KEY FIGURE

Highlights

• β-Cell insulin secretory capacity is central to T2DM onset, progression, and remission.

• Methods range from hyperglycemic clamps to fasting indices and mathematical models.

• CGM and AI-driven approaches enable non-invasive, real-world β-cell function assessment.

• β-Cell reserve predicts diabetes remission across dietary, pharmacologic, and surgical interventions.

• Precision medicine requires β-cell assessment beyond glycemic thresholds alone.

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a growing global health problem, with prevalence exceeding 589 million adults worldwide and continuing to rise across all age groups and ethnicities [1,2]. Central to its pathophysiology is the progressive failure of pancreatic β-cells to maintain adequate insulin secretion in response to metabolic demand—a process that begins well before overt hyperglycemia becomes clinically apparent [3,4].

Pancreatic β-cell dysfunction is the central determinant in both the onset and progression of T2DM [5,6]. In response to rising blood glucose, β-cells mount a characteristic biphasic insulin secretion response: an initial rapid first-phase release occurring within the first 10 minutes, driven by calcium-triggered exocytosis of a readily releasable pool of insulin granules, followed by a sustained second-phase secretion maintained over the subsequent 60 to120 minutes through mobilization of reserve granule pools and amplification of mitochondrial signals. This biphasic pattern is tightly coupled to intracellular glucose metabolism—glucose entry via glucose transporter (GLUT) transporters, glucokinase-mediated phosphorylation, glycolytic and mitochondrial adenosine triphosphate (ATP) production, closure of ATP-sensitive K+ channels, and membrane depolarization—collectively constituting the triggering pathway, which is further enhanced by the amplifying pathway involving metabolic coupling factors such as nicotinamide adenine dinucleotide phosphate (NADPH), glutamate, and malonyl-coenzyme A (CoA) [7]. Loss of first-phase insulin secretion is an early and sensitive indicator of β-cell deterioration, detectable in prediabetes and predictive of progression to overt T2DM. Subsequent attrition of second-phase secretion further compromises postprandial glucose regulation as disease advances. While insulin resistance contributes to disease heterogeneity, it is the failure of β-cells to compensate adequately for increased metabolic demand that drives the transition from normoglycemia to hyperglycemia [7,8]. These observations highlight the importance of accurately measuring β-cell insulin secretory capacity (ISC) in both research and clinical settings.

The etiology of β-cell dysfunction is markedly heterogeneous, reflecting complex interactions between genetic predisposition, ethnic background, and environmental exposures [9]. Monogenic forms of diabetes, particularly maturity-onset diabetes of the young (MODY), demonstrate how specific gene mutations can produce unique patterns of insulin secretory defects, sometimes permitting targeted therapeutic interventions such as sulfonylurea sensitivity in hepatocyte nuclear factor-1A (HNF1A) and HNF4A mutations. Ethnic and genetic variations exert substantial influence on β-cell function and diabetes susceptibility. Individuals of Black African ancestry exhibit lower basal insulin secretion compared to White Europeans, independent of insulin sensitivity and pancreatic fat content [10], yet paradoxically demonstrate unique adaptive patterns in secretory response. Similarly, East Asian and South Asian populations tend to have reduced β-cell reserve, contributing to earlier disease onset and more severe progression of T2DM [11,12]. Environmental factors—including obesity-induced glucolipotoxicity, chronic hyperglycemia, sedentary lifestyle, poor dietary patterns, and exposure to environmental pollutants—interact synergistically with genetic predispositions to accelerate β-cell stress and dysfunction [13], producing diverse clinical and molecular phenotypes that are particularly pronounced in younger populations .

Recent precision medicine approaches have challenged the traditional oversimplified dichotomy of diabetes classification. The landmark data-driven cluster analysis by Ahlqvist et al. [14] redefined diabetes into five distinct subgroups based on β-cell function, insulin resistance, and autoimmunity. These novel classifications include severe insulin-deficient diabetes (SIDD) and severe insulin-resistant diabetes (SIRD), which can be distinguished using homeostatic model assessment (HOMA)-derived indices combined with clinical features. Critically, these subgroups exhibit markedly different trajectories and risks for both microvascular complications (retinopathy, neuropathy) and macrovascular complications (nephropathy, cardiovascular disease), reinforcing the need for accurate β-cell function assessment to optimize diagnosis, prognosis, and individualized management strategies.

Despite this clear clinical need, accurate assessment of ISC remains technically challenging, and translating sophisticated laboratory findings into practical clinical utility presents ongoing difficulties. Gold-standard methods such as the hyperglycemic clamp provide direct and dynamic measurements of insulin secretion with excellent precision, but these procedures are resource-intensive, require specialized expertise, and remain impractical for routine clinical use or large-scale epidemiological studies. Surrogate indices derived from oral glucose tolerance tests (OGTT) [15-18], mixed-meal tolerance tests (MMT) [15,18], and fasting samples—including homeostatic model assessment of β-cell function (HOMA-β) [19,20], insulinogenic index (IGI) [21], and C-peptide/glucose ratio (CGR)—offer feasible and practical alternatives [22-24]. However, these measures have recognized limitations in sensitivity and specificity, and ongoing efforts toward standardization and validation across diverse populations are essential.

Given the complexity of diabetes pathophysiology and the key role of β-cell dysfunction, a comprehensive approach to assessing β-cell ISC is crucial for progress in the field. This review summarizes current methodologies—including in vitro and ex vivo models, animal studies, and clinical tools—and evaluates their relevance to translational research and clinical practice. By connecting laboratory insights with patient care, we aim to support precision medicine strategies that incorporate β-cell functional assessment in diagnosis, risk evaluation, and treatment planning (Fig. 1).

