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Lipoprotein (a) [Lp(a)] is a lipoprotein similar to low-density lipoprotein (LDL), which binds to a characteristic component: apolipoprotein (a). The plasma Lp(a) level is mainly determined by genetic factors, with variations across ethnic groups. In adults, various epidemiological and genetic studies have shown that elevated Lp(a) levels are an independent risk factor for atherosclerotic cardiovascular disease and aortic valve stenosis, associated with inflammatory, atherogenic, and thrombotic mechanisms. Given that the distribution, variability, and prognostic value of this marker in the pediatric population have been less investigated, the objective of this review is to analyze the available evidence on the behavior of Lp(a) as a risk marker in children and adolescents, current recommendations for its measurement in pediatrics, and treatment prospects. Show less

Cancer patients face a markedly elevated risk of thromboembolism (TE), including both venous thromboembolism (VTE) and arterial thromboembolism (ATE), which contribute substantially to morbidity and mortality in this population. This study examined sex disparities in associations between sleep, sedentary behavior (SB), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and TE risk, in cancer patients using data from the UK Biobank. A longitudinal cohort analysis of 6,765 cancer patients (2,774 men and 3,991 women) from the accelerometry subsample was conducted using Cox proportional hazards and isotemporal substitution models stratified by sex. The incidence of VTE was 3.0% in men versus 2.2% in women, while ATE incidence was 5.0% versus 2.2%, respectively. Compared with high LPA, medium and low durations were associated with 2.75- and 2.88-fold higher VTE risk only in men. Reallocating 1 h per day from sleep or SB to LPA reduced VTE risk by 24% and 19% in men. Low MVPA was associated with 3.35- and 1.59-fold higher ATE risk in women and men, respectively. Reallocating 1 h per day from sleep, SB, or LPA to MVPA reduced ATE risk by 71%, 70%, and 66%, respectively, only in women. LPA was associated with a lower risk of VTE only in male cancer patients, whereas MVPA was linked to a lower risk of ATE in female patients, indicating sex-specific associations between movement behaviors and TE risk. Show less

To evaluate the current status and latent profiles of caregiver self-care contributions for patients with chronic obstructive pulmonary disease (COPD) and examine the associations between demographic characteristics, health literacy, confidence in self-care contributions, family intimacy, and profile membership. We recruited 275 dyads of patients with COPD and their family caregivers from five tertiary hospitals between May and November 2022 using convenience sampling. Latent profile analysis (LPA) was used to identify distinct profiles of caregiver self-care contributions. Univariate analysis and multinomial logistic regression were subsequently conducted to examine associations between participant characteristics and profile membership. LPA identified four distinct profiles of caregiver self-care contributions: low-contributing, under-monitored, maintenance-prioritized, and high-contributing. Significant differences were observed across these profiles in terms of patients' symptom severity, exacerbation frequency, number of hospitalizations, caregivers' education levels, caregiving duration, health literacy, confidence in self-management contributions, and family intimacy using univariate analysis. Multinomial logistic regression analysis revealed that caregivers' education levels, caregiving duration, confidence in self-management contributions, and health literacy were significant predictors of profile membership. Caregiver self-care contributions for patients with COPD can be characterized by four distinct profiles, with caregivers' educational level, health literacy, and confidence in self-management identified as key factors associated with profile membership. Show less

Non-suicidal self-injury (NSSI) is highly prevalent among adolescents with depression, yet the heterogeneity of underlying temperamental risk factors remains poorly understood. Traditional variable-centered approaches fail to capture how distinct affective temperaments co-occur within individuals. This study aimed to identify latent profiles of affective temperaments and examine their association with NSSI, exploring the statistical mediating role of cognitive emotion regulation (CER). A cross-sectional study was conducted from February 2025 to September 2025 at the First Hospital of Hebei Medical University. A total of 290 adolescents (aged 10–19) diagnosed with Major Depressive Disorder were recruited, with 282 valid responses included in the final analysis. Participants completed the TEMPS-A, CERQ, and ASHS. Latent Profile Analysis (LPA) was utilized to identify temperament subgroups. Mediation analysis with bootstrapping was performed to test the indirect effects of CER strategies. LPA identified three distinct profiles: Resilient/Low-risk (Class 1, 32.6%), Anxious-Depressive (Class 2, 46.1%), and Mixed-Dysregulated (Class 3, 21.3%). The Mixed-Dysregulated group, characterized by simultaneous elevations in depressive, anxious, irritable, and cyclothymic temperaments, exhibited the highest frequency (45.2 ± 21.3 times/year) and prevalence (98.8%) of NSSI compared to other groups ( The findings delineate a specific “Mixed-Dysregulated” risk phenotype within adolescent depression that is associated with severe NSSI. Interventions should move beyond standard depression care to target cognitive flexibility and emotional regulation skills. Statistical mediation analysis suggests that this risk is mediated by maladaptive cognitive emotion regulation strategies. Not applicable. Show less

