Glycemic Control and Clinical Outcomes in U.S. Patients With COVID-19: Data From the National COVID Cohort Collaborative (N3C) Database

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

COVID-19 Vaccination Prior to SARS-CoV-2 Infection Reduced Risk of Subsequent Diabetes Mellitus: A Real-World Investigation Using U.S. Electronic Health Records

OBJECTIVE

Previous studies have indicated a bidirectional correlation between diabetes and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, no investigation has comprehensively explored the potential of coronavirus disease 2019 (COVID-19) vaccination to reduce the risk of new-onset diabetes in infected individuals.

RESEARCH DESIGN AND METHODS

In the first of 2 cohorts, we compared the risk of new-onset diabetes between individuals infected with SARS-CoV-2 and noninfected individuals (N = 1,562,606) using the TriNetX database to validate findings in prior literature. For the second cohort, we identified 83,829 vaccinated and 83,829 unvaccinated COVID-19 survivors from the same period. Diabetes, antihyperglycemic drug use, and a composite of both were defined as outcomes. We conducted Cox proportional hazard regression analysis for the estimation of hazard ratios (HRs) and 95% CIs. Kaplan-Meier analysis was conducted to calculate the incidence of new-onset diabetes. Subgroup analyses based on age (18-44, 45-64, ≥65 years), sex (female, male), race (White, Black or African American, Asian), and BMI categories (<19.9, 20-29, 30-39, ≥40), sensitivities analyses, and a dose-response analysis were conducted to validate the findings.

RESULTS

The initial cohort of patients infected with SARS-CoV-2 had a 65% increased risk (HR 1.65; 95% CI 1.62-1.68) of developing new-onset diabetes relative to noninfected individuals. In the second cohort, we observed that vaccinated patients had a 21% lower risk of developing new-onset diabetes in comparison with unvaccinated COVID-19 survivors (HR 0.79; 95% CI 0.73-0.86). Subgroup analyses by sex, age, race, and BMI yielded similar results. These findings were consistent in sensitivity analyses and cross-validation with an independent data set from TriNetX.

CONCLUSIONS

In conclusion, this study validates a 65% higher risk of new-onset diabetes in SARS-CoV-2-infected individuals compared to noninfected counterparts. Furthermore, COVID-19 survivors who received COVID-19 vaccinations experienced a reduced risk of new-onset diabetes, with a dose-dependent effect. Notably, the protective impact of COVID-19 vaccination is more pronounced among the Black/African American population than other ethnic groups. These findings emphasize the imperative of widespread vaccination to mitigate diabetes risk and the need for tailored strategies for diverse demographic groups to ensure equitable protection.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study

BACKGROUND

More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis.

METHODS

This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC.

RESULTS

Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls.

CONCLUSIONS

This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

Racial and Ethnic Disparities in Glycemic Control Among Insured US Adults

Importance

Poor access to care and lack of health insurance are important contributors to disparities in glycemic control. However expanding health insurance coverage may not be enough to fully address the high burden of poor glycemic control for some groups.

Objective

To characterize racial and ethnic disparities in glycemic control among adults with private and public insurance in the US over a 15-year timeframe and to evaluate whether social, health care, and behavioral or health status factors attenuate estimates of disparities.

Design, Setting, and Participants

This cross-sectional study used data from the National Health and Nutrition Examination Survey from 2003 to 2018. Participants included Hispanic or Latino, non-Hispanic Black, and non-Hispanic White adults aged 25 to 80 years with self-reported diabetes and health insurance. Data were analyzed from January 15 to August 23, 2023.

Exposure

Participants self-identified as Hispanic or Latino, non-Hispanic Black, or non-Hispanic White.

Main Outcomes and Measures

The main outcome, poor glycemic control, was defined as glycated hemoglobin A1c (HbA1c) of 7.0% or greater. Information about social (education, food security, and nativity), health care (insurance type, routine place for health care, insurance gap in past year, and use of diabetes medications), and behavioral or health status (years with diabetes, waist circumference, and smoking) factors were collected via questionnaires.

