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Published in final edited form as: J Psychiatr Res. 2020 Jan 2;122:97–104. doi: 10.1016/j.jpsychires.2019.12.016

Seasonal affective disorder and seasonal changes in weight and sleep duration are inversely associated with plasma adiponectin levels

Faisal Akram 1,2, Claudia Gragnoli 3,4,5, Uttam K Raheja 1,2, Soren Snitker 6,7, Christopher A Lowry 8,9,10,11, Kelly A Sterns-Yoder 8,10,12, Andrew J Hoisington 10,13, Lisa A Brenner 8,10,12,14, Erika Saunders 15, John W Stiller 1,2, Kathleen A Ryan 16,17,18, Kelly J Rohan 19, Braxton D Mitchell 16,17,18, Teodor T Postolache 1,2,8,12
PMCID: PMC7024547  NIHMSID: NIHMS1551582  PMID: 31981963

Abstract

Overlapping pathways between mood and metabolic regulation have been increasingly reported. Although impaired regulation of adiponectin, a major metabolism-regulating hormone, has been implicated in major depressive disorder, its role in seasonal changes in mood and seasonal affective disorder-winter type (SAD), a disorder characterized by onset of mood impairment and metabolic dysregulation (e.g., carbohydrate craving and weight gain) in fall/winter and spontaneous alleviation in spring/summer, has not been studied previously. We studied a convenience sample of 636 Old Order Amish (53.8 (±15.5) years; 50.1% males), a population isolate with self-imposed restriction on network electric light at home, and low prevalence of total SAD (t-SAD= syndromal+subsyndromal). We calculated the global seasonality score (GSS) and estimated SAD and subsyndromal-SAD after obtaining Seasonal Pattern Assessment Questionnaires (SPAQs) and measured overnight fasting plasma adiponectin levels. We then tested associations between plasma adiponectin levels and GSS, t-SAD, winter-summer difference in self-reported sleep duration and self-reported seasonal weight change, by using analysis of co-variance (ANCOVA) and linear regression analysis after adjusting for age, gender, and BMI. Participants with t-SAD (N = 14; 2.2%) had significantly lower plasma adiponectin levels (8.76 ±1.56 μg/mL) than those without t-SAD (11.93 ±0.22 μg/mL) (p=0.035). In addition, there was significant negative association between adiponectin levels and winter-summer difference in self-reported sleep duration (p=0.025) and between adiponectin levels and self-reported seasonal change in weight (p=0.006). There was no significant association between GSS and adiponectin levels (p=0.88). To our knowledge, this is the first study testing the association of SAD with adiponectin levels. Replication and extension of our findings longitudinally and, then, interventionally, may implicate low adiponectin as a novel target for therapeutic intervention in SAD.

Keywords: Seasonality, Seasonal Affective Disorder, Adiponectin

INTRODUCTION

Mood and metabolic regulation and dysregulation have strongly overlapping molecular pathways (de Melo et al., 2017), comorbid clinical manifestations, and reciprocally influence clinical course and prognosis (Dimitrova et al., 2017; Postolache et al., 2019). Perhaps, no other condition illustrates the associations among physiology, mental well-being, and physical health better than seasonal affective disorder, winter type (SAD).

Because the axis of the earth is tilted during the revolution of our planet around the sun, seasonal variations in photoperiod (day length) occur with a period of 1 calendar year. The majority of non-human animals have been selected to track photoperiod (“photoperiodic species”) and exhibit seasonal changes in appearance, physiology and behavior in anticipation of seasons, in particular to prepare in advance for the energetic bottleneck of winter, characterized by increased thermoregulatory demands and decreased environmental caloric availability (Nelson et al., 2010; Prendergast et al., 2002). Although humans have been increasingly isolated in a microclimate with artificial heating and lighting diminishing the environmental influences of winter, they manifest seasonal changes in appetite, weight, and behavior resembling those of seasonal mammals, albeit to a lesser degree (Davis and Levitan, 2005; Wehr, 2001). However, a sizable proportion of humans manifest large seasonal changes resembling seasonal changes in photoperiodic mammals with increased appetite, sleepiness and sleep duration, as well as decreased level of energy, interest in sex and social activities, with fewer individuals meeting criteria for recurrent depressive episodes in fall/winter and full remission in spring and summer, commonly known as SAD (Davis and Levitan, 2005; Rosenthal et al., 1984).

