Scope
Little is known about the changes that a very‐low‐calorie ketogenic diet (VLCKD) produces in gut microbiota or the effect of synbiotics during the diet. The aim of this study is to evaluate ...changes in gut microbiota produced by a VLCKD and synbiotic supplementation.
Methods and results
A randomized, single‐blind, parallel‐design trial is conducted in 33 obese patients who follow a weight‐loss program (PnK‐Method) that include a VLCKD followed by a low‐calorie diet (LCD). Subjects are randomly allocated to three groups: one supplemented with synbiotics, a second group supplemented with a placebo during the VLCKD and synbiotics during the LCD phase, and a control group given a placebo.
Although symbiotic administration do not produce an effect on microbial diversity, an increase in short‐chain fatty aciding producing bacteria and anti‐inflammatory mediator signals such as Odoribacter and Lachnospira is shown. The administration of Bifidobacterium animalis subsp. lactis and prebiotics fiber during the LCD is significantly associated with the percentage of weight loss and change in glucose, C‐reactive protein and lipopolysaccharide‐binding protein.
Conclusions
VLCKD produces important changes in gut microbiota. The administration of synbiotics during VLCKD can improve weight loss through the amelioration of inflammation, which may be mediated by the gut microbiota.
Gut microbiota changes produced by a very‐low‐calorie ketogenic diet in obese patients are shown. Although the administration of synbiotics during the intervention does not have an effect on microbial diversity, they can contribute to the weight loss through the amelioration of the inflammation. The inflammation improvement can be mediated through the gut microbiota.
Background: Identifying those parameters that could potentially predict the deterioration of metabolically healthy phenotype is a matter of debate. In this field, epigenetics, in particular DNA ...methylation deserves special attention. Results: The aim of the present study was to analyze the long-term evolution of methylation patterns in a subset of metabolically healthy subjects in order to search for epigenetic markers that could predict the progression to an unhealthy state. Twenty-six CpG sites were significantly differentially methylated, both at baseline and 11-year follow-up. These sites were related to 19 genes or pseudogenes; a more in-depth analysis of the methylation sites of these genes showed that CYP2E1 had 50% of the collected CpG sites differently methylated between stable metabolically healthy obesity (MHO) and unstable MHO, followed by HLA-DRB1 (33%), ZBTB45 (16%), HOOK3 (14%), PLCZ1 (14%), SLC1A1 (12%), MUC2 (12%), ZFPM2 (12.5%) and HLA-DQB2 (8%). Pathway analysis of the selected 26 CpG sites showed enrichment in pathways linked to th1 and th2 activation, antigen presentation, allograft rejection signals and metabolic processes. Higher methylation levels in the cg20707527 (ZFPM2) could have a protective effect against the progression to unstable MHO (OR: 0.21, 95%CI (0.067–0.667), p < 0.0001), whilst higher methylation levels in cg11445109 (CYP2E1) would increase the progression to MUO; OR: 2.72, 95%CI (1.094–6.796), p < 0.0014; respectively). Conclusions: DNA methylation status is associated with the stability/worsening of MHO phenotype. Two potential biomarkers of the transition to an unhealthy state were identified and deserve further investigation (cg20707527 and cg11445109). Moreover, the described differences in methylation could alter immune system-related pathways, highlighting these pathways as therapeutic targets to prevent metabolic deterioration in MHO patients.
Background:
Prospective longitudinal studies evaluating the relevance of “Metabolically Healthy but Obese” (MHO) phenotype at risk for type 2 diabetes mellitus (T2D) and cardiovascular diseases are ...few and results are contradictory.
Methods:
As a representative of the general population, 1051 individuals were evaluated in 1997–1998 and re-evaluated after 6 years and 11 years. Subjects without known T2D were given an oral glucose tolerance test. Anthropometric and biochemical variables were measured. Four sets of criteria were considered to define MHO subjects besides body mass index ≥30 kg/m2: A: Homeostatic Model of Assessment-Insulin Resistance Index (HOMA-IR) <90th percentile; B: HOMA-IR <90th percentile, high-density lipoprotein cholesterol >40 mg/dL in men and high-density lipoprotein cholesterol >50 mg/dL in women, triglycerides <150 mg/dL, fasting glucose <110 mg/dL, and blood pressure ≤140/90 mm Hg; C: HOMA-IR <90th percentile, triglycerides <150 mg/dL, fasting glucose <110 mg/dL, and blood pressure ≤140/90 mm Hg; D: HOMA-IR <90th percentile, triglycerides <150 mg/dL, and fasting glucose <110 mg/dL. Subjects with T2D at baseline were excluded from the calculations of incidence of T2D.
Results:
The baseline prevalence of MHO phenotype varied between 3.0% and 16.9%, depending on the set of criteria chosen. Metabolically nonhealthy obese subjects were at highest risk for becoming diabetic after 11 years of follow-up (odds ratio = 8.20; 95% confidence interval = 2.72–24.72; P < .0001). In MHO subjects the risk for becoming diabetic was lower than in metabolically nonhealthy obese subjects, but this risk remained significant (odds ratio = 3.13; 95% confidence interval = 1.07–9.17; P = .02). In subjects who lost weight during the study, the association between MHO phenotype and T2D incidence disappeared, even after adjusting for HOMA-IR.
