Vol 3-1 Research Article

HbA1c Evidence for a Prediabetes Diagnosis Delays Onset of Type 2 Diabetes

Maurice C. Johnson, Jr.1, Howard A. Fishbein1*, Rebecca Jeffries Birch1, Qilu Yu2, Russ Mardon1, Wilson Pace3, Natalie Ritchie4, Jennifer K. Carroll5, Daniella Meeker6

1Westat, Rockville, MD, USA

2National Center for Complementary and Integrative Health (NCCIH), National Institutes of Health (NIH), Bethesda, MD, USA

3DARTNet, Aurora, CO, USA

4University of Colorado/Denver Health and Hospitals Authority, USA

5University of Colorado, USA

6University of Southern California, USA 

Objective: This study examined the influential role of making a prediabetes diagnosis resulting in the subsequent delay in onset of type 2 diabetes.

Research Design and Methods: Using electronic medical records, a multivariable logistic regression model examined demographic and clinical risk factors associated with a prediabetes diagnosis among patients with HbA1c evidence of prediabetes. A multivariable non-proportional Cox regression examined development to type 2 diabetes (maximum 7 year follow-up).

Results: Analysis includes 40,970 patients with incident prediabetes (76.8% undiagnosed). Logistic regression showed higher baseline HbA1c levels significantly influenced assigning a prediabetes diagnosis: compared to patients with HbA1c level 5.7-5.9% (low), OR 1.66 (99% CI 1.54-1.78) for HbA1c level 6.0-6.2% (medium) and OR 1.62 (CI 1.43-1.83) for HbA1c level 6.3-6.4% (high). Cox model results, which included an interaction between HbA1c and prediabetes diagnosis, found HbA1c the most significant predictor. Patients with diagnosed prediabetes progressed to type 2 diabetes slower than those undiagnosed. Comparing diagnosed patients to undiagnosed within the same HbA1c level, HRs ranged from 0.47 (CI 0.37-0.61) in the high HbA1c level to 0.83 (CI 0.67-1.02) in the low HbA1c level.

Conclusions: From the LEADR cohort (1) HbA1c levels were the principle factor associated with risk for prediabetes diagnosis. Modeling development to diabetes, baseline HbA1c was the significant predictor of risk. Findings suggest assignment of a prediabetes diagnosis is associated with slower development of diabetes and this protective benefit of being diagnosed increases with a higher baseline HbA1c. Prediabetes diagnosis is useful for delaying onset of type 2 diabetes.

DOI: 10.29245/2767-5157/2021/1.1114 View / Download Pdf
Vol 3-1 Mini Review Article

Abnormal Glucose Tolerance in Prediabetes Patients with Acute Myocardial Infarction: Implications for Therapy

Nitchakarn Laichuthai1, Ralph A. DeFronzo2*

1Hormonal and Metabolic Disorders Research Unit and Excellence Center in Diabetes, Hormone, and Metabolism, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, and Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

2Diabetes Division, UT Health San Antonio and Texas Diabetes Institute, San Antonio, Texas, USA

Newly discovered abnormal glucose tolerance is common in patients who present with acute myocardial infarction (MI). These individuals are at very high risk for recurrent major adverse cardiovascular events (MACE), cardiovascular (CV) mortality, and all-cause mortality compared to normal-glucose-tolerant individuals who present with acute MI. Early and aggressive intervention with lifestyle and pharmacologic treatment are essential for the prevention of prediabetes progression to diabetes and recurrent cardiovascular events in this high risk population. Management, both with regard to prevention of recurrent cardiovascular events and development of diabetes, has been poorly addressed in current cardiology and diabetes guidelines. In this article, we review current evidence regarding the use of glucagon-like peptide 1 receptor agonists (GLP-1 RAs), sodium glucose cotransporter 2 inhibitors (SGLT2i), and pioglitazone to prevent recurrent cardiovascular events and propose areas of research to be explored in the future.

