Abstract
Background
Menopause contributes to central obesity and increases cardiovascular risk in women. Diet influences both menopausal symptoms and cardiovascular health, but the impact of chrononutrition, namely food timing, is not well understood. This cross-sectional study investigated whether the timing of food intake affected menopausal symptoms in 100 postmenopausal women with overweight or obesity.
Methods
Anthropometric and clinical parameters, and lifestyle habits were assessed. Menopausal symptoms were evaluated using the Menopause Rating Scale (MRS). Nutritional assessment utilized 7-day food records. Food intake was divided into morning intake (meals from breakfast to lunch) and evening intake (meals from afternoon snacks to dinner).
Results
The mean MRS score was 22.7 ± 7.8, showing a high prevalence of symptoms in the study population. Postmenopausal women under the median of morning energy intake showed a significantly a higher score for heart discomfort (p = 0.045), while those under the median of morning intake of lipids showed significantly higher scores for heart discomfort and lower scores for bladder problems (p = 0.013 and p = 0.040, respectively). Postmenopausal women above the median evening intake of lipids showed a significantly higher score for heart discomfort (p = 0.007). The heart discomfort score correlated negatively and positively with the morning (r = -0.210, p = 0.034) and evening (r = 0.210, p = 0.034) intakes of lipids, respectively, even after correction for confounding factors (r = -0.219 and r = 0.219, p = 0.028 for both).
Conclusion
Consuming most of the energy and lipids later in the day was linked to higher prevalence of menopausal symptoms in postmenopausal women with overweight or obesity. This eating pattern may potentially have adverse effects on the cardiovascular health of these women. Therefore, adopting chrononutrition behaviors, particularly favoring an earlier intake of energy and lipids, could prove beneficial as an additional measure in the nutritional therapy for postmenopausal women dealing with overweight or obesity.
Keywords: Menopause, Obesity, Menopausal symptoms nutrition, Diet, Food timing, Circadian rhythms, Chrononutrition
Introduction
Menopause, a natural phase in a woman’s life, marks the cessation of reproductive capacity and is characterized by a complex interplay of hormonal changes [1]. The menopausal transition, encompassing the period from the onset of menstrual irregularities to the final menstrual period, is associated with various physiological alterations, predominantly driven by hormonal fluctuations. These hormonal variations give rise to a spectrum of menopausal symptoms, affecting psychological, somato-vegetative, and urogenital domains [1], with documented associations to diminished quality of life [2–5]. Of particular concern is the impact of menopause on body composition and metabolic health. Hormonal shifts during menopause are implicated in weight gain, particularly in the accumulation of visceral fat, leading to the development of abdominal obesity [6]. Postmenopausal women, as a consequence, are predisposed to obesity-related metabolic disorders such as insulin resistance, dyslipidemia, and metabolic syndrome, thereby elevating the risk of type 2 diabetes and cardiovascular diseases [7–9].
Notably, several lifestyle factors, including dietary practices, have been implicated in influencing the severity and manifestation of menopausal symptoms [10–12]. Observational studies have shed light on the relationships between dietary habits and menopausal symptoms, offering valuable insights into potential avenues for symptom management. High consumption of fruits, vegetables, whole grains, and unprocessed foods has emerged as protective against various menopausal symptoms, while diets rich in sugar and saturated fats are associated with adverse outcomes [13]. These findings emphasize the importance of diet in mitigating the impact of the menopausal transition on women’s well-being. In the context of nutritional interventions, the Mediterranean diet (MD) has garnered attention for its potential benefits in ameliorating menopausal symptoms [14, 15]. Recent research highlights associations between specific dietary components, such as legume consumption and extra virgin olive oil intake, and lower menopausal symptom severity and psychological symptoms in postmenopausal women [15]. Additionally, the adherence to the MD has been inversely related to the severity of menopausal symptoms in this population [15].
Much research has been focused on ‘what to eat’ or ‘how much to eat’ to reduce the obesity burden, but increasingly evidence indicates that ‘when to eat’ is fundamental to human metabolism [16–19]. In recent years, numerous studies have highlighted how eating times and frequency can influence biological rhythms. Additionally, individuals’ chronotype, working shifts, and food intake can make a deep impact on people’s tendency to develop obesity and metabolic diseases [19, 20]. In this context, a single food and a specific combination of these can also affect the circadian rhythms and fasting cycle and consequently body weight and viceversa [16–18]. Lipid intake is particularly significant due to its role in influencing hormonal balance and metabolic processes. Recent animal studies have highlighted how the timing of lipid consumption affects circadian rhythms and metabolism [21, 22]. Mice fed an ad libitum high-fat diet during the light phase (the sleep phase for nocturnal animals) exhibit increased adiposity and impaired glucose tolerance [22]. Conversely, when the high-fat diet is restricted to the active phase, mice show protection against obesity, hyperinsulinemia, hepatic steatosis, and inflammation [21].
Chrononutrition, focusing on the timing of food intake in alignment with circadian rhythms [16], offers a nuanced approach to enhance overall health during the menopausal transition. However, to date there are no data available on the role of chrononutrition in the specific case of postmenopausal women.
Given their heightened risk for health disturbance during menopause due to the prevalence of obesity in this group, we aimed to investigate the impact of daily lipids consumption—specifically, whether lipid consumed predominantly in the morning or evening—affect menopausal symptoms in postmenopausal women with overweight or obesity.
Materials and methods
Study population and design
This cross-sectional study screened all postmenopausal women with overweight or obesity attending the Outpatient Clinic of the Endocrinology, Diabetology, and Andrology Unit at the Department of Clinical Medicine and Surgery, University of Naples Federico II, from January to December 2023 for eligibility. To be included in the study, participants had to be postmenopausal (defined as having had no menstrual period for at least 12 months without hormonal contraceptives or being hysterectomized with menopausal symptoms) and have a BMI greater than 25 kg/m². Exclusion criteria included premenopausal status, use of hormonal therapy, type 1 diabetes mellitus, diabetes managed with insulin, any chronic diseases (such as cardiopulmonary, brain, kidney, or liver diseases), severe mental illnesses (such as depression, anxiety, or schizophrenia), and any allergies, intolerances, or adherence to a specific dietary regimen. The purpose of the study was thoroughly explained to all potential participants, and written informed consent was obtained. The study received approval from the Local Ethical Committee and was conducted in compliance with the Declaration of Helsinki. Medical professionals and nutritionists collected participants’ demographic information, medical histories, lifestyle behaviors (including smoking and physical activity), and anthropometric measurements. Participants also completed an interview questionnaire to assess the severity of their menopausal symptoms and their dietary intake. The study followed the guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement checklist [23].
