**Diabetology: Feature Papers 2022**

Editor

**Peter Clifton**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editor* Peter Clifton University of South Australia Australia

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This is a reprint of articles from the Special Issue published online in the open access journal *Diabetology* (ISSN 2673-4540) (available at: https://www.mdpi.com/si/diabetology/Diabetology Feature Papers).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-7256-7 (Hbk) ISBN 978-3-0365-7257-4 (PDF)**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

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## **Contents**

Reprinted from: *Diabetology* **2022**, *3*, 48, doi:10.3390/diabetology3040048 .............. **129**

#### **Felice Strollo, Ersilia Satta and Sandro Gentile**

Insulin Injection-Related Skin Lipodystrophies: Blemish or Pathology? Reprinted from: *Diabetology* **2022**, *3*, 47, doi:10.3390/diabetology3040047 .............. **143**

## *Article* **Transitioning to Telehealth during COVID-19: Experiences and Insights from Diabetes Prevention and Management Program Providers in Los Angeles County**

**Sally L. Bullock 1,\*, Telma Menendez 2, Liz Schwarte 3, Lisa Craypo 3, Jennifer T. Mosst 4, Gabrielle Green 2, Noel C. Barragan 2 and Tony Kuo 5,6,7**

**Abstract:** The onset of the COVID-19 pandemic in March 2020 accelerated the efforts of several organizations providing the National Diabetes Prevention Program (National DPP) and the Diabetes Self-Management Education and Support (DSMES) program to rapidly transition from in-person service delivery to program administration via telehealth. Semi-structured interviews were conducted with 35 National DPP and DSMES experts and providers in Los Angeles County to gain a better understanding of the challenges and benefits associated with this transition. Interviews were completed during June to October 2021. Thematic analyses were performed using the Social-Ecological Model as a guiding framework. The analyses revealed several factors that influenced the transition, including at the individual (e.g., technology and health behaviors), interpersonal (e.g., social connections and support), organizational (e.g., provider workload and program enrollment and retention), community (e.g., recruitment), and policy (e.g., government support and reimbursement for telehealth services) levels. Findings suggest that the transition to telehealth was challenging for most National DPP and DSMES providers. However, because of its lower cost, ability to reach long distances virtually, and potential efficiency when employed as part of a hybrid approach, this delivery modality remains viable, offering benefits beyond the traditional program models.

**Keywords:** key informant interviews; prevention of type 2 diabetes; self-management of type 2 diabetes; National DPP; DSMES; telehealth; provider experiences; COVID-19

#### **1. Introduction**

In the United States, an estimated 34.1 million adults have diabetes, and 88 million have prediabetes [1]. In Los Angeles County, California, approximately 1 in 10 adults has been diagnosed with type 2 diabetes [2]. Individuals with diabetes are at risk for many serious health problems, including heart disease, stroke, chronic kidney failure, amputations of lower limbs, blindness, and premature death [3]. They are also at increased risk for severe disease and death from the coronavirus disease 2019 (COVID-19) [4]; thus, limiting exposure to the virus that causes COVID-19 has been a critical priority for protecting individuals with these conditions throughout the pandemic.

**Citation:** Bullock, S.L.; Menendez, T.; Schwarte, L.; Craypo, L.; Mosst, J.T.; Green, G.; Barragan, N.C.; Kuo, T. Transitioning to Telehealth during COVID-19: Experiences and Insights from Diabetes Prevention and Management Program Providers in Los Angeles County. *Diabetology* **2023**, *4*, 46–61. https://doi.org/10.3390/ diabetology4010006

Academic Editor: Peter Clifton

Received: 15 December 2022 Revised: 10 January 2023 Accepted: 22 January 2023 Published: 31 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Even before the pandemic, efforts to locally promote diabetes prevention and management have focused on increasing access to and use of the National Diabetes Prevention Program (National DPP) and Diabetes Self-Management Education and Support (DSMES) program services. The National DPP is a year-long lifestyle change program based on the original Diabetes Prevention Program [5–7], which has been shown to reduce the incidence of diabetes in the short term and over time [5,8]. Likewise, DSMES is an evidence-based service model designed for individuals diagnosed with diabetes, with education services that have been shown to improve eating patterns, activity levels, and hemoglobin A1C (HbA1c) levels among program participants [9].

While telehealth is not a novel approach to either program model, the onset of the COVID-19 pandemic in March 2020 accelerated its use by National DPP and DSMES providers across Los Angeles County (LAC) and elsewhere in the United States (U.S.). This rapid transition from in-person service delivery to a virtual format was unprecedented in this regard. Telehealth, by definition and by standard practice, includes the use of telecommunications technologies of various forms to provide healthcare and health education. The modality can involve live videoconferencing, the electronic transmission of health information, and the use of devices to collect and transmit data to providers to assist with clinical decision-making (e.g., remote glucose monitor, remote weight monitoring device) [10].

Prior to the pandemic, both National DPP and DSMES programming were offered in-person, sometimes via telehealth, and through other technology-based modalities [8,11]. Prior studies indicate that programs delivered via telehealth can result in observable improvements to health and behavioral outcomes [11–16]. However, the majority of organizations providing National DPP and DSMES in LAC at the start of the pandemic provided them primarily in-person [17,18]. Nationally, the use of telehealth for these two programs, and for healthcare services more broadly, had been low before the health crisis [19–21]. Reported barriers to telehealth included a lack of technical skills and/or equipment, privacy and security concerns, provider comfort and organizational support of the modality, and a lack of adequate reimbursement for such services [22–24]. The shelter-in-place orders and other efforts to decrease the spread of COVID-19, in a sense, forced providers to come up with innovative solutions to these barriers as they moved from a traditional in-person delivery model to telehealth.

Although pre-pandemic research on telehealth is not sparse in the health services research literature, it has not been conducted for transitions that took place on a very short timeline. For example, little is known about the challenges or the optimal practices that are needed to rapidly switch from primarily in-person sessions to virtual sessions that are facilitated by evolving technology platforms such as Zoom, GoToMeeting, or Doximity. This study explores the experiences and insights of 35 National DPP and DSMES experts and providers in LAC to identify opportunities where integration of telehealth into these 2 program models can help improve the delivery of diabetes prevention and management services, including ways to better increase participation and retention.

#### **2. Methods**

#### *2.1. Study Design*

A qualitative, key informant study was employed for this project. Thirty-five semistructured interviews were conducted between June and October 2021. A core project team guided the design, methods, data collection, and analysis of the interview data. The team consisted of staff from Ad Lucem Consulting and the Los Angeles County Department of Public Health (DPH). The DPH's Institutional Review Board reviewed and approved the study protocols and materials.

#### *2.2. Setting*

In LAC and at the time of the study, there were approximately 35 organizations that were either accredited by the American Association of Diabetes Care and Education Specialists (ADCES) or recognized by the American Diabetes Association (ADA) to provide DSMES services [17], and 43 organizations that provided the National DPP [18]. Some of these organizations provided both National DPP and DSMES services. Many that offered the National DPP had applied for and received recognition from the Centers for Disease Control and Prevention (CDC) Diabetes Prevention Recognition Program (DPRP), a program entity that reviews and affirms the effective delivery of DPP services. The organizations providing these services included hospitals and healthcare systems, community-based organizations, Federally Qualified Health Centers (FQHCs), community health centers, pharmacies, universities, and health plans/insurers.

#### *2.3. Participants*

Key informant interviews were conducted with experts and providers of National DPP and/or DSMES programs in LAC. The former were not restricted to local experts—they included subject matter experts from across the U.S. For both experts and providers, DPH assisted with recruitment. Some of the expert interviewees were identified through a literature review, and others were identified as "experts" by other interviewees. Experts were primarily selected based on their knowledge of and/or experience with delivering the National DPP and/or the DSMES program. All prospective interviewees were invited via email first, with follow-up phone calls as needed. In total, 56 experts and providers were contacted, and 35 were interviewed (62% response rate).

#### *2.4. Data Collection*

Two semi-structured interview guides were developed for the study, one for experts and the other for providers (see Supplementary Materials (S1) for interview guide questions). Both guides include questions about the impact of COVID-19 on National DPP/DSMES program delivery, challenges and optimal practices associated with integrating/implementing telehealth sessions, and policy and systems changes that are likely to be necessary to support and sustain telehealth practices. Experts were asked to assess the value or impacts of telehealth and what the future may look like for National DPP/DSMES that continue telehealth services. Providers were asked questions about the infrastructure and capacity needed to implement telehealth.

All interviewees provided consent prior to being interviewed and did not receive compensation for their participation. Each interview required approximately 45 min to complete, and all interviews were conducted and recorded via videoconferencing software by trained interviewers. Verbatim transcripts were generated, checked for accuracy, and loaded into ATLAS.ti (Version <IP_ADDRESS>, access on 15 December 2022) [25] for sorting and qualitative analysis.

#### *2.5. Qualitative Data Analysis*

Interview transcripts were initially coded in ATLAS.ti for themes and subthemes using thematic analysis [26]. A codebook was then developed based on the identified themes. The transcripts were double-coded, and any differences were resolved during team meetings. The lead author coded all the transcripts, and two trained undergraduate research assistants each coded approximately half of the transcripts.

The social-ecological model (SEM) was used as a framework to describe factors that influenced the transition from in-person service delivery to services offered via telehealth. The SEM is a theory-based framework that recognizes the complex interactions between individuals, their social networks, and broader structural factors through five levels: individual, interpersonal, organizational/institutional, community, and policy [27]. The SEM has been used previously to identify factors that impact health and well-being and as the guiding framework for health promotion interventions in the community. This model serves as the organizing framework for themes identified in this study.

#### **3. Results**

A total of 35 interviews were conducted with National DPP/DSMES experts and providers. Nine were conducted with experts who represented the following organization types: health management and training (*n* = 4), health professional association (*n* = 3), state public health (*n* = 1), and academia (*n* = 1). Twenty-six were with providers, in particular, with individuals from hospitals/healthcare systems (*n* = 8), community health centers (*n* = 7), private/small businesses (*n* = 4), universities (*n* = 3), Health Resources and Services Administration-funded FQHCs (*n* = 3), and health plans/insurers (*n* = 1). Many of the providers interviewed serve populations with a high burden of diabetes and other chronic diseases.

Two expert interviewees could speak to both National DPP and DSMES delivery, two could speak to just DSMES services, and six could speak to just National DPP programming. Of the experts, two were also DPP providers, and one was delivering both National DPP and DSMES.

Almost all providers were providing in-person sessions prior to the COVID-19 pandemic, and only 2 of the 26 providers were already offering primarily telehealth National DPP and/or DSMES services. Majority of providers switched to providing telehealth services shortly after the start of the pandemic. In most cases, telehealth delivery included videoconferences and/or telephone conference calls with groups or individuals. Experts and providers used different terminology to refer to the virtual delivery of services/programming (e.g., distance learning, telehealth visits, online sessions). For consistency, we use "telehealth sessions" to refer to these services. Table 1 provides an overview of the mode of delivery before and during the pandemic by program type.

**Table 1.** National Diabetes Prevention Program and Diabetes Self-Management Education and Support program delivery before and after the start of the COVID-19 pandemic among 26 providers that participated in the key informant interviews.

\* One provider was already offering the National DPP via telehealth, and another provider was offering both the National DPP and DSMES services via telehealth before the pandemic.

#### *3.1. Major Themes*

The themes identified during data analysis are described below and in Figure 1 and Table 2 according to the levels of the SEM. It is important to note that some themes included more than one level of influence and were organized according to where they fit best in the conceptual model.

**Figure 1.** Social-ecological model with factors influencing telehealth delivery of the National Diabetes Prevention Program and Diabetes Self-Management Education and Support program during the COVID-19 pandemic.

**Table 2.** Themes and representative quotes related to telehealth delivery of the National Diabetes Prevention Program and Diabetes Self-Management Education and Support program during the COVID-19 pandemic by level of the social-ecological model.

**Table 2.** *Cont.*

#### *3.2. Individual Factors*

#### 3.2.1. Participant Comfort with and Access to Technology

Interviewees reported a wide range of comfort with and access to technology across and within the populations they work with. While some interviewees described challenges in reaching older populations and lower-income groups because of discomfort with technology and/or limited access to the internet or a computer, others had the opposite experience. One National DPP expert stated:

*"Maybe they are an 89-year-old grandmother who's online all the time and is Queen of the internet* ... *or maybe there's someone that is not very familiar with technologies. We saw a big range within our communities."*

Participants with less technical knowledge experienced challenges connecting to telehealth platforms and sometimes attended meetings via phone/audio only. In a few cases, participants did not have permanent phones/phone numbers or email accounts. Participants also experienced technical difficulties such as unstable internet connections, dropped calls, or difficulties unmuting to speak. Some participants did have access to technology, but they had a strong preference for in-person learning.

