Category: 3. Business

Introduction

The 1973 Oil Embargo first revealed the deep interconnection between Middle Eastern stability and global energy security. In response to US military support to Israel during the Yom Kippur war against Egypt and Syria, the Organization of Arab Petroleum Exporting Countries (now OPEC) imposed a total oil embargo against any country that had supported Israel militarily during the aforementioned conflict. This triggered a global recession and a fourfold increase in oil prices. By that time, the world economy had become heavily dependent on oil supplies from the Middle East, with nearly 85% of US crude oil imports coming from OPEC nations

Although the embargo was lifted by March of 1974, two following Middle Eastern crises would disrupt global energy markets. In late 1978, the Iranian Revolution resulted in a significant drop in Iranian crude oil production and average loss of 3.9 million barrels per day. Neighboring Gulf producers restored the supply gap. Then, in August 1990, Iraq’s invasion of Kuwait disrupted oil exports from both countries, causing another sudden rise in crude oil prices. These crises underscored how regional instability in the Gulf can rapidly escalate into global economic crises.

This article argues that Western reliance on Gulf energy necessitates a proactive role in Middle East conflict mediation. As such, Western powers must recognize energy mediation as a core security function. As regional actors like Qatar, Egypt, and Oman increasingly assume key roles in facilitating dialogue, Western powers should pursue deeper partnerships with these mediators to ensure long-term stability in global energy markets.

Global Supply Growth and Contemporary Energy Security Challenges

Since these major disruptions, the oil market has changed in terms of global supply growth. Four countries in the Americas—the United States, Guyana, Canada, and Brazil—have the capacity to partially offset near-term voluntary production cuts from OPEC+ nations. In 2018, the United States became the world’s greatest oil producer. Concurrently, US imports of Gulf oil have fallen by 1.29 million barrels per day (56.8% reduction) in the last 20 years, while imports from Canada have increased by 2.4 million barrels per day (121.8% increase) over the same time period.

Although US dependence on Gulf oil has declined, the region remains critical for global energy stability and Western interests. For instance, the European Union has continued to depend heavily on Gulf oil, particularly from Iraq, Saudi Arabia, and Qatar, as alternatives to Russian energy sources. . Moreover, the region remains relevant due to its strategic export role and control over key trade routes. In early 2024, global oil prices increased amid growing tensions in the Middle East. Although the Israel-Hamas conflict did not directly impact supply, it raised fears of broader disruption. Houthi attacks in the Red Sea rerouted oil tankers, and concerns over potential escalation between Israel and Iran heightened fears about the Strait of Hormuz, which in 2022 carried roughly 21% of global petroleum liquids. These risks underscore the Gulf’s enduring influence on energy markets.

Middle East Mediation as an Essential Process for Global Energy Security

To prevent supply disruptions and a wider regional war, mediation has become a vital tool for Western powers. This is not only to advance peace but also to protect the stability of global energy markets, which remain vulnerable to Gulf-based conflicts and chokepoint disruptions. Because access to secure, continuous energy flows is a strategic necessity, Western powers are compelled to engage either directly or through partners when conflicts threaten this stability.

As a rule, the objective of mediation is for a neutral third party to help disputants reach an agreement on their own, rather than impose a solution. Mediators work with parties, sometimes together and sometimes separately, to assist in finding a voluntary and sustainable resolution. However, Western powers are often seen as lacking neutrality due to their strategic and political ties in the region. For example, the United States is frequently viewed as a biased actor due to its close alliance with Israel. Even when Western statements appear neutral and peace-oriented, they are often interpreted through the lens of underlying power dynamics and economic interests, particularly around energy.

This means that Western powers are pressed to act in partnership with a more diverse set of countries to achieve success in Middle East mediation. This is part of a broader trend in global peacemaking away from the United Nations and the group of traditional mediation countries—such as the United States, United Kingdom, Switzerland, Norway and Finland—that took the lead on international mediation for decades after World War II toward a more diverse set of peacemakers in partnership with traditional mediators. On one hand, traditional mediators are searching for trusted third parties to convey communications with the other side, as when the U.S. leans on Gulf states to communicate messages to Iran. On the other hand, states are increasingly taking on peace-facilitation roles as critical components of their foreign policy for both political prestige and national and regional security.

Looking Forward: Regional Mediation Partnerships and the Role of Qatar, Egypt, and Oman

This trend puts a new group of countries—such as Qatar, Egypt, and Oman—as a better conduit between Western powers and Middle East disputants. Qatar and Oman have confidentially mediated discussions for years between the United States and Iran that resulted in a 2023 deal involving the release of prisoners and the unfreezing of $6 billion in Iranian oil revenue. Additionally, since the Gaza war began in 2023, Qatar and Egypt, have emerged as central mediators between Israel and Hamas.. Qatar have initiated ideas for solutions, settled deadlines for replies, reminded both sides of the gravity of the situation, and intervened when it appeared that cease-first talks were stuck. These events demonstrated that Western powers should explore partnerships with this new group of countries to safeguard global energy security.

Should Western powers seek to ensure short- and long-term security in global energy markets, the role of Gulf state energy and mediation in Middle Eastern conflicts must not go unrecognized. Despite growth in the Americas, Western powers still retain significant reliance on Gulf energy, particularly for its strategic advantages. To mitigate future conflict-related disruptions in global energy markets, Western powers should intensify mediations in partnership with countries that hold regional credentials to achieve success. This includes formalizing mediation partnerships with states like Qatar and Egypt through multilateral agreements or frameworks that go beyond ad hoc collaboration. It also requires investing in capacity-building for regional mediators to strengthen their role in early-warning systems and de-escalation diplomacy. With support and collaboration from traditional mediators, Qatar appears ready, together with other Arab countries, to consolidate its position as a mediator that seeks both regional peace and global energy security.

. . .

Dr. Talal Abdulla Al-Emadi is the Energy Law Professor & Fourth Dean of the College of Law at Qatar University (QU). He holds a doctorate degree in law (DPhil) from the University of Oxford, where he wrote about joint ventures in the gas industry. He also holds an LLM from Harvard and an LLB from QU. The author would like to extend thanks to his RAs, Leandro Alves and Eduardo Pereira, for the research help provided, and Prof. Francis Botchway and Dr. Ezieddin Elmahjub for reading the drafts and invaluable comments.

Image Credit: Odile, Unsplash Content License, Unsplash.

Getty Images

Getty Images

PA Media

Transport for London

Givaudan has been recognised once again in the CDP Supplier Engagement Assessment Leaderboard, securing another A rating for engaging its supply chain on climate action. 

CDP’s annual Supplier Engagement Assessment (SEA) – formerly known as the Supplier Engagement Rating (SER) – evaluates corporate supply chain engagement on climate issues. The Supplier Engagement Assessment Leaderboard celebrates companies who achieve the highest ratings and recognises their crucial role in the transition towards the net-zero sustainable economy.

“Our supply chain accounts for over 90% of our total emissions (scope 3). While some may view this as a daunting challenge, we see it as a significant opportunity for collective action. Collaborating to reduce supply chain emissions is essential for our climate ambitions, and we are proud of the recognition we’ve received for our progress.”

Gilles Andrier, CEO of Givaudan

“As we celebrate this achievement, we must remember that climate action is urgent. Achieving our long-term goals depends on businesses and their supply chains working together to create meaningful impact not just in the distant future, but today and throughout our journey.”

Givaudan continues to deliver concrete progress in its climate journey. For example, in January 2025, Givaudan announced new milestones on its journey towards climate positivity with the validation of its net-zero targets by the Science Based Targets initiative.

For the latest on Givaudan’s climate progress, read our latest Integrated Report.

---

About Givaudan
Givaudan is a global leader in Fragrance & Beauty and Taste & Wellbeing. We celebrate the beauty of human experience by creating for happier, healthier lives with love for nature. Together with our customers we deliver food experiences, craft inspired fragrances and develop beauty and wellbeing solutions that make people look and feel good. In 2024, Givaudan employed over 16,900 people worldwide and achieved CHF 7.4 billion in sales with a free cash flow of 15.6%. With a heritage that stretches back over 250 years, we are committed to driving long-term, purpose-led growth by improving people’s health and happiness and increasing our positive impact on nature. This is Givaudan. Human by nature. Discover more at: www.givaudan.com.

