key: cord-0964831-nj2nhn6f
authors: Roy, Sumith; Demmer, Ryan T.
title: Impaired Glucose Regulation, SARS-CoV-2 Infections and Adverse COVID-19 Outcomes
date: 2021-11-09
journal: Transl Res
DOI: 10.1016/j.trsl.2021.11.002
sha: ff64e08776e204651283ea34998fef025864fb85
doc_id: 964831
cord_uid: nj2nhn6f

Impaired glucose regulation (IGR) is common world-wide, and is correlated with SARS-CoV-2 (the virus that causes COVID-19). However, no systematic reviews are available on the topic, and little is known about the strength of the evidence underlying published associations. The current systematic review identified consistent, reproducible associations but several limitations were observed including: i) a consistent lack of robust confounder adjustment for risk factors collected prior to infection; ii) lack of data on insulin resistance or glycemia measures (A1c or glucose; iii) few studies considering insulin resistance, glucose or A1c values in the clinically normal range as a predictor of SARS-CoV-2 risk; iv) few studies assessed the role of IGR as a risk factor for infection among initially uninfected samples; v) a paucity of population-based data considering SARS-CoV-2 as a risk factor for the onset of IGR. While diabetes status is a clear predictor of poor prognosis following a SARS-CoV-2 infection, causal conclusions are limited. It is uncertain whether interventions targeting dysglycemia to improve SARS-CoV-2 outcomes have potential to be effective, or if risk assessment should include biomarkers of diabetes risk (i.e., insulin and glucose or A1c) among diabetes-free individuals. Future studies with robust risk factor data collection, among population-based samples with pre-pandemic assessments will be important to inform these questions.

Diabetes Mellitus is believed to be an important risk factor predisposing to SARS-CoV-2 (the virus that causes infection as well as severity of infection and risk for hospitalization and mortality. Interestingly, less is known about the relationships between non-diabetic impaired glucose regulation (for the purposes of this review, prediabetes, and insulin resistance) as a potential risk factor for SARS-CoV-2 infection.

For example, Rosenthal et al reported that 29.9% of inpatients with a diagnosis of COVID-19 had no co-morbidities using the Charlson-Deyo comorbidity index suggesting the importance of phenotypic characteristics other than comorbidity burden. [1] One of the key factors in deciphering the above conundrum is to view Type 2 Diabetes (T2D) as a continuum of glucose dysregulation and not merely dichotomously by hyperglycemia exceeding thresholds (largely identified vis-à-vis their prediction of microvascular outcomes). [2] Tabak et al observed a modest linear increase in fasting and post prandial glucose, followed by a sharp rise in the aforementioned levels and a steeper decrease in HOMA insulin sensitivity in the 5 years prior to diagnosis of T2D, suggesting that individuals with prediabetes are already in the accelerated phase, trending towards overt diabetes. [3] The relative risk of cardiovascular events was 1.33 (95% CI:1.06-1.67) for impaired fasting glucose and 1.58 (95% CI: 1.19-2.10) for impaired glucose tolerance. [4] The current guidelines in the screening, diagnosis, and management of cardiometabolic disease have shifted towards prioritizing risk factors of preclinical entities such as insulin resistance and prediabetes. For instance, the United States Preventive Services Task Force updated their recommendations in 2015 to include screening for diabetes among those ages 40-70 years who are overweight or obese which was previously limited to asymptomatic individuals with hypertension. [5] Similarly, the American Association of Clinical Endocrinologists introduced the dysglycemia based chronic disease model (DBCD) to include stage 1 of insulin resistance, stage 2 of biochemical risk or prediabetes, stage 3 of type 2 diabetes, and stage 4 vascular complications, to encourage aggressive cardiovascular management as early as stage 1. [6] Although uncommon, the microvascular complications of T2D, specifically retinopathy has been reported in the impaired glucose regulation phase, before diabetes is diagnosed. [7] Thefore, considering impaired glucose regulation as a continuum in relation to SARS-CoV-2 outcomes could add great value to risk prediction as SARS-CoV-2 infection will likely become endemic. [8] Beyond the role of IGR as a predictor of SARS-CoV-2 infection and poor outcomes post-infection, there is preliminary evidence that SARS-CoV-2 infection itself might be a risk factor for impaired glucose regulation and subsequent diabetes development among diabetes-free individuals. There is a strong rationale to believe this is possible based on the broader literature linking infections to adverse metabolic outcomes including insulin resistance [9] [10] [11] , glucose intolerance [12] and diabetes development [2, [13] [14] [15] [16] .

