Nirmatrelvir/Ritonavir-Induced Glucose Fluctuation：A new adverse reaction

Nirmatrelvir/Ritonavir-Induced Glucose Fluctuation：A new adverse reaction | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 December 2025 V1 Latest version Share on Nirmatrelvir/Ritonavir-Induced Glucose Fluctuation：A new adverse reaction Authors : Ouyang Yi 0000-0001-8785-8779 [email protected] , Yao Bai , Ping Chen , Lifang Duan , Guilin He , and Jinsong Yuan Authors Info & Affiliations https://doi.org/10.22541/au.176521165.55219749/v1 231 views 85 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: During treatment with nirmatrelvir/ritonavir, the patient developed an adverse drug reaction (ADR) of glycemic fluctuation-a phenomenon not documented in the drug’s official labeling. Aim: To ascertain the correlation between nirmatrelvir/ritonavir and glycemic fluctuation, and to elucidate the underlying mechanism of this ADR. Methods: First, the Naranjo Algorithm for ADR Assessment was applied to evaluate the causal relationship between them; Second, a comprehensive literature review was conducted to interpret potential mechanisms contributing to nirmatrelvir/ritonavir-induced glycemic fluctuation; finally, evidence-based recommendations for the prevention and management of this ADR were proposed to assist clinicians in addressing similar clinical scenarios. Results: The results show: (1) The Naranjo Algorithm confirmed a definite causal relationship between nirmatrelvir/ritonavir and glycemic fluctuation. (2) Three potential mechanisms were identified to explain nirmatrelvir/ritonavir-induced glycemic disturbance: a) Ritonavir inhibits the activity of glucose transporter 4; b) Pharmacokinetic interactions between ritonavir and drugs metabolized by CYP3A4 may precipitate glycemic fluctuations; c) Nirmatrelvir enhance insulin activity excessively. (3) The study recommends: routine monitoring of blood glucose levels during nirmatrelvir/ritonavir treatment; avoidance of concurrent use of nirmatrelvir/ritonavir with drugs that are metabolized by CYP3A4 and have a known risk of inducing glycemic instability. Conclusion: In conclusion, this study identifies glycemic fluctuation as a previously unreported ADR to nirmatrelvir/ritonavir, preliminarily explores its potential mechanisms, and provides clinical recommendations for managing this ADR. Future work will include collecting additional clinical case for proactive post-marketing surveillance and conducting basic research, providing evidence for updating nirmatrelvir/ritonavir’s drug labeling and promoting safer clinical use of this medication. 1 Introduction On November 22, 2021, the United States Food and Drug Administration (FDA) granted emergency authorization for nirmatrelvir/ritonavir (Paxlovid) for the treatment of COVID-19. In February 2022, Paxlovid received conditional marketing approval in China via the accelerated special drug review and emergency evaluation pathway. Its approved indication is the treatment of mild to moderate COVID-19 in adult patients with high-risk factors for progression to severe disease. Pharmacologically, nirmatrelvir acts as a peptide-like inhibitor of the severe acute respiratory syndrome coronavirus 2 main protease M pro (SARS-CoV-2 M pro ). It binds to the active site of SARS-CoV-2 M pro and forms a covalent interaction with the cysteine residue at position 145 of the enzyme. This interaction inhibits the ability of SARS-CoV-2 M pro to process viral polyprotein precursors, thereby blocking SARS- CoV-2 replication. Ritonavir functions as a pharmacokinetic (PK) enhancer: it inhibits the cytochrome P450 3A4 (CYP3A4)-mediated metabolism of nirmatrelvir, leading to increased and sustained plasma concentrations of nirmatrelvir. The synergistic effect of this combination enhances antiviral efficacy, supporting its widespread clinical use. With the expanding global application of Paxlovid in recent years, reports of associated ADR have gradually increased. Among these, gastrointestinal ADR-including diarrhea, dyspepsia, and nausea-are the most frequently documented and are considered common adverse events. Notably, glycemic fluctuation as an ADR of Paxlovid is not listed in the drug’s official prescribing information or labeling in major markets, including the United States and China. To contextualize the novelty of this observation, we conducted a comprehensive literature search across key academic databases: Chinese databases (China National Knowledge Infrastructure [CNKI], Wanfang Data, VIP Chinese Science and Technology Periodical Database) and English databases (PubMed). This search yielded only a limited number of case reports or studies describing ritonavir-associated glycemic disturbances, indicating a paucity of evidence on this topic. We further queried the FDA Adverse Event Reporting System (FAERS) to assess real-world post-marketing safety data. As of June 30, 2025, FAERS had received a total of 31,154 reports of ADRs attributed to Paxlovid, among which 71 cases were specifically associated with glycemic abnormalities. The clinical manifestations of these cases varied in severity: mild cases were