A multinational study on artificial intelligence adoption: Clinical implementers' perspectives.

BACKGROUND: Despite substantial progress in AI research for healthcare, translating research achievements to AI systems in clinical settings is challenging and, in many cases, unsatisfactory. As a result, many AI investments have stalled at the prototype level, never reaching clinical settings. OBJECTIVE: To improve the chances of future AI implementation projects succeeding, we analyzed the experiences of clinical AI system implementers to better understand the challenges and success factors in their implementations. METHODS: Thirty-seven implementers of clinical AI from European and North and South American countries were interviewed. Semi-structured interviews were transcribed and analyzed qualitatively with the framework method, identifying the success factors and the reasons for challenges as well as documenting proposals from implementers to improve AI adoption in clinical settings. RESULTS: We gathered the implementers' requirements for facilitating AI adoption in the clinical setting. The main findings include 1) the lesser importance of AI explainability in favor of proper clinical validation studies, 2) the need to actively involve clinical practitioners, and not only clinical researchers, in the inception of AI research projects, 3) the need for better information structures and processes to manage data access and the ethical approval of AI projects, 4) the need for better support for regulatory compliance and avoidance of duplications in data management approval bodies, 5) the need to increase both clinicians' and citizens' literacy as respects the benefits and limitations of AI, and 6) the need for better funding schemes to support the implementation, embedding, and validation of AI in the clinical workflow, beyond pilots. CONCLUSION: Participants in the interviews are positive about the future of AI in clinical settings. At the same time, they proposenumerous measures to transfer research advancesinto implementations that will benefit healthcare personnel. Transferring AI research into benefits for healthcare workers and patients requires adjustments in regulations, data access procedures, education, funding schemes, and validation of AI systems.

Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project.

BACKGROUND: Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48-72 h after blood draw; thus, early treatment decisions are made in the absence of a definite diagnosis. METHODS: In this retrospective study, we assessed the performance of different supervised machine learning algorithms for the early differential diagnosis of candidemia and bacteremia in adult patients on a large dataset automatically extracted within the AUTO-CAND project. RESULTS: Overall, 12,483 episodes of candidemia (1275; 10%) or bacteremia (11,208; 90%) were included in the analysis. A random forest classifier achieved the best diagnostic performance for candidemia, with sensitivity 0.98 and specificity 0.65 on the training set (true skill statistic [TSS] = 0.63) and sensitivity 0.74 and specificity 0.57 on the test set (TSS = 0.31). Then, the random classifier was trained in the subgroup of patients with available serum ß-D-glucan (BDG) and procalcitonin (PCT) values by exploiting the feature ranking learned in the entire dataset. Although no statistically significant differences were observed from the performance measures obtained by employing BDG and PCT alone, the performance measures of the classifier that included the features selected in the entire dataset, plus BDG and PCT, were the highest in most cases. CONCLUSIONS: Random forest classifiers trained on large datasets of automatically extracted data have the potential to improve current diagnostic algorithms for candidemia. However, further development through implementation of automatically extracted clinical features may be necessary to achieve crucial improvements.

Investigating the inflammatory status in the patients candidate for second angiography after coronary stent implantation.

BACKGROUND: Inflammation has been shown to be an important feature of atherosclerosis. We aimed to assess a profile of inflammatory cytokines and growth factors in patients with established coronary artery disease (CAD), 12 months after stent implantation. METHODS: A total of 193 patients with CAD, who were candidates for angiography, 12 months after stent implantation (cases), were compared with 107 patients with CAD, who were candidates for their first angiography (controls). Fasting blood glucose (FBG), triglycerides (TGs), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and high-sensitive C-reactive protein (hs-CRP) were measured using routine methods. The serum concentrations of IL-1α, IL-1ß, IL-2, IL-4, IL-6, IL-8, IL-10, TNF-α, IFN-γ, MCP-1, EGF and VEGF were determined using competitive chemiluminescence immunoassays. RESULTS: Serum levels of FBG (p = .002), TG (p = .029) and hs-CRP (p = .005) were significantly lower in cases than controls. The cytokines and growth factor profiles in cases were significantly different from controls. After multivariate analysis, serum levels of IL-2 (p < .001), IL-4 (p = .028) were significantly lower in cases compared with the controls while serum levels of IL-8, TNF-α, MCP-1, EGF and VEGF were significantly higher in the cases (p < .001). CONCLUSIONS: In patients with CAD and higher consumption of drug used (statins, aspirin and glucose lowering agents) to mitigate the risk of a secondary event, the level of hs-CRP one year after stent implantation decreased despite of significant higher serum levels of pro- and anti-inflammatory cytokines and growth factors.

