Abstract
This hospital-based study aimed to investigate the relationship between dietary omega-3 (DHA, EPA) and omega-6 (linoleic acid) fatty acid intake and sleep quality in older adults with type 2 diabetes mellitus (T2DM), addressing gaps in research on habitual (non-supplemental) intake patterns and their associations with sleep disturbances in this high-risk population. A case-control study included 193 hospitalized participants, divided into with sleep impairment (Pittsburgh Sleep Quality Index, PSQI ≥5) and without sleep impairment (PSQI <5) groups. Dietary intake was assessed via a validated 14-item food frequency questionnaire incorporating local food profiles. Sleep quality was measured using the PSQI. Logistic regression models evaluated associations between fatty acid intake thresholds and sleep impairment risk. Sleep-impaired individuals exhibited significantly lower marine ω-3 intake: DHA (284.5 vs 884.8 mg/d, P <.001) and EPA (134.7 vs 405.2 mg/d, P <.001). Adjusted models revealed consistent associations: DHA intake <583.1 mg/d increased sleep impairment risk by 2.81-fold (95% CI:1.61–4.92, P = .006), while EPA <269.3 mg/d conferred 1.99-fold higher risk (95% CI:1.05–3.75, P = .012). Total polyunsaturated fatty acid (PUFA) (OR = 1.62, P = .182) and linoleic acid intake (OR = 1.79, P = .842) showed no significant associations. Insufficient marine ω-3 intake, particularly DHA and EPA, is a modifiable risk factor for sleep impairment in older adults with diabetes.
Keywords: DHA, diabetes, EPA, fatty acids, sleep quality
1. Introduction
The convergence of population aging and diabetes pandemic poses escalating challenges to global health systems.[1] Recent surveillance data estimate 129 million adults ≥65 years worldwide live with diabetes, exceeding 22% prevalence in industrialized nations.[2,3] Within this population, sleep disturbances – ranging from insomnia to reduced sleep efficiency – are prevalent yet frequently overlooked comorbidities.[4,5] Systematic reviews indicate 38% to 55% of elderly diabetics meet diagnostic criteria for clinically significant sleep disorders, representing nearly twice the risk of age-matched nondiabetics.[6–8] This reciprocal exacerbation cycle links dysglycemia and sleep disruption: chronic hyperglycemia aggravates nocturia and neuropathic pain, thereby fragmenting sleep architecture, whereas sleep deprivation impairs insulin sensitivity and amplifies systemic inflammation.[5,9]
Omega-3 (EPA/DHA) and omega-6 (AA) polyunsaturated fatty acids (PUFAs) jointly regulate inflammatory cascades and neuroendocrine signaling.[10] Beyond its pro-inflammatory role, ω-6-derived AA is essential for cellular membrane integrity, while ω-3-derived specialized pro-resolving mediators actively terminate inflammation.[11] Critically, elderly diabetics exhibit impaired PUFA metabolism due to oxidative stress-mediated suppression of Δ-5/Δ-6 desaturases, diminishing α-linolenic acid conversion to bioactive EPA/DHA and favoring pro-inflammatory ω-6 metabolite accumulation.[12]
Emerging evidence suggests PUFA imbalances may disrupt sleep regulation through neuroendocrine pathways.[13] DHA, constituting 30% to 40% of neuronal phospholipids, enhances serotonin receptor density in the hippocampus – a precursor for melatonin synthesis.[13,14] Conversely, elevated ω-6:ω-3 ratios impair pinealocyte melatonin production by altering membrane fluidity, supported by clinical observations that plasma ω-6:ω-3 >4:1 correlates with 30% to 40% reductions in urinary melatonin metabolites.[15] Nevertheless, population studies report inconsistent associations, potentially confounded by age-related comorbidities like chronic pain or depression.[14,16,17]
To bridge this translational gap, this hospital-based analysis investigates habitual dietary ω-3/ω-6 intake patterns and polysomnography-quantified sleep parameters in older diabetic inpatients. Unlike prior interventions using supratherapeutic ω-3 doses, we focus on achievable dietary ratios reflective of real-world nutritional practices, thereby addressing a critical evidence void in geriatric diabetes management.
