Abstract
Context
Slight elevations in plasma glucose (PG) manifest in advance of diabetes onset, but abnormalities in immunoreactive insulin (IRI), proinsulin (Pro), and adiponectin dynamics during this stage remain poorly understood.
Objective
The objective of this work is to investigate whether IRI and Pro dynamics become abnormal as glucose tolerance deteriorates from within the normal range toward impaired glucose tolerance (IGT), as well as the relationship between PG, and these dynamics and serum adiponectin levels.
Design
A cross-sectional study was designed.
Setting
This study took place at Jichi Medical University in Japan.
Participants and Measurements
PG, IRI, and Pro levels were determined in 1311 young Japanese individuals (age < 40 years) with normal or IGT before and at 30, 60, and 120 minutes during a 75-g oral glucose tolerance test. Participants were assigned to 4 groups according to glucose tolerance, and then background factors, adiponectin levels, insulin sensitivity (SI), and insulin secretion (β) indexes were determined.
Results
PG levels as well as IRI and Pro levels 60 and 120 minutes after glucose-loading increased incrementally with deteriorating glucose tolerance. All measures of β and the SI measure index of insulin sensitivity (ISI)-Matsuda decreased incrementally. Serum adiponectin levels were not significantly different among the glucose tolerance groups, but were independently and negatively correlated with fasting glucose.
Conclusions
Early β decreased and postloading Pro levels became excessive in a progressive manner as glucose tolerance deteriorated from within the normal range toward IGT.
Keywords: glucose tolerance, insulin sensitivity, insulin secretion, proinsulin, adiponectin
Glucose intolerance develops as a consequence of β-cell dysfunction and/or insulin resistance. Glucose tolerance is classified as normal glucose tolerance (NGT), impaired glucose tolerance (IGT), impaired fasting glucose (IFG), and diabetes mellitus (DM) according to plasma glucose (PG) levels during an oral glucose tolerance test (OGTT) [1]. Impaired glucose-stimulated insulin secretion (GSIS) is an indicator of β-cell dysfunction in the prediabetic categories of IGT and IFG [2, 3]. High triglyceride (TG) levels, hypertension, and high body mass index (BMI) are also risk factors for prediabetes and diabetes, even in individuals with NGT [4]. We previously revealed that the disposition index, which is the product of insulin sensitivity (SI) and insulin secretion (β), decreases with increasing BMI in young Japanese individuals with NGT, and that β-cell dysfunction can manifest with increasing BMI even when glucose tolerance is normal [5]. However, in a population of mainly NGT individuals, whether SI and β deteriorate with worsening glucose tolerance has been poorly elucidated.
Slight elevations in PG manifest in advance of glucose intolerance [6], but the understanding of abnormalities in immunoreactive insulin (IRI) and proinsulin (Pro) dynamics in this stage remains insufficient. A recent study in Chinese individuals with NGT or IGT (but not IFG or DM) showed a delayed peak in Pro level with deteriorated glucose tolerance despite no abnormalities in insulin dynamic patterns, even in those with NGT following a 75-g OGTT [7]. However, that study had a small sample size and did not seem to sufficiently characterize the relationship between glucose tolerance and insulin and Pro dynamics during OGTT. Differences in Pro assay procedures and background factors, including age and ethnicity, can also affect this relationship.
Adiponectin is an insulin-sensitizing hormone secreted by adipocytes. High circulating levels of adiponectin reduce the risk of prediabetes and diabetes [8, 9]. Serum adiponectin levels are low in individuals with visceral fat accumulation, insulin resistance, and diabetes [10, 11]. Hypoadiponectinemia was also found to be related to impaired β and diabetes onset in the participants, including those with prediabetes [12, 13]. It is unclear, however, whether serum adiponectin levels are related to PG, β, and SI in people with NGT.
In the present study, we used data from a large number of young Japanese individuals to test the hypothesis regarding whether insulin and Pro dynamics progressively become abnormal as glucose tolerance deteriorates from within the normal range toward IGT. We also investigated the relationship of serum adiponectin levels to PG, β, SI, and other parameters in this population.
1. Materials and Methods
A. Diagnosis of Glucose Tolerance
The criteria of the Japan Diabetes Society define NGT as a fasting PG of less than 110 mg/dL and a 120-minute value of less than 140 mg/dL, IGT as a fasting PG of less than 110 mg/dL and a 120-minute value greater than or equal to 140 mg/dL to less than 200 mg/dL, IFG as a fasting PG of greater than or equal to 110 mg/dL to less than 126 mg/dL, and DM as a fasting PG of greater than or equal to 126 mg/dL or a 120-minute value of greater than or equal to 200 mg/dL during a 75-g OGTT [1].
