Intermittent Fasting Improves Cardiometabolic Risk Factors and Alters Gut Microbiota in Metabolic Syndrome Patients

Abstract

Context

Intermittent fasting (IF) is an effective strategy to improve cardiometabolic health.

Objective

The objective of this work is to examine the effects of IF on cardiometabolic risk factors and the gut microbiota in patients with metabolic syndrome (MS).

Design and Setting

A randomized clinical trial was conducted at a community health service center.

Patients

Participants included adults with MS, age 30 to 50 years.

Intervention

Intervention consisted of 8 weeks of “2-day” modified IF.

Main Outcome Measure

Cardiometabolic risk factors including body composition, oxidative stress, inflammatory cytokines, and endothelial function were assessed at baseline and at 8 weeks. The diversity, composition, and functional pathways of the gut microbiota, as well as circulating gut-derived metabolites, were measured.

Results

Thirty-nine patients with MS were included: 21 in the IF group and 18 in the control group. On fasting days, participants in the IF group reduced 69% of their calorie intake compared to nonfasting days. The 8-week IF significantly reduced fat mass, ameliorated oxidative stress, modulated inflammatory cytokines, and improved vasodilatory parameters. Furthermore, IF induced significant changes in gut microbiota communities, increased the production of short-chain fatty acids, and decreased the circulating levels of lipopolysaccharides. The gut microbiota alteration attributed to the IF was significantly associated with cardiovascular risk factors and resulted in distinct genetic shifts of carbohydrate metabolism in the gut community.

Conclusion

IF induces a significant alteration of the gut microbial community and functional pathways in a manner closely associated with the mitigation of cardiometabolic risk factors. The study provides potential mechanistic insights into the prevention of adverse outcomes associated with MS.

intermittent fasting, cardiometabolic risk factors, gut microbiota, metabolic syndrome

Metabolic syndrome (MS), a constellation of metabolic abnormalities including insulin resistance, abdominal obesity, atherogenic dyslipidemia, and hypertension (1), is associated with elevated risks of incident cardiovascular morbidity and mortality (2-5). Early lifestyle intervention is regarded as an effective approach to reducing the burden of cardiovascular diseases in the population with MS (6).

As a strategy of dietary intervention, intermittent fasting (IF) is a practice of periodic energy restriction that has been shown to reduce the risks of aging and aging-related conditions in rodents and to exert positive effects on weight and metabolic health in human studies (7). The major challenge is a lack of standardization of paradigms for implementing various intermittent energy restriction strategies. Alternate-day fasting (ADF), defined as strict 36-hour periods without caloric intake (“fast days”) followed by 12-hour intervals with ad libitum food consumption (“feast days”), is a strict and well-studied alternative to IF (8, 9). Several studies have shown that short-term ADF (< 24 weeks) facilitates weight loss and alters chronic disease risks (10-12). However, the long-term (≥ 24 weeks) adverse effects of ADF, including hypoglycemia, damage to the digestive system, and compromised bone metabolism, are still controversial in humans (13, 14).

As another alternative to IF, “2-day” IF, which has been described as “an eating pattern in which there are 2 nonconsecutive days of moderate to complete calorie restriction a week and an ad libitum diet the other 5 days,” is the least restrictive form of IF (7). Studies of 2-day IF, in which 60% to 75% calorie restriction on 2 fasting days per week was required, demonstrated efficacy for weight loss (15). Clinical trials showed that 2-day IF led to a significant decrease in body fat mass and improved insulin resistance (16), and improved glycated hemoglobin and glycemic control in patients with obesity and type 2 diabetes (17). Recently, animal studies showed that IF might also directly affect gut microbial composition, function, and interaction with the host (18). IF selectively stimulates white adipose browning and decreases obesity by reshaping gut microbiota in mice (19). Fecal microbiota transplantation from IF-treated mice to naive recipients ameliorated antioxidative microbial metabolic pathways (20). However, the impact of 2-day IF on cardiometabolic biomarkers in patients with MS and on gut microbiota alteration in human studies and clinical trials has not yet been fully investigated.

Given that the perturbation of gut microbiota homeostasis is associated with the risk of developing cardiometabolic diseases in rodent models and humans (21-24), we conducted a randomized controlled trial to examine the effects of 2-day IF on cardiometabolic markers and gut microbiota in patients with MS to understand the benefits and possibility of application of 2-day IF in clinical MS prevention.

Materials and Methods

Study design

We conducted a randomized controlled trial to evaluate the effects of 2-day IF on cardiometabolic health in participants with MS. The eligible participants were randomly assigned to initially follow either a control diet (the CD group, which maintained a routine diet without dietary instructions) or a 2-day fasting dietary schedule (the IF group, which involved a 75% of energy restriction for 2 nonconsecutive days a week and an ad libitum diet the other 5 days) for 8 weeks. The sample size calculation indicated that for a well-controlled randomized trial, at least 17 completers per group were required to achieve 80% statistical power. Participants were stratified by sex and randomly assigned to either the IF or CD group at a 1:1 ratio (25).

The study was registered at ClinicalTrials.gov (identifier: NCT03608800) and approved by the ethics committee of the School of Public Health, Sun Yat-sen University. All participants provided written informed consent before study enrollment.

Study participants and recruitment

Participants were recruited from Qishi and Chashan community health service centers, Dongguan, China, which are 2 sites in the South China Cohort Study of Chronic Diseases. Individuals diagnosed with MS, according to the International Diabetes Federation diagnostic criteria (2005), were assessed for eligibility (26). The inclusion criteria and exclusion criteria were as follows.