2. Methods
2.1. Study design and participants
This hospital-based case-control study was conducted at The Second Affiliated Hospital of Zhejiang Chinese Medical University between January 2021 and December 2024. Ethical approval was obtained from the Institutional Review Board of The Second Affiliated Hospital of Zhejiang Chinese Medical University. Written informed consent was obtained from all participants. For participants who were unable to provide consent due to cognitive impairment, consent was obtained from their legal guardians. Participants aged ≥65 years with confirmed type 2 diabetes mellitus (T2DM) were recruited during outpatient visits or inpatient admissions. The study utilized a 1:1 ratio, with cases defined as individuals with sleep impairment (Pittsburgh Sleep Quality Index [PSQI] score ≥ 5) and controls as those with good sleep quality (PSQI score <5), matched for age (±3 years) and sex.
Inclusion criteria required: T2DM diagnosis confirmed by WHO criteria; stable glycemic control (HbA1c 7.0–11.0%) for ≥ 3 months prior to enrollment; and ability to complete validated sleep assessments.
Exclusion criteria included: severe cognitive impairment; active psychiatric disorders (e.g., major depression diagnosed); untreated thyroid dysfunction or obstructive sleep apnea; use of sedatives or omega-3/6 supplements within 3 months; and acute diabetic complications (e.g., ketoacidosis) or terminal illnesses.
Sample size calculation utilized the formula:
Where Z = 1.96, P = .25 (estimated prevalence of sleep impairment in diabetic elderly based on our prior studies), d = 0.1, and design effect (deff) = 2.5.[18] This yielded a minimum requirement of 180 participants; 200 were enrolled to account for 10% attrition.
2.2. T2DM diagnosis
The diagnosis of T2DM follows the WHO criteria,[19] which require fasting plasma glucose ≥ 7.0 mmol/L (126 mg/dL), 2-hour plasma glucose ≥ 11.1 mmol/L (200 mg/dL) during a 75-g oral glucose tolerance test, or hemoglobin A1c (HbA1c) ≥ 6.5% using standardized assays. For asymptomatic individuals, confirmatory testing on a subsequent day is mandatory. Random plasma glucose ≥ 11.1 mmol/L (200 mg/dL) in the presence of classic hyperglycemic symptoms (e.g., polyuria, polydipsia) also confirms diagnosis.[19]
2.3. Sleep quality assessment
Sleep quality was evaluated using the PSQI, a validated 19-item self-report questionnaire measuring 7 domains: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction.[20] Each domain is scored 0 to 3, with higher scores reflecting poorer sleep quality. Global scores (0–21) were calculated by summing component scores, where a threshold >5 indicates clinically significant sleep impairment.[21] The PSQI demonstrates robust psychometric properties, including a Cronbach α of 0.77 for internal consistency in population-based studies, and has been extensively validated in older adults with chronic conditions.[22] For hospital-based populations, standardized administration protocols were followed, including assessment of sleep patterns over the preceding month and exclusion of non-scorable partner-rated items.[22,23]
2.4. Food consumption survey and dietary ω-3 and ω-6 intake
Dietary intake of oω-3 andω-6 fatty acids was assessed using a validated 14-item food frequency questionnaire (FFQ), adapted to capture region-specific food habits and cooking methods. The FFQ included detailed categories of ω-3-rich foods (e.g., fish, walnuts) and ω-6 sources (e.g., vegetable oils, poultry), with portion sizes estimated using standardized household measures (e.g., bowls, spoons) and validated visual aids. Participants reported consumption frequency over the preceding year, categorized as daily, weekly, monthly, or rarely/never. Trained interviewers conducted face-to-face surveys to minimize recall bias, with cross-checks against 24-hour dietary recalls for 15% of participants to verify consistency.