characterized by poor glycemic control, including exacerbation of hyperglycemia or hypoglycemia in patients with pre-existing diabetes, while severe cases were linked to the de novo development of diabetes mellitus—including rare instances of type 1 diabetes mellitus. Given the lack of formal documentation in drug labeling, the limited existing literature, and the potential clinical impact of unmanaged glycemic fluctuations (especially in vulnerable populations such as elderly patients or those with comorbidities), this study presents a detailed case analysis of an elderly non-diabetic patient who developed glycemic fluctuation during Paxlovid treatment. Specifically, this manuscript: (1) clarifies the causal relationship between Paxlovid administration and glycemic fluctuation; (2) explores the potential underlying mechanisms contributing to this ADR; and (3) proposes evidence-based recommendations for the prevention and clinical management of Paxlovid-associated glycemic disturbances. Ultimately, this work aims to provide critical reference information to support safe and rational clinical use of Paxlovid. 2 Case description A 69-year-old male patient was admitted to our hospital with chief complaints of fever and dyspnea. His medical history included hypertension (Grade 3) and epilepsy, but no history of chronic diseases such as diabetes mellitus or coronary heart disease. On admission, the patient was diagnosed with: (1) severe pneumonia complicated with Type I respiratory failure; (2) sepsis, (3) epilepsy, (4) grade 3 hypertension, and (5) malnutrition. Initial comprehensive treatment for severe pneumonia included: antitussive therapy with ammonium bromide and acetylcysteine; bronchodilation via inhaled budesonide and ipratropium bromide; and antibacterial therapy with moxifloxacin combined with meropenem. Concomitant medications were administered as follows: levetiracetam and sodium valproate sustained-release tablets for antiepileptic control; pantoprazole for acid suppression and gastric protection; and enteral nutrition emulsion for nutritional support. On the 3rd day after admission, the patient tested positive for COVID-19. Antiviral treatment with Paxlovid (nirmatrelvir 150 mg/ritonavir 100 mg, once daily [qd] ) was initiated and continued for 6 days (from hospital DAY 3 to Day 8). On hospital Day 9, Paxlovid was discontinued, and a repeat COVID-19 test remained positive; the patient also exhibited persistent body temperature fluctuations. Therefore, Paxlovid was reinitiated at the same dosage (150/100 mg, qd ) from hospital Day 11 to Day 14. On hospital Day 15, Paxlovid was temporarily withheld for 1 day due to medication procurement issues. During this period, a key observation was made: glycemic levels were elevated above the normal reference range during Paxlovid administration, but returned to normal following temporary discontinuation of the drug. After 10 days of Paxlovid treatment (at the 150/100 mg, qd dosage), the patient’s clinical condition showed no improvement, and the therapeutic effect was deemed unsatisfactory. Therefore, the Paxlovid dosage was adjusted to 300 mg nirmatrelvir/100 mg ritonavir, twice daily [bid] , administered on hospital Days 16 and 17. Concurrently, the patient’s blood glucose levels decreased to 3.8 mmol/L and 3.2 mmol/L, respectively (below the normal fasting glucose threshold of 3.9 mmol/L, indicating asymptomatic hypoglycemia). Following the dosage adjustment, the patient’s body temperature normalized, and a repeat COVID-19 test on hospital Day 18 returned negative; Paxlovid was thus permanently discontinued. Consistent with prior observations, the patient’s blood glucose levels returned to the normal range after the final discontinuation of Paxlovid. Based on the temporal correlation between Paxlovid administration (including dosage adjustments) and the onset of glycemic fluctuations (hyperglycemia at the lower dosage, hypoglycemia at the higher dosage), as well as the resolution of glycemic abnormalities following drug discontinuation, we preliminarily concluded that the observed glycemic fluctuations were likely associated with Paxlovid treatment (Figure 1). 3 Discussion 3.1 Correlation between Paxlovid and Glycemic Fluctuations Throughout the patient’s hospitalization, a total of 26 medications were administered for the management of his comorbidities (e.g., hypertension, epilepsy) and acute conditions (e.g., severe pneumonia, sepsis). To isolate the potential causative agent of the observed glycemic fluctuations, a preliminary screening was first conducted to exclude drugs with low likelihood of association: specifically, we eliminated medications that were used both during periods of normal and abnormal glycemia (as their consistent use would not explain the temporal variation in blood glucose) and those administered only during periods of normal glycemia (as they could not contribute to glycemic disturbances). This screening process narrowed down the candidate medications to 6 drugs that were administered exclusively during the periods when the patient exhibited glycemic abnormalities. These included: Paxlovid, recombinant human brain natriuretic peptide, vancomycin, dexamethasone phosphate, triple live bacteria tablets (containing Bifidobacterium and Lactobacillus ), and trimetazidine sustained-release tablets (Table 1) To quantify the causal relationship between each candidate drug and the patient’s glycemic fluctuations, we applied the Naranjo Algorithm for ADR Assessment—a validated tool widely used in clinical pharmacology to evaluate the probability of a drug-induced adverse event. The Naranjo Algorithm assigns a total score based on responses to 10 questions (e.g., “Did the adverse event appear after the suspected drug was administered?”, “Is the adverse event dose-dependent?”), with predefined interpretation criteria: a total score of ≥ 9 indicates a definite causal relationship, 5–8 indicates a likely relationship, 1–4 indicates a possible relationship, and ≤ 0 indicates a suspect (unlikely) relationship. For this analysis, responses to each question were determined by cross-referencing the patient’s medication administration records, blood glucose monitoring data, drug package inserts, and relevant literature on each drug’s known ADR profile. The Naranjo assessment yielded critical findings (Table 2): Paxlovid received a total score of 10, which falls into the “definite” category—confirming that the patient’s glycemic fluctuations were most likely induced by Paxlovid. In contrast, the remaining 5 candidate drugs scored between 1 and 4, corresponding to a “possible” causal relationship. This lower score reflects several key observations: for example, none of these 5 drugs have well-documented associations with glycemic disturbances in their labeling or high-quality literature; additionally, their administration timing did not show the same tight temporal correlation with glycemic changes as Paxlovid (e.g., resolution of glycemia upon discontinuation was not consistently observed for these drugs). Notably, while the Naranjo Algorithm strongly supports Paxlovid as the primary cause of the patient’s glycemic fluctuations, the “possible” scores for the other 5 drugs mean their potential contributory role cannot be entirely ruled out—particularly given the patient’s complex clinical state (e.g., sepsis, malnutrition) and polypharmacy, which may increase the risk of drug-drug interactions or synergistic effects on glucose metabolism. Nevertheless, the “definite” score for Paxlovid, combined with the temporal association observed in the patient’s clinical course (Figure 1), provides compelling evidence that Paxlovid was the dominant factor driving the glycemic abnormalities. 3.2 Mechanism of Paxlovid-induced glycemic fluctuations Paxlovid is a fixed-dose combination of nirmatrelvir and ritonavir, where ritonavir acts as a pharmacokinetic enhancer to maintain therapeutic plasma concentrations of nirmatrelvir (via CYP3A4 inhibition). Below is a detailed analysis of the potential mechanisms by which each component contributes to glycemic fluctuations, integrated with clinical observations from this case. 3.2.1 Ritonavir-mediated hyperglycemia Glucose transporter 4 (GLUT4) is a key mediator of insulin-dependent glucose transmembrane transport, facilitating glucose uptake in peripheral tissues (e.g., adipose, skeletal muscle) and playing a critical role in systemic glucose homeostasis. Abnormal GLUT4 function is linked to insulin resistance in type 2 diabetes and altered glucose metabolism in pathological states such as cancer. Preclinical and clinical studies have demonstrated that ritonavir directly inhibits GLUT4 activity, reducing peripheral glucose uptake and thereby increasing circulating blood glucose levels[1]. Additionally, ritonavir indirectly impairs GLUT4-mediated glucose transport by upregulating the expression of heme oxygenase-1 (HO-1), which is accompanied by the release of pro-inflammatory cytokines (e.g., interleukin-8 [IL-8], tumor necrosis factor-α [TNF-α], C-C motif chemokine ligand 5 [CCL5], monocyte chemoattractant protein-1 [MCP-1]). These inflammatory factors interfere with GLUT4 translocation to the plasma membrane, further compromising glucose uptake efficiency[2]. Beyond GLUT4 inhibition, ritonavir disrupts insulin signaling and secretion: it reduces insulin sensitivity in peripheral tissues, decreases non-oxidative glucose disposal (a key pathway for glucose storage), and directly inhibits pancreatic β-cell function[3]. Specifically, ritonavir blocks voltage-dependent potassium (K⁺) channels and anion channels in β-cells, lowering cytoplasmic calcium concentrations—a critical step in insulin exocytosis—thereby suppressing insulin secretion [4]. Collectively, these effects of ritonavir provide a mechanistic basis for the hyperglycemia observed in the patient during initial Paxlovid treatment (150/100 mg, qd ). 