In this review, we systematically search the literature for published data on the interplay between impaired glucose regulation, diabetes mellitus and SARS-CoV-2. We summarize key findings and discuss a variety of methodological issues and their implications for understanding whether metabolic parameters predict risk of SARS-CoV-2 infection and poor COVID-19 outcomes, as well as the evidence that SARS-CoV-2 induces metabolic abnormalities increasing diabetes risk. We additionally summarize emerging concepts related to the biological plausibility of the relationships.

To assess the evidence from population-based and clinically-based human studies, we searched PubMed for literature published on impaired glucose regulation, diabetes, SARS-CoV-2, and COVID-19 between December 2019 and June 2021. The search strategy is summarized in Table 1 . Initially 3,008 publications were identified. A primary reviewer SR removed duplicate publications and reviewed titles for inclusion.

Ambiguous inclusion decisions were discussed by both authors (SR and RTD) to determine inclusion/exclusion. Review articles (systematic reviews, meta-analysis), case reports, studies that were done exclusively in children and adolescence or in pregnancy and studies that did not report a measure of association pertinent to the specific search criteria were excluded. The final selection flow is summarized in Figure   1 and 32 articles assessing metabolic parameters and SARS-CoV-2 infection were included.

Nearly all studies identified included only study samples with SARS-CoV-2 infected patients, while only five studies considered samples with and without SARS-CoV-2 infected individuals. Therefore, limited data exist considering the contribution of diabetes status to the risk of becoming SARS-CoV-2 infected. Among studies that do include samples with and without infection, most report on poor SARS-CoV-2 related outcomes rather than the cumulative incidence of infection. Nevertheless, while sparse, the limited data are informative. McGurnaghan et al [17] provide total population level data among 5,463,300 Scotland residents (5.8% with diabetes) and assessed whether diabetes status was associated with critical care unit-treated COVID-19 or mortality. The age and sex adjusted odds ratios for poor COVID outcomes was 2. 40 [18] . Similarly, studies from Mexico [19] , and Italy [20] also reported higher risk of SARS-CoV-2 outcomes (including hospitalization and mortality) among those with vs. without diabetes (Table 2) .

Most published studies to date, included only hospitalized patients with confirmed SARS-CoV-2 infection. Table 2 provides a summary of all studies and their key design features and findings. In total 26 inpatient studies were identified among which 20 reported mortality outcomes while the remaining 6 reported adverse outcomes such as ICU admission and/or length of stay, need for mechanical ventilation, and/or COVID-19 severity. Reports arose from 11 countries across diverse populations and included 28,311 patients. Reported mortality rates varied greatly but among eight such studies with a samples size of at least 1,000, six studies reported mortality rates >20%. Among 13 studies that reported a summary measure of association for mortality, 9 observed a statistically significantly increased risk for death among those with vs. without diabetes.

Limited reporting for T1 vs. T2 diabetes was available, although at least one large study with >6,000 patients [21] reported empirically higher risk for hospitalization among patients with T1DM (but seemingly equal risk for mortality between T1DM and T2DM).

In contrast, a separate study with only 238 patients reported lower mortality risk among T1DM although there was only 1 death in the T1DM group and the findings for both T1DM and T2DM were not statistically significant.

Two studies with sample sizes sufficient for subgroup analyses observed that the mortality risk among people with vs. without diabetes, decreased as age increased.

Woolcott et al [19] reported adjusted hazard ratios for death to 3.12 ( 64, and 65+ respectively (p-value for interaction<0.05). These interactions could be biologically driven although the underlying mechanisms are unclear. Alternatively, these findings are quite possibly a consequence of scaling as the measures of association were relative risk measures and not absolute risk differences. Regardless, despite attenuated relative risk in the oldest age groups, risk remained elevated among patients with vs. without diabetes and absolute risk for poor outcomes in older, SARS-CoV-2 infected patients with diabetes is quite high.

There were nine studies identified that assessed impaired glucose regulation and SARS-CoV-2 infection ( Table 3 ). The criteria used to define impaired glucose regulation varied considerably making between study comparisons challenging. Some studies used objective laboratory parameters while others relied on self-report physician diagnosis. Even among studies relying on laboratory measures, there was variation in the analytes (e.g., fasting blood glucose, A1c, and triglyceride and glucose index) and thresholds defining IGR. Most studies reported outcomes to be worse among patients with non-diabetic IGR vs. metabolically normal patients. However, study samples were small with seven of nine studies enrolling fewer than 500 patients and statistical significance was lacking in several reports.

