Tag: statistics

Drug Chem Toxicol. 2026 Jul 9:1-10. doi: 10.1080/01480545.2026.2638309. Online ahead of print.

ABSTRACT

Gestational diabetes mellitus (GDM) is a growing health problem causing a higher risk of delivering large infants and experiencing maternal and neonatal mortality. Studies have found a link between increased levels of mercury (Hg) and lead (Pb) in the blood and a higher risk of developing GDM. However, there are other reports contradicting the aforementioned findings. In this meta-analysis, we investigated the association between exposure to Hg and Pb and the elevated risk of being diagnosed with GDM. To that end, a systematic search was performed through Google Scholar, Web of Science, PubMed, and Scopus, from inception to May 01, 2024, to gather relevant studies. The association between maternal Hg and Pb exposure and the potential risks of GDM was evaluated through the use of pooled odds ratios (OR) and the corresponding 95% confidence intervals (CI). To make the calculations, the fixed-effects or random-effects models were also applied. Overall, 8 eligible studies met the inclusion criteria and were included in our meta-analysis. The results of our meta-analysis revealed that maternal Hg exposure increases the risk of GDM 1.27 times, which is statistically significant (OR = 1.27, 95% CI =1.10-1.46). Conversely, no significant association was observed between Pb levels and GDM. In conclusion, results showed that Hg exposure, unlike Pb, markedly gave rise to higher risks of GDM.

PMID:42426582 | DOI:10.1080/01480545.2026.2638309

J Chem Inf Model. 2026 Jul 9. doi: 10.1021/acs.jcim.6c00143. Online ahead of print.

ABSTRACT

The elastic properties of biological membranes can be described by mechanical constants like the bending modulus at flexure (k_c) and the area compressibility modulus (K_A), which quantify the energy cost associated with bending, compression, and stretching of the membrane area. These properties provide a means to describe phenomena such as the shape variation of a vesicle in response to pressure or the strain energy of a membrane influenced by lipid composition, interactions with ions, small molecules, or biomolecules. The determination of elastic moduli provides a quantitative basis for describing deformation-related processes in cellular systems at the molecular and mesoscopic levels. However, measurements of elastic constants, such as the bending modulus, exhibit a wide dispersion of reported values, ranging from approximately 10 k_BT (4 × 10^-20 J) to 100 k_BT (4 × 10^-19 J) for liquid disordered phospholipid membranes. Computational protocols for estimating elastic constants of lipid membranes commonly rely on Fourier analysis of the membrane surface, where the upper integration limit is determined by lipid molecular dimensions, making results sensitive to lipid composition and complicating cross-system comparisons. We have implemented the s_comp tool in the SuAVE software to estimate area compressibility from the direct integration of membrane surface areas, circumventing the dependence on wavevector integration limits that arises when elastic constants are extracted from Fourier mode amplitude spectra. The calculation does not require prior knowledge of lipid molecular dimensions and is therefore applicable to membranes of arbitrary composition and morphology. The calculated elastic constants are in reasonable agreement with values reported in the literature, falling within the variability observed across computational protocols and experimental techniques. As with any fluctuation-derived property, fully converged trajectories and appropriate statistical sampling are prerequisites for reliable estimates. The SuAVE software is freely available from https://github.com/SuAVE-Software.

PMID:42426567 | DOI:10.1021/acs.jcim.6c00143

Diabetes Obes Metab. 2026 Jul 9. doi: 10.1111/dom.71094. Online ahead of print.

ABSTRACT

BACKGROUND: The effects of individual sodium-glucose cotransporter-2 inhibitors (SGLT2 inhibitors) and glucagon-like peptide-1 receptor agonists (GLP-1 receptor agonists) on ventricular arrhythmias (VAs) remain uncertain. This study aimed to comprehensively compare their effects on VAs and cardiovascular outcomes in patients with type 2 diabetes mellitus (T2DM) and/or heart failure (HF).

METHODS: Four databases were systematically searched from inception through May 16, 2026, to identify randomised controlled trials. Nine outcomes were evaluated, including VAs, cardiovascular mortality, all-cause mortality and hospitalization for heart failure (HHF).