Total ω-3 and ω-6 intake was calculated using the Chinese Food Composition Table (2022 edition),[24] integrating updated values for local fish species and cooking oil profiles. For ω-3, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were quantified separately, while ω-6 intake focused on linoleic acid (LA). Quality control measures included outlier detection for implausible energy intakes (<500 or >4000 kcal/day) and manual verification of ambiguous entries.
2.5. Statistical analysis
Analyses were performed using R statistical software (version 4.4.0). Continuous variables were summarized as mean ± standard deviation and compared between groups using independent t-tests. Categorical variables, such as sex, were expressed as frequencies (%) and analyzed via chi-square or Fisher exact tests, as appropriate. Dietary fatty acid intake comparisons utilized independent t-tests for normally distributed variables (e.g., PUFA, DHA, EPA), with homogeneity of variance confirmed via Levene test. Logistic regression models evaluated associations between fatty acid intake thresholds and sleep impairment risk. Three sequential models were constructed: crude, adjusted for age and sex (adjusted effect1), and fully adjusted for demographic (race [recorded as Han or Other], education [recorded as ≤ 6 years, 7 to 9 years, or ≥ 10 years]), behavioral (smoking [current smoker vs nonsmoker], alcohol use [current drinker vs nondrinker]), and clinical confounders (comorbidities [number of chronic diseases: none, 1, 2, or ≥ 3], BMI, HbA1c, blood pressure) (Adjusted effect2). Odds ratios (ORs) with 95% confidence intervals were computed using the glm function in R. Model assumptions were verified through residual analysis and variance inflation factors (<2.0 for all covariates). Statistical significance was set at P <.05 (two-tailed).
3. Result
3.1. Characteristic of eligible participants
The study enrolled 193 hospitalized older adults with diabetes (mean age 72.2 ± 5.0 years), comprising 97 with sleep impairment and 96 without sleep impairment. The participant selection process is illustrated in Figure 1. Sex distribution was balanced overall (43% male, 57% female) with no significant between-group differences (P = .950). Over 96.0% of participants were of Han ethnicity. As expected, PSQI scores showed substantial between-group variation (11.5 ± 4.0 vs 2.7 ± 1.1, P <.001), validating the sleep status classification. Other baseline characteristics of the study cohort are presented in Table 1.
Figure 1.
Flow chart of selecting participants.
Table 1.
Characteristic of eligible participants.
| Variables | Total (n = 193) | With sleep impairment (n = 97) | Without sleep impairment (n = 96) | Statistic* | P-value |
|---|---|---|---|---|---|
| Age, year | 72.2 ± 5.0 | 73.0 ± 5.6 | 71.3 ± 4.2 | 1.82 | .064 |
| Sex, n (%) | |||||
| Male | 83 (43) | 41 (42.3) | 42 (43.8) | 0.01 | .950 |
| Female | 110 (57) | 56 (57.7) | 54 (56.2) | ||
| Race, n (%) | |||||
| Han | 187 (96.9) | 94 (96.9) | 93 (96.9) | Fisher | 1.000 |
| Other | 6 (3.1) | 3 (3.1) | 3 (3.1) | ||
| Education period, n (%) | |||||
| ≤6 yr | 28 (14.5) | 15 (15.5) | 13 (13.5) | 0.21 | .899 |
