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Introduction

Idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are progressive lung diseases that lead to irreversible respiratory decline.¹ COPD is significantly more prevalent, affecting an estimated 212 million people globally and contributing to over 3 million deaths annually, making it one of the leading causes of respiratory morbidity.² In contrast, IPF is a rarer condition, with a global annual incidence of 5–20 cases per 100,000 people, primarily affecting men over 50.³ While COPD is characterized by airflow limitation and chronic inflammation,⁴ IPF is marked by fibrosis of lung tissue, resulting in progressive respiratory dysfunction, severe breathing difficulties, and eventual respiratory failure.⁵ Both diseases share overlapping risk factors such as smoking, environmental exposures, and aging,⁶ yet these factors appear to direct individuals toward different pathological outcomes. In addition to environmental risks, recent evidence emphasizes the role of genetic predisposition in fibrotic lung diseases, such as familial pulmonary fibrosis.⁷ This highlights the importance of exploring genetic and environmental interactions that may differentiate COPD and IPF pathogenesis. Investigating both diseases together allows for a deeper understanding of potential shared or divergent pathways in their pathogenesis, which may uncover novel insights into targeted prevention and treatment strategies.

Recent studies have highlighted the potential role of saliva microbiota in respiratory diseases, as it contributes to the lung microbiota through microaspiration, influencing immune responses, inflammation, and epithelial integrity, which are critical in the pathogenesis of lung diseases.⁸ In COPD, changes in saliva microbiota composition are characterized by increased bacterial richness and diversity, with genera like Veillonella, Rothia, and Actinomyces frequently identified. These alterations are linked to disease progression and exacerbations through inflammatory pathways, as evidenced by associations between specific microbiota profiles and elevated salivary inflammatory markers, which correlate negatively with lung function.⁹ In IPF, the oral microbiota significantly contributes to lung microbial composition, with 32.84% of lung microbiota genes traced to the oral cavity. Enriched genera like Streptococcus, Pseudobutyrivibrio, and Anaerorhabdus are associated with microbial translocation, biofilm formation, antibiotic resistance, and metabolic changes, potentially promoting lung injury, fibrosis, and reduced microbial diversity, highlighting the oral-lung axis in IPF etiology.¹⁰ Repeated aspiration of oral or gastric contents due to gastroesophageal reflux may further contribute to lung inflammation and microbial imbalance in IPF.¹¹ These findings suggest that oral microbial changes may influence disease development and warrant investigation to determine their causal role in pathogenesis.

Mendelian Randomization (MR) is a robust genetic epidemiology method that utilizes genetic variants, typically single nucleotide polymorphisms (SNPs) identified through Genome-Wide Association Studies (GWAS), as instrumental variables (IVs) to assess the causal relationship between an exposure and an outcome.¹² By minimizing confounding and reverse causality, MR offers a powerful tool to identify causal links when randomized controlled trials may be impractical or unethical.¹³ This study employs a two-sample MR approach to investigate the genetically causal relationships between saliva microbiota abundance and IPF or COPD, aiming to identify potential shared and disease-specific microbial influences on pathogenesis. The findings may provide novel insights into the microbial mechanisms underlying these diseases, facilitating the understanding of their progression and enabling early risk stratification for targeted interventions.

Materials and Methods

Study Design

The methodology followed the STROBE-MR statement guidelines and employed a two-sample MR framework to assess bidirectional causal relationships between saliva microbiota abundance and respiratory diseases. Forward MR was used to estimate the effects of microbial taxa on COPD and IPF, while reverse MR tested whether genetic liability to COPD or IPF influenced microbial abundance. IVs were selected based on the three key principles of MR analysis: relevance, independence, and exclusion restriction.¹⁴ MR Steiger directionality testing was performed to validate the inferred causal direction. GWAS summary statistics for exposures and outcomes were obtained from publicly available datasets. Sensitivity analyses included heterogeneity testing, horizontal pleiotropy assessment, and outlier correction. A schematic overview of the workflow is shown in Figure 1.

Figure 1 Study flowchart for the Mendelian randomization (MR) analysis of saliva microbiota and respiratory diseases. This flowchart summarizes the bidirectional MR framework used to investigate potential causal relationships between salivary microbiota abundance and chronic obstructive pulmonary disease (COPD) or idiopathic pulmonary fibrosis (IPF). GWAS summary statistics for 44 saliva microbiota traits were screened, with 43 traits meeting the inclusion criteria for instrumental variable (IV) selection. Forward MR used the inverse variance weighted (IVW) method as the primary analysis. Sensitivity analyses included MR-PRESSO for outlier correction, Cochran’s Q test, MR-Egger intercept, and leave-one-out analysis. Steiger filtering was applied to confirm causal directionality. Reverse MR was performed using genome-wide significant SNPs from COPD and IPF GWAS to evaluate potential feedback effects on salivary microbiota composition.

Ethics Statement

All data used in this study were obtained from publicly available datasets containing de-identified human genetic or microbiome data. In accordance with Article 32, Items 1 and 2 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects (promulgated February 18, 2023, China), research using publicly available or legally obtained, de-identified human data is exempt from ethics review and informed consent. Therefore, no further institutional review board approval was required.

Data Sources

GWAS data on IPF were obtained from a published study via https://github.com/genomicsITER/PFgenetics/tree/master. The study analyzed individuals of European ancestry, including 2668 IPF cases and 8591 controls in the discovery phase.¹⁵ GWAS data for COPD were sourced from the FINNGEN R10 release (https://r9.risteys.finngen.fi/endpoints/J10_COPD), comprising 18,266 cases, predominantly of Finnish ancestry. Oral microbiome data were obtained from a study conducted on 610 unrelated adults of Danish ancestry from the ADDITION-PRO cohort. The study utilized 16S rRNA gene sequencing to investigate the salivary microbiota and performed GWAS to identify host genetic variants associated with oral bacterial traits. A total of 44 oral microbiome measurements were analyzed, including 43 univariate bacterial features (spanning taxonomic groups from phylum to species levels) and one multivariate bacterial community diversity metric.¹⁶ To our knowledge, there is no sample overlap between the exposure (oral microbiota) and outcome (COPD and IPF) GWAS datasets, as these were derived from distinct cohorts (ADDITION-PRO and FinnGen/multicenter studies, respectively). The information on the datasets is summarized in Table S1.

IV Selection

For forward MR analysis, IVs were selected from GWAS summary statistics of saliva microbiota traits. An initial threshold of genome-wide significance (P < 5×10⁻⁸) yielded insufficient variants for most taxa; therefore, a relaxed threshold of P < 5×10⁻⁶ was adopted, consistent with prior MR studies on gut microbiota, inflammatory factors, and related traits.¹⁷ To ensure adequate instrument strength and model stability, a minimum of 3 independent SNPs per exposure was required.¹⁸ For exposures with fewer than 3 SNPs at this threshold, the criterion was further relaxed to P < 1 × 10⁻⁵, as previously reported.^19–21 In contrast, for reverse MR analysis, instruments for COPD and IPF were selected using the standard genome-wide significance threshold (P < 5×10⁻⁸) without relaxation.

