A Systematic Review and Meta-Analysis of the Impacts of Time-Restricted Eating on Metabolic Homeostasis.
Dan QiXiaolu NieJianjun ZhangPublished in: Angiology (2024)
This meta-analysis investigated the effect of time-restricted eating (TRE) as an economical lifestyle intervention for the prevention of metabolic syndrome and improving the related metabolic variables. The Cochrane library, MEDLINE, EMBASE, clinical trials, and other databases were searched for randomized controlled trials (RCTs). We included 22 RCTs (1004 participants, aged 18-75 years, including healthy subjects, prediabetes and overweight patients) designed to evaluate the effect of TRE on metabolic parameters. Body mass index (BMI) (-0.56 kg/m 2 , 95% CI: -1.00, -0.13, P < .01), fasting blood glucose (-1.74 mmol/L, 95% CI: -3.34, -0.14, P < .01), and body weight (-0.48 kg, 95% CI: -0.74, -0.22, P < .01) in the TRE intervention group were decreased to varying degrees compared with controls. In contrast, high-density lipoprotein cholesterol (HDL-C) levels were significantly increased in the TRE group compared with the control group ( P < .01). The change in waist circumference, blood pressure, triglycerides, low-density lipoprotein cholesterol (LDL-C), and total cholesterol did not vary markedly across the groups. In conclusion, this meta-analysis found a significant reduction in BMI, weight, and fasting glucose, as well as a rise in HDL-C level with TRE compared with control. TRE could be used as an adjuvant treatment for metabolic dysfunctions.
Keyphrases
- body mass index
- blood glucose
- body weight
- physical activity
- weight gain
- systematic review
- weight loss
- blood pressure
- metabolic syndrome
- randomized controlled trial
- glycemic control
- clinical trial
- meta analyses
- insulin resistance
- early stage
- end stage renal disease
- magnetic resonance
- ejection fraction
- cardiovascular disease
- newly diagnosed
- type diabetes
- computed tomography
- machine learning
- adipose tissue
- combination therapy
- atomic force microscopy
- high resolution
- artificial intelligence
- big data
- cardiovascular risk factors
- phase ii