Episode 34 - Metabolic Syndrome Chapter 1: insulin resistance
Sponsor: This podcast is sponsored by Health IQ, the fastest growing life insurance agency in the United States. To see if you qualify, get your free quote today at healthiq.com/BDN or mention the promo code BDN when you talk to a Health IQ agent.
Gabor gives the classical definition of insulin resistance (IR) that focuses on glucose disposal and explains its limited because it ignores other metabolic parameters, such as lipids.
Raphael explains that the gold-standard method to measure (not estimate) IR is the hyperinsulinemic-euglycemic clamp. Yet even that is an imperfect measure
Raphael mentions that insulin resistance is a core mechanism (not pathology per se!) acting in Metabolic Syndrome. Metabolic Syndrome also isn’t a pathology but a “physiologic state” that’s more or less appropriate depending on the context.
Raphael asks Gabor to lay out the the strength and limitations of the gold-standard IR measuring method.
Gabor emphasizes there’s a great need for better surrogates measures that aren’t as involved (i.e. 3hr procedure) as the gold-standard measure of IR. See Figure 1. from Katalin 2013 – the further from the reference line a test is, the better is is (>Sensitive & >Specific)
Gabor also highlights that limitation whereby insulin levels used in this gold-standard method are so high and lipolysis so sensitive to insulin, that both of these facts need to be reconciled with newer, better measures of IR.
Raphael discusses the 5 criteria making up the Metabolic Syndrome ‘checklist’
- Waist circumference of ≥ 102 cm in men and ≥ 88 cm in women
- HDL-cholesterol of < 40 mg/dL in men and < 50 mg/dL in women
- Triglycerides of ≥ 150 mg/dL
- Systolic blood pressure (BP) of ≥ 130 mm Hg or Diastolic BP of ≥ 85 mm Hg
- Fasting plasma glucose of ≥ 100 mg/dL
**Please understand that these values are useful but still somewhat ‘arbitrary’. Also, the reference ranges for these values are subject of great debate (see the Nourish Balance Thrive podcast episode for a deeper dive into how one establishes better or worse reference ranges)**
Raphael talks about Dennis McGarry, known to him thanks to Dr.Bikman, and the importance of his scientific contributions to Metabolic Syndrome.
Raphael asks Gabor to comment on the usefulness or lack thereof of the following correlates of IR correlates:
- family history of diabetes
- blood pressure (BP), BMI (body mass index), waist-and-hip circumference
- fasting triglycerides, HDL-cholesterol, glucose, insulin and hepatic enzymes
Raphael reiterates that just because a method is good at measuring IR at a population level, doesn’t mean it will be good at doing so in any given individual (or vice-versa)
Raphael and Gabor discuss the 2017 study by Abbasi, Reaven et al. called “Relationship between Several Surrogate Estimates of Insulin Resistance and a Direct Measure of Insulin-Mediated Glucose Disposal: Comparison of Fasting versus Post-Glucose Load Measurements”.
Raphael recapitulates the fact that a higher SSPG (steady-state plasma glucose) result indicates more IR.
Gabor isn’t surprised that nearly all of the 11 IR estimates didn’t correlate to any useful degree with SSPG results.
Raphael explains guards people against assuming “there is a strong correlation” just because a strong p-value accompanies a correlation value. One must distinguish between
(a) the strength of a correlation itself. A negative correlation goes from and -1 and positive one goes from 0 to 1. 0 means no correlation and 1 (or -1) is a ‘perfect’ correlation.
(b) and its given p-value. The latter is the level of statistical confidence with which we take the result (in this case a correlation) didn’t happen by chance, but is in fact a ‘true’ measure.
Raphael quotes the study result claiming that the “best overall predictors of insulin resistance were the McAuley, Matsuda and Insulin AUC, which were essentially identical in predictive performance” .
HealthIQ mid-roll ad.
Raphael mentions that there was “a higher false positive rate (1 – specificity) associated with all fasting indices except the McAuley index, being comparable to the lower false positive rate observed with the post-glucose challenge indices” .
Raphael recaps the conclusions mentioned by the authors and then asks Gabor to comment on them.
Mffm/I = exp
**formula assuming it’s a continuous variable
**ln = log10
**insulin = fasting insulin (mIU/L)
**TAG = fasting triglycerides (mg/dL)
**M = average glucose disposal rate (mg/kg/min) during the last hour of a 2hr SSPG test.
**ISI (insulin sensitivity index using body-weight ) = average M divided by average insulin, both within the last hour of a 2hr SSPG test (M x miliunit x mL).
** Mffm (ISI corrected for fat-free mass ) = usually ~5% ⇒ so approximately ISI x 0.95.
Raphael recapitulates why we care about Metabolic Syndrome and insulin resistance, and asks Gabor to give his bird’s-eye view of the situation.
Gabor recommends people use these indices (specifically the McAuley index) to check up on their health.
Raphael mentions the Randle Cycle that describes the mutual inhibition of glucose oxidation and fatty acid oxidation, speculating that the McAuley index performs well because it ‘pays reconnaissance’ to the Randle Cycle.
Raphael asks Gabor if he wishes to venture a guess as to the what the next variable might be that could improve the McAuley index.