Inclusion criteria

• -

  Residents aged 30 to 50 years;

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  Stable body weight (change < ±10% of body weight) for 3 months before the study;

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  Central obesity: waist circumference 90 cm or greater in men or 80 cm or greater in women;

Plus any 2 of the following 4 conditions:

• 1) Elevated triglycerides: serum triglycerides 150 mg/dL or greater (1.70 mmol/L);

• 2) Reduced high-density lipoprotein cholesterol (HDL-c): serum HDL-c less than 40 mg/dL (1.03 mmol/L) in men or less than 50 mg/dL (1.29 mmol/L) in women;

• 3) Elevated blood pressure: systolic blood pressure of 130 mm Hg or greater or diastolic blood pressure of 85 mmHg or greater;

• 4) Elevated fasting plasma glucose: fasting plasma glucose of 100 mg/dL or greater (5.6 mmol/L);

Exclusion criteria

• -

  Presence of known cardiovascular and cerebrovascular diseases (defined on the form as coronary heart disease, stroke, or revascularization) and malignant tumors;

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  Acute or chronic infection within the previous 4 weeks;

• -

  Alcohol abuse (weekly consumption of alcohol is more than 70 g in women or 140 g in men);

• -

  Regular therapy with antihypertensive agents, hypolipidemic agents, hypoglycemic agents, hormonal agents or antidepressants within the previous 6 months;

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  Use of probiotics, prebiotics, or antibiotics within 3 months prior to or during the study;

• -

  Use of anti-inflammatory drugs within 3 months prior to or during the study;

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  Vegetarianism or following a vegan diet;

• -

  Women who were pregnant or intended to become pregnant during the study.

Calorie intake and energy expenditure

Participants in the IF group were asked to record what they ate and the cooking methods they used on every fasting day and to take pictures of the food in real time. The food pictures were required to be sent to the investigators the same day, with a fist beside the food as reference. Calorie restriction was defined as the difference in calorie consumption between nonfasting days and fasting days in the IF group or as the difference in calorie consumption between baseline and 8 weeks in the CD group. All participants were instructed to complete a 3-day (2 work days and 1 off-day) dietary recall interview at baseline and after 8-week IF (25). To avoid bias regarding energy expenditure, physical activity level was assessed by metabolic equivalents (METs) according to the time and intensity of activities within 7 days of the assessment.

Compliance monitoring

Compliance was quantified as the percentage of completers in each group (1): in the IF group: those who complied with the provided energy intake and the IF schedules and (2) in the CD group: those who maintained their habitual diet and lifestyle for 8 weeks. Imperfect compliance was defined as an energy intake of 20% or less of the recommended consumptions on fasting days, failure to follow the IF schedules for more than 1 week, and/or fasting days of fewer than 12 days (noncompliance ≥ 20%) during the intervention period. Owing to the nature of the intervention, neither participants nor staff could be blinded to allocation. All probable adverse effects and participant responses were closely monitored by primary doctors and researchers during the intervention.

Anthropometric measurements

Characteristics including body weight, body composition, circumference index, blood pressure, and pulse were measured by trained investigators and doctors at baseline and after 8 weeks according to standard operation procedures. Weight, percentage body fat, and visceral fat index were measured with an Innerscan Segmental Body Composition Monitor (No. 570509, Tanita Corporation). Neck circumference, waist circumference, and hip circumference were measured according to standard operation procedures (25). Blood pressure and pulse were measured using an automated electronic sphygmomanometer (HEM-752 FUZZY, Omron). All measurements were performed 3 times with a 2-minute interval between measurements and reported as an average of 2 similar records.

Biological sample collection and storage

Sample collections were performed the day after a nonfasting day. After overnight fasting (≥ 10 hours), peripheral venous blood samples were drawn and collected at baseline and at 8 weeks. Fecal samples were collected and stored in a natural, oil-based polyol–based stabilizer reagent. Samples were stored at –80°C within 12 hours of collection until analysis.

Metabolic explorations and laboratory analyses

The metabolic explorations and fecal microbiome analysis were performed at baseline and at 8 weeks. Measurements of (i) serum lipid profiles: total cholesterol (TC), HDL-c, low-density lipoprotein cholesterol (LDL-c), triglycerides (TGs), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB); (ii) serum glucose and insulin; and (iii) circulating biomarkers closely related to cardiovascular risks were assessed in this study. We measured 4 plasma cytokines of systemic inflammation: interleukin-6 (IL-6), tumor necrosis factor-α (TNFα), soluble CD40 ligand (sCD40L), and von Willebrand factor; 2 adipokines: leptin and adiponectin; 2 biomarkers of oxidative stress: malondialdehyde (MDA) and oxidized LDL; 3 biomarkers of endothelial function: total nitrate, asymmetrical dimethylarginine (ADMA), and vascular cell adhesion molecule-1 and 3 gut-derived metabolites: lipopolysaccharide (LPS), short-chain fatty acids (SCFAs), and trimethylamine N-oxide (TMAO). The diversity, composition, and functional capacity of gut microbiota in fecal samples were also determined. All of the measurements were performed according to the manufacturer’s instructions, which are described in detail in Supplementary Table 1 (25).

Statistical analysis

All continuous parameters were first examined for normality using the Shapiro-Wilk test. Variables with Gaussian distribution are presented as means ± SD; otherwise, they were presented as medians (25th percentile to 75th percentile). Baseline differences between the 2 groups were analyzed using a 2-tailed independent t test (log-transformed before analysis, if applicable). The paired t test was used to compare the means within each group. Fisher exact test or chi-square test was used to compare categorical variables. Comparison of the 8-week effects between groups was performed using analysis of covariance (ANCOVA) and the Levene test, with the baseline values of the characteristics of the 2 groups used as the covariate and the postintervention values used as the dependent variable. The bootstrap method of regression was used in the analysis of mediation effects. To discover the strength and direction of a link between 2 parameters, Spearman rank correlation coefficient was calculated. The false discovery rate (FDR) was calculated in multiple testing. A P value of less than .05 or an FDR of less than 0.1 was considered statistically significant.