#### 3.2.2. Participant Health Behaviors and Outcomes

The rapid transition to telehealth resulted in challenges in collecting health data from some program participants, so it was difficult for interviewees to determine the full impact of the pandemic on the transition and on telehealth services' delivery as it relates to changes in participant health behaviors and health outcomes.

When program sessions were held in-person, it had been easier for providers to collect participant health data such as weight and HbA1c levels. With the transition to telehealth, providers had to rely on self-reported weight from the participants. Some participants did not want to report their weight or did not have access to a scale at home. Despite lacking complete data on participant weight, some providers mentioned that participants were successful in maintaining their weight, and a minority lost weight. Other interviewees mentioned that participants gained weight. DSMES providers also did not have current data on HbA1c levels because patients were not coming into the office to have laboratory work performed. A few interviewees speculated that participants likely experienced higher HbA1c levels, at least initially. Among those interviewees who had data, they reported similar or better HbA1c levels with telehealth delivery.

Despite the difficulties with collecting health data, a minority of interviewees described how participants improved their eating habits and cooked more after the start of the pandemic due to the large amounts of time they were spending at home. Others mentioned that participants had worse eating habits and described how a few experienced food insecurity and were reliant on food pantries for food, which did not provide enough healthy options. National DPP providers also reported that participants' physical activity levels decreased, and participants had to be highly motivated to exercise during the pandemic. One National DPP provider stated:

*"We saw activity in minutes plunder, and we saw those not so good eating habits go up* ... *even though they knew staying healthy and maybe preventing diabetes would hopefully make them weather COVID better."*

#### 3.2.3. Meeting Participants Where They Are

To address issues with technology, health behaviors, and health outcomes that resulted from the transition to telehealth and the COVID-19 pandemic, interviewees stressed the need to "meet participants where they are". Technical assistance, health and behavior goals, and resources for participants needed to be tailored to the needs of each participant and/or group. In many cases, providers and participants could work around their initial discomfort with technology and the lack of access to technology, but in other cases, it was too difficult, and some National DPP cohorts ended, or some DSMES participants did not continue receiving services.

Providers described tremendous efforts, especially during the initial transition, to make sure that "everyone's on the same playing field" with technology. These efforts included conducting individual phone meetings with participants, creating simple handouts or guides for connecting to the platform, and following up frequently with participants to make sure they could connect and stay engaged in the program. National DPP providers

also hosted "Session Zeroes" before the start of the program to assess participants' readiness to participate and address their technology needs. In addition, providers loaned devices (e.g., tablets, Chromebooks, smartphones) with internet connections to participants without access to the technology.

Tailored assistance for participants was also helpful for overcoming barriers to healthy eating and physical activity habits. Providers helped participants create plans or set goals to work around challenges and provided them with resources when needed. One National DPP and DSMES provider stated:

*"People's lives had to change. People were fearful* ... *we would have to tell them these are some exercises you could do at home* ... *or these are healthy recipes that you can make with the food that you would get at a food pantry or Food Bank."*

#### *3.3. Interpersonal Factors*

Social Support and Engagement

Interviewees reported that interpersonal factors such as the relationships between providers and participants were important to the success of DSMES services and to the success that many National DPP cohorts enjoyed. However, interviewees noted that making a "human connection" was "a little harder to do over a virtual connection". One National DPP and DSMES provider stated:

*"The benefit of having an in-person workshop is the social aspect of it. People do feel more connected with each other, and they feel like they have the support of their peers. In the virtual [setting] that was a challenge* ... *They're just really not as connected with each other.*

Strategies to increase engagement in telehealth sessions and help participants make connections included having round-robin discussions, warm invitations to participate in the conversation, and using breakout rooms and poll functions provided by the telehealth platform. Some providers also mentioned changing the way they delivered content and modifying materials to work for telehealth. One National DPP and DSMES provider mentioned "the more colorful and interesting your materials are, the more you'll get their attention".

Maintaining connections and communication between telehealth sessions was also important for increasing engagement and social cohesion. National DPP providers created group text messages and used social media such as WhatsApp and Facebook to increase communication and connection.

Providers also noted the importance of "knowing your audience and what works for them". Some noted that a hybrid approach with both telehealth and in-person sessions might be good moving forward. One DSMES provider suggested "incorporating face-toface periodically," as in-person sessions may help establish a relationship with participants and make future telehealth sessions more successful. Despite efforts to engage participants during telehealth sessions, some participants and providers still had a strong preference for in-person sessions.

#### *3.4. Organizational Factors*

Providers faced several challenges at the organizational level when transitioning from in-person to telehealth services. Many successfully implemented a range of strategies or optimal practices to overcome these challenges.

#### 3.4.1. Telehealth Platforms and Technical Difficulties

There was a range in comfort levels with the platform (e.g., Zoom, GoToMeeting, Doximity) selected to provide telehealth services. Some providers were very familiar with their platform and had experience delivering telehealth sessions prior to the pandemic, whereas others had very limited or no experience.

Training sessions for providers were implemented at organizations to overcome challenges with platforms and telehealth delivery. Most organizations were also quick to supply providers with the necessary technological equipment (monitors, webcams, etc.) to conduct telehealth sessions. In most cases, providers were able to learn how to use the platform relatively quickly. One National DPP provider stated:

*"We had practice facilitation where I would have them take turns facilitating a class online and then provide feedback. So, there was a lot of training on that for my team."*

In other cases, interviewees noted it would have been helpful to have additional training provided by their organization or external experts to increase providers' comfort and effectiveness at delivering telehealth sessions.

Despite providers' quick adjustment to telehealth platforms and the new equipment, a small number of interviewees described remaining challenges with particular platforms that were not fully meeting their needs. Providers also experienced occasional challenges with internet connectivity and noted the need for contingency plans for technical issues or when participants cannot join telehealth sessions.

#### 3.4.2. Provider Workload and Organizational Support

Transitioning to telehealth required providers to spend additional time modifying materials for telehealth delivery, recruiting and retaining participants, and assisting participants. However, interviewees also noted that telehealth delivery of services can be more convenient for providers and save them time or allow them to meet with more cohorts or individuals. One DPP and DSMES provider stated:

*"You can provide a [telehealth] program. It's one hour for the coach. It's not all this travel and set up and talking time that really adds on an additional two hours for every meeting session. So, it's much more streamlined, and that can open the possibilities for delivering the program at hours more populations can attend."*

In most cases, providers noted that they felt sufficiently supported by their organizations in the transition to telehealth. A small number of DSMES providers did mention the need for additional organizational support, specifically having enough administrative staff to prepare participants for telehealth sessions and schedule future appointments.

#### 3.4.3. Recruitment and Enrollment

Interviewees reported that programs experienced an initial drop in enrollment because of a pause to program operations and/or recruitment due to the pandemic. Some programs continued to have low enrollment numbers because of recruitment challenges or participant discomfort with telehealth. However, several interviewees noted that telehealth increased access to individuals from a wider geographic area and to those who previously could not commit to a long program and/or had challenges attending in-person. With the enrollment of these additional participants, some programs maintained pre-pandemic numbers, and a few increased their enrollment. For example, one National DPP provider said:

*"Switching to virtual allowed us to reach members that wouldn't normally have the time to attend our in-person workshop. So, these are the working people and maybe the elderly who don't want to drive to a site at night."*

Interviewees noted several strategies that could be used to improve outreach and recruitment efforts for telehealth programs. They described the importance of contacting healthcare providers about telehealth services and encouraging them to make referrals, conducting direct marketing to patients through patient portals and newsletters, and marketing through social media. One National DPP and DSMES provider mentioned that regardless of the mode of outreach or recruitment, providers need to tailor the message to fit the needs of the individual participant.

#### 3.4.4. Retention

Interviewees reported two different experiences when it came to the retention of National DPP participants in telehealth programs in comparison to DSMES participants. National DPP providers and experts mentioned that retention rates were mixed across cohorts. Some National DPP providers reported that retention was better with telehealth, especially among well-established cohorts. However, others experienced lower retention rates, especially among participants with a strong preference for in-person sessions or who were not comfortable with technology.

In contrast, many DSMES providers stated that retention stayed the same or improved after transitioning to virtual/distance learning program provision. One DSMES provider commented, "it's about the same as before ... it's not like we saw them for a series of visits. We saw them for the initial and then maybe one follow-up or two. We're still staying about the same".

#### *3.5. Community Factors*

Factors external to organizations and occurring in the broader community as a result of the pandemic had an impact on the transition from in-person services to telehealth. The closure of various organizations, churches, senior centers, and other locations was especially challenging for providers that recruited through community outreach efforts. Community outreach often required substantial effort prior to the pandemic and was even more difficult when recruitment locations were closed because of COVID-19. Providers had to conduct other forms of outreach, and some experienced lower enrollments. One National DPP expert stated:

*"In a lot of these communities, the recruitment was very much this high-touch relationshipbuilding with the community where you're going out to health fairs, to congregate meal sites* ... *Well, senior centers closed, the congregate meal site has become Meals on Wheels, the church is not servicing, senior housing is not letting anyone in, and the assisted living programs are on lockdown."*

#### *3.6. Policy and Systems Factors*

Interviewees had several suggestions for changes that are needed to support telehealth National DPP and DSMES programs at the local, state, and federal governmental levels.

#### 3.6.1. Local Government

Suggestions for policy and systems changes that could be implemented by the local government or DPH included: assistance with marketing programs to increase enrollment, providing free training and materials for delivering telehealth programs, purchasing technology needed for telehealth and sharing with organizations and program participants, facilitating networking opportunities for providers to share best practices, assisting providers with program compliance, and advocating for policy change at the federal level to increase reimbursement for programs. For example, one National DPP and DSMES provider stated:

*"I think LA County Department of Public Health can continue being an advocate for health plans and vendors that provide the services and really take a role at the federal level to change some of these policies to provide more flexibility for populations covered by Medicare."*

#### 3.6.2. State and Federal Government

Interviewees mentioned some general policy changes at the state and federal level that would be helpful for both National DPP and DSMES, such as expanding access to healthcare/insurance and expanding broadband/internet availability. Interviewees also mentioned that insurance companies should cover the technology needed for participants to receive telehealth services. However, in most cases, they suggested program-specific changes.

#### 3.6.3. Policy and Systems Changes Needed for National DPP

Interviewees spoke to the need to increase reimbursement rates for National DPP from Medicaid (Medi-Cal in California) and Medicare, noting that the current reimbursement rates made it difficult for program providers to cover their costs. For example, one National DPP expert stated:

*"The reimbursement needs to align, actually align with the high fixed costs of [National DPP]* ... *It is very hard to start one of these programs and sustain it over time. You have to be incredibly efficient to be able to do that with Medi-Cal or Medicare DPP reimbursement."*

In addition, interviewees mentioned that the process for receiving Medicare reimbursement for National DPP needs to be "less onerous". They noted that few organizations are applying to provide Medicare DPP because of the stringent requirements.

Interviewees also had suggestions for policy and systems changes that need to be made by the CDC, particularly around the Diabetes Prevention Recognition Program (DPRP). Interviewees discussed the need for providing flexibility for maintaining DPRP recognition if organizations were not able to meet program requirements during the pandemic (for example, cohorts that do not achieve required weight loss and activity levels), or if organizations offered the program in a different modality than the one they originally applied for (i.e., offering distance learning versus in-person).

Interviewees also mentioned that National DPP eligibility requirements, the curriculum, and reporting requirements need to be changed or updated. Providers stated that the body mass index (BMI) eligibility requirement for National DPP needs to be removed, as some individuals with lower BMIs may be at risk for diabetes. Providers also recommended the CDC update the National DPP curriculum to meet current nutrition guidelines/science and provide guidance for adapting the curriculum to telehealth. Other providers would like the curriculum to be available in more languages than just English and Spanish. In addition, providers would like the CDC to streamline reporting requirements. One National DPP expert and provider suggested that the CDC should provide a platform for all National DPP programs from which they could provide distance learning, securely enter confidential participant information, and communicate with participants.

