Key words
insulin resistance - diabetes remission - non-alcoholic fatty liver - weight loss resistance - epigenetics
Introduction
Today, treatment of Type 2 Diabetes is glucose-centered and symptom-oriented. The
history of most patients with Type 2 Diabetes is a gradual rise in blood glucose
concentrations over time, despite medication, as reported by Madsen et al. [1]. Insulin resistance is the hallmark of Type
2 Diabetes, but is still an unmet medical need even though advances in diabetes
medicines have been made over the last several years. Ferrannini et al. [2] reported that the same is true for the fast
and simple detection of insulin resistance. Insulin resistance lies at the key
crossroads of metabolic and mitochondrial dysfunction, visceral fat, non-alcoholic
fatty liver (NAFL), muscle exercise and weight loss resistance, subclinical
inflammation, metabolic syndrome (MetS), overweight, and obesity, and is
characterized by epigenetically driven accelerated aging, as Nannini et al. [3] reported. Insulin resistance plays a key
role in a reduced capability of the immune system to kill pathogens such as viruses
and bacteria, as De Rosa et al. [4] and
Chávez-Reyes et al. [5] reported.
Vestergaard Jensen et al. [6] reported insulin
resistance as an important risk factor for community-acquired pneumonia. Insulin
resistance predisposes to cardiovascular disease and shortens human lifespan, as
data from Gardner et al. [7] showed. Insulin
resistance is reported to drive other major non-communicable diseases, such as high
blood pressure, chronic kidney disease (Chen et al. [8]), depression (Won Lee et al. [9]; Geraets et al. [10], Ford et al.
[12]), and stroke (Rundek et al. [11]). Alzheimer’s disease and dementia
are also highly correlated to increased levels of triglycerides at midlife as data
from Nägga et al. [13] showed. Insulin
resistance also underlies many obesity-related cancer developments, as reported by
Sung et al. [14].
Insulin resistance is independently correlated with increased risk of incident
diabetes in Chinese adults, as reported by Li et al. [15], and is closely related with higher
mortality for COVID-19, according to data reported by Ren et al. [16]. Diabetes is the most important cause of
mortality in COVID-19 hospitalized patients, as reported by Corona et al. [17]. A large-scale analysis reported by
McGovern et al. [18] found that the COVID-19
mortality risk increases in patients with Type 2 Diabetes due to drastic biological
age acceleration. The prevalence of Type 2 Diabetes in Switzerland is estimated to
be between 5.7 and 7.0%. Two-thirds of patients with Type 2 Diabetes are
aware of their status, and over three-quarters of those who are aware are treated,
according to data from Kaiser et al. [19]. In
the US, adults with optimal metabolic health are a small percentage of the
population: 17.6%, according to Araujo et al. [20].
Type 2 Diabetes is observed to correlate with obesity regardless of the genetic
risks. The currently available obesity-oriented treatment options for Type 2
Diabetes are either a bariatric operation, or a caloric weight-loss strategy that
involves a very low-calorie diet, as reported by Kerr et al. [21], Willi et al. [22], and Xin et al. [23]. As observed by Baskota et al. [24], a bariatric operation can only be proposed
for those with a BMI>30 and should not be proposes for non-diabetic obese
patients.
BMI is not the main driver for insulin resistance; insulin resistance drives
BMI
Diabetes primarily depends on visceral fat and not BMI, according to data from
Vistisen et al. [25]. Her group found that
the great majority of patients who had modest weight gains before diagnosis had
the highest diabetes risk. Wu et al. [26]
reported that, in Mexican Americans, metabolic health has a greater impact on
diabetes than overweight/obesity. Eckel et al. [27] reported that the quality of metabolic
health was decisive for diabetes progression, irrespective of BMI. Acquired
obesity independent of genetic risk is primarily related to deleterious
alterations in the lipid metabolism, per data found by Pitiläinen et al.
[28] in studies of twins. Insulin
resistance discriminates between healthy and unhealthy phenotypes of obesity and
leanness, as reported by Owei et al. [29]
and Mongraw-Chaffin et al. [30] and drives
the unhealthy obese phenotype into developing Type 2 Diabetes.
Insulin resistance has its root not only in disturbed glucose metabolism but also
in a derailed fatty acid metabolism leading to glucolipotoxic conditions and
beta-cell death, as reported by Bagnati et al. [31]. Increased blood triglycerides accompanied by lower HDL values
represent an insulin resistant metabolic status and are purely
nutrition-related. Triglyceride levels showed a significant negative correlation
with BMI and body fat. HDL cholesterol was significantly negatively correlated
with waist circumference and positively correlated with body fat, as data from
Telles et al. [32] showed.