MECHANISMS OF β-CELL INSULIN SECRETION

The primary function of β-cells is to secrete insulin in response to elevated blood glucose levels. There are two major pathways at play in this process: the ‘triggering’ pathway and the ‘amplifying’ pathway [25,26]. The triggering pathway includes the ‘first phase’ of insulin secretion and decline that occurs in the first 10 to 20 minutes after glucose stimulation [27,28]. The amplifying pathway includes the ‘second phase’ response and allows insulin secretion to continue at a lower sustained rate for several hours. The first phase results in approximately 1% of insulin release from primed granules, whereas the second phase results in the release of both the primed granules and granules that are located in an internal storage pool [27]. In both ways, ATP synthesis from glycolysis or/and mitochondria provide the energy to power the membrane depolarization, insulin granule docking and exocytosis [29].

GLUCOSE METABOLISM AND TRIGGERING OF GLUCOSE STIMULATED INSULIN SECRETION

Pancreatic β-cells are installed with glucose sensing machinery to maintain blood glucose levels as a euglycemic range. Glucose enters β-cells by facilitated transporter mediated by GLUT1 and GLUT3 in humans, or GLUT2 in rodent [25,30,31]. Glucose into the β-cell and glucokinase phosphorylate the glucose to glucose-6-phosphate [32]. After phosphorylation of the glucose, glucose is getting into the glycolysis which include pentose phosphate pathway [33]. Eventually, glycolysis produces pyruvate [34]. In the pancreatic β-cells, pyruvate is a primary source for mitochondrial tricarboxylic acid (TCA) cycle. The pyruvate entering cycle splits between its conversion to acetyl-CoA by pyruvate dehydrogenase (PDH) and its conversion to oxaloacetate by pyruvate carboxylase (PC) [35]. The conversion of pyruvate to lactate is limited in β-cells according to the low expression levels of lactate dehydrogenase A (LDHA) which classified as a one of ‘disallowed genes’ [36,37]. As the results, ATP production contributes to K_ATP-channel closure to membrane depolarization and the consequent activation of voltage-gated Ca²⁺ channels [25,38].

ASSESSMENT OF INSULIN SECRETION IN EXPERIMENTAL MODELS

Experimental model systems

Insulinoma cell lines

Over the decades, various rodent insulinoma cell lines have been developed through the introduction of oncoprotein transgenes into mouse β-cells [39-43] or through X-ray irradiation in rats [44,45]. Among these, cell lines expressing the SV40 large T antigen (Tag) have received considerable attention. Understanding how glucose stimulated insulin secretion (GSIS) is regulated in pancreatic β-cells is crucial for advancing diabetes research and treatment. While the MIN6 pancreatic β-cell line is capable of GSIS, this function progressively diminishes during extended culture [26]. Some of these cell lines were capable of producing substantial levels of insulin—up to one-third of that produced by normal β-cells—and secreted it in response to glucose stimulation. However, their glucose sensitivity differed widely: while a few demonstrated a normal response within physiological glucose levels [26,46], the majority of transformed β-cell lines exhibited an exaggerated sensitivity to glucose [47]. In cell lines that initially exhibited a normal glucose response, extended passage in tissue culture often led to a shift toward glucose hypersensitivity, which was linked to elevated hexokinase activity. This represents a significant limitation for functional studies using insulinoma cell lines (Fig. 1).

To address the translational limitations of rodent insulinoma cell lines, human pancreatic β-cell lines have been developed as physiologically relevant models. EndoC-βH1, the first genetically engineered human β-cell line, was produced by targeted oncogenesis of human fetal pancreatic buds using SV40 large T antigen under the insulin promoter and exhibited glucose-inducible insulin secretion, as well as restoration of normoglycemia following transplantation into chemically diabetic mice [48]. A conditionally immortalized derivative, EndoC-βH2, incorporated an excisable lentiviral oncogenic cassette; Cre-mediated transgene excision arrested proliferation and markedly increased insulin content and stimulated secretory capacity, providing a closer functional approximation to primary β-cells [49]. The EndoC-βH3 variant further refined this system with a tamoxifen-inducible CRE-ERT2 mechanism, enabling large-scale cellular expansion followed by on-demand transgene removal [50]. The EndoC-βH cell family is now employed worldwide and serves as an important complement to rodent insulinoma lines and primary human islets for functional assessment of β-cell ISC (Fig. 1).

Primary islet isolation and culture

Growing interest in islet replacement therapies for humans has driven progress in techniques for isolating islets from human donors, alongside the development of various animal research models [51,52]. Although numerous isolation protocols exist for rodents, few provide sufficient procedural detail for reliable replication. The primary goal of islet isolation—whether for transplantation or in vitro experimentation—is to obtain viable, purified islets with preserved functional responses [53]. Standardized isolation protocols typically include intraductal perfusion with collagenase (Liberase TL or collagenase P, 1.0–1.5 mg/mL), mechanical disruption at 37°C for 8 to 12 minutes, and purification by discontinuous Ficoll density gradient centrifugation (1.096/1.069 g/mL). Isolated islets are subsequently cultured overnight in Roswell Park Memorial Institute (RPMI) 1640 containing 11.1 mM glucose supplemented with 10% fetal bovine serum at 37°C in 5% CO₂ prior to functional assessment [53].

Functional assessment methods

Static glucose-stimulated insulin secretion assay

To assess GSIS, islets are initially cultured in a low glucose concentration—typically around 3 mM—to evaluate insulin release under basal or unstimulated conditions. To measure stimulated secretion, islets are then exposed to elevated glucose levels, such as 11.1 mM (eliciting a half-maximal response) or over 28 mM (maximal stimulation) (Fig. 1). The insulin secretion response to glucose follows a biphasic pattern: an initial rapid surge (first phase) is followed by a sustained, lower-level release (second phase) that persists throughout the glucose exposure. GSIS can be evaluated under static incubation conditions or through perfusion systems that allow for real-time monitoring of insulin secretion dynamics [53,54].

