Clinical and pharmacokinetics overview of intranasal administration of fentanyl.

Due to the presence of large surfaces and high blood supply, drug delivery through the nasal route of administration is the appropriate route to administrate drugs with rapid onsets of action. Bypassing first-pass metabolism can increase drug bioavailability. The physicochemical properties of fentanyl led to a need to develop formulations for delivery by multiple routes. Several approved inter-nasal fentanyl products in Europe and the USA have been used in prehospital and emergency departments to treat chronic cancer pain and used to treat severe acute abdominal and flank pain. Analgesia durations and onsets were not significantly different between intranasal and intravenous fentanyl in patients with cancer breakthrough pain and were well-tolerated in the long term. Intranasal Fentanyl (INF) at a 50 µg/ml concentration decreased renal colic pain to the lowest level in 30 min. Possible adverse effects specific to INF are epistaxis, nasal wall ulcer, rhinorrhea, throat irritation, dysgeusia, nausea, and vomiting. However, there is limited available literature about the serious adverse effects of INF in adults and children. Intranasal Fentanyl Spray (INFS) results in significantly higher plasma concentrations and has a lower Tmax than oral transmucosal formulation, and the bioavailability of fentanyl in intranasal formulations is very high (89 %), particularly in pectin-containing formulations such as PecFent and Lazanda.

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utilizes National Poison Data System (NPDS) data. The severity of outcomes was derived from the NPDS Coding Manual. Our database was divided into training (70%) and test (30%) sets. We used a light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) to predict the outcomes of methadone poisoning. A total of 3847 patients with methadone exposures were included. Our results demonstrated that machine learning models conferred high accuracy and reliability in determining the outcomes of methadone poisoning cases. The performance evaluation showed all models had high accuracy, precision, specificity, recall, and F1-score values. All models could reach high specificity (more than 96%) and high precision (80% or more) for predicting major outcomes. The models could also achieve a high sensitivity to predict minor outcomes. Finally, the accuracy of all models was about 75%. However, XGBoost and LGBM models achieved the best performance among all models. This study showcased the accuracy and reliability of machine learning models in the outcome prediction of methadone poisoning.

The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States.

Background: Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM), logistic regression (LR), and random forest (RF), in the outcome prediction of DPH poisoning. Materials and Methods: We used the National Poison Data System database and included all of the human exposures of DPH from January 01, 2017 to December 31, 2017, and excluded those cases with missing information, duplicated cases, and those who reported co-ingestion. Data were split into training and test datasets, and three ML models were compared. We developed confusion matrices for each, and standard performance metrics were calculated. Results: Our study population included 53,761 patients with DPH exposure. The most common reasons for exposure, outcome, chronicity of exposure, and formulation were captured. Our results showed that the average precision-recall area under the curve (AUC) of 0.84. LGBM and RF had the highest performance (average AUC of 0.91), followed by LR (average AUC of 0.90). The specificity of the models was 87.0% in the testing groups. The precision of models was 75.0%. Recall (sensitivity) of models ranged between 73% and 75% with an F1 score of 75.0%. The overall accuracy of LGBM, LR, and RF models in the test dataset was 74.8%, 74.0%, and 75.1%, respectively. In total, just 1.1% of patients (mostly those with major outcomes) received physostigmine. Conclusion: Our study demonstrates the application of ML in the prediction of DPH poisoning.

Clinical utility of lipid ratios as potential predictors of metabolic syndrome among the elderly population: Birjand Longitudinal Aging Study (BLAS).

