Category: Statistics

BMC Cardiovasc Disord. 2025 Aug 7;25(1):586. doi: 10.1186/s12872-025-05054-3.

ABSTRACT

BACKGROUND: Currently, there is insufficient evidence available to fully understand the correlation between cardiovascular disease(CVD) and peripheral arterial disease(PAD). The association between CVD and PAD holds significant importance within the realms of both healthcare and public health.The primary aim of this study is to elucidate the impact of CVD on PAD and to examine the examine the clinical implications of this association for preventive strategies.

METHODS: A retrospective cross-sectional study was conducted, encompassing 6123 participants from the National Health and Nutrition Examination Survey (NHANES) of the United States spanning the years 1999 to 2004. Data on demographics including age, gender, race, education level, marital status, poverty income ratio, physical activity, body mass index (BMI), smoking status, total cholesterol, C-reactive protein (CRP), glycosylated hemoglobin (HbA1c), hypertension and diabetes status were collected from all participants. Multiple logistic regression analysis was employed to quantify the independent association between CVD and PAD risk while adjusting for demographic, socioeconomic, and clinical confounders.

RESULTS: The mean age of the selected participants was 59.9 ± 13.0 years, with 51.2% of them being male. Multivariable analysis revealed a clinically significant 54% increase in PAD risk among CVD patients (adjusted OR = 1.54, 95%CI = 1.20-1.99; P = 0.001), indicating a strong positive association. Subgroup analyses testing age (< 65/≥65 years), sex, educational level, marital status, poverty income ratio, physical activity, smoking status, hypertension and diabetes status showed no effect modification (all P-interaction > 0.05).

CONCLUSIONS: This analysis has demonstrated a notable link between CVD and the likelihood of developing PAD, independent of other influencing factors. These results highlight the necessity of focusing on cardiovascular health to prevent the onset of PAD.

PMID:40770306 | DOI:10.1186/s12872-025-05054-3

BMC Nephrol. 2025 Aug 6;26(1):438. doi: 10.1186/s12882-025-04161-9.

ABSTRACT

BACKGROUND: In patients with chronic kidney disease, mineralocorticoid receptor antagonists (MRAs) exert a reno-protective effect through its anti-inflammatory and antifibrotic effects. Less is known about the efficacy of MRAs in kidney transplant (KT) recipients. This meta-analysis aims to systematically assess the efficacy of MRAs in KT recipients.

METHODS: PubMed, Embase and Cochrane databases were searched for randomized controlled trials (RCTs) that compared MRAs to placebo in KT recipients and reported the outcomes of (1) glomerular filtration rate (GFR); (2) serum creatinine; (3) systolic (SBP) and diastolic blood pressure (DBP); (4) hyperkalemia; and (5) interstitial fibrosis and tubular atrophy (IFTA) scores. Heterogeneity was examined with I2 statistics. A random-effects model was used for outcomes with high heterogeneity.

RESULTS: We included 5 RCTs with 293 patients, of whom 142 (48.5%) underwent treatment with a steroidal MRA. Mean follow-up ranged from 5 days to 36 months. There was no significant difference in GFR (MD 9.04 mL/min/1.73 m2; 95% CI – 2.76-20.85; p = 0.13) and serum creatinine between placebo and MRA groups (MD – 0.21 mg/dL; 95% CI – 0.62-0.20; p = 0.32). SBP (MD 0.69 mmHg; 95% CI – 0.69-2.08; p = 0.33), DBP (MD 0.45 mmHg; 95% CI – 0.69-1.59; p = 0.44) and IFTA scores exhibited no differences between groups (mild IFTA RR 1.21; 95% 0.83-1.74; p = 0.32) (moderate IFTA RR 0.82; 95% CI 0.45-1.50; P = 0.51) (severe IFTA RR 0.64; 95% CI 0.24-1.76; p = 0.39). MRAs were associated with a 4-fold increase in the risk of hyperkalemia compared with placebo (RR 4.06; 95% CI 1.46-11.28; p = 0.007).

CONCLUSION: Steroidal MRAs have no superior efficacy compared with placebo in KT recipients and are associated with a 4-fold increase in the risk of hyperkalemia despite preserved kidney function.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40770304 | DOI:10.1186/s12882-025-04161-9

BMC Gastroenterol. 2025 Aug 6;25(1):559. doi: 10.1186/s12876-025-04158-6.

NO ABSTRACT

PMID:40770303 | DOI:10.1186/s12876-025-04158-6

BMC Pediatr. 2025 Aug 6;25(1):603. doi: 10.1186/s12887-024-05068-4.

ABSTRACT

BACKGROUND: Patients undergoing cardiac surgery, particularly children, are at an increased risk of experiencing skin integrity disorders and pressure ulcers, attributed to the underlying disease and prolonged surgical durations. This study aims to examine the impact of a specialized care program on the incidence and advancement of skin integrity disorders in children aged 1 month to 6 years after open heart surgery.

