U.S. patent application number 16/212356 was filed with the patent office on 2019-04-11 for method of detection of clinically significant post-prandial hyperglycemia in normoglycemic patients.
The applicant listed for this patent is True Health IP, LLC. Invention is credited to Rebecca E. Caffrey, James V. Pottala, Steve Varvel.
Application Number | 20190107530 16/212356 |
Document ID | / |
Family ID | 50031603 |
Filed Date | 2019-04-11 |
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United States Patent
Application |
20190107530 |
Kind Code |
A1 |
Varvel; Steve ; et
al. |
April 11, 2019 |
METHOD OF DETECTION OF CLINICALLY SIGNIFICANT POST-PRANDIAL
HYPERGLYCEMIA IN NORMOGLYCEMIC PATIENTS
Abstract
This invention relates to a method for detecting the presence of
or likelihood of a patient of developing occult pancreatic beta
cell dysfunction, and a method for detecting the presence of or
likelihood of a patient of developing clinically significant
post-prandial hyperglycemia. The methods involve (a) measuring a
level of alpha-hydroxybutyrate (AHB) in a single fasting baseline
biological sample of the patient; (b) comparing the level of AHB in
the single fasting baseline biological sample to a reference AHB
level; and (c) determining the presence of or likelihood of
developing the disorder in the patient based on the comparison in
step (b). An increased AHB level at fasting baseline indicates that
a normoglycemic, normo-insulinemic and/or non-dyslipidemic patient
has developed or has an increased likelihood of developing occult
pancreatic beta cell dysfunction. An increased AHB level at fasting
baseline and an elevated glucose level of at least about 155 mg/dL
at 30 minutes and/or 1 hour indicates that a normoglycemic,
normo-insulinemic and/or non-dyslipidemic patient has developed or
has an increased likelihood of developing clinically significant
post-prandial hyperglycemia.
Inventors: |
Varvel; Steve; (Richmond,
VA) ; Caffrey; Rebecca E.; (N. Chesterfield, VA)
; Pottala; James V.; (Sioux Falls, SD) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
True Health IP, LLC |
Frisco |
TX |
US |
|
|
Family ID: |
50031603 |
Appl. No.: |
16/212356 |
Filed: |
December 6, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14154074 |
Jan 13, 2014 |
10191032 |
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16212356 |
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61831337 |
Jun 5, 2013 |
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61831405 |
Jun 5, 2013 |
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61751328 |
Jan 11, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/50 20130101;
G01N 33/6893 20130101; G01N 2800/50 20130101; G01N 2800/042
20130101; G01N 33/92 20130101; G01N 2800/044 20130101 |
International
Class: |
G01N 33/50 20060101
G01N033/50; G01N 33/92 20060101 G01N033/92; G01N 33/68 20060101
G01N033/68 |
Claims
1. A method for detecting the presence of or likelihood of
developing clinically significant post-prandial hyperglycemia in a
patient, comprising: a. measuring a level of alpha-hydroxybutyrate
(AHB) in a single fasting baseline biological sample of the
patient; b. comparing the level of AHB in the single fasting
baseline biological sample to a reference AHB level; and c.
determining the presence of or likelihood of developing clinically
significant post-prandial hyperglycemia based on the comparison in
step (b), wherein an increased AHB level at fasting baseline and an
elevated glucose level of at least about 155 mg/dL at 30 minutes
and/or 1 hour indicates that a normoglycemic, normo-insulinemic
and/or non-dyslipidemic patient has developed or has an increased
likelihood of developing clinically significant post-prandial
hyperglycemia.
2. The method of claim 1, wherein the level of AHB is greater than
4.5 mg/dl.
3. The method of claim 1, further comprising measuring one or more
biomarkers in one or more biological samples of the patient.
4. The method of claim 3, wherein the one or more biomarkers are
selected from the group consisting of glucose, insulin, HDL, HDL-c,
triglycerides, LDL, LDL-c, C-peptide, 1,5-anhydroglucitol, and
pro-insulin.
5. The method of claim 3, wherein the one or more biomarkers are
selected from the group consisting of auto-antibodies present in
type-1 diabetes; viral nucleic acids; biomarkers to autoimmune
diseases; viral DNAs, viral RNAs and antibodies to viral capsid
proteins for members of the Enterovirus family.
6. The method of claim 3, wherein the one or more biomarkers are
selected from the group consisting of glucose, insulin, anti-islet
cell cytoplasmic (anti-ICA) auto-antibodies, glutamic acid
decarboxylase (anti-GAD) auto-antibodies, 1,5-anhydroglucitol,
hemoglobin (Hb) A1c, fructosamine, mannose, D-mannose,
mannose-binding lectin (MBL) amount, mannose binding lectin (MBL)
activity, 1,5-anhydroglucitol (1,5 AG), glycation gap
(glycosylation gap), serum amylase, c-peptide, intact pro-insulin,
leptin, adiponectin, leptin/adiponectin ratio, ferritin, free fatty
acids, lipoprotein-associated phospholipase A2 (Lp-PLA2),
fibrinogen, myeloperoxidase, cystatin C, homocysteine,
F2-isoprostanes, .alpha.-hydroxybutyrate (AHB), linoleoyl
glycerophosphocholine (L-GPC), oleic acid (OA), analytes associated
with IR score, analytes associated with HOMA (Homeostasis Model
Assessment) IR score, analytes associated with CLIX score,
gamma-glutamic transferase (GGT), uric acid, vitamin B12,
homocysteine, 25-hydroxyvitamin D, TSH, estimated glomerular
filtration rate (eGFR), and serum creatinine.
7. The method of claim 3, wherein the one or more biomarkers are
selected from the group consisting of body mass index (BMI); free
fatty acids; low density lipoprotein particle number (LDL-P);
LDL-cholesterol (LDL-C); triglyceride; high density lipoprotein
particle number (HDL-P); high density lipoprotein-cholesterol
(HDL-C); high sensitivity C-reactive protein (hs-CRP); remnant-like
lipoproteins (RLPs); RLP-(cholesterol measures); apolipoprotein
A-1; HDL2; ApoB:ApoA-1 ratio; Lp(a) mass; Lp(a) cholesterol; large
VLDL-P; small LDL-P; large HDL-P; VLDL-size; LDL size; HDL size;
LP-IR score; apolipoprotein A-1 (ApoA-1); apolipoprotein B (ApoB);
apolipoprotein C (ApoC); apolipoprotein E (ApoE); and ApoE
sub-species, variations, fragments, PTMs and isoforms thereof.
8. The method of claim 3, wherein the one or more biomarkers are
selected from the group consisting of campesterol, sitosterol
(.beta.-sitosterol), cholestanol, desmosterol, lathosterol, and
squalene.
9. The method of claim 3, wherein the one or more biomarkers are
biomarkers for coagulation or dyslipidemia.
10. The method of claim 1, further comprising administering an oral
glucose tolerance test (OGTT), wherein a glucose level of at least
about 155 mg/dL and/or a decreased first phase insulin response
within one hour of taking OGTT and/or after food consumption
indicates that the patient has developed or has an increased
likelihood of developing clinically significant post-prandial
hyperglycemia.
11. The method of claim 1, wherein the presence of or increased
likelihood of developing clinically significant post-prandial
hyperglycemia also indicates that said patient is at risk of a
diabetic condition selected from the group consisting of
cardiodiabetes, gestational diabetes, latent autoimmune diabetes of
adults (LADA), mixed phenotype diabetic conditions, and atypical
form of type 1 diabetes.
12. The method of claim 1, wherein the presence of or increased
likelihood of developing clinically significant post-prandial
hyperglycemia further predicts an increased likelihood of a
requirement for exogenous insulin supplementation.
13. The method of claim 1, wherein the patient is at risk for a
cardiodiabetic disease associated with post-prandial
hyperglycemia.
14. The method of claim 13, wherein the cardiodiabetic disease is
selected from the group consisting of retinopathy, neuropathy,
nephropathy, atherosclerosis, stroke, myocardial infarction,
gestational diabetes, pre-term labor, and high birth-weight
infants.
15. The method of claim 6, further comprising measuring the
biological sample with the biomarker 1,5-anhydroglucitol, wherein
an elevated level of AHB and a normal level of 1,5-anhydroglucitol
at baseline are used as a guide to determine whether the
post-prandial hyperglycemia does not exceed the glucose renal
threshold.
16. The method of claim 15, wherein the glucose renal threshold is
at least about 180 mg/dL.
17. The method of claim 11, wherein the atypical form of type 1
diabetes is insulin autoimmune syndrome (IAS).
18. The method of claim 1, wherein the patient shows no clinically
significant post-prandial hyperglycemia, as detected by
conventional diagnostic techniques.
19. The method of claim 1, wherein the determination in step (c) is
performed without having the patient provide multiple biological
samples separated by a period of time.
20. The method of claim 1, further comprising assigning a health
risk value for the patient based on the determination in step (c),
wherein the health risk value is selected from the group consisting
of low risk, moderate risk and high risk of clinically significant
post-prandial hyperglycemia.
21-30. (canceled)
Description
[0001] PRIORITY CLAIM
[0002] This application is a continuation of U.S. patent
application Ser. No. 14/154,074 filed Jan. 13, 2014 which claims
priority to U.S. Provisional Application No. 61/751,328, filed Jan.
11, 2013, U.S. Provisional Application No. 61/831,337, filed Jun.
5, 2013, and U.S. Provisional Application No. 61/831,405, filed
Jun. 5, 2013, the entirety of each of which are incorporated herein
by reference and relied upon.
BACKGROUND
[0003] Currently a number of tests exist that can diagnose patients
as diabetic or pre-diabetic, including pre-diabetic conditions,
such as occult pancreatic beta cell dysfunction or post-prandial
hyperglycemia. Such tests include glucose, insulin, pro-insulin,
c-peptide, HbA1c, fructosamine, glycation gap, 1,5-anhydroglucitol
(1,5-AG), OGTT, CLIX scoring, HOMA IR scoring, and IRI scores based
on combinations of AHB, L-GPC, and Oleic Acid weighted by insulin
or BMI. Used alone or in combination some of these tests can detect
the presence of Type 2 diabetes, pre-diabetes (metabolic syndrome)
and early insulin resistance. Additionally, there are tests that
may detect some cases of Type 1 Diabetes (T1DM, sometimes referred
to as childhood-onset or early-onset) such as anti-GAD antibody and
other auto-antibodies to pancreatic islet cells, as Type 1 diabetes
usually involves development of auto-antibodies.
[0004] The best current predictors of fasting normoglycemic
patients who are actually at risk of developing diabetes are OGTTs
and CLIX scoring of OGTTs. Both of these techniques involve testing
multiple analytes at multiple time-points, requiring the patient to
have a blood sample drawn at baseline (fasting), drink a beverage
containing a known quantity of glucose, and subsequently contacting
patient blood samples and measuring the levels of various analytes
(e.g. glucose, insulin, pro-insulin, c-peptide, creatinine, etc . .
. ) at fasting baseline, and then at various time point intervals
after dosing with the glucose load. Most OGTTs and CLIX scoring
require a patient to remain in the doctor's office for 2 hours post
dose, and most clinicians only test baseline samples and the 2-hour
time point, and not the labor-intensive 3-5 additional times blood
draws during the 2-hour period necessary for CLIX scoring, due to
labor and cost constraints. Additionally, complicated and laborious
mathematical calculations need to be performed in order to optimize
detection of at-risk individuals with these techniques, and kidney
function (approximated by blood creatinine levels/eGFR) needs to be
accounted for, causing an additional step. In standard OGTTs,
1-hour time points are rarely obtained and tested for determination
of early insulin resistance and/or beta cell dysfunction, even
though it is known from the literature that impaired first-phase
insulin secretion response to glucose load at the 1 hour time point
is a predictor of risk of development of diabetes and resulting
cardio-diabetic complications such as atherosclerosis, coronary
artery disease, diabetic retinopathy, etc.
[0005] There therefore exists a need in the art for a better method
for detecting the presence of likelihood of developing occult
pancreatic beta cell dysfunction and post-prandial
hyperglycemia.
SUMMARY OF INVENTION
[0006] This invention relates to a method for detecting the
presence of or likelihood of developing occult pancreatic beta cell
dysfunction in a patient, comprising: (a) measuring a level. of
alpha-hydroxybutyrate (AHB) in a single fasting baseline biological
sample of the patient; (b) comparing the level of AHB in the single
fasting baseline biological sample to a reference AHB level; and
(c) determining the presence of or likelihood of developing occult
pancreatic beta cell dysfunction in said patient based on the
comparison in step (b). An increased AHB level at fasting baseline
indicates that a normoglycemic, normo-insulinemic and/or
non-dyslipidemic patient has developed or has an increased
likelihood of developing occult pancreatic beta cell dysfunction.
The level of AHB may be greater than 4.5 mg/dl.
[0007] The method may include measuring one or more additional
biomarkers in one or more biological samples of the patient.
Biomarkers may be selected from glucose, insulin, HDL, HDL-c,
triglycerides, LDL, LDL-c, C-peptide, 1,5-anhydroglucitol, or
pro-insulin. Alternatively, the biomarkers may be auto-antibodies
present in type-1 diabetes, viral nucleic acids, biomarkers to
autoimmune diseases, viral DNAs, or viral RNAs and antibodies to
viral capsid proteins for members of the Enterovirus family.