SAD, as now defined, was first characterized by a group including Thomas Wehr, Al Lewy, and Norman Rosenthal in 1984 (Rosenthal et al., 1984). The first scientific description of SAD was reported by the German physiologist Hellmut Marx in 1946, who reported “hypophyseal insufficiency associated with lack of light” in a patient (Marx, 1946), although the existence of maladaptive mood seasonality during the winter has long been recognized in Scandinavia (a part of the world where photoperiod in winter is particularly short) by the use of various denotations such as skamdegistunglindi (depression of the short days) (Overy and Tansey, 2014). SAD is currently conceptualized as a disorder of biological rhythms combined with an affective dysregulation (Lam et al., 2001), triggered by shortened photoperiods and decreased sunlight exposure in winters of temperate and higher latitudes (photoperiod hypothesis) (Levitan, 2007). Considering that light deprivation at critical times of the day represents the most important environmental trigger for seasonal changes of winter in seasonal nonhuman animals, treatment with bright light improves mood in humans and is recognized as a first line treatment for SAD (Golden et al., 2005; Kasper et al., 1989a; Lam et al., 2006; Rosenthal et al., 1984). Additionally, humans with seasonal affective disorder manifest an increased duration of nocturnal melatonin secretion in winter relative to summer, the biological signal responsible for conveying calendar information of winter in photoperiodic mammals and responsible for major seasonal changes in physiology (Wehr, 1991; Wehr et al., 2001). Lewy et al. (1988) proposed a phase-shift hypothesis, which implicates a mismatch between sleep/wake cycle and internal circadian rhythms that are externally cued by light/dark cycle (Lewy et al., 2006; Lewy et al., 1988). Decreased serotonin and catecholamine brain levels have been found in SAD patients (Brewerton, 1989; Neumeister et al., 1998), that appear to be restored with light treatment (Neumeister et al., 1998). More recently, a distinct retino-thalamic pathway from intrinsically photosensitive retinal ganglion cells (ipRGC) to the perihabenular nucleus (PHB) has been identified in mice, which is believed to mediate the effects of light on affect independent of the suprachiasmatic nucleus (Fernandez et al., 2018).

As the syndrome of depression includes changes in sleep and feeding behaviors, several studies have focused on identifying shared factors mediating abnormalities in regulating both mood and metabolic pathways. For example, it has been recently reported that body mass index (BMI) could predict response to light therapy in winter-SAD (Dimitrova et al., 2017). In particular, there has been a great interest in adipokines i.e. hormones secreted by the adipose tissue that can cross the blood brain barrier and act on central adipokine receptors to regulate appetite (Xu et al., 2018). Traditionally known for their role in neural regulation of appetite, adipokines, including leptin and adiponectin, are now believed to have broad neuroendocrine functions which include their role in neuronal excitability (Zhang et al., 2017), synaptic plasticity (Zhang et al., 2016), neurogenesis (Garza et al., 2012) and behavior (Liu et al., 2012; Liu et al., 2011). In addition, both leptin and adiponectin show circadian and ultradian rhythms (Gavrila et al., 2003; Licinio et al., 1997), which makes investigations into these hormones more relevant to depression, a condition known to be associated with dysfunctions in circadian rhythms such as disordered ACTH secretion (Young and Veldhuis, 2006) and dysfunctional hypothalamic-pituitary (HPA) axis (Juruena et al., 2018).