In the analysis of the microbiome data, P values were obtained by t test or Wilcoxon test, which was used to compare baseline and 8 weeks within each group. The P values were adjusted for multiple testing by the Benjamini and Hochberg method. An FDR of less than 0.1 was considered statistically significant.

Results

Study overview and participant characteristics

Participants were recruited between July 2018 and March 2019 via posters and advertisements in local and social media at the community health service centers. At trial enrollment, 63 interested people were screened, and 17 of them were excluded for ineligibility. Three refused to take part in the study after consideration. A total of 46 participants were randomly assigned to either the IF or CD group at a 1:1 ratio. Two individuals in the IF group did not complete the intervention because of poor compliance. Five participants in the CD group withdrew (1 got influenza and took antibiotics, 1 suffered from gout and used anti-inflammatory drugs, and 3 were lost to follow-up). Twenty-one participants in the IF group and 18 in the CD group ultimately completed the 8-week 2-day IF intervention (Fig. 1). A total of 91.3% of participants in the IF group and 78.3% in the CD group were compliant with the required IF schedules or maintained the routine diets during the trial (P = .414). There were no adverse events, including hunger, fainting, irritability, insomnia, or overeating, reported by participants during the 8-week trial.

Figure 1.

Study design and CONSORT (Consolidated Standards of Reporting Trials) diagram for the Intermittent Fasting (IF) Intervention Study. At enrollment, 63 participants were assessed for eligibility according to the inclusion and exclusion criteria. Thirteen individuals did not meet the study criteria, and 4 individuals refused to take part after consideration. After screening, 46 eligible participants were randomly assigned to either the IF or the control diet (CD) group at a 1:1 ratio. Two participants in the IF group and 5 in the CD group dropped out because of poor compliance or personal reasons during the study period. At the end of the intervention, 21 participants in the IF group and 18 in the CD group completed the 8-week schedule.

The baseline characteristics of the study participants in the IF and CD groups are shown in Table 1. The participants in the IF and CD groups were aged 40.2 ± 5.7 and 42.7 ± 4.1 years, respectively, with central obesity, elevated blood pressure, elevated TGs, and/or reduced HDL-c levels. The distribution of age, sex, body mass index, waist circumference, and components of MS were not significantly different between the 2 groups at baseline.

Table 1.

Demographic, anthropometric characteristics, and changes in glycolipid metabolism in the study

	Group	Baseline	8 wks	Δchange	Pa	Pb	qc
Age, y	IF	40.2 ± 5.7	–	–	.132	–	–
	CD	42.7 ± 4.1	–	–
Male, n (%)	IF	10 (47.6%)	–	–	.523	–	–
	CD	11 (61.1%)	–	–
Weight, kg	IF	77.8 ± 13.6	74.3 ± 12.9	–3.5 ± 1.5	.309	< .001	0.004
	CD	74.1 ± 8.6	72.9 ± 8.4	–1.2 ± 1.5		.103
BMI, kg/m2	IF	28.0 (25.8 to 32.9)	27.0 (24.5 to 31.1)	–1.3 (–1.7 to –1.1)	.183	< .001	0.002
	CD	27.7 (26.4 to 28.7)	27.2 (25.9 to 28.2)	–0.4 (–0.8 to –0.1)		.102
Neck circumstance, cm	IF	38.1 ± 4.1	37.8 ± 4.0	–0.3 ± 1.03	.907	.146	0.060
	CD	38.2 ± 3.2	38.8 ± 3.0	0.5 ± 1.2		.102
Waist circumference, cm	IF	94.6 ± 10.3	92.1 ± 10.6	–2.5 ± 3.9	.463	.007	0.427
	CD	92.7 ± 5.5	91.6 ± 5.3	–1.1 ± 5.1		.368
Hip circumference, cm	IF	102.6 ± 7.0	102.1 ± 8.9	–0.5 ± 4.5	.039	.606	0.531
	CD	98.6 ± 4.4	99.1 ± 5.0	0.5 ± 2.9		.476
Body fat mass, kg	IF	27.7 ± 9.6	24.9 ± 8.6	–2.4 ± 1.6	.043	< .001	0.017
	CD	22.5 ± 4.7	21.6 ± 5.2	–0.9 ± 1.3		.107
Fat-free mass, kg	IF	50.9 ± 10.4	49.4 ± 9.9	–1.0 ± 0.9	.838	< .001	0.015
	CD	51.5 ± 8.6	51.3 ± 8.5	–0.3 ± 1.1		.323
Visceral fat index	IF	11.0 (8.2 to 13.7)	10.0 (7.5 to 12.5)	–1.0 (–1.0 to –1.0)	.828	< .001	0.003
	CD	12.5 (8.0 to 14.0)	12.0 (7.7 to 14.0)	0.0 (–1.0 to 0.0)		.156
Serum glucose, mmol/L	IF	4.90 (4.48 to 5.13)	4.77 (4.48 to 5.03)	–0.19 (–0.38 to 0.41)	0.100	.703	0.318
	CD	5.11 (4.57 to 6.12)	5.15 (4.73 to 5.63)	–0.15 (–0.63 to 0.53)		.422
Insulin, mU/L	IF	12.68 ± 4.58	11.38 ± 7.85	–2.84 ± 3.75	.028	.018	0.478
	CD	9.00 ± 3.18	9.07 ± 3.10	–0.53 ± 2.77		.559
HOMA-IR	IF	2.47 (1.92 to 3.51)	1.84 (1.28 to 3.38)	–0.75 (–1.20 to –0.17)	.468	.012	0.615
	CD	1.90 (1.56 to 2.74)	2.02 (1.61 to 2.46)	–0.09 (–0.62 to –0.54)		.710
TC, mmol/L	IF	5.14 ± 0.87	5.10 ± 0.86	–0.04 ± 0.57	.660	.744	0.420
	CD	5.27 ± 0.85	4.99 ± 0.81	–0.27 ± 0.58		.060
HDL-c, mmol/L	IF	1.20 (0.98 to 1.33)	1.21 (1.06 to 1.35)	0.05 (–0.04 to 0.12)	.135	.104	0.800
	CD	0.90 (0.81 to 1.19)	1.11 (0.99 to 1.37)	0.16 (0.03 to 0.23)		.118
LDL-c, mmol/L	IF	3.11 ± 0.70	3.13 ± 0.68	0.02 ± 0.35	.182	.746	0.020
	CD	2.84 ± 0.52	3.38 ± 0.85	0.55 ± 0.625		.002
Triglyceride, mmol/L	IF	1.90 (1.50 to 3.16)	1.50 (1.16 to 2.22)	–0.22 (–1.00 to –0.11)	.130	.006	0.315
	CD	1.39 (0.96 to 2.32)	1.51 (0.96 to 2.21)	0.00 (–0.24 to 0.39)		.427
Apo B/apo A1	IF	0.78 ± 0.17	0.75 ± 0.19	0.02 ± 0.10	.453	.492	0.325
	CD	0.83 ± 0.20	0.90 ± 0.29	0.08 ± 0.15		.447
Systole, mm Hg	IF	130.3 ± 19.5	125.0 ± 16.4	–5.3 ± 11.6	.081	.051	0.294
	CD	140.9 ± 16.2	136.0 ± 12.0	–4.9 ± 15.0		.194
Diastole, mm Hg	IF	86.8 ± 12.3	84.3 ± 9.2	–2.5 ± 6.6	.062	.099	0.992
	CD	94.3 ± 11.7	89.2 ± 11.7	–5.1 ± 9.6		.144
Heart rate, /min	IF	72.0 (66.0 to 75.0)	72.0 (65.0 to 81.0)	0.0 (–3.5 to 5.5)	.306	.769	0.597
	CD	73.0 (67.0 to 84.2)	75.5 (70.5 to 85.5)	1.5 (0.0 to 5.7)		.147

