## 3 Diagnostic Advancements  
### Obesity Diagnostics  
While body mass index (BMI) has long been the cornerstone of diagnosing obesity, its limitations are well documented. BMI is a simple calculation based on weight and height, but it does not differentiate between muscle mass and fat, potentially misclassifying individuals with a high proportion of muscle as obese. This highlights the need for more sophisticated diagnostic tools. Recent advancements are offering a more nuanced picture of body composition, paving the way for a more personalized approach to obesity diagnosis [14]. **Technological progress:** Bioelectrical impedance analysis (BIA) is a well-established, non-invasive technique for obtaining insights into body composition, particularly fluid distribution, lean muscle mass, and body fat percentage. BIA devices send a low-level electrical current through the body, measuring the resistance encountered by different tissues. Muscle tissue conducts electricity more readily than fat, allowing BIA to estimate body fat percentage. Studies have shown BIA to be a relatively quick and non-invasive method for assessing body composition in clinical settings [15; 16]. The BIA device demonstrated superior diagnostic capabilities, as evidenced by a higher area under the curve (AUC \(\geq\) 0.94) compared to BMI (AUC 0.89), according to an ROC analysis [17]. Still, studies that have tested BIA devices in comparison to other methods have found that BIA underestimates body fat measurement compared with dual-energy X-ray absorptiometry (DXA), for example, so it does not yet have enough evidence to be considered an interchangeable method of assessing adiposity [18], especially in people for whom these devices have not been developed and approved, indicating that BIA could be a method with limited concordance and validity compared to DXA [19]. However, BIA accuracy can be influenced by hydration levels and requires careful interpretation by trained professionals [20]; as such, the forthcoming investigations have to address these issues more deeply. Dual-energy X-ray absorptiometry (DXA) scans utilize two different X-ray energies to precisely measure bone mineral density, lean muscle mass, and fat mass throughout the body. DXA scans offer a more detailed and comprehensive assessment of body composition compared to BIA [21]. While DXA scans are highly accurate, they are typically more expensive and time consuming than BIA, limiting their widespread use in primary care settings [19; 22]. Beyond these established techniques, the field is actively exploring the potential of novel biomarkers in blood tests for identifying individuals at risk of obesity-related complications. Emerging research suggests that specific blood markers may correlate with increased visceral fat accumulation, a major health concern associated with obesity. **Biomarkers:** The field of obesity diagnosis has seen significant advancements with the introduction of biomarkers. Biomarkers have provided a more nuanced understanding of obesity, going beyond traditional measures like BMI and waist circumference [23; 24]. Recent research has increasingly relied on new technologies to identify mechanisms in the development of obesity using various \"omics\" platforms. These include genomics, transcriptomics, proteomics, metabolomics, and lipidomics, which have led to the identification of genetic and epigenetic biomarkers that translate into changes in the transcriptome, proteome, and metabolome [25]. These biomarkers could serve as targets for obesity prevention. In the era of \"precision medicine\", there is an increasing interest in novel biomarkers such as adipokines, cytokines, metabolites, and microRNAs implicated in obesity [26]. In the context of the development of modern therapies, some of these biomarkers, such as glucagon-like peptide-1 (GLP-1), monocyte chemoattractant protein-1 (MCP-1), glucose-dependent insulinotropic peptide (GIP), and insulin-like growth factor-binding protein 7 (IGFBP-7), have already shown promising results in clinical trials [27].