Association Between Phase Angle and HbA1c in Non-elderly Patients with Type 2 Diabetes Mellitus: A Cross-sectional Study Using Outpatient Clinical Data

Abstract

Objective

This study investigated the association between the phase angle (PhA), a marker of cellular health and body composition quality, and hemoglobin A1c (HbA1c), an indicator of glycemic control, in non-elderly (＜65 years old) patients with type 2 diabetes mellitus (T2DM).

Methods

This cross-sectional study included 167 non-elderly T2DM patients. The participants were categorized into tertiles (low, middle, and high) based on their PhA values. A multiple logistic regression analysis was performed to examine the association between PhA tertiles and controlled HbA1c levels (defined as ＜7.0%) after adjusting for potential confounders, such as the age, sex, body mass index, waist circumference, and number of anti-diabetic medications used by the study participants.

Results

HbA1c levels were significantly lower in the middle (8.41%±2.06%) and high (8.35%±1.77%) PhA tertiles than in the low tertile (9.58%±2.86%). After adjusting for confounders, the odds ratios for achievement of controlled HbA1c in the middle and high PhA tertiles, compared with the low tertile, were 2.338 (95% confidence interval 1.006-5.432) and 2.666 (1.071-6.636), respectively. Furthermore, when analyzed as a continuous variable, PhA remained a significant and independent predictor of controlled HbA1c level.

Conclusion

PhA appears to be a useful independent indicator of glycemic control in non-elderly patients with T2DM. This non-invasive marker could contribute to the development of personalized therapeutic strategies that consider the cellular health and muscle quality.

Introduction

Type 2 diabetes mellitus (T2DM) is a major public health challenge with a persistently increasing prevalence worldwide (1). The cornerstone of disease management is prevention of associated complications, particularly cardiovascular diseases (2). Hemoglobin A1c (HbA1c), a measure that reflects average blood glucose levels over the preceding one to three months, has long been widely used as the “gold standard” for assessing glycemic control (3). Although HbA1c is a valuable marker in the monitoring of long-term glycemic fluctuations and is used to show risks for the development of complications from diabetes, it has limitations in providing a comprehensive assessment of a patient's overall health status (4). Specifically, it does not fully capture the physiological impact of hyperglycemia on cellular integrity nor does it comprehensively reflect nutritional status or body composition. Therefore, on a clinical basis, there is a strong need to explore simple, non-invasive biomarkers that offer a more multifaceted perspective on the pathophysiology of T2DM.

In recent years, the phase angle (PhA), a measure derived from a bioelectrical impedance analysis (BIA), has emerged as a promising indicator of systemic cellular health, reflecting the integrity and functional status of cell membranes (5). The PhA is a biophysical parameter that integrates the quantity and quality of cell membranes, as well as the balance of intracellular and extracellular water (5). A higher PhA suggests a more robust cellular function and good nutritional status (6), whereas a lower PhA indicates a frailer cellular state (7). Based on these properties, PhA has been demonstrated to be a useful indicator of the muscle mass and quality (8).

Previous studies have reported that the PhA is significantly lower in patients with T2DM than in healthy individuals, with some studies suggesting an inverse association between the PhA and HbA1c (9,10). However, interpretation of such findings requires caution, given that prior studies included a wide age range of participants, including the elderly, or did not sufficiently adjust for potential confounders, such as the body mass index (BMI) or anti-diabetic medications being used by the participant at the time. The PhA is known to decline with age, and the elderly tend to have an increased prevalence of comorbidities, such as sarcopenia, cardiovascular disease, and renal dysfunction. These conditions act as potent confounders that can independently affect the PhA, potentially obscuring the intrinsic association between glycemic control and cellular health in real-world outpatient clinical practice.

Given this context, we hypothesized that examining the relationship in a population in which the effects old-related physiological changes and comorbidities are minimized could more clearly elucidate the independent impact of glycemic control on cellular integrity. Therefore, this cross-sectional study investigated the association between HbA1c, an indicator of glycemic control, and the PhA, a marker of the cellular health and body composition quality, in a population of non-elderly patients with T2DM.

Materials and Methods

Study population and patient selection

A total of 390 patients with T2DM who regularly visited diabetes outpatient clinics at Kawasaki Medical School Hospital and a related medical institution between July 2021 and July 2024 underwent BIA measurements at the discretion of their attending physicians. Patients with active retinopathy, end-stage renal disease, steroid use, those who were ≥65 years old, those who had difficulty carrying out physical activity due to orthopedic and other impairments, and those deemed to be inappropriate for inclusion in the study by the attending physician were excluded from the study. The final study sample consisted of 167 participants.