Adiponectin, the adipokine under investigation in this study, is a 30-kDa monomeric glycoprotein which circulates in plasma in relatively higher concentrations as compared to other adipokines such as leptin (Fang and Judd, 2018; Pajvani et al., 2003). Serum adiponectin levels show a diurnal rhythm with peak levels during the day, followed by a nocturnal decline starting in the late evening and reaching a nadir in the early morning (Gavrila et al., 2003). Peripherally, adiponectin has insulin-sensitizing, anti-inflammatory, and anti-atherogenic actions (Fang and Judd, 2018; Fruhbeck et al., 2017; Hotta et al., 2001), while evidence of its actions on nervous system is growing rapidly, with preliminary evidence in modulation of neuronal excitability, neurogenesis and affect regulation (Liu et al., 2012; Zhang et al., 2016; Zhang et al., 2017). Unlike most other adipokines including leptin, adiponectin gene expression and blood levels are inversely associated with adiposity (Lee and Shao, 2014; Weyer et al., 2001). This relationship has also been observed in case of depression, where high serum leptin has been found in depressive disorders with atypical features (Gecici et al., 2005; Lamers et al., 2016), with some studies reporting the association independent of BMI (Lawson et al., 2012; Pasco et al., 2008). Conversely, adiponectin levels have been found to be low in individuals with major depression in many studies (Cao et al., 2018; Cizza et al., 2010; Diniz et al., 2012; Lehto et al., 2010; Leo et al., 2006; Liu et al., 2012), including reports of lower adiponectin in atypical vs. melancholic depression (Lamers et al., 2016) and in individuals with depression and BMI > 25 (Cao et al., 2018). Some studies have found no association (Bai et al., 2014; Rebelo et al., 2016; Syk et al., 2019) or higher adiponectin levels (Aliyazicioglu et al., 2011; Jeong et al., 2012; Oh et al., 2018; Yildiz et al., 2017) in depression. One reason for variability in results may be that major depressive disorder is a highly heterogeneous group of disorders with multiple subtypes (e.g. anxious, melancholic, atypical and seasonal) potentially having diverse etiologies. In this regard, investigation into adiponectin levels in SAD is advantageous as (1) SAD is a relatively homogenous subtype of depression; and (2) SAD predominantly includes atypical symptoms (increased appetite, weight gain & hypersomnia). To our knowledge, the association of adiponectin with SAD and mood seasonality has not been studied previously. Therefore, our study aims at exploring the links among adiponectin blood levels, mood seasonality, and SAD. The research participants in the current study were Old Order Amish, who culturally self-impose restrictions on network electric light and air conditioning at home, and spend more time outdoors considering predominant farming activity, and thus, theoretically, being more exposed to seasonal changes in photoperiod, light and temperature. Old Order Amish score high on morningness (Zhang et al., 2015), and, although many Amish manifest seasonality of mood, few perceive it as a “problem” (Patel et al., 2012), and have a lower prevalence of winter SAD, both syndromal (0.84 %) and subsyndromal (1.75 %) as compared to non-Amish populations at similar latitudes (Raheja et al., 2013).

Our hypothesis was that adiponectin levels will be lower in participants with SAD than those without SAD, and that will correlate negatively with GSS. We also secondarily expected a negative association of plasma adiponectin levels with self-reported seasonal changes in weight and winter–summer difference in sleep duration.

MATERIALS AND METHODS

Study Population

This report is based on data collected in the Old Order Amish of Lancaster County, Pennsylvania, as part of the University of Maryland studies of cardiovascular, metabolic, and bone health (Hsueh et al., 2000; Hsueh et al., 2007; Pollin et al., 2005; Raheja et al., 2013), analyzed to examine hypotheses not articulated at the time of funding. The Old Order Amish are a rural, agrarian population that eschews use of modern technologies, including residential use of electricity for lighting and appliances, thereby providing a convenient population to study seasonality and SAD without the confounding influence of network electric light (Walbert, 2002). The Amish individuals were all at least 18 years of age, had participated in University of Maryland studies (Hsueh et al., 2000; Hsueh et al., 2007; Pollin et al., 2005; Raheja et al., 2013) and had provided consent to be contacted for future studies. Seasonal Pattern Assessment Questionnaires (SPAQ) (Rosenthal et al., 1987) were mailed to Amish individuals in May 2010, with a second mailing to non-responders in September 2010 (Raheja et al., 2013). Included with the SPAQ there were: a letter instructing how to complete the SPAQ and stating that returning the filled questionnaire would document participant’s informed consent, a stamped and pre-addressed envelope, and a one-dollar bill for effort compensation. Overall, the response rate was 57.8%, with 1,307 SPAQs returned before December 31, 2011. The Institutional Review Board of the University of Maryland approved the study.

Seasonality Parameters

SPAQ is a well-known screening and research tool (32) used to calculate the global seasonality score (GSS) and determine the presence of SAD and subsyndromal SAD (s-SAD), based on criteria initially defined by Kasper et al. (1989) (Kasper et al., 1989b). SPAQ has shown good test-retest reliability in the Old Order Amish (Kuehner et al., 2013).

The seasonality-quantitative measure provided by the SPAQ, the GSS, is based on six parameters – sleep duration, social activity, mood, weight, appetite, and energy level – tested by 6 questions and rated on a scale of 0 to 4 reflecting “no change” to “extremely marked change” in each parameter with season (with GSS represented by the sum of the six individual scores). A proportion-based calculation is used to estimate GSS in cases where one or more parameters have not been rated.