#### 3.6.4. Policy and Systems Changes Needed for DSMES

Similar to the National DPP, DSMES experts and providers mentioned the need for adequate and sustained coverage for telehealth programs through Medicare. Interviewees noted that if Medicare continues to cover telehealth and allow a variety of providers to offer telehealth services, other insurers will follow. Interviewees also noted that because of the complexity of Medicare reimbursement, only a small percentage of Medicare beneficiaries utilize the program. For example, a DSMES expert stated:

*"[DSMES providers] are afraid to bill [Medicare] because they're afraid they're going to do something wrong.

Other interviewees mentioned that regulations need to be changed to allow providers to be licensed in multiple states, so they can provide services to participants that travel or live in another state.

#### *3.7. Beyond the Pandemic: The Future of Telehealth*

When asked about their future plans and whether they would continue to offer National DPP and/or DSMES via telehealth, some providers mentioned that they were excited to go back to all in-person sessions. However, many providers mentioned they were planning to offer both in-person and telehealth programming or some hybrid version. Interviewees noted that telehealth expanded the reach of the program to individuals who could not or would not participate in-person, but in-person was needed for others

who either preferred in-person sessions or had technical challenges. One National DPP expert stated:

*"[Telehealth] provides a way in another way into the program. And with 88 million people who have prediabetes, you need as many doors as possible into this program."*

After the initial challenges of the rapid transition to telehealth, many providers found that telehealth allowed them to expand their programs to new geographic areas and reduced travel and/or set-up necessary for in-person sessions. Providers noted that continued investment in telehealth modalities and the policy changes mentioned above would be necessary to continue to offer telehealth options.

#### **4. Discussion**

Our study analysis shows that several challenges emerged at all levels of the SEM as providers rapidly switched from in-person sessions to telehealth in LAC. Providers implemented several strategies or optimal practices to overcome these challenges and, in many instances, were able to successfully transition to telehealth, retain existing participants, and enroll new participants.

Similar to other studies [23,24], interviewees reported a wide range of comfort with and access to technology among participant populations. Other studies have also found that the elderly, lower-income individuals, and individuals who require translation services may experience greater difficulty in using telehealth services [23,24]. However, in this study, these differences in comfort and access were not always driven by age or income status, as interviewees provided examples of elderly individuals who were very tech-savvy and people with lower incomes who were quickly able to adjust to telehealth.

To overcome technological barriers to engaging in telehealth services, interviewees mentioned that participants needed intense, tailored assistance, at least initially. The extent to which this technical assistance will be necessary in the future, however, is not clear. Overall, participant comfort with and ability to use telehealth likely increased as a result of more frequent use during the pandemic. Studies indicate that interest in telehealth, willingness to use, and use of telehealth increased significantly among U.S. adults throughout the pandemic [28,29], especially among non-Hispanic Black adults and adults with lower education levels [29]. As a result, many new National DPP and DSMES participants may not need as much technical assistance to participate in programs that employ telehealth either exclusively or in a hybrid fashion.

Challenges in collecting participant health data via telehealth made it difficult for interviewees to determine the impact of the pandemic on the transition to telehealth or on individual health behaviors and outcomes. Similar challenges in conducting nutrition assessments and monitoring client health outcomes were also reported by registered dietitian nutritionists that had transitioned to telehealth services during the pandemic [30]. Increasing access to and reimbursement for remote monitoring devices may help overcome data collection challenges associated with telehealth usage [31,32]. Hybrid programming may allow for in-person assessments and data collection while maintaining many of the benefits of services delivered via telehealth.

Challenges with accessing healthy food and safely engaging in physical activity that were exacerbated by the pandemic [33–35] could have resulted in worse health behaviors and outcomes than might normally be seen in programs that utilize telehealth. Studies of telehealth diabetes management programs conducted prior to the pandemic have shown improvements in HbA1c levels and other behavioral and health outcomes [14,15,36]. Additionally, weight loss among individuals with prediabetes that participated in DPP-based programs that used telehealth has been equal to or greater than among those participating in in-person programs [12,13,37–40].

At the interpersonal level, it was challenging to create active, supportive online communities and personal connections among providers and program participants. Other healthcare providers have also had difficulty connecting via telehealth and have called for the development of best practices and training for building rapport [30,41]. There have also been recommendations for additional research to be conducted on strategies and devices that could help increase engagement and social connections to prevention programs that deliver services via telehealth [22].

At the organizational level, providers' comfort and familiarity with telehealth platforms and delivery was mixed, and transitioning to telehealth increased many providers' workloads, at least initially. Similarly, other healthcare providers implementing telehealth services have reported experiencing technical difficulties, a lack of support from administrative staff, and an increased workload [32,42]. Training sessions for providers can help overcome some of these challenges with platforms and telehealth delivery [43,44], and contingency plans are needed to deal with technical barriers. Additional administrative staff may be needed to support programs that rely on telehealth [32,42]. Overall, the number of healthcare providers using telehealth increased significantly during the pandemic [23,30], so provider comfort and familiarity with these virtual platforms have likely increased as well.

Despite the organizational challenges presented by transitioning to telehealth, interviewees noted that delivery via telehealth provided significant opportunities to expand the reach of the National DPP and DSMES programs, especially for individuals from various geographic areas, including rural communities, and for individuals who cannot or will not, otherwise, participate in in-person services. Telehealth delivery has been effective at reaching individuals who live in areas where transportation and access to in-person services can be limited [37–39,42,45]. As a modality, telehealth allows services to continue without disruption during travel or relocation [39]. Many telehealth participants are highly satisfied with these services because of the time savings, convenience, and reduction in travel time associated with using telehealth [24,46].

#### *4.1. Limitations*

This study has limitations. First, interviews were not conducted with program participants themselves, so the perspectives of individuals involved in the transition to telehealth were based on the impressions of experts and providers and not directly on the perspectives of participants. Since most providers interviewed were able to switch from in-person to telehealth services during the pandemic, additional perspectives may be needed from providers who were not successful in making this switch to complete a full story of this process. However, some of the experts who were interviewed could speak to the experiences of providers who were not able to transition to telehealth. The interviewees were also selected because of their positions as National DPP and/or DSMES experts and providers, and we did not collect demographic information from them. It is possible that responses to interview questions could have been influenced by demographic variables. Finally, while we did interview experts familiar with programs and services in other states and localities, majority of the interviewees were based in LAC. As a result, findings from this study may not be generalizable to other jurisdictions and areas across the U.S.

#### *4.2. Implications for Policy and Practice*

While interviewees made policy and systems change recommendations across different levels of government, the most critical changes identified were at the federal level. Specifically, interviewees mentioned the need for more flexible program requirements and adequate reimbursement of the telehealth services being delivered. Although great strides have been made in response to the COVID-19 pandemic, the rapid transition to telehealth for many of these programs remains a temporary change. Reimbursement policies for telehealth delivery instituted by Medicare, Medicaid, and private health insurers during the health crisis will likely need to continue to sustain these gains made in service delivery. In addition, these policies will need to be adjusted to allow for greater coverage and payment of services provided via telehealth, at the equivalent level as in-person sessions [47,48]. There have been calls for the Centers for Medicare and Medicaid Services, as well as for health plans, to make these changes permanent and expand telehealth delivery options

to increase coverage of chronic disease, dietary counseling, and remote support services that are needed to better manage chronic conditions [42,49,50]. To further increase access to and the convenience of telehealth services, policy changes may also be needed to allow providers to be licensed in multiple states and/or practice in a different state than their patients are receiving treatment. Policymakers can also improve upon the incentives used for providing more comprehensive services via telehealth. More research may be needed to study these policy options and to provide evidence of telehealth effectiveness for this type of coverage expansion, especially for Medicare and Medicaid populations across the nation.

#### **5. Conclusions**

The COVID-19 pandemic provided a rare opportunity for increasing access to and use of telehealth services for National DPP and DSMES programs. Despite the challenges of implementing telehealth under the difficult circumstances created by the health crisis, many providers appreciated the benefits of telehealth for participants and for their organizations. Additional program flexibility and adequate reimbursement will be key to these programs continuing to offer telehealth services.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/diabetology4010006/s1, File S1: Interview Guides.

**Author Contributions:** Conceptualization, T.K., N.C.B., T.M., J.T.M., G.G., L.S. and L.C.; methodology, L.S. and L.C.; formal analysis, S.L.B.; investigation, S.L.B.; data curation, S.L.B.; writing—original draft preparation, S.L.B.; writing—review and editing, all authors; visualization, S.L.B.; supervision, T.K. and N.C.B.; project administration, T.M. and J.T.M.; funding acquisition, T.K. and N.C.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project was supported in part by a cooperative agreement from the Centers for Disease Control and Prevention (CDC, U58DP006619).

**Institutional Review Board Statement:** The project was considered exempt from full review by the Los Angeles County Department of Public Health Institutional Review Board, as it was originally conducted for program fidelity/improvement purposes.

**Informed Consent Statement:** Each interviewee verbally consented to participate in the project prior to each interview.

**Data Availability Statement:** Data can be made available upon request if the request is appropriate and feasible.

**Acknowledgments:** The authors thank the Los Angeles County Diabetes Coalition for its partnership and efforts in helping to recruit and facilitate the key informant interviews with the various National Diabetes Prevention Program and Diabetes Self-Management Education and Support program experts and providers. The authors would also like to thank Caroline Wachino and Lizabella Nadelson for their assistance with the analysis. The decision to develop and publish this study's results was that of the authors and was not influenced by the funder nor any of the affiliated institutions. The contents presented do not represent the positions or views of the organizations or agencies mentioned in the text.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **Investigating the Protective Effects of a Rhenium (V) Compound with Uracil-Derived Ligands on Liver Damage Associated with Prediabetes in Diet-Induced Prediabetic Rats**

**Angezwa Siboto 1,\*, Akinjide Moses Akinnuga 1, Muhammed Bilaal Ismail 2, Irvin Noel Booysen 2, Ntethelelo Hopewell Sibiya 3, Phikelelani Ngubane 1 and Andile Khathi 1**

**Abstract:** Non-alcoholic fatty liver disease (NAFLD) is associated with prediabetes and can be treated by using a combination of metformin and dietary modification. However, people often fail to adhere to dietary modifications and become more dependent on pharmaceutical intervention, and this affects the effectiveness of the drug. In this study, we investigated the effects of rhenium (V) compound with uracil-derived ligands on liver health in diet-induced prediabetic rats in both the presence and absence of dietary modification. Prediabetic male Sprague Dawley rats were treated with the rhenium (V) compound for 12 weeks in both the presence and absence of dietary modification while monitoring fasting blood glucose levels. Antioxidant enzyme activity, inflammation markers and liver enzymes were measured together with liver glycogen and plasma triglycerides after sacrificing. The administration of rhenium (V) compound to prediabetic rats in both the presence and absence of dietary modification resulted in reduced concentrations of fasting blood glucose and triglycerides. There was also reduced liver glycogen, oxidative stress and liver enzymes while increasing antioxidant enzymes. Altogether, the rhenium (V) compound ameliorated liver injury and prevented hepatotoxicity.

**Keywords:** prediabetes; liver enzymes; rhenium (V) compound; triglycerides; NAFLD; fructose

#### **1. Introduction**

Non-alcoholic fatty liver disease (NAFLD) has recently become the most frequent chronic liver disease that occurs across all age groups due to the growing prevalence of obesity and prediabetes [1,2]. NAFLD is strongly associated with insulin resistance, dyslipidemia and hypertriglyceridemia. Diets that are high in carbohydrates and saturated fats have been shown to predispose individuals to developing both prediabetes and NAFLD [3,4]. Additionally, fizzy drinks that are high in fructose activate lipogenesis in the hepatocytes, resulting in a fatty liver [5–7]. Prediabetes is linked with moderate levels of insulin resistance and has been shown to play a primary role in the pathogenesis of NAFLD [8,9]. Liver dysfunction is associated with hepatic insulin resistance and an increase in hepatic glycogen production, whereas liver injury is shown by abnormal liver enzymes such as alanine transaminase (ALT), aspartate transaminase (AST) and lactic dehydrogenase [10,11]. These may be due to alternations in the permeability of the cell membrane and damage in the liver tissue [5,12].

