Biological Data Research Articles

KDM1A genetic alterations, a rare cause of Primary Bilateral Macronodular Adrenal Hyperplasia, strongly associated with food-dependent Cushing's syndrome: results of its systematic germline screening in 301 index cases and genotype/phenotype correlation.

ARMC5 is the most prevalent gene predisposing to Primary Bilateral Macronodular Adrenal Hyperplasia (PBMAH), but germline KDM1A variants have been identified in the rare PBMAH associated with food-dependent Cushing's syndrome (FDCS). The purpose of this work was to assess the frequency of KDM1A variants in a large series of PBMAH patients. 301 consecutive PBMAH index cases from 8 international Endocrinology departments were included. Clinical, biological and imaging data were collected retrospectively. 10 (3.3%) patients carried a germline KDM1A pathogenic or likely pathogenic variant, 60 (19.9%) carried a germline ARMC5 alteration, 231 (76.8%) had no identified genetic predisposition. FDCS was present in all patients with KDM1A variants and absent in the 2 other groups. KDM1A patients had a higher 24h urinary free cortisol (3.0-fold ULN vs 1.36 for ARMC5 patients and 0.66 for wild-type patients, respectively, p=0.0001). In accordance with FDCS pathophysiology, patients with KDM1A variants had a lower morning fasting plasma cortisol (192 nmol/L vs 407 and 428, respectively, p=0.0003) and a higher midnight plasma cortisol (487 nmol/L vs 297 and 171.96, respectively, p=0.0004). Morning/midnight plasma cortisol ratio below 0.65 holds 100% sensitivity and specificity for the detection of FDCS. All patients with KDM1A variants were women, vs 65% of ARMC5 patients and 67% of wild-type patients (p=0.0337). KDM1A germline pathogenic variants are rare in PBMAH and account for less than 5% of index cases. KDM1A seems constantly associated with FDCS, which can be evoked in front of a morning/midnight plasma cortisol ratio below 0.65.

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA.

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

Biological embodiment of educational attainment and future risk of breast cancer: findings from a French prospective cohort

BackgroundWomen with higher educational attainment have a higher risk of developing breast cancer (BC). Despite the acknowledged impact of reproductive and lifestyle factors, some excess risks remain unexplained. Many studies...

Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.

Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. Innovations in biological data and computational chemistry have spurred a shift from trial-and-error methods to holistic approaches to medicinal chemistry. Computational techniques, particularly artificial intelligence (AI), notably machine learning (ML) and deep learning (DL), have revolutionized drug development, enhancing data analysis and predictive modeling. Natural products (NPs) have long served as rich sources of biologically active compounds, with many successful drugs originating from them. Advances in information science expanded NP-related databases, enabling deeper exploration with AI. Integrating AI into NP drug discovery promises accelerated discoveries, leveraging AI's analytical prowess, including generative AI for data synthesis. This perspective illuminates AI's current landscape in NP drug discovery, addressing strengths, limitations, and future trajectories to advance this vital research domain.

PyBootNet: a python package for bootstrapping and network construction

Background Network analysis has emerged as a tool for investigating interactions among species in a community, interactions among genes or proteins within cells, or interactions across different types of data (e.g., genes and metabolites). Two aspects of networks that are difficult to assess are the statistical robustness of the network and whether networks from two different biological systems or experimental conditions differ. Methods PyBootNet is a user-friendly Python package that integrates bootstrapping analysis and correlation network construction. The package offers functions for generating bootstrapped network metrics, statistically comparing network metrics among datasets, and visualizing bootstrapped networks. PyBootNet is designed to be accessible and efficient with minimal dependencies and straightforward input requirements. To demonstrate its functionality, we applied PyBootNet to compare correlation networks derived from study using a mouse model to investigate the impacts of Polycystic Ovary Syndrome (PCOS) on the gut microbiome. PyBootNet includes functions for data preprocessing, bootstrapping, correlation matrix calculation, network statistics computation, and network visualization. Results We show that PyBootNet generates robust bootstrapped network metrics and identifies significant differences in one or more network metrics between pairs of networks. Our analysis of the previously published PCOS gut microbiome data also showed that our network analysis uncovered patterns and treatment effects missed in the original study. PyBootNet provides a powerful and extendible Python bioinformatics solution for bootstrapping analysis and network construction that can be applied to microbes, genes, metabolites and other biological data appropriate for network correlation comparison and analysis.

Uncovering alternative diagnoses in patients with clinical syndromes suggestive of multiple sclerosis: A transversal study from the prospective Barcelona CIS cohort.

