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doi: 10.1093/ageing/afn249
Published electronically 22 November 2008
Relationship between serum preheparin
lipoprotein lipase mass, plasma glucose and
metabolic syndrome in older subjects
SIR—The biomarkers regarding the pathophysiology of
metabolic syndrome (MetS), a risk factor for atherosclerosis, remain to be explored. Recently, low serum levels of
preheparin lipoprotein lipase mass (preLPL) in MetS have
been reported in a wide range of ages. Although the associations between MetS, metabolic measures including MetS
components and preLPL among older people can differ from
younger adults, the specific correlations in older individuals
have not been examined. In the present study, 125 Japanese
subjects (37 men and 88 women, mean 76.9 years) were investigated to observe the association between preLPL, MetS and
metabolic measures, such as body mass index, blood pressure, lipid panels and plasma glucose (PG). In simple correlation and multiple regression analysis, among metabolic
measures, only PG was significantly and inversely correlated to preLPL. Additionally, MetS subjects showed significantly lower preLPL levels than non-MetS subjects, even after
adjustments for age and sex. These results suggest that the
significant correlation of PG to preLPL might be reflective
of age-related metabolic features, and that preLPL might be a
biomarker connected with the pathophysiology of MetS, even
in older subjects.
Introduction
Recently, obesity/obesity-based disorders and ageing have
become two overlapping and mounting public health
problems. In particular, MetS, a cluster of metabolic
123
Research letters
abnormalities, such as obesity, dyslipidaemia, hypertension
and hyperglycaemia with an underlying condition of insulin
resistance, has drawn much attention [1, 2]. MetS is considered a risk factor for atherosclerosis and its sequelae, such as
cardiovascular and cerebrovascular diseases [1, 2]. Biological
markers regarding MetS pathophysiology, e.g. high-sensitive
C-reactive protein and adiponectin [3], have been explored,
but presently, the biomarkers of MetS remain to be further
elucidated.
Lipoprotein lipase (LPL) is a lipolytic enzyme involved
in catalysing the hydrolysis of triglycerides (TG) in chylomicrons and very low-density lipoprotein particles. LPL is produced mainly in adipocytes or skeletal muscle cells [4]. A small
quantity of LPL protein, preLPL, is measured in serum, and it
has been reported that preLPL is related negatively to TG or
visceral adiposity, and positively to HDL-cholesterol [5–7].
Also, preLPL is significantly lower in type 2 diabetes mellitus
and coronary atherosclerosis [5, 6]. Given these data, preLPL
may be considered one of the most important indicators of
insulin resistance. Furthermore, a recent study over a wide
range of ages reported a low preLPL level in MetS, proposing
that preLPL may be a biomarker of MetS [7].
We have recognised changes in body composition and fat
distribution with advancing age, resulting in visceral obesity
and insulin resistance development in older adults [8, 9]. Ageing also promotes increases in the occurrence of MetS and
abnormalities of metabolic measures such as components
used in the definition of MetS [10]. Thus, due to age-related
complex pathophysiologic mechanisms, the associations
between preLPL, metabolic measures including MetS components and MetS among older people can differ from
younger adults; however, the specific correlations in older
individuals have not been examined. In a prior report
examining preLPL in MetS, including older individuals, the
subjects’ mean age was 55.6 years (≥20 years younger than
in the present study). This study was aimed at describing the
associations among preLPL, metabolic measures and MetS
in a restricted population of ≥70 years.