Lipid indices, which can be easily calculated with routine laboratory tests, may
be useful markers for insulin resistance risk assessments in clinical settings,
according to Lee et al. [33]. The
triglyceride to HDL ratio allow identification of the insulin-resistant
metabolic state, and a high TG:HDL-C ratio at baseline may be a useful surrogate
indicator of future Type 2 Diabetes, as reported by Lim et al. [34]. The TyG index, calculated based on
fasting triglyceride and glucose blood values, correlates highly with insulin
resistance. Both markers, the TG:HDL-C and TyG present a significant association
with lifetime cardiovascular risks in adults reported by Rojas-Humpire
et al. [35] and Si et al.
[36]. The TyG index correlated highly
with the epigenetic age acceleration, as data from Arpon et al. [37] showed. HOMA-IR had a significant
positive correlation with the TyG and TG:HDL indices, as reported by Çin
et al. [38] and Kron
et al. [39].
Triglycerides are endogenously formed after an excess of glucose ingestion via a
de novo lipogenesis pathway forming malonyl-CoA, diacylglycerols, and palmitic
acid (16:0), and directly influence insulin resistance, as Lyu et al.
[40] reported. Lee [41] reported palmitic acid 16:0 was
positively associated with incident heart failure in older adults. Malonyl-CoA
is a master regulator for insulin sensitivity. Hyperglycemia with
hyperinsulinemia increases malonyl-CoA production and inhibits functional CPT1
activity, while and shunting long-chain fatty acids away from oxidation and
towards storage in human muscle, as reported by Rasmussen et al. [42]. Malonyl-CoA is the first reaction step
for the de novo lipogenesis of palmitic acid, which is positively associated
with Type 2 Diabetes, as data from Imamura et al. [43] showed. But de novo lipogenesis is also
positively associated with saturated fatty acid intake and is elevated in
patients with non-alcoholic fatty liver and Type 2 Diabetes, as reported by
Imamura et al. [43]. The TyG index
is significantly associated with incident NAFL, as reported by Kitae
et al. [44] and Jimenez-Rivera
et al. [45]. Kolb et al.
[46] reported detrimental consequences
of prolonged high insulin concentrations and argues for a lifestyle that limits
circadian insulin levels. Skipping breakfast a few times a week was associated
with general adiposity and with general and central adiposity, as reported by
Wadolowska et al. [47], and was
also associated with a higher risk of insulin resistance, as data from Joo
et al. [48] showed. Consuming a
three-meal diet with a carbohydrate-rich breakfast allows insulin dose
reduction, leading to weight loss and better glycemic control compared with an
isocaloric six-meal diet, as reported by Jakubowicz et al. [49].
Food choices for a molecular dietary pattern developing an insulin-resistant
metabolic state
Compared to normal glycemic adults, adults with Type 2 Diabetes make different
food choices, favoring a high saturated fat diet with higher total fat and
protein and less fiber, as reported by Breen et al. [50]. Increased insulin resistance favors
continued unhealthy food choices via aberrant central insulin action, as
reported by Tiedemann et al. [51],
leading to a vicious cycle in which insulin resistance drives changes in taste
perception, as observed by Pugnaloni et al. [52], leading to continued eating and
snacking behavior. Saturated fat increases intramuscular triglycerides, and a
decreased intake of saturated fatty acids could be beneficial in reducing
intramuscular triglycerides and the associated risk of diabetes, according to
data from Luukkonen et al. [53].
In obese subjects with normal glycemia, elevated circulating levels of free
fatty acids during fasting is the major metabolic derangement candidate driving
fasting hyperinsulinemia as a homeostatic response, as reported by Fryk
et al. [54].
Maintaining glycemic control via a healthy fatty acid metabolism may be an
emerging key factor for maintaining a healthy beta-cell formation for a normal
glucagon metabolism, as reported by Grubelnik et al. [55]. Lower fiber intake of all types is
associated with higher insulin levels. Triglyceride concentrations are
potentially sensitive to fiber consumption, as reported by Hannon et al.
[56]. Fiber intake at recommended
levels may be associated with significant cardiometabolic benefits, as data from
Dong et al. [57] showed. Higher
intakes of dietary choline and betaine are associated with lower insulin
resistance in the general population, as reported by Gao et al. [58].
The amount of dietary intake of animal protein was positively related to HOMA-IR,
while plant protein was not significantly related to insulin resistance, as
reported by Azemati et al. [59].
Plant-based proteins with polyphenols are reported by Meir et al. [60] and Castro-Barquero et al.
[61] to improve HDL cholesterol and to
have an inverse relationship with triglycerides. McKaye et al. [62] reported that a low intake of vitamin
D, folate, magnesium, and potassium have a negative relationship to BMI, while
other micronutrients, such as vitamins B12 and E, do not.
A personalized molecular feeding and control strategy for the remission of
insulin resistance
The molecular dietary pattern of humans is directly related to transcriptions
generating epigenetically the enzymatic pattern needed for a healthy metabolism
for maintaining energy homeostasis. An unhealthy dietary pattern leads via the
epigenetically driven transcription to an unfavorable enzymatic pattern, leading
to insulin resistance to rescue the energy homeostasis, as reported by Hall
et al. [63].