Dynamic islet perifusion

Microfluidic perifusion systems have been developed as advanced platforms for real-time monitoring of islet insulin secretion dynamics in vitro [55-57], enabling continuous perfusion with defined glucose concentrations and time-resolved sampling for biochemical or imaging-based analysis [58-61]. Commercially available islet perifusion systems have also been used in various GSIS studies. However, these platforms often face limitations, such as low temporal resolution or the need to pool multiple islets to obtain measurable insulin concentrations [62,63]. Highly integrated microfluidic devices capable of high-resolution quantification are technically demanding and inaccessible to non-specialist users, whereas simpler platform lack the design features necessary to resolve rapid secretory dynamics with adequate sensitivity (Fig. 1) [61]. Addressing this trade-off remains a central challenge in the development of next-generation islet perifusion platforms.

Mitochondrial bioenergetics

(1) Islet oxygen consumption rate

Oxygen consumption rate (OCR) is widely used as a surrogate marker of mitochondrial respiratory activity in pancreatic islets and reflects the capacity for oxidative phosphorylation [64]. In β-cells, mitochondrial glucose metabolism plays a central role in coupling nutrient sensing to insulin secretion through ATP generation [65,66]. Accordingly, OCR has been extensively applied to assess mitochondrial function, metabolic flexibility, and bioenergetic efficiency of islets under physiological and diabetic conditions (Fig. 1) [67]. Alterations in OCR are frequently observed in states of β-cell stress and dysfunction, highlighting impaired mitochondrial metabolism as a key contributor to diabetes pathogenesis [68,69]. From an ISC perspective, OCR-derived indices—including basal respiration, ATP-linked respiration, and spare respiratory capacity—serve as functional correlates of secretory competence, given that the magnitude of ATP production through oxidative phosphorylation directly governs the metabolic coupling of glucose to insulin release.

(2) Extracellular acidification rate

Extracellular acidification rate (ECAR) reflects the rate of proton release into the extracellular milieu, predominantly driven by lactate production during glycolysis. As such, ECAR is commonly used as an indirect measure of glycolytic activity in pancreatic islets (Fig. 1) [70]. Changes in ECAR provide insight into shifts in cellular energy metabolism, particularly the balance between glycolytic and mitochondrial pathways [71]. In the context of diabetes, elevated ECAR often accompanies reduced mitochondrial respiration, suggesting a compensatory or maladaptive increase in glycolytic flux in response to mitochondrial dysfunction and metabolic stress in β-cells.

(3) ¹³C-glucose metabolic flux

Stable isotope tracing with ¹³C-labeled glucose has become a widely used approach to examine glucose metabolism in pancreatic β-cells [70]. By tracking the incorporation of glucose-derived carbon into downstream metabolites, this method allows investigators to evaluate not only glycolytic activity but also mitochondrial metabolism, including flux through the TCA cycle and associated anaplerotic pathways [72]. Unlike static metabolite measurements, ¹³C-based flux analysis provides dynamic information on pathway utilization, capturing real-time shifts in metabolic routing. Following glucose uptake and glycolysis, pyruvate is metabolized through both PDH-mediated oxidation and PC-dependent anaplerosis; the latter contributes to the generation of metabolic coupling factors such as malate, citrate, and NADPH, which amplify insulin secretion beyond ATP-dependent signaling (Fig. 1) [70,71]. Alterations in ¹³C-glucose metabolic flux have been consistently reported in models of β-cell dysfunction and diabetes [72,73]. Reduced incorporation of glucose-derived carbon into TCA cycle intermediates, together with increased labeling of glycolytic or lactate-associated metabolites, reflects a shift away from mitochondrial oxidative metabolism and is consistent with impaired metabolic flexibility in failing β-cells [73]. From an ISC assessment perspective, ¹³C-glucose metabolic flux analysis can quantify the relative contributions of mitochondrial oxidative and anaplerotic pathways to fuel-coupled insulin release, thereby providing a mechanistic basis for impaired secretory responses that complements functional assays such as GSIS and perifusion.

ASSESSING β-CELL FUNCTION IN CLINICAL SETTINGS

Hyperglycemic clamp

A variable glucose infusion is acutely applied to increase blood glucose concentration to reach a targeted steady-state level [74]. The infusion rate is adjusted to reach the target. Blood samples are collected frequently during the clamp to measure insulin (or C-peptide) and glucose concentration for 2 hours. The acute increase in glucose leads to a sharp increase in insulin secretion (the first 10 minutes) and followed by a steady increase (afterward). The sharp secretion is called by the first-phase insulin secretion and includes readily released pools of insulin granules triggered by calcium influx. The latter is termed with the second-phase insulin secretion and is stimulated by insulin granule mobilization from reserved pools to docked pools. The first and second-phase insulin secretion are calculated by the area under the insulin curve for the first 10 and 60–120 minutes, respectively. The steady increase represents potentiation of glucose that stimulates insulin secretion. First-phase secretion is significantly decreased in prediabetes of impaired fasting glucose and impaired glucose tolerance (IGT) compared to normal glucose tolerance (NGT), and blunted with T2DM patients [75], assessed in a cross-sectional data. A longitudinal data set confirmed that diminished first-phase secretion is high risk for T2DM [76]. Thus, impaired first-phase secretion could be considered as an early sign of T2DM progression [77]. Second-phase secretion is also decreased in T2DM and further deteriorates as the disease progresses.

