ot;width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0" : "width=1100"' name='viewport'/> 2020 Update in Clinical Endocrinology: Diabetes Sept 2020: RAS and severity of SARS-COV-2. Precision medicine in diabetes.

Friday, September 4, 2020

Diabetes Sept 2020: RAS and severity of SARS-COV-2. Precision medicine in diabetes.

 Here the link

Renin-Angiotensin System and severity of SARS-COV-2. 

Aleksandr Obukhov et al have written a review about the possible pathophysiological mechanism of dysregulared Renin-Angiotensin System (RAS). Quite a lot have been commented about the role of the Angiotensin-2 converting enzyme 2 (ACE-2) as linking site to SARS-COV-2 spike glycoprotein and therefore a path for the virus to entry into the organism. In this article they draw the hypotesis that viral infection hijacks ACE-2 receptor dysregulating thereby the whole RAS system, unchaining cytokine storm and other deletereous consequences. In summary, RAS system works like this:

Figure 1

In this way, ACE2 sequestration by the evil coronavirus leads to increase of ATII effects, not only globally but also locally in gut, bone marrow and lung. This could have unwanted consequences:

  • Bone Marrow: Local RAS regulates hematopoiesis, and its dysregulation could lead to proinflammatory cells and cytokines, as well as prothrombotic state. The topic is complicated, there are quite a lot of implicated cells and humoral factors, I suspect there are no more that 10 people in the world that really master this topic. As usual, when something is complex, intricated and unexplainable someone made up a term to name it: myeloidosis.
  • Gut: It's the quantitatively most important ACE-2 expession site in the organism. As it of weren't enough tangled, there is the role of gut microbiota, that critically affects bone marrow activity throug bacterial antigen presentation.

Well, all this is pure speculation because so far there is not a single effective therepy or intervention on RAS system in SARS-COV-2 infection. 

PRECISION MEDICINE IN TYPE 2 DIABETES

Diabetes is a heterogeneous disease and precision medicine (PM) is more difficult than in other pathologies like cancer or monogenic diabetes, where genetic data leads to clear-cut subgroups as to response to therapies or side effects. 

Nevertheless, one field of PM is the Heterogeneity of Treatment Effect (HTE). In this regard T2DM is a very good example for HTE because 1.- It's a heterogeneous disease; 2.- There are multiple drugs for it; 3.- glycemic response is very heterogeneous; 4.- no drug class is clearly superior to other. This review focuses only on glycemic (A1c) responses of type 2 diabetes drugs and is only based on available clinical data, not in genetic or other research markers. 

No single available factor cam predict which drug may be more beneficial for a single patient. PD tries to identify combinations of factors that could be associated to it. In this sense, data from real-life clinical practice like UK Datalink as well as from RCT (both types have strengths and weaknesses) have been used to draw initial associations, all of them congruent with mechanism of action of drugs:

  • Lean males have a better initial response to SU than to TZD. the oposite for obese (BMI >30) females. 
  • DPP4-i get better response in patients with lower BMI and lower insulin resistance (IR). 
  • IR does not affect effect of GLP-RAs, but in insulin-treated patients low insulin secretion is associated to worse response. 
  • Response to SGLT2-i is, compared to SU or DPP4-i, directly proportional to basal glycemia (A1c) and eFGR, not only for those <60 mL/min but all through the GFR range (0-90)

Two strategies are proposed to benefit from this knowledge:

  • To create subgroups according to age, gender, BMI and other characterystics, as proposed by Ahlquist using Scandinavian registry data. 
  • To develop an individualized prediction approach i.e. consider available unique characterystics (diagnosis age, gender, BMI, and A1c as continuous variables) of every patient to predict his or her unique differential responses to every treatment. This approach can also be used to predict CV or microvascular risk. 

 


 The authors compared both strategies using external validation data from RCT and found better the second one. 

Future directions in this field are:

  • Incorporation of other individual data, like genomics, could refine the procedure.
  • Taking into account not only glycemic response but also individual propensity to side effects, microvascular complications, cardiovascular risk and eventually mortality. 
  • RCT to evaluate effectiveness of this approach.