Abbreviations: ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; BMI, body mass index; CD, control diet; HDL-c, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; IF, intermittent fasting; LDL-c, low-density lipoprotein cholesterol; TC, total cholesterol.

^aP values for the comparison of baselines between the 2 groups: independent t test.

^bP values for the comparison between baseline and 8 weeks within each group: paired t test.

^cq values of 8-week effects between the 2 groups: P values were obtained by analysis of covariance and adjusted for multiple testing with the false discovery rate.

Calorie intake and physical activity level

Calorie consumptions were evaluated during the study. There was no significant difference in baseline calorie or macronutrient intakes between the 2 groups. In the process of the trial, participants in the IF group had significantly reduced daily calorie intake on fasting days, averaging 31.0% (95% CI, 27.8%-34.2%) of the intake on nonfasting days. On nonfasting days, calorie intake was maintained compared to baseline, with no significant increase in the IF group (Fig. 2A). Participants in the CD group did not differentially modify their energy and macronutrient intakes during the trial (Fig. 2B). There were no significant differences in physical activity level or energy expenditure observed between groups (Fig. 2C).

Figure 2.

Levels of calorie restriction and physical activity in participants. A, Daily calorie intake (kcal/d) and macronutrient contents of the intermittent fasting (IF) and control diet (CD) groups. Energy consumed on fasting days was significantly reduced compared to nonfasting days within the IF group. B, The percentage of calorie restriction in the IF group during the study was reduced significantly compared to the CD group. C, The level of physical activity (metabolic equivalents per week; MET/w) in the IF group was similar to the CD group. Data are shown as Tukey boxplots (line at median and + at mean). BL indicates baseline.

Intermittent fasting changes anthropometric characteristics and body composition, but does not affect blood lipids, glucose metabolism, or blood pressure

At baseline, anthropometric characteristics and body composition were similar between the groups. There was no significant change in anthropometric characteristics or body composition observed in the CD group during the study. In the IF group, body mass index was significantly reduced from 29.3 kg/m² (95% CI, 27.4-31.2) to 28.0 kg/m² (95% CI, 26.2-29.8), and weight decreased from 77.8 kg (95% CI, 71.6-84.0) to 74.3 kg (95% CI, 68.4-80.2) (both P < .001. Table 1). Waist circumference was significantly reduced by 2.5 cm (95% CI, 0.8-4.3) (P = .007. Table 1). We also observed an improvement in body composition in the IF group. Fat mass and fat-free mass were both significantly reduced after 8 weeks (P < .001. Table 1). The visceral fat index, which indicated the deposit of abdominal fat, was lowered by 7.9% (95% CI, 5.6%-10.1%) from baseline (P < .001. Table 1). After adjusting for baseline values, the effects of 8 weeks of IF on weight were statistically significant compared to the CD group (q = 0.004. Table 1). We also found that the baseline-adjusted fat mass, fat-free mass, and the neck circumstance of the IF group were significantly lower than those of the CD group (q = 0.017, q = 0.015 and q = 0.060, respectively. Table 1).

Glucose metabolism measurements revealed that serum insulin was appreciably decreased by 2.84 mU/L (95% CI, 0.58-5.11) in the IF group after the intervention (P = .018. Table 1). The homeostatic model assessment of insulin resistance (HOMA-IR) was also significantly reduced from 2.60 (95% CI, 2.03-3.17) to 1.98 (95% CI, 1.39-2.56) (P = .012; Table 1). Lipid profiles, including serum TC, HDL-c, and Apo B/apo A1, were not significantly affected, except for a decrease in TGs of 0.49 mmol/L (95% CI, 0.15-0.82) in the IF group (P = .006; Table 1). An unexpected increase in LDL-c was observed in the CD group (P = .002; Table 1) and resulted in a significantly decreased 8-week LDL-c in the IF group after adjusting for baseline values (q = 0.020. Table 1). The improvement in glucose metabolism and serum TGs in the IF group was not statistically significant by ANCOVA. No significant improvement in blood pressure was found in the study.