Nutritional and food timing assessment
Nutritional assessment was performed by experienced nutritionists using food and nutrient intake data from the 7-day food records. Subjects received specific training to be able to properly complete the diary. Participants were encouraged to follow their normal diet and to record, in as many details as possible, all food and beverages consumed over a 7-day period, the method of preparation, and the weighed amount in grams or in other measures, such as household units, for each different food consumed. The analysis was conducted using WinFood nutrient analysis software. In the 7-day food records, a subdivision of meals was maintained, including breakfast, morning snacks, lunch, afternoon snacks, dinner, and evening snacks. Meals from breakfast to lunch were grouped as “morning intake”, while meals from the afternoon snack to the last evening meal were grouped into as “evening intake”. Subsequently, women were divided based on the median of morning and evening intakes of total energy and lipids. When selecting the cut-off values, we referred to Willett’s publication, which suggests that mean daily energy intakes between 500 and 4000 kcal are appropriate [24]. However, after considering the specific characteristics of our study population (postmenopausal women with a particular mean age and BMI), we opted for more conservative cut-off values, setting daily energy intakes between 600 and 2500 kcal.
Demographic information, medial history and lifestyle habits
Medical history evaluated the presence of conditions such as type 2 diabetes, hypertension, and dyslipidemia. Additionally, data were gathered on age at menarche, years since menopause, type of menopause (natural or induced), weight before and after menopause, and full-term pregnancies along with the birth weight of the children. For lifestyle habits, participants smoking at least one cigarette per day were classified as “current smokers.” Those regularly engaging in at least 30 min of aerobic exercise per day were considered physically active.
Anthropometric assessment
Anthropometric measurements were conducted between 8 a.m. and 10 a.m., following an overnight fast. A nutritionist skilled in nutritional assessment and body composition carried out these measurements according to standard procedures [25, 26]. Participants wore light clothing and no shoes. The measurements were taken while the participants stood upright with feet together, arms hanging loosely at their sides, and breathing normally. Weight and height measurements were then used to calculate the body mass index (BMI), which is weight in kilograms divided by height in meters squared (kg/m²). BMI categories were classified according to the World Health Organization (WHO) guidelines as follows: underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25–29.9 kg/m²), obesity class I (30–34.9 kg/m²), obesity class II (35–39.9 kg/m²), and obesity class III (≥ 40 kg/m²).
Menopausal symptoms
The Menopausal Rating Scale (MRS) includes a list of 11 symptoms [27]. Participants rated each symptom on a scale from 0 (no symptoms) to 4 (severe symptoms) based on their subjective experience. The MRS classifies symptoms into three groups: psychological (including depression, irritability, anxiety, and physical and mental exhaustion), somato-vegetative (such as sweating and hot flushes, cardiac complaints, sleeping disorders, and joint and muscle complaints), and urogenital (including sexual problems, urinary complaints, and vaginal dryness). Thus, the total MRS score ranges from 0 (no symptoms) to 44 (most severe complaints). The MRS score was used to define five categories: absence of symptoms (0–4), mild [5–8], moderate [9–12], marked [13–16], and severe (> 17). However, previous studies [28, 29] suggested that respondents might have difficulty accurately assessing low-to-moderate symptom perceptions. Therefore, to better examine the impact of food timing on the severity of menopausal symptoms, we combined the “no symptoms”, “mild”, and “moderate” categories into a single category (“none-to-moderate symptoms”). The MRS version used in this study was in Italian, matching the primary language of the participants. Validity and reliability for the translated versions have been established and are documented in the literature [30].
Sample size justification and power
The sample size was determined a priori, considering an effect size of 0.3, a type I error rate of 0.05, and a power of 80%. This calculation indicated that 82 subjects would be required. However, during the study, 100 eligible postmenopausal women with overweight or obesity were identified. Since this sample size exceeded the minimum requirement and would strengthen the study’s findings, all 100 women were included in the statistical analysis. The sample size calculation was conducted using G Power Software [31].
Statistical analysis
Continuous variables were presented as mean ± standard deviation (SD), while categorical variables were shown as numbers and percentages (%). Normality was assessed using the Shapiro-Wilk test and by visually inspecting histograms. Homogeneity of variance was evaluated using Levene’s test. Group differences were analyzed using the unpaired Student’s t-test, assuming normal distribution and equal variance between groups. When assumptions were not met, appropriate non-parametric alternatives were considered. The Chi-squared test for independence was employed to assess the association between categorical variables. Pearson’s correlation was used to evaluate the relationships between study variables, assuming linearity and homoscedasticity, while partial correlation was performed to adjust for confounding factors. A p-value < 0.05 (two-tailed) was considered statistically significant. Statistical analyses were conducted using the Statistical Package for Social Sciences software version 26.0 (SPSS/PC; SPSS, Chicago, IL, USA).
Results
A total of 100 postmenopausal women with overweight or obesity (BMI 36.0 ± 7.4 kg/m2, mean age 57.2 ± 7.3 years) were included in the analyses. The main characteristics of the women (medical data, lifestyle habits, anthropometric parameters, and diseases) are reported in Table 1. As for lifestyle habits, 23 women (23.0%) were smokers, and most of the participants were sedentary (81.0%). The prevalence of diseases was 13.0% type 2 diabetes, 43.0% hypertension, and 44.0% dyslipidemia.
Table 1.