It is essential to exclude alternative diagnoses to diagnose multiple sclerosis (MS). However, detailed descriptions of alternative diagnoses in patients with suspected MS presenting with clinically isolated syndrome (CIS) are limited. To describe alternative diagnoses in patients presenting with CIS suggestive of MS. We conducted a descriptive analysis of patients from the Barcelona CIS cohort including subjects under 50 years of age with a CIS suggestive of MS but later diagnosed with conditions other than MS. We collected clinical, biological, and radiological data, and described the alternative etiologies identified. Among 1468 patients in the Barcelona CIS cohort, 100 (6.8%) were diagnosed with an alternative condition. The most common neurological syndrome was optic neuritis (43.0%). Four patients (4.0%) had inflammatory-demyelinating lesions in at least two typical MS topographies on baseline magnetic resonance imaging (MRI), and 2 (2.0%) met the 2017 McDonald MS criteria. The most common etiologies were immune-mediated diseases (42.0%), especially MOGAD, followed by functional neurological disorders (15.0%) and vascular disease (10.0%). The range of alternative diagnoses encountered during the MS diagnostic process highlights the need to rule out better explanations than MS. However, current MS diagnostic criteria effectively identify patients without MS in this context.

Nutrition Knowledge, Food Insecurity, and Dietary Biomarkers: Examining Fruit and Vegetable Intake Among College Students

Objectives: Food insecurity among college students, combined with limited nutrition knowledge and barriers to healthy eating, significantly impacts diet quality and fruit and vegetable intake. Efforts to address these issues are further complicated by the challenges of accurately and efficiently collecting dietary data in research settings. This study aimed to explore the relationship between nutrition knowledge and fruit/vegetable intake using skin, plasma, and dietary carotenoid levels as biomarkers. Methods: Undergraduate and graduate students aged 18 years and older (n = 166) from a California public university were recruited. The sample was predominately female (n = 133, 80%), with 30 males (18%) and three individuals (2%) identifying as non-binary. Food security was assessed using the USDA’s 10-item Adult Food Security Survey Module and nutrition knowledge through a validated questionnaire. Biological data included blood samples and skin carotenoid measurements (Veggie Meter®). Dietary quality (HEI-2015) and carotenoid intake were assessed through Diet ID™, a photo-based assessment tool. Results: The mean nutrition knowledge scores were 36.55 ± 8.83 out of 58 points, and the mean skin carotenoid score was 307.07 ± 110.22. Higher knowledge scores were associated with increased plasma carotenoids, HEI-score, and Diet ID™ total carotenoids. Food security classification did not significantly impact nutrition knowledge but did influence HEI scores and skin carotenoid levels, with very low food security linked to poorer diet quality and lower carotenoid levels. Conclusions: Nutrition knowledge may serve as a significant predictor of fruit and vegetable intake in university students. Despite this correlation, the impact of overall diet quality is potentially hindered by an individual’s food security status. Therefore, while knowledge is critical, addressing food insecurity is essential for enhancing diet quality among college students.

Nursing Effects of Large-Scale Floating Raft Aquaculture Habitats on Conger myriaster: A Perspective from Marine Ranching

This study assessed the growth characteristics of Conger myriaster in large-scale floating raft aquaculture habitats and natural habitats. Monthly sampling in aquaculture and control areas, combined with biological and morphological data analyses, were used to investigate the growth, morphological differences, and seasonal distribution of Conger myriaster in different habitats. The results showed that the floating raft aquaculture habitat was dominated by juveniles, with a higher abundance compared to the control areas. The juveniles exhibited favorable levels of growth and ecological performance in the floating raft aquaculture habitat and experienced less environmental stress. This research shows that the floating raft aquaculture habitat provides a critical stage habitat for juvenile Conger myriaster in island reef areas, providing refuge from predation and facilitating juvenile development. The integration of surface floating rafts and bottom-set artificial reefs in marine ranching can create a fisheries model that focuses on the protection of juveniles and the exploitation of adults.

Leveraging public AI tools to explore systems biology resources in mathematical modeling

Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.

Why the South African National Health Research Ethics Council is wrong about ownership of human biological material and data.

