A blood test could potentially be used to assess a
patient's risk of type 2 diabetes. The most commonly
used inflammatory biomarker currently used to
predict the risk of type 2 diabetes is
high-sensitivity C-reactive protein (CRP). However,
emerging research has suggested that the joint
assessment of biomarkers, rather than assessing each
individually, would improve the chances of
predicting diabetes risk and diabetic complications.
Researcher investigated the connection between
systematic inflammation, assessed by joint
cumulative high-sensitivity CRP and another
biomarker called monocyte to high-density
lipoprotein ratio (MHR), and incident type 2
diabetes. Specifically, increases in the MHR in each
CRP stratum increased the risk of type 2 diabetes;
concomitant increases in MHR and CRP presented
significantly higher incidence rates and risks of
diabetes. Furthermore, the association between
chronic inflammation (reflected by the joint
cumulative MHR and CRP exposure) and incident
diabetes was highly age and sex-specific and
influenced by hypertension, high cholesterol, or
prediabetes. The addition of the MHR and CRP to the
clinical risk model significantly improved the
prediction of incident diabetes. The study found
that females had a greater risk of type 2 diabetes
conferred by joint increases in CRP and MHR.
Researcher noted that the chronic progressive nature
of diabetes and the enormous burden of subsequent
comorbidities further highlighted the urgent need to
address this critical health issue. Although aging
and genetics are non-modifiable risk factors, other
risk factors could be modified through lifestyle
changes. Inflammation is strongly influenced by life
activities and metabolic conditions such as diet,
sleep disruptions, chronic stress, and glucose and
cholesterol dysregulation, thereby indicating the
potential benefits of monitoring risk-related
metabolic conditions. Researcher said that the dual
advantages of cost effectiveness and the wide
availability of cumulative MHR and CRP in current
clinical settings, potentiated the widespread use of
these measures as a convenient tool for predicting
the risk of diabetes. |