Main characteristics of all women included in the analyses
| Parameters | N = 100 |
|---|---|
| Age (years) | 57.2 ± 7.3 |
| Smoking (n, %) | 23 (23.0) |
| Physical activity (n, %) | 19 (19.0) |
| BMI (kg/m2) | 36.0 ± 7.4 |
| Age at menarche (years) | 11.6 ± 1.6 |
| Age at menopause (years) | 48.7 ± 5.0 |
| Weight before menopause (kg) | 81.9 ± 19.0 |
| Weight gain after menopause (kg) | 11.0 ± 15.0 |
| Menopause type | |
| Natural (n, %) | 89 (89.0) |
| Induced (n, %) | 14 (14.0) |
| Diseases | |
| Type 2 diabetes (n, %) | 13 (13.0) |
| Hypertension (n, %) | 43 (43.0) |
| Dyslipidemia (n, %) | 44 (44.0) |
Data are expressed as mean ± SD or n (%). BMI, body mass index
Total MRS scores and the severity of symptoms in all women are reported in Table 2. The mean MRS score was 22.7 ± 7.8, with the following prevalence of the severity of symptoms: 9 women (9.0%) had none-to-moderate symptoms, 10 (10.0%) had marked symptoms, and 81 (81.0%) had severe symptoms. Regarding the somato-vegetative, psychological, and urogenital symptoms, most of the women were in the third (36.0% and 32.0%) and first (54.0%) of the severity of the symptoms, respectively. As for the specific menopausal symptoms, the most frequent disorders were anxiety (85.0%), sexual problems (85.0%), joint and muscular discomfort (85.0%), and sleep problems (81.0%).
Table 2.
MRS score and menopausal symptoms in all women included in the analyses
| MRS and symptoms | N = 100 |
|---|---|
| Total MRS score | 22.7 ± 7.8 |
| None-to-moderate symptoms (0–12) (n, %) | 9 (9.0) |
| Marked (13–16) (n, %) | 10 (10.0) |
| Severe (17+) (n, %) | 81 (82.0) |
| Somato-vegetative score | 8.7 ± 3.8 |
| 0–4 (n, %) | 14 (14.0) |
| 5–8 (n, %) | 33 (33.0) |
| 9–12 (n, %) | 36 (36.0) |
| 13–16 (n, %) | 17 (17.0) |
| Psychological score | 9.2 ± 4.3 |
| 0–4 (n, %) | 13 (13.0) |
| 5–8 (n, %) | 31 (31.0) |
| 9–12 (n, %) | 32 (32.0) |
| 13–16 (n, %) | 24 (24.0) |
| Urogenital score | 4.8 ± 2.9 |
| 0–4 (n, %) | 54 (54.0) |
| 5–8 (n, %) | 34 (34.0) |
| 9–12 (n, %) | 12 (12.0) |
| 13–16 (n, %) | 0 (0) |
| Symptoms | |
| Hot flashes and sweating (n, %) | 79 (79.0) |
| Heart discomfort (n, %) | 60 (60.0) |
| Sleep problems (n, %) | 81 (81.0) |
| Depressive mood (n, %) | 83 (83.0) |
| Irritability (n, %) | 79 (79.0) |
| Anxiety (n, %) | 85 (85.0) |
| Physical and mental exhaustion (n, %) | 56 (56.0) |
| Sexual problems (n, %) | 85 (85.0) |
| Bladder problems (n, %) | 41 (41.0) |
| Dryness of the vagina (n, %) | 64 (64.0) |
| Joint and muscular discomfort (n, %) | 85 (85.0) |
Data are expressed as mean ± SD or n (%). MRS, Menopause Rating Scale
Average total, morning and evening daily intake of kcal and macronutrients in all postmenopausal women are reported in Table 3. It appeared that a larger proportion of carbohydrates was consumed in the morning, while protein intake was higher in the evening. The lipid intake seemed to be relatively evenly distributed between morning and evening.
Table 3.
Average daily energy and macronutrient intake based on total, morning, and evening intake of all women included in the analyses
| N = 100 | Total intake | Morning intake | Evening intake |
|---|---|---|---|
| Energy (kcal) | 1436.3 ± 401.2 | 53.3 ± 13.5 | 46.7 ± 13.5 |
| Carbohydrates (g) | 170.7 ± 69.2 | 60.2 ± 17.1 | 39.8 ± 17.1 |
| Protein (g) | 64.3 ± 18.5 | 46.9 ± 19.4 | 53.1 ± 19.4 |
| Lipids (g) | 58.0 ± 21.2 | 48.9 ± 19.4 | 51.1 ± 17.9 |
Data are expressed as mean ± SD. “Morning intake” includes breakfast to lunch; “evening intake” includes afternoon snack to evening snack. Classification based on median lipid and energy intake in each time window
Table 4 reported main characteristics of postmenopausal women broken down by median value of evening energy intake. Notably, a significant difference was observed in smoking habits among postmenopausal women under the median and postmenopausal women above the median of evening energy intake (p = 0.002).
Table 4.
Main characteristics of postmenopausal women broken down by median value of evening energy intake
| Evening energy intake | |||
|---|---|---|---|
| Parameters | Postmenopausal women under the median (N = 50) | Postmenopausal women above the median (N = 50) | p-value |
| Age (years) | 57.2 ± 7.0 | 57.2 ± 7.7 | 0.987 |
| Smoking (n, %) | 5 (10.0) | 18 (36.0) | 0.002 |
| Physical activity (n, %) | 10 (20.0) | 9 (18.0) | 0.519 |
| BMI (kg/m2) | 36.0 ± 7.9 | 35.9 ± 6.7 | 0.930 |
| Age at menarche (years) | 11.6 ± 1.5 | 11.7 ± 1.7 | 0.871 |
| Age at menopause (years) | 48.5 ± 5.2 | 49.0 ± 4.8 | 0.600 |
| Weight before menopause (kg) | 80.5 ± 21.0 | 83.1 ± 17.1 | 0.482 |
| Weight gain after menopause (kg) | 11.2 ± 17.3 | 10.9 ± 12.5 | 0.931 |
| Menopause type | |||
| Natural (n, %) | 43 (86.0) | 44 (88.0) | 0.403 |
| Induced (n, %) | 7 (14.0) | 6 (12.0) | |
| Diseases | |||
| Type 2 diabetes (n, %) | 9 (18.0) | 4 (8.0) | 0.125 |
| Hypertension (n, %) | 21 (42.0) | 22 (44.0) | 0.467 |
| Dyslipidemia (n, %) | 21 (42.0) | 23 (46.0) | 0.388 |
A p-value in bold type denotes a significant difference (p < 0.05). Data are expressed as mean ± SD or n (%). “Morning intake” includes breakfast to lunch; “evening intake” includes afternoon snack to evening snack. Classification based on median lipid and energy intake in each time window
BMI, body mass index
Table 5 shows the menopausal symptoms (scores) and MRS scores in postmenopausal women broken down by the medians of morning energy and lipid intakes. Postmenopausal women under the median of morning energy intake showed a significantly higher score for heart discomfort (p = 0.045), while those under the median of morning intake of lipids showed significantly higher scores for heart discomfort and lower scores for bladder problems (p = 0.013 and p = 0.040, respectively).