The CPT1 gene expression is epigenetically silenced in the unhealthy obese
phenotype, as data from Maples et al. [64] show. The β-oxidation genes are also downregulated during
weight loss, preserving metabolic inflexibility. Metabolic flexibility is
lacking at the extreme of the metabolic phenotype in obese youth with
dysglycemia related to a defect in insulin sensitivity, limiting substrate
utilization, as reported by Bacha et al. [65]. An epigenomic study by Irvin
et al. [66] and Mamtani
et al. [67] found that the CPT1
locus influenced by methylation is strongly and robustly associated with
low-density lipoprotein cholesterol, triglycerides, as well as visceral fat and
waist circumference. Lai et al. [68] found that the proportion of total energy supplied by
carbohydrates and fat can have a causal effect on the risk of metabolic
diseases, via the epigenetic status of the CPT1A gene directly influencing
metabolic health through CPT1A gene activation or silencing. Epigenetic
signatures are associated with dyslipidemia and are strongly associated with
HOMA-IR, as a direct measure for insulin resistance. HOMA-IR values
≤ 3 as the threshold for Type 2 Diabetes showed a different
methylation pattern than for individuals with HOMA-IR>3, as reported by
Arpon et al. [69].
Below we present a precisely designed and personalized molecular feeding
strategy, integrating food research findings, and combining an epigenetic view
aimed at the TyG and TG:HDL indices, for simple metabolic steering and control,
for efficient insulin resistance normalization.
Compliance with ethical standards
Ethical approval: All patients were treated following an ethical approach to
select the optimal treatment procedure according to their medical needs and the
patients agreed before therapy began to possible reduction of their medications,
when necessary.
Informed consent: Patient data and information have been completely
anonymized.
Materials and Methods
A personalized nutritional method for insulin resistance and diabetes
remission
Hepatic intermittent fasting therapy consists of two parts, always with a three
meal per day diet. The first part is a very low calorie diet (VLCD) to reverse
Type 2 Diabetes. The VLCD is followed by a personalized, hepatic-focused whole
food diet, epigenetically oriented to stabilizing the metabolism that was
reprogrammed in the VLCD part. Both parts, VLCD followed by the whole food diet,
are used for diabetes remission therapy. The VLCD combines protein shakes
(Protiline) with selected vegetable intake focused mainly on sulforaphane,
betaine, and choline. The personalized, hepatic-focused whole food diet is
applied for the normalization of MetS and prediabetes. The whole food diet
steers and controls the personalized food intake, and is digitally supported on
a molecular level for the individual threshold values of selected nutrients,
calculated not just for the key influencing macro nutrition molecules of
saturated fats, glucose, fiber, and proteins, but also for selected
micronutrients, geared to achieve optimal TyG and TG:HDL values, according to
Rohner [70].
The key molecules related to insulin resistance of the food are digitally
calculated for molecular control. The healthy range of molecular concentration
of the key food ingredients is estimated and visualized for the client for easy
self-control, using the “EPIKonzept App.” The client can simply
follow daily his food selection and is informed about how well he is achieving
on a molecular level the targeted biomarkers. Possible deviations are monitored
and a buddy support system is put in place, for increasing the self-efficacy for
continuous optimization.
Supporting product formulas combined with therapy
Both parts of the therapy, VLCD and a personalized whole food diet, are supported
with tailored biochemically active product formulas, namely EPIGENOSAN and
METHYLOSAN.
Epigenosan is a tableted formula product consisting of mate tea extract, oil of
the microalgae schizochytrium, green tea extract, an isoflavone from soja
extract, l-arginine, magnesium, niacin, pantothenic acid, folic acid,
biotin, and vitamins D, E, B6, and B12.
The supplement's key ingredients are combined with l-carnitine
l-tartrate, assuring a cofactor for the CPT1 enzyme complex, and is
intermittently fed under fasting conditions. l-Carnitine supports
energy metabolism and counteracts metabolic inflexibility, synchronizing the
intermittent fasting method and supporting the functionality of the
β-oxidation to accelerate the clearance of triglycerides. The product
formula epigenosan is embedded into the diet, according to Rohner [71].
Epimethylosan is a capsuled formula product based on broccoli sprouts and white
asparagus and includes choline, magnesium, l-methionine, coenzyme Q10,
zinc, vitamins B2, B6 and B12, manganese, chrome, and folic acid mediating the
one-carbon metabolism and avoiding undernutrition of key molecules for optimal
redox reactions. Epimethylosan is always used together with epigenosan.
Analytics applied and calculation of medical indices
Commonly used analytical methods were applied to determine the measured
parameters in the figures in the result section. The TyG index was estimated
according to the formula Ln [fasting
triglycerides(mg/dl)×fasting glucose (mg/dl)/2],
as published by Simental-Mendia et al. [72]. The TG:HDL ratio was obtained by dividing the triglyceride level
(mg/dl) by the HDL-C level (mg/dl) according to Masson
et al. [73].
