Keywords: Continuous Glucose Monitoring, Estradiol, Insulin Sensitivity, Menstrual Cycle, Precision Medicine, Progesterone, Type 1 Diabetes, Women’s Metabolic Health
Introduction
Type 2 diabetes and related metabolic diseases differ between women and men in their risk factors, pathophysiology, and clinical course, and sex hormones are a central reason for these differences. Estrogens in particular exert broad protective effects on energy balance, insulin action, and pancreatic beta-cell function, which helps explain why premenopausal women are, on average, more insulin sensitive than men of similar age and adiposity [1,2].
Yet for roughly four decades of a woman’s life, these hormones are not constant. They rise and fall in a predictable monthly pattern, and this raises a practical question for clinicians, researchers, and developers of digital health tools: does the menstrual cycle meaningfully change blood sugar? Historically, studies relying on single or infrequent blood draws produced conflicting answers. The arrival of wearable continuous glucose monitors and large app-based cycle datasets now allows this question to be studied with far greater resolution [3,4].
This review aims to (i) outline the hormonal physiology of the menstrual cycle relevant to glucose metabolism, (ii) describe the mechanisms through which estradiol and progesterone influence glycaemia, (iii) summaries human evidence in women with and without diabetes, (iv) examine lifestyle modifiers and cycle irregularity as a marker of metabolic risk, and (v) consider implications for personalized, technology-enabled prevention.
The Menstrual Cycle as Metabolic Cycle
A typical menstrual cycle is divided into a follicular phase, beginning on the first day of menstruation and ending at ovulation, and a luteal phase, from ovulation until the next menses. Real-world data from more than 600,000 cycles show that cycle length and phase timing vary considerably both between and within individuals, and that the commonly assumed 28-day cycle with ovulation on day 14 applies to only a minority of cycles. This variability matters for any study, or algorithm, that attempts to assign metabolic changes to a specific phase [4].
Hormonally, the early follicular phase is characterized by low estradiol and progesterone. Estradiol rises steadily to a pre-ovulatory peak in the late follicular phase, followed by the luteinizing hormone surge and ovulation. During the luteal phase, the corpus luteum secretes large amounts of progesterone alongside a secondary rise in estradiol, before both decline if pregnancy does not occur. Metabolomic profiling has shown that many plasma metabolites, including amino acids and lipid species, follow rhythmic patterns across these phases, supporting the view that the menstrual cycle is also a metabolic cycle. Table 1 summarizes the main phases and their typical glycaemic tendencies [5].
| Phase (approx. days*) | Dominant hormonal profile | Typical glycaemic tendency |
|---|---|---|
| Early follicular / menstrual (1–5) | Low oestradiol, low progesterone | Relatively stable glucose; insulin sensitivity generally preserved |
| Late follicular (6–ovulation) | Rising to peak oestradiol; low progesterone | Lowest daily median glucose observed in CGM studies; favourable insulin sensitivity |
| Peri-ovulatory | Oestradiol peak, LH/FSH surge | Beginning of rise in hyperglycaemia risk in some women with type 1 diabetes |
| Luteal (ovulation–next menses) | High progesterone, secondary oestradiol rise | Higher glucose, reduced insulin sensitivity, greater insulin requirements in susceptible individuals |
Table 1. Menstrual cycle phases, hormonal profile, and glycaemic tendencies
*Days are indicative for a 28-day cycle; actual timing varies widely between and within individuals. Synthesised from [3,4,6,7].
Hormonal Mechanism Linking the Cycle to Glucose Homeostasis
Estradiol: a Largely Insulin-Sensitizing Hormone
Estradiol acts through estrogen receptors (ER-alpha, ER-beta) and the G protein-coupled oestrogen receptor in liver, skeletal muscle, adipose tissue, the hypothalamus, and pancreatic islets. Through these pathways it suppresses hepatic gluconeogenesis, enhances insulin-stimulated glucose uptake in muscle, promotes favorable fat distribution, and regulates appetite and energy expenditure. In the pancreas, estrogens enhance glucose-stimulated insulin secretion, increase insulin biosynthesis, and protect beta cells from apoptosis. Physiologically, the high-estradiol, low-progesterone milieu of the late follicular phase would therefore be expected to favor lower glucose levels [2,8].
