However, Huang et al. detected co-infection rates of 8 to 15% in COVID-19 patients [3]. With regard to fungi, the rate was around 3 to 6% [4]. The above invasive fungal infections (IFIs) include invasive aspergillosis, mucormycosis, candidiasis, and cryptococcosis [5].

Invasive Mucormycosis (IM), since the advent of the 21st century, has been deemed an emerging, angio-invasive fungal infection with drastically high mortality, of around 80% [6–8]. Historically speaking, India has been the principal hotbed of mucormycosis, recording a prevalence of 140 cases per million population. Globally, it is the highest and is eighty times higher than that of the affluent nations [6]. Hailing from the order *Mucorales*, Rhizopus is the most common species in the world. Apart from that, *Mucor* and *Lichtheimia* are the predominant causative agents in Europe, whereas *Apophysomyces* is the Asian, primarily Indian, counterpart [6,9]. The clinical spectrum includes five forms, primarily rhinocerebral, pulmonary, cutaneous, gastrointestinal, and disseminated disease [9]. Uncontrolled hyperglycaemia, steroid and immunomodulatory therapy, haematological malignancy (HMs) and haematopoietic stem cell transplantation (HSCT), solid organ malignancy (SOM) and transplant (SOT), persistent neutropenia, and iron overload are among the most notorious risk factors of IM [7,9].

In high-income countries (HIC) such as Europe, the USA, and Australia, HMs are the most common underlying cause of IM (38–62%) [7,9,10]. Acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL), which frequently necessitate HSCT, are prominent examples [9,10]. These cases are at increased risk of IM, especially in their neutropenic state. Additionally, in these above patients, antifungal prophylaxis with azoles and echinocandins predisposes to breakthrough mucormycosis—voriconazole (52%), fluconazole (25%), caspofungin (9.8%), itraconazole (7.6%), and posaconazole (5%) [7,10].

Meanwhile, in low-and-middle-income countries (LMICs) such as the Indian subcontinent, diabetes mellitus (DM) is the most prevalent predisposing factor to IM [6,7,9]. Patel et al. identified rhino-orbito-cerebral mucormycosis (ROCM), constituting 65.7% of their IM cohort, to be the most common form [8]. The diabetic population of India is on the rise, from 60 million in 2011 to around 100 million by 2030 [11]. This risk relationship with IM is directly proportional. Prakash et al. designed a multi-centre study in India where 56.8% of IM patients were diabetics and 18% had diabetic ketoacidosis (DKA). Approximately 65% of them manifested ROCM [6].

COVID-19 patients have a higher prevalence of DM and DKA than that of the general population [12]. Compared to the other IFIs, mucormycosis in COVID-19 has proven to be a better-documented disease. A systematic review by John et al. reported the incidence of COVID-associated Mucormycosis (CAM) to be 87% [13]. Until 13 May 2021, Singh et al. reported that out of 101 CAM cases, 82 were from India and the remaining 19 were from the rest of the world [14]. From then until July, around 42,000 cases and 4000 deaths have occurred as a result of CAM in the Indian population [15].

The prevalence of COVID-19 among patients with cancer was higher in HMs (10.9%) as compared to SOMs (7.8%). Among SOMs, prostate cancer showed the highest frequency (10%). Patients receiving cancer treatment within 6 months (6.2%) were less susceptible to COVID-19 than those exceeding the above time interval (7.8%). COVID-19 patients with cancer have higher morbidity and mortality rates as compared to their negative counterparts—hospitalizations (43.8% vs. 31.5%), intensive care unit admissions (19.7% vs. 7.8%), respiratory support (7.9% vs. 1.3%), and deaths (14% vs. 3.1%). Among these COVID-19-positive cancer patients, mortality was lower in those treated with immunotherapy (7.1%) as compared to chemotherapy (14%), hormone therapy (16.2%), or even targeted therapy (14.1%) [16]. Among the 30.6% COVID-19 cancer patients enrolled in the UK Coronavirus Cancer Monitoring Project (UKCCMP) who died, fatality rates were higher in HMs as compared to SOMs. Leukaemia had the highest case fatality rate of 2.25. These HM patients who received recent chemotherapy experienced higher death rates during COVID-19 hospital admissions (odds ratio (OR) 2.09) [17].

In this review article, we search for an inter-relationship between the COVID-19 pandemic, emerging mucormycosis, and cancer.
## 2. CAM: Clinical Features

Rhizopus, as already mentioned, is ubiquitously present in soil, manure and as moulds on food [18]. Transmission of the infection can occur by airborne fungal spores entering via the nasal, oral, and conjunctival mucosa or by ingesting contaminated food. It may also be a commensal on the skin and mucosa, thus precipitating in opportunistic infections among the immunocompromised [19].

As discussed before, ROCM accounts for about a third of the cases of mucormycosis, with a steep rise noted in the COVID-19 pandemic [20]. The second most common type is pulmonary mucormycosis (PM) [13,14,20–22]. ROCM and PM originate in the nose and paranasal sinuses. The hallmarks of the disease are vascular invasion, thrombosis, and tissue necrosis. It results in bony destruction in the walls of the sinuses, leading to orbital and cranial spread [20,23].

Smith and Kirchner's diagnostic criteria for ROCM from 1958 still remain the gold standard for clinical diagnosis [14]. In an immunocompromised setting, a combination of the following features ought to raise suspicion of mucormycosis:

* Facial pain and blood-tinged nasal discharge on the same side;

* Soft periorbital or peri-nasal swelling with skin and mucosal discoloration progressing to induration;

* Conjunctival suffusion, eyelid ptosis, eyeball proptosis, and complete ophthalmoplegia;

* Necrotic, black turbinates appearing like clotted, crusted blood;

* Cranial nerve palsies.

The other non-specific features include fever, unilateral headache, sinus tenderness, puffiness of the face, loosening of teeth, and nasal congestion [24].

PM has non-specific symptoms such as fever, cough, chest pain, dyspnoea, and haemoptysis [24]. The symptoms appear innocuously similar to the underlying viral infection. Hence, the early signs are written off as COVID-19. Suspicion of PM should arise in COVID-19 patients when these symptoms persist, or new symptoms develop after the resolution of the viral disease. Strict monitoring for reappearance or prolonged persistence of symptoms in diabetics and those receiving steroids and immunosuppressive therapies should prompt evaluation for mucormycosis [13,14,20].

In the immunocompromised population, the invasive nature of the fungus quickly turns the disease lethal [13,14,21,25]. A mortality rate of 30–50% is seen in CAM [13,14,20,21].

## 3. CAM: Road to Diagnosis

The first line of investigation performed is an imaging study [20]. The modalities recommended are magnetic resonance imaging of the paranasal sinuses with cerebral contrast for ROCM and simple computerized tomography (CT) thorax for pulmonary disease.

For pathologies of the paranasal sinuses, the imaging preferred is a CT scan [26]. IM may show as a unilateral hypodense opacification of the sinuses. It has a limited role as the soft tissue changes, and critical features such as perineural spread and cavernous sinus involvement can be missed. Its use is limited to picking up bony destruction only. Magnetic resonance imaging (MRI) has higher sensitivity than CT (86% vs. 57–69%) and similar specificity (83% vs. 81%) for acute invasive fungal sinusitis [26,27]. It produces hypo- or isointense lesions in all sequences with variable enhancement on contrast. The invasion of the fungus along the soft tissue and fatty planes is delineated with an increased clarity by T1-weighted images, aiding in the staging of the disease [28]. Detection of neurological complications such as cavernous sinus thrombosis, meningitis, necrosis, and subtle perineural invasion requires the utilization of cerebral contrast material such as gadolinium [27,29].

In pulmonary disease, with concomitant COVID-19 infection, it is exceedingly difficult to differentiate due to the similarities in the lesions and the acute respiratory distress syndrome picture. CT thorax shows ground-glass opacities and infiltration. Some cases may show features of consolidation and cavitation [30]. Angio-invasion in the lungs may cause thrombosis of the pulmonary vessels, appearing as wedge-shaped infarcts.
The suspicion of mucormycosis is based on direct microscopic examination of a wet mount. Staining with comparative fluorescent fungal stain, calcofluor white, and Giemsa is also performed on the clinical specimens at the outset [20,31].

The standard practice for confirmation of the diagnosis is a biopsy from the infected area demonstrating the presence of broad, wide ribbon-like aseptate hyphae branching at right angles, on a background of necrotic debris on histopathology [19,20,32,33]. Stains commonly used are haematoxylin-eosin, periodic acid-Schiff, or Grocott-Gomori's methenamine-silver stain (GMS) [20,31].

On culture, the presence of cotton white or grey-black colonies at 28-30 °C and 35-37 °C is characteristic. The media commonly employed are Sabouraud Dextrose Agar and Brain and Heart Infusion Agar. Morphological identification from the fungal culture can be carried out by microscopic examination or by DNA sequencing on the basis of barcodes 18s, ITS, and MALDI-TOF [5,34,35]. Isolation from a fungal culture may be performed for differentiation of genus and species, and for antimicrobial susceptibility testing [5,32]. The treatment, however, is independent of the genus and species and is of epidemiological importance [36].

Serological investigations such as galactomannan assays and 1,3-β-D glucan assays support a diagnosis of fungal infection [20,35]. These tests are, however, typically negative in a pure mucor infection [20]. The utility of using the negative test to rule out the disease is questionable, as the tests are positive in mixed fungal infections [34]. While, presently, there is no serological test available for the rapid diagnosis of mucormycosis, ELISA and lateral flow immunoassay (LFIA) are in the process of development [37].

The clinical features, though characteristic of a fungal infection, are unfortunately non-specific, and may be mistaken for aspergillosis. Further, there exists a degree of misidentification of the Mucorales for the more ubiquitous Aspergillus spp. on microscopy [20,25,38]. There is thus a challenge in obtaining a confirmation of the diagnosis without immunohistochemistry staining with monoclonal antibodies or DNA-PCR of the tissue section. Both of these modalities are not only expensive but also not readily available at the point of contact [20,39].

The utility of complete blood counts is not established, especially in the present cohort of COVID-19 patients, where the white cell picture is deranged as a result of the primary infection [30,32,36,40]. For any analysis into the association of the derangement of these parameters due to the secondary fungal infection, confounding variables of the primary disease will have to be considered. The data on this are insufficient to comment upon and require further study.

# 4. Treatment

## 4.1. Mucormycosis

As discussed above, the suspicion of IM in the immunocompromised must be treated as a medical emergency due to its lethality [20]. In the present cohort, individuals infected with SARS-CoV-2, receiving treatment with steroids and immunomodulatory drugs, fall into the aforementioned high-risk category. Hyperglycaemia, due to a primary diabetic status or as a consequence of steroid therapy, increases the risk of infection [8,13,14].

Early diagnosis can lead to early initiation of treatment for positive outcomes and decreased case fatality rates: 20% fatality in early diagnosis vs. 60% in delayed diagnosis. Treatment guidelines can be set up in a three-pronged approach involving the utilization of antifungal drugs, surgical debridement, and an effort in part to increase immunity levels [41].

Prompt diagnosis directs the initiation of antifungal therapy wherein the first-line treatment is 5-10 mg/kg of Liposomal Amphotericin B (LAmB). A higher dosage of 10 mg/kg/day is recommended for central nervous system (CNS) involvement. If LAmB is not available, amphotericin B lipid complex or amphotericin deoxycholate can be used, with caution because of their known nephrotoxicity, and should be subjected to stringent therapeutic drug monitoring [20,42,43].
Other broad-spectrum antifungals such as posaconazole and isavuconazole may be utilized in the initial induction therapy where the patient cannot tolerate amphotericin B. Although not studied in randomized trials, their efficacy has been suggested to be similar to amphotericin [44]. There is, however, a noticeable need for updating the medical guideline in terms of induction of these agents for the treatment of CAM and their role as prophylactic agents [45].

Although antifungal prophylaxis for mucormycosis has been used for diseases such as underlying haematological malignancies, it is not recommended in COVID-19 patients [20,46].

Studies on combination antifungal therapy, where amphotericin B is combined with posaconazole or other antifungals such as caspofungin, have been carried out, which have not been shown to enhance survival or be more effective than monotherapy (35% versus 39%, respectively), and their cost to benefit ratio needs to be further evaluated on a case-by-case basis [42,44,47–49].

Step-down therapy is initiated with posaconazole delayed-release tablets (300 mg every 12 h on the first day, then 300 mg once daily) or infusions, which is preferred over oral suspensions. Oral isavuconazole is also an acceptable alternative, with doses of 200 mg three times a day on day 1–2 and 200 mg once a day from day 3, which is continued for a minimum of 6 weeks [20,43,50].

Add-on aggressive surgical intervention paves the way for a favourable response, which can be seen on imaging and clinical stabilization, in a minimum of 4–6 weeks [20,42,43]. Surgical debridement is carried out with clean margins, which serves three purposes—controls the disease, obtains specimens for histopathology, and confirms the microbiological diagnosis [20]. In cases of ROCM, there is extensive surgical debridement of the affected craniofacial tissues and risk of orbital exenteration, which may be lifesaving and has been found to be helpful even in cases where the infection has spread intracranially, according to the stage of involvement. Retrobulbar injection of amphotericin B may be given before surgery, according to the stage of involvement [42,43]. Lobectomy has also been performed with success in cases of PM where the infection was localized to a single lobe. The debridement procedures might need to be repeated as and when required. Endoscopic sinus surgery with limited tissue removal has also been performed [20].

While immediate surgical debridement has been the convention for lung, Saraiya et al. found that, after response to antifungal therapy, delayed surgical debridement (10 days) showed better patient outcomes than conventional aggressive surgical management. All five patients where the new protocol was followed survived [47].

Other modalities of treatment include hyperbaric oxygen therapy, administration of cytokines concurrently with antifungals, and deferasirox, but their use is not routinely recommended in the treatment guidelines [20].

Even after aggressive therapy and immediate initiation of the treatment, the prognosis is still quite grim with multiple studies showing a 90-day mortality average of 50%, with multiple patients discontinuing treatment because of financial constraints [8,48,49].

## 4.2. CAM

Methylprednisolone and dexamethasone, the cornerstones of COVID-19 treatment, are known to cause immunosuppression [50]. The World Health Organization (WHO) and the National Institute of Health (NIH) have recommended judicious use of systemic corticosteroids in COVID-19, which is in cases with evidence of respiratory failure or with oxygen saturation below 93–94%. These drugs unfortunately are known to be the most common cause of drug-induced hyperglycaemia [46,51].

A widespread disruption of antibiotic stewardship programs was noted due to rampant and injudicious use of antimicrobial agents (AMAs). Increased exposure of patients to AMAs, due to fear of superadded bacterial and fungal infection on COVID pneumonia, poor infection control measures due to rapidly changing protocols and the resulting confusion, led to an upstroke of nosocomial superbug infections in the background of COVID itself [52].
Hyperglycaemia is the most common risk factor for developing mucormycosis. In CAM, there should be immediate induction of antifungal therapy along with COVID-19 treatment protocols, and control of hyperglycaemia. Diabetic ketoacidosis, if present, should be emergently managed along with the reversal of immunosuppression in these patients [13,24,43,46,49,53,54].

Tocilizumab is another immunomodulator that is being used in the treatment of the disease. Studies and guidelines have recommended against indiscriminatory use of tocilizumab, which targets the immune pathways. Its use is only recommended for severe refractory disease where inflammatory markers are increased (e.g., C-reactive protein, interleukin 6) and in the absence of active fungal or bacterial infections [20,55].

Hence, upon detection of CAM, steroid doses should be reduced and immunomodulating drugs such as tocilizumab should be discontinued for reversal of immunosuppression [43,46,51].

# 5. COVID-19, Mucormycosis, and Cancer: The Triple Threat

## 5.1. COVID and Mucormycosis

As explained above, high-risk factors for mucormycosis include DM and metabolic acidosis, steroid usage in COVID-19, neutropenia, elevated serum iron levels, immunomodulatory therapies, and concomitant chronic illness. An increased likelihood of fungal infection has also been noted with haematological malignancies, organ and bone marrow transplant recipients, steroid therapy, patients on maintenance haemodialysis and iron chelation, trauma and burn victims [22].

