Nevin Manimala

Alzheimers Dement. 2025 Feb;21(2):e70002. doi: 10.1002/alz.70002.

ABSTRACT

INTRODUCTION: Whether prescription opioid exposure, duration, and dose are associated with cognitive function remains inconclusive.

METHODS: A longitudinal cohort among 3097 older adults with chronic pain and without dementia was conducted using Health and Retirement Study (HRS) linked to Medicare data from 2006 to 2020. Prescription opioid exposure, cumulative use for ≥ 90 days, and high-dose use (≥ 90 morphine milligram equivalents [MME] daily) were assessed biennially. Memory score and dementia probability were derived from HRS cognitive measures and analyzed using linear mixed-effects models.

RESULTS: Adjusted memory decline and dementia probability were not statistically different between patients with (vs. without) opioid exposure and between patients with cumulative use for ≥ 90 days (vs. < 90 days) but were higher between participants with high-dose opioid use (vs. low-dose) at the end of the follow-up.

DISCUSSION: Prescription opioid exposure and duration were not associated, but high-dose opioid use was associated with greater memory decline and dementia probability.

HIGHLIGHTS: Opioid use versus no use was not related to memory decline and dementia probability. Long-term opioid use was not related to memory decline and dementia probability. High-dose opioid use was related to greater memory decline and dementia probability.

PMID:39989238 | DOI:10.1002/alz.70002

Ecology. 2025 Feb;106(2):e70046. doi: 10.1002/ecy.70046.

ABSTRACT

Spatial capture-recapture models (SCRs) provide an integrative statistical tool for analyzing animal movement and population patterns. Although incorporating home range formation with a theoretical basis of animal movement into SCRs can improve the prediction of animal space use in a heterogeneous landscape, this approach is challenging owing to the sparseness of recapture events. In this study, we developed an advection-diffusion capture-recapture model (ADCR), which is an extension of SCRs incorporating home range formation with advection-diffusion formalism, providing a new framework to estimate population density and landscape permeability. we tested the unbiasedness of the estimator using simulated capture-recapture data generated by a step selection function. We also compared the accuracy of population density estimates and home range shapes with those from SCR incorporating the least-cost path and basic SCR. In addition, ADCR was applied to a real dataset of Asiatic black bear (Ursus thibetanus) in Japan to demonstrate the capacity of the ADCR to detect geographical barriers that constrain animal movements. Population density and permeability of ADCR were substantially unbiased for simulated datasets. ADCR could detect environmental signals on connectivity more sensitively and could estimate population density, home range shapes, and size variations better than the existing models. For the application to the bear dataset, ADCR could detect the effect of water bodies as a barrier to movement, which is consistent with previous studies, whereas estimates by SCR with the least-cost path were difficult to interpret. ADCR provides unique opportunities to elucidate both individual- and population-level ecological processes from capture-recapture data. By offering a formal link with step selection functions to estimate animal movement, it is suitable for simultaneously modeling capture-recapture data and animal movement data. This study provides a basis for studies of the interplay between animal movement processes and population patterns.

PMID:39989236 | DOI:10.1002/ecy.70046

Protein Sci. 2025 Mar;34(3):e70055. doi: 10.1002/pro.70055.

ABSTRACT

Differential scanning fluorimetry (DSF) can be an effective high-throughput screening assay in drug discovery for detecting protein-compound interactions that stabilize or destabilize macromolecules. Due to the magnitude and quality of the data produced by this biophysical assay, analyzing and prioritizing compounds from large-scale DSF data sets has proven challenging to the research community. Here, we present ShiftScan-a powerful, stand-alone tool designed for the rapid analysis of DSF data and compound prioritization based on thermal transition patterns. ShiftScan accurately and quickly predicts melting temperatures (Tm values) from both canonical and non-canonical transition patterns, efficiently filtering out spurious data to minimize false positives. We report on the use of this tool for data analysis of screens involving both pure compound and natural product fraction libraries and provide the software to the screening community to aid in the discovery of molecularly-targeted compounds. Instructions for installation and usage of ShiftScan can be found at our GitHub repository: https://github.com/samche42/ShiftScan.

PMID:39989223 | DOI:10.1002/pro.70055

Cereb Cortex. 2025 Feb 5;35(2):bhaf025. doi: 10.1093/cercor/bhaf025.