This paper presents the Assimilation Modified Emotional (AME) algorithm, which is an enhanced version of the traditional label propagation algorithm (LPA) designed to address key challenges in social network analysis and emotional feature extraction. Traditional LPA methods, such as asynchronous label propagation and the Louvain algorithm, do not incorporate emotional representations and are often limited by local structural dependencies. The AME algorithm addresses these limitations by applying spectral algorithms, Markov chains, graph coarsening, and link prediction to simulate and optimize emotional transitions within the network. In addition, the AME algorithm enhances label representation through multi-label encoding, which allows for more accurate simulation of dynamic emotional states. Experimental results show that the AME algorithm achieves better performance than traditional LPA methods in terms of both accuracy and loss values. These findings indicate that the AME algorithm has strong potential for improving AI models used in social network analysis and emotional feature extraction. Show less

Identifying high-performing advanced practice nursing roles and understanding the factors that contribute to their effectiveness are critical for advancing professional development, optimizing workforce deployment, and ensuring long-term sustainability in nursing. This study aimed to (1) identify distinct latent profiles of advanced practice nursing among specialist nurses in mainland China, (2) quantitatively examine the individual and contextual factors associated with high performance, as characterized by these profiles, and (3) qualitatively confirm the significant factors using explanatory semistructured interviews in the high-performance groups. A mixed-methods sequential explanatory design was used, in which quantitative data were collected first and subsequently explained through qualitative interviews. Certified specialist nurses from 16 hospitals across urban and rural areas of Shanghai were included. Latent profile analysis (LPA) was conducted using the five domains from the Advanced Practice Role Delineation tool as manifest indicators to classify nurses into distinct performance profiles. Multinomial logistic regression was used to examine potential determinants (e.g., job position) of group membership. Additionally, a backpropagation neural network (BPNN) was developed to rank the importance of contributing factors. Specialist nurses identified as high performers in the quantitative phase were purposively sampled for explanatory semistructured qualitative interviews. Three latent profiles emerged: high performance (26.1%), moderate performance (46.3%), and low performance (27.6%). Compared to APNs, staff nurses had significantly lower odds of belonging to the high-performance group ( Identifying the profiles of advanced practice nursing roles provides valuable insights for optimizing APN performance and informing targeted management and policy strategies. High-performing specialist nurses are positioned at the nexus of individual capability, interdisciplinary collaboration, and institutional support. Show less

Lipoprotein(a) (Lp[a]) is an independent risk factor for atherosclerotic cardiovascular events and aortic stenosis. In Spain, the prevalence of elevated Lp(a) and its clinical impact remain poorly defined. We conducted a cross-sectional study including two cohorts: patients discharged after a non-fatal acute coronary syndrome (secondary prevention), and asymptomatic patients with subclinical atherosclerosis ("1.5 prevention"). The prevalence of elevated Lp(a) levels was assessed in both groups. Associations with multivessel coronary artery disease (secondary prevention) and with a coronary artery calcium (CAC) score ≥300 AU (1.5 prevention) were analyzed. A total of 1043 patients were included (788 secondary prevention). Median Lp(a) levels were 61 nmol/L in secondary prevention and 29 nmol/L in the 1.5 prevention cohort. In secondary prevention, 36.8%, 33.6%, 29.2%, and 24.5% had Lp(a) ≥125, ≥150, ≥175, and ≥ 200nmol/L, respectively; in the 1.5 prevention cohort the corresponding proportions were 27.5%, 24.3%, 17.6%, and 14.1%. In secondary prevention, Lp(a) ≥175 nmol/L was associated with multivessel disease after multivariable adjustment for age, sex, LDLc, and statin treatment (OR 1.45, 95% CI: 1.04-2.01; Elevated Lp(a) levels are common in both populations and correlate with greater atherosclerotic burden. These findings support the systematic assessment of Lp(a) to guide preventive strategies across both patient populations. Show less