Progesterone: a Counter-Regulatory Influence
Progesterone has broadly opposed metabolic actions. It has been shown to reduce insulin sensitivity at the post-receptor level and to increase insulin secretion as a compensatory response, contributing to the relative insulin resistance seen in pregnancy and in the luteal phase. In the BioCycle Study, both estradiol and progesterone were positively associated with insulin resistance (HOMA-IR) after adjustment for other hormones, while follicle-stimulating hormone and sex hormone-binding globulin were inversely associated. This suggests that the net glycaemic effect at any point in the cycle reflects the balance and interaction of several hormones rather than any single one [6.9].
The Integrated Luteal-Phase Picture
Early physiological studies using the euglycaemic clamp, the frequently sampled intravenous glucose tolerance test, and minimal-model analysis reported lower insulin-mediated glucose disposal and reduced insulin sensitivity in the luteal compared with the follicular phase. Not all studies have replicated these findings, and the effect size in healthy women is generally small. Taken together, the evidence supports a modest luteal-phase reduction in insulin sensitivity that is driven largely by progesterone and is only partly offset by the concurrent luteal rise in estradiol [6,10-13].
Evidence in women Without Diabetes
The BioCycle Study followed 257 healthy premenopausal women through one or two cycles with up to eight hormonally timed visits per cycle. Fasting insulin and HOMA-IR varied across the cycle, and these changes primarily reflected insulin rather than glucose; the overall variability was described as minor but statistically significant. This pattern is consistent with a healthy pancreas compensating for small hormone-driven changes in insulin action [6].
A larger analysis of 1,906 premenopausal women from NHANES (1999–2006) used cosinor rhythmometry to model glucose, insulin, triglycerides, HOMA-IR, and adipose tissue insulin resistance across the cycle. Rhythmicity in these markers was present and was modified by body mass index, physical activity, and cardiorespiratory fitness. This finding is important for prevention: women with higher adiposity or lower fitness may experience larger cyclical swings in metabolic control [13].
Continuous glucose monitoring now provides a much richer view. In a study of 49 non-diabetic participants who wore a CGM and smartwatch while measuring daily urinary hormones over an average of about 79 days, daily median glucose followed a biphasic pattern, peaking in the luteal phase and reaching its lowest point in the late follicular phase. This pattern persisted after adjustment for age, BMI, and self-reported symptoms. Higher daily estrogen levels and step counts were associated with lower glucose, whereas stronger food cravings were associated with higher glucose. These data illustrate how hormonal, behavioral, and physiological signals combine to shape everyday glycaemia [3].
Evidence in Women with Type 1 Diabetes
Women with type 1 diabetes provide a “magnified” view of cycle-related changes, because they cannot increase endogenous insulin secretion to compensate for reduced insulin sensitivity. Early CGM studies across multiple complete cycles showed that cyclic changes in glycaemia occur in many, but not all, women, and that patterns are often reproducible within individuals. Further CGM work reported higher glucose levels in the luteal phase compared with the follicular phase, and clamp studies in a pilot cohort suggested reduced insulin sensitivity during the luteal phase in some participants [14-16].
In women using insulin pumps and CGM over three consecutive cycles, with ovulation confirmed by prediction kits and laboratory testing, the risk of hyperglycaemia rose from the early follicular phase through the peri-ovulatory and early luteal phases, accompanied by a decrease in model-estimated insulin sensitivity during the luteal phase. Notably, total daily insulin, carbohydrate, and calorie intake did not change significantly, suggesting a physiological rather than behavioral driver. More recent ambulatory glucose profile analyses have similarly reported poorer time-in-range in the luteal phase [7,17].
A 2023 systematic review of 14 studies concluded, however, that heterogeneity in phase definitions, glucose metrics, insulin-sensitivity methods, and hormonal confirmation, together with a high overall risk of bias, prevented a meta-analysis or firm quantitative conclusions. The authors observed a possible luteal-phase worsening of insulin sensitivity and hyperglycaemia in a subset of patients and recommended an individualized, pattern-based strategy. The key message is that cycle effects are real but highly individual [18].
