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Outlining the 'brain-belly' connection that describes how sticky fat cells send mixed messages to the brain - and cause us to get fat as a result - Larry McCleary offers a unique approach that enables us to get in touch with the signals our bodies generate so that we work with, not against, our innate metabolic machinery.

Background On December 12th 2019, a new coronavirus (SARS-Cov2) emerged in Wuhan, China, sparking a pandemic of acute respiratory syndrome in humans (COVID-19). On the 24th of April 2020, the number of COVID-19 deaths in the world, according to the COVID-Case Tracker by Johns Hopkins University, was 195,313, and the number of COVID-19 confirmed cases was 2,783,512. The COVID-19 pandemic represents a massive impact on human health, causing sudden lifestyle changes, through social distancing and isolation at home, with social and economic consequences. Optimizing public health during this pandemic requires not only knowledge from the medical and biological sciences, but also of all human sciences related to lifestyle, social and behavioural studies, including dietary habits and lifestyle. Methods Our study aimed to investigate the immediate impact of the COVID-19 pandemic on eating habits and lifestyle changes among the Italian population aged ≥ 12 years. The study comprised a structured questionnaire packet that inquired demographic information (age, gender, place of residence, current employment); anthropometric data (reported weight and height); dietary habits information (adherence to the Mediterranean diet, daily intake of certain foods, food frequency, and number of meals/day); lifestyle habits information (grocery shopping, habit of smoking, sleep quality and physical activity). The survey was conducted from the 5th to the 24th of April 2020. Results A total of 3533 respondents have been included in the study, aged between 12 and 86 years (76.1% females). The perception of weight gain was observed in 48.6% of the population; 3.3% of smokers decided to quit smoking; a slight increased physical activity has been reported, especially for bodyweight training, in 38.3% of respondents; the population group aged 18–30 years resulted in having a higher adherence to the Mediterranean diet when compared to the younger and the elderly population (p < 0.001; p < 0.001, respectively); 15% of respondents turned to farmers or organic, purchasing fruits and vegetables, especially in the North and Center of Italy, where BMI values were lower. Conclusions In this study, we have provided for the first time data on the Italian population lifestyle, eating habits and adherence to the Mediterranean Diet pattern during the COVID-19 lockdown. However, as the COVID-19 pandemic is ongoing, our data need to be confirmed and investigated in future more extensive population studies.

Recent studies have suggested that the intestinal microbiome plays an important role in modulating risk of several chronic diseases, including inflammatory bowel disease, obesity, type 2 diabetes, cardiovascular disease, and cancer. At the same time, it is now understood that diet plays a significant role in shaping the microbiome, with experiments showing that dietary alterations can induce large, temporary microbial shifts within 24 h. Given this association, there may be significant therapeutic utility in altering microbial composition through diet. This review systematically evaluates current data regarding the effects of several common dietary components on intestinal microbiota. We show that consumption of particular types of food produces predictable shifts in existing host bacterial genera. Furthermore, the identity of these bacteria affects host immune and metabolic parameters, with broad implications for human health. Familiarity with these associations will be of tremendous use to the practitioner as well as the patient.