B. Participants
The participants were 1311 medical students of Jichi Medical University who were younger than 40 years and had NGT or IGT (but not IFG or DM) among the about 1400 medical students who underwent a 75-g OGTT from December 2002 to April 2015. The present study was approved by the ethics committee of Jichi Medical University (EKI 09-45). The participants were fully informed of the purpose of the study and gave written consent to participate. The participants were assigned into 4 groups according to their glucose tolerance (OGTT 120-minute value; PG120) (NGT0: PG120 < 100, n = 864; NGT1: 100 ≤ PG120 < 120, n = 331; NGT2: 120 ≤ PG120 < 140, n = 87, IGT: 140 ≤ PG120 < 200, n = 29, in mg/dL). Background factors, PG, insulin, SI, β, Pro, and adiponectin were determined.
C. Measurements and Calculation of Indices
PG was determined using a glucose oxidase assay, and insulin using an immunoradiometric assay for IRI (Insulin RIA Beads II; Yamasa), as described previously [5]. The manufacturer claims that there is little cross-reactivity with Pro in the immunoradiometric assay for IRI. Proinsulin was determined as intact Pro with the Intact-Proinsulin Assay (MLT Research, Ltd), a chemiluminescent immunoassay procedure, as described previously [14]. Interassay and intra-assay variability for insulin and intact Pro were less than 5% and 10%, respectively. Samples for insulin and intact-Pro analysis were frozen until immunoassays were performed at about 6-month intervals.
In the 75-g OGTT, PG, IRI, and proinsulin were measured under fasting conditions (preloading) and at 30, 60, and 120 minutes after glucose loading, and are abbreviated as PG0, PG30, PG60, and PG120 (PG), fasting immunoreactive insulin (FIRI), IRI30, IRI60, and IRI120 (IRI), and Pro0, Pro30, Pro60, and Pro120. The molar ratio of Pro to insulin (P/I) was calculated. P/I values are abbreviated as P/I0, P/I30, P/I60, and P/I120.
As in a previous study by the authors [5], the following measures of SI and β were used in the present study. Index of insulin sensitivity (ISI)-Matsuda, a measure of systemic SI, was calculated as follows: ISI-Matsuda = 10 000/[square root (PG0∙PG120∙FIRI∙IRI120)] [15]. In addition, 1/FIRI and 1/homeostasis model assessment of insulin resistance (HOMA-IR) were used primarily as measures of hepatic SI [16, 17]. HOMA-IR was calculated as [PG0·FIRI]/405 [16]. The insulinogenic index was used as a measure of early GSIS [18, 19]: Insulinogenic index = (IRI30 – FIRI)/(PG30 – PG0). For ISI-Matsuda, 1/FIRI, HOMA-IR, and the insulinogenic index, the units of PG and IRI were milligram per deciliter and microunits per milliliter, respectively. Stumvoll 1 and Stumvoll 2 indexes were used as early- and second-phase GSIS, respectively: Stumvoll 1 = 1283 + 1.829∙IRI30 – 138.7∙PG30 + 3.772∙FIRI; Stumvoll 2 = 287 + 0.4164∙IRI30 – 26.07∙PG30 + 0.9226∙FIRI [20]. Here, the units for PG and IRI were millimole per liter and picomole per liter, respectively. Negative or unusable insulinogenic index values (PG30 = PG0) and negative Stumvoll 1 and Stumvoll 2 values were treated as missing.
Total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL), and adiponectin levels of the participants were determined using serum collected under fasting conditions. Total adiponectin concentrations were determined using an enzyme-linked immunosorbent assay kit (Otsuka Pharmaceutical). Sitting heart rate and blood pressure were measured after the participants had been at rest for 5 minutes.
D. Statistical Analysis
JMP version 5.1 (SAS Institute Inc) was used to conduct the statistical analyses. Most variables lacked a normal distribution. Values are shown as median (25th-75th percentile). The Wilcoxon or chi-square test was used to test for differences among the 4 groups. Because serum adiponectin levels are susceptible to the effects of sex [21], the participants were classified into the 4 groups according to glucose tolerance described earlier and further classified according to sex when evaluating adiponectin.
Multiple regression analysis was used to calculate regressions of the background factors for PG120. When conducting multiple regression analysis, factors potentially contributing to PG120 other than PG, insulin, SI, and β—that is, age, sex, BMI, blood pressure, heart rate, lipids, and family history of diabetes—were selected as explanatory factors. At this time, pairs of variables with a variation inflation factor greater than 2 (systolic and diastolic blood pressure) were considered to have multicollinearity, and the variable with a lower coefficient of correlation with PG120 (systolic blood pressure) was excluded.
