Search

Introduction Given the high rates of obesity and poor glycemic control among individuals with type 2 diabetes (T2D), this study examines current trends in HbA1c and body mass index (BMI) as well as the association between HbA1c and BMI among adults with T2D. Methods Data from the IBM® MarketScan® Explorys® Claims-EMR Data were used to construct eight annual cohorts for the years 2012–2019. Each annual cohort included adults identified with T2D who had at least one recorded HbA1c laboratory result and BMI value in the year of interest. Given these cohorts, trends in HbA1c and BMI were described over time using generalized estimating equation (GEE) tests. Results Results indicate that, over the study period from 2012–2019, average BMI increased significantly and there was a decrease in the percentage of adults with T2D who achieved glycemic control. In addition, for all years, higher BMI classification was associated with higher HbA1c values. When examining results for patients in different age groups, the findings were generally consistent with the overall population. In each age group, but most notably the age 18–44 group, the mean BMI increased over time and higher BMI was associated with higher HbA1c. Conclusion Given the increase in BMI and decreasing percentage of individuals achieving glycemic control among adults with T2D found over the study period, therapies which decrease BMI as well as HbA1c can potentially have a significant impact on the management of T2D. The growing proportion of the younger age group with higher mean BMI may remain a key subgroup of interest.

Introduction Type 2 diabetes (T2D) is extremely burdensome to people with T2D and associated with impaired health-related quality of life. This study explores the impact of T2D and potentially relevant outcomes for new therapies using a unique approach to in-depth qualitative interviews where people with T2D are asked to think about their future with T2D. Methods A cross-sectional qualitative interview study among people with T2D from the USA and UK. Interviews explored their treatment journey, perceptions of their future with T2D, and the value of achieving normoglycemia (explored through presentation of two vignettes with hypothetical medications that reduced hemoglobin A1c [HbA1c] levels

Obesity is the most common chronic disease in the U.S. Patients with obesity have many cardiovascular risk factors, often modifiable. It is important to identify patients with obesity at high risk of atherosclerotic cardiovascular disease (ASCVD) to be able to appropriately direct treatment and resources. We conducted an observational study of EMR data of 399,360 adults with BMI ≥ 25 kg/m2 and without baseline ASCVD between 2000 and 2019. We first identified a literature-based pool of risk factors for ASCVD onset and conducted variable selection by applying least absolute shrinkage and selection operator (LASSO) penalized Cox regression with ten-fold cross-validation on an 80% training dataset. We estimated a regular Cox model on the training dataset with selected variables and evaluated model performance on the remaining 20% dataset. Over the mean of 7.4 years of follow-up, 37,424 (11.7%) of study patients developed ASCVD. The predictive model achieved a Harrell’s C-statistic of 0.81. The greatest risk of ASCVD onset was associated with older age (HR 2.16 per 1 SD = 15.4 years; 95% CI 2.13- 2.20), heart failure (HR 1.85; 95% CI 1.74-1.97), valvular heart disease (HR 1.61; 95% CI 1.53-1.68) and stimulant abuse (HR 1.60; 95% CI 1.41-1.81). Female sex was associated with a lower risk of ASCVD (HR 0.64; 95% CI 0.63-0.65). Patient characteristics found in >5% of patients that carried additional risk included history of smoking (39.9% of patients; HR 1.40; 95% CI 1.37-1.43), proteinuria (5.5%; HR 1.33; 95% CI 1.29-1.38) and hypertension (23.6%; HR 1.29; 95% CI 1.26-1.32). Each standard deviation increase in BMI (5.3 kg/m2) was associated with a 1.15-fold (95% CI 1.14-1.16) increase in the risk of ASCVD onset. It is feasible to use predictive modeling to identify patients with overweight and obesity at high ASCVD risk. This approach could be utilized to guide population management and clinical treatment decisions. Disclosure A. Turchin: Consultant; Self; Proteomics International, Research Support; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Pfizer Inc., Sanofi, Stock/Shareholder; Self; Brio Systems. F. J. Morrison: None. M. Shubina: None. S. Shinde: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. N. Ahmad: Employee; Self; Eli Lilly and Company. H. Kan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Bristol-Myers Squibb Company, GlaxoSmithKline plc. Funding Eli Lilly and Company

