fo00287a

This journal is © The Royal Society of Chemistry 2020