Vitamin D Deficiency in Association with Depression in Adults

Vitamin D Deficiency in Association with Depression in Adults

Serum 25-hydroxyvitamin D Deficiency in Association with Depression in Adults: A Systematic Review and Meta-Analysis.

Author: Benjamin R. Holmes

(NOT PEER-REVIEWED LITERATURE)

Abstract

Introduction: Depression is a prevalent and debilitating health issue within the modern world and correlative data has shown an association between rates of depression and vitamin D deficiency. There also appears to be a neurobiological link that offers grounding for causality. This systematic review was to look at the degree of association between vitamin D deficiency and depression and to explore the possible aetiologies in relation to both.

Methods: A systematic review and meta-analysis was conducted on 40 observational studies published between 2009 to 2018 over 22 different countries, comparing serum 25-hydroxyvitamin D levels in 118,569 participants and analysing the effect on rates of depression using established testing and questionnaires.

Results: The total OR for 18 studies showed that if vitamin D levels were < 50 nmol/L, there was a 68% greater chance for depression (OR = 1.68, 95% CI 1.41–2.01, P = < 0.00001). The total OR for another 13 studies showed that if vitamin D levels were in the highest quartile, then there is a 25% greater chance to not have depression (OR = 0.75, 95% CI 0.66–0.85, P = < 0.00001). The total HR for 4 cohort studies showed that if vitamin D levels were below 50 nmol/L, there was a 65% greater chance for depression (HR = 1.65, 95% CI 1.35–2.03, P = < 0.00001). 4 cross-sectional and 1 case control study showed the total β-coefficient did not significantly correlate with levels of vitamin D serum and rates of depression (β = -0.15, 95% CI -0.37 – 0.06, P = 0.17).

Conclusion: There is significant association between vitamin D deficiency and depression in adults. Although, conclusions based on causality should not be made without future investigation by means of randomised control trials and other interventional studies.

 

Introduction

      Depression is a common mental health disorder that can be very debilitating to many people, the prevalence of depression globally has continued to rise in the last 30 years (GHDx, 2016). In 2016 the global prevalence of depression was recorded at 3.77% which was 268.12 million people of the population at that time (GHDx, 2016).  Depression is a multifactorial and complex health issue, the entire aetiology of it is not clear and the intrapersonal variability makes treating it difficult (Saveanu and Nemeroff, 2012).

      The hypothesis that depression is a cause of chemical imbalances in the brain was first proposed by Schildkraut (1965) and then later by Coppen (1967), both suggested a link between monoamine neurotransmitters (serotonin, norepinephrine and dopamine) in the brain and depression. A more recent study concluded similar findings but suggested that a shift to investigating areas of the brain modulated by monoamine systems rather than the monoamines themselves may be more informative for understanding the neurobiology of depression (Delgado and Moreno, 2000). Further studies have revisited the serotonin hypothesis giving more support to the idea (Albert and Benkelfat, 2013). Vitamin D has been described as a neurosteroid hormone that effects the developing and adult brain (Groves, McGrath and Burne, 2014), and it was first discovered by Holick (1972). It has also been shown to modulate over 1,000 genes within the body once bound to the vitamin D receptor (VDR) (Haussler et al., 2010), and this receptor is present on neuronal plasma membranes (Dursun and Gezen-Ak, 2017) and other parts of the brain (Eyles et al., 2005, 2014). Vitamin D has been linked with promoting regions of serotonin receptors and tryptophan hydroxylase (Fernandes de Abreu, Eyles and Féron, 2009), it also protects against the dopamine- and serotonin-depleting effects of methamphetamine (Cass, Smith and Peters, 2006) which supports the hypothesis that vitamin D deficiency could have neurological implications for depression.

      There have been numerous reviews dating back to 2007 discussing this topic, some have found there to be an association between vitamin D deficiency and depression (Berk et al., 2007; Murphy and Wagner, 2008; Day et al., 2011; Anglin et al., 2013; Ju, Lee and Jeong, 2013; Alsofyani, Alharbi and Alanazi, 2017), others have been inconclusive, suggesting insufficient evidence for the association (Bertone-Johnson, 2009; Humble, 2010; Penckofer et al., 2010; Howland, 2011; Parker and Brotchie, 2011; Li et al., 2013; Spedding, 2014).