First- and second-phase secretion of hyperglycemic clamp are direct measurements of β-cell function, representing the capacity of β-cells to respond to glucose stimulation. Thus, the method is regarded as the gold standard for assessing β-cell function. However, the hyperglycemic clamp does not reflect physiological conditions due to supraphysiological glucose levels. Additionally, the procedure is resource-intensive, requires skilled personnel to adjust variable infusion rates, and the 2-hour duration can be burdensome for participants.

Intravenous glucose tolerance test

A bolus of glucose is intravenously injected over 1 to 2 minutes with glucose load (0.3 g/kg of body weight) [78]. Blood samples are collected every 2 minutes during the first 10 minutes and sparsely later time up to 2 hours to measure plasma glucose and insulin concentration. Acute insulin response to glucose is defined as the insulin incremental area under the curve (AUC) during the first 10 minutes, which represents first-phase insulin secretion. Intravenous glucose tolerance test (IVGTT) bypasses gastrointestinal absorption, allowing for more controlled and precise analysis. However, it is unable to assess the incretin effect of glucose load. Correlation coefficients between IVGTT-derived first-phase insulin response and hyperglycemic clamp measures generally range from r=0.75 to 0.88 [79]. Although less commonly used in routine clinical practice due to its invasive nature and procedural complexity, IVGTT remains valuable in research settings.

Mixed-meal tolerance test and model-derived indices

The MMT is designed to assess β-cell function in response to a standardized meal containing carbohydrates, proteins, and fats. Unlike all other challenge tests, the MMT closely mimics real-life nutrient intake and triggers the release of gastrointestinal hormones such as glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). These incretin hormones significantly enhance insulin secretion in a glucose-dependent manner. Thus, the MMT provides a physiologically relevant assessment of postprandial glucose metabolism, reflecting conditions encountered in daily life. After overnight fasting, upon ingestion of a mixed meal, blood sampling at t=0 minute and postprandial samplings are drawn at 15–30-minute interval and for up to 2–4 hours. Glucose, insulin, C-peptide, and incretins (GLP-1, GIP) are measured.

Classical estimates of β-cell function with MMT are IGI and the ration of the area under insulin curve to the area of glucose curve. Mathematical model-based estimates of β-cell function during MMT have been used by the C-peptide minimal model [15,18]. The two models utilize C-peptide concentration data to calculate insulin secretion rate (ISR) and use ISR to estimate dynamic (first phase) and static (second phase) secretion. Although the C-peptide minimal model does not reliably capture first-phase secretion, it shows moderate to strong correlation with second-phase insulin response (r=0.6 to 0.7). The MMT is more physiological than IVGTT or OGTT, which stimulates incretin pathways, giving a fuller picture of postprandial metabolism. Standardization across studies is difficult due to variable meal compositions. The test is labor-intensive, time-consuming, and requires modeling expertise with specialized software for parameter estimation, limiting its application to research settings rather than routine clinical practice.

Frequently sampled oral glucose tolerance test

A frequently sampled oral glucose tolerance test (FSOGTT) is an enhanced version of the standard OGTT, designed to capture detailed temporal profiles of glucose, insulin, and C-peptide responses following oral glucose ingestion. Unlike the standard OGTT, which typically includes measurements at t=0, 30, 60, 90, and 120 minutes, the FSOGTT involves measurements at shorter intervals (e.g., every 10–30 minutes) over a 2- to 5-hour period. Glucose, insulin, and C-peptide are measured. FSOGTT data are also analyzed using the C-peptide oral minimal model, an extension of the C-peptide minimal model for MMT [15] and the model by Mari et al. [18]. The two models show moderate to strong correlation with second-phase insulin response (r=0.6 to 0.8). The FSOGTT is less invasive and more physiologic alternative to the IVGTT, capturing both gastrointestinal and incretin-mediated effects on β-cell function. Compared to IVGTT, the FSOGTT better reflects real-world postprandial physiology but introduces greater variability due to gut absorption and incretin effects. Accurate modeling often requires individualized glucose absorption estimates or tracer use. Despite these challenges, when standardized and carefully implemented, the FSOGTT provides robust, clinically relevant indices of glucose homeostasis. Similar to the MMT, the procedural demands and need for modeling expertise limit its use primarily to research applications.

Standard OGTT

Under clinical settings and practices, glucose and insulin measurements are collected sparsely than the FSOGTT, where blood samplings are drawn at five or less time points up to 2 hours during an OGTT. It is termed by ‘The standard OGTT.’ The conventional OGTT-derived β-cell function are IGI calculated as the ratio of insulin change to glucose change from t=0 to 30 minutes, and the ratio of the area under insulin curve to the area of glucose curve during the standard OGTT. IGI showed moderate to strong correlation with both first-phase insulin secretion and steady-state secretion measured by hyperglycemic [80]. However, OGTT-derived surrogates use single or average of measurements of glucose and insulin which may not fully utilize dynamics of insulin secretion. A recently developed mathematical model-derived algorithm gives an estimation of β-cell function with a standard OGTT and even limited data sets (e.g., at t=0, 60, and 120) by fitting the mathematical model of glucose homeostasis to glucose and insulin measurements [16]. Importantly, the algorithm of the fitting has been extended to estimate relative β-cell function without insulin [17]. The relative β-cell function also has potential to be implemented to estimate a relative β-cell function during continuous glucose monitoring (CGM) because a CGM device does not provide insulin measurement. The model-derived β-cell function’s showed good correlation with hyperglycemic clamp. The mathematical model-derived β-cell function and relative β-cell function without insulin are cost-effective and has potential to be implemented in large scaled clinical studies as well as clinical practices [81,82].