Severity of problem is measured on a 5-point rating scale ranging from mild to disabling. Seasonal pattern is identified based on participants’ response to the request to indicate the month, or months, of “feeling worst”. Fall-winter pattern is defined as “feeling worst” in one or more months from September to February (for details see Raheja et al., 2013).

In addition, for secondary analyses, individuals were asked to provide the number of hours slept (including naps) in each season, while for seasonal changes in weight, individuals were asked to report fluctuation in weight over the course of a year on a 6 point rating scale 0–3 lbs to 18–20 lbs. Seasonal change in sleep duration was estimated through calculation of difference between self-reported sleep hours per day in winter and self-reported sleep hours per day in summer..

SAD cases are identified based on a seasonal fall-winter pattern with a total GSS ≥ 11 and a problem score representing moderate to disabling severity. Cases of subsyndromal-SAD (s-SAD) are identified based on GSS ≥ 11 but rating problem with changes in season, in contrast to syndromal SAD as less than moderate, including no problem. Another path to a categorization as s-SAD was GSS of 9–10 with at least a mild seasonal problem (Kasper et al., 1989b; Raheja et al., 2013).

Metabolic Variables

Participants were examined in their home or at the Amish Diabetes Research Clinic in Strasburg, Pennsylvania. Trained nurses measured height and weight using a stadiometer and calibrated scale in subjects wearing light clothing and without shoes. Weight in kilograms divided by height in meters squared was used to calculate BMI, as previously described (Akram et al., 2018; Mitchell et al., 2008).

Among the 1307 subjects who had completed the SPAQ, 636 had data available for fasting plasma adiponectin levels, originally collected for previous studies (Pollin et al., 2005; Rampersaud et al., 2008; Zupancic et al., 2012). Fasting plasma samples were derived from blood drawn after in early morning after an overnight fast. Blood samples were collected throughout the year. Subjects with type 2 diabetes (n = 58) were excluded due to potential diabetes effect on adiponectin levels (Li et al., 2009). Other exclusion criteria were current medication intake (e.g. anti - inflammatory agents, thiazolidinedione, glucocorticoids, or immune modulating medications), being currently pregnant or having been pregnant in the last 6 months; unwilling to discontinue supplements, vitamins or probiotics for at least 14 d; uncontrolled thyroid disease (thyroid stimulating hormone >5.5 or <0.4 IU mL−1); co-existing malignancy; renal insufficiency (serum creatinine >2 mg dL–1); hematocrit <32%; inflammatory bowel disease, celiac disease, lactose intolerance or other malabsorption disorders. Levels of adiponectin in fasting plasma samples were ascertained by radioimmunoassay (Linco, St. Louis, MO, USA) (interassay coefficient of variation = 6.90%–9.25%; intra-assay precision = 1.78%–6.21%).

Statistical Analysis

The analyzed variables were age, sex, BMI, GSS, seasonal weight change, seasonal change in sleep duration, SAD, t-SAD (combined SAD and s-SAD cases), and plasma adiponectin levels. We tested the association of the GSS, t-SAD and self-reported seasonal changes in sleep duration and weight with adiponectin levels by using analysis of co-variance (ANCOVA) and linear regression analysis after adjusting for age, sex, and BMI. Data was analyzed with the SYSTAT 13 software package (Systat Software, Inc., Chicago, IL) (SYSTAT, 2009) and α-criterion was set at a value of 0.05.

RESULTS

Demographic Characteristics

Our sample included 319 men (50.1%) and 317 women (49.9%) with a mean age (± SD) of 53.8 (± 15.5) years and mean body mass index (BMI) of 26.8 (± 4.6) kg/m2.

Descriptive Statistics

Table 1 shows mean characteristics of the study sample by gender (For demographic characteristics of original study population see (Raheja et al., 2013)). Females had higher age (p = 0.007), BMI (p < 0.001) and adiponectin levels (p < 0.001) as compared to males. Among the 636 subjects with adiponectin data available, 14 had t-SAD (SAD (n = 3) and s-SAD (n = 11), with no significant difference found between males and females (p = 0.27). Mean GSS was 4.4 (SD = 3.2) and no significant difference was found between males and females (p = 0.64).

Table 1.

Demographic characteristics of Amish men and women.