A BIA was performed using an InBody 770 (InBody Japan, Tokyo, Japan). The PhA was obtained as the ratio between the raw resistance (R) and reactance (Xc) values obtained by the BIA at a frequency of 50 kHz, in accordance with the formula PhA = arc tangent (Xc/R) × (180/π). Waist circumference (WC) was measured at the umbilical level in the late expiration phase with the study participants in the standing position using centimeters as units. The BMI was calculated by dividing the body weight in kilograms by height in meters squared. The impact of the PhA on HbA1c levels in T2DM treatment was cross-sectionally investigated in a clinical setting.

The ethics committee of the hospital approved the study protocol, and the requirement for informed consent was waived. Information pertaining to the study was provided to the public via the internet to allow participants the opportunity to refuse to have their data used in any retrospective scientific research (No. 5245-03).

Statistical analyses

Categorical variables are expressed as numerical data and percentages. Continuous variables are expressed as the mean values with standard deviation (SD). Chi-square test was used to test the associations between categorical variables. For statistical comparisons, HbA1c values were logarithmically transformed to ensure a normal distribution. However, for ease of interpretation, the data in the tables are presented as the original mean±SD. To examine the impact of PhA on HbA1c values, participants were stratified by sex and subsequently categorized into PhA tertiles, as PhA is known to differ by sex. The PhA tertiles for men were defined as low (＜5.4), middle (5.4-5.8), and high (＞5.8). For women, tertiles were defined as low (＜4.7), middle (4.7-5.0), and high (＞5.0). Continuous variables were compared using an analysis of covariance among tertiles after adjusting for the age, sex, BMI, WC, and number of antidiabetic medications being used. Tukey's test was performed for a post hoc analysis. In addition, to further investigate the association between PhA and glycemic control, the odds ratios (ORs) for controlled HbA1c values in the middle and high tertiles compared to the low tertile were estimated using logistic regression models. “Controlled” HbA1c (n=84) was defined as being lower than 7.0% (11), regardless of diabetes medications being used for glycemic control. Both univariate and multivariate logistic regression analyses were conducted. Initially, an unadjusted analysis was performed, followed by a series of multivariable models (Models 1-4) with cumulative adjustments for the age, sex, BMI, WC, and number of antidiabetic medications being used among the 10 classes of drugs indicated in Table 1. p＜0.05 was considered to indicate statistical significance.

Table 1.

Clinical Characteristics in Each Tertile Group Based on Phase Angle among Non-elderly Patients with T2DM.

	All	Phase angle
		Low	Middle	High
Male / Female (n)	100 / 67	31 / 21	30 / 26	39 / 20
Age (years)	53.5±9.3	56.9±7.7	54.6±7.9	49.6±10.5*,**
BMI (kg/m2)	27.1±4.3	25.9±4.4	27.3±4.3	27.9±4.0*,**
HbA1c (%)	8.75±2.31	9.58±2.86	8.41±2.06*	8.35±1.77*
Patients with controlled HbA1c (n, %)	84 (50.3)	21 (40.4)	33 (58.9)	30 (50.9)
SBP (mmHg)	129±17	130±15	127±17	131±18
DBP (mmHg)	82±13	81±12	82±16	84±12
AST (U/L)	27.4±16.9	23.2±11.7	25.5±12.6	32.7±22.3*,**
ALT (U/L)	36.6±34.7	26.9±16.9	31.7±21.2	49.4±49.7*,**
eGFR (mL/min/1.73m2)	81.0±22.6	81.0±29.1	79.4±19.2	82.6±19.4
T-Chol (mg/dL)	205±58	200±45	216±82	200±40
TG (mg/dL)	225±328	186±122	257±534	227±159
HDL-C (mg/dL)	51±14	55±16	50±14	48±11*
LDL-C (mg/dL)	115±38	112±42	118±40	114±33
WC (cm)	94.9±11.5	93.7±11.0	95.5±13.7	95.5±9.5
Treatment for diabetes (n)
Insulin / GLP-1RA or GIP/GLP-1RA	19 / 25	7 / 5	7 / 9	5 / 11
BG / α-GI / DPP-4I*	68 / 6 / 50	21 / 2 / 24	22 / 2 / 11	25 / 2 / 15
TZD / SGLT2I	10 / 74	5 / 23	4 / 26	1 / 25
SU / Glinides / Imeglimin	18 / 6 / 4	7 / 2 / 3	5 / 1 / 1	6 / 3 / 0

Data are shown as mean±SD. *: p<0.05 compared with category of the low tertile and **: p<0.05 compared with category of the middle tertile, after adjustment was made for confounders, respectively. “Controlled” HbA1c was defined as being lower than 7%. GS: grip strength, T2DM: type 2 diabetes mellitus, BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, T-Chol: total cholesterol, TG: triglycerides, HDL-C: high density lipoprotein cholesterol, LDL-C: low density lipoprotein cholesterol, WC: waist circumstance, SU: sulfonylureas, TZD: thiazolidinedione, BG: biguanide, α-GI: alpha-glucosidase inhibitors, DPP-4I: dipeptidyl peptidase-4 inhibitors, SGLT2I: sodium-glucose co-transporter 2 inhibitors, GLP-1RA: glucagon-like peptide-1 receptor agonist, GIP/GLP-1RA: glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist

Statistical analyses were performed using the JMP software program (version 13.2 for Windows; SAS Institute, Tokyo, Japan).