SARS-CoV-2-mediated immune dysregulation of the cytokine storm in the background of a compromised immune system predisposes to invasive fungal infections [56]. This hyper-inflammatory state is also noted as a consequence of some malignancies, autoimmune diseases, and immunosuppressive medications [57]. High inflammatory markers, interleukins, interferon-gamma, tumour necrosis factors and hyperferritinaemia produce a picture similar to secondary haemophagocytic lymphohistiocytosis [3,58]. This hypercytokinaemia of severe COVID-19 produces diffuse alveolar lung damage, microvascular thrombosis, hyaline membrane formation and fibrosis, leading to an acute respiratory distress picture [59–61]. This unregulated increase in inflammatory markers and acute phase reactants downregulates the CD4+ T lymphocyte count, causing lymphopenia, propagating a decrease in viral clearance [62,63]. Additionally, there is a diffuse systemic vasculitic endotheliitis and microvascular damage, causing multiorgan dysfunction [64,65].

The vascular endothelial and alveolar damage is due to angiotensinogen converting enzyme-2 (ACE-2) receptor-mediated viral entry into these cells. Other tissues with higher expression of these receptors such as gastrointestinal mucosa and proximal tubular cells of the kidney show a greater localised inflammatory response [66,67]. Patients with already compromised systems, i.e., with cardiovascular comorbidities such as hypertension, severe dyslipidaemia, obesity, DM, and those with chronic kidney disease thus have poorer outcomes in the event of disease progression to a cytokine storm [68,69]. Cardiac infection-producing myocarditis and infarctions are similarly hypothesized [70].

Immunosuppressive therapies such as steroids are among the few interventions proven to blunt this unchecked inflammation and improve survival. The high rate of usage of steroids in the period of cytokine storm in these patients exponentially increases the risk of them acquiring the IFIs in the recovery period [13,26].

The association of steroid therapy with mucormycosis has been the most constant in the recent upstroke of cases. Steroids are one of the only therapies proven to consistently reduce mortality in COVID-19 patients [13,26]. They are considered an essential therapy for COVID-19 patients on supplemental oxygen therapy [71]. Their use, however, has a multitude of complications attached. Worsening hyperglycaemia or new-onset DM and immunosuppression are among the common mechanisms that predispose to angio-invasive mucormycosis. Corticosteroids are implicated in immune system destruction by impairing leukocyte chemotaxis and prevention of phagolysosome fusion [72]. This steroid-
induced immune dysregulation is an added burden on the virus-induced lymphopenia and hypercytokinaemia, leaving the patient wide open to a host of super-added co-infections. This may be compounded by the fact that steroids are given as over-the-counter pills in India leading to rampant misuse of the drugs [14].

DM remains the maximally associated factor predisposing to mucormycosis, with a mortality of 46% [48]. SARS-CoV-2 has been seen to worsen the glycaemic profile in patients with DM through multiple mechanisms, though they are all not completely understood. Notably, the virus can infect the pancreatic islet cells causing damage to the β-cells, causing hypoinsulinaemia and worsening hyperglycaemia [22,73,74]. DM, by nature, causes a degree of endothelial dysfunction due to chronic inflammation, leading to microvascular complications. Direct viral invasion of the vascular lining, causing endothelial damage, is mediated by ACE-2 receptors. This results in apoptosis and pyroptosis of the endothelium predisposing to angio-invasive secondary infections [61].

Among diabetics, the risk multiplies in patients with diabetic ketoacidosis (DKA). The virus and the hyperimmune stress state themselves predispose to dysglycaemia and DKA, even in the absence of poorly controlled sugars [22,74]. Hyperglycaemia produces a state of secondary immune deficiency. The dysglycaemia and acidic pH in ketoacidosis results in phagocyte dysfunction and defective intracellular killing of the fungi. The low-grade chronic inflammation causes an impaired immune response—both innate and adaptive response to infections. This also paves the way for the cytokine storm. The acidotic serum pH of 6.88–7.3 in DKA is also conducive to florid fungal proliferation [75].

The endothelial dysfunction and cytokine storm are procoagulant states leading to thrombosis and tissue ischemia [59,61]. The ischemic necrosis prevents leukocyte chemo-taxis and effective delivery of antifungal agents to the foci of infection. The concomitant endotheliitis that occurs as a result of oxidative damage causes adherence of the fungal elements to the vessel walls. These factors combined with the angio-invasive nature of the fungus are responsible for the haematogenous dissemination of the disease [76].

Iron is required for fungal hyphal growth and development. One of the main host defence mechanisms against mucormycosis is limiting the availability of free iron to the fungus by having it bind to forms such as transferrin, ferritin, and lactoferrin. The acidic pH in DKA displaces the iron from its bound forms in the human body, increasing its availability to the fungal elements. The presence of increased unbound iron plays a vital role in predisposing patients with DKA to developing the disease. Some Mucorales secrete high-affinity iron chelators or siderophores such as rhizoferrin to acquire the iron from the host cells. Those species without rhizoferrin utilize exogenous xenosiderophores such as deferoxamine that are administered in DKA patients to treat iron overload, in order to fulfil their iron requirements. This explains why patients who are on long-term iron chelation therapy are at elevated risk of mucormycosis. Severe COVID-19 disease is also a hyperferritinaemic state, due to interleukin-six-mediated ferritin synthesis and downregulation of iron transport. There is an intracellular accumulation of iron in the hepatocytes, eventually causing necrosis and release of the accumulated iron. The high serum iron forms a fertile ground for florid fungal proliferation [77,78].

Tocilizumab, an immunomodulatory monoclonal antibody that targets the deregulated interleukin-six pathway implicated in the COVID-19 cytokine storm, is associated with an increased risk of infections. It modulates and suppresses the immune system, leaving the body vulnerable to other pathogens such as bacteria and fungi [79,80].

Neutrophil and phagocyte activity are the main defences against fungal infection. Mononuclear and polymorphonuclear cells generate reactive oxygen species and defensin peptides that inhibit fungal growth. Patients with impaired neutrophil function (primary immune deficiencies) are thus at higher risk. These are unaffected in COVID-19 infection; thus, a primary cause–association relationship is unlikely [72].
## 5.2. *Cancer and Mucormycosis*

Invasive fungal infections are a common cause of morbidity in patients with HMs. Second only to aspergillosis, zygomycosis occurs as a result of a confluence of multiple risk factors in this population [10,81]. Among the malignancies, acute leukaemia and lymphomas are found to be the most commonly associated with mucormycosis [82,83]. Other risk factors in the subgroup included haematopoietic stem cell transplants, myelodysplastic syndromes, concomitant graft versus host disease and high-dose steroid therapy [84]. The pulmonary and disseminated forms of the disease appear to be more common with haematologic malignancies [83,84].

Management of patients with HMs involves profound immunosuppression due to highly cytotoxic drugs, immunomodulatory therapies, and long-term high-dose steroids. All of these treatments break down the entire immune system to a virtually negligible existence, leaving the patient vulnerable to a host of opportunistic pathogenic infections [85].

These patients are at substantial risk for granulocytopenia and febrile neutropenia due to the chemotherapeutic medications. With neutrophils being the primary defence against the fungal elements, reduced numbers in the circulation lower the body's defence and immunity predisposing to infection [86]. These patients show extensive angio-invasion and hyphal elements, but inflammatory infiltrates are minimal. Conversely, non-neutropenic HM patients with steroid compromised immune systems have a far less invasive disease, but more inflammatory infiltration with polymorphonuclear leukocytes [87,88].

Febrile neutropenia patients are on concomitant broad-spectrum antibiotic therapy to prevent flare-ups of bacterial infections. This in conjunction with the primary cytotoxic immunosuppressive medications leave this cohort of patients at risk for fungal infections, especially in the absence of primary antifungal prophylaxis [89].

Steroids form an essential arm in the primary therapeutic regimens of some lymphomas and myelomas. They are additionally used as adjuvant therapy for pain management [90,91], reducing inflammation and oedema, and for symptomatic relief of refractory symptoms of obstructions and dyspnoea [92]. The presence of prolonged high-dose steroid therapy causes a drug-induced hyperglycaemic state and, consequently, DM and DKA, creating an environment conducive for fungal growth [93].

In patients planned for or having received stem-cell transplantation, the phase of rebuilding immunity can be a perilous one. The complex immune milieu in these patients that results from the cocktail of immunosuppressive therapies, antimicrobial prophylaxis, and graft-versus-host disease (GVHD) multiplies the risk of acquiring invasive fungal infections [88]. This risk does not appear to be ameliorated even with antifungal prophylaxis with some azole and echinocandin medications. Breakthrough mucor infections are not uncommon [94,95]. In the last few years, mortality due to breakthrough mucormycosis has reduced with the advent of prophylaxis regimens with posaconazole. Treatment of mucormycosis in the cohort with liposomal amphotericin B and isavuconazole has also led to improvements in morbidity and mortality statistics [6,44,48].

Patients receiving chemotherapeutic drugs such as folate antagonists, antipurines, and steroids tend to develop oral ulcerations in the oral cavity and gastrointestinal tract. This discontinuity in the mucosa can pave the way for disseminated fungal infection, beginning as mucocutaneous or gastrointestinal disease [96].

## 5.3. *COVID-19 and Cancer*

Patients with cancer and concomitant COVID-19 infection have a higher fatality rate (7.6%) as compared to those without cancer (3.8%), with risk doubling [72,97]. There is no clear evidence of any associative relation linking the virus to any modulatory oncogenic pathobiology. However, there are a few theories postulating reasons for this doubling in mortality and high rate of complications [98,99].

Cancer treatments significantly increased risk of COVID-19 infection in the form of radiation therapy, chemotherapy, stem cell transplantation, and chimeric-antigen-receptor T-cell (CAR-T) therapy. All of these therapies cause varying degrees of immunosuppression
in the body [100]. These treatments may sometimes cause a suppression of prodrome
symptoms such as fever, leading to a delay in diagnosis [101].

Cellular senescence, via its oncogenic mutations, is a proven risk factor for many can-
cers [102]. Increasing age is a determinant of various other comorbidities, such as DM and
hypertension that worsen the outlook of COVID-19 patients [73,102–104]. Inflammaging
in a setting of cancer leads to a progressive weakening of the body’s defence mechanisms
and healing [102,105]. This immune decline predisposes them to COVID-19 and also paves
the way for poorer outcomes [104]. Recent immunophenotyping studies also show the
virus promotes age-induced immune and genetic dysregulation, and this accounts for the
vulnerability of the geriatric population [106]. This is supported by the fact that paediatric
oncology patients show a lower infection rate as compared to their adult contacts and
caregivers (2.5% as compared to 14.7%) [107,108].

The SARS-CoV-2 virus enters host cells by using its spike protein (S), which binds to the ACE-2 receptors on human cells [109,110]. These receptors are highly populated on type 2 alveolar cells, leading to pulmonary inflammation. They are also present on other tissues, such as the endothelium of the blood vessels, smooth muscles of the gastrointestinal tract, heart, kidneys, and liver [111,112]. Interestingly, the expression of the ACE-2 receptors varies in patients with cancer, i.e., some cancers show an upregulation of the receptor on their cell surfaces. This suggests that oncology patients are more likely to be infected with the virus and also have a poorer prognosis [113,114].

Both cancer and COVID-19 share a few metabolic risk factors. Type 2 DM, obesity, and metabolic syndrome are amongst the widely known hazards implicated, notably, in liver, pancreatic and endometrial cancers [115,116]. The immunologic response in cancer depends on the T helper cells and cytotoxic T cells. This mechanism is impaired by DM [117].

Lymphopenia is noted in a subset of patients with advanced cancers such as pan-
creatic, breast, lymphomas, and melanomas [118,119]. The lymphocytes also control the
progression and therapeutic responses in cancer [120]. Additionally, the immune response
of the body, adaptive and innate systems alters the natural history of the malignancy. A
cytokine storm is a systemic inflammatory response syndrome that can be triggered in
cancer patients by a plethora of factors, such as CAR-T cell therapy, monoclonal anti-
bodies, infections, and some chemotherapeutic drugs [57,121,122]. The virus itself also
causes an immune dysregulation, as part of its core pathogenesis, resulting in hypercy-
tokinaemia [3,58]. This double-edged immune response may influence the pathogenesis
of both diseases. The therapeutic approaches used in oncology patients to combat this
syndrome can be considered as options in COVID-19-induced cytokine storm [123,124].

Coagulopathy is another common link between cancer and COVID-19 infection. Thrombosis and bleeding-related complications cause significant morbidity and mortality in severe COVID-19 infection [125,126]. They are also significantly accountable for cancer-related deaths, due to the direct thrombolytic properties of tumour cells, hypercoagulable state resulting from immune dysregulation, and release of pro-coagulant mediators such as cysteine proteases [127]. A combination of both entities can compound these procoagulant effects [98,99,128,129].

**6. Conclusions**

Despite there being a higher risk of morbidity, complications, and mortality, there is
still a paucity of data emphasising the need for stricter guidelines, prophylactic therapies,
and management strategies in cancer patients with COVID and systemic fungal infections
such as mucormycosis. Studies into the interactions of the virus with cancer cells and the
virus with chemotherapeutic drugs are also required to better understand the pathogenesis
of infection and the interplay of the human body with cancer and virus cells. The presence
of invasive fungal infection in this setting only complicates the treatment due to it being a
superadded infection in a doubly immunocompromised host.

For physicians to effectively control and manage the triple threat posed due to a
poor general condition from the malignancy, a viral infection and an invasive systemic
fungal infection, a thorough study into the interplay of physical factors, chemical and microbiological factors is the need of the hour. Moreover, not only do COVID-19 and mucormycosis pose a larger risk in cancer patients, the COVID-19 and mucormycosis status of a patient with an underlying malignancy may also have bearing on the cancer chemotherapy/immunotherapy or antibody therapy. Figure 1 illustrates the interwoven relationship between COVID-19, mucormycosis and cancer.

<figure><img src="image_74.png" /><figcaption>**Figure 1.** An illustration summarising the interwoven relationship between COVID-19, mucormycosis and cancer.</figcaption></figure>

It is therefore increasingly important to understand the interactions of the three entities to effectively manage them all together to reduce the morbidity presented. We hope this will invite further research to build up an evidence base for this triple threat—COVID-19, mucormycosis and cancer.

**Author Contributions:** Conceptualization, I.M. and A.G.; methodology, D.G.; software, M.M.; validation, S.B. (Saisha Bhandari), A.D. and E.S.; formal analysis, E.S.; investigation, S.B. (Saisha Bhandari); resources, E.S.; data curation, S.B. (Saisha Bhandari); writing—original draft preparation, I.M. and A.G.; writing—review and editing, S.B. (Stergios Boussios); visualization, D.G.; supervision, S.B. (Stergios Boussios); project administration, M.M.; funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

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

# Vitamin D Levels in Patients with Overlap Syndrome, Is It Associated with Disease Severity?

Kostas Archontogeorgis ¹, Athanasios Voulgaris ² ID, Evangelia Nena ³ ID, Athanasios Zissimopoulos ⁴, Izolde Bouloukaki ⁵ ID, Sophia E. Schiza ⁵ and Paschalis Steiropoulos ¹,²,* ID

1. MSc Program in Sleep Medicine, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece

2. Department of Pneumonology, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece

3. Laboratory of Social Medicine, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece

4. Laboratory of Nuclear Medicine, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece

5. Sleep Disorders Unit, Department of Respiratory Medicine, Medical School, University of Crete, 71110 Heraklion, Greece

* Correspondence: pstirop@med.duth.gr; Tel.: +30-551352096

**Abstract:** Background: The coexistence of chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA) has been defined as overlap syndrome (OVS). Recently, a link between OSA, COPD and Vitamin D (Vit D) serum concentration was reported, however, evidence regarding Vit D status in patients with OVS is scarce. The aim of the present study was to evaluate Vit D serum levels and to explore the association of those levels with anthropometric, pulmonary function and sleep parameters in patients with OVS. Methods: Vit D serum levels were measured in patients diagnosed with OVS, as confirmed by overnight polysomnography and pulmonary function testing. Results: A total of 90 patients (79 males and 11 females) were included in the analysis. The patients were divided into three groups matched for age, gender, and BMI: the control group that included 30 patients (27 males and 3 females), the OSA group that included 30 patients (26 males and 4 females), and the OVS group that included 30 patients (26 males and 4 females). Patients with OVS exhibited decreased serum 25(OH)D levels compared with OSA patients and controls (14.5 vs. 18.6 vs. 21.6 ng/mL, *p* < 0.001). In the OVS group, multiple linear regression analysis identified AHI and FEV₁, as predictors of serum 25(OH)D levels (*p* = 0.041 and *p* = 0.038, respectively). Conclusions: Lower Vit D levels have been observed in patients with OVS compared with OSA patients and non-apneic controls, indicating an increased risk of hypovitaminosis D in this population which might be associated with disease severity.