Men (n = 319) Women (n = 317) Total (n = 636) p-value*
Age 52.2 ± 15.9 55.5 ± 14.8 53.6 ± 14.8 0.007*
BMI 25.9 ± 3.5 27.8 ± 5.34 26.8 ± 4.6 <0.001*
GSS 4.4 ± 3.2 4.3 ±3.3 4.4 ± 3.2 0.64
Adiponectin 9.9 ± 4.1 13.7 ± 7.1 11.8 ±6.2 <0.001*
% with t-SAD 0.78 (n = 5) 1.42 (n = 9) 2.2 (n=14) 0.27
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BMI: Body Mass Index; GSS: Global Seasonality Score, t-SAD: (SAD + s-SAD)

“*”

denotes statistical significance

ANCOVA analysis using a univariate general linear model showed that participants with t-SAD had significantly lower plasma adiponectin levels (Mean ± SEM = 8.76 ± 1.56 μg/mL) than those without t-SAD (Mean ± SEM = 11.93 ± 0.22 μg/mL) after adjustment for age and gender (F(1,631) = 4.08; p = 0.044) and also after adjusting for age, gender and BMI (F(1,630) = 4.48; p = 0.035) (Fig. 1).

Figure 1.

Figure 1.

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Mean (± SEM) plasma adiponectin levels in individuals with and without total-SAD (combined syndromal and subsyndromal SAD)

Since blood samples were collected throughout the year, we compared mean adiponectin levels in each season to explore if timing of blood draw in a year had an effect on adiponectin levels. ANCOVA analysis showed that there was no significant association between seasons and mean plasma adiponectin levels in both healthy individuals (n: 622; p = 0.18) and those with t-SAD (n: 14; p = 0. 32) (N: 636). Similarly, to explore whether timing of administration of SPAQ in various seasons had an effect on GSS scores and t-SAD rates, we compared rates of t-SAD and GSS between the seasons of SPAQ administration. Results showed that season of administration of SPAQ had no effect on either GSS (p = 0.97) or rate of t-SAD (p = 0.32) (N: 636).

Average self-reported sleep duration of study population was 7.3 (±1.03) hours in summer, 7.6 (±1.03) hours in spring (±1.05), 7.7 hours in fall and 8.1(±1.1) hours in winter season. All participants with t-SAD reported increased sleep duration in winter as compared to summer. Average winter-summer difference in sleep duration was 0.89 (±0.78) hours. Linear regression analysis showed that there was a significant negative association of winter-summer differences in sleep duration with adiponectin levels (β = −0.09; p = 0.025), which remained significant even after adjustment for age, gender and BMI (β = −0.07; p = 0.04). Variance components model using maximum likelihood method showed that covariance between GSS and seasonal changes in sleep duration was 0.17 with a variance estimate of 0.019. We also found a significant negative association between self-reported seasonal weight changes and plasma adiponectin levels after adjusting for age and gender (β = −0.1; p = 0.006). The association between adiponectin levels and GSS after adjustment for age and gender was not significant (β = −0.005; p = 0.35) (see Table 2).

Table 2.

Adiponectin association with t-SAD, GSS, and self-reported seasonal changes in sleep duration and weight

Parameter (adjusted variables) (N = 636) Beta (β) p-value
t-SAD (Age, Gender) −0.076 0.034*
t-SAD (Age, Gender, BMI) −0.075 0.023
GSS (Age, Gender) −0.034 0.35
GSS (Age, Gender, BMI) −0.005 0.88
Seasonal Change in Sleep Duration (Age, Gender) −0.09 0.025*
Seasonal Weight Change (Age, Gender) −0.10 0.006*
Open in a new tab

BMI: Body Mass Index; GSS: Global Seasonality Score, t-SAD: (SAD + s-SAD)

“*”

denotes statistical significance. “Beta” denotes standardized coefficients of regression.

