Glycemic Control Across the Menstrual Cycle in Women with Type 1 Diabetes Using the MiniMed 780G Advanced Hybrid Closed-Loop System: The 780MENS Prospective Study

Abstract

Objective:

To evaluate whether the MiniMed™ 780G advanced hybrid closed-loop (AHCL) system maintains similar glycemic control across two different phases of the menstrual cycle in women with type 1 diabetes (T1D) and to analyze the system’s performance in these situations.

Methods:

Continuous glucose monitoring (CGM) and insulin delivery metrics from 12 participants with T1D using the MiniMed™ 780G AHCL were analyzed throughout 3 prospectively recorded, consecutive menstrual cycles (36 cycles in total). Mixed models were used to compare the different variables between the early follicular phase and late luteal phase.

Results:

A higher average glucose was found throughout the late luteal phase compared with the early follicular phase (139.5 [133.5, 145.2] vs. 131.5 [126.8, 140.2] mg/dl, respectively, P = 0.002), together with an increase in total daily insulin dose (37.2 ± 11.9 vs. 33.6 ± 12.2 IU, P < 0.001). However, similar values between phases were observed for most of the remaining CGM metrics, including time in range (83.0 [76.0, 87.5] vs. 85.0 [79.8, 89.0] %, P = 0.101).

Conclusion:

Our results suggest that differences in glycemic control may be found across the menstrual cycle in women with T1D using the MiniMed™ 780G AHCL. Although higher average glucose levels may be expected in the late luteal phase, the deterioration of glycemic control during this phase may be mild with the MiniMed™ 780G AHCL, given the similarities for most of the CGM metrics with respect to the early follicular phase.

Introduction

Women with type 1 diabetes (T1D) often report glycemic changes throughout the menstrual cycle. Studies suggest that 40%–60%^1,2 of women may experience these changes, particularly an increase in blood glucose during the late luteal phase,^3,4 although the underlying mechanisms are not yet fully understood. Despite the reported consistent pattern across cycles,⁵ the optimal insulin adjustment for this variation remains unclear. Early studies recommended increasing basal insulin doses,⁶ but recent advances in continuous glucose monitoring (CGM) and insulin infusion systems suggest that adjusting the sensitivity factor⁷ or bolus type⁸ may be more effective for women using multiple daily injections or sensor-augmented pumps.

The integrated use of CGM and insulin pump therapy, alongside control algorithms known as the advanced hybrid closed-loop (AHCL) system, has greatly enhanced the management of T1D.^9,10 This system automatically adjusts insulin delivery based on algorithms, enabling a more personalized and dynamic approach to insulin administration. Recent evaluations of AHCL systems during the menstrual cycle showed promising results. Levy et al. found no significant differences in glycemic control across menstrual phases in users of the Tandem Control-IQ system, positing that the insulin adjustments made by the system could have alleviated the hormonal influences on glucose levels.¹¹ Mesa et al. evaluated the performance of the MiniMed™ 780G system during the menstrual cycle in women with T1D prone to hypoglycemia who switched from a sensor-augmented pump system. Following the switch, the authors reported a reduction in time below range <70 mg/dL (TBR1)—primary outcome—and a significant increase in time in range (TIR) throughout all phases of the menstrual cycle, with this increase being more notable in the follicular phase.¹² However, to our knowledge, no additional studies have analyzed the performance of AHCL systems across the menstrual cycle, and further investigation is needed to shed light on this point.

Therefore, the present study aims to evaluate whether the MiniMed™ 780G AHCL system maintains similar glycemic control across two different phases of the menstrual cycle in women with T1D and to assess the system’s performance in these situations.