To guarantee sufficient genetic variability, only SNPs with a minor allele frequency (MAF) greater than 0.01 were included.²² Linkage disequilibrium (LD) effects were minimized by excluding SNPs with high LD (R² < 0.001) within a 10,000 kb window.²³ For cases where the selected IV was absent from the outcome summary data, proxy SNPs with strong LD (R² > 0.8) were identified and substituted to maintain integrity.²⁴ Furthermore, the F-statistic was calculated for each SNP to assess instrument strength and prevent weak instrument bias. The formula F = R² × (N – 2) / (1 – R²) was used. R² represents the proportion of variance in the exposure explained by the SNP. Only SNPs with an F-statistic > 10 were included to ensure the reliability of the IVs and their ability to capture the causal effect of the exposure on the outcome.²⁵

MR Analysis

In this study, the inverse variance weighted (IVW) random effects method was the primary approach for assessing the causal relationship between exposure and outcomes, providing odds ratios (ORs) with 95% confidence intervals (CIs). The IVW method calculates a weighted average effect size using each SNP’s inverse variance.²⁶ To ensure robustness, MR-Egger, weighted median, and weighted mode methods were also applied. MR-Egger accounts for pleiotropy by including an intercept term,¹⁸ while the weighted median method provides reliable estimates if at least 50% of IVs are valid.²⁷ The weighted mode method identifies the causal effect as the mode of the effect estimates, weighted by their precision, and is robust even when most IVs are invalid, provided that the largest subset of valid instruments produces consistent estimates.²⁸ Associations with IVW P < 0.05 were considered statistically significant. P values were adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) method.

Sensitivity Analysis

Sensitivity analyses were performed to evaluate the robustness of causal estimates and detect potential violations of MR assumptions. Heterogeneity among instrument-specific estimates was assessed using Cochran’s Q test under the IVW model. A P-value > 0.05 indicated low heterogeneity, suggesting consistency across SNP effects.²⁹ Horizontal pleiotropy was evaluated using the MR-Egger intercept. A non-significant deviation from zero indicated the absence of directional pleiotropy.¹⁸ MR-PRESSO identified and corrected for outlier variants contributing to pleiotropic bias.³⁰ Robustness was further assessed by leave-one-out (LOO) analysis, which sequentially removed individual SNPs to evaluate their influence on the overall causal estimates.³¹ To ensure correct causal direction, the Steiger directionality test was applied, and SNPs explaining greater variance in the outcome than in the exposure (R²_outcome > R²_exposure) were excluded prior to MR. All analyses were performed using the “TwoSampleMR” package. Diagnostic plots (scatter, forest, funnel) were used for visualization.

Results

IV Selection in Forward MR

For IV selection, out of 44 saliva microbiota abundance datasets, 43 were included in the final analysis. Beta diversity of salivary microbiota (GCST90429842) was excluded due to insufficient information. IVs were primarily selected using the threshold of P < 5×10⁻⁶. For unknown Streptococcus species (ASV0003, GCST90429825), species parvula (GCST90429829), genus Alloprevotella (GCST90429822), genus Streptococcus (GCST90429813), and unknown Rothia species (ASV0012, GCST90429834), fewer than 3 SNPs met this criterion. Therefore, the threshold was relaxed to P < 1×10⁻⁵. Unavailable SNPs in the abundance data for unknown Schaalia species (ASV0017), unknown Rothia species (ASV0016), and unknown Neisseria species (ASV0004) for IPF, and unknown Rothia species (ASV0016) and species parvula for COPD, were substituted with proxy SNPs, including rs73057773 for rs7807974, rs67487314 for rs61026851, rs2614710 for rs67577153, rs34837414 for rs7247650, rs4860383 for rs113621445, and rs9571821 for rs9564412. The number of IVs per dataset ranged from 3 to 13, with mean F-statistic between 20.53 and 27.10, confirming instrument strength (Table S2).

Forward MR Analysis

MR analysis using the IVW method revealed a significant inverse association between parvula abundance and COPD risk (OR = 0.9546, 95% CI: 0.9270–0.9831, P = 0.002). Higher abundances of class Bacilli (OR = 0.8447, 95% CI: 0.7402–0.9639, P = 0.0122) and genus Porphyromonas (OR = 0.8398, 95% CI: 0.7224–0.9764, P = 0.0231) were significantly associated with reduced risk of IPF (Table 1).

Table 1 Significant Associations Between Saliva Microbiota Abundance and Respiratory Diseases in Forward and Reverse Mendelian Randomization Analyses (IVW Method)

MR-PRESSO detected outliers in three associations: genus Fusobacterium with IPF (1 outlier), genus Rothia with COPD (1 outlier), and unknown Rothia species (ASV0012) with COPD (2 outliers) (Table S3). After removing outliers, a significant inverse association emerged between Fusobacterium and IPF (IVW OR = 0.9069, 95% CI: 0.8338–0.9865, P = 0.0227; Table 1). Other associations remained null. Notably, after FDR correction, only the association between parvula and COPD remained significant (adjusted P = 0.019), reinforcing its robustness. Full MR results, including post-correction estimates, are shown in Table S4. Scatter and forest plots illustrated the SNP-specific effects (Figures 2 and 3). These findings provide novel evidence linking specific salivary microbial taxa to COPD and IPF risk, suggesting disease-specific microbial contributions.

Figure 2 Scatter plots show genetic associations between saliva microbiota abundance and respiratory disease using different MR methods. Regression lines for IVW, MR-Egger, weighted median, and weighted mode methods are included when applicable. (A) Saliva microbiota abundance of species parvula and COPD risk. (B) Saliva microbiota abundance of class Bacilli and IPF risk. (C) Saliva microbiota abundance of genus Porphyromonas and IPF risk. (D) Saliva microbiota abundance of genus Fusobacterium and IPF risk. (E) COPD and saliva microbiota abundance of species periodonticum.

Figure 3 Forest plots demonstrate genetic associations between saliva microbiota abundance and respiratory disease. Forest plots summarize genetic associations between saliva microbiota abundance and respiratory disease risks, with odds ratios (ORs) and 95% confidence intervals (CIs). Each panel shows associations for different taxa, including individual SNP effects and the overall effect size derived from MR analyses. (A) parvula and COPD risk. (B) Bacilli and IPF risk. (C) Porphyromonas and IPF risk. (D) Fusobacterium and IPF risk. (E) COPD and saliva abundance of periodonticum.