Asthma is a common respiratory disease, and immune system dysregulation has direct relevance to asthma pathogenesis. Probiotics and prebiotics have immunomodulatory effects and can regulate immune responses and may attenuate allergic reactions. Therefore, in this study, we explored the role of probiotics and prebiotics in regulating acute airway inflammation and the TLR4/NF-kB pathway. Allergic asthma model of BALB/c mice was produced and treated with probiotics (LA-5, GG, and BB-12) and prebiotics (FOS and GOS). Then AHR, BALF cells count, EPO activity, IL-4, 5, 13, 17, 25, 33, as well as IFN-γ, total and OVA-specific IgE, IgG1, Cys-LT, LTB4, LTC4, and TSLP levels were measured. Also, the GTP/GOT assay was performed and gene expression of Akt, NLR3, NF-kB, PI3K, MyD88, TLR4, CCL11, CCL24, MUC5a, Eotaxin, IL-38, and IL-8 were determined. Finally, lung histopathological features were evaluated. Treatment with probiotics could control AHR, eosinophil infiltration to the BALF and reduce the levels of immunoglobulins, IL-17, GTP and also decrease mucus secretion, goblet cell hyperplasia, peribronchial and perivascular inflammation and also, EPO activity. It could reduce gene expression of TLR4 and CCL11. On the other hand, IL-38 gene expression was increased by both probiotic and prebiotic treatment. Treatment with probiotics and prebiotics could control levels of IL-4, 5, 13, 25, 33, leukotrienes, the gene expression of AKT, NLR3, NF-κB, MyD88, MUC5a. The prebiotic treatment could control peribronchial inflammation and PI3K gene expression. Both of the treatments had no significant effect on the GOT, TSLP and IL-8, eotaxin and CCL24 gene expression. Probiotics and prebiotics could induce tolerance in allegro-inflammatory reactions and alter immune responses in allergic conditions. Probiotics could also modulate cellular and humoral immune responses and prevent allergic disorders. Main findings of the study Probiotics controls AHR, eosinophil infiltration to the perivascular and BALF, levels of immunoglobulins, IL-17, GTP and also mucus secretion, goblet cell hyperplasia, EPO activity. It could reduce gene expression of TLR4 and CCL11, Probiotics and prebiotics control levels of cytokines (IL-4, 5, 13, 25, and 33), leukotrienes, the gene expression of AKT, NLR3, NF-κB, MyD88, MUC5a, peribronchial inflammation and increase IL-38 gene expression. Prebiotic controls and PI3K gene expression.

Alcoholism is a widespread and damaging behaviour of people throughout the world. Long-term alcohol consumption has resulted in alcoholic liver disease (ALD) being the leading cause of chronic liver disease. Many metabolic enzymes, including alcohol dehydrogenases such as ADH, CYP2E1, and CATacetaldehyde dehydrogenases ALDHsand nonoxidative metabolizing enzymes such as SULT, UGT, and FAEES, are involved in the metabolism of ethanol, the main component in alcoholic beverages. Ethanol consumption changes the functional or expression profiles of various regulatory factors, such as kinases, transcription factors, and microRNAs. Therefore, the underlying mechanisms of ALD are complex, involving inflammation, mitochondrial damage, endoplasmic reticulum stress, nitrification, and oxidative stress. Moreover, recent evidence has demonstrated that the gut-liver axis plays a critical role in ALD pathogenesis. For example, ethanol damages the intestinal barrier, resulting in the release of endotoxins and alterations in intestinal flora content and bile acid metabolism. However, ALD therapies show low effectiveness. Therefore, this review summarizes ethanol metabolism pathways and highly influential pathogenic mechanisms and regulatory factors involved in ALD pathology with the aim of new therapeutic insights.

ObjectiveTo systematically review the effect of oral intake of bacterial probiotics on 15 variables related to obesity, diabetes and non-alcoholic fatty liver disease.DesignSystematic review and meta-analysis.Data sourcesMedline, EMBASE and COCHRANE from 1990 to June 2018.Eligibility criteriaRandomised controlled trials (≥14 days) excluding hypercholesterolaemia, alcoholic liver disease, polycystic ovary syndrome and children <3 years.ResultsOne hundred and five articles met inclusion criteria, representing 6826 subjects. In overweight but not obese subjects, probiotics induced improvements in: body weight (k=25 trials, d=−0.94 kg mean difference, 95% CI −1.17 to −0.70, I²=0.0%), body mass index (k=32, d=−0.55 kg/m², 95% CI −0.86 to −0.23, I²=91.9%), waist circumference (k=13, d=−1.31 cm, 95% CI −1.79 to −0.83, I²=14.5%), body fat mass (k=11, d=−0.96 kg, 95% CI −1.21 to −0.71, I²=0.0%) and visceral adipose tissue mass (k=5, d=−6.30 cm², 95% CI −9.05 to −3.56, I²=0.0%). In type 2 diabetics, probiotics reduced fasting glucose (k=19, d=−0.66 mmol/L, 95% CI −1.00 to −0.31, I²=27.7%), glycated haemoglobin (k=13, d=−0.28 pp, 95% CI −0.46 to −0.11, I²=54.1%), insulin (k=13, d=−1.66 mU/L, 95% CI −2.70 to −0.61, I²=37.8%) and homeostatic model of insulin resistance (k=10, d=−1.05 pp, 95% CI −1.48 to −0.61, I²=18.2%). In subjects with fatty liver diseases, probiotics reduced alanine (k=12, d=−10.2 U/L, 95% CI −14.3 to −6.0, I²=93.50%) and aspartate aminotransferases (k=10, d=−9.9 U/L, 95% CI −14.1 to -5.8, I²=96.1%). These improvements were mostly observed with bifidobacteria (Bifidobacterium breve, B. longum), Streptococcus salivarius subsp. thermophilus and lactobacilli (Lactobacillus acidophilus, L. casei, L. delbrueckii) containing mixtures and influenced by trials conducted in one country.ConclusionsThe intake of probiotics resulted in minor but consistent improvements in several metabolic risk factors in subjects with metabolic diseases.Trial registration numberCRD42016033273.