Table 5.
Menopausal symptoms (scores) and MRS scores in postmenopausal women broken down by the medians of morning energy and lipids intakes
| Morning energy intake | Morning intake of lipids | |||||
|---|---|---|---|---|---|---|
| Scores | Under the median (N = 25) |
Above the median (N = 25) |
p-value | Under the median (N = 25) |
Above the median (N = 25) |
p-value |
| Somato-vegetative symptoms | 8.8 ± 3.9 | 8.6 ± 3.7 | 0.788 | 8.8 ± 3.8 | 8.5 ± 3.8 | 0.671 |
| Hot flashes and sweating | 2.5 ± 1.6 | 2.1 ± 1.6 | 0.242 | 2.4 ± 1.6 | 2.3 ± 1.6 | 0.867 |
| Heart discomfort | 1.5 ± 1.3 | 1.0 ± 1.2 | 0.045 | 1.5 ± 1.3 | 0.9 ± 1.2 | 0.013 |
| Sleep problems | 2.3 ± 1.7 | 2.6 ± 1.4 | 0.362 | 2.3 ± 1.6 | 2.5 ± 1.5 | 0.595 |
| Joint and muscular discomfort | 2.5 ± 1.5 | 2.9 ± 1.5 | 0.196 | 2.6 ± 1.5 | 2.8 ± 1.5 | 0.526 |
| Psychological symptoms | 9.0 ± 4.3 | 9.5 ± 4.4 | 0.582 | 9.3 ± 3.8 | 9.2 ± 4.8 | 0.860 |
| Depressive mood | 2.4 ± 1.5 | 2.3 ± 1.5 | 0.965 | 2.3 ± 1.5 | 2.4 ± 1.5 | 0.827 |
| Irritability | 2.0 ± 1.6 | 2.4 ± 1.4 | 0.184 | 2.1 ± 1.4 | 2.3 ± 1.6 | 0.592 |
| Anxiety | 2.4 ± 1.4 | 2.2 ± 1.5 | 0.651 | 2.4 ± 1.4 | 2.2 ± 1.5 | 0.469 |
| Physical and mental exhaustion | 2.3 ± 1.5 | 2.5 ± 1.4 | 0.443 | 2.5 ± 1.4 | 2.3 ± 1.5 | 0.565 |
| Urogenital symptoms | 4.8 ± 2.7 | 4.9 ± 3.2 | 0.769 | 4.8 ± 2.9 | 4.9 ± 3.1 | 0.820 |
| Sexual problems | 2.3 ± 1.6 | 1.9 ± 1.6 | 0.220 | 2.3 ± 1.5 | 2.0 ± 1.6 | 0.393 |
| Bladder problems | 0.7 ± 1.1 | 1.2 ± 1.4 | 0.059 | 0.7 ± 1.1 | 1.2 ± 1.3 | 0.040 |
| Dryness of the vagina | 1.8 ± 1.6 | 1.9 ± 1.6 | 0.770 | 1.9 ± 1.7 | 1.8 ± 1.5 | 0.751 |
| MRS total | 22.5 ± 7.4 | 23.0 ± 8.2 | 0.775 | 22.9 ± 7.0 | 22.6 ± 8.6 | 0.828 |
A p-value in bold type denotes a significant difference (p < 0.05). Data are expressed as mean ± SD. “Morning intake” includes breakfast to lunch; “evening intake” includes afternoon snack to evening snack. Classification based on median lipid and energy intake in each time window
MRS, Menopause Rating Scale
Table 6 shows the menopausal symptoms (scores) and MRS scores in postmenopausal women broken down by the medians of evening energy and lipid intakes. Postmenopausal women above the median of evening intake of lipids showed a significantly higher score for heart discomfort (p = 0.007).
Table 6.
Menopausal symptoms (scores) and MRS scores in postmenopausal women broken down by the medians of evening energy and lipids intakes
| Evening energy intake | Evening intake of lipids | |||||
|---|---|---|---|---|---|---|
| Scores | Under the median (N = 25) |
Above the median (N = 25) |
p-value | Under the median (N = 25) |
Above the median (N = 25) |
p-value |
| Somato-vegetative symptoms | 8.7 ± 3.8 | 8.6 ± 3.8 | 0.951 | 8.4 ± 3.8 | 8.9 ± 3.7 | 0.472 |
| Hot flashes and sweating | 2.2 ± 1.6 | 2.5 ± 1.6 | 0.338 | 2.3 ± 1.6 | 2.4 ± 1.6 | 0.648 |
| Heart discomfort | 1.0 ± 1.2 | 1.4 ± 1.3 | 0.117 | 0.9 ± 1.2 | 1.6 ± 1.3 | 0.007 |
| Sleep problems | 2.6 ± 1.4 | 2.2 ± 1.7 | 0.265 | 2.4 ± 1.5 | 2.4 ± 1.6 | 0.821 |
| Joint and muscular discomfort | 2.9 ± 1.5 | 2.5 ± 1.5 | 0.182 | 2.8 ± 1.5 | 2.6 ± 1.5 | 0.500 |
| Psychological symptoms | 9.6 ± 4.4 | 8.9 ± 4.2 | 0.416 | 9.2 ± 4.8 | 9.2 ± 3.8 | 0.996 |
| Depressive mood | 2.4 ± 1.5 | 2.3 ± 1.5 | 0.862 | 2.4 ± 1.5 | 2.3 ± 1.5 | 0.760 |
| Irritability | 2.4 ± 1.4 | 1.9 ± 1.5 | 0.116 | 2.3 ± 1.5 | 2.1 ± 1.4 | 0.519 |
| Anxiety | 2.3 ± 1.5 | 2.3 ± 1.4 | 0.823 | 2.2 ± 1.5 | 2.4 ± 1.4 | 0.529 |
| Physical and mental exhaustion | 2.5 ± 1.4 | 2.3 ± 1.5 | 0.398 | 2.4 ± 1.5 | 2.5 ± 1.4 | 0.716 |
| Urogenital symptoms | 5.0 ± 3.2 | 4.7 ± 2.7 | 0.661 | 4.9 ± 3.0 | 4.8 ± 2.9 | 0.864 |
| Sexual problems | 2.0 ± 1.6 | 2.3 ± 1.6 | 0.321 | 2.0 ± 1.7 | 2.2 ± 1.5 | 0.534 |
| Bladder problems | 1.1 ± 1.4 | 0.7 ± 1.1 | 0.082 | 1.2 ± 1.3 | 0.7 ± 1.1 | 0.057 |
| Dryness of the vagina | 1.9 ± 1.6 | 1.7 ± 1.7 | 0.660 | 1.7 ± 1.5 | 1.9 ± 1.7 | 0.592 |
| MRS total | 23.2 ± 8.4 | 22.2 ± 7.2 | 0.519 | 22.6 ± 8.6 | 23.0 ± 7.0 | 0.775 |
A p-value in bold type denotes a significant difference (p < 0.05). Data are expressed as score ± SD. “Morning intake” includes breakfast to lunch; “evening intake” includes afternoon snack to evening snack. Classification based on median lipid and energy intake in each time window
MRS, Menopause Rating Scale
Simple and after-adjustment for total energy intake, age, and BMI correlations between the heart discomfort score and the morning, evening, and total intakes of lipids are shown in Table 7. The heart discomfort score correlated negatively and positively with the morning (r = -0.210, p = 0.034) and evening (r = 0.210, p = 0.034) intakes of lipids, respectively. The correlations were maintained even after correction for confounding factors (r = -0.219 and r = 0.219 for morning and evening intakes of lipids, respectively, p = 0.028 for both).
Table 7.