DOI: 10.29245/2767-5157/2021/1.1115 View / Download Pdf
Vol 3-1 Systematic Review

Haemorrhagic suprarenal pseudocysts: a systematic review of a rare condition from family physicians’ perspective during the COVID-19 global pandemic

Adekunle Olowu1*, Adel Abbas Alzehairy2

1Consultant Family Medicine, Al Thumama Health Centre, Primary Health Care Corporation, Doha, Qatar

2Formerly Specialist Family Medicine, Al Thumama Health Centre, Primary Health Care Corporation, Doha, Qatar

Adrenal cysts are rare lesions that could be epithelial, endothelial, parasitic or haemorrhagic1, as well as pseudocysts. Haemorrhagic adrenal cysts are extremely rare and are often asymptomatic, so diagnosis can be really challenging. This can prove really difficult for primary care physicians who are often the frontline clinicians these patients tend to present to. They are usually benign lesions and do not often cause mortality if detected early and prompt surgery is done, as was the case with the patient in our case report4. When they do become symptomatic, they can present with different systemic symptoms as documented in literature, including in our case report2,4. Diagnosis is usually through Ultrasound and CT Scan and management is largely laparoscopic or open excision depending on the size of the lesion, surgical expertise and local protocol. Most patients make full recovery and mortality is extremely low3. The aim of this review is to provide a broader overview of the subject, highlight salient points in several studies relating to haemorrhagic cysts, provide an up to date follow up information on the index patient in our case report and to explore possible areas for future study4,6. This review also includes a suggested management algorithm and intends to emphasize the fact that patients who present in primary, urgent or emergency care settings with persistent non-specific symptoms should be investigated for rare diseases.

DOI: 10.29245/2767-5157/2021/1.1117 View / Download Pdf
Vol 3-1 Research Article

Latent Class Trajectory Analysis of Risk Factors Uncovers Progression to Type 2 Diabetes

Qilu Yu1,3, Maurice C. Johnson, Jr.1, Howard A. Fishbein1*, Rebecca J. Birch1, Xiaoshu Zhu1, Russ Mardon1, Wilson Pace2, Sunitha M. Mathew1, Holly L. Sawyer1, Lori S. Merrill1, Keith D. Umbel1, Sophia Jang1

1Westat, Rockville, MD, USA

2DARTNet, Aurora, CO, USA

3National Center for Complementary and Integrative Health (NCCIH), National Institutes of Health (NIH), Bethesda, MD, USA

We identified trajectories of diabetes risk factors in the Longitudinal Epidemiologic Assessment of Diabetes Risk (LEADR) cohort analyzing 8 years of electronic health records on 1.4 million patients, and investigated associations between trajectories and progression to new onset Type 2 diabetes.

Design and Methods: Analyzing LEADR data (2010-2016), we applied Latent Class Trajectory Analysis (LCTA) to classify patterns of risk factor change. There were 824,043 patients with BMIs; 955,128 patients with systolic blood pressures; 957,491 patients with diastolic blood pressures; 300,137 patients with HDLs; 267,553 patients with non-HDL cholesterols; and 297,026 patients with triglycerides. Patients had to have data for all risk factors being assessed. Association between trajectories and incidence of type 2 diabetes for 94,551 patients was assessed using negative binomial regression analysis.

Results: Compared to a static BMI trajectory, those with a sustained weight increase (25%+ from starting BMI) were at higher risk of type 2 diabetes over 4.8 years of follow-up (range 2.0 to 8.0 years) (adjusted rate ratios ranged 1.53-1.62, p-value<0.05). Patients with a BMI decrease trajectory (of ~10%), were at reduced risk of diabetes (adjusted rate ratios ranged 0.54-0.74, p-value<.05). BP and lipid trajectories had significant associations with diabetes onset.

Conclusions: Regardless of the starting BMI, those who increased their BMI by 25% within two years and maintained the higher weight were significantly at increased risk of type 2 diabetes. Monitoring BMI change and other known risk factor trajectories, BP and lipids, are additional tools for identifying patients at risk for type 2 diabetes.

DOI: 10.29245/2767-5157/2021/1.1118 View / Download Pdf