The proinsulin-to-C-peptide (PI:C) ratio is an index applied during the early stage of pancreatic β-cell dysfunction. The aim of this study was to identify the characteristics associated with the PI:C ratio to discuss pancreatic β-cell dysfunction progression during the natural course of type 2 diabetes and its relationship with glycemic management. This multicenter, prospective observational study included 272 outpatients with type 2 diabetes. Continuous glucose monitoring was performed and fasting blood samples were collected and analyzed. We identified the clinical factors associated with the PI:C ratio by multiple regression analysis. The mean age of the cohort was 68.0 years, mean hemoglobin A1c 7.1% (54 mmol/mol), and mean body mass index 24.9 kg/m². Multiple regression analysis showed that a prolonged time above the target glucose range (>180 mg/dL) and high body mass index contributed to a high PI:C ratio. However, no associations were found between the PI:C ratio and glucose variability indices. These findings suggested that the PI:C ratio is positively associated with a prolonged hyperglycemic time in type 2 diabetes, whereas its relationship with glucose variability remains unclear.

PANCREATIC β-CELL DYSFUNCTION progresses over several decades in the natural course of type 2 diabetes [1]. During this process, pancreatic β-cell overload plays a crucial role, which consists of compensation, decompensation, and eventual failure of β-cells [2]. In the early stages of pancreatic β-cell dysfunction, chronic hyperglycemia and pancreatic β-cell overload lead to metabolic stress, including oxidative and endoplasmic reticulum stress. These impair pancreatic β-cell proinsulin conversion mechanisms, resulting in the accumulation and secretion of inadequately processed proinsulin (PI) [3-5]. Consequently, during the early stages of pancreatic β-cell dysfunction, an increase in the ratio of fasting PI to fasting C-peptide (CPR) is observed. The serum PI-to-CPR (PI:C) ratio is a non-invasive index applied during the early stage of pancreatic β-cell dysfunction. Abnormally high PI:C ratios have been identified in subjects with prediabetes and type 2 diabetes [6, 7]. However, the relationships between PI:C ratios and characteristics following diabetes onset remain to be investigated.

Pancreatic β-cell dysfunction progresses with the duration of diabetes and hyperglycemia, suggesting that glucose management after diabetes onset can modify the PI:C ratio. To consider the development of pancreatic β-cell dysfunction in the natural progression of type 2 diabetes and the associated glycemic management, we aimed to investigate the relationship between the PI:C ratio and glucose management in type 2 diabetes by continuous glucose monitoring (CGM).

In this prospective observational study, data were collected from 311 participants with varying type 2 diabetes duration, irrespective of disease stage or treatment strategies [8, 9]. After excluding certain conditions, a total of 272 participants agreed to undergo ambulatory CGM and fasting blood sampling and were included in the analysis (Supplemental Fig. 1). Data and sample collections followed ethical review board approval (Hokkaido University Hospital: 017-0147) and adhered to the Declaration of Helsinki. Participants provided written informed consent. The study was registered with the University Hospital Medical Information Network Center (UMIN000029993).

Ambulatory CGM was conducted for 14 days using a FreeStyle Libre Pro sensor (Abbott Diabetes Care, Alameda, CA, USA). The blinded CGM system ensured anonymity of the participants’ data. Data from the first and final days of wearing the CGM device were excluded because of potential inaccuracies during attachment and removal [10]. GlyCulator2 software was used to analyze the remaining CGM data [11]. The percentage of readings and time per day within the target glucose range (TIR: 70–180 mg/dL), time below the target glucose range (TBR: <70 mg/dL), and time above the target glucose range (TAR: >180 mg/dL) were calculated [12]. Glucose variability was determined using the coefficient of variation (CV): 100 × [SD of glucose]/[mean glucose].

Blood samples collected after overnight fasting were immediately analyzed for the plasma glucose, hemoglobin A1c (HbA1c), estimated glomerular filtration rate, and CPR. The remaining serum was stored at –80°C until PI measurement. PI was measured using PI ELISA kit (Mercodia, Uppsala, Sweden), with a detection limit of 0.3 pmol/L. For the three cases in which PI values were undetectable, a value of 0.3 pmol/L was substituted. The PI:C ratios were calculated as molar ratios ×100.

All the participant variables, except for the log-transformed PI:C ratio, were not normally distributed; therefore, the results are presented as median (interquartile range) for continuous variables or number (percentage) of participants for categorical variables. To determine biochemical and anthropometric characteristics of the study cohort associated with the log-transformed PI:C ratio, spearman rank-order correlation and Mann-Whitney test were applied as appropriate. For the assessment of factors influencing the PI:C ratio, multiple regression analysis was performed on the variables that demonstrated significance (p < 0.05) in the univariate analysis, while excluding any indicators suggesting multicollinearity among these variables. The dependent variable in this analysis was the PI:C ratio. All tests were two-sided and p < 0.05 was considered to be statistically significant. Data analyses were conducted using JMP Pro version 16.0.0 (SAS Institute, Inc., Cary, NC, USA).

