Gene Expression Profiling in Metabolic Disorders

Gene expression profiling has emerged as a foundational approach for deciphering the molecular underpinnings of metabolic disorders such as obesity, type 2 diabetes and metabolic syndrome. By quantifying RNA transcripts on a genome-wide scale through technologies like microarrays and RNA sequencing, researchers can identify altered pathways of lipid synthesis and oxidation, insulin signalling, inflammation and energy homeostasis. Analyses span multiple tissues—including liver, muscle, adipose depots and accessible blood cells—to reveal both tissue‐specific and systemic signatures of disease. Integration of transcriptomic data with genetic variation (expression quantitative trait loci) and clinical phenotypes has begun to illuminate causal networks driving metabolic dysregulation. These insights hold promise for early detection, personalised dietary or pharmacological interventions and monitoring of therapeutic efficacy, thereby addressing a global burden of chronic metabolic conditions.

Research from Nature Portfolio

Recent studies have explored early biomarkers in PBMCs to predict later metabolic disturbances and monitor therapeutic interventions. One investigation used a gestational undernutrition model in rats to map PBMC transcriptomes and identify markers that were normalised by neonatal leptin supplementation, suggesting a window for reversing programming effects via targeted nutritional interventions. A pilot human study evaluated PBMC expression of lipid metabolism genes in overweight and obese individuals undergoing weight‐loss programmes. This work confirmed that expression of key regulators of fatty acid oxidation and synthesis in blood cells mirrors tissue‐level changes and correlates with adiposity and metabolic improvement, supporting PBMCs as surrogate reporters of metabolic health.

Gene Expression Profiling in Metabolic Disorders publication trend

The graph below shows the total number of articles in gene expression profiling in metabolic disorders across all publications each year (not limited to Nature Index journals).

Technical terms

Gene expression profiling: Measurement of mRNA levels across many genes to determine cellular responses or disease states.

Peripheral blood mononuclear cells (PBMCs): A mixed population of blood cells (lymphocytes and monocytes) used as accessible indicators of systemic gene expression.

Transcriptomic analysis: Comprehensive study of all RNA transcripts present in a cell or tissue at a given time.

Polymorphism: A genetic variant that may influence gene expression or protein function.

Biomarker: A measurable indicator of a biological condition or response, used for diagnosis, prognosis or monitoring.

References

1. Impact of the FTO Gene Variation on Appetite and Fat Oxidation in Young Adults. Nutrients (2023).
2. TLCD4 as Potential Transcriptomic Biomarker of Cold Exposure. Biomolecules (2024).
3. Identifying Candidate Genes for Type 2 Diabetes Mellitus and Obesity through Gene Expression Profiling in Multiple Tissues or Cells. Journal of Diabetes Research (2013).
4. Blood cell transcriptomic-based early biomarkers of adverse programming effects of gestational calorie restriction and their reversibility by leptin supplementation. Scientific Reports (2015).
5. Use of human PBMC to analyse the impact of obesity on lipid metabolism and metabolic status: a proof-of-concept pilot study. Scientific Reports (2021).