Subjective Sleep Quality and Trajectories of Interleukin-6 in Older Adults

Abstract

Background:

We aimed to identify trajectories of inflammation in older adults at elevated risk for syndromal depression and anxiety and to determine whether baseline physical, cognitive, and psychosocial factors could distinguish 15-month longitudinal trajectories.

Methods:

Older adults (N=195, mean age (±SD) = 74.4 years (9.0) participating in three depression and anxiety prevention protocols completed a comprehensive battery of psychosocial assessments and provided blood samples for analysis of interleukin-6 (IL-6) every 3 months over a maximum of 15 months. Group-based trajectory modeling identified trajectories. Adjusted logistic regression examined associations between baseline factors and trajectory groups.

Results:

Two 15-month trajectories were identified: stable lower IL-6 levels (84%; mean (±SD) = 3.2 (2.1) pg/mL); and consistently higher IL-6 levels (16%; mean = 9.5 (7.4) pg/mL). Poor sleep quality predicted consistently higher levels of IL-6 (OR=1.9, 95% CI = 1.03 – 3.55).

Conclusion:

Poor sleep quality may represent a therapeutic target to reduce inflammation.

Chronic low-grade inflammation increases risk for many age-related diseases including cardiovascular disease, Alzheimer’s disease, diabetes, osteoporosis, and cancer.¹ It predicts both frailty and mortality in older adults, even in the absence of clinical disease.² Compared with younger and middle-aged adults, older adults have higher concentrations of inflammatory cytokines, especially interleukin-6 (IL-6). These observations suggest inflammation may be a therapeutic target to minimize age-related disease and frailty.

Putative mechanisms by which inflammation is associated with pathological change and disease progression are still unclear. Nevertheless, evidence suggests chronic inflammation may be reduced with calorie restriction, zinc, resveratrol, Epimedium total flavonoids, and icariin.³ Characterizing inflammatory profiles across multiple domains of function could suggest new targets for age-related diseases associated with chronic inflammation. We sought to identify trajectories of inflammation in older adults at elevated risk for syndromal depression and anxiety⁴ and determine whether physical, cognitive, and/or psychosocial factors could distinguish different 15-month longitudinal trajectories of chronic inflammation.

METHODS

Study design overview

This secondary analysis uses data collected in three pilot randomized controlled trials (RCTs) that we conducted concurrently to evaluate potential strategies to prevent syndromal depression and anxiety in older adults at risk for developing these conditions (e.g., groups had: mild cognitive impairment, disability requiring a social service, or chronic knee pain). The methods of these three trials have already been published.^5–7 In brief, the primary objective was to determine whether focused learning-based interventions could prevent new episodes of major depressive disorder and syndromal anxiety disorders. Interventions were delivered over a 12–16-week period. Psychosocial assessments and blood samples were collected at baseline and 3, 6, 9, 12, and 15 months after baseline. All studies administered a similar battery of assessments to participants and followed the same standard operating procedures for blood sampling and transportation. Samples were drawn blindly by a laboratory technician, kept upright and cool, and delivered to a university-lab where they were processed and stored at −80C until assayed.

Participants

Participants were 60 years and older; they scored above 80 on the Modified 3MS cognitive screen; and met criteria for mild depressive symptoms (PHQ-9 score of 1–9 with at least a score of “1” for the questions of anhedonia or dysphoria), as well as the study specific risk factor. Exclusion criteria included the regular use of benzodiazepines (>4 times/week); MDD in the past 12 months; history of central nervous system disease, bipolar disorder, or schizophrenia; or drug or alcohol treatment in the past 12 months. A total of 277 adults (across the 3 trials) completed the baseline assessment and were randomized to a learning-based intervention or to enhanced usual care. The focus of this report is the 195 adults who provided 2+ blood samples over the course of the 15-month follow-up (Table 1 describes the distribution of blood samples provided by participants.) Participants with missing plasma samples (n=53) had more mobility limitations and were more socially isolated than participants who provided 2+ samples (n=195).

Table 1.

Logistic Regression Analysis that Examined the Association between Psychosocial Variables and 15-month Trajectories of Interleukin-6 (Stable Low versus Consistently High) (N=195)