Intermittent fasting induced modulation of cardiometabolic biomarkers

We did not observe significant differences in biomarkers between the 2 groups at baseline. Short-term 2-day IF did not influence the levels of IL-6 (Fig. 3A) and TNF-α (Fig. 3B). After adjusting for baseline values, plasma sCD40L in the IF group was significantly decreased compared to that of the CD group (q = 0.090; Fig. 3C). There was a significant improvement in adipokines in the IF group. Plasma leptin was significantly reduced from 14.05 ng/mL (95% CI, 7.15-20.94) to 6.87 ng/mL (95% CI, 4.65-9.09) (P = .004; Fig. 3D), whereas adiponectin increased from 19.89 μg/mL (95% CI, 10.03-29.74) to 30.54 μg/mL (95% CI, 22.72-38.37) in the IF group (P = .022; Fig. 3E). The baseline-adjusted levels of leptin and adiponectin in the IF group were significantly improved compared to those in the CD group (q = 0.053 and q = 0.026, respectively).

Figure 3.

Impact of “2-day” intermittent fasting (IF) on cardiovascular metabolic biomarkers. Plasma A, interleukin-6 (IL-6); B, tumor necrosis factor α (TNFα); C, soluble CD40 ligand (sCD40L); D, leptin; E, adiponectin; F, malondialdehyde; G, oxidized low-density lipoprotein (OxLDL); H, total nitrate; I, asymmetrical dimethylarginine (ADMA); J, vascular cell adhesion molecule-1 (VCAM-1); and K, von Willebrand factor (vWF) in the 2 groups are shown. All data are visualized as Tukey boxplots (the line at median and + at mean). BL indicates baseline. P values were obtained by paired t test for comparison within each group. Analysis of covariance was used in the comparison between poststudy values in the 2 groups after adjusting for the baseline values. q values were calculated for multiple comparisons.

In the IF group, there was a significant postintervention reduction by 7.35 nmol/mL (95% CI, 0.66-14.04) in plasma MDA (P = .038; Fig. 3F). After adjusting for baseline values, the MDA in the IF group was significantly lower than that in the CD group (q = 0.074; Fig. 3F). There were no significant differences in plasma oxidized LDL observed within each group or between the 2 groups (Fig. 3G). In the IF group, plasma total nitrate was significantly increased after 8 weeks by 275.1% (95% CI, 118.5%-431.8%) (P < .001; Fig. 3H). After adjusting for baseline values, total nitrate in the IF group was significantly higher than that in the CD group (q = 0.062; Fig. 3H). At 8 weeks, the baseline-adjusted ADMA was significantly lower than that in the CD group (q = 0.068; Fig. 3I). However, no significant differences in plasma levels of vascular cell adhesion molecule-1 (Fig. 3J) and von Willebrand factor (Fig. 3K) were observed within each group or between the 2 groups.

We performed the mediation effects analysis with the bootstrap method to determine the mediating effect of weight loss in the regression of circulating biomarkers on the treatment. When variations in weight loss were controlled, the regression coefficient between the intervention and the changes in circulating biomarkers was not statistically significant, indicating that the weight loss had no significant mediating effect on the changes in biomarkers (Supplementary Table 2) (25).

Diversity and composition of gut microbiota is altered by intermittent fasting

We assessed the diversity, composition, and functional capacity of gut microbiota to determine whether IF might induce alteration of gut microbiota in humans. The ecological organization of the gut microbiota communities was calculated using the Shannon index and Simpson index of 1-D. Generally, no significant differences in the α diversity of the gut microbiota were seen at baseline between the 2 groups. The number of observed species (operational taxonomic units, OTUs) did not differ significantly between baseline and postintervention within each group (Fig. 4A). No statistically significant changes in the IF group were observed with the Shannon index (P = .983; Fig. 4B) or Simpson index of 1-D (P = .977; Fig. 4C), which indicated diversity and evenness, respectively. Unweighted UniFrac distance-based principal coordinate analysis revealed that the community composition of the gut microbiota significantly diverged from that at baseline, after the intervention (P = .011; Fig. 4D). Weighted UniFrac analysis, which was performed based on differences in OTUs and relative abundance, revealed that the community structure of the gut microbiota was not significantly influenced (Fig. 4E).

Figure 4.

Impact of “2-day” intermittent fasting (IF) on gut microbiota diversity. A, Observed species values (OTUs) in study groups. B, Community diversity was assessed by the Shannon index. C, Community evenness was assessed by the Simpson index. D, Principal coordinate analysis (PCoA) of unweighted UniFrac distances based on OTU data from the phylotype sequencing run. PCoA plot shows a significant shift in the microbial community compositions after the intervention within the IF group (IF BL vs IF 8 weeks, P = .005). E, PCoA of weighted UniFrac distances based on OTUs data and the relative abundances. There was no significant alteration in the composition of the gut microbiota within each group. Data shown in A through C are expressed as median with interquartile range (IQR). BL indicates baseline.

We then comprehensively compared the hierarchical abundance of gut microbiota. Outputs were typified as relative abundance percentages of phyla. The top 9 phyla represented approximately 90% of the OTUs (Fig. 5A). Firmicutes and Bacteroidetes occupied the majority of phyla in the gut microbiota. Compared to baseline, the relative abundance of Spirochaetes increased significantly after the intervention in the IF group (FDR = 0.026; Fig. 5B), whereas most other phyla decreased. Linear discriminant analysis (LDA) effect size detected 17 bacterial clades at the order level with LDA scores higher than 3, showing statistically significant and biological differences in the IF group (Fig. 5C). After the intervention, the largest increases in abundance were found in Ruminococcaceae at the family level and Roseburia at the genus level, which both belong to Firmicutes. The cladogram showed that the taxonomic among the taxa was well differentiated (Fig. 5D). In the CD group, only 7 bacterial clades at the order level with LDA scores higher than 3 were detected after 8 weeks (Supplementary Fig. 1) (25). At the species level, in the IF group, 23 species were significantly influenced relative to baseline (FDR < 0.1; Fig. 6). The relative abundances of Ruminococcus gnavus, Chitinophagaceae bacterium, Roseburia faecis, Paraburkholderia caribensis, Verrucomicrobiae bacterium Ellin516, Neisseria dentiae, and Streptococcus ferus were increased after the intervention, whereas the other 16 species decreased. No significant difference in the hierarchical abundance of the gut microbiota was observed within the CD group.