BACKGROUND: Elderly adults are at higher risk of developing metabolic syndrome (MetS). The present study aims to investigate the relationship between lipid ratios and MetS in the elderly population. METHODS: This study was conducted on elderly population of Birjand during 2018-2019. The data of this study was driven from Birjand Longitudinal Aging Study (BLAS). The participants were selected based on multistage stratified cluster sampling. Patients were categorized into quartiles according to the lipid ratios (TG/HDL-C, LDL-C/HDL-C, non-HDL/HDL-C), and the relationship between lipid ratio quartiles and MetS was determined by Logistic Regression using Odds Ratio. Finally, the optimal cut-off for each lipid ratio in MetS diagnosis was calculated according to the Area Under the Curve (AUC). RESULTS: This study included 1356 individuals, of whom 655 were men and 701 were women. In our study, the crude prevalence of MetS was 792 (58%), including 543 (77.5%) women and 249 (38%) men. Increasing trends were observed in quartiles of all lipid ratios for TC, LDL-C, TG, and DBP. TG/HDL was also the best lipid ratio to diagnose the MetS, based on NCEP ATP III criteria. One unit increased in level of TG/HDL resulted in 3.94 (OR: 3.94; 95%CI: 2.48-6.6) and 11.56 (OR: 11.56; 95%CI: 6.93-19.29) increasing risk of having MetS in quartile 3 and 4 compared to quartile 1, respectively. In men and women, the cutoff for TG/HDL was 3.5 and 3.0, respectively. CONCLUSIONS: Our results showed that the TG/HDL-C is superior to the LDL-C/HDL-C and the non-HDL /HDL-C to predict MetS among the elderly adults.

Associations of emotional social support, depressive symptoms, chronic stress, and anxiety with hard cardiovascular disease events in the United States: the multi-ethnic study of atherosclerosis (MESA).

BACKGROUND: Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality around the globe and psychosocial factors are not sufficiently understood. AIM: In the current study, we aimed to evaluate the role of different psychosocial factors including depressive symptoms, chronic stress, anxiety, and emotional social support (ESS) on the incidence of hard CVD (HCVD). METHODS: We examined the association of psychosocial factors and HCVD incidence amongst 6,779 participants in the Multi-Ethnic Study of Atherosclerosis (MESA). Using physician reviewers' adjudication of CVD events incident, depressive symptoms, chronic stress, anxiety, emotional social support scores were measured by validated scales. We used Cox proportional Hazards (PH) models with psychosocial factors in several of the following approaches: (1) Continuous; (2) categorical; and (3) spline approach. No violation of the PH was found. The model with the lowest AIC value was chosen. RESULTS: Over an 8.46-year median follow-up period, 370 participants experienced HCVD. There was not a statistically significant association between anxiety and HCVD (95%CI) for the highest versus the lowest category [HR = 1.51 (0.80-2.86)]. Each one point higher score for chronic stress (HR, 1.18; 95% CI, 1.08-1.29) and depressive symptoms (HR, 1.02; 95% CI, 1.01-1.03) was associated with a higher risk of HCVD in separate models. In contrary, emotional social support (HR, 0.98; 95% CI, 0.96-0.99) was linked with a lower risk of HCVD. CONCLUSIONS: Higher levels of chronic stress is associated with greater risk of incident HCVD whereas ESS has a protective association.

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System.

BACKGROUND: Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases. METHODS: The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model's performance. RESULTS: Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%. CONCLUSION: Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.

The relationship between dietary patterns and insomnia in young women.

AIM: There is mounting evidence that eating habits affect sleeping patterns and their quality. The goal of this study was to evaluate the associations between major dietary patterns, identified using principal component analysis (PCA) and insomnia in young women. METHODS: The study subjects comprised 159 healthy young women aged 18-25 years. Neuropsychological assessment was performed using standard instruments, including a cognitive ability questionnaire (CAQ), depression and anxiety stress scales (DASS-21), insomnia severity index (ISI), Epworth sleepiness scale (ESS), and quality of life questionnaire (QLQ). Dietary patterns were obtained from a 65-item validated food frequency questionnaire (FFQ) in this study, using PCA. RESULTS: Two major dietary patterns were identified that were termed: "Traditional" and "Western." The Western pattern was characterized by a high intake of snacks, nuts, dairy products, tea, fast foods, chicken, and vegetable oils. Subjects with moderate/severe insomnia were found to have lower scores for total cognitive ability task, nocturnal sleep hours, and physical and mental health, but higher scores for depression, anxiety, stress, and daytime sleepiness compared to those without insomnia (p < 0.05). After adjustment for potential confounders, high adherence to the Western dietary pattern was associated with higher odds of insomnia (OR = 5.9; 95% confidence intervals: 1.9-18.7; p = 0.003). CONCLUSION: Our findings indicated adherence to Western pattern may increase the odds of insomnia. Prospective research is required to determine the feasibility of targeting dietary patterns to decrease the odds of insomnia.