Alternatively, the biomarkers may be glucose, insulin, anti-islet
cell cytoplasmic (anti-ICA) auto-antibodies, glutamic acid
decarboxylase (anti-GAD) auto-antibodies, 1,5-anhydroglucitol,
hemoglobin (Hb) Ale, fructosamine, mannose, D-mannose,
mannose-binding lectin (MBL) amount, mannose binding lectin (MBL)
activity, 1,5-anhydroglucitol (1,5 AG), glycation gap
(glycosylation gap), serum amylase, c-peptide, intact pro-insulin,
leptin, adiponectin, leptin/adiponectin ratio, ferritin, free fatty
acids, lipoprotein-associated phospholipase A2 (Lp-PLA2),
fibrinogen, myeloperoxidase, cystatin C, homocysteine,
F2-isoprostanes, .alpha.-hydroxybutyrate (AHB), linoleoyl
glycerophosphocholine (L-GPC), oleic acid (OA), analytes associated
with IR score, analytes associated with HOMA (Homeostasis Model
Assessment) IR score, analytes associated with CLIX score,
gamma-glutamic transferase (GGT), uric acid, vitamin B12,
homocysteine, 25-hydroxyvitamin D, TSH, estimated glomerular
filtration rate (eGFR), or serum creatinine. Alternatively, the
biomarkers may be biomarkers associated with body mass index (BMI),
free fatty acids, low density lipoprotein particle number (LDL-P),
LDL-cholesterol (LDL-C), triglyceride; high density lipoprotein
particle number (HDL-P), high density lipoprotein-cholesterol
(HDL-C), high sensitivity C-reactive protein (hs-CRP), remnant-like
lipoproteins (RLPs), RLP-(cholesterol measures), apolipoprotein
A-1, HDL2, ApoB:ApoA-1 ratio, Lp(a) mass, Lp(a) cholesterol, large
VLDL-P, small. LDL-P, large HDL-P, VLDL-size, LDL size, HDL size,
LP-IR score, apolipoprotein A-1 (ApoA-1), apolipoprotein B (ApoB),
apolipoprotein C (ApoC), apolipoprotein E (ApoE), ApoE sub-species,
or variations, fragments, PTMs and isoforms thereof. Alternatively,
biomarkers may be campesterol, sitosterol (.beta.-sitosterol),
cholestanol, desmosterol, lathosterol, or squalene. Alternatively,
the biomarkers may be biomarkers for coagulation or
dyslipidernia.
[0008] A determination of increased likelihood of an impaired first
phase insulin secretion response can be based on the determination
in 1 (c).
[0009] The presence of or increased likelihood of developing occult
pancreatic beta cell dysfunction also indicates that said patient
is at risk of a diabetic condition, such as cardiodiabetes,
gestational diabetes, latent autoimmune diabetes of adults (LADA),
mixed phenotype diabetic conditions, or atypical forms of type 1
diabetes, such as insulin autoimmune syndrome (IAS).
[0010] The presence of or increased likelihood of developing
pancreatic beta cell dysfunction can also be used to predict an
increased likelihood of a requirement for exogenous insulin
supplementation. The method can also be used to show that the
patient is at risk for a cardiodiabetic disease associated with
post-prandial hyperglycemia. Types of cardiodiabetic disease
include retinopathy, neuropathy, nephropathy, atherosclerosis,
stroke, myocardial infarction, gestational diabetes, pre-term
labor, and the birth of high birth-weight infants.
[0011] The patient may or may not show signs associated with any
apparent beta cell. dysfunction, as detected by conventional
diagnostic techniques.
[0012] Determination step (c) may be performed without having the
patient provide multiple biological samples separated by a period
of time.
[0013] A health risk value may be assigned for the patient based on
the determination in step (c), The health risk value may be low
risk, moderate risk and high risk of occult pancreatic beta cell
dysfunction.
[0014] In one embodiment, an AHB level of less than 4.5 mg/dL
indicates a low risk of occult pancreatic beta cell dysfunction; an
AHB level of about 4.5 mg/dL to about 5.7 mg/dL indicates an
intermediate to a high risk of occult pancreatic beta cell
dysfunction; and an AHB level of more than 5.7 mg/dL indicates a
high risk of occult pancreatic beta cell dysfunction.
[0015] The method may include measuring the anti-ICA or anti-GAD
auto-antibodies biomarkers in the biological sample, wherein a
positive reaction to one of the biomarkers indicates an increased
risk of occult pancreatic beta cell dysfunction.
[0016] A therapy guidance may be effectuated based on the
determination in step (c). Suitable therapy guidance includes one
or more of the following: performing a confirmatory OGTT and/or
additional diagnostic testing, prescribing a drug therapy,
increasing monitoring frequency of patient condition, and
recommending appropriate risk-reduction therapy such as making or
maintaining diet and lifestyle choices based on the determination
in step (c). The therapy guidance may involves administration of
antioxidants, administration of anti-coagulants, administration of
anti-dyslipidemic drugs, avoidance of drugs or agents known to
damage pancreatic cells; discontinued administration of current
drug therapy, administration of agents specific for post-prandial
hyperglycemia (e.g. cycloset), administration of drugs that
enhance, and/or augment, and/or spare pancreatic beta cell
function, administration of an anti-viral agent, an
immunosuppressant or insulin or an insulin analog or combinations
thereof. The therapy guidance may also include one or more of the
following: increased frequency of physician's follow-up, referral
for oral glucose tolerance test (OGTT) and/or CLIX test, repetition
of tests for monitoring disease progression, patient referral for
comprehensive testing for type 1 diabetes; testing for
auto-antibodies to pancreatic cell antigens, other biomarkers for
autoimmune diseases, viral DNA/RNA and/or antibodies to viral
capsid proteins for Enterovirus family members or combinations
thereof. A lifestyle choices involve changes in diet and nutrition,
changes in exercise, smoking elimination or a combination
thereof.
[0017] The biological sample may be a blood component, saliva or
urine.
[0018] Another embodiment of the invention relates to a method for
detecting the presence of or likelihood of a patient of developing
occult pancreatic beta cell dysfunction, comprising: (a) measuring
a level of alpha-hydroxybutyrate (AHB) in a biological sample of
the patient; (b) comparing the level of AHB in the baseline
biological sample to a reference AHB level; and (c) determining the
presence of or likelihood of the patient to develop occult
pancreatic beta cell dysfunction based on the comparison in step
(b). The determination in step (c) is performed without having the
patient provide multiple biological samples separated by a period
of time. An elevated AHB baseline level indicates that a
normoglycemic, normo-insulinemic and/or non-dyslipidemic patient
has developed or has an increased likelihood of developing occult
pancreatic beta cell dysfunction.
[0019] Another embodiment of the invention relates to a method for
monitoring the progression or remission or a patient's response to
treatment of a diabetic condition due to occult pancreatic beta
cell dysfunction in a patient, comprising: (a) measuring a first
level of alpha-hydroxybutyrate (AHB) in a biological sample of the
patient; (b) measuring a second level of alpha-hydroxybutyrate
(AHB) in the biological sample of the patient after a period of
time; (c) comparing the first level and the second level of AHB in
the biological sample based on the measurements in steps (a) and
(b) to determine whether the level of AHB has changed; and (d)
monitoring the patient's progression or remission or the patient's
response to treatment of the diabetic condition based on the
comparison in step (c). An increased AHB level or an. unchanged AHB
level indicates that the diabetic condition is still in progression
and/or a normoglycemic, normo-insulinemic and/or non-dyslipidemic
patient is not responding to the treatment. A decreased AHB level
indicates that the diabetic condition is in remission and/or a
normoglycemic, normo-insulinemic and/or non-dyslipidemic patient is
responding to the treatment. The measurement in step (b) may be
taken at least one day after the measurement in step (a).
[0020] When relating to monitoring a patient's response to a
treatment, the method may further comprise the step of adding a
treatment, after the measurement in step (a), to treat the diabetic
condition; the method may also further comprises the step of
changing and/or discontinuing a treatment, after the measurement in
step (a), to treat the diabetic condition.
[0021] Another embodiment of this invention relates to a method for
detecting the presence of or likelihood of developing clinically
significant post-prandial hyperglycemia in a patient, comprising:
(a) measuring a level of alpha-hydroxybutyrate (AHB) in a single
fasting baseline biological sample of the patient; (b) comparing
the level of AHB in the single fasting baseline biological sample
to a reference AHB level; and (c) determining the presence of or
likelihood of developing clinically significant post-prandial
hyperglycemia based on the comparison in step (b). An increased AHB
level at fasting baseline and an elevated glucose level of at least
about 155 mg/dL at 30 minutes and/or 1 hour indicates that a
normoglycemic, normo-insulinemic and/or non-dyslipidemic patient
has developed or has an increased likelihood of developing
clinically significant post-prandial hyperglycemia. The level of
AHB may be greater than 4.5 mg/dl.
[0022] The method may include measuring one or more additional
biomarkers in one or more biological samples of the patient.
Biomarkers may be selected from glucose, insulin, HDL, HDL-c,
triglycerides, LDL, LDL-c, C-peptide, 1,5-anhydroglucitol, or
pro-insulin. Alternatively, the biomarkers may be auto-antibodies
present in type-1 diabetes, viral nucleic acids, biomarkers to
autoimmune diseases, viral DNAs, or viral RNAs and antibodies to
viral capsid proteins for members of the Enterovirus family.
Alternatively, the biomarkers may be glucose, insulin, anti-islet
cell cytoplasmic (anti-ICA) auto-antibodies, glutamic acid
decarboxylase (anti-GAD) auto-antibodies, 1,5-anhydroglucitol,
hemoglobin (Hb) A1c, fructosamine, mannose, D-mannose,
mannose-binding lectin (MBL) amount, mannose binding lectin (MBL)
activity, 1,5-anhydroglucitol (1,5 AG), glycation gap
(glycosylation gap), serum amylase, c-peptide, intact pro-insulin,
leptin, adiponectin, leptin/adiponectin ratio, ferritin, free fatty
acids, lipoprotein-associated phospholipase A2 (Lp-PLA2),
fibrinogen, myeloperoxidase, cystatin C, homocysteine,
F2-isoprostanes, .alpha.-hydroxybutyrate (AHB), linoleoyl
glycerophosphocholine (L-GPC), oleic acid (OA), analytes associated
with IR score, analytes associated with HOMA (Homeostasis Model
Assessment) IR score, analytes associated with CLIX score,
gamma-glutamic transferase (GGT), uric acid, vitamin B12,
homocysteine, 25-hydroxyvitamin D, TSH, estimated glomerular
filtration rate (eGFR), or serum creatinine. Alternatively, the
biomarkers may be biomarkers associated with body mass index (BMI),
free fatty acids, low density lipoprotein particle number (LDL-P),
LDL-cholesterol (LDL-C), triglyceride; high density lipoprotein
particle number (HDL-P), high density lipoprotein-cholesterol
(HDL-C), high sensitivity C-reactive protein (hs-CRP), remnant-like
lipoproteins (RLPs), RLP-(cholesterol measures), apolipoprotein
A-1, HDL2, ApoB:ApoA-1 ratio, Lp(a) mass, Lp(a) cholesterol, large
VLDL-P, small LDL-P, large HDL-P, VLDL-size, LDL size, HDL size,
LP-IR score, apolipoprotein A-1 (ApoA-1), apolipoprotein B (ApoB),
apolipoprotein C (ApoC), apolipoprotein E (ApoE), ApoE sub-species,
or variations, fragments, PTMs and isoforms thereof. Alternatively,
biomarkers may be campesterol, sitosterol (.beta.-sitosterol),
cholestanol, desmosterol, lathosterol, or squalene. Alternatively,
the biomarkers may be biomarkers for coagulation or
dyslipidemia.
[0023] The method may further comprise administering an oral
glucose tolerance test (OGTT). If the patient exhibits a glucose
level of at least about 155 mg/dL and/or a decreased first phase
insulin response within one hour of taking OGTT and/or after food
consumption, this is an additional indication of clinically
significant post-prandial hyperglycemia, or that the patient has
developed or has an increased likelihood of developing clinically
significant post-prandial hyperglycemia.
[0024] The presence of or increased likelihood of developing
clinically significant post-prandial. hyperglycemia also indicates
that said patient is at risk of a diabetic condition, such as
cardiodiabetes, gestational diabetes, latent autoimmune diabetes of
adults (LADA), mixed phenotype diabetic conditions, or atypical
forms of type 1 diabetes, such as insulin autoimmune syndrome
(IAS).
[0025] The presence of or increased likelihood of developing
clinically significant post-prandial hyperglycemia can also be used
to predict an increased likelihood of a requirement for exogenous
insulin supplementation. The method can also be used to show that
the patient is at risk for a cardiodiabetic disease associated with
post-prandial hyperglycemia. Types of cardiodiabetic disease
include retinopathy, neuropathy, nephropathy, atherosclerosis,
stroke, myocardial infarction, gestational diabetes, pre-term
labor, and the birth of high birth-weight infants.
[0026] The method may further comprise measuring the biological
sample with the biomarker 1,5-anhydroglucitol. An elevated level of
AHB and a normal level of 1,5-anhydroglucitol at baseline can be
used as a guide to determine whether the post-prandial
hyperglycemia does not exceed the glucose renal threshold, for
instance a glucose renal threshold of at least about 180 mg/dL.
[0027] The patient may show no clinically significant post-prandial
hyperglycemia, as detected by conventional diagnostic
techniques.
[0028] Determination step (c) may be performed without having the
patient provide multiple biological samples separated by a period
of time.
[0029] A health risk value may be assigned for the patient based on
the determination in step (c), The health risk value may be low
risk, moderate risk and high risk of occult pancreatic beta cell
dysfunction.
[0030] In one embodiment, an AHB level of less than 4.5 mg/dL
indicates a low risk of clinically significant post-prandial
hyperglycemia; an AHB level of about 4.5 mg/dL to about 6.0 mg/dL
indicates an intermediate to a high risk of clinically significant
post-prandial hyperglycemia; and an AHB level of more than 6.0
mg/dL indicates a high risk of clinically significant post-prandial
hyperglycemia.
[0031] The method may include measuring the anti-ICA or anti-GAD
auto-antibodies biomarkers in the biological sample, wherein a
positive reaction to one of the biomarkers indicates an increased
risk of clinically significant post-prandial hyperglycemia.
