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Metabolic syndrome in type 1 diabetes: higher time above range and glycemic variability revealed by continuous glucose monitoring (CGM)

Diabetology & Metabolic Syndrome volume 17, Article number: 49 (2025) Cite this article

Abstract

Aims

To investigate the glucose profile of Chinese individuals with type 1 diabetes (T1D) who also have metabolic syndrome.

Materials and methods

Type 1 diabetes participants from Peking University People’s Hospital were recruited from Jan 2017 to Jan 2024. The diagnosis of metabolic syndrome was developed based on the updated National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) criteria. Demographic data, anthropometric measurements, clinical information and continuous glucose monitoring (CGM) data were collected and compared between participants with metabolic syndrome and those without.

Results

The median age of the participants was 50.0 years (IQR 35.0-63.3), and the median duration was 10.0 years (IQR 2.0–17.0). Compared to those without metabolic syndrome, participants with metabolic syndrome were older (63.0 years, IQR 41.0–69.0 vs. 48.5 years, IQR 35.0–60.0; P < 0.001) and had a longer duration (13.0 years, IQR 5.0–22.0 vs. 9.0 years, IQR 2.0–15.0; P = 0.011). The comparison of CGM metrics suggested significantly higher time above range (TAR, 48.9%, IQR 35.3–59.5 vs. 32.8%, IQR 16.1–47.6; P < 0.001), standard deviation (SD, 3.6 ± 0.9 mmol/L vs. 3.2 ± 1.0 mmol/L; P = 0.022) and interquartile range (IQR, 4.2 mmol/L, IQR 3.2–4.8 vs. 3.7 mmol/L, IQR 3.0-4.5; P = 0.046) in those with metabolic syndrome. And the Logistic regression analysis showed that TAR (OR 1.53, 95% CI 1.02–2.23, per 20% increase), SD ( OR 1.75, 95% CI 1.07–2.84, P = 0.025) and IQR (OR 1.50, 95% CI 1.03–2.19, P = 0.036) were positively associated with metabolic syndrome after adjusting for age, sex, diabetes duration, BMI and complication status.

Conclusions

Our findings suggested that in T1D participants, metabolic syndrome was associated with higher glucose level and glycemic variability. Personalized diabetes education including optimal meal planning and sufficient physical activity should be emphasized to improve glycemic control in T1D with metabolic syndrome.

Introduction

With the discovery of insulin, type1 diabetes (T1D) transitioned from a fatal condition to a manageable chronic disease. Recent studies suggest that cardiovascular diseases have now become the leading cause of mortality among adults with long standing T1D [1]. As a result, the management of T1D has evolved beyond merely focusing on glycemic control to include the maintenance of overall metabolic health. Nonetheless, emerging epidemiological data reveal a concerning trend, that metabolic syndrome is relatively common among T1D patients, with approximately 25% of those diagnosed with T1D also affected by this condition [2]. This co-occurrence poses new challenges in diabetes care and highlights the importance of adopting a comprehensive approach to metabolic health in T1D management.

Previous studies have examined how metabolic syndrome influences the development of chronic complications in individuals with T1D, identifying it as a risk factor for both macrovascular and microvascular complications [3, 4]. T1D patients with metabolic syndrome face an elevated risk of diabetic complications compared to those without, regardless of their HbA1c levels [5, 6]. This suggests that glucose levels, as indicated by HbA1c, do not fully explain the increased complication rates in individuals with T1D and metabolic syndrome. Therefore, further investigation into the relationship between metabolic syndrome and dynamic glycemic profiles in T1D is needed. Our study aims to compare continuous glucose monitoring (CGM) data between T1D patients with and without metabolic syndrome. By identifying the distinct characteristics in the glucose profiles of those with metabolic syndrome, we seek to provide insights that could enhance diabetes management strategies.