      This systematic review specifically looks at the association between vitamin D deficiency and depression, and not the therapeutic effects that vitamin D intake has on depression. Therefore, observational studies were the focus that fit within the set criteria to ensure the totality of evidence was critically reviewed.

 

Method

Search Strategy

      PubMed, CINAHL and Cochrane were the databases used within the search, a similar search strategy was used for all three but adapted to suit each database. Search terms were decided upon by first identifying the different elements of the question posed, using a PICO like model (Huang, Lin and Demner-Fushman, 2016) and then searching for related terms by using PubMed’s MeSH search function and performing publication searches with individual terms to gather other related terms and to identify potential exclusionary terms to refine the search further. The final search was constructed using the Search Builder function in the advanced search settings of PubMed, this string was then copy and pasted into the other databases with the appropriate filters in each database to fulfil the eligibility criteria.

Eligibility Criteria

      Cross-sectional, cohort and case control type studies published within the last 10 years, written in English were included. All studies that analysed data from adult subjects of both genders (19+ years) that were tested for depression and vitamin D status using established testing methods were included. Studies that analysed other psychological issues like autism, psychosis, schizophrenia, cognition, anorexia or post-partum depression were excluded. Intervention studies like RCTs were excluded as a link between deficiency and depression were reviewed, not the therapeutic effect of vitamin D intake or supplementation on depression. Systematic reviews and meta analyses were also excluded from the review.

Exposure

      Serum 25-hydroxyvitamin D was the required exposure measure, any studies that used a different measurement to determine vitamin D status or used vitamin D intake as a measure were excluded. The threshold that defined vitamin D deficiency varied across studies, there was no accepted threshold for vitamin deficiency in this review that dictated eligibility but papers that used different thresholds were stratified into similar subgroups for the analysis so that fair comparisons could be made. If studies used multiple thresholds, odds and hazard ratios were chosen that used < 50 nmol/L over other thresholds if it was available as this was considered the most widely acknowledged value among other papers (Holick, 2011; Scragg, 2018). Baseline values were included into the statistical comparisons for all cohort and longitudinal studies, follow ups were not assessed in this review.

Outcome

      Studies that diagnosed depression using an established cut off scale were included, any studies that didn’t use a specific scale to measure depression or used a generalised health/wellbeing scale were excluded. No discrimination between types of depression scales used or whether they were self-reported, as this either limited the amount papers for the review or was difficult to ascertain for most studies.

Meta-Analysis

      Odds ratio, hazard ratio and beta coefficients were used to determine the effect of the association. Studies that didn’t provide sufficient data required for the analysis were excluded.

Risk of Bias & Quality Assessment

      Risk of bias and quality was assessed using the Newcastle-Ottawa scale for all studies (cross-sectional studies used an adapted scale) (Stang, 2010). Results of the risk of bias and quality can be found in appendix B.

 

Results

      All databases used identified and total of 1,699 studies (Fig 1), and after filters from corresponding databases were applied and duplicates removed, there remained a total of 464 studies for title review. Out of those studies, 58 abstracts were screened, and 51 studies went forward for full text review. 11 studies were excluded leaving 40 studies that met the eligibility criteria (Fig 2).       

Studies were arranged into groups based on their method of analysis and sub-grouped based on their method of defining vitamin D status. Their characteristics and the confounders they adjusted for are presented in tables above their corresponding forest plots. There was a total of 118,569 participants across all 40 studies that were published between 2009 and 2018 over 22 different countries. 32 out 40 studies observed both genders, 4 studies looked at just males and 4 studies looked at just females.

