Eating Patterns, Chronotypes, and Their Relationship with Metabolic Health in the Early Postpartum Period in Women after Gestational Diabetes Mellitus.
Anna Lesniara-StachonMariana Treviño MontemayorTinh-Hai ColletMagali AndreyDan Yedu QuansahJardena J PuderPublished in: Nutrients (2024)
Observational studies have shown a relationship between eating patterns and chronotypes with metabolic health in the general population and in healthy pregnancies. Data are lacking in the postpartum period, which is characterized by an externally driven misalignment of sleep and food intake. We investigated the associations between eating patterns, chronotypes, and metabolic health in the early postpartum period in women who had gestational diabetes mellitus (GDM). We prospectively included 313 women who completed their 6-8 weeks postpartum visit between January 2021 and March 2023 at the Lausanne University Hospital. Women filled questionnaires on the timing of food intake, sleep (a shortened Pittsburgh Sleep Quality Questionnaire), and the chronotype (the Morningness-Eveningness Questionnaire) and underwent HbA1c and fasting plasma glucose measurements. After adjustments for weight, sleep quality, or breastfeeding, the later timing of the first and last food intake were associated with higher fasting plasma glucose and HbA1c levels 6-8 weeks postpartum (all p ≤ 0.046). A higher number of breakfasts per week and longer eating durations were associated with lower fasting plasma glucose levels (all p ≤ 0.028). The chronotype was not associated with metabolic health outcomes. Eating patterns, but not the chronotype, were associated with worsened metabolic health in the early postpartum period in women with previous GDM.
Keyphrases
- sleep quality
- physical activity
- pregnancy outcomes
- blood glucose
- weight loss
- polycystic ovary syndrome
- public health
- healthcare
- depressive symptoms
- mental health
- health information
- pregnant women
- clinical trial
- preterm infants
- cervical cancer screening
- randomized controlled trial
- type diabetes
- risk assessment
- cross sectional
- preterm birth
- mass spectrometry
- metabolic syndrome
- human health
- gestational age
- breast cancer risk
- electronic health record
- machine learning
- social media
- weight gain
- deep learning
- body weight
- skeletal muscle
- patient reported