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The Lancet. Psychiatry Jan 2020Antipsychotic treatment is associated with metabolic disturbance. However, the degree to which metabolic alterations occur in treatment with different antipsychotics is... (Comparative Study)
Comparative Study
Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysregulation, and association with psychopathology: a systematic review and network meta-analysis.
BACKGROUND
Antipsychotic treatment is associated with metabolic disturbance. However, the degree to which metabolic alterations occur in treatment with different antipsychotics is unclear. Predictors of metabolic dysregulation are poorly understood and the association between metabolic change and change in psychopathology is uncertain. We aimed to compare and rank antipsychotics on the basis of their metabolic side-effects, identify physiological and demographic predictors of antipsychotic-induced metabolic dysregulation, and investigate the relationship between change in psychotic symptoms and change in metabolic parameters with antipsychotic treatment.
METHODS
We searched MEDLINE, EMBASE, and PsycINFO from inception until June 30, 2019. We included blinded, randomised controlled trials comparing 18 antipsychotics and placebo in acute treatment of schizophrenia. We did frequentist random-effects network meta-analyses to investigate treatment-induced changes in body weight, BMI, total cholesterol, LDL cholesterol, HDL cholesterol, triglyceride, and glucose concentrations. We did meta-regressions to examine relationships between metabolic change and age, sex, ethnicity, baseline weight, and baseline metabolic parameter level. We examined the association between metabolic change and psychopathology change by estimating the correlation between symptom severity change and metabolic parameter change.
FINDINGS
Of 6532 citations, we included 100 randomised controlled trials, including 25 952 patients. Median treatment duration was 6 weeks (IQR 6-8). Mean differences for weight gain compared with placebo ranged from -0·23 kg (95% CI -0·83 to 0·36) for haloperidol to 3·01 kg (1·78 to 4·24) for clozapine; for BMI from -0·25 kg/m (-0·68 to 0·17) for haloperidol to 1·07 kg/m (0·90 to 1·25) for olanzapine; for total-cholesterol from -0·09 mmol/L (-0·24 to 0·07) for cariprazine to 0·56 mmol/L (0·26-0·86) for clozapine; for LDL cholesterol from -0·13 mmol/L (-0.21 to -0·05) for cariprazine to 0·20 mmol/L (0·14 to 0·26) for olanzapine; for HDL cholesterol from 0·05 mmol/L (0·00 to 0·10) for brexpiprazole to -0·10 mmol/L (-0·33 to 0·14) for amisulpride; for triglycerides from -0·01 mmol/L (-0·10 to 0·08) for brexpiprazole to 0·98 mmol/L (0·48 to 1·49) for clozapine; for glucose from -0·29 mmol/L (-0·55 to -0·03) for lurasidone to 1·05 mmol/L (0·41 to 1·70) for clozapine. Greater increases in glucose were predicted by higher baseline weight (p=0·0015) and male sex (p=0·0082). Non-white ethnicity was associated with greater increases in total cholesterol (p=0·040) compared with white ethnicity. Improvements in symptom severity were associated with increases in weight (r=0·36, p=0·0021), BMI (r=0·84, p<0·0001), total-cholesterol (r=0·31, p=0·047), and LDL cholesterol (r=0·42, p=0·013), and decreases in HDL cholesterol (r=-0·35, p=0·035).
INTERPRETATION
Marked differences exist between antipsychotics in terms of metabolic side-effects, with olanzapine and clozapine exhibiting the worst profiles and aripiprazole, brexpiprazole, cariprazine, lurasidone, and ziprasidone the most benign profiles. Increased baseline weight, male sex, and non-white ethnicity are predictors of susceptibility to antipsychotic-induced metabolic change, and improvements in psychopathology are associated with metabolic disturbance. Treatment guidelines should be updated to reflect our findings. However, the choice of antipsychotic should be made on an individual basis, considering the clinical circumstances and preferences of patients, carers, and clinicians.
FUNDING
UK Medical Research Council, Wellcome Trust, National Institute for Health Research Oxford Health Biomedical Research Centre.
