Evangelos C. Rizos

Background/objectives: Overweight and obesity in children and adolescents is a global health issue with increasing prevalence. The Mediterranean diet (Med diet) is an extensively studied dietary pattern with benefits on various health conditions such as cardiovascular disease, diabetes mellitus and obesity in adult populations but the evidence on children and adolescents remains limited. The aim of this systematic review and meta-analysis is to collect, assess, and synthesize evidence from randomized controlled trials (RCTs) on the efficacy of Med diet on anthropometric and cardiometabolic parameters in children and adolescents with excess body weight. Methods: Three electronic databases (PubMed, Cochrane Central Register of Controlled Trials, Scopus) and references in relevant studies and systematic reviews were searched. Random-effects meta-analysis models were used to synthesize study effect estimates. Heterogeneity was assessed using the I ² . The quality of evidence was evaluated using the Cochrane Risk of Bias tool v2.0. Results: Five RCTs involving 330 children and adolescents affected by overweight or obesity were included. Mean study duration was 12 weeks (range: 8–16 weeks). Compared with the control group, Med diet was significantly associated with lower body mass index (BMI) (Standardized mean difference [SMD] = −0.26, 95% confidence intervals [CI] = −0.49 to −0.03, I ² = 0%) and lower systolic blood pressure (SMD = −0.48, 95% CI = −0.87 to −0.09, I ² = 45.9%). No statistically significant associations were observed for any other anthropometric or cardiometabolic parameter studied. Regarding risk of bias, all RCTs were judged to have some concerns for each outcome assessed. Conclusion: This systematic review suggests that Med diet could have a potentially beneficial effect on the reduction of BMI among youths with excess body weight. Nevertheless, further research, with greater methodological rigor, higher participation rates and longer follow-up is needed. Systematic Review Registration: This systematic review and meta-analysis is registered with PROSPERO (CRD420251143115).

Adherence to statin therapy remains suboptimal and is significantly influenced by demographic and clinical factors. Targeted strategies are needed to improve adherence, particularly in high-risk groups. Adherence to statin therapy is a key determinant of cardiovascular risk reduction, yet real-world adherence remains uncertain across populations. We conducted a systematic review and meta-analysis to estimate the prevalence of good adherence to statin monotherapy and to identify demographic and clinical factors associated with adherence among adults prescribed lipid-lowering therapy for the prevention of atherosclerotic cardiovascular disease. PubMed and Scopus were searched through May 2025 for randomized controlled trials, cohort, nested case control, and cross-sectional studies assessing statin adherence. Data were extracted on study design, participant characteristics, comorbidities, adherence measurement methods and duration, and statin type. Study quality was assessed using the Newcastle-Ottawa Scale, and risk of bias in randomized trials was evaluated with the Cochrane RoB 2 tool. Random-effect meta-analysis, along with subgroup and sensitivity analyses by follow-up duration, adherence thresholds, and study quality, was performed. Seventy-six studies including 5,898,141 participants with a median follow-up of 24 months were analysed. The pooled prevalence of good adherence (≥80% medication use) was 62.4% (95% CI: 58.3–66.5%), with lower adherence in primary prevention compared with secondary prevention settings. Lower adherence was associated with female sex, Black race, smoking, depression, and heart failure, whereas higher adherence was observed among older adults, those with myocardial infarction or hypertension, individuals with multiple comorbidities, and patients receiving polypharmacy. Primary non-adherence was not reported in any included study, and subgroup and sensitivity analyses showed consistent findings. Overall, statin adherence remains suboptimal and is strongly influenced by demographic and clinical factors, underscoring the need for targeted strategies to improve adherence, particularly among high-risk groups.