Many of the studies in this review included only hospitalized patients. Such studies are potentially susceptible to differential selection bias according to diabetes status. In some settings, it is possible thatall else equalpatients with diabetes are potentially more likely to be tested for SARS-CoV-2 and/or admitted as the result of perceived higher risk. This could result in a bias such that the risk profile of admitted DM patients was more favorable compared to non-DM patients who perhaps required more severe infection for admission. Such bias could attenuate study findings. While selection bias is a separate issue from confounding, it is possible to mitigate these biases analytically through statistical adjustment if sufficient data collection is available [23] particularly on factors related to prognosis. Given the limited adjustment in most studies, selection bias is a concern that remains to be addressed in future studies.

The assessment method for SARS-CoV-2 infection varied across studies. Most studies defined infection as physician diagnosed with a positive nasopharyngeal and/or oropharyngeal swab RT-PCR confirmed infection. However, several studies (particularly those from early in the pandemic when testing capacity was limited) often relied on physician diagnosis based on symptomology and radiological findings suggestive of SARS-CoV-2 infection. Information bias of this nature is likely to bias study findings towards the null as the SARS-CoV-2 positivity rates during most of the pandemic has been <50% and frequently <5%among symptomatic individuals. [24] No studies were identified that considered asymptomatic SARS-CoV-2 infection.

The lack of population-based research that systematically monitors for any infection (asymptomatic or otherwise) is a major limitation in the literature to date. In the context of risk factor studies, such as those considering metabolic parameters as a risk factor for SARS-CoV-2 infection, mildly symptomatic and asymptomatic infections can bias study results by misclassifying participants as uninfected when in fact they were truly infected but not diagnosed. This form of classical information bias in epidemiological studies will generally bias towards the null if it occurs equally among those with vs.

without the risk factor of interest (e.g., diabetes).

It remains poorly understood whether asymptomatic cases are more or less likely to occur among individuals with IGR. This is in part due to the need for costly serial testing to enhance the likelihood of detecting asymptomatic/mildly symptomatic cases, since gold-standard PCR testing can only detect active viral infection. To address this issue, an alternative to frequent (~weekly) PCR testing, is less frequent antibody (~bimonthly) testing to assess recent history of infection. While promising, important limitations to this approach also remain. For example, the lack of definitive knowledge about the duration of antibody responses coupled with the large number of assays available with substantial performance variability is an important current limitation [25] .

Additionally, lack of data related to antibody response and duration that might be differential by IGR status is another important concern. Ironically, while antibody studies might be conceptually helpful for identifying prior asymptomatic infections, if their performance is dependent on IGR status, their use would simply lead to another form of differential misclassification bias. Published data on this topic are conflicting with at least one study suggesting antibody response to be similar [26] among those with vs.

without diabetes while another study reported impaired seroconversion [27] in diabetes (which would lead to SARS-CoV-2 under-estimation in diabetes). Finally, in the age of vaccination, assays that can reliably distinguish natural infection from vaccination based on serology will be necessary [28] . Ultimately, while reliance on antibody assays alone could be problematic, their use in combination with other assessment methods could enhance sensitivity of infection identification methods.

All studies included in this review were observational study designs which are susceptible to confounding, in which associations between IGR status and either SARS-CoV-2 infection, or poor COVID-19 outcomes, might be spurious and due to differential distributions of causal risk factors for outcomes among those with vs. without IGR. The epidemiological study design that is least susceptible to confounding is a randomized controlled trial. However, for hypotheses of IGR status as a risk factor for infection and infection severity, RCTs are not possible as the IGR status in humans cannot be randomized. Therefore, well conducted observational studies will be necessary to inform causality. Of central importance to addressing this limitation is the robust data collection of including important potential confounders. The relevant data collection likely extends beyond standard data available in medical records and includes information on lifestyle. As importantly, many of these factors should be assessed prior to infection (and prior to pandemic times) to ensure that baseline risk is rigorously characterized ( Figure. 2.).

Overall, the level of confounder adjustment observed in most reported studies (Tables 2 and 3 ) was quite limited. For example, only one third of studies adjusted for either adiposity or smoking and fewer adjusted for both. Lack of adiposity adjustments is particularly concerning given the early data that adiposity is likely a strong risk factor for diabetes, as well as for hospitalization and death [29] among patients diagnosed with COVID-19. (Figure 2 ) Similarly, rigorous adjustment for medication use was also limited.