Figure 5.

Impact of “2-day” intermittent fasting (IF) on relative abundance of gut microbiota at each taxonomic level. A, Bar graph of community structure at the phylum level. B, Heat map of the relative abundances of observed species values (OTUs) at the phylum level. Color intensity indicates Z scores of the relative abundances. *False discovery rate less than 0.1 compared to baseline (BL). C, Histogram of linear discriminant analysis (LDA) scores computed for features with differential abundance within the IF group. Horizontal bars represent the effect size for each taxon. The length of the bar represents the log10-transformed LDA score. The threshold on the logarithmic LDA score for discriminative features was set to 3. The name of the taxon level is abbreviated as c-class; o-order; f-family; g-genus; and s-species. D, Taxonomic representation of statistically and biologically consistent differences between baseline and postintervention within the IF group. Differences are represented in the color of the most abundant class (red indicating baseline, green indicating postintervention). The diameter of each circle is proportional to the taxon’s abundance.

Figure 6.

Associations between changes in relative abundances of main species and a series of cardiovascular metabolic biomarkers. Heat map of the Spearman rank correlation between alteration at the species level and changes in metabolic indices with the adjustment for weight loss. Only species with significant alterations (false discovery rate [FDR] < 0.1) in the intermittent fasting group are shown. The coefficient is defined as red (positive) or blue (negative). Yellow and green in the left column indicate reduced and increased relative abundance, respectively. ⁺P less than .05, ^*FDR < 0.1, ^**FDR < 0.05.

Relationship of gut microbiota and changes in cardiometabolic indices

We performed Spearman correlation analysis to investigate the relationship between the abundances of 23 significantly altered gut microbial species (FDR < 0.1) and changes in cardiovascular indices in the IF group (Fig. 6). Close associations were found between glucose metabolism, lipid profiles, inflammatory cytokines, and these species, independent of weight loss. Acidobacteria bacterium and Mitsuokella jalaludinii showed the strongest positive association with HOMA-IR. Neisseria dentiae was negatively related to serum glucose. Among links to the lipid profiles, we found significant negative associations between Paenibacillus aestuarii and serum TC. Eubacterium sp 1_3 was positively related to serum TC and LDL-c. Ruminococcus gnavus and Christensenellaceae bacterium YE57 were positively associated with serum TGs. The relative abundance of Tepidimonas fonticaldi and Roseburia faecis was positively related to plasma adipokine levels. Paenibacillus campinasensis and Paraburkholderia caribensis were significantly negatively associated with plasma adiponectin. In general, Acidobacteria bacterium, Roseburia faecis, and Eubacterium sp 1_3 exhibited the closest association with improvements in cardiovascular risk factors. In the CD group, the relative abundance of these species remained unchanged, and no significant association between microbial species and biomarkers was observed.

Changes in metabolic pathways and functional profiles of the gut microbiota

The impact of gut microbiota on functional capabilities and metabolic pathways was subsequently examined. After 8 weeks of IF, in comparison to baseline, the gut microbiota was altered in a cluster of metabolic pathways. Based on KEGG and COG categories, the most abundant pathways were involved in “genetic information processing,” “environmental information processing,” and “metabolism” at the top level of the gut microbiota community (Supplementary Fig. 2A) (25). Modest but not significant segregation was also observed in microbial functional pathways in subcategories (Supplementary Fig. 2B) (25). Of the 35 mainly altered pathways, 24 were implicated in carbohydrate metabolism, genetic information processing, and protein biosynthesis, especially starch and sucrose metabolism as well as glycolysis/gluconeogenesis, and enriched in the gut microbiota of participants in the IF group (Supplementary Fig. 2C) (25).

Statistically significant changes were observed at the genetic level. More than 70 genes shifted after the intervention. Four of these pathways were predicted to be significantly upregulated in samples of the IF group (FDR < 0.1): bacillary synthase trans-acting acyltransferase (KO15328), thioredoxin-like protein biosynthesis (KO06434), ferredoxin biosynthesis (KO00204), and phenylacetate CoA-transferase (KO13607). These genes were functionally related to the biosynthesis of antibacterial compounds, defense against oxidative and nitrosative components of the immune response, and mediation of electron transfer in a range of carbohydrate metabolic reactions in the microorganism (Supplementary Fig. 2D) (25, 27-29).

Intermittent fasting improves plasma short-chain fatty acids and lipopolysaccharide but not trimethylamine N-oxide

Gut microbial metabolites, including LPS, TMAO, and SCFAs, have been reported to participate in immunity and inflammation in the host, and are closely related to cardiovascular health (24). Plasma SCFAs in the IF group was increased from 4.97 ng/mL (95% CI, 4.09-5.86) to 6.14 ng/mL (95% CI, 4.90-7.37) after the intervention. The ANCOVA results indicated that baseline-adjusted SCFAs were significantly increased in the IF group, compared to the CD group (q = 0.064, Fig. 7A). IF also significantly lowered plasma LPS from 156.1 ng/L (95% CI, 69.1-243.1) to 74.6 ng/L (95% CI, 36.5-112.7) (P = .011). After adjusting for baseline, the 8-week LPS level in the IF group was significantly decreased compared with that in the CD group (q = 0.015; Fig. 7B). The plasma TMAO concentration remained unchanged in both groups throughout the study (Fig. 7C).

