Category: 3. Business

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.

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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

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• Gold price turns south early Tuesday but remains in a familiar range below $3,350.
• US Dollar reverses the previous upswing alongside US Treasury bond yields as traders assess Trump’s latest tariffs.
• Gold price closes Monday above 50-day SMA but sellers lurk at 21-day SMA amid a neutral daily RSI.

Gold price is back in the red below $3,350 early Tuesday, remaining stuck in a familiar range since last Friday as investors assess the implications of the latest tariff threats by US President Donald Trump.

Gold price remains at the mercy of tariff talks, US Dollar

US President Donald Trump announced on social media on Monday that 25% tariffs will be imposed on imports from Japan and the Republic of Korea, respectively, beginning August 1.

Later, he announced that similar letters were sent to the leaders of 12 other countries, informing them that tariffs ranging 25% to 40% will be charged starting next month.

Risk sentiment took a further hit on Trump’s announcements, fuelling a fresh rebound in the traditional safe-haven Gold price as the renewed tariff threats revived trade war fears globally.

Gold price bounced off five-day lows of $3,297 and recovered to near $3,350 but its renewed upside was capped by the resurgent US Dollar demand as a safe-haven.

The Greenback got an additional boost from a big rally in the USD/JPY pair as the Japanese Yen fell hard on Trump’s 25% tariffs announcement on Japan.

Further, the US Treasury bond yields firmed up on expectations that the US Federal Reserve (Fed) will stand pat on interest rates for an extended period if fresh tariffs stoke inflationary fears.

This narrative also aided the buck’s turnaround while the non-interest-bearing Gold price’s recovery.

In Tuesday’s trading so far, Gold sellers have made a comeback as trade concerns continue to cast a cloud on the US economic and inflation outlook, backing the case for an extended pause by the Fed.

However, the Gold price downside appears cushioned as a lack of certainty on the US tariff plans continues to keep investors scurrying for safety in the bright metal while the US Dollar faces headwinds amid renewed worries over the economic prospects if a trade war kick starts.

When asked if the trade negotiations deadline, extended until August 1, was firm, Trump said: “I would say firm, but not 100% firm. If they call up and they say we’d like to do something a different way, we’re going to be open to that.”

Heightened uncertainty around Trump’s tariff plans will continue to keep a floor under Gold price.

In the absence of any top-tier US economic data releases, tariff updates will likely keep Gold traders entertained as they eagerly await the release of the Fed’s June meeting Minutes on Wednesday.

Gold price technical analysis: Daily chart

Gold price remains confined in a narrow range, with the 21-day Simple Moving Average (SMA) at $3,350 acting as a tough nut to crack for buyers.

Meanwhile, the 50-day SMA at $3,322 continues to guard the downside as the 14-day Relative Strength Index (RSI) turns neutral, clinging to the midline at the time of writing.

The leading indicator indicates a lack of a clear directional bias.

A break below the 50-day SMA on a daily closing basis is needed to negate any near-term bullish momentum.

The next support is aligned at the 38.2% Fibonacci Retracement (Fibo) level of the April record rally at $3,297.

A sustained move below the latter will target the monthly low of $3,248.

Alternatively, recapturing the 21-day SMA is critical to reviving the recovery from five-week lows.

Further up, the 23.6% Fibo level of the same advance at $3,377 could offer stiff resistance to Gold buyers.

The next topside hurdle is seen at the $3,400 threshold.

China stands at a pivotal point in its climate transition journey. While the road to carbon neutrality remains long and demanding, the country’s rapid and remarkable progress in renewable energy deployment has already started to transform its emissions profile. The key challenge now is to sustain this momentum through deeper decarbonization, especially by tackling key infrastructure and policy constraints.

Between 2020 and 2024, China experienced strong growth in both energy consumption and carbon emissions, even as overall economic growth slowed. Energy consumption rose by 4.7 percent annually on average, while emissions grew by 3.9 percent, both growing faster than in the preceding five years. These trends have put pressure on meeting China’s interim climate targets under the 14th Five-Year Plan, as energy and carbon intensity reductions are both lagging behind schedule.

At the core of this dynamic is surging electricity demand. As sectors such as transportation and manufacturing accelerate electrification, electricity consumption has risen by nearly 7 percent annually in recent years. The power and transport sectors now account for most of the country’s emissions growth, while industrial emissions—particularly from steel and cement—have stabilized due to the ongoing property market adjustment.

Yet, amid this challenge lies a powerful opportunity. China is rapidly emerging as the world’s leading force in renewable energy development. In 2023 and 2024, China added a combined 649 gigawatts of new solar and wind capacity, representing around 60 percent of total global additions. Since 2020, its installed solar and wind capacity has tripled. The electricity generated by renewables reached 1,830 terawatt-hours in 2024, roughly equivalent to the total electricity consumption of China’s entire tertiary sector.

However, the growth in clean power generation has not fully kept pace with the rapid expansion in installed capacity, and fossil fuels—especially coal—remain a critical backstop for meeting China’s soaring electricity demand. Between 2021 and 2024, approximately 45 percent of incremental power consumption was met by coal or other fossil fuel-based generation.

This presents a paradox: despite record growth in installed renewables capacity, reliance on fossil generation remains substantial.

The main constraint is inadequate transmission infrastructure. Many renewable resources are concentrated in remote inland provinces, far from coastal areas which have high power demand, creating geographical imbalances in supply and demand. In contrast to the rapid expansion of renewable generation, investment in power transmission has also been lagging, with total power sector investment falling from 66 percent in 2018 to just 35 percent in 2023.

As a result, grid limitations are increasingly hindering renewable electricity from reaching end users. Curtailment rates for wind and solar, i.e. the proportion of generated electricity that was wasted or not dispatched due to grid limitations or oversupply, have risen in the past two years, signaling that transmission capacity is struggling to keep pace with generation. Without a stronger grid backbone, much of China’s clean energy potential remains underutilized.

Addressing this challenge will require stepped-up investment in ultrahigh-voltage transmission lines, smart grid technologies, and energy storage solutions to enhance flexibility and manage intermittency.

Integrating renewables into grid operations is a necessary first step, but doing that alone would not be sufficient. The recent Spain–Portugal blackout highlights the operational risks associated with rising renewable penetration. To enhance overall grid resilience and ensure system stability, China must also invest in advanced voltage control, synthetic inertia, distributed energy storage, and stronger inter-regional transmission links, gradually reducing its reliance on fossil backup.

In parallel, deeper reforms in China’s power sector can help ensure clean energy generated is delivered efficiently and fairly. Building a truly integrated national electricity market—through the development of real-time and spot trading platforms, greater regional interconnection, and more flexible electricity pricing—will be essential to maximize the value of clean power. Reforming dispatch protocols to prioritize low-carbon generation and fostering competition across provincial boundaries can further accelerate the green transition.

The good news is that the turning point may be near. In the first quarter of 2025, the increase in China’s renewable electricity generation outpaced the growth in overall power demand. If this trend continues, China’s power sector emissions could peak this year—a major milestone on the path to reach its 2030 and 2060 climate goals.

Today, China’s clean energy transformation is already reshaping the global energy system. Its next phase—which is set to emphasize on integration, efficiency, and reform—will determine how quickly and effectively the transformation leads to sustaining emissions reductions.

If China can align its infrastructure and institutions with the pace of its renewable expansion, the country will not only meet its climate targets, but also set a new global benchmark for how to scale decarbonization in a fast-growing, energy-hungry economy.