The treatment of NAFLD involves a combination of dietary modification to enhance weight loss, along with the use of different insulin-sensitizing agents such as metformin [13–16]. However, studies have shown that patients become more dependent on

**Citation:** Siboto, A.; Akinnuga, A.M.; Ismail, M.B.; Booysen, I.N.; Sibiya, N.H.; Ngubane, P.; Khathi, A. Investigating the Protective Effects of a Rhenium (V) Compound with Uracil-Derived Ligands on Liver Damage Associated with Prediabetes in Diet-Induced Prediabetic Rats. *Diabetology* **2022**, *3*, 524–538. https:// doi.org/10.3390/diabetology3040040

Academic Editor: Peter Clifton

Received: 1 August 2022 Accepted: 8 October 2022 Published: 17 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the pharmaceutical interventions while neglecting the lifestyle modification, resulting in a reduction in the efficacy of metformin [17,18]. Therefore, there exists a need for novel pharmacological compounds that can work in both the presence and absence of lifestyle modifications. Transition metals have been used to try and manage diabetes and other metabolic conditions, but they have been shown to disrupt lipid and protein metabolism as well as induce oxidative stress in the liver [5]. However, recent studies from our laboratory have shown that the incorporation of organic ligands to the transition metals results in reduced cellular toxicity [19,20]. We have previously shown that our novel rhenium (V) compound with uracil-derived ligands improves glucose homeostasis in high-fat–highcarbohydrate diet-induced prediabetic animals in both the absence and presence of dietary modifications [20,21]. In this study, we sought to further investigate the effects of the rhenium (V) compound on selected liver function markers in diet-induced prediabetic rats.

#### **2. Methods and Materials**

#### *2.1. Animals*

Thirty-six (36) male Sprague Dawley rats (150–180 g) obtained from Biomedical Research Unit, University of KwaZulu-Natal (UKZN), were kept under standard environmental conditions, i.e., constant humidity (55 ± 5%), temperature (22 ± 2 ◦C), 12 h day:12 h night cycle. The animals were acclimatized for 2 weeks with free access to a standard rat chow (Meadow Feeds, South Africa) and water ad libitum before being fed on the experimental high-fat–high-carbohydrate (HFHC) diet (AVI Products (Pty) Ltd., Waterfall, South Africa) to induce prediabetes. The HFHC diet consists of carbohydrates (55% kcal/g), fats (30% kcal/g) and proteins (15% kcal/g), as described in our previous study [3,20]. All the experimental designs and procedures were carried out according to the ethics and guidelines of the Animal Research Ethics Committee (AREC, ethical clearance code: AREC/039/018M) of the UKZN, Durban, South Africa.

#### 2.1.1. Induction of Prediabetes on Male Sprague Dawley Rats

Sprague Dawley rats (*n* = 6 per group) were divided into groups based on the diet they received: a standard rat chow with normal drinking water (ND + H2O), high-fat–highcarbohydrate diet with drinking water supplemented with fructose (HFHC + fructose). Prediabetes induction took 20 weeks using a previously established protocol [3,22]. Rats with fasting blood levels that were higher than 5.6 mmol/L were considered prediabetic and grouped further for pharmacological studies [23,24]. The treatment commenced on the subsequent day, and this was considered as day 1 of treatment.

#### 2.1.2. Study Design for Experiments

In this study, rats were randomly divided into 6 groups of 6 animals in each (30 prediabetic persisting, 6 normal). Group 1: normal healthy control rats received vehicle (NC); Group 2: prediabetic control rats continued with HFHC diet and received vehicle (PD); Group 3: prediabetic treated rats switched to the STD diet and received metformin (MET + DI); Group 4: prediabetic treated rats continued with HFHC diet received metformin (MET + HFHC); Group 5: prediabetic rats switched to the STD diet and received rhenium (V) compound (Re + DI); Group 6: prediabetic treated rats continued with diet received rhenium (V) compound (Re + HFHC). See Figure 1.

#### 2.1.3. Treatment of Prediabetic Animals

After 20 weeks of inducing prediabetes, the treatment period started and lasted an additional 12 weeks. During the treatment period, the animals were treated once every third day at 9:00 a.m., where metformin (500 mg/kg) was given through oral dosing to the MET + HFHC and MET + DI, while rhenium (V) compound (15 mg/kg) was given via subcutaneous injection to the Re + HFHC and Re + DI groups. Parameters including fasting blood glucose (FBG) concentration, food intake and body weights were monitored every 4 weeks during the treatment period. See Figure 1.

**Figure 1.** The experimental protocol for the study.

#### 2.1.4. Blood Collection and Tissue Harvesting

For blood collection, all animals were anesthetized with Isofor (100 mg/kg) (Safeline Pharmaceuticals (Pty) Ltd., Roodeport, South Africa) via a gas anesthetic chamber (Biomedical Resource Unit, UKZN, Durban, South Africa) for 3 min in line with the guidelines for use of anesthesia. While the rats were unconscious, blood was collected by cardiac puncture into individual pre-cooled heparinized containers. The blood was then centrifuged (Eppendorf centrifuge 5403, Germany) at 4 ◦C, 503× *g* for 15 min. Plasma was collected and stored at −80 ◦C in a Bio Ultra freezer (Snijers Scientific, Holland, Netherlands) until ready for biochemical analysis. Thereafter, liver tissue was removed, weighed and rinsed with cold normal saline solution and snap frozen in liquid nitrogen before storage in a BioUltra freezer (Snijers Scientific, Tilburg, Netherlands) at −80 ◦C until biochemical analysis.

#### 2.1.5. Relative Liver Weight

The relative liver weights of all the animals in each experimental group were determined from the percentage of the ratio of liver weight to the body weight using the formula below:

$$\text{Relative liver weight} = \frac{\text{liver weight}}{\text{body weight}} \times 100\% \tag{1}$$

#### *2.2. Biochemical Analysis*

2.2.1. Quantification of Hepatic Glycogen, Plasma Triglycerides, TNF α and Liver Function Enzymes

Glycogen concentration analysis was performed in liver tissues. The glycogen assay was conducted using a well-established laboratory protocol [20].

Triglycerides (TGs) were measured using a colorimetric assay kit (catalog No: E-BC-K238; Manufacturer: Elabscience), single reagent, GPO-PAP method. Briefly, 50 mg of liver tissue was homogenized on 0.9% saline in a ratio of 9:1. The tissue was homogenized on ice for 2 min, and then the sample was centrifuged at 1000× *g* for 5–10 min. After centrifugation, the aqueous phase extraction was used in the assay. The protein quantification was conducted using Bradford assay. The assay was carried out according to the manufacturer's instructions. Furthermore, for TNF α measurements, 50 mg of liver tissue was homogenized, and the concentration was determined using an ELISA kit following the manufacturer's instructions (catalog no.: E-EL-R2856; manufacturer: Elabscience). Liver AST and ALT concentrations were measured using the Catalyst One Chemistry Analyzer (IDEXX Laboratories, Westbrook, ME, USA).

#### 2.2.2. Antioxidant Activity Profile

Antioxidant activity in the liver was measured on selected antioxidant markers. Glutathione peroxidase (GPx) (catalog no.: E-EL-R2491; manufacturer: Elabscience) and superoxide dismutase (SOD) (catalog no.: E-EL-R1424; manufacturer: Elabscience) concentrations were determined using assay kits as per manufacturer's instructions.

#### *2.3. Statistical Analysis*

All data are expressed as means ± SD. Statistical comparisons were performed with GraphPad InStat Software (version 5.00, GraphPad Software, Inc., San Diego, CA, USA) using two-way analysis of variance (ANOVA) followed by Tukey–Kramer multiple comparison test. A value of *p* < 0.05 was considered statistically significant.

#### **3. Results**

#### *3.1. Fasting Blood Glucose Concentration*

Figure 2 shows fasting blood glucose concentrations in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. By comparison with the NC, there was a significant increase in fasting blood glucose concentration in the PD group (*p* < 0.05, Figure 2). The administration of the rhenium (V) compound in both the HFHC and diet intervention groups resulted in significant reduction in fasting blood glucose concentration by comparison to PD (*p* < 0.05, Figure 1). A similar observation was shown by the MET + DI treated group as compared to the PD group (see Figure 2).

**Figure 2.** Fasting blood glucose in NC, PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC at 12 weeks of treatment. Values are presented as means ± SD (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### *3.2. Liver Glycogen Concentration*

Figure 3 shows liver glycogen levels in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. By comparison with the NC group, the PD group showed a significant increase in liver glycogen concentration (*p* < 0.05) (Figure 3). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant decrease of liver glycogen concentration when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 3).

**Figure 3.** Liver glycogen concentration in NC, PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### *3.3. Liver Triglycerides (TGs) Concentration*

Figure 4 shows liver triglyceride concentration in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. By comparison with the NC group, the PD group showed a significant increase in liver triglycerides concentration (*p* < 0.05) (Figure 4). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant decrease of liver triglycerides concentration when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 4).

**Figure 4.** Liver triglycerides concentration in NC, PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PC, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### *3.4. Relative Liver Weight*

Figure 5 shows relative liver weight in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. In comparison with the NC group, the PD group showed a significant increase in relative liver weight (*p* < 0.05) (Figure 5). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant decrease of relative liver weight when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 5).

**Figure 5.** Relative liver weight in normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### *3.5. Liver Antioxidant Activity*

Figure 6 shows antioxidant SOD activity in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. In comparison with the NC group, the PD group showed a significant decrease in SOD activity (*p* < 0.05) (Figure 6). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant increase of SOD activity when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 6).

Figure 6 shows antioxidant GPx activity in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. In comparison with the NC group, the PD group showed a significant decrease in GPx activity (*p* < 0.05) (Figure 7). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant increase of GPx activity when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 7).

**Figure 6.** SOD activity in normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

**Figure 7.** GPx activity in normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### *3.6. Liver TNF α Concentration*

Figure 8 shows TNF α concentrations in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. In comparison with the NC group, the PD group showed a significant increase in TNF α concentration (*p* < 0.05) (Figure 8). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant decrease of TNF α concentration when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 8).

**Figure 8.** TNF α concentration in normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PC, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### *3.7. Plasma ALT Concentration*

Figure 9 shows plasma ALT concentration in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) after 12 weeks of treatment. In comparison with the NC group, the PD group showed a significant increase in plasma ALT concentration (*p* < 0.05) (Figure 9). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant decrease of plasma ALT concentration when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 9).

**Figure 9.** Plasma ALT concentration in normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat– high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PC, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### *3.8. Plasma AST Concentration*

Figure 10 shows plasma AST concentration in the normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. In comparison with the NC group, the PD group showed a significant increase in plasma AST concentration (*p* < 0.05) (Figure 10). The administration of rhenium (V) compound in both diet intervention and high fat–high carbohydrate groups resulted in a significant decrease of plasma AST concentration when compared with the PD group (*p* < 0.05). A similar effect was observed with the MET + DI treated group when compared with the PD group (*p* < 0.05) (see Figure 10).

**Figure 10.** Plasma AST concentration in normal control (NC), prediabetic control (PD), metformin and diet intervention (MET + DI), metformin and high fat–high carbohydrate (MET + HFHC), rhenium (V) compound and diet intervention (Re + DI) and rhenium (V) compound and high fat–high carbohydrate (Re + HFHC) groups after 12 weeks of treatment. Values are presented as means ± SD and individual data points (*n* = 6). *p* < 0.05 by comparison with NC, α *p* < 0.05 by comparison with PD, MET + DI, MET + HFHC, Re + DI and Re + HFHC.

#### **4. Discussion**

There is growing evidence that associates prediabetes with disorders that were previously thought to co-exist with type 2 diabetes [25,26]. These disorders include impaired glucose homeostasis, dyslipidemia, NAFLD and cardiovascular disease [27,28]. The development of NAFLD is strongly linked with chronic consumption of high calorie diets and is considered the most frequent liver disease in developing countries [28]. Prediabetes is associated with insulin resistance, which can increase peripheral lipolysis, triglyceride synthesis and hepatic uptake of free fatty acids, which ultimately leads to NAFLD [27,29]. Studies have revealed that individuals with prediabetes and NAFLD have a higher risk of progressing to T2DM [27].

Studies have shown that anti-diabetic drugs can be used to manage NAFLD and to prevent it from developing into non-alcoholic steatohepatitis (NASH) [12,30]. Metformin has been used to treat NAFLD, as it is known to increase insulin action and reduce plasma glucose levels [31]. However, metformin has been shown to reach optimal efficacy when combined with lifestyle modifications that increase physical activity and lower caloric intake [30,32]. However, studies on patients show that there is an over-reliance on pharmacological interventions leading to a neglect on the lifestyle modification that results in reduced efficacy of the drugs [33]. Therefore, there is a great need for drugs which could perhaps assist those individuals who struggle with lifestyle modification [15,18].