Patients with diabetes struggle to control blood glucose with appropriate insulin doses. Classic prediction methods such as multi-linear regression often lead to ineffective blood’s glucose control, because they are not capable to identify the correct neighborhood (other similar patients) of the target patient, since they do not take under consideration demographic (age, gender, etc.) or other clinical similarities of patients’ therapeutics (blood glucose levels, etc.). In this paper, we use deep reinforcement learning (DRL), and define a transition probability function to intelligently select the right neighbors of the target patient. We also define a reward and policy function for our DRL method, which can predict the optimal insulin dose for a patient and achieves optimal glucose control, while decreasing hypoglycemia risk. Our experimental results show that our Deep Q Network (DQN) algorithm can assist doctors to administer optimal insulin dose for diabetes treatment and keep the glucose levels in blood for longer time periods in accepted values range.

Introduction: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination and axonal degeneration. Evidence has shown that lipid metabolism is associated with MS clinical outcomes. The aim was to systematically investigate the association between high-density lipoprotein cholesterol (HDL-C) and disease activity in MS.Material and methods: Medline via PubMed, the Cochrane Library and Clinicaltrials.gov databases were searched for studies with data on HDL-C in patients with MS. A qualitative synthesis of published prospective and retrospective studies on the role of HDL-C and other lipid profile parameters in MS was performed. Additionally, a meta-analysis on HDL-C mean differences (MD) between relapsing remitting MS (RRMS) cases and healthy controls (HC) was performed.Results: Literature search identified 13 eligible studies evaluating the HDL-C levels of RRMS patients. In total, 1692 participants were included, 859 of which were RRMS patients, and 833 were HC. The pooled analysis demonstrated that RRMS patients had significantly lower HDL-C levels compared with HC (MD: –3.35 mg/dl, 95% CI (–5.69, –1.01), p = 0.005). Regarding triglycerides (TG) and total cholesterol (TC), the pooled analysis from 11 and 13 studies, respectively, showed significantly higher TG (MD = 26.33, 95% CI (15.03, 37.62), p < 0.00001) and TC levels (MD = 11.03, 95% CI (0.45, 21.60), p = 0.04) in RRMS versus HC. No significant differences were observed in low-density lipoprotein cholesterol levels between RRMS and HC.Conclusions: HDL-C levels were significantly decreased in RRMS patients compared with HC. HDL-C and other lipid profile parameters should be considered in the assessment of patients with RRMS.

Introduction—Aim: Adopting a lifestyle that incorporates regular physical activity confers substantial benefits to both physical and mental health and is recommended for prediabetic individuals. The aim of this study is to investigate the impact of activity tracking apps on increasing physical activity and its effect on glycemic control in people with prediabetes. Materials and Methods: This pilot study included 37 participants, 18 in the prediabetic group and 19 in the normoglycemic group matched for age and gender (mean age 53 years, 40% males). Participants used the Google Fit app for 3 months. The number of daily steps was recorded via the app, and blood and urine tests as well as body fat measurements were conducted before and following 3 months of app use. The co-primary outcome was the change in steps, and the change in HbA1c in both groups. Secondary outcomes were the change in fasting plasma glucose (FPG) (main secondary outcome), as well as lipid parameters, body mass index, visceral fat, and kidney function indices among the two groups. Results: Both groups increased the daily step count following the app intervention, without any statistically significant difference when we compared the steps change between the two groups. We found a statistically significant difference between HbA1c levels in favor of the prediabetic compared to the normoglycemic group [mean difference (MD) 0.16, 95%CI 0.04, 0.28, p-value 0.01)], following the 3-month intervention. Additionally, there was a statistically significant difference between FPG levels in favor of the prediabetic compared to the normoglycemic group (MD 9.06, 95%CI 4.16, 13.96, p-value 0.001). Conclusions: The present study suggests that the use of activity tracking apps, combined with tailored activity goals and monthly supportive phone calls, may contribute to improved glycemic control among individuals with prediabetes.