DISCUSSION

To the best of our knowledge, this is the first study linking adiponectin with seasonality of mood and behavior. We found that participants with t-SAD had significantly lower adiponectin levels than those without t-SAD. The association was resilient to adjustment for age and gender and for age, gender, and BMI. The resilience of the association to BMI adjustment is important, as it does suggest that the link is not driven by BMI, which is inversely associated with adiponectin levels and an important predictor of response of SAD to light treatment (Dimitrova et al., 2017). We also found that the winter-summer difference between self-reported seasonal sleep duration was inversely related to adiponectin levels, i.e., those with lower adiponectin levels had greater winter-summer differences in sleep duration. This association could be a representation of bidirectional causality, as lower adiponectin is associated with depression, and depression with changes in sleep. In consequence, sleep impairment may lead to changes in appetite and metabolic rate (Van Cauter et al., 2008), and thus influence weight gain and carbohydrate craving that are components of the SAD syndrome (Rosenthal et al., 1984). These bidirectional links could also explain our secondary finding of an inverse association between seasonal weight changes and plasma adiponectin levels. In addition, since SAD syndrome predominantly includes atypical symptoms (weight gain and hypersomnia), our findings could also be explained by the atypical component of SAD, which corroborate with previous reports finding low adiponectin in atypical depression (Lamers et al., 2016). However, the resilience of our findings to BMI adjustment suggest that adiponectin is probably associated with both seasonal and atypical components of SAD.

Adiponectin is expressed more by subcutaneous than visceral fat (Chandran et al., 2003) and has two primary receptors, adiponectin receptor 1(AdipoR1) and adiponectin receptor 2 (AdipoR2), the former being found primarily in muscle and the latter in liver. Importantly, these receptors are also found in the central nervous system, including hypothalamus, hippocampus and cerebral cortex (Fry et al., 2006; Thundyil et al., 2012). One mechanism of affect regulation by adiponectin involves modulation of hippocampal neurogenesis (Yau et al., 2014). For example, Yau et al. (2014) showed that adiponectin-deficient mice showed decrease in exercise induced hippocampal neurogenesis and reduction in exercise-induced depressive-like behavior (Yau et al., 2014). Apart from modulation of neurogenesis, adiponectin also possesses anti-inflammatory actions. In fact, it decreases interleukin-6 (IL-6) and tumor necrosis factor (TNF) (thereby inhibiting the TH1 immune pathway) and increases interleukin-10 (IL-10) which is anti-inflammatory (Surendar et al., 2019; Wolf et al., 2004; Wulster-Radcliffe et al., 2004). The main finding that t-SAD is associated with low adiponectin levels is consistent with inflammation being potentially implicated in seasonality and seasonal depression (Leu et al., 2001; Mohyuddin et al., 2017; Song et al., 2015) as well as in non-seasonal depression (Kappelmann et al., 2018; Kim et al., 2018; Köhler et al., 2017; Liu et al., 2012; Mahajan et al., 2018).

However, based on our present study, we cannot assert a causal role of low adiponectin in SAD-pathogenesis. Furthermore, it is still unclear which of the two main components of SAD (seasonality and depression) is associated with low adiponectin level. Animal studies indicate that a shortened photoperiod can directly reduce adiponectin levels; for instance, Syrian hamsters (Weitten et al., 2013) and yellow-bellied marmots (Marmosa flaviventris) (Florant et al., 2004) have decreased adiponectin levels during shortened photoperiods. Thus, shortened photoperiod, a putative SAD-triggering factor could lead to lower adiponectin levels in vulnerable individuals (Wehr et al., 2001). This vulnerability may be genetic. For instance, in animal models, mice lacking one copy of the adiponectin gene, thus being haploinsufficient for APM1, show depressive like behaviors, and, of interest, administration of exogenous adiponectin in normal weight and diet-induced obese mice produces antidepressant effects in both (Liu et al., 2012). It is possible that overlapping genetic factors implicated in seasonality, SAD and adiponectin may represent an upstream cause of our reported association (Byrne et al., 2015), and in that scenario, interventional elevation of adiponectin is unlikely to prevent or treat seasonal exacerbation of depression in SAD.

In this study, the association between adiponectin levels and global seasonal scores was not significant. This discrepancy between associations of GSS and t-SAD with plasma adiponectin levels may be explained by the dimensional nature of the seasonality construct. In general, seasonality is experienced by most individuals to variable extents, whereas, seasonal affective disorder affects only certain individuals. It is possible that the association of low adiponectin levels with t-SAD but not with overall GSS, points to severity of seasonality in individuals with winter SAD, suggesting that distinct mechanisms may be responsible for causing more ample or impactful seasonality of mood, i.e. perceived as a problem by participants. In addition, GSS, in contrast to the estimation of SAD diagnosis does not account for a specific seasonal pattern, i.e. individuals with either fall-winter or summer-spring pattern can have high GSS. It may be likely that low adiponectin is only associated with a fall-winter pattern of seasonality – typically including the manifestation of increasing appetite and weight gain, and not the spring-summer pattern (Akram et al., 2019) (not analyzed in this study considering the very small number of cases). Similarly, not finding associations with GSS may appear internally inconsistent with finding significant associations with seasonal changes in sleep duration and weight. Yet, the components of GSS rated on the item one of the SPAQ, are different than those rated on section four (weight fluctuations) and five (specific sleep duration during each season) of the SPAQ, which include a more precise anchoring of the answers. Moreover, the GSS calculation based on the item one of the SPAQ also includes, besides sleep and weight, ratings of mood, energy, appetite, socializing, that might blur the individual associations of sleep duration and weight with adiponectin levels.

