Category: Nevin Manimala

Pain. 2024 Oct 16. doi: 10.1097/j.pain.0000000000003445. Online ahead of print.

ABSTRACT

Neuropathic pain remains difficult to treat, with drug development hampered by an incomplete understanding of the pathogenesis of the condition, as well as a lack of biomarkers. The problem is compounded by the scarcity of relevant human peripheral tissues, including skin, nerves, and dorsal root ganglia. Efforts to obtain such samples are accelerating, increasing the need for standardisation across laboratories. In this white paper, we report on a consensus meeting attended by neuropathic pain experts, designed to accelerate protocol alignment and harmonization of studies involving relevant peripheral tissues. The meeting was held in London in March 2024 and attended by 28 networking partners, including industry and patient representatives. We achieved consensus on minimal recommended phenotyping, harmonised wet laboratory protocols, statistical design, reporting, and data sharing. Here, we also share a variety of relevant standard operating procedures as supplementary protocols. We envision that our recommendations will help unify human tissue research in the field and accelerate our understanding of how abnormal interactions between sensory neurons and their local peripheral environment contribute towards neuropathic pain.

PMID:39432804 | DOI:10.1097/j.pain.0000000000003445

Proc Natl Acad Sci U S A. 2024 Oct 29;121(44):e2408715121. doi: 10.1073/pnas.2408715121. Epub 2024 Oct 21.

ABSTRACT

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

PMID:39432782 | DOI:10.1073/pnas.2408715121

Proc Natl Acad Sci U S A. 2024 Nov 5;121(45):e2403309121. doi: 10.1073/pnas.2403309121. Epub 2024 Oct 21.

ABSTRACT

We chose the “natural laboratory” provided by high-altitude native ethnic Tibetan women who had completed childbearing to examine the hypothesis that multiple oxygen delivery traits were associated with lifetime reproductive success and had genomic associations. Four hundred seventeen (417) women aged 46 to 86 y residing at ≥3,500 m in Upper Mustang, Nepal, provided information on reproductive histories, sociocultural factors, physiological measurements, and DNA samples for this observational cohort study. Simultaneously assessing multiple traits identified combinations associated with lifetime reproductive success measured as the number of livebirths. Women with the most livebirths had distinctive hematological and cardiovascular traits. A hemoglobin concentration near the sample mode and a high percent of oxygen saturation of hemoglobin raised arterial oxygen concentration without risking elevated blood viscosity. We propose ongoing stabilizing selection on hemoglobin concentration because extreme values predicted fewer livebirths and directional selection favoring higher oxygen saturation because higher values had more predicted livebirths. EPAS1, an oxygen homeostasis locus with strong signals of positive natural selection and a high frequency of variants occurring only among populations indigenous to the Tibetan Plateau, associated with hemoglobin concentration. High blood flow into the lungs, wide left ventricles, and low hypoxic heart rate responses aided effective convective oxygen transport to tissues. Women with physiologies closer to unstressed, low altitude values had the highest lifetime reproductive success. This example of ethnic Tibetan women residing at high altitudes in Nepal links reproductive fitness with trait combinations increasing oxygen delivery under severe hypoxic stress and demonstrates ongoing natural selection.

PMID:39432765 | DOI:10.1073/pnas.2403309121

Nutr Rev. 2024 Oct 21:nuae150. doi: 10.1093/nutrit/nuae150. Online ahead of print.

ABSTRACT

CONTEXT: Individual variability in oral vitamin D supplement response hinders the understanding of its clinical impact, and while ethnicity has been implicated in this variability it has not been well described.

OBJECTIVE: The aim was to systematically assess the impact of ethnicity on response to oral vitamin D supplementation.

DATA SOURCE: The Web of Science and PubMed databases were searched for articles published from 1960 to the end of 2020. All trials in adults measuring 25(OH)D3 blood levels were included.

DATA EXTRACTION: Two reviewers independently extracted the data from the eligible studies. The change in 25(OH)D3 blood levels (95% CI) and P values were extracted, and grouped according to ethnicity, then subjected to random-effects meta-analysis. The primary outcome measurement was mean serum 25(OH)D3 levels and the secondary outcome was dose-adjusted mean serum 25(OH)D3 levels, both compared with baseline.

