PD-1/PD-L1 inhibitor-induced metabolic and nutritional disorders: a real-world pharmacovigilance analysis using FAERS database

PD-1/PD-L1 inhibitor-induced metabolic and nutritional disorders: a real-world pharmacovigilance analysis using FAERS database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article PD-1/PD-L1 inhibitor-induced metabolic and nutritional disorders: a real-world pharmacovigilance analysis using FAERS database Liyan Zhou, Xiaohui Liu, Na Xiao, Guiying Bai, Hongli Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9051493/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Immune checkpoint inhibitors targeting PD-1/PD-L1 have transformed oncology, yet their association with metabolic and nutritional disorders (MNDs) remains poorly characterized in real-world populations. While classic endocrine immune-related adverse events are recognized, the comprehensive spectrum, temporal dynamics, and differential risks between PD-1 and PD-L1 inhibitors require systematic elucidation to inform evidence-based monitoring strategies. Methods We conducted a retrospective pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) from Q1 2011 to Q2 2025. Reports of MNDs with eight PD-1/PD-L1 inhibitors as primary suspected drugs were identified. Disproportionality analysis employed four algorithms (ROR, PRR, BCPNN, MGPS) with concordant positivity required for signal detection. Preferred terms (PTs) were analyzed at the System Organ Class level. Time-to-onset (TTO) was characterized using Kaplan-Meier estimation and Weibull shape parameter (β) modeling to identify failure patterns. Results Among 21,712,563 total records, 5,160 MNDs reports were identified. Males (53.22%) and older adults (65–85 years, 45.19%) predominated. Disproportionality signals were detected for 29 PTs with PD-1 inhibitors versus 13 PTs with PD-L1 inhibitors. Fulminant type 1 diabetes mellitus (FT1DM) emerged as the strongest consistent signal. Nivolumab, pembrolizumab, and atezolizumab demonstrated the broadest metabolic risk spectra (24, 16, and 17 positive PTs, respectively). Weibull analysis revealed an early-failure pattern (β < 1), with 80% of events occurring within 120 days. Conclusion This study provides comprehensive real-world evidence delineating distinct metabolic toxicity profiles between PD-1 and PD-L1 inhibitors, with FT1DM as a critical early-onset complication. The concentrated risk window within initial treatment cycles supports front-loaded metabolic surveillance to enhance patient safety. Biological sciences/Cancer Health sciences/Diseases Biological sciences/Drug discovery Health sciences/Endocrinology Health sciences/Oncology immune checkpoint inhibitors PD-1/PD-L1 inhibitors metabolic disorders fulminant type 1 diabetes mellitus pharmacovigilance time-to-onset risk stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Cancer remains a major global health challenge, with 20 million new cases and 9.7 million deaths reported in 2022.Projections indicate 35 million annual cases by 2050, imposing substantial economic and societal burdens [ 1 ]. Immune checkpoint inhibitors (ICIs), particularly those targeting the programmed cell death protein-1(PD-1)/programmed cell death ligand-1 (PD-L1) pathway, have transformed oncology by blocking T-cell inhibitory signals to enhance anti-tumor immunity [ 2 – 4 ]. However, this efficacy is accompanied by immune-related adverse events (irAEs) affecting virtually any organ system, compromising quality of life and treatment continuity [ 5 , 6 ]. Current clinical focus predominantly centers on organ-specific irAEs, such as pneumonitis, colitis, hepatitis, and classic endocrinopathies (thyroiditis, hypophysitis), for which management guidelines are established [ 7 – 9 ]. In contrast, metabolic and nutritional disorders (MNDs), including hyperglycemic crises, severe electrolyte imbalances, and dyslipidemia, remain systematically overlooked despite their rapid progression to life-threatening complications (e.g., diabetic ketoacidosis) and profound impact on patient outcomes[ 10 – 15 ]. These disorders encompass a heterogeneous spectrum of systemic disturbances, all of which may be induced by ICIs through disruption of endocrine-immune crosstalk and systemic metabolic homeostasis[ 16 ]. While sporadic case reports have linked PD-1/PD-L1 inhibitor to diabetes and electrolyte imbalances, the full spectrum, incidence patterns, and temporal dynamics of these metabolic toxicities in real-world populations are entirely uncharacterized. This knowledge deficit directly contributes to the absence of evidence-based metabolic monitoring guidelines, leaving clinicians without actionable guidance for early detection and intervention. The FDA Adverse Event Reporting System (FAERS) is a system for collecting and storing individual case safety report, and is highly valuable due to their ability to gather spontaneous reports[ 17 ]. Continuous post-marketing surveillance of PD-1/PD-L1 inhibitors are crucial for evaluating the long-term safety and identifying rare or severe AEs. While prior FAERS studies have signaled ICI-associated MNDs, they relied on single-algorithm analyses without temporal stratification [ 18 , 19 ], limiting clinical utility for risk-adapted monitoring. This study leverages FAERS data (Q1 2011–Q2 2025) to conduct a comprehensive pharmacovigilance analysis of MNDs associated with eight PD-1/PD-L1 inhibitors. Using four disproportionality algorithms (ROR, PRR, BCPNN, MGPS), we quantify risk signals at system organ class (SOC) and preferred terms (PT) levels, characterize demographic and clinical determinants, and define time-to-onset dynamics. These findings aim to inform precision monitoring strategies and enhance the safe use of PD-1/PD-L1 inhibitors in oncology practice. Methods Data source and extraction The FAERS database is a publicly accessible spontaneous reporting system that compiles safety reports on marketed drugs and biologics from countries worldwide. These reports are submitted by healthcare professionals, drug manufacturers, patients, and other stakeholders such as attorneys. FAERS contains seven datasets: demographic and management information (DEMO), drug information (DRUG), adverse event information (REAC), patient outcome information (OUTC), report source information (RPSR), drug therapy start and end dates (THER), and indication/diagnosis information (INDI). The study was performed involving cumulative data available from the FAERS database collected from the first quarter of 2011 to the second quarter of 2025, following FDA guidelines and official recommendations. FAERS files are made publicly available on a quarterly basis and are accessed at the website: https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD QDE-FAERS.html. Given that the FAERS constitutes a publicly accessible and anonymized dataset, the requirement for institutional review board approval and informed consent has been waived. The present study was conducted in strict accordance with the FDA official website guidance document for data cleaning. The data cleaning rules involved removing duplicate reports using the method recommended by the FDA[ 17 ]. Study drugs and adverse events Study drugs included five PD-1 inhibitors (nivolumab, pembrolizumab, tislelizumab, toripal imab, and dostarlimab), and three PD L1 inhibitors (atezolizumab, avelumab, and durvalumab). Reports were extracted where any of these agents were listed as the “primary suspect” in DRUG file. To identify the target AEs, we used the standardized Medical Dictionary for Regulatory Activities (MedDRA) terminology (version 27.0), which provides standardized and precise descriptions of medical conditions. All reports where any PT belonging to the SOC “Metabolism and nutrition disorders” (code 10027433) was recorded in the REAC file were included for subsequent analysis. Duplicate reports were identified and removed based on the FDA-recommended primary key (case id), with the most recent version retained for analysis. Descriptive and clinical characterization analysis Descriptive statistics were performed on all included reports. We summarized patient de mographics (age, gender, body weight), reporter qualification, reporting country/region, and report year. Clinical outcomes were assessed based on the “serious” outcomes as defined by the FDA (e.g., hospitalization, life-threatening, death. Disproportionality analysis (Signal Detection) A disproportionality analysis was employed to detect potential safety signals by comparing the reporting proportion of a specific drug-AE pair against the background reporting proportion for all other drugs in the database. Four complementary statistical algorithms were used to enhance robustness and minimize method-specific bias: Reporting odds ratio (ROR) and proportional reporting ratio (PRR), with their corresponding 95% confidence intervals (95% CI). Bayesian confidence propagation neural network (BCPNN), generating the Information Component (IC) with its 95% credibility interval (95% CI). Multi-item gamma Poisson shrinker (MGPS), generating the empirical bayes geometric mean (EBGM) with its 95% CI [ 7 ]. A positive signal was conservatively defined if all four of the following criteria were met simultaneously: lower limit of the 95% CI for ROR > 1, lower limit of the 95% CI for PRR > 1, lower limit of the 95% CI for IC > 0, and lower limit of the 95% CI for EBGM05 > 2. This stringent, multi-method consensus approach aims to increase specificity and reduce false-positive signals. Analyses were performed at three levels: (1) Class level: comparing all PD-1 inhibitors versus all PD-L1 inhibitors against the background; (2) Individual drug level: analyzing each of the eight inhibitors separately; (3) Intersection analysis: identifying common signals shared among the three drugs (nivolumab, pembrolizumab, atezolizumab) with the highest number of positive PTs. Time-to-onset (TTO) Analysis For reports with available start and event dates for the primary suspect drug, the TTO was calculated as the interval (in days) between these dates. Reports with illogical TTO (e.g., ≤ 0 or > 365 days after therapy cessation) were excluded from this specific analysis. Kaplan-Meier curves were generated to visualize the cumulative probability of metabolic disorder onset over time after treatment initiation. Log-rank tests were used to compare TTO distributions across pre-specified subgroups. Median TTO with interquartile range (IQR) was calculated for overall events. To model the hazard function and characterize the failure pattern, a Weibull distribution was fitted to the TTO data for the overall metabolic events and for key drugs. The shape parameter (β) was estimated: β 1 indicates a “wear-out failure type” (risk increasing over time). Statistical and Software All statistical analyses and data processing were conducted using R software (version 4.3.1). The ggplot2 (version 3.4.4) package was used for visualization. To compare the frequency of these AEs with their background frequency, a classic 2 × 2 contingency table Table S1 ) was utilized to establish statistical associations. The standard formulas for ROR, PRR, BCPNN, and MGPS and evaluation criteria are outlines in Table S2 . A two-sided p-value < 0.05 was considered statistically significant for comparative tests. Results Overview of reports and clinical characteristics Among a total of 21, 712, 563 records in