Prevalence and Risk Factors of Anemia in the MASHAD Cohort Study.

BACKGROUND: Anemia is a serious public health problem which may be associated with cardiovascular diseases (CVDs) and brain damage. This survey aims to determine the prevalence of anemia and its association with demographic and biochemical factors and metabolic syndrome in a human sample derived from the MASHAD cohort study. METHODS: This survey was conducted on a sub-sample of 9847 individuals aged 35 to 65 as part of the MASHAD cohort study. Demographic characteristics and biochemical and anthropometrics indices were recorded. Data were analyzed using SPSS version 20. RESULTS: Anemia was seen in 11.5% of the population. Anemia was significantly more prevalent in younger subject (P<0.001), females (P<0.001) and those with elevated body mass index (BMI) (P<0.001). Mean high-density lipoprotein (HDL) was higher in anemic participant (P=0.032). The incidence of anemia was significantly lower in smokers (P<0.001) and also participant with hypertension (HTN) (P<0.001), diabetes mellitus (DM) (P<0.001) and metabolic syndrome (MetS) (P<0.001). Mean FBG (P<0.001), TG (P<0.001), total cholesterol (P<0.001), LDL (P<0.001) and uric acid (P<0.001) were significantly lower in anemic subjects. Cholesterol, MetS, low-density lipoprotein (LDL), BMI, uric acid, diabetes mellitus and also TG remained significantly different after multivariate analysis between anemic and healthy participants. CONCLUSION: The studied population had a lower prevalence of anemia compared to the previous WHO report for Iranians. Iron deficiency is recognized as the most important cause of anemia in Iran; however, further investigations will be need to confirm this pattern. We demonstrated that anemia is adversely associated with MetS and DM.

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators.

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention's generalizability and interoperability with existing systems, as well as the inner settings' data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

Factors Associated With Trabecular Bone Score and Bone Mineral Density; A Machine Learning Approach.

INTRODUCTION: Bone indexes including trabecular bone score (TBS) and bone mineral density (BMD) have been shown to be associated with wide spectrum of variables including physical activity, vitamin D, liver enzymes, biochemical measurements, mental and sleep disorders, and quality of life. Here we aimed to determine the most important factors related to TBS and BMD in SUVINA dataset. METHODS: Data were extracted from the Survey of Ultraviolet Intake by Nutritional Approach (SUVINA study) including all 306 subjects entered this survey. All the available parameters in the SUVINA database were included the analysis. XGBoost modeler software was used to define the most important features associated with bone indexes including TBS and BMD in various sites. RESULTS: Applying XGBoost modeling for 4 bone indexes indicated that this algorithm could identify the most important variables in relation to bone indexes with an accuracy of 92%, 93%, 90% and 90% respectively for TBS T-score, lumbar Z-score, neck of femur Z-score and Radius Z-score. Serum vitamin D, pro-oxidant-oxidant balance (PAB) and physical activity level (PAL) were the most important factors related to bone indices in different sites of the body. CONCLUSIONS: Our findings indicated that XGBoost could identify the most important variables with an accuracy of >90% for TBS and BMD. The most important features associated with bone indexes were serum vitamin D, PAB and PAL.

