The prevalence of diabetes and prediabetes in the adult population of Jeddah, Saudi Arabia- a community-based survey

BACKGROUND: Type 2 (T2DM) is believed to be common in Saudi Arabia, but data are limited. In this population survey, we determined the prevalence of T2DM and prediabetes. MATERIALS AND METHODS: A representative sample among residents aged ≥ 18 years of the city of Jeddah was obtained comprising both Saudi and non-Saudi families (N = 1420). Data on dietary, clinical and socio-demographic characteristics were collected and anthropometric measurements taken. Fasting plasma glucose and glycated hemoglobin (HbA1c) were used to diagnose diabetes and prediabetes employing American Diabetes Association criteria. Multiple logistic regression analysis was used to identify factors associated with T2DM. RESULTS: Age and sex standardized prevalence of prediabetes was 9.0% (95% CI 7.5-10.5); 9.4% (7.1-11.8) in men and 8.6% (6.6-10.6) in women. For DM it was 12.1% (10.7-13.5); 12.9% (10.7-13.5) in men and 11.4% (9.5-13.3) in women. The prevalence based on World Population as standard was 18.3% for DM and 11.9% for prediabetes. The prevalence of DM and prediabetes increased with age. Of people aged ≥50 years 46% of men and 44% of women had DM. Prediabetes and DM were associated with various measures of adiposity. DM was also associated with and family history of dyslipidemia in women, cardiovascular disease in men, and with hypertension, dyslipidemia and family history of diabetes in both sexes. DISCUSSION: Age was the strongest predictor of DM and prediabetes followed by obesity. Of people aged 50 years or over almost half had DM and another 10-15% had prediabetes leaving only a small proportion of people in this age group with normoglycemia. Since we did not use an oral glucose tolerance test the true prevalence of DM and prediabetes is thus likely to be even higher than reported here. These results demonstrate the urgent need to develop primary prevention strategies for type 2 diabetes in Saudi Arabia

Hookah smoking is strongly associated with diabetes mellitus, metabolic syndrome and obesity: a population-based study

Objectives The adverse effects of cigarette smoking have been widely studied before, whilst the effects of hookah smoking has received less attention, although it is a common habit in the Middle East. Here we have investigated the effects of cigarette and hookah smoking on biochemical characteristics in a representative population sample derived from the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study, from Northeastern Iran. Study design A total of 9840 subjects from the MASHAD population study were allocated to five groups; non-smokers (6742), ex-smokers (976), cigarette smokers (864), hookah smokers (1067), concomitant cigarette and hookah smokers (41). Methods Baseline characteristics were recorded in a questionnaire. Biochemical characteristics were measured by routine methods. Data were analyzed using SPSS software and p < 0.05 was considered significant. Results After adjustment for age and sex; the presence of CVD, obesity, metabolic syndrome, DM and dyslipidemia were significantly (p < 0.001) related to smoking status. After multivariate analysis, HDL (p < 0.001), WBC (p < 0.001), MCV (p < 0.05), PLT (p < 0.01) and RDW (p < 0.001), and the presence of CVD (p < 0.01), obesity (p < 0.001), metabolic syndrome (p < 0.05) and DM (p < 0.01) remained significant between cigarette smokers and non-smokers. Between hookah smokers and non-smokers; uric acid (p < 0.001), PLT (p < 0.05) and RDW (p < 0.05), and the presence of obesity (p < 0.01), metabolic syndrome (p < 0.001), diabetes (p < 0.01) and dyslipidemia (p < 0.01) remained significant after logistic regression. Conclusion There was a positive association between hookah smoking and metabolic syndrome, diabetes, obesity and dyslipidemia which was not established in cigarette smoking

A multidisciplinary systematic review of the use of diagrams as a means of collecting data from research subjects: application, benefits and recommendations