For the nutritional analysis, the DGExpert program was used.
The liver index was calculated according to the formula liver index:
(e0.953*loge
(triglycerides)+0.139*BMI+0.718*loge
(ggt)+0.053*waist
circumference–15.745)/(1+e0.953*loge
(triglycerides)+0.139*BMI+0.718*loge
(ggt)+0.053*waist
circumference–15.745)×100 according to Bedogni
et al. [74].
Diabetes remission group
The Type 2 Diabetes group consisted of 13 patients ([Table 1]). The inclusion criteria for the
diabetes remission group were to be not older than 70 years old; diagnosed with
Type 2 Diabetes for not longer than 7 years; no heart attack history; no
depressive disorders at the time of application; no insulin treatment; and
BMI≥25. Diabetes remission was defined as glycated haemoglobin
(HbA1c) of less than 47.5 mmol/mol
(<6.5%) and fasting glucose of less than
7.0 mmol/l after at least 2 month off all antidiabetic
medications.
Table 1 Diabetes remission group participants
overview.
|
Patient
|
BMI initially
|
BMI after 60 d
|
BMI after 150 d
|
Sex
|
Age
|
Antidiabetic medication
|
Years diagnosed
|
|
Diab 1
|
33.3
|
29.2
|
29.2
|
F
|
55
|
Metfin
|
5
|
|
Diab 2
|
34.3
|
31.2
|
25.9
|
F
|
62
|
Metformin
|
1
|
|
Diab 3
|
26.8
|
22.2
|
21.3
|
M
|
54
|
Glavumet
|
2
|
|
Diab 4
|
32.8
|
27.3
|
26.1
|
M
|
55
|
None
|
<1
|
|
Diab 5
|
31.4
|
26.5
|
24.6
|
F
|
56
|
Metfin
|
7
|
|
Diab 6
|
31.5
|
27.1
|
25.2
|
M
|
47
|
Metfin
|
3
|
|
Diab 7
|
35.9
|
29.4
|
26.7
|
M
|
46
|
Metfin
|
4
|
|
Diab 8
|
26.8
|
24.5
|
22.6
|
F
|
47
|
None
|
<1
|
|
Diab 9
|
26.8
|
24.4
|
22.0
|
M
|
64
|
Metfin
|
Unknown
|
|
Diab 10
|
33.3
|
26.5
|
26.7
|
M
|
61
|
None
|
Unknown
|
|
Diab 11
|
54.3
|
47.6
|
46.5
|
M
|
50
|
Janumet
|
2
|
|
Diab 12
|
38.9
|
36.0
|
29.7
|
F
|
68
|
Januvia
|
2
|
|
Diab 13
|
41.8
|
36.2
|
31.1
|
F
|
35
|
None
|
<1
|
|
Mean BMI
|
34.4
|
29.9
|
27.5
|
|
|
|
|
MetS/prediabetic therapy group
The MetS group consisted of 21 patients ([Table
2]). The inclusion criteria for the MetS/prediabetic group
were no depressive disorders at the time of application; and
BMI≥20 kg/m2.
Table 2 MetS/prediabetic group participant
overview.
|
Patient
|
Age
|
Sex
|
Medication
|
BMI initially
|
BMI after Therapy (150 d)
|
Initial metabolic status
|
|
MetS 1
|
47
|
w
|
None
|
33.2
|
29.3
|
Severe risk for insulin resistance
|
|
MetS 2
|
60
|
m
|
None
|
37.2
|
32.5
|
Severe risk for insulin resistance
|
|
MetS 3
|
49
|
w
|
Antiepileptics
|
31.9
|
30.7
|
Severe risk for insulin resistance
|
|
MetS 4
|
70
|
w
|
None
|
31.4
|
26.1
|
Insulin resistant
|
|
MetS 5
|
71
|
m
|
None
|
26.5
|
22.9
|
Insulin resistant
|
|
MetS 6
|
84
|
w
|
None
|
27.8
|
26.2
|
Prediabetic
|
|
MetS 7
|
82
|
w
|
Statin
|
39.6
|
36.1
|
Prediabetic
|
|
MetS 8
|
51
|
w
|
None
|
23.0
|
20.7
|
Insulin resistant
|
|
MetS 9
|
53
|
m
|
None
|
33.6
|
29.1
|
Prediabetic
|
|
MetS 10
|
32
|
w
|
None
|
24.6
|
21.3
|
Insulin resistant
|
|
MetS 11
|
66
|
m
|
None
|
32.1
|
29.1
|
Insulin resistant
|
|
MetS 12
|
63
|
m
|
Statin
|
30.4
|
27.0
|
Prediabetic
|
|
MetS 13
|
69
|
m
|
Statin
|
37.5
|
33.6
|
Severe risk for insulin resistance
|
|
MetS 14
|
76
|
m
|
None
|
22.9
|
22.2
|
Prediabetic
|
|
MetS 15
|
79
|
w
|
None
|
26.9
|
25.5
|
Insulin resistant
|
|
MetS 16
|
60
|
w
|
None
|
32.7
|
28.8
|
Insulin resistant
|
|
MetS 17
|
56
|
m
|
Antidepressive
|
32.8
|
28.1
|
Prediabetic
|
|
MetS 18
|
57
|
m
|
None
|
31.4
|
25.4
|
Insulin resistant
|
|
MetS 19
|
49
|
m
|
None
|
26.9
|
23.8
|
Insulin resistant
|
|
MetS 20
|
70
|
m
|
None
|
38.8
|
34.2
|
Insulin resistant
|
|
MetS 21
|
43
|
w
|
Statin
|
36.9
|
32.0
|
Insulin resistant
|
|
Mean BMI
|
|
|
|
31.3
|
27.8
|
|
Statistics
A two-sample t-test for dependent samples (pairwise comparison test) was
applied using Excel. Statistical significance was considered at
p+< 0.05.