---

For further information please contact
Claudia Pedretti, Head of Investor and Media Relations
T +41 52 354 0132
E claudia.pedretti@givaudan.com

Sara Neame, Sustainability Communications
E sara.neame@givaudan.com

Introduction

Postoperative delirium (POD) is an acute brain dysfunction characterized by sudden onset, altered consciousness, and cognitive impairment. It frequently occurs within 24~72 hours post-surgery, particularly in intensive care unit (ICU) settings, with an incidence rate ranging from 11% to 42%,¹ depending on patient populations. In liver transplant patients, the risk of POD is even higher due to preexisting hepatic encephalopathy, hepatorenal syndrome, and the complexity of liver transplantation procedures, which include prolonged surgery, significant blood loss, and immunosuppressive therapy. Studies have shown that delirium prevalence in this population ranges from 17% to 47.4%.^2,3 POD has been associated with adverse outcomes, such as prolonged ICU stays, increased mortality rates, long-term cognitive decline, and elevated healthcare costs.^4,5 As such, POD has become a critical area of focus in clinical practice.

Although various models like PRE-DELIRIC and E-PRE-DELIRIC have been developed to predict ICU delirium, they are rarely tailored to liver transplant recipients.^6,7 Therefore, a prediction model tailored to liver transplant patients is needed to better assess the risk of delirium, facilitate early detection, and guide appropriate preventive and therapeutic interventions.

This study aimed to develop a nomogram-based prediction model that integrates liver-specific parameters to provide early identification of high-risk liver transplant patients.

Methods

Study Design and Population

This was a retrospective, single-center study aimed at developing a prediction model for POD in liver transplant patients in the ICU. Patients who received liver transplantation between June 2018 and June 2020 and were aged ≥18 years were included. Exclusion criteria were preoperative delirium, ICU stays <3 days, combined organ transplantation, or conditions impeding delirium assessment (eg, coma, severe mental disabilities).

All operations were performed by the same surgical team. Intraoperative anaesthesia was performed according to a uniform anaesthesia protocol: sevoflurane, alfentanil, sufentanil, rocuronium, cyclopofol. All patients received standard immunosuppressive therapy with basiliximab, methylprednisolone, mycophenolate mofetil, and tacrolimus.

This study was approved by the Medical Ethics Committee of Zhongshan Hospital (Approval No. B2022-447) and was conducted in accordance with the applicable regulations for research ethics committee review and informed consent. Our study complies with the Declaration of Helsinki. All organs were donated voluntarily with written informed consent, and that these were conducted in accordance with the Declaration of Istanbul.

Data Collection

We collected a range of preoperative, intraoperative and postoperative data from the enrolled patients. Preoperative data included: age, gender, primary disease, BMI, Child-Pugh grade, Model for End-stage Liver Disease (MELD) score, number of complications, use of artificial liver treatment. Intraoperative data included operation duration, blood loss volume, blood transfusion volume, liver cold ischemia duration, hepatic-free phase. Postoperative data included the Acute Physiology and Chronic Health Evaluation (APACHE) II score, use of vasoactive agent and lab results within 24 hours of ICU admission.

Definitions

Delirium was assessed using the Confusion Assessment Method for the ICU (CAM-ICU), performed three times daily by trained nurses. A CAM-ICU positive result confirmed a POD diagnosis.

Statistical Analysis

Descriptive Statistics

For the small number of missing values in the predictors (one case each of glucose, sodium and hepatic-free stage, accounting for 0.2%), we employed Multiple Imputation by Chained Equations (MICE).

Frequencies and percentages were used for categorical variables, and chi-squared tests for comparisons between groups. For continuous variables, mean ± standard deviation (mean ± SD) was used for description, and median and interquartile range (median [Q1; Q3]) were used when data did not conform to a normal distribution. For continuous variables that followed a normal distribution, independent samples t-tests were used to compare differences between groups. For continuous variables that did not follow a normal distribution, the Mann–Whitney U-test was used to compare differences between groups.

Development and Assessment of the Nomogram

All analyses were performed using R (version 4.1.3) and associated packages. Univariate logistic regression was used to identify potential predictors of postoperative delirium (POD). Variables with a p-value <0.15 in univariate analysis were included in the multivariate logistic regression model to refine the prediction model. The final model’s performance was evaluated using the area under the receiver operating characteristic curve (AUROC). The optimal classification threshold was determined based on the maximum Youden index of the ROC curve. Internal validation was performed using bootstrapping with 200 iterations to assess the model’s stability and generalisability. In addition, the Hosmer-Lemeshow goodness of fit test (p > 0.05) was used to further validate the fit of the model. A nomogram was created to visualize the prediction model for practical application.

Results

General Characteristics

Of the 493 screened patients, 480 were included in the study, with 148 (30.8%) developing POD (Figure 1). The presence of delirium was used as a grouping variable to compare differences in each patient characteristic. Differences in patient characteristics between POD group and non-POD group are detailed in Table 1.

Table 1 General Characteristics of the Patients

Figure 1 Flow chart for patient selection.

Development of Prediction Model

Using delirium as the dependent variable, we considered 31 potential factors as independent variables, including age, gender, primary disease, BMI, APACHE score, Child-Pugh classification, MELD score, artificial liver treatment, number of complications, operation duration, volume of hemorrhage, blood transfusion volume, liver cold ischemia duration, hepatic-free phase, vasoactive drugs, and various blood markers. Blood markers included total bilirubin (TB), albumin (Alb), glutamic oxalacetic transaminase (AST), blood glucose (Glu), sodium (Na), blood Urea nitrogen (BUN), creatinine (Cr), C-reactive protein (CRP), blood ammonia(AMON), lactate(LAC), prothrombin time (PT), International normalized ratio(INR), hemoglobin (Hb), platelet (PLT), white blood cell (WBC) and procalcitonin (PCT). Univariate logistic regression analysis identified six variables with P < 0.15: age, APACHE score, albumin, AST, BUN, and AMON. These variables were considered for multivariate analysis (Table 2).

Table 2 Single Factor Logistic Regression Analyses for Screening Predictors

Multivariate logistic regression revealed that all six variables were independent predictors of postoperative delirium. The regression coefficients, odds ratios (OR), and 95% confidence intervals (CI) are shown in Table 3. The area under receiver operating characteristic curve (AUROC) of this model was 0.757 (95% CI: 0.709 ~ 0.806), with good differentiation ability. The optimal threshold for classifying patients at risk of delirium was 0.341, with a sensitivity of 66.2% and specificity of 77.7% (Figure 2). These data demonstrated that our nomogram had a significant potential for clinical decision-making.

Table 3 Results of Multivariate Unconditioned Logsitic Regression Analysis

Figure 2 ROC curves. Abbreviations: ROC, receiver operating characteristic; AUC, area under the ROC curve.

Validation of Prediction Model

To assess the model’s stability, we used bootstrapping (B=200) for internal validation, generating a calibration curve (Figure 3). The calibration curve closely aligned with the reference line, indicating good consistency and stability. The Hosmer-Lemeshow test also confirmed the model’s goodness of fit (χ² =2.1505, P=0.3412). The AUROC was 0.757 before the calibration using the bootstrapping technique, and 0.743 after the calibration.

Figure 3 Calibration curve for predicting probability of LTP-delirium. Abbreviations: LTP-delirium, delirium for liver transplantation patients.

The regression coefficients before and after the calibration are detailed in Table 4. The equations for predicting the risk of delirium are as follows:

1. The risk of delirium before the calibration: 1/[1+exp-(−1.785+0.035×Age+0.821×APACHE≧15-0.073×ALB-0.0002×AST+0.113×BUN+0.024×AMON]

Table 4 Model Variables and Corresponding Regression Coefficients

(2) The risk of delirium After the calibration:

1/[1+exp-(−1.940+0.037×Age+0.793×APACHE≧15-0.078×ALB-0.0002×AST+0.124×BUN+0.024×AMON)]

Visualization of Risk Prediction Model for Delirium

A nomogram was created based on the multivariate logistic regression model to visually predict the risk of delirium (Figure 4). Each predictor variable was assigned a score, and the total score was used to determine the likelihood of a patient developing delirium. The model’s performance was confirmed by an AUROC of 0.757 (95% CI: 0.709–0.806), demonstrating good consistency and clinical applicability for decision-making.

Figure 4 Nomogram for the prediction of LTP-delirium.

Discussion

This study presents a novel nomogram-based prediction model for assessing the risk of postoperative delirium (POD) in liver transplant patients. The model incorporates six significant perioperative factors – age, APACHE score, albumin (ALB), glutamic oxalacetic transaminase (AST), blood urea nitrogen (BUN) and ammonia (AMON) – and demonstrates good discriminatory ability with an AUROC of 0.757. By enabling early identification of high-risk patients, the model facilitates timely clinical intervention, potentially reducing the incidence and severity of POD.