The South African National Health Research Ethics Council (NHREC) states in its 2024 Ethics Guidelines that human biological material (HBM) and data cannot be privately owned under South African law. This position conflicts with established legal principles, guidelines by the Health Professions Council of South Africa (HPCSA), and South African university policies, all of which support private ownership of HBM and data. Private ownership is not only legally sound but also ethically necessary, providing a framework for accountability, ensuring fair recognition of institutional contributions, and enabling responsible custodianship over these valuable resources. The NHREC's denial of private ownership of HBM and data undermines South African research institutions' ability to control their research assets and leaves them vulnerable to exploitation by foreign entities. The NHREC should issue a corrigendum to delete its incorrect position on private ownership of HBM and data.

Descriptive analysis of the efficacity of R-GEMOX /R-IE/R-CEPP in patients with relapsed/refractory (R/R) transplant-ineligible diffuse large B-cell lymphoma (DLBCL).

Patients with Relapsed/Refractory (R/R) Diffuse Large B-Cell Lymphoma (DLBCL) ineligible for Hematopoietic Stem Cell Transplantation (HSCT) may benefit from a second line anticancer drug regimen but real-life outcomes are lacking. To describe the efficacity of 3 anticancer drug regimens (Gemcitabine and Oxaliplatin GEMOX; Ifosfamide and Etoposide IE; Cyclophosphamide, Etoposide, Procarbazine and Prednisone CEPP) combined with Rituximab (R-) in terms of progression-free (PFS) and overall survival (OS). Retrospective study including R/R DLBCL patients HSCT ineligible who received at least one cycle of R-GEMOX, R-IE or R-CEPP between 2010 and 2022. Demographic, clinical, biological and survival data were collected. Univariate and multivariate analysis were performed to identify associated variables with survival outcomes. Sixty-two patients (median age 78 [40-102] with predominantly stage III-IV DLBCL (n = 49, 79%), non-germinal center-B like (n = 27, 44%) were included. Median OS and PFS were 9 (CI95% [3-10]) and 4 months (CI95% [2-13]) for R-GEMOX, 4 (CI95% [2-6]) and 1 month (CI95% [1-4]) for R-IE and 5 (CI95% [3-6]) and 3 months (CI95% [2-15]) for R-CEPP, respectively. Univariate analysis selects ECOG, aa-IPI scores, LDH rate and Ann-Arbor stage with no independently association in multivariate analysis. All three regimens show modest survival benefit especially between last anticancer treatment course and death. Emergence of bispecific antibodies and Cart-cells for which real-life benefits have yet to be demonstrated could be coupled with improved access to early palliative care.

Mx. BIOME: A bioinformatics and big data analysis platform in a data explosion era

With the development of next-generation sequencing and other technologies, there has been an exponential increase in DNA, RNA, and protein sequences and protein structures in the last two decades, paralleled with the advancement of user-friendly bioinformatics tools. Furthermore, the enormous improvement in computational power and accumulation of massive amounts of data has given rise to the development of big data analysis, which can unveil novel patterns, associations, and trends. In view of the unprecedented biological data explosion, we have recently developed a platform known as Mx. BIOME, which provides a collection of some of the most popular and cutting-edge tools in the fields of bioinformatics and big data analysis. Such a collection would facilitate end-users to identify suitable tools for analyzing their specific datasets. Further studies and reviews on various in silico tools are necessary to compare the advantages and limitations.

Comparative analysis of allowable total error specifications for coagulation factor assays utilizing China National External quality assessment scheme data and biological variation data.

It is necessary and challenging to establish reasonable and feasible total error specifications for coagulation factor assays for quality control and assessment. The aim of this study is to establish new total error specifications for coagulation factor assays by combining External Quality Assessment data with reliable biological variation data. Data from China National External Quality Assessment Scheme (28,408 results from 1,381 laboratories) were analyzed, stratifying External Quality Assessment data by reference intervals to establish concentration-dependent total error specifications. A "state-of-the-art" total error was defined at 80% best results of the participants. Then these specifications were compared with biological variation-derived total error to determine recommended total error specifications for different concentration levels. None of the measurands could meet biological variation-derived optimum total error. Eight parameters (FII, FVIII, FX at both high and low levels; FVII and FIX at high level) reached the desirable criteria. Only FXI at low level can't met the minimum criteria. The establishment of total error specifications for coagulation factor assays should be set separately according to different concentration levels and attainability of current testing technologies. Setting total error specifications should also consider clinical requirements and regulatory standards across different regions. Analyte concentration significantly influences laboratory performance, particularly for low-concentration coagulation factors. Total error specifications need updated periodically in the further. Tea, allowable total error; BV, biological variation; SOTA, state of the art; EQAS, External quality assessment schemes; ME, measurement error; RIs, reference intervals; BIVAC, Biological Variation Data Critical Appraisal Checklist; QIs, quality items; EFLM, European Federation of Clinical Chemistry and Laboratory Medicine; APS, analytical quality specifications; ISO, International Organization for Standardization; EQA, External quality assessment; PT, proficiency testing; RCPA, Royal College of Pathologists of Australasia; RfB, Reference Institute for Bioanalytics; ECAT, External Quality Control for Assays and Tests.