Fasting-based surrogates indices

The hyperglycemic clamp and other glucose challenge tests are rarely feasible in routine clinical practice. As a result, fasting-based measurements are often the only practical option. A widely used surrogate for β-cell function in clinical research is the HOMA-β, which is calculated using fasting glucose and insulin levels: HOMA-β=[360×insulin (μU/mL)]/[glucose (mg/ dL)–63]. While HOMA-β does not reflect the dynamic β-cell response to postprandial glucose, it has shown moderate correlations with β-cell function estimates derived from hyperglycemic clamps (r=0.4–0.7) between HOMA-β and first-phase insulin secretion, depending on the population studied [19,20].

C-peptide-based surrogates indices

A substantial proportion of secreted insulin undergoes first-pass hepatic extraction, whereas C-peptide is not significantly cleared by the liver. Consequently, circulating C-peptide concentrations more accurately reflect β-cell secretory capacity. Accumulating evidence also supports the use of C-peptide as a surrogate marker of functional β-cell mass [24,83]. Beyond C-peptide–based minimal modeling approaches [15,18], several C-peptide–derived surrogate indices of β-cell function have been widely adopted. These include the fasting CGR (fasting C-peptide/fasting glucose), the postprandial CGR (e.g., 2-hour C-peptide/2-hour glucose), the acute C-peptide response (mean C-peptide concentration at 3–10 minutes minus basal C-peptide during IVGTT), and the C-peptide–derived IGI [22-24]. The C-peptide–derived IGI is strongly correlated with the insulin-derived IGI. However, when validated against gold-standard assessments such as the hyperglycemic clamp, the C-peptide–derived IGI does not outperform the insulin-derived IGI [80].

TECHNOLOGICAL ADVANCES IN β-CELL FUNCTION ASSESSMENT

Continuous glucose monitoring

CGM-derived metrics—including glycemic variability, postprandial glucose excursions, and time in range (TIR)—have emerged as indirect indices of β-cell ISC. Elevated glycemic variability reflects inadequate or poorly coordinated insulin secretion, while exaggerated postprandial excursions indicate insufficient early secretory response to nutrient stimulation. These metrics allow partial estimation of β-cell function without direct measurement of insulin or C-peptide, making CGM particularly suited for large-scale, non-invasive studies in real-world and outpatient settings. However, interpretation as an ISC surrogate is limited by confounding from variable meal composition, physical activity, and psychological stress, and requires standardized conditions to enable meaningful comparisons across individuals. Overall, CGM represents a promising and increasingly accessible tool for evaluating β-cell function outside traditional laboratory settings—offering new opportunities for early detection, monitoring, and personalized intervention in metabolic disorders.

Artificial intelligence and machine learning approaches

Ahlqvist et al. [14] reclassified newly diagnosed diabetes patients into five distinct subgroups using six clinical variables, glutamic acid decarboxylase (GAD) antibodies, age at diagnosis, body mass index (BMI), glycosylated hemoglobin (HbA1c), and HOMA2 estimates of β-cell function and insulin resistance, establishing a foundation for data-driven diabetes subclassification as detailed in the Introduction. Building on this framework, recent machine learning models apply CGM-derived features (e.g., peak glucose, slope of rise) to predict β-cell function [84]. This study shows that individuals with prediabetes have distinct metabolic sub phenotypes—such as muscle or liver insulin resistance, β-cell dysfunction, and impaired incretin action—which can be predicted using CGM during athome OGTTs. Machine learning models trained on glucose curve shapes accurately identified these subtypes, with AUCs up to 95%. This approach offers a non-invasive method for early risk stratification and personalized intervention in glucose dysregulation. A summary of methods applicable to clinical practices is presented in Table 1 [16,17,19,80,85].

β-CELL FUNCTION ASSESSMENT FOR PRECISION MEDICINE

Diagnostic applications in diabetes classification

Glycemic thresholds alone are insufficient to delineate the complex pathophysiological heterogeneity and mechanistic subtypes of diabetes. To better understand the underlying pathophysiological mechanisms and guide precision therapeutic strategies and prognostic evaluation, accurate assessment of pancreatic β-cell function is essential. Conventional diagnostic frameworks often fail to capture the functional diversity inherent in T2DM. The integration of ISC markers—including C-peptide levels, CGR, IGI, and disposition index—enhances diagnostic resolution and enables differentiation of biologically distinct disease phenotypes [86,87].

Dynamic secretion indices support differentiation among type 1 diabetes mellitus, T2DM, latent autoimmune diabetes in adults, and monogenic forms like MODY. Data-driven subclassification approaches—such as those identifying β-cell dysfunction (SIDD) or insulin resistance (SIRD)—offer a more personalized framework for care, informing tailored therapy and complication screening. The Diagnostic Optimization and Treatment of Diabetes and its Complications in the Chernihiv Region, Ukraine (DOLCE) study validated these mechanistic subtypes in a large cohort, confirming that SIDD and severe autoimmune diabetes (SAID) groups were associated with higher risks of retinopathy and nephropathy, reinforcing the clinical relevance of β-cell–based classification [87,88]. These mechanistic categories can inform tailored treatment choices, monitoring strategies, and complication screening, potentially improving both metabolic control and long-term outcomes.

Prediction and prevention of diabetes progression

Understanding pancreatic β-cell dynamics is increasingly recognized as critical for predicting diabetes progression. The transition from normoglycemia to overt T2DM typically follows a two-step process, beginning with insulin resistance and culminating in a progressive decline in β-cell function [89]. Impaired first-phase insulin secretion [77], detectable through dynamic testing such as the OGTT, represents one of the strongest predictors of progression from normoglycemia to overt T2DM.