3.2.2 Drug-Drug Interactions via CYP3A4 Inhibition Nirmatrelvir is primarily metabolized by CYP3A4, and ritonavir (a potent, irreversible CYP3A4 inhibitor) significantly increase nirmatrelvir plasma concentration[5]. Consequently, most clinically relevant drug-drug interactions (DDI) with Paxlovid arise from ritonavir-mediated CYP3A4 inhibition. CYP3A4 is a major isoform of the cytochrome P450 enzyme system, responsible for the metabolism of various drugs-including several agents that regulate glucose metabolism. For example, the oral hypoglycemic agent repaglinide is metabolized by both CYP2C8 and CYP3A4; ritonavir inhibits both CYP450 enzymes and organic anion transporters (OATP), reducing repaglinide clearance and potentiating its hypoglycemic effect[6]. In another case, a kidney transplant recipient with COVID-19 developed severe hyperglycemic (fasting blood glucose: 16.7 mmol/L) after initiating Paxlovid, concurrent with a marked increase in tacrolimus plasma concentration (to 43.6ng/mL)-likely due to CYP3A4-mediated DDI between ritonavir and tacrolimus[7]. This highlights the need for caution when co-administering Paxlovid with CYP3A4-metabolized drugs, as such interactions may precipitate glycemic fluctuations. In the current case, the patient had no prior history of diabetes or hypoglycemic medication use, so initial hyperglycemia was primarily attributed to ritonavir’s direct effects on glucose metabolism. However, glycemic abnormalities observed on Days 4-5 may also involve a DDI between Paxlovid and dexamethasone. To explore this, we analyzed the metabolic profiles of the 5 other candidate drugs administered during glycemic fluctuations: vancomycin and trimetazidine are excreted primarily as unchanged drug via the kidneys, with no reliance on enzymatic metabolism; recombinant human brain natriuretic peptide is cleared via receptor-mediated endocytosis and lysosomal degradation, independent of the CYP450 system or glomerular filtration; and triple live bacteria tablets (a probiotic) colonize the gastrointestinal tract without systemic metabolic activity. Only dexamethasone phosphate sodium is predominantly metabolized by hepatic CYP3A4. Dexamethasone was administered exclusively on hospital Days 4-5, and FAERS data indicate it I associated with glycemic disturbances: of 134,608 dexamethasone-related adverse event reports, 2,907(2.19%) involved blood glucose abnormalities (a well-documented common ADR). Mechanistically, dexamethasone promotes hepatic gluconeogenesis by inducing Kruppel-like factor 9 (KLF9) expression[8] and enhances fructose absorption in the small intestine by promoting nuclear translocation of carbohydrate response element binding protein (ChREBP)—which upregulates the activity of the fructose transporter GLUT5 [9]. Thus, the transient hyperglycemia on Days 4–5 may reflect a synergistic effect of ritonavir (direct GLUT4 inhibition) and dexamethasone (gluconeogenesis promotion), exacerbated by CYP3A4-mediated DDI between ritonavir and dexamethasone. 3.2.3 Hypoglycemia Following Paxlovid Dose Escalation A critical observation was the patient’s hypoglycemia (blood glucose: 3.8 mmol/L on Day 16, 3.2 mmol/L on Day 17) after increasing the Paxlovid dose to 300/100 mg, bid . This change could be ruled out as a DDI-related effect: concurrent medications included agents administered consistently before and after glycemic fluctuations (e.g., levetiracetam) or during both hyperglycemic and hypoglycemic periods (e.g., triple live bacteria tablets)—none of which are metabolized by CYP3A4. Thus, hypoglycemia was likely attributed to the higher Paxlovid dose itself. To further investigate, we measured the patient’s insulin, C-peptide (0-hour, fasting), and diabetes antibody levels after Paxlovid discontinuation (Table 4). C-peptide is a byproduct of insulin synthesis, and fasting C-peptide levels reflect basal pancreatic β-cell function—making it a valuable marker for differentiating diabetes subtypes, insulin resistance, and hypoglycemic etiologies. Test results showed normal blood glucose and insulin levels, negative diabetes antibodies (ruling out autoimmune diabetes), and elevated fasting C-peptide (above the normal reference range). This suggests the patient had transient early-phase insulin hyperactivity during high-dose Paxlovid treatment. Notably, existing literature predominantly reports ritonavir-mediated hyperglycemia, with no documented cases of ritonavir directly causing hypoglycemia. Given this, we hypothesize that nirmatrelvir (the antiviral component) may contribute to hypoglycemia, potentially via modulation of insulin activity. While the exact mechanism remains unclear, the dose-dependent nature of the hypoglycemia (observed only at 300 mg nirmatrelvir, bid ) suggests a concentration-dependent effect of nirmatrelvir on β-cell function or insulin signaling—an observation that warrants further preclinical investigation. In summary, Paxlovid-induced glycemic fluctuations appear to be multifactorial: ritonavir drives hyperglycemia via GLUT4 inhibition, insulin resistance, and β-cell suppression (possibly exacerbated by CYP3A4-mediated DDIs with co-administered drugs like dexamethasone), while higher doses of nirmatrelvir may precipitate hypoglycemia via a novel mechanism involving insulin hyperactivity. This dual effect—hyperglycemia at low doses and hypoglycemia at standard doses—highlights the complexity of Paxlovid’s metabolic effects and the need for close glycemic monitoring. 