BACKGROUND: In research, diagrams are most commonly used in the analysis of data and visual presentation of results. However there has been a substantial growth in the use of diagrams in earlier stages of the research process to collect data. Despite this growth, guidance on this technique is often isolated within disciplines. METHODS: A multidisciplinary systematic review was performed, which included 13 traditional healthcare and non-health-focused indexes, non-indexed searches and contacting experts in the field. English-language articles that used diagrams as a data collection tool and reflected on the process were included in the review, with no restriction on publication date. RESULTS: The search identified 2690 documents, of which 80 were included in the final analysis. The choice to use diagrams for data collection is often determined by requirements of the research topic, such as the need to understand research subjects' knowledge or cognitive structure, to overcome cultural and linguistic differences, or to understand highly complex subject matter. How diagrams were used for data collection varied by the degrees of instruction for, and freedom in, diagram creation, the number of diagrams created or edited and the use of diagrams in conjunction with other data collection methods. Depending on how data collection is structured, a variety of options for qualitative and quantitative analysis are available to the researcher. The review identified a number of benefits to using diagrams in data collection, including the ease with which the method can be adapted to complement other data collection methods and its ability to focus discussion. However it is clear that the benefits and challenges of diagramming depend on the nature of its application and the type of diagrams used. DISCUSSION/CONCLUSION: The results of this multidisciplinary systematic review examine the application of diagrams in data collection and the methods for analyzing the unique datasets elicited. Three recommendations are presented. Firstly, the diagrammatic approach should be chosen based on the type of data needed. Secondly, appropriate instructions will depend on the approach chosen. And thirdly, the final results should present examples of original or recreated diagrams. This review also highlighted the need for a standardized terminology of the method and a supporting theoretical framework

Effects of fungicides and bactericides on orchid seed germination and shoot tip cultures in vitro

Amphotericin B, benomyl, gentamycin, nystatin, quintozene penicillin G, sodium omadine, and vancomycin singly and in several combinations have no deleterious effects on the germination of orchid seeds, but inhibit the growth in vitro of shoot tip explants. © 1981 Martinus Nijhoff/Dr W. Junk Publishers

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies

Horses damp the spring in their step

The muscular work of galloping in horses is halved by storing and returning elastic strain energy in spring-like muscle–tendon units1, 2.These make the legs act like a child\u27s pogo stick that is tuned to stretch and recoil at 2.5 strides per second. This mechanism is optimized by unique musculoskeletal adaptations: the digital flexor muscles have extremely short fibres and significant passive properties, whereas the tendons are very long and span several joints3, 4. Length change occurs by a stretching of the spring-like digital flexor tendons rather than through energetically expensive length changes in the muscle5. Despite being apparently redundant for such a mechanism5, the muscle fibres in the digital flexors are well developed. Here we show that the mechanical arrangement of the elastic leg permits it to vibrate at a higher frequency of 30–40 Hz that could cause fatigue damage to tendon and bone. Furthermore, we show that the digital flexor muscles have minimal ability to contribute to or regulate significantly the 2.5-Hz cycle of movement, but are ideally arranged to damp these high-frequency oscillations in the limb

Bruikbaarheid en validiteit van de Nederlandse versie van de Montreal Cognitive Assessment (MoCA-D) bij het diagnosticeren van Mild Cognitive Impairment,Applicability and validity of the Dutch version of the Montreal Cognitive Assessment (moCA-d) in diagnosing MCI

Contains fulltext : 87418.pdf (publisher's version ) (Closed access)OBJECTIVE: The MoCA is a new screening test to detect Mild Cognitive Impairment (MCI). Purpose of this study is validating the Dutch version (MoCA-D). METHOD: We administered the MoCA-D to healthy control subjects and to elderly with MCI or dementia from a memory disorder outpatient clinic and a geriatric (outpatient) clinic (n = 30, 32, 37 respectively, age > or = 60). Neuropsychological testing was part of the standard procedure for patients to diagnose MCI. Sensitivity, specificity and predictive values (positive: PPV and negative: NPV) of the MoCA-D were assessed. RESULTS: A significant effect of group was found on MoCA-D total score (F (2.95) =67.9; p < 0.01). With a cutoff score of < or = 25, sensitivity and specificity to detect MCI in relation to healthy controls were 72% and 73%, respectively. PPV and NPV were 84% and 56%, respectively. With a cut-off score of < or = 20, sensitivity to detect dementia in relation to MCI was 100% for severe dementia and 75% for mild dementia. Specificity for dementia was 81%, PPV 94% and NPV 55%. CONCLUSION: The MoCA-D distinguishes between healthy elderly, MCI patients and dementia patients. However, in this study, insufficient sensitivity and poor specificity were found. For the present, applying a broader and flexible screening procedure in order to detect MCI seems a more useful method than the interpretation of one test result in particular.10 p