[0032] A therapy guidance may be effectuated based on the
determination in step (c). Suitable therapy guidance includes one
or more of the following: performing a confirmatory OGTT and/or
additional diagnostic testing, prescribing a drug therapy,
increasing monitoring frequency of patient condition, and
recommending appropriate risk-reduction therapy such as making or
maintaining diet and lifestyle choices based on the determination
in step (c). The therapy guidance may involves administration of
antioxidants, administration of anti-coagulants, administration of
anti-dyslipidemic drugs, avoidance of drugs or agents known to
damage pancreatic cells; discontinued administration of current
drug therapy, administration of agents specific for post-prandial
hyperglycemia (e.g. cycloset), administration of drugs that
enhance, and/or augment, and/or spare pancreatic beta cell
function, administration of an anti-viral agent, an
immunosuppressant or insulin or an insulin analog or combinations
thereof. The therapy guidance may also include one or more of the
following: increased frequency of physician's follow-up, referral
for oral glucose tolerance test (OGTT) and/or CLIX test, repetition
of tests for monitoring disease progression, patient referral for
comprehensive testing for type 1 diabetes; testing for
auto-antibodies to pancreatic cell antigens, other biomarkers for
autoimmune diseases, viral DNA/RNA and/or antibodies to viral
capsid proteins for Enterovirus family members or combinations
thereof. A lifestyle choices involve changes in diet and nutrition,
changes in exercise, smoking elimination or a combination
thereof.
[0033] The biological sample may be a blood component, saliva, or
urine.
[0034] Additional aspects, advantages and features of the invention
are set forth in this specification, and in, part will become
apparent to those skilled in the art on examination of the
following, or may learned by practice of the invention. The
inventions disclosed in this application are not limited to any
particular set of or combination of aspects, advantages and
features. It is contemplated that various combinations of the
stated aspects, advantages and features make up the inventions
disclosed in this application.
BRIEF DESCRIPTION OF THE DRAWINGS
[0035] FIG. 1 shows the traditional and modern models for onset of
type 1 diabetic mellitus (T1DM), adapted from Atkinson and
Eisenbarth, Type 1 diabetes: new perspective on disease
pathogenesis and treatment, The Lancet, 358 (9277):221-229
(2009).
[0036] FIG. 2 illustrates a concept showing methods that accurately
predict T1DM development early in the course of the disease may be
clinically useful in the prevention of full-blown diabetes, adapted
from Atkinson and Eisenbarth, Type 1 diabetes: new perspective on
disease pathogenesis and treatment, The Lancet, 358 (9277):221-229
(2009).
[0037] FIG. 3 shows the pathways that lead normal beta cells to
become dysfunctional in T1DM versus Type 2 diabetic mellitus
(T2DM).
[0038] FIG. 4 shows CLIX-IR and CLIX-B graphs for patients assigned
within each of the glycemic status (NGT, IFG, IGT and CGI).
[0039] FIG. 5 shows graphs of biomarker levels for hemoglobin A1c,
glucose, fructosamine and glycation gap for patients assigned
within each of the glycemic status (NGT, IFG, IGT and CGI).
[0040] FIG. 6 shows graphs of biomarker levels for baseline
insulin, pro-insulin, c-peptide and pro-insulin:c-peptide ratio for
patients assigned within each of the glycemic status (NGT, IFG, IGT
and CGI).
[0041] FIG. 7 shows graphs of biomarker levels for leptin,
adiponectin, ferritin, free-fatty acid, HDL-2 and hs-CRP for
patients assigned within each of the glycemic status (NGT, IFG, IGT
and CGI).
[0042] FIG. 8 shows graphs of biomarker levels for
alpha-hydroxybutyrate (AHB), linoleoyl-GC, oleic acid, IRI, HOMA-IR
and LP-IR score for patients assigned within each of the glycemic
status (NGT, IFG, IGT and CGI).
[0043] FIG. 9 shows graphs of biomarkers levels for glucose,
insulin, free fatty acids and c-peptide for NGT patients assigned
to the following categories: insulin sensitive (IS), insulin
resistant (IR) by high baseline trig/HDLc ratio only, IR by high
baseline AHB only or IR by both trig/HDLc, and AHB high at
baseline.
[0044] FIG. 10 is a graph depicting 1-hour glucose values measured
against traditional GTT glycemic categories. The graph shows that a
1-hour glucose value of above 155 mg/dL was strongly associated
with diabetes incidence in the Botnia and SAHS studies (during 8
years of f/u). Adapted from Abdul-Ghani et al., 2009.
[0045] FIG. 11 is a graph showing a linear relationship between a
one (1)-hour glucose "bump" (mg/dL) and AHB (.mu.g/mL) over the
range 1.7 to 12.2 .mu.g/mL in 87 normoglycemic and
normo-insulinemic subjects tested.
[0046] FIG. 12 shows an ROC curve comparing the relationship
between AHB measurement and one (1)-hour glucose held regardless of
age, gender or baseline glucose levels.
[0047] FIG. 13 shows oral glucose tolerance test (OGTT) response
area under the curve (AUC) using cubic regression with 95% mean
confidence bands by AHB levels (i.e., normal, intermediate,
high).
[0048] FIG. 14 shows oral glucose tolerance test (OGTT) response
area under the curve (AUC) using cubic regression with 95% mean
confidence bands by AHB levels (i.e., normal, high).
[0049] FIG. 15 shows a distribution of beta cell CLIX score with
two identified outlier observations.
[0050] FIGS. 16A-C show lipid profiles of Patient X at six time
points. FIG. 16A shows that no lipid test was conducted on Feb 28,
2012 and Apr. 3, 2012. FIG. 16B shows the results of the lipid
tests conducted on May 2, 2012 and Jul. 10, 2012. FIG. 16C shows
the results of the lipid tests conducted on Jan. 8, 2013 and Feb.
27, 2013.
[0051] FIGS. 17A-C show the test results for biomarkers of Glycemic
Control, Beta Cell Function and Insulin Resistance of Patient X at
six time points. FIG. 17A shows that biomarker test results from
Feb. 28, 2012 and Apr. 3, 2012. FIG. 17B shows biomarker test
results from May 2, 2012 and Jul. 10, 2012. FIG. 17C shows
biomarker test results from Jan. 8, 2013 and Feb. 27, 2013.
[0052] FIG. 18 shows an oral glucose tolerance test (OGTT) insulin
response area under the curve (AUC) over time using cubic
regression with 95% mean confidence bands for normal, overweight,
and obese BMI categories by AHB tertiles.
[0053] FIG. 19 shows graphs of fitted oral glucose tolerance test
(OGTT) insulin response 1.sup.st phase linear slope estimate with
95% mean confidence intervals for normal, overweight, and obese BMI
groups by AHB tertiles.
[0054] FIG. 20 shows an oral glucose tolerance test (OGTT) glucose
response area under the curve (AUC) over time using cubic
regression with 95% mean confidence bands for normal, overweight,
and obese BMI categories by AHB tertiles.
[0055] FIG. 21 shows graphs of fitted oral glucose tolerance test
(OGTT) glucose response 1.sup.st phase linear slope estimate with
95% mean confidence intervals for normal, overweight, and obese BMI
groups by AHB tertiles.
[0056] FIG. 22 shows ROC curves for classifying subjects having a
1-hour glucose .gtoreq.155 mg/dL during oral glucose tolerance test
(OGTT).
DETAILED DESCRIPTION
[0057] This invention provides a diagnostic tool that enables the
detection and identification of a subset of apparently normal
(normoglycemic and non-dyslipidemic) patients, from a single
fasting baseline sample, who have occult pancreatic beta cell
dysfunction resulting in impaired first-phase insulin response. The
clinical utility of the invention arises from identification of
asymptomatic patients at increased risk of developing full-blown
diabetes from pancreatic insufficiency early in the progression of
the disease. This test identifies forms of diabetes with features
of both Type 1 and Type 2 and slow progression to insulin
insufficiency such as (LADA), "skinny diabetes," which is more
frequently observed in Asian populations, and atypical forms of
Type 1 diabetes such as Insulin Autoimmune Syndrome (IAS) in
apparently normal, healthy patients. Detecting patients who would
be mis-diagnosed as "normal" (no apparent beta cell dysfunction) by
conventional diagnostic testing procedures will result in earlier
identification of at-risk patients so that they can be targeted for
optimal therapeutic intervention to delay or prevent disease
progression, and improve clinical outcomes.
Existing Test Cut-offs and Definitions of Disease
[0058] The control of blood glucose levels is critical. Insulin is
the hormone that brings blood glucose into cells. Without
sufficient insulin to bring glucose into the cells, blood glucose
becomes elevated, and the cells "starve" for glucose and the body
must use alternative pathways to produce energy for vital organs,
like generating ketone bodies and free fatty acids (FFA's) to fuel
the brain and heart, respectively. The pancreatic beta cells
normally secrete insulin in response to a meal or a "glucose load"
during an oral glucose tolerance test (OGTT), thus bringing down
the level of blood glucose by bringing it into the cells of the
body. This process of glucose homeostasis can be dysregulated in a
number of ways, resulting in poor control of blood glucose levels.
When glucose balance is dysregulated such that blood glucose varies
to higher than normal for short or long periods of time, this means
that the patient has developed or is developing diabetes.
[0059] Type 1 Diabetes (T1DM). There are multiple types of
diabetes; it is not a single disease. Decades ago, the predominant
type of diabetes was known as "early onset" and it was an acute
illness usually occurring in childhood or adolescence in which the
patient would suddenly go from healthy to very sick, with high
blood sugar due to rapid and catastrophic failure of the pancreas
to produce enough insulin. The patient would require injections of
insulin in order to maintain normal levels of blood sugar and
survive. Today we call this Type 1 Diabetes Mellitus (T1DM) and
recognize that the cause is usually viral infection and/or
autoimmunity, and that this form occurs in adults as well as
children. Full-blown T1DM requires that patients be treated with
exogenous insulin, because patients do not make enough insulin by
themselves to survive. However, there are milder forms of T1DM that
progress more slowly to insulin-dependence, or in which a patient
may need insulin for a short period of time, and then go off of the
insulin and maintain their normal blood glucose regulation.
[0060] Type 2 Diabetes (T2DM) is completely different
physiologically to T1DM. T2DM is characterized by abnormally high
blood glucose and abnormally high insulin levels. Also, T2DM does
not have an acute onset of symptoms like T1DM. In contrast, it
develops gradually over time, usually years, and therefore used to
be called "adult onset" diabetes. T2DM is related to diet and
lifestyle factors such as eating a high-sugar, high-carbohydrate
diet, lack of exercise, and development of obesity, in particular
abdominal obesity. Because of the sedentary lifestyle and poor diet
in the Western world, there is an epidemic of T2DM in the US and
Europe that parallels the rise in the number of obese and morbidly
obese adults. Because more children are also becoming obese, we now
see more cases of T2DM developing in childhood. The consequences
for development of T2DM is a radical increase in the risk of
cardiovascular disease, termed cardio-diabetes, such as increased
risk of heart attacks, strokes, high blood pressure,
atherosclerosis, coronary artery disease, etc . . .
[0061] Insulin Resistance. The development of T2DM is preceded by
years of abnormal metabolism during which lifestyle and diet
intervention, including weight loss, can completely prevent and
reverse the development of the disease in most people. The earliest
stage of T2DM is called "insulin resistance" and most patients
exhibit signs of the "metabolic syndrome." The initial clinical
presentation associated with insulin resistance is
hyperinsulinemia, impaired glucose tolerance, dyslipidemia
[hypertriglyceridemia and decreased high-density lipoprotein (HDL)
cholesterol] and hypertension. We also know that chronic
inflammation can help drive the development of insulin resistance.
Insulin resistance is a change in physiologic regulation such that
a fixed dose of insulin causes less of an effect on glucose
metabolism than occurs in normal individuals (blood glucose does
not drop as much or as fast as it should in response to increases
in insulin). The normal compensatory response to insulin resistance
is an even higher increase in insulin secretion that results in
hyperinsulinemia. If the hyperinsulinemia is sufficient to overcome
the insulin resistance, glucose regulation remains normal; if not,
type 2 diabetes ensues.
[0062] "Metabolic syndrome" is associated with insulin resistance;
this is a cluster of metabolic abnormalities involving body fat
distribution, lipid metabolism, thrombosis, blood pressure
regulation, and endothelial cell function. This cluster of
abnormalities is referred to as the insulin resistance syndrome or
the metabolic syndrome. Eventually, blood glucose remains elevated
even in the fasting state as the insulin resistant patient
progresses towards T2DM. The pancreatic beta cells must work very
hard to pump out this much insulin, and over time, the pancreatic
islets (and the beta cells they contain) are damaged due to what
can be thought of as exhaustion. The beta cells begin to secrete
more immature insulin (pro-insulin) in an attempt to keep up with
the demand, and therefore in the blood of people who are insulin
resistant and well on their way to developing T2DM we see
biomarkers of pancreatic beta cell dysfunction such as higher
levels of insulin, pro-insulin and c-peptide. An excellent review
of Insulin Resistance and all the various tests and indices used to
diagnose insulin resistance and gauge its severity is "Surrogate
markers of insulin resistance: A review" by Bhawna Singh and Alpana
Saxena, 2010.
[0063] "Pre-diabetes". This term is essentially synonymous with
insulin resistance and metabolic syndrome of Type 2 Diabetes, but
has specific lab values associated with it. Doctors screen patients
for diabetes if they have known risk factors, a family history of
diabetes, high blood pressure, BMI greater than 25, or if they have
abnormal cholesterol levels (defined as HDL-C below 35 mg/dL (0.9
mmol/L) or triglyceride level above 250 mg/dL (2.83 mmol/L). Tests
used to diagnose pre-diabetes include the Glycated hemoglobin
(HbA1C) test (6.0 to 6.5 percent is the pre-diabetes range), a
fasting blood glucose level from 100 to 125 mg/dL (5.6 to 6.9
mmol/L), or a blood glucose value of 140 to 199 mg/dL (7.8 to 11.0
mmol/L) at the 2-hour time point of an OGTT. It is this elevation
of 2-hour blood glucose value that defines a patient as having
"impaired glucose tolerance" (IGT. If the patient has pre-diabetes,
doctors will usually test fasting blood glucose, HbA1C, total
cholesterol, HDL cholesterol, low-density lipoprotein (LDL)
cholesterol and triglycerides at least once a year. Note that the
above lab values do not describe the patient population in which
HDL is claiming utility of elevated baseline AHB for pancreatic
beta cell dysfunction.