Results

A total of 4070 individuals (weighted mean [SE] age, 61.4 [0.27] years; 1970 [weighted proportion, 49.3%] were women) were included, representing 16 337 362 US adults, including 1146 Hispanic or Latino individuals (weighted proportion, 13.2%), 1196 non-Hispanic Black individuals (weighted proportion, 15.7%), and 1728 non-Hispanic White individuals (weighted proportion, 71.1%). In models adjusted for age, sex, and survey year, Hispanic or Latino and non-Hispanic Black individuals had significantly higher odds of poor glycemic control than non-Hispanic White individuals (Hispanic or Latino: odds ratio [OR], 1.46; 95% CI, 1.16-1.83; Black: OR, 1.28; 95% CI, 1.04-1.57). There was some attenuation after adjustment for social factors, especially food security (Hispanic or Latino: OR, 1.39; 95% CI, 1.08-1.81); Black: OR, 1.39; 95% CI, 1.08-1.81). However, accounting for health care and behavioral or health status factors increased disparities, especially for Hispanic or Latino individuals (OR, 1.63; 95% CI, 1.24-2.16), with racial and ethnic disparities persisting even among those with private insurance (OR, 1.66; 95% CI, 1.10-2.52).

Conclusions and Relevance

In this cross-sectional study of insured adults with diabetes in the US, disparities in poor glycemic control persisted despite adjustment for social, health care, and behavioral factors. Research is needed to identify the barriers contributing to poor control even in populations with access to care.

Long-term effects of COVID-19 on the endocrine system - a pilot case-control study

Background

Coronavirus disease 2019 (COVID-19) has permanently changed the world. Despite having been a pandemic for nearly 3 years, the mid- and long-term complications of this disease, including endocrine disorders, remain unclear. Our study aimed to evaluate the lasting effects of COVID-19 on the endocrine system 6 months after initial infection.

Methods

We compared patients who underwent COVID-19 to age- and sex-matched subjects from a population-based study conducted before the pandemic. We evaluated differences in multiple parameters related to metabolism and the endocrine system including fasting glucose, insulin, lipids, body composition, thyroid stimulating hormone (TSH), free thyroxine (fT4), free triiodothyronine (fT3), anti-thyroglobulin (aTG) and anti-thyroid peroxidase (aTPO) antibodies, prolactin, cortisol, testosterone, and estradiol.

Results

We found significantly lower levels of fT3 and fT4, accompanied by higher levels of TSH and aTPO antibodies, in COVID-19 survivors. Moreover, we found that patients who underwent SARS-CoV2 infection had higher levels of prolactin and lower levels of testosterone than controls. Interestingly, differences in testosterone levels were observed only in male subjects. We did not detect significant differences in body composition or metabolic and glycemic parameters between cases and controls, except for significantly higher values of the HOMA2-B index in COVID-19 survivors.

Conclusion

Our study indicates that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might have long-term consequences on the endocrine system, including the suppressed function of the thyroid gland, prolactin, and male sex hormone secretion. Moreover, we showed that in a 6-month follow-up, COVID-19 had no consequences on glycemic parameters, lipid profiles, liver function, body composition, cortisol levels, and estradiol levels.

Sex Differences among Overweight/Obese Kidney Transplant Recipients Requiring Oxygen Support Amid the COVID-19 Pandemic

Background and Objectives: Overweight/obesity puts individuals at greater risk for COVID-19 progression and mortality. We aimed to evaluate the impact of overweight/obesity on oxygen (O2) requirement outcomes of male and female kidney transplant recipients (KTRs) during the COVID-19 pandemic. Materials and Methods: We conducted a retrospective analysis of a cohort of KTRs diagnosed with COVID-19. Participants were stratified based on BMI categories, and data on the need for O2 therapy outcome were collected and analyzed separately for male and female KTRs. Results: In total, 284 KTRs (97 males and 187 females) were included in the study. Overweight/obesity was observed in 60.6% of male KTRs and 71% of female KTRs. Strikingly, overweight/obese women had a significantly higher requirement for supplemental O2 (63.3% vs. 41.7%, OR = 2.45, p = 0.03), particularly among older individuals (OR = 1.05, p = 0.04), smokers (OR = 4.55, p = 0.03), those with elevated lactate dehydrogenase (LDH) levels (OR = 1.01, p = 0.006), and those with lower admission and basal estimated glomerular filtration rate (eGFR) levels. Within this cohort, the necessity for O2 supplementation was correlated with more unfavorable outcomes. These included heightened mortality rates, transfers to the intensive care unit, employment of invasive mechanical ventilation, and the emergence of acute kidney injury requiring hemodialysis. On the other hand, although overweight/obese male KTRs had a higher prevalence of hypertension and higher fasting blood glucose levels, no significant association was found with COVID-19-related outcomes when compared to lean male KTRs. Conclusions: Overweight/obesity is highly prevalent in KTRs, and overweight/obese women demonstrated a higher need for supplemental O2. Therefore, the early identification of factors that predict a worse outcome in overweight/obese female KTRs affected by COVID-19 contributes to risk stratification and guides therapeutic decisions.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Comorbidities, multimorbidity and COVID-19