Figure 7.

Impact of “2-day” intermitten fasting (IF) on plasma short-chain fatty acids (SCFAs), lipopolysaccharide (LPS), and trimethylamine N-oxide (TMAO). Plasma levels of A, SCFAs; B, LPS; and C, TMAO at baseline (BL) and after 8-week intervention are shown. Data are visualized as Tukey boxplots (line at median and + at mean). Analysis of covariance was used in the comparison between postintervention values between the 2 groups after adjustment for baseline values. q values were calculated for multiple comparisons.

Discussion

In this well-controlled randomized intervention trial conducted in medication-naive participants with MS, we show that short-term 2-day IF caused significant changes in circulating biomarkers, including those for inflammation, oxidative stress, and endothelial function. We also found an improvement in gut-related metabolites, including LPS and SCFAs. Importantly, IF resulted in gut bacteria alteration and activated microbial metabolic pathways that were strongly associated with improvements in cardiovascular biomarkers in the study. Our trial shows that short-term 2-day IF improves some aspects of cardiometabolic health and gut microbiota homeostasis, thus providing novel mechanistic insight into the metabolic response to calorie restriction.

In consistence with previous findings that 3 months of 2-day IF results in a 5.0% to 10.0% reduction in body weight in overweight individuals (30), in our study, 8 weeks of 2-day IF not only caused similar effects on body weight with a 4.0% reduction but also led to a significant decrease in fat mass and visceral fat. The results from recent studies on IF revealed that IF/ADF regimens influenced visceral fat and body fat more compared to weight (31). The limited weight loss in our study may be explained by the fact that food was chosen by the participants and not provided by investigators. Another factor that appears to affect the amount of weight loss is the number of fasting days per week. Although significant improvements were observed in serum TGs, insulin, and HOMA-IR within the IF group, we did not find a significant effect of 8-week 2-day IF on dyslipidemia and glucose metabolism compared to the CD group. The unexpected increase in LDL-c in the CD group was possibly attributed to the moderate but not significant increase in total energy intake. Previous studies have shown that improved lipid profiles and insulin sensitivity mainly resulted from ADF for at least 6 to 12 weeks. A study of 6-month IF was found with a reduction in LDL-c, insulin, and HOMA-IR (30). The less pronounced changes in glucose and lipids in the present study appear to be attributed to the short intervention period and mild restriction of calorie intake.

The central role of a chronic low-grade inflammatory state, prooxidative status, and adipokine dysregulation in metabolic disorders has been demonstrated (32). In our study, an IF intervention resulted in systemic anti-inflammatory effects, as evidenced by significantly decreased circulating levels of sCD40L, which is known to play an essential role in platelet activation and atherogenesis (33). In this study, IF significantly reduced circulating levels of leptin and elevated adiponectin levels, which is consistent with previous studies showing that energy restriction regulates adipokine homeostasis (23). However, we failed to observe a significant reduction in IL-6 or TNF-α. These findings might be explained by the study by Klempel and Varady, in which leptin was more sensitive than other inflammatory cytokines to diet-induced loss of visceral fat in humans (34). The impact of IF on cytokines must be investigated in trials with larger sample sizes and longer durations. On the other hand, the significantly decreased MDA in our study supports the fact that IF alleviates oxidative damage in MS (35). More important, IF reduced ADMA and increased total nitrate, thus improving nitric oxide production and bioavailability in cardiovascular health. Combined with recent evidence, our study supports that short-term of constant and moderate calorie restriction exerts a beneficial impact on endothelial function in obese participants (20). Accumulating evidence has revealed that gut microbiota plays an essential role in human health and cardiometabolic diseases (23). Alterations in the composition and function of gut microbiota have been identified as a contributing factor in the pathogenesis of cardiovascular diseases (20, 33). Studies on ADF-treated db/db mice found that ADF restructured gut microbiota and microbial metabolites. The removal of microbiota partly abolished the protective effects of IF on insulin resistance and cognitive function (36). ADF-mediated changes in the gut microbiota produced beneficial metabolites and prevented the development of diabetic retinopathy in db/db mice (18). To date, few reports have addressed the effects of short-term 2-day IF on the gut microbiota and their metabolism in humans. 16S ribosomal RNA (rRNA) profiling based on high-throughput next-generation technology sequencing provides taxonomic resolution at the species and strain levels and has the potential to estimate microbial functional capacity (37). Amplicon sequencing methods targeting the 16S rRNA gene are highly prevalent in the field of microbiota research, especially in comparing populations under different exposure or health states (38, 39). In the present study, we therefore used 16S rRNA sequencing to assess the alteration of gut microbiota and functional pathways. Although the average species diversity of the gut microbiota was not influenced, IF induced favorable alterations in the composition of the gut microbiota and increased the relative abundances of Rumonococcaceae, Roseburia, and Clostridium at the genus and family levels. Significant alterations in the gut microbiota were found at the species level. We found that the range of 23 gut microbes was significantly influenced at the species level after 8-week IF. Among them, 10 species belong to Firmicutes, and 7 belong to Proteobacteria. These data are consistent with the present results that IF mainly induces a shift of the gut microbiota in Firmicutes (22). Here, we reveal a close relationship between these species and the metabolic impact induced by IF for the first time. Spearman correlation of the gut microbiota and cardiometabolic markers exhibited a close association with glucose metabolism and lipid profiles, together with inflammatory cytokines. Among the links, Acidobacteria bacterium, Eubacterium sp 1_3, and Roseburia faecis exhibited the most notable and general associations with improvements in cardiometabolic markers. Acidobacteria bacterium is a key taxon of valeric acid production. Valeric acid is a straight-chain alkyl carboxylic acid produced by gut microbiota and is reported to be a valuable predictor of diabetes (40). Increased valeric acid and enriched Acidobacteria bacteria are closely associated with impaired glucose homeostasis and cardiometabolic dysfunction (40, 41). In the present study, IF contributed to a decreased relative abundance of Acidobacteria bacterium, which was closely associated with serum glucose and HOMA-IR. This result indicates the potential role of Acidobacteria bacteria in the metabolic changes induced by IF. The genus Roseburia constitutes a group of dominant butyrate-producing Firmicutes. Roseburia faecis plays an important role in the control of gut inflammatory processes, amelioration atherosclerosis, and maturation of the immune system, primarily through the production of SCFAs. Humans with metabolic and inflammatory diseases frequently harbor lower levels of Roseburia in their gut (42). Our results are consistent with the findings that an increased abundance of Roseburia is negatively associated with metabolic disorders and diabetes. Eubacterium sp 1_3 was another key species found in our study. Eubacterium species are key flora within the intestinal trophic chain and have the potential to affect metabolic balance as well as the gut microbiota/host homeostasis by the formation of different SCFAs (43). Patients with pulmonary arterial hypertension have lower levels of Eubacterium than healthy individuals (44). These results further confirm that IF enriches specific gut microbial species involved in metabolic dysfunction. Along with those studies, the present study bolsters the notion and further shows that a shift in gut microbiota attributed to short-term 2-day IF is relevant to the elimination of metabolic disorders in MS.