The demographics of the study cohort are shown in Table 1. The cohort had a mean age of 68.0 years and 43% were female. The mean HbA1c level was 7.1% (54 mmol/mol) and the mean body mass index (BMI) 24.9 kg/m². The monotonic relationships between the log-transformed PI:C ratio and clinical variables are presented in Table 2. The log-transformed PI:C ratio showed significant negative correlation with age, diabetes duration, the TBR, the TIR, and the CV. In addition, the log-transformed PI:C ratio had a significant positive correlation with BMI, the level of fasting plasma glucose, HbA1c, CPR, PI, and the TAR. The log-transformed PI:C ratio in participants treated with sodium-glucose cotransporter 2 inhibitors was significantly greater than that of participants not treated with such inhibitors. There was a strong correlation between CPR and PI, with a correlation coefficient of 0.59. This result indicated the multicollinearity in PI and CPR associated with the PI:C ratio. There were also relatively strong correlations between the TAR and HbA1c, TAR and TIR, and CV and TBR, with correlation coefficients of 0.67, –0.95, and 0.55, respectively. These results suggested multicollinearity among these indices. After excluding these variables, multiple regression analysis revealed that both a high BMI and TAR were independently associated with a high log-transformed PI:C ratio (Table 3). However, the multiple regression results did not indicate a significant correlation between the CV and log-transformed PI:C ratio (Table 3).

The present study aimed to identify characteristics associated with the PI:C ratio and to investigate its association with glucose management after diabetes onset. Our findings indicated that a high log-transformed PI:C ratio is positively associated with both a prolonged hyperglycemic time and high BMI. The early stages of pancreatic β-cell dysfunction may be associated not with unstable glucose variability, rather with constant hyperglycemia.

While recognizing the limitations related to this prospective observational study design, the present findings suggested that hyperinsulinemia owing to obesity and hyperglycemia could affect the PI:C ratio. Previous studies have shown that pancreatic β-cell overload and hyperglycemia are closely related [1–3, 13]. Hyperglycemia and obesity-induced insulin resistance add to the endoplasmic reticulum stress applied to pancreatic β-cells [14, 15]. Impaired PI processing, resulting from this stress, leads to elevated PI levels in type 2 diabetes [4, 16]. Therefore, a high TAR and BMI may be associated with a high PI:C ratio, which serves as an index during the early stage of pancreatic β-cell dysfunction. These findings provide support for our results.

The participants in this study had diabetes for a relatively long period, specifically, a mean time of 14 years. To investigate whether diabetes duration influenced the relationship of interest, we also conducted a similar analysis by dividing the group based on diabetes duration, either within 3 years or longer. In participants with a short duration of type 2 diabetes, a high BMI was independently associated with a high log-transformed PI:C ratio, whereas the results did not show a significant correlation between the TAR and log-transformed PI:C ratio (Supplemental Table 1). For participants with a long duration, a high BMI and TAR were both independently associated with a high log-transformed PI:C ratio (Supplemental Table 2). These results provided further support for the present results showing that consistent hyperglycemia over many years, although not immediately noticeable after the diabetes diagnosis, was associated with the PI:C ratio.

Even though the early stage of pancreatic β-cell dysfunction and hyperglycemia are closely related, the role of glucose variability in the PI:C ratio remains unclear. Given the current results, no cross-sectional association exists between glucose variability and the early stage of pancreatic β-cell dysfunction, which is characterized by pancreatic β-cell overload. Constant hyperglycemia has detrimental effects on pancreatic β-cell mass and function, which leads to pancreatic β-cell failure and exacerbates hyperglycemia. This vicious cycle is thought to be responsible for the gradual decline in pancreatic β-cell function [17]. Our results and previous reports suggested that unstable glucose variability may be associated with impaired endogenous insulin secretion resulting from pancreatic β-cell failure, rather than with pancreatic β-cell overload [9, 18, 19]. Further basic research is needed to confirm these findings.

The current study had several limitations. First, the observational study design precludes the investigation of causality. Second, the participants were all Japanese. Japanese ethnic groups have lower insulin secretory capacities than people of Western ethnicities; therefore, it is unclear whether our findings apply to other populations [20].

In conclusion, a high PI:C ratio was associated with a prolonged hyperglycemic time and high BMI. This result suggested that pancreatic β-cell dysfunction is associated with constant hyperglycemia rather than with unstable glucose variability.

The authors would like to express special gratitude to all the participants and staff who participated in this study. We thank Carol Wilson, PhD, and Robert Blakytny, DPhil, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Young Scientists [Grant Number 22K17766].

None of the authors have any potential conflicts of interest associated with this research.

