Background The prevalence of myopia has increased in recent years, with changes being dynamic and uneven in different regions. The purpose of this study is to evaluate the prevalence of visual impairment caused by myopia in Bulgarian school children, associated risk factors, and health care coverage. Methods A cross-sectional study among 1401 children (mean age 10.38, standard deviation 2.70) is conducted in three locations in Bulgaria from 2016 to 2020. Refractive error is measured with an auto-refractor in the absence of cycloplegia, the visual acuity is assessed without refractive error correction. A paper-based preliminary questionnaire is used to collect data on previous eye examinations, prescribed optical vision correction, regularity of wearing corrective glasses and risk factors. Results Children with myopic objective refraction ≤ -0.75 D and decimal visual acuity ≤ 0.8 on at least one of the eyes are 236 out of 1401 or 16.85%. The prevalence of myopia varies depending on age, geographical location, and school profile. The rate of myopic children in age group 6–10 is 14.2% compared to 19.9% in age group 11–15. The prevalence of myopic children in the urban populations is 31.4% (capital) and 19.9% (medium-sized town) respectively, and only 8.4% in the rural population. Our results show 53% increase of the prevalence of myopia in the age group 11–15 compared to a 2009 report. The analysis of data associated with health care coverage factors of all myopic pupils shows that 71.6% had a previous eye examination, 43.2% have prescription for corrective glasses, 27.5% wear their glasses regularly. Risk factors for higher odds of myopia are gender (female), age (adolescence), and parents with impaired vision. Residence in a small town and daily sport activities correspond to lower odds for myopia. The screen time (time in front of the screen calculated in hours per day) is self-reported and is not associated with increased odds of myopia when accounted for the other risk factors. Conclusions The prevalence of myopia in this study is higher compared to previous studies in Bulgaria. Additional studies would be helpful in planning adequate prevention and vision care.

The Health and Retirement Study was designed to evaluate changes in health and labor force participation during and after the transition from working to retirement. Every 2 years, participants provided information about their self-rated health (SRH), body mass index (BMI), smoking status, and other characteristics. Our goal was to assess the effects of smoking and gender on trajectories of change in BMI and SRH over time. Joint longitudinal analysis of outcome measures is preferable to separate analyses because it allows to account for the correlation between the measures, to test the effects of predictors while controlling type I error, and potentially to improve efficiency. However, because SRH is an ordinal measure while BMI is continuous, formulating a joint model and parameter estimation is challenging. A joint correlated probit model allowed us to seamlessly account for the correlations between the measures over time. Established estimating procedures for such models are based on quasi-likelihood or numerical approximations that may be biased or fail to converge. Therefore, we proposed a novel expectation-maximization algorithm for parameter estimation and a Monte Carlo bootstrap approach for standard errors approximation. Expectation-maximization algorithms have been previously considered for combinations of binary and/or continuous repeated measures; however, modifications were needed to handle combinations of ordinal and continuous responses. A simulation study demonstrated that the algorithm converged and provided approximately unbiased estimates with sufficiently large sample sizes. In the Health and Retirement Study, male gender and smoking were independently associated with steeper deterioration in self-rated health and with lower average BMI. Copyright © 2016 John Wiley & Sons, Ltd.

CYP2D6 and CYP2C19 are enzymes of essential significance for the pharmacokinetics of a multitude of commonly used antidepressants, antipsychotics, antiemetics, β-blockers, opioids, antiestrogen, antacids, etc. Polymorphisms in the respective genes are well established as resulting in functional differences, which in turn can impact safety and efficacy. Importantly, the prevalence of genetic CYP2D6 and CYP2C19 variability differs drastically between populations. Drawing on the limited information concerning genotype frequencies in Bulgaria, we here analyzed 742 Bulgarian psychiatric patients predominantly diagnosed with depression and/or anxiety. Specifically, we analyzed frequencies of CYPC19*2, *4 and *17, as well as of CYP2D6*2, *3, *4, *5, *6, *10 and *41. In total, 571 out of 742 patients (77%) carried at least one variant which impacts metabolizer status. Overall, 48.6% of the studied individuals were classified as non-normal metabolizers of CYP2D6 with most exhibiting reduced function (38.2% intermediate metabolizers and 6.6% poor metabolizers). In contrast, for CYP2C19, the majority of non-normal metabolizers showed increased functionality (28.9% rapid and 5.5% ultrarapid metabolizers), while reduced activity metabolizer status accounted for 25.6% (23.8% intermediate and 1.8% poor metabolizers). These results provide an important resource to assess the genetically encoded functional variability of CYP2D6 and CYP2C19 which may have significant implications for precision medicine in Bulgarian psychiatry practice.

