The Effect of Microbiome Therapies on Waist Circumference, a Measure of Central Obesity, in Patients with Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

BACKGROUND: Microbiome therapies (probiotic, prebiotic, and synbiotics) have been proposed as adjuvants in the control of central obesity; however, their results for patients with type 2 diabetes (T2D) remain inconclusive. OBJECTIVE: The aim of this systematic review and meta-analysis was to evaluate the effect of microbiome therapies on central obesity as measured by waist circumference (WC), and to evaluate the effect of microbiome therapies for glycemic parameters (fasting glucose [FPG], fasting insulin [FPI], hemoglobin A1c [HbA1c], and insulin resistance [HOMA1-IR]) in patients with T2D. METHODS: SCOPUS, Pubmed, EBSCO, and LILACS databases were searched for studies that investigated the effect of microbiome therapies on WC up to June 1, 2022. Heterogeneity was determined using Cochran's Q test and quantified using the inconsistency index. The random effects model was used to calculate the pooled difference in means (DM) and 95% confidence intervals (95%CI). Egger's test and Beggs-Muzamar's test were used to assess publication bias. RESULTS: Fifteen reports were included (443 treated and 387 controls). Overall, a significant decrease in WC was found (DM = -0.97 cm; 95% confidence interval [95%CI] = -1.74 to -0.20; P = 0.014); however, when stratified by type of microbiome therapy, only probiotics significantly decreased WC (DM = -0.62 cm; 95%CI = -1.00 to -0.24; P = 0.002). No effect was observed for prebiotics and synbiotics. With respect to glycemic parameters, HbA1c, FPG, and HOMA1-IR significantly decrease with microbiome therapies (P ≤ 0.001). When stratified by the type of therapy, for probiotic treatments, HbA1c, FPG, and HOMA1-IR scores decrease (P < 0.001). For prebiotic treatments, HbA1c and FPG (P ≤ 0.001) levels decrease, whereas FPI increased (P = 0.012). Synbiotic treatments were only associated with an increase in FPI (P = 0.031). CONCLUSION: Findings indicate that using probiotics alone improved WC in patients with T2D. Both probiotics and prebiotics decreased HbA1c and FPG; however, prebiotics and synbiotics resulted in an increase in FPI. The formulation of the therapy (single vs multi) had no difference on the effect.

Probiotics' effect on visceral and subcutaneous adipose tissue: a systematic review of randomized controlled trials.

Probiotics are shown to alter the microbiota, leading to a favorable environment, in which weight loss and metabolic parameters are improve. However, the results on probiotics' effect on specific types of central adipose tissues, mainly visceral (VAT) and subcutaneous adipose tissue (SAT), are conflicting. Therefore, we conducted a systematic review, aimed to evaluate the effects of probiotics on VAT and SAT. PubMed, SCOPUS, EBSCO, and LILACS databases were searched for studies that investigated the effect of probiotics on VAT and SAT. Fixed effects were used to calculate the pooled difference in means (DM) and 95% confidence intervals (95%CI). Fourteen publications met the inclusion criteria, which consisted of 1523 participants. For VAT, overall, there was a significant decrease (DM = -3.63 cm2, 95% CI: -5.08 to -2.17, p < 0.001). When stratified by type of probiotic, single Bifidobacterium (DM = -4.49 cm2, 95% CI:-7.37 to -1.61, p = 0.002) and single Lactobacillus probiotics (DM = -3.84 cm2, 95% CI:-5.74 to -1.93, p < 0.001) resulted in significant reductions. Mixed probiotics had no effect. For SAT, overall, there was a significant decrease (DM = -2.91 cm2, 95% CI:-4.82 to -1.01, p = 0.003), and when stratified by type of probiotic, single Lactobacillus (DM = -3.39 cm2, 95% CI:-5.90 to -0.88, p = 0.008) and mixed probiotics (DM = -5.97 cm2, 95% CI:-10.32 to -1.62, p = 0.007) resulted in a significant decrease. Single Bifidobacterium probiotics had no effect. Using meta-regression, no association was observed between the total daily probiotic dose and VAT or SAT reduction. This study shows that probiotics have a beneficial effect on central adiposity. Single Lactobacillus-based probiotics reduced VAT and SAT, whereas Bifidobacterium-based probiotics reduce VAT.

