Safoora Gharibzadeh

Aims: To compare cardiovascular outcomes associated with the initiation of insulin degludec versus insulin glargine using a target trial emulation approach in nationwide real‐world data. Materials and Methods: We conducted a nationwide observational cohort study using Swedish health registers, including the National Diabetes Register. Individuals with type 2 diabetes initiating insulin degludec or insulin glargine between 2012 and 2022 were included. Two complementary approaches were applied: a trial‐eligible emulation cohort reflecting the eligibility criteria of the DEVOTE trial, and a trial‐ineligible emulation cohort representing routine clinical practice. The primary outcome was major adverse cardiovascular events (MACE, i.e., myocardial infarction, stroke and cardiovascular mortality). Secondary outcomes included the individual components of MACE, all‐cause mortality and heart failure. Average treatment effects were estimated using longitudinal targeted minimum loss‐based estimation. Results: Among 96 731 individuals identified, 44 890 met the study eligibility criteria and were included in the analyses. In the trial‐eligible cohort, degludec was associated with a similar risk of MACE compared with glargine (risk ratio: 1.10; 95% CI: 0.91–1.34; p = 0.34), but a significantly lower risk of all‐cause mortality (risk ratio: 0.75; 95% CI: 0.62–0.92; p = 0.002). In the trial‐ineligible emulation cohort, degludec was associated with lower risks of MACE (risk ratio: 0.80; 95% CI: 0.67–0.95; p = 0.004) as well as reduced risks of all‐cause mortality, cardiovascular mortality, non‐fatal stroke and heart failure. Conclusions: In this nationwide study, degludec was associated with a lower risk of all‐cause mortality compared with glargine, consistently observed in both trial‐eligible and trial‐ineligible populations, despite similar cardiovascular risk in the trial‐eligible cohort.

Hypertension is a major global health concern associated with chronic conditions, particularly in aged adults. Managing these conditions often requires mul

Aims: Diabetic foot ulcer disease ( DFUD ) is common, life‐changing and associated with a lower 5‐Year survival rate than many cancers. However, the risk factors for DFUD have generally been identified in small, single‐centre, clinic‐based studies, many of which are cross‐sectional. This study aims to assess the incidence of DFUD and its related risk factors in two large, contemporary UK cohorts. Materials and Methods: We investigated common sociodemographic and clinical factors affecting the incidence rates of DFUD in two large representative independent cohorts of people with diabetes in England ( CPRD , n = 131 042) and Scotland (Scottish Diabetes Research Network‐National Diabetes Dataset [ SDRN ‐ NDS ] n = 260 748). The methods of case ascertainment differed between the two cohorts: in England, both primary and secondary care data were used, whereas in Scotland, secondary care and foot clinic data were used. Results and Conclusions: In the English cohort, 4.7% developed DFUD over a median of 4.3years (incidence rate 9.0[95%CI: 8.8–9.2] per 1000 person‐years) follow‐up; in the Scottish cohort, the equivalent figure was 2.9% over a median of 6.3 years (incidence rate 4.4 [95% CI: 4.3–4.5] per 1000 person‐years). Despite different methods of case ascertainment, multivariable analysis in both populations indicated that those who developed DFUD were more likely to be older, male, smokers, of White ethnicity, with higher systolic blood pressure and baseline HbA1c. These findings provide a robust evidence base for identifying people with diabetes at risk of DFUD for targeted efforts for prevention.

Background & aim: Cardiovascular disease (CVD) is the most important cause of death in the world and has a potential impact on health care costs, this study aimed to evaluate the performance of machine learning survival models and determine the optimum model for predicting CVD-related mortality.Method: In this study, the research population was all participants in Tehran Lipid and Glucose Study (TLGS) aged over 30 years. We used the Gradient Boosting model (GBM), Support Vector Machine (SVM), Super Learner (SL), and Cox proportional hazard (Cox-PH) models to predict the CVD-related mortality using 26 features. The dataset was randomly divided into training (80%) and testing (20%). To evaluate the performance of the methods, we used the Brier Score (BS), Prediction Error (PE), Concordance Index (C-index), and time-dependent Area Under the Curve (TD-AUC) criteria. Four different clinical models were also performed to improve the performance of the methods.Results: Out of 9258 participants with a mean age of (SD; range) 43.74 (15.51; 20–91), 56.60% were female. The CVD death proportion was 2.5% (228 participants). The death proportion was significantly higher in men (67.98% M, 32.02% F). Based on predefined selection criteria, the SL method has the best performance in predicting CVD-related mortality (TD-AUC > 93.50%). Among the machine learning (ML) methods, The SVM has the worst performance (TD-AUC = 90.13%). According to the relative effect, age, fasting blood sugar, systolic blood pressure, smoking, taking aspirin, diastolic blood pressure, Type 2 diabetes mellitus, hip circumference, body mss index (BMI), and triglyceride were identified as the most influential variables in predicting CVD-related mortality.Conclusion: According to the results of our study, compared to the Cox-PH model, Machine Learning models showed promising and sometimes better performance in predicting CVD-related mortality. This finding is based on the analysis of a large and diverse urban population from Tehran, Iran.