Xian et al demonstrated in mouse models, the anti-inflammatory role of metformin via inhibition of the NLRP3 inflammasome and interlekin-1 induced acute respiratory distress syndrome in SARS-CoV-2 infection. [30] Similarly statins such as simvastatin and lovastatin were found to be most effective in severe SARS-CoV-2 infection exerting its influence via cytotoxic T lymphocytes and regulatory T cell induction. [31] As the literature assessing the effect of medications targeting dysglycemia, hypertension, thrombosis, [32] and hypercholesterolemia on SARS-CoV-2 infection and severity tends to suggest modest protection against infection and mortality, future studies will be enhanced by accounting for medications to avoid negative confounding towards the null (eg, weaker associations due to the protective effect of medications being more common among patients with vs. without diabetes). The need for more robust consideration of several additional important factors including socio-demographic (access to care, income, education, isolation), behavioral (diet, activity), ( Figure 2 ) and biological (d-dimer, blood pressure, renal function) was also largely lacking in published studies limiting interpretation and conclusions.

The systematic review discussed above supports the preliminary mechanistic evidence linking IGR to risk for infection as briefly discussed below.

First, SARS-CoV-2 is postulated to bind to ACE2 (Angiotensin Converting Enzyme-2) receptor which is found on human lung epithelium, vascular endothelium, intestinal lining, pancreatic beta cells etc. 12 Secondly, diabetes has shown to significantly alter cell-mediated immunity [40, 41] lowering the chemotactic and phagocytic function leading to a diminished response to infection. For example, impaired interferon production (IFN-alpha and IFN-gamma) was observed in non-obese diabetic and prediabetic mouse models [42] . Another hypothesized mechanism is that elevated glucose levels increase adherence of microorganisms to cellular receptors [43] , thereby increasing the prevalence of infections in patients with diabetes. Increased SARS-CoV-2 viral replication and cytokine expression were also observed in monocytes of diabetic/obese patients in an environment of elevated glucose levels [44] . Codo et al observed that the hypoxiainducible factor 1 alpha(HIF1A) was overexpressed in the monocytes of SARS-CoV-2 infection in greater intensity that that of RSV or influenza-infected monocytes [44] .This might explain the severity of COVID-19 infection and poorer outcomes commonly reported in patients with diabetes [45] . The concurrent responses resemble that of the severe and prolonged infective period observed in MERS-CoV infected mice models with diabetes [46] .

Despite, a reasonable biological premise for IGR to increase risk for poor outcoms among SARS-CoV-2 infected individuals, future studies are necessary to: i) confirm biological mechanisms in animal models and extend findings to humans; ii) identify biomarker phenotypes that identify elevated risk; and iii) translate these findings to interventional approaches that reduce infection risk and improve outcomes postinfection.

There are also several biologically plausible mechanisms through which SARS-CoV-2 might predispose to the development of impaired glucose regulation (as opposed to the reverse). Key mechanisms discussed to date are outlined below.

Chronic inflammation is a plausible biological mechanism linking infections and insulin resistance. Animal models have shown that inflammatory cytokines such as TNF- can induce a state of insulin resistance [47] , possibly as a consequence of TNF-'s ability to interrupt serine phosphorylation of IRS-1 [48] , and data in humans have repeatedly shown inflammation to be an independent risk factor for both insulin resistance [49] and T2DM [50] [51] [52] . If a chronic inflammatory phenotype emerges in some COVID-19 patients, it is plausible that insulin resistance may follow along with increased risk for incident prediabetes of diabetes. Notably, in studies that lack pre-infection phenotyping, it is not possible rule out reverse causality (i.e., SARS-CoV-2 induced IGR rather than IGR increase risk for infection and poor outcomes).

underlying poor metabolic outcomes among individuals with symptomatic COVID-19.