Sensitivity and Directionality Analysis in Forward MR

Sensitivity analysis confirmed the robustness of our findings. No evidence of heterogeneity (Cochran’s Q P = 0.7748 for parvula–COPD, P = 0.7179 for Bacilli–IPF, P = 0.2210 for Porphyromonas–IPF, and P = 0.3145 for Fusobacterium–IPF) or horizontal pleiotropy (MR-Egger intercept P = 0.7327, 0.4638, 0.7139, and 0.4938, respectively) was detected in significant associations (Table S5). Funnel plots showed symmetrical SNP distributions (Figure 4). LOO analysis confirmed that no individual SNP disproportionately influenced the results (Figure 5). MR-PRESSO distortion tests supported the validity of the Bacilli–IPF association (OR = 0.8447, 95% CI: 0.7732–0.9228, P = 0.0334). No significant pleiotropy or distortion effects were detected for the significant associations, indicating that horizontal pleiotropy is unlikely to influence these findings (Table S3). To ensure correct causal direction, SNPs explaining more variance in the outcome than in the exposure were excluded prior to MR (Table S6), and all retained associations passed the Steiger directionality test (Table 2), supporting causality from microbiota to disease.

Table 2 Results of Steiger Directionality Testing for Salivary Microbiota Traits and Respiratory Disease Outcomes

Figure 4 Funnel plots assess horizontal pleiotropy for the association between saliva microbiota abundance and respiratory disease. (A) parvula and COPD risk. (B) Bacilli and IPF risk. (C) Porphyromonas and IPF risk. (D) Fusobacterium and IPF risk. (E) COPD and saliva abundance of periodonticum.

Figure 5 Leave-one-out (LOO) analysis for the association between saliva microbiota abundance and respiratory disease. (A) parvula and COPD risk. (B) Bacilli and IPF risk. (C) Porphyromonas and IPF risk. (D) Fusobacterium and IPF risk. (E) COPD and saliva abundance of periodonticum.

Reverse MR

For reverse MR analysis, genetic instruments were selected from GWAS summary data of COPD and IPF at genome-wide significance (P < 5×10⁻⁸). For COPD, 20 independent SNPs were initially identified. After removing 5 SNPs unavailable in the microbial GWAS datasets (rs538515410, rs60892124, rs9271399, rs28929474, rs141669463) and 2 additional SNPs due to palindromic structure or weak strength (rs1095705, rs6874581), a total of 13 IVs were retained. Similarly, 15 SNPs were selected for IPF, among which three (rs78238620, rs35705950, rs41308092) were not available in the outcome datasets, yielding 12 final IVs. The mean F-statistics were 51.53 (range: 29.89–330.93) for COPD and 115.52 (range: 36.96–927.06) for IPF, confirming sufficient instrument strength. COPD showed a potential positive association with species periodonticum abundance (IVW OR = 1.5446, 95% CI: 1.0170–2.3460, P = 0.041, adjusted P = 0.979) (Table 1). The reverse MR results are provided in Table S7.

Sensitivity analysis revealed heterogeneity (IVW Q statistic P < 0.05) or evidence of directional pleiotropy (MR-Egger P < 0.05) in several associations, including COPD with family Pasteurellaceae (Q P = 0.032), genus Haemophilus (Q P = 0.016), genus Leptotrichia (Q P = 0.023), genus schaalia (Egger P = 0.049), and unknown Schaalia species (ASV0017) (Egger P = 0.048), as well as IPF with unknown Streptococcus species (ASV0003) (Q P = 0.037) (Table S8). MR-PRESSO identified outliers in the associations of COPD with Haemophilus, Leptotrichia, and Pasteurellaceae (Table S9). After outlier removal, these associations remained non-significant (Table S7). LOO identified rs16969968 as an influential SNP in association with Schaalia (genus and ASV0017). Its exclusion eliminated potential pleiotropy and heterogeneity without altering the null results (Table S7). No horizontal pleiotropy or heterogeneity was detected after correction, except for COPD–Haemophilus (P = 0.03) (Table S9).

Discussion

This bidirectional MR study explored potential causal links between salivary microbiota composition and two chronic respiratory diseases, COPD and IPF. In forward MR, higher abundances of species parvula were associated with a lower risk of COPD, while class Bacilli, genus Porphyromonas, and genus Fusobacterium were inversely associated with IPF. These associations showed no evidence of horizontal pleiotropy or heterogeneity, and all passed the Steiger directionality test, suggesting a possible causal effect of specific microbial taxa on respiratory disease risk. In contrast, reverse MR identified an association between COPD and increased abundance of species periodonticum. While no pleiotropy or heterogeneity was detected for this pair, several other reverse MR associations showed sensitivity to instrument outliers, limiting interpretability. Taken together, these findings suggest microbiota-to-disease directionality is more plausible than the reverse, providing preliminary evidence for salivary microbial involvement in the pathogenesis of COPD and IPF.

After multiple testing corrections, the inverse associations of Bacilli, Porphyromonas, and Fusobacterium with IPF did not remain statistically significant (all adjusted P > 0.05), suggesting limited robustness. In contrast, parvula exhibited a significant negative association with COPD risk (P = 0.002; adjusted P = 0.019), indicating a potentially protective role. However, this finding appears to contrast with previous observational and experimental evidence implicating Veillonella parvula in COPD pathogenesis. V. parvula isolated from the saliva of COPD frequent exacerbators has been shown to impair epithelial barrier integrity, increase cytotoxicity, and activate IL-1β/NF-κB signaling in bronchial epithelial cells.³² It has also been identified as a dominant species enriched in the lower airways of COPD patients, positively correlated with neutrophilic inflammation and reduced lung function.³³ As a predominant subgingival species, V. parvula can translocate to the lower respiratory tract via microaspiration of saliva, particularly in individuals with periodontal disease or impaired mucociliary clearance.⁹ This apparent discrepancy may reflect differences in taxonomic resolution, microbial niche, or study design. The MR framework infers lifetime genetic predisposition to salivary parvula abundance, which may not directly reflect the local effects of transient airway colonization or infection. Additionally, salivary parvula may serve as a proxy for broader microbial community features or immune homeostasis that modulate COPD risk. Future studies integrating strain-level metagenomics, mucosal immunity, and longitudinal sampling are needed to clarify the context-dependent role of parvula in respiratory health.

IPF is characterized by dysfunction of alveolar epithelial cells, leading to impaired barrier integrity, persistent inflammation, and a profibrotic microenvironment that drives fibroblast activation and irreversible lung remodeling.³⁴ Emerging evidence suggests that microbial communities may influence these processes. In our analysis, Bacilli, Porphyromonas, and Fusobacterium showed nominal inverse associations with IPF risk, although none remained significant after multiple testing corrections. These taxa are common components of the oral and respiratory microbiota and have been implicated in maintaining mucosal and immune balance and microbial diversity,³⁵ both of which are considered protective against chronic inflammation and epithelial injury in IPF.³⁶ Preclinical studies suggest that members of the class Bacilli, such as Lactobacillus, can enhance epithelial barrier function and modulate immune responses via the production of lactic acid and bacteriocins.³⁷ These metabolites may suppress pro-inflammatory cytokines and reduce tissue injury, potentially mitigating fibrotic progression. For example, Lactobacillus have been shown to stabilize epithelial monolayers and suppress inflammatory signaling in vitro.³⁸ These properties may counteract the inflammation and tissue remodeling observed in fibrosis.