The role of decision tree and machine learning models for outcome prediction of bupropion exposure: A nationwide analysis of more than 14 000 patients in the United States.

Bupropion is widely used for the treatment of major depressive disorder and for smoking cessation assistance. Unfortunately, there are no practical systems to assist clinicians or poison centres in predicting outcomes based on clinical features. Hence, the purpose of this study was to use a decision tree approach to inform early diagnosis of outcomes secondary to bupropion overdose. This study utilized a dataset from the National Poison Data System, a 6-year retrospective cohort study on toxic exposures and patient outcomes. A machine learning algorithm (decision tree) was applied to the dataset using the sci-kit-learn library in Python. Shapley Additive exPlanations (SHAP) were used as an explainable method. Comparative analysis was performed using random forest (RF), Gradient Boosting classification, eXtreme Gradient Boosting, Light Gradient Boosting (LGM) and voting ensembling. ROC curve and precision-recall curve were used to analyse the performance of each model. LGM and RF demonstrated the highest performance to predict outcome of bupropion exposure. Multiple seizures, conduction disturbance, intentional exposure, and confusion were the most influential factors to predict the outcome of bupropion exposure. Coma and seizure, including single, multiple and status, were the most important factors to predict major outcomes.

Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS).

Clinical effects of antihyperglycemic agents poisoning may overlap each other. So, distinguishing exposure to these pharmaceutical drugs may take work. This study examined the application of machine learning techniques in identifying antihyperglycemic agent exposure using the national poisoning database in the USA. In this study, the data of single exposure due to Biguanides and Sulfonylureas (n=6183) was requested from the National Poison Data System (NPDS) for 2014-2018. We have tried five machine learning models (random forest classifier, k-nearest neighbors, Xgboost classifier, logistic regression, neural network Keras). For the multiclass classification modeling, we have divided the dataset into two parts: train (75%) and test (25%). The performance metrics used were accuracy, specificity, precision, recall, and F1-score. The algorithms used to get the classification results of different models to diagnose antihyperglycemic agents were very accurate. The accuracy of our model in determining these two antihyperglycemic agents was 91-93%. The precision-recall curve showed average precision of 0.91, 0.97, 0.97, and 0.98 for k-nearest neighbors, logistic regression, random forest, and XGB, respectively. The logistic regression, random forest, and XGB had the highest AUC (AUC=0.97) among both biguanides and sulfonylureas groups. The negative predictive values (NPV) for all the models were between 89 and 93%. We introduced a practical web application to help physicians distinguish between these agents. Despite variations in accuracy among the different types of algorithms used, all of them could accurately determine the specific exposure to biguanides and sulfonylureas retrospectively. Machine learning can distinguish antihyperglycemic agents, which may be useful for physicians without any background in medical toxicology. Besides, Our suggested ML-based Web application might help physicians in their diagnosis.

Prevalence of Metabolic Syndrome and Its Associated Potential Factors among the Elderly in the East of Iran.