PG, insulin, Pro, SI, β, and P/I were considered to constitute glucose-insulin-Pro profiles, and their correlation with serum adiponectin levels was tested using the Spearman rank correlation for bivariate analysis. Factors for which a significant correlation was identified were analyzed with multiple regression analysis after adjustment for background factors (age, sex, BMI, diastolic blood pressure, heart rate, lipids, family history of diabetes). In all statistical tests, a P value less than .05 was taken to indicate statistical significance.
2. Results
A. Characteristics of Participants’ Background Factors According to Glucose Tolerance
The participants’ background factors according to glucose tolerance are shown in Table 1. The participants were young, and no significant differences in age, BMI, high-density lipoprotein cholesterol, or TG were seen among the glucose tolerance groups. Blood pressure and TC increased from NGT0 to NGT2, but tended to decrease in IGT. As glucose tolerance deteriorated, the proportion of men decreased (NGT0: 79%, NGT1: 73%, NGT2: 69%, IGT: 59%), heart rate increased (NGT0: 62 beats per minute [bpm], NGT1: 64 bpm, NGT2: 67 bpm, IGT: 69 bpm), and the proportion of participants with a family history of diabetes (in a first- or second-degree relative) increased (NGT0: 32%, NGT1: 39%, NGT2: 46%, IGT: 59%). No significant difference among the glucose tolerance groups was seen in the proportion with a family history of diabetes in a first-degree relative.
Table 1.
Background factors of participants according to glucose tolerance
| NGT0 (n = 864) | NGT1 (n = 331) | NGT2 (n = 87) | IGT (n = 29) | P | |
|---|---|---|---|---|---|
| Age, y | 23 (22-23) | 23 (22-24) | 23 (22-24) | 22 (22-23) | .58 |
| Male, % | 684 (79%) | 243 (73%) | 60 (69%) | 17 (59%) | < .01a |
| BMI, kg/m2 | 21 (20-23) | 22 (20-23) | 22 (20-25) | 22 (19-24) | .10 |
| SBP, mm Hg | 118 (111-126) | 119 (111-126) | 123 (116-130) | 119 (107-124) | < .05 |
| DBP, mm Hg | 66 (62-71) | 67 (62-73) | 70 (63-75) | 67 (63-70) | < .05 |
| HR, bpm | 62 (56-69) | 64 (58-71) | 67 (60-76) | 69 (61-77) | < .0001a |
| HDL, mg/dL | 61 (53-70) | 60 (53-70) | 61 (54-69) | 62 (56-69) | .98 |
| TG, mg/dL | 58 (45-79) | 62 (47-85) | 63 (48-82) | 53 (33-84) | .19 |
| TC, mg/dL | 165 (148-183) | 169 (154-187) | 180 (160-202) | 174 (150-191) | < .0001 |
| Positive FH No., % | 278 (32%) | 129 (39%) | 40 (46%) | 17 (59%) | < .001a |
| Positive FH1 No., % | 102 (12%) | 47 (14%) | 11 (13%) | 7 (24%) | .19 |
Data are shown as median (25th percentile to 75th percentile). P values for the continuous variables and categorical variables were determined by the Wilcoxon and chi-square tests, respectively.
Abbreviations: BMI, body mass index; bpm, beats per minute; DBP, diastolic blood pressure; FH, family history of diabetes (first and/or second degree); FH1, family history of diabetes (first degree); HDL, high-density lipoprotein; HR, heart rate; IGT, impaired glucose tolerance; NGT, normal glucose tolerance; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides.
aThe association between the 4 groups is linear.
B. Glucose, Insulin, Proinsulin, Insulin Sensitivity, and Insulin Secretion in Participants According to Glucose Tolerance
PG, IRI, Pro, and P/I levels in the participants according to glucose tolerance are shown in Fig. 1. IRI30 (Fig. 1B), Pro0, Pro30 (Fig. 1C), P/I30, and P/I60 (Fig. 1D) were not significantly different among the glucose tolerance groups. FIRI (Fig. 1B) showed an increasing tendency and P/I0 (Fig. 1D) showed a decreasing tendency from NGT0 to NGT1. PG0, PG30, PG60, and PG120 (Fig. 1A), IRI60 and IRI120 (Fig. 1B), and Pro60 and Pro120 (Fig. 1C) increased incrementally with deteriorating glucose tolerance. A clearly delayed peak in IRI and excessive postloading Pro levels were observed in the participants with IGT. P/I120 (Fig. 1D) decreased with deteriorating glucose tolerance and was significantly lower for NGT1, NGT2, and IGT than for NGT0.