Topics: Antipsychotic Agents; Blood Glucose; Body Mass Index; Humans; Lipid Metabolism; Network Meta-Analysis; Randomized Controlled Trials as Topic; Schizophrenia; Weight Gain
PubMed: 31860457
DOI: 10.1016/S2215-0366(19)30416-X -
Signal Transduction and Targeted Therapy Feb 2021The abnormal regulation of alternative splicing is usually accompanied by the occurrence and development of tumors, which would produce multiple different isoforms and...
The abnormal regulation of alternative splicing is usually accompanied by the occurrence and development of tumors, which would produce multiple different isoforms and diversify protein expression. The aim of the present study was to conduct a systematic review in order to describe the regulatory mechanisms of alternative splicing, as well as its functions in tumor cells, from proliferation and apoptosis to invasion and metastasis, and from angiogenesis to metabolism. The abnormal splicing events contributed to tumor progression as oncogenic drivers and/or bystander factors. The alterations in splicing factors detected in tumors and other mis-splicing events (i.e., long non-coding and circular RNAs) in tumorigenesis were also included. The findings of recent therapeutic approaches targeting splicing catalysis and splicing regulatory proteins to modulate pathogenically spliced events (including tumor-specific neo-antigens for cancer immunotherapy) were introduced. The emerging RNA-based strategies for the treatment of cancer with abnormally alternative splicing isoforms were also discussed. However, further studies are still required to address the association between alternative splicing and cancer in more detail.
Topics: Alternative Splicing; Carcinogenesis; Gene Expression Regulation, Neoplastic; Humans; Neoplasms; RNA Splicing; RNA Splicing Factors; RNA, Circular
PubMed: 33623018
DOI: 10.1038/s41392-021-00486-7 -
Advances in Nutrition (Bethesda, Md.) Dec 2022The timing and nutritional composition of food intake are important zeitgebers for the biological clocks in humans. Thus, eating at an inappropriate time (e.g., during... (Review)
Review
The timing and nutritional composition of food intake are important zeitgebers for the biological clocks in humans. Thus, eating at an inappropriate time (e.g., during the night) may have a desynchronizing effect on the biological clocks and, in the long term, may result in adverse health outcomes (e.g., weight gain, obesity, and poor metabolic function). Being a very late or early chronotype not only determines preferred sleep and wake times but may also influence subsequent mealtimes, which may affect the circadian timing system. In recent years, an increased number of studies have examined the relation between chronotype and health outcomes, with a main focus on absolute food intake and metabolic markers and, to a lesser extent, on dietary intake distribution and eating behavior. Therefore, this review aimed to systematically determine whether chronotype indirectly affects eating behaviors, dietary intake (timing, choice, nutrients), and biomarkers leading to body composition outcomes in healthy adults. A systematic literature search on electronic databases (PubMed, CINAHL, MEDLINE, SCOPUS, Cochrane library) was performed (International Prospective Register of Systematic Reviews number: CRD42020219754). Only studies that included healthy adults (aged >18 y), classified according to chronotype and body composition profiles, using outcomes of dietary intake, eating behavior, and/or biomarkers, were considered. Of 4404 articles, 24 met the inclusion criteria. The results revealed that late [evening type (ET)] compared with early [morning type (MT)] chronotypes were more likely to be overweight/obese with poorer metabolic health. Both MT and ET had similar energy and macronutrient intakes, consuming food during their preferred sleep-wake timing: later for ET than MT. Most of the energy and macronutrient intakes were distributed toward nighttime for ET and exacerbated by unhealthy eating behaviors and unfavorable dietary intakes. These findings from our systematic review give further insight why higher rates of overweight/obesity and unhealthier metabolic biomarkers are more likely to occur in ET.
Topics: Adult; Humans; Overweight; Chronotype; Energy Intake; Circadian Rhythm; Feeding Behavior; Eating; Obesity; Sleep; Body Composition
PubMed: 36041181
DOI: 10.1093/advances/nmac093 -
Biomedicine & Pharmacotherapy =... Jul 2020This study provides a critical overview of experimental studies in vitro, in humans, and in animals that evaluated the efficacy of Berberine and its effect on management...
