Background
Adiponectin, the circulating peptide hormone secreted by adipocytes, reportedly has an important role in insulin resistance, glucose and lipid metabolism and cardiovascular morbidity and mortality [
1‐
4]. In previous studies, adiponectin showed an inverse relationship to adipose tissue mass, especially central adiposity [
5‐
7]. Despite evidence of adiponectin as a cardiovascular risk marker supported by early reports of a strong inverse association with incident coronary heart disease (CHD) in healthy middle-aged males [
8], the inverse relationship between adiponectin and CHD was comparatively moderate in other populations [
9]. Furthermore, in older populations, higher adiponectin concentrations were associated with greater risk of CHD, stroke or mortality or, conversely, no association was observed between adiponectin and risk of stroke [
10‐
12]. Possible explanations for these conflicting associations were reported as reverse causation from reactive increases [
13] or different proportions of high-molecular-weight (HMW) adiponectin [
12]. However, the reason for this paradoxical finding in older adults remains unclear.
Visceral adiposity is strongly related to insulin resistance and cardiometabolic disease risk [
14,
15]. Some researchers reported that not only visceral adipose tissue but also abdominal subcutaneous adipose tissues are both associated with adverse cardiometabolic risk [
16,
17]. In recent years, research has focused on muscle’s protective effect from metabolic syndrome because higher muscle mass has been reported to be associated with better insulin sensitivity and lower risk of insulin resistance [
18,
19]. Body composition changes with age [
20]. The gender-based difference of body composition change in older adults can influence the adiponectin level.
In this study we determined which body composition component was associated with adiponectin concentration and the gender difference of the association in older adults.
Results
The subjects showed differences in several metabolic parameters, body composition, comorbidities and health behaviour between males and females (Table
1). Plasma adiponectin, fasting insulin and HOMA-IR were significantly higher in females than in males. BMI, waist/height ratio and body fat percentage were also significantly higher in females than in males. Conversely, total lean body mass, skeletal muscle mass, bone mineral content of limbs and testosterone were significantly higher in males than in females. Regarding health behaviour, males showed a much higher prevalence of smoking and alcohol drinking than females and were more physically active than females.
Table 1
General characteristics of the subjects
Age (year) | 75.67 ± 5.36 | 74.44 ± 4.73 | 0.030 |
BMI (kg/m2) | 24.07 ± 2.99 | 25.26 ± 3.57 | 0.001 |
Waist/height ratio | 0.54 ± 0.05 | 0.58 ± 0.07 | < 0.001 |
Body fat (%) | 24.89 ± 7.74 | 34.02 ± 8.52 | < 0.001 |
Fat mass (g) | 16935.33 ± 4953.03 | 19463.03 ± 5665.38 | < 0.001 |
SBP (mmHg) | 137.8 ± 16.12 | 138.95 ± 16.74 | 0.535 |
DBP (mmHg) | 78.04 ± 9.05 | 77.77 ± 8.44 | 0.781 |
Fasting glucose (mg/dL) | 100.68 ± 25.76 | 98.82 ± 29.44 | 0.550 |
HbA1C (%) | 5.96 ± 0.91 | 5.93 ± 0.75 | 0.798 |
Fasting insulin (μU/mL) | 4.48 ± 2.25 | 5.87 ± 2.01 | 0.002 |
HOMA-IR | 1.09 ± 2.46 | 1.40 ± 2.16 | 0.009 |
