The current classification system presents challenges to the diagnosis and treatment of patients with diabetes mellitus (DM), in part due to its conflicting and confounding definitions of type 1 DM, type 2 DM, and latent autoimmune diabetes of adults (LADA). The current schema also lacks a foundation that readily incorporates advances in our understanding of the disease and its treatment. For appropriate and coherent therapy, we propose an alternate classification system. The β-cell–centric classification of DM is a new approach that obviates the inherent and unintended confusions of the current system. The β-cell–centric model presupposes that all DM originates from a final common denominator—the abnormal pancreatic β-cell. It recognizes that interactions between genetically predisposed β-cells with a number of factors, including insulin resistance (IR), susceptibility to environmental influences, and immune dysregulation/inflammation, lead to the range of hyperglycemic phenotypes within the spectrum of DM. Individually or in concert, and often self-perpetuating, these factors contribute to β-cell stress, dysfunction, or loss through at least 11 distinct pathways. Available, yet underutilized, treatments provide rational choices for personalized therapies that target the individual mediating pathways of hyperglycemia at work in any given patient, without the risk of drug-related hypoglycemia or weight gain or imposing further burden on the β-cells. This article issues an urgent call for the review of the current DM classification system toward the consensus on a new, more useful system.

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Ecological Society of America is collaborating with JSTOR to digitize, preserve and extend access to Ecology. Abstract.Plant communities of large portions of the southwestern United States have changed from grassland to desert shrubland. Previous studies have demonstrated that soil nutrient resources become spatially more heterogeneous and are redistributed into islands of fertility with the shift in vegetation. The research presented here addressed the question of whether soil resources become more temporally heterogeneous as well as more spatially heterogeneous when grassland undergoes desertification to form shrubland. Within adjacent grassland and creosotebush sites, soil profiles were described at three soil pits, and samples were collected for description of nutrient resources within the profile. Relative abundance of plant cover and bare soil was determined within each site using line transects. Surface samples (0-20 cm depth) of bare soil and soil beneath the canopies of grasses and creosotebush were collected 17 times during 1992-1994. Soil samples were analyzed for moisture, extractable ammonium and nitrate, nitrogen mineralization potential, microbial biomass carbon, total organic carbon, microbial respiration, dehydrogenase activity, the ratio of microbial C to total organic C (Cmic/Corg), and the ratio of microbial respiration to biomass carbon (metabolic quotient). The major differences in the structure of soils between sites were the apparent loss of 3-5 cm depth of sandy surface soil at the creosotebush site and an associated increase in calcium carbonate content at a more shallow depth. Soils under plants at both sites had greater total and available nutrient resources, with higher concentrations under creosotebush than under grasses. Greatest temporal variation in available soil resources was observed in soils under creosotebush. When expressed on the basis of area, available soil resources were higher in the grassland than in the creosotebush shrubland, primarily due to the difference in plant cover (45% in grassland, 8% in creosotebush shrubland).

Glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RAs) are injectable glucose-lowering medications approved for the treatment of adult patients with type 2 diabetes mellitus (T2DM). This article provides practical information to guide primary care physicians on the use of GLP-1RAs in patients with T2DM. Two short-acting (once- or twice-daily administration; exenatide and liraglutide) and three long-acting (weekly administration; albiglutide, dulaglutide and exenatide) GLP-1RAs are currently approved in the US. These drugs provide levels of GLP-1 receptor agonism many times that of endogenous GLP-1. The GLP-1RAs have been shown to significantly improve glycemic parameters and reduce body weight. These agents work by activating GLP-1 receptors in the pancreas, which leads to enhanced insulin release and reduced glucagon release-responses that are both glucose-dependent-with a consequent low risk for hypoglycemia. Effects on GLP-1 receptors in the CNS and the gastrointestinal tract cause reduced appetite and delayed glucose absorption due to slower gastric emptying. The most common adverse effects are gastrointestinal, which are transient and less common with the long-acting drugs. GLP-1RAs are recommended as second-line therapy in combination with metformin, sulfonylureas, thiazolidinediones or basal insulin, providing a means of enhancing glucose control while offsetting the weight gain associated with insulin and some oral agents. GLP-1RAs represent a useful tool that the primary care physician can use to help patients with T2DM achieve their therapeutic goals.