This study provides a critical overview of experimental studies in vitro, in humans, and in animals that evaluated the efficacy of Berberine and its effect on management of obesity and the related metabolic consequences. As a result of this review, we summarized the effects of Berberine in different models and the related mechanism of actions. In preclinical models, Berberine demonstrates that it affects gut microbiota by reducing diversity of microbes starting at a dosage of 100 mg/kg/day. Moreover, in animal models, Berberine explicates an action on glucose through the inhibition of α-glycosidase at a dose of 200 mh/kg/day. Berberine is also known to be effective against differentiation of adipocytes through a decrease in LXRs, PPARs, and SREBPs expression at 150 mg/kg/day. Other mechanism ascribed to Berberine are related to its inhibition of hepatic gluconeogenesis through the Phospheoenolpyruvate carboxykinase (PEPCK), Glucose-6-phosphate (G6Pase) and AMP-activated protein kinase (AMPK). Furthermore, Berberine (associated to Red Yeast Rice) is effective in decreasing lipid levels in rats, which consequently lowers the change of weight gain at dosage of 40 mg/kg to 380 mg/kg/day. All the above preclinical data are confirmed in human studies where Berberine can modulate the diversity of gut microbes at the dose of 500 mg/day. In addition, Berberine is found to have a beneficial impact on gene regulation for the absorption of cholesterol at a daily dose of 300 mg in humans, an amelioration on glucose accumulation at 1.0 g daily dose was also observed. For all these reasons, this review gives an important good account of the impact of Berberine in obesity treatment and prevention.
Topics: Adipocytes; Berberine; Blood Glucose; Cholesterol; Gastrointestinal Microbiome; Gluconeogenesis; Humans; Insulin Resistance; Obesity; Weight Loss
PubMed: 32353823
DOI: 10.1016/j.biopha.2020.110137 -
International Journal of Molecular... May 2023Heart failure (HF) is a progressive chronic disease that remains a primary cause of death worldwide, affecting over 64 million patients. HF can be caused by... (Review)
Review
Heart failure (HF) is a progressive chronic disease that remains a primary cause of death worldwide, affecting over 64 million patients. HF can be caused by cardiomyopathies and congenital cardiac defects with monogenic etiology. The number of genes and monogenic disorders linked to development of cardiac defects is constantly growing and includes inherited metabolic disorders (IMDs). Several IMDs affecting various metabolic pathways have been reported presenting cardiomyopathies and cardiac defects. Considering the pivotal role of sugar metabolism in cardiac tissue, including energy production, nucleic acid synthesis and glycosylation, it is not surprising that an increasing number of IMDs linked to carbohydrate metabolism are described with cardiac manifestations. In this systematic review, we offer a comprehensive overview of IMDs linked to carbohydrate metabolism presenting that present with cardiomyopathies, arrhythmogenic disorders and/or structural cardiac defects. We identified 58 IMDs presenting with cardiac complications: 3 defects of sugar/sugar-linked transporters (GLUT3, GLUT10, THTR1); 2 disorders of the pentose phosphate pathway (G6PDH, TALDO); 9 diseases of glycogen metabolism (GAA, GBE1, GDE, GYG1, GYS1, LAMP2, RBCK1, PRKAG2, G6PT1); 29 congenital disorders of glycosylation (ALG3, ALG6, ALG9, ALG12, ATP6V1A, ATP6V1E1, B3GALTL, B3GAT3, COG1, COG7, DOLK, DPM3, FKRP, FKTN, GMPPB, MPDU1, NPL, PGM1, PIGA, PIGL, PIGN, PIGO, PIGT, PIGV, PMM2, POMT1, POMT2, SRD5A3, XYLT2); 15 carbohydrate-linked lysosomal storage diseases (CTSA, GBA1, GLA, GLB1, HEXB, IDUA, IDS, SGSH, NAGLU, HGSNAT, GNS, GALNS, ARSB, GUSB, ARSK). With this systematic review we aim to raise awareness about the cardiac presentations in carbohydrate-linked IMDs and draw attention to carbohydrate-linked pathogenic mechanisms that may underlie cardiac complications.