the FAERS pharmacovigilance database included, 5,160 reports were documented for MNDs after receiving eight target PD-1/PD-L1 inhibitors from Q1-2011 to Q2-2025. The demographic and clinical characteristics of these reports are summarized in Table 1 . In terms of gender distribution, male patients exhibited a significantly higher rate of MNDs (53.22%) compared to females (35.39%). When categorized by age, patients between 45 and 65 ages accounted for the highest proportion (34.22%) of MNDs cases. Physicians constituted the largest group of reporters (40.17%), followed by health professional (23.26%). Geographically, most reports originated from the United States (30.62%) and Japan (14.98%). The annual number of reports demonstrated a marked increase starting in 2015, coinciding with the broader clinical adoption of PD-1/PD-L1 inhibitors. Regarding patient outcomes, hospitalization was the most frequently reported, accounting for 44.48% of all cases. Alarmingly, a large proportion of these events were classified as serious (97.83%), with a considerable subset resulting in death (18.31%). Overall, reports of MNDs events are significantly higher for PD-1 inhibitors than for PD-L1 inhibitors. Table 1 Demographic and clinical characteristics of reports for MNDs associated with PD-1/PD-L1 inhibitors. Characteristics Total (n = 5160) PD-1 inhibitors (n = 3931) PD-L1 inhibitors (n = 1229) Gender, n (%) Female 1827 (35.41) 1406 (35.77) 421 (34.26) Male 2746 (53.22) 2223 (56.55) 523 (42.55) Unknown 587 (11.36) 302 (7.68) 285 (23.19) Age (years), n (%) =18 and =45 and =65 and =75 853 (16.53) 651 (16.56) 202 (16.44) Unknown 708 (13.72) 448 (11.40) 260 (21.16) Weight (Kg), n (%) =50 and =70 and =100 193 (3.74) 141 (3.59) 52 (4.23) Unknown 2416 (56.51) 2405 (61.18) 511 (41.58) Reporter type, n (%) Physician 2073 (40.17) 1120 (28.49) 953 (77.54) Health Professional 1200 (23.26) 988 (25.13) 212 (17.25) Consumer 724 (14.03) 708 (18.01) 16 (1.30) Pharmacist 156 (3.02) 145 (3.69) 11 (0.90) Other health-professional 906 (17.56) 884 (22.49) 22 (1.79) Lawyer 1 (0.02) 1 (0.03) 0 (0.0) Missing 100 (1.94) 85 (2.16) 15 (1.22) Reported countries, n (%) United States 1580 (30.62) 1251 (31.82) 329 (26.77) Japan 773 (14.98) 433 (11.02) 340 (27.66) France 644 (12.48) 571 (14.53) 73 (5.94) China 342 (6.63) 257 (6.54) 85 (6.92) Germany 319 (6.18) 269 (6.84) 50 (4.07) Others 1502 (29.11) 1150 (29.25) 352 (28.64) Reporting year, n (%) 2014 44 (0.85) 44 (1.12) 0 (0.00) 2015 111 (2.15) 111 (2.82) 0 (0.00) 2016 226 (4.38) 215 (5.47) 11 (0.90) 2017 424 (8.22) 380 (9.67) 44 (3.58) 2018 575 (11.14) 504 (12.82) 71 (5.78) 2019 623 (12.07) 533 (13.56) 90 (7.32) 2020 640 (12.40) 533 (13.56) 107 (8.71) 2021 628 (12.17) 480 (12.21) 148 (12.04) 2022 626 (12.13) 377 (9.59) 249 (20.26) 2023 532 (10.31) 237 (6.03) 295 (24.00) 2024 453 (8.78) 319 (8.11) 134 (10.90) 2025 (January-June) 278 (5.39) 198 (5.04) 80 (6.51) Outcome, n (%) Death 945 (18.31) 692 (17.60) 253 (20.59) Disability 19 (0.37) 19 (0.48) 0 (0.0) Hospitalized 2295 (44.48) 1762 (44.82) 533 (43.37) Life-threatening 474 (9.19) 390 (9.92) 84 (6.83) Others 1427 (27.66) 1068 (27.17) 359 (29.21) Serious outcome, n (%) NO 112 (2.17) 99 (2.52) 13 (1.06) YES 5048 (97.83) 3832 (97.48) 1216 (98.94) Fatal outcome, n (%) NO 4215 (81.69) 3239 (82.40) 976 (79.41) YES 945 (18.31) 692 (17.60) 253 (20.59) Disproportionality analysis and spectrum of signals At the drug class level, disproportionality analysis revealed a significantly broader spectrum of metabolic risk associated with PD-1 inhibitors compared to PD-L1 inhibitors. Using the stringent four-method consensus criterion, 29 unique PTs showed positive signals for PD-1 inhibitors as a class (Fig. 1 A and Table S3), compared to 13 PTs for PD-L1 inhibitors (Fig. 1 B and Table S4). The all signals for each class, ranked by ROR, are listed in Fig. 1 C-D. Fulminant type 1 diabetes mellitus (FT1DM) emerged as the most prominent signal for both classes, with the highest ROR and meeting all signal detection criteria. Comparison of the toxicity profile in different PD-1/PD-L1 inhibitors For disproportionality at PT level, a wide array of signals emerged in specific PD-1/PD-L1 inhibitors with different occurrence frequencies. At the individual drug level, nivolumab, pembrolizumab, and atezolizumab accounted for the vast majority of reports and exhibited the most extensive metabolic risk profiles, with 24, 16, and 17 positive PTs, respectively (Fig. 2 A-C). The remaining five agents had between 2 and 4 positive PTs each. An intersection analysis of the positive signals from the three most reported drugs (nivolumab, pembrolizumab, atezolizumab) identified nine PTs shared among all three, including hyperlipasaemia, hypercalcaemia, hyponatraemia, hypoalbuminaemia, tumor lysis syndrome, diabetic ketoacidosis, type 1 diabetes mellitus, decreased appetite, and FT1DM (Fig. 2 D). Among them, the strongest signal was FT1DM, highlighting its role as a core, pan-category metabolic irAE. Comparison of the median onset times in different PD-1/PD-L1 inhibitors The median TTO for MNDs varied among three most reported agents: 43 days for nivolumab, 32.5 days for pembrolizumab, and 22 days for atezolizumab (Table 2 ). Stratification by age revealed distinct onset pattern for each drug. For nivolumab, the median TTO was 28 days (IQR: 14-70.5) in the 18 to 45 years group, showing a trend of prolonged onset times with increasing age (P = 0.097, Fig. 3 A). For pembrolizumab, the shortest TTO was observed in patients under 18 years old, with a median of 15 days. In contrast, patients aged 45–65 years had a significantly longer median TTO of 43 days (IQR: 14.5–117.75; P = 0.019) (Fig. 3 B). For atezolizumab, a similar pattern of earlier onset in younger patients was evident. The shortest median TTO was 11 days (IQR: 9–13) in patients under 18 years old, while the longest median TTO was 45 days (IQR: 11.25–87.25) in patients over 85 years old (P = 0.019, Fig. 3 C). To investigate the potential influence of body weight on the onset kinetics of metabolic disorders, we performed a stratified analysis for the three most reported agents (nivolumab, pembrolizumab, atezolizumab) based on available patient weight data. Notably, a distinct pattern emerged across the different agents. For nivolumab, the median TTO was not significantly different across weight strata (P = 0.47, Fig. 3 D). In contrast, a statistically significant association was observed for both pembrolizumab and atezolizumab. For pembrolizumab, patients in the low-weight group (≤ 50 kg) experienced the shortest median TTO of 20 days (IQR: 7–68), which was significantly earlier than those in the intermediate-weight group and the high-weight group (≥ 100 kg, 97.5 days, IQR: 20-274.5; p < 0.001, Fig. 3 E). A similar trend was observed for atezolizumab, where the low-weight group had the shortest median TTO of 16 days (IQR: 6–17), compared to 44 days (IQR: 33.5-130.5) in the high-weight group (P = 0.002, Fig. 3 F). The TTO analysis showed no differences among the three drugs in terms of gender and fatal groups (Figure S1 ). Weibull distribution analysis of TTO in different PD-1/PD-L1 inhibitors To quantitatively characterize the hazard-over-time profile for metabolic disorders associated with each agent, Weibull distribution models were fitted to the available time-to-onset data. The shape parameter (β) and its 95% confidence interval (CI) for each of the eight PD-1/PD-L1 inhibitors are summarized in Table 2 and Fig. 4 A. The analysis revealed two distinct hazard patterns among the agents with sufficient data for modeling. For five drugs—nivolumab, pembrolizumab, atezolizumab, tislelizumab, and durvalumab—the estimated β values were significantly less than 1 (upper limit of 95% CI < 1), indicating a consistent "early failure" type hazard. This pattern signifies that the instantaneous risk of developing a metabolic disorder is highest immediately following treatment initiation and decreases thereafter. Notably, dostarlimab exhibited a diametrically opposite hazard pattern. Its β value was significantly greater than 1 (lower limit of 95% CI > 1), defining a "wear-out failure" pattern. This suggests that for this agent, the risk of metabolic events increases with the duration of therapy. Weibull models could not be reliably fitted for toripalimab and avelumab, primarily due to the limited number of reported cases with documented onset times, precluding a stable statistical estimation of their temporal risk profiles. Table 2 Weibull distribution parameter analysis for the time-to-onset of MNDs. Drug class Median (IQR) Scale parameter α (95%CI) Shape parameter β (95%CI) Failure Type Nivolumab 43 (15–120) 85.42 (80.09–90.76) 0.75 (0.72–0.77) Early Failure Pembrolizumab 32.5 (10–105) 67.8 (60.2-75.41) 0.7 (0.66–0.74) Early Failure Tislelizumab 10 (5–25) 23.46 (11.7-35.23) 0.63 (0.51–0.76) Early Failure Toripalimab 49.5 (4.5–95.5) 43.07 (-16.72-102.86) 0.75 (0.14–1.35) NA Dostarlimab 184 (141.25-269.25) 230.62 (129.37-331.87) 1.65 (0.71–2.59) Wear-out Failure Atezolizumab 22 (8–80) 53.59 (47.31–59.87) 0.67 (0.63–0.71) Early Failure Avelumab 72 (18.75–88.5) 66.23 (-8.19-140.66) 0.75 (0.24–1.26) NA Durvalumab 45 (27.75–65.5) 79.95 (41.36-118.53) 0.76 (0.58–0.94) Early Failure NA: A reliable Weibull model could not be fitted due to an insufficient number of reports with valid time-to-onset data. Clinical characterization of fulminant type 1 diabetes mellitus events A total of 561 individual reports of FT1DM associated with PD-1/PD-L1 inhibitors were identi fied, accounting for 10.87% of all positive MNDs events, underscoring its status as a critical metabolic irAE. The majority of these reports were linked to PD-1 inhibitors (n = 494, 88.06%), compared to PD-L1 inhibitors (n = 67, 11.94%). In terms of gender distribution, male patients exhibited a significantly higher rate of MNDs (57.58%) compared to females (32.98%) (P < 0.05). Consistent with its acute and severe nature, 100% of FT1D reports were classified as serious adverse events, with a considerable proportion resulting in a fatal outcome (n = 39, 6.95%). We collected FT1DM events with prognostic information and found significant differ ences in the distribution of FT1DM reported for different drugs (Table 3 ). Among PD-1 inhibitors, nivolumab accounted for the highest number of reports (n = 334), followed by pembrolizumab (n = 70). Notably, the fatality rate among FT1DM reports also differed, ranging from 2.70% for atezolizumab to 50.00% for avelumab, though these differences were only statistically significant in the nivolumab and atezolizumab cohort. The peak onset periods also varied, with nivolumab peaking within 0–120 days, pembrolizumab and atezolizumab both reach their peak within 0–30days (Fig. 4 B). The median TTO for MNDs in PD-1/PD-1 inhibitors was 93 days. Table 3 Characteristics of FT1DM reports by individual PD-1/PD-L1 inhibitors. Drug Target FT1DM reports n(%) Fatal reports n(%)* P value Nivolumab PD-1 334 (59.54) 13 (3.89) < 0.001 Pembrolizumab PD-1 70 (12.48) 14 (20.00) 0.96 Tislelizumab PD-1 2 (0.36) 0 (0.00) - Toripalimab PD-1 1 (0.18) 0 (0.00) - Dostarlimab PD-1 0 (0.00) 0 (0.00) - Atezolizumab PD-L1 37 (6.60) 1 (2.70) 0.002 Avelumab PD-L1 4 (0.71) 2 (50.00) 0.27 Durvalumab PD-L1 14 (2.50) 0 (0.00) - *The percentage is calculated as the number of fatal reports for this drug divided by the num ber of FT1DM reports. P ˂ 0.05 were considered