Methods

Study population

The study employed a cross-sectional design and continuously recruited T1D patients at Peking University People’s Hospital from January 2017 to January 2024. The diagnosis of T1D was developed independently by two endocrinologists based on clinical manifestations, including diabetic ketoacidosis at onset, the initiation of insulin therapy within six months of diagnosis (and its continuation thereafter), and the presence of positive diabetes autoantibodies including islet cell autoantibody (ICA), insulin autoantibody (IAA), and glutamic acid decarboxylase (GAD) autoantibody.

Initially, 776 individuals with T1D were enrolled in the study. Of these, 536 participants were excluded due to the lack of CGM data. Out of the remaining 240 participants, 3 were excluded due to not meeting the required minimum of 72 h for CGM data collection. Additionally, 26 participants were excluded because of missing data necessary for assessing metabolic syndrome, including waist circumference, hypertension status, and lipid profile. Five female participants who were pregnant or breastfeeding were also excluded. Ultimately, 206 eligible participants were included in the final study cohort (see Fig. 1). The study adhered to the ethical principles outlined in the Declaration of Helsinki and received approval from the Peking University People’s Hospital Ethics Committee (2022PHB407-001). Informed consent was obtained from all participants or their legal guardians.

Fig. 1
figure 1

Inclusion Flowchart of 206 Eligible Participants with Type 1 Diabetes. A total of 206 participants were assessed for eligibility, of which 58 met the criteria for metabolic syndrome. The remaining participants did not fulfill the criteria. Abbreviations: ICA, islet cell autoantibody; IAA, insulin autoantibody; GADA, glutamic acid decarboxylase autoantibody

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Diagnostic criteria for the metabolic syndrome

The diagnosis of metabolic syndrome was established according to the updated NCEP-ATPIII criteria [7]. Participants were considered to have metabolic syndrome if they satisfied at least two of the following additional criteria: (1) central obesity, defined as a waist circumference of ≥ 90 cm for males or ≥ 80 cm for females; (2) hypertriglyceridemia, characterized by triglyceride levels ≥ 1.7 mmol/L or use of relevant medications; (3) low HDL-C, specified as HDL-C levels < 1.03 mmol/L for males or < 1.3 mmol/L for females, or use of related medications; and (4) hypertension, defined as a systolic blood pressure of ≥ 130 mmHg, diastolic blood pressure of ≥ 85 mmHg, or use of antihypertensive medications.

CGM data collection

CGM data was collected from 206 participants (Freestyle Libre H, Abbott, US; Sibionics, China). To ensure the accuracy of the analysis, CGM data was required to have ≥ 90% sensor activation for a minimum of 3 days. Glucose metrics including standard deviation (SD), mean glucose (MG), coefficient of variance (CV), interquartile range (IQR), glucose risk indicator (GRI), glucose management indicator (GMI), time below range (TBR, < 3.9 mmol/L), time above range (TAR, > 10.0 mmol/L) and time in range (TIR, 3.9–10.0 mmol/L) were calculated.

Statistical analysis

Statistical analyses were conducted as follows, unless otherwise specified: continuous variables with normal distributions were expressed as mean ± standard deviation (SD), while those with non-normal distributions were presented as median with interquartile range (IQR). Categorical variables were represented as proportions. For normally distributed variables, one-way ANOVA tests were used; for non-normally distributed variables, Mann-Whitney U tests were applied. Categorical variables were analyzed using Chi-squared tests. To further investigate the relationships between CGM metrics and metabolic syndrome, we performed a binary logistic regression analysis. The dependent variable was the presence or absence of metabolic syndrome, coded as a binary outcome. The CGM metrics, including SD, IQR, CV, TIR, TAR, TBR, MG, GMI, and GRI, were treated as continuous independent variables. Based on existing literature and their potential relevance to both CGM metrics and metabolic syndrome, we included sex, age, duration of diabetes, body mass index (BMI), fasting C-peptide levels, daily insulin dosage, and the presence of diabetic nephropathy as potential confounders.

Statistical analyses were conducted using SPSS software (version 27.0), with a P-value of < 0.05 considered statistically significant. Graphical presentations were created using GraphPad Prism (version 10.2.3).