Methods

Study design and participants

The 780MENS was a prospective observational study performed at Virgen de la Victoria University Hospital, Málaga, Spain. Inclusion criteria were women with T1D followed up at the Diabetes and Technology outpatient clinic of Virgen de la Victoria University Hospital, aged between 18 and 55 years, with premenopausal status (having spontaneous menstrual cycles), and users of the MiniMed™ 780G AHCL system (>95% of use in automatic mode) with the Medtronic Guardian G4 CGM sensor for at least 1 month. Exclusion criteria were: women with irregular cycles or a history of amenorrheic episodes; use of medications or interventions that could influence the menstrual cycle, such as contraceptives, cyclic progestogens, or intrauterine devices; concurrent illnesses or treatments affecting blood glucose levels during the study period; pregnancy; breastfeeding; previous diagnosis of polycystic ovary syndrome; participants with other physical or psychological conditions that could impede their ability to manage the AHCL system or affect result interpretation, as determined by the research team. The study was approved by the Provincial Research Ethics Committee of Málaga on May 25, 2023, and was conducted according to the Declaration of Helsinki. Written informed consent was obtained before study inclusion.

Enrolled participants were invited to download a menstrual cycle-tracking app and were instructed to track menstruations using the app. Over 4 months, menstrual cycles were prospectively registered. The definitions of menstrual cycle phases were as follows: early follicular phase—the first 7 days starting from the first day of menstruation and late luteal phase—the last 7 days preceding the onset of menstruation within a complete menstrual cycle.

Data collection and follow-up

This study primarily aimed to evaluate differences in CGM metrics across the menstrual cycle in women with T1D using the MiniMed™ 780G AHCL. We also aimed to evaluate differences in insulin delivery metrics.

Age, diabetes duration, AHCL therapy duration, weight, body mass index (BMI) (calculated as weight in kg divided by square of height in meters), and glycated hemoglobin (HbA1c) (determined by point-of-care immunoassay, DCA Vantage Analyzer, Siemmens Healthineers) were recorded at baseline.

Over the next 4 months following the inclusion, biweekly phone calls were scheduled, and menstrual occurrences registered by the participants were assessed. CGM metrics, automatic mode usage, insulin delivery metrics, meals per day, and carbohydrate intake were collected from Carelink™ in each phase of the menstrual cycle. CGM metrics were recorded according to international consensus standards,¹³ that is, TIR (70–180 mg/dL), time above range level 1 (TAR1) (>180 mg/dL), time above range level 2 (TAR2) (>250 mg/dL), TBR1 (<70 mg/dL), time below range level 2 (TBR2) (<54 mg/dL), mean glucose level, coefficient of variation (CV), and glucose management indicator (GMI).

At each contact, participants were asked if they perceived glycemic changes, specifically a tendency toward hypoglycemia or hyperglycemia, in the week before or after the onset of menstruation. They were also asked about medication use, illnesses, changes in exercise routines, or daily activities that could influence their glycemic control. Only three consecutive menstrual cycles without intercurrent events were included in the analysis.

Statistical analysis

Data were analyzed using R (version 4.3.1) through RStudio (version 2022.02.0). Continuous variables are reported as mean and standard deviation (SD) if normally distributed, or as median (25th, 75th percentiles) otherwise. To study the differences in CGM metrics between the follicular and luteal phases, mixed models were employed. This approach accommodates measurements within the same menstrual cycle and across multiple cycles per participant. Linear mixed models were utilized for numerical dependent variables, whereas logistic mixed models were applied for binary categorical variables. Model fit was assessed for each model, and robust linear mixed models were generated to mitigate the impact of poorly fitted data points on estimated coefficients. Statistical significance was set at P < 0.05.

Results

Characteristics of the study participants

Between June 2023 and January 2024, 20 potential candidates for the study were identified. Finally, data from three menstrual cycles of 12 patients (a total of 36 consecutive menstrual cycles) were analyzed. The flowchart of the study participants is shown in Figure 1. The participants of this study were 37.6 ± 9.4 years old (mean ± SD), with 26.0 ± 9.4 years of diabetes duration, body weight of 63.4 ± 10.0 kg, and a BMI of 23.6 ± 3.5 kg/m². Their HbA1c was 6.8% ± 0.5%. They had been using the MiniMed™ 780G AHCL system for a median of 7 months (P25: 5.3, P75: 17). Regarding the configurable parameters of the system, all patients maintained a glucose target of 100 mg/dL. The duration of active insulin was set to 2 h in 10 of the 12 patients, while in 2 patients, it was set to 3 h. This setting was maintained throughout the study period. No changes were made to the bolus calculator settings throughout the menstrual cycles studied.