MATERIALS AND METHODS: This quasi-experimental study, registered with the Iranian Registry of Clinical Trials (IRCT) under the identifier IRCT202008250458515N12 on November 11, 2020, involved 80 children admitted to the critical care unit of a teaching hospital. The participants were divided into two groups: an intervention group and a control group, each consisting of 40 children. The intervention group received targeted care program measures, while the control group followed standard care procedures. The risk of pressure ulcer development, incidence of skin damage, and pressure ulcer status were assessed and recorded using the Braden Q Scale and the Pressure Ulcer Scale for Healing (PUSH) both before the intervention and seven days after hospitalization.

FINDINGS: The incidence of skin damage in the intervention group (45%) was significantly lower than that in the control group (62.5%) (P < 0.001). Additionally, the average scores for PUSH and Braden Q in the intervention group were 2.95 ± 0.67 and 27.48 ± 1.50, respectively, whereas in the control group, they were 5.70 ± 1.07 and 25.05 ± 2.22, respectively. These differences were statistically significant (P < 0.001).

CONCLUSION: According to the results, the designed focused care program can lead to the reduction of the risk of skin damage and the improvement in pressure ulcers; thus, it is recommended to be considered in the special care units.

PMID:40770300 | DOI:10.1186/s12887-024-05068-4

BMC Cardiovasc Disord. 2025 Aug 7;25(1):585. doi: 10.1186/s12872-025-05035-6.

ABSTRACT

BACKGROUND: Hypertension is a major public health issue globally, affecting hundreds of millions of people. Although the association between alcohol consumption and hypertension has been widely demonstrated, uncertainties remain regarding the dose-response relationship, potential confounding factors, and population-specific differences.

METHODS: Data from NHANES participants aged 20 and above from 2007 to 2018 were analyzed. A weighted multivariate logistic regression model assessed the association between daily alcohol intake and hypertension prevalence, calculating adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic splines (RCS) examined the potential non-linear association. Subgroup analyses evaluated interaction effects across demographic variables to identify differences in the alcohol-hypertension association among populations.

RESULTS: This study included 30,127 eligible participants, representing 228,198,440 individuals in the United States population after weighting. In the fully adjusted Model 3, a significant positive association was observed between daily alcohol intake and hypertension prevalence (OR: 1.007, 95% CI: 1.005-1.009; P < 0.001). Compared to the abstainers group (0 g/d), participants in the very heavy consumers group (> 40 g/d) had a significantly higher prevalence of hypertension (OR: 1.873, 95% CI: 1.585-2.213; P < 0.001). RCS analysis further confirmed the positive association between daily alcohol intake and hypertension prevalence, with no significant non-linearity detected. In the subgroup analysis, a significant interaction was detected in the racial/ethnic subgroup (P for interaction < 0.05).

CONCLUSION: A significant association exists between very heavy alcohol consumers (> 40 g/d) and an increased prevalence of hypertension. These findings underscore the importance of including alcohol intake assessments in hypertension management and prevention strategies. Further research is needed to explore the causal association between alcohol intake and hypertension and to investigate its potential biological mechanisms.

PMID:40770292 | DOI:10.1186/s12872-025-05035-6

Sci Rep. 2025 Aug 6;15(1):28710. doi: 10.1038/s41598-025-09972-2.

ABSTRACT

Mammography is important for breast cancer detection, and calcifications and masses are the most common mammographic features. In this exploratory study, we evaluated the association between these findings and immune gene expression to determine the role that inflammation may play in mass- or calcification-associated breast cancers. This study included 205 breast cancer patients in the Carolina Breast Cancer Study and the Carolina Mammography Registry. Hierarchical clustering and logistic regression were used to examine associations between radiologist-reported imaging features and tumor RNA expression of 48 immune-related genes. The Bonferroni correction adjusted for multiple comparisons. There were statistically significant associations between CCL7 and calcifications (odd ratio [OR] = 0.52, 95% confidence interval [CI] = 0.35, 0.75) and between CCL7 and the presence of a mass (OR = 1.48 95% CI = 1.18, 1.88). Furthermore, lower expression of genes with anti-inflammatory function was associated with calcifications (p = 0.04), but not masses. However, neither mammographic masses nor calcifications were associated with global gene expression patterns using hierarchical clustering. This analysis of bulk immune markers suggests plausible associations between immune alterations and mammographic findings. Larger studies and immunoprofiling approaches sensitive to subtle variation should be conducted to identify targetable imaging characteristics, which alongside immunotherapeutic advances, may facilitate targeted breast cancer treatment.

PMID:40770282 | DOI:10.1038/s41598-025-09972-2

Sci Data. 2025 Aug 6;12(1):1371. doi: 10.1038/s41597-025-05723-0.

ABSTRACT

Changing crop patterns are primary driver of land use change and can impact global atmospheric cycles. While existing studies have mapped the distribution of several crops in China, harvest area maps for a complete set of crops over the past decades are lacking. This study pioneered the development of a spatiotemporal dataset of harvest area maps for 16 crop types in China at a 1-km resolution from 1990 to 2020 with 5-year intervals. Prefecture-level crop statistics were allocated to grids based on synthetical crop suitability score, which is evaluated by natural and socioeconomic factors. County-level validations demonstrated the built dataset is highly consistent with statistics, especially for primary grains and oilseed. Moreover, crop harvest area at sub-pixel level can better represent gradient changes within urban-rural zones. The built crop maps revealed the harvest zones for maize, rice and soybeans in Northern China have steadily expanded since 1990. This dataset fully supports identification of spatiotemporal changes in China’s crop patterns and can serve as critical input for biogeochemical and agricultural models.