Gintegrator: Enhancing biological sequences data integration with real-time identifier translation

Gintegrator: Enhancing biological sequences data integration with real-time identifier translation

Common issues of data science on the eco-environmental risks of emerging contaminants.

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Cohort profile: Outcome Monitoring After Cardiac Surgery (OMACS) - a prospective UK cohort study of cardiac surgery patients at the Bristol Heart Institute.

The Outcome Monitoring After Cardiac Surgery (OMACS) cohort study was set-up with the aim of establishing a rich source of biological samples and health status data from patients who undergo cardiac surgery. The objectives were to use these data to inform the design of new clinical studies and to provide samples and data to answer research questions. Recruitment began on 23 May 2016 and ended 31 May 2022. Adult patients undergoing cardiac surgery at University Hospitals Bristol and Weston NHS Foundation Trust were screened and approached for consent. Participants could optionally consent to provide biological samples (urine, blood and waste tissue) in addition to data. A total of 4068 patients consented to participate in the study with 2027 consenting to donate samples. Participants were sent quality-of-life follow-up questionnaires at 3 and 12 months after surgery. The clinical data were collected from hospital records/databases. The OMACS population appears to be representative of the wider cardiac surgery population with similar preoperative demography to those reported on the UK surgery population. To date, eight studies have been carried out by research teams using data from a total of 1165 OMACS participants. Two Studies Within A Trial have been performed by the OMACS study team. The format of the study patient information leaflet was not significantly associated with an increased recruitment rate, and an alternative theory-informed cover letter included with the 12-month follow-up questionnaires was not associated with an increase in questionnaire completion. Additional exploratory research carried out within the OMACS study has been presented at international conferences. The OMACS study is now closed. The cohort's data and samples will be available to share with researchers, providing an opportunity to answer a variety of research questions (eg, evaluating predictors of adverse outcomes after cardiac surgery such as biomarkers, surgical methods and pre-existing conditions). The data and samples will be available for sharing in a linked anonymised format. ISRCTN90204321 (date assigned: 21 January 2015).

Comparative study of radio-clinical parameters in pediatric forms of fibrous dysplasia and chronic recurrent multifocal osteomyelitis.

Chronic recurrent multifocal osteomyelitis (CRMO) is an autoinflammatory disease with clinical and radiological symptoms that overlap with fibrous dysplasia (FD), particularly in children. This study aimed to compare the clinical and radiological features of craniofacial CRMO and FD in a pediatric population. Seven children with CRMO and 14 with FD were retrospectively identified in our tertiary centre between 2012 and 2022. Their clinical, radiological, and biological (when available) data were collected. Two experienced radiologists reviewed imaging modalities following a standardized form, including characteristics of the lesions, soft-tissue involvement, and signal abnormalities (MRI). Swelling and pain were common symptoms in the CRMO group (7/7 and 5/5, respectively), compared with 11/14 and 2/14 patients in the FD group. Imaging (CT scan) comparisons revealed a predominance of erosive lesions, cortical interruption, periosteal apposition, and soft-tissue involvement in CRMO compared with FD (4/5 vs 0/6, p1=0.01; 5/5 vs 1/6, p2=0.01; 5/5 vs 0/6, p3=0.002; and 4/5 vs 0/6, p4=0.02, respectively). Hyperosteosis was mainly associated with FD lesions (6/6 vs 2/5, p=0.06). Knowledge of the clinical and radiographic differences between CRMO and FD could help clinicians to differentiate between the two diseases.

Calculation of pair distribution functions from small-angle X-ray scattering protein data by direct transform

In a small-angle X-ray scattering analysis of protein molecules in solution the calculation of the pair distribution function, P(r), is invariably performed by an indirect Fourier transform. This approach models a P(r) to fit the available intensity data, I(q). The determination of P(r) via a direct transform from I(q) has been dismissed as unworkable since the range of q that is experimentally measured is necessarily incomplete. Here, it is shown that, provided suitable measures are taken to estimate unmeasured low-resolution data and avoid a sharp data truncation at the high-resolution data limit, the appearance of significant artifacts in the resulting P(r) may be circumvented. Using several examples taken from the Small Angle Scattering Biological Data Bank, it is demonstrated that the P(r) obtained by a direct transform are in close agreement with the P(r) obtained using the popular indirect transform program GNOM.