Results

Characteristics of the participants

In the final study cohort, 58 participants were identified as having metabolic syndrome, while the remaining 148 did not have the condition. The characteristics of these two groups were detailed in Table 1. The median age of the cohort was 50.0 years (IQR 35.0-63.3), and the median duration of T1D was 10.0 years (IQR 2.0–17.0). Among the participants, 95 (46.1%) were male. The mean BMI was 23.4 3.3 kg/m2 and the mean HbA1c was 8.8 2.0%

Table 1 Characteristics of the study participants according to the presence of metabolic syndrome
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Participants with metabolic syndrome were older (63.0 years, IQR 41.0–69.0 vs. 48.5 years, IQR 35.0–60.0; P < 0.001) and had a longer duration of diabetes (13.0 years, IQR 5.0–22.0 vs. 9.0 years, IQR 2.0–15.0; P = 0.011) compared to those without the condition. Additionally, individuals with metabolic syndrome exhibited a higher BMI (25.5 ± 3.1 kg/m² vs. 22.4 ± 2.9 kg/m²; P < 0.001), along with a higher prevalence of hypertension (60.3% vs. 8.3%; P < 0.001) and elevated systolic blood pressure (139 ± 20 mmHg vs. 125 ± 16 mmHg; P < 0.001). They also had lower HDL-C levels (1.3 mmol/L, IQR 1.0-1.6 vs. 1.5 mmol/L, IQR 1.2–1.7; P < 0.001) and higher triglyceride levels (1.13 mmol/L, IQR 0.75–1.60 vs. 0.83 mmol/L, IQR 0.62–1.06; P < 0.001). Although it might seem counterintuitive, LDL-C levels were lower in the metabolic syndrome group (2.2 mmol/L, IQR 1.9–2.9 vs. 2.6 mmol/L, IQR 2.2–3.2; P = 0.003), which may be explained by a higher rate of statin use in this group (46.6% vs. 37.2%; P = 0.268), despite the difference not being statistically significant.

No significant differences were observed in HbA1c levels (9.0 ± 1.9% vs. 8.7 ± 2.0%; P = 0.446) or fasting C-peptide levels (0.02 ng/mL, IQR 0-0.25 vs. 0.06 ng/mL, IQR 0-0.34; P = 0.173) between the two groups. However, the metabolic syndrome group had significantly higher fasting glucose levels (12.0 mmol/L, IQR 7.9–15.9 vs. 8.8 mmol/L, IQR 6.0-12.3; P < 0.001) and required higher insulin dosages (0.60 U/kg/day, IQR 0.49–0.72 vs. 0.55 U/kg/day, IQR 0.43–0.70; P = 0.036).

Additionally, the metabolic syndrome group demonstrated a higher prevalence of diabetic nephropathy (24.6% vs. 12.0%; P = 0.032), yet no significant difference was noted in the prevalence of diabetic retinopathy (37.3% vs. 25.7%; P = 0.187).

CGM metrics

The CGM metrics of study participants, categorized by the presence of metabolic syndrome, revealed significant differences. Participants with metabolic syndrome demonstrated notably higher MG levels (10.2 mmol/L, IQR 8.8–11.1) compared to those without the condition (8.8 mmol/L, IQR 7.1–10.0; P < 0.001). They also showed increased TAR (48.9%, IQR 35.3–59.5 vs. 32.8%, IQR 16.1–47.6; P < 0.001) and GMI (7.7%, IQR 7.1–8.1 vs. 7.1%, IQR 6.4–7.6; P < 0.001). Conversely, TIR was significantly lower in the metabolic syndrome group (49.8 ± 18.8% vs. 59.7 ± 19.2%; P < 0.001), as depicted in in Fig. 2. Meanwhile, these participants exhibited higher SD (3.6 ± 0.9 mmol/L vs. 3.2 ± 1.0 mmol/L; P = 0.022) and a broader IQR (4.2 mmol/L, IQR 3.2–4.8 vs. 3.7 mmol/L, IQR 3.0-4.5; P = 0.046).