Simple and after-adjustment for total energy intake, age, and BMI correlations between the heart discomfort score and the morning, evening, and total intakes of lipids in postmenopausal women
| Intakes of lipids | Heart discomfort score | |||
|---|---|---|---|---|
| Simple Correlation | After adjusted for total energy intake, age, and BMI | |||
| r | p-value | r | p-value | |
| Morning (%) | -0.210 | 0.034 | -0.219 | 0.028 |
| Evening (%) | 0.210 | 0.034 | 0.219 | 0.028 |
| Total (g) | 0.041 | 0.681 | 0.047 | 0.644 |
A p-value in bold type denotes a significant difference (p < 0.05). “Morning intake” includes breakfast to lunch; “evening intake” includes afternoon snack to evening snack. Classification based on median lipid and energy intake in each time window
BMI, body mass index
Discussion
In this cross-sectional observational study, we focused on the timing of total energy and lipid intakes and menopausal symptoms in 100 postmenopausal women with overweight or obesity. Firstly, we observed that women with a morning total energy intake below the median had notably higher scores for cardiac discomfort. Similarly, those with a morning lipids intake below the median demonstrated higher scores for cardiac discomfort and lower scores for bladder problems.
These results showed for the first time the importance of considering not only the amount but also the timing of total energy and lipid intakes in managing menopausal symptoms in postmenopausal women with overweight or obesity. In this regard, consuming food late at night has been linked to an increased risk of poor cardiovascular and metabolic health in various studies that analyze different populations [32]. For instance, in a study involving 3610 Swedish individuals, those who ate late at night had a 1.62 times higher risk of obesity compared to those who did not eat late at night [33]. Similarly, in a smaller study of 239 US adults, those who consumed more than 33% of their total daily energy intake in the evening were twice as likely to suffer from obesity compared to those who consumed less than 33% of their energy intake at night [34]. Additionally, a combination of late-night eating and skipping breakfast was associated with a higher risk of developing metabolic syndrome in a study of 60,800 Japanese adults aged 20 to 75 years [35]. Compared to individuals with healthier eating habits, those who ate dinner within 2 h of bedtime and skipped breakfast had a 1.17 times higher risk of metabolic syndrome [35].
The impact of eating frequency and timing on inflammation and insulin resistance markers was examined in a study involving 2212 female participants in NHANES 2009 to 2010 [36]. The study looked at variables such as the number of eating occasions per day, the percentage of total energy intake consumed between 5 pm and midnight, and the duration of nighttime fasting. Results showed that a 10% increase in the proportion of total energy intake consumed in the evening was associated with a 3% increase in C-reactive protein concentrations. Conversely, having one additional eating occasion per day was associated with an 8% decrease in C-reactive protein levels. However, there was no significant relationship found with nighttime fasting duration and HOMA-IR. Interestingly, favorable effects of nighttime fasting on inflammation and insulin resistance were observed only in women who stopped eating before 6 pm, while prolonging nighttime fasting by skipping breakfast did not yield the same beneficial effects. It’s important to note that a limitation of these studies is the variation in the definition of late-night eating across different studies. Additionally, the causality of the associations between eating patterns and cardiovascular risk factors remains unclear due to the cross-sectional nature of the studies [36].
Evidence from both animal and human research suggests that the timing of food consumption throughout the day can significantly influence the metabolic rhythms crucial for human well-being [37]. Food intake exerts a profound impact by influencing molecular oscillators known as circadian clocks, which are present in nearly all cells and tissues and synchronize rhythmic metabolic processes [38]. It is now well understood that eating at irregular times can disrupt the circadian system, leading to a mismatch in circadian rhythms [39, 40]. This misalignment, caused by improper timing of meals, disrupts normal metabolic regulation and homeostasis, thereby increasing the risk of cardiometabolic conditions such as obesity, type 2 diabetes, and ultimately cardiovascular diseases [41, 42]. Precise mechanisms likely involve the transmission of multiple reinforcing signals, the expression of numerous energy-regulating hormones, and changes in adipose tissue regulation, all contributing to the promotion of obesity [40, 43].