**Keywords:** chronic obstructive pulmonary disease; obstructive sleep apnea; overlap syndrome; vitamin D

## 1. Introduction

Chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA) are both highly prevalent pulmonary diseases [1,2]. COPD affects approximately 11.7% of the global adult population, and OSA is estimated to affect 10–17% men and 3–9% women [1,2]. The coexistence of the two conditions has been defined as “overlap syndrome (OVS)”, a distinct clinical syndrome, which may be different to the simple aggregate of OSA and COPD [3]. Epidemiological studies indicate an OVS prevalence of approximately 1% among adult males [4]. Both COPD and OSA share common risk factors such as tobacco smoking, and are associated with systemic inflammation and oxidative stress [4]. Additionally, patients with OVS are known to exhibit a more profound hypoxia during sleep

**Citation:** Archontogeorgis, K.; Voulgaris, A.; Nena, E.; Zissimopoulos, A.; Bouloukaki, I.; Schiza, S.E.; Steiropoulos, P. Vitamin D Levels in Patients with Overlap Syndrome, Is It Associated with Disease Severity? *J. Pers. Med.* **2022**, *12*, 1693. https://doi.org/10.3390/jpm12101693

Academic Editor: Anne-Marie Caminade

Received: 16 September 2022

Accepted: 6 October 2022

Published: 11 October 2022

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

<figure><img src="image_77.png" /></figure>

**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/).
compared with patients with COPD or OSA alone, further contributing to inflammatory
activation and the potential development and progression of cardiovascular disease and
other comorbidities [5,6]. OSA and COPD have also been associated with an increased
risk of endocrinal and metabolic conditions, namely insulin resistance, depression of the
somatotropic axis, hypogonadism, imbalance of the adrenal axis, bone loss and hypothy-
roidism [7–9].

Recently, a link between OSA, COPD and Vitamin D serum concentration was reported in the literature [10–12]. Vitamin D (Vit D) is a fat-soluble vitamin that can be found in some foods and is synthesized in the skin after exposure to sunlight [13]. The classical function of Vit D is the regulation of bone metabolism; however, accumulating evidence suggests that it also possesses anti-inflammatory and immune-modulating properties [13]. Serum 25-hydroxyvitamin D (25(OH)D) concentration is recommended as the best indicator of Vit D status [13]. Vit D insufficiency has exponentially increased over the years, with its prevalence estimated between 13% and 40.4% in European countries, depending on the definition of Vit D insufficiency used [14]. Decreased serum 25(OH)D has been associated with a variety of pulmonary diseases, including respiratory infections, asthma and cancer [13]. Recently, OSA has been associated with hypovitaminosis D, and adequate use of CPAP is likely to augment Vit D levels [10,15]. Similarly, decreased serum 25(OH)D levels have been described in COPD patients, while a protective role against exacerbations has been attributed to Vit D supplementation [11,16]. However, evidence regarding Vit D status in patients with OVS is scarce. Therefore, the aim of the present study is to evaluate Vit D serum levels and to explore the association of those levels with anthropometric, pulmonary function and sleep parameters in patients with OVS.

**2. Methods**

**2.1. Study Population**

The present study protocol was approved by the Alexandroupolis University General Hospital Ethics Committee and all performances were carried out in accordance with the Helsinki Declaration of Human Rights [17]. Participants who enrolled between January of 2019 and December of 2020 from the sleep laboratory of our institution were referred for evaluation of suspected sleep-disordered breathing. Informed consent was provided prior to each participant's enrollment.

Patients diagnosed with both OSA and COPD, as confirmed by overnight polysomnography and pulmonary function testing were included in the study. Exclusion criteria were as follows: refusal to participate, previously diagnosed OSA under continuous positive airway pressure (CPAP) therapy or evidence of exclusively central apneas documented in polysomnography, Vit D supplementation, conditions known to affect calcium, phosphorus and Vit D metabolism and absorption, serious heart diseases, chronic kidney or hepatic disease, cancer, inflammatory diseases, severe cognitive or psychiatric disorders, or any respiratory disorder other than COPD.

**2.2. Study Variables**

Information on previous medical history, current medication use (with an emphasis
on Vit D supplementation) and tobacco smoking were obtained from all participants.
Anthropometrical data including age, sex, neck, waist and hip circumference were also
recorded, and a waist-to-hip ratio (WHR) was calculated. Body mass index (BMI) was
calculated using the following formula: BMI = Weight (in kilograms) / Height (in meters)².
Subjective sleepiness was assessed using the Greek version of the Epworth Sleepiness Scale
(ESS), which assesses the tendency to fall asleep during eight typical daytime situations [18].
A score >10 was considered indicative of excessive daytime sleepiness [18].

**2.3. Polysomnography**

All participants underwent an overnight, in-laboratory supervised polysomnography
from 22:00 to 06:00 and the data were recorded on a computer system (Alice® 4, Philips
Respironics, Murrysville, PA, USA). In each patient, electroencephalogram, electroocu-
logram, electromyogram (submental and bilateral tibial), and electrocardiogram were
recorded. Airflow was detected using combined oronasal thermal sensors. Additionally,
continuous pulse oximetry was performed, and impedance belts were used to assess the
thoracic and abdomen respiratory effort. Respiratory events (apneas and hypopneas)
and electroencephalogram recordings were manually scored according to international
guidelines [19]. Apnea was defined as a cessation of airflow or ≥90% of airflow reduction
for at least 10 s [19]. Obstructive apneas were defined based on the presence of thoracic
efforts. Hypopnea was defined as ≥50% decrement in airflow for at least 10 s in combi-
nation with oxyhemoglobin desaturation of at least 4% [19]. The apnea-hypopnea index
(AHI) was calculated as the average number of apneas and hypopneas per hour of recorded
sleep time [19]. A diagnosis of OSA was made when the AHI in the recorded study was
>5 events/h of sleep accompanied by related symptoms (sleepiness, snoring, witnessed
apneas) [20]. Subjects with AHI <5/h of sleep were considered as controls.

2.4. *Pulmonary Function Testing*

Pulmonary function testing (Screenmate, Erich Jaeger GmbH & Co., Hochberg, Germany) and arterial blood gasses analysis were performed the day before PSG. Forced expiratory volume in the first second (FEV₁) and forced vital capacity (FVC) were measured and the FEV₁/FVC ratio was calculated. The diagnosis of COPD was based on the presence of persistent airflow limitation, as defined by a post-bronchodilator FEV₁/FVC ratio of less than 70%, associated with symptoms such as dyspnea, cough and/or sputum production and a compatible history of exposure to risk factors [21].

2.5. *Blood Samples and Analysis*

All participants provided a fasting blood sample upon awakening the morning after polysomnography. Blood was then centrifuged (3000 rpm for 10 min) and the serum obtained was conserved frozen at -80 °C for future analysis. Serum 25(OH)D was measured using a commercial radioimmunoassay kit (DiaSorin, Stillwater, MN, USA). Biochemical examinations regarding renal, hepatic function, as well as lipidemic profile were also performed.

2.6. *Statistical Analysis*

All analyses were performed using the IBM Statistical Package for Social Sciences version 17.0 (SPSS Inc., Chicago, IL, USA, 2008). Study participants were divided into three groups matched for age, gender and BMI, as controls, OSA and OVS. All variables were presented as median (25th–75th percentile) for continuous variables and proportions for categorical variables. Comparison of baseline characteristics among groups was performed by a chi-square test for categorical variables, or a one-way ANOVA for continuous variables. Post-hoc analysis was performed using the Tukey’s post-hoc test when homogeneity of variances was assumed, and the Games-Howell’s post-hoc test when the assumption of homogeneity of variances was violated. Multiple linear regression analysis was used to test the association of serum 25(OH)D levels with sleep, anthropometric and respiratory function parameters.

**3. Results**

A total of 90 patients (79 males and 11 females) were included in the analysis. In general, participants were middle-aged (median age 57 years) and obese (median BMI 36.2 kg/m²) with a median value of serum 25(OH)D concentration of 18.1 (13.4–24.4) ng/mL. The sample was divided into three groups matched for age, gender and BMI: the control group that included 30 subjects (27 males and 3 females), the OSA group that included 30 patients (26 males and 4 females) and the OVS group that included 30 patients (26 males and 4 females). Neck and waist circumference differed between the groups (*p* = 0.005 and *p* = 0.023, respectively), and patients with OVS were more likely to
be current or ex-smokers. The baseline characteristics of the three groups are shown in Table 1.

<table><caption>**Table 1.** Comparison of anthropometric characteristics between control, obstructive sleep apnea (OSA) and overlap syndrome (OVS) groups.</caption><thead><tr><th></th><th>Control Group (n = 30)</th><th>Patients with OSA (n = 30)</th><th>Patients with OVS (n = 30)</th><th>p</th></tr></thead><tbody><tr><td>Gender (males/females)</td><td>27/3</td><td>26/4</td><td>26/4</td><td>0.902</td></tr><tr><td>Age (years)</td><td>56 (48.8–64.3)</td><td>56 (52.8–65)</td><td>60 (54.8–67.3)</td><td>0.386</td></tr><tr><td>Neck circumference (cm)</td><td>41 (38–45.5)</td><td>46 (43.3–48.8) *</td><td>45 (44–50) *</td><td>0.005</td></tr><tr><td>Waist circumference (cm)</td><td>119 (104–131.5)</td><td>122 (119–129)</td><td>130 (119–135.5) *</td><td>0.023</td></tr><tr><td>Hip circumference (cm)</td><td>119 (107.5–125.5)</td><td>117 (112.3–122.8)</td><td>119 (110–123.5)</td><td>0.554</td></tr><tr><td>WHR</td><td>1.01 (0.94–1.05)</td><td>1.03 (1–1.08)</td><td>1.08 (1.04–1.11) *</td><td>0.040</td></tr><tr><td>BMI (kg/m²)</td><td>33.6 (29.9–40.2)</td><td>36.9 (34.5–41.6)</td><td>36.5 (32–42)</td><td>0.105</td></tr><tr><td>Tobacco smoking</td><td></td><td></td><td></td><td></td></tr><tr><td>Non-smokers</td><td>36.7%</td><td>30%</td><td>0% **, #</td><td>0.001</td></tr><tr><td>Ex-smokers</td><td>36.7%</td><td>13.3% *</td><td>40% #</td><td>0.049</td></tr><tr><td>Current smokers</td><td>26.7%</td><td>56.7% *</td><td>60% *</td><td>0.017</td></tr></tbody></table>

Abbreviations: BMI, body mass index; WHR, waist to hip ratio; *: *p* < 0.05 compared with control group; **: *p* < 0.001 compared with control group; #: *p* < 0.05 compared with OSA group.

Regarding sleep parameters, AHI was similar between OSA and OVS patients (40.2 vs. 37.1 events/h of sleep, *p* > 0.05). Patients with OVS exhibited the worst oxygenation during sleep compared with OSA patients, as assessed by the average oxygen saturation during sleep (90.8% vs. 92%, *p* < 0.05) and the percentage of time with oxygen saturation during sleep < 90% (27.3% vs. 19.7%, *p* < 0.05). No difference was noted between the groups in terms of daytime sleepiness, as expressed by the Epworth sleepiness scale score (*p* = 0.106). Comparisons of sleep parameters between the groups are presented in Table 2.

<table><caption>**Table 2.** Comparison of sleep characteristics between control, obstructive sleep apnea syndrome (OSA) and overlap syndrome (OVS) groups.</caption><thead><tr><th></th><th>Control Group (n = 30)</th><th>Patients with OSA (n = 30)</th><th>Patients with OVS (n = 30)</th><th>p</th></tr></thead><tbody><tr><td>TST (min)</td><td>326.3 (280.8–358.5)</td><td>310.1 (260.8–346.3)</td><td>320.5 (278.3–360.8)</td><td>0.843</td></tr><tr><td>N1 (%)</td><td>10 (5.6–17.2)</td><td>11.4 (6.6–23.4)</td><td>11 (4.8–18.2)</td><td>0.191</td></tr><tr><td>N2 (%)</td><td>62.2 (56.9–71)</td><td>66.7 (56.2–77.3)</td><td>66.4 (60.7–71.4)</td><td>0.676</td></tr><tr><td>N3 (%)</td><td>11.7 (4.4–20.5)</td><td>7 (2.1–15.8)</td><td>9.9 (6.6–19.1)</td><td>0.391</td></tr><tr><td>REM (%)</td><td>13.5 (4.9–15.1)</td><td>6.2 (1.2–13.7) *</td><td>4.1 (0.3–12.3) *</td><td>0.011</td></tr><tr><td>Sleep efficiency (%)</td><td>84.6 (75–91.9)</td><td>86.4 (74.2–91.9)</td><td>83.2 (72.4–92.2)</td><td>0.871</td></tr><tr><td>Arousal index</td><td>14.4 (9.1–18.2)</td><td>36.5 (21.5–47.4) **</td><td>26.1 (10.3–44.9) *</td><td>&lt;0.001</td></tr><tr><td>AHI (events/h)</td><td>2.9 (1.5–4)</td><td>40.2 (22.1–70) **</td><td>37.1 (18–62.6) **</td><td>&lt;0.001</td></tr><tr><td>Aver SaO₂ (%)</td><td>94.3 (93.8–95.3)</td><td>92 (89–93) **</td><td>90.8 (84.9–92.1) **, #</td><td>&lt;0.001</td></tr><tr><td>Min SaO₂ (%)</td><td>88 (84–89)</td><td>74.5 (71.8–83.8) **</td><td>74.5 (63.5–81.3) **, #</td><td>&lt;0.001</td></tr><tr><td>T &lt; 90% (%)</td><td>0.1 (0–0.4)</td><td>19.7 (3.8–39.4) *</td><td>27.3 (7.7–83.1) **, #</td><td>&lt;0.001</td></tr><tr><td>ESS score</td><td>8 (4.8–12.3)</td><td>11.5 (7.8–15.3)</td><td>9 (6–13.8)</td><td>0.106</td></tr></tbody></table>

Abbreviations: AHI, apnea hypopnea index; Aver SaO₂, average oxyhemoglobin saturation; ESS, Epworth sleepiness scale; Min SaO₂, minimum oxyhemoglobin saturation; N1, sleep stage 1; N2, sleep stage 2; N3, sleep stage 3; REM, rapid eye movement; TST, total sleep time; T < 90%, time with oxyhemoglobin saturation <90%; *: *p* < 0.05 compared with control group; **: *p* < 0.001 compared with control group; #: *p* < 0.05 compared with OSA group.

In the OVS group, the median COPD assessment test (CAT) score was 9 (5–14.3). According to the GOLD classification, the OVS group included 7 patients with mild, 16 patients with moderate, 6 patients with severe and 1 patient with very severe airflow limitation [21]. Patients with OVS had a lower FEV₁ compared with the OSA patients and control patients (64.7 vs. 89.9 vs. 105.5%, respectively, *p* < 0.001) and presented increased PaCO₂ during wake in respect to both the OSA and control patients (46.5 vs. 41 vs. 40.5 mmHg, respectively, *p* < 0.001). Patients with OVS also exhibited decreased serum 25(OH)D levels compared with the OSA patients and control patients (14.5 vs. 18.6 vs.
21.6 ng/mL, *p* < 0.001) (Figure 1). Comparisons of measurements and laboratory analyses between the groups are presented in Table 3.