LDL cholesterol (mg/dL) | 111.32 ± 34.62 | 115.69 ± 32.98 | 0.251 |
HDL cholesterol (mg/dL) | 46.99 ± 1.30 | 47.94 ± 1.28 | 0.411 |
Triglyceride (mg/dL) | 120.30 ± 1.75 | 127.74 ± 1.70 | 0.320 |
Albumin (g/dL) | 4.47 ± 0.36 | 4.47 ± 0.31 | 0.923 |
hs-CRP (mg/L) | 0.38 ± 1.4 | 0.22 ± 0.65 | 0.194 |
Testosterone (ng/ml) | 5.20 ± 1.86 | 0.12 ± 0.13 | < 0.001 |
e-GFR (mL/min/per 1.73 m2) | 78.54 ± 18.14 | 85.42 ± 19.18 | 0.001 |
Adiponectin (μg/mL) | 8.00 ± 1.75 | 10.38 ± 1.67 | < 0.001 |
Body composition | | | |
Total LBM (gram) | 43924.49 ± 6008.97 | 34271.89 ± 4551.19 | < 0.001 |
Arm LBM (gram) | 4962.42 ± 880.34 | 3614.25 ± 747.13 | < 0.001 |
Leg LBM (gram) | 13828.23 ± 2326.93 | 10294.68 ± 1699.85 | < 0.001 |
Arm SM (gram) | 4592.98 ± 856.51 | 3382.68 ± 726.57 | < 0.001 |
Leg SM (gram) | 12933.74 ± 2192.03 | 9681.23 ± 1591.48 | < 0.001 |
Arm BMC (gram) | 344.3 ± 72.24 | 221 ± 82.16 | < 0.001 |
Leg BMC (gram) | 894.49 ± 168.19 | 613.45 ± 143.95 | < 0.001 |
Comorbidities on treatment | | | |
Hypertension (%) | 75 (49.34) | 90 (53.57) | 0.450 |
DM (%) | 26 (17.11) | 22 (13.1) | 0.316 |
Dyslipidemia (%) | 63 (42.28) | 107 (63.69) | < 0.001 |
CVA (%) | 15 (9.87) | 11 (6.55) | 0.278 |
MI (%) | 14 (9.21) | 5 (2.98) | 0.018 |
Lifestyle | | | |
Smoking (pack/year) | 30.27 ± 28.56 | 0.65 ± 4.23 | < 0.001 |
Alcohol (g/week) | 152.74 ± 362.32 | 7.94 ± 33.44 | < 0.001 |
Exercise (%) | | | < 0.001 |
< 3 times/week | 105 (69.08) | 152 (90.48) | |
3-4 times/week | 14 (9.21) | 7 (4.17) | |
> 4 times/week < | 33 (21.71) | 9 (5.36) | |
In correlation analysis, the most significant correlations were observed between adiponectin and HDL-C, triglyceride and HOMA-IR in both genders. Gender differences in older adults were observed in the relationship between adiponectin concentrations and phenotypes, including metabolic parameters and body composition (Table
2). Age was positively associated with adiponectin in males (P < 0.01), but there was no significant relationship between adiponectin and age in females. Fasting glucose and albumin were negatively associated with adiponectin in males (P < 0.05), but not in females. Testosterone was negatively associated with adiponectin in females (P < 0.05), but not in males. Arm skeletal muscle mass and bone mineral content were negatively associated with adiponectin in males (P < 0.05), but not in females. Leg bone mineral content was negatively associated with adiponectin in females (P < 0.05), but not in males in univariate analysis.
Table 2
Relationships between adiponectin concentrations and phenotypes including metabolic parameters and body composition
Age (year) | 0.250 (0.093, 0.395) | 0.002 | 0.129 (−0.023, 0.275) | 0.095 |
BMI (kg/m2) | −0.305 (−0.444, −0.152) | < 0.001 | −0.294 (−0.427, −0.150) | < 0.001 |
Waist/height ratio | −0.202 (−0.353, −0.042) | 0.014 | −0.304 (−0.435, −0.160) | < 0.001 |
Body fat (%) | -0.295 (-0.435, −0.140) | < 0.001 | −0.295 (−0.428, −0.151) | < 0.001 |
Fat mass (g) | −0.311 (−0.450, −0.157) | <0.001 | −0.329 (−0.458, −0.185) | < 0.001 |
SBP (mmHg) | 0.101 (−0.061, 0.257) | 0.222 | −0.059 (−0.208, 0.093) | 0.449 |
DBP (mmHg) | 0.067 (−0.095, 0.225) | 0.419 | −0.045 (−0.195, 0.107) | 0.559 |
Fasting glucose (mg/dL) | −0.174 (−0.326, −0.014) | 0.033 | −0.095 (−0.243, 0.057) | 0.220 |