FIG. 1.

Flowchart of the study participants.

Differences in CGM and insulin delivery metrics across the menstrual cycle

CGM and insulin delivery metrics in the early follicular and late luteal phases of the menstrual cycle, and mixed models evaluating the differences between these two phases are shown in Table 1 and Table 2, respectively.

Table 1.

Continuous Glucose Monitoring and Insulin Delivery Metrics in Early Follicular and Late Luteal Phases

	Early follicular phase	Late luteal phase
Automatic mode usage (%)	100 (99, 100)	100 (99, 100)
Average glucose (mg/dL)	131.5 (126.8, 140.2)	139.5 (133.5, 145.2)
GMI (%)	6.6 (6.4, 6.7)	6.7 (6.5, 6.8)
CV (%)	29.8 (3.8)	30.8 (6.2)
TIR (%)	85 (79.8, 89)	83 (76, 87.5)
TBR1 (%)	1 (1, 2.3)	1 (0.8, 2)
TBR2 (%)	0 (0, 0.3)	0 (0, 0.3)
TAR1 (%)	12.4 (6.2)	13.4 (5.2)
TAR2 (%)	1 (0, 2)	2 (0, 4.3)
Total insulin (IU)	33.6 (12.2)	37.2 (11.9)
Insulin (IU/kg)	0.5 (0.2)	0.6 (0.2)
Basal insulin
IU	13.7 (5.8)	14.9 (5.2)
%	42.6 (6.7)	40.3 (6.5)
Insulin bolus^a
IU	17.6 (9.4)	22.3 (8.1)
%	57.4 (6.7)	59.7 (6.5)
Autocorrection bolus
IU	5.5 (3.3)	5.8 (3.4)
%	26.5 (11.1)	26.9 (9.2)
Meals per day (n)	4.8 (1.5)	5.1 (1.6)
Carbohydrate intake (g/day)	113 (26.5)	124 (32.4)
Cannula change (d)	4.0 (1.3)	4.1 (2.7)

Data are presented as mean (SD), median (P25–75), or %. Early follicular phase was defined by the first 7 days from the onset of menstruation; late luteal phase was defined by the last 7 days preceding the onset of menstruation. TIR, time in range 70–180 mg/dL; TAR1, time above range level 1 (>180 mg/dL); TAR2, time above range level 2 (>250 mg/dL); TBR1, time below range level 1 (<70 mg/dL); TBR2, time below range level 2 (<54 mg/dL); CV, coefficient of variation; GMI, glucose management indicator.

The autocorrection bolus is included in the total bolus administered by the system.

Table 2.

Mixed Models to Evaluate Differences Between Menstrual Cycle Phases

	Β	SE	95% CI	t	P
Average glucose (mg/dL)	6.93	2.09	[2.83, 11.03]	3.31	0.002
GMI (%)	0.08	0.03	[0.02, 0,14]	2.29	0.025
CV (%)	0.19	0.78	[−1.34, 1.72]	0.24	0.805
TIR (%)	−1.97	1.17	[−4.26, 0.32]	−1.69	0.101
TBR1 (%)	−0.19	0.17	[−0.52, 0.14]	−1.11	0.274
TBR2 (%)	−0.00	−0.00	0	−0.43	0.664
TAR1 (%)	1.08	0.87	[−0.63, 2.79]	1.23	0.225
TAR2 (%)	0.86	0.32	[0.23, 1.49]	2.64	0.010
Total insulin (IU)	2.48	0.79	[0.93, 4.03]	3.10	<0.001
Basal insulin (IU)	0.52	0.52	[−0.50, 1.54]	1.0	0.317
Basal insulin (%)	−2.22	0.98	[−4.14, −0.30]	−2.26	0.029
Insulin bolus (IU)	3.52	0.91	[1.74, 5.30]	3.89	<0.001
Insulin bolus (%)	2.22	0.98	[0.30, 4.14]	2.26	0.029
Autocorrection bolus (IU)	0.47	0.32	[−0.16, 1.10]	1.49	0.146
Autocorrection (%)	0.21	0.99	[−1.73, 2.15]	0.22	0.827
Meals per day (n)	0.38	0.16	[0.07, 0.70]	2.33	0.026
Carbohydrate intake (g)	12.44	4.30	[4.01, 20.87]	2.88	0.007
Cannula change (d)	−0.27	0.31	[−0.88, 0.34]	−0.87	0.385