<figure><img src="image_78.png" /><figcaption>**Figure 1.** Comparison of 25(OH)D serum levels between control, obstructive sleep apnea syndrome (OSA) and overlap syndrome (OVS) groups.</figcaption></figure>

<table><caption>**Table 3.** Comparison of measurements and laboratory analyses between control, obstructive sleep apnea syndrome (OSA) and overlap syndrome (OVS) groups.</caption><thead><tr><th></th><th>Control Group (n = 30)</th><th>Patients with OSA (n = 30)</th><th>Patients with OVS (n = 30)</th><th>p</th></tr></thead><tbody><tr><td>FEV<sub>1</sub> (% predicted)</td><td>105.5 (91.3–113.8)</td><td>89.9 (74.6–100.3)</td><td>64.7 (48.8–76) **, ##</td><td>&lt;0.001</td></tr><tr><td>FVC (% predicted)</td><td>102.5 (87.5–111.4)</td><td>85.6 (71–97.3) *</td><td>77.9 (62.4–89.1) **</td><td>&lt;0.001</td></tr><tr><td>FEV<sub>1</sub>/FVC (%)</td><td>83 (80.4–87.8)</td><td>84.6 (79.1–90.3)</td><td>67.1 (62.5–69.2) **, ##</td><td>&lt;0.001</td></tr><tr><td>pO<sub>2</sub> (mmHg)</td><td>79 (73.5–84.3)</td><td>75 (66.8–79.5) *</td><td>67 (60.8–76) **</td><td>&lt;0.001</td></tr><tr><td>pCO<sub>2</sub> (mmHg)</td><td>40.5 (37–43)</td><td>41 (37–44.5)</td><td>46.5 (42–52) **, #</td><td>&lt;0.001</td></tr><tr><td>Glucose (mg/dL)</td><td>95 (81–112.5)</td><td>112.5 (96.5–151.3)</td><td>112.5 (95.3–129.3)</td><td>0.080</td></tr><tr><td>Creatinine (mg/dL)</td><td>0.8 (0.75–1)</td><td>0.9 (0.8–1.1)</td><td>0.9 (0.8–1.1)</td><td>0.707</td></tr><tr><td>SGOT (U/L)</td><td>20 (17.5–22.3)</td><td>22 (17.3–27)</td><td>20 (17–24)</td><td>0.296</td></tr><tr><td>SGPT (U/L)</td><td>19.5 (16.8–28)</td><td>24 (18–30.5)</td><td>21.5 (16.8–26.3)</td><td>0.624</td></tr><tr><td>Cholesterol(mg/dL)</td><td>207 (182–258)</td><td>210.5 (162.5–242.8)</td><td>185.5 (149–214.5)</td><td>0.154</td></tr><tr><td>Triglycerides (mg/dL)</td><td>158 (117.5–214.3)</td><td>158 (120.5–190)</td><td>156.5 (117–244.8)</td><td>0.166</td></tr><tr><td>LDL-C (mg/dL)</td><td>128.2 (101.9–159.4)</td><td>124.2 (95.4–156.1)</td><td>96.1 (77.3–126.2) *</td><td>0.030</td></tr><tr><td>HDL-C (mg/dL)</td><td>46 (41.5–56)</td><td>44.5 (35.8–52.5)</td><td>50 (43–56.3)</td><td>0.411</td></tr><tr><td>25(OH)D (ng/mL)</td><td>21.6 (17.8–33.6)</td><td>18.6 (13.2–25.2) *</td><td>14.5 (12.3–17.9) **, #</td><td>&lt;0.001</td></tr></tbody></table>

Abbreviations: FEV₁, forced expiratory volume in first second; FVC, forced vital capacity; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; pCO₂, carbon dioxide partial pressure; pO₂, oxygen partial pressure; SGOT, serum glutamate-oxaloacetate transaminase; SGPT, serum glutamate-pyruvate transaminase; 25(OH)D, 25-hydroxyvitamin D; *: *p* < 0.05 compared with control group; **: *p* < 0.001 compared with control group; #: *p* < 0.05 compared with OSA group; ##: *p* < 0.001 compared with OSA group.

In order to explore the factors predicting serum 25(OH)D levels, a regression model was created with 25(OH)D serum levels used as a dependent variable. Anthropometric (age, gender, BMI), pulmonary function parameters (FEV₁, FVC and CAT score), and sleep parameters (AHI, average and minimum saturation during sleep, time spent with SaO₂ < 90%, arousal index and ESS score) were used as independent variables. Multiple linear regression identified that serum 25(OH)D levels could be predicted by AHI (β = −0.758, *p* = 0.041, 95% CI: −0.282–−0.006) and FEV₁ (β = 0.698, *p* = 0.038, 95% CI: 0.014–0.410).
4. Discussion

The OVS is characterized by the co-existence of COPD and OSA, both conditions
that seem to have an adverse effect on calcium homeostasis. In this study, we found
that 25(OH)D serum levels were decreased in patients with OVS, compared with both
OSA patients and non-apneic controls. Additionally, AHI and FEV₁ were independently
associated with 25(OH)D serum levels in OVS patients.

The definition of Vit D status in OSA patients remained controversial for a long time, with some studies reporting decreased Vit D levels in those patients while others concluded otherwise [22–25]. A meta-analysis conducted by Neighbors et al., [26] including 14 studies with 1513 controls and 3424 OSA patients, reported decreased Vit D serum levels in the latter, with Vit D insufficiency being incrementally exacerbated with increasing severity of OSA (mean differences were −2.7% for mild OSA, −10.1% for moderate OSA and −17.4% for severe OSA). Similarly, results from a more recent meta-analysis conducted by Li et al. [10] showed decreased Vit D levels only in patients with moderate (p = 0.002) and severe (p < 0.0001) OSA compared with non-apneic controls. OSA treatment with CPAP seems to possess a beneficial effect on Vit D serum levels. Liguori et al. found increased levels of 25(OH)D after 7 nights of CPAP therapy in male OSA patients, and after 1 year of treatment mainly in obese OSA patients [27,28]. Similar conclusions were reached in a randomized sham-controlled trial, where 24 weeks of CPAP treatment improved Vit D serum levels (p = 0.045) in patients with severe OSA [29]. In the present study, Vit D serum levels were strongly associated with AHI. These results are in line with those from previous studies. In a study that included 139 OSA patients and 30 controls, OSA patients had lower 25(OH)D levels compared with controls (17.8 ± 7.8 vs. 23.9 ± 12.4 ng/mL respectively, p = 0.019) and there was a significant inverse association between AHI and 25(OH)D levels (r = −0.187, p = 0.045) [22]. Similarly, in another study that included 75 OSA patients and 31 controls, serum 25(OH)D levels decreased with OSA severity (p = 0.003) and were negatively associated with AHI (r = −0.40, p = 0.0001) [30]. Overall, current evidence suggests that hypovitaminosis D is frequent among OSA patients. Multiple factors such as BMI, gender and disease severity may play a regulatory role in this relationship.

Previous studies have shown that, compared with controls, patients with COPD fre-
quently presented with decreased Vit D levels, with a prevalence that ranged between 33%
and 77% according to disease severity [31–33]. In a recent study that included 1609 COPD
patients, Vit D deficiency was present in 21% and was associated with a 4.11% decrease
in predicted FEV₁ at enrollment (95% CI: –6.90% to –1.34% predicted FEV₁, p = 0.004),
a 1.27% predicted greater rate of FEV₁ decline after 1 year (95% CI: –2.32% to –0.22%
predicted/year, p = 0.02), and increased odds of any COPD exacerbation in the prior year
(OR: 1.32, 95% CI: 1.00–1.74, p = 0.049) [11]. Studies evaluating the effect of Vit D supple-
mentation in lung function produced contradictory results. In a meta-analysis that included
8 studies and 687 COPD patients, Vit D treatment resulted in no significant improvements
in FEV₁ (p = 0.144), FVC (p = 0.299), and FEV₁/FVC (p = 0.995) in COPD patients [34].
On the other hand, another meta-analysis that included 25 studies involving 2670 COPD
patients concluded that Vit D supplementation was significantly associated with FEV₁
(p < 0.01), FEV₁/FVC (p < 0.01), exacerbations (p < 0.01), sputum volume (p < 0.01), 6-min
walk distance (p = 0.02) and CAT score (p < 0.01) [35].

Vit D insufficiency in OVS is multifactorial and represents the result of the interaction
of individual conditions present in COPD and OSA. In particular, altered cutaneous syn-
thesis due to aging, toxic smoke effects and reduced sun exposure because of disability to
perform outdoor activities, sequestration in the adipose tissue, increased Vit D catabolism
caused by glucocorticoid therapy (together with impaired liver and renal activation and
reduced gastrointestinal absorption) are all factors known to reduce Vit D serum levels in
COPD patients [36]. Reduced outdoor activities due to exertional dyspnea may also nega-
tively affect Vit D cutaneous synthesis. In addition, low 25(OH)D levels in COPD correlate
with genetic variants of the Vit D-binding gene [32]. Conversely, OSA may represent a risk
factor for Vit D insufficiency. Lack of outdoor activity due to obesity and excessive daytime
sleepiness might result in reduced Vit D synthesis because of insufficient sun exposure [37]. Moreover, Vit D is stored in fat tissue, thus reducing the release of Vit D into the circulation and decreasing its bioavailability [37]. Finally, Vit D receptor gene variations affect both 25(OH)D serum levels and disease susceptibility in OSA [38].

A previous study has shown that OVS patients had a similar Vit D status compared to the controls, OSA, or COPD patients and Vit D deficiency as associated with overall mortality among groups [39]. Diagnosis of Vit D insufficiency and correction of Vit D status has several clinical implications in patients with both OSA and COPD. Notably, hypovitaminosis D is highly prevalent in COPD patients and has been associated with worse lung function, increased symptoms and rate of exacerbations, and worse prognosis [11,35]. Moreover, decreased Vit D serum levels were related to COPD severity, and acute exacerbations may be prevented with adequate Vit D supplementation [16,40]. Finally, there is evidence to suggest that a normal Vit D status could improve prognosis in patients with COPD exacerbations caused by respiratory tract infections [41]. Serum 25(OH)D levels were decreased in OSA patients compared with controls, and have been associated with disease severity [10]. On the other hand, with increasing OSA severity serum 25(OH)D levels further decreased, this suggests that serum 25(OH)D might be a risk factor for OSA [10]. Finally, markers of inflammation and HOMA-IR were significantly decreased after Vit D supplementation in a small group of patients with mild OSA [42].

The relationship between an adequate Vit D status and cardiovascular risk is controversial, with data suggesting a negative association between Vit D levels and the risk of cardiovascular disease. Whereas, other studies failed to demonstrate an association between Vit D serum levels and a reduced risk for adverse cardiovascular events [43,44]. In a study conducted in Southern Italy that included 451 participants, the Vit D level did not correlate with cardiovascular risk, as assessed using the Framingham cardiovascular risk charts [45]. On the contrary, the parathyroid hormone was in direct correlation (p < 0.001) with cardiovascular risk, and increased parathyroid hormone levels identified a population with a higher risk for cardiovascular events (p < 0.001) [45]. Results from this study suggest increased parathyroid hormone levels might be a better predictor of cardiovascular risk in patients with hypovitaminosis D.

Nonetheless, the present study has limitations. First, the study was cross-sectional in design, and consequently, it is not possible to infer any causal relationships. Second, the sample of OVS patients included was rather small and was not representative of the female population. This is largely due to the low prevalence of OVS in the general population and to the disparity in gender-specific estimates of COPD and OSA prevalence [4,46,47]. Thus, larger scale studies are necessary to better assess Vit D status in OVS patients. Third, 25(OH)D was not des seasonalized and the parathyroid hormone was not measured. However, all participants were Caucasian, living in a single region of northern Greece, a relatively small geographical area, with no variations in latitude and with similar conditions regarding cloudiness and air pollution. Moreover, participants had no variations regarding skin pigmentation and in their way of dressing, had similar dietary habits, similar sun exposure and sun-bathing behavior. Furthermore, OVS patients were middle-aged, so caution is warranted in order to extrapolate this data to elderly OVS patients. Finally, only one patient with very a severe air-flow limitation was included in the OVS group. However, it has been shown that Vit D levels decrease with increasing air-flow limitation severity [11]. An analysis of sub-populations based on disease severity in the OSA group was not performed due to the limited sample size.

## 5. Conclusions

In conclusion, lower Vit D levels have been observed in patients with OVS compared with OSA patients and non-apneic controls, indicating an increased risk of hypovitaminosis D in this population. This is expected since both the components of OVS, COPD and OSA are also associated with decreased Vit D levels. Given the protective role of Vit D against FEV₁ decline and COPD exacerbations, as well as the association of low Vit D levels
with cardiovascular and metabolic diseases, screening of OVS patients for Vit D might be
beneficial in the management of this population of patients.

**Author Contributions:** Conceptualization, P.S.; writing—original draft preparation, K.A. and A.V.; writing—review and editing, E.N., A.Z., I.B., S.E.S. and P.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Alexandroupolis University General Hospital (approval code 1/27-01-2017, date of approval 27 January 2017).

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

**Data Availability Statement:** Not applicable.

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

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<figure><img src="image_79.png" /></figure>

Article

# Diagnostic Efficacy of Ultra-Short Term HRV Analysis in Obstructive Sleep Apnea

Seung-Su Ha<fnref n="1" /> and Dong-Kyu Kim <fnref n="1" />,<fnref n="2" />,*

**Abstract:** Heart rate variability (HRV) is the standard method for assessing autonomic nervous system (ANS) activity and is considered a surrogate marker for sympathetic overactivity in obstructive sleep apnea (OSA). Although HRV features are usually obtained from the short-term segment method, it is impossible to evaluate rapid dynamic changes in ANS activity. Herein, we propose the ultra-short-term analysis to detect the balance of ANS activity in patients with OSA. In 1021 OSA patients, 10 min HRV target datasets were extracted from polysomnographic data and analyzed by shifting the 2 min (ultra-short-term) and 5 min (short-term) segments. We detected frequency-domain parameters, including total power (Ln TP), very low frequency (Ln VLF), low frequency (Ln LF), and high frequency (Ln HF). We found that overall HRV feature alterations indicated sympathetic overactivity dependent on OSA severity, and that this was more pronounced in the ultra-short-term methodology. The apnea-hypopnea index, oxygen desaturation index, and Epworth sleepiness scale correlated with increased sympathetic activity and decreased parasympathetic activity, regardless of the methodology. The Bland-Altman plot analyses also showed a higher agreement of HRV features between the two methodologies. This study suggests that ultra-short-term HRV analysis may be a useful method for detecting alterations in ANS function in OSA patients.

**Keywords:** heart rate variability; autonomic nervous system; sleep; obstructive sleep apnea

## 1. Introduction

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder and is characterized by total or partial upper airway collapse. Patients with OSA usually experience frequent hypoxic events and sleep fragmentation, resulting in repeated exposure to hypoxemia and hypercapnia. Additionally, this phenomenon could induce sympathetic overactivity in patients [1-3]. These alterations in autonomic nervous system (ANS) activity also contribute to the development of cardiovascular diseases in patients with OSA [4-6]. Heart rate variability (HRV) is a noninvasive method for the assessment of changes in ANS activity and represents the balance between the parasympathetic nervous system (PNS) and sympathetic nervous system (SNS) [7]. Most studies regarding HRV have shown a higher low-frequency power (LF) and a higher ratio of LF to high-frequency power (HF) in patients with OSA than in healthy subjects [8-10]. These findings support the idea that the balance shift in ANS activity moves toward sympathetic overactivity in these patients. In fact, evidence suggests that HRV can assess the shift in ANS activity and could be a useful marker of OSA severity [11-14].

