Dietary diversity and its associations with sleep quality and chronotype in young and middle-aged adults – Frontiers

Report on the Associations Between Dietary Diversity, Sleep Quality, and Chronotype in Young and Middle-Aged Adults: Emphasis on Sustainable Development Goals

Introduction

Sleep is fundamental to maintaining physical and mental health, aligning with Sustainable Development Goal (SDG) 3: Good Health and Well-being. Healthy sleep is regulated by homeostatic mechanisms and circadian rhythms, with chronotype—individual preference for timing of daily activities—strongly influencing sleep quality. Poor sleep quality and evening chronotype are linked to increased risks of obesity, diabetes, cardiovascular diseases, and mortality, highlighting the importance of addressing lifestyle factors to promote health and well-being.

Diet, a modifiable lifestyle behavior, is closely associated with sleep quality and chronotype. Evidence suggests that specific nutrients and dietary patterns improve sleep quality and promote earlier chronotypes. Dietary diversity, defined as the variety of food groups consumed, serves as an indicator of overall diet quality and nutrient adequacy, contributing to SDG 2: Zero Hunger and SDG 3.

Despite its importance, limited research has examined the relationship between dietary diversity and sleep outcomes, particularly among young and middle-aged adults who face unique lifestyle pressures. Additionally, depressive symptoms, which relate to both diet and sleep, may mediate these associations but have not been systematically studied in this context.

This study aims to investigate the associations between dietary diversity and sleep quality and chronotype in a population-based sample of young and middle-aged adults, exploring the potential mediating role of depressive symptoms and interaction effects by sociodemographic, behavioral, and psychological characteristics.

Methods

Study Design and Population

The study utilized data from the China Nutrition and Sleep Survey (CNSS), a large-scale, ongoing research initiative aligned with SDG 3 and SDG 10: Reduced Inequalities, aiming to explore diet and sleep health associations. Data from the 2024 and 2025 cross-sectional waves were combined, encompassing 4,128 participants aged 20–59 years from across China’s seven geographic regions, ensuring demographic representativeness.

Assessment of Dietary Diversity

Dietary diversity was assessed using a validated Food Frequency Questionnaire (FFQ) covering nine major food groups, excluding cereals and oils due to their ubiquitous consumption. Dietary Diversity Scores (DDS) and related indices were calculated:

1. Total DDS: Scores range 0–9 based on intake frequency of vegetables, fruits, legumes, nuts, meat, eggs, fish, dairy, and tea.
2. Animal-based DDS: Includes meat, fish, eggs, dairy (0–4 scale).
3. Plant-based DDS: Includes vegetables, fruits, legumes, nuts (0–4 scale).
4. Anti-inflammatory Diet Diversity Index (AIDDI): Based on food groups known to reduce inflammation (0–5 scale).
5. Protein-Enriched Diet Diversity Index (PEDDI): Sum of protein-rich food consumption scores (0–6 scale).

Assessment of Sleep Quality and Chronotype

• Sleep Quality: Measured by the Pittsburgh Sleep Quality Index (PSQI), with scores >7 indicating poor sleep quality.
• Chronotype: Assessed by the Morning and Evening Questionnaire (MEQ-5), classifying participants as evening, intermediate, or morning types.

Assessment of Depression

Depressive symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9), with scores ≥10 indicating depression.

Covariates and Statistical Analysis

Analyses adjusted for sociodemographic and lifestyle covariates including age, sex, ethnicity, residence, education, overweight/obesity status, smoking, drinking, physical exercise, and survey wave. Propensity Score Matching (PSM) was employed to reduce confounding. Linear and logistic regression models estimated associations, while mediation analyses explored the role of depressive symptoms. Sensitivity and stratified analyses assessed robustness and effect modification.

Results

Participant Characteristics

• 4,128 participants included; 42.95% had good sleep quality, 57.05% poor sleep quality.
• Poor sleep quality was more prevalent among younger adults, females, rural residents, smokers, and drinkers.
• After PSM, groups were balanced on key covariates.

Associations Between Dietary Diversity and Sleep Quality

• Higher dietary diversity scores were significantly associated with better sleep quality (lower PSQI scores) and reduced odds of poor sleep quality.
• Associations were consistent across total DDS, animal-based DDS, plant-based DDS, AIDDI, and PEDDI indices.

Associations Between Dietary Diversity and Chronotype

• Greater dietary diversity was linked to a morning chronotype preference and lower odds of evening chronotype.
• Findings were consistent across dietary diversity indices and robust after PSM.

Associations Between Dietary Diversity and Depression

• Higher dietary diversity was associated with lower depression scores and reduced odds of depression.
• These associations persisted after controlling for confounders and PSM.

Stratified and Interaction Analyses

• Stronger associations between dietary diversity and sleep quality were observed among females, adults aged ≥45 years, non-drinkers, physically active individuals, and those with depressive symptoms.
• Positive associations between dietary diversity and morning chronotype were stronger in overweight/obese participants for animal-based DDS and PEDDI.