HbA1C (%) | −0.205 (−0.354, −0.045) | 0.012 | −0.167 (−0.310, −0.016) | 0.031 |
Fasting insulin (μU/mL) | −0.329 (−0.465, −0.178) | < 0.001 | −0.428 (−0.544, −0.295) | < 0.001 |
HOMA-IR | −0.339 (−0.474, −0.188) | < 0.001 | −0.425 (−0.541, −0.292) | < 0.001 |
LDL cholesterol (mg/dL) | −0.096 (−0.253, 0.066) | 0.244 | −0.033 (−0.183, 0.119) | 0.673 |
HDL cholesterol (mg/dL) | 0.425 (0.284, 0.548) | < 0.001 | 0.354 (0.214, 0.480) | < 0.001 |
Triglyceride (mg/dL) | −0.399 (−0.526, −0.255) | < 0.001 | −0.362 (−0.487, −0.223) | < 0.001 |
Albumin (g/dL) | −0.184 (−0.335, −0.024) | 0.025 | −0.087 (−0.236, 0.065) | 0.260 |
hs-CRP (mg/L) | −0.010 (−0.170, 0.151) | 0.905 | 0.009 (−0.143, 0.160) | 0.909 |
Testosterone (ng/ml) | 0.122 (−0.040, 0.277) | 0.139 | −0.183 (−0.325, −0.032) | 0.018 |
e-GFR (mL/min/per 1.73 m2)
| −0.016 (−0.177, 0.145) | 0.842 | −0.09 (−0.238, 0.062) | 0.247 |
Body composition | | | | |
Total LBM | −0.123 (−0.278, 0.039) | 0.136 | −0.134 (−0.279, 0.018) | 0.084 |
Arm LBM | −0.200 (−0.350, −0.041) | 0.014 | -0.163 (−0.307, −0.012) | 0.034 |
Leg LBM | −0.117 (−0.273, 0.045) | 0.156 | −0.136 (−0.282, 0.015) | 0.078 |
Arm SM | −0.229 (−0.376, −0.071) | 0.005 | −0.146 (−0.291, 0.006) | 0.059 |
Leg SM | −0.113 (−0.269, 0.049) | 0.170 | −0.124 (−0.270, 0.028) | 0.110 |
Arm BMC | −0.187 (−0.338, −0.027) | 0.023 | −0.107 (−0.255, 0.046) | 0.169 |
Leg BMC | −0.145 (−0.299, 0.016) | 0.077 | −0.24 (−0.378, −0.092) | 0.002 |
Comorbidities on treatment | | | | |
Hypertension | | 0.652 | | 0.013 |
Yes | 2.098 (1.966, 2.230) | | 2.249 (2.148, 2.351) | |
No | 2.057 (1.938, 2.176) | | 2.442 (2.330, 2.555) | |
DM | | 0.002 | | 0.005 |
Yes | 1.770 (1.600, 1.940) | | 2.056 (1.851, 2.261) | |
No | 2.142 (2.045, 2.239) | | 2.382 (2.301, 2.462) | |
Dyslipidemia | | < 0.001 | | 0.002 |
Yes | 1.815 (1.691, 1.939) | | 2.248 (2.157, 2.338) | |
No | 2.269 (2.160, 2.379) | | 2.499 (2.368, 2.630) | |
CVA | | 0.188 | | 0.145 |
Yes | 1.898 (1.705, 2.090) | | 2.123 (1.808, 2.438) | |
No | 2.097 (2.002, 2.193) | | 2.354 (2.275, 2.433) | |
MI | | 0.630 | | 0.104 |
Yes | 2.006 (1.656, 2.356) | | 1.975 (1.497, 2.453) | |
No | 2.084 (1.993, 2.175) | | 2.350 (2.273, 2.428) | |
Lifestyle | | | | |
Smoking (pack/year) | 0.102 (−0.060, 0.259) | 0.215 | 0.134 (−0.018, 0.279) | 0.084 |
Alcohol (g/week) | −0.009 (−0.170, 0.152) | 0.912 | 0.006 (−0.145, 0.157) | 0.938 |
Exercise | | 0.716 | | 0.451 |
< 3 times/week | 2.100 (1.991, 2.208) | | 2.324 (2.243, 2.405) | |
3-4 times/week | 1.982 (1.765, 2.198) | | 2.548 (2.204, 2.891) | |
> 4 times/week | 2.047 (1.849, 2.245) | | 2.430 (2.125, 2.734) | |
Multiple linear regression models assessed the association of body fat percentage, regional muscle mass and bone mineral content of body composition and waist/height ratio with adiponectin concentration (Table
3). After adjusting the metabolic parameters, body fat percentage and albumin (P < 0.05) were negatively associated with adiponectin in males, but not in females. HDL-C (P < 0.001) and age (P < 0.01) were positively associated with adiponectin in older males. In older females, the only factors that correlated significantly with adiponectin concentration were HOMA-IR (P < 0.001) and HDL-C (P < 0.05). The waist/height ratio and bone mineral content were not associated with adiponectin in either gender.