Results

A comparison of clinical characteristics by PhA tertile

Table 1 shows the clinical characteristics of the participants stratified by PhA tertile (low, middle, and high). Among the participants in the three tertiles for PhA, those in the high tertile were significantly younger than those in the low and middle tertiles after adjusting for confounders. The BMI, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) values for participants in the high tertile were significantly higher than those in the low and middle tertiles after adjusting for confounders. Furthermore, the HbA1c levels were significantly lower in the middle and high tertiles than in the low tertile (Table 1).

Association between PhA and controlled HbA1c

Unadjusted and multiple logistic regression analyses were performed using the PhA as a continuous variable. While the unadjusted OR for controlled HbA1c in relation to the PhA was 1.212 (95% confidence interval 0.750-1.958, p=0.433), the OR became statistically significant after adjusting for all confounders [2.290 (1.151-4.556, p=0.018)].

Next, we examined the association between PhA tertiles and HbA1c levels (Table 2). In the unadjusted model, the ORs for achieving controlled HbA1c were not statistically significant for the middle [2.118 (0.982-4.567)] or high [1.527 (0.719-3.243)] PhA tertiles compared with the low tertile. However, after adjusting for potential confounders, this association became significant and robust. In the fully adjusted model (Model 4), the ORs were 2.338 (1.006-5.432) for the middle tertile and 2.666 (1.071-6.636) for the high tertile.

Table 2.

Multivariate-adjusted Odds Ratios for HbA1c <7% by PhA Tertile.

	Low	Middle	p value	High	p value
Unadjusted	1 (Ref)	2.118 (0.982-4.567)	0.056	1.527 (0.719-3.243)	0.271
Model 1	1 (Ref)	2.479 (1.121-5.482)	0.025	2.313 (1.007-5.309)	0.048
Model 2	1 (Ref)	2.460 (1.109-5.453)	0.027	2.359 (1.024-5.436)	0.044
Model 3	1 (Ref)	2.652 (1.175-5.988)	0.019	2.626 (1.102-6.260)	0.029
Model 4	1 (Ref)	2.338 (1.006-5.432)	0.048	2.666 (1.071-6.636)	0.035

Number of patients with HbA1c <7%: Low (n=21), Middle (n=33), High (n=30). Odds ratios and corresponding 95% CIs by tertile were presented.

Note:

Model 1: adjusted for age

Model 2: adjusted for age and gender

Model 3: adjusted for age, gender, BMI, and WC

Model 4: adjusted for age, gender, BMI, WC, and anti-diabetic medication

Discussion

The data in this cross-sectional study indicated that the PhA was a significant indicator for glycemic control, defined as HbA1c ＜7.0%, among non-elderly patients with T2DM after adjusting for body composition, such as the BMI and WC, in addition to the use of anti-diabetic medications. These findings suggest that cell membrane integrity, as reflected by the PhA, is associated with glycemic control in non-elderly patients with T2DM.

The most plausible hypothesis for our findings lies in the physiological role of muscle, as reflected by PhA, an established indicator of cellular mass and membrane integrity (5), and correlated with the muscle quantity and quality, which are factors that are inversely related to sarcopenia (8,12). Given that the muscle is the primary tissue for the uptake of glucose in the body, a higher PhA, suggesting healthier and more abundant muscle tissue, might be associated with a greater glucose uptake capacity and, consequently, favorable insulin sensitivity. This mechanism may provide a plausible physiological explanation for the association observed in this study.

Previous studies have established that the PhA is associated with the nutritional status and prognosis in elderly populations (13), as well as in patients with other clinical conditions, such as cancer (14) or kidney disease leading to dialysis treatment (15). These reports support the role of the PhA as a key indicator of cellular health. Our findings suggest that the PhA may also serve as a novel marker for assessing glucose tolerance and sensitivity. However, prior research on T2DM (9,10,16) focused on elderly participants. The scarcity of previous reports on the link between the PhA and glucose metabolism might be partly attributable to challenges in participant selection, an issue highlighted by our findings in a non-elderly cohort. Elderly individuals often present with multiple comorbidities, medicines being used, and a variety of other factors beyond poor nutrition that could lower the PhA; in other words, a variety of confounding factors, which make it difficult to interpret the relationship between PhA and glucose metabolism. It is well established that there is a strong negative correlation between age and the PhA (17,18). Therefore, our finding of this association in a non-elderly population suggests that, even before the significant impact of aging, factors such as lifestyle, physical activity, and nutritional status might be linked to glycemic control and muscle quality, as reflected by PhA, potentially playing a role in this relationship. Indeed, similar logistic regression analyses performed on the entire cohort (n=390) and elderly subgroup (n=223) showed that PhA was not a significant determinant of glycemic control in either the unadjusted or fully adjusted models (data not shown).