To date, linear and non-linear methods of HRV analysis have been used. There are several algorithms of the non-linear method, such as Hurst exponent and Higuchi fractal dimension. However, HRV analysis is usually performed as a function of the time or temporal frequencies from linear analysis methods: peaks below 0.04 Hz (very

**Citation:** Ha, S.-S.; Kim, D.-K.
Diagnostic Efficacy of Ultra-Short Term HRV Analysis in Obstructive Sleep Apnea. *J. Pers. Med.* **2022**, *12*, 1494. https://doi.org/10.3390/jpm12091494

Academic Editor: Anne-Marie Caminade

Received: 24 August 2022
Accepted: 9 September 2022

Published: 13 September 2022

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**Copyright:** © 2022 by the authors.
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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/).

<footnote n="1">¹ Department of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Korea</footnote>

<footnote n="2">² Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Korea</footnote>

<footnote>* Correspondence: doctordk@naver.com; Tel.: +82-33-240-5180</footnote>
low frequency, VLF), between 0.04 and 0.15 Hz (low frequency, LF), and between 0.15 and 0.4 (high frequency, HF). Previously, the majority of these studies used a short-term window, which investigates the degree of fluctuations in the time and frequency domains, to evaluate the HRV analysis. Short-term segment HRV is typically calculated over 5 min and the magnitude of the frequency components obtained by spectral analysis is useful for assessing ANS activity [15]. However, HRV analysis based on short-term segments has inevitable limitations in clinical applications because its spectral estimates cannot reflect rapid ANS fluctuations. For this reason, several recent studies have suggested that ultra-short-term segments (shorter than 5 min) may be effective alternatives to short-term HRV analysis for mental stress detection [16–18]. Herein, we investigated the frequency spectral analysis of HRV features using the ultra-short-term methodology and evaluated whether the methodology could be more effective as a diagnostic marker for the alteration of ANS activity in patients with OSA. To the best of our knowledge, no studies have investigated ultra-short HRV as a valid surrogate in patients with OSA.

## 2. Materials and Methods

We consecutively enrolled patients diagnosed with OSA at the Sleep Center of Hallym Medical University Chuncheon Sacred Hospital and the informed consent was obtained from all subjects involved in the study. We excluded patients who had previously been diagnosed with a psychological disorder, a history of any medical condition that could influence sympathovagal activity, and serious comorbidities (e.g., cancer, severe depression or insomnia, severe cardiac or respiratory failure, and severe renal or hepatic insufficiency). We performed overnight full polysomnography (PSG) and obtained the Epworth Sleepiness Scale (ESS) scores for all enrolled patients to assess daytime sleepiness. In this study, sleep stage and respiratory events were scored according to the American Academy of Sleep Medicine [19]. The following sleep parameters were assessed: total sleep time (TST), sleep efficiency (TST/time in bed × 100), sleep latency, oxygen desaturation index (ODI), and apnea-hypopnea index (AHI). ODI was defined as the number of episodes of oxygen desaturation per hour of sleep, with oxygen desaturation defined as a decrease in blood oxygen saturation (SpO₂) to lower than 3% below baseline. The AHI was defined as the sum of the number of apneas and hypopneas per hour of sleep. In this study, we defined apnea as when the peak signal excursions, measured using an oronasal thermal sensor, dropped by ≥90% of baseline before the event, lasting for more than 10 s. Conversely, hypopnea was scored when the peak signal excursions using nasal pressure dropped by ≥30% of baseline before the event for at least 10 s, followed by a ≥ 3% decrease in oxygen desaturation or accompanied by arousal. Based on AHI, we classified the severity of OSA as follows: normal (AHI < 5), mild OSA (5 ≤ AHI < 15), moderate OSA (15 ≤ AHI < 30), and severe OSA (AHI ≥ 30).

HRV is characterized by the variability in beat-to-beat intervals of the heart and is typically measured using RR intervals extracted from an ECG signal. Thus, in this study, we extracted the electrocardiography data from PSG recordings visually inspected for accuracy and quality. Specifically, we eliminated ectopic beats and artifacts and selected only normal-to-normal beats for the HRV analysis. Generally, time-domain HRV parameters showed variability in beat-to-beat intervals, which influenced both sympathetic and parasympathetic activities [20]. Thus, these parameters are not typically used to discriminate the balance of ANS activity (sympathetic and parasympathetic functions). For this reason, we obtained the frequency-domain HRV parameters. We used four spectral power analyses by shifting predetermined segments (ultra-short-term, 2 min and short-term, 5 min) forward by 2 s during the entire 10 min HRV target dataset, which were collected from each patient: total power (Ln TP), very low frequency (Ln VLF), low frequency (Ln LF), and high frequency (Ln HF) in frequency-domain measures.
Numerical variables are expressed as mean ± standard deviation, and Student's t-test was used to compare HRV parameters after the normality test. ANOVA with post hoc analysis was performed to compare the specific values according to the OSA severity group. Pearson's coefficient test was used to identify the relationships between HRV and sleep parameters according to OSA severity. To compare the agreement of the HRV parameters between the ultra-short-term and short-term segments, we used Bland-Altman plots. All statistical analyses were conducted using R version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at *p* < 0.05.

### 3. Results

In total, 1021 patients with OSA were enrolled in this study. The patients were divided into three groups: mild OSA (n = 238), moderate OSA (n = 331), and severe OSA (n = 452). The demographics and PSG parameters are presented in Table 1. Age and ESS were significantly higher in patients with severe OSA than in the other groups (*p* < 0.001), whereas there were no significant group differences in TST, sleep latency, and sleep efficiency.

<table><caption>**Table 1.** Baseline characteristics and polysomnographic parameters.</caption><thead><tr><th></th><th>Mild</th><th>Moderate</th><th>Severe</th></tr></thead><tbody><tr><td>Number</td><td>238 (23.3%)</td><td>331 (32.4%)</td><td>452 (44.3%)</td></tr><tr><td>Age</td><td>53.87 ± 7.55</td><td>56.14 ± 7.91</td><td>57.06 ± 8.53</td></tr><tr><td>Sex</td><td></td><td></td><td></td></tr><tr><td>Male</td><td>206 (86.6%)</td><td>237 (71.6%)</td><td>403 (89.2%)</td></tr><tr><td>Female</td><td>32 (13.4%)</td><td>94 (28.4%)</td><td>49 (10.8%)</td></tr><tr><td>Body mass index<br>(kg/m²)</td><td>24.2 ± 3.8</td><td>26.4 ± 4.8</td><td>27.2 ± 3.1</td></tr><tr><td>Hypertension</td><td>168</td><td>216</td><td>285</td></tr><tr><td>ESS</td><td>5.06 ± 1.34</td><td>5.56 ± 2.24</td><td>11.11 ± 4.04</td></tr><tr><td>Total sleep Time (min)</td><td>363.93 ± 56.94</td><td>367.12 ± 56.43</td><td>375.67 ± 55.23</td></tr><tr><td>Sleep latency (min)</td><td>44.70 ± 9.64</td><td>45.94 ± 8.19</td><td>45.76 ± 7.72</td></tr><tr><td>Sleep efficiency (%)</td><td>79.74 ± 6.63</td><td>79.59 ± 6.42</td><td>79.30 ± 6.53</td></tr><tr><td>AHI</td><td>9.34 ± 2.37</td><td>20.50 ± 3.86</td><td>60.69 ± 17.38</td></tr><tr><td>ODI</td><td>7.87 ± 2.23</td><td>9.43 ± 3.96</td><td>33.62 ± 17.21</td></tr></tbody></table>

Epworth sleepiness scale, ESS; apnea-hypopnea index, (AHI); oxygen desaturation index, (ODI).

A 10 min HRV target segment was divided into consecutive individual ultra-short-term and short-term segments every 2 s, and all frequency-domain HRV parameters were averaged according to OSA severity (Tables 2–4). In mild and moderate OSA, Ln TP and Ln HF were significantly higher in the ultra-short term than in the short term (Tables 2 and 3), whereas the values of Ln TP, Ln VLF, and Ln LF based on the ultra-short term were significantly higher in those based on the short term (Table 4). However, there was no significant difference in the value of Ln LF/Ln HF between the ultra-short-term and short-term segments, regardless of OSA severity. We also confirmed that patients with OSA had overall alterations in HRV measures, indicating sympathetic overactivity, and this tendency was more pronounced in those with severe OSA. Additionally, these findings were more clearly detected in the ultra-short-term analysis than in the short-term analysis.
<table><caption>**Table 2.** Heart rate variability parameters between ultra-short and short term in mild obstructive sleep apnea.</caption><thead><tr><th></th><th>Ultra-Short Term</th><th>Short Term</th><th>p Value</th></tr></thead><tbody><tr><td>Ln TP</td><td>7.47 ± 0.34</td><td>7.29 ± 0.47</td><td>&lt;0.001</td></tr><tr><td>Ln VLF</td><td>6.97 ± 0.57</td><td>6.93 ± 0.61</td><td>0.504</td></tr><tr><td>Ln LF</td><td>7.24 ± 0.91</td><td>7.20 ± 1.21</td><td>0.733</td></tr><tr><td>Ln HF</td><td>7.10 ± 0.29</td><td>6.97 ± 0.61</td><td>0.002</td></tr><tr><td>Ln LF/Ln HF</td><td>1.02 ± 0.15</td><td>1.04 ± 0.20</td><td>0.221</td></tr></tbody></table>

Total power, Ln TP; very low frequency, Ln VLF; low frequency, Ln LF; high frequency, Ln HF.

<table><caption>**Table 3.** Heart rate variability parameters between ultra-short and short term in moderate obstructive sleep apnea.</caption><thead><tr><th></th><th>Ultra-Short Term</th><th>Short Term</th><th>p Value</th></tr></thead><tbody><tr><td>Ln TP</td><td>7.43 ± 0.35</td><td>7.30 ± 0.46</td><td>&lt;0.001</td></tr><tr><td>Ln VLF</td><td>7.19 ± 0.58</td><td>7.13 ± 0.53</td><td>0.152</td></tr><tr><td>Ln LF</td><td>7.56 ± 1.08</td><td>7.41 ± 1.00</td><td>0.071</td></tr><tr><td>Ln HF</td><td>5.18 ± 1.24</td><td>4.71 ± 1.45</td><td>&lt;0.001</td></tr><tr><td>Ln LF/Ln HF</td><td>1.60 ± 0.64</td><td>1.78 ± 0.75</td><td>0.001</td></tr></tbody></table>

Total power, Ln TP; very low frequency, Ln VLF; low frequency, Ln LF; high frequency, Ln HF.

<table><caption>**Table 4.** Heart rate variability parameters between ultra-short and short term in severe obstructive sleep apnea.</caption><thead><tr><th></th><th>Ultra-Short Term</th><th>Short Term</th><th>p Value</th></tr></thead><tbody><tr><td>Ln TP</td><td>7.45 ± 0.34</td><td>7.22 ± 0.50</td><td>&lt;0.001</td></tr><tr><td>Ln VLF</td><td>7.19 ± 0.52</td><td>7.02 ± 0.58</td><td>&lt;0.001</td></tr><tr><td>Ln LF</td><td>8.36 ± 0.97</td><td>7.95 ± 0.57</td><td>&lt;0.001</td></tr><tr><td>Ln HF</td><td>1.80 ± 0.92</td><td>1.82 ± 1.06</td><td>0.849</td></tr><tr><td>Ln LF/Ln HF</td><td>6.93 ± 7.17</td><td>6.33 ± 4.14</td><td>0.125</td></tr></tbody></table>

Total power, Ln TP; very low frequency, Ln VLF; low frequency, Ln LF; high frequency, Ln HF.

The correlation between sleep parameters and HRV parameters is shown in Figures 1–3. We found that, in both HRV analyses (ultra-short or short-term segments), AHI was positively correlated with Ln LF and Ln LF/Ln HF, whereas there was a negative correlation between AHI and Ln HF. This trend was also detected in the ODI and ESS, although the Pearson's correlation coefficients slightly decreased from AHI to the ODI and ESS. This means that AHI, ODI, and ESS were well correlated with increased sympathetic activity and decreased parasympathetic activity, a tendency that was more prominent in the ultra-short-term analysis.

Moreover, we used Bland-Altman plots to analyze the consistency of the HRV parameters. The Bland-Altman plots for HRV analyses based on ultra-short and short-term segments are shown in Figure 4. We found that the major HRV parameters, such as Ln LF, Ln HF, and Ln LF/Ln HF, were mostly located within the upper and lower 2.0 SD lines. This means that the agreement of HRV parameters between the ultra-short and short-term segments was substantial.
<figure><img src="image_81.png" /><figcaption>**Figure 1.** Correlation between AHI (A) Ln LF, (B) Ln HF, (C) Ln LF/Ln HF. Apnea-hypopnea index, AHI; total power, Ln TP; very low frequency, Ln VLF; low frequency, Ln LF; high frequency, Ln HF.</figcaption></figure>
<figure><img src="image_82.png" /><figcaption>Short term
r = 0.357, p < 0.001</figcaption></figure>

<figure><img src="image_83.png" /><figcaption>(B) Ln HF</figcaption></figure>

<figure><img src="image_84.png" /><figcaption>Short term
r = -0.722, p < 0.001</figcaption></figure>

<figure><img src="image_85.png" /><figcaption>(C) Ln LF/Ln HF</figcaption></figure>

<figure><img src="image_86.png" /><figcaption>Short term
r = 0.767, p < 0.001</figcaption></figure>

<figure><img src="image_87.png" /></figure>

**Figure 2.** Correlation between ODI and (A) Ln LF, (B) Ln HF, (C) Ln LF/Ln HF. Oxygen desaturation index, ODI; total power, Ln TP; very low frequency, Ln VLF; low frequency, Ln LF; high frequency, Ln HF.
<figure><img src="image_88.png" /><figcaption>**Figure 3.** Correlation between ESS and (A) Ln LF, (B) Ln HF, (C) Ln LF/Ln HF. Epworth sleepiness scale ESS, total power; Ln TP, very low frequency; Ln VLF, low frequency; Ln LF, high frequency; Ln HF, low frequency.</figcaption></figure>
<figure><img src="image_89.png" /><figcaption>**Figure 4.** *Cont.*</figcaption></figure>
<figure><img src="image_90.png" /><figcaption>**Figure 4.** Agreement rate of heart rate variability parameters between ultra-short- and short-term methodology using Bland-Altman plots: (A) Ln LF, (B) Ln HF, (C) Ln LF/Ln HF (standard deviation, SD; low frequency, Ln LF; high frequency, Ln HF).</figcaption></figure>

**4. Discussion**

HRV is widely used as a marker related to the ANS and is classically divided into two categories based on the length of the data recording. One is short-term HRV, typically calculated over 5 min, and the magnitude of its frequency components obtained by spectral analysis is used for the assessment of ANS function [21–24]. Another is long-term HRV computed over a nominal 24 h and its time-frequency domain is used mainly for mortality risk prediction [25–28]. However, the demand for rapid detection of ANS alterations has increased in several medical areas with the development of wearable devices [29,30]. Thus, ultra-short-term HRV analysis has been actively applied in various medical fields [16, 17, 31–33]. In the present study, to overcome the shortcomings of short-term methodology in HRV analysis, we investigated the effectiveness of ultra-short-term methodology in the detection of ANS alterations in patients with OSA. We found that, in both ultra-short- and short-term methodologies, all HRV parameters showed a similar tendency, which indicated sympathetic overactivity along with increased OSA severity. Additionally, we detected a significant correlation between HRV parameters and AHI, ODI, and ESS scores. The HRV feature also showed a higher agreement between the ultra-short- and short-term methodologies.