3.3 Prevention and management of Paxlovid-induced glycemic fluctuations Given the dose-dependent and multifactorial nature of Paxlovid-induced glycemic fluctuations (hyperglycemia at low doses, hypoglycemia at standard doses), targeted prevention and management strategies are critical to ensure patient safety. Below is a structured approach tailored to different patient populations. 3.3.1 Routine Glycemic Monitoring: A Universal Recommendation All patients receiving Paxlovid-regardless of preexisting diabetes status-should undergo regular glycemic monitoring to detect early abnormalities. For non-diabetic patients, baseline fasting blood glucose (FBG) and postprandial blood glucose (2-hour post-meal) should be measured before initiating Paxlovid, with subsequent monitoring at least once daily during treatment and for 3–5 days after discontinuation. For patients with pre-existing diabetes, more frequent monitoring (e.g., fasting, preprandial, postprandial, and bedtime glucose) is recommended, as Paxlovid may interact with hypoglycemic agents to potentiate or diminish their effects. 3.3.2 Medication Management: Avoiding and Mitigating Drug-Drug Interactions (DDIs) (A) Non-Diabetic Patients Non-diabetic patients on Paxlovid require special attention to co-administered drugs that are metabolized by CYP3A4 or CYP2D6—enzymes inhibited by ritonavir (a component of Paxlovid)—as these DDIs can alter drug concentrations and precipitate glycemic fluctuations. Table 5 summarizes high-risk drug classes and examples: • Glucocorticoids (e.g., dexamethasone): Promote hepatic gluconeogenesis and may exacerbate hyperglycemia when combined with Paxlovid[10]. • Beta-blockers (e.g., metoprolol): Mask hypoglycemic symptoms (e.g., tachycardia) and may delay recognition of low blood glucose[11]. • Statins (e.g., atorvastatin): While not direct glycemic modulators, CYP3A4 inhibition by ritonavir increases statin concentrations, and some statins have been associated with incident hyperglycemia[12]. • Immunosuppressants (e.g., tacrolimus): CYP3A4-mediated DDI with Paxlovid increases immunosuppressant levels, which may indirectly affect glucose metabolism (as observed in prior case reports) [13, 14]. • Antipsychotics (e.g., olanzapine): Known to induce insulin resistance; CYP2D6 inhibition by ritonavir may enhance this effect[15]. (B) Patients with Pre-Existing Diabetes For diabetic patients, the primary concern is DDI between Paxlovid and hypoglycemic agents-many of which are metabolized by CYP enzymes. Table 6 systematically classifies commonly used hypoglycemic drugs by their interaction risk with Paxlovid, based on metabolic pathways and clinical evidence: • High-risk drugs : Agents primarily metabolized by CYP3A4, with a high likelihood of concentration elevation and increased hypoglycemic risk. Examples include saxagliptin (a DPP-4 inhibitor). These drugs should be avoided during Paxlovid treatment; alternatives (e.g., linagliptin for saxagliptin) are preferred. • Medium-risk drugs : Agents partially metabolized by CYP3A4 or CYP2D6, with moderate DDI potential. Examples include glimepiride (a sulfonylurea) and repaglinide (a glinine class). Caution is advised: dose reductions and frequent glycemic monitoring are recommended to prevent severe hypoglycemia. • Low-risk drugs : Agents not metabolized by CYP enzymes, with no clinically significant DDI with Paxlovid. Examples include metformin (excreted unchanged via the kidneys) and insulin detemir (metabolized via non-enzymatic pathways). These drugs can be used at standard doses without additional monitoring beyond routine diabetes care. 3.3.3 Management of Established Glycemic Abnormalities (A) Hyperglycemia For non-diabetic patients with Paxlovid-induced hyperglycemia: Impaired glucose regulation (prediabetes): Defined as FBG≥ 6.1 mmol/L and 32.5 kg/m², age 25-59 years, or a history of gestational diabetes should undergo HbA1c testing. In addition, it is recommended that they maintain a balanced diet, cook with vegetable oil, limit saturated fatty acid intake, and appropriately increase the intake of coarse grains and dietary fibers. If their HbA1c is still poorly controlled after lifestyle intervention, metformin may be initiated for prevention of type 2 diabetes[16]. Persistent hyperglycemia: Clinicians should consult an endocrinologist to weigh the risk (e.g., ketoacidosis, osmotic diuresis) and benefits of continuing Paxlovid. If Paxlovid is deemed essential (e.g., severe COVID-19), short-acting insulin may be initiated, with dose adjustments based on glucose monitoring. (B) Hypoglycemia Hypoglycemia definitions vary by population: Non-diabetic patients:Blood glucose < 2.8 mmol/L Diabetic patients on pharmacologic treatment: Blood glucose < 3.9 mmol/L. Untreated hypoglycemia can cause neurological symptoms (e.g., confusion, seizures) and cardiovascular complications (e.g., arrhythmias), making prompt management critical. The following steps are consistent with the treatment of hypoglycemia as described in the “Guidelines for the prevention and treatment of diabetes mellitus in china”[17]: 1. Conscious patient: Administer 15-20g carbohydrate. 