statistically significant. Discussion The expanding clinical application of ICIs has unmasked a spectrum of irAEs, with MNDs emerging as a critical concern that impacts treatment continuity and long-term patient outcomes[ 20 , 21 ]. Unlike the controlled setting of clinical trials, real-world pharmacovigilance data offer an unfiltered view of their epidemiological landscape. Our study represents the first systematic, multi-method disproportionality analysis (ROR, PRR, BCPNN, MGPS) leveraging the FAERS database to quantify the association between PD-1/PD-L1 inhibitors and MNDs. This consensus-based approach minimizes false-positive signals inherent to single-algorithm analyses and enhances reproducibility. By analyzing 5,160 reports across eight agents, we provide a comprehensive toxicity map revealing distinct risk spectra, temporal dynamics, and clinically critical subgroups. Demographic vulnerabilities and clinical severity Our study demonstrated higher MNDs reporting frequency in male and older (≥ 45 years) patient, trends consistently observed across nivolumab, pembrolizumab, and atezolizumab. These findings underscore the need for heightened vigilance in these subgroups. Notably, clinical outcomes were severe in over 97% of reports, with an 18% fatality rate among serious cases, emphasizing the life-threatening nature of ICI-induced metabolic toxicities and the imperative for proactive risk mitigation. Two pillars of metabolic toxicity: electrolyte abnormalities and diabetes Electrolyte abnormalities represent a critical MNDs subset associated with poor prognosis in oncology populations [ 22 ]. While sporadically reported in ICI-treated patients [ 10 , 23 – 25 ], systematic real-world data remain limited. Our findings align with existing literatures showing predominantly manifestation within the first three months [ 22 , 26 – 28 ], reinforcing the need for early electrolyte monitoring. ICI-induced diabetes represents a distinct clinical entity[ 29 ]. The median time to diabetes on set was 49 days, with 71% of cases developing within 3 months, varying by agent: pembrolizumab (42 days), nivolumab (73.5 days), and PD-L1 inhibitors (84 days)[ 30 ]. FT1DM emerged as the cornerstone metabolic irAE, characterized by rapid onset, severe metabolic disturbance, and disproportionately elevated blood glucose relative to near-normal glycated hemoglobin (HbA1c), reflecting near-total, immune-mediated β-cells destruction [ 31 ].The 6.95% fatality rate underscores its acute lethality. Weibull analysis (β < 1) and Kaplan-Meier estimates (80% of events within 120 days) define a critical high-risk window mandating recognition of FT1DM as a medical emergency, with protocol-driven glucose monitoring and patient education on cardinal symptoms (polyuria, polydipsia, weight loss, abdominal pain) during initial cycles [ 29 , 32 ]. PD-1 versus PD-L1: biological distinction beyond utilization patterns Our most salient finding is the significantly broader metabolic signal spectrum with PD-1 ver sus PD-L1 inhibitors, a disparity pointing to fundamental biological differences. The pancreatic islet represents a unique immunological niche where PD-1/PD-L1 signaling maintains tolerance checkpoints. Islet-infiltrating and adipose tissue immune cells universally express PD-1, rendering them susceptible to checkpoint modulation disrupting established immune tolerance [ 33 – 36 ]. Notably, β-cells with high baseline PD-L1 expression demonstrate remarkable resistance to T-cell-mediated apoptosis, enabling prolonged survival under autoimmune attack[ 37 ]. We propose that systemic PD-1 blockade delivers a “dual hit”: globally reinvigorates effector T-cells while simultaneously depriving peripheral metabolic tissues of protective PD-1 signaling. This disrupts the intrinsic “immune-metabolic dialogue” within pancreas and adipose tissue, predisposing them to autoimmune attack and functional dysregulation[ 38 – 40 ], explaining the predominance of severe, organ-specific endocrinopathies like FT1DM. In contrast, PD-L1 blockade primarily interferes with ligand-receptor interactions without directly altering intracellular PD-1 signaling, potentially yielding narrower metabolic effects. This tissue-specific vulnerability underscores the necessity of pre-treatment islet autoantibody screening and intensive glycemic surveillance, particularly within 120 days when immune reconstitution is most dynamic. Our findings provide crucial epidemiological support for the hypothesis that PD-1 plays a non-redundant role in systemic metabolic homeostasis, warranting dedicated experimental dissection. Temporal dynamics and personalized monitoring Despite ICIs clinical significance, temporal dynamic of ICI-induced MNDs remain poorly characterized. We employed Weibull distribution analysis and demonstrated an early-failure pattern (β < 1), confirming hazard peaks shortly after treatment initiation. Kaplan-Meier analysis showed that 80% of all MNDs occurred within 120 days, strongly advocating front-loaded surveillance with intensive metabolic screening (fasting glucose, lipid panel, electrolytes) during early cycles. Beyond the class-effect, we identified important drug-specific and host-specific modifiers. The shorter median TTO for atezolizumab compared to nivolumab and pembrolizumab warrants heightened early vigilance. Furthermore, lower body weight predicted significantly shorter TTO for pembrolizumab and atezolizumab, possibly reflecting altered pharmacokinetics, pharmacodynamics, or distinct immune-metabolic states, suggesting weight-stratified monitoring intensity. The association between fatal outcomes and shorter TTO reinforces that early-onset events are particularly virulent. Clinical implications: toward precision surveillance Our findings collectively argue for a paradigm shift from indefinite, uniform monitoring to personalized, phase-specific surveillance. We propose: (1) mandatory baseline metabolic profiling; (2) intensive monitoring every 2–4 weeks during the first 4 months, especially for PD-1 inhibitors recipients, low body weight patients on pembrolizumab, atezolizumab, and older males; (3) patient-held “FT1DM alert cards” detailing cardinal symptoms; and (4) explicit incorporation of metabolic parameter into irAE management guidelines. Limitations This study shares the limitations inherent to spontaneous reporting systems, including under-reporting, potential reporting bias toward severe events, and the inability to establish true incidence rates or confirm causality. Confounding by malignancies, concomitant medications, and pre-existing metabolic conditions cannot be fully excluded. Absence of detailed laboratory values and tumor response data limits deeper correlative analysis. Despite these limitations, our analysis of over twenty million real-world records provides important signals to inform prospective validation studies and clinical practice guidelines. Conclusions The large-scale pharmacovigilance study provides the first comprehensive, real-world evidence characterizing spectrum, risk signals, and temporal dynamics of PD-1/PD-L1 inhibitor-associated metabolic toxicities. We delineate a wider risk profile for PD-1 inhibitors, pinpoint FT1DM as a critical early-onset emergency, and quantitatively define the highest-risk period within 120 days. By integrating these findings, we advance from toxicity description towards a framework for precision monitoring, aiming to enhance the safety and sustainability of these transformative anticancer treatments. Declarations Supplementary Materials : The following supporting information can be downloaded at: https://www.mdpi.com/article/doi/s1 Institutional Review Board Statement Ethical review and approval were waived for this study due to the analysis of publicly available, anonymized data from the FAERS, where individual patient identification is not possible and the data are exempt from institutional review board oversight. Informed Consent Statement Patient consent was waived due to the retrospective analysis of publicly available, anonymized data from the FAERS, where individual patient identification is not possible. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This work is supported by the National Natural Science Foundation of China (Grant No. 82002816), Tianjin Key Medical Discipline Construction Project (Grant No. TJYXZDXK-3-003), and Hospital-level Project of Tianjin Medical University Cancer Institute and Hospital (Grant No. MS2510). Author Contribution Conceptualization, L.Z.; methodology, L.Z.; software, X.L.; formal analysis, N.X.; data curation, G.B.; writing—original draft preparation, L.Z.; writing—review and editing, H.L.; supervision, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript. Acknowledgments: The authors acknowledge the FAERS for providing the publicly available pharmacovigilance data used in this study. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74 (3), 229–263 (2024). Khosravi, G. R. et al. Immunologic tumor microenvironment modulators for turning cold tumors hot. Cancer Commun. (Lond) . 44 (5), 521–553 (2024). Sharma, P. et al. Immune checkpoint therapy-current perspectives and future directions. Cell 186 (8), 1652–1669 (2023). Yi, M. et al. Combination strategies with PD-1/PD-L1 blockade: current advances and future directions. Mol. Cancer . 21 (1), 28 (2022). Barron, C. C. et al. Chronic immune-related adverse events in patients with cancer receiving immune checkpoint inhibitors: a systematic review. J Immunother Cancer. ; 11(8). (2023). Rajak, P. Immune checkpoint inhibitors: From friend to foe. Toxicol. Rep. 14 , 102033 (2025). Corsello, A., Paragliola, R. M., Torino, F. & Salvatori, R. Hypophysitis Associated with Immune Checkpoint Inhibitors. Endocrinol. Metab. Clin. North. Am. 54 (4), 685–702 (2025). Garcia-Goni, M. et al. Thyroid dysfunction caused by immune checkpoint inhibitors improves cancer outcomes. Endocr. Relat. Cancer ; 31 (10). (2024). Wu, L. et al. Hyperglycemia in patients treated with immune checkpoint inhibitors: key clinical challenges and multidisciplinary consensus recommendations. J. Immunother Cancer ; 13 (6). (2025). Tao, C. et al. PD-1/PD-L1 inhibitor-induced hyponatremia: a real-world pharmacovigilance analysis using FAERS database. Front. Immunol. 16 , 1561942 (2025). Quandt, Z. E. et al. Immune Checkpoint Inhibitor-Induced Diabetes Across National Cancer Institute Trials That Included PD-1 or PD-L1 Agents. JAMA Oncol. (2025). Manohar, S. et al. Programmed cell death protein 1 inhibitor treatment is associated with acute kidney injury and hypocalcemia: meta-analysis. Nephrol. Dial Transpl. 34 (1), 108–117 (2019). Ruiz-Esteves, K. N. et al. Identification of Immune Checkpoint Inhibitor-Induced Diabetes. JAMA Oncol. 10 (10), 1409–1416 (2024). Han, M. et al. Immune Checkpoint Inhibitors-Associated Diabetes and Ketoacidosis Were Found in FDA Adverse Event Reporting System: A Real-World Evidence Database Study. Endocr. Pract. 30 (9), 887–892 (2024). Karra, P. et al. Metabolic dysfunction and obesity-related cancer: Beyond obesity and metabolic syndrome. Obes. (Silver Spring) . 30 (7), 1323–1334 (2022). Garcia-Garcia, F. J., Monistrol-Mula, A., Cardellach, F. & Garrabou, G. Nutrition, Bioenergetics, and Metabolic Syndrome. Nutrients ; 12 (9). (2020). Potter, E., Reyes, M., Naples, J. & Dal Pan, G. FDA Adverse Event Reporting System (FAERS) Essentials: A Guide to Understanding, Applying, and Interpreting Adverse Event Data Reported to FAERS. Clin. Pharmacol. Ther. 118 (3), 567–582 (2025). Zhai, Y. et al. Metabolic and Nutritional Disorders Following the Administration of Immune Checkpoint Inhibitors: A Pharmacovigilance Study. Front. Endocrinol. (Lausanne) . 