Topics: Humans; Cardiomyopathies; Metabolic Diseases; Heart Defects, Congenital; Glycosylation; Carbohydrates; Sugars; Chondroitinsulfatases; Pentosyltransferases; Mannosyltransferases; Acetyltransferases
PubMed: 37239976
DOI: 10.3390/ijms24108632 -
JAMA Psychiatry Mar 2021Precise estimation of the drug metabolism capacity for individual patients is crucial for adequate dose personalization. (Meta-Analysis)
Meta-Analysis
IMPORTANCE
Precise estimation of the drug metabolism capacity for individual patients is crucial for adequate dose personalization.
OBJECTIVE
To quantify the difference in the antipsychotic and antidepressant exposure among patients with genetically associated CYP2C19 and CYP2D6 poor (PM), intermediate (IM), and normal (NM) metabolizers.
DATA SOURCES
PubMed, Clinicaltrialsregister.eu, ClinicalTrials.gov, International Clinical Trials Registry Platform, and CENTRAL databases were screened for studies from January 1, 1990, to June 30, 2020, with no language restrictions.
STUDY SELECTION
Two independent reviewers performed study screening and assessed the following inclusion criteria: (1) appropriate CYP2C19 or CYP2D6 genotyping was performed, (2) genotype-based classification into CYP2C19 or CYP2D6 NM, IM, and PM categories was possible, and (3) 3 patients per metabolizer category were available.
DATA EXTRACTION AND SYNTHESIS
The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed for extracting data and quality, validity, and risk of bias assessments. A fixed-effects model was used for pooling the effect sizes of the included studies.
MAIN OUTCOMES AND MEASURES
Drug exposure was measured as (1) dose-normalized area under the plasma level (time) curve, (2) dose-normalized steady-state plasma level, or (3) reciprocal apparent total drug clearance. The ratio of means (RoM) was calculated by dividing the mean drug exposure for PM, IM, or pooled PM plus IM categories by the mean drug exposure for the NM category.
RESULTS
Based on the data derived from 94 unique studies and 8379 unique individuals, the most profound differences were observed in the patients treated with aripiprazole (CYP2D6 PM plus IM vs NM RoM, 1.48; 95% CI, 1.41-1.57; 12 studies; 1038 patients), haloperidol lactate (CYP2D6 PM vs NM RoM, 1.68; 95% CI, 1.40-2.02; 9 studies; 423 patients), risperidone (CYP2D6 PM plus IM vs NM RoM, 1.36; 95% CI, 1.28-1.44; 23 studies; 1492 patients), escitalopram oxalate (CYP2C19 PM vs NM, RoM, 2.63; 95% CI, 2.40-2.89; 4 studies; 1262 patients), and sertraline hydrochloride (CYP2C19 IM vs NM RoM, 1.38; 95% CI, 1.27-1.51; 3 studies; 917 patients). Exposure differences were also observed for clozapine, quetiapine fumarate, amitriptyline hydrochloride, mirtazapine, nortriptyline hydrochloride, fluoxetine hydrochloride, fluvoxamine maleate, paroxetine hydrochloride, and venlafaxine hydrochloride; however, these differences were marginal, ambiguous, or based on less than 3 independent studies.
CONCLUSIONS AND RELEVANCE
In this systematic review and meta-analysis, the association between CYP2C19/CYP2D6 genotype and drug levels of several psychiatric drugs was quantified with sufficient precision as to be useful as a scientific foundation for CYP2D6/CYP2C19 genotype-based dosing recommendations.