2. Unconscious or confused patients: Administer 20-40mL of 50% glucose intravenously (IV) or 0.5-1.0 mg of glucagon intramuscularly. Check blood glucose every 15 minutes until level return to normal (≥3.9 mmol/L for diabetics, ≥2.8 mmol/L for non-diabetics). After blood glucose recovery, Investigate the cause (e.g., Paxlovid dose, concurrent medications) and adjust the treatment regimen (e.g., reduce Paxlovid dose if clinically appropriate, switch to a low-risk hypoglycemic agent). Evaluate for potential complications, such as myocardial ischemia or cognitive impairment, especially in elderly patients. In summary, the prevention and management of Paxlovid-induced glycemic fluctuations require a personalized approach—combining routine monitoring, DDI avoidance, targeted pharmacologic intervention, and lifestyle modifications. Tables 5 and 6 provide practical references for clinicians to guide medication selection, while the stepwise management of hyperglycemia and hypoglycemia ensures timely and safe resolution of glycemic abnormalities. 4 Conclusion This study reports a case of Paxlovid-induced glycemic fluctuation—a ADR not documented in Paxlovid’s official prescribing information, thus fulfilling the definition of a “new ADR”. Notably, this study is the first to confirm, through a standardized assessment tool, that Paxlovid as a fixed-dose combination (rather than ritonavir alone) is significantly associated with glycemic disturbances. This finding addresses a critical gap in the existing literature, which has primarily focused on ritonavir’s individual effects on glucose metabolism. Further investigation into the underlying mechanisms of Paxlovid-induced glycemic fluctuations identified three key pathways. On the basis of these findings, targeted clinical recommendations are proposed to mitigate the risk of Paxlovid-induced glycemic fluctuations. Despite these contributions, this study has limitations, primarily the single case analysis, which precludes generalization of the findings and full elucidation of the mechanisms underlying Paxlovid-induced glycemic fluctuations. Future work will include: (1) Basic research to clarify the specific molecular pathways (e.g., the effect of nirmatrelvir on β-cell insulin secretion) that drive these glycemic abnormalities; (2) Proactive post-marketing surveillance to collect additional clinical cases, thereby enabling quantitative analysis of the incidence and risk factors of this ADR. Collectively, these efforts aim to provide robust evidence for updating Paxlovid’s drug labeling and guiding safer clinical use of this critical COVID-19 therapeutic. Acknowledgements The authors thank the frontline medical staff for their observations and treatments of adverse reactions that occurred during the use of Paxlovid in treating patients with COVID-19. Statements and declarations Competing interests: The authors declared no competing interests for this work. Data availability statement: All data supporting the findings of this study are available within the paper. Funding This work was supported by the Shenzhen Science and Technology Innovation Commission General Project [grant numbers: JCYJ20250604183821029] Author contributions O.Y.Y. wrote the manuscript; L.F.D and S.J.Y. designed the research; G.L.H and P.C. performed the research; Y.B. analyzed the data. References: 1. Vyas AK, Koster JC, Tzekov A, Hruz PW. Effects of the HIV protease inhibitor ritonavir on GLUT4 knock-out mice. J Biol Chem. 2010 Nov 19;285(47):36395-400.2. Taylor N, Kremser I, Auer S, Hoermann G, Greil R, Haschke-Becher E, et al. Hemeoxygenase-1 as a Novel Driver in Ritonavir-Induced Insulin Resistance in HIV-1-Infected Patients. J Acquir Immune Defic Syndr. 2017 May 1;75(1):e13-e20.3. Lee GA, Rao M, Mulligan K, Lo JC, Aweeka F, Schwarz JM, et al. Effects of ritonavir and amprenavir on insulin sensitivity in healthy volunteers. AIDS. 2007 Oct 18;21(16):2183-90.4. Neye Y, Dufer M, Drews G, Krippeit-Drews P. HIV protease inhibitors: suppression of insulin secretion by inhibition of voltage-dependent K+ currents and anion currents. J Pharmacol Exp Ther. 2006 Jan;316(1):106-12.5. Gerhart J, Cox DS, Singh RSP, Chan PLS, Rao R, Allen R, et al. A Comprehensive Review of the Clinical Pharmacokinetics, Pharmacodynamics, and Drug Interactions of Nirmatrelvir/Ritonavir. Clin Pharmacokinet. 2024 Jan;63(1):27-42.6. Goud T, Maddi S, Nayakanti D, Thatipamula RP. Altered pharmacokinetics and pharmacodynamics of repaglinide by ritonavir in rats with healthy, diabetic and impaired hepatic function. Drug Metab Pers Ther. 2016 Jun 1;31(2):123-30.7. Wei Zhou JJ, Facai Wang. Pharmacological monitoring of a case of paxlovid interacting with tacrolimus leading to elevated blood sugar in a patient. Practical Medicines and Clinical Practice. 2024;27(05):370-3.8. Cui A, Fan H, Zhang Y, Zhang Y, Niu D, Liu S, et al. Dexamethasone-induced Kruppel-like factor 9 expression promotes hepatic gluconeogenesis and hyperglycemia. J Clin Invest. 