Fig. 2
figure 2

Comparison of Glucose Profiles Between Groups With and Without Metabolic Syndrome. The blue solid line represents the median glucose concentrations at each hour for the group without metabolic syndrome; the pink solid line represents the median glucose concentrations at each hour for the group with metabolic syndrome; the blue shaded area reflects the blood glucose range from the 25th to the 75th percentile for the group without metabolic syndrome; the pink shaded area reflects the blood glucose range from the 25th to the 75th percentile for the group with metabolic syndrome; the blue box represents the glucose range from 5th to the 95th percentile for the group without metabolic syndrome; the pink dashed box represents the blood glucose range of 5th to the 95th percentile for the group with metabolic syndrome. MS, metabolic syndrome; TBR, Time below range (< 3.9 mmol/L); TIR, time in range (3.9–10.0 mmol/L); TAR, time above range (> 10.0 mmol/L)

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Associations between CGM metrics and metabolic syndrome

To further explore the potential association between CGM metrics and metabolic syndrome, a binary logistic regression analysis performed, adjusting for confounding variables including sex, age, duration of diabetes, BMI, fasting C-peptide levels, daily insulin dosage, and the presence of diabetic nephropathy. After these adjustments, differences in MG, TAR, and GMI remained statistically significant (MG: OR 1.21, 95% CI 1.00-1.46, P = 0.048; TAR: OR 1.53, 95% CI 1.02–2.23, per 20% increase, P = 0.039; GMI: OR 1.55, 95% CI 1.00-2.40, P = 0.048; see Table 2; Fig. 3), suggesting that metabolic syndrome is an independent risk factor for unsatisfied glucose control. Moreover, the differences in SD and IQR also remained significant (SD: OR 1.75, 95% CI 1.07–2.84, P = 0.025; IQR: OR 1.50, 95% CI 1.03–2.19, P = 0.036; see Table 2; Fig. 3), independently contributes to increased glycemic variability.

Fig. 3
figure 3

Analysis of Odds Ratios for Continuous Glucose Monitoring (CGM) Metrics Associated with Metabolic Syndrome. This figure presents the results of binary logistic regressions analysis examining the association between CGM metrics and the presence of metabolic syndrome. Odds ratios and 95% confidence intervals are provided for the following CGM-derived metrics: (A) SD; (B) IQR; (C) TIR; (D) TAR; (E) TIR; (F) GRI; (G) MG; (H) GMI; (I) CV. These models were adjusted for age, duration of diabetes, BMI, fasting C peptide level, presence of diabetic nephropathy and daily insulin dosage. Abbreviations: OR, odds ratio; CI, confidence interval; SD, standard deviation; IQR, interquartile range; TIR, time in range (3.9–10.0 mmol/L); TAR, time above range (> 10.0 mmol/L); TBR, time below range (< 3.9 mmol/L); GRI, glucose risk index; MG, mean glucose; GMI, glucose management indicator; CV, coefficient variation; T1D, type 1 diabetes; BMI, body mass index

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Table 2 CGM metrics of study participants based on the presence of metabolic syndrome
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Glucose profile in T1D with metabolic syndrome

Participants with metabolic syndrome showed elevated glucose levels throughout the day (see Fig. 2). Additionally, the area between the interquartile ranges of glucose levels the monitoring period was larger in those with metabolic syndrome (see Fig. 2). These findings are consistent with the comparison of CGM metrics, which indicated that both glucose levels and glucose variability were higher in individuals with metabolic syndrome.

Discussion

Our study revealed that patients with T1D who also had metabolic syndrome experienced greater glycemic variability and higher levels of hyperglycemia in free-living condition, as demonstrated by CGM metrics. Even after adjusting for potential confounding factors, metabolic syndrome remained significantly associated with increased MG, TAR, SD, and IQR.