ABSTRACT

In the constantly changing environment that characterizes our daily lives, the ability to predict and adapt to new circumstances is crucial. This study examines the influence of sequence and knowledge adaptiveness on predictive coding in skill learning and rewiring. Participants were exposed to two different visuomotor sequences with overlapping probabilities. By applying temporal decomposition and multivariate pattern analysis, we dissected the neural underpinnings across different levels of signal coding. The study provides neurophysiological evidence for the influence of knowledge adaptiveness on shaping predictive coding, revealing that these are intricately linked and predominantly manifest at the abstract and motor coding levels. These findings challenge the traditional view of a competitive relationship between learning context and knowledge, suggesting instead a hierarchical integration where their properties are processed simultaneously. This integration facilitates the adaptive reuse of existing knowledge in the face of new learning. By shedding light on the mechanisms of predictive coding in visuomotor sequences, this research contributes to a deeper understanding of how the brain navigates and adapts to environmental changes, offering insights into the foundational processes that underlie learning and adaptation in dynamic contexts.

PMID:39989199 | DOI:10.1093/cercor/bhaf025

Diabetol Metab Syndr. 2025 Feb 22;17(1):67. doi: 10.1186/s13098-025-01637-4.

ABSTRACT

BACKGROUND: Diabetes prevalence in China is significant, with a large proportion in the prediabetes stage. Dyslipidemia is associated with abnormal glucose metabolism, and the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) shows potential in diabetes risk assessment, but its role in prediabetes progression is understudied.

METHODS: A longitudinal study from 2011 to 2015 using CHARLS data was conducted. After exclusions, 1408 participants were included. NHHR was calculated from serum TC and HDL – C levels. Diabetes and prediabetes were defined based on standard criteria. Covariates and mediators were assessed, and statistical analyses included logistic regression and mediation analysis, and mediation analysis was conducted to evaluate the involvement of BMI in the association between NHHR and the risk of prediabetes progression.

RESULTS: Among the 1423 people in the cohort analysis, 339 (23.8%) were diagnosed with prediabetes progression. The median NHHR was significantly larger in the progression group (136.99 vs. 124.95, p < 0.05). In the fully adjusted model, NHHR one-unitincrease led to a 10% higher risk. Subgroup analyses showed consistent associations in most subgroups. BMI mediated 33.8% of the NHHR – prediabetes progression association.

CONCLUSION: NHHR is correlated with the risk of prediabetes progressing to diabetes, and BMI may mediate this association. NHHR monitoring could help assess the risk of progression in prediabetes participants.

PMID:39987453 | DOI:10.1186/s13098-025-01637-4

Subst Abuse Treat Prev Policy. 2025 Feb 22;20(1):9. doi: 10.1186/s13011-025-00637-x.

ABSTRACT

OBJECTIVES: Canada continues to experience an epidemic of toxic drug-related overdose deaths. Public health messaging emphasizes the dangers of using drugs alone as it restricts timely overdose response or renders it impossible, yet this practice remains prevalent among people who use drugs. While drug use practices and associated harms are known to be highly gendered, little is known about how factors shaping solitary drug use may differ across genders (including cisgender men, cisgender women, transgender women, Two-Spirit people and gender diverse people). Thus, we sought to explore solitary drug use practices according to gender in Vancouver, Canada.

METHODS: Data were collected through Vancouver Injection Drug Users Study, a prospective cohort study between June 2019 and May 2023. We used gender-stratified multivariable generalized estimating equation models to identify factors associated with using drugs alone.

RESULTS: Among the 697 participants, 297 (42.6%) reported using drugs alone in the previous 6 months at baseline. In multivariable analyses, we found that being in a relationship was negatively associated with using alone for both cisgender men and cisgender women (adjusted odds ratio [AOR] = 0.25 and 0.34, respectively), while homelessness was negatively associated for cisgender men only (AOR = 0.45). Factors positively associated for cisgender men included daily illicit stimulant use (AOR = 1.90), and binge drug use (AOR = 2.18). For cisgender women, only depression was positively associated with using drugs alone (AOR = 2.16). All p-values < 0.05. While unable to conduct a multivariable analysis on transgender, Two-Spirit and gender diverse people due to small sample sizes, bivariate analyses showed larger impact of depression on using alone for Two-Spirit (OR = 8.00) and gender diverse people (OR = 5.05) compared to others, and only gender diverse people’s risk was impacted by experiences of violence (OR = 9.63). All p-values < 0.05.