There is a growing interest in using transition metal compounds to treat complications associated with metabolic disorders due to their high biological activity [9,34]. Our novel transition metal compound, rhenium (V) compound with uracil-derived ligands, has been previously shown to have anti-diabetic and anti-oxidant effects [20]. However, the effects of this metal-based compound on prediabetes induced NAFLD are yet to be investigated. In this study, we sought to further investigate the effects of this rhenium (V) compound on selected liver function markers in diet-induced prediabetic rats.

To maintain an individual's health, processes such as glucose homeostasis are tightly regulated to meet the energy needs for important organs. The liver plays an important role in the maintenance of glucose homeostasis by being involved many pathways of glucose metabolism including glycogenesis, glycogenolysis, glycolysis and gluconeogenesis [6,35]. During fasting conditions, the liver has a vital role in producing glucose through gluconeogenesis as a fuel for other tissues, such as the brain, red blood cells and muscles [35]. The main function of insulin is to inhibit glycogenolysis and gluconeogenesis in the hepatocytes [33]. However, in the prediabetic state, there is increased energy demand due to insulin resistance in some tissues, while there is also dysregulation of metabolic pathways such as gluconeogenesis [36,37]. Gluconeogenesis is increased in the liver due to peripheral insulin resistance [38]. Insulin-resistant muscle and other tissues require energy; therefore, signals are sent to the liver to produce glucose [39,40]. Gluconeogenesis is activated, resulting in glucose production from the liver to the plasma, while hepatic steatosis leads to increased hepatic gluconeogenesis [41,42]. Indeed, the untreated prediabetic group in this study showed high fasting plasma glucose levels. This could be due to unregulated gluconeogenesis and energy demand from insulin-resistant peripheral tissues [32,43]. Furthermore, the treated groups with rhenium (V) compound were shown to have reduced plasma glucose in both the presence and absence of dietary intervention. The rhenium (V) compound has been previously shown to restore peripheral insulin sensitivity through increased GLUT 4 expression, thus ameliorating insulin sensitivity and improving glycemic control [37]. Another transition metal with an organic ligand such as the dioxidovanadium complex (V) for this compound was shown to lower plasma glucose by increasing glucose transport and insulin-receptor tyrosine-kinase activity in NAFLD [44].

The hepatocytes play a vital role in regulating carbohydrate metabolism [45]. The storage of glycogen in the liver during feeding conditions provides a storage form of glucose that can be used during fasting periods. Normally, stored glycogen is critical for maintaining glucose homeostasis in individuals during an overnight fasting period [11,45]. However, in the prediabetic state, the liver has to store glucose due to insulin resistance in other tissues such as muscle and adipose tissues [11]. GLUT 2 transports glucose independently of insulin, and this results in a higher rate of hepatic glycogenesis occurring due to the shunting of glucose during the prediabetic state [46]. In this study, the untreated prediabetic group was shown to have high hepatic glycogen concentration. This could be due to high glucose uptake through GLUT 2. Further observations show that the treated groups with the rhenium (V) compound in both the presence and absence of dietary intervention had reduced glycogen concentration. A possible reason for the observed reduction in glycogen levels in the rhenium (V) compound treated group may be because this compound has been shown to improve insulin sensitivity by increasing the expression of GLUT4 in the skeletal muscle and fat tissue, as shown in a study conducted by Siboto, A. et al., 2020 [20]. This reduction in the amount of glucose being shunted to the liver could result in reduced storage of hepatic glycogen [34]. Other studies on transition metals on NAFLD treatment showed that metal complexes including ruthenium and vanadium reduce hepatic glycogen concentration by channeling excess glucose to be metabolized in skeletal muscle and adipose tissue [19,44]. Glucose from excess dietary carbohydrates goes through glycolysis in hepatocytes and is later converted into fatty acids to be esterified into triglycerides (TGs), which are eventually secreted via very low-density lipoproteins [45].

In NAFLD patients with insulin resistance, the increased lipolysis in adipose tissue causes enhanced liver glucose synthesis, which further activates de novo lipogenesis, resulting in hepatic fat deposition [12,38]. Enhanced de novo lipogenesis and reduced fatty acid oxidation had been reported in patients with insulin resistance and contribute to a critical biochemical pathway for the pathogenesis of NAFLD [47,48].

Consumption of diets high in fructose also results in the activation of the de novo lipogenesis pathway. PPARγ-coactivator-1β (PGC-1β), which acts as a co-activator of SREBP1c, can be stimulated by fructose. Moreover, fructose inhibits hepatic fatty acid β-oxidation, which mainly occurs by inhibiting the transcriptional activities of PPARα [7,49]. Thus, the shift towards lipogenesis over fatty acid oxidation contributes to hepatic steatosis. In a hyper-insulinemic state, insulin continues to drive lipogenesis via the SREBP1 pathway in addition to failing to suppress gluconeogenesis, contributing to exacerbating hepatic steatosis. Due to hyperinsulinemia and hyperglycemia, SREBP-1c is activated resulting in high storage of TGs observed in the untreated prediabetic group. However, the administration of the rhenium (V) compound in both the presence and absence of diet intervention resulted in reduced hepatic TGs storage on the treated prediabetic rats. This may be because the rhenium (V) compound facilitated weight loss through suppressing the secretion of ghrelin and reducing food intake. The food consumption results are on a glucose homeostasis paper by Siboto, A. et al., 2020 [20].

Due to a decrease in hepatic TGs concentration in the treated groups, we can also speculate that the rhenium (V) compound may decrease free fatty deposition to the liver by divergence of the substrates to other tissues for metabolism, increased β oxidation of fat or increased triglyceride disposal via very low-density lipoprotein (VLDL) exportation from the liver. Other metals such a dioxidovanadium lowered plasma TGs in diabetic rats via improving insulin sensitivity in adipose tissue and skeletal muscle [44]. Literature research on metal complexes such as ruthenium (ii) complex facilitated weight loss in prediabetic rat resulting in less fatty acid deposition in the liver [19].

The administration of rhenium (V) compound in both the presence and the absence of diet intervention on treated prediabetic rats resulted in both reduction of hepatic glycogen concentration and hepatic TGs storage. This positive effect caused by the administration of the rhenium (V) compound resulted in reduced liver weight as compared to the untreated prediabetic group. Several studies suggest that increased liver weights in prediabetes and NAFLD are associated with increased hepatic lipid accumulation [33]. Furthermore, this may imply that the rhenium (V) compound manages NAFLD delaying from becoming NASH. Relative liver weights results may also be a reflection of triacylglycerol and liver weights due to hepatic very-low-density lipoprotein–triglyceride (VLDL-TG) secretion rates. Studies have shown that liver weight is directly related to hepatic VLDL-TG secretion, independently of body weight. Therefore, since there is reduction of liver TGs in the prediabetic treated group, the liver weight is also reduced. We speculate that with improved insulin sensitivity on the adipose tissue, fat molecules are metabolized, resulting in enhanced HDL reducing the risk of hepatic steatosis associated with insulin resistance.

Oxidative stress is balanced by a number of antioxidant enzymes [50]. Antioxidant enzymes are ROS scavenging agents to protect the cells against oxidative stress under physiological conditions [4]. Uncontrolled oxidative stress can result in liver injury [51]. Increased oxidative stress is independently associated with NAFLD [50]. NAFLD is characterized by insulin resistance, which results in elevated concentrations of free fatty acids, providing substrates for triglyceride formation and subsequent progression of the disease in the liver [51]. It is suggested that increased accumulation of liver triglycerides leads to increased oxidative stress in the hepatocytes [52,53]. NAFLD is also associated with mitochondrial dysfunction, and an increase of mitochondrial β-oxidation activity, due to a lipid overload, may induce an impairment of electron transport chain, resulting in electron leakage and increased ROS [54]. Oxidative stress causes hepatocellular damage through many different mechanisms, including lipid peroxidation, that can directly stimulate cell necrosis and activation of apoptosis [41,55]. Oxidation stress can directly lead to the synthesis of reactive oxygen species (ROS) that are usually removed by antioxidant pathways; however, in the prediabetic state, antioxidant activity is reduced. Indeed, this

is also observed in the untreated prediabetic group. The administration of the rhenium (V) compound in both the presence and the absence of diet intervention in the prediabetic rats showed reduced lipid peroxidation and improved antioxidant activity in both SOD and GPx. We speculate that since the rhenium (V) compound reduces fat deposition in the liver, this prevents mitochondrial dysfunction. Therefore, the rhenium (V) compound can prevent liver injury that can be caused by oxidative stress. Vanadium and ruthenium complexes have also been shown to prevent oxidative stress through enhanced glycemic control [19,21,44].

Inflammation, oxidative stress and insulin resistance are involved in NAFLD. The progression of NAFLD to NASH is identified through increased inflammation. NASH patients with increased serum TNF-a concentrations also show higher levels of interleukin (IL)-6 [54]. The administration of the rhenium (V) compound in both the presence and the absence of diet intervention was showed to reduce oxidative stress and improve antioxidant activity, reducing liver injury. The rhenium (V) compound has been shown to have antiinflammatory properties by reducing plasma TNF α in the treated prediabetic group, protecting progressive liver damage.

Liver enzymes, AST and ALT, are used as clinical biomarkers to identify the degree of hepatocyte damage occurring in the liver [45]. The reason the liver enzyme levels better reflect the presence of injury is that these enzymes are components of hepatocytes that are released into circulation upon hepatocyte damage [45]. Literature trends have variably shown that high AST and ALT occur in blood due to necrosis of the hepatocyte during liver damage [33,55]. Metals are strongly known to be toxic on the liver [39]. The rhenium (V) compound was administered via subcutaneous injection in order for the drug to bypass liver metabolism and be absorbed in the bloodstream. This allows the drug to avoid firstpass metabolism in the liver, as the rhenium complex is known to be relatively harmless. The literature has shown that metal complexes are often associated with increased plasma AST and ALT levels, suggesting liver toxicity [33,55]. However, the rhenium (V) compound is synthesized with uracil-derived ligands that increase bioactivity and uracil-derived ligands. This has been shown to have the capability of coordination through a variety of donor atoms [56]. These uracil-derived ligands make the rhenium (V) compound have reduced cellular toxicity [56,57].

Liver enzymes such as AST and ALT are released into the bloodstream whenever hepatocytes are damaged, and this has been reported to occur during prediabetes [55]. The untreated prediabetic group had a high level of liver AST and ALT [27]. This observation of increased liver enzymes in the plasma suggested that liver cells are damaged through oxidative stress and increased hepatic lipogenesis or glycogenesis. However, administration of the rhenium (V) compound in prediabetic rats resulted in a decreased concentration of liver enzymes. This can be a result that is observed in this study. The rhenium (V) compound seems to improve hepatic health via its antioxidant, antilipidemic and antiinflammatory effects. Studies have shown that metal-based compounds such as ruthenium (II) compounds ameliorated liver disarrangement and prevented hepatotoxicity [19,58].

#### **5. Conclusions**

The administration of the rhenium compound is associated with an improved liver health as evidenced by decreased liver triglyceride, glycogen and fasting blood glucose concentrations. Additionally, the observations suggest that this compound may attenuate liver injury associated with prediabetes, as evidenced by the reduction of oxidative stress, liver function marker enzymes and inflammatory markers in the liver. Given the beneficial effects of the rhenium (V) compound presented in this study, further investigations are warranted to fully assess the therapeutic value of rhenium (V) administration during prediabetes.

**Author Contributions:** Conceptualization, A.S., A.K. and A.M.A.; methodology, A.S., M.B.I. and A.M.A.; software, A.S.; validation, A.S., A.K. and N.H.S.; formal analysis, A.S. and A.K.; investigation, A.S.; resources, A.K., P.N. and I.N.B.; data curation, A.S.; writing—original draft preparation, A.S.; writing—review and editing, I.N.B., A.K., N.H.S. and P.N.; visualization, A.S.; supervision, N.H.S. and A.K.; project administration A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** National Research Foundation (NRF) (grant 106041) and University of KwaZulu Natal College of Health Science funding.