The genus Porphyromonas, commonly associated with periodontal disease, produces short-chain fatty acids such as butyrate,³⁹ which regulate immune responses by increasing regulatory T cells and reducing pro-inflammatory cytokines.⁴⁰ Butyrate has also been shown to inhibit TGF-β1-induced myofibroblast differentiation and enhance mitochondrial function, thereby mitigating fibrotic progression.⁴¹ These mechanisms align with the observed nominal inverse association between Porphyromonas abundance and IPF risk, suggesting a possible protective role in maintaining mucosal homeostasis. Fusobacterium, a facultative anaerobe frequently detected in the lower respiratory tract,⁴² has been linked to altered lung microbiota in IPF.¹⁰ However, our findings suggest a nominal negative association between salivary Fusobacterium and IPF risk. This may indicate that oral Fusobacterium contributes to microbial stability and barrier defense. For example, Fusobacterium nucleatum is known to support biofilm structure and induce antimicrobial peptides and chemokines that modulate host responses.⁴³ These observations raise the question of whether specific oral commensals exert context-dependent effects—protective in the oral niche but potentially pathogenic upon translocation. One hypothesis is that higher oral abundance of these genera may reflect a more balanced or resilient microbiome state, indirectly influencing systemic or mucosal immune tone relevant to lung disease. Collectively, these findings provide preliminary evidence for the potentially protective roles of specific oral taxa in fibrotic lung disease. Given the exploratory nature of our analysis, further mechanistic and longitudinal studies are needed to clarify their functional relevance and therapeutic potential.

This study establishes potential genetically causal links between specific saliva microbiota and COPD and IPF. However, several limitations should be acknowledged. The reliance on GWAS data from predominantly European populations may restrict the generalizability. Due to limited variant availability in current oral microbiota GWAS datasets based on a relatively small sample size (n = 610), we applied a relaxed significance threshold for IV selection, which enabled broader analysis but introduced weak instrument bias. Larger, ancestry-diverse GWAS are needed to improve instrument strength. Although meta-GWAS or pooled datasets could address this, no suitable resources are currently available. Similarly, triangulation using gut or nasal microbiota is constrained by niche-specific microbial differences and the lack of harmonized cross-site data. At present, replication using independent microbiota GWAS or observational cohorts is not feasible due to the limited availability of comparable salivary microbiota datasets with genetic data. Although oral microbiome data exist for East Asian populations, they were not used for replication because of ancestry differences that may introduce bias. This limits the generalizability and reinforces the exploratory nature of the findings. Some microbial traits, such as ASV0012, remain taxonomically ambiguous. However, re-annotation was not feasible due to the absence of full-length 16S sequences, raw FASTQ files, or shotgun metagenomic data in the original dataset. Additionally, the use of salivary taxa as proxies for respiratory exposure warrants further validation. Moreover, the reliance on broad taxonomic categories may obscure the functional roles of individual microbial species. Addressing these limitations through diverse cohorts, expanded GWAS datasets, advanced microbial characterization, and functional assessments will provide deeper insights into the role of saliva microbiota in respiratory diseases.

Conclusion

This study provides exploratory evidence for genetically inferred associations between specific salivary microbiota and the risk of COPD and IPF, offering new insights into potential microbiome–host interactions in chronic respiratory disease. Increased abundance of species parvula was significantly associated with reduced COPD risk, while Bacilli, Porphyromonas, and Fusobacterium showed nominal inverse associations with IPF. Reverse MR provided limited evidence for disease-to-microbiota effects, further supporting a directional influence of oral microbes on disease susceptibility. These findings suggest the potential of salivary microbiota as biomarkers or modulators of chronic lung disease, warranting further validation in diverse populations and functional studies to clarify their mechanistic relevance.

Data Sharing Statement

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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

There is no funding to report.

Disclosure

The authors declare that they have no competing interests.

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A new study links excessive screen time in children and teens to higher risks of high blood pressure, cholesterol, and insulin resistance, especially when sleep is limited

Children and teens who spend long hours on screens may be setting themselves up for serious long-term health risks. New research published in the Journal of the American Heart Association reveals that more time spent on phones, TVs, or gaming devices is linked to elevated markers for heart disease, diabetes, and other metabolic conditions, particularly in those who also get less sleep.

“Limiting discretionary screen time in childhood and adolescence may protect long-term heart and metabolic health,” said study lead author David Horner, M.D., PhD., a researcher at the Copenhagen Prospective Studies on Asthma in Childhood (COPSAC) at the University of Copenhagen in Denmark. “Our study provides evidence that this connection starts early and highlights the importance of having balanced daily routines.”

Extracting data to understand the link between screentime and cardiometabolic risk factors

The researchers used data from a group of ten-year-olds in 2010 and a group of 18-year-olds in 2000 that were part of the Copenhagen Prospective Studies on Asthma in Childhood cohorts, to understand the relationship between excessive screen time and cardiometabolic risk factors. 

The team developed a composite score based on a cluster of metabolic syndrome components, including waist size, blood pressure, high-density lipoprotein or HDL “good” cholesterol, triglycerides, and blood sugar levels, and adjusted for sex and age. The cardiometabolic score reflected a participant’s overall risk relative to the study group average (measured in standard deviations): 0 means average risk, and 1 means one standard deviation above average.

Each extra hour of screentime increased children’s risk

The researchers found that each subsequent hour of screen time increased the cardiometabolic score by around 0.08 standard deviations in the 10-year-olds and 0.13 standard deviations in the 18-year-olds. “This means a child with three extra hours of screen time a day would have roughly a quarter to half a standard deviation higher risk than their peers,” Horner said.

“It’s a small change per hour, but when screen time accumulates to three, five or even six hours a day, as we saw in many adolescents, that adds up,” he said. “Multiply that across a whole population of children, and you’re looking at a meaningful shift in early cardiometabolic risk that could carry into adulthood.”

The analysis revealed that both sleep duration and sleep timing play a crucial role in the relationship between excessive screen time and cardiometabolic risk. Shorter sleep duration and going to sleep later intensified this relationship, with children and adolescents who had less sleep showing a significantly higher risk associated with the same amount of screen time.

“In childhood, sleep duration not only moderated this relationship but also partially explained it: about 12% of the association between screen time and cardiometabolic risk was mediated through shorter sleep duration,” Horner said. “These findings suggest that insufficient sleep may not only magnify the impact of screen time but could be a key pathway linking screen habits to early metabolic changes.”