[0064] If full-blown T2DM develops and is left undiagnosed and
untreated, patients must be treated with insulin-sensitizing drugs
which may help make their cells more responsive to insulin, and the
pancreas does not have to work as hard. Blood glucose balance can
be maintained with insulin sensitizing drugs, or maintained and/or
reversed by the addition of diet and lifestyle modifications and
weight loss. Unlike full-blown T1DM, T2DM may be reversible in many
patients. However, if T2DM progresses far enough, the pancreatic
beta cells become unable to secrete enough insulin on their own due
to exhaustion and the patient may progress to the last stage of
T2DM wherein they cannot make enough insulin, and therefore will
become insulin-dependent and must inject exogenous insulin to
survive because their pancreatic beta cells no longer function.
This is the worst stage of T2DM and can be fatal because while a
patient can be administered exogenous insulin, their body may still
be resistant to its effects. These patients are at dramatically
increased risk for cardio-diabetic morbidity and mortality.
Disorders of Glucose Metabolism
[0065] Disorders of glucose metabolism on the sliding scale of T2DM
are defined per the following laboratory test values:
[0066] Insulin resistance (IR): a state in which higher
concentrations of insulin are required to exert normal effects;
blood glucose levels may be normal but fasting insulin levels may
be high because of compensatory insulin secretion by the pancreas.
Optimal fasting insulin level is defined by HDL as 3-9 .mu.U/mL,
intermediate is defined as >9 and <12, and high is defined as
>12.
[0067] "Pre-diabetes"=Impaired glucose tolerance (IGT): glucose
140-199 mg/dL 2 hours after a 75 g oral glucose load
[0068] Impaired fasting glucose: glucose 100-125 mg/dL after an
8-hour fast
[0069] Diabetes mellitus (DM): any of the following four criteria
may be used (results must be confirmed by retesting on a subsequent
occasion): fasting glucose .gtoreq.126 mg/dL; glycosylated
hemoglobin (HbA1c) level .gtoreq.6.5%; 2-hour glucose level
.gtoreq.200 mg/dL during glucose tolerance testing; or random
glucose values .gtoreq.200 mg/dL in the presence of symptoms of
hyperglycemia.
[0070] It will be appreciated by those skilled in the art of
diabetes diagnostics and treatment that the patient population in
which this invention has clinical utility do not fit the above
clinical definitions for insulin resistance, pre-diabetes,
metabolic syndrome, impaired fasting glucose, T2DM, or T1DM
(insulin-dependent). This test does not detect insulin resistance
or place a patient on a scale between normal glucose tolerance
(NGT) and diabetes, because the patient population in which the
test predicts abnormal first-phase insulin response are by
definition NGT with normal glucose and insulin levels at baseline
and the 2 hour time point, and do not meet the definition of
insulin resistance based on their lipid values on the LP-IR scale.
Thus, while for the purpose of illustrating the utility of the
invention we split the patients into groups by glucose tolerance
and degree of insulin resistance for the purpose of analyzing data
in the different groups, this is for illustrative purposes to show
the utility of the test in the NGT, non-insulin resistant "normal"
group. The test does not have clinical utility as an early
predictor of risk once the patient has met the criteria for
Impaired Glucose Tolerance (IGT) or Diabetes.
Relationship of First and Second-Phase Insulin Response to Beta
Cell Function in Health and Diabetes
[0071] When patients are challenged with a glucose load in an OGTT,
and when they eat meals, blood glucose rises, and the pancreatic
beta cells detect the post-prandial rise in blood glucose. The beta
cells normally react by releasing a rapid burst of insulin termed
the "first phase" response from 2-15 minutes after a glucose load
or mixed meal. The first phase response is followed by a second
phase response, which is a slower and more sustained release of
insulin sufficient to return the blood glucose to normal fasting
levels, usually by 120 minutes (2 hours). Insulin should also
return to normal levels by the 2 hour time point in a healthy
individual with no pancreatic beta cell dysfunction and normal
glucose tolerance.
[0072] In non-diabetic individuals, half of the total daily insulin
is secreted during basal periods; this suppresses lipolysis,
proteolysis, and glycogenolysis. The other half of insulin
secretion occurs postprandially. In response to a meal, the first
phase insulin secretion response should be a rapid and sizable
release of preformed insulin from storage granules within the beta
cell in non-diabetic individuals. The first phase of insulin
secretion promotes peripheral utilization of the prandial glucose
load, and also suppresses hepatic glucose production, thus limiting
postprandial glucose elevation.
[0073] Comparison of inter-individual first-phase insulin response
to an intravenous glucose bolus serves as a standardized way to
measure beta cell function among different subjects. A blunting of
or loss of first phase response signals beta-cell declines early in
the development of type 1 or later in the development of type 2
diabetes, even while responses to amino acid and other stimuli may
be preserved.
[0074] In Type 1 and Type 2 diabetics, first phase and second phase
insulin responses are altered in different ways during disease
onset and progression. In T2DM (as illustrated in the FIG. 1,
published in The Lancet, 2009)), note that years before diagnosis
of T2DM the overall insulin secretion is greater than normal
(mostly due to increased second phase response). During this
period, the cells of the patient become more and more resistant to
the effects of insulin, causing blood glucose to rise and despite
the hyperinsulinemic state. In the classical view of onset of T2DM,
the beta cells do begin to fail later in the disease and is
accompanied by insulin resistance and (usually) components of the
metabolic syndrome.
[0075] T1DM has a very different presentation to T2DM. In T1DM, the
disease progression does not have an early hyperinsulinemic phase
of increased beta cell function prior to the decline in function,
and insulin resistance and metabolic syndrome generally do not
contribute to the pathology. In T1DM, the beta cell function begins
to deteriorate, usually slowly over time prior to overt symptoms of
hypo-insulinemia, the resulting hyper-glycemia, and disease
diagnosis. The figure below compares first and second phase insulin
response in a normal patient vs. T1DM and T2DM patients late in the
course of the disease. Note that the T2DM patients have a blunted
first-phase response but enhanced second phase, whereas the
full-blown T1DM patient has no first or second phase insulin
response and would therefore be dependent on exogenous insulin for
survival.
[0076] The complete ablation of beta cell function and insulin
response in T1DM is, however, preceded by measurable incremental
declines, especially in the case of adult-onset T1DM, which tends
to be more gradual and less acute than childhood onset T1DM. The
figure below illustrates the general time-course of development of
T1DM and correlates the loss of beta cell mass (loss of cells, loss
of function) to various events and triggers in the time period
leading up to prior to detectable blunted insulin secretion and
diagnosis of T1DM. Currently an asymptomatic patient with normal
baseline glucose and normal baseline insulin levels would be
considered normal and would not be screened for T1DM; in the event
that an OGTT test was performed on such a patient and the beta
cells were not sufficiently compromised to give abnormal 2-hour
time point values, the early onset of blunted first phase response
would be missed. However, testing at earlier time points in an OGTT
such as prior to 1 hour would reveal blunted first phase response
which is the earliest indicator of deterioration of beta cell on
the continuum of T1DM development. However, in a patient with no
discernible risk factors for development of diabetes, and/or no
abnormal baseline values in fasting blood glucose or insulin, a
physician would not order and OGTT and thus the sub-clinical
deterioration in beta cell function would not be detected,
resulting in a missed opportunity to identify an at-risk patient
and intervene clinically to prevent disease progression.
[0077] Until recently it was thought that the destruction of the
beta cells was wholly attributable to an auto-immune attack, and
that diagnosis of disease onset was measurable by detection of
anti-pancreatic island auto-antibodies (IAA) such as anti-GAD and
others, and that onset of disease and destruction of beta cell
function was triggered by the auto-immune reaction. Thus it was
thought in the past that there was no impaired first-phase response
until after development of auto-antibodies.
[0078] Now it is thought that the deterioration in beta cell
function and impaired first-phase response begins prior to
appearance of auto-immunity and that the development of
auto-immunity is an adaptive, protective response to ongoing beta
cell damage, rather than a precipitating event in and of itself. It
is now recognized that there are metabolic abnormalities and
environmental insults that, in combination with genetic risk
factors predisposing a patient to susceptibility to development of
T1DM, combine to initiate development of the disease.
[0079] FIG. 1 shows the traditional model as well as the modern
model for onset of T1DM and illustrates that there is a clear
window of beta cell decline which, if detected early, could provide
an opportunity for therapeutic intervention to delay or prevent
further loss of beta cell function.
[0080] FIG. 2 (also published in The Lancet, 2009) illustrates the
concept that tests which are able to accurately predict T1DM
development early in the course of the disease, such as tests for
early beta cell dysfunction, would be clinically useful in
preventing full-blown diabetes. The early detection of subclinical
beta cell dysfunction, including impaired first phase insulin.
secretory response resulting in post-prandial hyperglycemia in the
onset of any form of diabetes, is the area of clinical utility for
the test described herein.
[0081] FIG. 3 illustrates how beta cells become dysfunctional in
T1DM vs. T2DM. In T1DM, oxidative stress and inflammation damage
the beta cells; some individuals seem to be more susceptible to
these stressors whereas others are more resistant and therefore
less likely to develop T1DM The damage and recovery or progression
is a multi-factorial process that is partially due to genetic risk
factors, lifestyle, diet/nutrition, inflammatory events such as
infections or autoimmune conditions, metabolic alterations, etc.
Ultimately the beta cells cannot adequately recover and repair and
begin to secrete less insulin and also die by apoptosis, resulting
in fewer functional cells and hypo-insulinemia. In T2DM by contrast
there is an intermediate step before beta cell dysfunction and
failure wherein the beta cells may proliferate and hypertrophy as
the result of hyperglycemia and insulin resistance; this is the
early phase in which the patient is hyper-insulinemic to compensate
for the hyperglycemia. The demand for insulin production as well as
the metabolic changes occurring during progression of insulin
resistance to T2DM generates oxidative stress and inflammatory
responses, and these then drive the progression of beta cell
failure just as in T1DM, but later in the disease state.
Alpha Hydroxybutyrate (AHB)
[0082] Current theories about AHB elevations in the context of
diabetes are that this metabolite is related to glucose disposal
rate resulting from insulin resistance (metabolic syndrome), and
that higher levels of AHB particularly with other metabolites such
as L-GPC and Oleic Acid are useful for placing patients on a
spectrum of insulin resistance (glucose tolerance) in the
development of Type 2 diabetes. However, our results clearly show
that AHB may not be related to insulin resistance and glucose
disposal rate but rather to impaired first phase insulin secretion
response that results in clinically significant post-prandial
hyperglycemia in apparently NGT individuals.
[0083] AHB is also a ketone though it is not glucogenic, and
therefore not directly related to metabolism of glucose or
alternative substrates. The 2 main ketone bodies are
3-hydroxybutyrate (3HB) and acetoacetate (AcAc). Therefore AHB is
not produced like other ketone bodies in the context of altered
glucose metabolism.
[0084] It is believed that AHB is produced in the liver as a
byproduct during the formation of .alpha.-ketobutyrate (a product
of either threonine catabolism or methionine metabolism via
cystathione) under conditions of excess glutathione demand
resulting from high oxidative stress, or conditions that promote
high dihydronicotinamide adenine dinucleotide/nicotinamide adenine
dinucleotide (NADH/NAD+) levels, such as increased fatty acid
oxidation. Glutathione (GSH) is one of the most important molecules
for fighting oxidative stress in the human body. Oxidative stress
may be caused by inflammation, infection, and environmental
factors, and the imbalance between the generation of free radicals
and a biological system's ability to readily neutralize the free
radicals or to repair the resulting damage results cellular damage
and disease. Oxidative stress causes disturbances in the normal
redox state of cells resulting in production of toxic peroxides and
free radicals that damage proteins, lipids, and DNA by oxidation.
It is known that oxidative stress can cause apoptosis and necrosis
in cells, including beta cells, which are very sensitive to
oxidative damage.
[0085] Oxidative stress is involved in all aspects of damage to the
body, from pancreatic beta cell damage to atherosclerosis,
inflammation, and neuropathy. Autoimmunity and the chronic
inflammation it causes also result in significant oxidative stress.
Under conditions of metabolic stress, the liver tries to synthesize
as much glutathione as possible from precursors L-glutamate and
L-Cysteine. L-cysteine becomes rate-limiting for production of GSH
under metabolic stress conditions, so more cysteine is made by
diverting homocysteine away from methionine synthesis and into a
trans-sulfuration pathway to form cystathione. When cystathione is
cleaved to cysteine to make glutathione, AHB is released as a
by-product and can be detected in blood and urine.
[0086] Therefore, the higher the oxidative and metabolic stress
(such as from inflammation), the more AHB is released. Therefore it
is believed that the appearance of elevated levels of AHB does not
signal insulin resistance or the existence of Type 2 diabetes as
taught in the current literature, but rather signals the oxidative
stress leading to beta cell damage and dysfunction, as opposed to
only "insulin resistance" from high levels of insulin and high
blood glucose. It is clear that in the studies described above,
elevated AHB in the context of baseline normoglycemia and
non-dyslipidemia is diagnostic for increased likelihood of impaired
beta cell function resulting in impaired first-phase insulin
response. Again this is in contrast to the teachings of current
literature which regards elevated AHB as a biomarker of impaired
glucose disposal rates with utility for classifying patients on the
continuum of insulin resistance towards T2DM.
[0087] It should be noted that in one study, when AHB was added to
culture medium of an immortalized cell-line derived from beta
cells, insulin secretion was suppressed. Conversely, when L-GPC was
added to the same culture system, insulin secretion was stimulated.
In our study, elevated plasma AHB even in the context of normal
plasma L-GPC levels were predictive of impaired first phase
response and significant post-prandial hyperglycemia. This suggests
a dominant effect of AHB to suppress insulin secretion response
even in the presence of potentiators such as L-GPC that work in
vitro, which was not investigated or predicted by the in vitro
study using cell lines. Furthermore, in the in vivo physiological
milieu of the human organism, it is known that many substances from
hormones to metabolites to toxisn and drugs) may affect beta cell
function and insulin secretion. For example, metabolites such as
glutamate and GABA may be toxic to beta cells in vitro and in vivo
(and these have been related to diabetes development and
progression), and beta cells may be damaged by infections (e.g.
enteroviral) and auto-immune processes as well. There are thus many
factors interacting in a complex system in a human organism that
contribute to beta cell health and number and secretory ability.