Of particular note are metabolic hormones that convey information about meal timing to circadian clocks, thereby regulating energy metabolism [40]. These hormones, including cortisol, insulin, insulin-like growth factor 1 (IGF-1), ghrelin, leptin, pancreatic peptide YY (PYY), gastric inhibitory polypeptide (GLP-1), and adiponectin, exhibit circadian rhythmicity, with their peak circulating levels showing time-of-day-dependent variations crucial for efficient nutrient metabolism. This metabolic activity appears to be most optimal earlier in the day rather than during rest hours. For instance, cortisol peaks at 8 a.m., ghrelin (which increases appetite) peaks at 8 a.m., 1 p.m., and 6 p.m., adiponectin peaks at 11 a.m., insulin peaks at 5 p.m., and leptin (which inhibits fat accumulation) peaks at 7 p.m. Therefore, concentrating the majority of daily food intake earlier in the day may better align with the pulsatile rhythm of the circadian clock [40].
Taking a closer look at lipid intake, in our study, postmenopausal women above the median of evening intake exhibited significantly higher scores for cardiac discomfort. The correlation between the cardiac discomfort score and morning lipids intake was significantly negative, while the correlation with evening lipid intake was significantly positive. These correlations remained significant even after adjustment for confounding variables such as total energy intake, age, and BMI, reinforcing the hypothesis that timing of lipid intake could have an independent impact on cardiac symptoms.
There is substantial evidence indicating that the timing of dietary fat intake plays a crucial role, but data are currently available only for obesity. To our knowledge, no one has yet studied the specific setting of postmenopausal women. High-fat diets can exert significant effects on both obesity and chronobiology, as the consumption of saturated and unsaturated fats influences the circadian cycle differently. Additionally, there is a notable risk of developing metabolic syndrome and insulin resistance associated with such dietary patterns. An observational study involving 52 healthy volunteers revealed that consuming a higher percentage of fat after 8 p.m. was linked to adverse outcomes, including a higher BMI and increased total calorie intake, compared to those who consumed fat within 4 h of sleeping [44]. The detrimental impact of elevated fat intake during the evening was also investigated in mice [45]. A study highlighted that the consumption of a high-fat diet at the end of the active phase, akin to an evening meal in humans, compromised metabolic plasticity. This led to increased total energy intake, resulting in weight gain, elevated adiposity, hypertriglyceridemia, and hyperleptinemia [45]. Regarding morning fat intake, an in vivo study using mouse models demonstrated that a high-fat diet during the light phase significantly contributed to greater weight gain compared to mice fed only during the dark phase over a 6-week period [46]. However, despite equivalent calorie intake and similar levels of locomotor activity between the two groups, mice fed during the light phase were consistently less active than those fed during the dark period, indicating a potential decrease in energy expenditure. Furthermore, the light-fed group also consumed slightly more than the dark-fed group. The combined impact of these effects could explain the observed differences in body weight between the two groups [46].
Bandin and colleagues reported that consistently delaying meals may have an impact on the daily variation of free cortisol levels [47]. Cortisol, a hormone with a significant daily fluctuation, typically peaks in the morning, close to wake-up time, and decreases in the evening, close to bedtime. These fluctuations play a crucial role in regulating the sleep/awake cycle and are often used to monitor circadian rhythms [48]. A robust daily cortisol variation indicates a typical diurnal rhythm, while reduced variability or flattened cortisol rhythmicity has been associated with chronic emotional or physical stress, obesity, and metabolic syndrome. The diminished daily variability in cortisol, as observed with delayed meals, can lead to a decrease in circadian signaling to peripheral clocks. As the amplitude of cortisol decreases, cells may gradually lose synchronization, contributing to lower glucose tolerance and oxidation. If this misalignment persists over extended periods, it may also elevate plasma concentrations of pro- and anti-inflammatory proteins [41, 49]. This, in turn, contributes to the low-grade inflammation commonly found in obesity and metabolic syndrome, which retroactively worsens glucose and lipid metabolism, creating a snowball effect that could affect cardiovascular health [50].
It is important to acknowledge that this study was observational in nature, limiting our ability to establish causality. The identified associations may be influenced by unmeasured confounding variables or reverse causation, preventing definitive cause-and-effect conclusions. Additionally, the study’s geographic homogeneity, with all participants attending the same hospital in Naples, Italy, and the relatively small sample size further restrict generalizability. While the sample size calculation was performed using G*Power Software and inclusion criteria were appropriately applied, future studies with larger, geographically diverse populations are needed. Moreover, although our study focused on lipid intake timing, we did not collect specific data on lipid types (e.g., saturated vs. unsaturated fats), which could offer further insights into their differential impact on metabolic outcomes. Future research using longitudinal or experimental designs, such as randomized controlled trials, and considering confounding factors like sleep quality, metabolic status, and physical activity, would be necessary to confirm causality and the directionality of these findings. Finally, although participants were trained to complete the dietary diaries and all entries were reviewed by expert nutritionists, the use of self-reported dietary data remains subject to recall bias and potential misreporting. Despite these limitations, the novelty of the evidence generated by this study holds significant potential. This new insight can serve as a guiding force for future research and interventions aimed at enhancing women’s health during the menopausal transition.
In conclusion, our findings revealed that a predominantly later intake of energy and lipids was associated with an increased prevalence of menopausal symptoms in postmenopausal women with overweight or obesity. This eating pattern may potentially have adverse effects on the cardiovascular health of these women. Therefore, adopting chrononutrition behaviors, particularly favoring an earlier intake of energy and lipids, could prove beneficial as an additional measure in the nutritional therapy for postmenopausal women dealing with overweight or obesity. However, given the observational nature of the study, further research, possibly through randomized controlled trials, is needed to confirm the associations found and better understand the effects of “when to eat” on menopausal symptoms.
Abbreviations
- BMI
Body Mass Index
- GLP-1
Glucagon-Like Peptide 1
- HOMA-IR
Homeostatic Model Assessment of Insulin Resistance
- IGF-1
Insulin-Like Growth Factor 1
- MD
Mediterranean Diet
- MRS
Menopause Rating Scale
- NHANES
National Health and Nutrition Examination Survey
- PYY
Pancreatic Peptide YY
- SD
Standard Deviation
Author contributions
The authors’ responsibilities were as follows LB, LV, and GM: were responsible for the concept and design of the study and interpreted data and drafted the manuscript; LB conducted statistical analyses; RZV, DSR, PM, MG, SS, AC: provided a critical review of the manuscript. All authors contributed to and agreed on the final version of the manuscript.