TIR, time in range 70–180 mg/dL; TAR1, time above range level 1 (>180 mg/dL); TAR2, time above range level 2 (>250 mg/dL); TBR1, time below range level 1 (<70 mg/dL); TBR2, time below range level 2 (<54 mg/dL); CV, coefficient of variation; GMI, glucose management indicator. Β, Estimated coefficient value for each variable, the model takes the value in the follicular phase as a reference; SE, standard error; CI, confidence interval, t: t value; P, statistical significance of the coefficient estimate; g, grams; d, interval of days. Statistical significance was set at P < 0.05.

Regarding CGM, we did not find significant differences between the late luteal and early follicular phases in TIR (83.0 [76.0, 87.5] vs. 85.0 [79.8, 89.0]%, respectively, P = 0.101). Similarly, no differences were found for TBR1, TBR2, TAR1, or CV. In contrast, a higher average glucose was observed in the late luteal phase compared with the early follicular phase (139.5 [133.5, 145.2] vs. 131.5 [126.8, 140.2] mg/dL, respectively, P = 0.002). Moreover, significant differences were detected in GMI and TAR2, which were higher in the late luteal phase.

When analyzing differences in insulin delivery metrics, total daily insulin dose was higher in the late luteal phase than in the early follicular phase (37.2 ± 11.9 vs. 33.6 ± 12.2 IU, P < 0.001). These differences were mainly observed at the expense of bolus insulin dose. Time in automatic mode remained consistent between phases. Also, a slight but significant difference in announced carbohydrate intake and meals per day was detected between phases.

Study participants were also asked to register and report factors with potential impact on glycemic control or insulin needs (Table 3). In this regard, the use of acetaminophen was reported in 1 out 36 early follicular phases and 1 out of 36 luteal phases. Intercurrent illnesses were reported in these same proportions in both phases. Also, the reported physical activity was similar in both phases of the menstrual cycle.

Table 3.

Percentage of Menstrual Cycles in Which Participants Described Changes or Intercurrent Events

	Early follicular^a	Late luteal^b	P
Perceived glycemic changes	11.1	8.3	0.662
Acetaminophen use	2.8	2.8	0.998
Intercurrent processes	2.8	2.8	1.000
Exercise performance	44.4	41.7	0.812

Early follicular: the first 7 days from the onset of menstruation.

Late luteal: the last 7 days preceding the onset of menstruation.

Self-perceived glycemic changes in both the early follicular and late luteal with respect to rest of the menstrual cycle were recorded. During the evaluated periods, women reported changes in 4 out of 36 (11.1%) early follicular phases and in 3 out of 36 (8.3%) late luteal phases, with this difference being not statistically significant. In Figure 2, the glycemic changes perceived by the participants are shown.

FIG. 2.

Self-perceived glycemic changes during the study. Hypo: Tendency to hypoglycemia; Hyper: Tendency to hyperglycemia. The blank cells indicate that the participants did not perceive any changes.

Discussion

In this cohort of women with T1D using the MiniMed™ 780G AHCL system, a higher average glucose was found throughout the late luteal phase, together with an increase in total insulin dose. Despite this, a similar TIR was observed in the early follicular phase and late luteal phase. These results reinforce the fact that glycemic oscillations may occur across the menstrual cycle, and the use of the MiniMed™ AHCL 780G system might partially attenuate some of these differences in glycemic control.