Thus, the results shown in these examples could not have been
predicted based on the current literature.
Preferred Embodiments
[0088] This invention comprises measurement of alpha-hydroxy
butyrate (AHB) in blood or biological fluid of a fasted patient,
wherein elevated levels of AHB compared to a healthy population or
previous test values of a given patient are indicative of occult
beta cell damage and predictive of impaired first-phase insulin
response. The test may comprise measurement of AHB as a single
analyte, and optionally other measurements of other biomarkers of
glycemic control and/or dyslipidemia. In the one embodiment the
test comprises measurement of alpha-hydroxybutyrate (AHB) alone. In
a second embodiment, a panel of 3 core analytes may be used:
alpha-hydroxybutyrate (AHB), and the ratio of triglycerides (trigs)
to HDL-cholesterol (HDL-c). This invention requires only 1 baseline
fasting blood sample. The sample is contacted and the amounts of
the analytes are measured. A triglyceride to HDL-c ratio is
calculated. An elevated amount of AHB (greater than about 4.5
mgs/dl) measured in biological fluid from a fasting patient,
particularly in a patient who is normoglycemic and/or
non-dyslipidemic (defined as normal trig/HDLc ratio (less than 3)),
indicates the presence of beta cell dysfunction and/or impaired
first-phase insulin response, and thus enables said patient's risk
level for progression to diabetes and the co-morbidities associated
with development of diabetes to be determined. As an example, in an
apparently normal individual, a baseline elevation of AHB greater
than about 4.5 mgs/dl would therefore cause the individual's risk
to be increased from optimal to intermediate, or optimal to
high.
[0089] The elevation of AHB in the context of normoglycemia and
normal trig/HDL-c serves as a proxy for detection of the same
individuals who have abnormally high elevations of 1-hour blood
glucose (greater than 155 mg/dl) and impaired first-phase insulin
response (which is indicative of beta cell dysfunction due to
suppression or pancreatic islet damage). Because this test is able
to identify a subset of at-risk patients currently missed by
standard diagnostic techniques, these patients can be treated
earlier such that the onset of forms of diabetes likely to require
eventual treatment with exogenous insulin can be delayed or
prevented by lifestyle and diet modifications, as well as
pharmacologic intervention.
[0090] Treatment options for patients identified at risk by this
diagnostic method may comprise causing one or more of the
following: increased frequency of follow-up, referral for OGTT
including time points between baseline and 2 hours, and/or CLIX,
repetition of tests for monitoring disease progression, lifestyle
and diet changes, and treatment with agents to improve beta cell
function such as DPP-4 inhibitors and/or GLP-1 agonists, agents to
treat post-prandial glucose excursions, and/or administration of
insulin. Treatment may further comprise not administering typical
first-line drugs that would normally be used to treat insulin
resistance with no beneficial effect on pancreatic beta cell
function, such as metformin, or adding agents which protect and
enhance beta cell function to a regimen including metformin.
Because the various embodiments of this test are proxies for
decreased first-phase insulin response in response to glucose load
of an OGTT, treatment may also comprise causing patient referral
for comprehensive testing for development of Type-1 diabetes, such
as tests for auto-antibodies to pancreatic islet beta cell antigens
or other biomarkers of autoimmune disease (e.g. rheumatoid factor
as a non-limiting example), and/or viral DNA/RNA and/or antibodies
to viral capsid proteins for members of the enterovirus family that
are known to cause pancreatic beta cell death and/or impairment of
function. In the case of patients who are presumptively positive
for any degree of enteroviral infection, and/or Type-1 Diabetes,
and/or LADA, the treatment may comprise administering one or more
of the following: an anti-viral agent, an immunosuppressant,
insulin or an insulin analog, agents known to those skilled in the
art to preserve beta cell function, agents that prevent
post-prandial glucose excursions (e.g. Cycloset), and lifestyle and
diet changes commonly prescribed for avoidance of development of
Type 2 diabetes such as low-carbohydrate diets. Treatment may
further comprise causing the avoidance of drugs or agents known to
damage pancreatic islet cells.
[0091] Other treatments to reduce or ameliorate cardiovascular risk
(cardiodiabetes) based on the results of this test further comprise
contacting the patient sample, measuring the analytes included on
HDL's panel tests for dyslipidemia, inflammatory biomarkers, and/or
other biomarkers of cardiovascular disease, and determining the
associated risk levels (optimal, intermediate, or high) for one or
more of these analytes, and recommending appropriate risk-reduction
therapy based on said determining. Therapy may comprise causing
treatment with agents or lifestyle/diet changes for the improvement
of these conditions. This improved risk stratification for future
cardiodiabetes morbidity and mortality allows for therapeutic
strategies such as those listed above, but not limited to those
listed above, to be prescribed in order to ameliorate risk of
development of cardiodiabetes and improvement of disease condition
in existing cardiodiabetes.
[0092] The test panel may be used once, or repeatedly, for initial
diagnosis of occult pancreatic beta cell dysfunction and/or for
monitoring disease progression and/or for monitoring response to
treatment. The biological sample is contacted, tested by means
known to those skilled in the art, and the results are measured and
reported to a qualified healthcare provider and/or patient. The
report may take the form of a written report, a verbal discussion,
a faxed report, or an electronic report accessed by a computing
device or hand-held smart-phone device. A diagnostic and
therapeutic nomogram based on the initial analyte measurements and
the corresponding risk levels as well as other incidental
laboratory test values and patient history, and comments may be
added to the report based on this diagnostic nomogram that aid in
data interpretation, diagnosis, and choice of therapy. Qualified
healthcare provider is defined as a physician (MD, DO), nurse,
registered dietician, pharmacist, or other appropriately trained
individual qualified to counsel patients on health-related
issues.
[0093] Beyond use of the test described herein to detect occult
beta cell dysfunction and identify patients likely to display
impaired first phase insulin response and progress to full-blown
diabetes, additional biomarkers may be added to further improve
diagnostic sensitivity/specificity and detection of and/or
determination of risk of developing T1DM and the future
cardiodiabetes complications. Short term post-prandial glucose
elevations such as those demonstrated in patients with impaired
first-phase insulin response are a known risk factor for
development of many cardiodiabetic complications. As a non-limiting
examples, biomarkers from the group comprising 1,5-AG,
auto-antibodies related to type-1 diabetes, viral nucleic acids,
antigens and/or antibodies to viral capsid proteins, biomarkers of
dyslipidemia, metabolites related to the altered metabolism
resulting from oxidative stress, and inflammatory biomarkers may be
measured in addition to the core analytes previously described to
further improve determination of patient risk level. In some
embodiments only measurements of the core diagnostic analytes are
used to classify patients' risks of progression as being optimal,
intermediate, or high. In other embodiments the core analytes plus
one or more additional analytes may be used to classify patients as
optimal, intermediate, or high. In some cases a score may be
calculated based on the measurement of core analytes plus
additional analytes, and the mathematically derived score may be
utilized to determine patient risk level, and said determining
shall be used to guide treatment decisions.
[0094] In the preferred embodiment, the biological sample contacted
is a blood component (serum or plasma). In other embodiments, other
biological fluids comprising urine, saliva, or a combination of any
biological fluids, may be contacted, and measurements of the
analytes determined. It will be understood that all analytes need
not be measured in the same fluid, i.e. 1,5 AG may be measured in
urine or plasma and viral genetic material or proteins may be
measured in cellular material, regardless of the biological sample
type in which the other analytes are measured.
[0095] There have been no previous studies or reports in the
literature of a test based on AHB alone or in combination with
other analytes useful for predicting a priori which patients who
are apparently normoglycemic (NGT) with apparently normal
pancreatic beta cell function, are more likely to have clinically
significant post-prandial glucose excursions greater than 155 mg/dl
at 1 hour time point, and who are therefore at increased risk of
cardiodiabetic complications. This novel test enables
re-classification of "low- or optimal-risk" patients who would be
considered normal by conventional diagnostics to be re-assigned to
a higher risk category based on baseline elevations of AHB alone or
in combination with other analytes. Taken together, the low insulin
and elevated blood glucose at the "halfway point" of an OGTT are
indicative of impaired beta cell function that is undetectable in
the fasting state using conventional screening test methods. This
test is unique in its ability to identify via elevated. AHB, at a
fasting baseline time point, the normoglycemic, normo-insulinemic,
non-dyslipidemic patients who would be missed by existing
diagnostic techniques who have impaired beta cell function
sufficient to cause a decreased first-phase insulin response, and
who are therefore at higher risk of beta cell exhaustion and.
progression to an insulin-dependent form of diabetes mellitus
(IDDM) in the future. Because some forms of diabetes, such as LADA,
may follow a relapsing/remitting pattern common to other
auto-immune diseases, measuring baseline AHB will provide a means
to monitor deterioration or improvement of pancreatic function and
response to therapy even in intermediate forms of diabetes.
[0096] The measurement of AHB at baseline, without or without
additional baseline analytes, allows for the identification of
patients who have impaired beta cell function who would not be
detected using the conventional diagnostic analytes (glucose,
HbA1c, and insulin) at baseline. Because measurement of AHB at
baseline reliably predicts which patients have beta cell
dysfunction/impaired first phase response, OGTTs may be avoided in
some cases. This would result in fewer blood samples being drawn
from the patient due to elimination of multiple time points, a
shorter time to obtain samples (no 2-hour waiting period for
patient), fewer analytes needing to be measured, lower testing
costs, shorter turn-around times for laboratory test results, no
additional calculations needed such as CLIX scores to interpret
data, and no need to account for impairment of kidney function
(creatinine, eGFR). Measurement of elevated AHB can re-classify
patients who are apparently low-risk to intermediate or high-risk
of developing future cardiodiabetic disease due to beta cell
dysfunction.
EXAMPLES
[0097] Study No. 1
[0098] A study was done wherein 100 patients were sampled at
baseline (fasting) and again at 30 minutes, 1 hour, 90 minutes, and
2 hours post glucose load in an OGTT. Analytes listed in FIGS. 1-4
were measured. For each figure, glycemic status of the each patient
was categorized into NGT, IFG, IGT, and COI according to standard
guidelines issued by the American Diabetes Association (see
DIABETES CARE, vol. 20, sup. 7, Jul. 1997). The figures then show
whether the 1 hour glucose was above or below 155 mg/dL, which is
the cutoff value established in the literature as a post-prandial
hyperglycemic excursion value associated with increased risk of
diabetes and resulting cardio-diabetic complications; this 1 hour
cutoff value is further associated with decreased first phase
insulin secretion response due to occult beta cell dysfunction in
NOT individuals (see Abdul-Ghani and DeFronzo, DIABETES CARE, vol.
32, sup. 2, Nov. 2009). CLIX scores were calculated based on the
measured analytes, as a measure of insulin sensitivity. Patients
were assigned to a group based on their CLIX scores: Normal Glucose
Tolerance (NGT), Impaired Fasting Glucose (IFG), Impaired Glucose
Tolerance (IGT), and Complete Glucose Intolerance (CGI). As noted
in the figures below, approximately 20% of normoglycemics at
baseline will be detected as at-risk (insulin resistant) by CLIX
scoring. See FIG. 4.
[0099] FIGS. 5-8 show the values of various markers measured in
each patient within the four glycemic status category and with
1-hour glucose cutoff distinctions. A marker that effectively
predicts occult beta cell dysfunction will have a large value for
the NOT red bar (high 1-hour glucose, the indication of beta cell
dysfunction) and a low value for the NOT blue bar (regular 1-hour
glucose).
[0100] Individual biomarkers measured at baseline were then studied
to determine how well they would be able to predict which patients
would have blood glucose elevated above 155 mg/dl at one hour, a
value which has been associated in the literature and in
longitudinal clinical studies (Botnia, SAHS) to be associated with
development of diabetes and cardiodiabetic complications in the
future. 1 hour glucose levels also strongly stratified risk within
each traditional GTT glycemic category (Abdul-Ghani, et. al, 2008,
2009).
[0101] FIG. 5 shows the lack of predictive power of common
diagnostic tests for diabetes, namely HbA1c, Glucose, Fructosamine,
and Glycation Gap. FIG. 6 shows the lack of predictive value of
baseline insulin, pro-insulin, c-peptide, and proinsulin:c-peptide
ratio. FIG. 7 shows the lack of predictive power for leptin,
adiponectin, ferritin, free fatty acid, HDL-2, and hs-CRP.
[0102] Other biomarkers used to quantify insulin resistance, namely
alpha hydroxybutyrate, Linoleoyl-GPC, Oleic Acid, IRI Score,
HOMA-IR score, and LP-1R score were further studied. AHB in
isolation was the only single biomarker that had significant
predictive power for classifying which NGT patients would have
blood glucose above 155 at 1 hour.
[0103] FIG. 8 shows a lack of predictive value for common
biomarkers Linoleoyl-GPC, Oleic Acid, HOMA-IR and LP-IR Score. AHB
(.alpha.-hydroxybutyrate) shows strong predictive power in tins
figure. IRI score, which incorporates measurement of AHB, L-GPC,
Oleic Acid, and a mathematical weighting by either BMI (in this
study) or baseline insulin (formula currently in clinical
diagnostic use) also shows statistically relevant results, however,
the score's weighting by BMI limits its clinical utility to
detection of patients with significant insulin resistance in the
context of metabolic syndrome and/or hyper-insulinemia and
dyslipidemia.
[0104] In FIG. 9, the NGT patients were assigned to categories of
IS (insulin sensitive), IR (insulin resistant) by high baseline
trig/HDLc ratio only, IR by high baseline AHB only, or IR by both
trig/HDL-c and AHB high at baseline. The levels of Glucose,
Insulin, Free Fatty Acids, and C-peptide at each of the time points
in the study were then compared for each of these groups. In the
upper left panel, there is a clear difference between 1 hour
glucose actually measured between the IS group (AHB not elevated at
baseline, blue) and the IR-AHB group (only AHB elevated at
baseline, green). The group with only elevated baseline AHB has
significantly higher 1 hour blood glucose than the IS controls,
demonstrating the utility of this biomarker as a proxy for 1 hour
glucose measurements. In the upper right panel, it can be clearly
seen that by 1 hour the insulin secretion in the group with
elevated baseline AHB was markedly lower than controls,
demonstrating that 1) the ability of the pancreatic beta cell to
secrete insulin in response to a glucose challenge is impaired, and
2) this less than optimal insulin secretion may be the cause of the
elevated blood glucose values at 1 hour in this group.