Conclusions:
The results suggest that MHO is a dynamic concept that should be taken into account over time. As a clinical entity, it may be questionable.
Bariatric surgery induces changes in gut microbiota that have been suggested to contribute to weight loss and metabolic improvement. However, whether preoperative gut microbiota composition could ...predict response to bariatric surgery has not yet been elucidated.
Seventy-six patients who underwent sleeve gastrectomy were classified according to the percentage of excess weight loss (%EWL) 1 year after surgery in the responder group: >50%EWL (n=50) and the nonresponder group: <50%EWL (n=26). Patients were evaluated before surgery, and 3 months and 1 year after surgery. Gut microbiota composition was analyzed before surgery (n=76) and 3 months after bariatric surgery (n=40).
Diversity analysis did not show differences between groups before surgery or 3 months after surgery. Before surgery, there were differences in the abundance of members belonging to Bacteroidetes and Firmicutes phyla (nonresponder group: enriched in Bacteroidaceae, Bacteroides, Bacteroides uniformis, Alistipes finegoldii, Alistipes alistipes, Dorea formicigenerans, and Ruminococcus gnavus. Responder group: enriched in Peptostreptococcaceae, Gemmiger, Gemiger formicilis, Barnesiella, Prevotellaceae, and Prevotella; linear discriminant analysis >2; p < 0.05). Prevotella-to-Bacteroides ratio was significantly lower in the nonresponder group compared to the responder group (p = 0.048). After surgery, the responder group showed an enrichment in taxa that have been shown to have beneficial effects on host metabolism. Before surgery, PICRUSt analysis showed an enrichment in pathways involved in the biosynthesis components of the O-antigen polysaccharideunits in lipopolysaccharides in the nonresponder group.
Preoperative gut microbiota could have an impact on bariatric surgery outcomes. Prevotella-to-Bacteroides ratio could be used as a predictive tool for weight loss trajectory. Early after surgery, patients who experienced successful weight loss showed an enrichment in taxa related to beneficial effects on host metabolism.
Little is known about the potential role of epigenetic marks as predictors of the resolution of obesity-related comorbidities after bariatric surgery. In this study, 20 patients were classified ...according to the metabolic improvement observed 6 months after sleeve gastrectomy, based on the diagnosis of metabolic syndrome, into responders if metabolic syndrome reversed after bariatric surgery (n = 10) and non-responders if they had metabolic syndrome bariatric surgery (n = 10). Blood DNA methylation was analyzed at both study points using the Infinium Methylation EPIC Bead Chip array-based platform. Twenty-six CpG sites and their annotated genes, which were previously described to be associated with metabolic status, were evaluated. Cg11445109 and cg19469447 (annotated to Cytochrome P450 2E1 (CYP2E1) gene) were significantly more hypomethylated in the responder group than in the non-responder group at both study points, whilst cg25828445 (annotated to Nucleolar Protein Interacting With The FHA Domain Of MKI67 Pseudogene 3 (NIFKP3) gene) showed to be significantly more hypermethylated in the non-responder group compared to the responder group at both study points. The analysis of the methylation sites annotated to the associated genes showed that CYP2E1 had 40% of the differentially methylated CpG sites, followed by Major Histocompatibility Complex, Class II, DR Beta 1 (HLA-DRB1) (33.33%) and Zinc Finger Protein, FOG Family Member 2 (ZFPM2) (26.83%). Cg11445109, cg19469447 and cg25828445 could have a role in the prediction of metabolic status and potential value as biomarkers of response to bariatric surgery.
Gestational diabetes mellitus (GDM) increases the risk of developing metabolic disorders in both pregnant women and their offspring. Factors such as nutrition or the intrauterine environment may play ...an important role, through epigenetic mechanisms, in the development of GDM. The aim of this work is to identify epigenetic marks involved in the mechanisms or pathways related to gestational diabetes. A total of 32 pregnant women were selected, 16 of them with GDM and 16 non-GDM. DNA methylation pattern was obtained from Illumina Methylation Epic BeadChip, from peripheral blood samples at the diagnostic visit (26-28 weeks). Differential methylated positions (DMPs) were extracted using ChAMP and limma package in R 2.9.10, with a threshold of FDR <0.05, deltabeta >|5|% and B >0. A total of 1.141 DMPs were found, and 714 were annotated in genes. A functional analysis was performed, and we found 23 genes significantly related to carbohydrate metabolism. Finally, a total of 27 DMPs were correlated with biochemical variables such as glucose levels at different points of oral glucose tolerance test, fasting glucose, cholesterol, HOMAIR and HbA1c, at different visits during pregnancy and postpartum. Our results show that there is a differentiated methylation pattern between GDM and non-GDM. Furthermore, the genes annotated to the DMPs could be implicated in the development of GDM as well as in alterations in related metabolic variables.
