Application of Magnetic Resonance Imaging in the Evaluation of Nutritional Status: A Literature Review with Focus on Dialysis Patients

Investigation of accordance between nutritional assessment tools, and bio-electrical impedance-derived phase angle, with the global leadership initiative on malnutrition criteria in hemodialysis patients

BACKGROUND

Malnutrition (MN) is a major health concern for patients with chronic kidney disease (CKD) who receive maintenance hemodialysis (MHD). These patients are particularly vulnerable to MN due to their compromised health status, which in turn increases the risk of morbidity and mortality. However, there is limited evidence on the use of reliable and effective tools for assessing MN in this population. This lack of sufficient data highlights the crucial need to assess MN within these patients, considering the significant mortality risk it poses. The first aim of this study was to compare the concurrent validity of three nutritional methods: the 7-point Subjective Global Assessment (7p-SGA), the Nutritional Risk Index (NRI), and the Nutritional Risk Screening from 2002 (NRS-2002) with the Global Leadership Initiative on Malnutrition (GLIM) criteria in MHD patients. The second aim was to investigate the advantage of the bio-electrical impedance-derived phase angle (PhA) in predicting MN.

METHODS

one hundred sixty-eight outpatients (31% women) with a mean age of 56.9 ± 14.7 years and a median dialysis vintage of 48 months were included in this retrospective study. Nutritional scores, anthropometric measurements, biological markers, and body composition parameters were collected.

RESULTS

According to GLIM standards, MN was identified in 80% of these patients. Using logistic regression (LR) analysis, all nutritional scores were significantly associated with GLIM criteria, with optimal sensitivity (94.4%) and specificity (85.7%) for 7p-SGA and NRI, respectively. For discriminating the nutritional risk, the GLIM criteria demonstrated a good agreement with 7p-SGA (Kappa concordance coefficient (κ) = 0.677, p-value<0.001) with a good level of accuracy (Area Under the Curve (AUC) = 0.841; 95% Confidence Interval (CI) = 0.705-0.977; p-value <0.001) when compared to the NRI and NRS-2002 (κ = 0.522, p-value<0.001 and κ = 0.411, p-value = 0.006, respectively). An excellent accuracy was found between PhA and the GLIM-defined MN, with an optimal cut-off value of 5.5° for males and 4.5° for females. The LR showed that arm circumference is the parameter that most influences the decrease of PhA (odds ratio 2.710, 95% CI = 1.597-4.597, p-value <0.001).

CONCLUSION

Based on the results of the present study, 7p-SGA is the most sensitive score in identifying MN diagnosed by GLIM criteria. Nonetheless, NRI exhibits greater specificity. PhA is a valuable marker for MN in MHD patients.

Body Composition Evaluation and Clinical Markers of Cardiometabolic Risk in Patients with Phenylketonuria

Cardiovascular diseases are the main cause of mortality worldwide. Patients with phenylketonuria (PKU) may be at increased cardiovascular risk. This review provides an overview of clinical and metabolic cardiovascular risk factors, explores the connections between body composition (including fat mass and ectopic fat) and cardiovascular risk, and examines various methods for evaluating body composition. It particularly focuses on nutritional ultrasound, given its emerging availability and practical utility in clinical settings. Possible causes of increased cardiometabolic risk in PKU are also explored, including an increased intake of carbohydrates, chronic exposure to amino acids, and characteristics of microbiota. It is important to evaluate cardiovascular risk factors and body composition in patients with PKU. We suggest systematic monitoring of body composition to develop nutritional management and hydration strategies to optimize performance within the limits of nutritional therapy.

Low Tissue Creatine: A Therapeutic Target in Clinical Nutrition

Low tissue creatine characterizes many conditions, including neurodegenerative, cardiopulmonary, and metabolic diseases, with a magnitude of creatine shortfall often corresponds well to a disorder’s severity. A non-invasive monitoring of tissue metabolism with magnetic resonance spectroscopy (MRS) might be a feasible tool to evaluate suboptimal levels of creatine for both predictive, diagnostic, and therapeutic purposes. This mini review paper summarizes disorders with deficient creatine levels and provides arguments for assessing and employing tissue creatine as a relevant target in clinical nutrition.

A Role for Data Science in Precision Nutrition and Early Brain Development

Multimodal brain magnetic resonance imaging (MRI) can provide biomarkers of early influences on neurodevelopment such as nutrition, environmental and genetic factors. As the exposure to early influences can be separated from neurodevelopmental outcomes by many months or years, MRI markers can serve as an important intermediate outcome in multivariate analyses of neurodevelopmental determinants. Key to the success of such work are recent advances in data science as well as the growth of relevant data resources. Multimodal MRI assessment of neurodevelopment can be supplemented with other biomarkers of neurodevelopment such as electroencephalograms, magnetoencephalogram, and non-imaging biomarkers. This review focuses on how maternal nutrition impacts infant brain development, with three purposes: (1) to summarize the current knowledge about how nutrition in stages of pregnancy and breastfeeding impact infant brain development; (2) to discuss multimodal MRI and other measures of early neurodevelopment; and (3) to discuss potential opportunities for data science and artificial intelligence to advance precision nutrition. We hope this review can facilitate the collaborative march toward precision nutrition during pregnancy and the first year of life.

Application of first-order feature analysis of DWI-ADC in rare malignant mesenchymal tumours of the maxillofacial region

BACKGROUND

To research the first-order features of apparent diffusion coefficient (ADC) values on diffusion-weighted magnetic resonance imaging (DWI) in maxillofacial malignant mesenchymal tumours.

METHODS

The clinical data of 12 patients with rare malignant mesenchymal tumours of the maxillofacial region (6 cases of sarcoma and 6 cases of lymphoma) treated in the hospital from May 2018 to June 2020 and were confirmed by postoperative pathology were retrospectively analyzed. The patients were all examined by 1.5T magnetic resonance imaging. PyRadiomics were used to extract radiomics imaging first-order features. Group differences in quantitative variables were examined using independent-samples t-tests.

RESULTS

The voxels number of ADC_mean and ADC_median of sarcoma tissues were 44.9124 and 44.2064, respectively, significantly higher than those in lymphoma tissues (ADC_mean (- 68.8379) and ADC_median (- 74.0045)), the difference considered statistically significant, so do the ADC_kurt and ADC_skew.

CONCLUSIONS

The statistical difference of ADC_mean and ADC_median is significant, it is consistent with the outcome of the manual measurement of the ADC mean value of the most significant cross-section of twelve cases of lymphoma. Development of tumour volume based on the ADC parameter map of DWI demonstrates that the first-order ADC radiomics features analysis can provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Therefore, first-order ADC features of ADC_kurt combined ADC_skew may improve the diagnosis level.