Factors determining the serum 25-hydroxyvitamin D response to vitamin D supplementation: Data mining approach.

Vitamin D supplementation has been shown to prevent vitamin D deficiency, but various factors can affect the response to supplementation. Data mining is a statistical method for pulling out information from large databases. We aimed to evaluate the factors influencing serum 25-hydroxyvitamin D levels in response to supplementation of vitamin D using a random forest (RF) model. Data were extracted from the survey of ultraviolet intake by nutritional approach study. Vitamin D levels were measured at baseline and at the end of study to evaluate the responsiveness. We examined the relationship between 76 potential influencing factors on vitamin D response using RF. We found several features that were highly correlated to the serum vitamin D response to supplementation by RF including anthropometric factors (body mass index [BMI], free fat mass [FFM], fat percentage, waist-to-hip ratio [WHR]), liver function tests (serum gamma-glutamyl transferase [GGT], total bilirubin, total protein), hematological parameters (mean corpuscular volume [MCV], mean corpuscular hemoglobin concentration [MCHC], hematocrit), and measurement of insulin sensitivity (homeostatic model assessment of insulin resistance). BMI, total bilirubin, FFM, and GGT were found to have a positive relationship and homeostatic model assessment for insulin resistance, MCV, MCHC, fat percentage, total protein, and WHR were found to have a negative correlation to vitamin D concentration in response to supplementation. The accuracy of RF in predicting the response was 93% compared to logistic regression, for which the accuracy was 40%, in the evaluation of the correlation of the components of the data set to serum vitamin D.

Association between serum Vitamin E concentrations and the presence of Metabolic Syndrome: A population-based cohort study.

BACKGROUND: Metabolic syndrome (MetS) is a cluster of clinical and metabolic features that include central obesity, dyslipidemia, hypertension and impaired glucose tolerance. These features are accompanied by increased oxidative stress and impaired antioxidant defenses. Vitamin E is a major factor in the non-enzymatic antioxidant defenses. The aim of present study was to investigate the association between serum levels of vitamin E and the presence of MetS and its components in a sample population of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. METHODS: This cross-sectional study was carried out in 128 subjects with MetS and 235 subjects without MetS. MetS was defined according to the International-Diabetes-Federation criteria. Serum levels of vitamin E were measured using the HPLC method. Anthropometric and biochemical parameters were measured using standard protocols.  Results. MetS patients had significantly lower serum levels of vitamin E (Vit E), Vit E/Total cholesterol (TC), and Vit E/ (TC+triglyceride(TG)) compared to the control group (P < 0.05).  Vit E/ (TG+TC) was also significantly lower in diabetics or those with elevated levels of high sensitive C-reactive protein (hs-CRP). Additionally, there was a significant association between Vit E/ (TG + Total Cho) and the number of components of the metabolic syndrome (p= 0.02) Conclusions. There is a significant inverse association between indices of Vit E status and the presence of MetS. Moreover, a significantly lower Vit E/ (TC+TG) was observed along with individuals with increasing numbers of components of the MetS.

Association between a genetic variant in scavenger receptor class B type 1 and its role on codon usage bias with increased risk of developing coronary artery disease.

OBJECTIVE: Coronary artery disease (CAD) as an important cause of morbidity and mortality globally. The scavenger receptor class B type 1 (SCARB1) plays an essential role in the reverse cholesterol transport. We have explored the association between a genetic variant, rs5888, in the SCARB1 gene with CAD and serum HDL-C levels. METHODS: Patients were categorized into two groups' angiogram positive (>50% coronary stenosis) and angiogram negative (<50% coronary stenosis). Genotyping was carried out using polymerase chain reaction-amplification refractory mutation system. The association between the SNP rs5888 and serum HDL-C was analyzed using a logistic regression model. RESULTS: The results showed that the subjects carrying a T allele was associated with a decreased serum HDL-C levels compared to the C allele in total population (p < 0.001). The risk of angiogram positivity in subjects carrying a T allele was 3.1-fold higher than for the control group (p < 0.001). CONCLUSION: CVD patients carrying the T allele of rs5888 variant in the SCARB1 gene was associated with decreased serum level of HDL.