[0105] This line of evidence is further reinforced by data on
blunted C-peptide secretion (FIG. 9 bottom right panel) in the
group with elevated baseline AHB. C-pep is released when
pro-insulin is cleaved to insulin and declining levels of C-pep and
increased pro-insulin to C-pep ratio is associated with a decline
in beta cell function. The abnormally low levels of C-pep at 1 hour
post-glucose challenge correspond to the abnormally low insulin
levels and serve as confirmation that there is indeed less insulin
secreted in the NGT group with elevated baseline AHB. Because
elevated AHB only, at baseline, in normoglycemic non-dyslipidemic
subjects, is able to predict which patients will have impaired
insulin secretion and therefore 1 hour post-challenge glucose
excursions, it has clinical utility in identifying patients that
would normally be mis-classified as normal and at low risk of
developing diabetes and reclassifying them properly into a category
of increased risk. The data trends shown in FIG. 9 support the
conclusion that elevated baseline AHB is strongly associated with
beta cell dysfunction and impaired first phase insulin
response.
[0106] It is also worth noting in FIG. 9 that all of the patients
were tested for anti-GAD antibody, which is the most common
auto-antibody to pancreatic beta cells in Type 1 diabetics. All of
the patients were negative for anti-GAD, which is significant
because impaired insulin secretion on glucose challenge could be
indicative of the presence of Type 1 diabetes. However, just
because the patients were negative for the most common
auto-antibody detected in type 1 diabetics does not mean that they
are not early-stage Type 1's or LADAs (Latent Autoimmune Diabetes
in Adults) or suffering from Insulin Auto-immune Syndrome (IAS)
wherein the body produced auto-antibodies to insulin that can also
result in slow progression to insulin-dependent diabetes. IAS
patients typically have hypoglycemic episodes and normal or
low-normal insulin levels at baseline, meaning that the standard
tests for glycemic control such as HbA1c, fructosamine, and
glycation gap would be normal and not raise any suspicion of
presence of IAS until it progresses to abnormally low levels of
insulin and pancreatic beta cell dysfunction. sufficient to cause
symptoms.
[0107] So it is possible that some of the patients with elevated
baseline AHB who exhibit abnormally low insulin levels and
abnormally high glucose levels at 1 hour in an OGTT could be
positive for one of the other autoantibodies detectable in Type 1
diabetics that HDL did not test for, or they may have occult damage
to their pancreatic beta cells from a past or current viral
infection, such as enterovirus infections, that is/was not severe
enough to be detected at baseline and is only observable when the
patient is challenged with a glucose load in an OGTT.
[0108] Additional validation of the 1 hour glucose levels as a
marker for future diabetes risk is presented in FIG. 10. The data
show that a 1-hour glucose level of 155 mg/dL or higher is strongly
associated with diabetes incidence in the Botnia and SAHS (San
Antonio Heart Study). It is noted that the 1-hour glucose reading
strongly stratified risk within each traditional GTT glycemic
category (see Abdul-Ghani and DeFronzo DIABETES CARE, vol. 32, sup.
2, Nov. 2009).
Study No. 2
[0109] OGTTs with multiple time points were performed on 222
subjects. Patients with any signs of impaired glucose tolerance, or
T1DM were excluded per the table below (Table 1), leaving a total
of 87 subjects who were normoglycemic and normo-insulinemic.
Patient population was mixed sex, mixed race, various ages, and on
various medications. This was an "all-comers" study to test the
strength of the predictive power of AHB in an apparently normal
population.
TABLE-US-00001 TABLE 1 Define non-insulin resistant subjects
Condition (in order) N All oral glucose tolerance tests with AHB
222 AntiGad > 5 6 Insulin 0 hr > 12 78 Glucose 0 hr .gtoreq.
100 20 Glucose 2 hr .gtoreq. 140 17 HbA1c missing 6 HbA1c .gtoreq.
5.7 6 Glucose 1 hr missing 1 BMI missing 1 Total sample size 87
[0110] There were 87 subjects that were not insulin resistant by
current clinical definitions (Table 1). The mean (SD) glucose
elevation above baseline at 1-hour was 33 (34) mg/dL. The
relationship between the 1-hour glucose `bump` and
alpha-hydroxybutyrate (AHB) was linear over the range 1.7 to 12.2
ug/mL (FIG. 11).
[0111] FIG. 11 shows that AHB predicts a change in glucose at
1-hour using linear regression with 95% mean confidence band. Each
1 ug/mL increase in fasting AHB was correlated with 6.4 mg/dL
higher 1-hour glucose levels (p<0.0001) following an oral
glucose tolerance test (Table 2). Furthermore, the variability in
AHB explained about 16% of the variability in elevated 1-hour
glucose levels. Per these results, if a subject is just below a
fasting glucose of 100 mg/dL, then a mean 55 mg/dL elevated 1-hour
glucose would equate to an AHB of 8.2 ug/mL. This relationship
between AHB measurement and 1-hr glucose held regardless of age,
gender, or baseline glucose levels (Tables 3 & 4, FIG. 12).
[0112] The strength of the relationship between AHB and elevated
1-hour glucose levels remained, but was slightly attenuated in
subgroups of the healthiest patients. One of these subgroups was
defined by reducing the baseline insulin level to .ltoreq.9 for
inclusion. Then a 1 unit increase in AHB was correlated with 5.3
mg/dL higher 1-hour glucose levels (p=0.0044, N=69). The healthiest
patients were also defined by excluding dyslipidemias measured by
triglyceride to HDL cholesterol ratio (Tg/HDLC) or LP-IR score.
When subjects with Tg/HDLC .gtoreq.3 were excluded, then a 1 unit
increase in AHB was correlated with 5.1 mg/dL higher 1-hour glucose
levels (p=0.0031, N=61). Similar results were obtained when
subjects with LP-IR .gtoreq.50 were excluded, then a 1 unit
increase in AHB was correlated with 4.3 mg/dL higher 1-hour glucose
levels (p=0.027, N=52).
[0113] Fasting AHB levels were also used to predict the probability
of having a 1-hour glucose level .gtoreq.155 mg/dL. For each 1 unit
increase in AHB a patient was 1.6 times as likely to have levels
above this threshold (Table 5, p=0.0005). Also the model fit was
calibrated across risk deciles of having an elevated 1-hour glucose
(Hosmer-Lemeshow p=0.99). AHB was effective in discriminating
patients; the area under the ROC curve was 0.79 for AHB alone,
which was greater than chance (p<0.0001, FIG. 12).
[0114] FIG. 12 shows discrimination of patients with 1-hour glucose
.gtoreq.155 mg/dL. In that figure the base model included age,
gender, BMI, and baseline glucose. Adding AHB to a model with age,
baseline glucose and gender increased the AUC from 0.62 to 0.79
(p=0.027). The sum of sensitivity and specificity were at a maximum
with an AHB cut point .gtoreq.6.8; 53% and 93%, respectively (Table
12). This resulted in a positive likelihood ratio (PLR) of 7.8,
which meant a patient with an AHB.gtoreq.6.8 was almost 8 times as
likely to have a 1-hour glucose .gtoreq.155 mg/dL. A good clinical
test has a PLR>3 and an excellent test has a PLR>6.
[0115] These data are evidence that fasting alpha-hydroxybutyrate
is an effective risk marker for elevated glucose levels at 1-hour
following an oral glucose tolerance test. The results are
consistent when 1-hour glucose levels are modeled continuously or
dichotomously using a known threshold (.gtoreq.155 mg/dL) of
increased risk for development of cardiodiabetic diseases.
TABLE-US-00002 TABLE 2 AHB predicts change in 1-hour glucose using
linear regression Analysis of Variance Sum of Mean Source DF
Squares Square F Value Pr > F Model 1 17481 17481 18.08
<.0001 Error 86 83172 967.11064 Corrected Total 87 100652 Root
MSE 31.09840 R-Square 0.1737 Dependent Mean 33.36364 Adj R-Sq
0.1641 Coeff Var 93.21047 Parameter Estimates Parameter Standard
Variable DF Estimate Error t Value Pr > |t| Intercept 1 2.52508
7.97521 0.32 0.7523 AHB 1 6.37190 1.49874 4.25 <.0001
TABLE-US-00003 TABLE 3 AHB predicts change in 1-hour glucose using
linear regression model adjusted for age, gender, baseline glucose
and their interactions with AHB. AHB, age, and glucose were mean
centered. Analysis of Variance Sum of Mean Source DF Squares Square
F Value Pr > F Model 7 20675 2953.54743 2.95 0.0083 Error 80
79978 999.71915 Corrected Total 87 100652 Root MSE 31.61834
R-Square 0.2054 Dependent Mean 33.36364 Adj R-Sq 0.1359 Coeff Var
94.76885 Parameter Estimates Parameter Standard Variance Variable
DF Estimate Error t Value Pr > |t| Inflation Intercept 1
33.49118 4.69663 7.13 <.0001 0 ahb_cl 1 6.26981 2.41269 2.60
0.0111 2.50697 age_cl 1 0.01641 0.24530 0.07 0.9468 1.06289
gluc0_cl 1 0.08321 0.48601 0.17 0.8645 1.07353 Male 1 0.29552
6.96140 0.04 0.9662 1.05763 ahb_age 1 0.15014 0.12292 1.22 0.2255
1.40144 ahb_gluc0 1 0.13073 0.25092 0.52 0.6038| 1.30816 ahb_male 1
-0.04190 3.51283 -0.01 0.9905 2.82172
TABLE-US-00004 TABLE 4 AHB predicts change in 1-hour glucose using
linear regression model adjusted for age, gender, and baseline
glucose. Analysis of Variance Sum of Mean Source DF Squares Square
F Value Pr > F Model 4 17569 4392.14936 4.39 0.0029 Error 83
83084 1001.00923 Corrected Total 87 100652 Root MSE 31.63873
R-Square 0.1745 Dependent Mean 33.36364 Adj R-Sq 0.1348 Coeff Var
94.82998 Parameter Estimates Parameter Standard Variance Variable
DF Estimate Error t Value Pr > |t| Inflation Intercept 1
-3.47725 40.81224 -0.09 0.9323 0 AHB 1 6.34436 1.53574 4.13
<.0001 1.01443 Age 1 0.01727 0.24161 0.07 0.9432 1.02983 GLUC0 1
0.05390 0.48127 0.11 0.9111 1.05134 Male 1 1.60269 6.89453 0.23
0.8168 1.03607
TABLE-US-00005 TABLE 5 AHB predicts 1-hour glucose .gtoreq.155
mg/dL using logistic regression model adjusted for age, gender, and
baseline glucose Testing Global Null Hypothesis: BETA = 0 Test
Chi-Square DF Pr > ChiSq Likelihood Ratio 17.9144 4 0.0013
Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF
Estimate Error Chi-Square Pr > ChiSq Intercept 1 -10.9372 4.7377
5.3294 0.0210 AHB 1 0.5007 0.1433 12.2000 0.0005 Age 1 0.0139
0.0239 0.3374 0.5613 Male 1 0.0484 0.6594 0.0054 0.9415 GLUC0 1
0.0689 0.0518 1.7707 0.1833 Odds Ratio Estimates 95% Wald
Confidence Effect Point Estimate Limits AHB 1.650 1.246 2.185 Age
1.014 0.968 1.063 Male 1.050 0.288 3.822 GLUC0 1.071 0.968 1.186
Hosmer and Lemeshow Goodness-of-Fit Chi-Square DF Pr > ChiSq
1.5677 8 0.9915
[0116] The prior AHB results are supported in statistical models
adjusted for age, gender, baseline glucose, and BMI (Tables 6 &
7, FIG. 12). The relations between AHB and various OGTT endpoints
including glucose, insulin, C-peptide, and proinsulin arc
summarized in Table 8. The AHB slope estimates to 30-minute and
1-hour glucose excursion (FIGS. 5 & 6). AHB has the following
results:
[0117] 1) Linear relation with the 1-hour and 30-minute glucose
measurement (Table 8, p<0.0009)
[0118] 2) Associated with the probability of having a 1-hour
glucose .gtoreq.155 mg/dL (Table 7, p=0.0005)
[0119] 3) Association is calibrated across the range of data (Table
7, Hosmer-Lemeshow p=0.69)
[0120] 4) AHB adds to the discrimination of subjects having a
1-hour glucose .gtoreq.155 mg/dL; the area under the ROC curve
increases by 16% (FIG. 12, p=0.039) compared to a model with age,
gender, BMI, and baseline glucose (FIG. 12).
[0121] Several variable selection methods were explored to
determine if other variables could assist AHB in explaining
variability in the 1-hour glucose `bump` and classifying patients
with 1-hour glucose .gtoreq.155 mg/dL. LGPC had a significant
linear relation with the 1-hour glucose measurement (Table 9,
p=0.038); however, it did not add any information to classifying
subjects above the 155 mg/dL 1-hour threshold (Table 10,
p=0.35).
[0122] Correlations were determined with AHB and FFA, CLIX-IR,
CLIX-Beta Cell, CRP, and Lp-PLA2 (Table 11). Pearson correlations
assume a bivariate normal distribution, such that at least both
variables are normally distributed. These assumptions can be
investigated using several methods; basic tools available are the
skewness and kurtosis measures. As a rule-of-thumb when the
absolute value of the skewness is greater than 3, or kurtosis is
greater than 10, the normality assumption is not tenable. In these
data the CLIX-Beta Cell measure had absolute skew=6.6 and
kurtosis=44 (Table 11), which identified two outlier observations
(FIG. 15). When normality is not reasonable and the variables are
continuous measures, then Spearman's Rank correlation should be
used instead. In these raw data, the Pearson's correlation for
CLIX-Beta Cell with AHB was r=0.12, p=0.28. However, since the data
are non-normal Spearman's correlation was a more accurate measure
r=-0.30, v0.0046. When the two outliers were removed, which may or
may not be appropriate depending if they are viable measurements,
then Pearson's correlation correctly identified the magnitude and
direction of the linear relation, r=-0.32, p=0.00289 (Table
11).