An interesting finding from our study was the clinical profile of subjects in the high PhA tertiles. These individuals were significantly younger than others yet paradoxically displayed a cluster of characteristics associated with higher insulin resistance. They had significantly higher BMI, AST, and ALT values and significantly lower high-density lipoprotein cholesterol levels than the low PhA group; however, despite this profile, they achieved better glycemic control, as indicated by their significantly lower HbA1c levels. Although the causality and directionality of this relationship are not fully understood (19), this apparent paradox leads us to speculate that a high PhA, which is an indicator of the muscle quality, might signify a greater mass of high-quality muscle with enhanced glucose uptake capacity, even in individuals with a high BMI. This hypothesis could explain why the strong association between high PhA and controlled HbA1c persisted even after adjusting for confounders, such as the BMI and WC.

Another cross-sectional study on T2DM (20) reported that the combination of a low PhA and high BMI was associated with the most severe insulin resistance. Conversely, researchers found that the group with a high PhA and high BMI tended to be younger and had lower HbA1c values than the group with a low PhA and high BMI. This finding supports our results and suggests that a high BMI, particularly when associated with a high PhA, might reflect not only fat mass, but also the quality and quantity of skeletal muscle. In other words, our study findings suggest that even in the presence of a certain degree of insulin resistance, a high PhA could indicate that the glucose-regulating function of skeletal muscle is predominant. Further prospective studies are warranted to explore the interplay between the PhA and insulin resistance and the pancreatic β-cell function.

The finding that the association between PhA and glycemic control persisted after adjusting for the BMI, WC, and antidiabetic medications seems to highlight its potential as a novel indicator of the muscle quality and cellular health. As an objective, noninvasive, and rapidly measured BIA-derived parameter, PhA could have particular utility in the routine clinical management of non-elderly patients with T2DM. This suggests that the PhA has the potential to provide a more detailed physiological assessment than conventional body composition analysis, offering insights beyond conventional metrics to aid in the differentiation of patients with similar physiques. In light of these findings, it might be worth considering a broader therapeutic approach that targets not only weight loss but also the improvement of PhA as a clinical endpoint. This approach may provide a scientific rationale for prescribing specific exercises and nutritional interventions designed to maintain and improve the muscle quality.

Several limitations associated with the present study warrant mention. First, because this was a cross-sectional study with a limited number of participants, the generalizability of the results is limited. In addition, the study population was characterized by relatively poor glycemic control, with a mean HbA1c level higher than that of typical clinic-based cohorts in Japan (21). This finding suggests that our participants represent a group with an increased need for intensive therapeutic intervention; thus, caution is warranted when generalizing these findings. Second, lifestyle habits and comorbidity factors such as smoking and daily activities were not considered. Third, our analysis did not consider the duration of diabetes. As diabetes duration could have a negative correlation with PhA (22), its potential confounding effect with age should be considered. Finally, the anti-diabetic medications used by participants might have modified the PhA. Assessing the effect independently of these medications was difficult owing to the study design, despite the statistical adjustments made. Consequently, a further investigation in a population not undergoing antidiabetic therapy is warranted to clarify the direct relationship between these two variables.

In conclusion, the PhA is a useful indicator that is independently associated with favorable glycemic control in non-elderly patients with T2DM. This non-invasive marker might contribute to the development of personalized therapeutic strategies that consider the cellular health and muscle quality.

Author’s disclosure of potential Conflicts of Interest (COI).

H.K. received honoraria for lectures and scholarship grants from Novo Nordisk Pharma, Sanofi, Eli Lilly, Boehringer Ingelheim, Sumitomo Pharma, Daiichi Sankyo, Mitsubishi Tanabe Pharma, Manpei Suzuki Diabetes Foundation, Japan Arteriosclerosis Prevention Fund, and the Japan Association for Diabetes Education and Care. K.K. has been an advisor to, received honoraria for lectures from, and received scholarship grants from Novo Nordisk Pharma, Sanwa Kagaku, Taisho Pharma, Sumitomo Pharma, Astellas and Boehringer Ingelheim. S.N. received honoraria for lectures from Novo Nordisk Pharma, Kowa, and Daiichi Sankyo. T.K. received honoraria for lectures from Sumitomo and Novo Nordisk Pharma. The authors declare no conflicts of interest.

Funding Statement

This study was supported in part by Research Project Grants R03B-014 and R04B-028 from Kawasaki Medical School, and by JSPS KAKENHI Grant Number 25K14684.