CONCLUSION: The findings of this study suggest significant heterogeneity in gender-specific factors associated with using drugs alone. Factors exclusively impacting cisgender men’s risk included homelessness and daily stimulant use, and depression having a significant impact on cisgender women’s, but not cisgender men’s, risk. Ultimately, gender-specific factors must be recognized in public health messaging, and in developing policies and harm reduction measures to address the risks associated with using alone.

PMID:39987447 | DOI:10.1186/s13011-025-00637-x

Eur J Med Res. 2025 Feb 22;30(1):123. doi: 10.1186/s40001-025-02340-w.

ABSTRACT

BACKGROUND: Visceral adiposity index (VAI) and diets are associated with the risk of cardiovascular disease (CVD). It is unclear how well VAI and diet predict CVD.

METHODS: Data were obtained from the National Health and Nutrition Examination Survey (NHANES 2017-2018). Demographic data, diets, biochemical examination, and questionnaire information were collected. VAI was calculated using body mass index, waist circumference, triglycerides, and high-density lipoprotein cholesterol. Binary logistic regression was adopted to examine the correlation of VAI and diets with CVD. A decision tree model was developed to predict CVD risk according to different factors.

RESULTS: 2104 participants (mean age: 50.87 ± 17.35 years, 48.38% males) were included. Participants with high levels of VAI (≥ 2.18) had an elevated risk of CVD compared to those with low levels of VAI (≤ 0.76) (OR = 1.654, 95% CI: 1.025-2.669, P = 0.039). Compared with the low protein intake level (≤ 50.34 g), the upper intermediate (72.10-99.92 g) (OR = 0.445, 95% CI: 0.257-0.770, P = 0.004) and high (≥ 99.93 g) levels of protein intake (OR = 0.450, 95% CI: 0.236-0.858, P = 0.015) reduced CVD risk. The decision tree model unveiled that VAI, protein intake, and dietary fiber intake were predictors for CVD.

CONCLUSION: VAI and protein intake levels are independently associated with CVD risk and have predictive power for CVD. These findings can provide insights into the development of appropriate lifestyle and treatment strategies for patients to reduce the incidence of CVD.

PMID:39987443 | DOI:10.1186/s40001-025-02340-w

Nutr J. 2025 Feb 22;24(1):28. doi: 10.1186/s12937-025-01097-6.

ABSTRACT

BACKGROUND: Cancer-related deaths and environmental issues pose significant global challenges. The Planetary Health Diet (PHD) is a healthy dietary pattern that simultaneously promotes human health and ecology. This study aims to investigate the association between the Planetary Health Diet Index (PHDI) and mortality among cancer survivors, as well as the mediating role of inflammation between PHDI and all-cause mortality.

METHODS: This study analyzed data from 3,442 cancer survivors enrolled in the United States National Health and Nutrition Examination Survey between 1999 and 2018. To investigate the association between PHDI and mortality, we applied weighted multivariate Cox proportional hazards regression, restricted cubic spline analysis, subgroup analysis, and sensitivity analysis. The mediating effects of the Systemic Immune-Inflammation Index (SII) and Neutrophil-to-Lymphocyte Ratio (NLR) were assessed using the bootstrap method with 1000 simulations.

RESULTS: In the fully adjusted model, each 10-point PHDI increase correlated with a 9% decrease in all-cause mortality (HR, 0.91; 95% CI, 0.86-0.95), a 10% decrease in cancer mortality (HR, 0.90; 95% CI, 0.83-0.99), and a 10% decrease in non-cancer mortality (HR, 0.90; 95% CI, 0.85-0.96). The PHDI was significantly inversely correlated with SII and NLR, which were positively related to all-cause mortality. The mediation proportions of SII and NLR between the PHDI and all-cause mortality were 6.52% and 8.52%, respectively.

CONCLUSIONS: Adherence to the PHD is associated with reduced all-cause, cancer, and non-cancer mortality among cancer survivors. Additionally, SII and NLR may mediate the relationship between PHDI and all-cause mortality.