12 , 809063 (2021). Li, R., Mu, X., Liu, Z., Huang, R. & Peng, X. Association of type 2 diabetes, hypertension, and hyperlipidemia with immune-related adverse events in patients undergoing immune checkpoint inhibitors therapy. Front. Immunol. 16 , 1472197 (2025). Wright, J. J., Powers, A. C. & Johnson, D. B. Endocrine toxicities of immune checkpoint inhibitors. Nat. Rev. Endocrinol. 17 (7), 389–399 (2021). Chen, X. et al. Immune Checkpoint Inhibitors and Risk of Type 1 Diabetes. Diabetes Care . 45 (5), 1170–1176 (2022). Seethapathy, H. et al. Hyponatremia and other electrolyte abnormalities in patients receiving immune checkpoint inhibitors. Nephrol. Dial Transpl. 36 (12), 2241–2247 (2021). Izzedine, H., Chazal, T., Wanchoo, R. & Jhaveri, K. D. Immune checkpoint inhibitor-associated hypercalcaemia. Nephrol. Dial Transpl. 37 (9), 1598–1608 (2022). Gu, Y. et al. Refractory hypokalemia and metabolic acidosis induced by undifferentiated connective tissue disease secondary to immune checkpoint inhibitors: a case report and literature review. Front. Oncol. 14 , 1442605 (2024). Chiu, T. J., Huang, T. L., Chien, C. Y., Huang, W. T. & Li, S. H. Hypoalbuminemia and hypercalcemia are independently associated with poor treatment outcomes of anti-PD-1 immune checkpoint inhibitors in patients with recurrent or metastatic head and neck squamous cell carcinoma. World J. Surg. Oncol. 22 (1), 242 (2024). Matsushiro, M., Shibue, K., Osawa, K. & Hamasaki, A. Isolated Adrenocorticotropic Hormone (ACTH) Deficiency as an Immune-Related Adverse Event Following Combination Immune Checkpoint Inhibitor Therapy. Cureus 16 (6), e62863 (2024). Ikeda, Y. et al. Isolated Adrenocorticotropic Hormone Deficiency Associated with Atezolizumab and Bevacizumab Administration for Treating Hepatocellular Carcinoma: A Case Series. Intern. Med. 62 (22), 3341–3346 (2023). Kafley, S., Tamrakar, S., Samra, M., Shanmugar, S. & Gupta, I. Immune Checkpoint Inhibitors and Endocrine Disruption: A Case of Hyponatremia and Adrenal Insufficiency. Cureus 16 (9), e70089 (2024). Venetsanaki, V., Boutis, A., Chrisoulidou, A. & Papakotoulas, P. Diabetes mellitus secondary to treatment with immune checkpoint inhibitors. Curr. Oncol. 26 (1), e111–e114 (2019). Akturk, H. K. et al. Immune checkpoint inhibitor-induced Type 1 diabetes: a systematic review and meta-analysis. Diabet. Med. 36 (9), 1075–1081 (2019). Ying, L. et al. Classic Type 1 Diabetes Mellitus and Fulminant Type 1 Diabetes Mellitus: Similarity and Discrepancy of Immunological Characteristics and Cytokine Profile. Diabetes Metab. Syndr. Obes. 14 , 4661–4670 (2021). Imagawa, A. & Hanafusa, T. Fulminant Type 1 Diabetes-East and West. J. Clin. Endocrinol. Metab. 108 (12), e1473–e1478 (2023). Loaiza Naranjo, J. D. et al. PD-1 expressing islet-specific CD4(+) T cells promote bystander tolerance and prevent autoimmunity. Immunol. Cell. Biol. 103 (7), 738–751 (2025). Sun, X. et al. Transcriptional switch of hepatocytes initiates macrophage recruitment and T-cell suppression in endotoxemia. J. Hepatol. 77 (2), 436–452 (2022). Ham, J. et al. Modulating the PD-1-FABP5 axis in ILC2s to regulate adipose tissue metabolism in obesity. Mol. Ther. 33 (4), 1842–1859 (2025). Patsoukis, N. et al. PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nat. Commun. 6 , 6692 (2015). Kawata, S. et al. Inflammatory Cell Infiltration Into Islets Without PD-L1 Expression Is Associated With the Development of Immune Checkpoint Inhibitor-Related Type 1 Diabetes in Genetically Susceptible Patients. Diabetes 72 (4), 511–519 (2023). Li, S. et al. Metabolic regulation of immunity in the tumor microenvironment. Cell. Rep. 44 (11), 116463 (2025). Falcone, M. & Fousteri, G. Role of the PD-1/PD-L1 Dyad in the Maintenance of Pancreatic Immune Tolerance for Prevention of Type 1 Diabetes. Front. Endocrinol. (Lausanne) . 11 , 569 (2020). Sun, J. et al. Metabolic regulator LKB1 controls adipose tissue ILC2 PD-1 expression and mitochondrial homeostasis to prevent insulin resistance. Immunity 57 (6), 1289–1305 (2024). e1289. Additional Declarations No competing interests reported. Supplementary Files LiyanZhouSupplementaryFigure.docx Figure S1: Kaplan-Meier curve analysis of factors associated with the TTO of the MNDs; LiyanZhouSupplementaryTables.docx Table S1: The classic two-by-two contingency table; Table S2: Overview of algorithms utilized for signal detection; Table S3: Positive disproportionality signals for MNDs of PD-1 inhibitors; Table S4: Positive disproportionality signals for MNDs of PD-L1 inhibitors. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9051493","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612034689,"identity":"167594aa-a1ba-427e-8a7f-9d89219dde83","order_by":0,"name":"Liyan Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACCSB+YAAk2BsYQFQCcVoSQGp5DpOkBcxIBvMJa5Gf3XxMIqHgsLy55PsDRTfb7PIY2A8f3YBPC+OcY2kSCQaHDXfOTmYwzm1LLmbgSUu7gU8Ls0SOGUgL44bbYC0HEhskeMzwamGDarHfcPMwkVp4oFoSN9xgJlKLhERaskWCQXryhjPJBsY555IT2wj5RX5G8sEbH/5Y2244fvCZcU6ZXWI/++FjeLVAQTPYXwZgkgjlIFAHIpgfEKl6FIyCUTAKRhgAAP9dScAReRQgAAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Liyan","middleName":"","lastName":"Zhou","suffix":""},{"id":612034690,"identity":"7aa379c8-9af9-4c94-ad9f-d1f09721540c","order_by":1,"name":"Xiaohui Liu","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Liu","suffix":""},{"id":612034691,"identity":"8baf8bc2-e65c-4f7b-8ada-db2bb3acd90f","order_by":2,"name":"Na Xiao","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Xiao","suffix":""},{"id":612034692,"identity":"5674438e-209b-4357-8086-04a9cd0ccc1f","order_by":3,"name":"Guiying Bai","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guiying","middleName":"","lastName":"Bai","suffix":""},{"id":612034693,"identity":"606aadd1-6d52-4a7f-9843-ead88c88dd9b","order_by":4,"name":"Hongli Li","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongli","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-06 14:12:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9051493/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9051493/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105548402,"identity":"6e255436-e890-4cba-be4f-5e7e20e62173","added_by":"auto","created_at":"2026-03-27 09:31:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75704,"visible":true,"origin":"","legend":"\u003cp\u003eDisproportionality analysis and spectrum of signals of PD-1/PD-L1 inhibitors. (A-B) Venn diagram illustrating the intersection of positive MNDs signals for PD-1(A) /PD-L1 (B) inhibitors. (C-D) Signal detection analysis of MNDs associated with PD-1 (C)/PD-L1 (D) inhibitors using ORR methods.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9051493/v1/9bfa95358ff490f5376f3b27.png"},{"id":105548401,"identity":"ec608b25-53ab-40f5-bb23-11cd47318b0f","added_by":"auto","created_at":"2026-03-27 09:31:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94624,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the toxicity profile in different PD-1/PD-L1 inhibitors.\u003cstrong\u003e \u003c/strong\u003e(A-C) Signal detection analysis of MNDs associated with nivolumab (A), pembrolizumab (B), atezolizumab (C) using ROR, PRR, BCPNN, and MGPS methods. (D) Venn diagram illustrating the intersection of positive MNDs PTs for nivolumab, pembrolizumab, and atezolizumab.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9051493/v1/3cc2e605bac8ac1593814729.png"},{"id":105548403,"identity":"1a8c1381-1ce9-40cf-8e66-b904ffcc7537","added_by":"auto","created_at":"2026-03-27 09:31:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124492,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve analysis of factors associated with the TTO of the MNDs. (A-C) Time of the first MNDs in different age groups with nivolumab (A), pembrolizumab (B), atezolizumab (C). (D-F) Time of the first MNDs in different weight body groups with nivolumab (D), pembrolizumab (E), atezolizumab (F).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9051493/v1/4484f215c064d69e7ece7671.png"},{"id":105548405,"identity":"c182d663-3a7b-4353-a7d5-6363cb439eaf","added_by":"auto","created_at":"2026-03-27 09:31:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70393,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of different onset times for MNDs and FT1DM associated with dif ferent drugs. (A) Proportional distribution of MNDs incidence across different time intervals. (B) Proportional distribution of FT1DM incidence across different time intervals.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9051493/v1/8ef6d7fc20c4efc04cb3ddcd.png"},{"id":105567142,"identity":"b433ccb4-6c3b-485c-9f0e-595c87d88c6c","added_by":"auto","created_at":"2026-03-27 12:58:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1587361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9051493/v1/44a40ed0-1482-4483-b544-6992f20a80e1.pdf"},{"id":105548406,"identity":"ff1b6283-9699-4b4f-bda0-9f89eaf5b23d","added_by":"auto","created_at":"2026-03-27 09:31:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":433576,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1: Kaplan-Meier curve analysis of factors associated with the TTO of the MNDs;\u003c/p\u003e","description":"","filename":"LiyanZhouSupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-9051493/v1/789bb50de9a8184c519f0228.docx"},{"id":105548404,"identity":"b950beaa-0495-4e27-8414-4ee170005cfc","added_by":"auto","created_at":"2026-03-27 09:31:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33289,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1: The classic two-by-two contingency table; Table S2: Overview of algorithms utilized for signal detection; Table S3: Positive disproportionality signals for MNDs of PD-1 inhibitors; Table S4: Positive disproportionality signals for MNDs of PD-L1 inhibitors.\u003c/p\u003e","description":"","filename":"LiyanZhouSupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9051493/v1/bcbfc23fafd73877bc51766b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"PD-1/PD-L1 inhibitor-induced metabolic and nutritional disorders: a real-world pharmacovigilance analysis using FAERS database","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer remains a major global health challenge, with 20\u0026nbsp;million new cases and 9.7\u0026nbsp;million deaths reported in 2022.Projections indicate 35\u0026nbsp;million annual cases by 2050, imposing substantial economic and societal burdens [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Immune checkpoint inhibitors (ICIs), particularly those targeting the programmed cell death protein-1(PD-1)/programmed cell death ligand-1 (PD-L1) pathway, have transformed oncology by blocking T-cell inhibitory signals to enhance anti-tumor immunity [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, this efficacy is accompanied by immune-related adverse events (irAEs) affecting virtually any organ system, compromising quality of life and treatment continuity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent clinical focus predominantly centers on organ-specific irAEs, such as pneumonitis, colitis, hepatitis, and classic endocrinopathies (thyroiditis, hypophysitis), for which management guidelines are established [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast, metabolic and nutritional disorders (MNDs), including hyperglycemic crises, severe electrolyte imbalances, and dyslipidemia, remain systematically overlooked despite their rapid progression to life-threatening complications (e.g., diabetic ketoacidosis) and profound impact on patient outcomes[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These disorders encompass a heterogeneous spectrum of systemic disturbances, all of which may be induced by ICIs through disruption of endocrine-immune crosstalk and systemic metabolic homeostasis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While sporadic case reports have linked PD-1/PD-L1 inhibitor to diabetes and electrolyte imbalances, the full spectrum, incidence patterns, and temporal dynamics of these metabolic toxicities in real-world populations are entirely uncharacterized. This knowledge deficit directly contributes to the absence of evidence-based metabolic monitoring guidelines, leaving clinicians without actionable guidance for early detection and intervention.