	mean (SD) or n (%)	95% CI for Exp (B)
		OR	Lower	Upper	p-value*
Demographics
Age	74.31 (8.89)	1.068	1.016	1.123	0.010
Male sex	86 (31.0)	1.009	0.395	2.580	0.985
Black/minority status	62 (22.4)	3.136	1.170	8.402	0.023
Education	14.2 (2.64)	0.976	0.833	1.143	0.762
Lifestyle/risk factors
Body mass index	30.27 (7.65)	1.059	1.003	1.117	0.038
Co-morbid physical illnessa	9.78 (3.86)	1.096	0.973	1.233	0.130
Cognitive status
3MSb	93.72 (4.54)	-	-	-	-
RBANSc	94.48 (12.61)	-	-	-	-
Physical performance
Lower extremity functiond		0.907	0.769	1.070	0.246
Psychosocial scales
Depression symptomse	5.90 (2.44)	-	-	-	-
Anxiety symptomsf	3.24 (2.71)	-	-	-	-
Paing	7.95 (4.58)	-	-	-	-
Social isolation/supporth	38.79 (6.42)	-	-	-	-
Daily activityi	52.04 (6.83)	0.954	0.873	1.042	0.293
Participation in social rolesj	47.67 (8.36)	-	-	-	-
Sleep qualityk		1.911	1.029	3.548	0.040
Very bad	5 (2.6)
Fairly bad	55 (28.5)
Fairly good	99 (51.3)
Very good	34 (17.6)
Study Characteristics
Learning-based interventionl		0.777	0.338	1.787	0.552

Notes. Of the 277 participants, 53 (19.1%) did not provide a plasma sample; 30 (10.8%) provided 1 sample, 10 (0.5%) provided 2 samples; 22 (11.3%) provided 3 samples; 29 (15.5%) provided 4 samples; 59 (30.3) provided 5 samples; and 74 (38.0%) provided 6 samples. Chi-square (df = 10, N = 195) = 30.265; Negelkerke R²: = 0.246

^*p-values are based on Wald chi-square tests with df = 1

^aCumulative Illness Rating Scale – Geriatrics (CIRS-G): total score

^bModified Mini-mental State Exam (3MS): total score

^cRepeatable Battery for the Assessment of Neuropsychological Status (RBANS): total score

^dShort Performance Physical Battery (SPPB): total score

^ePatient Health Questionnaire (PHQ-9): total score

^fGeneralized Anxiety Disorder (GAD-7): total score

^gNumeric Rating Scale (NRS) for Pain: total score

^hInterpersonal Support Evaluation List (ISEL): total score

ⁱLate-Life Functional Disability Instrument (LLFDI): activity limitations score

^jLate-Life Functional Disability Instrument (LLFDI): participations limitations score

^kPittsburgh Sleep Quality Index (PSQI): sleep quality composite score

^lReference group is enhanced usual care

Measures

Inflammation.

Our main outcome variable was circulating levels of the proinflammatory cytokine interleukin-6 (IL-6). Laboratory analyses were performed on blood samples using a high sensitivity IL-6 Human ELISA kit (R&D Systems Catalog #HS600C) to evaluate IL-6 as a marker of systemic inflammation. If needed, samples were diluted according to manufacturer’s specification. The mean detectable level was 0.031 pg/ml; intra-assay coefficient of variation (CV) % = 3.6–4.7; and inter-assay CV % = 3.9–10.8. We attempted to collect blood samples six times over the course of 15 months. We analyzed 941 blood samples (an average of 4.8 samples per participant).

Correlates and predictors of inflammation.

Sociodemographic information (age, sex, race/ethnicity, and education) was collected via self-report questionnaires at baseline. A comprehensive battery of assessment scales in the domains of physical function, cognition, and psychosocial assessment was administered by masters-level clinicians at baseline. Cognitive assessments included measures of general cognitive function and neuropsychological status. Physical function included measures of lower extremity function. Psychosocial scales included measures of depression symptoms, anxiety symptoms, pain, sleep quality, social isolation, limitations in daily activity, and participation in social roles. All measures are listed in Table 1.

Statistical Analyses

We first identified subgroups that followed similar IL-6 patterns over time using a group-based trajectory approach in SAS, a latent growth-curve approach.⁸ Participants (n=82) with zero/single time point measurements were excluded from the model to preserve the longitudinal aspect of the analyses. Model selection involved the iterative estimation of the number of trajectory groups and the shape of each trajectory group Statistical criteria for determining the number of trajectory groups and polynomial degrees for each group included the Bayesian Information Criterion (BIC). Smaller values of the BIC indicate better model fit. Other criteria included non-overlapping confidence intervals and reasonable sample sizes in each identified trajectory group; solutions that included small trajectory groups (<10% of the sample) were rejected. After identifying the optimal trajectory group solution, we compared our battery of assessments between the different trajectories using logistic regression. Variables that were significantly different between the trajectory groups (p<.05) in these univariate analyses were then entered into a multiple logistic regression model to identify the unique predictors of group membership. We standardized each variable (to a mean of zero and standard deviation of one) to facilitate effect size comparisons. Sociodemographic variables, body mass index, and co-morbid physical conditions were included in the regression model as covariates because of their well-established impact on inflammation markers.⁹ We also controlled for group assignment (intervention or control). Analyses were performed using SAS 9.4 and SPSS 26.0

RESULTS

The mean age of participants was 74.3 years (SD = 8.9); 69% (n = 191) were women and 22% (n= 62) were black. Average years of education was 14.2 (SD = 2.6).

