Proteomic metabolic health related to socioeconomic deprivation
Background:
Understanding how socioeconomic status influences access to health care and other resources is essential to making sustainable and practical metabolic health and nutritional recommendations. Assessing and monitoring patients holistically and understanding the health impacts of socioeconomic status can enable optimal decision making and recommendations as it relates to nutrition. Large-scale proteomic profiling using a novel aptamer-based technology to measure 7,000 proteins has facilitated the development and validation of blood-based proteomic signatures for 11 different cardiometabolic SomaSignalTM Tests (SST) in plasma.
Method:
We used clinical data and SST results in the psychosocial, behavioral, and biological determinants of ill health (pSoBid) study to identify differences in health status between socioeconomically advantaged and deprived participants from Glasgow, Scotland. We used logistic regression analyses to compare SST results between advantaged and deprived study participants.
Results:
After correcting for multiple comparisons, socioeconomic deprivation was associated with the following proteomic predictions: decreased cardiorespiratory fitness (odds ratio (OR): 6.07, 90% confidence interval (CI): 3.09-11.90), p-value: 0.0003), decreased estimated renal function (OR: 3.00, 95% CI: 1.42-6.33, p-value: 0.008), increased risk for cardiovascular disease (OR: 1.86, 95% CI: 1.56-2.21), increased liver fat (OR: 1.26, 95% CI: 1.06-1.50, p-value: 0.02), and impaired glucose tolerance (OR: 1.18, 95% CI: 1.06-1.31, p-value: 0.007). The following test predictions were not associated with socioeconomic status: resting energy rate, percent body fat, lean body mass, visceral adiposity, and alcohol impact.
Conclusions:
These results demonstrate that participants with lower socioeconomic status have proteomic signatures that are consistent with decreased cardiometabolic health compared to participants with higher socioeconomic status. This study shows that SST can be used to identify proteomic phenotypes, which will be invaluable to help guide precision metabolic health and nutrition recommendations that address the unique needs of people with lower socioeconomic status.
Presentation format: Flash presentation & Poster