[0123] These data are evidence that fasting alpha-hydroxybutyrate
is an effective risk marker for elevated glucose levels at 30
minutes and 1-hour following an oral glucose tolerance test. The
results are consistent when 1-hour glucose levels are modeled
continuously or dichotomously using a known threshold (.gtoreq.155
mg/dL) of increased risk for progression to diabetes. Using the
current empirically determined HDL guidelines for AHB high risk
level (i.e. >5.7 ug/mL), produces a positive likelihood ratio
(PLR) of 3.1. This means a patient with an AHB>5.7 is about 3
times as likely to have a 1-hour glucose .gtoreq.155 mg/dL.
[0124] These results were not influenced by age, gender, BMI, or
baseline glucose levels. These results were also not affected by
any anti-diabetic medications, lipid altering medications, or fish
oil. However, the medication status of 40 (45%) of the subjects was
unknown. The strength of the relationship between AHB and elevated
1-hour glucose levels remained in subgroups of the healthiest
patients defined as those without dyslipidemias measured by
triglyceride to HDL cholesterol ratio (Tg/HDLC) or LP-IR score, or
when lowering the level of fasting insulin from .ltoreq.12 to
.ltoreq.9 for inclusion.
[0125] FIG. 13 shows oral glucose tolerance test responses using
cubic regression with 95% confidence bands by AHB levels (i.e.
normal, intermediate, high). FIG. 14 shows oral glucose tolerance
test responses using cubic regression with 95% confidence bands by
AHB levels (i.e. normal, high). FIG. 15 shows a distribution of
beta cell CLIX score with extreme IDs=834498, 924352.
TABLE-US-00006 TABLE 6 AHB predicts change in 1-hour glucose using
linear regression model adjusted for age, gender, BMI, and baseline
glucose. Analysis of Variance Sum of Mean Source DF Squares Square
F Value Pr > F Model 5 17402 3480.33690 3.44 0.0071 Error 81
81839 1010.35298 Corrected Total 86 99240 Root MSE 31.78605
R-Square 0.1753 Dependent Mean 33.79310 Adj R-Sq 0.1244 Coeff Var
94.06076 Parameter Estimates Parameter Standard Variance Variable
DF Estimate Error t Value Pr > |t| Inflation 95% Confidence
Limits Intercept 1 -14.21410 43.89913 -0.32 0.7469 0 -101.55959
73.13140 AHB 1 6.28384 1.54464 4.07 0.0001 1.01576 3.21048 9.35720
Age 1 0.03713 0.24473 0.15 0.8798 1.04269 -0.44982 0.52407 GLUC0 1
0.12484 0.49249 0.25 0.8005 1.06678 -0.85506 1.10474 Male 1 1.03406
7.10739 0.15 0.8847 1.08040 -13.10741 15.17553 BMI 1 0.16036
0.57133 0.28 0.7797 1.05721 -0.97642 1.29713
TABLE-US-00007 TABLE 7 AHB predicts 1-hour glucose .gtoreq.155
mg/dL using logistic regression model adjusted for age, gender,
BMI, and baseline glucose Testing Global Null Hypothesis: BETA = 0
Test Chi-Square DF Pr > ChiSq Likelihood Ratio 17.9447 5 0.0030
Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF
Estimate Error Chi-Square Pr > ChiSq Intercept 1 -11.2959 4.9384
5.2319 0.0222 AHB 1 0.4978 0.1431 12.1004 0.0005 Age 1 0.0147
0.0240 0.3718 0.5420 Male 1 0.0116 0.6735 0.0003 0.9862 GLUC0 1
0.0722 0.0526 1.8850 0.1698 BMI 1 0.00290 0.0546 0.0028 0.9576 Odds
Ratio Estimates 95% Wald Confidence Effect Point Estimate Limits
AHB 1.645 1.243 2.178 Age 1.015 0.968 1.064 Male 1.012 0.270 3.787
GLUC0 1.075 0.970 1.192 BMI 1.003 0.901 1.116 Hosmer and Lemeshow
Goodness-of-Fit Test Chi-Square DF Pr > ChiSq 5.6005 8 0.69
TABLE-US-00008 TABLE 8 Summary of AHB relations in linear
regression models adjusted for age, gender, and BMI Response N
Slope 95% CI P-value (1 hr-0 hr) Glucose 87 6.3 3.2 to 9.4
<0.0001 (30 min-0 hr) 75 4.7 2.0 to 7.5 0.0009 Glucose (1 hr-0
hr) Insulin 87 0.2 -4.5 to 0.94 4.9 (30 min-0 hr) Insulin 75 -2.0
-6.2 to 0.34 2.1 (1 hr-0 hr) C-peptide 87 0.2 -0.1 to 0.21 0.4 (30
min-0 hr) C- 75 -0.1 -0.3 to 0.38 peptide 0.1 Proinsulin 86 -0.3
-1.3 to 0.48 0.6 Proinsulin/C-peptide 86 -0.2 -0.6 to 0.32 0.2
TABLE-US-00009 TABLE 9 AHB and LGPC predict change in 1-hour
glucose using linear regression model adjusted for age, gender,
BMI, and baseline glucose. Analysis of Variance Sum of Mean Source
DF Squares Square F Value Pr > F Model 6 21730 3621.68424 3.74
0.0025 Error 80 77510 968.87713 Corrected Total 86 99240 Root MSE
31.12679 R-Square 0.2190 Dependent Mean 33.79310 Adj R-Sq 0.1604
Coeff Var 92.10989 Parameter Estimates Parameter Standard Variance
95% Confidence Variable DF Estimate Error t Value Pr > |t|
Inflation Limits Intercept 1 27.41477 47.28563 0.58 0.5637 0
-66.68663 121.51618 AHB 1 5.53930 1.55308 3.57 0.0006 1.07085
2.44857 8.63003 Age 1 0.02658 0.23971 0.11 0.9120 1.04315 -0.45046
0.50362 Male 1 4.92029 7.19874 0.68 0.4963 1.15580 -9.40566
19.24624 BMI 1 -0.17545 0.58160 -0.30 0.7637 1.14246 -1.33288
0.98198 GLUC0 1 0.09669 0.48246 0.20 0.8417 1.06759 -0.86343
1.05681 LGPC 1 -1.36742 0.64695 -2.11 0.0377 1.23672 -2.65489
-0.07995
TABLE-US-00010 TABLE 10 AHB and LGPC predict 1-hour glucose
.gtoreq.155 mg/dL using logistic regression model adjusted for age,
gender, BMI, and baseline glucose Testing Global Null Hypothesis:
BETA = 0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 18.8646
6 0.0044 Analysis of Maximum Likelihood Estimates Standard Wald
Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1
-8.3845 5.5756 2.2613 0.1326 AHB 1 0.4641 0.1447 10.2890 0.0013
LGPC 1 -0.0680 0.0728 0.8709 0.3507 Age 1 0.0124 0.0240 0.2680
0.6047 Male 1 0.1278 0.6948 0.0339 0.8540 GLUC0 1 0.0651 0.0523
1.5477 0.2135 BMI 1 -0.0230 0.0614 0.1397 0.7086 Odds Ratio
Estimates 95% Wald Confidence Effect Point Estimate Limits AHB
1.591 1.198 2.112 LGPC 0.934 0.810 1.078 Age 1.013 0.966 1.061 Male
1.136 0.291 4.436 GLUC0 1.067 0.963 1.183 BMI 0.977 0.866 1.102
TABLE-US-00011 TABLE 11 Correlations with AHB Variable N Mean Std
Dev Minimum Maximum Skewness Kurtosis AHB 88 4.840 2.225 1.700
12.200 1.325 1.745 FFA0 88 0.558 0.243 0.120 1.250 0.825 0.423
CLIX_IR 88 7.642 3.472 1.880 17.200 0.926 0.318 CLIX_Bcell 88 2.883
49.047 -360.890 36.660 -6.610 44.426 hsCRP 72 2.206 2.850 0.300
15.000 2.544 6.698 Lp_PLA2_DSX 67 140.537 40.382 54.000 249.000
0.306 0.048 CLIX_Bcell 86 10.177 5.885 2.260 36.660 1.577 3.846
FFA0 CLIX_IR CLIX_Bcell hsCRP Lp_PLA2_DSX Pearson Correlation
Coefficients Prob > |r| under H0: Rho = 0 AHB 0.45767 -0.35768
0.11665 0.10306 0.17367 <.0001 0.0006 0.2791 0.3890 0.1599 88 88
88 72 67 Spearman Correlation Coefficients Prob > |r| under H0:
Rho = 0 AHB 0.55913 -0.39366 -0.29947 0.19634 0.11334 <.0001
0.0001 0.0046 0.0983 0.3611 88 88 88 72 67 Pearson Correlation
Coefficients Prob > |r| under H0: Rho = 0 AHB -0.31780 0.0029
86
TABLE-US-00012 TABLE 12 Diagnostic metrics for AHB thresholds AHB
Threshold .gtoreq. True False [ug/mL] Positive Positive Sensitivity
Specificity 1.5 15 73 100.0 0.0 1.8 15 72 100.0 1.4 2.2 15 70 100.0
4.1 2.3 15 68 100.0 6.8 2.4 15 65 100.0 11.0 2.6 15 64 100.0 12.3
2.8 15 63 100.0 13.7 2.9 15 62 100.0 15.1 3.0 15 60 100.0 17.8 3.1
15 58 100.0 20.5 3.2 15 54 100.0 26.0 3.3 14 52 93.3 28.8 3.4 14 50
93.3 31.5 3.5 14 46 93.3 37.0 3.7 14 44 93.3 39.7 3.8 13 43 86.7
41.1 3.9 12 38 80.0 47.9 4.1 12 35 80.0 52.1 4.3 12 34 80.0 53.4
4.4 12 33 80.0 54.8 4.5 12 31 80.0 57.5 4.6 11 27 73.3 63.0 4.7 11
26 73.3 64.4 4.8 11 23 73.3 68.5 5.0 10 21 66.7 71.2 5.1 10 19 66.7
74.0 5.4 9 18 60.0 75.3 5.5 9 17 60.0 76.7 5.6 9 16 60.0 78.1 5.7 9
15 60.0 79.5 6.0 9 14 60.0 80.8 6.1 9 13 60.0 82.2 6.2 9 11 60.0
84.9 6.5 8 10 53.3 86.3 6.6 8 9 53.3 87.7 6.7 8 7 53.3 90.4 6.8 8 5
53.3 93.2 7.1 7 5 46.7 93.2 7.2 6 4 40.0 94.5 7.5 6 3 40.0 95.9 7.7
6 2 40.0 97.3 7.8 5 2 33.3 97.3 9.3 5 1 33.3 98.6 9.5 3 1 20.0 98.6
10.5 2 1 13.3 98.6 11.0 1 1 6.7 98.6
[0126] Case Study: Patient X is a female aged 40 with normal
weight, normal fasting glucose, insulin levels, free fatty acid,
and lipid levels at the time of the first serial blood draw for
metabolic testing on Feb. 8, 2012. Tests were repeated at 6 time
points with the last test being performed on Feb. 27, 2013. Patient
has no evidence of metabolic syndrome or Type-2 diabetes but does
have a history of late gestational diabetes in 2 pregnancies 8 and
6 years previously which required insulin therapy. Gestational
diabetes resolved after delivery and insulin therapy was no longer
required. Patient tested negative for anti-GAD antibodies, the
classical test for detection of Type 1 diabetes (auto-immune), but
patient is positive for Rheumatoid Factor and has been diagnosed
with an auto-immune connective tissue disorder (data not shown).
However, despite her auto-immune status, tests for biomarkers of
inflammation during the course of the year-long follow were
unremarkable, however it is possible that auto-immune flares may
still skew metabolic test results (tests included Myeloperoxidase,
Lp-PLA2, hs-CRP, and Fibrinogen, data not shown).
[0127] FIG. 16 shows lipids of Patient X at 6 time points. In FIG.
16A it is indicated that tests were not performed on Feb. 28, 2012
and Apr. 3, 2012.
[0128] In FIG. 16B, results from May 2, 2012 (far right column
under previous results) and Jul. 10, 2012 (values to left of risk
ranges) are shown indicating that the patient is
normolipidemic.
[0129] In FIG. 16C, results from Jan. 28, 2013 (far right column
under previous results) and Feb. 27, 2013 (values to left of risk
ranges) are shown indicating that the patient is
normolipidemic.
[0130] FIG. 17 shows biomarkers of Glycemic Control, Beta Cell
Function, and Insulin Resistance. In FIG. 17A, results from Feb. 8,
2012 (far right column under previous results) and Apr. 3, 2012
(results to left of risk ranges) are shown. Feb. 28, 2012: Fasting
blood glucose, FFA, and insulin arc all normal. Apr. 3, 2012:
Patient was not fasting, so blood glucose and insulin values as
well as scores derived therefrom (HOMA IR) cannot be compared to
other panels. However, AHB and FFA levels are optimal, and measures
of glycemic control such as HbA1c, Fructosamine and Glycation Gap
are normal, indicating that blood glucose is well-controlled.
[0131] In FIG. 17B, results from May 2, 2012 (far right column
under previous results) and Jul. 10, 2012 (values to left of risk
ranges) are shown. May 2, 2012: patient is still normoglycemic,
normo-insulinemic and has normal levels of FFA, and AHB. There
would be no reason to suspect on the face of these common screening
test results that this patient had compromised pancreatic beta cell
dysfunction and no evidence of deteriorating condition. Jul. 10,
2012: AHB and FFA levels increase for the first time above the
optimal range into the high-risk range. Patient is mildly
hypoglycemic (69, close to optimal range of 70 and within
experimental error), with optimal insulin levels, and is still
normolipidemic. A standard screening test for fasting blood glucose
and insulin would not pick up any deterioration in beta cell
function or cause a physician to suspect the onset of deterioration
of the patient's condition.
