The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition

Journal article

Spinal cord injury (SCI) leads to complex nutritional alterations, including energy imbalance, skewed macronutrient and micronutrient intake, and disrupted nutrient absorption and metabolism. These changes contribute to increased risks of obesity, cardiovascular disease, metabolic syndrome, and other comorbidities, profoundly affecting long-term recovery and quality of life. Despite the growing recognition of these challenges, nutritional assessment methods for SCI patients remain fragmented and insufficient. This review first outlines the major nutritional consequences and clinical implications of SCI, then focuses on current methods for assessing nutritional status in this population. Three major domains are discussed: body composition analysis, nutrient intake and absorption assessment, and energy metabolism monitoring. Traditional tools such as anthropometry, food diaries, and indirect calorimetry are discussed alongside advanced technologies including magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry (DXA), and metabolomics. By highlighting both current limitations and emerging solutions, this review underscores the importance of personalized, technology-assisted nutritional assessment strategies to guide clinical decision-making and optimize outcomes for individuals with SCI

Authors: van der Meer, D; Gurholt, TP; Sønderby, IE; Shadrin, AA; Hindley, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Communications Biology
• Volume: 5
• Issue: 1
• Pages: 1271-1271
• Article number: 1271
• Publication date: 2022-11-19
• DOI: 10.1038/s42003-022-04237-4
• EISSN: 2399-3642
• ISSN: 2399-3642
• Language: English
• Keywords: Gene; Genetic association; Genetic architecture; Genotype; Physiology; Obesity; Heritability; Biology; Internal medicine; Genetics; Body mass index; Anthropometry; Medicine; Genome-wide association study; Quantitative trait locus; Single-nucleotide polymorphism; Body fat percentage; Bioinformatics