| 7–9 yr | 62 (32.1) | 30 (30.9) | 32 (33.3) | ||
| ≥10 yr | 103 (53.4) | 52 (53.6) | 51 (53.1) | ||
| Smoking, n (%) | |||||
| Never | 130 (67.4) | 69 (71.1) | 61 (63.5) | 1.28 | .526 |
| Ever | 13 (6.7) | 6 (6.2) | 7 (7.3) | ||
| Current | 50 (25.9) | 22 (22.7) | 28 (29.2) | ||
| Alcohol drinking, n (%) | |||||
| Never | 101 (52.3) | 51 (52.6) | 50 (52.1) | 0.06 | .972 |
| Ever | 27 (14) | 14 (14.4) | 13 (13.5) | ||
| Current | 65 (33.7) | 32 (33) | 33 (34.4) | ||
| Number of chronic comorbidities, n (%) | |||||
| No | 72 (37.3) | 28 (28.9) | 44 (45.8) | Fisher | .007 |
| 1 diseases | 78 (40.4) | 46 (47.4) | 32 (33.3) | ||
| 2 diseases | 35 (18.1) | 22 (22.7) | 13 (13.5) | ||
| ≥3 diseases | 8 (4.2) | 1 (1.0) | 7 (7.3) | ||
| BMI | 24.2 ± 2.8 | 24.6 ± 2.8 | 23.9 ± 2.8 | 1.59 | .113 |
| HbA1c, % | 9.9 ± 2.2 | 10.1 ± 2.1 | 9.6 ± 2.4 | 1.84 | .067 |
| FPG, mmol/L | 13.3 ± 3.5 | 13.6 ± 3.6 | 12.9 ± 3.4 | 1.35 | .177 |
| Diastolic blood pressure, mm Hg | 120.1 ± 16.3 | 121.5 ± 16.9 | 118.7 ± 15.8 | 1.17 | .242 |
| Systolic blood pressure, mm Hg | 74.8 ± 11.6 | 75.2 ± 11.5 | 74.4 ± 11.8 | 0.49 | .622 |
| PSQI | 7.1 ± 5.3 | 11.5 ± 4.0 | 2.7 ± 1.1 | 15.65 | <.001 |
Bold values indicate that the result is statistically significant (P < .05).
BMI = body mass index, CI = confidence interval, FPG = fasting plasma glucose, HbA1c = hemoglobin A1c, OR = odds ratio, PSQI = Pittsburgh sleep quality index.
For continuous variables including Age, BMI, HbA1c, FPG, diastolic blood pressure, and systolic blood pressure, independent t-tests were used. For categorical variables including sex, education period, smoking and alcohol drinking, chi - square tests were performed. For categorical variables including Race and number of chronic comorbidities, Fisher exact tests were used.
3.2. Comparison of dietary fatty acids intake between 2 groups
The analysis revealed significant differences in PUFA consumption patterns between diabetic older adults with and without sleep impairment. Total fatty acid intake showed no between-group differences (51.4 ± 18.8 vs 52.2 ± 16.2 g/d, P = .747). While SFA (17.1 ± 7.7 vs 15.6 ± 6.0 g/d, P = .143) and MUFA (5.8 ± 6.3 vs 16.1 ± 5.0 g/d, P = .764) exhibited comparable levels, PUFA intake differed significantly (18.5 ± 6.0 vs 20.7 ± 6.2 g/d, P = .016), as showed in Table 2.
Table 2.
Comparison of dietary fatty acids intake between 2 groups.
| Fatty acids | Total (n = 193) | With sleep impairment(n = 97) | Without sleep impairment (n = 96) | Statistic* | P-value |
|---|---|---|---|---|---|
| Total fatty acides, g/d | 51.8 ± 17.5 | 51.4 ± 18.8 | 52.2 ± 16.2 | 0.32 | .747 |
| SFA, g/d | 16.4 ± 6.9 | 17.1 ± 7.7 | 15.6 ± 6.0 | 1.47 | .143 |
| MUFA, g/d | 15.9 ± 5.7 | 15.8 ± 6.3 | 16.1 ± 5.0 | 0.30 | .764 |
| PUFA, g/d | 19.6 ± 6.2 | 18.5 ± 6.0 | 20.7 ± 6.2 | 2.43 | .016 |
| LA, g/d | 14.5 ± 4.4 | 14.2 ± 4.6 | 14.8 ± 4.2 | 0.77 | .441 |
| DHA, mg/d | 583.1 ± 729.5 | 284.5 ± 368.6 | 884.8 ± 869.1 | 15.65 | <.001 |
| EPA, mg/d | 269.3 ± 327.5 | 134.7 ± 166.2 | 405.2 ± 389.4 | 9.42 | <.001 |
Bold values indicate that the result is statistically significant (P < .05).