Large-scale intervention trials have established that β-cell function preservation is the cornerstone of successful diabetes prevention. The Diabetes Prevention Program (DPP) and its follow-up study the Diabetes Prevention Program Outcomes Study (DPPOS) demonstrated that among 3,234 adults with prediabetes, individuals who achieved regression to normal glucose regulation exhibited a 56% reduction in subsequent diabetes incidence (hazard ratio [HR], 0.44; 95% confidence interval [CI], 0.37 to 0.55; P<0.0001) [90]. Critically, higher baseline β-cell function—measured by corrected insulin response and disposition index—was the strongest predictor of both regression achievement and sustained diabetes protection, with investigators concluding that ‘preservation of β-cell function is more closely related than insulin sensitivity to the long-term prevention of diabetes’ [90]. The Restoring Insulin Secretion (RISE) consortium further substantiated this principle, demonstrating that progressive β-cell decline plays a central role in progression from normal to abnormal glucose tolerance [91]. Notably, individuals with IGT had already lost 70% to 80% of β-cell function at diagnosis, with even modest further declines resulting in marked hyperglycemia [89].

Traditional diagnostic criteria relying on fasting or 2-hour post-load glucose thresholds often fail to capture early β-cell deterioration. The 1-hour post-load glucose (1h-PG) measurement has emerged as a sensitive and practical surrogate marker of ISC. Studies across diverse populations—including African [92], African American [93], Native American [94], and East Asian cohorts [95-98]—have consistently demonstrated superior predictive value of 1h-PG for identifying individuals at heightened diabetes risk. In the San Antonio Heart Study, 1h-PG was the strongest predictor of progression from NGT to prediabetes over 7.5 years, with individuals exhibiting 1h-PG between 120 and 155 mg/dL showing significant metabolic abnormalities and early β-cell dysfunction despite not meeting conventional prediabetes criteria [99]. In the Africans in America study, a 1h-PG threshold of 183 mg/dL demonstrated superior sensitivity for detecting diabetes compared to the 2-hour threshold, particularly in non-obese individuals where β-cell failure predominated [92]. Longitudinal analyses from the Korean Genome and Epidemiology Study (KoGES) cohort reinforced these findings, demonstrating that elevated 1h-PG (≥144–155 mg/dL) independently predicted diabetes development and marked impairment in β-cell compensatory capacity [96,98].

Contemporary approaches to assessing β-cell function extend beyond conventional indices. While validated measures derived from OGTT and MMT—such as corrected insulin response, IGI, and disposition index—remain fundamental for quantifying ISC, emerging technologies may enhance prediction. CGM metrics, including TIR and glycemic variability, provide dynamic insights into β-cell reserve and metabolic stability [100]. Integration of machine learning algorithms with CGM data shows promise for identifying subtle patterns of β-cell deterioration [84], and assessment of incretin hormone secretory patterns (GIP/GLP-1 ratios) may further refine early detection strategies.

These findings indicate that β-cell dysfunction precedes overt hyperglycemia and represents a modifiable therapeutic target. Incorporating 1h-PG measurement into routine clinical assessment, combined with validated indices of β-cell function, enables earlier identification of high-risk individuals and supports timely intervention before irreversible β-cell loss occurs. This approach facilitates precise risk stratification and allows mechanism-based interventions—including lifestyle modification and targeted pharmacotherapy—that may preserve β-cell function, delay diabetes onset, and improve long-term metabolic outcomes.

Therapeutic monitoring and treatment selection

Comprehensive assessment of β-cell secretory capacity is essential for guiding both initial therapeutic choices and ongoing treatment adjustments in T2DM management. The Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness (GRADE) study demonstrated that commonly used glucose-lowering agents—glargine, glimepiride, liraglutide, and sitagliptin—exert differential effects on distinct components of β-cell function, including ISR, glucose sensitivity, rate sensitivity, and potentiation [101]. Liraglutide showed the greatest enhancement in ISR and glucose sensitivity, while sitagliptin primarily improved early insulin response. However, all β-cell function parameters declined over time across treatment groups, reflecting the progressive nature of β-cell dysfunction [101].

Understanding individual ISC is critical for personalizing pharmacotherapy. Patients with marked insulin deficiency and low ISC may benefit from early initiation of exogenous insulin or β-cell–protective agents such as GLP-1 receptor agonists. Conversely, individuals with preserved insulin secretion but predominant insulin resistance may respond better to insulin sensitizers like metformin or thiazolidinediones, or to incretin-based therapies including dipeptidyl peptidase-4 inhibitors [102]. In clinical practice, even when patients initially present with hyperglycemia and impaired insulin secretion, intensive glycemic control can lead to reversible recovery of β-cell function [103]. This is largely due to the alleviation of glucotoxicity, which impairs β-cell responsiveness. Studies have shown that early and aggressive treatment can restore first-phase insulin secretion and improve overall β-cell performance, supporting the value of timely intervention not only for glycemic control but also for preserving endogenous insulin production capacity.

As novel disease-modifying and β-cell regenerative therapies continue to emerge, incorporating dynamic functional indices such as mixed-meal C-peptide responses and model-based β-cell parameters may become increasingly relevant for assessing therapeutic efficacy and guiding treatment intensification or de-escalation [104]. However, broader acceptance of these surrogate endpoints remains a challenge, necessitating further validation in clinical trials.

β-Cell reserve as a determinant of durable diabetes remission

Emerging evidence from dietary, surgical, and pharmacologic interventions has supported the concept that T2DM as a potentially reversible condition, with accurate β-cell function assessment emerging as critical for predicting and sustaining remission (Table 2) [105-115].