**Institutional Review Board Statement:** The animal study protocol was approved by the Institutional Animal Research Ethics Committee of University of KwaZulu-Natal UKZN (protocol code AREC/00003221/2021 (previous AREC reference number: AREC/039/018M) and date of approval: 23 June 2022).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors acknowledge the personnel at the UKZN Biomedical Resource Unit for their technical assistance.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **An Evaluation of Point-of-Care HbA1c, HbA1c Home Kits, and Glucose Management Indicator: Potential Solutions for Telehealth Glycemic Assessments**

**Dessi P. Zaharieva 1,\*, Ananta Addala 1, Priya Prahalad 1,2, Brianna Leverenz 1, Nora Arrizon-Ruiz 1, Victoria Y. Ding 3, Manisha Desai 3, Amy B. Karger 4 and David M. Maahs 1,2**

**Abstract:** During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (*n* = 99), GMI, (*n* = 88), and POC HbA1c (*n* = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland–Altman analyses and Lin's concordance correlation coefficient (ߩc) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (ߩc = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (ߩc = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.

**Keywords:** type 1 diabetes; pediatrics; HbA1c; telemedicine; continuous glucose monitoring

#### **1. Introduction**

For individuals with type 1 diabetes (T1D), hemoglobin A1c (HbA1c), self-monitoring of blood glucose (SMBG), and continuous glucose monitoring (CGM) are common metrics used to assess glycemic management. HbA1c has long been considered the gold standard in glucose monitoring [1]. Although HbA1c remains an important measure of glucose values over time and overall diabetes management, various conditions can impact the accuracy of HbA1c measures, including hemoglobinopathies, chronic kidney disease, or iron-deficiency anemia [1]. In routine clinical care, point-of-care (POC) HbA1c measurements using fingerstick samples with National Glycohemoglobin Standardization Program (NGSP) certification offer healthcare providers a method for real-time assessment of glycemic control [2].

With the increasing use of CGM technology [3], various CGM-derived estimations of HbA1c have evolved over the years. The term "estimated A1c" has also been replaced with terms such as Glucose Management Indicator (GMI) to reduce confusion if laboratory HbA1c and estimated A1c differ. CGM-derived glucose metrics offer promising, real-time, and effective data to better manage T1D [4] by focusing on the glucose time-in-range (i.e., time spent between 70–180 mg/dL).

**Citation:** Zaharieva, D.P.; Addala, A.; Prahalad, P.; Leverenz, B.; Arrizon-Ruiz, N.; Ding, V.Y.; Desai, M.; Karger, A.B.; Maahs, D.M. An Evaluation of Point-of-Care HbA1c, HbA1c Home Kits, and Glucose Management Indicator: Potential Solutions for Telehealth Glycemic Assessments. *Diabetology* **2022**, *3*, 494–501. https://doi.org/10.3390/ diabetology3030037

Academic Editor: Jie Hu

Received: 27 June 2022 Accepted: 31 August 2022 Published: 13 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

CGM-derived metrics have become particularly relevant during the COVID-19 pandemic, where fewer in-person clinic visits resulted in fewer POC HbA1c measurements. Therefore, as reported by Beck and colleagues [5], there is an increased need for alternate methods to assess HbA1c, including academic laboratories' fingerstick capillary blood collection kits suitable for home use (HbA1c home kit) and CGM-derived GMI [6]. This work aimed to assess the accuracy and concordance between POC HbA1c, HbA1c home kit, and GMI in pediatric patients with T1D within the 4T Study when the COVID-19 pandemic required creative solutions to monitor glucose control. We report these findings as they may expand options for evaluating diabetes care.

#### **2. Materials & Methods**

This sub-study was part of the larger 4T Study, which aims to initiate CGM (Dexcom G6; Dexcom, San Diego, CA, USA) shortly after T1D diagnosis, approved by the Stanford University Institutional Review Board (clinicaltrials.gov: NCT03968055 and NCT04336969) [7–16]. Inclusion criteria for the 4T Study included all youth within one month of the T1D diagnosis seen in the Stanford Lucile Packard Children's Hospital ages six months to 21. A formal diagnosis of T1D included at least one positive autoantibody. The exclusion criteria for the 4T Study included a diabetes diagnosis other than T1D, diagnosis greater than one month before the initial study visit, individuals to obtain diabetes care at another clinic, individuals who do not consent to CGM use, and individuals greater than 21 years of age.

HbA1c measurements were collected from 71 youth with T1D (age 9.7 ± 4.6 years, 41% female, and 48% Non-Hispanic White, Table 1). Of the 71 unique participants, 23 (32.4%) contributed more than one HbA1c home kit measurement (University of Minnesota's Advanced Research and Diagnostic Laboratory [ARDL]) for a total of 99 HbA1c values for which there were GMI (*n* = 88) and concurrent POC HbA1c (*n* = 32; DCA Vantage® Analyzer, Siemens, Germany) to evaluate these three methods for measuring glycemic control. The ARDL HbA1c measurements were performed on the Tosoh G8 HPLC system in variant mode (Tosoh Bioscience, Inc., South San Francisco, CA, USA). Although there are a variety of methods for computing a HbA1c value from CGM data [17–19], we chose GMI in this study because all of the participants enrolled in the 4T Study were started on a Dexcom G6 CGM system, and we were able to determine GMI readily using Dexcom Clarity reports.

The HbA1c home kit and POC HbA1c (*n* = 32) measurements were collected on the same day in the clinic with the guidance of the study staff. The HbA1c home kit and GMI (*n* = 88) and POC A1c and GMI (*n* = 27) were also collected on the same day.  The HbA1c home kits were collected, packaged according to instructions, and mailed via United States Postal Service (USPS) as first-class mail to the ARDL at the University of Minnesota for analysis.

The POC vs. GMI only had 27 paired samples (versus 32 for POC vs. the home kit) due to missing GMI data in five participants. Similarly, for the home kit vs. GMI, there were 88 paired samples (of the 99 total home kits) due to the 11 participants missing GMI data. Overall, there were fewer matched comparisons available for the POC HbA1c because fewer families attended in-clinic visits during the COVID-19 pandemic. GMI values were captured on the same day the home kit and POC HbA1c were collected, and we used a 90-day retrospective GMI measurement period via Dexcom Clarity reports.

**Table 1.** Participant Demographics. Note: N = 71 unique participants (25 contributed more than one measurement).

#### **3. Statistical Analysis**

Matched pairs were used for Bland–Altman analyses and Lin's concordance correlation coefficient (ߩc) to evaluate the agreement among glycemic control measures. Paired measures include the following: POC vs. home kit = 32 paired; POC vs. GMI = 27 paired; and home kit vs. GMI = 88 paired samples. Bias was defined as the difference between the alternative glycemic control measure and the reference POC A1c as presented for each measure. When comparing home kits' A1c and GMI, the home kit was used as the reference measure since the standard of care POC HbA1c was not available for these matched pairs. All matched HbA1c measurements were collected on the same day, and 90-day GMI data were captured using the CGM mean glucose value from Dexcom Clarity. To confirm that the observed differences fell within the equivalence bounds (i.e., between −0.5 and 0.5%), a TOST analysis for equivalency was conducted using paired *t*-tests.

#### **4. Results**

The median CGM wear time to evaluate GMI was 98.9% (IQR 86.7, 100%). In relation to POC HbA1c (Figure 1), both HbA1c home kit (panel (A)) and GMI (panel (B)) showed a slight positive bias (mean difference 0.18% and 0.34%, respectively). The HbA1c home kit and GMI showed strong concordance with POC HbA1c (ߩc = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). The GMI (panel (C)) also showed a slight positive bias (mean difference 0.28%) and fair concordance (ߩc = 0.750 [0.658, 0.820]) with the HbA1c home kit.

In addition, the percentage of the paired HbA1c values that deviated by a clinically meaningful amount (>0.5%) was 0% (POC versus home kit, 0/32), 44% (POC versus GMI, 12/27), and 27% (home kit versus GMI, 24/88); Figure 2. For each pairwise comparison, we tested the composite null hypotheses H01: Δ ≤ −0.5 and H02: Δ ≥ 0.5. Upon rejection at the 0.05 alpha level, we concluded that the observed differences fall within the predefined equivalence bounds of −0.5 and 0.5%.

**Figure 1.** Bland–Altman comparison of the HbA1c home kit, GMI, and POC HbA1c measurements. The solid line (blue) represents the mean difference between comparator and reference, and the dashed lines (red) represent the 95% limits of agreement. In (**A**), the HbA1c home kit is the comparator, and POC HbA1c is the reference. In (**B**), GMI is the comparator, and POC HbA1c is the reference. In (**C**), GMI is the comparator, and HbA1c home kit is the reference. GMI = Glucose Management Indicator; POC = point-of-care HbA1c.

**Figure 2.** Paired HbA1c values are as follows: (**A**) POC versus Home Kit A1c, (**B**) POC versus GMI, and (**C**) home kit versus GMI. The green shaded area denotes HbA1c values that did not deviate by a clinically meaningful amount (>0.5%).

The Bland–Altman analyses revealed that 30 and 90 days of GMI data showed slightly less bias than using 14-day GMI data (mean difference of 0.34% for both 30 and 90 days of the GMI data versus 0.38% for 14-day GMI data, respectively).

#### **5. Discussion**

In this sub-study, we assessed the accuracy and concordance between POC HbA1c, HbA1c home kit, and Glucose Management Indicator (GMI) values in pediatric patients with type 1 diabetes. The COVID-19 pandemic has led to changes and rapid adoption of diabetes care delivery in a telehealth model. Even with more openings, patients and families often choose telehealth for convenience [9]. Therefore, implementation and accessibility to HbA1c home kits will allow for regular glycemia and patient-centered care monitoring. This

sub-study demonstrates that the HbA1c home kit and GMI show a strong concordance with the standard of care POC HbA1c, and these data support the use of the ARDL home HbA1c kits within the 4T study [7–16], as well as verified home HbA1c kits as an option for clinical care for telehealth diabetes care. Beck et al. [5] recently reported similar findings with two capillary blood collection kits (one of which was the ARDL HbA1c home kit described here) and venous HbA1c. They show compelling evidence that HbA1c measurements from these two capillary blood collection kits can be used interchangeably with venous HbA1c. In our analysis, we extend these findings and demonstrate the added utility of GMI in youth wearing CGM technology.

There are limitations in this sub-study worth noting. We used the Dexcom G6 CGM system in the present study, so these conclusions may not be generalizable across different types of CGM systems. In addition, our POC reference device used in this analysis was the DCA Vantage analyzer based on latex immunoagglutination inhibition, a commonly used POC A1c measurement system. Different POC HbA1c instruments may work by different methodologies (e.g., boronate affinity chromatography); therefore, we also note the lack of generalizability of these conclusions across other HbA1c POC systems that were not tested. Similarly, in this sub-study, we obtained a relatively small number of matched pairs (specifically for POC measurements due to the COVID-19 pandemic). This may warrant a more robust analysis to confirm the current findings.

The use of telehealth-specific options for A1c measures (i.e., GMI and home kit) has its own unique set of pros and cons. In our analysis, the A1c home kit outperformed GMI in concordance. However, the A1c home kit also requires additional collection kit instructions for patients, the possibility of user error in sample collection, and challenges around timely shipment and analysis of samples. GMI calculations do not correct glycation rates, and studies have shown that similar methods may yield more accurate A1c estimations [17–19]. Therefore, future studies might consider different methods for calculating HbA1c from CGM tracings that may correct the rate of glycation and, in turn, the lifetime of red blood cells. The convenience of GMI is that it does not require additional testing or associated costs like the A1c home kit and is obtainable from the CGM device already in use.

#### **6. Conclusions**

In conclusion, these data demonstrate that the HbA1c home kit and GMI show strong concordance with POC HbA1c. The use of GMI data in this analysis is not intended to replace future POC or venous blood samples for HbA1c. However, using GMI may be particularly helpful for families and individuals that may not have access to POC HbA1c or the HbA1c home kit and to facilitate telehealth visits. CGM data also provide additional information on hypoglycemia and glucose variability [20]. Overall, the HbA1c home kit and GMI may be potential solutions to glycemic assessment during the COVID-19 pandemic and for future telehealth visits.

**Author Contributions:** D.P.Z. wrote and edited the technical report. D.P.Z. performed statistical analyses that were reviewed and approved by V.Y.D. and M.D. All authors (D.P.Z., V.Y.D., A.A., P.P., B.L., N.A.-R., A.B.K., D.M.M. and M.D.) contributed to the revisions and approved the submitted letter. All authors have read and agreed to the published version of the manuscript.