Lipoprotein(a) [Lp(a)] is a recognized risk factor for atherosclerotic cardiovascular disease (ASCVD), but the shape and potential nonlinearity of its association remain uncertain. We assessed the linear and nonlinear associations between Lp(a) levels and ASCVD risk using observational and Mendelian randomization (MR) approaches. We analyzed 351,858 UK Biobank participants (2006-2023), stratified into Lp(a) percentiles: <70th, 70th-<80th, 80th-<90th, and ≥90th. Outcomes included ASCVD events from hospital, primary care, self-report, and death registry data. Cox models estimated the hazard ratios (HRs). MR analyses used a polygenic risk score from 10 Lp(a)-associated single-nucleotide polymorphisms, with nonlinearity tested by doubly ranked MR. Higher Lp(a) levels were associated with increased ASCVD risk. Compared with the <70th percentile, adjusted HRs (95% confidence interval) were 1.11 (1.07-1.16), 1.18 (1.14-1.22), and 1.25 (1.21-1.30) for the 70th-<80th, 80th-<90th, and ≥90th groups. Kaplan-Meier curves diverged early by group. Spline models suggested nonlinearity with an inflection near 130 nmol/L (P=0.007). MR showed a 2% higher ASCVD risk per 10 nmol/L genetically predicted Lp(a) (P<2×10 Elevated Lp(a) concentrations were causally associated with ASCVD risk, showing a predominantly graded relationship with possible nonlinearity at very high levels, supporting routine Lp(a) measurement and the development of Lp(a)-lowering therapies. Show less

Coronary microvascular dysfunction (CMD) constitutes an increasingly acknowledged aspect of coronary artery disease. Even though traditional cardiovascular risk factors have been implicated in CMD pathogenesis, data on lipoprotein (a) [Lp(a)] is limited. This cross-sectional study aimed to investigate whether Lp(a) levels are associated with CMD in patients with angina and nonobstructive coronary arteries. Coronary physiology assessment was performed with the standard bolus thermodilution technique, allowing for coronary flow reserve (CFR) and index of microvascular resistance estimation. Participants were categorized into 3 groups based on Lp(a) levels (<30, [30 to 50], and ≥50 mg/dl) as well as into 2 groups based on the presence of CMD. CMD was defined as CFR ≤2.5 and/or index of microvascular resistance ≥25. A total of 127 patients were recruited. No significant differences in baseline characteristics were observed between the groups. In unadjusted analysis, no significant associations were found. In multivariable analysis adjusting for age and sex, participants with Lp(a) values ≥50 mg/dl displayed a trend for a 4.25 increased CMD risk when compared to participants with Lp(a) values <30 mg/dl (odds ratio 4.25, confidence interval 0.81 to 22.28, p = 0.087). The same group of patients tended to have lower CFR than controls with Lp(a) <30 mg/dl, with a median CFR that was 1.05 units lower (p = 0.086). In conclusion, patients with high Lp(a) levels tended to display a higher prevalence of CMD and lower CFR. More studies are needed in order to better elucidate the relationship between Lp(a) and CMD. Show less

Dyslipidemia remains a major contributor to atherosclerosis and cardiovascular events. While low-density lipoprotein cholesterol (LDL-C) lowering is the primary treatment target, lipoprotein(a) [Lp(a)] has emerged as an independent, genetically determined risk factor, often unaffected by standard treatment. This study aimed to analyze the relationship between body composition parameters and lipid profile, including Lp(a). Clinically stable high cardiovascular risk patients (n = 207) receiving lipid-lowering pharmacotherapy were enrolled in this cross-sectional study. Anthropometric data and body composition were assessed using bioelectrical impedance analysis, including body mass index (BMI), fat mass (FM%) and fat-free mass percentage (FFM%). Lp(a) and lipid profile were measured. Patients were stratified by Lp(a) concentration: <75 nmol/L, 75-125 nmol/L, and >125 nmol/L. Lp(a) levels showed no significant association with body composition, age, and sex. In contrast, HDL-C was significantly inversely correlated with BMI (R = -0.25, p < 0.001) and this relationship was independent of sex (β = -0.68, p < 0.001), while triglycerides were positively correlated with FM% (R = 0.17, p = 0.02) and BMI (R = 0.28, p < 0.001) and negatively with FFM% (R = -0.17, p = 0.02). LDL-C was not associated with body composition. No significant differences in lipid profile or body composition were observed across Lp(a) strata. In high cardiovascular risk patients, Lp(a) appears unrelated to body composition, supporting its role as a non-modifiable, genetically driven risk factor. Conversely, despite pharmacotherapy, HDL-C and triglycerides demonstrated significant associations with body fat distribution. These findings suggest clinical role of body composition assessment in cardiovascular risk management, particularly in addressing residual risk beyond LDL-C. Show less