The Diabetes Remission Clinical Trial (DiRECT) demonstrated that weight loss-induced remission is driven by recovery of first-phase insulin response, which increased significantly from baseline 0.04 (–0.05 to 0.32) to 0.11 (0.0005 to 0.51) nmol/min/m² at 5 months (P<0.0001) in remission responders, independent of weight loss magnitude [106,116]. Maximal insulin secretion capacity recovered progressively to near-normal levels (baseline 0.62 to 12 months 0.94 nmol/min/m², comparable to controls at 1.016 nmol/min/m²), with the prolonged trajectory suggesting reversal of β-cell de-differentiation rather than simple metabolic normalization. Shorter diabetes duration emerged as the only significant baseline predictor of remission success (2.7±0.3 years vs. 3.8±0.4 years, P=0.02) [116].

In contrast, the Prediabetes Lifestyle Intervention Study (PLIS) challenges conventional weight-centric prevention paradigms by demonstrating that remission can be achieved independently of weight loss [117,118]. Among 234 participants (21.2% of 1,105 total) who did not lose weight or gained weight during 12-month intervention, 51 (21.8%) achieved remission to normal glucose regulation [118]. Despite nearly identical body weight trajectories between responders and non-responders (BMI change 1.0 kg/m² vs. 0.8 kg/m², P=0.24), responders exhibited marked improvements in ISC. Specifically, the C-peptide-to-glucose AUC ratio during the first 30 minutes of OGTT increased from 159.5±17.13 to 169.96±19.3 pmol/mmol in responders while remaining unchanged in non-responders (P(group×time)=0.043), and the adaptation index similarly increased only in responders (P(group×time)=0.025) [118]. Enhanced β-cell GLP-1 sensitivity and improved glucagon suppression further characterized the responder phenotype [118].

Importantly, these insulin secretory improvements occurred alongside enhanced whole-body insulin sensitivity (oral glucose insulin sensitivity index increased from 336.45±15.41 to 358.23±18.88 mL/min/m² in responders, P(group×time)=0.0035), creating simultaneous gains in both β-cell function and insulin action—a pattern not consistently observed in weight loss-induced remission [90,118]. This translated into substantial long-term protection: 71% relative risk (RR) reduction for incident T2DM over 10 years (RR, 0.29; 95% CI, 0.09 to 0.91; P=0.02), equivalent to that conferred by weight loss remission [118]. External validation in the U.S. Diabetes Prevention Program cohort confirmed similar results (73% risk reduction: RR, 0.27; 95% CI, 0.11 to 0.66; P=0.0001) [90,118]. Collectively, these findings establish that restoration of β-cell secretory capacity represents the mechanistic foundation of durable remission and long-term diabetes prevention, whether achieved through weight loss or other pathways. These findings support a shift in clinical prevention and remission strategies toward β-cell functional and glycemic endpoints rather than relying on weight-focused metrics alone [90,116-118].

Bariatric surgery studies have identified specific β-cell function indices with strong predictive value for remission. C-peptide AUC during OGTT demonstrated the highest discriminative power (AUC=0.76, P<0.001), with preoperative C-peptide levels showing dose-dependent remission rates: <3 ng/mL (55.3%), 3–6 ng/mL (82.0%), and >6 ng/mL (90.3%, P<0.001) [119,120]. Additional predictive measures included IGI, Stumvoll indices, and fasting C-peptide, all maintaining significance after adjustment for age and HbA1c [120]. Importantly, medication use did not independently predict remission when adjusted for β-cell function indices, indicating that residual secretory capacity—not treatment history—determines remission potential [120].

Pharmacologic remission trials have demonstrated that early, intensive combination therapy can induce drug-free glycemic remission in a substantial subset of patients with T2DM. Short-term regimens combining basal insulin, metformin, and incretin-based agents or SGLT2 inhibitors, often with structured lifestyle support, have reported remission rates ranging from 17% to 68% at 6 to 12 months post-therapy cessation, with outcomes correlating closely to baseline β-cell function [89,121-125].