RESULTS: Thirty-seven publications, corresponding to 32 independent RCTs and 140 156 participants, were included. Most SGLT2 inhibitors and GLP-1 receptor agonists did not significantly increase VA risk; empagliflozin showed a statistically significant but exploratory signal for lower VA risk in the T2DM network (OR 0.31, 95% CI 0.11-0.86). Dapagliflozin in the HF network and empagliflozin and liraglutide in the T2DM network, were associated with lower cardiovascular and all-cause mortality. SGLT2 inhibitors consistently reduced HHF across both networks. Dapagliflozin was associated with lower AKI risk, while albiglutide and liraglutide were associated with lower hypoglycemia risk; however, these safety findings should be interpreted cautiously because adverse-event reporting was not uniform across trials. Several safety outcomes were based on sparse events and non-uniform adverse-event reporting; no statistically significant increase in diabetic ketoacidosis risk was detected for empagliflozin and the semaglutide fracture signal should be interpreted cautiously. Because the evidence networks were largely placebo-centered and lacked closed loops, treatment rankings and between-drug comparisons depend heavily on the transitivity assumption. These rankings, including P-score rankings, should be regarded as exploratory and should not be interpreted as head-to-head comparative evidence.

CONCLUSIONS: SGLT2 inhibitors consistently reduced HHF risk across the HF and T2DM networks and selected agents showed mortality benefits in clinically relevant populations. Empagliflozin showed an exploratory signal for lower VA risk in the T2DM network; however, this finding requires confirmation in trials with prespecified and adjudicated arrhythmia endpoints. Safety signals, including DKA and fracture, should be interpreted cautiously because adverse-event ascertainment was not uniform across trials.

PMID:42426564 | DOI:10.1111/dom.71094

Oncologist. 2026 Jul 9:oyag224. doi: 10.1093/oncolo/oyag224. Online ahead of print.

ABSTRACT

BACKGROUND: HER2 amplification identifies a subgroup of colorectal cancer with poorer prognosis and resistance to anti-EGFR therapy. We conducted a systematic review and a metaanalysis of available data on anti-HER2 treatments (HER2Tx) in mCRC patients (pts).

METHODS: A systematic literature search was performed, encompassing phase II/III clinical trials (CTs) investigating HER2Tx in HER2-overexpressed mCRC. CTs reporting HER2Tx plus chemotherapy were excluded. Primary endpoints were objective response rate (ORR) and disease control rate (DCR). Fixed and random-effect models were applied according to heterogeneity assessed through I 2 statistics. Progression free survival (PFS) and overall survival (OS) were compared descriptively and pooled using the weighted median of medians (WM) with approximated 95% CIs. Subgroup analyses by HER2Tx were carried out.

RESULTS: The analysis included 10 CTs evaluating Trastuzumab-Pertuzumab (T + P, 5 CTs,), Trastuzumab Deruxtecan (T-DXd, 2 CTs), Trastuzumab-Lapatinib (T + L, 1 CT), Pertuzumab-TDM1 (P+TDM1, 1 CT), and Trastuzumab-Tucatinib (T-Tu, 1 CT), for a total of 467 pts. The pooled ORR was 33.7% (29.6%-38.1%), and the pooled DCR was 68.5% (58.1%-77.4%). The WM OS was 13.4 months (10-24.1) and WM PFS was 5.5 months (4.1-6.9). The T-DX and T-Tu groups showed higher ORR and DCR (38% and 39% respectively, 85.1% and 73.2% respectively) compared to the T + P group (ORR: 30%, DCR: 52.6%).

CONCLUSIONS: HER2Tx demonstrated efficacy in pretreated CRC pts, exhibiting good DCR and ORR alongside promising PFS and OS. T-DXd and T-Tu appears to outperform T + P; however, further studies are needed.

PMID:42426557 | DOI:10.1093/oncolo/oyag224

Eur Heart J Qual Care Clin Outcomes. 2026 Jul 10:qcag108. doi: 10.1093/ehjqcco/qcag108. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess the impact of Continuity of Care (COC)-care provided by the same Primary Care Physician (PCP)-on outcomes in patients with atrial fibrillation (AF).