Results
Typical dietary pattern of MetS, prediabetes and Type 2 Diabetes patients
observed
The macromolecular dietary pattern of patients with MetS, prediabetes, or Type 2
Diabetes of both patient groups who had an initial TyG value higher than
baseline shows a similar initial pathological nutritional pattern irrespective
of BMI. An average intake of saturated fats of 35% and unsaturated fats
of 46%; an average glucose intake of 82 g/d, and an
average fiber intake of 20 g/d was observed.
Type 2 Diabetes remission results
The hepatic intermittent fasting therapy delivered efficient results, reversing
Type 2 Diabetes within 60 days for all participants as measured by fasting
glucose ([Fig. 1]). Antidiabetic
medications could be omitted for all patients at the latest at day 60 of the
VLCD part of the therapy. The reprogrammed metabolism in these 60 days was
stabilized during the personalized whole food nutrition part of the therapy up
to 150 days, as shown in [Fig. 1]. All
patients could avoid their antidiabetic medications during the 90 days of the
personalized whole food phase, and the diabetes remission rate was estimated to
be 85%, according to HbA1c and fasting glucose levels.
Fig. 1 Median results of the diabetic remission group
(n=13) at three points in time: start (t=0), after 60
days of VLCD, and after 150 days with the personalized whole food
application. The single lead parameters and the corresponding surrogate
biomarkers TyG and TG:HDL, as the leading medical indices for
nutritional health, are shown. All data with p<0.001, confidence
interval 95%. a Triglycerides, threshold value
1.7 mmol/l; b fasting glucose, threshold value
5.6 mmol/l for normal value, threshold value for
diabetes remission<7.0 mmol/l; c TyG,
calculated from a and b, threshold value 8.73; d
HbA1c, threshold value for
diabetes<47.54 mmol/mol; e HOMA Index,
threshold value for Type 2 Diabetes>3; f HDL values,
threshold value>1.0 mmol/l; g TG:HDL
ratio, threshold value for insulin resistance>3.0; h LDL,
threshold value 3.3 mmol/l. HbA1c and fasting
glucose are the relevant Type 2 Diabetes remission parameters, as
defined.
Adjuvant Type 2 Diabetes therapy results for uncontrolled diabetes
[Table 4] shows results achieved in a
patient with uncontrolled diabetes.
Table 4 Time course of a Type 2 Diabetes patient with
uncontrolled diabetes (male, 55 years).
|
Analysis
|
Before
|
One month after
|
Norm value
|
Unit
|
|
Cholesterol
|
2.74*
|
2.4*
|
<5.2
|
mmol/l
|
|
HDL cholesterol
|
0.77
|
0.79
|
>1.0
|
mmol/l
|
|
LDL cholesterol
|
1.14*
|
1.21*
|
<3.3
|
mmol/l
|
|
Triglycerides
|
3.58*
|
1.55*
|
<1.7
|
mmol/l
|
|
Glucose
|
12.28**
|
5.77**
|
3.9–5.6
|
mmol/l
|
|
TyG
|
10.45
|
8.86
|
<8.73
|
–
|
|
ASAT/GOT
|
0.62
|
0.63
|
<0.85
|
μmol/s*l
|
|
ALAT/GPT
|
0.88
|
0.69
|
<0.85
|
μmol/s*l
|
|
GGT
|
1.24
|
1.05
|
<1.19
|
μmol/s*l
|
|
Cortisol
|
56
|
<28
|
|
nmol/l
|
|
Insulin (ip)
|
0.52
|
0.09
|
0.02–0.12
|
nmol/l
|
|
HOMA Index
|
39.2**
|
3.2**
|
<3
|
–
|
|
Waist circumference
|
115
|
109
|
<94
|
cm
|
|
BMI
|
31.6
|
29.4
|
20–25
|
kg/m2
|
|
Bodyweight
|
99.8
|
92.9
|
|
kg
|
* Statins; **
Antidiabetics.