Obesity is the most common chronic disease in the U.S. Patients with obesity are at high risk for type 2 diabetes mellitus (T2DM); however, many of their risk factors for T2DM are modifiable. It is important to identify patients with obesity at high risk for T2DM to appropriately direct treatment and resources. We conducted an observational study of EMR data of 399,711 adults with BMI ≥ 25 kg/m2 and without baseline T2DM between 2000 and 2019. We first identified a literature-based pool of risk factors for T2DM onset and conducted variable selection by applying LASSO penalized Cox regression with ten-fold cross-validation on an 80% training dataset. We estimated a regular Cox model on the training dataset with selected variables and evaluated model performance on the remaining 20% dataset. Over a mean of 7.4 years of follow-up, 38,898 (9.8%) study patients developed T2DM. The predictive model achieved a Harrell’s C-statistic of 0.81. The greatest risk of T2DM was associated with gestational diabetes (HR 2.53; 95% CI 2.12-3.01), Asian and Hispanic race/ethnicity (HRs 2.38 and 1.94; 95% CIs 2.24-2.53 and 1.86-2.02, respectively), family history of DM (HR 2.00; 95% CI 1.95-2.04) and PCOS (HR 1.84; 95% CI 1.63-2.07). Female sex (HR 0.77; 95% CI 0.75-0.79), having a partner (HR 0.90; 95% CI 0.88-0.92) or commercial insurance (HR 0.86; 95% CI 0.84-0.88) were associated with a lower risk of T2DM. Characteristics found in >5% of patients that carried additional risk included proteinuria (5.0% of patients; HR 1.53; 95% CI 1.47-1.59), hypertension (23.9%; HR 1.21; 95% CI 1.18-1.25), high cholesterol (11.9%; HR 1.19; 95% CI 1.15-1.23), and history of smoking (40.5%; HR 1.18; 95% CI 1.15-1.21). Each standard deviation increase in BMI (5.2 kg/m2) was associated with a 1.51-fold (95% CI 1.49-1.52) increase in T2DM onset. It is feasible to use predictive modeling to identify patients with overweight and obesity at high risk of T2DM. This approach could be utilized to guide population management and clinical treatment decisions. Disclosure A. Turchin: Consultant; Self; Proteomics International, Research Support; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Pfizer Inc., Sanofi, Stock/Shareholder; Self; Brio Systems. F. J. Morrison: None. M. Shubina: None. S. Shinde: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. N. Ahmad: Employee; Self; Eli Lilly and Company. H. Kan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Bristol-Myers Squibb Company, GlaxoSmithKline plc. Funding Eli Lilly and Company

In low and middle-income countries, access to cancer diagnosis and treatment is suboptimal. Further, compliance to cancer treatment is a major issue due to various reasons including financial barriers, lack of family support and fear of treatment. This article discusses the determinants of treatment completion in cancer patients of a government-run hospital, in a rural part of Punjab in India. The Sangrur hospital-based cancer registry data for the year 2018 have been used. We have registered 2,969 cancer cases, out of which 2,528 (85%) cases were eligible for the analysis. Of the total 2,528 cases, 1,362 (54%) cases completed the cancer directed treatment and 1,166 (46%) did not. The data have been collected from the electronic medical record (EMR) department and entered into CanReg5 software. The bivariate and multivariate binary logistic regression analysis was performed to see the effect of variables on the treatment completion. The results indicate that the elderly age group (>60 years) (odds ratio (OR): 0.52, (95% confidence interval (CI): 0.31–0.86)), distance from hospital (OR: 0.67, (95% CI: 0.50–0.89)) and access to government health schemes (OR: 0.13, (95% CI: 0.10–0.19)] have direct correlation with the treatment completion. The educated patients (OR: 1.49, (95% CI: 1.13–1.96)) and patients who received curative treatment (OR: 2.7, (95% CI: 1.88–3.88)) have shown 58% and 84% compliance to treatment completion, respectively. The other variables like the clinical extent of disease, religion, gender and income do not have any significant effect on the treatment completion. Determinants like age (young), education, distance from the hospital, curative treatment and availability of government health schemes for financial support have shown positive effects on treatment completion. These factors have to be considered by the cancer hospitals, health departments and policymakers while planning for cancer care or control in India.

Aims To examine body mass index (BMI) and HbA1c for individuals with type 2 diabetes (T2D) in the United States. Methods The retrospective study utilized data from IBM® MarketScan® Explorys® Claims-EMR Data for the years 2012–2019. Individuals with T2D and a recorded HbA1c laboratory result and BMI were included. The relationship between BMI and HbA1c was assessed descriptively and logistic regressions examined the relationship between benchmark BMI and the probability of having HbA1c above targets of 7% or 8% in the 1 year post-period. Results In our sample of 44,723 patients, results indicated that compared to individuals of normal weight, those classified as obese class I or obese class II were 24% more likely to have a last HbA1c ≥ 7% (Odds Ratio [OR] = 1.24; 95% Confidence Interval [CI] 1.14–1.35 for both obese class I and obese class II), while those classified as obese class III were 16% more likely (OR = 1.16; 95% CI 1.06–1.27). Results were similar when using a HbA1c threshold of 8%. Conclusions Given the importance of glycemic control for people with T2D, these results suggest that treatments which reduce rates of obesity may help to improve health outcomes.