Figure 1.
Glucose, insulin, proinsulin, and the proinsulin/insulin ratio according to glucose tolerance. Median bar graphs are shown for A, glucose; B, insulin; C, proinsulin; and D, the proinsulin/insulin ratio according to glucose tolerance. FIRI, fasting IRI; IGT, impaired glucose tolerance; IRI, immunoreactive insulin; NGT, normal glucose tolerance; NGT0, PG120 < 100; NGT1, 100 ≤ PG120 < 120; NGT2, 120 ≤ PG120 < 140, in mg/dL; PG, plasma glucose; P/I, proinsulin/insulin ratio; Pro, proinsulin. P values for the variables were determined by paired Wilcoxon test. *P is less than .05.
The SI and β values of the participants according to glucose tolerance are shown in Fig. 2. The SI measure ISI-Matsuda (Fig. 2B) and all β measures (Fig. 2D-2F) incrementally decreased with deteriorating glucose tolerance. Few significant differences in 1/HOMA-IR (Fig. 2A) and 1/FIRI (Fig. 2C) were seen among the glucose tolerance groups.
Figure 2.
Insulin sensitivity (SI) and β according to glucose tolerance. Median bar graphs are shown for A to C, SI indices, and D to F, β indices, according to glucose tolerance. Abbreviations are the same as those in Fig. 1 FIRI, fasting IRI; IGT, impaired glucose tolerance; IRI, immunoreactive insulin; NGT, normal glucose tolerance; NGT0, PG120 < 100; NGT1, 100 ≤ PG120 < 120; NGT2, 120 ≤ PG120 < 140, in mg/dL. P values for the variables were determined by paired Wilcoxon test. *P is less than .05.
C. Relationship Between Background Factors and 120-Minute Plasma Glucose Value
The results of multiple regression analysis of the background factors for PG120 are shown in Table 2. Multiple regression analysis revealed negative correlations of PG120 with the proportion of men and TG, and positive correlations with BMI, heart rate, TC, and family history of diabetes.
Table 2.
Multiple regression analysis for PG120 in all participants
| t | P | |
|---|---|---|
| Age, y | 0.49 | .62 |
| Male, % | –2.73 | < .01 |
| BMI, kg/m2 | 3.01 | < .01 |
| DBP, mm Hg | 0.31 | .76 |
| HR, bpm | 3.82 | < .001 |
| HDL, mg/dL | –1.86 | .063 |
| TG, mg/dL | –2.07 | < .05 |
| TC, mg/dL | 2.94 | < .01 |
| FH, % | 2.62 | < .01 |
Abbreviations: BMI, body mass index; bpm, beats per minute; DBP, diastolic blood pressure; FH, family history of diabetes (first and/or second degree); HDL, high-density lipoprotein; HR, heart rate; PG, plasma glucose; TC, total cholesterol; TG, triglycerides.
D. Serum Adiponectin Levels According to Glucose Tolerance and Sex
Serum adiponectin levels according to glucose tolerance in all participants, women and men, are shown in Fig. 3. Serum adiponectin levels in the participants with NGT were significantly higher in women than in men (P < .0001 for NGT0, P < .0001 for NGT1, P < .001 for NGT2). Serum adiponectin levels in the participants with IGT tended to be higher in women than in men (P = .058). Serum adiponectin levels in all participants, women and men, were not significantly different among the glucose tolerance groups.
Figure 3.
Serum adiponectin levels according to glucose tolerance and sexual status. Median bar graphs are shown for serum adiponectin levels according to glucose tolerance and sex. Abbreviations are the same as those in Fig. 1 IGT, impaired glucose tolerance; NGT, normal glucose tolerance; NGT0, PG120 < 100; NGT1, 100 ≤ PG120 < 120; NGT2, 120 ≤ PG120 < 140, in mg/dL. No differences in any comparison were significant. P values for the variables were determined by paired Wilcoxon test.
E. Relationship Between Serum Adiponectin Levels and Glucose-Insulin-Proinsulin Profiles
The results of the Spearman rank correlation analysis of serum adiponectin levels to glucose-insulin-Pro profiles and multiple regression analysis following adjustment for background factors are shown in Table 3. In the Spearman rank correlation analysis, serum adiponectin levels were negatively correlated with some PG, IRI, Pro, and SI measures in OGTT, but not with measures of β. Serum adiponectin levels were independently and negatively correlated with PG0 in the multiple regression analysis. However, the other measures of PG, IRI, Pro, P/I, and SI, as well as β, were not independently correlated with serum adiponectin levels.