METHODS: We conducted a retrospective cohort study including all patients with AF referred by PCPs (n=17,889) between January 2010 and December 2023 in the Santiago de Compostela healthcare area (Spain). COC was categorized as “PCP stability” (care by the assigned PCP) or “interrupted COC” (care by multiple rotating PCPs). The association between COC and outcomes (hospitalization, mortality, stroke, and haemorrhage) was estimated using Cox regression and Fine-Gray competing risk models, adjusted for potential confounders.

RESULTS: Patients with PCP stability had a significantly lower annual referral rate (1.5 vs. 1.8, p<0.001) and a higher rate of adequate Oral Anticoagulation (OAC) indication according to the CHA2DS2-VASc score (79.5% vs. 69.1%, p<0.001). COC was independently associated with reduced all-cause mortality (Hazard Ratio [95% CI]: 0.79 [0.69-0.91]), a benefit that remained robust after sensitivity analyses. No significant differences were observed in stroke or haemorrhagic complications; however, competing risk analysis suggests that the higher mortality rate in the interrupted COC group likely precluded the observation of non-fatal events in this high-risk subset.

CONCLUSIONS: Longitudinal COC in Primary Care is associated with improved clinical management, including better OAC indication, and a significant reduction in all-cause mortality among patients with AF. The survival benefit of seeing the same physician appears to extend beyond anticoagulation optimization, highlighting the pleiotropic value of the patient-physician relationship.

PMID:42426552 | DOI:10.1093/ehjqcco/qcag108

Metabolomics. 2026 Jul 9;22(4):123. doi: 10.1007/s11306-026-02504-7.

ABSTRACT

INTRODUCTION: Saliva biomarker research requires understanding of how saliva collection and processing techniques affect results and reproducibility. Direct comparison of the effect of processing and storage conditions on downstream assay outcomes is important to understanding the ways that delay in freezing, methods of freezing, filtration protocols, and other factors can affect results and thus, interpretations about stress response and adaptation. This type of study compliments the work that is required to understand how saliva compares to tissue and blood responses and what the implications of changes in saliva biomarkers mean to our understanding of biomarkers of stress during exercise and environmental stress exposures.

OBJECTIVE: In this pilot study, we evaluated the effects of different processing methods on metabolomics results and discuss the implications of these findings for future experimental design.

METHODS: We assessed the effects of centrifugation, time-of-day collection, filtration, and mucinase treatments and determined that there are quantifiable differences in metabolomics results with different treatments.

RESULTS: Adding processing steps did not increase the number of metabolites detected or the sensitivity and specificity of results.

CONCLUSION: Comprehensive descriptions of methods in this area will support better interpretation and reproducibility in this field.

PMID:42426550 | DOI:10.1007/s11306-026-02504-7

Acta Neurol Belg. 2026 Jul 10. doi: 10.1007/s13760-026-03134-5. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: The predictive value of the atherogenic index of plasma (AIP) and high-sensitivity C-reactive protein (hsCRP) for stroke is established; however, evidence is largely derived from diabetic cohorts, limiting the generalizability of findings to non-diabetic populations. We therefore conducted this study to specifically assess their combined and interactive associations with stroke in individuals without diabetes.

METHODS: This study included 8,721 participants from the CHARLS baseline (wave1) with no history of stroke or diabetes at baseline. The AIP was calculated as lg[Triglycerides (mmol/L)/HDL-C(mmol/L)]. In a subset of 5,763 participants with repeated measurements, we further analyzed the associations of CumAIP and CumhsCRP with incident stroke.

RESULTS: The results showed that compared to individuals with both low AIP and low hsCRP, those with elevated levels of both had the highest overall risk of stroke (adjusted Hazard Ratio [aHR]: 1.715; 95% Confidence Interval (CI): 1.380-2.130). Compared with the traditional risk factor model, the model adding AIP and hsCRP improved the AUC from 0.664 to 0.673 (P < 0.05), with a continuous Net Reclassification Improvement (NRI) of 0.182 (95% CI: 0.075-0.289) and Integrated Discrimination Improvement (IDI) of 0.009 (95% CI: 0.003-0.015).A total of 435 stroke events were observed during the 5-year follow-up subanalysis, individuals with high levels of both cumulative exposures also had a significantly increased risk (aHR: 1.421; 95% CI: 1.133-1.783).Furthermore, using repeated measurements, mediation analyses demonstrated no statistically significant mediating effect of hsCRP in the association between AIP and stroke. In contrast, AIP exerted a significant mediating effect, accounting for 11.6% of the total effect of hsCRP on stroke.