Subjects and methods
In total, 125 Japanese subjects (37 men and 88 women),
aged 70–95 [mean 76.9 ± 6.4 (SD)] years, participated in this
cross-sectional study, approved by the Institutional Ethics
Committee of Kyoto Medical Center. All subjects were consecutively recruited from outpatient and community-living
volunteers, and gave informed consent. Eligible subjects
were in a subjectively asymptomatic state without any clinical features of ischaemic heart, cerebrovascular, kidney, liver
and cognitive diseases. All subjects had a similar lifestyle:
non-smoking and non-sedentary/non-athletic. Subjects currently taking drugs known to affect blood pressure (BP)
and lipid/glucose metabolism were excluded. In each subject, the following metabolic measures were determined
after an overnight fast: seated systolic blood pressure (SBP)
and diastolic blood pressure (DBP) (by a standard sphyg-
124
momanometer), PG, serum total cholesterol (TC), TG (by
standard methods, respectively) and high-density lipoproteincholesterol (HDL-C) (by a homogeneous method). PreLPL
was determined by an enzyme-linked immunosorbent
assay [11]. The within-run coefficient variation (CV) was
2.8% and between-day CV was 4.3%. According to the
NCEP-ATP III report [12] with a minor modification of
obesity for Japanese [13], MetS was defined as the presence
of at least three of the following five conditions: (i) obesity [identified by body mass index (BMI) of ≥25 kg/m2 ],
(ii) elevated BP (identified by SBP of ≥130 and/or DBP of
≥85 mmHg), (iii) hypertriglyceridaemia (identified by TG of
1.69 mmol/l), (iv) low HDL cholesterolaemia (identified by
HDL-C of <1.04 in men and <1.29 mmol/l in women) and
(v) elevated glucose (identified by PG of ≥6.1 mmol/l).
Data are expressed as the mean ± SD. To compare differences among groups, the unpaired t-test, χ 2 -test or analysis
of variance followed by Tukey’s post hoc test was used as
appropriate. To observe the correlations between preLPL
and metabolic measures, we used Pearson’s rank coefficient
test as well as multiple regression analysis adjusted for values in all metabolic measures. In these correlation analyses,
TG had a skewed distribution, so TG was logarithmically
transformed. In addition, a general linear model was made to
determine the relationship between preLPL (as a dependent
variable) and the MetS category (as a fixed variable), with
adjustments for age and sex (as covariates). P < 0.05 was
considered significant.
Results
In the whole study population, mean levels of the respective
measures were as follows: BMI, 21.9 ± 3.3 kg/m2 (range,
15.0–34.6); SBP, 144.1 ± 16.3 mmHg (93–190); DBP, 79.3 ±
7.7 mmHg (60–108); TC, 5.03 ± 0.78 mmol/l (3.06–7.67);
TG, 1.24 ± 0.63 mmol/l (0.37–3.53); HDL-C, 1.49 ± 0.45
mmol/l (0.65–2.67); PG, 5.56 ± 0.93 mmol/l (3.89–8.88);
preLPL, 35.8 ± 7.3 ng/ml (21.0–57.3). No significant difference in preLPL levels between men and women was observed
(35.7 ± 8.0 vs. 35.8 ± 7.0 ng/ml, P > 0.05).
In the simple correlation test for preLPL, only PG
was significantly and inversely correlated to preLPL among
metabolic measures (Table 1). Other measures did not
show any relative significance. In multiple regression analysis
(Table 1), PG remained significantly and inversely correlated
to preLPL.
Among all subjects, 23 subjects with MetS (8 men and
15 women; age: 75.0 ± 4.3 years) and 102 without MetS
(29 men and 73 women; age: 77.4 ± 6.7 years) were detected,
without significant differences in the mean age and number
distribution by sex (P > 0.05 in all). Subjects with MetS
showed a significant lower preLPL level than those without
MetS (31.7 ± 6.2 vs. 36.7 ± 7.3 ng/ml, P = 0.003). In a
general linear model adjusted for age and sex, this difference
remained significant (F = 7.9, P = 0.006).
Research letters
Table 1. Correlations of preheparin LPL mass levels with
metabolic measures
Measures
r (P-value)
β (P-value)
................................................................
Age (years)
0.139 (0.123)
0.086 (0.361)
Sex (men)
−0.001 (0.987)
0.052 (0.597)
Body mass index (kg/m2 )
−0.008 (0.930)
0.062 (0.519)
Systolic blood pressure (mmHg)
0.123 (0.170)
0.194 (0.078)
Diastolic blood pressure (mmHg)
0.005 (0.954)
−0.099 (0.355)
Total cholesterol (mmol/l)
0.017 (0.850)
−0.048 (0.658)
Log-triglyceride (mmol/l)
−0.120 (0.184)
−0.027 (0.802)
HDL-cholesterol (mmol/l)
0.123 (0.171)
0.073 (0.521)
Plasma glucose (mmol/l)
−0.306 (0.001∗ ) −0.301 (0.001∗ )
HDL, high-density lipoprotein; r, Pearson’s rank correlation coefficient; β, multiple regression coefficient (adjusted for all measures).