[0132] In FIG. 17C, results from Jan. 28, 2013 (far right column
under previous results) and Feb. 27, 2013 (values to left of risk
ranges) are shown. Jan. 28, 2013: AHB is again elevated beyond the
threshold of 4.5 into the intermediate risk category while FFA are
still in the optimal range. Glucose and insulin are still within
the optimal range. Interestingly, a new test for post-prandial
glucose index (1,5-anhydro-glucitol levels, also known as
GlycoMark) is only very slightly elevated over the optimal range
(6.1 vs. 6.0). Feb. 26, 2013: AHB levels have increased to a new
high of 7.7, and FFA are also again elevated to the high risk
range. On this date the patient was hypoglycemic but the estimated
daily average glucose was within the optimal range, as was
insulin.
Discussion of Data in FIGS. 16 and 17
[0133] These figures collectively show that elevated AHB can be
detected at baseline in a patient whose fasting glucose, insulin,
and blood lipids are all within normal limits. In FIG. 16C, there
are a number of observations worth pointing out. For instance,
despite other normal values of glycemic control and the patient
being hypoglycemic on Feb. 26, 2013, and though HbA1c is in the
optimal range at 5.1, this is the highest value for HbA1c recorded
over a 1 year period where all other values fell between 4.7 and
4.9; taken together with the elevated Jan. 28, 2013 intermediate
elevation in post-prandial glucose index, the indicate that
post-prandial glucose excursions are occurring, possibly causing
elevations in glycosylated hemoglobin over the previous months.
These results suggest that if an OGTT had been done in the
preceding months, abnormal elevations of blood glucose (and blunted
secretion of insulin and C-peptide) would have been detected at 1
hour and/or 30 minutes. Also worth noting: while fasting insulin
levels are normal, on the last blood draw date, pro-insulin and
c-peptide were abnormally high for the first time, and the
pro-insulin: c-peptide ratio was also elevated to the high risk
range; this is significant. The appearance of pro-insulin and
c-peptide are indicators of beta cell dysfunction; these are
released when the pancreas is working to produce insulin as fast as
possible in response to high blood sugar (for example in the
context of Type 2), or due to deterioration of pancreatic beta
cells that then spill immature forms of insulin into the
bloodstream (such as in beta cell lysis/damage in auto-immune
context of Type 1), or a combination of both conditions. In
conditions like this where pancreatic beta cells are being
destroyed or exhausted, low and low-normal levels of insulin
production do not evidence health, but rather progressing disease.
It is in this case that one may observe normal levels of fasting
insulin together with lower-than-normal levels of insulin at 1 hour
in an OGTT because the pancreatic beta cells "cannot keep up with
the demand" in response to elevated blood sugar. Because the first
abnormal elevation of AHB and FFA occurred in July 2012 and the
first evidence of pancreatic beta cell dysfunction occurred in
February 2013, there was an 8-month window from the time elevated
AHB signaled a decline in pancreatic beta cell function and the
time such dysfunction could be definitively measured by detection
of immature forms of insulin in the bloodstream.
[0134] The invention described herein would allow for detection of
abnormal beta cell function in a patient who otherwise showed no
signs of impending beta cell dysfunction by standard diagnostic
screening methods, and would have allowed for therapeutic
intervention 8 months earlier than conventional diagnostic
techniques. It is also worth noting that because this patient was
thin and the weight and BMI were lowest for the last test wherein
the AHB was highest and the beta cell function had deteriorated the
greatest, elevated fasting AHB may a biomarker for the onset of
"skinny diabetes", which is a form of adult-onset Type 1 (most
commonly observed in Asian populations) requiring exogenous insulin
therapy and completely different in etiology to Type 2/metabolic
syndrome.
Study No. 3
[0135] In study 3, 217 consecutive nondiabetic subjects underwent a
75 g oral glucose tolerance test (OGTT) and fasting blood
collection to evaluate risk of diabetes between March 2012 and May
2013 at several outpatient centers across the US (Madison, Wis.;
Jackson, Miss.; Montgomery, Ala.; Charleston, S.C.; Seattle, Wash.;
and Salt Lake City, Utah). Clinical indications for testing
included obesity, history of first-degree family members with
diabetes, and presence of one or more components of the metabolic
syndrome, including impaired fasting glucose, Samples were sent by
overnight courier to Health Diagnostic Laboratory, Inc. (Richmond,
Va.) for measurement of glucose, insulin, metabolites, and other
biomarkers. Subjects with detectable anti-GAD antibody (titer >5
IU/ml) were excluded. Patient characteristics and results are shown
in table 13.
[0136] Insulin resistance (IR) was defined by one or more of the
following conditions: fasting glucose .gtoreq.100 mg/dL, 2-hour
glucose .gtoreq.140 mg/dL, HbA1c .gtoreq.5.7%, fasting insulin
.gtoreq.12 .mu.U/mL. Transient hyperglycemia (TH) was defined as
30, 60, or 90-minute glucose .gtoreq.140 mg/dL during OGTT. The
final study group consisted of 90 IR subjects and 85 healthy
subjects with normal levels for all these criteria.
[0137] General linear mixed models were used with restricted
maximum likelihood (REML) estimation to analyze the mean response
profiles for insulin and glucose changes over the 3- or 5-time
point 2-hour OGTT. A cubic regression model was fit to the data
since the curve's characteristics were known to include two
inflection points. The unstructured repeated measures covariance
matrix was chosen since it minimized Akaike's Information Criterion
(AIC). The insulin response was transformed using the natural
transformation to improve the normality and homoscedasticity of the
residual errors. To determine if AHB modified the insulin or
glucose response, interactions were tested between tertiles of AHB
with time, time, and time using F-tests and Wald tests.
Interactions were also tested between BMI categories (i.e. normal
<25, 25.ltoreq.overweight<30, and obese.gtoreq.30 kg/m2) and
the cubic time response.
[0138] Multivariable logistic regression was used to test the
association (i.e. odds ratio) and incremental improvement in
discrimination (i.e. c-statistic) of subjects with 1-hour glucose
>155 mg/dL when AHB was added to age, gender, BMI, fasting
glucose, Ln(fasting insulin), Ln(triglycerides), HDL-C, and LDL-C.
Fasting insulin and triglycerides were natural logarithm
transformed to reduce leverage of extreme observations. When
testing the usefulness of a novel biomarker, the American Heart
Association recommends reporting the marker's statistical
association, discrimination, calibration, and reclassification
performance. Hosmer-Lemeshow was used as a measure of model
calibration. The reclassification was tested when AHB was added to
the fully adjusted logistic regression model with the integrated
discrimination improvement (IDI) metric, which can be described as
the average increase in sensitivity given no change in specificity.
The percentage of subjects who had model probabilities changed in
the correct direction (i.e., increased for those with events and
decreased for non-events) due to the addition of AHB to the fully
adjusted model was tested with the continuous net reclassification
index (NRI). SAS.RTM. version 9.3 (Cary, N.C.) was used for all
analyses, with the critical level set to 0.05 to prescribe
statistical significance. Results are shown in table 14.
[0139] FIG. 18 shows results from Study 3. OGTT insulin response
over time shown with cubic regression and 95% mean confidence bands
for normal, overweight, and obese BMI categories by AHB tertiles.
In linear mixed models, the 1.sup.st phase insulin linear slopes
were independent of BMI (p=0.16) in models adjusted for age,
gender, BMI, fasting glucose, Ln(HbA1c), Ln(triglycerides), HDL-C,
and LDL-C. The lowest AHB tertile had a 1.sup.st phase linear slope
that was 1.67 and 1.33 units greater than the 2.sup.nd and 3.sup.rd
tertiles, respectively (minimum p=0.0008). The increased slope in
the lowest AHB tertiles compared to the higher tertiles shows that
the first phase insulin secretion response is suppressed in terms
of amount of insulin released and rate of release by increasing
amounts of plasma AHB. There was no difference in the 1.sup.st
phase linear slopes between the 2.sup.nd and 3.sup.rd AHB tertiles
(p=0.39) in fully adjusted models.
[0140] FIG. 19 Shows fitted OGTT insulin response 1.sup.st phase
linear slope estimate with 95% mean confidence intervals for
normal, overweight, and obese BMI groups by AHB tertiles. *
p-value<0.05 compared to 1.sup.st tertile; there were no
differences between 2.sup.nd and 3.sup.rd tertiles (minimum p-value
=0.12).
[0141] FIG. 20 shows OGTT glucose response over time shown with
cubic regression and 95% mean confidence bands for normal,
overweight, and obese BMI categories by AHB tertiles. In linear
models, the glucose area under the curve (AUC) was independent of
BMI (p=0.55) in models adjusted for age, gender, BMI, fasting
insulin, Ln(triglycerides), HDL-C, and LDL-C. The lowest AHB
tertile had a glucose AUC that was 32 and 42 units lower than the
2.sup.nd and 3.sup.rd tertiles, respectively (minimum p=0.0065),
further supporting the decreased first phase insulin response due
to beta cell dysfunction as AHB levels increase. The independence
of the effect from BMI further underscores the assertion herein
that increased levels of AHB are not related to metabolic
syndrome/insulin resistance phenomena as currently taught in the
literature. There was no difference in the glucose AUC between the
2.sup.nd and 3.sup.rd AHB tertiles (p=0.37) in fully adjusted
models.
[0142] FIG. 21 shows OGTT glucose response area under the curve
(AUC) shown with 95% mean confidence intervals for normal,
overweight and obese BMI groups by AHB tertiles; *p-value<0.05
compared to 1.sup.st tertile; there were no differences between
2.sup.nd and 3.sup.rd tertiles (minimum p-value=0.39). There were
no significant differences between corresponding AHB tertiles
between BMI groups.
[0143] FIG. 22 shows ROC curves for classifying subjects having a
1-hour glucose .gtoreq.155 mg/dL during OGTT. The area increased by
0.039 (95% CI: 0.008 to 0.070, p=0.015) when AHB was added to age,
gender, BMI, fasting glucose, Ln(fasting insulin),
Ln(Triglycerides), HDL-C, and LDL-C in the logistic regression
model.
TABLE-US-00013 TABLE 13 Patient characteristics grouped by BMI
category [kg/m.sup.2] Linear BMI < 25 25 .ltoreq. BMI < 30
BMI .gtoreq. 30 Trend Variable n = 37 n = 66 n = 114 P-value*
P-value Age [years] 53.6 (17.8) 53.5 (15.0) 49.3 (13.1) 0.098 0.12
Male: n (%) 14 (38) 43 (65) 39 (34) 0.0002 n/a Fasting Glucose
[mg/dL] 84 (9) 92 (14) 95 (16) 0.0003 <0.0001 1-hr Glucose
[mg/dL] 130 (55) 139 (50) 157 (53) 0.0083 0.0060 2-hr Glucose
[mg/dL] 103 (53) 119 (62) 126 (50) 0.078 0.024 HbA1c.dagger. [%]
5.2 (0.4) 5.4 (0.5) 5.6 (0.8) 0.0010 0.0005 Fasting Insulin.dagger.
[uU/mL] 5.8 (3.6) 10.2 (7.1) 19.2 (16.1) <0.0001 <0.0001
Triglycerides.dagger. [mg/dL] 78 (44) 121 (117) 149 (122)
<0.0001 <0.0001 HDL Cholesterol [mg/dL] 68 (20) 56 (18) 52
(14) <0.0001 <0.0001 LDL Cholesterol [mg/dL] 93 (34) 112 (40)
104 (36) 0.040 0.13 CLIX-IR.dagger. 9.8 (10.2) 6.5 (3.6) 4.3 (2.4)
<0.0001 <0.0001 alpha-hydroxybutyrate [ug/mL] 5.4 (3.3) 5.3
(2.7) 5.1 (2.2) 0.77 0.56 Anti-GAD Positive: n (%) 1 (2.7) 3 (4.6)
4 (3.5) 0.88 n/a Insulin Resistant: n (%) 11 (30) 32 (48) 78 (68)
<0.0001 n/a 1-hr Glucose .gtoreq.155 [mg/dL]: n (%) 11 (30) 19
(29) 58 (51) 0.0049 n/a Transient Hyperglycemia: n (%) 17 (46) 34
(52) 77 (68) 0.023 n/a Data are mean (SD) unless stated otherwise;
*One-way ANOVA and Chi-squared test for continuous and categorical
data, respectively; .dagger.Used natural logarithm transformation
for improved normality and homoscedasticity of residual errors in
linear models.
TABLE-US-00014 TABLE 14 ROC curve comparisons for classifying
subjects having a 1-hour glucose .gtoreq.155 mg/dL during OGTT
(Study #3) AUC (c-statistic) Without With AUC Difference P-value
AHB AHB (95% CI) Difference Model 1: Age, 0.632 0.739 0.107 (0.043
to 0.172) 0.0011 Gender Model 1 + BMI 0.702 0.775 0.073 (0.021 to
0.126) 0.0066 Model 1 + fasting 0.786 0.836 0.050 (0.014 to 0.086)
0.0069 glucose Model 1 + 0.753 0.807 0.054 (0.011 to 0.098) 0.014
Ln(HbA1c) Model 1 + 0.731 0.821 0.071 (0.026 to 0.115) 0.0019
Ln(fasting insulin) Model 1 + Ln(trigs), 0.727 0.787 0.060 (0.015
to 0.106) 0.0098 HDL-C, LDL-C All covariates 0.821 0.857 0.037
(0.005 to 0.069) 0.025 The area increased by 0.037 (95% CI: 0.005
to 0.069, p = 0.025) when AHB was added to age, gender, BMI,
fasting glucose, Ln(HbA1c), Ln(fasting insulin), Ln(Triglycerides),
HDL-C, and LDL-C in the logistic regression model.
[0144] Although preferred embodiments have been depicted and
described in detail herein, it will be apparent to those skilled in
the relevant art that various modifications, additions,
substitutions, and the like can be made without departing from the
spirit of the invention and these are therefore considered to be
within the scope of the invention as defined in the claims which
follow.
* * * * *