**Funding:** Funding was provided in part by the NIDDK (R18 DK122422), Stanford Diabetes Research Center (P30DK116074), Leona M. and Harry B. Helmsley Charitable Trust (G-2002-04251), Lucile Packard Children's Hospital Auxiliaries, and ISPAD-JDRF Research Fellowship.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Stanford University (protocol code clinicaltrials.gov: NCT03968055 and NCT04336969 on 30 May 2019, and 7 April 2020, approval).

**Informed Consent Statement:** Informed consent was obtained from all individuals involved in the study.

**Data Availability Statement:** Data is contained within the article.

**Acknowledgments:** We would like to thank the other members of the 4T Study Group for their help with this project. Study team members include: Julie Hooper, Ana Cortes, Erica Pang, Carolyn Herrera, Rachel Tam, Dom Mitchell" Liz Heckard, Andrea Bonilla Ospina, Franziska Bishop, Natalie Pageler, Jeannine Leverenz, Piper Sagan, Anjoli Martinez-Singh, Barry Conrad, Annette Chmielewski, Julie Senaldi, Rebecca Gardner, Kim Clash, Erin Hodgson, Diana Naranjo, Molly Tanenbaum, Johannes Ferstad, Ryan Pi, Michael Gao, Annie Chang, Ransalu Senanayake, Anastasiya Vitko, Simrat Ghuman, Esli Osmanlliu, Emily Fox, Carlos Guestrin, Alex Wang, and Juan Langlois.

**Conflicts of Interest:** D.P.Z. has received speaker's honoraria from Medtronic Diabetes, Ascensia Diabetes, and Insulet Canada; and research support from the Helmsley Charitable Trust and ISPAD-JDRF Research Fellowship. She is also on the Dexcom Advisory board. A.B.K. has served as an external consultant for Roche Diagnostics and received research funding from Siemens Healthcare Diagnostics and Kyowa Kirin Pharmaceutical Development. A.B.K. has also received speaker honoraria from the National Kidney Foundation and the American Kidney Fund. All funding is unrelated to this manuscript. D.M.M. has had research support from the National Kidney Foundation, Juvenile Diabetes Research Foundation, NSF, and the Helmsley Charitable Trust and his institution has had research support from Medtronic, Dexcom, Insulet, Bigfoot Biomedical, Tandem, and Roche. D.M.M. has consulted for Abbott, Aditxt, the Helmsley Charitable Trust, Lifescan, Mannkind, Sanofi, Novo Nordisk, Eli Lilly, Medtronic, Insulet, Dompe, and Biospex. The remaining authors have no conflicts of interest.

#### **References**

## *Article* **Diabetic Ketoacidosis Was Associated with High Morbidity and Mortality in Hospitalized Patients with COVID-19 in the NYC Public Health System**

**Sahana Parthasarathy 1,2, Natalia Chamorro-Pareja 1,2, Amrin Kharawala 1,2, Kenneth H Hupart 1,3,4, Joan Curcio 1,5,6, Christina Coyle 1,2, Daniel Buchnea 1,7, Dimitris Karamanis 8, Robert Faillace 1,2, Leonidas Palaiodimos 1,2 and Preeti Kishore 1,2,\***

**Abstract:** Background: COVID-19 has been associated with a higher risk of death in patients with diabetes mellitus (DM). However, there is a dearth of data regarding the effects of diabetic ketoacidosis (DKA) in these patients. We explored the in-hospital outcomes of patients who presented with COVID-19 and DKA. Methods: A propensity score-matched observational retrospective cohort study was conducted in hospitalized patients with COVID-19 in the public healthcare system of New York City from 1 March 2020 to 31 October 2020. Patients were matched, and a subgroup analysis of patients with DKA and COVID-19 and patients without COVID-19 was conducted. Results: 13,333 (16.0%) patients with COVID-19 and 70,005 (84.0%) without COVID-19 were included in the analysis. The in-hospital mortality rate was seven-fold in patients with DKA and COVID-19 compared to patients with COVID-19 and without DKA (80 (36.5%) vs. 11 (5.4%), *p* < 0.001). Patients with COVID-19 and DKA had a two-fold higher likelihood for in-hospital death (OR: 1.95; 95% CI: 1.41–2.70; *p* < 0.001) after adjusting for multiple variables. Conclusions: DKA was associated with significantly higher in-hospital mortality in hospitalized patients with COVID-19.

**Keywords:** coronavirus disease 2019; COVID-19; severe acute respiratory syndrome coronavirus 2; SARS-CoV-2; diabetes; diabetic ketoacidosis; DKA; hyperglycemia; mortality; innate immunity; cytokine storm; angiotensin-converting enzyme 2; pancreatic β-cell damage

#### **1. Introduction**

New-onset diabetes and severe metabolic complications such as diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS) have been observed in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) [1], the virus that causes Coronavirus disease 2019 (COVID-19). Initially, DKA was more commonly associated with patients with type 1 diabetes mellitus (T1DM) as compared to type 2 diabetes mellitus (T2DM). However, some studies have shown that patients with T2DM can present with DKA, but they are usually older, require more insulin, and tend to have a longer course of stay [2,3]. Interestingly, patients with COVID-19 have presented with DKA without a previously known diagnosis of DM [4,5]. Since the beginning of the pandemic, there have been reports of worse outcomes and increased mortality in patients with DKA and COVID-19 [6,7]. It has been proposed that the higher mortality in these patients can be attributed to advanced age, the higher severity of COVID-19, and the use

**Citation:** Parthasarathy, S.;

Chamorro-Pareja, N.; Kharawala, A.; Hupart, K.H.; Curcio, J.; Coyle, C.; Buchnea, D.; Karamanis, D.; Faillace, R.; Palaiodimos, L.; et al. Diabetic Ketoacidosis Was Associated with High Morbidity and Mortality in Hospitalized Patients with COVID-19 in the NYC Public Health System. *Diabetology* **2022**, *3*, 477–493. https:// doi.org/10.3390/diabetology3030036

Academic Editor: Yung-Chih Chen

Received: 10 July 2022 Accepted: 25 August 2022 Published: 6 September 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of steroids [8,9]. COVID infection resulted in an increased incidence of DKA in patients with T2DM. These patients required a longer ICU stay and were found to have worse mortality [10]. We previously reported significantly higher mortality in patients with COVID-19 that presented with DKA compared to the previously known mortality rate of DKA [11]. In this study, we compared the in-hospital outcomes of patients with DKA prior to the start of the COVID-19 pandemic in our hospital system to the mortality of patients that presented with DKA in the setting of COVID-19 during the first phase of the pandemic in 2020.

#### **2. Materials and Methods**

#### *2.1. Study Design and Patient Population*

This was a propensity score-matched observational cohort study performed at the eleven acute care hospitals of the New York City Health + Hospitals (NYC H + H) system. The study group included patients ≥18 years of age who presented to the emergency room and were admitted to any inpatient service, including intensive care units (ICU), with laboratory-confirmed COVID-19 from 1 March 2020 to 31 October 2020. The control group included patients that were admitted from 1 July 2019 to 31 December 2019, before the pandemic was declared and prior to the first diagnosed case of COVID-19 in the USA. These populations were matched, and a subgroup analysis of patients with COVID-19 and DKA and patients with DKA but without COVID-19 was performed. DKA was defined as blood glucose > 250 mg/dL, pH < 7.3, a bicarbonate level of <18 mEq/L, an elevated anion gap, and positive ketones in blood or urine [12]. Laboratory-confirmed COVID-19 was defined as a SARS-CoV-2 positive result in the real-time reverse transcriptase-polymerase chain reaction (RT-PCR) analysis of nasopharyngeal or nasal swab samples (Bio Reference Laboratories, Elmwood Park, NJ, USA). We excluded patients that had the following criteria: patients that were discharged from the emergency department, patients without laboratory-confirmed COVID-19, and patients who were still hospitalized at the time of data collection. The study was approved by the Biomedical Research Alliance of New York Institutional Review Board with a waiver of informed consent (IRB #20-12-318-373). The data were fully de-identified and anonymized before they were accessed, and IRB waived the requirement for informed consent.

#### *2.2. Data Sources*

The study data were obtained from electronic health records via appropriate diagnostic codes (Epic systems, Verona, WI, USA). The dataset was reviewed by two independent investigators for missing data and completeness. The extracted data included baseline demographic variables (age, sex, race), clinical characteristics (body mass index (BMI), history of tobacco use, hypertension, hyperlipidemia, asthma, coronary artery disease (CAD), heart failure, stroke/transient ischemic attack (TIA), chronic kidney disease (CKD), end-stage renal disease (ESRD)), medication list (including insulin, biguanides, dipeptidyl peptidase- 4 (DPP-4) inhibitors, glucagon like peptide-1 (GLP-1) agonists, sulfonylureas, insulin, angiotensin-converting enzyme (ACE) inhibitors, and statins), laboratory data (hemoglobin A1C,C-reactive protein (CRP), lactate dehydrogenase (LDH), Ferritin, D-Dimer, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT)), and outcomes including invasive mechanical ventilation, admission to the intensive care unit (ICU), the need for renal replacement therapy (RRT), and death from all causes. The data were processed and analyzed without any personal identifiers to maintain patient confidentiality, as per the Health Insurance Portability and Accountability Act (HIPAA).

#### *2.3. Exposure of Interest and Outcomes*

The primary outcome was the in-hospital mortality in patients with COVID-19 and DKA. The secondary outcomes were length of stay (LOS), invasive mechanical ventilation, admission to the ICU, and need for RRT.

#### *2.4. Statistical Analysis*

Propensity score matching was conducted to create comparable groups controlling for imbalances of covariates [13] [Supplemental Material Figures S1 and S2]. The propensity score was estimated using a logistic regression model and is the subject-specific probability of being infected with COVID-19, in which the following covariates were used: age, sex, BMI, race, DKA, comorbidities (history of diabetes mellitus, hypertension, hyperlipidemia, Chronic obstructive pulmonary disease (COPD), asthma, CAD, heart failure, stroke TIA, ESRD, CKD), and medications (biguanides, DPP4 inhibitors, SGLT2 inhibitors, GLP-1 agonists, insulin, ACE inhibitors, sulfonylureas, and statins). A nearest-neighbor matching without replacement was performed using a caliper of 0.2, which has been found to be optimal [14,15].

A stepwise logistic regression model identified the baseline variables associated with in-hospital mortality, invasive mechanical ventilation/intubation [Tables S1–S3] admission to ICU [Tables S4–S6], and the need for RRT [Tables S7–S9] in three different data panels: (a) matched groups (COVID-19 and control), (b) COVID-19 group, and (c) DKA group. Univariate analysis was performed, and four multivariate models with different definitions of our variable of interest are presented for robustness: model 1: age, sex, and BMI; model 2: age, sex, BMI, and all comorbidities; model 3: age, sex, BMI, all comorbidities, and diabetes medications; model 4: age, sex, BMI, all comorbidities, diabetes medications, and all medications used during inpatient status. Additional variables are included in the multivariable models 1–4 for every different panel, specifically: for panel (a), models 1–4 also include positive COVID-19 status and the presence of DKA; for panel (b), models 1–4 also include the presence of DKA; and for panel (c), models 1–4 also include positive COVID-19 status.

T-tests compared continuous variables, while Chi-Square tests compared discrete variables. Continuous data are presented as median values with the interquartile range (IQR) specified, and categorical data are presented as absolute and relative frequencies. A *p*-value < 0.05 was considered statistically significant.

#### **3. Results**

#### *3.1. Descriptive Analyses*

#### 3.1.1. Baseline Characteristics

A flowchart of the analysis is presented in Figure 1. In total, 83,338 patients were included in our analysis. A total of 13,333 (16.0%) were patients with COVID-19, and 70,005 (84.0%) were patients without COVID-19. The propensity score matching between the COVID-19 and control groups yielded a cohort of 22,694 patients (COVID-19 group: 11,347 and control group: 11,347). In the COVID-19 group, 58.8% were men, the median age was 62 (IQR 49–74) years, and the median BMI was 28.1 (24.4–32.8) kg/m2. From the non-COVID-19 group, 57.8% were men, the median age was 62 (IQR 48–75) years, and the median BMI was 27.9 (23.8–33.3) kg/m2. The matching in DKA patients yielded a cohort of 422 patients (DKA/COVID-19 group: 219 and control group: 203). In the DKA/COVID-19 group, 63.5% were men, the median age was 56 (69–41) years, and the median BMI was 25.7 (22.6–30.2) kg/m2. In the DKA/non-COVID-19 group, 67.0% were men, the median age was 50 (38–63) years, and the median BMI was 26.1 (22.4–30.8) kg/m2. Detailed baseline characteristics are included in Table 1.