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Objective To evaluate glycemic control among patients with type 2 diabetes mellitus (T2DM) treated with canagliflozin (CANA) vs. dipeptidyl peptidase-4 (DPP-4) inhibitors. Methods Using integrated claims and lab data from a US health plan of commercial and Medicare Advantage enrollees, this matched-control cohort study assessed adult T2DM patients receiving treatment with CANA or DPP-4 inhibitors (1 April 2013-31 December 2013). Cohorts were chosen hierarchically; the first pharmacy claim for CANA was identified as the index date; then the first pharmacy claim for a DPP-4 inhibitor was identified and index date set. Eligible patients had 6 months of continuous health plan enrollment before the index date (baseline) and 9 months after (follow-up) and no evidence of index drug in baseline. Patients were matched 1:1 using propensity score matching. Changes in glycated hemoglobin (HbA1c) and percentages of patients with HbA1c <8% and <7% during the follow-up were evaluated. Results The matched CANA and DPP-4 inhibitor cohorts (53.2% treated with sitagliptin) included 2766 patients each (mean age: 55.7 years). Among patients with baseline and follow-up HbA1c results, mean baseline HbA1c values were similar, 8.62% and 8.57% (p = 0.615) for the CANA (n = 729) and DPP-4 inhibitor (n = 710) cohorts, respectively. Change in HbA1c was greater among patients in the CANA cohort than for those in the DPP-4 inhibitor cohort (-0.92% vs. -0.63%, p < 0.001), and also among the subset of patients with baseline HbA1c ≥7% (-1.07% [n = 624] vs. -0.79% [n = 603], p = 0.004). During follow-up, greater percentages of the CANA cohort relative to the DPP-4 inhibitor cohort achieved HbA1c of <8% (66.0% vs. 58.6%, p = 0.004) and <7% (35.4% vs. 29.9%, p = 0.022). Limitations This study was observational and residual confounding remains a possibility. Conclusions In this real-world study of patients with T2DM, CANA use was associated with greater HbA1c reduction and higher percentages of patients attaining HbA1c goals than those treated with DPP-4 inhibitors.

Importance: Currently, there is no unified framework linking disease progression to established viral levels, clinical tests, inflammatory markers, and investigational treatment options. Objective: It may take many weeks or months to establish a standard treatment approach. Given the growing morbidity and mortality with respect to COVID-19, we present a treatment approach based on a thorough review of scholarly articles and clinical reports. Our focus is on staged progression, clinical algorithms, and individualized treatment. Evidence Review: We followed the protocol for a quality review article proposed by Heyn et. al.1 A literature search was conducted to find all relevant studies related to COVID-19. The search was conducted between April 1, 2020 and April 13, 2020 using the following electronic databases: PubMed (1809 to present), Google Scholar (1900 to present), MEDLINE (1946 to present), CINAHL (1937 to present), and Embase (1980 to present). Keywords used included COVID-19, 2019-nCov, SARS-CoV-2, SARS-CoV, and MERS-CoV, with terms such as efficacy, seroconversion, microbiology, pathophysiology, viral levels, inflammation, survivability, and treatment and pharmacology. No language restriction was placed on the search. Reference lists were manually scanned for additional studies. Findings: Of the articles found in the literature search, 70 were selected for inclusion in this study (67 cited in the body of the manuscript and 3 additional unique references in the Figures). The articles represent work from China, Japan, Taiwan, Vietnam, Rwanda, Israel, France, the United Kingdom, the Netherlands, Canada, and the United States. Most of the articles were cohort or case studies, but we also drew upon information found in guidelines from hospitals and clinics instructing their staff on procedures to follow. In addition, we based some decisions on data collected by agencies such as the CDC, FDA, IHME, ISDA, and Worldometer. None of the case studies or cohort studies used a large number of participants. The largest group of participants numbered less than 500 and some case studies had fewer than 30 patients. However, the review of the literature revealed the need for individualized treatment protocols due to the variability of patient clinical presentation and survivability. A number of factors appear to influence mortality: the stage at which the patient first presented for care, pre-existing health conditions, age, and the viral load the patient carried. Conclusion and Relevance: COVID-19 can be divided into three distinct Stages, beginning at the time of infection (Stage I), sometimes progressing to pulmonary involvement (Stage II, with or without hypoxemia) and less frequently to systemic inflammation (Stage III). In addition to modeling the stages of disease progression, we have also created a treatment algorithm which considers age, comorbidities, clinical presentation, and disease progression to suggest drug classes or treatment modalities. This paper presents the first evidence-based recommendations for individualized treatment for COVID-19.