DATA ANALYSIS: A total of 18 studies were identified, and data from 1131 participants were extracted. Body mass index (BMI) and dose were significant covariates (Pearson correlation coefficient, P = .016 and .017) and were normalized in the meta-analysis to minimize heterogeneity, but latitude was not (P = .66). Meta-analysis showed an effect of ethnicity on dose and BMI-adjusted mean serum 25(OH)D3 levels compared with baseline (P < .00001, I2 = 98%). Asian and White study participants demonstrated a statistically higher increase in dose and BMI-adjusted 25(OH)D3 blood levels (183 nmol/L [95% CI, 163-203] and 173 nmol/L [95% CI, 152-194], respectively), compared with Arab and Black study participants (37 nmol/L [95% CI, 35-39] and 99 nmol/L [95% CI, 90-108]) using repeated t tests. Sensitivity analysis demonstrated that these findings were not impacted by potential study bias or the inclusion of immigrant populations.

CONCLUSION: Ethnicity had an impact on oral vitamin D response. Further prospective studies should examine if ethnicity-based dose stratification in both clinical practice and clinical trials is warranted.

SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42023410076.

PMID:39432764 | DOI:10.1093/nutrit/nuae150

J Leukoc Biol. 2024 Oct 21:qiae223. doi: 10.1093/jleuko/qiae223. Online ahead of print.

ABSTRACT

Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk and enhance clinical management. Optimal efficacy of COVID-19 antiviral medications relies on early administration within the initial five days of symptoms, assisting high-risk patients in avoiding hospitalization and improving survival chances. The complete blood count can be an efficient and affordable option to find biomarkers that predict the COVID-19 prognosis due to infection-induced alterations in various blood parameters. This study aimed to associate hematological parameters with different COVID-19 clinical forms and utilize them as disease outcome predictors. We performed a complete blood count in blood samples from 297 individuals with COVID-19 from Belo Horizonte, Brazil. Statistical analysis, as well as ROC Curves and machine learning Decision Tree algorithms were used to identify correlations, and their accuracy, between blood parameters and disease severity. In the initial four days of infection, traditional hematological COVID-19 alterations, such as lymphopenia, were not yet apparent. However, the monocyte percentage and granulocyte-to-lymphocyte ratio proved to be reliable predictors for hospitalization, even in cases where patients exhibited mild symptoms that later progressed to hospitalization. Thus, our findings demonstrate that COVID-19 patients with monocyte percentages lower than 7.7% and a granulocyte-to-lymphocyte ratio higher than 8.75 are assigned to the hospitalized group with a precision of 86%. This suggests that these variables can serve as important biomarkers in predicting disease outcomes and could be used to differentiate patients at hospital admission for managing therapeutic interventions, including early antiviral administration. Moreover, they are simple parameters that can be useful in minimally equipped health care units.

PMID:39432758 | DOI:10.1093/jleuko/qiae223

Trends Psychiatry Psychother. 2024 Oct 22. doi: 10.47626/2237-6089-2024-0908. Online ahead of print.

ABSTRACT

OBJECTIVE: Approximately 6 to 13% of women suffer from antenatal depression (AD) around the world. AD can lead to several health problems for mother-baby. Vitamin D is a molecule that appears to have great preventive/therapeutic potential against neuropsychiatric disorders. The present study aimed to analyze the association between deficiency of vitamin D and AD in pregnant women in a city in the south of Brazil (Pelotas, RS). We hypothesize that pregnant women with a positive AD diagnosis have deficient levels of 25-hydroxyvitamin D (25(OH)D).

METHODS: This cross-sectional study was conducted in a cohort study (CEP/UCPEL 47807915.4.0000.5339). From this cohort, 180 pregnant women at up to 24 weeks gestation were selected (130 non-depressed and 50 depressed), and the diagnosis of depression was made using the MINI-Plus. Blood was collected and stored for the later analysis of vitamin D (25(OH)D) by chemiluminescence method. The SPSS program was used for data analysis, and p<0.05 was considered statistically significant.

