The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in ...easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D.
Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts.
We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling.
Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.
The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, ...but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal.
We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments.
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•Soft clustering based on 32 phenotypes identified 4 quantitative archetypes•These reflect different patterns of dysfunction across T2D etiological processes•The four archetypes are different in disease progression, GRSs, and omics signals•Some patients are dominated by one archetype, but many have etiological combinations
Wesolowska-Andersen et al. represent the clinical heterogeneity of newly diagnosed T2D as four quantitative archetype profiles reflecting patterns of dysfunction in disease etiological processes, rather than clustering individuals into categorical subgroups as attempted by others. The archetype profiles differ in genetic risk scores, disease progression, and circulating omics biomarkers.
Although pulmonary rehabilitation results in improvement in multiple outcome areas, relatively few studies in the United States have evaluated its effect on healthcare utilization. This study ...compared aspects of healthcare utilization during the year before to the year after outpatient pulmonary rehabilitation in patients with chronic obstructive pulmonary disease referred to 11 hospital-based centers in Connecticut and New York. Utilization data from 128 of 132 patients who originally gave informed consent were evaluated; their mean age was 69 years and their forced expiratory volume in 1 second was 44% of predicted. Forty-five percent had 1 or more hospitalizations in the year before beginning pulmonary rehabilitation. In the year after pulmonary rehabilitation, there were 0.25 fewer total hospitalizations (P = .017) and 2.18 fewer hospital days (P = .015) per patient and 271 fewer hospital days for the group. Hospitalizations for respiratory reasons also decreased significantly. Most of the reduction in hospital utilization was due to a decrease in intensive care unit days. The number of physician visits decreased by 2.4 in the year after pulmonary rehabilitation (P < .0001); most of this reduction was due to decreased visits to primary care providers. The estimated costs/charges for the aspects of healthcare utilization that we studied decreased by a mean of 4,694 dollars and a median of 390 dollars (P = .0002). This study suggests that pulmonary rehabilitation leads to a reduction in healthcare utilization.
An increasing number of ideal cardiovascular health factors have been associated with a lower prevalence and incidence of ASCVD events, heart failure, atrial fibrillation, cancer, depression, and ...cognitive impairment (S1-7). ...moving individuals toward ideal cardiovascular health is critically important for prevention of many important health conditions. ...it is difficult, perhaps impossible, to assess long-term effectiveness in terms of survival or quality-adjusted life-years (QALYs) or associated costs because of increasing uncertainty about outcome the further one tries to look into the future.” ...social determinants of ASCVD risk—and their impact on the patient’s ability to prevent or treat risk factors—must be taken into account. High blood pressure ▪Short sleep duration (<6 h) and poor-quality sleep are associated with high blood pressure and should be considered (S2.1-41). Because other lifestyle habits can impact blood pressure, access to a healthy, low-sodium diet and viable exercise options should also be considered.
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