In view of the high prevalence of delirium in ICU patients^8,9 and its serious consequences,^10,11 predicting ICU delirium is clinically important. Early identification of patients at risk can inform caregivers and families, helping them make decisions regarding preventive measures. Researchers have attempted to develop delirium prediction models for different patient groups. The PRE-DELIRIC (PREdiction of DELIRium in ICu patients)⁶ model was constructed using 3056 patients from five intensive care units across the Netherlands, the model contains 10 risk factors—age, APACHE-II score, admission group, coma, infection, metabolic acidosis, use of sedatives and morphine, urea concentration, and urgent admission. The model had an AUC of 0.87 and outperformed the clinical prediction by nurses and physicians. Varga-Martínez et al incorporated factors such as low cognitive function, advanced age, low physical activity, and insomnia into the postoperative delirium prediction model for patients undergoing cardiac surgery,¹² with an AUC of 0.833. Although existing studies have developed high-quality delirium prediction models for general ICU populations,¹³ their applicability to liver transplant recipients remains limited. This population faces unique delirium risks rooted in both preoperative comorbidities (eg, decompensated cirrhosis, hepatic encephalopathy, hepatorenal syndrome) and perioperative stressors (eg, prolonged anhepatic phase, major bleeding). Importantly, liver-specific pathways—such as ammonia metabolism dysregulation³ during graft dysfunction or neurotoxicity from calcineurin inhibitors¹⁴ —are rarely incorporated into general ICU models. Previous studies have identified specific risk factors for delirium in liver transplant patients. Si-Yuan Wu et al¹⁵ reported that Liver recipients’age, body mass index, Child-Pugh class C, history of preoperative hepatic encephalopathy or mental disorders, day 7 tacrolimus level > 8.9 ng/mL, and postoperative intra-abdominal infection were more likely associated with early neurologic complications after liver transplantation. Our model addresses this gap by incorporating liver-specific parameters, providing a tailored risk assessment tool for this high-risk population.

We collected 31 items of data included the patient demographics, health conditions, and postoperative laboratory results within 24 hours of admission to intensive care. This study revealed that age, APACHE score, ALB, AST, BUN and AMON were predictors of delirium in patients who experienced liver transplantation. Age and APACHE scores are widely recognized as predictors of delirium across different patient populations.¹⁶ The liver-specific markers, including ALB, AST, BUN, and AMON, are closely related to the liver’s functioning and reflect complications commonly seen in liver transplant patients. For example, low albumin levels, high urea nitrogen, and elevated ammonia levels are frequently observed in patients with significant ascites, hepatorenal syndrome, and hepatic encephalopathy, conditions common among liver transplant recipients. These findings align with previous research, which has also identified these markers as significant predictors of postoperative delirium in liver transplant patients. Albumin (ALB) is synthesized exclusively by hepatocytes, reflects hepatic synthetic function. Sung Ae Park et al found that a low plasma albumin level was an independent predictor of postoperative delirium in patients undergoing hepatectomy.¹⁷ The study by Rudolph et al found that abnormal albumin is useful for predicting postoperative delirium in patient with cardiac surgery.¹⁸ By interfering with glutamate metabolism and pyruvate metabolism, high blood ammonia alters the concentration and mutual balance of certain neurotransmitters in the brain, causes astrocyte swelling and neuroinflammation, thereby disrupting the normal function of the central nervous system. Studies such as those by Zhou have further emphasized the role of ammonia in the development of delirium post-liver transplant.² Low BUN in advanced cirrhosis reflects impaired urea cycle (hepatocyte dysfunction). Excessive peak AST after transplantation suggests ischaemia-reperfusion injury and predicts graft dysfunction. Our model incorporates these variables specific to liver transplant patients. The inclusion of these liver-specific markers enhances the model’s applicability and predictive power for this unique patient group.

The developed nomogram provides a practical tool for early identification of high-risk patients, enabling clinicians to implement targeted preventive measures, such as optimizing perioperative care and closely monitoring identified risk factors. The visual representation of the nomogram enhances its usability in clinical settings, supporting real-time decision-making and improving patient outcomes.

Our study has several limitations. First, due to time constraints and other reasons, the final number of cases included was small at 480. Nevertheless, we believe that this sample size is representative within the scope of this study and that the results of the statistical analyses have a certain degree of reliability. When analysing the data, we fully considered the potential impact of insufficient sample size and used appropriate statistical methods to make adjustments: 1. Use of one-way regression to screen for variables with strong predictive ability, avoiding the introduction of too many irrelevant variables with a small sample size; 2. Variable selection in the multifactorial regression model to improve the accuracy of the model; 3. Use of the bootstrap method to generate multiple data sets through self-sampling to improve the robustness of the model and the reported confidence intervals. These methods helped us to maximise the use of the available samples and ensure the reliability of the model assessment. Second, it is a single-center, retrospective study, which may limit the generalizability of the findings. While the model demonstrated good performance in internal validation, external validation using data from multiple centers is needed to confirm its robustness and applicability across diverse patient populations. Third, the reliance on traditional statistical methods limits the model’s potential compared to machine learning approaches,^19–21 which could capture more complex interactions among variables. Future studies incorporating machine learning could potentially improve prediction accuracy. A multicenter cohort study with a larger sample size would also be valuable for further validating our model.

Conclusion

This study presents a novel prediction model tailored to liver transplant patients, offering a significant advancement in postoperative delirium risk stratification. Future studies should focus on multicentre validation to improve the generalisability of the model. Incorporation of advanced machine learning techniques could further improve predictive performance.

Data Sharing Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Requests to access the datasets should be directed to the corresponding Author.

Informed Consent Statement

Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Zhongshan Hospital, Fudan University, (approval No. B2022-447). Donor livers are derived from organ donation after cardiac death and allocated by the National Donor Allocation System.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was funded by [Shanghai Municipal Key Clinical Specialty] grant number [shslczdzk03603], and [the Key Discipline Construction Fund of Health System in Shanghai Xuhui District] grant number [SHXHZDXK202324].

Disclosure

The authors report no conflicts of interest in this work.

References

1. Siddiqi N, House AO, Holmes JD. Occurrence and outcome of delirium in medical in-patients: a systematic literature review. Age Ageing. 2006;35(4):350–364. doi:10.1093/ageing/afl005

2. Zhou J, Xu X, Liang Y, Zhang X, Tu H, Chu H. Risk factors of postoperative delirium after liver transplantation: a systematic review and meta-analysis. Minerva Anestesiol. 2021;87(6):684–694. doi:10.23736/s0375-9393.21.15163-6

3. Chen J, Wang H, He Z, Li T. Analysis of risk factors for postoperative delirium after liver transplantation. Neuropsychiatr Dis Treat. 2020;16:1645–1652. doi:10.2147/ndt.S254920

4. Goldberg TE, Chen C, Wang Y, et al. Association of delirium with long-term cognitive decline. JAMA Neurol. 2020;77(11). doi:10.1001/jamaneurol.2020.2273

5. Hughes CG, Boncyk CS, Culley DJ, et al. American society for enhanced recovery and perioperative quality initiative joint consensus statement on postoperative delirium prevention. Anesthesia Analg. 2020;130(6):1572–1590. doi:10.1213/ane.0000000000004641

6. Boogaard MVD, Pickkers P, Slooter AJC, et al. Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study. BMJ. 2012;344(feb09 3):e420–e420. doi:10.1136/bmj.e420

7. Wassenaar A, van den Boogaard M, van Achterberg T, et al. Multinational development and validation of an early prediction model for delirium in ICU patients. Intensive Care Med. 2015;41(6):1048–1056. doi:10.1007/s00134-015-3777-2

8. Stollings JL, Kotfis K, Chanques G, Pun BT, Pandharipande PP, Ely EW. Delirium in critical illness: clinical manifestations, outcomes, and management. Intensive Care Med. 2021;47(10):1089–1103. doi:10.1007/s00134-021-06503-1

9. Marcantonio ER, Solomon CG. Delirium in hospitalized older adults. N Engl J Med. 2017;377(15):1456–1466. doi:10.1056/NEJMcp1605501

10. Bhattacharya B, Maung A, Barre K, et al. Postoperative delirium is associated with increased intensive care unit and hospital length of stays after liver transplantation. J Surg Res. 2017;207:223–228. doi:10.1016/j.jss.2016.08.084