Fig 1. Study Selection Process

 

 

7 studies were excluded based off their abstract:

1 cohort and 1 cross sectional study didn’t report vitamin D deficiency as an exposure measure

1 cohort and 1 case control study didn’t report depression as an outcome measure

2 meta-analysis was excluded based on study type

1 case report were excluded due to being written in a non-English language

 

11 studies were excluded based off their full-text:

7 cross sectional studies reported insufficient results for comparison

3 cross sectional studies didn’t report depression as an outcome measure

1 cross sectional study didn’t report vitamin D deficiency as an exposure measure


Fig 2.
Study exclusion reasons at abstract and full-text review stage

 

 

Study Characteristics & Meta-Analysis

 

Table 1.  Study Characteristics for studies included in Fig 4 and adjustments made for confounders.

Study, year

Study Type

Country

Population

n

Vitamin D
Category

Diagnosis of Depression

Confounder Adjustments

De Oliveira, Hirani and Biddulph, 2017

Cross-Sectional

UK

Male & Female, ≥50 years

5870

8.5 - 34.3 nmol/L

CES-D, ≥16

Age, sex, season, waist circumference, wealth, smoking, exercise, CVD, non-CVD conditions, difficulties in activities of daily living and memory score

Han et al., 2014

Cross-Sectional

China

Male & Female, 18-80 years

189

≤ 37 nmol/l

HDS, ≥7

Sex, education, BMI, smoking, alcohol, NHISS score, mRS score, BI score, MMSE score

Kojima et al., 2016

Cross-Sectional

Hawaii

Male, 60.8 years (x̅)

64

< 50nmol/L

GDS, ≥5

Age, BMI and race

Krysiak, Gilowska and Okopień, 2016

Case
Control

Poland

Female, 20-40 years

42

< 50nmol/L

BDI-II, ≥14

None

Krysiak, Szwajkosz and Okopień, 2018

Case
Control

Poland

Male, 18 - 40 years

47

< 50nmol/L

BDI-II, ≥14

None

Lee et al., 2010

Cohort

IT, BE, PL, SE, UK, ES, HE and EE

Male, 40-79 years

3369

25.0 - 49.9 nmol/L

BDI-II, ≥14

Age, BMI, smoking, alcohol, activity, physical function, morbidities, adverse life events and psychotropic drug use.

Lee et al., 2016

Cross-Sectional

Korea

Male & Female, 20-88 years

7198

< 50 nmol/L

KNHANES V

Age, sex, BMI, season, smoking, alcohol, exercise, income, education, marital status, changes in bodyweight, perceived body shape and cholesterol

Moy et al., 2016

Cross-Sectional

Netherlands

Male & Female, 18 - 65 years

770

< 50 nmol/L

IDS, ≥26

BMI, smoking, alcohol, physical activity, chronic diseases and creatinine clearance

Park, Yang, Won Park and Chung, 2015

Cross-Sectional

Malaysia

Female, ≥20 years

15695

< 50 nmol/L

DASS, >9

Age, race, BMI, metabolic syndrome, depression, PTH and MCS and PCS scores.

Polak et al., 2014

Cross-Sectional

Korea

Male & Female ≥20 years

615

< 50 nmol/L

KNHANES-V

Age, sex, obesity, education, occupation, economic status, marital status, exercise, chronic disease, subjective health status, alcohol, and smoking

Sherchand et al., 2018

Cross-Sectional

New Zealand

Male & Female, 17-25 years

300

< 43.9nmol/L

CES-D, ≥16

Age, sex, race, BMI, and time spent outdoors

Shin et al., 2016

Cross-Sectional

Nepal

Male & Female, ≥18 years

52228

< 50 nmol/L

BDI, ≥10

BMI, geographical region, marital status, activity, socioeconomic status, education and employment

Vidgren et al., 2018

Cross-Sectional

Finland

Male & Female, 53 - 73 years

1602

< 50nmol/L

DSM-III, ≥4

Age, sex, examination year/month, economic status, marital status, leisure-time, activity, energy intake, alcohol, intake of fruits, berries, vegetables, omega-3 and mental diseases.