BACKGROUND AND AIMS: This cross-sectional study aimed to determine potential factors with a strong association with metabolic syndrome (MetS) among obesity and lipid-related parameters, and liver enzymes, fasting blood glucose (FBG), and blood pressure (BP) as well as some sociodemographic factors in elderly over 60 years old from a sample of Birjand Longitudinal Aging Study (BLAS). METHODS: A total of 1366 elderly Birjand participants were enrolled and divided into non-MetS (n = 512) and MetS (n = 854) groups based on the status of MetS from January 2018 to October 2018. The anthropometric parameters, blood lipid profiles, liver enzymes, and disease history were evaluated and recorded. RESULTS: 62.5% of the participants from our sample of elderly Birjand have MetS (33.4% in males and 66.6% in females). The prevalence of MetS in females was significantly higher than in males (P < 0.001). The increasing trend in the number of MetS components (from 0 to 5) was observed in females (p < 0.001). Odds ratio showed a strong association between female gender [8.33 (5.88- 11.82)], obesity [8.00 (4.87-13.14)], and overweight [2.44 (1.76-3.40)] with MetS and acceptable association between TG/HDL [(1.85 (1.62-2.12)] with MetS. CONCLUSION: This study indicated that the female sex, overweight and obesity have a strong association with MetS and TG/HDL has an acceptable association found in the sample of the elderly Birjand population. However, due to the obvious limitations of our study including the homogeneous sex and race of population, and no adjustment for several important confounding factors including sex, different ages, stage in the elderly, alcohol consumption, smoking, married status, physical activity, diet, and family history of CVD, more epidemiological investigations are needed to address this question.

Curcumin and blood lipid levels: an updated systematic review and meta-analysis of randomised clinical trials.

The present study was designed to indicate the protective effects of curcumin on dyslipidemia. Main databases were searched to recognise randomised clinical trials evaluating the effect of curcumin on blood lipid profiles. The pooled odds ratio with a 95% confidence interval (CI) was used to evaluate the effect of curcumin on blood lipid parameters. HDL-C levels in the curcumin group were 0.04-fold lower than placebo (95% CI:-0.36-0.29; Z = 0.23; p = .82). LDL-C levels in the curcumin group reduced by 0.17 versus the placebo group (95% CI: -0.43-0.09; Z = 1.27; p = .2). TC levels in the curcumin group were 0.21 lower versus the placebo group (95% CI: -0.55-0.13; Z = 1.22; p = .22). TG level in the curcumin group were 0.05 lower versus the placebo (95% CI: -0.20-0.11; Z = 0.58; p = .56). This study suggests that curcumin may reduce blood lipid levels and can be used as a hypolipidemic agent.

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System.

BACKGROUND: With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT). METHODS: This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation. RESULTS: Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively. CONCLUSIONS: In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases.

Distinguishing characteristics of exposure to biguanide and sulfonylurea anti-diabetic medications in the United States.

OBJECTIVES: Biguanides and sulfonylureas are anti-hyperglycemic drugs commonly used in the United States. Poisoning with these drugs may lead to serious consequences. The diagnosis of biguanide and sulfonylurea poisoning is based on history, clinical manifestations, and laboratory studies. METHODS: This study is a six-year retrospective cohort analysis based on the National Poison Data System. Clinical effects of 6183 biguanide and sulfonylurea exposures were identified using binary logistic regression. RESULTS: The mean age of patients with biguanide and sulfonylurea exposure was 39.27 ± 28.91 and 28.91 ± 30.41 years, respectively. Sulfonylurea exposure is most commonly seen via unintentional exposure, while biguanide exposure frequently occurs as a result of intentional ingestion. Minor and moderate outcomes commonly developed following biguanide and sulfonylurea exposure, respectively. Sulfonylurea exposure was less likely to develop clinical effects abdominal pain, metabolic acidosis, diarrhea, nausea, vomiting, and elevated creatinine than patients ingesting biguanides. However, sulfonylurea exposure was more likely to cause dizziness or vertigo, tremor, drowsiness or lethargy, agitation, diaphoresis, and hypoglycemia. CONCLUSIONS: Our study is the first to use a wide range of national data to describe the clinical characteristics that differentiate the toxicologic exposure to biguanides and sulfonylureas. Sulfonylurea exposure is commonly seen via unintentional exposure, while metformin exposure is frequently seen via intentional exposure. Sulfonylurea toxicity is more likely to cause agitation, dizziness or vertigo, tremor, diaphoresis, and hypoglycemia, while metformin exposure induces abdominal pain, acidosis, diarrhea, nausea, vomiting, and elevated creatinine.