11. Mattison MLP. Delirium. Ann Internal Med. 2020;173(7):ITC49–ITC64. doi:10.7326/aitc202010060

12. de la Varga-Martínez O, Gómez-Pesquera E, Muñoz-Moreno MF, et al. Development and validation of a delirium risk prediction preoperative model for cardiac surgery patients (DELIPRECAS): an observational multicentre study. J Clin Anesth. 2021;69:110158. doi:10.1016/j.jclinane.2020.110158

13. Wang ML, Kuo YT, Kuo LC, et al. Early prediction of delirium upon intensive care unit admission: model development, validation, and deployment. J Clin Anesth. 2023;88:111121. doi:10.1016/j.jclinane.2023.111121

14. Bican Demir A, Erer Özbek S, Bora I, Hakyemez B, Tırnova I, Kaya E. Two cases with developing neurologic complications after liver transplant. Exp Clin Transplant. 2016;14(6):685–687. doi:10.6002/ect.2014.0204

15. Wu S-Y, Chen T-W, Feng A-C, Fan H-L, Hsieh C-B, Chung K-P. Comprehensive risk assessment for early neurologic complications after liver transplantation. World J Gastroenterol. 2016;22(24). doi:10.3748/wjg.v22.i24.5548

16. Cai S, Cui H, Pan W, Li J, Lin X, Zhang Y. Two-stage prediction model for postoperative delirium in patients in the intensive care unit after cardiac surgery. Eur J Cardiothorac Surg. 2022;63:1. doi:10.1093/ejcts/ezac573

17. Park SA, Tomimaru Y, Shibata A, Miyagawa S, Noguchi K, Dono K. Incidence and risk factors for postoperative delirium in patients after hepatectomy. World J Surg. 2017;41(11):2847–2853. doi:10.1007/s00268-017-4079-3

18. Rudolph JL, Jones RN, Levkoff SE, et al. Derivation and validation of a preoperative prediction rule for delirium after cardiac surgery. Circulation. 2009;119(2):229–236. doi:10.1161/circulationaha.108.795260

19. Girard TD, Thompson JL, Pandharipande PP, et al. Clinical phenotypes of delirium during critical illness and severity of subsequent long-term cognitive impairment: a prospective cohort study. Lancet Respir Med. 2018;6(3):213–222. doi:10.1016/s2213-2600(18)30062-6

20. Potter KM, Kennedy JN, Onyemekwu C, et al. Data-derived subtypes of delirium during critical illness. eBioMedicine. 2024;100. doi:10.1016/j.ebiom.2023.104942

21. Potter KM, Prendergast NT, Boyd JG. From traditional typing to intelligent insights: a narrative review of directions toward targeted therapies in delirium. Crit Care Med. 2024;52(8):1285–1294. doi:10.1097/ccm.0000000000006362

New research uncovers widespread confusion about ultra-processed foods among Australians, revealing that clear labelling and better education are essential for helping shoppers navigate the modern food landscape.

Study: Concerned but confused: Australian consumers’ awareness, understanding, and recognition of ultra-processed foods. Image Credit: voronaman / Shutterstock

In a recent article published in the journal Appetite, researchers investigated the perceptions and recognition ability of Australian adults regarding ultra-processed foods (UPFs).

Many participants expressed concern about UPFs and supported clearer labelling policies, but also showed confusion about what UPFs are, highlighting the need for public education to support labelling strategies.

Background

UPFs are increasingly associated with poor health outcomes and now account for a large portion of energy intake in many high-income countries, including 56% in Australia.

These foods are industrially produced using processes and ingredients not typically used in home cooking, such as additives and artificial flavourings, making them hyper-palatable and convenient. They are often high in refined starches, salt, and sugar, but research suggests that their health risks extend beyond their nutrient content alone.

Disruption of the food matrix and adverse effects of additives on gut health are among the proposed mechanisms.

Despite a growing global consensus, as evidenced by recent guidance from the World Health Organization (WHO), urging a reduction in the consumption of UPFs, strategies to achieve this goal are still emerging.

One promising approach is front-of-pack labelling (FoPL), which has shown moderate success in guiding healthier consumer choices. However, current labels, such as Australia’s Health Star Rating (HSR) system, are based on nutrient profiles and may conflict with processing-based frameworks, like the Nova classification.

As a result, consumers may receive mixed messages; for example, a product could have a high health star rating despite being ultra-processed. Compounding this issue is the lack of Australian research on how consumers perceive UPFs, which is essential for designing effective and comprehensible labelling strategies that reflect both nutritional and processing concerns.

About the Study

This qualitative study employed an exploratory design to understand how Australian adults perceive and interpret UPFs and their views on FoPL in relation to processing. Across five Australian states, 112 adults participated in 12 online focus groups in 2024.

Participants were recruited by a professional agency using demographic quotas to ensure equal representation across gender and age groups. About 78% lived in metropolitan regions. Eligibility required participants to shop for food at least twice a month.

Each focus group was moderated by an experienced researcher using a semi-structured interview guide. Discussions began with general food selection habits, then explored perceptions of terms like “processed” and “ultra-processed.”

Participants were shown images of food products with similar HSR but different Nova classifications to assess recognition of UPFs. Data were analysed inductively by a single coder, before codes and themes emerged through repeated reading and comparative analysis within and across groups.

The moderator reviewed the coding framework to ensure validity. The analysis was supported by matrix searches to explore trends by demographic group. It is essential to note that the recognition findings were based on only two food categories (bread and milk), which may limit generalizability to other products.

Key Findings

Two main themes emerged: concern and confusion about UPFs, and support for labeling UPFs with concerns about the practicality. Participants expressed concerns about health issues related to food additives, unfamiliar ingredients, and preservatives, often using ingredient lists to inform their food choices. However, many people misunderstood or conflated the terms “processed” and “ultra-processed,” with most having never heard of the term “ultra-processed” before.

The few familiar with the term had typically encountered it through media or books. Recognition of UPFs based on packaging was limited, and judgments were often based on perceived naturalness and visual packaging cues rather than actual processing level.

For example, in the stimuli task, nearly all participants incorrectly identified a supermarket-brand white bread made in-store (processed) as ultra-processed, rather than an industrially produced packaged bread (also ultra-processed), because the latter had packaging that appeared more ‘artisan’ and natural. However, most correctly identified oat milk (ultra-processed) as more processed than dairy milk (minimally processed).

Participants generally supported including information on UPFs in food labelling, but were unsure how best to present it. Many suggested clearer, simpler ingredient lists or labels showing the number of ingredients or production steps. However, they emphasized that education was needed to help consumers distinguish between processed and ultra-processed foods.

There was also concern about conflicting signals from existing health ratings, such as the HSR. Some participants expressed strong distrust in the HSR’s credibility when foods with very different processing levels had the same rating, with several proposing to integrate UPF classification directly into the HSR algorithm.

Others feared a UPF label might unfairly stigmatize foods like oat milk. Many proposed integrating information about UPFs into current systems to avoid confusion and make the labels more meaningful and practical for informed decision-making.

Conclusions

This study highlights the confusion Australian consumers have about UPFs, despite growing concerns about their health impacts. While participants supported the idea of FoPL to identify UPFs, unfamiliarity with the term and difficulty distinguishing UPFs from other processed foods could limit the effectiveness of such strategies.

Public education campaigns were deemed necessary to enhance consumer understanding. Some participants suggested using familiar ingredient names or integrating UPF classification into Australia’s existing HSR system to avoid conflicting messages. However, concerns were raised about oversimplifying complex products and misrepresenting the fact that not all UPFs have the same health impact. This includes the potential value of some fortified foods or plant-based alternatives, such as oat milk, in specific dietary contexts, as well as the recognition that UPFs exist on a spectrum of health impacts.

The study’s strength lies in being the first of its kind in Australia; however, its qualitative and exploratory nature, as well as the limited food categories in the stimuli testing, limit its generalisability.

Future research should test specific labelling formats and their influence on consumer choices. Ultimately, FoPLs should be part of a broader policy framework addressing affordability, access, and marketing of UPFs.