Mediation Analyses

• Depressive symptoms partially mediated the associations between dietary diversity and both sleep quality and chronotype, suggesting psychological well-being as a relevant factor.
• Due to the cross-sectional design, causal mediation cannot be confirmed.

Sensitivity Analyses

• Findings were robust across survey waves and when varying the PSQI cutoff for poor sleep quality.

Discussion

This study provides novel evidence linking greater dietary diversity with improved sleep quality and earlier chronotype among young and middle-aged adults, supporting SDG 3 by promoting health and well-being through sustainable dietary practices. The mediation by depressive symptoms underscores the importance of integrating mental health considerations in lifestyle interventions.

Potential mechanisms include balanced nutrient intake supporting circadian regulation, healthier lifestyle behaviors associated with diverse diets, and beneficial effects on gut microbiota influencing sleep and circadian rhythms. The stronger associations in specific subgroups highlight the need for tailored public health strategies, aligning with SDG 10 by addressing health disparities.

Strengths of the study include a large, representative sample, comprehensive dietary diversity assessment, rigorous confounding control via PSM, and exploration of psychological mediators. Limitations include the cross-sectional design limiting causal inference, potential selection bias, reliance on self-reported measures, and generalizability restricted to Chinese populations.

Conclusions

The findings suggest that promoting dietary diversity may be a feasible, sustainable approach to enhance sleep health, psychological well-being, and circadian alignment in working-age adults, contributing to multiple SDGs including SDG 3 (Good Health and Well-being), SDG 2 (Zero Hunger), and SDG 10 (Reduced Inequalities). Future longitudinal and interventional research incorporating objective assessments is warranted to establish causality and inform evidence-based public health policies.

1. Sustainable Development Goals (SDGs) Addressed

1. SDG 3: Good Health and Well-being
   • The article focuses on sleep quality, mental health (depression), and chronic disease risks (obesity, diabetes, cardiovascular diseases), which are central to SDG 3.
   • Sleep quality and dietary diversity are linked to physical and mental health outcomes.
2. SDG 2: Zero Hunger
   • Dietary diversity and nutrient adequacy are discussed, relating to SDG 2’s aim to end hunger and ensure access to nutritious food.
3. SDG 10: Reduced Inequalities
   • The study considers sociodemographic factors such as urban/rural residence, education, and ethnicity, addressing health inequalities.

2. Specific Targets Under the Identified SDGs

1. SDG 3: Good Health and Well-being
   • Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
     • The article links poor sleep quality and chronotype to risks of obesity, diabetes, cardiovascular diseases, and depression.
   • Target 3.5: Strengthen the prevention and treatment of substance abuse, including harmful use of alcohol.
     • Associations with drinking behavior and sleep quality are discussed.
   • Target 3.8: Achieve universal health coverage, including access to quality essential health-care services.
     • Focus on population-based health surveys and assessments of mental and physical health.
2. SDG 2: Zero Hunger
   • Target 2.2: By 2030, end all forms of malnutrition, including achieving targets on stunted and wasted children and addressing the nutritional needs of adolescent girls, pregnant and lactating women, and older persons.
     • Dietary diversity as an indicator of nutrient adequacy and diet quality is emphasized.
3. SDG 10: Reduced Inequalities
   • Target 10.2: Empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.
     • The study addresses differences in sleep quality and diet across demographic groups (age, sex, ethnicity, urban/rural residence).

3. Indicators Mentioned or Implied to Measure Progress

1. Sleep Quality Indicators
   • Pittsburgh Sleep Quality Index (PSQI) – a validated scale measuring sleep quality, with scores >7 indicating poor sleep quality.
   • Used to assess prevalence and severity of poor sleep quality in the population.
2. Chronotype Indicator
   • Morning and Evening Questionnaire (MEQ-5) – measures individual chronotype (morningness-eveningness preference).
   • Classifies participants as evening, intermediate, or morning types.
3. Dietary Diversity Indicators
   • Dietary Diversity Score (DDS) – based on intake frequency of nine major food groups.
   • Sub-indices: animal-based DDS, plant-based DDS, Anti-inflammatory Diet Diversity Index (AIDDI), Protein-Enriched Diet Diversity Index (PEDDI).
   • Used as indicators of diet quality and nutrient adequacy.
4. Mental Health Indicator
   • Patient Health Questionnaire-9 (PHQ-9) – assesses depressive symptoms, with scores ≥10 indicating depression.
5. Behavioral and Sociodemographic Covariates
   • Age, sex, ethnicity, residence, education, overweight/obesity status, smoking, drinking, physical exercise.
   • Used to analyze disparities and effect modification.

4. Table of SDGs, Targets, and Indicators

Source: frontiersin.org