Background/Objectives: Lifestyle interventions such as dietary changes have been proposed to control the cardiometabolic risk factors and thus prevent cardiovascular (CV) disease (CVD). We performed an umbrella review to investigate whether different dietary patterns affect CV risk in individuals with at least one cardiometabolic risk factor (hypertension, dyslipidemia, obesity, diabetes, metabolic syndrome) but not established CVD. Methods: We systematically searched the PubMed and Scopus databases (up to August 2024) for the systematic reviews and meta-analyses of randomized controlled trials (RCTs). Articles should be written in English and refer to a specific dietary pattern (such as Mediterranean diet, etc.). The population studied referred to adults with at least one cardiovascular (CV) risk factor. Results: From 4512 records identified, we finally included 25 meta-analyses with a total of 329 associations. Strong evidence for a benefit was found for LCD with reductions in BW [MD: −4.79 (95% CI −5.85, −3.72) kg, p ≤ 0.001], SBP [MD: −6.38 (95% CI −7.84, −4.93) mmHg, p ≤ 0.001], TG [WMD: −5.81 (95% CI −7.96, −3.66) mg/dL, p ≤ 0.001], and fasting plasma insulin [MD: −15.35 (95% CI −19.58, −11.12) pmol/L, p ≤ 0.001], as well as for low-GI diet for the reduction of BW [SMD: −0.66 (95% CI −0.90, −0.43) kg, p ≤ 0.001]. Conclusions: Across many dietary patterns, LCD showed strong or highly suggestive evidence for a benefit on SBP, BW reduction, and lipid profile improvement. Secondarily, low-GI, DASH, and Portfolio and Nordic diets suggested beneficial effects on controlling CV risk.

This paper addresses the critical challenge of predicting future insulin dosages for diabetic patientsin Intensive Care Units (ICUs), a setting where precise glycemic control is crucial. The work proposes aunique methodology that sidesteps the complications associated with traditional glucose-insulin interactionmodels, due to data sparsity. We introduce a simplified version of Bergman’s Insulin-Glucose Interaction Modeland construct an extended dataset based on the MIMIC III database. This dataset includes 870 predictivefeatures encompassing demographic data, prior insulin administrations, and average glucose levels. The paperalso introduces a Reinforcement Learning approach, utilizing Deep Q-Learning, to optimize both the thetraining population and the feature selection for individualized predictions. We found that our CompositeMultilinear Regression Model algorithm outperforms the single-patient regression model in terms of MeanAbsolute Error (MAE). Specifically, the MAE values for the Type I and Type II Diabetic groups were 2.33 and3.68, respectively, significantly better than the single-patient regression model. The work contributes a novelapproach to insulin dose prediction, offering a promising pathway for more effective glucose management inICU settings.

Background: Fertilizers are widely used to supply nutrients to crops, thereby increasing yields and soil fertility. However, the effects of their production and application on human health through occupational, residential, and environmental exposure remain unclear. Objective: To conduct a systematic review of epidemiological studies on the association between exposure to fertilizers and health-related outcomes. Methods: We searched in PubMed, Scopus, and Web of Science for cohort, case-control, cross-sectional, and ecological studies (up to May 2024) related to exposure to fertilizers and any reported human health endpoints across all age groups, without language or geographical limitations. Data were extracted for population and study characteristics, type of fertilizer used, exposure assessment, sample size, outcome and its definition, effect estimate, and quality characteristics from the eligible studies, and they were descriptively synthesized. Results: We found 65 eligible publications, with 407 postulated associations. Forty-six publications (321 associations) assessed exposure to inorganic fertilizers, and nineteen studies (93 associations) assessed organic fertilizers. Exposure assessed was related to occupation, residence, and/or proximity. The assessed outcomes were diverse, with considerable harmonization challenges. Inorganic fertilizers have been associated with an increased risk of cancerous outcomes in a small number of studies with methodological limitations and low replication validity, while organic fertilizers have been associated with infections and diarrhea. Conclusions: The epidemiological evidence suggests possible associations between inorganic fertilizers with solid organ tumors and hematological malignancies and organic fertilizers with infections and diarrhea. However, the available evidence is limited, and heterogeneity prevails. Further research is needed to enlarge the evidence base and increase the replication validity and robustness of the results.