Background Intermittent fasting (IF) is an increasingly popular dietary approach used for weight loss and overall health. While there is an increasing body of evidence demonstrating beneficial effects of IF on blood lipids and other health outcomes in the overweight and obese, limited data are available about the effect of IF in athletes. Thus, the present study sought to investigate the effects of a modified IF protocol (i.e. time-restricted feeding) during resistance training in healthy resistance-trained males. Methods Thirty-four resistance-trained males were randomly assigned to time-restricted feeding (TRF) or normal diet group (ND). TRF subjects consumed 100 % of their energy needs in an 8-h period of time each day, with their caloric intake divided into three meals consumed at 1 p.m., 4 p.m., and 8 p.m. The remaining 16 h per 24-h period made up the fasting period. Subjects in the ND group consumed 100 % of their energy needs divided into three meals consumed at 8 a.m., 1 p.m., and 8 p.m. Groups were matched for kilocalories consumed and macronutrient distribution (TRF 2826 ± 412.3 kcal/day, carbohydrates 53.2 ± 1.4 %, fat 24.7 ± 3.1 %, protein 22.1 ± 2.6 %, ND 3007 ± 444.7 kcal/day, carbohydrates 54.7 ± 2.2 %, fat 23.9 ± 3.5 %, protein 21.4 ± 1.8). Subjects were tested before and after 8 weeks of the assigned diet and standardized resistance training program. Fat mass and fat-free mass were assessed by dual-energy x-ray absorptiometry and muscle area of the thigh and arm were measured using an anthropometric system. Total and free testosterone, insulin-like growth factor 1, blood glucose, insulin, adiponectin, leptin, triiodothyronine, thyroid stimulating hormone, interleukin-6, interleukin-1β, tumor necrosis factor α, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides were measured. Bench press and leg press maximal strength, resting energy expenditure, and respiratory ratio were also tested. Results After 8 weeks, the 2 Way ANOVA (Time * Diet interaction) showed a decrease in fat mass in TRF compared to ND (p = 0.0448), while fat-free mass, muscle area of the arm and thigh, and maximal strength were maintained in both groups. Testosterone and insulin-like growth factor 1 decreased significantly in TRF, with no changes in ND (p = 0.0476; p = 0.0397). Adiponectin increased (p = 0.0000) in TRF while total leptin decreased (p = 0.0001), although not when adjusted for fat mass. Triiodothyronine decreased in TRF, but no significant changes were detected in thyroid-stimulating hormone, total cholesterol, high-density lipoprotein, low-density lipoprotein, or triglycerides. Resting energy expenditure was unchanged, but a significant decrease in respiratory ratio was observed in the TRF group. Conclusions Our results suggest that an intermittent fasting program in which all calories are consumed in an 8-h window each day, in conjunction with resistance training, could improve some health-related biomarkers, decrease fat mass, and maintain muscle mass in resistance-trained males.

Dysbiosis represents changes in composition and structure of the gut microbiome community (microbiome), which may dictate the physiological phenotype (health or disease). Recent technological advances and efforts in metagenomic and metabolomic analyses have led to a dramatical growth in our understanding of microbiome, but still, the mechanisms underlying gut microbiome–host interactions in healthy or diseased state remain elusive and their elucidation is in infancy. Disruption of the normal gut microbiota may lead to intestinal dysbiosis, intestinal barrier dysfunction, and bacterial translocation. Excessive uremic toxins are produced as a result of gut microbiota alteration, including indoxyl sulphate, p -cresyl sulphate, and trimethylamine-N-oxide, all implicated in the variant processes of kidney diseases development. This review focuses on the pathogenic association between gut microbiota and kidney diseases (the gut–kidney axis), covering CKD, IgA nephropathy, nephrolithiasis, hypertension, acute kidney injury, hemodialysis and peritoneal dialysis in clinic. Targeted interventions including probiotic, prebiotic and symbiotic measures are discussed for their potential of re-establishing symbiosis, and more effective strategies for the treatment of kidney diseases patients are suggested. The novel insights into the dysbiosis of the gut microbiota in kidney diseases are helpful to develop novel therapeutic strategies for preventing or attenuating kidney diseases and complications.