2019 Apr 29;129(6):2266-78.9. Hwang S, Park S, Kim J, Oh AR, Lee HJ, Cha JY. Role of Carbohydrate response element-binding protein in mediating dexamethasone-induced glucose transporter 5 expression in Caco-2BBE cells and during the developmental phase in mice. Anim Cells Syst (Seoul). 2024;28(1):15-24.10. Chang M, Wang JC. Hepatic Glucocorticoid Receptor Action and Glucose Homeostasis. Endocr Rev. 2025 Sep 2.11. Parker N, Flowers R, Vickery K, Stolfi A, Bugnitz C. Assessing the Risk of Hypoglycemia Secondary to Propranolol Therapy for the Treatment of Supraventricular Tachycardia in Infants. Pediatr Cardiol. 2023 Apr;44(4):836-44.12. She J, Tuerhongjiang G, Guo M, Liu J, Hao X, Guo L, et al. Statins aggravate insulin resistance through reduced blood glucagon-like peptide-1 levels in a microbiota-dependent manner. Cell Metab. 2024 Feb 6;36(2):408-21 e5.13. Li S, Zhou H, Xie M, Zhang Z, Gou J, Yang J, et al. Regenerating islet-derived protein 3 gamma (Reg3g) ameliorates tacrolimus-induced pancreatic beta-cell dysfunction in mice by restoring mitochondrial function. Br J Pharmacol. 2022 Jun;179(12):3078-95.14. Abd-Ellah HF, Abou-Zeid NR. Role of alpha-lipoic acid in ameliorating Cyclosporine A-induced pancreatic injury in albino rats: A structural, ultrastructural, and morphometric study. Ultrastruct Pathol. 2017 Mar-Apr;41(2):196-208.15. Kapse S, Ando H, Fujiwara Y, Suzuki C, Ushijima K, Kitamura H, et al. Effect of a dosing-time on quetiapine-induced acute hyperglycemia in mice. J Pharmacol Sci. 2017 Mar;133(3):139-45.16. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. Prevention or Delay of Type 2 Diabetes and Associated Comorbidities: Standards of Care in Diabetes-2023. Diabetes Care. 2023 Jan 1;46(Suppl 1):S41-S8.17. Society CMAD. Guideline for the prevention and treatment of diabetes mellitus in China (2024 edition). Chinese Journal of Diabetes. 2025;17(01):16-139. Figure 1: Timeline of Paxlovid administration and corresponding changes in the patient’s blood glucose levels during hospitalization. Table 1: Timeline of blood glucose measurements, COVID-19 test and concurrent medication administration during the patient’s hospitalization Days 1 2 3 4 5 6 7 9 10 12 15 16 17 22 Glucose（mmol/L) 4.44 6.01 9.31 6.32 7.71 5.47 6.45 4.96 4.62 6.39 4.3 3.8 3.2 5.78 COVID-19 test （+） （+） （+） （-） 1.Levetiracetam (g) 0.25 qd 0.25 qd 0.25 qd 0.25 qd 0.25 qd 0.25 qd 0.25 qd 0.25 Bid 0.25 Bid 0.25 Bid 0.25 qd 0.25 qd 0.25 qd 0.25 qd 2.Sodium Valproate (g) 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 0.5 qn 3.Ambroxol Hydrochloride Injection (mg) 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 30 bid 0 4.Inhalation form of acetylcysteine (g) 0.3 bid 0.3 bid 0.3 bid 0.3 bid 0.3 bid 0.3 bid 0.3 bid 0.3 bid 0.3 bid 0.3 bid 0 0 0 0 5.Pantoprazole (mg) 40 qd 40 qd 40 qd 40 qd 40 qd 0 0 0 0 0 0 0 0 0 6.Meropenem (g) 1 q6h 1 q6h 1 q6h 1 q6h 1 q6h 0 0 0 0 0 0 0 0 0 7.Moxifloxacin (g) 0.4 qd 0.4 qd 0 0 0 0 0 0 0 0 0 0 0 0 8.Enteral nutrition emulsion (TPF) (ml) 0 500 qd 500 qd 500 qd 500 qd 0 0 0 0 0 0 0 0 0 9.Inhalation form of budesonide (g) 0 1 bid 1 bid 1 bid 0 0 0 0 0 0 0 0 0 0 10.Inhalation form of ipratropium bromide (ml) 0 2 bid 2 bid 2 bid 0 0 0 0 0 0 0 0 0 0 11.That heparin (AXaIU) 0 2050 q12h 2050 q12h 0 0 2050 q12h 2050 q12h 0 0 0 0 0 0 0 12.Levofloxacin (g) 0 0 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 0.5 qd 13.Paxlovid（mg） 0 0 150 qd 150 qd 150 qd 150 qd 150 qd 0 0 150 qd 0 300 bid 300 bid 0 14.Recombinant human brain natriuretic peptide (g) 0 0 1 qd 1 qd 0 0 0 0 0 0 0 0 0 0 15.Vitamin D (IU) 0 0 0 400 qd 400 qd 400 qd 400 qd 400 qd 400 qd 400 qd 400 qd 400 qd 400 qd 400 qd 16.Acetylcysteine Granules (g) 0 0 0 0.2 tid 0.2 tid 0.2 tid 0.2 tid 0.2 tid 0.2 tid 0.2 tid 0.2 tid 0.2 tid 0.2 tid 0.2 tid 17.Vancomycin (g) 0 0 0 0.5 q12h 0.5 q12h 0.5 q12h 0.5 q12h 0 0 0 0 0 0 0 18.Dexamethasone (mg) 0 0 0 6 qd 6 qd 0 0 0 0 0 0 0 0 0 19.Ceftriaxone (g) 0 0 0 0 2 qd 2 qd 2 qd 2 qd 2 qd 2 qd 2 qd 2 qd 2 qd 2 qd 20.Rabeprazole (mg) 0 0 0 0 10 qd 10 qd 10 qd 10 qd 10 qd 10 qd 10 qd 10 qd 10 qd 10 qd 21.Enteral nutrition suspension (fiber type) (ml) 0 0 0 0 1000 qd 1000 qd 1000 qd 1000 qd 1000 qd 1500 qd 0 0 0 0 22.Inhalation form of ambroxol (mg) 0 0 0 0 0 30 bid 30 bid 30 bid 30 bid 30 bid 0 0 0 0 23.Shulodite (LSU) 0 0 0 0 0 0 0 600 qd 600 qd 600 qd 600 qd 600 qd 600 qd 600 qd 24.Triple Live Bacteria Tablets Containing Bifidobacterium and Lactobacillus(g) 0 0 0 0 0 0 0 0 0 2 tid 0 2 tid 2 tid 0 25.Trimetazidine (mg) 0 0 0 0 0 0 0 0 0 35 bid 0 0 0 0 26.Lactobacillus acidophilus (g) 0 0 0 0 0 0 0 0 0 0 0 0 0.5 bid 0.5 bid Table 2: Naranjo Algorithm scores for evaluating the association between suspected medications and glycemic fluctuations Related issues Question score Score Yes No Unknown Paxlovid Recombinant human brain natriuretic peptide Vancomycin Dexamethasone Triple Live Bacteria Tablets Trimetazidine 1.Have there been any similar reports before? +1 0 0 0 (Unknown) 0 (Unknown) 0 (Unknown) +1 (Yes) 0 (Unknown) 0 (Unknown) 2.Did ADR occur after using the suspected drug? +2 -1 0 +2 (Yes) +2 (Yes) 0 (No) 0 (No) 0 (No) 0 (No) 3.Did ADR symptoms subside after discontinuation the medication or using the antagonist? +1 0 0 +1 (Yes) 0 (No) +1 (Yes) 0 (No) +1 (Yes) +1 (Yes) 