∗ P < 0.05: statistical significance.
According to the number of components used in the
definition of MetS, the study subjects were grouped into
the following three categories: category 0, no components;
category 1, 1–2 components and category 3, ≥3 components.
Category 3 showed a significant lower preLPL level (n = 23;
31.7 ± 6.2 ng/ml) than category 0 (n = 11; 34.7 ± 8.0 ng/
ml, P > 0.05) or category 1 without MetS (n = 91; 36.9 ±
7.2 ng/ml, P = 0.007). In a general linear model adjusted for
age and sex, this difference remained significant (F = 4.6,
P = 0.012).
clarified, low preLPL may reportedly reflect insulin resistance
[5–7], which underlies MetS. It is also known that older people
generally become more insulin resistant with age [20], which
is thought reasonable to partly explain the results.
In this study, there were potential limitations, such as
a cross-sectional design and a relatively small sample size.
Further studies with a prospective design and larger sample
size are needed to clarify our theory.
In summary, this study showed that PG was the only
significant measure correlated to preLPL among various
metabolic measures, maybe reflecting metabolism on lipid
and obesity in older subjects. We also disclosed lower preLPL
levels in MetS than non-MetS, suggesting that preLPL might
be a biomarker connected with the pathophysiology of MetS,
even in an older population. This knowledge will help us
to better understand the relationships between MetS and
atherosclerotic diseases in older people.
Key points
r Plasma glucose is only one significant correlate of
serum preheparin lipoprotein lipase mass among various
metabolic measures, and this might be a feature reflective
of metabolism on lipid and obesity in older subjects.
r Lower levels of serum preheparin lipoprotein lipase
mass in metabolic syndrome suggest that it might be
a biomarker connected with the pathophysiology of
metabolic syndrome, even in an older population.
Discussion
Our study found that PG showed the best correlation to
preLPL among all metabolic measures. This association has
already been shown in a prior study [7], and can be supported
by the observation that LPL expression and production in
adipocytes are controlled by insulin [14, 15]; however, we
confirmed that there was no significant association between
preLPL and other metabolic measures relevant to lipid panels and body weight, although the previous report indicated
that preLPL was associated with not only PG but also lipid
metabolism and body weight [7]. An inconsistency might
just reflect an age-related metabolic feature. A progressive
increase of glucose intolerance with ageing [16] and change
in lipid metabolism, that is, lipid levels decline after reaching a peak level in middle- and early older-aged individuals,
has been documented [17]. In addition, body weight-related
measures, especially BMI, are claimed to become a poor measure of obesity in older people [18, 19]. These age-dependent
changes, in contrast to younger adults, can attenuate the correlations between preLPL, lipids and BMI.
Our study further disclosed lower preLPL levels in MetS
compared to non-MetS, even in an older population. Our
data are noteworthy in expanding on the prior result of a
significant association between MetS and preLPL [7] in older
people. Accordingly, in older as well as in younger adults,
preLPL might be a biomarker of MetS. Although the detailed
mechanism of the low preLPL level in MetS remains to be
Acknowledgements
This study was supported in part by a grant-in-aid from the
Foundation for the Development of the Community, Japan.
Conflicts of interest
None declared.
KAZUHIKO KOTANI1,∗ , SEIJI ADACHI2,3 , KOKORO TSUZAKI1 ,
NAOKI SAKANE1
1
Division of Preventive Medicine, Clinical Research Institute for
Endocrine and Metabolic Disease, National Hospital Organization
Kyoto Medical Center, Kyoto 612-8555, Japan
Email: [email protected]
2
Division of General Practice and Community Medicine, Fujii
Masao Memorial Hospital, Kurayoshi 682-0023, Japan
3
Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
∗
To whom correspondence should be addressed
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Published electronically 11 November 2008