Association of Healthy Eating Index and the Alternative Healthy Eating Index with the cell blood count indices.

OBJECTIVES: There is an association between diet quality and markers of inflammation. We aimed to investigate the relationship between diet quality with cell blood count (CBC) and inflammatory indices such as red cell distribution width (RDW) and white blood cell count (WBC). STUDY DESIGN: A total of 367 male subjects aged 20-69 yrs, who were employees of Shahid Hasheminejad Gas Processing Company (SGPC) completed the study. METHODS: All participants completed a questionnaire that contained questions about demography. Standard protocols were used for measuring anthropometric indices in all subjects. Blood samples were collected after a 12 hrs fast from all participants. Biochemical parameters were determined in all participants using an auto-analyzer (Eppendorf, Germany). Systolic and diastolic blood pressure were measured using a standard mercury sphygmomanometer. CBC was measured using the Sysmex auto analyser system (KX-21 N). The HEI/AHEI scores extracted from a validated food frequency questionnaire (FFQ) to evaluate the diet quality. Data analyses were performed using SPSS 16 (SPSS Inc., IL, and USA). RESULTS: A total of 674 men (aged 43.68±9.09 yrs) completed the study. The CBC indices were not significantly different between the HEI/AHEI classifications among the Iranian men (p-value >0.05 for all variables). Moreover, there was no association between HEI/ AHEI with the CBC indices in our population (p-value >0.05 for all variables). CONCLUSIONS: In summary, there was no association between the diet quality with the cell blood count parameters among Iranian men.

Potential value and impact of data mining and machine learning in clinical diagnostics.

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Effect of low-fat dairy products fortified with 1500IU nano encapsulated vitamin D3 on cardiometabolic indicators in adults with abdominal obesity: a total blinded randomized controlled trial.

BACKGROUND & AIMS: Vitamin D deficiency is widespread worldwide. In this study, we aimed to evaluate the effect of a nano encapsulated form of vitamin D used for fortifying low-fat dairy products (milk and yogurt) on anthropometric indices, glycemic status, and lipid profile in subjects with abdominal obesity. METHODS: In a totally (quadruple) blinded, randomized, and parallel-controlled trial, 306 individuals with abdominal obesity were randomly allocated to one of four groups: fortified low-fat yogurt (FY, 1500 IU nano encapsulated vitamin D3 per 150 g/d), non-fortified low-fat yogurt (nFY), fortified low-fat milk (FM, 1500 IU nano encapsulated vitamin D3 per 200 g/d), non-fortified low-fat milk (nFM), for 10 weeks (nFM and nFY, were considered as the control groups). Anthropometric and biochemical parameters were measured at baseline and after a ten-week trial in Mashhad, Iran. RESULTS: After the ten-week intervention, we found a significant increase in serum concentration of 25(OH)D in both the FM and FY groups compared to the respective control groups (19.10 ± 5.69 ng/mL and 20.88 ± 5.76 ng/mL respectively, p < .001). We observed a significant reduction in weight to hip ratio (p = .04) and a significant improvement in triglyceride (p < .001) and HDL-C (p = .01) only in FM group compared to nFM group. Also, we found a significant reduction in fasting serum insulin (p < .001), and a significant improvement of HOMA-IR (p < .001) and QUICKI (p < .001) in both intervention groups compared to their placebos. CONCLUSIONS: An intake of fortified dairy products containing nano-encapsulated vitamin D3 was associated with an improvement in some measures of anthropometric indices, glucose homeostasis, and lipid profiles, particularly in individuals receiving fortified milk. Hence, along with other benefits, fortification of dairy products with vitamin D may be an effective approach to improve some cardiometabolic indicators, such as insulin resistance. TRIAL REGISTRATION NUMBER: IRCT20101130005280N27.