**Figure 1.** Flowchart of the analysis.

**Table 1.** Baseline characteristics.

**Table 1.** *Cont.*

Notes: BMI in kg/m2. *p-*values refer to t-test or Chi-Square test between COVID-19 and control groups. Significance at *p-*value < 0.05. Abbreviations and symbols: BMI = body mass index; kg = kilograms; m = meter; N = no = number; IQR = interquartile range; DKA = Diabetic Ketoacidosis; DM = Diabetes Mellitus; HTN = hypertension; HLD = hyperlipidemia; COPD = Chronic Obstructive Pulmonary Disease; CAD = coronary artery disease; TIA = Transient Ischemic Attack; ESRD = End-Stage Renal Disease.

#### 3.1.2. Inflammatory Markers

The baseline concentrations of the available inflammatory markers are presented in Table 2. The median values of LDH, D-dimer, ferritin, and CRP were higher in patients with COVID-19 compared to patients without COVID-19 (*p* < 0.001). Significant and consistent differences in the baseline concentrations of inflammatory markers among patients with DKA/COVID-19 compared to patients with DKA but not COVID-19 were not noted.

#### **Table 2.** Laboratory values.

Note: *p-*values refer to T-test between COVID-19 and control groups. Significance at *p*-value < 0.05. Abbreviations and symbols: N = number; IQR = interquartile range; U/L = unit/liter; mg = milligram; ng = nanogram; ml = milliliter; dL = deciliter; DKA = Diabetic Ketoacidosis; CRP = C-Reactive Protein; LDH = Lactate Dehydrogenase; AST = Aspartate Aminotransferase; ALT = Alanine Aminotransferase; HbA1c = Hemoglobin A1c.

#### 3.1.3. Outcomes

The median LOS in patients with COVID-19 was 6 days vs. 4 days in the control group (*p* < 0.001). In total, 9.1% of patients with COVID-19 required intubation compared to 3.3% in the non-COVID-19 control group (*p* < 0.001). ICU admission happened for 23.4% of patients in the COVID-19 group vs. 14.7% of patients in the non-COVID-19 group (*p* < 0.001). A total of 8.5% of patients with COVID-19 required RRT compared to 4.2% in the control group (*p <* 0.001). In-hospital death occurred in 26.0% of patients with COVID-19 compared to 9.3% in the control group (*p* < 0.001). Outcomes are presented in Table 3 and Figure 2.

**Table 3.** Outcomes in COVID-19 patients vs. control group.

Notes: *p-*values refer to T-test or Chi-Square test between COVID-19 and control groups. Significance at *p*-value < 0.05. Abbreviations and symbols: N = no = number; IQR = interquartile range; ICU = Intensive Care Unit.

**Figure 2.** Outcomes in COVID-19 vs. control groups after propensity score matching.

#### *3.2. Subgroup Analysis for Patients with DKA*

In patients with DKA and COVID-19, the LOS was 7 days compared to 4 days in patients with DKA and without COVID-19. Intubation happened for 14.6% of patients with DKA and COVID-19 vs. 5.9% of patients with DKA and without COVID-19 (*p* = 0.003). The mortality rates were 36.5% for patients with DKA and COVID-19 compared to 5.4% for patients with DKA and without COVID-19 (*p* < 0.001). The rate of requiring RRT was significantly higher in the DKA/COVID 19 group compared to the DKA/non-COVID-19 group (*p* = 0.005). There was no statistical significance of ICU admission between the two groups (*p* = 0.207). The outcomes of the subgroup analysis are presented in Table 4 and Figure 3.

Notes: *p-*values refer to T-test or Chi-Square test between COVID-19 and control groups. Significance at *p-*value < 0.05. Abbreviations and symbols: N = no = number; IQR = interquartile range; ICU = Intensive Care Unit; DKA = Diabetic Ketoacidosis.

#### *3.3. Logistic Regression Analysis*

#### 3.3.1. Matched Cohort (COVID-19 and Non-COVID-19)

Univariate associations with in-hospital mortality were examined for the available baseline demographic and clinical characteristics and are presented in Table 5. Older age, male sex, higher BMI, ESRD, COVID-19, diabetes mellitus, and DKA were all associated with a higher likelihood for in-hospital mortality in all models. Hypertension, CAD, asthma, and a history of stroke were shown to have an inverse association with in-hospital death. The use of biguanides, DPP4-inhibitors, ACE inhibitors, sulfonylureas, and statins were also shown to have an inverse association with in-hospital death.

**Table 5.** Baseline patient characteristics: univariate and multivariate logistic regression analysis for the outcome of mortality.

**Table 5.** *Cont.*

Notes: Table 5 shows univariate analysis and four multivariate models with different definitions of our variable of interest. Model 1 includes the variables: age, sex, BMI, diabetic ketoacidosis, and COVID-19. Model 2 includes the variables: age, sex, BMI, diabetic ketoacidosis, COVID-19, and comorbidities including a history of diabetes, hypertension, hyperlipidemia, pulmonary hypertension, chronic obstructive pulmonary diseases, asthma, coronary artery disease, heart failure, stroke/TIA, end-stage renal disease, and chronic kidney disease. Model 3 includes the variables: age, aex, BMI, diabetic ketoacidosis, COVID-19, comorbidities including a history of diabetes, hypertension, hyperlipidemia, pulmonary hypertension, chronic obstructive pulmonary diseases, asthma, coronary artery disease, heart failure, stroke/TIA, end-stage renal disease, and chronic kidney disease, and antidiabetic medication classes including biguanides, DPP4 inhibitors, SGLT-2 inhibitors, GLP-1 agonists, insulin, ACE inhibitors, sulfonylureas, and statins. Model 4 includes the variables: age, sex, BMI, diabetic ketoacidosis, COVID-19, comorbidities including a history of diabetes, hypertension, hyperlipidemia, pulmonary hypertension, chronic obstructive pulmonary diseases, asthma, coronary artery disease, heart failure, stroke/TIA, end-stage renal disease, and chronic kidney disease, antidiabetic medication classes including biguanides, DPP4 inhibitors, SGLT-2 inhibitors, GLP-1 agonists, insulin, ACE inhibitors, sulfonylureas, and statins, and all the medications used during inpatient status: heparin, enoxaparin, apixaban, steroids, tocilizumab, remdesivir, convalescent plasma, and cefepime. \*\* *p* < 0.01, \* *p* < 0.05.

Abbreviations and symbols: n = number; BMI in kg/m2; DM = Diabetes Mellitus; DKA = Diabetic Ketoacidosis; COPD = Chronic Obstructive Pulmonary Disease; CAD = coronary artery disease; TIA = Transient Ischemic Attack; ESRD = End-Stage Renal Disease; DPP 4 = Dipeptidyl Peptidase 4; GLP 1 = Glucagon-like peptide 1; SGLT 2 = Sodium Glucose Transporter 2; ACE = Angiotensin-Converting Enzyme

#### 3.3.2. COVID-19 Cohort

Univariate associations with in-hospital mortality for the COVID-19 cohort were examined for the available baseline demographics and clinical characteristics and are presented in Table 6. Older age, male sex, higher BMI, diabetes mellitus, and DKA were associated with a higher likelihood for in-hospital death in all models. A history of type 1 diabetes, hypertension, asthma, heart failure, and stroke/TIA were shown to have an inverse association with in-hospital mortality. The use of biguanides, DPP4-inhibitors, and ACE inhibitors was also inversely associated with in-hospital mortality.

**Table 6.** COVID-19 Cohort: univariate and multivariate logistic regression analysis for the outcome of mortality.

**Table 6.** *Cont.*

Notes: Table 6 shows univariate analysis and four multivariate models with different definitions of our variable of interest. Model 1 includes the variables: age, sex, BMI, and diabetic ketoacidosis. Model 2 includes the variables: age, sex, BMI, diabetic ketoacidosis, and comorbidities including a history of diabetes, hypertension, hyperlipidemia, pulmonary hypertension, chronic obstructive pulmonary diseases, asthma, coronary artery disease, heart failure, stroke/TIA, end-stage renal disease, and chronic kidney disease. Model 3 includes the variables: age, sex, BMI, diabetic ketoacidosis, comorbidities including a history of diabetes, hypertension, hyperlipidemia, pulmonary hypertension, chronic obstructive pulmonary diseases, asthma, coronary artery disease, heart failure, stroke/TIA, end-stage renal disease, and chronic kidney disease, and antidiabetic medication classes including biguanides, DPP4 inhibitors, SGLT-2 inhibitors, GLP-1 agonists, insulin, ACE inhibitors, sulfonylureas, and statins. Model 4 includes the variables: age, sex, BMI, diabetic ketoacidosis, comorbidities including a history of diabetes, hypertension, hyperlipidemia, pulmonary hypertension, chronic obstructive pulmonary diseases, asthma, coronary artery disease, heart failure, stroke/TIA, end-stage renal disease, and chronic kidney disease, antidiabetic medication classes including biguanides, DPP4 inhibitors, SGLT-2 inhibitors, GLP-1 agonists, insulin, ACE inhibitors, sulfonylureas, and statins, and all the medications used during inpatient status: heparin, enoxaparin, apixaban, steroids, tocilizumab, remdesivir, convalescent plasma, and cefepime. \*\* *p* < 0.01, \* *p* < 0.05.

Abbreviations and symbols: n = number; BMI in kg/m2; DM = Diabetes Mellitus; DKA = Diabetic Ketoacidosis; COPD = Chronic Obstructive Pulmonary Disease; CAD = coronary artery disease; TIA = Transient Ischemic Attack; ESRD = End-Stage Renal Disease; DPP 4 = Dipeptidyl Peptidase 4; GLP 1 = Glucagon-like peptide 1; SGLT 2 = Sodium Glucose Transporter 2; ACE = Angiotensin-Converting Enzyme

#### 3.3.3. DKA Cohort

Univariate associations with in-hospital mortality for the DKA cohort were examined for the available baseline demographics and clinical characteristics and are presented in Table 7. Older age, higher BMI, and hyperlipidemia were associated with a higher likelihood for in-hospital mortality in all models. COVID-19, in this cohort, was associated with an almost ten-fold likelihood for in-hospital mortality in all cohorts. In this cohort, no association between other comorbidities or the use of medications and in-hospital death was noted.

**Table 7.** DKA Cohort: univariate and multivariate logistic regression analysis for the outcome of mortality.

**Table 7.** *Cont.*

and chronic kidney disease, and antidiabetic medication classes including biguanides, DPP4 inhibitors, SGLT-2 inhibitors, GLP-1 agonists, insulin, ACE inhibitors, sulfonylureas, and statins. Model 4 includes the variables: age, sex, BMI, COVID-19, comorbidities including a history of diabetes, hypertension, hyperlipidemia, pulmonary hypertension, chronic obstructive pulmonary diseases, asthma, coronary artery disease, heart failure, stroke/TIA, end-stage renal disease, and chronic kidney disease, antidiabetic medication classes including biguanides, DPP4 inhibitors, SGLT-2 inhibitors, GLP-1 agonists, insulin, ACE inhibitors, sulfonylureas, and statins, and all the medications used during inpatient status: heparin, enoxaparin, apixaban, steroids, tocilizumab, remdesivir, convalescent plasma, and cefepime. \*\* *p* < 0.01, \* *p* < 0.05.

Abbreviations and symbols: n = number; BMI in kg/m2; DM = Diabetes Mellitus; DKA = Diabetic Ketoacidosis; COPD = Chronic Obstructive Pulmonary Disease; CAD = coronary artery disease; TIA = Transient Ischemic Attack; ESRD = End-Stage Renal Disease; DPP 4 = Dipeptidyl Peptidase 4; GLP 1 = Glucagon-like peptide 1; SGLT 2 = Sodium Glucose Transporter 2; ACE = Angiotensin-Converting Enzyme

#### **4. Discussion**

Diabetic ketoacidosis is a severe metabolic complication attributable to severe insulin deficiency. From previous studies, it is well established that COVID-19 is associated with ketosis, ketoacidosis, and diabetic ketoacidosis [16]. Our study investigated the association of DKA with in-hospital outcomes among patients admitted with COVID-19 in the largest public health care system of the United States. The key findings of our descriptive analysis showed that the mortality rate was seven times higher in patients with DKA and COVID-19 compared to the DKA control group, i.e., patients without COVID-19. The likelihood of death in patients with DKA and COVID-19 was found to be significantly higher compared to patients with DKA and without COVID-19.