Elevated concentrations of both low-density lipoprotein (LDL)-cholesterol and lipoprotein(a) [Lp(a)] is probably the most detrimental lipid profile in terms of cardiovascular health. Our primary objective was to review the reports published before January 2026 pertaining to the metabolism of lipoprotein(a) and associated cardiovascular disease (CVD) risk specifically in familial hypercholesterolemia. Lp(a) has consistently been found elevated in familial hypercholesterolemia (FH) cohorts. To a large extent, this results from the fact that elevated Lp(a) increases the likelihood for a patient to be clinically diagnosed as FH. For long, increases in Lp(a) concentrations observed in FH patients have been regarded as the consequence of impaired Lp(a) plasma clearance by the LDL receptor. However, recent studies strongly advocate against a significant role for the LDL receptor in mediating Lp(a) hepatic uptake. The molecular mechanisms by which Lp(a) is cleared from blood still remain elusive. Finally, mounting clinical evidence indicates that lowering LDL-C pharmacologically will not offset the specific cardiovascular risk stemming from elevated Lp(a) in FH. It is highly recommended to systematically measure Lp(a) in FH patients. These patients should be treated with high-dose statins, when necessary, in combination with a proprotein convertase subtilisin/kexin type 9 inhibitor to reach LDL-C therapeutic goals. Hopefully, the Lp(a) lowering therapies currently under development will prove instrumental for adequate treatment of FH patients with concomitantly elevated Lp(a) in coming years. Show less

High-risk pregnancy requires effective self-management strategies to prevent adverse outcomes. Understanding the heterogeneity in pregnant women's self-management behaviors through latent profile analysis (LPA) may inform targeted interventions. We sought to identify latent profiles of self-management behaviors among women with high-risk pregnancies and examine their associations with adverse pregnancy outcomes. A cross-sectional study was conducted among high-risk pregnant women recruited from 2 tertiary maternal and child health hospitals in Heilongjiang, China. A total of 503 eligible participants completed questionnaires assessing demographic characteristics, clinical features, self-management behaviors, and preconception health behaviors. LPA was performed to identify distinct self-management patterns. Logistic regression analysis examined the influencing factors of profiles, and chi-squared tests were applied to examine differences in adverse pregnancy outcomes across profiles. Three distinct latent profiles were identified: "high-engagement type" (38.6%), "compliant executors" (36.4%), and "low-engagement type" (25.0%). Factors associated with class membership included preconception health behaviors, residential location, educational level, and history of chronic diseases. High-engagement type demonstrated significantly lower rates of adverse pregnancy outcomes than other classes. Women with high-risk pregnancies exhibit distinct patterns of self-management behaviors. Women classified as high-engagement type show superior pregnancy outcomes, suggesting the importance of comprehensive self-management interventions targeting all behavioral dimensions. Show less

To explore the association between 24-h movement behaviours and fundamental motor skills in children with intellectual disabilities using compositional data analyses and to investigate the 'dose-effect' characteristics of the reallocation between 24-h movement behaviours and fundamental motor skills. A cross-sectional study was conducted among 306 children with intellectual disabilities aged 6-10 years from 12 special education schools in Beijing and Jinan between 10 September 2023 and 27 March 2024. The ActiGraph GT3X+ accelerometer was used to estimate the amount of time spent in 24-h movement behaviours. The Test of Gross Motor Development-2 was applied to assess fundamental motor skills. The compositional isotemporal substitution was utilized to analyse the relationship between 24-h movement behaviours and fundamental motor skills. (1) After controlling the gender, age and intellectual disability level, MVPA of children with intellectual disabilities was positively associated with their FMS total score, locomotor skills and object control skills (β Special education school administrators, teachers, parents and guardians should consider 24-h movement behaviours as a whole and pay attention to their impact on children with intellectual disabilities. In the process of promoting FMS in children with intellectual disabilities, ensuring adequate sleep and trying to reallocate time from SB to MVPA and LPA may be effective methods. Show less

In recent years, young adults have navigated multiple, simultaneous crises - COVID-19, war in Ukraine, economic turbulence, climate change, and rapid AI growth - which pose complex mental-health risks. Drawing on multisystemic resilience models and the dual-factor model of mental health, we examine how individual (emotion-regulation difficulties), relational (attachment, social support), and contextual resources (social engagement, place attachment, socioeconomic status) relate to distinct emotional-response profiles and their change across three waves (July 2023, February 2024, September 2024) in a representative Polish sample ( The online version contains supplementary material available at 10.1186/s12992-026-01199-8. Show less

