By Max Bingham, PhD
Socioeconomic Factors May Explain High Rates of Type 2 Diabetes in Racial/Ethnic Minorities in the U.S.
An analysis by Piccolo et al. (p. 1208) reveals that racial/ethnic disparities in rates of type 2 diabetes in the U.S. are not likely to be associated with minority populations per se. Rather, socioeconomic factors, as well as others, likely explain the excess prevalence of the disease in these populations. Using a technique called structural equation modeling, the authors report the relative contributions of six groups of factors on racial/ethnic disparities in type 2 diabetes. Many previous studies have identified risk factors for type 2 diabetes but usually this has been done independently and never previously in one multilevel risk model, according to the authors. Applying the approach to the Boston Area Community Health (BACH) III cohort, they could then evaluate both direct and indirect factors affecting rates of type 2 diabetes according to racial/ethnic grouping (black, Hispanic, or white). BACH III is a random, population-based sample that included 2,764 subjects for this analysis. According to the model, black race or Hispanic ethnicity had no direct effect on type 2 diabetes outcomes. However, risk scores for a range of factor groupings could explain ∼40–45% of the total effects of black race or Hispanic ethnicity (i.e., indirect effects associated with race/ethnicity may well influence prevalence of type 2 diabetes in these groups). According to this analysis, socioeconomic factors may account for a significant proportion of risk in these groups. The authors suggest their data point to possible interventions at policy, community, and primary prevention levels. They also suggest future research may pinpoint exactly which risk factors can be tackled to bring about maximum reductions in type 2 diabetes rates.
A Call for Standardized Outcome Measures Reporting in Imminent Artificial Pancreas Clinical Trials
A consensus report by Maahs et al. (p. 1175) calls for the adoption of a basic set of core outcome measures to be used in upcoming clinical trials of artificial pancreas devices. This, the authors say, is needed to allow for the comparison of outcomes between trials and, as a consequence, should accelerate the widespread adoption of the technology. The so-called artificial pancreas is an automated insulin delivery system consisting at its core of an insulin pump, a continuous glucose monitoring device, and control software. The technology is widely regarded as a potential treatment option for type 1 diabetes patients, and various pivotal trials will start soon to assess further the efficacy and safety of a number of devices. The authors report that members of the JDRF Artificial Pancreas Project Consortium as well as a wider community of stakeholders were involved in defining the outcomes that should be reported. In particular, they call for specific continuous glucose monitoring metrics, in addition to HbA1c, to enable detailed reporting of glycemic excursions and glucose control. Based on the currently available technology, it is now possible to obtain glucose measures every five minutes and at a minimum these should be reported in ranges over the study period. A number of safety and technical performance metrics should also be reported. Consensus on a broader range of outcomes was reportedly challenging and, as such, the authors recommend reporting additional metrics as appropriate for individual study designs. Specific issues covered include standards for graphical illustrations, meaningful reporting of disease burden, and technical reporting for each system. In short, they call for maximum disclosure to allow outcomes to be fully evaluated. Commenting more widely on the recommendations, author David M. Maahs stated: “The authors hope that this article will lead to further discussions to improve outcome measures reporting for artificial pancreas research and to contribute to the process of making these technologies widely available to patients with diabetes.”
Cognitive Function May Be Decreased in Individuals with Type 2 Diabetes and Diabetic Foot
A case-control study by Natovich et al. (p. 1202) suggests that individuals with type 2 diabetes and diabetic foot ulcers may possess fewer cognitive resources than individuals with type 2 diabetes alone—a situation they suggest should be remedied by assessing cognitive status regularly to optimize treatment and care. The study examined 99 individuals with type 2 diabetes and foot ulcers (the case subjects) and 95 individuals with type 2 diabetes alone, matched for sex and diabetes duration. All underwent an extensive battery of neuropsychological evaluations to assess cognition on various levels. Scores were all reduced significantly in individuals with diabetic foot ulcers. This was with the exception of premorbid cognition, which suggested individuals with foot ulcers suffered a decline in cognition whereas those with type 2 diabetes but without this complication did not. Prior to this study little was known about diabetic foot ulcers and cognition, although some evidence suggested a link may exist. Current guidelines relating to the treatment of diabetic foot do not carry any advice on cognitive function in patients. According to author Rachel Natovich: “Our study demonstrates that ‘diabetic foot’ may refer not only to the physical condition of the limb but rather to a more generalized complex state involving significant cognitive changes as well. This new information is an important contribution to the health care of these patients who are at significantly increased risk for medical complications and the unique challenge that they present to health providers. Our results support the conclusion that cognitive changes must be addressed as part of the diabetic foot complication and monitored regularly. We believe this practice will lead to early identification of cognitive changes and initiation of prompt treatment. We hope that our findings will help improve communication between health providers and this unique patient group and help develop and implement new interventions for improved patient care and outcome.”
Implications of the Precision Medicine Initiative for Diabetes Research
A central component of the Precision Medicine Initiative (PMI) is the development by the National Institutes of Health (NIH) of a national research cohort of 1 million U.S. volunteers that will be characterized according to numerous health, medical, and other outcomes. Fradkin et al. (p. 1080) examine the potential implications of the initiative on diabetes research and discuss ongoing related NIH projects that are likely to contribute to the development of precision medicine in the near term. The authors detail the considerable challenges involved in the PMI program and the progress made to date. In total, they report that NIH aims to recruit 1 million people to the cohort by the end of 2019. While the PMI has considerable potential for novel insights into diabetes, certain current studies may also prove informative from the perspective of precision medicine. The examples given include The Environmental Determinants of Diabetes in the Young (TEDDY) cohort, the Accelerating Medicines Partnership (AMP) portal, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) initiative, the Trans-Omics for Precision Medicine (TOPMed) program, and the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness (GRADE) study. The article points to the potential of the PMI cohort to address the enduring issue of why some individuals develop diabetes complications and others do not. The sheer number of potential recruits should also help uncover biomarkers for diabetes complications and potential targets for disease treatment. The inclusion of advanced technologies (i.e., continuous glucose monitoring) in the cohort may help uncover glycemic and other data patterns that could prove informative for treatment. According to the authors, one of the biggest issues for diabetes care is that the current classification of diabetes into type 1 and type 2 appears to be a gross oversimplification that may result in less than optimal patient care. The PMI may therefore present a great opportunity to identify subgroups within a diabetes spectrum and on that basis develop more refined treatments and interventions. Commenting more widely, author Judith E. Fradkin told Diabetes Care: “The substantial individual variability in development and progression of diabetes must inform its treatment. We anticipate the PMI, coupled with emerging high throughput technologies, will move us toward this goal.”