Previous studies have reported glycemic changes across the menstrual cycle in women with T1D on multiple daily injections or continuous subcutaneous insulin infusion, showing higher glucose levels during the luteal phase, compared with the follicular phase.^3,5,14,15 More recently, Tatulashvili et al. evaluated 62 menstrual cycles of 24 women with T1D (58.3% on sensor-augmented insulin pump therapy) and observed a decrease in TIR and an increase in TAR in the late luteal phase compared with the early follicular phase.⁴ Despite that, the involved mechanisms are yet to be fully elucidated; some authors have attributed these glycemic oscillations across the menstrual cycle to changes in insulin sensitivity, with the increase of progesterone related to insulin resistance in the luteal phase, and a higher insulin sensitivity due to the sudden decrease of progesterone levels in the early follicular phase.^15
–17 Interestingly, similar fluctuation patterns in glycemic control and hormonal profile (with a negative association between glucose and estrogen levels) during the menstrual cycle have also been reported in healthy women,¹⁸ and some of these glycemic changes might be related to a “brain insulin resistance” in the luteal phase.¹⁹

The performance of AHCL systems across the menstrual cycle in women with T1D remains poorly explored. A secondary analysis from a randomized controlled trial that included 16 Tandem Control-IQ users (4 of them using hormonal contraception) evaluated insulin delivery and glycemic metrics throughout menstrual cycle phases.¹¹ It was noteworthy that no differences were found for these outcomes during the cycle phases, and even the evaluation of intraparticipant data showed similar results. In this regard, the authors hypothesized some potential reasons for these findings: on the one hand, they argued that the study might have not been robust enough to detect changes in these parameters, and that the significant improvement in glycemic control following the AHCL use could have blunted changes in insulin sensitivity across the phases, leading to less meaningful differences in insulin delivery or glycemic metrics. Another hypothesis was that a more strategic insulin delivery by the system might have attenuated differences in glycemic control.

Although the Tandem Control-IQ AHCL system uses a model-based predictive algorithm,²⁰ the MiniMed™ 780G AHCL uses an adaptative one,²¹ and some differences between the performance of these AHCL systems across the menstrual cycle might be expected. Therefore, in our study, we showed no differences in TIR between phases, although a higher average glucose was detected in the late luteal phase. To our knowledge, the recent ambispective study by Mesa et al. was the only previous report on the performance on the MiniMed™ 780G AHCL system across the menstrual cycle.¹² This study included 39 menstrual cycles from 13 participants with T1D that switched from sensor-augmented insulin pump to AHCL, showing a significant increase in TIR and decrease in TBR1 and TBR2 globally. Surprisingly, while a similar TIR was reported across the menstrual cycle with sensor-augmented insulin pump therapy, a significant difference was detected between the early follicular and late luteal phases for this parameter (79.1% ± 9.3% vs.74.5% ± 10.0%, respectively) when participants were on the AHCL system.¹² The contradictory findings between the aforementioned study and ours may be explained, in part, by the characteristics of the study participants (patients prone to hypoglycemia) and AHCL system settings (a glucose target of 110 mg/dL and active insulin of 3 h).¹² In fact, a lower TIR in both phases was observed in this study compared with ours. In addition to this, the primary outcome of this study was change in TBR1 for each phase, before and after the initiation of the AHCL system, and the rest of the outcomes were considered exploratory.¹² However, despite not having detected differences in TIR between phases, it should be noted that a higher average glucose across the luteal phase was found in our study, also accompanied by a greater total insulin dose in this phase. In agreement to Mesa et al.¹² and previous studies,⁵ bolus insulin was significantly higher in the late luteal phase than in the early follicular phase, which suggests that glycemic fluctuations during the menstrual cycle may predominantly occur in the postprandial state. We also found that announced carbohydrate intake was significantly higher in the late luteal phase than in the early follicular phase. In this regard, a previous systematic review and meta-analysis showed that energy intake was higher during the luteal phase than the follicular phase in healthy women.²² However, given the small differences in carbohydrate intake in our study, which were also similar to those reported in previous works, further factors should be considered to explain these results. Indeed, factors such as entering false carbohydrate intake to correct hyperglycemia should also be attempted to be quantified in future studies.