Acute ischemic stroke (AIS) poses a substantial risk of permanent disability and death globally, with neuroinflammation being a key driver of secondary brain damage post-stroke. Proprotein convertase subtilisin/kexin type 9 (PCSK9), beyond its well-accepted role in cholesterol metabolism through low-density lipoprotein receptor (LDLR) degradation, has emerged as an important mediator of neuroinflammation, making it an attractive new therapeutic target. This has sparked broader discussions about the potential pleiotropic effects of PCSK9 inhibitors on brain function. Proprotein convertase subtilisin/kexin type 9 mediates inflammation post-ischemia directly and indirectly by disrupting mTOR pathways. This stimulates signaling cascades associated with inflammation. For example, the nuclear factor-κB (NF-κB), toll-like receptor 4 (TLR4), and mitogen-activated protein kinase (MAPK) pathways in microglia activation. It also brings about reaction in astrocytes and increases the release of cytokines like interleukin-1β, interleukin-6, and tumor necrosis factor-α. Proprotein convertase subtilisin/kexin type 9 interacts with apolipoprotein E receptor 2 (ApoER2) present on neurons cells, leading to further inflammatory effects. Proprotein convertase subtilisin/kexin type 9 indirectly increases lipoprotein(a) [Lp(a)], which promotes inflammation through the Lp(a)-TLR4 axis and induces endothelial dysfunction. Monoclonal antibodies (evolocumab, alirocumab) and small interfering RNA (siRNA) agents (inclisiran) are examples of PCSK9 inhibitors. According to preclinical studies, these inhibitors can mitigate neuroinflammation by blocking the M1 polarization of microglia and downregulating key pro-inflammatory factors while preserving the blood-brain barrier (BBB). They also inhibit neuronal apoptosis via the Bcl-2/Bax-caspase cascade and reduce the aggregation of β-amyloid (Aβ). Evidently, the findings from cardiac ischemia-reperfusion models show that pretreatment with PCSK9 inhibitors is effective with optimal neuroprotection. Recent clinical data support these mechanisms: PCSK9 inhibitors not only lower LDL-C and Lp(a) but also reduce systemic inflammatory markers (e.g., high-sensitivity C-reactive protein [hs-CRP], interleukin-6). Early adjunctive use of evolocumab in AIS is associated with reduced early neurological deterioration, highlighting that its effects extend beyond lipid lowering to modulating immune pathways in both the central and peripheral systems. As a promising multitarget therapeutic strategy for AIS, PCSK9 inhibitors target the interconnected pathways of lipid metabolism and neuroinflammation. Future studies should address critical challenges such as defining the optimal therapeutic time window, improving BBB penetrability, and refining patient stratification to translate their neuroprotective effects into clinical benefits for stroke patients. Show less

Prior research has utilised person-centred approaches to identify parent feeding profiles distinguished by controlling and structure-based practices, but less research has examined autonomy support-based practices, or how social and family contextual factors differ between feeding profiles. This study aimed to identify profiles of parents with similar patterns of feeding practices and to examine whether profiles differ on family contextual factors. In 2022, 989 UK parents of children aged 3-6 years (M = 4.1 years) completed an online survey, which included the Comprehensive Feeding Practices Questionnaire (CFPQ), measuring parental feeding practices, and validated questionnaires capturing family contextual variables. Latent Profile Analysis (LPA) was conducted to identify parent feeding profiles using the CFPQ. A MANCOVA assessed differences in family contextual variables between profiles. LPA identified three profiles based on common model fit indices and theoretical considerations. Profile 1 'moderate control' (25.2%) showed moderate use of controlling practices and low use of structure-based and autonomy support-based practices. Profile 2 'structured and supportive' (29.6%) showed low use of controlling practices and high use of structure-based and autonomy support-based practices. Profile 3 'using everything' (45.2%) showed high use of all three types of feeding practices. Parents in the 'moderate control' profile had significantly lower parental wellbeing and reported more barriers of time and energy for meal planning compared to other profiles. In contrast, parents in the 'structured and supportive' profile had significantly lower household chaos and lower parental stress. Mothers had a higher proportion of membership to the 'structured and supportive' profile (33.9%) compared to other profiles, whereas fathers had a higher membership proportion to the 'using everything' profile (60.9%). Future interventions should be tailored to parent feeding practice profiles and associated family contextual factors. Show less

Muscular strength is a marker of current health and a predictor of long-term health outcomes in young populations, supporting the inclusion of muscle-strengthening activities into current guidelines and recommendations. Over the last decade, muscular strength has been included in several fitness-test batteries in children and adolescents. However, little is known about its relevance and the feasibility of assessing it in preschool children aged 3-5 years. Therefore, in this cross-sectional study, we aimed to generate reference values for handgrip strength in Swedish preschool children and to examine the associations of device-measured movement behaviours (sedentary time [ST], light physical activity [LPA], moderate-to-vigorous physical activity [MVPA], and sleep duration) with handgrip strength using compositional data analysis. A total of 3,218 preschool children (48.53% female) aged 3.0-5.5 years from Sweden were included. Handgrip strength was measured using a validated analog dynamometer following standardized procedures. Movement behaviours were assessed in a subsample of 2,328 children who had both handgrip data and valid accelerometer recordings. Compositional data analysis was used to examine associations between handgrip strength and the 24-hour time-use composition, adjusting for age, sex, body mass index, parental education, and wear time. Age- and sex-specific percentiles for handgrip strength were developed. Boys showed higher handgrip values than girls at all ages (e.g., median increased from 4.08 to 7.42 kg in boys and from 3.45 to 6.87 kg in girls between ages 3 and 5 years). When the proportion of time spent in MVPA increased relative to the other behaviours, handgrip strength rose by + 1.22 kg; the opposite was observed for ST, which related to - 0.84 kg lower handgrip strength. No significant associations were observed for LPA or sleep duration (LPA: β =-0.48 kg, 95% CI: -1.23, 0.27; sleep: β = 0.10 kg, 95% CI: -0.37, 0.57). This study provides the first normative reference values for handgrip strength from Northern Europe. These values offer a useful reference for screening and contextual interpretation of muscular strength in preschool children. Show less

Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to investigate the combined effect of elevated Lp(a) and DM on survival outcomes in CAD patients. This study included 65 547 CAD patients (62.6 ± 10.7 years, 27.7% female) from CIN-II and RED-CARPET cohorts. Patients were stratified into four groups by Lp(a) levels (< or ≥ 30 mg/dL) and DM status. Multivariable Cox regression models estimated associations with cardiovascular and all-cause mortality, examining additive and multiplicative interactions. During a median follow-up of 5.5 years, 10 686 (16.3%) patients died from all causes and 5106 (7.8%) died from cardiovascular causes. Patients with Lp(a) ≥ 30 mg/dL and DM were independently associated with cardiovascular mortality (adjusted hazard ratio [aHR]: 1.28, 95% CI: 1.20-1.35; aHR: 1.53, 95% CI: 1.44-1.62, all p < 0.001, respectively). Compared to patients with Lp(a) < 30 mg/dL without DM, the aHRs were 1.26 (95% CI: 1.16-1.36, p < 0.001), 1.51 (95% CI: 1.40-1.62, p < 0.001) and 2.00 (95% CI: 1.83-2.18, p < 0.001) for those with Lp(a) ≥ 30 mg/dL without DM, Lp(a) < 30 mg/dL with DM and Lp(a) ≥ 30 mg/dL with DM, respectively. Significant additive interaction between elevated Lp(a) and DM on cardiovascular mortality was observed, with 12% of the excess risk attributed. Similar associations were observed in all-cause mortality. In patients with CAD, elevated Lp(a) and DM act synergistically to increase the risk of cardiovascular and all-cause mortality, suggesting that both risks should be considered to integrate management. Show less

Osteoporosis has emerged as a growing public health concern due to its high prevalence and substantial economic burden on both individuals and society. Recent studies have identified the serum uric acid to high-density lipoprotein cholesterol ratio (UHR) as a novel predictive biomarker for various diseases. However, its association with bone mineral density (BMD) remains unclear. This study evaluated the association of the UHR and forearm BMD (FR-BMD) in a middle-aged and elderly cohort. We also assessed the interaction effects of age, sex, and body mass index (BMI). A total of 4,958 adults aged ≥50 years were enrolled from health examinees at Heze Municipal Hospital (2022-2025). We collected demographic data, serum lipids, and uric acid levels. Measurements of FR-BMD were performed on the left distal radius (1/3 site) utilizing dual-energy X-ray absorptiometry. Multivariate linear regression analyses evaluated the UHR-BMD relationship, supplemented by subgroup analyses and interaction tests. Nonlinear associations were assessed using generalized additive models with smoothing curves. After adjusting for age, sex, BMI, Alb, ALP, ALT, BUN, TP, Scr, Lp(a), TC, GGT and hypertension, a higher UHR was significantly associated with lower FR-BMD [β=-0.076, 95%CI(-0.138~-0.015), P = 0.015]. Significant interaction effects were observed for age and sex ( The association of UHR with FR-BMD is significantly modified by age and sex in middle-aged and elderly populations. Nonlinear relationships exist in males <60 years, females ≥60 years and non-overweight individuals. The potential of UHR as a novel indicator for bone health assessment in select populations is highlighted by our results. Show less

Circulating lipoprotein(a) [Lp(a)] levels are highly heritable and linked to atherosclerotic cardiovascular disease, yet clinical measurement rates remain low (<1%) in the United States. The high heritability of Lp(a) across populations makes genetic prediction an attractive approach for closing this testing gap, but existing polygenic scores transfer poorly across populations. Haplotype-based prediction models, which use standard genome-wide genotype data to capture common-, rare-, and structural-variation at the LPA locus, could bridge this gap, enabling opportunistic identification of individuals with elevated Lp(a) levels across diverse populations within existing large, genotyped cohorts. This study sought to develop and validate a haplotype-based prediction model using genome-wide genotype data to identify individuals with elevated Lp(a) levels across diverse populations. We developed an Among PMBB (n = 1856), MGBB (n = 1401), and BioMe (n = 1686) participants with available genotype and Lp(a) measurements, average age was 60 years, and 51% were female. Overall r A haplotype-based genetic model effectively identified individuals with elevated Lp(a) levels across diverse populations, with potential utility for opportunistic screening among cohorts where genotype data is available, but Lp(a) testing rates are low. Show less