Metabolic impact of saffron and crocin: an updated systematic and meta-analysis of randomised clinical trials.

The present systematic and meta-analysis study was designed to show the protective impact of saffron and crocin supplementation on hyperlipidaemia and hyperglycaemia in randomised and clinical trials (RCTs). A pooled analysis using a model for random-effects showed that HDL-C levels were 0.21 fold higher in the saffron and 0.01 fold higher in the crocin group than placebo. LDL-C levels in the saffron group reduced by 0.51 and 0.04 fold in the crocin group versus the placebo. Moreover, TC levels in the saffron group were 0.19 lower and 0.11 fold lower in crocin group than in the placebo group. TG level in saffron group was 0.04 lower and 0.02 fold lower in crocin than the control group. The blood glucose levels did not significantly differ from the control group. This study suggests that saffron and crocin may modulate the serum lipid profile in patient with metabolic disorders.

Decision tree outcome prediction of acute acetaminophen exposure in the United States: A study of 30,000 cases from the National Poison Data System.

Acetaminophen is one of the most commonly used analgesic drugs in the United States. However, the outcomes of acute acetaminophen overdose might be very serious in some cases. Therefore, prediction of the outcomes of acute acetaminophen exposure is crucial. This study is a 6-year retrospective cohort study using National Poison Data System (NPDS) data. A decision tree algorithm was used to determine the risk predictors of acetaminophen exposure. The decision tree model had an accuracy of 0.839, an accuracy of 0.836, a recall of 0.72, a specificity of 0.86 and an F1_score of 0.76 for the test group and an accuracy of 0.848, a recall of 0.85, a recall of 0.74, a specificity of 0.87 and an F1_score of 0.78 for the training group. Our results showed that elevated serum levels of liver enzymes, other liver function test abnormality, anorexia, acidosis, electrolyte abnormality, increased bilirubin, coagulopathy, abdominal pain, coma, increased anion gap, tachycardia and hypotension were the most important factors in determining the outcome of acute acetaminophen exposure. Therefore, the decision tree model is a reliable approach in determining the prognosis of acetaminophen exposure cases and can be used in an emergency room or during hospitalization.

Prognostic factors of acetaminophen exposure in the United States: An analysis of 39,000 patients.

Acetaminophen is a frequently used over-the-counter or prescribed medication in the United States. Exposure to acetaminophen can lead to acute liver cytolysis, acute liver failure, acute kidney injury, encephalopathy, and coagulopathy. This retrospective cohort study (1/1/2012 to 12/31/2017) investigated the clinical outcomes of intentional and unintentional acetaminophen exposure using the National Poison Data System data. The frequency of outcomes, chronicity, gender, route of exposure, the reasons for exposure, and treatments as described. Binary logistic regression was used to estimate the prognostic factors and odds ratios (OR) with 95% confidence intervals (CI) for outcomes. This study included 39,022 patients with acetaminophen exposure. Our study demonstrated that the likelihood of developing severe outcomes increased by aging (OR = 1.12, 95% CI: 1.08-1.015) and was lower in females (OR = 0.88, 95% CI: 0.78-0.99). Drowsiness/lethargy (OR = 1.48, 95% CI: 1.22-1.82), agitation (OR = 1.66, 95% CI: 1.11-2.50), coma (OR = 23.95, 95% CI: 17.05-33.64), bradycardia (OR = 2.29, 95% CI: 1.22-4.32), rhabdomyolysis (OR = 8.84, 95% CI: 3.71-21.03), hypothermia (OR = 4.1, 95% CI: 1.77-9.51), and hyperthermia 2.10 (OR = 2.10, 95% CI: 1.04-4.22) were likely associated with major outcomes or death. Treatments included intravenous N-acetylcysteine (61%), oral N-acetylcysteine (10%), vasopressor (1%), hemodialysis (0.7%), fomepizole (0.1%), hemoperfusion (0.03%), and liver transplant (0.1%). In conclusion, it is important to consider clinical presentations of patients with acetaminophen toxicity that result in major outcomes and mortality to treat them effectively.