A machine learning analysis also identified a unique metabolic signature in the blood that appeared to be associated with excessive screen time.

The study was able to identify a set of blood-metabolite changes, or a ‘screentime fingerprint’, which validates the potential biological impact of screen time behavior. Using the same data, the study also assessed whether screen time was linked to predicted cardiovascular risk in adulthood, finding a positive trend in childhood and a significant association in adolescence. This suggests that screen-related metabolic changes may provide early signals of long-term heart health risk.

“Recognizing and discussing screen habits during pediatric appointments could become part of broader lifestyle counseling, much like diet or physical activity,” he said. “These results also open the door to using metabolomic signatures as early objective markers of lifestyle risk.”

“If cutting back on screen time feels difficult, start by moving screentime earlier and focusing on getting into bed earlier and for longer,” said Amanda Marma Perak, an assistant professor of pediatrics and preventive medicine at Northwestern University Feinberg School of Medicine in Chicago.

Adults can also set an example, she said. “All of us use screens, so it’s important to guide kids, teens and young adults to healthy screen use in a way that grows with them. As a parent, you can model healthy screen use – when to put it away, how to use it, how to avoid multitasking. And as kids get a little older, be more explicit, narrating why you put away your devices during dinner or other times together.

“Make sure they know how to entertain and soothe themselves without a screen and can handle being bored! Boredom breeds brilliance and creativity, so don’t be bothered when your kids complain they’re bored. Loneliness and discomfort will happen throughout life, so those are opportunities to support and mentor your kids in healthy ways to respond that don’t involve scrolling.”

Introductions

Nephrolithiasis, or kidney stone disease, is a serious condition that affects approximately 12% of the global population, impacting health-care systems worldwide.^1–3 Symptomatic kidney stone management requires a comprehensive approach that includes both medical and surgical interventions.⁴ Unlike surgical interventions, extracorporeal shock wave lithotripsy (ESWL) is a noninvasive modality and has become the preferred treatment for uncomplicated renal and ureteral calculi, typically less than 20 mm, due to its effectiveness, low morbidity, and high patient preference.^5,6 Nevertheless, stone-free rates (SFRs) after ESWL range from 46% to 91%.⁷ Therefore, obtaining crucial patient information for preoperative surgical planning should be a mandatory step in determining the probability of procedural success and the necessity of ancillary procedures.

Various scoring systems for classifying the complexity of renal stones and nomograms that predict outcomes after renal stone surgery have been developed and externally validated.⁸ However, limitations exist. The Clinical Research Office of the Endourological Society (CROES) nomogram is complex and requires extensive preoperative data, while the S.T.O.N.E. (stone size, tract length, obstruction, number of involved calices, and essence/ stone density) was validated in a small cohort, potentially limiting its generalizability. For ESWL, Tran et al⁹ devised the Triple D scoring system, which is based on CT imaging and takes into account three parameters: stone density, stone volume, and skin-to-stone distance (SSD). Any scoring system must be simple, fast, and accurate to be widely used in daily practice.

The modified Seoul National University Renal Stone Complexity (S-ReSC-R) scoring system is a modified version of the original Seoul National University Renal Stone Complexity system that includes one additional point when the stone located lower calyx is proven to be a precise and rapid assessment in the predictive model for both percutaneous nephrolithotomy and flexible ureteroscopy.^10,11 We hypothesize that the S-ReSC-R is an accurate tool for predicting stone-free rates after ESWL.

We aimed to evaluate the predictive value of the S-ReSC-R scoring system for determining treatment outcomes following ESWL, using stone-free status as the primary endpoint.

Materials and Methods

This was a retrospective observational study approved by the Ethics Committee of Ranong Hospital (no. COA_PHRN013/2564) and conducted in accordance with the principles outlined in the Declaration of Helsinki. Consent for chart review was not required because of the retrospective nature of the study. All data were encrypted and remained confidential.

A total of 332 patients undergoing ESWL for kidney stones at a tertiary hospital between January 2019 and November 2021 were reviewed for inclusion in the study. The inclusion criteria were an age of 18 years or older, previously untreated renal stones greater than 4 mm in size, and available preoperative CT imaging. The exclusion criteria were a stone in a solitary kidney, uncorrectable bleeding disorders, active urinary tract infection, urinary tract obstruction or abnormalities, and pregnancy. Twenty-eight patients were excluded because of insufficient imaging, and seven were excluded due to incomplete clinical data. Thus, 297 patients were included in the analysis.

The data collected included medical history, physical examination, complete blood count, serum biochemical profile, urinalysis, and coagulation profile. Non-contrast CT was used preoperatively to determine the stone characteristics. The largest dimension of a stone visualized on a soft tissue window in a coronal view represented the stone’s size. Stone Hounsfield units (HU) were calculated by taking the mean attenuation of three consistent, nonoverlapping regions of interest chosen from stones in bone windows. SSD measurements were performed using axial CT as the average distance from the skin to the surface of the targeted stone at 0°, 45°, and 90° angles.

S-ReSC-R scores were calculated using the method described by Jung et al.⁹ The S-ReSC-R score is calculated by summing the points assigned to specific stone locations: renal pelvis (#1); superior and inferior major calyceal groups (#2–#3); and anterior and posterior minor calyceal groups of the superior (#4–#5), middle (#6–#7), and inferior calyx (#8–#9). If a stone is situated in inferior locations (#3, #8–#9), one extra point is added for each site. Thus, the total S-ReSC-R score ranges from 1 to 12 points. A higher score indicates greater stone complexity and may be associated with less favorable procedural outcomes. Based on these scores, the patients were divided into a low score (1–2 points) group, an intermediate score (3–4 points) group, and a high score (5–12 points) group.

Triple D scores were calculated based on the formula devised by Tran et al.⁸ Values were calculated for each of the three stone parameters (stone volume, stone density, and SSD). One point was assigned for any parameter that was below the cutoff value (150 μ L for stone volume, 600 HU for stone density, and 12 cm for SSD). Thus, the Triple D score ranged from 0 (worst) to 3 (best).

Before ESWL, urine examinations and cultures were evaluated. If the results indicated urinary tract infection, an antibiotic was prescribed, and the procedure was postponed. Following the standard protocol, all patients underwent ESWL as outpatients in the supine position without anesthesia or sedation. A Siemens Modularis Vario Lithostar electromagnetic lithotripter was used in the procedures. Stone localization and real-time tracking during the procedure were performed using both ultrasound and fluoroscopy. The frequency applied was 60–90 shock waves/min. The total number of shock waves applied in one session ranged from 3000 to 5000, or the session was stopped when significant stone fragmentation was detected. The patients were advised to increase fluid intake and to take analgesics consistently after the procedure. A follow-up X-ray KUB and ultrasonography examination was scheduled four to six weeks later to assess residual fragments. Treatment success was defined as complete stone clearance without any residual fragments.