Prevalence of ABO and Rh blood groups and their association with demographic and anthropometric factors in an Iranian population: Mashad study.

BACKGROUND: Blood groups appear to be markers for various human diseases and their distribution among different communities, ethnic groups and geographical boundaries varies over time. AIMS: We aimed to investigate the frequency of ABO and Rh blood groups and their relationship with demographic and anthropometric characteristics among Iranian residents in Mashad. METHODS: ABO and Rh blood groups were determined among 7268 participants from the MASHAD cohort study and their relationships with demographic and anthropometric parameters were evaluated. This part of the study was done in January 2017. Student t-test, ANOVA, Bonferroni' and Chi-squared were used for comparison of quantitative and qualitative variables. RESULTS: The most common blood group was O (33.8%); AB was the least common (8.3%). The prevalence of Rh-positive and Rh-negative was 88.2% and 11.8% respectively. There were statistically significant associations between ABO blood groups and demi-span (P = 0.03), even after correction for multiple comparisons. CONCLUSION: Our findings showed there was no relationship between ABO blood groups and demographic characteristics although there was an association with anthropometric measurements such as demi-span.

Comprehensive laboratory reference intervals for routine biochemical markers and pro-oxidant-antioxidant balance (PAB) in male adults.

BACKGROUND: Reference values of biochemical markers are influenced by various parameters including age, sex, region, and lifestyle. Hence, we aimed to determine age- and BMI-specific reference intervals (RIs) for important clinical biomarkers in a healthy adult male population from northeastern Iran. This is also the first study to investigate reference values for pro-oxidant-antioxidant balance (PAB). METHODS: Seven hundred and twenty (720) healthy men, aged 20-60 years, were recruited from Sarakhs in the northeast region of Iran. Reference values for lipid profiles (total cholesterol, triglyceride, HDL-C and LDL-C), fasting blood glucose, inflammatory factors (hs-CRP and PAB), minerals (zinc and copper), uric acid, and blood pressure were measured and statistically analyzed to establish accurate age- and BMI-specific RIs in alignment with CLSI Ep28-A3 guidelines. RESULTS: RIs for lipid profiles, inflammatory factors, minerals, and uric acid required no age partitioning with the exception of fasting blood glucose and blood pressure, which demonstrated significantly higher values in subjects aged 50 years and older. Among these biomarkers, only uric acid, blood pressure, and triglycerides demonstrated statistically significant increases in reference value concentrations with increasing BMI. CONCLUSION: In this study, age- and BMI-specific RIs for several biochemical markers were determined in healthy adult Iranian men. Partitioning by age and BMI was only required for a few analytes with most demonstrating no statistically significant changes with these covariates. These data can be useful to monitor various diseases in male adults with varying BMI in this region and others.

Dietary Inflammatory Index is associated with Healthy Eating Index, Alternative Healthy Eating Index, and dietary patterns among Iranian adults.

BACKGROUND: Recent investigations have evaluated the effect of the inflammatory potential of diet in several populations by calculating the Dietary Inflammatory Index (DII) score. We aimed to evaluate the association of the DII with the Healthy Eating Index (HEI), the Alternative Healthy Eating Index (AHEI), and dietary pattern (DP) among healthy Iranian adults. METHODS: A cross-sectional study was conducted among 4365 middle-aged adults. Major DPs and DII score were identified using a validated semi-quantitative food frequency questionnaire (FFQ). Poisson regression was used to evaluate the association of DPs, HEI, and AHEI across tertiles of DII. RESULTS: After adjustment for confounding variables, a low HEI (HEI < 55) and AHEI (AHEI < 56.5) were more prevalent among the participants in the highest tertile of DII compared to the first tertile (PR: 1.13, P-value <.05; PR: 1.10, P-value <.05; respectively). Adherence to a balanced healthy dietary pattern was significantly lower in subjects with a diet that was more pro-inflammatory compared to those with anti-inflammatory diet (PR: 0.85, P-value P < .01). No significant association was found between the DII and a western DP. High levels of HDL and hip and waist circumference were observed in the highest tertile of DII, and high levels of dietary intake of protein and fiber, minerals, fasting blood glucose, and monounsaturated fat were reported in the lowest tertile of DII. CONCLUSION: The highest tertile of the DII (a pro-inflammatory diet) was associated with a lower HEI, AHEI, and lower adherence to balanced DP in a representative sample of adults in Iran.