The gut microbiota interacts continuously with nutrients to digest and shape intestinal immune responses during the development of cardiovascular disease through metabolic activities (45). Imbalances in the gut microbiota can trigger several immune disorders and facilitate inflammatory diseases (46). We observed more active metabolic pathways involving carbohydrate metabolism and glycolysis after IF in the trial. Microbial carbohydrate degradation is linked to the production of SCFAs, which are the bacterial metabolites derived from indigestible carbohydrates. A previous study reported the critical role of SCFAs in maintaining intestinal homeostasis and modulating immunity in the host (43, 47). Taking the results regarding the enriched relative abundance of SCFA-produced species and the enhanced carbohydrate fermentation in the gut microbiota together, our findings indicate that IF exerts a beneficial effect in modulating the gut microbiome and reversing metabolic disorders. In the present study, IF was also shown to significantly reduce plasma LPS, which has been recognized as a putative trigger for the systemic inflammatory response and atherosclerotic cardiovascular disease (46, 48). Obese and diabetic mice displayed enhanced intestinal permeability and increased LPS, which participate in the occurrence of insulin resistance (49). The lowered level of LPS following IF results in improvements in gut barrier function and alleviation of systemic inflammation in the host. Thus, our results further support the notion that IF might change the circulating levels of microbiota-derived metabolites, which may contribute to the regulation of the host immune response.

Several limitations of this study should be acknowledged. First, the relatively small sample size might restrict the wide applicability of our findings, which needs further validation in a larger cohort. Nevertheless, the present study was well controlled and has sufficient power to demonstrate the preventive effects of 2-day IF in individuals with MS, which is more clinically relevant. Second, similar to other exploratory clinical studies, the present study remains largely observational and does not infer the molecular mechanism underlying the impact of IF. However, the 16S rRNA gene–based association study allowed us to gain novel insight into the role of gut microbiota in the improvement of cardiometabolic risk factors in response to IF intervention. Animal studies are needed to further explore the molecular mechanism by which IF regimens affect the gut microbiota in the future. Finally, our trial was designed to evaluate the physiologic effects of IF on individuals with MS during a specific, short time period. Future studies are needed to validate the optimal extent of calorie restriction and the effectiveness of IF in a long-term clinical study.

In summary, our study demonstrates that short-term 2-day IF improves levels of adipokines, prevents lipid peroxidation, and improves vascular endothelial function among populations with MS, and the effects appear to be associated with alternations in gut microbiota composition, microbial-related metabolites, and activated metabolic pathways in the gut microbiome. Our trial confirms that 2-day IF is an effective strategy with a limited extent of energy restriction and few side effects for preventing cardiometabolic diseases in patients with MS.

Abbreviations

Abbreviations
 
• ADF

  alternate-day fasting

 
• ADMA

  asymmetrical dimethylarginine

 
• ANCOVA

  analysis of covariance

 
• ApoA1

  apolipoprotein A1

 
• ApoB

  apolipoprotein B

 
• CD

  control diet

 
• FDR

  false discovery rate

 
• HDL-c

  high-density lipoprotein cholesterol

 
• HOMA-IR

  homeostatic model assessment of insulin resistance

 
• IF

  intermittent fasting

 
• IL-6

  interleukin-6

 
• LDL-c

  low-density lipoprotein cholesterol

 
• LPS

  lipopolysaccharide

 
• MDA

  malondialdehyde

 
• MET

  metabolic equivalent

 
• MS

  metabolic syndrome

 
• OTU

  operational taxonomic unit

 
• rRNA

  ribosomal RNA

 
• sCD40L

  soluble CD40 ligand

 
• SCFAs

  short-chain fatty acids

 
• TC

  total cholesterol

 
• TGs

  triglycerides

 
• TMAO

  trimethylamine N-oxide

 
• TNFα

  tumor necrosis factor-α.

Acknowledgments

We would like to gratefully acknowledge the support of the study participants, investigators, doctors, and cooperation partners in Qishi and Chashan Community Health Service Center, Dongguan, China during the process of participant recruitment and intervention period.

Financial Support: This work was supported by the National Key Research and Development Program of China (No. 2017YFC0907100 and 2017YFC0907101), the Natural Science Foundation of China-Guangdong Joint Fund (No. U1801281), and Science and Technology Innovation Talents.

Clinical Trial Information: Clinical trial registration No. NCT03608800 (registered July 22, 2018).

Additional Information

Disclosure Summary: The authors have nothing to disclose.

Data Availability

The data sets generated during and/or analyzed during the present study are not publicly available but are available from the corresponding author on reasonable request.