Behavioral and Lifestyle Modifier
Glycaemic changes across the cycle are not purely hormonal. Energy intake and appetite tend to increase in the luteal phase, accompanied by a modest rise in resting energy expenditure. Dietary composition also shifts; in the BioCycle cohort, intake of protein and certain food groups varied across phases. Food cravings, as noted above, were independently associated with higher glucose in CGM data [3,19,20].
Substrate use during exercise changes across the cycle as well. estradiol promotes fat oxidation and may spare muscle glycogen, while the combined hormonal environment of the luteal phase can alter carbohydrate utilization and glucose kinetics during exercise. Importantly, physical activity and cardiorespiratory fitness appear to dampen cycle-related fluctuations in insulin sensitivity, and higher daily step counts are associated with lower glucose. These findings position exercise as a practical lever for stabilizing glycaemia across the cycle [3,13,21].
Cycle Irregularity as an Early Marker of Metabolic Risk
Beyond within-cycle variation, the pattern of the cycle itself carries prognostic information. In the Nurses’ Health Study II, women who reported long (40 days or more) or highly irregular cycles in early adulthood had a substantially increased risk of later type 2 diabetes, an association that was not fully explained by obesity. A subsequent analysis of 75,546 women found that irregular and long cycles across the reproductive life span were associated with greater type 2 diabetes risk, with the excess risk further amplified by an unhealthy lifestyle [22,23].
Polycystic ovary syndrome (PCOS) is a major contributor to this relationship. PCOS is characterized by ovulatory dysfunction, hyperandrogenism, and insulin resistance, and the 2023 international evidence-based guideline recommends routine assessment of glycaemic status in women with PCOS together with lifestyle intervention as a cornerstone of management. From a prevention standpoint, menstrual history is therefore a low-cost, readily available “vital sign” for identifying women who may benefit from earlier metabolic screening [24].
Implications for Digital Health and AI-Driven Precision Prevention
Postprandial glycaemic responses to identical foods differ markedly between individuals, and machine-learning models that integrate personal features can predict these responses more accurately than conventional carbohydrate-based approaches. The menstrual cycle adds a further, largely unmodelled, layer of within-person variability. For women, a glucose response measured in the late follicular phase may not represent the same individual’s response in the late luteal phase [3,25].
This has several practical implications. First, CGM-based wellness programs should interpret glucose trends in the context of cycle phase, so that a predictable luteal rise is not misclassified as a deterioration in metabolic health. Second, cycle-aware algorithms could personalize nutrition, activity, and, in insulin-treated diabetes, dosing recommendations by phase, as individual patterns appear reproducible across cycles. Third, glucose itself may contribute to cycle-phase estimation, as suggested by its biphasic pattern. Finally, because cycle length and ovulation timing vary widely, any such model should rely on measured or well-estimated phases rather than calendar assumptions [3,4,7,14,26].
Research Gaps and Methodological Considerations
Despite growing interest, the field remains limited by small sample sizes, inconsistent phase definitions, and frequent reliance on self-reported cycle days without hormonal confirmation. Methodological guidance recommends verifying phases using a combination of calendar counting, urinary luteinizing hormone testing, and serum hormone measurement, and clearly reporting hormonal contraceptive use, which suppresses the natural hormonal rhythm [18,26].
Key unanswered questions include: the magnitude of cycle-related glycaemic variation in women with prediabetes, type 2 diabetes, or PCOS; how hormonal contraception and perimenopause alter these patterns; the role of inflammation, sleep, and gut physiology as mediators; and whether cycle-adjusted interventions improve long-term outcomes. Large, longitudinal studies combining CGM, wearable data, and hormonal measurements in diverse populations, including Asian cohorts that remain underrepresented, are needed.
Conclusion
The menstrual cycle exerts a real, measurable influence on blood glucose regulation. estradiol generally enhances insulin action, whereas progesterone opposes it, producing a modest luteal-phase rise in glucose and fall in insulin sensitivity that is most evident with continuous monitoring and in women with type 1 diabetes. These fluctuations are shaped by body composition, fitness, and behavior, and long or irregular cycles signal elevated future risk of type 2 diabetes. For clinicians, researchers, and AI-driven health platforms focused on longevity and metabolic disease prevention, recognizing the menstrual cycle as a core physiological variable, rather than background noise, is an essential step toward truly personalized care for women [3,6,7,13,22,23].
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