Obesity is the most common chronic disease in the U.S. Patients with obesity have many risk factors for cancer, often modifiable. It is important to identify patients with obesity at high risk of cancer to be able to appropriately direct treatment and resources. Given that the risk pool is large, it is imperative to identify a clinically meaningful metric for risk stratification to help guide interventions. We conducted an observational study of electronic medical records data for 394,161 adults aged between 18 and 80, with BMI ≥ 25 kg/m2 and without baseline history of cancer between 2000 and 2019. We first identified a literature-based pool of risk factors for cancer onset and conducted variable selection by applying least absolute shrinkage and selection operator (LASSO) penalized Cox regression with ten-fold cross-validation on an 80% training dataset. Effects of the selected variables on risk of cancer (excluding non-melanoma skin cancer) onset were assessed using Cox regression on the 80% training dataset. The resulting model accuracy was evaluated using Cox regression on a withheld 20% validation dataset. Participants had a mean age of 46.7 (SD: 15.5) years and mean body mass index (BMI) of 30.5 (SD: 5.4) kg/m2; 51.9% were women. Over a mean of 7.5 years of follow-up, 34,679 (8.8%) of study patients developed cancer. The predictive model achieved a Harrell’s C-statistic of 0.73. The greatest risk of cancer incidence was associated with HIV infection (HR 2.22; 95% CI 1.88–2.63; 0.27% of patients), older age (HR 2.05 per 1 SD = 15.5 years; 95% CI 2.01- 2.09), hepatitis C infection (HR 1.48; 95% CI 1.34–1.63; 0.96% of patients), and family history of cancer (HR 1.44; 95% CI 1.41–1.48; 42.5% of patients). Additional patient characteristics found in >5% of patients that also carried risk included proteinuria (5.8% of patients; HR 1.23; 95% CI 1.18–1.29) and history of smoking (40.7% of patients; HR 1.20; 95% CI 1.17–1.23). Each standard deviation increase in BMI (5.4 kg/m2) was associated with a hazard ratio of 1.06 (95% CI 1.05–1.07) for incident cancer. It is feasible to use predictive modeling to identify patients with overweight and obesity at high cancer risk. This approach could be utilized to guide population management and clinical treatment decisions.

Most published reports of patient safety in clinical practice focus largely on the culture of safety in complex health systems, separate from pre-approval and postmarketing research-related safety considerations for drugs, biologics, and other medical products. The science of safety requires a linked integrated perspective, i.e., an iterative process examining and relating safety concerns from drug or biologic discovery and development in preclinical stages, clinical trials and post-market use, research, surveillance, and potential regulatory changes. This commentary addresses the science of safety across the lifecycle of drug and biological products, regulatory considerations, barriers, and research needs. This paper provides a brief overview on how the functioning of healthcare systems affects the safety environment and describes how stakeholder involvement, research participation, and targeted education and training can help facilitate better safety measures and practices, provide improved quality of care to patients, and contribute to the science of safety. Conflict of Interest We declare no conflicts of interest or financial interests that the authors or members of their immediate families have in any product or service discussed in the manuscript, including grants (pending or received), employment, gifts, stock holdings or options, honoraria, consultancies, expert testimony, patents and royalties". Type: Commentary

Objective: To examine the association between treprostinil sodium and pneumonia in patients with pulmonary arterial hypertension, utilizing and analyzing the reports submitted to the Food and Drug Administration (FDA) Adverse Even Reporting System (AERS) and the published literature. Methods: A total of 5,332926 reports of adverse events between January 2006 and June 2012 were downloaded from the FDA AERS. These adverse events were associated with all other drugs inclusive of Treprostinil Sodium. A literature review was conducted on PubMed using the terms treprostinil and pneumonia. Authorized pharmacovigilance tools were used to determine the proportional reporting ratio, the reporting odds ratio, and the information component given by the Bayesian confidence propagation neural network. Results: Based on the numerous adverse event pairs, 144 adverse events were listed as treprostinil sodium associated with pneumonia. The majority of the cases were seen in females (74%) and those between the ages of 51 and 75 years (63%). The most common route of drug administration among the cases was inhalation (73%). Conclusion: This study helps indicate an association between the treatment with Treprostinil sodium and the development of pneumonia in pulmonary hypertensive patients. Additional research is needed to confirm with the medical relevance of the analysis.