Funding
The authors declare that financial support was received for the research, authorship and/or publication of this article. The research leading to these results has received funding from PRIN n.2022BASPYN.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study has been approved by the Local Ethical Committee and carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments that involved humans. The aim of the study was clearly explained to all the study participants and a written informed consent was obtained.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Luigi Barrea and Ludovica Verde have equal contributed to this article and should be considered as co-first authors.
References
- 1.Nelson HD, Menopause. Lancet. 2008;371(9614):760–70. [DOI] [PubMed] [Google Scholar]
- 2.Avis NE, Colvin A, Bromberger JT, Hess R, Matthews KA, Ory M, et al. Change in health-related quality of life over the menopausal transition in a multiethnic cohort of middle-aged women: study of women’s health across the Nation. Menopause. 2009;16(5):860–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Gallicchio L, Miller S, Zacur H, Flaws JA. Race and health-related quality of life in midlife women in baltimore, Maryland. Maturitas. 2009;63(1):67–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Karacam Z, Seker SE. Factors associated with menopausal symptoms and their relationship with the quality of life among Turkish women. Maturitas. 2007;58(1):75–82. [DOI] [PubMed] [Google Scholar]
- 5.Williams RE, Kalilani L, DiBenedetti DB, Zhou X, Fehnel SE, Clark RV. Healthcare seeking and treatment for menopausal symptoms in the united States. Maturitas. 2007;58(4):348–58. [DOI] [PubMed] [Google Scholar]
- 6.Davis SR, Castelo-Branco C, Chedraui P, Lumsden MA, Nappi RE, Shah D, et al. Understanding weight gain at menopause. Climacteric. 2012;15(5):419–29. [DOI] [PubMed] [Google Scholar]
- 7.El Khoudary SR, Aggarwal B, Beckie TM, Hodis HN, Johnson AE, Langer RD, et al. Menopause transition and cardiovascular disease risk: implications for timing of early prevention: A scientific statement from the American heart association. Circulation. 2020;142(25):e506–32. [DOI] [PubMed] [Google Scholar]
- 8.Nappi RE, Simoncini T. Menopause transition: a golden age to prevent cardiovascular disease. Lancet Diabetes Endocrinol. 2021;9(3):135–7. [DOI] [PubMed] [Google Scholar]
- 9.Hirahatake KM, Jiang L, Wong ND, Shikany JM, Eaton CB, Allison MA, et al. Diet quality and cardiovascular disease risk in postmenopausal women with type 2 diabetes mellitus: the women’s health initiative. J Am Heart Assoc. 2019;8(19):e013249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Gold EB, Flatt SW, Pierce JP, Bardwell WA, Hajek RA, Newman VA, et al. Dietary factors and vasomotor symptoms in breast cancer survivors: the WHEL study. Menopause. 2006;13(3):423–33. [DOI] [PubMed] [Google Scholar]
- 11.Kupferer EM, Dormire SL, Becker H. Complementary and alternative medicine use for vasomotor symptoms among women who have discontinued hormone therapy. J Obstet Gynecol Neonatal Nurs. 2009;38(1):50–9. [DOI] [PubMed] [Google Scholar]
- 12.Somekawa Y, Chiguchi M, Ishibashi T, Aso T. Soy intake related to menopausal symptoms, serum lipids, and bone mineral density in postmenopausal Japanese women. Obstet Gynecol. 2001;97(1):109–15. [DOI] [PubMed] [Google Scholar]
- 13.Herber-Gast GC, Mishra GD, Fruit. Mediterranean-style, and high-fat and -sugar diets are associated with the risk of night sweats and hot flushes in midlife: results from a prospective cohort study. Am J Clin Nutr. 2013;97(5):1092–9. [DOI] [PubMed] [Google Scholar]
- 14.Flor-Alemany M, Marin-Jimenez N, Coll-Risco I, Aranda P, Aparicio VA. Influence of dietary habits and mediterranean diet adherence on menopausal symptoms. The FLAMENCO project. Menopause. 2020;27(9):1015–21. [DOI] [PubMed] [Google Scholar]
- 15.Vetrani C, Barrea L, Rispoli R, Verde L, De Alteriis G, Docimo A, et al. Mediterranean diet: what are the consequences for menopause?? Front Endocrinol (Lausanne). 2022;13:886824. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Barrea L, Frias-Toral E, Aprano S, Castellucci B, Pugliese G, Rodriguez-Veintimilla D, et al. The clock diet: a practical nutritional guide to manage obesity through chrononutrition. Minerva Med. 2022;113(1):172–88. [DOI] [PubMed] [Google Scholar]
- 17.Bernardes da Cunha N, Teixeira GP, Madalena Rinaldi AE, Azeredo CM, Crispim CA. Late meal intake is associated with abdominal obesity and metabolic disorders related to metabolic syndrome: A chrononutrition approach using data from NHANES 2015–2018. Clin Nutr. 2023;42(9):1798–805. [DOI] [PubMed] [Google Scholar]
- 18.Verde L, Barrea L, Vetrani C, Frias-Toral E, Chapela SP, Jayawardena R, et al. Chronotype and sleep quality in obesity: how do they change after menopause?? Curr Obes Rep. 2022;11(4):254–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Verde L, Di Lorenzo T, Savastano S, Colao A, Barrea L, Muscogiuri G. Chrononutrition in type 2 diabetes mellitus and obesity: A narrative review. Diabetes Metab Res Rev. 2024;40(2):e3778. [DOI] [PubMed] [Google Scholar]
- 20.Barrea L, Vetrani C, Altieri B, Verde L, Savastano S, Colao A et al. The Importance of Being a ‘Lark’ in Post-Menopausal Women with Obesity: A Ploy to Prevent Type 2. Diabetes Mellitus? Nutrients. 2021;13(11). [DOI] [PMC free article] [PubMed]
- 21.Hatori M, Vollmers C, Zarrinpar A, DiTacchio L, Bushong EA, Gill S, et al. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 2012;15(6):848–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kohsaka A, Laposky AD, Ramsey KM, Estrada C, Joshu C, Kobayashi Y, et al. High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metab. 2007;6(5):414–21. [DOI] [PubMed] [Google Scholar]
- 23.von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7. [DOI] [PubMed] [Google Scholar]
- 24.Willett W. Nutritional epidemiology. 3rd ed. Oxford Academic; 2012.