As the rates of overweight and obesity have increased in the T1D population [8, 9], the prevalence of metabolic syndrome seemed to follow this trend [2, 10,11,12]. Previous research has identified several characteristics associated with metabolic syndrome, including older age, female sex, longer diabetes duration, and higher insulin dosages [5, 8, 13]. Our study also suggested that participants with metabolic syndrome were generally older, predominantly female, had a longer duration of diabetes, and required higher insulin dosages. While previous studies comparing beta-cell function in individuals with and without metabolic syndrome have yielded inconsistent results [14, 15], our study found that beta-cell function, as indicated by fasting C-peptide levels, was similar between the two groups.

The presence of metabolic syndrome in T1D patients was associated with poorer glycemic control and a higher incidence of complications. Previous studies have shown a connection between metabolic syndrome and elevated HbA1c levels [3, 5, 13]. Our research, utilizing CGM data, further confirmed that metabolic syndrome was associated with unsatisfied glycemic control, suggested by higher TAR and increased glucose variability. Metabolic syndrome in T1D patients was associated with a higher risk of both macrovascular and microvascular complications [3, 5, 6, 13, 15,16,17,18,19]. While the role of insulin resistance and metabolic syndrome in the development of cardiovascular diseases was well established in type 2 diabetes [20,21,22], it was also associated with an increased likelihood of macrovascular complications in T1D [8]. Even with good glycemic control, T1D patients with metabolic syndrome showed a higher prevalence of macrovascular complications such as stroke, peripheral arterial disease, and diabetic foot syndrome compared to those without metabolic syndrome [8, 23]. Meanwhile, there was a higher prevalence of diabetic nephropathy and retinopathy in T1D patients with metabolic syndrome [5, 8, 23], which was also observed in our study. This highlighted the importance of considering patients’ overall metabolic health, beyond just glucose control, to mitigate the risk associated with metabolic syndrome and complications in T1D.

Our study demonstrated that metabolic syndrome is associated with an increased risk of diabetic nephropathy in individuals with type 1 diabetes, suggesting that metabolic syndrome may contribute to the progression of diabetic nephropathy. Previous research has indicated that dyslipidemia is linked to diabetic nephropathy [24, 25]. The underlying mechanisms may involve abnormal lipid accumulation, which can damage podocytes, proximal tubular epithelial cells, and renal interstitial tissues through processes such as inflammation, mitochondrial dysfunction, autophagy impairment, endoplasmic reticulum stress, and apoptosis [26,27,28,29]. Therefore, in the long-term management of T1D, it is essential to adopt an integrated approach that emphasizes the comprehensive management of glycemic levels, lipid profiles and blood pressure.

As previously reported, remnant cholesterol in T1D has been linked to reduced TIR, increased TAR, and a higher prevalence of diabetic nephropathy and severe diabetic retinopathy [24, 25]. Our study also observed that metabolic syndrome in T1D was correlated with higher TAR and an increased prevalence of diabetic nephropathy. These results suggest that lipid metabolism abnormalities, including elevated remnant cholesterol, may play a role in the development of diabetic nephropathy in T1D. Further research is needed to validate these associations.

Our study revealed that, consistent with previous research [30,31,32], TAR was associated with metabolic syndrome in free-living conditions. Since increased TAR was linked to both long-term microvascular and acute complications [33,34,35], the observed higher TAR in individuals with metabolic syndrome may partly explain their association with a poorer prognosis. However, another study conducted on Chinese individuals with T1D found no significant difference in TAR between the metabolic syndrome group and the non-metabolic syndrome group after adjusting for age, sex, and duration of diabetes [15]. This discrepancy may be attributable to the fact that the participants in that study were instructed to adhere to a structured lifestyle intervention, which included regular dietary habits and physical exercise, whereas the participants in our study were observed under free-living conditions. In a study with 547 participants with T1D, it was shown that those in the obesity/overweight group were associated with a lower CV [30]. However, in a study conducted in Greece with 73 T1D participants, glucose metrics indicating variability were similar in obese individuals [36]. Another study conducted on Chinese individuals with T1D suggested that CV was higher in T1D patients with metabolic syndrome after adjusting for age, sex, and duration of diabetes using nearest neighbor matching [15]. Moreover, a study involving 895 children and youths with T1D found that obesity was associated with a higher CV [37]. Further research is necessary to elucidate the causal relationship between glycemic variability and metabolic syndrome in T1D patients.