Introduction: Pharmacogenetics in psychiatry is currently gaining momentum. The efficiency of antipsychotic therapy is often limited by the lack of response and the presence of side effects. Pharmacogenetic variation is probably one of the causative factors for the observed interindividual differences in the response to and the side effects of antipsychotics, which could be addressed and whose negative effects could be avoided or mitigated. Aim: The present study aimed to conduct a comprehensive analysis of the frequency of DRD2 rs1799732, COMT rs4680, MC4R rs489693, and HTR2C rs3813929 in Bulgarian psychiatric patients. Materials and methods: The frequency of genotypes and the alleles of variants DRD2 rs1799732, COMT rs4680, MC4R rs489693, and HTR2C rs3813929 were studied in a cohort of 515 Bulgarian psychiatric patients using the polymerase chain reaction (PCR) method. Results: We found no significant difference between our cohort and the dataset of the 1000 Genomes Project. Moreover, we found that 433 out of 515 patients carried at least one, and 191 out of 515 carried at least two variants which, based on multiple scientific sources with consistent findings, could potentially alter the expected response rate, time to respond and/or risk of side effects to antipsychotic medications. Conclusions: Considering the consistent data about the frequency of these pharmacogenetic variants, testing these genetic variants may prove useful in clinical practice. Further studies regarding the clinical interpretation and frequency distribution in larger cohorts and different populations are warranted.

BackgroundThere is a strong case for de‐risking neurodegenerative agent development through highly informative experimental medicine studies early in the disease process. These types of studies are dependent on a research infrastructure that includes volunteer registries holding highly granular phenotypic and genotypic data to allow stratified study selection. Examples of such registries include the Brain Health Registry, Great Minds and PROTECT cohorts which rely on remote cognitive, self‐reported medical history and genetic data. This requires the development of effective algorithms to predict the presence of preclinical dementia pathology. In this study we sought to address this need by building a machine learning (ML) ATN risk prediction algorithm which incorporates data typically collected in such registries.MethodsTo build a ML algorithm that is validated against an existing regression‐based model (Calvin et al. 2020), we used the EPAD LCS cohort (V1500.0). We excluded participants with 1) known diagnosis of dementia or Mild Cognitive Impairment or Clinical Dementia Rating scale ≥ 0.5 and 2) no cerebrospinal fluid biomarkers. Participants were categorised into 5 ATN categories: (i) Normal AD biomarkers: A−T−(N)−; (ii) Alzheimer’s pathologic change: A+T−(N)−; (iii) Alzheimer’s disease: A+T+(N)±; (iv)Alzheimer’s and concomitant non‐Alzheimer’s pathologic change: A+T−(N)+; (v)Non‐AD pathologic change: A−T ± (N)+; A−T+(N)−. Using a Weight of Evidence and Information Value method we identified 13 significant features for testing differences between each of the four neurodegeneration‐related groups vs. controls (A‐T‐N‐). Random Forest and XGBoost with 5‐fold cross validations were used to optimise the Area Under the Curve (AUC) metric.ResultThe study dataset included 927 individuals. Our optimal results outperformed the regression models in the Calvin et al. 2020 paper by between 2 and 12%. The optimal feature sets were not consistent across the 4 models with the A+T−(N)+ vs A−T−(N)− differing the most from the rest.ConclusionOur study demonstrates the gains offered by ML in generating ATN risk prediction over logistic regression models among pre‐dementia individuals. The reliance of the model on variables that can be collected remotely demonstrates its utility for research registers. An openly available version of the ML algorithm for use by research registries is under development.