The frequency analysis of HRV typically uses three parameters: Ln VLF, Ln LF, and Ln HF. Additionally, Ln TP can be measured by the sum of the total spectral power, and it is thought to represent the global autonomic function. The Ln LF/HF ratio, as a major marker for sympathovagal balance, can also be calculated during HRV analysis. Among HRV studies of OSA, there is widespread evidence demonstrating that the alteration of ANS balance toward sympathetic predominance is well reflected in the Ln LF/HF ratio, Ln LF [8, 10, 34–36]. In addition, a shift in ANS function towards parasympathetic predominance was observed with increases in Ln HF [34–36]. Thus, the shift toward sympathetic predominance demonstrated by HRV analysis of patients with OSA could suggest a higher cardiovascular risk during waking status.
It is well known that HRV features obtained from short-term segments show smoother spectral profiles than those obtained from ultra-short-term segments. Thus, short-term methodology may be easy to evaluate in an entirely autonomic state during sleep. However, short-term HRV analysis has inherent limitations in real-world settings. First, short-term HRV-based studies are highly susceptible to noise. Second their failure to detect dynamic ANS activity, especially the rapid changes in Ln LF and Ln HF. However, ultra-short-term HRV analysis could assess the immediate changes in RR intervals on electrocardiograms before and after the period of respiratory events. For these reasons, to investigate sophisticated sleep studies, short-term HRV analysis is not recommended. Although ultra-short-term HRV analyses have not yet been adopted as a standard tool in OSA, a recent study showed that higher ultra-short-term HRV was strongly related to longer respiratory event durations [32]. In this study, we detected substantial agreement in HRV parameters between ultra-short and short-term methodologies. Interestingly, this alteration in HRV value was more prominent with the ultra-short-term method, regardless of OSA severity. Consistent with our findings, ultra-short-term HRV parameters were generally higher with longer respiratory events, regardless of the respiratory event type [37]. Therefore, our findings may be used as a diagnostic marker for the alteration of ANS activity in patients with OSA.

However, our study had several limitations. First, only time-domain HRV parameters were assessed for ANS function, because frequency-domain HRV parameters require a longer RR interval segment for the analysis. Second, this study did not include data on HRV parameters in control subjects. However, to include the control group, we should match the age, sex, BMI, and comorbidities; thus, it is very difficult in the real-world study. Third, in this study, we obtained HRV features from only the linear method, not using the non-linear method. Thus, our further study should include the comparison of HRV features between the linear and the non-linear methodologies. Fourth, several comorbidities could influence the inflammatory status of OSA patients and these conditions could be confounding factors in the HRV analysis [38,39]. Finally, our data need to be used to further evaluate the effect of OSA treatment on HRV features.

**5. Conclusions**

In conclusion, ultra-short-term HRV features are valid surrogates of short-term HRV features for investigating ANS function in OSA patients. Therefore, this study provides valuable insights into the balance of ANS activity in OSA patients.

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

**Funding:** This research was funded by the Bio and Medical Technology Development Program of the National Research Foundation, the Korean Government (MSIT, NRF-2021R1C1C1005746), and a grant from the Hallym University Research Fund.

**Institutional Review Board Statement:** This study adhered to the tenets of the Declaration of Helsinki and was approved by the institutional review board of Hallym Medical University Chuncheon Sacred Hospital (IRB No. 2020-03-022).

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

**Data Availability Statement:** The authors confirm that data supporting the findings of this study are available within the article.

**Acknowledgments:** The authors would like to thank all the members of the New Frontier Research Team for their assistance.

**Conflicts of Interest:** The authors declare no conflict of interest.
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<figure><img src="image_91.png" /></figure>

Article

# Risk Factors of 30-Day All-Cause Mortality in Patients with Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infection

Keh-Sen Liu<fnref n="1" />, Yao-Shen Tong<fnref n="2" />, Ming-Tsung Lee<fnref n="3" />,<fnref n="4" />, Hung-Yu Lin<fnref n="3" /> ID and Min-Chi Lu<fnref n="5" />,<fnref n="6" />,*

**Citation:** Liu, K.-S.; Tong, Y.-S.; Lee, M.-T.; Lin, H.-Y.; Lu, M.-C. Risk Factors of 30-Day All-Cause Mortality in Patients with Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infection. *J. Pers. Med. 2021*, *11*, 616. https://doi.org/10.3390/jpm11070616

Academic Editor:
Anne-Marie Caminade

Received: 12 May 2021

Accepted: 27 June 2021

Published: 29 June 2021

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

<figure><img src="image_92.png" /></figure>

**Copyright:** © 2021 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/).

**Abstract:** An optimal antimicrobial regimen for the treatment of patients with carbapenem-resistant Klebsiella pneumoniae (CRKP) bloodstream infection (BSI) is currently unavailable. This study aimed to identify the appropriate antibiotics and the risk factors of all-cause mortality for CRKP BSI patients. This retrospective cohort study included the hospitalized patients with CRKP BSI. Primary outcome was 30-day all-cause mortality. Cox regression analysis was used to evaluate the risk factors of 30-day mortality. A total of 89 patients were included with a 30-day mortality of 52.1%. A total of 52 (58.4%) patients were treated with appropriate antimicrobial regimens and 58 (65.2%) isolates carried blaKPC-2 genes. Microbiologic eradication within 7 days (adjusted hazard ratio [HR] = 0.09, *p* < 0.001), platelet count (per 1 × 10⁴/mm<fnref n="3" />, adjusted HR = 0.95, *p* = 0.002), and Pitt bacteremia scores (adjusted HR = 1.40, *p* < 0.001) were independently associated with 30-day all-cause mortality. No effective antimicrobial regimens were identified. In conclusion, risk factors of 30-day mortality in patients with CRKP BSI included microbiologic eradication > 7 days, lower platelet count, and a higher Pitt bacteremia score. These findings render a new insight into the clinical landscape of CRKP BSI.

**Keywords:** bacteremia; carbapenemase; carbapenem-resistant *Enterobacteriaceae*; Klebsiella pneumoniae; mortality; risk factors

## 1. Introduction

The rapidly increasing prevalence of antibiotic resistance in *Enterobacteriaceae* is currently a major threat to public health worldwide [1]. Recently, the European Survey of Carbapenemase-Producing *Enterobacteriaceae* (EuSCAPE) Working Group investigated 2703 clinical isolates of carbapeneme-resistant *Enterobacteriaceae* (CRE) submitted from 455 sentinel hospitals in 36 countries and reported that 15% of these were *Escherichia coli* and 85% were *Klebsiella pneumoniae*. Among these Klebsiella isolates, 37% were carbapenemase-producers [2]. Four gene families encoding carbapenemase-production have been identified: *Klebsiella pneumoniae* carbapenemase (KPC), New Delhi metallo-β-lactamase, oxacillinase 48-like, and Verona integron-encoded metallo-β-lactamase [2]. Carbapenem-resistant *Klebsiella pneumoniae* (CRKP) has attracted particular attention since it was first identified as one of the multidrug resistant bacteria strains [3]. Previous studies reported that the

<footnote n="1">¹ Division of Infectious Diseases, Department of Internal Medicine, Show Chwan Memorial Hospital, Changhua 500, Taiwan; liumilka2@gmail.com</footnote>

<footnote n="2">² Department of Medical Laboratory, Show Chwan Memorial Hospital, Changhua 500, Taiwan; yaushen51@gmail.com</footnote>

<footnote n="3">³ Research Assistant Center, Show Chwan Memorial Hospital, Changhua 500, Taiwan; lee6717kimo@yahoo.com.tw (M.-T.L.); linhungyu700218@gmail.com (H.-Y.L.)</footnote>

<footnote n="4">⁴ Department of Nursing, Hungkuang University, Taichung 433, Taiwan</footnote>

<footnote n="5">⁵ Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, Taichung 404, Taiwan</footnote>

<footnote n="6">⁶ Department of Microbiology and Immunology, School of Medicine, China Medical University, Taichung 404, Taiwan</footnote>

<footnote>* Correspondence: luminchi@gmail.com</footnote>
KPC-producing *Klebsiella pneumoniae* was independently associated with higher mortal-
ity [4,5]. Mortality rate of CRKP bloodstream infection (BSI) is reported to range from 42%
to 84% [6]. Mechanistically, the acquisition of genes encoding carbapenemase-production is
the predominant mechanism of carbapenem-resistance through inactivation or degradation
of carbapenems [7].

Although the innovation of new drugs combating resistant bacteria may create a
glimmer of hope, the evolution of drug-resistant genes soon overwhelms progression.
For instance, drug resistance-combating ceftazidime-avibactam was first approved under
the Generating Antibiotic Incentives Now (GAIN) Act, whereas its clinical use was soon
followed by reports of resistance caused by CRKP [8–11]. Due to the rapid progression and
high mortality rate, the role of CRKP can be better understood by an early examination.
Nonetheless, the clinical value of CRKP and its related risk factors are yet to be clarified.

Many studies found that CRKP BSI patients treated with combined antibiotics had better survival outcomes than those treated with monotherapy [12,13]. However, potentially effective regimens are varied across studies and those that are available are very limited [14–17]. Since there is no consensus on optimal antimicrobial regimens for CRKP infection, it is crucial to understand the possible risk factors for unfavourable outcomes and potentially optimal antibiotics when treating CRKP BSI patients. In this retrospective cohort study, we attempted to identify the appropriate antimicrobial regimens and to investigate the risk factors of all-cause mortality for CRKP bloodstream infection (BSI) patients.

## 2. Materials and Methods

### 2.1. *Study Design and Patients*

This retrospective cohort study was ethically approved by the Institutional Review
Board of Show Chwan Memorial Hospital, which is a regional hospital offering an esti-
mated 750 beds in Taiwan (approval number: SCMH_IRB No. 1061202). Hospitalized
patients diagnosed with CRKP BSI (number of patients) between June 2014 and August
2017 in Show Chwan Memorial Hospital in Taiwan were included in the study. CRKP BSI
was defined by at least one blood-culture positive for a CRKP strain. The patients with
polymicrobial BSI and those aged < 20 years were excluded.

### 2.2. *Data Collection*

All patient data were collected through a retrospective review of medical records.
Variables included demographics, medical history, Charlson Comorbidity Index [18], ad-
mission history, initial presentations (quick sepsis related organ failure assessment [qSOFA]
score [19], systemic inflammatory response syndrome [SIRS] criteria [20], Pitt bacteremia
score [21], vital signs, and laboratory data), length of hospitalization, antibiotic regimens,
microbiologic results, and drug susceptibilities. Sepsis was defined by qSOFA ≥ 2 [22] and
shock was as mean arterial pressure ≤ 65 mmHg [23]. Only antimicrobial agents used for
more than 48 h would be included for analysis. Antimicrobial regimens were classified as
empiric or definitive. Empiric regimen included the prescription of antibiotics before the
blood culture result was available. Definitive regimen referred to prescription of antibiotics
based on drug susceptibility results. An appropriate regimen was defined as including
one or more in vitro active drugs against the CRKP isolates. Primary outcome was 30-day
all-cause mortality, measured from the day on which the first blood culture revealing CRKP
was taken. Microbiologic eradication was defined as negative blood cultures for CRKP
during follow-up.

### 2.3. *Microbiology and Antimicrobial Susceptibilities*

The CRKP isolates were collected from the first positive blood culture of each patient.
Bacterial identification and antimicrobial susceptibility tests were performed using Phoenix
Automated Microbiology System (Becton, Dickinson and Company, USA) and the inter-
pretative criteria of the Clinical and Laboratory Standards Institute (CLSI) guidelines was
applied. CRKP was defined as an isolate with a minimum inhibitory concentration (MIC)
of ≥2 µg/mL for ertapenem, ≥4 µg/mL for meropenem, or ≥4 µg/mL for imipenem. Due to no available CLSI breakpoints for *Enterobacteriaceae*, MIC breakpoints by the European Committee on Antimicrobial Susceptibility Testing were used for the interpretation of colistin susceptibility (susceptible: ≤2 mg/L, resistant: >2 mg/L) [24] and tigecycline susceptibility was determined by MIC breakpoints by the USA Food and Drug Administration (susceptible: MIC ≤ 2 µg/mL, intermediate: 4 µg/mL, resistant: MIC ≥ 8 µg/mL) [25]. Detection for *bla*<sub>KPC</sub> gene and *bla*<sub>OXA-48</sub> gene were performed by polymerase chain reaction with specific primers, as previously described [26]. Pulse-field gel electrophoresis (PFGE) was used to detect the relatedness of the CRKP strains. The profiles of XbaI macro-restricted fragments of each strain were determined by a standardized PulseNet PFGE protocol [27]. The BioNumerics version 6.6 (Applied Maths, Belgium) was used to analyze the PFGE profiles. The relatedness was based on PFGE profiles using the dice coefficients and the unweighted pair group method with arithmetic mean algorithm. The optimization value and position tolerance were set at 1.5% and 0.75%, respectively.

## 2.4. Statistical Analysis

Continuous variables were presented as median with interquartile range (IQR) and categorical variables were presented as a percentage. The comparison between the survival group and mortality group was performed using the Mann-Whitney U test and Fisher's exact test for continuous and categorical variables, respectively. Mortality rate was estimated using the Kaplan-Meier method and compared using the log-rank test. A univariate and multivariate Cox proportional hazard model was used to evaluate the risk factors of 30-day mortality. The variables with *p* < 0.1 in the univariate model were manually selected into the multivariate model in a backward stepwise manner. The results of the Cox proportional hazard model were presented as hazard ratio (HR) with 95% confidence interval (CI). A *p*-value of <0.05 was considered statistically significant. IBM SPSS Statistics for Windows, version 24.0 (IBM Corp., Armonk, NY, USA) was used for the statistical analyses.

# 3. Results

## 3.1. Patient Characteristics and Antimicrobial Treatment

As shown in Table 1, a total of 89 patients (59.6% male, median age 75.6 years) with CRKP BSI were included: 43 (48.3%) patients in the 30-day survival group and 46 (51.7%) in the 30-day mortality group. The 7-day, 14-day, and 30-day all-cause mortality of all patients were 32.6%, 43.9%, and 51.7%, respectively. Within 48 h of admission, 23 out of 89 (25.8%) had a positive sign of blood culture testing for CRKP, indicating that it was community acquired. The 66 out of 89 (74.1%) patients who presented a positive sign of blood culture testing for CRKP after 48 h of admission were regarded as nosocomial infections. Survival group patients had a significantly lower Charlson comorbidity index, Pitt bacteremia score, and albumin levels. The proportion of sepsis and septic shock was lower in the survival group, while platelet counts were higher. Pre-existing cardiovascular disease and hospitalizations during the prior year was more uncommon among survivors (Table 1).