DHA = docosahexaenoic acid, EPA = eicosapentaenoic acid, LA = linoleic acid, MUFA = monounsaturated fatty acid, PUFA = polyunsaturated fatty acid, SFA = saturated fatty acid.
For continuous variables including Total fatty acids, SFA, MUFA, PUFA, LA, DHA, and EPA, independent t-tests were used.
Notably, ω-3 subtypes demonstrated marked disparities. The non-impaired group had higher DHA (884.8 ± 869.1 vs 284.5 ± 368.6 mg/d, P <.001) and EPA intake (405.2 ± 389.4 vs 134.7 ± 166.2 mg/d, P <.001). In contrast, LA (ω-6 FA) showed no significant variation (14.8 ± 4.2 vs 14.2 ± 4.6 g/d, P = .441).
3.3. Association between dietary PUFA, ω-3 and ω-6 intake and sleep impairment
Logistic regression analyses identified distinct associations between PUFA subtypes and sleep impairment in older adults with diabetes (Table 3). Reduced DHA intake (<583.1 mg/d) showed consistent associations with sleep impairment across all models, with elevated ORs in crude (OR = 2.78, 95% CI: 1.28–6.04, P <.001), age-sex-adjusted (OR = 2.64, 95% CI: 1.46–4.78, P <.001), and fully adjusted analyses (OR = 2.81, 95% CI: 1.61–4.92, P = .006). Similarly, lower EPA intake (<269.3 mg/d) was significantly associated with increased risk of sleep impairment, demonstrating consistent effects in crude (OR = 2.13, 95% CI: 1.14–3.97, P <.001), partially adjusted (OR = 2.06, 95% CI: 1.08–3.94, P <.001), and fully adjusted models (OR = 1.99, 95% CI: 1.05–3.75, P = .012).
Table 3.
Association between dietary PUFA, ω-3 and ω-6 intake and sleep impairment.
| Fatty acids | Dietary intake | Crude effect | Adjusted effect1 | Adjusted effect2 | |||
|---|---|---|---|---|---|---|---|
| OR(95%CI) | P-value | OR(95%CI) | P-value | OR(95%CI) | P-value | ||
| PUFA | ≥51.8 g/d | Ref. | Ref. | Ref. | |||
| <51.8 g/d | 1.67 (0.99–2.82) | .072 | 1.62 (0.87–3.03) | .101 | 1.62 (0.79–3.32) | .182 | |
| LA | ≥14.5 g/d | Reference | Reference | Reference | |||
| <14.5 g/d | 1.83 (0.76–4.43) | .715 | 1.66 (0.73–3.78) | .857 | 1.79 (0.68–4.71) | .842 | |
| DHA | ≥583.1 mg/d | Ref. | Ref. | Ref. | |||
| <583.1 mg/d | 2.78 (1.28–6.04) | <.001 | 2.64 (1.46–4.78) | <.001 | 2.81 (1.61–4.92) | .006 | |
| EPA | ≥269.3 mg/d | Ref. | Ref. | Ref. | |||
| <269.3 mg/d | 2.13 (1.14–3.97) | <.001 | 2.06 (1.08–3.94) | <.001 | 1.99 (1.05–3.75) | .012 | |
Bold values indicate that the result is statistically significant (P < .05).
Adjusted effect1: adjusted for age and sex.
Adjusted effect2: adjusted for age, sex, race, education period, smoking, alcohol drinking, number of chronic comorbidities, BMI, HbA1c, FPG, diastolic blood pressure, and systolic blood pressure.
BMI = body mass index, CI = confidence interval, DHA = docosahexaenoic acid, EPA = eicosapentaenoic acid, FPG = fasting plasma glucose, HbA1c = hemoglobin A1c, LA = linoleic acid, OR = odds ratio, PUFA = polyunsaturated fatty acid, Ref. = reference category.
By contrast, neither total PUFA intake <median level (51.8 g/d) nor reduced LA consumption (<14.5 g/d) demonstrated statistically significant associations with sleep impairment in any model (P >.05 for all). These nonsignificant associations remained stable after progressive adjustments for demographic (age, sex, race, education) and clinical confounders (comorbidities, metabolic parameters).
