Blood Glucose & Insulin Resistance · 1 week profiling Dietitian compiled 4–6 isocaloricoptions...
Transcript of Blood Glucose & Insulin Resistance · 1 week profiling Dietitian compiled 4–6 isocaloricoptions...
Blood Glucose & Insulin Resistance
Barr et al., Diabetologia, 2009, Mar;52(3):415-24
All-
cau
se m
orta
lity
ra
te
(per
1,0
00 p
.y)
Fasting Glucose (mmol/L)
< 5.1 ≥ 5.1 ≥ 5.3 ≥ 5.6 ≥ 6.1 ≥ 7.0
5
10
25
All-
cau
se m
ort
ali
ty r
ate
(p
er 1
,000
p.y
)
Glucose Tolerance – 2h PG (mmol/L)
< 4.8 ≥ 4.8 ≥ 5.6 ≥ 6.3 ≥ 7.8 ≥ 11.1
5
10
25
Barr et al., Diabetologia, 2009, Mar;52(3):415-24
All-
cau
se m
ort
ali
ty r
ate
(p
er 1
,000
p.y
)
HbA1C (%)
< 4.9 ≥ 4.9 ≥ 5.0 ≥ 5.2 ≥ 5.4
5
10
25
Barr et al., Diabetologia, 2009, Mar;52(3):415-24
Image from: Kim et al., J Korean Endocr Soc. 2009 Jun;24(2):75-83
Measurement of Insulin Sensitivity
• At present, hyperinsulinemic euglycemic clamp and intravenous glucose tolerance test are the most reliable methods available for estimating insulin resistance [1]
• Hyperinsulinemic euglycemic clamp = “gold standard”
• Various indices of insulin sensitivity/resistance using the data from oral glucose tolerance test (OGTT): e.g. homeostasis model assessment (HOMA), quantitative insulin sensitivity check index (QUICKI)
• Fasting insulin alone was as accurate at predicting insulin resistance in the normoglycemic population as HOMA, insulin-to-glucose ratio, and the Bennett index [2]
[1] Gutch et al., Indian J Endocrinol Metab. 2015 Jan-Feb; 19(1): 160–164
[2] McAuley et al., Diabetes Care 2001 Mar; 24(3): 460-464
Image from Diabetes Ireland
Insulin Resistance – Practical Strategies
Wing et al., Diabetes Care. 2011 Jul; 34(7): 1481–1486
Cha
ng
e in
Hb
A1c
(%)
-0.6
-1
- 0.2
Wing et al., Diabetes Care. 2011 Jul; 34(7): 1481–1486
Cha
ng
e in
Fa
stin
g G
luco
se (
mg
/dl)
-30
-50
- 10
~ 800 kcal formula
~ 300 people with T2DM, BMI > 27, from 49 primary care practices
Best Practice Care
Lean et al., Lancet. 2018 Feb 10;391(10120):541-551
825-853 kcal diet for 3 months (up to 5)
Gradual food reintroduction (2–8 weeks)
Structured support for long-term weight loss maintenance
DiRECT Trial
Lean et al., Lancet. 2018 Feb 10;391(10120):541-551
Lean et al., Lancet. 2018 Feb 10;391(10120):541-551
Remission of diabetes: HbA1c less than 6·5% (<48 mmol/mol) after at least 2 months offall antidiabetic medications
Lean et al., Lancet. 2018 Feb 10;391(10120):541-551
Exercise
Peri-Workout
Pre-Loading
Circadian Rhythms
CHO Timing
Carbohydrate
Supplementation
Exercise
Peri-Workout
Pre-Loading
Circadian Rhythms
CHO Timing
Carbohydrate Low to moderate intake, high fibre
Supplementation
Exercise tl;dr = exercise is good
Peri-Workout
Pre-Loading
Circadian Rhythms
CHO Timing
Carbohydrate
Supplementation[1] Richter & Hargreaves, Physiol Rev. 2013 Jul;93(3):993-1017
[2] Duncan et al., Diabetes Care March 2003 vol. 26 no. 3 557-562
Exercise
Peri-Workout Bias carbohydrates here
Pre-Loading
Circadian Rhythms
CHO Timing
Carbohydrate
Supplementation
Exercise
Peri-Workout
Pre-Loading Protein or fibre/vegetables
Circadian Rhythms
CHO Timing
Carbohydrate:
Supplementation[1] Shukla et al., Diabetes Obes Metab. 2019 Feb;21(2):377-381
[2] Jakubowicz et al., Diabetologia. 2014 Sep;57(9):1807-11
[3] Frid et al., Am Jour Clin Nutr, Vol 82, Issue 1, 2005, pg 69–75
Exercise
Peri-Workout
Pre-Loading
Circadian Rhythms Alignment > Misalignment
CHO Timing
Carbohydrate:
Supplementation
Exercise
Peri-Workout
Pre-Loading
Circadian Rhythms
CHO Timing Carbs biased to earlier in the day
Carbohydrate:
Supplementation
Exercise
Peri-Workout
Pre-Loading
Circadian Rhythms
CHO Timing
Carbohydrate:
Supplementation a) Berberine
[1] Pérez-Rubio et al., Metab Syndr Relat Disord. 2013 Oct;11(5):366-9
[2] Dong et al., Evid Based Complement Alternat Med. 2012;2012:591654[3]
1,000 – 2,000 mg/d
Divided into 3 – 4 doses
High doses can lead to GI distress
Possible drug interactions
Exercise
Peri-Workout
Pre-Loading
Circadian Rhythms
CHO Timing
Carbohydrate:
Supplementation a) Berberine; b) Resistant Starch (10 – 45 g)
Wang et al., Nutrition & Diabetes (2019), vol 9, no. 19∆ Fasting glucose
R.S.