- 25.Barrea L, Muscogiuri G, Pugliese G, Laudisio D, de Alteriis G, Graziadio C et al. Phase angle as an easy diagnostic tool of Meta-Inflammation for the nutritionist. Nutrients. 2021;13(5). [DOI] [PMC free article] [PubMed]
- 26.Khazem S, Itani L, Kreidieh D, El Masri D, Tannir H, Citarella R et al. Reduced lean body mass and cardiometabolic diseases in adult males with overweight and obesity: A pilot study. Int J Environ Res Public Health. 2018;15(12). [DOI] [PMC free article] [PubMed]
- 27.Schneider HP, Heinemann LA, Rosemeier HP, Potthoff P, Behre HM. The menopause rating scale (MRS): reliability of scores of menopausal complaints. Climacteric. 2000;3(1):59–64. [DOI] [PubMed] [Google Scholar]
- 28.Tao M, Shao H, Li C, Teng Y. Correlation between the modified Kupperman index and the menopause rating scale in Chinese women. Patient Prefer Adherence. 2013;7:223–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Rahman SA, Zainudin SR, Mun VL. Assessment of menopausal symptoms using modified menopause rating scale (MRS) among middle age women in kuching, sarawak, Malaysia. Asia Pac Fam Med. 2010;9(1):5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Heinemann K, Ruebig A, Potthoff P, Schneider HP, Strelow F, Heinemann LA, et al. The menopause rating scale (MRS) scale: a methodological review. Health Qual Life Outcomes. 2004;2:45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Kang H. Sample size determination and power analysis using the G*Power software. J Educ Eval Health Prof. 2021;18:17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Dashti HS, Jansen EC, Zuraikat FM, Dixit S, Brown M, Laposky A et al. Advancing chrononutrition for cardiometabolic health: A 2023 National heart, lung, and blood Institute workshop report. J Am Heart Assoc. 2025:e039373. [DOI] [PMC free article] [PubMed]
- 33.Berg C, Lappas G, Wolk A, Strandhagen E, Toren K, Rosengren A, et al. Eating patterns and portion size associated with obesity in a Swedish population. Appetite. 2009;52(1):21–6. [DOI] [PubMed] [Google Scholar]
- 34.Wang JB, Patterson RE, Ang A, Emond JA, Shetty N, Arab L. Timing of energy intake during the day is associated with the risk of obesity in adults. J Hum Nutr Diet. 2014;27(Suppl 2):255–62. [DOI] [PubMed] [Google Scholar]
- 35.Kutsuma A, Nakajima K, Suwa K. Potential association between breakfast skipping and concomitant Late-Night-Dinner eating with metabolic syndrome and proteinuria in the Japanese population. Scientifica (Cairo). 2014;2014:253581. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Marinac CR, Sears DD, Natarajan L, Gallo LC, Breen CI, Patterson RE. Frequency and circadian timing of eating May influence biomarkers of inflammation and insulin resistance associated with breast Cancer risk. PLoS ONE. 2015;10(8):e0136240. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Wehrens SMT, Christou S, Isherwood C, Middleton B, Gibbs MA, Archer SN, et al. Meal timing regulates the human circadian system. Curr Biol. 2017;27(12):1768–75. e3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Pickel L, Sung HK. Feeding rhythms and the circadian regulation of metabolism. Front Nutr. 2020;7:39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Charlot A, Hutt F, Sabatier E, Zoll J. Beneficial effects of early Time-Restricted feeding on metabolic diseases: importance of aligning food habits with the circadian clock. Nutrients. 2021;13(5). [DOI] [PMC free article] [PubMed]
- 40.Ruddick-Collins LC, Morgan PJ, Johnstone AM, Mealtime. A circadian disruptor and determinant of energy balance? J Neuroendocrinol. 2020;32(7):e12886. [DOI] [PubMed] [Google Scholar]
- 41.Mason IC, Qian J, Adler GK, Scheer F. Impact of circadian disruption on glucose metabolism: implications for type 2 diabetes. Diabetologia. 2020;63(3):462–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Chellappa SL, Vujovic N, Williams JS, Scheer F. Impact of circadian disruption on cardiovascular function and disease. Trends Endocrinol Metab. 2019;30(10):767–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Heyde I, Begemann K, Oster H. Contributions of white and brown adipose tissues to the circadian regulation of energy metabolism. Endocrinology. 2021;162(3). [DOI] [PMC free article] [PubMed]
- 44.Baron KG, Reid KJ, Horn LV, Zee PC. Contribution of evening macronutrient intake to total caloric intake and body mass index. Appetite. 2013;60(1):246–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Bray MS, Tsai JY, Villegas-Montoya C, Boland BB, Blasier Z, Egbejimi O, et al. Time-of-day-dependent dietary fat consumption influences multiple cardiometabolic syndrome parameters in mice. Int J Obes (Lond). 2010;34(11):1589–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Arble DM, Bass J, Laposky AD, Vitaterna MH, Turek FW. Circadian timing of food intake contributes to weight gain. Obes (Silver Spring). 2009;17(11):2100–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Bandin C, Scheer FA, Luque AJ, Avila-Gandia V, Zamora S, Madrid JA, et al. Meal timing affects glucose tolerance, substrate oxidation and circadian-related variables: A randomized, crossover trial. Int J Obes (Lond). 2015;39(5):828–33. [DOI] [PubMed] [Google Scholar]
- 48.Hastings M, O’Neill JS, Maywood ES. Circadian clocks: regulators of endocrine and metabolic rhythms. J Endocrinol. 2007;195(2):187–98. [DOI] [PubMed] [Google Scholar]
- 49.Zinna L, Verde L, Tolla MFD, Barrea L, Parascandolo A, D’Alterio F, et al. Chronodisruption enhances inflammatory cytokine release from visceral adipose tissue in obesity. J Transl Med. 2025;23(1):231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Guarner V, Rubio-Ruiz ME. Low-grade systemic inflammation connects aging, metabolic syndrome and cardiovascular disease. Interdiscip Top Gerontol. 2015;40:99–106. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.