Table 3.
Relationship between serum adiponectin levels and glucose-insulin-proinsulin profiles in all participants
| Nonparametric Spearman rank coefficient | Multiple regression analysis after adjusting for background factors | |||
|---|---|---|---|---|
| ρ | P | t | P | |
| PG0 | –0.0868 | < .01 | –2.13 | < .05 |
| PG30 | –0.1298 | < .0001 | –0.76 | .45 |
| PG60 | –0.0486 | .098 | ||
| PG120 | –0.0236 | .42 | ||
| FIRI | –0.1078 | < .001 | 0.18 | .86 |
| IRI30 | –0.07 | < .05 | –0.20 | .84 |
| IRI60 | –0.0699 | < .05 | 0.07 | .94 |
| IRI120 | 0.0034 | .91 | ||
| Pro0 | –0.134 | < .0001 | –0.98 | .33 |
| Pro30 | –0.0773 | <0.01 | –0.97 | .33 |
| Pro60 | –0.0948 | < .01 | –1.05 | .29 |
| Pro120 | –0.0511 | .081 | ||
| P/I0 | 0.0164 | .58 | ||
| P/I30 | –0.0047 | .87 | ||
| P/I60 | –0.0075 | .80 | ||
| P/I120 | –0.0694 | < .05 | –0.89 | .38 |
| 1/HOMA-IR | 0.1183 | < .0001 | –0.49 | .63 |
| ISI-Matsuda | 0.0458 | .12 | ||
| 1/FIRI | 0.1078 | < .001 | –0.71 | .48 |
| Stumvoll-1 | –0.0321 | .27 | ||
| Stumvoll-2 | –0.0419 | .15 | ||
| Insulinogenic index | 0.0276 | .36 | ||
Abbreviations: FIRI, fasting immunoreactive insulin; HOMA-IR, homeostasis model assessment of insulin resistance; IRI, immunoreactive insulin; ISI, index of insulin sensitivity; PG, plasma glucose; P/I, proinsulin/insulin ratio; Pro, proinsulin.
3. Discussion
Slight elevations in PG in NGT and prediabetes have been reported to be associated with the long-term risk of diabetes onset [6, 22, 23]. Elucidating the relationship between slight elevations in PG and β-cell function in NGT and prediabetes is therefore important to clarify pathogenic factors for progression from NGT to diabetes. However, it is not well known whether slight elevations in PG are correlated with β-cell function or markers related to β-cell function in individuals with NGT. The present study showed that in a large sample of young Japanese participants, early β progressively decreases, and postloading Pro levels become excessive as glucose tolerance deteriorates from within the normal range toward IGT. Serum adiponectin levels were inversely correlated with fasting glucose.
Chinese individuals with NGT or IGT showed no abnormalities in insulin dynamic patterns across the NGT categories of glucose tolerance, but the peak of Pro level was delayed [7]. In the present study, even participants with NGT had delayed and/or excessive insulin and Pro levels as glucose tolerance deteriorated, but the peak of the Pro level was not delayed (Fig. 1C). In the previously mentioned study [7], those with NGT (OGTT 120-min value < 120 mg/dL; equivalent to NGT0 or NGT1 in the present study) had a Pro peak at 60 minutes after glucose loading, but intact Pro levels in the present study continued to increase through 120 minutes after glucose loading, even in the NGT0 and NGT1 categories (Fig. 1C). Differences in the Pro assay procedures, age, or sample size may explain this disparity. In the present study, SI was used to evaluate systemic and hepatic insulin sensitivity, and β was used to evaluate GSIS. ISI-Matsuda and all β measures decreased with deteriorating glucose tolerance. It cannot be concluded from the earlier mentioned study [7] that β abnormalities did not manifest over the glucose tolerance categories of NGT because that study did not evaluate SI and β in detail, as in the present study.
Other studies have found that in people with prediabetes or diabetes, Pro levels increase as glucose tolerance deteriorates, and that high Pro levels precede diabetes onset [24-26]. Those studies, however, did not evaluate insulin and Pro dynamics in different glucose-tolerance categories within NGT. Fasting Pro and the P/I ratio did not essentially differ across the NGT categories and IGT in the present study (Fig. 1C and 1D). Postloading insulin and Pro levels became delayed and/or excessive as the NGT categories approached IGT (Fig. 1B and 1C). P/I120 values from NGT1 to IGT were lower than that of NGT0 (Fig. 1D), whose PG120 level had returned to baseline (Fig. 1A). This is consistent with the results that P/I120 values were lower in IFG/IGT and diabetic participants than those in NGT [26]. These findings suggest that β exceeds Pro secretion in response to sustained postloading elevated glycemia in different NGT categories or in IGT, which seems to be a plausible response [27].