MetS, NAFL, and prediabetes therapy results
In the MetS group, 21 patients were insulin resistant according to the TyG index
value, and six were prediabetic. The duration of the therapy needed to reprogram
the metabolisms of all patients in the MetS group and get them stably out of the
diseased path was up to 150 days ([Fig.
2]). The method shows fast results within 30 to 50 days.
Fig. 2 Median results of the MetS/prediabetic group
(n=21) at four-time windows: start t=0, at 30 days, at
50 days, and at 150 days. The single lead parameters and the
corresponding surrogate biomarkers TyG and TG:HDL ratio as the leading
medical index for nutritional health, are shown. All data with
p<0.001, confidence interval 95% a Triglyceride
course, threshold value 1.7 mmol/l; b fasting
glucose course, threshold value 5.6 mmol/l for normal;
c TyG course, calculated from a and b,
threshold value 8.73; d HDL values, threshold
value>1.0 mmol/l; e TG:HDL ratio,
threshold value for insulin resistance>3.0; f LDL,
threshold value 3.3 mmol/l.
Discussion and Conclusions
Discussion and Conclusions
The paradox of satiety and nutrient hunger irrespective of BMI
A similar macro- and micromolecular dietary pattern observed for MetS,
prediabetes, or Type 2 Diabetes patients with too high intake of saturated fats,
unsaturated fats and glucose intake and a too low intake of fiber is in close
agreement as reported by Breen et al. [50]. At the outset, all participants also deviated from the RDA of
micronutrients magnesium, potassium, and vitamin D, as also reported by McKaye
[63]. This may point to a new paradox
for insulin-resistant risks, showing an abundance of glucose and palmitic acid
intake, a shortage in unsaturated fat intake, and a scarcity for micronutrients,
irrespective of BMI. No strong correlation with a typical macromolecular nor
micromolecular dietary pattern could be extracted concerning the severity of the
insulin resistance, nor BMI nor waist circumference. But a molecular nutrition
deviation irrespective of BMI leading to insulin resistance is particular
congruent with nutritional stress for hypertriglyceridemia, hyperinsulinemia and
low HDL-cholesterol. As a consequence a combination of TyG and TG:HDL as lead
biomarkers for leading the food intake to structure the personalized program for
optimal diabetes remission was chosen.
Type 2 Diabetes remission is not driven by calories only
TyG as the lead surrogate biomarker for nutritional feeding allows structuring
personalized whole food nutrition for diabetes remission for all BMI categories
(≥25), and a nutritional structure that is not just focused on calories
(as is the main approach in today’s marketplace). Caloric aspects are
taken into account of the personalization algorithm of the diet, but calories
are not the leading factor only and are calculated according to the
body’s energy needs. For Patient 8 ([Table 1]) this is shown in detail ([Table 3]). Patient 8 presented a starting BMI of 26.8. The
patient’s diet before therapy, with a high intake of saturated fats and
glucose, and low fiber intake with a typical in-between snacking behavior,
showed similar calorie intake (isocaloric) and macronutrient distribution to her
diet after the therapy ([Table 3]).
Table 3 BMI is not the driver for Type 2 Diabetes
remission. Isocaloric nutrition of a patient before and at the end
of therapy, no medication,<1-year diagnosis, female, 47
years, initial BMI 26.8.
|
Isocaloric nutrition, composition
|
Before therapy
|
After therapy
|
|
Total calories kcal/day
|
1676
|
1560
|
|
Carbohydrate g/d
|
188
|
160
|
|
Carbohydrate kcal/d
|
771
|
656
|
|
% carbohydrate of total calorie intake
|
53
|
54
|
|
Proteins g/d
|
80
|
83
|
|
Proteins as kcal/d
|
330
|
338
|
|
% protein of total calorie intake
|
20
|
22
|
|
Total fat g/d
|
66
|
63
|
|
Fat as kcal/d
|
611
|
590
|
|
% of total fat intake
|
36
|
38
|
|
Saturated fat g/d
|
27.6
|
12.5
|
|
Monounsaturated fat g/d
|
21.6
|
28.2
|
|
Polyunsaturated fat g/d
|
8.6
|
17.6
|
|
Omega-3 fatty acids g/d
|
0.8
|
3.3
|
|
Fiber g/d
|
26.7
|
44.9
|
|
% fiber of total calorie intake
|
6.5
|
11.8
|
|
Vitamin D µg/day
|
1.3
|
9.2
|
|
Magnesium mg/day
|
279.0
|
577.0
|
|
Potassium g/d
|
2.4
|
4.3
|
|
Fasting blood values/anthropometric data
|
Before therapy
|
After therapy
|
|
Triglyceride mmol/l
|
2.7
|
1.4
|
|
HDL mmol/l
|
1.2
|
1.3
|
|
LDL mmol/l
|
3.9
|
3.1
|
|
Glucose mmol/l
|
9.1
|
5.4
|
|
HbA1c mmol/mol
|
50.8
|
38.8
|
|
HOMA
|
8.5
|
2.7
|
|
TyG
|
9.9
|
8.7
|
|
TG:HDL
|
5.1
|
2.4
|
|
BMI kg/m2
|
26.8
|
22.6
|
|
Waist circumference cm
|
97.0
|
90.0
|
As can be seen, the molecular distribution of the fatty acid fraction was
completely changed after therapy. After therapy, saturated fat intake values
were normal (<13 g); intake of fiber was above the recommended
value of 40 g/d; and vitamin D, magnesium, and potassium all
reached values above the RDA. The HOMA index continued to improve after the end
of the therapy (after 150 days) and was estimated to be 1.57 after 323 days.