The results of antimicrobial susceptibility revealed that 52 (58.4%) were treated with the appropriate antimicrobial regimens, including 9 (10.1%) as empiric therapy and additionally 50 (56.2%) as definite regimens. Colistin (31.5%) and carbapenem (41.6%) were the most commonly prescribed antibiotics. Patients in the survival group had a significant proportion of microbiological eradication within 7 days, as compared to the mortality group (72.1% vs. 13.0%, *p* < 0.001) (Table 2). However, the proportion of appropriate regimen, length of hospitalization, and the pattern of antibiotics utilization did not significantly differ between the two groups (Table 2).
<table><caption>**Table 1.** Demographics and baseline characteristics in patients with carbapenem-resistant Klebsiella pneumoniae bloodstream infection according to 30-day mortality.</caption><thead><tr><th>Variables</th><th>All<br>(n = 89)</th><th>Survival Group<br>(n = 43) (48.3%)</th><th>Mortality Group<br>(n = 46) (51.7%)</th><th>p Value</th></tr></thead><tbody><tr><td><strong>Demographics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Male; n (%)</td><td>53 (59.6%)</td><td>26 (60.5%)</td><td>27 (58.7%)</td><td>1.000</td></tr><tr><td>Age; y/o (IQR)</td><td>75.6 (63.8–83.7)</td><td>74.5 (62.3–83.4)</td><td>80.5 (65.8–85.2)</td><td>0.153</td></tr><tr><td>BMI; kg/m<sup>2</sup> (IQR)</td><td>21.3 (18.3–25.9)</td><td>20.8 (18.2–24.8)</td><td>21.4 (19.2–30.0)</td><td>0.223</td></tr><tr><td><strong>Comorbidities</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Charlson comorbidity index; score (IQR)</td><td>8 (6–10)</td><td>7 (6–9)</td><td>8 (7–11)</td><td>0.008</td></tr><tr><td>Diabetes mellitus; n (%)</td><td>53 (59.6%)</td><td>24 (55.8%)</td><td>29 (63.0%)</td><td>0.523</td></tr><tr><td>Cardiovascular disease; n (%)</td><td>57 (64.0%)</td><td>34 (79.1%)</td><td>23 (50.0%)</td><td>0.008</td></tr><tr><td>Chronic obstructive pulmonary disease; n (%)</td><td>21 (23.6%)</td><td>9 (21.4%)</td><td>12 (26.1%)</td><td>0.628</td></tr><tr><td>Chronic liver disease; n (%)</td><td>15 (16.9%)</td><td>4 (9.5%)</td><td>11 (23.9%)</td><td>0.092</td></tr><tr><td>Chronic kidney disease; n (%)</td><td>15 (16.9%)</td><td>9 (21.4%)</td><td>6 (13.0%)</td><td>0.397</td></tr><tr><td>Malignancy; n (%)</td><td>18 (20.2%)</td><td>7 (16.7%)</td><td>11 (23.9%)</td><td>0.439</td></tr><tr><td>Steroid use ≥ 3 months; n (%)</td><td>15 (16.9%)</td><td>4 (9.5%)</td><td>11 (23.9%)</td><td>0.092</td></tr><tr><td>Immunocompromised condition; n (%)</td><td>8 (9.0%)</td><td>3 (7.1%)</td><td>5 (10.9%)</td><td>0.716</td></tr><tr><td><strong>Events in the prior year</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Hospitalization; events (%)</td><td>65 (73.0%)</td><td>36 (85.7%)</td><td>29 (64.4%)</td><td>0.028</td></tr><tr><td>Admitted to intensive care units; events (%)</td><td>30 (33.7%)</td><td>16 (38.1%)</td><td>14 (30.4%)</td><td>0.504</td></tr><tr><td>Nursing home residence; events (%)</td><td>31 (34.8%)</td><td>14 (32.6%)</td><td>17 (37.0%)</td><td>0.824</td></tr><tr><td>CRKP colonization; events (%)</td><td>14 (15.7%)</td><td>7 (16.7%)</td><td>7 (15.2%)</td><td>1.000</td></tr><tr><td>Surgery; events (%)</td><td>24 (27.0%)</td><td>12 (27.9%)</td><td>12 (26.1%)</td><td>1.000</td></tr><tr><td><strong>Initial presentation</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Pitt bacteremia score; score (IQR)</td><td>4 (2–6)</td><td>3 (1–4)</td><td>6 (4–8)</td><td>&lt; 0.001</td></tr><tr><td>SIRS; score (IQR)</td><td>3 (2–4)</td><td>3 (2–3)</td><td>3 (2–4)</td><td>0.273</td></tr><tr><td>SIRS ≥ 2; n (%)</td><td>76 (85.4%)</td><td>35 (81.4%)</td><td>41 (89.1%)</td><td>0.375</td></tr><tr><td>qSOFA; score (IQR)</td><td>2 (1–3)</td><td>1 (1–2)</td><td>2 (2–3)</td><td>&lt; 0.001</td></tr><tr><td>Sepsis; n (%)</td><td>54 (60.7%)</td><td>18 (41.9%)</td><td>36 (78.3%)</td><td>0.001</td></tr><tr><td>Septic shock; n (%)</td><td>27 (30.3%)</td><td>7 (16.3%)</td><td>20 (43.5%)</td><td>0.006</td></tr><tr><td>Body temperature ≥38 °C; n (%)</td><td>54 (60.7%)</td><td>27 (62.8%)</td><td>27 (58.7%)</td><td>0.828</td></tr><tr><td>White blood cell count (10<sup>3</sup>/mm<sup>3</sup>); count (IQR)</td><td>13.2 (8.7–17.8)</td><td>13.7 (10.1–17.8)</td><td>10.7 (6.4–18.0)</td><td>0.249</td></tr><tr><td>Hemoglobin (g/dL); value (IQR)</td><td>9.8 (9.0–10.6)</td><td>9.8 (9.1–11)</td><td>9.7 (8.6–10.5)</td><td>0.352</td></tr><tr><td>Platelet count (10<sup>4</sup>/mm<sup>3</sup>); count (IQR)</td><td>12.4 (5.2–23.8)</td><td>17.5 (10.8–26.3)</td><td>8.15 (3.8–18.2)</td><td>0.002</td></tr><tr><td>Creatinine (mg/dL); value (IQR)</td><td>1.3 (0.8–2.8)</td><td>1 (0.7–2.8)</td><td>1.4 (0.9–2.8)</td><td>0.088</td></tr><tr><td>Albumin (g/dL); value (IQR)</td><td>2.6 (2.2–2.9)</td><td>2.9 (2.5–3.0)</td><td>2.4 (2.1–2.7)</td><td>&lt; 0.001</td></tr><tr><td>C-reactive protein (mg/dL); value (IQR)</td><td>11.6 (4.9–19.4)</td><td>10.8 (3.6–19.3)</td><td>12.7 (8.0–19.6)</td><td>0.256</td></tr><tr><td><strong>Microbiology</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Presence of KPC gene; n (%)</td><td>58 (65.2%)</td><td>22 (51.2%)</td><td>36 (78.3%)</td><td>0.008</td></tr><tr><td>Clonal relatedness of CRKP strain</td><td></td><td></td><td></td><td>0.006</td></tr><tr><td>Cluster I; n (%)</td><td>11 (12.4%)</td><td>8 (18.6%)</td><td>3 (6.5%)</td><td></td></tr><tr><td>Cluster II; n (%)</td><td>62 (69.7%)</td><td>23 (53.5%)</td><td>39 (84.8%)</td><td></td></tr><tr><td>Others; n (%)</td><td>16 (18.0%)</td><td>12 (27.9%)</td><td>4 (8.7%)</td><td></td></tr></tbody></table>

Abbreviations: BMI, body mass index; CRKP, carbapenem-resistant Klebsiella pneumoniae; IQR, interquartile range; KPC, Klebsiella pneumoniae carbapenemase; qSOFA, quick sepsis related organ failure assessment; SIRS, systemic inflammatory response syndrome.

## 3.2. Microbiology Revealed Reduced Proportion of KPC Genes and KPC Cluster II in the Survival Group

Among the 89 CRKP strains, 58 (65.2%) isolates carried KPC genes, and all were blaKPC-2 genes. According to the results of PFGE, two distinct clusters could be identified from the CRKP strains (Figure 1): 11 (12.4%) strains in the cluster I, 62 (69.7%) in cluster II, and others (18.0%). All the strains carrying KPC genes were in cluster II. None of the cluster I and II strains had blaOXA-48 genes. Notably, we noted that the survival group had a reduced proportion of KPC genes (51.2% vs. 78.3%, *p* = 0.008) and the cluster II strains (53.5% vs. 84.8%, *p* = 0.006) as compared to the mortality group (Table 1).
<table><caption>**Table 2.** Treatment in patients with carbapenem-resistant Klebsiella pneumoniae bloodstream infection according to 30-day mortality.</caption><thead><tr><th>Variables</th><th>All (n = 89)</th><th>Survival Group (n = 43)</th><th>Mortality Group (n = 46)</th><th>p Value</th></tr></thead><tbody><tr><td>Appropriate antimicrobial regimen; n (%)</td><td>52 (58.4%)</td><td>28 (65.1%)</td><td>24 (52.2%)</td><td>0.283</td></tr><tr><td>Colistin included; n (%)</td><td>28 (31.5%)</td><td>10 (23.3%)</td><td>18 (39.1%)</td><td>0.006</td></tr><tr><td>Amikacin included; n (%)</td><td>13 (14.6%)</td><td>9 (20.9%)</td><td>4 (8.7%)</td><td>0.336</td></tr><tr><td>Carbapenem included; n (%)</td><td>37 (41.6%)</td><td>17 (39.5%)</td><td>20 (43.5%)</td><td>0.124</td></tr><tr><td>Tigecycline included; n (%)</td><td>3 (3.4%)</td><td>0 (0%)</td><td>3 (6.5%)</td><td>0.092</td></tr><tr><td>Monotherapy; n (%)</td><td>12 (13.5%)</td><td>11 (25.6%)</td><td>1 (2.2%)</td><td>0.003</td></tr><tr><td>Appropriate empiric regimen; n (%)</td><td>9 (10.1%)</td><td>3 (7.0%)</td><td>6 (13.0%)</td><td>0.487</td></tr><tr><td>Appropriate definitive regimen; n (%)</td><td>50 (56.2%)</td><td>28 (65.1%)</td><td>22 (47.8%)</td><td>0.135</td></tr><tr><td>Microbiologic eradication within 7 days; n (%)</td><td>37 (41.6%)</td><td>31 (72.1%)</td><td>6 (13.0%)</td><td>&lt;0.001</td></tr><tr><td>Length of hospitalization; days (IQR)</td><td>21.5 (13.0–34.0)</td><td>21.5 (14.0–40.0)</td><td>21.5 (13.0–33.0)</td><td>0.483</td></tr></tbody></table>

Abbreviations: CRKP, carbapenem-resistant Klebsiella pneumoniae; IQR, interquartile range; KPC, Klebsiella pneumoniae carbapenemase.

### 3.3. Comparison of Antimicrobial Susceptibility

All of the 89 isolates were resistant to at least one of the three carbapenems. For the 89 isolates, colistin exerted the highest susceptibility rate (96.6%), followed by amikacin (88.8%), tigecycline (82.0%), meropenem (22.5%), levofloxacin (11.2%), ertapenem (7.9%), and imipenem (5.6%) (Figure 2A). The pattern of drug susceptibility in the survival group differed from that of the mortality group (Figure 2A). The pattern difference was observed between CRKP strains with and without KPC genes (Figure 2B). The survival group had a significantly higher susceptibility rate of imipenem (11.6% vs. 0%, *p* = 0.023) and meropenem (34.9% vs. 10.9%, *p* = 0.01) than the mortality group (Figure 2A). Except for colistin, tigecycline, and amikacin, the susceptibility rate of all drugs were significantly lower in the CRKP strains with KPC genes than in the strains without KPC genes.

### 3.4. Risk Factors of 30-Day All-Cause Mortality

For patients with CRKP BSI, multivariate analysis revealed that microbiologic eradication within 7 days (adjusted HR = 0.09, *p* < 0.001), platelet count (per 1 × 10⁴/mm³, adjusted HR = 0.95, *p* = 0.002), and Pitt bacteremia score (adjusted HR = 1.40, *p* < 0.001) were independently associated with 30-day all-cause mortality. The univariate Kaplan-Meier method revealed that CRKP patients with KPC genes had significantly higher 30-day mortality than those without KPC genes (62.6% vs. 32.4%, *p* = 0.022) (Figure 3). However, the predictive role of the presence of KPC genes was not confirmed by the multivariate model. None of the antimicrobial regimens was significantly associated with 30-day mortality (Table 3).

<table><caption>**Table 3.** Multivariate analysis for risk factors of 30-day mortality in patients with carbapenem-resistant Klebsiella pneumoniae bloodstream infection.</caption><thead><tr><th>Variables</th><th>Adjusted Hazard Ratio</th><th>95% Confidence Interval</th><th>p</th></tr></thead><tbody><tr><td>Microbiologic eradication within 7 days (yes vs. no)</td><td>0.09</td><td>0.03–0.26</td><td>&lt;0.001</td></tr><tr><td>Platelet count (per 1 × 10<sup>4</sup>/mm<sup>3</sup>)</td><td>0.95</td><td>0.92–0.98</td><td>0.002</td></tr><tr><td>Pitt bacteremia score (per 1 unit)</td><td>1.40</td><td>1.21–1.61</td><td>&lt;0.001</td></tr></tbody></table>
<figure><img src="image_93.png" /><figcaption>**Figure 1.** Clonal relatedness of the carbapenemase-producing *Klebsiella pneumoniae* strains. Pulse-field gel electrophoresis (PFGE) was used to determine the profiles of XbaI macro-restricted fragments of each strain were determined by a standardized PulseNet PFGE protocol.</figcaption></figure>
A

<figure><img src="image_94.png" /></figure>

B

<figure><img src="image_95.png" /><figcaption>**Figure 2.** Antimicrobial susceptibility of carbapenemase-producing *Klebsiella pneumonia* isolates according to 30-day mortality (A) and the presence of *Klebsiella pneumonia* carbapenemase (KPC) genes (B). KPC, *Klebsiella pneumoniae* carbapenemase.</figcaption></figure>

<figure><img src="image_96.png" /><figcaption>**Figure 3.** Kaplan-Meier curve showing the 30-day in-hospital survival for bacteremic patients with carbapenem-resistant *Klebsiella pneumoniae* (CRKP) carrying KPC gene as compared to CRKP not carrying KPC gene.</figcaption></figure>
## 4. Discussion

CRKP BSI is a clinical challenge as no effective antimicrobial regimens are currently available. This study observed a 30-day all-cause mortality of 52.1% in patients with CRKP BSI. The lack of microbiologic eradication within 7 days, a lower platelet count, and a higher Pitt bacteremia score were independently associated with higher 30-day mortality. Notably, the predictive role of KPC genes and appropriate antibiotic regimens were not identified in this study.

All the KPC genes detected in this study were *bla*<sub>KPC-2</sub>, which was consistent with the previous nationwide surveillance in Taiwan that *bla*<sub>KPC-2</sub> accounted for the majority of KPC genes [28]. However, in the present study, the prevalence of *bla*<sub>KPC-2</sub> gene among CRKP strains was 65.2%, much higher than the previous prevalence of 36.2% in 2011–2015 [29]. The results suggested the KPC-2-producing CRKP strains disseminate rapidly at an alarming rate in Taiwan. In addition, this study reveals that non-KPC-producing strains present high susceptibility to imipenem, meropenem, ertapenem, ceftriaxone, cefepime, levofloxacin, and piperacillin/tazobactam, as compared to that of KPC-producing strains. Although the results might suggest the role of KPC in the treatment of CRKP BSI, multivariate analysis did not find an association of KPC with mortality. Wang et al. reported similar results that KPC-gene positive and negative strains had different MICs of antibiotics, but mortality did not differ between these two CRKP strains [30]. Some studies suggested CRKP infection as an important risk factor of hospital mortality [4,5]; however, the role of KPC deserves further investigations for patients with CRKP BSI.

This study revealed that a higher Pitt bacteremia score was independently associated with 30-day mortality in patients with CRKP BSI. The result was comparable with the study of Shen et al. and Xiao et al. [31,32] Gomez-Simmonds et al. and Lee et al. also reported that the patients with Pitt bacteremia score > 4 had significantly higher mortality [15,17] Additionally, our study revealed an association of lower platelet count with higher mortality, which might also reflect the severity of illness [33]. Some studies used different tools to evaluate the severity of illness at the presence of CRKP BSI, for example, APACHE II score; a higher APACHE II score was reported to be independently associated with 30-day mortality [13,34]. All these results supported the predictive value of the severity of illness for 30-day mortality in patients with CRKP BSI.

Both our results and that of Nguyen et al. revealed that the patients with microbiologic eradication within 7 days had a significantly better survival rate [35]. Falcone et al. further evaluated the effect of time to appropriate antibiotic therapy on 30-day mortality and showed that time to appropriate antibiotic therapy was an independent predictor of 30-day mortality in patients with CRKP BSI. Falcone et al. suggested that appropriate antibiotic therapy was preferably initiated within the first 24 h after collection of the blood culture [36]. From our study and the previous research, the importance of timely control of the disease is evident.