\u003c/p\u003e \u003cp\u003eThe FDA Adverse Event Reporting System (FAERS) is a system for collecting and storing individual case safety report, and is highly valuable due to their ability to gather spontaneous reports[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Continuous post-marketing surveillance of PD-1/PD-L1 inhibitors are crucial for evaluating the long-term safety and identifying rare or severe AEs. While prior FAERS studies have signaled ICI-associated MNDs, they relied on single-algorithm analyses without temporal stratification [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], limiting clinical utility for risk-adapted monitoring.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study leverages FAERS data (Q1 2011\u0026ndash;Q2 2025) to conduct a comprehensive pharmacovigilance analysis of MNDs associated with eight PD-1/PD-L1 inhibitors. Using four disproportionality algorithms (ROR, PRR, BCPNN, MGPS), we quantify risk signals at system organ class (SOC) and preferred terms (PT) levels, characterize demographic and clinical determinants, and define time-to-onset dynamics. These findings aim to inform precision monitoring strategies and enhance the safe use of PD-1/PD-L1 inhibitors in oncology practice.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and extraction\u003c/h2\u003e \u003cp\u003eThe FAERS database is a publicly accessible spontaneous reporting system that compiles safety reports on marketed drugs and biologics from countries worldwide. These reports are submitted by healthcare professionals, drug manufacturers, patients, and other stakeholders such as attorneys. FAERS contains seven datasets: demographic and management information (DEMO), drug information (DRUG), adverse event information (REAC), patient outcome information (OUTC), report source information (RPSR), drug therapy start and end dates (THER), and indication/diagnosis information (INDI). The study was performed involving cumulative data available from the FAERS database collected from the first quarter of 2011 to the second quarter of 2025, following FDA guidelines and official recommendations. FAERS files are made publicly available on a quarterly basis and are accessed at the website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD\u003c/span\u003e\u003cspan address=\"https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e QDE-FAERS.html. Given that the FAERS constitutes a publicly accessible and anonymized dataset, the requirement for institutional review board approval and informed consent has been waived. The present study was conducted in strict accordance with the FDA official website guidance document for data cleaning. The data cleaning rules involved removing duplicate reports using the method recommended by the FDA[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy drugs and adverse events\u003c/h3\u003e\n\u003cp\u003eStudy drugs included five PD-1 inhibitors (nivolumab, pembrolizumab, tislelizumab, toripal imab, and dostarlimab), and three PD L1 inhibitors (atezolizumab, avelumab, and durvalumab). Reports were extracted where any of these agents were listed as the \u0026ldquo;primary suspect\u0026rdquo; in DRUG file. To identify the target AEs, we used the standardized Medical Dictionary for Regulatory Activities (MedDRA) terminology (version 27.0), which provides standardized and precise descriptions of medical conditions. All reports where any PT belonging to the SOC \u0026ldquo;Metabolism and nutrition disorders\u0026rdquo; (code 10027433) was recorded in the REAC file were included for subsequent analysis. Duplicate reports were identified and removed based on the FDA-recommended primary key (case id), with the most recent version retained for analysis.\u003c/p\u003e\n\u003ch3\u003eDescriptive and clinical characterization analysis\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics were performed on all included reports. We summarized patient de mographics (age, gender, body weight), reporter qualification, reporting country/region, and report year. Clinical outcomes were assessed based on the \u0026ldquo;serious\u0026rdquo; outcomes as defined by the FDA (e.g., hospitalization, life-threatening, death.\u003c/p\u003e\n\u003ch3\u003eDisproportionality analysis (Signal Detection)\u003c/h3\u003e\n\u003cp\u003eA disproportionality analysis was employed to detect potential safety signals by comparing the reporting proportion of a specific drug-AE pair against the background reporting proportion for all other drugs in the database. Four complementary statistical algorithms were used to enhance robustness and minimize method-specific bias: Reporting odds ratio (ROR) and proportional reporting ratio (PRR), with their corresponding 95% confidence intervals (95% CI). Bayesian confidence propagation neural network (BCPNN), generating the Information Component (IC) with its 95% credibility interval (95% CI). Multi-item gamma Poisson shrinker (MGPS), generating the empirical bayes geometric mean (EBGM) with its 95% CI [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A positive signal was conservatively defined if all four of the following criteria were met simultaneously: lower limit of the 95% CI for ROR\u0026thinsp;\u0026gt;\u0026thinsp;1, lower limit of the 95% CI for PRR\u0026thinsp;\u0026gt;\u0026thinsp;1, lower limit of the 95% CI for IC\u0026thinsp;\u0026gt;\u0026thinsp;0, and lower limit of the 95% CI for EBGM05\u0026thinsp;\u0026gt;\u0026thinsp;2. This stringent, multi-method consensus approach aims to increase specificity and reduce false-positive signals.\u003c/p\u003e \u003cp\u003eAnalyses were performed at three levels: (1) Class level: comparing all PD-1 inhibitors versus all PD-L1 inhibitors against the background; (2) Individual drug level: analyzing each of the eight inhibitors separately; (3) Intersection analysis: identifying common signals shared among the three drugs (nivolumab, pembrolizumab, atezolizumab) with the highest number of positive PTs.\u003c/p\u003e\n\u003ch3\u003eTime-to-onset (TTO) Analysis\u003c/h3\u003e\n\u003cp\u003eFor reports with available start and event dates for the primary suspect drug, the TTO was calculated as the interval (in days) between these dates. Reports with illogical TTO (e.g., \u0026le;\u0026thinsp;0 or \u0026gt;\u0026thinsp;365 days after therapy cessation) were excluded from this specific analysis. Kaplan-Meier curves were generated to visualize the cumulative probability of metabolic disorder onset over time after treatment initiation. Log-rank tests were used to compare TTO distributions across pre-specified subgroups. Median TTO with interquartile range (IQR) was calculated for overall events.\u003c/p\u003e \u003cp\u003eTo model the hazard function and characterize the failure pattern, a Weibull distribution was fitted to the TTO data for the overall metabolic events and for key drugs. The shape parameter (β) was estimated: β\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates a \u0026ldquo;early failure type\u0026rdquo; (highest risk initially, decreasing over time), β\u0026thinsp;=\u0026thinsp;1 indicates a \u0026ldquo;random failure type\u0026rdquo; (constant hazard), and β\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates a \u0026ldquo;wear-out failure type\u0026rdquo; (risk increasing over time).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical and Software\u003c/h2\u003e \u003cp\u003eAll statistical analyses and data processing were conducted using R software (version 4.3.1). The ggplot2 (version 3.4.4) package was used for visualization. To compare the frequency of these AEs with their background frequency, a classic 2 \u0026times; 2 contingency table Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) was utilized to establish statistical associations. The standard formulas for ROR, PRR, BCPNN, and MGPS and evaluation criteria are outlines in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for comparative tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOverview of reports and clinical characteristics\u003c/h2\u003e \u003cp\u003eAmong a total of 21, 712, 563 records in the FAERS pharmacovigilance database included, 5,160 reports were documented for MNDs after receiving eight target PD-1/PD-L1 inhibitors from Q1-2011 to Q2-2025. The demographic and clinical characteristics of these reports are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In terms of gender distribution, male patients exhibited a significantly higher rate of MNDs (53.22%) compared to females (35.39%). When categorized by age, patients between 45 and 65 ages accounted for the highest proportion (34.22%) of MNDs cases. Physicians constituted the largest group of reporters (40.17%), followed by health professional (23.26%). Geographically, most reports originated from the United States (30.62%) and Japan (14.98%). The annual number of reports demonstrated a marked increase starting in 2015, coinciding with the broader clinical adoption of PD-1/PD-L1 inhibitors. Regarding patient outcomes, hospitalization was the most frequently reported, accounting for 44.48% of all cases. Alarmingly, a large proportion of these events were classified as serious (97.83%), with a considerable subset resulting in death (18.31%). Overall, reports of MNDs events are significantly higher for PD-1 inhibitors than for PD-L1 inhibitors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of reports for MNDs associated with PD-1/PD-L1 inhibitors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5160)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD-1 inhibitors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3931)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePD-L1 inhibitors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1229)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1827 (35.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1406 (35.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e421 (34.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2746 (53.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2223 (56.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e523 (42.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e587 (11.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302 (7.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e285 (23.