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Purpose: When examining the underlying processes of obesity, evaluation of gut flora and energy homeostasis can be crucial since disruption of the normal gut microbiota community and energy imbalances are significant factors in the development of obesity. Therefore, this study aimed to compare the relative abundance of important obesity modulator gut microbiota (including Firmicutes, Bacteroidetes, Bifidobactrium spp. , Lactobacillus spp. , Bacteroides fragilis , Faecalibacterium prausnitzii , Akkermansia muciniphila , and Escherichia coli ) in fecal samples of normometabilic and hypometabolic overweight/obese individuals., Methods: This matched case-control study conducted on 36 healthy women aged 18-50 years old. An indirect calorimeter and impedance body analyzer were used to assess resting metabolic rate (RMR) and body composition, respectively. Dietary intake and physical activity were assessed using questionnaires. To determine the abundance of the abovementioned gut microbiota, quantitative polymerase chain reaction (qPCR) method was performed. Moreover, ELISA kits were used to assess leptin, ghrelin, and insulin hormones., Results: The results highlighted higher load of Firmicutes ( p  = 0.02), F. prausnitzii ( p  < 0.001), and B. fragilis ( p  = 0.02) in the normometabolic individuals compared to the hypometabolic ones. Besides, the positive correlation between the abundance of Firmicutes (β = 7.76 × 10 -1 , p  = 0.01), F. prausnitzii (β = 1.29 × 10 -5 , p  = 0.01), and B. fragilis (β = 4.13 × 10 -6 , p  = 0.04) with the RMR have been shown. Whereas the abundance of Bacteroidetes, A. muciniphila , Lactobacillus spp. , Bifidobactrium spp. , and E. coli showed no significant difference ( p  > 0.05) and no significant correlation with the RMR except Lactobacillus spp. (β = 1.73 × 10 -4 , p  = 0.01)., Conclusion: It seems that gut microbiota can be a potential target for refining host energy homeostasis and treating obesity and its consequences., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s), under exclusive licence to Tehran University of Medical Sciences 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Background: Nowadays, use of continuous metabolic syndrome (cMetS) score has been suggested to improve recognition of metabolic syndrome (MetS). The aim of this study was to evaluate the validity of cMetS scores for predicting MetS., Methods: We searched the electronic databases included MEDLINE/PubMed, Embase, ISI Web of Science, and Scopus from 1 January 1980 to 30 September 2020. Observational studies on participants with different cMetS scores were included in this meta-analysis. The sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR) and diagnostic odds ratio (DOR) with 95% CI were calculated., Results: Ten studies involving a total of 25,073 participants were included. All studies had cross-sectional design. The pooled sensitivity and specificity of cMetS scores for predicting MetS were 0.90 (95% CI: 0.83 to 0.95) and 0.86 (95% CI: 0.83 to 0.89), respectively. Moreover, cMetS scores had the pooled LR+ of 6.5 (95% CI: 5.0 to 8.6), and a pooled (LR-) of 0.11 (95% CI: 0.063 to 0.21). The pooled DOR of cMetS scores to predict MetS were 57 (95% CI: 26 to 127)., Conclusions: The high sensitivity and specificity of cMetS scores indicates that it has a high accuracy to predict the risk of MetS. Furthermore, the cMetS scores has a good ability to rule out healthy people., Study Registration: This study was registered as PROSPERO CRD42020157273., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© Springer Nature Switzerland AG 2021.)