Statistical Analysis

Statistical analysis was performed using IBM SPSS Statistics for MacOS, version 25.0 (IBM Corp., Armonk, NY, USA). Descriptive variables were represented as numbers and percentages. Associations between continuous variables, such as stone size, HU, SSD, and BMI, were assessed using the Student’s t-test. Categorical variables, such as gender, stone laterality, and stone location, were compared using the chi-square test. Values of p < 0.05 were considered statistically significant. Univariate and multivariate logistic regression analyses were used to identify significant predictors of stone-free status. Receiver operator characteristic (ROC) curves were drawn to assess the predictive ability of the S-ReSC-R and Triple D scoring systems.

Results

Among the 297 patients, 156 were male, and 141 were female. The procedure was successful in 62.3% (185/297) of the patients and unsuccessful in 37.7% (112/297). In the univariate analysis, there were no significant differences between the two groups in terms of age, gender, stone laterality, stone density, or SSD. Conversely, statistically significant differences were observed in stone size, presence of a lower pole stone, and mean S-ReSC-R score (all p < 0.001; Table 1).

Table 1 Baseline Demographic and Clinical Characteristics of the Study Cohort

The SFRs were significantly lower with higher S-ReSC-R scores (p < 0.001). The low score (1–2) group had an SFR of 72.4% (165/228), the intermediate score (3–4) group had an SFR of 36.0% (18/50), while the high score (5–12) group had an SFR of 10.5% (2/19). The SFRs determined by the individual S-ReSC-R scores differed significantly (p < 0.001) from those obtained using the three-tier S-ReSC-R classification (Table 2).

Table 2 Stone-Free Rates Following ESWL by Individual S-ReSC-R Scores and Low, Intermediate, and High Score Groups

As shown in Table 3, multivariate logistic stepwise regression revealed significant inverse associations between the SFR and both the S-ReSC-R score (p < 0.001) and the Triple D score (p < 0.001). The ROC curves drawn to assess the accuracies of the S-ReSC-R and Triple D scoring systems in predicting stone-free status are shown in Figures 1 and 2, respectively. While both scoring systems exhibited high predictive accuracy, the S-ReSC-R system had a higher area under the curve (AUC) than the Triple D system (0.767 vs 0.694).

Table 3 Multivariate Analysis of Stone-Free Status Predictors

Figure 1 ROC curve of the S-ReSC-R score. Abbreviations: ROC, receiver operator characteristic; S-ReSC-R, modified Seoul National University Renal Stone Complexity.

Figure 2 ROC curve of the Triple D score. Abbreviation: ROC, receiver operator characteristic.

Discussion

The use of ESWL in the management of urolithiasis marks a significant milestone in urology. Since its introduction in 1980, it has been established as the primary modality for renal calculus management.¹² However, SFRs after ESWL vary widely, depending on several factors. To obtain the best results, ensure safety, and reduce re-treatment rates, a combination of technological advancements, improved procedural techniques, and proper patient selection is essential. Advanced lithotripters equipped with expanded focal zones and automatic image-based renal stone tracking mechanisms enhance the accuracy of shock wave delivery to the target while reducing potential harm to adjacent renal parenchymal tissue.^13,14 Notably, lowering the shock wave rate has proven beneficial for fragmenting stones, especially those exceeding 1 cm in size.¹⁵ Li et al¹⁶ showed that ESWL at a rate of 60–90 shock waves/min outperformed a rate of 120 shock waves/min in terms of fragmentation. A gradual increase in the device’s energy voltage, termed “ramping”, not only reduces the risk of tissue injury but also augments stone fragmentation.¹⁷

Proper patient selection is also crucial for the success of ESWL. The key selection factors are stone size, stone location, SSD, and Hounsfield units. A larger stone size is associated with a higher risk of ESWL failure. In a study of 2954 patients with single or multiple radiopaque renal stones undergoing ESWL monotherapy, Abdel-Khalek et al¹⁸ reported success rates of 89.7% for stones of <15 mm and 78% for stones of >15 mm (p < 0.001). Similarly, in a study of 427 patients with renal stones, Al-Ansari et al¹⁹ reported success rates of 90% for stones of ≤10 mm and 70% for stones of >10 mm (p < 0.05). Our multivariate analysis confirms that stone size is an independent predictor of ESWL success.

The location of kidney stones, particularly those situated in the lower pole, also plays a crucial role in the success of ESWL. Lower pole stones present a distinct challenge due to their relatively low clearance rates, which range from 47% to 75%.²⁰ Tarawneh et al²¹ reported an ESWL success rate for lower calyceal stones of only 47%, which was significantly lower than that for stones in other locations (79%; p = 0.012). Similarly, Samir et al²² found that a lower calyceal location was an independent predictor of ESWL success (p = 0.017). In line with these results, a statistically significant difference in SFRs between lower pole and non–lower pole stones was observed in our study (p < 0.001), confirming the significant influence of a lower pole location on ESWL outcomes. We believe that scoring systems in which stone location plays an important role, such as the S-ReSC-R system, are suitable for predicting tone-free status after ESWL.

ESWL failure has also been associated with a greater SSD, especially in Western populations. Pareek et al²³ found that an SSD of up to 10 cm was a powerful predictor of stone-free status. This is because with greater SSDs, shock waves travel longer distances, which results in shock wave attenuation. However, studies involving Asian populations have reported contradictory results. Because Asian populations have thinner bodies than Western populations, it has been argued that SSD cannot be applied to Asian patients.²⁴ Our results are in line with those of other studies, without significant differences in both groups. Thus, the SSD may not be universally applicable as a predictor of stone-free status following ESWL.

Stone fragility is determined by the stone’s composition and mineral content. Several studies have examined the relationship between stone density on radiological imaging and stone composition, suggesting that stone density can predict stone characteristics.^25,26 In our study, the mean stone density did not differ significantly between the successful and unsuccessful ESWL groups. However, 71.1% of the patients with a stone density below 500 HU had favorable treatment outcomes, compared to only 47.4% of those with a stone density above 500 HU. Different studies have proposed varying stone density thresholds. Gupta et al²⁷ reported optimal ESWL outcomes with a mean stone density of ≤750 HU. Similarly, a prospective study of 50 patients with urinary stones reported that a threshold of 970 HU had high specificity and sensitivity in predicting ESWL success.²⁸

Cutting-edge technologies such as artificial intelligence and virtual reality are increasingly becoming integral to a range of surgical procedures and assist in predictive analyses of various interventions.^29–31 Despite these advancements, numerous regions around the world still lack internet access, highlighting disparities in access to technology. Traditional tools, such as scoring systems and nomograms, have been developed using multiple clinical variables to assess procedural complexity and predict outcomes. However, nomograms have not been widely adopted in clinical practice due to their complexity and lack of practicality. Clinical scoring systems are generally easy to use and require minimal training and no specialized software, which has made them more popular in routine practice. Tran et al⁹ introduced the Triple D scoring system, a straightforward system based on CT imaging that evaluates three stone-related parameters. However, it does not fully account for stone location, particularly a lower pole location, an important variable considered in other scoring systems, such as the S-ReSC-R system.

