Association of point in range with β-cell function and insulin sensitivity of type 2 diabetes mellitus in cold areas

Yanan Ni; Dan Liu; Xiaona Zhang; Hong Qiao

doi:10.2478/fzm-2023-0031

Association of point in range with β-cell function and insulin sensitivity of type 2 diabetes mellitus in cold areas

doi: 10.2478/fzm-2023-0031

1.
Department of Endocrinology, NO.2 Affiliated Hospital of Harbin Medical University, Harbin 150081, China
2.
Department of Biostatistics, Harbin Medical University, Harbin 150086, China

Funds: None of the authors accepted external funding or financial support

More Information

Corresponding author: Hong Qiao, E-mail: qiaohong@hrbmu.edu.cn

摘要

Abstract: Background and Objective Self-monitoring of blood glucose (SMBG) is crucial for achieving a glycemic target and upholding blood glucose stability, both of which are the primary purpose of anti-diabetic treatments. However, the association between time in range (TIR), as assessed by SMBG, and β-cell insulin secretion as well as insulin sensitivity remains unexplored. Therefore, this study aims to investigate the connections between TIR, derived from SMBG, and indices representing β-cell functionality and insulin sensitivity. The primary objective of this study was to elucidate the relationship between short-term glycemic control (measured as points in range [PIR]) and both β-cell function and insulin sensitivity. Methods This cross-sectional study enrolled 472 hospitalized patients with type 2 diabetes mellitus (T2DM). To assess β-cell secretion capacity, we employed the insulin secretion-sensitivity index-2 (ISSI-2) and (ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index, while insulin sensitivity was evaluated using the Matsuda index and HOMA-IR. Since SMBG offers glucose data at specific point-in-time, we substituted TIR with PIR. According to clinical guidelines, values falling within the range of 3.9–10 mmol were considered "in range, " and the corresponding percentage was calculated as PIR. Results We observed significant associations between higher PIR quartiles and increased ISSI-2, (ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index, Matsuda index (increased) and HOMA-IR (decreased) (all P < 0.001). PIR exhibited positive correlations with log ISSI-2 (r = 0.361, P < 0.001), log (ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index (r = 0.482, P < 0.001), and log Matsuda index (r = 0.178, P < 0.001) and negative correlations with log HOMA-IR (r = -0.288, P < 0.001). Furthermore, PIR emerged as an independent risk factor for log ISSI-2, log (ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index, log Matsuda index, and log HOMA-IR. Conclusion PIR can serve as a valuable tool for assessing β-cell function and insulin sensitivity.
- time in range /
- points in range /
- self-monitoring of blood glucose /
- β-cell function /
- insulin sensitivity

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Figure 1. Different levels of insulin secretion-sensitivity index-2 (ISSI-2), logISSI-2, (ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index and log(ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index among patients with different point in range (PIR) quartiles. Comparisons of ISSI-2(A), logISSI-2(B), (ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index (C) and log(ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index (D) among patients with different PIR quartiles (Q1-Q4). P-value for the significant difference among the groups was determined by Wilcoxon rank sum test (A, C), one-way ANCOVA (B, D). Q1: PIR ≤ 51.5%, Q2: 51.5% < PIR ≤ 68.0%, Q3: 68.0% < PIR ≤ 80.0%, Q4: PIR > 80.0%. Data are presented as the median and interquartile range (25^th–75^th) (A, C), mean ± SD (B, D). P < 0.001 vs. group Q1; ^#P < 0.001 vs. group Q2; ^& P < 0.001 vs. group Q3 in panel B; P < 0.001 vs. group Q1; ^#P < 0.05, ^##P < 0.001 vs. group Q2; ^& P < 0.001 vs. group Q3 in panel D.

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Figure 2. Different levels of Matsuda index and log Matsuda index among patients with different PIR quartiles. Comparisons of Matsuda index(A) and log Matsuda index(B) among patients with different point in range (PIR) quartiles (Q1-Q4). P-value for the significant difference among the groups was determined by Wilcoxon rank sum test (A), one-way ANCOVA (B). Q1: PIR ≤ 51.5%, Q2: 51.5% < PIR ≤ 68.0%, Q3: 68.0% < PIR ≤ 80.0%, Q4: PIR > 80.0%. Data are presented as the median and interquartile range (25^th–75^th) (A), mean ± SD (B). P < 0.001 vs. group Q1.

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Figure 3. Different levels of HOMA-IR and log HOMA of insulin resistance (HOMA-IR) among patients with different point in range (PIR) quartiles. Comparisons of HOMA-IR(A) and log HOMA-IR(B) among patients with different PIR quartiles (Q1-Q4). P-value for the significant difference among the groups was determined by Wilcoxon rank sum test(A), one-way ANCOVA(B). Q1: PIR ≤ 51.5%, Q2: 51.5% < PIR ≤ 68.0%, Q3: 68.0% < PIR ≤ 80.0%, Q4: PIR > 80.0%. Data are presented as the median and interquartile range (25^th–75^th) (A), mean ± SD (B). ^***P < 0.001 vs. group Q1; ^#P < 0.05 vs. group Q2.

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Figure 4. The relationships of point in range (PIR) and log Insulin Secretion-Sensitivity Index-2 (ISSI-2) (A), log (ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index (B), log Matsuda index (C), and log HOMA-IR (D) in the participants. Pearson's correlation test was used to determine the relationship.