Cardiovascular (CV) disease risk is increased in rheumatoid arthritis (RA) and is the leading cause of mortality. Improved CV risk stratification tools in RA could enhance use of preventative care and improve outcomes. We previously studied biomarkers of CV disease - adiponectin, hsCRP, Lp(a), osteoprotegerin (OPG), high-sensitivity cardiac troponin T (hsTnT), serum amyloid A (SAA), YKL-40, soluble TNF receptor1 (sTNFR1) -- that were associated with CV risk. In the current study, these biomarkers were tested in an unrelated external cohort of RA patients followed at a single academic medical center without a history of CV events. CV events were identified through Medicare and Medicaid administrative data or through medical record review of self-reported events.Biomarkers were assessed at cohort entry among a nested cohort of cases and controls, matched 1:1 on sex and age. Analyses were conducted using conditional logistic regression. We examined whether the candidate biomarkers added to clinical CV risk factors improved model prediction, using the area under the curve (AUC) as well as the net reclassification index (NRI). From a cohort of 1,345 eligible patients with RA, we identified 123 patients with confirmed CV events. Cases and matched controls were typical of RA: median age 63 years, 77% women, RA disease duration 11 years, 72% seropositive, 85% used a biologic or conventional disease modifying anti-rheumatic drug, 58% non-steroidal anti-inflammatory drugs, and 30% oral glucocorticoids. From the candidate biomarkers, LASSO regression selected hsTnT and sTNFR1 as associated with CV events. The AUC for models that included only clinical risk factors was 0.758 (95% CI 0.689-0.829); after adding hsTnT and sTNFR1, the AUC increased to 0.802 (95% CI 0.718-0.998). The NRI of the model with biomarkers was 16.3%, with improvement only observed in patients who did not have CV events during follow-up. Adding selected biomarkers to clinical risk factors enhances the discrimination of models predicting CV events among patients with RA. These risk models require prospective testing to see if they have value in clinical practice decision-making regarding preventative care. Show less

Background With the prevalence of coronary artery diseases (CAD) on the rise, especially in the younger population, characterization of non-conventional risk factors remains essential, especially in the inherently predisposed Southeast Asian population. This study aimed at clinical and biochemical profiling in angiographically proven CAD in young Gujarati Indians without conventional risk factors such as tobacco/alcohol consumption. Methodology This single-center, descriptive, cross-sectional case series included consecutive Gujarati patients aged ≤45 years presenting with symptomatic, angiographically significant CAD over a 15-month period. Patients with tobacco or alcohol exposure and those with concomitant pre-existing diabetes mellitus and hypertension were excluded. Clinical characteristics, biochemical parameters (glycated hemoglobin, lipid profile, lipoprotein A (LpA), homocysteine, apolipoproteins), and coronary angiographic findings were recorded. Analyses were primarily descriptive, with limited exploratory comparisons. Results Overall, 2/4 obese patients (50%) and 3/4 obese patients (75%) were newly diagnosed with dysglycemia and dyslipidemia, respectively. Among patients with single-vessel disease (SVD; n = 16), eight (50%) patients presented with ST-segment elevation myocardial infarction, whereas among those with multi-vessel disease (MVD; n = 6), three (50%) patients presented with non-ST-segment elevation myocardial infarction. Elevated low-density lipoprotein cholesterol levels were observed in 8/16 (50%) patients with SVD and 3/6 (50%) patients with MVD. More than 5/6 (83.3%) patients with elevated LpA had SVD. Conclusions The study showed that non-conventional risk factors, such as obesity and family history of CAD, when combined with LpA and lipid profiles, can help in earlier identification of a predisposed individual in a high-risk population. Show less

The study explores the interconnection between the latent categories of mobile phone dependency and self-control in the sub-healthy urban older adults practicing Tai Chi. The findings aim to provide a reference for preventing mobile phone dependence, enhancing self-control and improving sub-health status in this population. A multi-stage cluster sampling method was employed to screen 560 sub-healthy urban older adults from 2,946 valid survey responses in Xuzhou City, Jiangsu Province. Sub-health status was verified using the SHMS V1.0 scale. Data were collected between September and October 2025. Latent profile analysis (LPA) was used to categorize mobile phone dependency and self-control. Pearson correlation analysis measured the relationship between these two variables. Additionally, chi-square test examined demographic differences across the identified latent profiles. Finally, multivariate logistic regression analyzed the associations between mobile phone dependency, self-control, and Tai Chi exercise. LPA identified four distinct profiles: Low dependency-Medium control (109 individuals, 19.5%), High dependency-No control (207 individuals, 37.0%), No dependency-High control (191 individuals, 34.1%), and Moderate dependency-Low control (53 individuals, 9.5%). These categories had statistically significant differences ( Tai Chi exercise exerts differential effects on urban sub-healthy older adults across distinct latent profiles of mobile phone dependency and self-control. Societal stakeholders should strengthen Tai Chi programs for these diverse categories to promote their physical and mental wellbeing. Show less