A robust secondary finding was the significant association of winter-summer difference in self-reported sleep duration with lower adiponectin. Martinez-Gomez et al. (2011), by studying the association of sleep duration with various inflammatory markers, including C-reactive protein (CRP) and adiponectin, only found a correlation of sleep duration with CRP, but not with adiponectin (Martinez-Gomez et al., 2011). St-Onge et al. (2012) did not find a correlation of short sleep duration with adiponectin; however, the sample size (n = 27) was small in that study (St-Onge et al., 2012). On the other hand, Simpson et al. (2010) identified decreased adiponectin associated with sleep restriction in healthy adults (Simpson and Singh, 2008). Variability among findings may be due to the fact that sleep parameters in many studies may have been affected by heterogeneity of samples, subjective nature of self-reports, comorbid conditions, and differences in consumption of caffeinated beverages, medications, supplements, and other substances (Fernandez-Mendoza et al., 2011).

If adiponectin has indeed a protective role in SAD, this may also provide one possible explanation for the low prevalence of SAD in the Amish (Magnusson, 2000; Raheja et al., 2013), and a potential mediator associated with increased sunlight exposure and higher activity levels in the Amish as reported by actigraphy studies (Esliger et al., 2010; Evans et al., 2011). In fact, exercise and diet-induced weight loss both increase adiponectin levels (Abbenhardt et al., 2013; Kriketos et al., 2004), although these associations need further study (Simpson and Singh, 2008).

As the study was performed in the Old Order Amish, compared to a study of similar size performed in a demographically more diverse group, the homogenous genetic and cultural background of the Amish makes it less likely that the findings are explained by certain non-measured confounders (e.g. alcohol intake, smoking, late evening/night exposure to network electric light, short wavelength light exposure to from laptops, tablets and cell phone screens, tv sets, etc.). However, further studies will be needed to ascertain generalizability outside the Amish population. Other limitations include a self-appraisal of behavioral change with seasons over a long period of time, thus carrying liability to multiple biases, aggravated especially in our population considering previously reported apprehension of the Amish toward pen-and-pencil questionnaires (Kraybill et al., 2012). However the formal analysis of test -retest reliability of SPAQ measures is satisfactory (Kuehner et al., 2013), and there is no indication to suggest that biases in individuals with high adiponectin would be different from those with low adiponectin. Another limitation is that the data for adiponectin levels was available for only 14 out of 34 cases of t-SAD and 622 out of 1270 healthy individuals in the original study (Raheja et al., 2013), thereby raising a possibility of bias due to missing data at random. We also did not explore other adipokines, leptin in particular, in our study. Ideally, future studies will include longitudinal measurements of adiponectin and mood, weight and sleep (self-report logs, and actigraphy). Future studies on adiponectin and SAD could clarify the connection between adiponectin, inflammation, metabolic factors and seasonal depression with implications for the treatment of SAD, and potentially broadening the study of seasonality towards interactive models of mood, BMI and metabolic syndrome, and cardiovascular morbidity and mortality.

CONCLUSIONS

To the best of our knowledge, this is the first report on adiponectin levels in seasonal depression. Further studies elucidating a pathophysiological role for adiponectin in SAD, with longitudinal measurements of adiponectin and weight, actigraphic measurements of sleep, and investigator-based evaluations of mood and anthropomorphic measurements, as well as potential genetic and epigenetic contributions to the current finding may have important prognostic and treatment implications.

ACKNOWLEDGMENTS

The authors would like to thank the participants and the Amish community for supporting the study, and the staff and Amish liaisons at the Amish Research Clinic in Lancaster, PA, USA for their exceptional contribution to this project.

Footnotes

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Declaration of interest:

Authors declare no conflict of interest. SS is currently an employee of Novo Nordisk A/S, which did not contribute to the study monetarily or in any kind.

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