Environmental temperature has been described to affect plasma glucose levels after oral glucose tolerance testing (OGTT).
We evaluated the relationship between seasons and environmental temperature ...and gestational diabetes mellitus (GDM) diagnosis and treatment.
We analyzed data from 2374 women retrospectively. GDM was diagnosed in 473 patients by a 100-g OGTT. OGTT results and needing of insulin therapy were evaluated in relation to seasons and environmental temperature (mean temperature and temperature change) the day of the OGTT and the preceding 14 and 28 days.
We found significant seasonal differences in the percentage of GDM: 24.4% in summer vs. 15.6% in autumn (p < 0.01). The odds ratio (OR) for being diagnosed with GDM was 1.78 in summer relative to autumn, after controlling for age. A higher mean temperature the day of the OGTT and the preceding 14 and 28 days increased the risk of being diagnosed with GDM the months in which temperature was rising (March–August) but not the months in which temperature was decreasing (September–February). We observed a negative correlation between temperature and fasting glucose and a positive correlation with post-load glucose. Neither the season nor the environmental temperature affected the risk of requiring insulin therapy.
There is a higher prevalence of GDM diagnosis at warmer seasons and at rising temperatures the 2–4 weeks prior to the OGTT. The impact of temperature is different between fasting and post-load glucose.
Display omitted
•Higher prevalence of gestational diabetes mellitus in warmer seasons.•Preceding 14 and 28 days mean temperature as independent predictors of being diagnosed with gestational diabetes mellitus.•Environmental temperature is inversely correlated with fasting glucose and directly with post-load glucose.•Environmental temperature is not associated with the risk of requiring insulin therapy.
Background/aim
Alterations in gut microbiota are associated with the pathogenesis of metabolic diseases, including metabolic-associated fatty liver disease (MAFLD). The aim of this study was to ...evaluate gut microbiota composition and functionality in patients with morbid obesity with different degrees of MAFLD, as assessed by biopsy.
Subjects/methods
110 patients with morbid obesity were evaluated by biopsy obtained during bariatric surgery for MAFLD. Stool samples were collected prior to surgery for microbiota analysis.
Results
Gut microbiota from patients with steatosis and non-alcoholic steatohepatitis (NASH) were characterized by an enrichment in
Enterobacteriaceae
(an ethanol-producing bacteria),
Acidaminococcus
and
Megasphaera
and the depletion of
Eggerthellaceae
and
Ruminococcaceae
(SCFA-producing bacteria). MAFLD was also associated with enrichment of pathways related to proteinogenic amino acid degradation, succinate production, menaquinol-7 (K2-vitamin) biosynthesis, and saccharolytic and proteolytic fermentation. Basic histological hepatic alterations (steatosis, necroinflammatory activity, or fibrosis) were associated with specific changes in microbiota patterns. Overall, the core microbiome related to basic histological alterations in MAFLD showed an increase in
Enterobacteriaceae
and a decrease in
Ruminococcaceae
. Specifically,
Escherichia coli
was associated with steatosis and necroinflammatory activity, whilst
Escherichia-shigella
was associated with fibrosis and necroinflammatory activity.
Conclusions
We established a link between gut microbiota alterations and histological injury in liver diagnosis using biopsy. Harmful products such as ethanol or succinate may be involved in the pathogenesis and progression of MAFLD. Thus, these alterations in gut microbiota patterns and their possible metabolic pathways could add information to the classical predictors of MAFLD severity and suggest novel metabolic targets.
Objective
To analyze the reference range of thyroid‐stimulating hormone (TSH) in different BMI categories and its impact on the classification of hypothyroidism.
Methods
The study included 3,928 ...individuals free of thyroid disease (without previous thyroid disease, no interfering medications, TSH <10 µUI/mL and thyroid peroxidase antibodies TPO Abs <50 IU/mL) who participated in a national, cross‐sectional, population‐based study and were representative of the adult population of Spain. Data gathered included clinical and demographic characteristics, physical examination, and blood and urine sampling. TSH, free thyroxine, free triiodothyronine, and TPO Ab were analyzed by electrochemiluminescence (E170, Roche Diagnostics, Basel, Switzerland).
Results
The reference range (p2.5‐97.5) for TSH was estimated as 0.6 to 4.8 µUI/mL in the underweight category (BMI<20 kg/m2), 0.6 to 5.5 µUI/mL in the normal‐weight category (BMI 20‐24.9 kg/m2), 0.6 to 5.5 µUI/mL in the overweight category (BMI 25‐29.9 kg/m2), 0.5 to 5.9 µUI/mL in the obesity category (BMI 30‐39.9 kg/m2), and 0.7 to 7.5 µUI/mL in the morbid obesity category (BMI ≥40). By using the reference criteria for the normal‐weight population, the prevalence of high TSH levels increased threefold in the morbid obesity category (P < 0.01).
Conclusions
Persons with morbid obesity might be inappropriately classified if the standard ranges of normality of TSH for the normal‐weight population are applied to them.
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