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=18 and \u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e266 (5.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237 (6.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (2.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=45 and \u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1766 (34.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1391 (35.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e375 (30.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=65 and \u0026lt;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1529 (29.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1178 (29.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e351 (28.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e853 (16.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e651 (16.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202 (16.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e708 (13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e448 (11.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e260 (21.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (Kg), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e292 (5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198 (5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (7.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=50 and \u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e906 (17.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e600 (15.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e306 (24.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=70 and \u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e853 (16.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e587 (14.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e266 (21.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193 (3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141 (3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (4.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2416 (56.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2405 (61.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e511 (41.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReporter type, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2073 (40.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1120 (28.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e953 (77.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1200 (23.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e988 (25.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212 (17.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e724 (14.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e708 (18.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharmacist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156 (3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145 (3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther health-professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e906 (17.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e884 (22.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (1.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLawyer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReported countries, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1580 (30.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1251 (31.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e329 (26.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e773 (14.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433 (11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e340 (27.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e644 (12.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e571 (14.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (5.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342 (6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257 (6.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (6.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319 (6.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269 (6.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (4.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1502 (29.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1150 (29.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352 (28.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReporting year, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111 (2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111 (2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226 (4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215 (5.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e424 (8.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e380 (9.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (3.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e575 (11.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e504 (12.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71 (5.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e623 (12.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e533 (13.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90 (7.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e640 (12.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e533 (13.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (8.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e628 (12.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e480 (12.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148 (12.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e626 (12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e377 (9.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249 (20.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e532 (10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237 (6.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295 (24.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e453 (8.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e319 (8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (10.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025 (January-June)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278 (5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198 (5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (6.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e945 (18.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e692 (17.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e253 (20.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2295 (44.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1762 (44.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e533 (43.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife-threatening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474 (9.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390 (9.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (6.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1427 (27.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1068 (27.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e359 (29.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSerious outcome, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112 (2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5048 (97.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3832 (97.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1216 (98.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFatal outcome, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4215 (81.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3239 (82.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e976 (79.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e945 (18.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e692 (17.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e253 (20.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDisproportionality analysis and spectrum of signals\u003c/h2\u003e \u003cp\u003eAt the drug class level, disproportionality analysis revealed a significantly broader spectrum of metabolic risk associated with PD-1 inhibitors compared to PD-L1 inhibitors. Using the stringent four-method consensus criterion, 29 unique PTs showed positive signals for PD-1 inhibitors as a class (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table S3), compared to 13 PTs for PD-L1 inhibitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Table S4). The all signals for each class, ranked by ROR, are listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D. Fulminant type 1 diabetes mellitus (FT1DM) emerged as the most prominent signal for both classes, with the highest ROR and meeting all signal detection criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the toxicity profile in different PD-1/PD-L1 inhibitors\u003c/h2\u003e \u003cp\u003eFor disproportionality at PT level, a wide array of signals emerged in specific PD-1/PD-L1 inhibitors with different occurrence frequencies. At the individual drug level, nivolumab, pembrolizumab, and atezolizumab accounted for the vast majority of reports and exhibited the most extensive metabolic risk profiles, with 24, 16, and 17 positive PTs, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). The remaining five agents had between 2 and 4 positive PTs each. An intersection analysis of the positive signals from the three most reported drugs (nivolumab, pembrolizumab, atezolizumab) identified nine PTs shared among all three, including hyperlipasaemia, hypercalcaemia, hyponatraemia, hypoalbuminaemia, tumor lysis syndrome, diabetic ketoacidosis, type 1 diabetes mellitus, decreased appetite, and FT1DM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Among them, the strongest signal was FT1DM, highlighting its role as a core, pan-category metabolic irAE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the median onset times in different PD-1/PD-L1 inhibitors\u003c/h2\u003e \u003cp\u003eThe median TTO for MNDs varied among three most reported agents: 43 days for nivolumab, 32.5 days for pembrolizumab, and 22 days for atezolizumab (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Stratification by age revealed distinct onset pattern for each drug. For nivolumab, the median TTO was 28 days (IQR: 14-70.5) in the 18 to 45 years group, showing a trend of prolonged onset times with increasing age (P\u0026thinsp;=\u0026thinsp;0.097, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For pembrolizumab, the shortest TTO was observed in patients under 18 years old, with a median of 15 days. In contrast, patients aged 45\u0026ndash;65 years had a significantly longer median TTO of 43 days (IQR: 14.5\u0026ndash;117.75; P\u0026thinsp;=\u0026thinsp;0.019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For atezolizumab, a similar pattern of earlier onset in younger patients was evident. The shortest median TTO was 11 days (IQR: 9\u0026ndash;13) in patients under 18 years old, while the longest median TTO was 45 days (IQR: 11.25\u0026ndash;87.25) in patients over 85 years old (P\u0026thinsp;=\u0026thinsp;0.019, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo investigate the potential influence of body weight on the onset kinetics of metabolic disorders, we performed a stratified analysis for the three most reported agents (nivolumab, pembrolizumab, atezolizumab) based on available patient weight data. Notably, a distinct pattern emerged across the different agents. For nivolumab, the median TTO was not significantly different across weight strata (P\u0026thinsp;=\u0026thinsp;0.47, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In contrast, a statistically significant association was observed for both pembrolizumab and atezolizumab. For pembrolizumab, patients in the low-weight group (\u0026le;\u0026thinsp;50 kg) experienced the shortest median TTO of 20 days (IQR: 7\u0026ndash;68), which was significantly earlier than those in the intermediate-weight group and the high-weight group (\u0026ge;\u0026thinsp;100 kg, 97.5 days, IQR: 20-274.