Table 3
Multiple linear regressions of adiponectin (dependent variables) with age and body composition for older males and females
Age | 0.026b
| 0.018a
| 0.018a
| 0.024b
| 0.014 | 0.004 | 0.001 | 0.011 |
Body fat (%) | - | −0.021b
| −0.020b
| −0.013a
| - | −0.011a
| −0.011a
| −0.003 |
Arm SM (g) | - | 0.000 | 0.000 | 0.000 | - | 0.000 | 0.000 | 0.000 |
Arm BMC (g) | - | −0.002 | −0.001 | −0.001 | - | 0.000 | 0.000 | 0.000 |
Leg SM (g) | - | 0.000 | 0.000 | 0.000 | - | 0.000 | 0.000 | 0.000 |
Leg BMC (g) | - | 0.000 | 0.000 | 0.000 | - | −0.001a
| −0.001a
| −0.001 |
Waist/Height | - | −0.712 | −0.496 | 0.955 | - | −0.565a
| −0.424a
| −0.516 |
Albumin | - | - | −0.200 | −0.251a
| - | - | −0.041 | −0.009 |
Testosterone | - | - | 0.027 | 0.028 | - | - | −0.630 | −0.412 |
HOMA-IR | - | - | - | 0.008 | - | - | - | −0.172b
|
HDL-Cholesterol | - | - | - | 0.876c
| - | - | - | 0.423a
|
Triglycerides | - | - | - | −0.127 | - | - | - | −0.153 |
Model adjusted R2
| 0.056 | 0.152 | 0.167 | 0.322 | 0.011 | 0.136 | 0.142 | 0.285 |
Discussion
In the present analysis, a gender difference was found in the relationship between adiponectin concentration and body fat in older adults. Age and gender are two of the most important confounding or effect modifying factors for most diseases. In particular, gender-specific body adiposity and age-related changes in body composition can influence metabolic profiles and cardiovascular diseases [
20]. Increased abdominal fat is associated with insulin resistance and atherogenic metabolic profiles [
27]. Adiponectin has been reported to have an important role in glucose and lipid metabolism [
1‐
3] and the most pronounced correlations were observed between adiponectin and HDL-C, triglycerides and HOMA-IR in the present study. The inverse relationship of circulating adiponectin to adipose tissue mass, especially central adiposity is well known [
5‐
7]. However, paradoxical results have been reported in older populations, such as higher adiponectin concentration associated with greater risk of CHD, stroke or mortality [
10‐
12]. In the result of multiple linear regression analysis for the relationship between adiponectin concentrations and body composition in our study, the body fat percentage was negatively associated with adiponectin in males, but not in females (Table
3). Except for a small contribution of body fat percentage in males, the waist/height ratio and body composition, including muscle and bone mineral content, had no relationship with adiponectin levels in either gender. In the previous study, bone mineral density was negatively associated with adiponectin levels in males > 60 years with a BMI > 27 kg/m
2 [
28]. However, in that study, the influence of waist circumference and body composition, including body fat and muscle, was not adjusted.
In the previous studies, age was positively associated with adiponectin [
12,
29,
30]; however, in our analysis of older adults, age was positively associated with adiponectin in males, but not in females (Tables
2 and
3). Adipocytes and their products, including leptin and adiponectin, play an important role in the interaction between energy balance and the reproductive axis [
31]. In the previous studies, the association of adiponectin with testosterone and estradiol was inconsistent. In a Caucasian population, higher levels of testosterone and lower estradiol concentrations predicted higher adiponectin levels in both genders [
32]. However, in an Asian population, adiponectin levels were negatively correlated with testosterone but were not correlated with estradiol in postmenopausal females (more than 1 year since menopause) [
33]. In our study, testosterone and adiponectin showed a significant negative association in the correlation analysis for older females. However, in multiple linear regression analysis, the correlation between testosterone and adiponectin in females was insignificant, even though the estimate of correlation changed from −0.183 to −0.412. The results of our study suggested that hormonal changes according to age and gender were not important factors that influenced the paradoxical finding of adiponectin in older Asian adults. In an
in vitro study using human fat cells, increasing concentrations of testosterone or estradiol did not influence adiponectin mRNA expression and secretion or the intracellular levels of high-, middle-, and low-molecular-weight adiponectin multimers. However, stimulation with human male and female serum downregulated adiponectin expression, with male serum exerting significantly stronger inhibitory properties than female serum, suggesting the presence of a serum factor that causes the gender dimorphism in circulating adiponectin levels [
34].
One strength of our study was that the data of older males and females were analysed separately. In several other studies, researchers did not analyse the male and female data separately, considering gender difference as a confounding factor [
12,
35], or they analysed data from males only [
10,
11]. However, our results suggested that gender difference was not only a confounding factor, but also an effect modifier in older adults; therefore, data from older males and females should be analysed separately. Another strength of our study was that we considered not only weight but also body composition using DXA and waist circumference in older adults. In previous studies, adiponectin was associated with weight change only in females [
36] or was not associated with changes in weight in older adults [
37]. However, in those studies, the amount of fat was not examined, which may explain the inconsistency between our results and previous studies.
The weakness of our study was the relatively small sample size. To minimise the effects of this limitation, participants were selected by systematic sampling from 200 areas that were selected randomly in Chuncheon, a small city in South Korea. Nevertheless, the sampling method cannot completely overcome the limitation from small sample size. Compared with other populations, the contribution of body fat and muscle to the adiponectin level in an older population was very small or insignificant after multiple regression analysis. Therefore, a larger study population is needed to determine the small but significant contribution of body fat and muscle to adiponectin levels in an older population.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
HJS suggested the study. HJS, SO and DHK designed the study and developed the study protocol. SO and HJS analysed the data. All authors interpreted the results. HJS drafted the manuscript. All authors contributed to the critical revision of the manuscript. DHK has full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.