Table 2.  Study Characteristics for studies included in Fig 3. and adjustments made for confounders

Study, year

Study Type

Country

Population

n

Vitamin D
Category

Diagnosis of Depression

Confounder Adjustments

Jhee et al., 2017

Cross- Sectional

Korea

Male & Female, 60-80 years

533

< 25nmol/L

KNHANES V, VI

Age, sex, BMI, alcohol, smoking, suicidal idea. EQ5D index, HTN, DM, hemoglobin, glucose, total cholesterol, eGFR and proteinuria

Lapid, Takahashi and Cha, 2013

Cross- Sectional

USA

Male & Female, ≥60 years

1618

< 25nmol/L

HICDA

Age, sex and ERA score

Milaneschi et al., 2013

Cohort

Korea

Male & Female, 20-70 years

2316

< 20 nmol/L

IDS, ≥26

Age, sex, BMI, season marriage status, smoking, DM, stroke, angina and CRP level.

Song et al., 2016

Longitudinal 

Korea

Male & Female, 65 - 95 years

2853

< 25 nmol/L

CES-D, ≥16

Age, BMI, study year, month of assay, PTH, comorbidities, smoking, alcohol, exercise, sleep, income, education, cohabitation status, and residential area

Stewart and Hirani, 2010

Cross- Sectional

UK

Male & Female, ≥65 years

2070

< 25 nmol/L

GDS, ≥5

Age, sex, BMI, season, vitamin D supplement intake, social class, smoking, long-standing limiting illness and subjective general health status

 

      18 studies were analysed that reported odds ratio with specific cut off values for vitamin D deficiency (Fig 3), the data was stratified into subgroups of those thresholds. The < 50 nmol subgroup that contained 8 cross sectional, 2 case control and 1 cohort study showed if vitamin D levels were < 50 nmol/L then there is a 68% greater chance for depression (OR = 1.68, 95% CI 1.34–2.10, P = < 0.00001), the < 37.5 nmol/L subgroup that contained 2 cross sectional studies showed that if vitamin D levels were < 37.5nmol/L then there is a 187% greater chance for depression (OR = 2.87, 95% CI 0.49–16.89, P = 0.24). The < 25 nmol/L subgroup that contained 3 cross sectional, 1 cohort and 1 longitudinal study showed that if vitamin D levels were below 25 nmol/L then there is an 82% greater chance for depression (OR = 1.82, 95% CI 1.19–2.80, P = 0.006). The total OR for all the subgroups showed that if vitamin D levels were < 50 nmol/L, then there was a 68% greater chance for depression (OR = 1.68, 95% CI 1.41–2.01, P = < 0.00001). A random effects model was used for this analysis as heterogeneity was assumed (Tau2 = 0.07; Chi2 = 42.09, df = 17, (P = 0.0007), I2 = 60%).

Fig 3. A forest plot displaying the odds ratio (OR) of depression for three subgroups with different categories for vitamin D deficiency. The blue squares to the right of the mid line indicate that the category for vitamin D deficiency listed was associated with an increased risk for depression. The red diamonds indicate the subtotal OR for each subgroup and the black diamond indicates the total OR for the entire dataset. The horizontal lines represent the 95% confidence intervals for the associated OR.

* - Serum 25-hydroxyvitamin D categories used in the subgroup are listed in Table 1.

** - Serum 25-hydroxyvitamin D categories used in the subgroup are listed in Table 2.

A random effects model was used in the forest plot as heterogeneity was assumed (appendix A).

 

 

Table 3.  Study Characteristics for studies included in Fig 4. and adjustments made for confounders

Study, year

Study Type

Country

Population

n

Vitamin D
Category

Diagnosis of Depression

Confounders Adjusted

Barbonetti et al., 2017

Cross-Sectional

Italy

Male & Female, 20-89 years

100

Serum Level increase

BDI-II, ≥14

Sex, BMI, season, activity, time since injury, intake of psychotropic medications, leisure time, and spinal cord independence measure

Chung, Cho, Choi and Shin, 2014

Cross-Sectional

Korea

Male & Female, ≥20 years

3570

≥50 nmol/

KNHANES-V

Age, sex, BMI, season, income, education, marital status, body weight control, perceived body shape, alcohol behaviour, smoking status and physical activity

Hoang et al., 2011

Cross-Sectional

USA

Male & Female, ≥40 years

12594

Serum level increase (+25 nmol/L)