• 1   Kendall  DM,  Cuddihy  RM,  Bergenstal  RM (2009) Clinical application of incretin-based therapy: therapeutic potential, patient selection and clinical use. Am J Med 122: S37–S50.
• 2   Poitout  V,  Robertson  RP (2008) Glucolipotoxicity: fuel excess and β-cell dysfunction. Endocr Rev 29: 351–366.
• 3   Prentki  M,  Peyot  ML,  Masiello  P,  Murthy Madiraju  SR (2020) Nutrient-induced metabolic stress, adaptation, detoxification, and toxicity in the pancreatic β-cell. Diabetes 69: 279–290.
• 4   Ozawa  S,  Katsuta  H,  Suzuki  K,  Takahashi  K,  Tanaka  T, et al. (2014) Estimated proinsulin processing activity of prohormone convertase (PC) 1/3 rather than PC2 is decreased in pancreatic β-cells of type 2 diabetic patients. Endocr J 61: 607–614.
• 5   Eizirik  DL,  Miani  M,  Cardozo  AK (2013) Signalling danger: endoplasmic reticulum stress and the unfolded protein response in pancreatic islet inflammation. Diabetologia 56: 234–241.
• 6   Bergman  RN,  Finegood  DT,  Kahn  SE (2002) The evolution of beta-cell dysfunction and insulin resistance in type 2 diabetes. Eur J Clin Invest 32: 35–45.
• 7   Loopstra-Masters  RC,  Haffner  SM,  Lorenzo  C,  Wagenknecht  LE,  Hanley  AJ (2011) Proinsulin-to-C-peptide ratio versus proinsulin-to-insulin ratio in the prediction of incident diabetes: The Insulin Resistance Atherosclerosis Study (IRAS). Diabetologia 54: 3047–3054.
• 8   Nakamura  A,  Miyoshi  H,  Ukawa  S,  Nakamura  K,  Nakagawa  T, et al. (2020) Proinsulin is sensitive to reflect glucose intolerance. J Diabetes Investig 11: 75–79.
• 9   Miya  A,  Nakamura  A,  Handa  T,  Nomoto  H,  Kameda  H, et al. (2021) Impaired insulin secretion predicting unstable glycemic variability and time below range in type 2 diabetes patients regardless of glycated hemoglobin or diabetes treatment. J Diabetes Investig 12: 738–746.
• 10   Miya  A,  Nakamura  A,  Handa  T,  Nomoto  H,  Kameda  H, et al. (2021) Log-linear relationship between endogenous insulin secretion and glycemic variability in patients with type 2 diabetes on continuous glucose monitoring. Sci Rep 11: 9057.
• 11   Bailey  T,  Bode  BW,  Christiansen  MP,  Klaff  LJ,  Alva  S (2015) The performance and usability of a factory-calibrated flash glucose monitoring system. Diabetes Technol Ther 17: 787–794.
• 12   Pagacz  K,  Stawiski  K,  Szadkowska  A,  Mlynarski  W,  Fendler  W (2018) GlyCulator2: an update on a web application for calculation of glycemic variability indices. Acta Diabetol 55: 877–880.
• 13   Battelino  T,  Alexander  CM,  Amiel  SA,  Arreaza-Rubin  G,  Beck  RW, et al. (2023) Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol 11: 42–57.
• 14   Bensellam  M,  Laybutt  DR,  Jonas  JC (2012) The molecular mechanisms of pancreatic β-cell glucotoxicity: recent findings and future research directions. Mol Cell Endocrinol 364: 1–27.
• 15   Brooks-Worrell  BM,  Palmer  JP (2019) Setting the stage for islet autoimmunity in type 2 diabetes: obesity-associated chronic systemic inflammation and endoplasmicreticulum (ER) stress. Diabetes Care 42: 2338–2346.
• 16   Sharma  RB,  Landa-Galvan  HV,  Alonso  LC (2021) Living dangerously: protective and harmful ER stress responses in pancreatic b-cells. Diabetes 70: 2431–2443.
• 17   Kahn  SE,  Halban  PA (1997) Release of incompletely processed proinsulin is the cause of the disproportionate proinsulinemia of NIDDM. Diabetes 46: 1725–1732.
• 18   Nakamura  A (2022) Effects of sodium-glucose co-transporter-2 inhibitors on pancreatic β-cell mass and function. Int J Mol Sci 23: 5104.
• 19   Weir  GC,  Bonner-Weir  S (2004) Five stages of evolving beta-cell dysfunction during progression to diabetes. Diabetes 53: S16–S21.
• 20   Funakoshi  S,  Fujimoto  S,  Hamasaki  A,  Fujiwara  H,  Fujita  Y, et al. (2011) Utility of indices using C-peptide levels for indication of insulin therapy to achieve good glycemic control in Japanese patients with type 2 diabetes. J Diabetes Investig 2: 297–303.