Overall, our results may indicate that the MiniMed™ AHCL 780G system might be able to partially attenuate some potential differences in glycemic control between the early follicular phase and the late luteal phase by increasing insulin delivery in the latter, as no differences in TIR were detected between phases. However, the correction of the hyperglycemic state related to the late luteal phase may not be complete, as shown by the greater mean glucose levels in this phase. Recently, an in-silico analysis using data of women with T1D undergoing euglycemic clamp studies reported that informing dosing algorithms of insulin delivery technologies about changes in insulin sensitivity across the menstrual cycle may lead to a stabilization of glycemic metrics.¹⁷ Therefore, future advances in AHCL systems considering this point may help to improve glycemic control throughout the cycle in clinical settings.

The participants of this study reported having perceived changes in glycemic control in only 3 out 36 (8.3%) of the analyzed late luteal phases, which was similar to the self-reported changes during the early follicular phases. This low percentage of self-perceived glycemic changes contrasts which previous works.^2,4,5,23 To our knowledge, no previous studies conducted in women with T1D on AHCL systems have evaluated the prevalence of self-perceived changes across the menstrual cycle. Our findings suggest that, in women with T1D on the MiniMed™ 780G AHCL system, self-perceived changes in glycemic control are low, a fact that may be related to the absence of significant differences in TIR between phases due to the system performance. Another possible explanation lies in the advantages provided by the system in reducing the burden of diabetes. Current studies suggest that AHCL systems reduce the psychological burden related to the disease and the fear of hypoglycemia.^24,25 Women may have been less focused on glycemic changes because the system primarily manages them, rather than requiring their direct intervention. In the study by Herranz et al., where the women evaluated were users of non-semi-automated insulin pumps, 79.6% reported perceiving glycemic changes associated with menstruation, without differences in the perception rate between those who objectively showed glycemic changes between phases and those who did not.⁵

This study has some limitations. First, given the small sample size and analyzed menstrual cycles, larger studies considering representative populations are needed to confirm these results. Also, the generalizability of these results may be limited, as the included women had an excellent glycemic control (TIR >80%) and a mean age of 37.6 years (different glycemic patterns might be observed in younger adults). Although glycemic variability throughout the menstrual cycle has predominantly been described in relation to the early follicular and late luteal phases, glycemic changes might also have occurred during the late follicular and early luteal phases, which were not evaluated in this study. In addition, hormonal determinations across the cycles were not assessed. Physical activity and carbohydrate intake were self-reported, and no dietary records were collected. However, some important strengths should also be highlighted, including the prospective method of reporting and evaluating menstrual cycles. In addition, a careful selection of participants was conducted (excluding those with contraceptive use), and considering potential confounders across the cycles, such as medication intake, intercurrent illnesses, or physical activity.

In conclusion, in a cohort of women with T1D on the MiniMed™ 780G AHCL system, no differences in TIR were detected between the early follicular phase and late luteal phase, although a higher average glucose was observed in the latter, together with a higher total insulin dose. This study adds valuable information regarding the role of AHCL systems in glycemic control across the menstrual cycle.

Footnotes

Acknowledgments

The authors acknowledge all the study participants for their kind collaboration. The authors also acknowledge Sol Balsells-Mejía, Fundació de Recerca Sant Joan de Déu, Barcelona, Spain, and Vicente Lustres-Pérez and Sergio Antón Fuente, Biostatech, A Coruña, Spain, for their collaboration in the statistical analyses performed in this study.

Authors’ Contributions

G.M., M.J.P.-C., and J.I.M.-M.: Conceptualization and study design; G.M. and M.J.P.-C.: Data collection; G.M., M.J.P.-C., and J.I.M.-M.: Data analysis and interpretation; G.M., M.J.P.-C., and J.I.M.-M.: Investigation; G.M. and J.I.M.-M.: Original draft preparation; G.M., M.J.P.-C., J.G.-A., F.J.T., and J.I.M.-M.: Article review and editing; G.M., M.J.P.-C., J.G.-A., F.J.T., and J.I.M.-M.: Visualization. All authors read and approved the final version of this article.

Author Disclosure Statement

The authors declare no potential conflicts of interest relevant to this article.

Funding Information

J.I.M.-M. was supported by a Rio Hortega grant (CM22/00217) and a Juan Rodés grant (JR24/00006) from Instituto de Salud Carlos III, Madrid, Spain.

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