CES-D, ≥10

Age, BMI, education, and thyrotropin

Jääskeläinen et al., 2015

Cross-Sectional

Finland

Male & Female, 30-79 years

5371

56–134 nmol/L

BDI, ≥10

Age, sex, BMI, season, education, leisure-time, activity, smoking, alcohol, BP, HDL, TAG, fasting glucose

Kjærgaard, Joakimsen and Jorde, 2011

Cross-Sectional

Norway

Male & Female, 30-87 years

8120

56.7–182.50 nmol/L

SCL-10, ≥1.85

Age, sex, BMI, GFR, marital status, education, alcohol consumption, physical activity and chronic disease

Maddock et al., 2013

Cohort

UK

Male & Female, ≥45 years

7401

≥100nmol/L

MHI-5, < 53

Sex, BMI, season, socioeconomic position, smoking, activity, leisure time, sun-cover, blistering after sunburn and actively seeking sun tan.

Mizoue et al., 2014

Cross-Sectional

Japan

Male & Female, 19–69 years

1786

≥ 75nmol/L

CES-D, ≥16

Age, sex, BMI, smoking, alcohol, factory, marital status, job, overtime/shift work, job strain, sleep, activity, kcal intake, folate, vitamin B6, n–3 PUFA, magnesium, iron and leisure-time

Nanri et al., 2009

Cross-Sectional

Japan

Male & Female, 21-67 years

527

82 nmol/L

CES-D, ≥16

Age, sex, BMI, job, marital status, occupation, non-job activity, smoking, alcohol and folate

Pan et al., 2009

Cross-Sectional

China

Male & Female, 50-70 years

3262

65.1± 16.0 nmol/L

CES-D, ≥16

Age, sex, BMI, urban/rural, activity, smoking, number of chronic diseases, social activity level, marital status, income, geographic location

Pu et al., 2017

Cross-Sectional

China

Male & Female, 25-75 years

161

Serum Level increase

HAMD ≥ 8

Age, sex, BMI, marital status, education, disease duration, DAS28-ESR, HAQ, VAS, CRP, treated by TNFi and treated by prednisone

Rabenberg et al., 2016

Cross-Sectional

Germany

Male & Female, 18-79 years

6331

60-347 nmol/L

PHQ-9, ≥ 10

Age, sex, socioeconomic status, partnership, municipality size, country of birth, obesity, physical functioning, eGFR, smoking, alcohol and sport activity.

Wang, Yang, Meng and Han, 2017

Cross-Sectional

China

Male & Female, 25 - 80 years

2786

> 47.25 nmol/L

PHQ-9, ≥10

Age, sex, BMI, season, education, marital status, family history of mental illness, use of insulin, activity, medication adherence score, Hs-CRP, FBG and HbA1c

Zhao, Ford, Li and Balluz, 2010

Cross-Sectional

USA

Male & Female, ≥20 years

3916

≥65nmol/L

PHQ-9, ≥10

Age, sex, race, BMI, alcohol, activity, education, marital status, cotinine, and chronic diseases


      12 cross sectional and 1 cohort study were analysed that reported odds ratio for depression that compared the highest v. lowest quartiles of vitamin D levels in participants (Fig 4), The total OR for all studies in this analysis showed that if vitamin D levels were in the highest quartile, then there is a 25% greater chance for no depression (OR = 0.75, 95% CI 0.66–0.85, P = < 0.00001). A random effects model was used for this analysis as heterogeneity was assumed (Tau2 = 0.03; Chi2 = 53.85, df = 12, (P = < 0.00001), I2 = 78%).

 

Fig 4. A forest plot displaying the odds ratio (OR) of depression for the highest v. lowest quartiles of serum 25-hydroxyvitamin D with the lowest quartile signifying the reference (1). The blue squares to the left of the mid line indicate that as levels of serum 25-hydroxyvitamin D increase there is a reduced odd for depression. The blue squares to the right of the mid line indicate that as levels of serum 25-hydroxyvitamin D increase there is an increased odd for depression. The red diamond and dotted line indicate the total OR for the entire dataset and the horizontal lines represent the 95% confidence intervals for the associated OR.