Summary In a population of 1156 men aged ≥ 60 years, opioid drug use was reported by 4.1% (n = 47) of participants. Among opioids, opium was the most prevalent consuming drug (83%). Adjusting for potential confounders, opioid consumption showed a positive association with osteoporosis. Purpose Limited evidence suggest a relationship between opioid consumption and osteoporosis. This study aims to investigate the possible association of osteoporosis and drug use among older adult men of Bushehr, Iran. Methods In this study, 1156 men aged ≥ 60 years of Bushehr Elderly Health (BEH) were included. Bone density and trabecular bone score (TBS) were measured using dual-energy X-ray absorptiometry. Total osteoporosis was noted based on osteoporosis at either site of the lumbar spine, femoral neck, and total hip densitometry. Drug use was defined as a self-reported current use of opioid drugs, either regular (daily) use or irregular consumption. Multivariable modified Poisson regression analysis was used for investigating the association of opioids and osteoporosis, reporting the adjusted prevalence ratio (APR) with 95% confidence interval (CI). The association between drug use and TBS was evaluated using a linear regression model. Results Opioid drug use was reported by 4.1% (n = 47) of participants. Among drug users, opium was the most prevalent consuming drug (83%). In all, 38.3% of drug users and 22.4% of non-users had osteoporosis (p-value = 0.011). Multivariable analysis showed that adjusting by age, education, smoking, alcohol consumption, body mass index, diabetes, and physical activity, a positive and significant association was detected between opioid drug use and the likelihood of osteoporosis (APR: 1.46, 95%CI: 1.02–2.10). Considering the potential confounders, the results also showed a negative association between drug consumption and TBS (β: − 0.027, 95%CI: − 0.053, − 0.001). Conclusion Opioid drug use has a positive association with osteoporosis in elderly men, independent of other conventional risk factors. Elderly drug users might be at a higher risk for osteoporotic fractures, given the effect of substance use on cognition. So, osteoporosis among drug users would be of importance, especially in countries where opium consumption is prevalent.

Background: This paper presents the protocol of the 4^th round of Iranian Multi-center Osteoporosis Study (IMOS), a national survey with the primary objective of estimating the prevalence of osteoporosis and sarcopenia and their risk factors in a representative sample of urban and rural populations. Methods: The target population of the survey is all individuals ≥ 50 years in Iran. A multi-stage random sampling method has been used in the study. We stratified the 31 provinces of the country into 5 strata based on the distribution of their potential risk factors for osteoporosis and randomly selected one or two provinces from each stratum. Then, we invited 2530 people aged ≥ 50 years recruited in the 8^th National Survey of None Communicable Diseases (NCD) Risk Factors (STEPs-2021) in the selected provinces to participate in IMOS. Body composition measurements including bone mineral density, muscle mass, and fat mass are measured through Dual-energy X-ray Absorptiometry (DXA) method using HOLOGIC (Discovery and Horizon) devices; and Trabecular Bone Score (TBS) is measured on the DXA scans using iNsight software. Anthropometric measurement and physical examinations are made by a trained nurses and other required information are collected through face-to-face interviews made by trained nurses. Laboratory measurements are made in a central lab. The prevalence of osteoporosis and sarcopenia will be estimated after applying sampling design, non-response, and post-stratification weights to the data. Discussion: IMOS will provide valuable information on the prevalence and determinants of osteoporosis and sarcopenia at the national level, and the results can be used in evaluating health system interventions and policymaking in the field of musculoskeletal diseases.

Salmonella is one of the most common causes of foodborne outbreaks and infection worldwide. The gold-standard detection method of Salmonella is cultivation. There is a need to investigate rapid and accurate processes with time-consuming cultivation. The study evaluated different approaches to detect Salmonella in poultry feces samples. Poultry farm feces samples from 21 cities in Iran were collected from January 2016 to December 2019. Microbiological cultures, serological assays, and multiplex PCR (m-PCR) were used to detect and characterize Salmonella spp. isolates. Serological assays and m-PCR were used to determine the serogroups A, B, C1, C2, D1, E, H, and FliC. The m-PCR was used to detect seven Salmonella serovars, and a Chi-square test was performed to compare the discriminatory power of the methods. Of 2300 poultry feces samples, 173 (7.5%) and 166 (7.2%) samples were detected as Salmonella spp. by cultivation and m-PCR, respectively. The sensitivity of the molecular method was equal to cultivation at 0.96 (CI = 95%). Assessment of H antigenic subgroups showed the same for both m-PCR and serological tests. Therefore, the matching rate of the two methods for detecting all H antigenic subgroups was 100%. Thus, the relationship between the results obtained from both methods was significant in the contingency table test (P < 0.01). The PCR-based approach confirmed the detection of Salmonella in a shorter period (24–36 h) compared to the conventional microbiological approach (3–8 days).

Purpose Our study examined whether T2DM and glucocorticoids treatment affect bone quality and quantity that are measured by Bone Mineral Density (BMD) and Trabecular Bone Score (TBS). Materials & methods Participants in this study were 2294 women and men aged over 60 years who participated in stage II of the Bushehr Elderly Health (BEH) program. Patients with T2DM and those who received glucocorticoids were included. BMD was detected using the DXA method and the TBS of L1-L4 was evaluated by TBS iNsight® software. To evaluate the correlation between TBS and BMD levels with diabetes and taking corticosteroids sex-specific multivariable linear regression models were appplied. Results TBS and BMD were not significantly different in those who had received glucocorticoids versus those who did not.T2DM revealed a significant association with both BMD and TBS in men (beta = 0.12, p < 0.001 and beta = 0.063, p = 0.03, respectively). BMD values were significantly higher in diabetic women (beta = 0.073, p < 0.01). BMI had a significant association with both TBS and BMD but in an opposite direction, in women and men (BMD: beta = -0.22, -0.24, and regarding TBS: beta = 0.37, 0.25, all p-values < 0.001). Conclusion Our findings showed that T2DM had major effects on BMD in both men and women. However, T2DM only affects TBS in men. Furthermore, neither BMD nor TBS were affected by GC intake in men or women.Based on the variable importance of covariates, BMI was the most influential factor on both BMD and TBS, although in opposite directions, in both sexes.