Metabolic age correlates better than chronological age with waist-to-height ratio, a cardiovascular risk index / La edad metabólica se correlaciona mejor que la edad cronológica con la relación cintura-altura, un índice de riesgo cardiovascular

Objectives: Chronological age confers an increased risk for cardiovascular disease; however, chronological age does not reflect the subject's current health status. Therefore, we assessed whether Metabolic age (Met-age), based on free fat mass, is a predictor of cardiovascular risk (CVR).MethodsSubjects attending either IMSS UMF-2 or CUSC-1 were asked to participate. CVR was assessed using the waist-to-height ratio (WHtR), whereas Met-age was determined using the TANITA bio-analyser (model: BC-545F Fitscan). The strengthen of association was determined by calculating Pearson's r and predictability was determined by the area-under-a-receiver-operating characteristic curve (AUC).Results284 subjects participated in this study, of which 61.6% had increased CVR. As expected, the chronological age was significantly higher in the CVR(+) group than the CVR(−) group (47.3±14.4 v. 35.2±12.7, respectively, p<.001) as well as Met-age (59.3±15.5 v. 34.3±14.3, respectively, p<.001). There was a strong association between WHtR and Met-age (r=.720, p<.001) and a moderate association for chronological age (r=.407 p<.001); however, the correlation between WHtR and Met-age was significantly better than chronological age (Z=−5.91, p<.01). Met-age was a good predictor of CVR (AUC=.88, 95%CI: .83–.92, p<.001), whereas chronological age was a fair predictor (AUC=.72, 95%CI: .66–.78, p<.001). However, Met-age showed a higher discriminatory capacity for CVR than chronological age (z=−4.597, p<.001).ConclusionsHere, we determined that Met-age correlated with a CVR index, WHtR, and was able to predict subjects with increased CVR better than chronological age. (AU)

Objetivos: La edad cronológica confiere un mayor riesgo a la enfermedad cardiovascular; sin embargo, la edad cronológica no refleja el estado de salud actual del individuo. Por lo tanto, evaluamos si la edad metabólica (Met-age), basada en masa de grasa libre, es un factor predictivo del riesgo cardiovascular (RCV).MétodosSe solicitó su participación a individuos que asistían a IMSS UMF-2 o CUSC-1. Se evaluó el RCV utilizando el índice cintura-altura (ICA), mientras que Met-age se determinó utilizando el bioanalizador TANITA (modelo: Bc-545F Fitscan). La fuerza de asociación se determinó calculando la r de Pearson, y la predictibilidad se determinó mediante el índice de área bajo la curva (AUC).ResultadosDoscientos ochenta y cuatro sujetos participaron en este estudio, de los cuales el 61,6% reflejó un aumento del RCV. Como se esperaba, la edad cronológica fue significativamente mayor en el grupo del RCV+ que en el grupo del RCV− (47,3±14,4 vs. 35,2±12,7, respectivamente; p<0,001), así como en Met-age (59,3±15,5 vs. 34,3±14,3, respectivamente; p <0,001). Se produjo una fuerte asociación entre el ICA y la Met-age (r=0,720; p<0,001) y una asociación moderada con la edad cronológica (r=0,407; p<0,001); sin embargo, la correlación entre el ICA y la Met-age fue significativamente mejor que la edad cronológica (Z=−5,91; p<0,01). La Met-age fue un buen predictor del RCV (AUC=0,88, IC 95%: 0,83-0,92; p<0,001), mientras que la edad cronológica fue un factor predictivo moderado (AUC=0,72; IC 95%: 0,66-0,78; p<0,001). Sin embargo, la Met-age mostró una mayor capacidad discriminatoria para identificar el RCV que la edad cronológica (z=−4,597; p<0,001).ConclusionesEn este estudio determinamos que la Met-age se correlacionó con el índice ICA del RCV, y fue capaz de predecir sujetos con RCV mejor que la edad cronológica. (AU)

Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome.