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Table 1. Clinical characteristics of the participants according to PIR quartiles

Variables	Q1	Q2	Q3	Q4	P
Age (years)	53.0 (47.0-62.0)	55.0 (46.0-64.0)	55.0 (47.0-61.5)	53.0 (43.0-60.0)	0.395
Male (n, %)	69.0 (61.6)	67.0 (51.5)	65.0 (54.2)	56.0 (50.9)	0.345
BMI (kg/m²)	25.0 (23.3-27.5)	25.4 (23.8-28.4)	25.5 (23.0-27.5)	26.1 (23.6-28.4)	0.203
Risk factors
SBP (mmHg)	135.5 (125.5-149.0)	138.0 (125.0-151.0)	139.0 (124.0-150.0)	138.0 (124.0-153.0)	0.898
DBP (mmHg)	86.5 (78.0-95.5)	86.0 (79.0-93.0)	85.5 (79.0-97.0)	89.0 (82.0-95.0)	0.263
Current Smoking (n, %)	33.0 (29.5)	34.0 (26.2)	24.0 (20.0)	17.0 (15.5)	0.057
Current alcohol drinker (n, %)	20.0 (17.9)	13.0 (10.0)	17.0 (14.2)	11.0 (10.0)	0.221
Family history of diabetes (n, %)	21.0 (18.8)	27.0 (20.8)	16.0 (13.3)	18.0 (16.4)	0.451
Diabetes duration (years) (n, %)
< 5 years	51.0 (45.5)	53.0 (40.8)	63.0 (52.5)	72.0 (65.4)	0.001
5-10 years	26.0 (23.2)	34.0 (26.1)	26.0 (21.7)	19.0 (17.3)	0.001
> 10 years	35.0 (31.3)	43.0 (33.1)	31.0 (25.8)	19.0 (17.3)	0.001
Laboratory data
HbA₁c (%)	10.2 (9.1-11.1)	9.6 (8.2-10.7)	8.7 (7.8-10.7)	8.3 (7.1-9.6)	< 0.001
eGFR (mL/min)	94.0 (73.9-124.7)	96.4 (75.2-120.8)	101.8 (74.0-130.8)	104.6 (83.2-126.6)	0.270
TG (mmol/L)	2.07 (1.28-3.21)	1.78 (1.30-2.79)	1.76 (1.13-2.75)	1.67 (1.14-2.47)	0.089
TC (mmol/L)	4.98 (4.27-5.80)	4.69 (3.91-5.47)	4.84 (3.82-5.73)	5.04 (4.02-5.61)	0.204
HDL-C (mmol/L)	1.07 (0.92-1.26)	1.04 (0.90-1.19)	1.09 (0.90-1.27)	1.14 (1.01-1.33)	0.018
LDL-C (mmol/L)	3.03 (2.43-3.70)	2.85 (2.08-3.35)	2.65 (2.20-3.71)	2.95 (2.20-3.67)	0.216
Data are expressed as the median and interquartile range (25^th–75^th) for variables without a normal distribution. Categorical data are expressed as frequency and percentage. Between the four groups, the P-values were calculated using Wilcoxon rank sum test. BMI: body mass index; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; HbA₁c: glycated hemoglobin; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride; PIR: point in range; SBP: systolic blood pressure.

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Table 2. Multiple linear regression models for logISSI-2 and log(ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index

Variables	β	standardized regression coefficient	P-value
For logISSI-2
PIR (%)	0.110	0.243	< 0.001
Diabetes duration (years)	-0.120	-0.206	< 0.001
HbA₁c (%)	-0.054	-0.209	< 0.001
LDL-C (mmol/L)	-0.064	-0.125	0.001
For log(ΔC-peptide_0–120/Δglucose_0–120) × Matsuda index
PIR (%)	0.269	0.307	< 0.001
Diabetes duration (years)	-0.231	-0.206	< 0.001
HbA₁c (%)	-0.165	-0.329	< 0.001
eGFR (mL/min)	0.002	0.104	0.015
TC (mmol/L)	-0.059	-0.078	0.043
eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin; ISSI-2: insulin secretion-sensitivity index-2; LDL-C: low-density lipoprotein cholesterol; PIR: point in range; TC: total cholesterol.

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Table 3. Multiple linear regression models for log Matsuda index

Variables	β	standardized regression coefficient	P-value
PIR (%)	0.076	0.166	< 0.001
Male (%)	-0.156	-0.157	< 0.001
BMI (kg/m²)	-0.035	-0.252	< 0.001
TG (mmol/L)	-0.032	-0.163	< 0.001
HDL-C (mmol/L)	0.214	0.119	0.011
LDL-C (mmol/L)	-0.049	-0.096	0.030
BMI: body mass index; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; PIR: point in range; TG: triglyceride.

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Table 4. Multiple linear regression models for log HOMA-IR

Variables	β	standardized regression coefficient	P-value
PIR (%)	-0.146	-0.269	< 0.001
Male (%)	0.107	0.089	0.039
BMI (kg/m²)	0.036	0.215	< 0.001
TC (mmol/L)	0.079	0.169	< 0.001
HDL-C (mmol/L)	-0.357	-0.167	< 0.001
BMI: body mass index; HDL-C: high-density lipoprotein cholesterol; TC: total cholesterol; HOMA-IR: HOMA of insulin resistance; PIR: point in range.

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