Many studies have attempted to identify the best combination of antibiotics for the treatment of CRKP BSI, but the results are mixed and there is no consistent conclusion [14–17]. Our results revealed that the most susceptible drug was colistin, followed by tigecycline and amikacin [13,15,31,35,37]. Medeiros et al. observed that the combination of colistin and amikacin could provide survival benefits for the patients with CRKP BSI [38]. However, both colistin [39] and amikacin [40] are nephrotoxic agents. Most patients with CRKP BSI are older, have multiple comorbid diseases, and frequently present with septic shock [13,35,41]. The use of colistin or amikacin, or in combination, can further aggravate nephrotoxicity [39,40]. This combination is reasonable on the basis of only antimicrobial susceptibility without consideration of patients’ condition and drug adverse effects. Clinicians are usually reluctant to use the combination because of the high risk of renal injury, which may lead to dialysis and increase subsequent morbidity and mortality. Although tigecycline causes minimal organ toxicity, the use of tigecycline for Gram-negative bacteremia remains controversial. The serum concentrations provided by standard doses of tigecycline are below the MICs of most Gram-negative pathogens [42]. Therefore,
the most common combinations are colistin/tigecycline, aminoglycoside/tigecycline, colistin/carbapenem, aminoglycoside/carbapenem, and a combination of three drugs (e.g., colistin/tigecycline/carbapenem) [12,13]. However, it lacks consensus on the best combination for the treatment of CRKP BSI. Ceftazidime-avibactam is a new combination of third generation cephalosporin and non-β-lactam β-lactamase inhibitor, and has promising in vitro activity against many Gram-negative pathogens, including KPC-producing *Enterobacteriaceae* [43]. It is hoped that the new drugs will not be misused or overused to avoid the development of antibacterial resistance [44].

The major caveat of this study is the observational design, in which potential reporting bias and selection could not be avoided. Secondly, all patients were diagnosed and treated in a single institute, which limited the external validity of the results. Thirdly, this study focused on the patients with CRKP BSI instead of other types of infection, such as urinary tract infection, pneumonia, or intra-abdominal infection. Fourthly, the definition of appropriate regimens in this study was the inclusion of one or more efficient antibiotics, instead of two or more, and the time to appropriate regimens was not evaluated. This might explain the results that the use of appropriate regimen was not significantly associated with 30-day mortality.

**5. Conclusions**

This retrospective cohort study observed that the risk factors of 30-day all-cause mortality in patients with CRKP BSI included microbiologic eradication > 7 days, a lower platelet count, and a higher Pitt bacteremia score. These findings render new insights into the clinical landscape of CRKP BSI.

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

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Show Chwan Memorial Hospital, Taiwan (protocol code SCMH_IRB No. 1061202; date of approval: 2 December 2017).

**Informed Consent Statement:** Patient consent was waived due to the retrospective study design.

**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.

**Acknowledgments:** The authors would like to thank the technical support from Hsing-Ju Wu, director of the Research Assistant Center, Show Chwan Memorial Hospital, Taiwan.

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

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Article

# Thiopurine S-Methyltransferase Polymorphisms Predict Hepatotoxicity in Azathioprine-Treated Patients with Autoimmune Diseases

Heh-Shiang Sheu¹, Yi-Ming Chen ¹,²,³,⁴,⁵,⁶ ID, Yi-Ju Liao ⁶, Chia-Yi Wei ², Jun-Peng Chen ², Hsueh-Ju Lin ², Wei-Ting Hung ¹,⁴,⁷,*, Wen-Nan Huang ¹,³,⁴,⁸ and Yi-Hsing Chen ¹,³ ID

1 Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan

2 Department of Medical Research, Taichung Veterans General Hospital, Taichung 40705, Taiwan

3 School of Medicine, National Yang-Ming Chiao Tung University, Taipei 30010, Taiwan

4 Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 40227, Taiwan

5 Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung 40227, Taiwan

6 Department of Pharmacy, Taichung Veterans General Hospital, Taichung 40705, Taiwan

7 Department of Medical Education, Taichung Veterans General Hospital, Taichung 40705, Taiwan

8 College of Business and Management, Ling Tung University, Taichung 408284, Taiwan

* Correspondence: wtinghung@gmail.com; Tel.: +886-4-2359-2525 (ext. 4304)

**Abstract:** Thiopurine methyltransferase (TPMT) is the rate-limiting enzyme in Azathioprine (AZA) metabolization. Although studies have discussed the association between the TPMT polymorphisms and myelosuppression, the data about the relationship between TPMT genotypes and hepatotoxicity in Asian patients remain limited. This study investigated the correlation between TPMT polymorphisms and AZA-related hepatotoxicity. This study enrolled the patients who had prior exposure to AZA from the Taichung Veterans General Hospital (TCVGH)-Taiwan Precision Medicine Initiative (TPMI) cohort. Genetic variants were determined using a single nucleotide polymorphism (SNP) array. Participants were accordingly categorized into normal metabolizer (NM) and non-normal metabolizer (non-NM) groups. From the TCVGH-TPMI cohort, we included 50 TPMT non-NM patients, including 1 poor metabolizer (PM), 49 intermediate metabolizers (IMs), and 1000 NM patients. The non-NM genotype was associated with hepatotoxicity compared with the NM genotype (hazard ratio (HR): 3.85, 95% confidence interval (CI): 1.83–8.10). In the non-NM group, the 3-year cumulative incidence of hepatotoxicity was higher than that in the NM group at 8.5% in the first year and 18.6% in the second and third years (p < 0.001). A TPMT non-NM genotype was associated with the occurrence of hepatotoxicity following AZA therapy. Preemptive testing helps individualize AZA therapy by minimizing the risk of hepatotoxicity.

**Keywords:** TPMT genotype; TPMT poor metabolizers; TPMT intermediate metabolizers; AZA; hepatotoxicity; cumulative incidence of hepatotoxicity; pharmacogenomics; autoimmune disease; preemptive genotyping; TPMI

**Citation:** Sheu, H.-S.; Chen, Y.-M.; Liao, Y.-J.; Wei, C.-Y.; Chen, J.-P.; Lin, H.-J.; Hung, W.-T.; Huang, W.-N.; Chen, Y.-H. Thiopurine S-Methyltransferase Polymorphisms Predict Hepatotoxicity in Azathioprine-Treated Patients with Autoimmune Diseases. *J. Pers. Med.* **2022**, *12*, 1399. https://doi.org/10.3390/jpm12091399

Academic Editor: Anne-Marie Caminade

Received: 15 July 2022
Accepted: 26 August 2022
Published: 28 August 2022

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4.0/).

## 1. Introduction

Azathioprine (AZA) is a valuable steroid-sparing immunosuppressant that is broadly used for systemic lupus erythematosus (SLE) [1–3], severe rheumatoid arthritis (RA), inflammatory bowel disease (IBD) [4–7], autoimmune hepatitis (AIH) [8], dermatomyositis (DM)/polymyositis, pemphigus [9], and post-transplant rejection. Azathioprine is a prodrug. Initially, AZA is metabolized to 6-mercaptopurine (6-MP) by glutathione S-transferase (GST). Three competitive enzymatic pathways metabolize 6-MP. First, xanthine oxidase (XO) catalyzes 6-MP to thiouric acid, an inactive metabolite. Second, thiopurine S-methyltransferase (TPMT), methylates 6-MP into 6-methylmercaptopurine (6-MMP).
Third, hypoxanthine guanine phosphoribosyltransferase (HPRT) converts 6-MP into 6-thioinosine monophosphate (6-TIMP). Inosine monophosphate dehydrogenase (IMPDH) dehydrogenizes 6-TIMP into 6-thioxanthosine monophosphate (6-TXMP). Subsequently, guanosine monophosphate synthetase (GMPS) metabolizes 6-TXMP to 6-thioguanine nucleotides (6TGNs). 6TGNs, the primary active metabolites, integrate into DNA and RNA molecules to generate cytotoxic and therapeutic effects [10,11]. Patients with intermediate TPMT activity show 50% more 6-TGNs than those with normal or high TPMT activity. Low or absent TPMT activity can lead to further accumulation of 6-TGNs. Deficient enzyme activity may increase AZA-related side effects. AZA-related side effects include myelosuppression, which occurs in 3–17% of the patients [12,13], hepatotoxicity in up to 10% of patients [14], gastrointestinal adverse reactions (nausea, vomiting, and diarrhea), and hair loss.

Genetic polymorphisms of the TMPT gene have been studied extensively [15]. In the Caucasian population, approximately 4–11% of individuals have intermediate TPMT activity, while approximately 1 in 300 (0.3%) have very low or absent TPMT activity [16,17]. In the Asian population, the frequency of TPMT gene mutations is approximately 1.5–3%. TPMT polymorphisms were significantly associated with AZA-induced bone marrow toxicity [18–20]. Two meta-analyses by Liu et al. [19,20] reported the incidence of the TPMT polymorphisms was not associated with AZA-induced hepatotoxicity in patients with autoimmune diseases and IBD. However, the populations in these studies discussing AZA-induced hepatotoxicity were mainly Caucasian. The enrolled Asian population was limited. We may need a larger Asian population to evaluate the association between the incidence of AZA-induced hepatotoxicity and TPMT polymorphisms.

This retrospective case-control study aimed to evaluate whether there is a relationship between TPMT polymorphisms and the incidence of AZA-related hepatotoxicity.

## 2. Material and Methods

### 2.1. Study Design

This retrospective case-control study was performed in a single medical center in Taiwan with the approval of the Ethics Committee of Taichung Veterans General Hospital (SF19153A). Each participant offered written informed consent before study participation. This study was conducted according to the Declaration of Helsinki.

### 2.2. Study Population

The study participants were from the Taiwan Precision Medicine Initiative (TPMI), which collected information and specimens from Taiwanese volunteers from 15 hospitals throughout the nation. The tertiary referral medical center, Taichung Veterans General Hospital (TCVGH), contributed to the majority of the TPMI cohort. A total of 43,035 patients who were >20 years old were enrolled in the TPMI study from June 2019 to August 2021. A total of 2128 patients with present or previous exposure to AZA were selected. Patients who had not yet started therapy at the time of laboratory analysis were excluded. We excluded patients without a white blood cell (WBC) count, those without alanine transaminase (ALT) data, and those with a cancer diagnosis before taking AZA.

### 2.3. Data Collection

The date of AZA prescription was defined as index day. Clinical data after the index day, including age, gender, azathioprine dosage, first biochemistry profile, use of other immunosuppressive agents, comorbidities, and AZA prescription department were collected from the electric health record.

Comorbidities diagnosed once during hospitalization or at least twice in the outpatient department and six months before and after AZA prescription were included. These included hepatitis B (International classification of diseases (ICD) 10/9: B16, B18.0–B18.1, B19.10–B19.11/070.2–070.3), SLE (M32/710), Sjogren's syndrome (M35/710.2), DM (M33.0–33.1, M33.9, M36.0/710.3), polymyositis (M33.2/710.4), pemphigus (L10.0–10.9/694.4), RA (M05.7–M05.9,
M06.0, M06.2–M06.3, M06.8–M06.9, M08/714), systemic sclerosis (SSc) (M34/710.1), mixed connective tissue disease (M35.1/710.8), vasculitis (M30.0, M31.0, M31.3-M31.7/446.0, 446.2, 446.4–446.5, 446.7), pemphigoid (L12.1–12.9, K05.11/694.5–694.6), Behcet's disease (M35.2/136.1), and IBD (K50, K51, K52.9/555.0–555.9, 556.0–556.9, 558.9).

Following the AZA therapy index day, the first onset time of leukopenia and hepatotoxicity was analyzed. Leukopenia was defined as a WBC count $\le$ 4000/uL. Hepatotoxicity was defined as an ALT level $\ge$ 150 U/L. The AZA prescription division was also analyzed and included the divisions of immunology and rheumatology (IMRH), nephrology (NEPH), neurology (NEUR), dermatology (DERM), chest medicine (CM), cardiology (CV), gastroenterology and hepatology (GI), hematology (HEMA), metabolism, endocrinology and nutrition (META), gastrointestinal surgery (GS), Thoracic Surgery (CS), colorectal surgery (CRS), otolaryngology (ENT), ophthalmology (OPH), and pediatrics (PENP).

## 2.4. TMPT Genotyping

Genotyping was performed using the Taiwan Biobank (TWB, established in 2012) version 2 array [21], a single nucleotide polymorphism (SNP) array designed for the Taiwanese population by choosing optimized SNPs for imputation from the whole-genome sequencing (WGS) of the Taiwan Biobank participants. The TWBv2 array straightly genotypes more than 100,000 functional variants. The SNPs rs1142345 representing TPMT alleles *3A or *3C were analyzed. Alleles *3A and *3C were the common inactivating alleles in East Asia, accounting for 98% of all no-function alleles [22]. Genotyping tests including these variants are likely to be informative for the TPMT phenotype. A patient carrying one allele with the rs1142345 variant was reported as *1/*3 and was defined as intermediate metabolizer (IM). The patient carrying two alleles with the rs1142345 variant was reported as *3/*3 and was defined as a poor metabolizer (PM). According to genotyping, the patients were classified into TPMT normal metabolizers (NMs) and non-normal metabolizers (non-NMs), which included IMs and PMs.

## 2.5. Statistical Analysis

Data are presented as either numbers (percentages) or as the mean $\pm$ standard deviation (SD) or as the median (interquartile range). Comparisons were performed using the Mann–Whitney U test. Categorical data are presented as numbers and percentages and were compared using the chi-square test. Cox regression analysis was used to analyze which factors are related to hepatotoxicity with adjustment for covariates (TPMT genotype, age, gender, AZA dosage, other medication (methotrexate (MTX)), and comorbidity of hepatitis B). Kaplan–Meier curves were used to determine differences in cumulative incidence of hepatotoxicity between the TPMT non-NM and NM groups. A *p*-value $\le 0.05$ was considered statistically significant. All data were analyzed using the Statistical Package for the Social Sciences (SPSS, IBM Corp., Armonk, NY, USA) version 22.0.

# 3. Results

## 3.1. *Study Patient Flow*

Of the 43,035 patients enrolled in the TPMI, 2128 patients with AZA exposure were selected (Figure 1). A total of 628 patients without a WBC count, without ALT data, or with a cancer diagnosis before taking AZA were excluded. The TPMT non-NM and NM groups were matched by age and gender. There were 50 TPMT non-NM patients, including 49 IMs and 1 PM, and 1000 TPMT NM patients among the AZA users.
<figure><img src="image_100.png" /><figcaption>**Figure 1.** Patient enrollment flowchart. % 1. Exclude patients without WBC count and ALT data; 2. Exclude patients with a diagnosis of cancer before taking AZA. & Match by age and sex at 1:20. TPMI, Taiwan Precision Medicine Initiative; TPMT, thiopurine S-methyltransferase; NM, normal metabolizer; IM, intermediate metabolizer; PM, poor metabolizer; WBC, white blood cell; ALT, alanine transaminase; AZA, azathioprine.</figcaption></figure>

## 3.2. Demographics of the Selected Patients

The patient characteristics are summarized in Table 1. Patients were classified into TPMT non-normal metabolizer (non-NM) (n = 50) and normal metabolizer (NM) (n = 1000) groups. Age, gender, AZA dosage, post-AZA treatment first mean WBC count and ALT, and AZA prescription division were similar among the TPMT non-NM and NM groups. In terms of comorbidities, more patients had hepatitis B in the TPMT non-NM group than in the NM group.

<table><caption>**Table 1.** Clinical and demographic characteristics of patients in the TPMT non-NM and NM groups.</caption><thead><tr><th>Variable</th><th>TPMT Non-NM (n = 50)</th><th>TPMT NM (n = 1000)</th><th>p-Value</th></tr></thead><tbody><tr><td>Age</td><td>51.5 ± 11.7</td><td>51.4 ± 11.6</td><td>0.95</td></tr><tr><td>Gender</td><td></td><td></td><td></td></tr><tr><td>Female</td><td>43 (86.0)</td><td>858 (85.8)</td><td rowspan="2">0.97</td></tr><tr><td>Male</td><td>7 (14.0)</td><td>142 (14.2)</td></tr><tr><td>AZA dose (mg)</td><td>36.4 ± 17.9</td><td>39.6 ± 23.47</td><td>0.35</td></tr></tbody></table>