Reducose Experiments

this is a draft posting…I’ll be updating it as I complete my experiments and analysis

Some friends at Phynova heard about my CGM experiments and asked me to try their new supplement, Reducose, which contains a proprietary extract of Mulberry leaves which they claim can reduce a person’s glucose response by up to 40% with no side effects. I’ve been testing it on myself to find out.

It seemed very effective when I tried it with different types of bread.

Whole Wheat Bread

Experiment	IAUC	Min	Max	Rise
1234-4/1-Whole Wheat bread	2371	-11	72	-11
1234-4/4-Whole Wheat bread	2536	-14	63	-14
1234-4/2-Whole Wheat bread	3158	0	54	26

Granola

So I tried with some Quaker Oats Granola and milk, whose high sugar content usually produces a big glucose spike:

Quaker Oats Granola with milk (two servings)

Experiment	IAUC	Min	Max	Rise
1234-5/23-Granola cereal	1222	-2	32	-2
1234-5/23-Granola cereal w/milk	1222	-2	32	-2
1234-5/25-Granola cereal	1496	0	26	5
1234-5/25-Granola cereal w/milk	1496	0	26	5
1234-5/25-Granola cereal w/milk \(Reducose\)	1496	0	26	5
1234-5/22-Granola cereal	3135	0	49	16
1234-5/22-Granola cereal w/milk	3135	0	49	16
1234-5/22-Granola cereal w/milk \(Reducose\)	3135	0	49	16

Salmon and Rice

Then I tried it with some salmon and rice. This rice was boiled with a mixture of water and coconut milk for flavor as well as to add fat.

Salmon plus rice made with coconut milk

Experiment	IAUC	Min	Max	Rise
1234-5/23-Salmon \+ coconut rice	2	-44	1	-3
1234-5/23-Salmon \+ coconut rice \(Reducose\)	1751	0	26	26

Banana

Reducose with a banana

Code

foodname <- list( "Banana \\(Reducose\\)", "Banana \\(Reducose T\\-10\\)", "Banana \\(Reducose T\\-20\\)")

food_results_a <- cgmr::food_times_df(cgm_data = taster, foodnames=foodname)


cgmr::plot_food(food_results_a, foodname = foodname) + psi_theme

Code

cgmr::food_table(food_results_a)  %>% 
    kable(digits = 0) %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

Experiment	IAUC	Min	Max	Rise
1234-5/28-Banana \(Reducose T\-10\)	2541	0	46	11

Code

cgmr::plot_intervention(food_results_a, "banana")

Oatmeal

Code

 foodname <- list("Oatmeal \\(plain\\)","Oatmeal w/milk","Oatmeal w/butter", "Oatmeal w/cinnamon", "Oatmeal \\(Reducose\\)")
 
food_results_a <- taster %>%
  {
    .$notes_records <- .$notes_records %>% 
      filter(lubridate::year(Start) == 2019, lubridate::month(Start) == 5)
    .
  } %>%
  cgmr::food_times_df(foodnames = foodname)



cgmr::plot_food(food_results_a, foodname = foodname) + psi_theme

Code

cgmr::food_table(food_results_a)  %>% 
    kable(digits = 0) %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

Experiment	IAUC	Min	Max	Rise
1234-5/26-Oatmeal \(Reducose\)	318	-10	16	-10

Each of these experiments was performed on a separate day, on an empty stomach.

I also tried to understand how much Reducose will affect meals later in the day. After one of the banana experiments above, I waited three hours for my glucose to stablize again and then I ate some whole wheat chips.

Multigrain chips, with a carefully measured serving size

Code

foodname <- list("Chips", "Chips \\(Reducose\\)", "chips and guacamole")

food_results_a <- taster %>%
  {
    .$notes_records <- .$notes_records %>% 
      filter(lubridate::year(Start) == 2019, lubridate::month(Start) == 5)
    .
  } %>%
  cgmr::food_times_df(foodnames = foodname)


cgmr::plot_intervention(food_results_a, intervention="Reducose") + psi_theme

Code

cgmr::food_table(food_results_a)  %>% 
    kable(digits = 0) %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

Experiment	IAUC	Min	Max	Rise
1234-5/27-Chips	70	-24	4	1
1234-5/23-Chips	1568	0	27	26
1234-5/23-chips and guacamole	1568	0	27	26
1234-5/29-Chips	1944	-4	43	28
1234-5/29-Chips \(Reducose\)	1951	-3	40	28
1234-5/30-Chips	2190	0	38	25

Conclusions

I don’t have conclusions yet because so many factors can influence this data and I’m not certain about my analysis. Stay tuned!