Background Sepsis is defined as a systemic inflammatory response to microbial infections with multiple organ dysfunction. This study analysed untargeted metabolomics combined with proteomics of serum from patients with sepsis to reveal the underlying pathological mechanisms involved in sepsis. Methods A total of 63 patients with sepsis and 43 normal controls were enrolled from a prospective multicentre cohort. The biological functions of the metabolome were assessed by coexpression network analysis. A molecular network based on metabolomics and proteomics data was constructed to investigate the key molecules. Results Untargeted metabolomics analysis revealed widespread dysregulation of amino acid metabolism, which regulates inflammation and immunity, in patients with sepsis. Seventy-three differentially expressed metabolites (|log 2 fold change| > 1.5, adjusted P value < 0.05 and variable importance in the projection (VIP) > 1.5) that could predict sepsis were identified. External validation of the hub metabolites was consistent with the derivation results (area under the receiver operating characteristic curve (AUROC): 0.81–0.96/0.62–1.00). The pentose phosphate pathway was found to be related to sepsis-associated encephalopathy. Phenylalanine metabolism was associated with sepsis-associated acute kidney injury. The key molecular alterations of the multiomics network in sepsis compared to normal controls implicate acute inflammatory response, platelet degranulation, myeloid cell activation involved in immune response and phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis. Conclusions Integrated analysis of untargeted metabolomics and proteomics revealed characteristic metabolite and protein alterations in sepsis, which were mainly involved in inflammation-related pathways and amino acid metabolism. This study depicted the pathological characteristics and pathways involved in sepsis and potential therapeutic targets.

Background The 2019 coronavirus disease pandemic (COVID-19) poses an enormous threat to public health worldwide, and the ensuing management of social isolation has greatly decreased opportunities for physical activity (PA) and increased opportunities for leisure sedentary behaviors (LSB). Given that both PA and LSB have been established as major influencing factors for obesity, diabetes and cardiometabolic syndrome, whether PA/LSB in turn affects the susceptibility to COVID-19 by disrupting metabolic homeostasis remains to be explored. In this study, we aimed to systematically evaluate the causal relationship between PA/LSB and COVID-19 susceptibility, hospitalization and severity using a Mendelian randomization study. Methods Data were obtained from a large-scale PA dataset (N = 377,000), LSB dataset (N = 422,218) and COVID-19 Host Genetics Initiative (N = 2,586,691). The causal effects were estimated with inverse variance weighted, MR-Egger, weighted median and MR-PRESSO. Sensitivity analyses were implemented with Cochran’s Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis and the funnel plot. Risk factor analyses were further conducted to investigate the potential mediators. Results Genetically predicted accelerometer-assessed PA decreased the risk for COVID-19 hospitalization (OR = 0.93, 95% CI 0.88–0.97; P = 0.002), while leisure television watching significantly increased the risk of COVID-19 hospitalization (OR = 1.55, 95% CI 1.29–1.88; P = 4.68 × 10 –6 ) and disease severity (OR = 1.85, 95% CI 1.33–2.56; P = 0.0002) after Bonferroni correction. No causal effects of self-reported moderate to vigorous physical activity (MVPA), accelerometer fraction of accelerations > 425 milligravities, computer use or driving on COVID-19 progression were observed. Risk factor analyses indicated that the above causal associations might be mediated by several metabolic risk factors, including smoking, high body mass index, elevated serum triglyceride levels, insulin resistance and the occurrence of type 2 diabetes. Conclusion Our findings supported a causal effect of accelerometer-assessed PA on the reduced risk of COVID-19 hospitalization as well as television watching on the increased risk of COVID-19 hospitalization and severity, which was potentially mediated by smoking, obesity and type 2 diabetes-related phenotypes. Particular attention should be given to reducing leisure sedentary behaviors and encouraging proper exercise during isolation and quarantine for COVID-19.