Wang et al., Nutrition & Diabetes (2019), vol 9, no. 19∆ Fasting insulin
Prebiotic Fiber
Raw: 8.6%
Cooked: 5%
17.5%
11.7%
5%1%
Exercise
Peri-Workout
Pre-Loading
Circadian Rhythms
CHO Timing
Carbohydrate:
Supplementation a) Berberine; b) Resistant Starch; c) Inositol?
Personalized Nutrition: The Future?
n = 800
Gut Microbiome Blood Tests Questionnaires Anthropometrics
Zeevi et al., Cell, Nov 2015, Volume 163, Issue 5, p1079–1094
Food Diary CGM
Day 1 Day 3 Day 5 Day 7
G F
Day 2 Day 4 Day 6
50 gCHO
Zeevi et al., Cell, Nov 2015, Volume 163, Issue 5, p1079–1094
Time
Glu
cose
(m
g/d
l)
160
120
80
08:00 12:00 16:00 20:00
2-hour PPGR AUC
Standardized meal
Lunch
Snack
Dinner
Zeevi et al., Cell, Nov 2015, Volume 163, Issue 5, p1079–1094
Zeevi et al., Cell, Nov 2015, Volume 163, Issue 5, p1079–1094Time
Bloo
d G
luco
se (
mg
/dl)
)
120
40
160
200
240
20 12060 80 100
Participant 67 – Test 1
Participant 67 – Test 2
Participant 358 - Test 1
Mean iAUC = 139
Mean iAUC = 15
Participant 358 - Test 2
Zeevi et al., Cell, Nov 2015, Volume 163, Issue 5, p1079–1094
PPGR - AUC (mg/dl/h)
Freq
uen
cy (
# p
art
icip
an
ts)
20
20
40
60
80
060400
85
Time (minutes)
Bloo
d G
luco
se (
mg
/dl)
Banana
Cookies
100
115
85
100
115Participant 445
Participant 644
0 120
Zeevi et al., Cell, Nov 2015, Volume 163, Issue 5, p1079–1094
Meal Carbohydrates (g)
PP
Glu
cose
Res
pon
se (
AUC)
Zeevi et al., Cell, Nov 2015, Volume 163, Issue 5, p1079–1094
25
Participant 49
Participant 14575
50
603015 45
PPGRs to the standardized meals (all types) positively associated with:
• HbA1c
• BMI
• Systolic blood pressure
• Alanine aminotransferase (ALT) activity
• CRP
Meal Response Predictor
Decision Tree XYZ
Meal Response Prediction20
BMI > 25 ?
Carbs > 10 g?N
250 5 30
Y
N
N
Y
YN
Firmicutes > 5% ?
Y
HbA1c > 5.7% ?
X 4,000
Main Cohortn = 800
Validation Cohortn = 100
Use meal response data to train the meal response predictor
algorithm
Validate meal response predictor
Intervention Trialn = 26
Test impact of “personalized nutrition” interventions
26 independent participants
12 14
1 week profiling
Dietitian compiled 4–6 isocaloric options for each meal
Good Diet Bad Diet Good Diet Bad Diet
1 week of each diet
Prediction Algorithm Expert Selection
Day 1 2 3 4 5 6
Breakfast B1 B2 B3 B4 B5 B6
Lunch L1 L2 L3 L4 L5 L6
Snack S1 S2 S3 S4 S5 S6
Dinner D1 D2 D3 D4 D5 D6
Low response High response
Dietitian prescribed meals
Day 1 2 3 4 5 6
Breakfast B1 B2 B3 B4 B5 B6
Lunch L1 L2 L3 L4 L5 L6
Snack S1 S2 S3 S4 S5 S6
Dinner D1 D2 D3 D4 D5 D6
Low response High response
Dietitian prescribed meals
Day 1 2 3 4 5 6
Breakfast B1 B2 B3 B4 B5 B6
Lunch L1 L2 L3 L4 L5 L6
Snack S1 S2 S3 S4 S5 S6
Dinner D1 D2 D3 D4 D5 D6
Low response High response
Dietitian prescribed meals
Time (days)
Blo
od G
luco
se (
mg
/dl)
Good Diet
Bad Diet
PPGR
iAU
C(m
g/d
l/h
)
Good Diet Bad Diet
Glu
cose
Flu
ctu
ati
on
s
Ma
x PP
GR iA
UC
(mg
/dl/
h)
0
80
40
0.2
120
60