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). A similar trend was observed for atezolizumab, where the low-weight group had the shortest median TTO of 16 days (IQR: 6\u0026ndash;17), compared to 44 days (IQR: 33.5-130.5) in the high-weight group (P\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The TTO analysis showed no differences among the three drugs in terms of gender and fatal groups (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eWeibull distribution analysis of TTO in different PD-1/PD-L1 inhibitors\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo quantitatively characterize the hazard-over-time profile for metabolic disorders associated with each agent, Weibull distribution models were fitted to the available time-to-onset data. The shape parameter (β) and its 95% confidence interval (CI) for each of the eight PD-1/PD-L1 inhibitors are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The analysis revealed two distinct hazard patterns among the agents with sufficient data for modeling. For five drugs\u0026mdash;nivolumab, pembrolizumab, atezolizumab, tislelizumab, and durvalumab\u0026mdash;the estimated β values were significantly less than 1 (upper limit of 95% CI\u0026thinsp;\u0026lt;\u0026thinsp;1), indicating a consistent \"early failure\" type hazard. This pattern signifies that the instantaneous risk of developing a metabolic disorder is highest immediately following treatment initiation and decreases thereafter. Notably, dostarlimab exhibited a diametrically opposite hazard pattern. Its β value was significantly greater than 1 (lower limit of 95% CI\u0026thinsp;\u0026gt;\u0026thinsp;1), defining a \"wear-out failure\" pattern. This suggests that for this agent, the risk of metabolic events increases with the duration of therapy. Weibull models could not be reliably fitted for toripalimab and avelumab, primarily due to the limited number of reported cases with documented onset times, precluding a stable statistical estimation of their temporal risk profiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeibull distribution parameter analysis for the time-to-onset of MNDs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale parameter α (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShape parameter β (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFailure Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNivolumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (15\u0026ndash;120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.42 (80.09\u0026ndash;90.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.72\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.5 (10\u0026ndash;105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.8 (60.2-75.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.66\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.46 (11.7-35.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.51\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToripalimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.5 (4.5\u0026ndash;95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.07 (-16.72-102.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.14\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDostarlimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184 (141.25-269.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230.62 (129.37-331.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65 (0.71\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWear-out Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtezolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (8\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.59 (47.31\u0026ndash;59.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.63\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvelumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (18.75\u0026ndash;88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.23 (-8.19-140.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.24\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurvalumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (27.75\u0026ndash;65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.95 (41.36-118.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76 (0.58\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNA: A reliable Weibull model could not be fitted due to an insufficient number of reports with valid time-to-onset data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical characterization of fulminant type 1 diabetes mellitus events\u003c/h2\u003e \u003cp\u003eA total of 561 individual reports of FT1DM associated with PD-1/PD-L1 inhibitors were identi fied, accounting for 10.87% of all positive MNDs events, underscoring its status as a critical metabolic irAE. The majority of these reports were linked to PD-1 inhibitors (n\u0026thinsp;=\u0026thinsp;494, 88.06%), compared to PD-L1 inhibitors (n\u0026thinsp;=\u0026thinsp;67, 11.94%). In terms of gender distribution, male patients exhibited a significantly higher rate of MNDs (57.58%) compared to females (32.98%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Consistent with its acute and severe nature, 100% of FT1D reports were classified as serious adverse events, with a considerable proportion resulting in a fatal outcome (n\u0026thinsp;=\u0026thinsp;39, 6.95%).\u003c/p\u003e \u003cp\u003eWe collected FT1DM events with prognostic information and found significant differ ences in the distribution of FT1DM reported for different drugs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among PD-1 inhibitors, nivolumab accounted for the highest number of reports (n\u0026thinsp;=\u0026thinsp;334), followed by pembrolizumab (n\u0026thinsp;=\u0026thinsp;70). Notably, the fatality rate among FT1DM reports also differed, ranging from 2.70% for atezolizumab to 50.00% for avelumab, though these differences were only statistically significant in the nivolumab and atezolizumab cohort. The peak onset periods also varied, with nivolumab peaking within 0\u0026ndash;120 days, pembrolizumab and atezolizumab both reach their peak within 0\u0026ndash;30days (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The median TTO for MNDs in PD-1/PD-1 inhibitors was 93 days.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of FT1DM reports by individual PD-1/PD-L1 inhibitors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFT1DM reports\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFatal reports\u003c/p\u003e \u003cp\u003en(%)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNivolumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334 (59.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (12.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToripalimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDostarlimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtezolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (6.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvelumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurvalumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*The percentage is calculated as the number of fatal reports for this drug divided by the num ber of FT1DM reports. P ˂ 0.05 were considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe expanding clinical application of ICIs has unmasked a spectrum of irAEs, with MNDs emerging as a critical concern that impacts treatment continuity and long-term patient outcomes[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Unlike the controlled setting of clinical trials, real-world pharmacovigilance data offer an unfiltered view of their epidemiological landscape. Our study represents the first systematic, multi-method disproportionality analysis (ROR, PRR, BCPNN, MGPS) leveraging the FAERS database to quantify the association between PD-1/PD-L1 inhibitors and MNDs. This consensus-based approach minimizes false-positive signals inherent to single-algorithm analyses and enhances reproducibility. By analyzing 5,160 reports across eight agents, we provide a comprehensive toxicity map revealing distinct risk spectra, temporal dynamics, and clinically critical subgroups.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDemographic vulnerabilities and clinical severity\u003c/h2\u003e \u003cp\u003eOur study demonstrated higher MNDs reporting frequency in male and older (\u0026ge;\u0026thinsp;45 years) patient, trends consistently observed across nivolumab, pembrolizumab, and atezolizumab. These findings underscore the need for heightened vigilance in these subgroups. Notably, clinical outcomes were severe in over 97% of reports, with an 18% fatality rate among serious cases, emphasizing the life-threatening nature of ICI-induced metabolic toxicities and the imperative for proactive risk mitigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTwo pillars of metabolic toxicity: electrolyte abnormalities and diabetes\u003c/h2\u003e \u003cp\u003eElectrolyte abnormalities represent a critical MNDs subset associated with poor prognosis in oncology populations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While sporadically reported in ICI-treated patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], systematic real-world data remain limited. Our findings align with existing literatures showing predominantly manifestation within the first three months [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], reinforcing the need for early electrolyte monitoring.