PMID:40770232 | DOI:10.1038/s41597-025-05723-0

Sci Rep. 2025 Aug 6;15(1):28698. doi: 10.1038/s41598-025-14698-2.

ABSTRACT

The adoption of Artificial Intelligence (AI) is reshaping the labor market; however, individuals’ perceptions of its impact remain inconsistent. This study investigates the presence of the Invulnerability Bias (IB), where workers perceive that AI will have a greater impact on others’ jobs than on their own, and Optimism Bias by Type of Impact (OBTI), where individuals perceive AI’s future impact on their own job as more positive than on others’. The study analyzes survey data collected from 201 participants, recruited through social media using convenience sampling. The data were analyzed using a combination of statistical and machine learning methods, including the Wilcoxon test, ordinary least squares regression, clustering, random forests, and decision trees. Results confirm a significant IB, but not OBTI; only 31.8% perceived AI’s future impact on their own job as more positive than on others’. Analysis shows that greater knowledge of AI correlates with lower IB, suggesting that familiarity with AI reduces the tendency to externalize perceived risk. Furthermore, bias levels vary across professional sectors: healthcare, law, and public administration exhibit the highest IB, while technology-related professions show lower levels. These findings highlight the need for interventions to improve workers’ awareness of AI’s potential future impact on employment.

PMID:40770226 | DOI:10.1038/s41598-025-14698-2

Sci Rep. 2025 Aug 6;15(1):28723. doi: 10.1038/s41598-025-14406-0.

ABSTRACT

Alzheimer’s disease (AD) presents a pressing global health challenge, demanding improved strategies for early detection and understanding its progression. In this study, we address this need by employing survival analysis techniques to predict transition time from Cognitive Normal (CN) to Mild Cognitive Impairment (MCI) in elderly individuals, considering the predictive value of baseline comorbidities. Leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) databases, we construct feature sets encompassing demographics, cognitive scores, and comorbidities. Various machine learning and deep learning methods for survival analysis are employed. Our top-performing model, fast random forest, achieves a concordance index of 0.84 when considering all feature modalities, with comorbidity data emerging as a significant predictor. The top features identified by the best-performing model include one demographic feature (age), seven cognitive scores (ADAS13, RAVLT learning, FAQ, ADAS11, RAVLT immediate, CDRSB, ADASQ4), and two comorbidities (Endocrine & Metabolic, Renal & Genitourinary). Age is highlighted as the most influential predictor, while cognitive scores are crucial indicators of Alzheimer’s disease. External validation against the AIBL dataset affirms the robustness of our approach. Overall, our study contributes to a deeper understanding of the role of baseline comorbidities in AD risk prediction and emphasizes the importance of incorporating comprehensive feature assessment in clinical practice for early diagnosis and personalized treatment planning.

PMID:40770222 | DOI:10.1038/s41598-025-14406-0

Sci Rep. 2025 Aug 6;15(1):28719. doi: 10.1038/s41598-025-09508-8.

ABSTRACT

With the instantaneous economic development, air quality keeps on dwindling. Some key factors for the emergence and evolution of air pollution are high-intensity pollution emissions and adverse weather circumstances. In air pollutants, Particulate Matter (PM) possessing less than 2.5Mu is considered the most severe health issue, resulting in respiratory tract illness and cardiovascular disease. Therefore, it is mandatory to predict PM 2.5 concentrations accurately to ward off the general public from the desperate influence of air pollution in advance owing to its complex nature. Aiming at the complexity of air quality prediction, a new method called Hebbian Concordance and Attention-based Long Short-Term Memory (HC-ALSTM) is proposed. The HC-ALSTM method is split into four sections. They are preprocessing using the Statistical Normalization-based Preprocessing model, feature extraction employing the Generalised Hebbian Spatio Temporal Feature extraction model, feature selection using Concordance Correlation function, and Attention-based Long Short-Term Memory for air quality prediction. First, the Statistical Normalization-based Preprocessing model is applied to the raw dataset to normalize the impact of distinct air pollutants on the bordering factor. Second, with the Generalised Hebbian Spatio Temporal Feature extraction algorithm, processed samples are applied to extract the dimensionality-reduced spatio-temporal feature. Third, with the extracted features, essential or significant features are selected using Concordance Correlation analysis that determines the impact of pollutant concentration of bordering places for predicting air quality index involving both city and state, daily and hourly. Finally, Attention-based Long Short-Term Memory is applied to the extracted and selected features to predict air quality accurately. Through evaluation and analysis using two other evaluation methods, the proposed HC-ALSTM method performs better in error and time. Our method has dramatically improved air quality prediction accuracy and overhead compared with other methods.

PMID:40770205 | DOI:10.1038/s41598-025-09508-8