RESULTS: In our study, we showed a significant association between Major Depressive Episode current in the antenatal period and vitamin D deficiency (OR: 0.9; CI 95%: 0.9;1.0, p=0.003).

CONCLUSION: Our results demonstrate that vitamin D deficiency may be involved in major depressive disorder in the antenatal period, in this way it advised a follow-up of vitamin D levels in the pregnancy-puerperal cycle to minimize mental health problems in women and prevent developmental deficits in children.

PMID:39432746 | DOI:10.47626/2237-6089-2024-0908

Chaos. 2024 Oct 1;34(10):103138. doi: 10.1063/5.0237801.

ABSTRACT

Analysis of correlation between time series is an essential step for complex system studies and dynamical characteristics extractions. Martingale difference correlation (MDC) theory is mainly concerned with the correlation of conditional mean values between response variables and predictor variables. It is the generalization and deepening of the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and other statistics. In this paper, on the basis of phase space reconstruction, the generalized dependence index (GDI) is proposed by using MDC and martingale difference divergence matrix theories, which can measure the degree of dependence between time series more effectively. Moreover, motivated by the theoretical framework of the refined distance correlation method, the corresponding dependence measure (DE) is employed in this paper to construct the DE-GDI plane, so as to comprehensively and intuitively distinguish different types of data and deeply explore the operating mechanism behind the relevant time series and complex systems. According to the performances tested by the different simulated and real-world data, our proposed method performs relatively reasonably and reliably in dependence measuring and data distinguishing. The proposal of this complex data clustering method can not only recognize the features of complex systems but also distinguish them effectively so as to acquire more relevant detailed information.

PMID:39432719 | DOI:10.1063/5.0237801

Medicine (Baltimore). 2024 Oct 18;103(42):e40131. doi: 10.1097/MD.0000000000040131.

ABSTRACT

BACKGROUND: Endometriosis is a common benign disease in women of childbearing age, with a malignant change rate of about 1%. Endometriosis associated ovarian cancer (EAOC), which usually occurs in the ovaries, is a serious threat to women’s health. Early identification of high-risk groups of EMs malignant transformation is of great significance for the prevention and treatment of EAOC. However, there is still a lack of specific and sensitive prediction factors. In recent years, scholars at home and abroad have used traditional statistical methods and machine learning to explore EAOC related prediction factors and prediction models. This paper mainly reviews and evaluates the diagnosis and prediction model of EAOC.

METHODS: Studies were identified by searching the CNKI, PubMed and Web of Science Core Collection, (WOSCC) till 2023, Data which met the inclusion criteria of clinical studies were evaluated about the quality. This paper analyzes and summarizes the prediction factors and prediction models in the literature.

RESULTS: After screening, 7 relevant studies were finally obtained. Prediction factors included: age, menstruation, menopausal status, course of disease, infertility associated with endometriosis, history of single estrogen use during menopause, serological indexes: human epididymis protein 4, carbohydrate antigen 125(CA125), ovarian malignancy risk algorithm, indications for ultrasound examination: cyst shape, structure and blood flow signal, etc. Prediction models: Alignment diagram, Multivariate logistic regression model, Gail model, Gradient Boosting Decision Tree and Lasso-logistics regression.

CONCLUSION: Related models were in good agreement with the actual situation, and have good sensitivity and specificity. The relevant prediction factors and prediction models were summarized to provide reference and new thinking for the research of prediction models in the field of EAOC, in order to develop standardized long-term management strategies for high-risk groups of EAOC and realize the advance of the diagnosis threshold of patients with EAOC.

PMID:39432664 | DOI:10.1097/MD.0000000000040131

Medicine (Baltimore). 2024 Oct 18;103(42):e40091. doi: 10.1097/MD.0000000000040091.

