XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Importance/Background: The coronavirus disease (COVID-19) pandemic is a critical public health issue. Evidence has shown that metformin favorably influences COVID-19 outcomes. This study aimed to assess the benefits and risks of metformin in COVID-19 patients. Methods: We searched the PubMed, Embase, Cochrane Library, and Chinese Biomedical Literature Database from inception to February 18, 2021. Observational studies assessing the association between metformin use and the outcomes of COVID-19 patients were included. The primary outcome was mortality, and the secondary outcomes included intubation, deterioration, and hospitalization. Random-effects weighted models were used to pool the specific effect sizes. Subgroup analyses were conducted by stratifying the meta-analysis by region, diabetic status, the adoption of multivariate model, age, risk of bias, and timing for adding metformin. Results: We identified 28 studies with 2,910,462 participants. Meta-analysis of 19 studies showed that metformin is associated with 34% lower COVID-19 mortality [odds ratio (OR), 0.66; 95% confidence interval (CI), 0.56-0.78; I 2 = 67.9%] and 27% lower hospitalization rate (pooled OR, 0.73; 95% CI, 0.53-1.00; I 2 = 16.8%). However, we did not identify any subgroup effects. The meta-analysis did not identify statistically significant association between metformin and intubation and deterioration of COVID-19 (OR, 0.94; 95% CI, 0.77-1.16; I 2 = 0.0% for intubation and OR, 2.04; 95% CI, 0.65-6.34; I 2 = 79.4% for deterioration of COVID-19), respectively. Conclusions: Metformin use among COVID-19 patients was associated with a reduced risk of mortality and hospitalization. Our findings suggest a relative benefit for metformin use in nursing home and hospitalized COVID-19 patients. However, randomized controlled trials are warranted to confirm the association between metformin use and COVID-19 outcomes. Study Registration: The study was registered on the PROSPERO on Feb 23, 2021 (CRD42021238722).

/pdf/metformin-in-patients-with-covid-19-a-systematic-review-and-6afkt849o4.pdf

Metformin in Patients With COVID-19: A Systematic Review and Meta-Analysis.

The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes major challenges to the healthcare system. SARS-CoV-2 infection leads to millions of deaths worldwide and the mortality rate is found to be greatly associated with pre-existing clinical conditions. The existing dataset strongly suggests that cardiometabolic diseases including hypertension, coronary artery disease, diabetes and obesity serve as strong comorbidities in coronavirus disease (COVID-19). Studies have also shown the poor outcome of COVID-19 in patients associated with angiotensin-converting enzyme-2 polymorphism, cancer chemotherapy, chronic kidney disease, thyroid disorder, or coagulation dysfunction. A severe complication of COVID-19 is mostly seen in people with compromised medical history. SARS-CoV-2 appears to attack the respiratory system causing pneumonia, acute respiratory distress syndrome, which lead to induction of severe systemic inflammation, multi-organ dysfunction, and death mostly in the patients who are associated with pre-existing comorbidity factors. In this article, we highlighted the key comorbidities and a variety of clinical complications associated with COVID-19 for a better understanding of the etiopathogenesis of COVID-19. 

https://link.springer.com/content/pdf/10.1007/s10238-022-00821-4.pdf

Comorbidities and clinical complications associated with SARS-CoV-2 infection: an overview

Introduction Diabetes mellitus (DM) is a leading cause of morbidity and mortality worldwide. At the same time, digital health technologies (DHTs), which include mobile health apps (mHealth) have been rapidly gaining popularity in the self-management of chronic diseases, particularly following the COVID-19 pandemic. However, while a great variety of DM-specific mHealth apps exist on the market, the evidence supporting their clinical effectiveness is still limited. Methods A systematic review was performed. A systematic search was conducted in a major electronic database to identify randomized controlled trials (RCTs) of mHealth interventions in DM published between June 2010 and June 2020. The studies were categorized by the type of DM and impact of DM-specific mHealth apps on the management of glycated haemoglobin (HbA1c) was analysed. Results In total, 25 studies comprising 3,360 patients were included. The methodological quality of included trials was mixed. Overall, participants diagnosed with T1DM, T2DM and Prediabetes all demonstrated greater improvements in HbA1c as a result of using a DHT compared with those who experienced usual care. The analysis revealed an overall improvement in HbA1c compared with usual care, with a mean difference of –0.56% for T1DM, –0.90% for T2DM and –0.26% for Prediabetes. Conclusion DM-specific mHealth apps may reduce HbA1c levels in patients with T1DM, T2DM and Prediabetes. The review highlights a need for further research on the wider clinical effectiveness of diabetes-specific mHealth specifically within T1DM and Prediabetes. These should include measures which go beyond HbA1c, capturing outcomes including short-term glycemic variability or hypoglycemic events.

