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Journal Club
Adam G Tabak, Markus Jokela, Tasnime N Akbaraly, Eric J Brunner, Mika Kivimaki,
Daniel R Witte
Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of
type 2 diabetes: an analysis from the Whitehall II study
Lancet online June 8, 2009
Robert R Henry, A Michael Lincoff , Sunder Mudaliar, Michael Rabbia, Cathy Chognot,
Matthias Herz
Effect of the dual peroxisome proliferator-activated receptor-α/γ agonist aleglitazar on
risk of cardiovascular disease in patients with type 2 diabetes (SYNCHRONY): a phase
II, randomised, dose-ranging study
Lancet online June 8, 2009
2009年6月25日 8:30-8:55
８階 医局
埼玉医科大学 総合医療センター 内分泌・糖尿病内科
Department of Endocrinology and Diabetes,
Saitama Medical Center, Saitama Medical University
松田 昌文
Matsuda, Masafumi
Department of Epidemiology and Public Health, University College
London, London, UK (A G Tabak MD, M Jokela PhD, T N Akbaraly PhD, E
J Brunner PhD, Prof M Kivimaki PhD, D R Witte MD); Semmelweis
University Faculty of Medicine, 1st Department of Medicine, Budapest,
Hungary (A G Tabak); Department of Psychology, University of Helsinki,
Helsinki, Finland (M Jokela); INSERM U888 and University Montpellier 1,
Montpellier, France (T N Akbaraly); Finnish Institute of Occupational
Health, Helsinki, Finland (Prof M Kivimaki); and Steno Diabetes Center,
Gentofte, Denmark (D R Witte)
www.thelancet.com Published online June 8, 2009
All 35–55-year-old non-industrial British
civil servants working in London offices
(UK) of 20 departments were invited to
participate in this study.
10 308 (6895 men) were recruited between August, 1985, and April, 1988 (phase
1).26 Between August, 1991, and December, 1994 (phase 3), all participants known
to be alive and in the country were invited to the screening clinic for an oral
glucose tolerance test, and 6058 men and 2758 women (85・5% of the original
sample) attended.
Background
Little is known about the timing of
changes in glucose metabolism before
occurrence of type 2 diabetes. We
aimed to characterise trajectories of
fasting and postload glucose, insulin
sensitivity, and insulin secretion in
individuals who develop type 2
diabetes.
Methods
We analysed data from our prospective
occupational cohort study (Whitehall II study) of
6538 (71% male and 91% white) British civil
servants without diabetes mellitus at baseline.
During a median follow-up period of 9・7 years, 505
diabetes cases were diagnosed (49・1% on the
basis of oral glucose tolerance test). We assessed
retrospective trajectories of fasting and 2-h
postload glucose, homoeostasis model
assessment (HOMA) insulin sensitivity, and HOMA
β-cell function from up to 13 years before diabetes
diagnosis (diabetic group) or at the end of followup (non-diabetics).
103 94
12797
mg/dl
10.2 6.5
65.9 36.1
microU/ml
126mg/dl
108mg/dl
Figure 1: Fasting (A) and 2-h postload (B) glucose trajectories before diagnosis of diabetes or the end of
follow-up
Numbers are 505 incident diabetes cases and 6033 non-diabetics. Time 0 is diagnosis for incident
diabetes cases or end of follow-up for non-diabetics. Multilevel longitudinal modelling was done using
linear growth model for non-diabetic and piecewise approach, including cubic terms for time, for incident
diabetic individuals with oral glucose tolerance test fasting glucose (A) and 2-h glucose (B) as outcomes.
Analysis was adjusted for age, sex, ethnic origin, and study phase. Estimations were done for a
hypothetical population consisting of 71% male, 91% white individuals aged 63 years at time 0 years.
Error bars show 95% CI for the fixed effects. Tables show the number of measurements for each year at
and before diabetes diagnosis or the end of follow-up.
198mg/dl
Figure 1: Fasting (A) and 2-h postload (B) glucose trajectories before diagnosis of diabetes or the end of
follow-up
Numbers are 505 incident diabetes cases and 6033 non-diabetics. Time 0 is diagnosis for incident
diabetes cases or end of follow-up for non-diabetics. Multilevel longitudinal modelling was done using
linear growth model for non-diabetic and piecewise approach, including cubic terms for time, for incident
diabetic individuals with oral glucose tolerance test fasting glucose (A) and 2-h glucose (B) as outcomes.
Analysis was adjusted for age, sex, ethnic origin, and study phase. Estimations were done for a
hypothetical population consisting of 71% male, 91% white individuals aged 63 years at time 0 years.
