Concomitant iGlarLixi and Sodium-Glucose Co-transporter-2 Inhibitor Therapy in Adults with Type 2 Diabetes: LixiLan-G Trial and Real-World Evidence Results

Introduction: iGlarLixi, the once-daily fixed-ratio combination of insulin glargine 100 U/ml and lixisenatide, robustly improves glycaemic control in adults with type 2 diabetes irrespective of previous treatment [oral antihyperglycaemic drugs (OADs), basal insulin or glucagon-like peptide-1 receptor agonists (GLP-1 RAs)]. Sodium-glucose co-transporter-2 inhibitors (SGLT2is) are a recommended treatment option for people with type 2 diabetes with cardiovascular disease, kidney disease and/or heart failure because of their cardio- and renoprotective benefits. Herein, we assessed the effects of concomitant iGlarLixi and SGLT2i therapy. Methods: We conducted subgroup analyses according to SGLT2i use in: (1) adults with suboptimally controlled type 2 diabetes on GLP-1 RAs and OADs switching to iGlarLixi in the 26-week LixiLan-G randomised controlled trial (RCT; NCT02787551) and (2) adults switching to or adding iGlarLixi in a 6-month, retrospective real-world evidence (RWE) observational study using data from the US Optum-Humedica electronic medical records database. Changes in HbA1c and hypoglycaemia prevalence and event rates were assessed. Results: There were no major differences in baseline characteristics for those who initiated iGlarLixi while already using SGLT2i (n = 346) and those initiating iGlarLixi without concomitant SGLT2i therapy (n = 1285). HbA1c reductions from baseline to time of assessment and hypoglycaemia prevalence and event rates were similar for iGlarLixi users regardless of SGLT2i therapy. Conclusion: Evidence from an RCT and an RWE analysis supports the efficacy/effectiveness and safety of iGlarLixi when used concomitantly with SGLT2i. Trial Registration: NCT02787551

Managing Ebola from rural to urban slum settings: experiences from Uganda.

Managing Ebola in Uganda.Five outbreaks of ebola occurred in Uganda between 2000-2012. The outbreaks were quickly contained in rural areas. However, the Gulu outbreak in 2000 was the largest and complex due to insurgency. It invaded Gulu municipality and the slum- like camps of the internally displaced persons (IDPs). The Bundigugyo district outbreak followed but was detected late as a new virus. The subsequent outbreaks in the districts of Luwero district (2011, 2012) and Kibaale (2012) were limited to rural areas

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction

Two Novel Susceptibility Loci for Prostate Cancer in Men of African Ancestry.

Prostate cancer incidence is 1.6-fold higher in African Americans than in other populations. The risk factors that drive this disparity are unknown and potentially consist of social, environmental, and genetic influences. To investigate the genetic basis of prostate cancer in men of African ancestry, we performed a genome-wide association meta-analysis using two-sided statistical tests in 10 202 case subjects and 10 810 control subjects. We identified novel signals on chromosomes 13q34 and 22q12, with the risk-associated alleles found only in men of African ancestry (13q34: rs75823044, risk allele frequency = 2.2%, odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.37 to 1.76, P = 6.10 × 10-12; 22q12.1: rs78554043, risk allele frequency = 1.5%, OR = 1.62, 95% CI = 1.39 to 1.89, P = 7.50 × 10-10). At 13q34, the signal is located 5' of the gene IRS2 and 3' of a long noncoding RNA, while at 22q12 the candidate functional allele is a missense variant in the CHEK2 gene. These findings provide further support for the role of ancestry-specific germline variation in contributing to population differences in prostate cancer risk

A Germline Variant at 8q24 Contributes to Familial Clustering of Prostate Cancer in Men of African Ancestry

Although men of African ancestry have a high risk of prostate cancer (PCa), no genes or mutations have been identified that contribute to familial clustering of PCa in this population. We investigated whether the African ancestry–specific PCa risk variant at 8q24, rs72725854, is enriched in men with a PCa family history in 9052 cases, 143 cases from high-risk families, and 8595 controls of African ancestry. We found the risk allele to be significantly associated with earlier age at diagnosis, more aggressive disease, and enriched in men with a PCa family history (32% of high-risk familial cases carried the variant vs 23% of cases without a family history and 12% of controls). For cases with two or more first-degree relatives with PCa who had at least one family member diagnosed at age <60 yr, the odds ratios for TA heterozygotes and TT homozygotes were 3.92 (95% confidence interval [CI] = 2.13–7.22) and 33.41 (95% CI = 10.86–102.84), respectively. Among men with a PCa family history, the absolute risk by age 60 yr reached 21% (95% CI = 17–25%) for TA heterozygotes and 38% (95% CI = 13–65%) for TT homozygotes. We estimate that in men of African ancestry, rs72725854 accounts for 32% of the total familial risk explained by all known PCa risk variants. Patient summary: We found that rs72725854, an African ancestry–specific risk variant, is more common in men with a family history of prostate cancer and in those diagnosed with prostate cancer at younger ages. Men of African ancestry may benefit from the knowledge of their carrier status for this genetic risk variant to guide decisions about prostate cancer screening. © 2020 The AuthorsThe African ancestry–specific prostate cancer risk variant at 8q24, rs72725854, is enriched in men diagnosed at younger ages and men with a prostate cancer family history. Carriers of this risk allele would benefit from regular and earlier prostate cancer screening

Mapping the medical outcomes study HIV health survey (MOS-HIV) to the EuroQoL 5 Dimension (EQ-5D-3L) utility index

10.1186/s12955-019-1135-8Health and Quality of Life Outcomes1718

Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

Given the growing use of machine learning (ML) technologies in health care, regulatory bodies face unique challenges in governing their clinical use. Under the regulatory framework of the Food and Drug Administration, approved ML algorithms are practically locked, preventing their adaptation in the ever-changing clinical environment, defeating the unique adaptive trait of ML technology in learning from real-world feedback. At the same time, regulations must enforce a strict level of patient safety to mitigate risk at a systemic level. Given that ML algorithms often support, or at times replace, the role of medical professionals, we have proposed a novel regulatory pathway analogous to the regulation of medical professionals, encompassing the life cycle of an algorithm from inception, development to clinical implementation, and continual clinical adaptation. We then discuss in-depth technical and nontechnical challenges to its implementation and offer potential solutions to unleash the full potential of ML technology in health care while ensuring quality, equity, and safety. References for this article were identified through searches of PubMed with the search terms “Artificial intelligence,” “Machine learning,” and “regulation” from June 25, 2017, until June 25, 2022. Articles were also identified through searches of the reference list of the articles. Only papers published in English were reviewed. The final reference list was generated based on originality and relevance to the broad scope of this paper