CONCLUSIONS: The study findings confirm that AIP and hsCRP exert combined effects on stroke risk among non-diabetic middle-aged and older adults. Notably, AIP plays a significant mediating role in the association between elevated hsCRP and stroke. Therefore, integrating both markers into risk assessment is recommended to refine primary stroke prevention and address residual risk among non-diabetic populations, particularly middle-aged adults.

PMID:42426492 | DOI:10.1007/s13760-026-03134-5

Aesthetic Plast Surg. 2026 Jul 9. doi: 10.1007/s00266-026-06144-z. Online ahead of print.

NO ABSTRACT

PMID:42426478 | DOI:10.1007/s00266-026-06144-z

Methods Mol Biol. 2026;2999:183-201. doi: 10.1007/978-1-0716-5050-9_15.

ABSTRACT

The purpose of this chapter is to provide a comprehensive, step-by-step methodology for the analysis of circulating tumor cell (CTC) interactions within the blood microenvironment, from the acquisition of CTCs to single-cell transcriptomic sequencing and subsequent data analysis. Our methodological approach involved the following steps: (1) Data Preprocessing: The raw sequencing data were subjected to stringent quality control and preprocessing to remove artifacts and outliers that could potentially skew the analysis. (2) Seurat analysis: Utilizing the Seurat package, we performed dimensionality reduction techniques such as principal component analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to visualize and cluster CTCs based on their gene expression profiles. (3) CellphoneDB integration: We employed CellphoneDB to analyze the interactions between CTCs and other cell types within the blood, elucidating potential interaction pairs that could be targeted for therapeutic intervention. (4) Statistical graphics: The ggplot2 package was used to create informative and visually appealing graphs that summarized the results of our analysis, facilitating the interpretation of complex data for both oncologists and biologists. By following this methodological framework, we were able to provide a comprehensive analysis of CTC interactions, offering valuable insights that could inform the development of targeted therapies in oncology.

PMID:42426458 | DOI:10.1007/978-1-0716-5050-9_15

Pharm Res. 2026 Jul 9. doi: 10.1007/s11095-026-04143-y. Online ahead of print.

ABSTRACT

PURPOSE: Systematic covariate modeling in nonlinear mixed-effects (NLME) analysis is computationally intensive due to repeated refitting to concentration-time data. Although empirical Bayes estimates (EBEs) facilitate screening, η-shrinkage attenuates between-subject variability and distorts covariance structures, leading to shrinkage bias. We propose a variance-consistent framework enabling covariate modeling from a single base-model fit.

METHODS: The proposed approach incorporates subject-specific posterior means and covariances from an NLME base model. A variance-matching penalty enforces consistency between the total between-subject covariance (model-explained and unexplained) and the base model estimates, preserving the covariance structure without refitting. Performance was compared with EBE regression, two-stage Bayesian estimation, and NLME covariate modeling. Stepwise covariate selection was evaluated using likelihood ratio tests, with the resulting structure compared against the NLME-identified structure as the gold-standard reference.

RESULTS: Under substantial η-shrinkage of approximately 30%, EBE regression and two-stage Bayesian estimation attenuated covariate-effect parameter estimates. The proposed method provided unbiased estimates, mitigating shrinkage bias and recovering covariate-effect parameter estimates obtained with NLME. It also reproduced NLME-based stepwise covariate selection with high computational scalability by avoiding repeated refitting to time-course data.

CONCLUSIONS: Variance-consistent posterior-based covariate modeling provides a statistically coherent and computationally scalable framework for systematic covariate identification in population PKPD analysis.

PMID:42426415 | DOI:10.1007/s11095-026-04143-y