Error bars show 95% CI for the fixed effects. Tables show the number of measurements for each year at
and before diabetes diagnosis or the end of follow-up.
Figure 2: Homoeostasis model assessment (HOMA) insulin sensitivity (A) and HOMA β-cell function trajectories (B) before
diagnosis of diabetes or the end of follow-up
Numbers are 505 incident diabetes cases and 6033 non-diabetics. Time 0 is diagnosis for incident diabetes cases or end of
follow-up for non-diabetics. Multilevel longitudinal modelling was done using linear growth model for non-diabetic and
non-piecewise or piecewise approach, including linear or quadratic terms for time, for incident diabetic individuals with
HOMA2-%S (A) and HOMA2-%B (B) as outcomes. Analysis was adjusted for age, sex, ethnic origin, and study phase.
Estimations were done for a hypothetical population consisting of 71% male, 91% white individuals aged 63 years at time 0
years. Error bars show 95% CI for the fixed effects. Tables show the number of measurements for each year at and before
diabetes diagnosis or the end of follow-up. HOMA2-%S=homoeostasis model assessment insulin sensitivity. HOMA2%B=homoeostasis model assessment β-cell function.
Figure 2: Homoeostasis model assessment (HOMA) insulin sensitivity (A) and HOMA β-cell function trajectories (B) before
diagnosis of diabetes or the end of follow-up
Numbers are 505 incident diabetes cases and 6033 non-diabetics. Time 0 is diagnosis for incident diabetes cases or end of
follow-up for non-diabetics. Multilevel longitudinal modelling was done using linear growth model for non-diabetic and
non-piecewise or piecewise approach, including linear or quadratic terms for time, for incident diabetic individuals with
HOMA2-%S (A) and HOMA2-%B (B) as outcomes. Analysis was adjusted for age, sex, ethnic origin, and study phase.
Estimations were done for a hypothetical population consisting of 71% male, 91% white individuals aged 63 years at time 0
years. Error bars show 95% CI for the fixed effects. Tables show the number of measurements for each year at and before
diabetes diagnosis or the end of follow-up. HOMA2-%S=homoeostasis model assessment insulin sensitivity. HOMA2%B=homoeostasis model assessment β-cell function.
Results
Multilevel models adjusted for age, sex, and ethnic
origin confirmed that all metabolic measures followed
linear trends in the group of non-diabetics (10 989
measurements), except for insulin secretion that did not
change during follow-up. In the diabetic group (801
measurements), a linear increase in fasting glucose was
followed by a steep quadratic increase (from 5・79
mmol/L to 7・40 mmol/L) starting 3 years before
diagnosis of diabetes. 2-h postload glucose showed a
rapid increase starting 3 years before diagnosis (from 7・
60 mmol/L to 11・90 mmol/L), and HOMA insulin
sensitivity decreased steeply during the 5 years before
diagnosis (to 86・7%). HOMA β-cell function increased
between years 4 and 3 before diagnosis (from 85・0% to
92・6%) and then decreased until diagnosis (to 62・4%).
Conclusion
In this study, we show changes in glucose
concentrations, insulin sensitivity, and insulin
secretion as much as 3–6 years before
diagnosis of diabetes. The description of
biomarker trajectories leading to diabetes
diagnosis could contribute to more-accurate
risk prediction models that use repeated
measures available for patients through
regular check-ups.
Funding Medical Research Council (UK); Economic and Social Research Council
(UK); British Heart Foundation (UK); Health and Safety Executive (UK);
Department of Health (UK); National Institute of Health (USA); Agency for Health
Care Policy Research (USA); the John D and Catherine T MacArthur Foundation
(USA); and Academy of Finland (Finland).
Department of Medicine, University of California at San Diego and VA San
Diego Healthcare System, San Diego, CA, USA (Prof R R Henry MD, S
Mudaliar MD); Department of Cardiovascular Medicine, Cleveland Clinic,
Cleveland, OH, USA (Prof A M Lincoff MD); Hoff mann-La Roche, Nutley, NJ,
USA (M Rabbia MA); and F Hoff mann-La Roche AG, Basel, Switzerland (C
Chognot PhD, M Herz MD)
www.thelancet.com Published online June 8, 2009
Figure: Synergistic beneficial
actions of balanced PPAR-α/γ
agonists
Apo AI=apolipoprotein A1.
Apo AII=apolipoprotein A2.
Apo CIII=apolipoprotein C3.
FA=fatty acids.
FFA=free fatty acids.