Topics: Antidepressive Agents; Antipsychotic Agents; Cytochrome P-450 CYP2C19; Cytochrome P-450 CYP2D6; Humans; Pharmacogenomic Variants
PubMed: 33237321
DOI: 10.1001/jamapsychiatry.2020.3643 -
Cell Death & Disease Feb 2023Sepsis is a life-threatening disorder disease defined as infection-induced dysregulated immune responses and multiple organ dysfunction. The imbalance between... (Review)
Review
Sepsis is a life-threatening disorder disease defined as infection-induced dysregulated immune responses and multiple organ dysfunction. The imbalance between hyperinflammation and immunosuppression is a crucial feature of sepsis immunity. Epigenetic modifications, including histone modifications, DNA methylation, chromatin remodeling, and non-coding RNA, play essential roles in regulating sepsis immunity through epi-information independent of the DNA sequence. In recent years, the mechanisms of histone modification in sepsis have received increasing attention, with ongoing discoveries of novel types of histone modifications. Due to the capacity for prolonged effects on immune cells, histone modifications can induce immune cell reprogramming and participate in the long-term immunosuppressed state of sepsis. Herein, we systematically review current mechanisms of histone modifications involved in the regulation of sepsis, summarize their role in sepsis from an immune perspective and provide potential therapeutic opportunities targeting histone modifications in sepsis treatment.
Topics: Humans; Histones; Histone Code; Epigenesis, Genetic; Sepsis; DNA Methylation
PubMed: 36774341
DOI: 10.1038/s41419-023-05656-9 -
Sports Medicine (Auckland, N.Z.) Feb 2022Body-fluid loss during prolonged continuous exercise can impair cardiovascular function, harming performance. Delta percent plasma volume (dPV) represents the change in... (Meta-Analysis)
Meta-Analysis
The Hydrating Effects of Hypertonic, Isotonic and Hypotonic Sports Drinks and Waters on Central Hydration During Continuous Exercise: A Systematic Meta-Analysis and Perspective.
BACKGROUND
Body-fluid loss during prolonged continuous exercise can impair cardiovascular function, harming performance. Delta percent plasma volume (dPV) represents the change in central and circulatory body-water volume and therefore hydration during exercise; however, the effect of carbohydrate-electrolyte drinks and water on the dPV response is unclear.
OBJECTIVE
To determine by meta-analysis the effects of ingested hypertonic (> 300 mOsmol kg), isotonic (275-300 mOsmol kg) and hypotonic (< 275 mOsmol kg) drinks containing carbohydrate and electrolyte ([Na] < 50 mmol L), and non-carbohydrate drinks/water (< 40 mOsmol kg) on dPV during continuous exercise.
METHODS
A systematic review produced 28 qualifying studies and 68 drink treatment effects. Random-effects meta-analyses with repeated measures provided estimates of effects and probability of superiority (p) during 0-180 min of exercise, adjusted for drink osmolality, ingestion rate, metabolic rate and a weakly informative Bayesian prior.
RESULTS
Mean drink effects on dPV were: hypertonic - 7.4% [90% compatibility limits (CL) - 8.5, - 6.3], isotonic - 8.7% (90% CL - 10.1, - 7.4), hypotonic - 6.3% (90% CL - 7.4, - 5.3) and water - 7.5% (90% CL - 8.5, - 6.4). Posterior contrast estimates relative to the smallest important effect (dPV = 0.75%) were: hypertonic-isotonic 1.2% (90% CL - 0.1, 2.6; p = 0.74), hypotonic-isotonic 2.3% (90% CL 1.1, 3.5; p = 0.984), water-isotonic 1.3% (90% CL 0.0, 2.5; p = 0.76), hypotonic-hypertonic 1.1% (90% CL 0.1, 2.1; p = 0.71), hypertonic-water 0.1% (90% CL - 0.8, 1.0; p = 0.12) and hypotonic-water 1.1% (90% CL 0.1, 2.0; p = 0.72). Thus, hypotonic drinks were very likely superior to isotonic and likely superior to hypertonic and water. Metabolic rate, ingestion rate, carbohydrate characteristics and electrolyte concentration were generally substantial modifiers of dPV.
CONCLUSION
Hypotonic carbohydrate-electrolyte drinks ingested continuously during exercise provide the greatest benefit to hydration.