4. Discussion
The present hospital-based case-control study provides novel insights into the differential associations between specific dietary PUFA subtypes and sleep quality in older adults with diabetes. Three key findings emerge: Insufficient intake of marine-derived ω-3 fatty acids (DHA <583.1 mg/d and EPA <269.3 mg/d) is independently associated with higher odds of sleep impairment, with dose-response relationships robust to comprehensive covariate adjustment; total PUFA and ω-6 LA intake show no significant associations with sleep outcomes; the protective effects of ω-3 fatty acids persist despite prevalent metabolic dysregulation in this population. These results align with emerging evidence on PUFA-mediated neuroendocrine regulation while highlighting critical distinctions between general and diabetic aging populations.
The observed 2.6 to 2.8-fold increased sleep impairment risk with low DHA/EPA intake corroborates experimental evidence of their roles in sleep-wake regulation. DHA constitutes 30% to 40% of neuronal membrane phospholipids, facilitating serotonin synthesis through enhanced tryptophan hydroxylase activity – a rate-limiting step in melatonin production.[13,25] Animal models demonstrate DHA-deficient diets reduce hippocampal 5-HT1A receptor density by 22%, impairing sleep onset latency.[26] In diabetic populations, impaired Δ-5/Δ-6 desaturase activity (reducing ALA-to-DHA conversion by 30%–50%) likely exacerbates ω-3 deficiencies, creating a pro-inflammatory state that disrupts suprachiasmatic nucleus function.[17] EPA’s anti-inflammatory effects through NOD-, LRR- and PYD-containing protein 3 (NLRP3) inflammasome suppression may mitigate hyperglycemia-induced neuroinflammation, which amplifies sleep fragmentation via blood-brain barrier dysfunction.[27,28]
Contrary to hypotheses, ω-6 LA intake showed no association with sleep outcomes despite its role as an arachidonic acid precursor. This null finding may reflect threshold effects: the cohort median LA intake (14.5 g/d) corresponds to typical Chinese dietary patterns, whereas detrimental impacts manifest at higher intake levels (>20 g/d) common in Western diets. Additionally, diabetes-associated Δ-6 desaturase inhibition may limit LA-to-AA conversion, attenuating pro-inflammatory eicosanoid production.[29,30]
The nutrient-specific associations observed align with recent cohort studies demonstrating DHA/EPA – but not total PUFA – predicts better sleep metrics in cardiometabolic populations. A study found plasma DHA levels inversely correlated with sleep latency (β=−0.34, P = .01), consistent with our OR estimates.[31] However, intervention trials using pharmacological ω-3 doses (2–4 g/d) report mixed results, likely due to supraphysiological effects overriding dietary pathways. Our findings suggest habitual intake thresholds (DHA ~500–800 mg/d) may optimize sleep benefits without inducing lipid peroxidation risks associated with high-dose supplementation.
The lack of association between ω-6:ω-3 ratios and sleep quality contrasts with general population studies where ratios >4:1 correlate with melatonin suppression. This discrepancy may stem from diabetic dyslipidemia altering PUFA metabolism: elevated triglycerides and nonesterified fatty acids in diabetes preferentially incorporate ω-6 PUFAs into adipose tissue rather than neural membranes, blunting their central nervous system effects.
For older adults with diabetes, achieving DHA/EPA intake above population medians (~600 mg/d and ~300 mg/d, respectively) could reduce sleep impairment risk by 50% to 65%, based on fully adjusted ORs. This aligns with the American Heart Association’s recommendation of ≥ 2 fish servings/week (providing ~500 mg/d EPA + DHA) for cardiometabolic health. However, traditional Chinese diets average only 200 to 300 mg/d marine ω-3 PUFAs, necessitating targeted dietary modifications. Practical strategies include promoting small fatty fish (e.g., mackerel, sardines) over larger species to minimize mercury exposure and adapting cooking methods to preserve ω-3 content.