The influence of comorbidities on COVID-19 outcomes has been recognized since the earliest days of the pandemic. But establishing causality and determining underlying mechanisms and clinical implications has been challenging-owing to the multitude of confounding factors and patient variability. Several distinct pathological mechanisms, not active in every patient, determine health outcomes in the three different phases of COVID-19-from the initial viral replication phase to inflammatory lung injury and post-acute sequelae. Specific comorbidities (and overall multimorbidity) can either exacerbate these pathological mechanisms or reduce the patient's tolerance to organ injury. In this Review, we consider the impact of specific comorbidities, and overall multimorbidity, on the three mechanistically distinct phases of COVID-19, and we discuss the utility of host genetics as a route to causal inference by eliminating many sources of confounding. Continued research into the mechanisms of disease-state interactions will be crucial to inform stratification of therapeutic approaches and improve outcomes for patients.

Endocrine Disorders and COVID-19

The multifaceted interaction between coronavirus disease 2019 (COVID-19) and the endocrine system has been a major area of scientific research over the past two years. While common endocrine/metabolic disorders such as obesity and diabetes have been recognized among significant risk factors for COVID-19 severity, several endocrine organs were identified to be targeted by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). New-onset endocrine disorders related to COVID-19 were reported while long-term effects, if any, are yet to be determined. Meanwhile, the "stay home" measures during the pandemic caused interruption in the care of patients with pre-existing endocrine disorders and may have impeded the diagnosis and treatment of new ones. This review aims to outline this complex interaction between COVID-19 and endocrine disorders by synthesizing the current scientific knowledge obtained from clinical and pathophysiological studies, and to emphasize considerations for future research.

COVID-19 complications among patients with opioid use disorder: a retrospective cohort study across five major NYC hospital systems

BACKGROUND AND AIMS

Individuals with opioid use disorder (OUD) suffer disproportionately from COVID-19. To inform clinical management of OUD patients, research is needed to identify characteristics associated with COVID-19 progression and death among this population. We aimed to investigate the role of OUD and specific comorbidities on COVID-19 progression among hospitalized OUD patients.

DESIGN

Retrospective cohort study of merged electronic health records (EHR) from five large private health systems.

SETTING

New York City, New York, USA, 2011-21.

PARTICIPANTS

Adults with a COVID-19 encounter and OUD or opioid overdose diagnosis between March 2020 and February 2021.

MEASUREMENTS

Primary exposure included diagnosis of OUD/opioid overdose. Risk factors included age, sex, race/ethnicity and common medical, substance use and psychiatric comorbidities known to be associated with COVID-19 severity. Outcomes included COVID-19 hospitalization and subsequent intubation, acute kidney failure, severe sepsis and death.

FINDINGS

Of 110 917 COVID-19+ adults, 1.17% were ever diagnosed with OUD/opioid overdose. OUD patients had higher risk of COVID-19 hospitalization [adjusted risk ratio (aRR) = 1.40, 95% confidence interval (CI) = 1.33, 1.47], intubation [adjusted odds ratio (aOR) = 2.05, 95% CI = 1.74, 2.42], kidney failure (aRR = 1.51, 95% CI = 1.34, 1.70), sepsis (aRR = 2.30, 95% CI = 1.88, 2.81) and death (aRR = 2.10, 95% CI = 1.84, 2.40). Among hospitalized OUD patients, risks for worse COVID-19 outcomes included being male; older; of a race/ethnicity other than white, black or Hispanic; and having comorbid chronic kidney disease, diabetes, obesity or cancer. Protective factors included having asthma, hepatitis-C and chronic pain.

CONCLUSIONS

Opioid use disorder patients appear to have a substantial risk for COVID-19-associated morbidity and mortality, with particular comorbidities and treatments moderating this risk.

Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program

Background

More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID).

Objective

To identify risk factors associated with PASC/long-COVID.

Design

Retrospective case-control study.

Setting

31 health systems in the United States from the National COVID Cohort Collaborative (N3C).

Patients

8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system.

Measurements

Risk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC.

Results

Among 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls.

Conclusions

This national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.