Introduction

Plastics are one of the indispensable and fundamental materials in modern society and are widely used in all aspects of daily life.¹ However, the chemical stability of plastics leads to their extensive accumulation in aquatic and terrestrial ecosystems, which in turn contributes to persistent environmental pollution.^2,3 The global production of plastic waste has reached 353 million tons per year and increased to 367 million tons in 2020.^4,5 As the consumption of plastics continues to rise, the future generation of plastic waste will show an increasing trend.⁶ Various additives are often added during the production of plastics to achieve the desired physical properties.⁷

Used as a plasticizer for plastics such as polyvinyl chloride (PVC), Di-(2-ethylhexyl)-Phthalate (DEHP) has become a core chemical additive in the plastics industry due to its low cost and excellent flexibility.⁸ DEHP is mainly used in food packaging, children’s products, and medical devices, which are involved in various aspects of life.^9,10 DEHP leaches out of the material over time and ultimately enters the environment, which has an undesirable effect on humans.^2,11 Exposure of newborns to DEHP during medical care has been found to begin after birth based on quantitative measurements of urinary DEHP metabolites.¹² PBPK (physiologically based pharmacokinetics) modelling has found that the cumulative distribution of DEHP in different organs and tissues may lead to various harmful health outcomes.¹³

Metabolic syndrome (MetS) is a common metabolic disorder characterized by a series of interrelated cardiovascular risk factors, including abdominal obesity, insulin resistance, hypertension, dyslipidemia, and disorders of glucose metabolism.^14,15 Recent studies have shown that environmental pollutants, especially plasticizer-like chemicals (eg, DEHP), may play an essential role in the development and progression of MetS.^16–18 DEHP acts as an endocrine disruptor (EDC) that may induce MetS through multiple biochemical pathways. It may interfere with hormone action through anti-androgenic or estrogenic mechanisms, thereby triggering early puberty.¹⁹ Studies have also shown that DEHP can upregulate the expression of hepatic PPARγand SREBP-1c, promote lipid accumulation, reduce insulin sensitivity, and cause inflammation by activating the NF-κB pathway.²⁰ Since DEHP is widely present in the environment and can be exposed to humans, primarily through plastic products, an in-depth understanding of its potential health effects is of great public health importance.

Derived from Network Pharmacology, Network Toxicology is based on systems biology theory and analyzes biological systems using bioinformatics and network analysis methods.²¹ Although studies have preliminarily revealed the association between DEHP and MetS, its complex multi-target mechanism of action has not been systematically analyzed. Therefore, this study aimed to systematically explore how DEHP exposure may affect the occurrence and development of MetS through multiple biological pathways and molecular targets using cyber toxicology techniques.²² Establishing the molecular network between DEHP exposure and MetS will provide a theoretical basis for scientific research.

Methods

DEHP Composition and Target Acquisition

We first searched the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) for the chemical structure of DEHP and the canonical SMILES representation. Using the obtained SMILES symbols, we searched the STITCH database (http://stitch.embl.de/), SwissTargetPrediction database (http://swisstargetprediction.ch/) (Probability >0), and ChEMBL database (https://www.ebi.ac.uk/chembl/), specifying “Homo sapiens” as the target species.

MetS Related Targets Collection

Using “Metabolic syndrome” as the search term, we searched and collected MetS-related target genes in the GeneCards database (https://www.genecards.org/), OMIM database (https://www.omim.org/), and TTD database (https://db.idrblab.net/ttd/). All results were integrated into Excel, and data were merged and deduplicated to obtain a list of disease targets.

Protein–Protein Interaction (PPI) Network Analysis

Enter the common predicted targets of DEHP and MetS in the program corresponding to the protein interaction platform STRING (https://string-db.org),²³ The species was set as “Homo sapiens”, and the minimum interaction score was set as “high confidence (0.700)” to obtain the PPI protein interactions map, which was simultaneously visualized using the Cytoscape 3.7.1 software was used for visualization.²⁴ Finally, topology analysis was performed using the Network Analyzer plug-in in Cytoscape 3.7.1 to assess the degree (DC), betweenness centrality (BC), and closeness centrality (CC) of the network nodes to filter out the core targets.

Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment

We entered the common targets into the DAVID database to elucidate the potential functions and enrichment pathways of MetS induced by DEHP exposure (https://david.ncifcrf.gov/).²⁵ The identifier was set to “OFFICIAL GENE SYMBOL”, the species was selected as “Homo sapiens”, and other defaults were kept, and gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. After downloading the relevant data, we logged into the Microbiome Visualization Cloud Platform (https://www.bioinformatics.com.cn/) and plotted the bar and bubble plots of the enrichment results. Items with P < 0.05 were considered statistically significant.

Molecular Docking

To confirm the effect of DEHP on core genes, molecular docking explored potential binding interactions. The 3D structures of the proteins were downloaded from the PDB database (https://www.rcsb.org/), while the PubChem database was used to download the 3D structural information of the compounds. The protein structures were obtained by searching the protein structure database, and after removing redundant structures such as small molecules and water, they were converted to pdbqt files using MGLTools. Download the small molecule structure file and process it into a pdbqt file using MGLTools. Construct the docking box so that it contains the entire protein. Use Autodock Vina 1.1.2 to dock the small molecule and protein. Receptor molecules with higher negative molecular docking binding energies are more stable in the docked conformation.

Molecular Dynamics Simulations

This study selected one set of protein-ligand complexes with the lowest molecular docking binding energies for molecular dynamics simulations using GROMACS 2022 software. The CHARMM36 force field was used for the proteins, and the ligand topology was constructed from the GAFF2 force field. The system was solvated using the TIP3P water model in a cubic box with periodic boundary conditions.²⁶ Particle mesh Ewald (PME) and Verlet algorithms were used to handle electrostatic interactions. Subsequently, 100,000 steps of isothermal isovolumic (NVT) equilibrium and isothermal isobaric (NPT) equilibrium were performed.²⁷ The van der Waals and Coulomb cutoffs were set to 1.0 nm. Ultimately, the system was subjected to 100 ns of molecular dynamics simulations at constant temperature and pressure.

Result

DEHP Affects the Target Identification of MetS

From the GeneCards database, we initially obtained 19,282 potential disease targets, and after four rounds of rigorous median screening, we finally screened 1205 disease targets (score>26.7). The OMIM database contains 650 disease-related gene targets. TTD contains nine disease targets. By querying the STITCH database, we identified potential targets for 10 compounds. Using the SwissTargetPrediction tool, we further predicted and obtained 100 potential compound targets. Finally, we collected 1165 compound targets from the ChEMBL database. After merging and de-weighting, we received 1772 MetS and 1177 DEHP targets. Venn diagram analysis was performed on DEHP and MetS targets, and 150 intersecting targets were finally obtained (Figure 1). A detailed list is provided in the Supplementary File.

Figure 1 Venn diagram of common targets of DEHP and MetS.

Potential Target Interaction Networks and Core Gene Acquisition

PPI network analysis was performed on 150 potential action targets using the STRING database, and the results were imported into Cytoscape 3.7.1 software to construct a PPI network and obtain a PPI network graph. The network graph contains 127 nodes and 385 edges, and the darker color of the nodes indicates the more important the target is in the network (Figure 2).

Figure 2 (A) PPI network of DEHP interfering with MetS target obtained by STRING. (B) PPI network processed by Cytoscape. The darker the color, the greater the degree value.

To identify the core targets, we further refined the selection of core targets based on three key parameters: “degree”, “betweenness centrality”, and “closeness centrality”. After two rounds of median screening, the core targets of Tumor Protein p53 (TP53), Estrogen Receptor 1 (ESR1), Epidermal Growth Factor Receptor (EGFR), Tumor Necrosis Factor (TNF), Interleukin 6 (IL6) were finally obtained. These may be potential key targets for DEHP to interfere with human metabolism (Table 1).

Table 1 Core Targets Screened from PPI

GO and KEGG Pathway Enrichment Analysis

The 150 DEHP and MetS intersecting targets were entered into the DAVID database for GO and KEGG enrichment analysis. Among them, 53 entries were obtained for cellular components (CC), 158 entries for molecular functions (MF), and 514 entries for biological processes (BP) (Figure 3). Among the BPs, metabolism, transcriptional regulation, insulin-like growth factor, etc. were mainly involved. Cellular components, on the other hand, focus on membrane-related structures and organelles, such as the endoplasmic reticulum and mitochondria. Various redox reactions, steroid metabolism, and other functions are involved in molecular functions.

Figure 3 DEHP exposure interferes with the GO of MetS target genes (BP, CC, MF).

125 signalling pathways were obtained from KEGG analysis, from which the top 20 were selected for ranking (p < 0.05). Among the top 20 ranked signalling pathways, steroid hormone biosynthesis, AGE-RAGE signalling pathway in diabetic complications, FoxO signalling pathway, metabolic pathway, and insulin resistance showed significant enrichment (Figure 4).