A systematic review of clinical and laboratory findings of lead poisoning: lessons from case reports.

Lead is one of the most toxic heavy metals in the environment. The present review aimed to highlight hazardous pollution sources, management, and review symptoms of lead poisonings in various parts of the world. The present study summarized the information available from case reports and case series studies from 2009 to March 2020 on the lead pollution sources and clinical symptoms. All are along with detoxification methods in infants, children, and adults. Our literature compilation includes results from 126 studies on lead poisoning. We found that traditional medication, occupational exposure, and substance abuse are as common as previously reported sources of lead exposure for children and adults. Ayurvedic medications and gunshot wounds have been identified as the most common source of exposure in the United States. However, opium and occupational exposure to the batteries were primarily seen in Iran and India. Furthermore, neurological, gastrointestinal, and hematological disorders were the most frequently occurring symptoms in lead-poisoned patients. As for therapeutic strategies, our findings confirm the safety and efficacy of chelating agents, even for infants. Our results suggest that treatment with chelating agents combined with the prevention of environmental exposure may be an excellent strategy to reduce the rate of lead poisoning. Besides, more clinical studies and long-term follow-ups are necessary to address all questions about lead poisoning management.

Impact of Metformin on Cancer Biomarkers in Non-Diabetic Cancer Patients: A Systematic Review and Meta-Analysis of Clinical Trials.

INTRODUCTION: Our aim was to investigate and evaluate the influence of metformin on cancer-related biomarkers in clinical trials. METHODS: This systematic study was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Major databases, including Scopus, Web of Sciences, PubMed, Ovid-Medline, and Cochrane, were systematically reviewed by February 2020. Clinical trials investigating metformin effects on the evaluation of homeostatic models of insulin resistance (HOMA-IR), Ki-67, body mass index (BMI), fasting blood sugar (FBS), and insulin were selected for further analysis. Quality assessment was performed with version 2 of the Cochrane tool for determining the bias risk for randomized trials (RoB 2). Heterogeneity among the included studies was assessed using the Chi-square test. After quality assessment, a random effects model was performed to summarize the data related to insulin, HOMA-IR, Ki-67, and a fixed-effect model for FBS and BMI in a meta-analysis. RESULTS: Nine clinical trials with 716 patients with operable breast and endometrial cancer and 331 with primary breast cancer were involved in the current systematic and meta-analysis study. Systematic findings on the nine publications indicated metformin decreased insulin levels in four studies, FBS in one, BMI in two, Ki-67 in three studies, and HOMA-IR in two study. The pooled analysis indicated that metformin had no significant effect on the following values: insulin (standardized mean differences (SMD) = -0.87, 95% confidence intervals (CI) (-1.93, 0.19), p = 0.11), FBS (SMD = -0.18, 95% CI (-0.30, -0.05), p = 0.004), HOMA-IR (SMD = -0.17, 95% CI (-0.52, 0.19), p = 0.36), and BMI (SMD = -0.13, 95% CI (-0.28, 0.02), p = 0.09). Metformin could decrease Ki-67 in patients with operable endometrial cancer versus healthy subjects (SMD = 0.47, 95% CI (-1.82, 2.75), p = 30.1). According to Egger's test, no publication bias was observed for insulin, FBS, BMI, HOMA-IR, and Ki-67. CONCLUSIONS: Patients with operable breast and endometrial cancer under metformin therapy showed no significant changes in the investigated metabolic biomarkers in the most of included study. It was also found that metformin could decrease Ki-67 in patients with operable endometrial cancer. In comparison to the results obtained of our meta-analysis, due to the high heterogeneity and bias of the included clinical trials, the present findings could not confirm or reject the efficacy of metformin for patients with breast cancer and endometrial cancer.