Automated radiomics model for preoperative pancreatic neuroendocrine tumor grade prediction.

655 Background: Pancreatic neuroendocrine tumors (PNETs) have varying biologic behavior based on factors including tumor grade. Many prognostic factors can only be determined post-pancreatectomy; thus, are unable to guide clinical decision making preoperatively. This is particularly relevant in small, non-functional (NF)-PNETs. Imaging analysis with radiomics may be able to elucidate biologic data such as tumor grade, without the need for tissue sampling. Our study aims to create an automatic pipeline from segmentation to radiomics-signature building to preoperatively predict tumor grade. Methods: Patients that underwent resection between 2003-21 with adequate preoperative arterial phase CT scans were divided into training and validation cohorts. In the training cohort, the pancreas and tumor region were manually segmented and used to train an auto-segmentation model. The validation cohort then underwent automatic segmentation. A total of 255 radiomics features were extracted from the tumor region. Correlated features were removed, and the minimum redundancy maximum relevance (mRMR) method was used to select the final feature set. These features were used to train a Support Vector Machine (SVM)-based classifier through the training cohort to predict grade, which was dichotomized into grade I versus II/III. The radiomic based prediction model was then evaluated in the automatically segmented validation cohort. Results: A total of 186 patients were identified and divided into training (n = 140) and validation (n = 46) cohorts. Median age was 57 (27, 85) and 52% were male. 113 (62%) and 70 (38%) patients were grades I and II/III, respectively. Median tumor size was 24 mm (6, 200) and 43 (26%) patients had positive nodal disease. The auto-segmentation model was able to accurately segment the tumor region in 33 (72%) patients of the validation cohort. In the training cohort (n = 140), the radiomics-signature produced an area under the curve (AUC) of 0.87 (0.81, 0.93). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.94 (0.9, 0.98), 0.74 (0.66, 0.81), 0.72 (0.65, 0.80) and 0.97 (0.94, 1.00) respectively. In the auto-segmented validation cohort (n = 33), the radiomics model produced an AUC of 0.75 (0.61, 0.90). Sensitivity, specificity, PPV and NPV were 0.88 (0.77, 0.99), 0.62 (0.46, 0.79), 0.71 (0.56, 0.87) and 0.83 (0.71, 0.96), respectively. Conclusions: The proposed auto-segmentation model was able to automatically identify tumor regions with a high degree of accuracy. Similarly, the subsequent radiomics-signature was also able to strongly discern PNET grade. This automatic pipeline bypasses manual segmentation and could help incorporate a radiomics-signature into preoperative clinical decision-making for PNETs.

Correlation of the Geriatric Nutritional Risk Index (GNRI) With Other Indicators of Nutrition in Chronic Hemodialysis Patients.

The GNRI (Geriatric Nutritional Risk Index1) is an index used in geriatrics to predict the risk of complications and mortality associated with malnutrition. It considers serum albumin levels and the ratio of current weight or BMI to the ideal theoretical weight/BMI. The aim of this study was to evaluate this index in a population of metabolically stable chronic hemodialysis patients aged > 60 years and associate it with other nutritional markers. The studied patient cohort was divided into two groups based on their Geriatric Nutritional Risk Index (GNRI) scores: Gr 1 with GNRI score < 97 and Gr 2 with GNRI ≥ 97. We registered the anthropometric, clinical, and biological data of the study population. One hundred seventy-seven patients (102 M-75F) undergoing chronic hemodialysis were included. There were no differences in age, muscle mass estimated by bioimpedance analysis, potassium levels, phosphorus levels, and nPCR between the groups. However, there were significant differences between the two groups concerning the primary disease. Gr 1 presented with a higher prevalence of diabetes and cardiovascular comorbidities. Additionally, Gr 1 presented with lower handgrip strength (Mean ± standard deviation in kg, 19.79 ± 9.37 vs. 26.83 ± 11.63, p = 0.05), lower fat mass index estimated by bioimpedance analysis (Mean ± standard deviation in kg/m2, 7.31 ± 4.55 vs. 15.24 ± 6.47, p < 0.001), and higher CRP levels (Mean ± standard deviation in mg/l, 22.27 ± 23.49 vs. 8.13 ± 10.14, p < 0.001). In conclusion, the GNRI, an easy calculation tool for nutrition assessment, is associated with important nutritional status parameters in chronic hemodialysis patients.