Patient safety competency (PSC) is a core element of nursing practice, essential for ensuring high-quality and safe patient care. Newly recruited nurses often face challenges such as transition shock, limited clinical experience, and fragmented safety education, which may hinder their ability to maintain patient safety. Most studies have assessed PSC using total scale scores, overlooking internal heterogeneity within this group. This study aimed to identify latent profiles of PSC among newly recruited nurses and explore the influencing factors to provide evidence for targeted competency development and management strategies. From July to August 2023, a convenience sample of newly recruited nurses was obtained from seven tertiary grade-A hospitals in Shandong Province, China. Data were collected using the General Information Questionnaire, the Transition Shock Scale of Newly Graduated Nurses, the Nurses' Perception of Organizational Support Scale, and the Patient Safety Nurse Competency Evaluation Scale. Latent Profile Analysis (LPA) was conducted to identify the potential subgroups of patient safety competency among newly recruited nurses. Univariate analysis and multivariate logistic regression were performed to examine the influencing factors associated with different latent profile categories. The patient safety competency of newly recruited nurses was categorized into 3 potential profiles: "high safety competency group" (36.9%), "medium safety competency group" (49.4%), and "low safety competency group" (13.7%). The results of the logistic regression analysis revealed that education level, average number of night shifts per week, participation in safety training, involvement in patient safety-related projects, transition shock, and perceived organizational support were significant predictors of patient safety competency among newly recruited nurses (P < 0.05). This study identified three distinct latent profiles of patient safety competency among newly recruited nurses, revealing a moderate overall competency level with notable heterogeneity. Nursing managers should pay particular attention to nurses with moderate and low competency levels and implement targeted, evidence-based interventions to strengthen their patient safety competency and promote safer clinical practice. Not applicable. Show less

Lipoprotein(a) [Lp(a)] is a causal, genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve stenosis (CAVS). Although elevated Lp(a) affects approximately 20% of the global population, specific pharmacological options have long been unavailable, leaving a major gap in residual risk management. This review synthesizes current understanding of Lp(a) molecular architecture, genetics, and metabolism, and integrates mechanistic evidence linking Lp(a) to pro-atherogenic, pro-inflammatory, and pro-thrombotic pathways. We summarize epidemiological and genetic data associating Lp(a) with a broad spectrum of cardiovascular outcomes and discuss current clinical guidelines on screening and risk stratification. Furthermore, we provide an up-to-date overview of the emerging therapeutic landscape, including RNA-targeted therapies and novel oral small molecules. With pivotal phase 3 outcome trials nearing completion, the field is transitioning from viewing Lp(a) as an untreatable biomarker to an actionable therapeutic target, with important implications for precision cardiovascular prevention. Show less

Nurses in traditional Chinese medicine (TCM) departments face significant sleep challenges associated with occupational stressors. However, person-centered analyses classifying these sleep patterns remain scarce. This study aimed to identify heterogeneous sleep disturbance subgroups via latent profile analysis (LPA) and evaluate the performance of explainable machine learning models in discriminating these subgroups based on demographic and occupational features. A cross-sectional survey enrolled 7721 nurses from 130 TCM healthcare institutions in Liaoning Province (December 2024). Data encompassed demographic, occupational, and psychological variables obtained via self-administered questionnaires, including the Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance short form 8a. LPA was employed to categorize sleep disturbance patterns. Recursive feature elimination with random forest (RFE-RF) was used to select features associated with subgroup membership for five machine learning models. Models were trained on 70% of the data and evaluated on a 30% independent test set. The optimal classification model (XGBoost) underwent interpretability analysis using Shapley additive explanations (SHAP). LPA identified three subgroups: mild-stable (29.8%), moderate-fluctuating (60%), and severe-persistent (10.2%). Machine learning models achieved test AUCs of 0.71-0.84, with XGBoost demonstrating the highest discriminatory performance (AUC = 0.84, 95%CI: 0.83-0.85) in classifying subgroups. SHAP analysis indicated that monthly income, organizational support, hospital level, self-compassion, and resilience were the top five features contributing to the model's classification output. This study characterized three distinct sleep disturbance subgroups among TCM nurses, with the majority exhibiting moderate symptoms. The sequential application of LPA and explainable machine learning demonstrated robust performance in distinguishing sleep disturbance patterns. Identifying correlates-such as lower income and resilience-may assist nurse managers in stratifying risk and tailoring interventions for those most likely to fall into the severe subgroup. Future longitudinal studies are required to validate the stability of these subgroups and establish causal relationships. Show less