We also investigated the relationship of various clinical background factors to glucose tolerance (OGTT 120-minute value; PG120). As glucose tolerance deteriorated, the proportion of men decreased, and the proportion of participants with a family history of diabetes and resting heart rate increased. Multivariable analysis showed these factors to be independently correlated with PG120. Studies have associated sex and a family history of diabetes with glucose intolerance in participants, including those with IGT [28, 29]. In those studies, IGT was more prevalent in women and those with a family history of diabetes, consistent with our findings. One possible explanation for the preponderance of women with IGT is assumed to be lower body mass and skeletal muscle mass in female participants [30]. A recent genome-wide assessment of the relationship between resting heart rate and the risk of diabetes onset found resting heart rate to be strongly associated with the onset of type 2 diabetes [31]. Consistent with that study, increased resting heart rate was associated with deteriorated glucose tolerance in the present study. Elevated resting heart rate in young Japanese individuals with NGT could therefore be a risk factor for future glucose intolerance.
We also investigated whether serum adiponectin levels were associated with glucose-insulin-Pro profiles in our mainly NGT participants. As in previous studies, serum adiponectin levels were higher in women, although serum adiponectin levels across the glucose-tolerance categories showed no differences in all participants or in women or men. In bivariate analysis with glucose-insulin-Pro profiles and multivariable analysis adjusted for background factors, serum adiponectin levels were independently and negatively correlated with fasting glucose. This correlation between serum adiponectin levels and glucose-insulin-Pro profiles has often been examined in people with prediabetes or diabetes, but less often in those with NGT [32-34]. A study in a non-DM population found low serum adiponectin levels to be associated with IFG [33]. Consistent with that study, our findings suggest that low serum adiponectin levels could be associated with moderately elevated fasting glucose in non-IFG individuals consisting mainly of those with NGT. Other studies have associated hypoadiponectinemia with impaired β [12, 13], but the present study found no association between serum adiponectin levels and impaired β in the stages of NGT or IGT.
Several limitations must be noted. Owing to the cross-sectional nature of this study, a direct causal relationship of β-cell function and Pro dynamics affecting glucose tolerance was not proven. However, the strength of the present study is that β-cell function and Pro dynamics were examined using a large sample of participants with common characteristics (mainly NGT and young). Abnormalities in insulin and Pro dynamics have been associated with aging [35], but no age differences were observed in this study across the different glucose-tolerance categories. Because we measured peripheral venous levels of insulin and Pro and the half-lives of those in serum are different, serum levels may not accurately reflect secretion rates. However, comparison of those levels at the same time during OGTT could allow us to estimate secretion abnormalities [17-20]. Because measurements were sparse and restricted within 120 minutes after glucose loading, the true insulin and Pro peak by differences in glucose tolerance may not be captured. Some of the participants in the present study could possibly experience glucose intolerance and develop diabetes in the future, and a comparative study between this study and disease onset may be of potential interest.
In conclusion, all measures of β and ISI-Matsuda decreased with deteriorating glucose tolerance, even within individuals with NGT. Delayed and/or excessive Pro and insulin levels and a decrease in the P/I ratio with deteriorating glucose tolerance are plausible responses to postloading elevated glycemia. The proportion of women, proportion of participants with a family history of diabetes, and elevated resting heart rate were associated with deteriorated glucose tolerance (ie, high PG120), and low serum adiponectin levels were associated with elevated fasting glucose. Insulin and Pro dynamics progressively became abnormal as glucose tolerance deteriorated incrementally from within the normal range toward IGT.
Acknowledgments
Financial Support: The authors received no financial support for the research, authorship, and/or publication of this article.
Glossary
Abbreviations
- β
insulin secretion
- BMI
body mass index
- bpm
beats per minute
- DBP
diastolic blood pressure
- DM
diabetes mellitus
- FH
family history of diabetes (first and/or second degree)
- FH1
family history of diabetes (first degree)
- FIRI
fasting immunoreactive insulin
- GSIS
glucose-stimulated insulin secretion
- HDL
high-density lipoprotein
- HOMA-IR
homeostasis model assessment of insulin resistance
- HR
heart rate
- IFG
impaired fasting glucose
- IGT
impaired glucose tolerance
- IRI
immunoreactive insulin
- ISI
index of insulin sensitivity
- LDL
low-density lipoprotein
- NGT
normal glucose tolerance
- OGTT
oral glucose tolerance test
- PG
plasma glucose
- P/I
proinsulin to insulin ratio
- Pro
proinsulin
- SBP
systolic blood pressure
- SI
insulin sensitivity
- TC
total cholesterol
- TG
triglyceride
Additional Information
Disclosure Summary: The authors have nothing to disclose.