Evidence is accumulating suggesting that SARS-CoV-2 causes endothelial dysfunction [53] . Importantly, endothelial dysfunction is believed to directly contribute to insulin resistance, possibly by impairing transcapillary passage of insulin to target tissues [54] . This primarily occurs through viral infection induced activation of the integrated stress response pathway (ISR). Phosphorylation of the IRS1 receptor by IRS regulator, PKR kinases, can attenuate insulin action, leading to insulin resistance. [55] Tropisms for beta-cells is another mechanism linking infection to metabolic abnormalities [37, 56, 57] . Very recent evidence suggests that SARS-Cov-2 can infect human beta-cells leading to morphological, transcriptional, and functional changes such as reduced insulin-secretory granules and reduced glucose stimulated insulin secretion [37, 56] . Accordingly, numerous prior studies have hypothesized a variety of viral infections in the etiology of type 1 diabetes due to beta-cell infection and impairment [58] . In support of this notion, impaired glucose metabolism and acute-onset diabetes have been reported to be higher among those with SARS coronavirus 1 pneumonia as compared to individuals with non-SARS pneumonia [59] . Adjustments: 1=Age; 2=Sex; 3=Race; 4=Ethnicity; 5=Obesity; 6=Comorbidities; 6a=chronic respiratory disease 6b=Asthma; 6c=HT; 6d=chronic heart disease/cardiovascular disease/CAD; 6e=chronic renal disease; 6f=chronic liver disease; 6g=chronic neurological disease; 6h=Immunosuppression; 6i=COPD; 6j=heart failure; 6k=Atrial fibrillation; 6l=hematological disorders; 6m=rheumatologic/autoimmune disorder; 7=deprivation; 8=geographical region; 9=previous hospital admissions with coronary heart disease, cerebrovascular disease, or heart failure; 10=History of diabetes; 11=LFT; 12=eGFR; 13=glucose on admission; 14=NT-proBNP (per 100 pg/mL); 15=BMI; 16=History of cancer ;17=CRP; 18=LDH (per 100 U/L); 19=smoking; 20=Lymphocyte count (per 1 × 109/L); 21=WBC count; 22=Treatment with ACE/ARB's; 23=blood glucose; 24=Serum ferritin (per 100 μg/L); 25=Hba1c; 26=Number of comorbidities; 27=Differential neutrophil count; 28=Hematuria; 29=Initial serum globulin (g/L); 30=Fever with chills; 31=Marijuana use; 32=Platelet count (x109 cells/L); 33=CD3, CD4, CD8; 34=Coagulation function (34a, Fbg; 34b, D dimer); 35=IL-6; 36=Chest distress/dyspnea/chest tightness; 37=BUN; 38=Creatinine kinase; 39=CTnI; 40=CRB-65 measures the severity of pneumonia on a 0 to 4 scale; 41=QTc prolongation; 42=≥2 comorbidities; 43=Prothrombin time; 44=Procalcitonin; 45=aspartate aminotransferase (AST); 46=hospital; 47=systolic blood pressure; 48=total cholesterol; 49=antihypertensive drugs, lipid-lowering agents; 50=admission to ICU; 51=invasive mechanical ventilation; 52=glucose-lowering drugs before inpatients and during inpatients; 53=corticosteroid use; 54=cognitive impairment;55=Area of lung injury>50% Figure 2 

Risk Factors Associated With In-Hospital Mortality in a US National Sample of Patients With COVID-19

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6f=chronic liver disease; 6g=chronic neurological disease; 6h=Immunosuppression; 6i=COPD; 6j=heart failure; 6k=Atrial fibrillation; 6l=hematological disorders; 6m=rheumatologic/autoimmune disorder; 7=deprivation; 8=geographical region; 9=previous hospital admissions with coronary heart disease, cerebrovascular disease, or heart failure

14=NT-proBNP (per 100 pg/mL); 15=BMI; 16=History of cancer ;17=CRP; 18=LDH (per 100 U/L); 19=smoking; 20=Lymphocyte count (per 1 × 109/L); 21=WBC count

24=Serum ferritin (per 100 μg/L); 25=Hba1c; 26=Number of comorbidities; 27=Differential neutrophil count; 28=Hematuria; 29=Initial serum globulin (g/L); 30=Fever with chills

35=IL-6; 36=Chest distress/dyspnea/chest tightness; 37=BUN; 38=Creatinine kinase; 39=CTnI

40=CRB-65 measures the severity of pneumonia on a 0 to 4 scale

52=glucose-lowering drugs before inpatients and during inpatients; 53=corticosteroid use; 54=cognitive impairment;55=Area of lung injury>50%

All authors have read the journal's policy on disclosure of potential conflicts of interest.The authors have declared that no conflict of interest exists. S.R., R.D. conceptualized, wrote, and edited the manuscript. All authors have read the journal's authorship agreement and that the manuscript has been reviewed by and approved by all named authors.