A comparison of dietary intake between personnel of a gas processing company and a sample population of public employees from Mashhad.

INTRODUCTION: An imbalanced dietary pattern may result in a number of non-communicable disorders that include: obesity, diabetes, metabolic syndrome, cardiovascular diseases, and subsequently impaired quality of life (QOL). Micro- and macro-nutrients play some important roles in maintaining human health. The objective of this study was to compare dietary intake of employees of a gas processing company, who were provided with meals by their employee, with staff from other public employers from Mashhad, in northeastern Iran. METHODS: Twenty-four-hour food recall and a food frequency questionnaire (FFQ) were used to evaluate dietary intakes of 654 male employees of Shahid Hasheminejad Gas Processing (SHGP) Company (case group), and 681 randomly selected male public employees from Mashhad as control subjects in this observational, analytic, longitudinal, prospective cohort study. The analysis of the dietary intakes was performed using Dietplan6 software. RESULTS: Dietary macro and micro-nutrients were evaluated: including total energy, fat, MUFA, PUFA, trans fatty acids, cholesterol, starch, fiber, protein, total nitrogen, zinc, phosphor, potassium, calcium, manganese, selenium, iodine, sodium, copper, retinol, carotene, thiamin, riboflavin, niacin, pantothen, vitamin B6, biotin, folate, vitamin B12, vitamin C and vitamin D. The intake of macro-nutrients, including: energy, fat, monounsaturated fatty acids, polyunsaturated fatty acids, carbohydrates, starch, and protein; minerals including zinc, iron, selenium; and vitamins including niacin and vitamin D were higher in the SHGP group, compared to the control group (P < 0.05). The dietary intake of the SHGP group contained lower amounts of several mineral and vitamins, which included: iodine, sodium, carotene, thiamin, pantothenic acid, vitamin B6, biotin, folic acid, and vitamin C, in comparison with the control group (P < 0.05). CONCLUSION: In conclusion, we suggest it will be important to evaluate the composition of meals provided by companies and organizations, especially those that provide their employees with the majority of their food intake.

Adherence to a Dietary Approach to Stop Hypertension (DASH)-Style in Relation to Daytime Sleepiness.

BACKGROUND: The beneficial impact of adherence to a DASH diet on several metabolic conditions and psychological well-being has been shown previously. Dietary modification can affect sleep quality. Thus, the aim of this present study was to investigate the correlation between adherence to the DASH diet and daytime sleepiness score in adolescent girls. METHODS: A total of 535 adolescent girls aged between 12 and 18 years old were recruited from different regions of Khorasan Razavi in northeastern of Iran, using a random cluster sampling method. DASH scores were determined according to the method of Fung et al. A Persian translation of the Epworth Sleepiness Scale (ESS-IR) was used to assess of daytime sleepiness. To investigate the correlation between DASH-style diet and daytime sleepiness score, we applied logistic regression analysis in crude and adjusted models. RESULTS: As may be expected, participants with the greatest adherence to the DASH diet had significantly higher intakes of fruits, vegetables, low-fat dairy products, fish and nuts, and lower consumption of refined grains, red and processed meat, sugar-sweetened beverages and sweets. There was an inverse correlation between adherence to the DASH-style diet and scores for daytime sleepiness in crude model (ß= -0.12; P=0.005). These findings were remained significant after adjustment for confounding variables (ß= -0.08 P=0.04). CONCLUSION: There is an inverse correlation between adherence to DASH diet and daytime sleepiness score. Further studies, particularly longitudinal studies, are required to determine whether dietary intervention may improve daytime sleepiness.