\u003c/p\u003e \u003cp\u003eICI-induced diabetes represents a distinct clinical entity[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The median time to diabetes on set was 49 days, with 71% of cases developing within 3 months, varying by agent: pembrolizumab (42 days), nivolumab (73.5 days), and PD-L1 inhibitors (84 days)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. FT1DM emerged as the cornerstone metabolic irAE, characterized by rapid onset, severe metabolic disturbance, and disproportionately elevated blood glucose relative to near-normal glycated hemoglobin (HbA1c), reflecting near-total, immune-mediated β-cells destruction [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].The 6.95% fatality rate underscores its acute lethality. Weibull analysis (β\u0026thinsp;\u0026lt;\u0026thinsp;1) and Kaplan-Meier estimates (80% of events within 120 days) define a critical high-risk window mandating recognition of FT1DM as a medical emergency, with protocol-driven glucose monitoring and patient education on cardinal symptoms (polyuria, polydipsia, weight loss, abdominal pain) during initial cycles [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePD-1 versus PD-L1: biological distinction beyond utilization patterns\u003c/h2\u003e \u003cp\u003eOur most salient finding is the significantly broader metabolic signal spectrum with PD-1 ver sus PD-L1 inhibitors, a disparity pointing to fundamental biological differences. The pancreatic islet represents a unique immunological niche where PD-1/PD-L1 signaling maintains tolerance checkpoints. Islet-infiltrating and adipose tissue immune cells universally express PD-1, rendering them susceptible to checkpoint modulation disrupting established immune tolerance [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Notably, β-cells with high baseline PD-L1 expression demonstrate remarkable resistance to T-cell-mediated apoptosis, enabling prolonged survival under autoimmune attack[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We propose that systemic PD-1 blockade delivers a \u0026ldquo;dual hit\u0026rdquo;: globally reinvigorates effector T-cells while simultaneously depriving peripheral metabolic tissues of protective PD-1 signaling. This disrupts the intrinsic \u0026ldquo;immune-metabolic dialogue\u0026rdquo; within pancreas and adipose tissue, predisposing them to autoimmune attack and functional dysregulation[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], explaining the predominance of severe, organ-specific endocrinopathies like FT1DM. In contrast, PD-L1 blockade primarily interferes with ligand-receptor interactions without directly altering intracellular PD-1 signaling, potentially yielding narrower metabolic effects. This tissue-specific vulnerability underscores the necessity of pre-treatment islet autoantibody screening and intensive glycemic surveillance, particularly within 120 days when immune reconstitution is most dynamic. Our findings provide crucial epidemiological support for the hypothesis that PD-1 plays a non-redundant role in systemic metabolic homeostasis, warranting dedicated experimental dissection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTemporal dynamics and personalized monitoring\u003c/h2\u003e \u003cp\u003eDespite ICIs clinical significance, temporal dynamic of ICI-induced MNDs remain poorly characterized. We employed Weibull distribution analysis and demonstrated an early-failure pattern (β\u0026thinsp;\u0026lt;\u0026thinsp;1), confirming hazard peaks shortly after treatment initiation. Kaplan-Meier analysis showed that 80% of all MNDs occurred within 120 days, strongly advocating front-loaded surveillance with intensive metabolic screening (fasting glucose, lipid panel, electrolytes) during early cycles.\u003c/p\u003e \u003cp\u003eBeyond the class-effect, we identified important drug-specific and host-specific modifiers. The shorter median TTO for atezolizumab compared to nivolumab and pembrolizumab warrants heightened early vigilance. Furthermore, lower body weight predicted significantly shorter TTO for pembrolizumab and atezolizumab, possibly reflecting altered pharmacokinetics, pharmacodynamics, or distinct immune-metabolic states, suggesting weight-stratified monitoring intensity. The association between fatal outcomes and shorter TTO reinforces that early-onset events are particularly virulent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications: toward precision surveillance\u003c/h2\u003e \u003cp\u003eOur findings collectively argue for a paradigm shift from indefinite, uniform monitoring to personalized, phase-specific surveillance. We propose: (1) mandatory baseline metabolic profiling; (2) intensive monitoring every 2\u0026ndash;4 weeks during the first 4 months, especially for PD-1 inhibitors recipients, low body weight patients on pembrolizumab, atezolizumab, and older males; (3) patient-held \u0026ldquo;FT1DM alert cards\u0026rdquo; detailing cardinal symptoms; and (4) explicit incorporation of metabolic parameter into irAE management guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study shares the limitations inherent to spontaneous reporting systems, including under-reporting, potential reporting bias toward severe events, and the inability to establish true incidence rates or confirm causality. Confounding by malignancies, concomitant medications, and pre-existing metabolic conditions cannot be fully excluded. Absence of detailed laboratory values and tumor response data limits deeper correlative analysis. Despite these limitations, our analysis of over twenty million real-world records provides important signals to inform prospective validation studies and clinical practice guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe large-scale pharmacovigilance study provides the first comprehensive, real-world evidence characterizing spectrum, risk signals, and temporal dynamics of PD-1/PD-L1 inhibitor-associated metabolic toxicities. We delineate a wider risk profile for PD-1 inhibitors, pinpoint FT1DM as a critical early-onset emergency, and quantitatively define the highest-risk period within 120 days. By integrating these findings, we advance from toxicity description towards a framework for precision monitoring, aiming to enhance the safety and sustainability of these transformative anticancer treatments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eSupplementary Materials\u003c/b\u003e: The following supporting information can be downloaded at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mdpi.com/article/doi/s1\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e \u003cp\u003eEthical review and approval were waived for this study due to the analysis of publicly available, anonymized data from the FAERS, where individual patient identification is not possible and the data are exempt from institutional review board oversight.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e \u003cp\u003ePatient consent was waived due to the retrospective analysis of publicly available, anonymized data from the FAERS, where individual patient identification is not possible.\u003c/p\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work is supported by the National Natural Science Foundation of China (Grant No. 82002816), Tianjin Key Medical Discipline Construction Project (Grant No. TJYXZDXK-3-003), and Hospital-level Project of Tianjin Medical University Cancer Institute and Hospital (Grant No. MS2510).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, L.Z.; methodology, L.Z.; software, X.L.; formal analysis, N.X.; data curation, G.B.; writing\u0026mdash;original draft preparation, L.Z.; writing\u0026mdash;review and editing, H.L.; supervision, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the FAERS for providing the publicly available pharmacovigilance data used in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e74\u003c/b\u003e (3), 229\u0026ndash;263 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhosravi, G. R. et al. Immunologic tumor microenvironment modulators for turning cold tumors hot. \u003cem\u003eCancer Commun. 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Metabolic regulator LKB1 controls adipose tissue ILC2 PD-1 expression and mitochondrial homeostasis to prevent insulin resistance. \u003cem\u003eImmunity\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e (6), 1289\u0026ndash;1305 (2024). e1289.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"immune checkpoint inhibitors, PD-1/PD-L1 inhibitors, metabolic disorders, fulminant type 1 diabetes mellitus, pharmacovigilance, time-to-onset, risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-9051493/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9051493/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eImmune checkpoint inhibitors targeting PD-1/PD-L1 have transformed oncology, yet their association with metabolic and nutritional disorders (MNDs) remains poorly characterized in real-world populations. While classic endocrine immune-related adverse events are recognized, the comprehensive spectrum, temporal dynamics, and differential risks between PD-1 and PD-L1 inhibitors require systematic elucidation to inform evidence-based monitoring strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) from Q1 2011 to Q2 2025. Reports of MNDs with eight PD-1/PD-L1 inhibitors as primary suspected drugs were identified. Disproportionality analysis employed four algorithms (ROR, PRR, BCPNN, MGPS) with concordant positivity required for signal detection. Preferred terms (PTs) were analyzed at the System Organ Class level. Time-to-onset (TTO) was characterized using Kaplan-Meier estimation and Weibull shape parameter (β) modeling to identify failure patterns.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 21,712,563 total records, 5,160 MNDs reports were identified. Males (53.22%) and older adults (65\u0026ndash;85 years, 45.19%) predominated. Disproportionality signals were detected for 29 PTs with PD-1 inhibitors versus 13 PTs with PD-L1 inhibitors. Fulminant type 1 diabetes mellitus (FT1DM) emerged as the strongest consistent signal. Nivolumab, pembrolizumab, and atezolizumab demonstrated the broadest metabolic risk spectra (24, 16, and 17 positive PTs, respectively). Weibull analysis revealed an early-failure pattern (β\u0026thinsp;\u0026lt;\u0026thinsp;1), with 80% of events occurring within 120 days.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides comprehensive real-world evidence delineating distinct metabolic toxicity profiles between PD-1 and PD-L1 inhibitors, with FT1DM as a critical early-onset complication. The concentrated risk window within initial treatment cycles supports front-loaded metabolic surveillance to enhance patient safety.\u003c/p\u003e","manuscriptTitle":"PD-1/PD-L1 inhibitor-induced metabolic and nutritional disorders: a real-world pharmacovigilance analysis using FAERS database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 09:31:31","doi":"10.21203/rs.3.rs-9051493/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-14T03:36:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80867383054002773513342211364272311098","date":"2026-04-14T03:28:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T02:51:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76238431049914571297839627055866219649","date":"2026-04-11T00:28:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246656026736973355335789311606557074256","date":"2026-04-09T02:35:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T00:29:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289583613182645600217413371089284729580","date":"2026-03-25T02:01:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-25T01:16:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-11T13:45:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-07T06:48:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-07T06:47:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-06T14:05:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3d3c40a-63a7-40ee-8e81-18c78dd23e4c","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65121267,"name":"Biological sciences/Cancer"},{"id":65121268,"name":"Health sciences/Diseases"},{"id":65121269,"name":"Biological sciences/Drug discovery"},{"id":65121270,"name":"Health sciences/Endocrinology"},{"id":65121271,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-27T09:31:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 09:31:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9051493","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9051493","identity":"rs-9051493","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}