In our cohort of 297 patients undergoing ESWL, we found that the S-ReSC-R score was significantly associated with stone-free status in both univariate and multivariate logistic regression analyses (p < 0.001). The score demonstrated better discriminative performance than the Triple D score, with an AUC of 0.767 compared to 0.694. This finding supports the clinical utility of incorporating stone location, as accounted for in the S-ReSC-R system, when predicting ESWL outcomes. As a practical and efficient tool, the S-ReSC-R scoring system has been applied to various surgical treatments, including flexible ureteroscopy and percutaneous nephrolithotomy. Therefore, its application to ESWL is reasonable.

Our research has certain limitations. First, the sample size was small. Moreover, because of the study’s retrospective nature, patients with missing CT scans were excluded, as without CT images, stone attenuation and SSD could not be measured. Second, although metabolic evaluation is an important parameter for assessing stone disease outcomes, such data were not included in this study. However, to the best of our knowledge, this is the first study to validate the S-ReSC-R score for the ESWL procedure. We believe that our findings provide a strong foundation for future research on the clinical applications of this system.

Conclusion

Our study demonstrates that the S-ReSC-R score is a reliable predictor of ESWL outcomes. Thus, the S-ReSC-R scoring system is a valuable tool for clinical planning in patients undergoing ESWL, offering both simplicity in calculation and proven effectiveness.

Acknowledgments

We would like to thank everyone involved in this study, as well as the Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, for its support in the statistical analysis.

Disclosure

The authors declare that they have no competing interests in this work.

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Europe is experiencing increasing prevalence of arbovirus diseases — those transmitted by arthropod vectors such as mosquitoes, ticks, or sand flies. These diseases, including dengue, chikungunya, and Zika, have traditionally been endemic to the tropical and subtropical regions of South America, the Caribbean, Africa, and Asia. Their frequency and scale have increased globally in the past two decades, and the geographic range of transmission has expanded into areas previously unaffected, including non-endemic regions in Europe.

While most cases are imported, mosquito species and ticks are establishing themselves further northwards and westwards in Europe. According to the European Centre for Disease Prevention and Control (ECDC), between 2021 and 2024, the number of autochthonous (locally acquired) dengue outbreaks and cases increased considerably, and chikungunya and Zika infections have also now been reported in Mediterranean regions.

Arbovirus Diseases Becoming More Common

Tamás Bakonyi, ECDC principal expert on vector-borne and zoonotic diseases, told Medscape Medical News that arbovirus diseases have become more common in Europe due to a combination of environmental, biological, and societal factors.

Climate conditions can change the environment in which infectious diseases thrive, contributing to their increase and impact, he said. Rising temperatures, milder winters, more frequent extreme weather events, and changing rainfall patterns may create more favorable conditions for the transmission of several vector-, food-, and waterborne diseases.

“Vector-borne diseases like dengue, chikungunya virus disease, West Nile virus infections, Lyme borreliosis, and tick-borne encephalitis are particularly sensitive to changes in temperature, humidity, and rainfall,” he explained. Warmer temperatures increase mosquito and tick survival and shorten pathogen incubation times, which in turn accelerates disease transmission cycles.

Most Cases Imported

International travel has led to the importation of arboviruses from endemic regions to Europe, Bakonyi said. This underlies most reported arbovirus infections in Europe but may spark local outbreaks. The ECDC collects data on imported cases on an annual basis. Its latest interactive surveillance atlas, for the year 2023, showed across the EU:

• Dengue: 5027 travel-associated cases vs 129 locally acquired cases.
• Chikungunya: 320 imported cases including in Spain (191), Germany (44), and France (39), with no reported locally acquired cases.
• Zika virus infection: 79 travel-associated cases with none locally acquired.

In addition, the ECDC collects seasonal data over the summer on locally acquired cases of various arboviral diseases. Its latest communicable disease threats surveillance for the week ending August 01, 2025, showed:

• Locally acquired dengue in three European countries in 2025 so far: France (6, including 2 new cases since the previous week), Italy (3), and Portugal (2 in Madeira).
• Locally acquired chikungunya in France (49) and Italy (2) so far during 2025.
• Up to 30 July, Crimean-Congo hemorrhagic fever cases were reported in Greece (2) and Spain (2).
• Up to 30 July, human cases of West Nile virus infection were reported in Bulgaria, France, Greece, Italy, and Romania.

Be Alert for Symptoms

Clinicians across Europe this summer should be on the alert for symptoms of arbovirus infections, both mosquito-borne and tick-borne diseases, Bakonyi advised.

Dengue — Most cases are imported by travelers returning to the EU from endemic areas, but these may generate local, mosquito-borne transmission in areas with competent vectors and supportive climatic/weather conditions. Transmission is primarily by Aedes aegypti globally and Aedes albopictus in Europe, where the species is increasingly frequent. Symptoms include an acute, high fever, occasionally progressing to hemorrhagic fever, with headache, myalgia, arthralgia, and a maculopapular rash. Up to 5% of cases can be severe, with increased vascular permeability that can lead to life-threatening hypovolemic shock.

Chikungunya — A notifiable disease at the EU level. Around a third of cases are asymptomatic; the remainder are characterized by sudden onset fever, chills, headache, myalgia, nausea, photophobia, incapacitating joint pain, and petechial or maculopapular rash. Recovery may take months but typically occurs within 10 days and gives lifelong immunity.

Zika — Generally asymptomatic, but may cause mild fever, arthralgia, and fatigue, with a maculopapular rash, conjunctivitis, myalgia, and headache. It is usually short (2-7 days) and self-limiting, but infection during pregnancy may lead to congenital central nervous system malformations such as microcephaly, with a raised risk for fetal loss.

West Nile fever — About 80% of infections are asymptomatic but may cause fever, headache, malaise, myalgia, fatigue, and eye pain, sometimes with a rash. Some 1%-10% of cases may be severe, especially among older people. Most cases in humans occur between July and September, when mosquitoes are active.

Diagnosis More Difficult

Diagnosis should be on the basis of clinical presentation and epidemiologic context, as well as laboratory tests, which vary by disease, Bakonyi said. Testing has become more difficult recently due to the global expansion of arboviruses, leading to antibodies that cross-react on serological assays.

As for treatment, Bakonyi recommended referring to the World Health Organization (WHO), which issued its first global arbovirus guidelines in July. These also point to the difficulty in distinguishing between arboviral infections because early symptoms often overlap.

Treatment Largely Symptomatic

Treatment is largely symptomatic in mild infections. With suspected or confirmed nonsevere dengue, chikungunya, Zika, or yellow fever, the WHO recommends oral rehydration, with paracetamol or dipyrone for managing pain and fever. Corticosteroids are not recommended in nonsevere infections, and nonsteroidal anti-inflammatory drugs should be avoided in all cases.