The optimal trajectory solution identified two trajectories of IL-6 over 15 months. The BIC indicated this two-group model fit the data better than a one group solution (Supplemental Table); adding quadratic polynomials did not result in better model fit. The two trajectories of IL-6 were: (1) stable lower IL-6 levels (84% or n=177; mean [±SD] = 3.2 [2.1] pg/mL); and (2) consistently higher levels of IL-6 (16% or n=34; mean [±SD] = 9.5 [7.4] pg/mL). There were several differences between participants in each trajectory. Participants with chronically high IL-6 were older (77.7 years [10.4] versus 73.7 [8.5], F [1, 193] = 5.68, p=.02) and more likely to be black (33% versus 16%, χ² [1] = 5.33, p=.02). They also had more co-morbid physical illness (CIRS-G score of 11.03 [3.07] versus 9.35 [3.68], F [1, 193] = 6.03, p=.02), worse sleep quality (48.5% poor sleep versus 27.2%, χ² [1] = 9.89, p=.02), worse lower extremity function (SPPB score of 6.33 (3.45) versus 8.56 [2.92], F [1, 193] = 14.92, p<.001), and more limitations in daily activity (LLFDI score of 49.09 [5.81] versus 53.37 [6.83], F [1, 190] = 10.94, p<.001). The two groups were similar in terms of cognitive status, depression symptoms, anxiety symptoms, pain, and social isolation/support.

After adjusting for confounding factors including age, body mass index, and co-morbid physical conditions, sleep quality was the only variable that remained in the logistic regression model that was significantly associated with inflammation (Table 1). Older adults who reported worse sleep quality were more likely to have chronically high levels of IL-6 (Wald chi-square = 4.15, df = 1, OR=1.62 [95% Confidence Interval = 1.02 – 2.56], p = 0.04)). Our model accounted for a moderate proportion of variance in IL-6 (Nagelkerke R²=0.25)

DISCUSSION

Our goal was to identify trajectories of inflammation in older adults and determine whether physical, cognitive, or psychosocial predictors could distinguish between different 15-month longitudinal trajectories. We demonstrated that after adjusting for potential confounders, poor sleep quality was associated with chronically high levels of IL-6 over 15-months. This finding is consistent with a recent meta-analysis showing that sleep disturbances and sleep duration are associated with elevated inflammatory markers.¹⁰ The association between sleep quality and inflammation may have implications for older adults and health practitioners, namely that attention to subjective sleep may serve as an indicator for chronic inflammation and/or undetected clinical disease. For example, sleep disturbance is a risk factor for both major depression and Alzheimer’s disease. Systemic inflammation may be one of several physiological mediators through which poor self-reported sleep impacts clinical disease.

One mechanism that may explain the association between poor sleep quality and inflammation is the circadian timing system (CTS), or “biological clock.” The CTS regulates sleep-wake cycles as well as physiological, metabolic, and immune system functions. A disrupted sleep-wake cycle leads to dysregulation of the immune response and increases inflammatory activity.¹¹ Systemic levels of IL-6 have a circadian profile: they peak at 19:00 and 5:00. Sleep deprivation shifts this pattern leading to an over-secretion of IL-6 during the day.¹² Another possibility is that obstructive sleep apnea is an underlying cause of sleep disturbance, mood, and inflammation.

Our finding extends those of previous studies in several ways. First, we analyzed a comprehensive battery of physical, cognitive, and psychosocial assessments that are implicated in age-related chronic disease. This allowed us to describe variance related to IL-6. Second, these findings over a long follow-up period (15 months) demonstrate the chronicity of inflammatory burden in older adults. Despite these strengths, limitations must be acknowledged. First, the number of statistical tests performed results in an increased risk for Type 1 error. Second, we examined only one inflammatory cytokine, while there are others which may be associated with poor sleep (e.g., TNF-a and C-reactive protein). Second, participants with missing plasma samples had more mobility limitations and were more socially isolated; thus, our results may not generalize to older adults with disabilities requiring social service assistance. Third, the lack of objective sleep assessments including assessment of sleep duration and 24-hour sleep-wake rhythm calls for cautious interpretation of these findings.

In conclusion, the relation between self-reported sleep quality and inflammation may have implications for older adults’ vulnerability to age-related chronic disease. Future research should determine whether changes in subjective sleep quality are associated with changes in IL-6.

Highlights.

• What is the primary question addressed by this study? — We sought to identify trajectories of inflammation in older adults at elevated risk for syndromal depression and anxiety and determine whether physical, cognitive, and/or psychosocial factors could distinguish different 15-month longitudinal trajectories of chronic inflammation.

• What is the main finding of this study? — After adjusting for confounding factors including age, body mass index, and co-morbid physical conditions, sleep quality was the only variable that remained in the logistic regression model that was significantly associated with inflammation. Older adults who reported worse sleep quality were more likely to have chronically high levels of IL-6

• What is the meaning of the finding? — The relation between self-reported sleep quality and inflammation may have implications for older adults’ vulnerability to age-related chronic disease. Future research should determine whether changes in subjective sleep quality are associated with changes in IL-6.