Data Availability: The data sets generated and/or analyzed during the present study are not publicly available, but are available from the corresponding author on reasonable request.
References and Notes
- 1. The Committee of the Japan Diabetes Society on the Diagnostic Criteria of Diabetes Mellitus; Seino Y, Nanjo K, Tajima N, et al. Report of the Committee on the Classification and Diagnostic Criteria of Diabetes Mellitus. J Diabetes Investig. 2010;1(5):212-228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Aizawa T, Yamauchi K, Yamada M. Longitudinal changes in insulin sensitivity, insulin secretion, beta cell function and glucose effectiveness during development of non-diabetic hyperglycemia in a Japanese population. Springerplus. 2014;3:252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Salunkhe VA, Veluthakal R, Kahn SE, Thurmond DC. Novel approaches to restore beta cell function in prediabetes and type 2 diabetes. Diabetologia. 2018;61(9):1895-1901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Neumann A, Norberg M, Schoffer O, et al. Risk equations for the development of worsened glucose status and type 2 diabetes mellitus in a Swedish intervention program. BMC Public Health. 2013;13:1014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Murai N, Saito N, Kodama E, et al. Association of ghrelin dynamics with beta cell function in Japanese subjects with normal glucose tolerance. Clin Endocrinol (Oxf). 2019;91(5):616-623. [DOI] [PubMed] [Google Scholar]
- 6. Malmström H, Walldius G, Carlsson S, et al. Elevations of metabolic risk factors 20 years or more before diagnosis of type 2 diabetes: experience from the AMORIS study. Diabetes Obes Metab. 2018;20(6):1419-1426. [DOI] [PubMed] [Google Scholar]
- 7. Yang Y, Wang M, Tong J, et al. Impaired glucose-stimulated proinsulin secretion is an early marker of β-cell impairment before prediabetes stage. J Clin Endocrinol Metab. 2019;104(10):4341-4346. [DOI] [PubMed] [Google Scholar]
- 8. Jiang Y, Owei I, Wan J, Ebenibo S, Dagogo-Jack S. Adiponectin levels predict prediabetes risk: the Pathobiology of Prediabetes in A Biracial Cohort (POP-ABC) study. BMJ Open Diabetes Res Care. 2016;4(1):e000194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Wang Y, Meng RW, Kunutsor SK, et al. Plasma adiponectin levels and type 2 diabetes risk: a nested case-control study in a Chinese population and an updated meta-analysis. Sci Rep. 2018;8(1):406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Yatagai T, Nagasaka S, Taniguchi A, et al. Hypoadiponectinemia is associated with visceral fat accumulation and insulin resistance in Japanese men with type 2 diabetes mellitus. Metabolism. 2003;52(10):1274-1278. [DOI] [PubMed] [Google Scholar]
- 11. Kim SM, Cho KH, Park HS. Relationship between plasma adiponectin levels and the metabolic syndrome among Korean people. Endocr J. 2006;53(2):247-254. [DOI] [PubMed] [Google Scholar]
- 12. Fagerberg B, Kellis D, Bergström G, Behre CJ. Adiponectin in relation to insulin sensitivity and insulin secretion in the development of type 2 diabetes: a prospective study in 64-year-old women. J Intern Med. 2011;269(6):636-643. [DOI] [PubMed] [Google Scholar]
- 13. Nakamura A, Miyoshi H, Ukawa S, et al. Serum adiponectin and insulin secretion: a direct or inverse association? J Diabetes Investig. 2018;9(5):1106-1109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Pfützner A, Kunt T, Langenfeld M, Löbig M, Knesovic M, Forst T. Clinical and laboratory evaluation of specific chemiluminescence assays for intact and total proinsulin. Clin Chem Lab Med. 2003;41(9):1234-1238. [DOI] [PubMed] [Google Scholar]
- 15. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22(9):1462-1470. [DOI] [PubMed] [Google Scholar]
- 16. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412-419. [DOI] [PubMed] [Google Scholar]
- 17. Hermans MP, Levy JC, Morris RJ, Turner RC. Comparison of tests of beta-cell function across a range of glucose tolerance from normal to diabetes. Diabetes. 1999;48(9):1779-1786. [DOI] [PubMed] [Google Scholar]