As hyperglycemia and glucose fluctuation were the primary challenges in the glucose profiles of T1D individuals with metabolic syndrome, treatment strategies focusing on these two factors should be prioritized. Despite advancements in technology, such as artificial pancreas systems, insulin dosage guidance systems, and novel insulin formulations [38,39,40,41], there remain significant unmet clinical needs in the management of patients with T1D. Our findings demonstrated that TAR was strongly correlated with diabetic complications and was associated with metabolic syndrome in T1D. These results underscored the critical importance of comprehensive long-term management strategies, including medical nutrition therapy, individualized meal planning strategy, and appropriate physical activity, to achieve favorable outcomes in T1D care. Furthermore, proper lifestyle management remained crucial and fundamental for T1D management. This included regular physical activity and personalized medical nutrition therapy. For individuals with metabolic syndrome, higher glucose variability often reflected inadequate meal planning and insufficient physical activity [8, 9, 42,43,44,45,46,47], which were key contributors to the development of metabolic syndrome. Therefore, for T1D patients with metabolic syndrome, comprehensive diabetes education should be emphasized. This education should focus on empowering patients with the knowledge and skills necessary to effectively manage their condition through lifestyle modifications.

Our study had several limitations. First, as a cross-sectional study, it could not establish causality between CGM metrics and metabolic syndrome. Further prospective studies are needed to clarify the relationship. Second, this was a single-center study with a relatively small sample size, highlighting the need for multi-center studies in diverse settings to validate our conclusions. Third, over five hundred participants were excluded due to insufficient CGM data or missing information necessary for assessing metabolic syndrome, which might introduce selection bias.

Conclusion

Metabolic syndrome was associated with significantly higher TAR, SD and IQR in individuals with T1D. Our study suggested that personalized diabetes education including optimal meal planning and sufficient physical activity should be emphasized to improve glycemic control in T1D with metabolic syndrome.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors express their gratitude to the study participants, research staff, physicians, and nurses at Peking University People’s Hospital, as well as the students who contributed to this work.

Funding

Peking University People’s Hospital Scientific Research Development Funds (RDX2023-02).

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Author notes

  1. Yayu Fang and Wei Liu contributed equally to this manuscript.

Authors and Affiliations

  1. Department of Endocrinology and Metabolism, Peking University People’s Hospital, No. 11, Xizhimen Nan Da Jie, Xicheng District, Beijing, 100044, PR China

    Yayu Fang, Wei Liu, Xiaoling Cai, Yu Zhu, Mingxia Zhang, Siqian Gong, Xiangqing Wang, Chu Lin, Rui Zhang, Sai Yin, Juan Li, Yongran Huo, Xiaodan Hu, Xiaoqi Xie & Linong Ji

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  1. Yayu Fang
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Contributions

L.W., F.Y.Y., C.X.L. and J.L.N. conceived and designed the experiments; Z.Y., Z.M.Y., G.S.Q., W.X.Q., L.C., Z.R., Y.S., L.J., H.Y.R., H.X.D. and X.X.Q. collected samples; L.W. and F.Y.Y. analysed the data; L.W. and F.Y.Y. wrote the manuscript; All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Xiaoling Cai or Linong Ji.

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Ethics approval and consent to participate

All the participants or their guardians were informed of sample collection and usage. This study was approved by the Ethics Committee of Peking University People’s Hospital (2022PHB407-001), and all the research methods were conducted in compliance with the ethical guidelines of the Declaration of Helsinki.

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Fang, Y., Liu, W., Cai, X. et al. Metabolic syndrome in type 1 diabetes: higher time above range and glycemic variability revealed by continuous glucose monitoring (CGM). Diabetol Metab Syndr 17, 49 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01602-1

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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