4.Does ADR reoccur after using the suspected drug again? +2 -1 0 +2 (Yes) 0 (Unknown) 0 (Unknown) 0 (Unknown) +2 (Yes) 0 (Unknown) 5. Are there any other reasons causing this ADR? -1 +2 0 +2 (No) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 6. Did this ADR recur after taking the placebo? -1 +1 0 +1 (No) +1 (No) +1 (No) +1 (No) +1 (No) +1 (No) 7.Whether the drug has reached a toxic concentration in the blood or other body fluids? +1 0 0 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 8.Does the ADR worsen as the dosage increases (or decreases)? +1 0 0 +1 (Yes) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 9. Did the patient experience similar reaction after using this drug or similar drugs? +1 0 0 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 0 (Unknown) 10. Is there any objective evidence to confirm this reaction? +1 0 0 +1 (Yes) 0 (Unknown) 0 (Unknown) +1 (Yes) 0 (Unknown) 0 (Unknown) Total score 10 3 2 3 4 2 Table 3: Fasting (0 h) insulin, C-peptide levels (measured on hospital Day 22) and diabetes antibody test results (measured on hospital Day 24) in the patient Inspection items Result Reference range Insulin（Ins） 9.8μU/mL 3-17 C-peptide（C-P） 2164.4pmol/L 260-1730 Tyrosine phosphatase antibody（IA-2A） 0.79 IU/mL 0-10 Glutamic acid decarboxylase antibody（GADA） 1.24 IU/mL 0-10 Islet cell antibody（ICA） 0.14 COI Inertness <0.9，Grey area 0.9-1.1，Reactive≥1.1 Zinc transporter 8 antibody（ZnT8A） 1.18 AU/mL 0-10 Table 4: Drugs associated with glycemic fluctuations that are metabolized by CYP3A4 or CYP2D6 (relevant to Paxlovid co-administration) Drug category Representative drug Mechanism Metabolic enzymes Glucocorticoid Prednisone, Hydrocortisone, Dexamethasone Promote liver gluconeogenesis, reduce glucose utilization in peripheral tissues, and induce insulin resistance CYP3A4 β receptor blockaders Propranolol Non-selective beta-blockers block the sympathetic nerve response mediated by beta2 receptors, thereby masking hypoglycemic symptoms CYP2D6 Statins Atorvastatin, Simvastatin Increase insulin resistance CYP3A4 Immunosuppressant Tacrolimus, Cyclosporine Direct toxicity to pancreatic β cells CYP3A4 Antipsychotic drugs Clozapine, Quetiapine, Olanzapine Insulin resistance CYP3A4、CYP2D6 Table 5: Metabolic pathways of commonly used hypoglycemic drugs and their interaction risk with Paxlovid Types of drugs Representative drug Main metabolic pathways/enzymes Interaction risk with Paxlovid DPP-4 inhibitor Saxagliptin It is mainly metabolized into an active product by CYP3A4/5. High risk Sitagliptin A small amount is metabolized by CYP3A4 and CYP2C8, while the majority is excreted in its original form. Medium risk Linagliptin It is almost not metabolized by CYP enzymes. Most of it is excreted in its original form. Low risk Alogliptin Not metabolized by CYP enzymes, it is mainly excreted directly by the kidneys. Low risk Vildagliptin Mainly undergoes hydrolytic metabolism Low risk Sulfonylurea Glibenclamide Mainly via CYP2C9, with secondary involvement of CYP3A4 Medium risk Glimepiride Mainly via CYP2C9 Medium risk Gliclazide Mainly via CYP2C9 Medium risk Glinine class Repaglinide CYP3A4 Mainly via CYP2C8, with secondary involvement of CYP3A4 Medium risk Nateglinide Mainly via CYP2C9（70%）, with secondary involvement of CYP3A4（30%） Medium risk Thiazolidinediones Pioglitazone Mainly via CYP2C8, with secondary involvement of CYP3A4 Medium risk Rosiglitazone Mainly via CYP2C8, with secondary involvement of CYP2C9 Medium risk Biguanides Metformin Not metabolized by the liver’s CYP enzymes Low risk SGLT2 inhibitors Dapagliflozin Mainly through UGT1A9 glucuronidation） Low risk Empagliflozin Mainly through UGT2B7, UGT1A3, and UGT1A8 for glucuronidation Low risk Canagliflozin The main process involves glucuronidation by UGT1A9 and UGT2B4 Low risk GLP-1 receptor agonists All varieties (such as liraglutide, semaglutide, etc.) Protein hydrolysis metabolism Low risk Insulin All types (such as insulin glargine, insulin detemir, etc.) Being hydrolyzed by proteases in the target tissue and the kidneys Low risk Information & Authors Information Version history V1 Version 1 08 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Ouyang Yi 0000-0001-8785-8779 [email protected] Peking University Shenzhen Hospital Department of Pharmacy View all articles by this author Yao Bai Peking University Shenzhen Hospital Department of Pharmacy View all articles by this author Ping Chen Peking University Shenzhen Hospital Department of Pharmacy View all articles by this author Lifang Duan Peking University Shenzhen Hospital Department of Pharmacy View all articles by this author Guilin He Peking University Shenzhen Hospital Department of Pharmacy View all articles by this author Jinsong Yuan Peking University Shenzhen Hospital Department of Pharmacy View all articles by this author Metrics & Citations Metrics Article Usage 231 views 85 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ouyang Yi, Yao Bai, Ping Chen, et al. Nirmatrelvir/Ritonavir-Induced Glucose Fluctuation：A new adverse reaction. Authorea . 08 December 2025. 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