For hospitalized patients with suspected or confirmed severe arboviral disease, the WHO recommends:

• Prefer crystalloid fluids over colloids for intravenous (IV) rehydration.
• Use capillary refill time and serum lactate levels to guide decisions on IV fluid management, with a passive leg raise test for patients in shock.
• Avoid systemic corticosteroids and immunoglobulins.
• Avoid prophylactic platelet transfusions in patients with platelet counts < 50,000/μL unless there is active bleeding.

Sheena Meredith is an established medical writer, editor, and consultant in healthcare communications, with extensive experience writing for medical professionals and the general public. She is qualified in medicine and in law and medical ethics.

Researchers using patient-derived stem cells reveal a rare ALS mutation triggers a chronic stress response in motor neurons, blocking it reverses damage in lab models, paving the way for new treatment strategies

A breakthrough study using patient-derived stem cells has identified how a rare ALS-causing mutation disrupts cellular communication, activating a chronic protective mechanism that ultimately damages motor neurons. Crucially, researchers were able to reverse this damage in the lab by inhibiting the stress response, suggesting a promising new therapeutic pathway for ALS treatment. This discovery opens up a new horizon of hope for potential ALS treatments.

The findings are detailed in the peer-reviewed journal EMBO Molecular Medicine.

Providing vital clues for treating ALS

A new study by Case Western Reserve University researchers used stem cells created from ALS patients to target a specific gene as a kind of shut-off valve for a stress that affects nerve cells, and this was successful. This success provides a solid foundation for further research and potential treatments.

Whilst the research involved a rare type of ALS, the scientists are hopeful the results could provide insight into potentially treating the condition more widely. This potential for broader ALS therapies is a promising step forward in the field of ALS research.

“This work could help lay the foundation for genetically informed clinical trials,” said lead researcher Helen Cristina Miranda, an associate professor of genetics and genome sciences at Case Western Reserve’s School of Medicine.

Blocking the stress response reversed damage

The researchers studied an inherited type of ALS caused by a mutation in a gene (vesicle-associated membrane protein B, or VAPB). The VAPB gene provides instructions for making a protein that helps link different parts of the cell so they can communicate and respond to stress.

“This is especially important in nerve cells,” Miranda said. “When they break down, the neurons become more vulnerable to degeneration.”

iPSCs, or induced pluripotent stem cells, are special cells created in the lab from a person’s skin or blood that can be turned into almost any cell type in the body. In this study, the researchers used iPSCs from ALS patients to grow their motor neurons in a dish, allowing them to study the disease using real human cells.

The researchers found a mutation in the VAPB gene that disrupted communication between key parts of the cell, specifically between the endoplasmic reticulum (ER) and mitochondria. The ER acts like a quality control centre and helps produce and fold proteins, ensuring everything within the cells runs smoothly. Mitochondria generate the energy that cells, especially nerve cells, need to stay alive.  This disruption leads to chronic activation of a mechanism called the Integrated Stress Response (ISR).

Although it was helpful, sustained ISR activation reduces protein production and impairs cell survival, leading to damaged motor neurons and contributing to this rare form of ALS. The researchers were able to identify the ISR as a potential therapeutic target.

“We also showed that blocking this stress response can reverse damage in the lab, a promising step toward future treatments,” she said. “That’s a promising proof-of-concept for future therapeutic strategies.”

The team’s study focused on a particular rare type of ALS, but they hope to expand the research to test whether the target might work on other forms of the disorder.

“It’s very rare, more prevalent in Brazil, but studying it gives us a window into how ALS motor neurons respond to stress,” Miranda said. “We are now testing ISR inhibitors in more complex neuromuscular models and exploring how this approach might benefit other ALS subtypes.”

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A new study led by Liverpool School of Tropical Medicine shows that mosquitoes are killed when they feed on blood and then land on a surface sprayed with nitisinone, a drug currently used to treat a rare genetic condition in humans.

The research shows that this is true even when the mosquitoes are highly resistant to existing insecticides, which opens a promising new avenue for formulating nitisinone for indoor spraying or bed nets at a time when resistance poses a significant threat to vector control programs worldwide.

Nitisinone is lethal to mosquitoes as it blocks an enzyme that they need to safely process the protein and amino acids they get from blood. 

The new paper, published in Parasites & Vectors, follows on from a study earlier this year that showed that nitisinone is deadly to mosquitoes when they drink the blood from someone on nitisinone therapy. The drug is safe and already approved for widespread human use and is currently the only treatment for the rare genetic disorders tyrosinemia type 1 and alkaptonuria.

In this new study, nitisinone was shown to be mosquitocidal to several mosquito species (Anopheles, Aedes and Culex), including those that transmit malaria, reemerging infections such as dengue and Zika, and emerging viral threats like Oropouche and Usutu viruses.

The research also proved that nitisinone killed mosquitoes regardless of whether exposure occurred before or after a blood meal, tapping into mosquito resting behaviour before or after feeding.  Because nitisinone works by disrupting the mosquito’s bloodmeal digestion (tyrosine metabolism), which is not a pathway targeted by any of our current insecticides, it could help public health campaigns eliminate mosquitoes where insecticide resistance has made other products fail.

Dr Lee Haines, senior author and Honorary Research Fellow at LSTM said: “Our conclusions are exciting.  Working with a drug like nitisinone, and its versatility, bodes well for creating new products to combat mosquitoes.  The fact that it effectively kills insecticide-resistant mosquitoes could be a game-changer in areas where resistance to current insecticides is causing public health interventions to fail.

“This project proved how important it is to think outside the box.  We don’t know yet why nitisinone is absorbed through the mosquito’s feet, and why the other similar compounds are not.  But it is going to be exciting to solve this mystery!”

Zachary Stavrou-Dowd, Research Assistant and PhD student at LSTM, and lead author on the new paper, said: “Nitisinone acts to clog up the mosquito digestive system. When a mosquito gorges on your arm, that blood contains a massive protein load. What we have shown here is that we can turn that key trait against them. The mosquito can’t digest the blood; it becomes overloaded by its own meal; it dies.”

The research was part funded by the Jean Clayton Fund for early career researchers at LSTM.

Zachary Stavrou-Dowd said: “Receiving the Jean Clayton Early Career Researcher Award at LSTM was a pivotal moment, it not only helped fund part of this study but also strengthened our application for a Rosetrees Seedcorn Grant. It’s a great example of how internal support can catalyse external funding opportunities and boost visibility for early career researchers like me.”

Reference: Stavrou-Dowd ZT, Parsons G, Rose C, et al. The β-triketone, nitisinone, kills insecticide-resistant mosquitoes through cuticular uptake. Parasites Vectors. 2025;18(1):316. doi: 10.1186/s13071-025-06939-0

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