INTRODUCTION: Every 10 years, an adult's basal metabolic rate (BMR), independent of their BMI, decreases 1-2% due to skeletal muscle loss, thus decreasing an adult's energy requirement and promoting obesity. Increased obesity augments the risk of developing Metabolic Syndrome (MetS); however, an adult's healthy lifestyle, which increases BMR, can mitigate MetS development. To compare different BMRs for certain ages, Metabolic age (Met-age) was developed. AIM: To assess the association between Met-age and MetS and to determine if Met-age is an indicator of high-risk individuals for MetS. METHODS: Four hundred thirty-five attendees at 2 clinics agreed to participate and gave signed informed consent. MetS risk was assessed by the ESF-I questionnaire. Met-age was determined using a TANITA bio-analyzer. Strengthen of association was determined by calculating Spearman's rho and predictability was evaluated by the area-under-a-receiver-operating characteristic curve (AUC). Difference-in-age (DIA) = [chronological age - Met-age]. RESULTS: There was a difference between the low-risk (n = 155) and the high-risk (n = 280) groups' Met-age (37.8±16.7 v. 62.9±17.3) and DIA (1.3±17.4 v. - 10.5±20.8, p < 0.001). There was a positive correlation between the ESF-I questionnaire and Met-age (rho = - 0.624, p < 0.001) and a negative correlation for DIA (rho = - 0.358, p < 0.001). Met-age was strongly predictive (AUC = 0.84, 95% CI 0.80-0.88), suggesting a 45.5 years cutoff (sensitivity = 83.2%, specificity = 72.3%). DIA was a good predictor (AUC = 0.68, 95% CI 0.63-0.74) with a - 11.5 years cutoff (sensitivity = 52.5%, specificity = 82.8%). CONCLUSION: Met-age highly associated with and is an indicator of high-risk individuals for MetS. This would suggest that increases in Met-age are associated with augmented MetS severity, independent of the individual's chronological age.

Metabolic age correlates better than chronological age with waist-to-height ratio, a cardiovascular risk index.

OBJECTIVES: Chronological age confers an increased risk for cardiovascular disease; however, chronological age does not reflect the subject's current health status. Therefore, we assessed whether Metabolic age (Met-age), based on free fat mass, is a predictor of cardiovascular risk (CVR). METHODS: Subjects attending either IMSS UMF-2 or CUSC-1 were asked to participate. CVR was assessed using the waist-to-height ratio (WHtR), whereas Met-age was determined using the TANITA bio-analyser (model: BC-545F Fitscan). The strengthen of association was determined by calculating Pearson's r and predictability was determined by the area-under-a-receiver-operating characteristic curve (AUC). RESULTS: 284 subjects participated in this study, of which 61.6% had increased CVR. As expected, the chronological age was significantly higher in the CVR(+) group than the CVR(-) group (47.3±14.4 v. 35.2±12.7, respectively, p<.001) as well as Met-age (59.3±15.5 v. 34.3±14.3, respectively, p<.001). There was a strong association between WHtR and Met-age (r=.720, p<.001) and a moderate association for chronological age (r=.407 p<.001); however, the correlation between WHtR and Met-age was significantly better than chronological age (Z=-5.91, p<.01). Met-age was a good predictor of CVR (AUC=.88, 95%CI: .83-.92, p<.001), whereas chronological age was a fair predictor (AUC=.72, 95%CI: .66-.78, p<.001). However, Met-age showed a higher discriminatory capacity for CVR than chronological age (z=-4.597, p<.001). CONCLUSIONS: Here, we determined that Met-age correlated with a CVR index, WHtR, and was able to predict subjects with increased CVR better than chronological age.