Topics: Bayes Theorem; Dehydration; Exercise; Humans; Osmolar Concentration; Sodium; Water-Electrolyte Balance
PubMed: 34716905
DOI: 10.1007/s40279-021-01558-y -
Brazilian Journal of Physical Therapy 2017Physical exercise has been used to mitigate the metabolic effects of diabetes mellitus. (Review)
Review
BACKGROUND
Physical exercise has been used to mitigate the metabolic effects of diabetes mellitus.
OBJECTIVE
To evaluate the effect of resistance exercise when compared to aerobic exercise without insulin therapy on metabolic and clinical outcomes in patients with type 2 diabetes mellitus.
METHODS
Papers were searched on the databases MEDLINE/PubMed, CINAHL, SPORTDiscus, LILACS, and SCIELO, without language or date of publication limits. Clinical trials that compared resistance exercise to aerobic exercise in adults with type 2 diabetes mellitus who did not use insulin therapy were included. The quality of evidence and risk of bias were assessed using the GRADE system and the Cochrane Risk of Bias tool, respectively. Meta-analysis was also used, whenever possible. Two reviewers extracted the data independently. Eight eligible articles were included in this study, with a total of 336 individuals, with a mean age of 48-58 years. The protocols of aerobic and resistance exercise varied in duration from eight to 22 weeks, 30-60min/day, three to five times/week.
RESULTS
Overall the available evidence came from a very low quality of evidence and there was an increase in Maximal oxygen consumption (mean difference: -2.86; 95% CI: -3.90 to -1.81; random effect) for the resistance exercise and no difference was found in Glycated hemoglobin, Body mass index, High-density lipoprotein cholesterol, Low-density lipoprotein cholesterol, triglycerides, and total cholesterol.
CONCLUSIONS
Resistance exercise appears to be more effective in promoting an increase in Maximal oxygen consumption in protocols longer than 12 weeks and there is no difference in the control of glycemic and lipid levels between the two types of exercise.
Topics: Diabetes Mellitus, Type 2; Exercise; Exercise Therapy; Humans; Insulin; Oxygen Consumption; Resistance Training
PubMed: 28728958
DOI: 10.1016/j.bjpt.2017.06.004 -
PloS One 2020The need to control for the potential influence of menstrual cycle phase on resting metabolism (RMR) places a burden on research participants who must self-report onset... (Meta-Analysis)
Meta-Analysis
BACKGROUND
The need to control for the potential influence of menstrual cycle phase on resting metabolism (RMR) places a burden on research participants who must self-report onset of menstruation and researchers who must schedule metabolic testing accordingly.
PURPOSE
To systematically review and analyze existing research to determine the effect of menstrual cycle on RMR.
METHODS
We searched PubMed, CINAHL, MEDLINE, SPORTDiscus, and Scopus databases using the search terms "menstrual cycle and metabolic rate" and "menstrual cycle and energy expenditure." Eligibility criteria were English language, single-group repeated measures design, and RMR as either a primary or secondary outcome. Risk of bias was assessed based on study sample, measurement, and control of confounders. Differences between the follicular and luteal phases of the menstrual cycle were analyzed using the standardized mean difference in effect size.
RESULTS
Thirty English-language studies published between 1930 and December 2019 were included in the systematic review, and 26 studies involving 318 women were included in the meta-analysis. Overall, there was a small but significant effect favoring increased RMR in the luteal phase (ES = 0.33; 95% CI = 0.17, 0.49, p < 0.001).
DISCUSSION
Limitations include risk of bias regarding measurement of both menstrual cycle and RMR. Sample sizes were small and studies did not report control of potential confounders. Sub-group analysis demonstrated that in more recent studies published since 2000, the effect of menstrual phase was reduced and not statistically significant (ES = 0.23; 95% CI = -0.00, 0.47; p = 0.055). Until larger and better designed studies are available, based on our current findings, researchers should be aware of the potential confounding influence of the menstrual cycle and control for it by testing consistently in one phase of the cycle when measuring RMR in pre-menopausal women.
Topics: Basal Metabolism; Female; Humans; Menstrual Cycle; Rest
PubMed: 32658929
DOI: 10.1371/journal.pone.0236025