The Canadian multicenter REMIT-iGlarLixi trial randomized patients with early T2DM to 12 weeks of intensive treatment (insulin glargine/lixisenatide, metformin, and lifestyle modification) versus standard care. At 6 months, 38% of the intervention group achieved partial or complete remission (RR, 1.92; 95% CI, 1.14 to 3.24) with 43% lower relapse risk (HR, 0.57; 95% CI, 0.40 to 0.81) [123]. Importantly, remission success was strongly associated with preserved baseline β-cell secretory capacity, underscoring insulin secretion as the primary determinant of treatment response. Parallel trials employing similar durations with dapagliflozin (RR, 1.5 at 24 weeks; HR, for relapse 0.57) or sitagliptin (RR, 2.7 at 36 weeks; HR, for relapse 0.60) showed comparable effects, with preserved β-cell function—measured by C-peptide or disposition index—independently predicting remission durability [122,126]. Addition of insulin degludec/liraglutide to metformin significantly reduced relapse (HR, 0.63; 95% CI, 0.45 to 0.88) but did not achieve significantly higher sustained remission at 52 weeks, suggesting a ceiling effect without robust β-cell responsiveness [124]. In Asian patients with recent-onset disease, intensive short-term continuous subcutaneous insulin infusion (CSII) combined with a low-carbohydrate diet produced remission rates of 68% versus 3% for conventional CSII, with improvements in HOMA-β and C-peptide AUC strongly predicting remission and glucose stability [121]. These findings consistently demonstrate that baseline β-cell function—assessed by fasting and stimulated C-peptide—represents the clearest prognostic marker for successful, durable remission across different therapeutic regimens. Effect sizes for combination induction therapies are substantially reduced in patients with lower β-cell reserve. Contemporary pharmacologic remission protocols thus emphasize not only potent glucose-lowering but early, aggressive restoration of β-cell rest and functional recovery.The Canadian multicenter REMIT-iGlarLixi trial randomized patients with early T2DM to 12 weeks of intensive treatment (insulin glargine/lixisenatide, metformin, and lifestyle modification) versus standard care. At 6 months, 38% of the intervention group achieved partial or complete remission (RR, 1.92; 95% CI, 1.14 to 3.24) with 43% lower relapse risk (HR, 0.57; 95% CI, 0.40 to 0.81) [123]. Importantly, remission success was strongly associated with preserved baseline β-cell secretory capacity, underscoring insulin secretion as the primary determinant of treatment response. Parallel trials employing similar durations with dapagliflozin (RR, 1.5 at 24 weeks; HR, for relapse 0.57) or sitagliptin (RR, 2.7 at 36 weeks; HR, for relapse 0.60) showed comparable effects, with preserved β-cell function—measured by C-peptide or disposition index—independently predicting remission durability [122,126]. Addition of insulin degludec/liraglutide to metformin significantly reduced relapse (HR, 0.63; 95% CI, 0.45 to 0.88) but did not achieve significantly higher sustained remission at 52 weeks, suggesting a ceiling effect without robust β-cell responsiveness [124]. In Asian patients with recent-onset disease, intensive short-term continuous subcutaneous insulin infusion (CSII) combined with a low-carbohydrate diet produced remission rates of 68% versus 3% for conventional CSII, with improvements in HOMA-β and C-peptide AUC strongly predicting remission and glucose stability [121]. These findings consistently demonstrate that baseline β-cell function—assessed by fasting and stimulated C-peptide—represents the clearest prognostic marker for successful, durable remission across different therapeutic regimens. Effect sizes for combination induction therapies are substantially reduced in patients with lower β-cell reserve. Contemporary pharmacologic remission protocols thus emphasize not only potent glucose-lowering but early, aggressive restoration of β-cell rest and functional recovery.

The remission paradigm establishes a practical hierarchy of β-cell function indices for clinical application. C-peptide measurements during OGTT emerge as the most robust predictive biomarker, offering methodologic stability and strong correlation with β-cell mass [119,120]. First-phase insulin response provides the most mechanistically informative measure, distinguishing remission responders with high sensitivity [116]. Disposition index integrates both insulin secretion and sensitivity, predicting sustained remission particularly when assessed post-intervention [92]. These insights establish accurate β-cell function assessment as essential for: (1) identifying optimal remission candidates based on preserved secretory capacity; (2) monitoring intervention efficacy with mechanistic precision; (3) predicting long-term remission sustainability; and (4) personalizing intervention selection—dietary, pharmacologic, or surgical—to individual β-cell phenotype. Implementation of accessible, standardized β-cell assessment protocols in routine clinical practice may enable precision, remission-oriented diabetes care tailored to underlying pathophysiology rather than glycemic thresholds alone.

CONCLUSIONS

Pancreatic β-cell dysfunction is a central feature of T2DM and strongly influences disease onset, progression, and treatment response. Evidence accumulated over the past decade indicates that impairment of ISC often begins before persistent hyperglycemia becomes clinically evident. In this review, we summarized experimental and clinical approaches for assessing β-cell function, from mechanistic laboratory assays to clinically applicable surrogate indices. Although clamp-based techniques remain the reference standard, their limited feasibility highlights the importance of OGTT-, mixed-meal–, and fasting-derived measures in routine settings. Recent intervention studies consistently show that preservation or recovery of β-cell secretory capacity is closely linked to diabetes prevention and durable remission, in some cases independent of weight loss. Emerging tools, including CGM and model-based analyses, provide new opportunities to capture β-cell dysfunction under real-world conditions. Together, these findings support a shift toward β-cell–centered evaluation as a foundation for earlier intervention and more individualized diabetes care.

NOTES

CONFLICTS OF INTEREST

Jun Sung Moon has been an editorial board member of the Diabetes & Metabolism Journal since 2016. He was not involved in the review process of this article. Otherwise, there was no conflict of interest.

FUNDING

This work was supported by dkNET Pilot Program of the National Institute of Diabetes & Digestive & Kidney Disease (Joon Ha), National Science Foundation (DMS 2401921) (Joon Ha), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. IRIS RS-2023-00219725) (Jun Sung Moon).

ACKNOWLEDGMENTS

During the preparation of this manuscript, the authors used ChatGPT5 for the purposes of language correction. The authors have reviewed and edited the output and take full responsibility for the content of this publication. All figures were created using BioRender.com.

Fig. 1.

In vitro and ex vivo functional platforms for assessing pancreatic β-cell function. Insulinoma cell lines (MIN6, INS-1, β-TC, and EndoC-β) are used for in vitro glucose-stimulated insulin secretion assays, with insulin release quantified by enzymelinked immunosorbent assay (ELISA). Ex vivo analyses are performed using isolated pancreatic islets to evaluate insulin secretion, mitochondrial respiration, and glycolytic activity through glucose stimulated insulin secretion, perifusion assays, and measurements of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), complemented by metabolic flux analysis (Created by BioRender).

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Kim Y, Ha J, Moon JS. Redefining β-Cell Function in Type 2 Diabetes Mellitus: From Comprehensive Assessment to Precision Medicine. Diabetes Metab J. 2026;50(2):235-252.

Received: Jan 10, 2026; Accepted: Feb 25, 2026

DOI: https://doi.org/10.4093/dmj.2026.0034.

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