ABSTRACT

Early diagnosis of gestational diabetes mellitus (GDM) reduces the risk of adverse perinatal and maternal outcomes. At present, the value of serum adiponectin (ADP) and pregnancy-associated plasma protein A (PAPP-A) in clinical practice for the diagnosis of GDM in early pregnancy is unclear. To investigate the predictive value of serum ADP and PAPP-A in GDM. The electronic medical record data of all pregnant women from Zhongshan People’s Hospital from 2018 to 2021 were retrospectively collected and divided into GDM group and control group according to whether GDM occurred. ADP and PAPP-A levels of the 2 groups were detected in early pregnancy, and the related factors of GDM were analyzed by binary logistic regression analysis. Receiver operating characteristic (ROC) curves of ADP and PAPP-A in predicting GDM in the early pregnancy were plotted and their clinical predictive value was analyzed. The significance level for all statistical tests is 0.05. Compared with the non-GDM group, the ADP of the GDM group was significantly lower than that of the non-GDM group [(8.19 ± 2.24) vs. (10.04 ± 2.73)]mg/L, the difference between groups was statistically significant (P < .05), and the multiple of median (MoM) of PAPP-A was significantly lower than that of the non-GDM group (1.13 ± 0.52) versus (1.45 ± 0.61) (P < .05). Binary logistic regression analysis showed that elevated serum ADP and PAPP-A levels were negatively correlated with the subsequent development of GDM [odds ratio (OR) 95% confidence interval (95% CI)] was 0.626 (0.536, 0.816), 0.934 (0.908, 0.961), respectively, P < .05.ROC curve analysis showed that the sensitivity and specificity of ADP and PAPP-A in predicting gestational diabetes were79.1% and 58.6%, respectively, 92.7% and 73.1%, and respectively. The area under curve (AUC) is 0.755 for ADP and 0.770 for PAPP-A. The AUC of the combined detection was 0.867, both of which were higher than that of single index diagnosis, and the sensitivity and specificity of the combined detection were 0.958 and 0.853, respectively. In summary, PAPP-A and ADP levels are independent related factors affecting the occurrence of GDM. The combined detection of PAPP-A and ADP should be utilized in diagnosing GDM to improve pregnancy outcomes for pregnant women.

PMID:39432661 | DOI:10.1097/MD.0000000000040091

Medicine (Baltimore). 2024 Oct 18;103(42):e39877. doi: 10.1097/MD.0000000000039877.

ABSTRACT

Although several studies have discussed the relationships among digital health literacy, health, and exercise behavior, few have integrated these 3 factors into a single model. This study aims to address this research gap. This article aims to analyze the impact of digital health literacy on the health of older adults, as well as the mediating mechanisms related to exercise frequency and duration. A cross-sectional survey was conducted in Luoyang and Zhengzhou urban areas from December 2023 to January 2024. Utilizing random sampling methods, data were collected from 661 older adults through the “digital health literacy scale,” “health scale,” and “count of exercise duration and frequency” questionnaires. The data were processed by employing SPSS 20 and Process, v3.0, and analyzed through independent samples t test, 1-way ANOVA (F-test), and mediation testing methods. The results indicate that no statistical significance (P > .05) is observed in terms of the 3 dimensions of digital health literacy, exercise behavior, and health status among older adults with different genders, living conditions, educational backgrounds, and economic status. In contrast, statistical significance (P < .05) is observed in terms of exercise frequency and health status among older adults with varying levels of smoking and drinking. The 3 dimensions of digital health literacy among older adults statistically impact (P < .05) their exercise duration, frequency, and health. The dimension of access and assessment exerts the most significant influence on exercise duration (β = 0.415) and a considerable impact on health (β = 0.214). Furthermore, the impact of exercise duration and frequency on health status is statistically significant (P < .05). In terms of the interactive capability dimension, exercise frequency exerts the most significant influence (β = 0.199). Digital health literacy has a significant impact on the health of older adults. The duration and frequency of exercise play a partial mediating role between older adults’ digital health literacy and their physical health status. Digital health literacy can encourage older adults to increase the duration and frequency of exercise, which, in turn, promotes their physical health.

PMID:39432656 | DOI:10.1097/MD.0000000000039877