The null findings for total PUFA intake underscore the importance of differentiating PUFA subtypes in dietary guidelines. While total PUFA recommendations (e.g., 10% of total energy) benefit cardiovascular health, sleep-specific guidance should emphasize marine ω-3 sources.
Strengths include rigorous adjustment for diabetes-specific confounders (HbA1c, comorbidities) and use of habitual intake assessments rather than supplements. The hospital-based design enabled recruitment of frail older adults typically excluded from community studies, enhancing clinical relevance.
Limitations warrant consideration. First, the cross-sectional design precludes causal inference – reverse causation (poor sleep altering dietary patterns) remains plausible. Second, FFQ-based intake estimates may underreport ω-3 consumption due to regional variations in fish preparation methods not fully captured in food composition tables. Third, residual confounding by unmeasured factors (e.g., circadian misalignment, gut microbiome diversity) cannot be excluded. Fourth, all participants were recruited from a single center, which may limit generalizability. Additionally, the study population was predominantly of Han ethnicity, and findings may not apply to other racial or ethnic groups. Finally, information on the causes and duration of hospitalization was not collected, and individual comorbidities were reported as aggregated counts rather than specified diagnoses, which may limit the precision of clinical interpretation.
Prospective cohorts should validate these findings using objective sleep measures (actigraphy, polysomnography) and biomarker-based PUFA assessments. Mechanistic studies in diabetic models should investigate: DHA/EPA effects on hypothalamic orexin signaling; Interactions between ω-3 intake and antidiabetic medications on sleep architecture. Clinical trials testing achievable dietary modifications (e.g., replacing red meat with fatty fish twice weekly) could establish feasibility and effect sizes for sleep improvement.
5. Conclusion
In older adults with diabetes, insufficient dietary intake of marine-derived ω-3 fatty acids – particularly DHA and EPA – emerges as a modifiable risk factor for sleep impairment, independent of cardiometabolic comorbidities. These observational findings suggest a potential association between low ω-3 levels and sleep disturbances in this population, highlighting the need for further longitudinal research. If confirmed, such evidence could inform strategies like ω-3 screening in routine diabetic nutritional assessments and targeted dietary interventions to address the interrelated risks of diabetes and sleep impairment.
Author contributions
Conceptualization: Li-jiao Wang, Tong-tong Cao.
Data curation: Li-jiao Wang, Yu-ping Shen, Man-fei Xu.
Investigation: Li-jiao Wang, Yu-ping Shen, Man-fei Xu.
Resources: Tong-tong Cao.
Software: Li-jiao Wang, Yu-ping Shen.
Writing – original draft: Li-jiao Wang.
Writing – review & editing: Yu-ping Shen, Man-fei Xu, Tong-tong Cao.
Abbreviations:
- AA
- arachidonic acid
- BMI
- body mass index
- CI
- confidence interval
- DHA
- docosahexaenoic acid
- DM
- diabetes mellitus
- EPA
- eicosapentaenoic acid
- FFQ
- food frequency questionnaire
- FPG
- fasting plasma glucose
- HbA1c
- hemoglobin A1c
- LA
- linoleic acid
- NLRP3
- NOD-, LRR- and PYD-containing protein 3
- OGTT
- oral glucose tolerance test
- OR
- odds ratio
- PSQI
- Pittsburgh sleep quality index
- PUFA
- polyunsaturated fatty acid
- SD
- standard deviation
- SFA
- saturated fatty acid
- T2DM
- type 2 diabetes mellitus
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Wang L-j, Shen Y-p, Xu M-f, Cao T-t. The relationship between intake of dietary omega-3 and omega-6 fatty acids and sleep quality in older adults with diabetes: A hospital-based analysis. Medicine 2026;105:7(e47670).
Contributor Information
Li-jiao Wang, Email: 852498703@qq.com.
Yu-ping Shen, Email: 202104049@zcmu.edu.cn.
Man-fei Xu, Email: 20194039@zcmu.edu.cn.
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