Figure adapted from Balakumar and colleagues
and Fievet and colleagues
Aleglitazar is a peroxisome proliferatoractivated receptor agonist (hence a PPAR
modulator ) with affinity to PPARα and
PPARγ,
This class includes muraglitazar and tesaglitazar. Both were discontinued
owing to safety concerns, including increase in serum creatinine and
decrease in glomerular filtration rate (tesaglitazar) or increased risk of
cardiovascular events (muraglitazar).
BACKGROUND
Despite previous reports of potential adverse
cardiovascular effects of peroxisome
proliferator-activated receptor (PPAR)
agonists, the promise for PPAR agonists to
positively affect risk of cardiovascular
disease in patients with type 2 diabetes is of
continued interest. The SYNCHRONY study
aimed to establish the glucose-lowering and
lipid-modifying effects, and safety profile, of
the dual PPAR-α and PPAR-γ agonist
aleglitazar.
METHODS
In this double-blind study, patients with type 2 diabetes
(either drug-naive or pre-treated with ≤two oral agents) were
enrolled from 47 sites in seven countries. After a single-blind,
4–5-week placebo run-in period, 332 patients were
randomised double-blind (via an interactive voice-response
system) to 16 weeks’ treatment with aleglitazar at once-daily
doses of 50 μg, 150 μg, 300 μg, or 600 μg, or matching
placebo (n=55 in each group), or to open-label pioglitazone 45
mg once daily (n=57) as a reference. The primary efficacy
endpoint was the change in glycosylated haemoglobin
(HbA1c) concentration from baseline to the end of treatment.
Patients who received at least one dose of study drug and
had at least one evaluable post-baseline HbA1c measurement
were included in the efficacy analysis.
This study is registered with ClinicalTrials.gov, number NCT00388518.
Figure 1: Trial profile
Table 1: Baseline (after placebo runin period) demographic and clinical
characteristics (safety population)
Figure 2: Effect on haemoglobin A1c concentration (A) Absolute change from
baseline to end of treatment period (week 16) and (B) over time. Analysis
undertaken in the intention-to-treat population, LOCF. p values are versus placebo.
LS=least squares. HbA1c=haemoglobin A1c.
Figure 2: Effect on haemoglobin A1c concentration (A) Absolute change from
baseline to end of treatment period (week 16) and (B) over time. Analysis
undertaken in the intention-to-treat population, LOCF. p values are versus placebo.
LS=least squares. HbA1c=haemoglobin A1c.
Figure 3: Effect on fasting plasma glucose (A) Absolute change from baseline to
end of treatment period (week 16) and (B) over time. Analysis undertaken in the
intention-to-treat population, LOCF. p values are versus placebo. LS=least
squares. FPG=fasting plasma glucose.
Figure 3: Effect on fasting plasma glucose (A) Absolute change from baseline to
end of treatment period (week 16) and (B) over time. Analysis undertaken in the
intention-to-treat population, LOCF. p values are versus placebo. LS=least
squares. FPG=fasting plasma glucose.
Figure 4: Eff ect on lipid parameters Percentage change from baseline lipid concentrations
to end of treatment period (week 16) for (A) triglycerides, (B) HDL cholesterol, (C) LDL
cholesterol, and (D) apolipoprotein B. Analysis undertaken in the intention-to-treat
population, LOCF. p values are versus placebo. LS=least squares.
RESULTS
The efficacy analysis excluded six patients (n=0 in
pioglitazone group; n=1 in each of placebo, 50 μg, 150 μg,
and 600 μg aleglitazar groups; and n=2 in 300 μg aleglitazar
group). Aleglitazar significantly reduced baseline HbA1c
versus placebo in a dose-dependent manner, from –0・36%
(95% CI 0・00 to –0・70, p=0・048) with 50 μg to –1・35% (–0・99
to –1・70, p<0・0001) with 600 μg. The trend of changes over
time suggests that the maximum effect of aleglitazar on
HbA1c concentration was not yet reached after 16 weeks of
treatment. Oedema, haemodilution, and weight gain occurred
in a dose-dependent manner. However, at aleglitazar doses
less than 300 μg, no patients had congestive heart failure,
frequency of oedema was similar to placebo (one case at 50
μg, two at 150 μg, and three with placebo) and less than with
pioglitazone (four cases), and bodyweight gain was less than
with pioglitazone (0・52 kg at 150 μg vs 1・06 kg).
CONCLUSION
The favourable balance in the safety and
efficacy profile of aleglitazar represents
encouraging short-term clinical data for
this agent and provides good evidence
to enter phase III investigation.
Funding: F Hoff mann-La Roche AG (Switzerland).