- 18. Seltzer HS, Allen EW, Herron AL Jr, Brennan MT. Insulin secretion in response to glycemic stimulus: relation of delayed initial release to carbohydrate intolerance in mild diabetes mellitus. J Clin Invest. 1967;46(3):323-335. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Phillips DI, Clark PM, Hales CN, Osmond C. Understanding oral glucose tolerance: comparison of glucose or insulin measurements during the oral glucose tolerance test with specific measurements of insulin resistance and insulin secretion. Diabet Med. 1994;11(3):286-292. [DOI] [PubMed] [Google Scholar]
- 20. Stumvoll M, Mitrakou A, Pimenta W, et al. Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity. Diabetes Care. 2000;23(3):295-301. [DOI] [PubMed] [Google Scholar]
- 21. Boyne MS, Bennett NR, Cooper RS, et al. Sex-differences in adiponectin levels and body fat distribution: longitudinal observations in Afro-Jamaicans. Diabetes Res Clin Pract. 2010;90(2):e33-e36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Selvin E, Steffes MW, Zhu H, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med. 2010;362(9):800-811. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Tricò D, Galderisi A, Mari A, Santoro N, Caprio S. One-hour post-load plasma glucose predicts progression to prediabetes in a multi-ethnic cohort of obese youths. Diabetes Obes Metab. 2019;21(5):1191-1198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Wareham NJ, Byrne CD, Williams R, Day NE, Hales CN. Fasting proinsulin concentrations predict the development of type 2 diabetes. Diabetes Care. 1999;22(2):262-270. [DOI] [PubMed] [Google Scholar]
- 25. Zethelius B, Hales CN, Lithell HO, Berne C. Insulin resistance, impaired early insulin response, and insulin propeptides as predictors of the development of type 2 diabetes: a population-based, 7-year follow-up study in 70-year-old men. Diabetes Care. 2004;27(6):1433-1438. [DOI] [PubMed] [Google Scholar]
- 26. Vangipurapu J, Stančáková A, Kuulasmaa T, Kuusisto J, Laakso M. Both fasting and glucose-stimulated proinsulin levels predict hyperglycemia and incident type 2 diabetes: a population-based study of 9396 Finnish men. PloS One. 2015;10(4):e0124028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Yoshioka N, Kuzuya T, Matsuda A, Taniguchi M, Iwamoto Y. Serum proinsulin levels at fasting and after oral glucose load in patients with type 2 (non-insulin-dependent) diabetes mellitus. Diabetologia. 1988;31(6):355-360. [DOI] [PubMed] [Google Scholar]
- 28. Zhang J, Yang Z, Xiao J, et al. ; China National Diabetes and Metabolic Disorders Study Group Association between family history risk categories and prevalence of diabetes in Chinese population. PloS One. 2015;10(2):e0117044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Kautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocr Rev. 2016;37(3):278-316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Someya Y, Tamura Y, Suzuki R, et al. Characteristics of glucose metabolism in underweight Japanese women. J Endocr Soc. 2018;2(3):279-289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Guo Y, Chung W, Zhu Z, et al. Genome-wide assessment for resting heart rate and shared genetics with cardiometabolic traits and type 2 diabetes. J Am Coll Cardiol. 2019;74(17):2162-2174. [DOI] [PubMed] [Google Scholar]
- 32. Bacha F, Saad R, Gungor N, Arslanian SA. Adiponectin in youth: relationship to visceral adiposity, insulin sensitivity, and beta-cell function. Diabetes Care. 2004;27(2):547-552. [DOI] [PubMed] [Google Scholar]
- 33. Kim SY, Lee SJ, Park HK, et al. Adiponectin is associated with impaired fasting glucose in the non-diabetic population. Epidemiol Health. 2011;33:e2011007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Tsuboi A, Minato S, Yano M, et al. Higher circulating adiponectin and lower orosomucoid were associated with postload glucose ≤ 70 mg/dL, a possible inverse marker for dysglycemia, in young Japanese women. BMJ Open Diabetes Res Care. 2019;7(1):e000596. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Shimizu M, Kawazu S, Tomono S, et al. Age-related alteration of pancreatic beta-cell function. Increased proinsulin and proinsulin-to-insulin molar ratio in elderly, but not in obese, subjects without glucose intolerance. Diabetes Care. 1996;19(1):8-11. [DOI] [PubMed] [Google Scholar]