Personalized molecular nutrition for Type 2 Diabetes remission
A highly efficient and effective insulin resistance remission result was achieved
for all patients across all BMI categories, according to the lead biomarker:
85% of the patients (11 out of 13) achieved remission, and only one
patient did not reach the lower threshold value of the TG:HDL ratio of 3.0. The
TG:HDL ratio combined with TyG are the leading biomarkers but are weighted more
to stabilizing the metabolism reprogramming that occurs during the VLCD part of
the therapy. Our finding confirmed Achilike et al. [75], who found that TG:HDL has the power to
produce a metabolically healthy phenotype. The personalized nutritional strategy
reduces the triglyceride levels, and the molecular personalized whole food diet
reduces the buildup of new triglycerides, leading to a negative balance that
reaches normal levels over time. It is of utmost importance to keep the
triglyceride buildup under control since triglycerides can cross the blood-brain
barrier and contribute to decreased satiety, as reported by Banks et al.
[76].
Improvements in triglycerides were very fast during VLCD, averaging 48%.
The fasting glucose adaptation during VLCD was also very fast, and all patients
reached the threshold value<7 mmol/l within the first 14
days.
Ten patients reached fasting glucose values<5.6 mmol/l,
and all patients were better than 6.3 mmol/l after completing
the therapy (150 days). On average, fasting glucose values improved by
33% (2.66 mmol/l).
Diabetes remission was always accompanied by weight loss as a symptomatic effect
of the metabolic improvements. Kelly et al. [77] reported a mean diabetes remission rate
of 49.4% applying very low calorie diets. The VLCD part of the
intermittent hepatic therapy, which is focused on the cause of insulin
resistance, delivers more effective results, as our reported data show ([Fig. 1]).
The weight loss reached was not homogenous, since the initial BMI was not, and
the therapy approach is not focused on calories only. The statistical
significance and regression coefficient for diabetes reversion against the HOMA
Index was highest for the TyG Index (0.61, p<0.001), compared to the
TG:HDL Index (0.41, p<0.05). The weight loss achieved also showed a
lower regression correlation to the HOMA Index (0.43, p<0.05). This
implies that the TyG index is the key biomarker for diabetes remission, with a
strong focus on triglyceride normalization as a key success factor. This does
reflect the current research that triglycerides are driving hyperinsulinemic
conditions that disturb the glucose metabolism, leading to weight increase as a
homeostatic defending mechanism.
The HOMA Index as a medically accepted analytical value for insulin resistance
was always surpassed, and on average was reduced by 79%, or 6.7 units
over the entire therapy length. For one hyperinsulinemia patient, an improvement
in the HOMA Index of 12 times was observed. All patients reached the<3
threshold point of the HOMA Index (3.8 reached for this patient, with a starting
HOMA Index of 28). Over the full course of the therapy, a success rate
of>90% could be estimated according to the HOMA Index. All
patients of the diabetes group reached HbA1c
values<47.54 mmol/mol, at the end of the 150 days time
window. The average improvement in HbA1c was 17%, or
21.8 mmol/ml, which is higher than the
6.65 mmol/mol achieved in the caloric diabetes remission study
reported by Ades et al. [78] that
had a VLCD with a similar therapy duration of six months.
The importance of the triglyceride metabolism as presented is further supported
by Ma et al. [79] and Lim
et al. [80] who report that
triglyceride levels are independently correlated with insulin resistance and
islet beta-cell function in individuals with dyslipidemia.
We observed a relationship between the time required to achieve the threshold
value of fasting glucose and the prior duration of Type 2 Diabetes and
medication taken, but due to the limited number of patients this observation is
in conclusive.
Adjuvant diabetes therapy to the uncontrolled medicated patient
Uncontrolled diabetes can be improved efficiently by restoring normal
triglyceride levels, leading to normal insulin levels, by applying the
personalized whole food part of the therapy ([Table 4]).