/pdf/the-effectiveness-of-digital-health-technologies-for-2sycnq4d.pdf

The effectiveness of digital health technologies for patients with diabetes mellitus: A systematic review

This article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.

/pdf/the-age-structure-stringency-policy-income-and-spread-of-3y9qqiapxs.pdf

The Age Structure, Stringency Policy, Income, and Spread of Coronavirus Disease 2019: Evidence From 209 Countries

Background: Diabetes has been found to increase severity and mortality under the current pandemic of coronavirus disease of 2019 (COVID-19). Up to date, the clinical characteristics of diabetes patients with COVID-19 and the risk factors for poor clinical outcomes are not clearly understood. Methods: The study was retrospectively carried out on enrolled diabetes patients with laboratory confirmed COVID-19 infection from a designated medical center for COVID-19 from January 25th, 2020 to February 14th, 2020 in Wuhan, China. The medical record was collected and reviewed. Univariate and multivariate analyses were performed to assess the risk factors associated with the severe events which were defined as a composite endpoint of admission to intensive care unit, the use of mechanical ventilation, or death. Results: A total of 52 diabetes patients with COVID-19 were finally included in the study. 21 (40.4%) patients had developed severe events in 27.50 (IQR 12.25-35.75) days follow-up, 15 (28.8%) patients experienced life-threatening complications and 8 patients died with a recorded mortality rate of 15.4%. Only 13 patients (41.9%) were in optimal glycemic control with HbA1c value of <7.0%. In addition to general clinical characteristics of COVID-19, the severe events diabetes patients showed higher counts of white blood cells and neutrophil, lower lymphocytes (40, 76.9%), high levels of hs-CRP, erythrocyte sedimentation rate (ESR) and procalcitonin (PCT) as compared to the non-severe diabetes patients. Mild higher level of cardiac troponin I (cTNI) (32.0 pg/ml; IQR 16.80-55.00) and D-dimer (1.70 μg/L, IQR 0.70-2.40) were found in diabetes patients with severe events as compared to the non-severe patients (cTNI:20.00 pg/ml, IQR5.38-30.00, p = 0.019; D-dimer: 0.70 μg/L, IQR 0.30-2.40, p = 0.037). After adjusting age and sex, increased level of cTNI was found to significantly associate with the incidence of severe events (HR: 1.007; 95% CI: 1.000-1.013; p = 0.048), Furthermore, using of α-glucosidase inhibitors was found to be the potential protectant for severe events (HR: 0.227; 95% CI: 0.057-0.904; p = 0.035). Conclusion: Diabetes patients with COVID-19 showed poor clinical outcomes. Vigorous monitoring of cTNI should be recommended for the diabetes patients with COVID-19. Usage of α-glucosidase inhibitors could be a potential protectant for the diabetes patients with COVID-19.

/pdf/risk-factors-for-poor-outcomes-of-diabetes-patients-with-5bliwzn0x0.pdf

Risk Factors for Poor Outcomes of Diabetes Patients With COVID-19: A Single-Center, Retrospective Study in Early Outbreak in China.

