Validation of new anthropometry-based standard for metabolic syndrome and nutritional status screening: A pilot study.

Background and aims: Metabolic syndrome is one of the greatest health threats in the modern world. Challenges associated with diagnostics and absence of a preventive strategy contribute to the evolution of metabolic syndrome towards central obesity and type 2 diabetes. Indicators such as body mass index (BMI) and body fat percentage (BF%) have limited clinical applications. Although anthropometrical indicators strongly correlate with risk of mortality, they have limited clinical applicability due to their inability to grade risk of cardiovascular and metabolic disease. We evaluated the ability of an anthropometry-based method, the Morphogram, that integrates body segment circumferences with validated cut-offs from the literature, to estimate body composition (BF% and lean mass percentage, LM%) and compute a score for metabolic syndrome risk (MSR). The aims of our study were (1) to assess the extent to which BF% and LM% measured by dual energy X-ray absorptiometry (DXA) can be captured by Morphogram and (2) to propose a novel method to quantify the stage of MSR.

Methods: We tested 52 study participants (26 males, 26 females; age: 39.2 ±8.4; BMI: 28.9 ±2.6). We compared BF% and LM% estimated by Morphogram vs. DXA and the MSR score vs. health risks associated with DXA adiposity parameters and anthropometric variables. Although we expected Morphogram to under-estimate BF% and, consequently, over-estimate LM% estimated by DXA, we hypothesized the MSR scores to exhibit stronger correlations with anthropometric variables than DXA parameters.

Results: BF% and LM% estimated by Morphogram (mean ±S.E.: 31.37 ±1.09% and 68.50 ±1.08%, respectively) significantly under-estimated and over-estimated BF% and LM%, estimated by DXA (34.68 ±1.30% and 65.41 ±1.35%, respectively; p < 0.001). The largest under-estimation discrepancies (> -5% of BF%) were caused by excessive subcutaneous fat and relative fat-free mass deficits, and/or excessive android fat in a subset of participants (35%). Lastly, we found stronger correlations between MSR scores and risk factors that have been linked by epidemiological studies to anthropometric variables than adiposity parameters measured by DXA.

Conclusion: Morphogram has significant potential as a screening and monitoring tool of metabolic status, thus making it a clinically relevant approach for prevention of metabolic diseases.