Figure 4 DEHP exposure interferes with the first 20 KEGG pathways of MetS target genes.

Molecular Docking of DEHP with MetS Core Target Proteins

Lower binding energy implies higher binding activity between large and small molecules. When the binding energy is less than −4.25 kcal/mol, it indicates a certain degree of affinity between the two. When the binding energy is further reduced to below −5 kcal/mol, it means a more significant affinity.²⁸ We performed molecular docking of the core targets of the screen, TP53, ESR1, EGFR, IL6, and DEHP (CHEMBL1242017). The Vina scores of TP53, ESR1, EGFR, and IL6 were −5.6 kcal/mol, −6.1 kcal/mol, −5.4 kcal/mol, and −4.8 kcal/mol, respectively (Figure 5). This indicates that these targets are highly bound and conformationally stable for DEHP. The detailed results of molecular docking are shown in Table 2.

Table 2 The Docking Results of Core Genes and DEHP Molecules

Figure 5 Two-dimensional and three-dimensional maps of molecular docking of DEHP with central targets (A) TP53-DEHP, (B) ESR1-DEHP, (C) EGFR-DEHP, (D) IL6-DEHP.

Molecular Dynamics Simulations Validation

The equilibrium state of the simulated system was assessed using root mean square deviation (RMSD) (Figure 6A), and the ESR1-DEHP complex system reached equilibrium after about 65 ns and ultimately fluctuated above and below 2.1 Å, suggesting that the complex has a high structural stability. Further analysis showed that the radius of gyration (Rg) and solvent-accessible surface area (SASA) of the complex fluctuated less during the simulation (Figure 6B and C), suggesting that no apparent structural contraction or expansion occurred. The number of hydrogen bonds between small molecules and target proteins (Figure 6D) and between complex systems ranged from 0 to 2, suggesting the existence of stable hydrogen bonding interactions between the two. In addition (Figure 6E), the root-mean-square rise and fall (RMSF) values were relatively low (mostly below 3 Å), reflecting their overall low flexibility and high stability.

Figure 6 Molecular dynamics simulations of protein-ligand complexes. (A) Root mean square deviation, (B) Radius of gyration, (C) Solvent accessible surface area, (D) Number of hydrogen bonds, (E) Root-mean-square fluctuation.

Discussion

The widespread use of DEHP in various plastic products has raised increasing concerns about environmental pollution and health risks, which continue to grow, as many studies have linked it to adverse health manifestations in humans.²⁹ DEHP has been detected at high frequencies and concentrations in soil, air, and water.^30,31 Plasticizers such as DEHP released from the slow degradation of discarded plastic products penetrate the soil,³² and various persistent organic pollutants (POPs) are introduced into the aquatic environment, resulting in water contamination.³³ After contaminating the environment, DEHP enters the human body through the air, food, water, dermal contact, and medical devices and accumulates in the body, potentially causing endocrine disruption to reproductive and immune systems with long-term health effects on health.³⁴

In this study, we systematically explored the potential mechanisms by which DEHP exposure may contribute to the pathogenesis of MetS through network toxicology. By integrating DEHP and MetS-related genes, we identified 150 overlapping targets and revealed key biological pathways, providing new insights into the molecular interactions between environmental pollutants and metabolic disorders. PPI network and topological analyses highlighted key targets, such as TP53, ESR1, EGFR, TNF, and IL6, which may serve as central hubs for DEHP-induced metabolic disorders.

The TP53 gene encodes the tumor suppressor protein p53 and is traditionally thought to play an essential role in apoptosis and cancer suppression.³⁵ Emerging evidence emphasizes that certain activities are also involved in the homeostatic regulation of energy metabolism.^36,37 For example, p53 enhances gluconeogenesis in human and mouse hepatocytes.³⁸ DEHP activates p53 by inducing oxidative stress and inhibiting Mdm2, and the p53-dependent apoptotic pathway plays a key role in DEHP-induced hepatotoxicity.³⁹ Estrogen Receptor Alpha (ERα), encoded by the ESR1 gene, is a nuclear hormone receptor that plays a key role in regulating gene expression, cell proliferation, and differentiation. The function of ESR1 is closely related to metabolic processes. ESR1 directly regulates the obesity disparity gene MMAA to improve the prognosis of patients with hepatocellular carcinoma in terms of liver metabolism and tumor suppression.⁴⁰ ERα knockout mice have increased adipose tissue and insulin resistance, indicating that the E2/Erα signalling pathway is essential in adipose tissue.⁴¹ Abnormalities in its function may lead to metabolic disorders and related diseases. EGFR genes play a key role in cell proliferation, differentiation, and survival. Recently, mutations in the EGFR gene have been found to affect metabolic processes.⁴² EGFR-sensitive mutations cause metabolic reprogramming in tumor cells, such as enhancement of aerobic glycolysis and the pentose phosphate pathway, up-regulation of glutamine metabolism, and increased synthesis of lipids and adenosine, among many other metabolic pathways.⁴³ EGFR-mediated activation of adipose tissue macrophages promotes obesity and insulin resistance and thus encourages a low-grade inflammatory state in the MetS.⁴⁴ Animal studies suggest that EGFR may play an essential role in lipid metabolism in mice. EGFR inhibitors reduce serum lipid levels and hepatic steatosis in high-fat diet-induced obese mice.^45,46 The pro-inflammatory cytokines TNF and IL6 are known mediators of chronic low-grade inflammation in the MetS.⁴⁷ Metabolic inflammation is characterized by elevated serum levels of pro-inflammatory cytokines, predominantly IL-6 and TNF-α, which are derived from chronically inflamed adipose tissue and are associated with oxidative stress.^48,49 Inhibition of IL-6 and TNF-α alleviates hypertension, hyperuricemia, dyslipidemia, and insulin resistance in MetS rats induced by a high-fat diet.⁵⁰ DEHP exposure may exacerbate their expression, further contributing to oxidative stress and metabolic dysfunction. These findings suggest that DEHP disrupts metabolic homeostasis by targeting multifunctional nodes involved in inflammation, hormonal signalling, and cellular stress responses.

Our KEGG pathway enrichment analysis showed that DEHP exposure disrupts multiple key metabolic homeostatic pathways, including steroid hormone biosynthesis, AGE-RAGE signalling, FoxO signalling, and insulin resistance. ESR1 is a steroid hormone receptor, and DEHP interferes with adipocyte differentiation and lipid storage by enhancing ESR1 activity, leading to abnormal adipose tissue distribution and inducing insulin resistance.⁵¹ EGFR, on the other hand, induces the expression of inflammatory factors TNF and IL6 through activation of the NF-κB signalling pathway.⁵² In the inflammatory response, the sustained activation of the AGE-RAGE pathway further induces pathological processes such as inflammation, oxidative stress, and insulin resistance, significantly increasing the risk of MetS-associated cardiovascular complications.^53,54 The metabolic disruptions induced by DEHP are dependent on FoxO1. DEHP-induced metabolic disturbances depend on the overexpression of FoxO1, which drives hepatic gluconeogenesis and lipid accumulation.¹⁸ Abnormal enhancement of FoxO signalling further contributes to disturbed energy metabolism and exacerbates the phenotype of MetS, and inhibition of FoxO1 reverses the metabolic disturbances induced by DEHP.⁵⁵ The disruption of these metabolic pathways is a direct driver of lipid accumulation and insulin resistance, which are central pathological features of MetS.

Although the present study revealed the potential mechanism of action of DEHP exposure and MetS through network toxicology, molecular docking, and kinetic simulations, there are still some limitations. First, cyber toxicology analysis is highly dependent on data from public databases, which may have data bias or incompleteness and cannot fully reflect the complex biological processes in the human body. Second, the results of molecular docking and kinetic simulations are computer simulations, which are difficult to adequately model the complex metabolic environment and its dynamic changes in the body. Finally, DEHP’s metabolizing ability and sensitivity differ in different populations (eg, children, pregnant women, and the elderly). It is also impossible to clarify the effect of DEHP exposure dose or exposure route on MetS. This is likewise a direction in which future research needs to focus on breakthroughs. Our findings emphasize the urgent need to regulate the use of DEHP, especially in products with a high risk of human exposure (eg, medical devices and food packaging). Future work should prioritize in vivo and in vitro validation of key targets. Epidemiological studies should also be conducted in different populations to establish dose-response relationships between DEHP exposure and MetS, to clarify the differential effects of DEHP in specific populations, and to track the impact of long-term DEHP exposure to MetS so that these heterogeneities can be fully assessed. In addition, there is an urgent need to find safer and more environmentally friendly alternatives and to systematically evaluate the differences in environmental persistence, bioaccumulation, and health risks between alternatives and DEHP. These directions will deepen the mechanistic understanding of the association between DEHP and MetS and provide a scientific basis for environmental health policy and precision medicine.