The study aimed to determine the relationships between the basal metabolic rate (BMR) and body composition of overweight and obese Chinese adults with type 2 diabetes mellitus (DM). This cross-sectional clinical study enrolled 193 Chinese adults with type 2 DM who were overweight (24 kg/m(2)=BMI≤28 kg/m(2), n=99), or obese (BMI ≥28 kg/m(2), n=94). Ninety-seven adults with normal BMIs, including 50 DM patients and 47 healthy adults, were recruited as a control group. BMR was measured by indirect calorimetry; predicted BMR was calculated according to the Schofield equation; and the relationships between BMR, body composition, and biochemical results were determined by the Pearson correlation. The results showed that obese DM patients had significantly higher BMRs than both overweight patients (P<0.05) and patients with normal BMI did (P<0.05). The measured BMR was significantly lower than the predicted BMR (P<0.05) in all groups. Obese and overweight DM patients had significantly greater weight, waist circumference, hip circumference, BMI, body surface area, body fat percentage, fat mass, and fat-free mass than patients with normal BMI. Except for waist circumference, these body composition measurements were significantly increased in obese DM patients when compared with those in overweight DM patients (P<0.05). Fat-free mass was closely correlated with BMR in both DM patients (r=0.874, P<0.01) and in healthy controls (r=0.902, P<0.01). It was concluded that overweight and obese Chinese adults with type 2 DM had increased BMRs compared with normal-weight controls, which may result from the difference in fat-free mass.

Increased BMR in overweight and obese patients with type 2 diabetes may result from an increased fat-free mass.

Objective
To investigate whether height is associated with peripheral arterial disease (PAD) in patients with type 2 diabetes.

/pdf/association-of-height-with-peripheral-arterial-disease-in-4xjxuz45w6.pdf

Association of height with peripheral arterial disease in type 2 diabetes.

Objective: Hypoglycemia has been shown to promote inflammation, a common pathogenic process, in many chronic health conditions including diabetes and cardiovascular disease. The aim of this study was to investigate the association of hypoglycemia, assessed by continuous glucose monitoring (CGM) with major adverse cardiovascular event (MACE) outcomes and all-cause mortality. Methods: A retrospective cohort study was conducted with 1,520 patients with type 2 diabetes mellitus (T2DM). The severity of hypoglycemia event was assessed by CGM system. Results: Three hundred and forty-seven participants experienced hypoglycemia events (323 with mild hypoglycemia and 24 with severe hypoglycemia). A fraction of 72.62% hypoglycemia was asymptomatic. During a median follow-up of 31 months, 380 participants reached the primary outcome of MACE (61 cardiovascular death, 50 non-fatal myocardial infarction [MI], 116 non-fatal stroke, 153 unstable angina requiring hospitalization), 80 participants died before the end of the study. In multivariate Cox regression models, hypoglycemia was associated with cardiovascular death (HR 2.642[95CI% 1.398-4.994]), non-fatal stroke (HR 1.813 [95CI% 1.110-2.960]) and all-cause mortality (HR 1.960 [95 CI% 1.124- 3.418]) after the full adjustment. Hypoglycemia was not associated with non-fatal MI and unstable angina. The HR of severe hypoglycemia was higher than mild hypoglycemia for cardiovascular death. Patients with symptomatic and asymptomatic hypoglycemia had similar MACE outcomes and all-cause mortality. Conclusions: CGM is effective to detect asymptomatic and nocturnal hypoglycemia. Hypoglycemia is associated with an increased risk of non-fatal stroke, cardiovascular related death, and total mortality. The cardiovascular mortality is dose-dependent on the severity of hypoglycemia.

/pdf/the-association-of-hypoglycemia-assessed-by-continuous-4sitn9v28s.pdf

The Association of Hypoglycemia Assessed by Continuous Glucose Monitoring With Cardiovascular Outcomes and Mortality in Patients With Type 2 Diabetes.

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Risk Factors for Poor Outcomes of Diabetes Patients With COVID-19: A Single-Center, Retrospective Study in Early Outbreak in China.

Increased BMR in overweight and obese patients with type 2 diabetes may result from an increased fat-free mass.

Association of height with peripheral arterial disease in type 2 diabetes.

The Association of Hypoglycemia Assessed by Continuous Glucose Monitoring With Cardiovascular Outcomes and Mortality in Patients With Type 2 Diabetes.