Association of a genetic variant in the AKT gene locus and cardiovascular risk factors.

Cardiovascular disease (CVDs) is the leading cause of morbidity and death worldwide. Most genetic variants could be identified by several genome-wide-association-studies (GWAS), including within genes encoding proteins involved in the AKT/PI3K pathways that are related with an increased risk of metabolic syndrome and CVDs. Therefore, due to the importance of genetic variants in the prognosis of diseases, we examined the genetic polymorphism of AKT-rs1130233 located on chromosome 14 with cardiovascular risk factors. In this cross-sectional study, 721 subjects recruited from the Mashhad-Stroke and Heart-Atherosclerotic-Disorders (MASHAD) cohort study. The participants including 257 subjects with metabolic syndrome, 144 subjects with cardiovascular disease and 320 subjects as a control group. Anthropometric, biochemical and demographic information measures were prepared. Dietary assessment was managed by 24h dietary recall. DNA extraction and genotyping were carried out by using the TaqMan real-time-PCR based method. The association of AKT rs1130233 locus with dietary intakes, metabolic syndrome and cardiovascular risk factors were assessed. Data were analyzed by using SPSS 21 software. Frequencies of genotypes AA, AG and GG of the AKT rs1130233 polymorphism were 12.6%, 44.5% and 42.9% in subjects with metabolic syndrome and 9.7%, 39.6% and 50.7% in subjects with cardiovascular disease, respectively. The frequency of allele A and G in cardiovascular disease and metabolic syndrome population were 29.5%, 70.5% and 34.8%, 65.2%, respectively. We have found no significant association between the AKT rs1130233 polymorphism with cardiovascular risk factors and metabolic syndrome. The results of dietary intake showed that the levels of phosphorus intake (p=0.008), calcium intake (p=0.007) and iodine intake (p=0.04) were different in subjects with and without metabolic syndrome. And also, energy intake was significantly different in subjects with cardiovascular disease (p=0.01) compared to the control group. Our findings suggest that AKT rs1130233 was not associated with the risk of metabolic syndrome and cardiovascular disease in the Iranian population. More studies are needed to validate our results. We did functional analysis, due to certify our investigation about value of this genetic biomarker for CVD risk.

The relationship between short stature and menstrual pattern in a large cohort of Iranian girls.

The aim of this study was to investigate the relationship between stature and menstrual pattern. Nine hundred and seventy six girls were selected from regions within two cities in north-eastern Iran in 2015. They were assessed with respect to: their stature and menstrual pattern, age of menstruation, severity of dysmenorrhoea, duration of their menstrual cycle and flow. 841 girls had experienced menarche. 10.5% had a short stature. There were significant differences in age, height, weight and body mass index between those who had experienced their first menstrual cycle and others (p < .001). There were weak and negative correlation between age of menarche and height percentiles (r = -0.12, p < .001). Premenstrual syndrome, duration of menstruation cycle and bleeding period did not differ between these two groups (p > .05). We found that dysmenorrhoea was significantly influenced by height. Although the mean age at menarche was statistically significant among different statures, the range was narrow (12.2-12.7). Further studies considering environmental factors, including socioeconomic status and nutrition concurrently, are also required.Impact statementWhat is already known on this subject? Various menstrual characteristics, including age of menarche, severity of dysmenorrhoea and duration of menstrual period may be associated with height. But, there have been few studies on the relationship between short stature and its impact on health status and menstrual patterns in adolescents.What do the results of this study add? There were significant differences in age, height, weight and body mass index between adolescent girls who had experienced their first menstrual cycle, and others. There were weak, negative, significant correlations between age of menarche and height percentiles. Dysmenorrhoea was significantly influenced by height.What are the implications of these findings for clinical practice and/or further research? Life style modification and nutritional interventions that optimise the height of girls may resolve their menstrual problems and dysmenorrhoea.