Conclusion

In this study, we revealed through network toxicology that DEHP exposure may promote MetS by regulating key target proteins (eg, TP53, ESR1, EGFR) and interfering with lipid metabolism, insulin signalling pathway, and inflammatory response. These findings not only elucidate the metabolic toxicity mechanism of DEHP but also provide new perspectives for understanding the association between environmental pollutants and metabolic diseases. Identification of DEHP-associated biomarkers of metabolic disorders may be helpful for early diagnosis and personalized intervention, especially in populations chronically exposed to plasticizers. It points the way for subsequent toxicological studies and provides a scientific basis for improving public health policy and clinical practice.

Data Sharing Statement

The data supporting the findings of this study are available from the corresponding author, Dr. Maoyuan Wang, upon reasonable request.

Ethics Approval and Informed Consent

The data used are de-identified public datasets that cannot be traced back to any individual and do not involve direct interaction with human subjects. According to Article 32, Item 1 of the Measures for Ethical Review of Human Life Science and Medical Research (February 18, 2023, China), this type of research meets the conditions for exemption from ethical review.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The project is funded by the Postgraduate Innovation Special Fund Project of Gannan Medical University (grant number YC2024-X014).

Disclosure

The authors declare that the study has no conflicts of interest.

References

1. Andrady AL, Neal MA. Applications and societal benefits of plastics. Philos Trans R Soc London Ser B. 2009;364(1526):1977–1984. doi:10.1098/rstb.2008.0304

2. Hahladakis JN, Velis CA, Weber R, Iacovidou E, Purnell P. An overview of chemical additives present in plastics: migration, release, fate and environmental impact during their use, disposal and recycling. J Hazard Mater. 2018;344:179–199. doi:10.1016/j.jhazmat.2017.10.014

3. Kushwaha M, Shankar S, Goel D, et al. Microplastics pollution in the marine environment: a review of sources, impacts and mitigation. Mar Pollut Bull. 2024;209(Pt A):117109. doi:10.1016/j.marpolbul.2024.117109

4. Kubíková Ľ, Rudý S. The current global situation of plastics and forecast of plastic waste. 2024.

5. Pandey P, Dhiman M, Kansal A, Subudhi SP. Plastic waste management for sustainable environment: techniques and approaches. Waste Dispos Sustain Energy. 2023:1–18.

6. Lebreton L, Andrady A. Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 2019;5(1):6. doi:10.1057/s41599-018-0212-7

7. Wensing M, Uhde E, Salthammer T. Plastics additives in the indoor environment–flame retardants and plasticizers. Sci Total Environ. 2005;339(1–3):19–40. doi:10.1016/j.scitotenv.2004.10.028

8. Puri M, Gandhi K, Kumar MS. The occurrence, fate, toxicity, and biodegradation of phthalate esters: an overview. Water Environ Res. 2023;95(1):e10832. doi:10.1002/wer.10832

9. Griffiths WC, Camara P, Lerner KS. Bis-(2-ethylhexyl) phthalate, an ubiquitous environmental contaminant. Ann Clin Lab Sci. 1985;15(2):140–151.

10. Erythropel HC, Maric M, Nicell JA, Leask RL, Yargeau V. Leaching of the plasticizer di(2-ethylhexyl)phthalate (DEHP) from plastic containers and the question of human exposure. Appl Microbiol Biotechnol. 2014;98(24):9967–9981.

11. Kavlock R, Boekelheide K, Chapin R, et al. NTP center for the evaluation of risks to human reproduction: phthalates expert panel report on the reproductive and developmental toxicity of di(2-ethylhexyl) phthalate. Reprod Toxicol. 2002;16(5):529–653.

12. Koch HM, Preuss R, Angerer J. Di(2-ethylhexyl)phthalate (DEHP): human metabolism and internal exposure – an update and latest results. Int JAndrology. 2006;29(1):155–165. doi:10.1111/j.1365-2605.2005.00607.x

13. Li A, Kang L, Li R, Wu S, Liu K, Wang X. Modeling di (2-ethylhexyl) Phthalate (DEHP) and its metabolism in a body’s organs and tissues through different intake pathways into human body. Int J Environ Res Public Health. 2022;19(9).

14. Neeland IJ, Lim S, Tchernof A, et al. Metabolic syndrome. Nat Rev Dis Primers. 2024;10(1):77.

15. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome–a new world-wide definition. A consensus statement from the international diabetes federation. Diabetic Med. 2006;23(5):469–480. doi:10.1111/j.1464-5491.2006.01858.x

16. Ashaari S, Jamialahmadi T, Davies NM, Almahmeed W, Sahebkar A. Di (2-ethyl hexyl) phthalate and its metabolite-induced metabolic syndrome: a review of molecular mechanisms. Drug Chem Toxicol. 2024:1–19.

17. Medic Stojanoska M, Milankov A, Vukovic B, et al. Do diethyl phthalate (DEP) and di-2-ethylhexyl phthalate (DEHP) influence the metabolic syndrome parameters? Pilot study. Environ Monit Assess. 2015;187(8):526. doi:10.1007/s10661-015-4754-5

18. Wei X, Yang D, Zhang B, et al. Di-(2-ethylhexyl) phthalate increases plasma glucose and induces lipid metabolic disorders via FoxO1 in adult mice. Sci Total Environ. 2022;842:156815. doi:10.1016/j.scitotenv.2022.156815

19. Freire C, Castiello F, Babarro I, et al. Association of prenatal exposure to phthalates and synthetic phenols with pubertal development in three European cohorts. Int J Hyg Environ Health. 2024;261:114418. doi:10.1016/j.ijheh.2024.114418

20. Huang YQ, Tang YX, Qiu BH, Talukder M, Li XN, Li JL. Di-2-ethylhexyl phthalate (DEHP) induced lipid metabolism disorder in liver via activating the LXR/SREBP-1c/PPARα/γ and NF-κB signaling pathway. Food Chem Toxicol. 2022;165:113119. doi:10.1016/j.fct.2022.113119

21. Lan Y, Peng Q, Fu B, Liu H. Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking and machine learning strategies. Toxicology. 2024;511:154029. doi:10.1016/j.tox.2024.154029

22. He N, Zhang J, Liu M, Yin L. Elucidating the mechanism of plasticizers inducing breast cancer through network toxicology and molecular docking analysis. Ecotoxicol Environ Saf. 2024;284:116866. doi:10.1016/j.ecoenv.2024.116866

23. Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–d612. doi:10.1093/nar/gkaa1074

24. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504. doi:10.1101/gr.1239303

25. Huang DW, Sherman BT, Tan Q, et al. DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007;35(Web Server issue):W169–175. doi:10.1093/nar/gkm415

26. Ren J, Liu Z, Qi X, et al. Active ingredients and potential mechanism of additive sishen decoction in treating rheumatoid arthritis with network pharmacology and molecular dynamics simulation and experimental verification. Drug Des Devel Ther. 2025;19:405–424. doi:10.2147/DDDT.S489323

27. Zhang S, Shao Y, Jin R, Ma B. Combining network pharmacology, molecular docking, molecular dynamics simulation, and experimental validation to uncover the efficacy and mechanisms of Si-Miao-Yong-An decoction in diabetic wound healing. J Inflamm Res. 2025;18:4087–4101. doi:10.2147/JIR.S506739

28. Istyastono EP, Radifar M, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS: a molecular interaction fingerprinting tool for docking results of AutoDock Vina and PLANTS. J Chem Inf Model. 2020;60(8):3697–3702. doi:10.1021/acs.jcim.0c00305

29. Zhang Y, Lyu L, Tao Y, Ju H, Chen J. Health risks of phthalates: a review of immunotoxicity. Environ Pollut. 2022;313:120173. doi:10.1016/j.envpol.2022.120173

30. Wang X, Zhang Y, Huang B, et al. Phthalate pollution and migration in soil-air-vegetable systems in typical plastic agricultural greenhouses in northwestern China. Sci Total Environ. 2022;809:151101. doi:10.1016/j.scitotenv.2021.151101

31. Dou Y, Hu W, Wang J, et al. Spatial distribution and chronic ecological risk assessment of typical phthalate esters in the surface waters of China. Bull Environ Contam Toxicol. 2024;114(1):11. doi:10.1007/s00128-024-03988-6