PMID:39987440 | DOI:10.1186/s12937-025-01097-6

J Orthop Surg Res. 2025 Feb 22;20(1):192. doi: 10.1186/s13018-025-05585-7.

ABSTRACT

BACKGROUND: Intraoperative endplate injury (IEI) is a type of fracture and a potential complication during lumbar interbody fusion (LIF). Osteoporosis diagnosed by bone mineral density (BMD) is a well-known risk factor for fracture itself and IEI also. The bone turnover markers (BTMs) are parameters of bone qualities and have some correlations with fractures, but there is no study about the BTMs and intraoperative fractures especially IEI. This study aims to identify the correlation between IEI and BTMs, especially in misTLIF.

METHODS: We retrospectively reviewed 184 patients (230 spine levels). The IEI was diagnosed as the breakage of the endplate observed on postoperative 1 mm thin-cut CT scans. All surgical and endogenous risk factors of IEI were also checked including the bone resorption marker (serum CTX) and bone formation marker (serum P1NP) of BTMs. Additionally, the ratio (P1NP/CTX) and the subtype groups of BTMs were analyzed.

RESULTS: The rate of total IEI was 38%. The sex, osteoporosis, spine BMD, femur BMD, CTX, P1NP/CTX, preoperative disc height, and the discrepancy between preoperative disc height and cage size were risk factors in multivariate logistic regression analyses. The subtypes according to BTMs showed a different rate of IEI, resulting in subtype 2 A (low CTX and P1NP and high P1NP/CTX ratio) having the lowest incidence and statistically significant odds ratios compared to other subtype groups.

CONCLUSION: This study demonstrated that the IEI is related to BTMs regardless of BMD in misTLIF. In addition, the P1NP/CTX ratio or subtypes could be helpful in predicting the risk of IEI due to the parallel dynamics of BTMs.

PMID:39987433 | DOI:10.1186/s13018-025-05585-7

BMC Med Res Methodol. 2025 Feb 22;25(1):50. doi: 10.1186/s12874-025-02489-2.

ABSTRACT

BACKGROUND: Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the treatment effect. However, individual significant interactions may not always yield clinically actionable subgroups, particularly for continuous covariates. Non-parametric causal machine learning approaches are flexible alternatives for estimating HTEs across many possible treatment effect modifiers in a single analysis.

METHODS: We conducted a secondary analysis of the VANISH RCT, which compared the early use of vasopressin with norepinephrine on renal failure-free survival for patients with septic shock at 28 days. We used classical (separate tests for interaction with Bonferroni correction), data-adaptive (hierarchical lasso regression), and non-parametric causal machine learning (causal forest) methods to analyse HTEs for the primary outcome of being alive at 28 days. Causal forests comprise honest causal trees, which use sample splitting to determine tree splits and estimate treatment effects separately. The modal initial (root) splits of the causal forest were extracted, and the mean value was used as a threshold to partition the population into subgroups with different treatment effects.

RESULTS: All three models found evidence of HTE with serum potassium levels. Univariable logistic regression OR 0.435 (95%CI [0.270, 0.683]. p = 0.0004), hierarchical lasso logistic regression standardised OR: 0.604 (95% CI 0.259, 0.701), lambda = 0.0049. Hierarchical lasso kept the interaction between the treatment and serum potassium, sodium level, minimum temperature, platelet count and presence of ischemic heart disease. The causal forest approach found some evidence of HTE (p = 0.124). When extracting root splits, the modal split was on serum potassium (mean applied threshold of 4.68 mmol/L). When dividing the patient population into subgroups based on the mean initial root threshold, risk differences in being alive at 28 days were 0.069 (95%CI [-0.032, 0.169]) and – 0.257 (95%CI [-0.368, -0.146]) with serum potassium ≤ 4.68 and > 4.68 respectively.

CONCLUSIONS: The causal forest agreed with the data-adaptive and classical method of subgroup analysis in identifying HTE by serum potassium. Whilst classical and data-adaptive methods may identify sources of HTE, they do not immediately suggest subgroup splits which are clinically actionable. The extraction of root splits in causal forests is a novel approach to obtaining data-derived subgroups, to be further investigated.

PMID:39987431 | DOI:10.1186/s12874-025-02489-2