The initial fasting glucose value of 12.28 mmol/l was reduced to
5.7 mmol/l in 30 days, while keeping the antidiabetic medication
unchanged. Insulin resistance according to the HOMA Index was reduced by a
factor of more than 10 (from 39.2 to 3.2), almost reaching the lower threshold
value of 3.0 within 30 days of application. The TyG index estimated was 8.86
after application (threshold 8.73) and indicates improved status or almost no
remaining insulin resistance. The triglycerides level was reduced from
3.58 mmol/l to 1.55 mmol/l (but still taking
statins). Cortisol was reduced from 56 to<28. The vicious cycle of
increased cortisol paired with insulin resistance leading to hyperinsulinemia
driving visceral fat was changed from a pathologic insulin level from 0.52 to a
normal insulin level of 0.09 within 30 days of application.
Triglycerides have emerged as a new risk factor for cardiovascular disease in
Type 2 Diabetes, as reported by Ye et al. [81]. According to Halldin et al.
[82], the risk factor cholesterol has
shifted more toward the risk factor triglycerides. Statins do not lower
triglycerides adequately, as reported by Toth et al. [83] and, as reported by Shimoda
et al. [84], the durability of the
glucose-lowering effects from DPP-4 inhibitors can be better maintained if
strict triglyceride management takes place.
Insulin resistance remission for MetS and prediabetes patients normalizing
triglycerides
Normalization of MetS, prediabetes, and amelioration of NAFL is mediated by the
reduction of triglycerides, similar to what is observed in Type 2 diabetes
patients. The patients reported in their anamnesis a weight loss resistance
which can be overcome with a fast normalization of the dyslipidemic state and
triglyceride levels within the two first weeks of the personalized whole food
part of the therapy. The molecular nutrition focused on the “one
carbon” and CPT1 metabolism of the whole food part of the intermittent
hepatic therapy delivers fast triglyceride amelioration. After 30 days of
application of the personalized whole food intermittent fasting therapy part,
the average TyG index was improved by 9%. Sixteen patients reached the
lower threshold value<8.73 concerning insulin resistance but also
TG:HDL<3. This corresponds to>76% efficiency for insulin
resistance normalization respectively remission. Those five patients who were
still slightly above the threshold showed good improvement but needed more time
to reach the lower threshold. All initially prediabetic patients were no longer
prediabetic after 150 days of application. The average improvement in
triglyceride reduction was 33% within 150 days. This is in accordance to
Farell et al. [85] of key
importance in breaking weight loss resistance, since early and established NAFL
is responsible for this effect due to triglyceride accumulation. We measured an
average improvement of the liver index within the first 50 days of 29 units. The
initial median liver index was estimated as 81 units. According to Khan
et al. [86], the modulation of
triglycerides and insulin resistance represents a potential new strategy for
NAFL treatment.
Triglyceride values stabilized after 50 days, HDL cholesterol increased, and LDL
cholesterol decreased, stabilizing the metabolic and weight loss goals that had
been reached.
Weight reduction occurred as a result of the metabolic improvement, with a mean
BMI reduction of about 8% within 50 days and up to 11% within
150 days. To date, weight loss is the only existing therapy for NAFL according
to Hydes et al. [87]. Our results
suggest that, with a triglyceride and insulin resistance focus paired for weight
loss, a new efficient NAFL treatment option is available.
As conclusion, the applied therapeutically method allows reversing the Type 2
Diabetic metabolism within 60 days and remission within 150 days with high
efficiency. This shifts the treatment paradigm for Type 2 Diabetes from
management to cure. Applying these method to patients with MetS and/or
prediabetes can normalize their insulin resistance or prediabetes completely
within 50 to 150 days. Our results favor a molecular nutritional diabetes
prevention and remission strategy that is focused on triglyceride normalization
with TyG as the lead biomarker, but that also combines a caloric approach
enabling dietary molecular nutrition with an epigenetic point of view as a
completely new diabetes prevention and remission strategy, and also a new
treatment option for NAFL patients.
The pilot study has strengths and also weaknesses. Its major strength is the
achievement of insulin resistance and Type 2 Diabetes remission in a
significant, fast, and highly efficient way. The method is completely digitally
supported for easy application and combines smart formulas of active ingredients
to support the nutritional and metabolic adaptation. The presented method can
establish a new treatment option that cures Type 2 Diabetes. The weakness of the
pilot study is a limited number of persons and inhomogeneous patient
characteristics.
Additional studies might be of interest from a scientific point of view, both to
measure further data from more homogenous patient groups, and also to explore
the epigenetic effects involved. However, from a practical and an application
point of view, the methodology is very robust, proven, simply to apply,
completely digitally supported, and ready to be used on a larger scale. Applying
this new paradigm of a personalized molecular dietary pattern control for
insulin resistance remission could revolutionize diabetes medicine and could
also contribute beneficially to reduce the economic burden of Type 2 Diabetes
and its related diseases.