32. Manatunga DC, Sewwandi M, Perera KI, et al. Plasticizers: distribution and impact in aquatic and terrestrial environments. Environ Sci Processes Impacts. 2024;26(12):2114–2131. doi:10.1039/D4EM00317A

33. Jiang J, Han D, Xiao Y, Song X. Occurrence, migration, and assessment of human health and ecological risks of PFASs and EDCs in groundwater of Northeast China. Water Res. 2025;269:122810. doi:10.1016/j.watres.2024.122810

34. Rowdhwal SSS, Chen J. Toxic effects of Di-2-ethylhexyl phthalate: an overview. Biomed Res Int. 2018;2018(1):1750368. doi:10.1155/2018/1750368

35. Aubrey BJ, Kelly GL, Janic A, Herold MJ, Strasser A. How does p53 induce apoptosis and how does this relate to p53-mediated tumour suppression? Cell Death Differ. 2018;25(1):104–113. doi:10.1038/cdd.2017.169

36. Olovnikov IA, Kravchenko JE, Chumakov PM. Homeostatic functions of the p53 tumor suppressor: regulation of energy metabolism and antioxidant defense. Semi Cancer Biol. 2009;19(1):32–41. doi:10.1016/j.semcancer.2008.11.005

37. Joerger AC, Stiewe T, Soussi T. TP53: the unluckiest of genes? Cell Death Differ. 2025;32(2):219–224. doi:10.1038/s41418-024-01391-6

38. Goldstein I, Yizhak K, Madar S, Goldfinger N, Ruppin E, Rotter V. p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production. Cancer Metab. 2013;1(1):9. doi:10.1186/2049-3002-1-9

39. Ha M, Wei L, Guan X, Li L, Liu C. p53-dependent apoptosis contributes to di-(2-ethylhexyl) phthalate-induced hepatotoxicity. Environ Pollut. 2016;208:416–425. doi:10.1016/j.envpol.2015.10.009

40. Zhang Y, Cheng J, Zhong C, et al. ESR1 regulates the obesity- and metabolism-differential gene MMAA to inhibit the occurrence and development of hepatocellular carcinoma. Front Oncol. 2022;12:899969. doi:10.3389/fonc.2022.899969

41. Heine PA, Taylor JA, Iwamoto GA, Lubahn DB, Cooke PS. Increased adipose tissue in male and female estrogen receptor-alpha knockout mice. Proc Natl Acad Sci USA. 2000;97(23):12729–12734. doi:10.1073/pnas.97.23.12729

42. Sigismund S, Avanzato D, Lanzetti L. Emerging functions of the EGFR in cancer. Mol Oncol. 2018;12(1):3–20. doi:10.1002/1878-0261.12155

43. Wu Y, Gao W, Liu H. Role of metabolic reprogramming in drug resistance to epidermal growth factor tyrosine kinase inhibitors in non-small cell lung cancer. Zhong Nan da Xue Xue Bao Yi Xue Ban. 2021;46(5):545–551. doi:10.11817/j.issn.1672-7347.2021.200529

44. Cao S, Pan Y, Tang J, et al. EGFR-mediated activation of adipose tissue macrophages promotes obesity and insulin resistance. Nat Commun. 2022;13(1):4684. doi:10.1038/s41467-022-32348-3

45. Scheving LA, Zhang X, Garcia OA, et al. Epidermal growth factor receptor plays a role in the regulation of liver and plasma lipid levels in adult male mice. Am J Physiol Gastrointest Liver Physiol. 2014;306(5):G370–381. doi:10.1152/ajpgi.00116.2013

46. Choung S, Kim JM, Joung KH, Lee ES, Kim HJ, Ku BJ. Epidermal growth factor receptor inhibition attenuates non-alcoholic fatty liver disease in diet-induced obese mice. PLoS One. 2019;14(2):e0210828. doi:10.1371/journal.pone.0210828

47. Hotamisligil GS. Inflammation, metaflammation and immunometabolic disorders. Nature. 2017;542(7640):177–185. doi:10.1038/nature21363

48. Silveira Rossi JL, Barbalho SM, Reverete de Araujo R, Bechara MD, Sloan KP, Sloan LA. Metabolic syndrome and cardiovascular diseases: going beyond traditional risk factors. Diabetes/Metab Res Rev. 2022;38(3):e3502. doi:10.1002/dmrr.3502

49. Mohammadi M, Gozashti MH, Aghadavood M, Mehdizadeh MR, Hayatbakhsh MM. Clinical significance of serum IL-6 and TNF-α levels in patients with metabolic syndrome. Rep Biochem Mol Biol. 2017;6(1):74–79.

50. Said MA, Nafeh NY, Abdallah HA. Spexin alleviates hypertension, hyperuricaemia, dyslipidemia and insulin resistance in high fructose diet induced metabolic syndrome in rats via enhancing PPAR-ɣ and AMPK and inhibiting IL-6 and TNF-α. Arch Physiol Biochem. 2023;129(5):1111–1116. doi:10.1080/13813455.2021.1899242

51. Zhou Z, Moore TM, Drew BG, et al. Estrogen receptor α controls metabolism in white and brown adipocytes by regulating Polg1 and mitochondrial remodeling. Sci Trans Med. 2020;12(555). doi:10.1126/scitranslmed.aax8096

52. Fang Q, Zou C, Zhong P, et al. EGFR mediates hyperlipidemia-induced renal injury via regulating inflammation and oxidative stress: the detrimental role and mechanism of EGFR activation. Oncotarget. 2016;7(17):24361–24373. doi:10.18632/oncotarget.8222

53. Zhou M, Zhang Y, Shi L, et al. Activation and modulation of the AGEs-RAGE axis: implications for inflammatory pathologies and therapeutic interventions – A review. Pharmacol Res. 2024;206:107282. doi:10.1016/j.phrs.2024.107282

54. Vianello E, Beltrami AP, Aleksova A, et al. The Advanced Glycation End-Products (AGE)–Receptor for AGE System (RAGE): an inflammatory pathway linking obesity and cardiovascular diseases. Int J Mol Sci. 2025;26(8):3707. doi:10.3390/ijms26083707

55. Wang M, Wang Y, Han J, et al. Gestational and lactational co-exposure to DEHP and BPA impairs hepatic function via PI3K/AKT/FOXO1 pathway in offspring. Toxics. 2023;11(3).

By Tomi Kilgore

MicroStrategy has acquired more than 69,000 bitcoins since it last took a pause, to bring its total holdings to nearly 600,000 bitcoins

It is now bigger news when MicroStrategy Inc. doesn’t buy any bitcoin than when it does.

The self-proclaimed largest bitcoin treasury company, which is technically still a software company (MSTR), disclosed Monday that it didn’t acquire any bitcoin during the week of June 30 to July 6.

That’s the first week MicroStrategy, which is doing business as Strategy, didn’t acquire any bitcoin since the week of March 31 to April 6.

The stock slipped 2% on Monday, as the price of bitcoin (BTCUSD) declined about 1.5% over the past 24 hours.

From April 7 through June 29, MicroStrategy spent $6.77 billion to buy 69,140 bitcoins, at an average price of about $97,906 per bitcoin. At current prices, the value of those purchases has increased by 10.4% to $7.49 billion.

The company currently owns 597,325 bitcoins, which it purchased for $42.4 billion at an average price of $70,982 per bitcoin. That holding is currently worth $64.71 billion.

MicroStrategy also didn’t issue any common or preferred shares in the latest week to raise money for bitcoin purchases. But it did announce a sales agreement in which it may issue and sell shares of 10% preferred stock with a total offering price of up to $4.2 billion.

The company said it plans to use proceeds from the sales agreement for general corporate purposes, including bitcoin purchases.

MicroStrategy’s stock has soared 38.5% in 2025, while bitcoin has rallied 16.1% and the S&P 500 index SPX has tacked on 6.1%.

Separately, in the world of bitcoin treasury companies, Singapore-based developer of artificial-intelligence education Genius Group Ltd. (GNS) said it has increased its bitcoin treasury target by 10 times, to 10,000 bitcoins.

The company said it plans to use debt financing, issue convertible bonds and preferred shares, and use money made from its business to raise money for the bitcoin purchases.

Genius’s stock dropped about 7% on Monday. It has lost 6.5% so far this month after rocketing 331.8% in June.

-Tomi Kilgore

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

  (END) Dow Jones Newswires

  07-07-25 2321ET
Copyright (c) 2025 Dow Jones & Company, Inc.