A random effects model was used in the forest plot as heterogeneity was assumed (appendix A).


Table 4.  Study Characteristics for studies included in Fig 5. and adjustments made for confounders

Study, year

Study Type

Country

Population

n

Vitamin D
Category

Diagnosis of Depression

Confounders Adjusted

Almeida et al., 2019

Cohort

Australia

Men, 71–88 years

2740

< 50 nmol/L

PHQ-9, ≥10

Age, smoking, season living arrangements, and CHD or stroke

Milaneschi et al., 2010

Cohort

Italy

Male & Female, ≥ 65 years

954

< 50 nmol/L

CES-D, ≥16

Age, season baseline CES-D, ADL disabilities, use of antidepressants, number of chronic diseases, SPPB and high PTH

Williams et al., 2014

Cohort

US

Male & Female, 70–79 years

2598

< 50nmol/L

CES-D, ≥16

Age, sex, race, BMI, site, season, education, DM, CVD, 3MS score, kidney disease, smoking, alcohol, marital status, activity, and history of depression.

May et al., 2010

Cohort

US

Male & Female, ≥50 years

7358

< 37.5 nmol/L

ICD-9

Age, sex, DM, hypertension, hyperlipidaemia, CAD, prior MI, heart failure, CVA, TIA, A-fib, prior fracture, PVD and renal failure

 

      4 cohort studies were analysed that reported hazard ratio for depression with specific cut off values for vitamin D deficiency (Fig 5), the data was stratified into subgroups of those thresholds so that fair comparisons could be made. The < 50 nmol subgroup that contained 3 cohort studies showed that if vitamin D levels were < 50 nmol/L then there is 58% greater chance for depression (HR = 1.58, 95% CI 1.27–1.96, P = < 0.0001), the < 37.5 nmol/L subgroup that contained 1 cohort study showed that if vitamin D levels were < 37.5nmol/L then there is a 170% greater chance for depression (HR = 2.70, 95% CI 1.35–5.40, P = 0.005). The total HR for all the subgroups showed that if vitamin D levels were < 50 nmol/L, then there is a 65% greater chance for depression (HR = 1.65, 95% CI 1.35–2.03, P = < 0.00001). A fixed effects model was used for this analysis as heterogeneity was not assumed (Chi2 = 4.85, df = 4, (P = 0.18), I2 = 38%).

Fig 5. A forest plot displaying the hazard ratio (HR) of depression for two subgroups with different categories for vitamin D deficiency. The blue squares to the right of the mid line indicate that the category for vitamin D deficiency listed was associated with an increased risk for depression. The red diamonds indicate the subtotal OR for each subgroup and the black diamond indicates the total OR for the entire dataset. The horizontal lines represent the 95% confidence intervals for the associated OR.

* - Serum 25-hydroxyvitamin D categories used in the subgroup are listed in Table 4.
A fixed effects model was used in the forest plot as heterogeneity was not assumed (appendix A).

 

Table 5.  Study Characteristics for studies included in Fig 6. and adjustments made for confounders

Study, year

Study Type

Country

Population

n

Vitamin D
Category

Diagnosis of Depression

Confounders Adjusted

Callegari et al., 2017

Cross-Sectional

Australia

Female, 16-25 years

353

Vitamin D serum level

PHQ-9

Age, season, height, Weight, COC use, vitamin D supplementation and sun exposure

Dana-Alamdari, Kheirouri and Noorazar, 2014

Case Control

Iran

Male & Female, 18–63 years

85

Vitamin D serum level

HRSD, ≥10

Not stated

Grzegorzewska et al., 2016

Cross-Sectional

Poland

Male & Female,
> 18 years

112

Vitamin D serum level

GHQ-28 ≥5

Age, sex, GG genotype of GC rs7041, treatment with HF-HD, chronic GN, IPAQ-L, T2D nephropathy, QLI-D, serum PTH, and serum albumin

Knippenberg et al., 2010

Cross-Sectional

Netherlands

Male & Female, 21.1–79.9 years

59

Vitamin D serum level

HADS ≥ 8

Age, EDSS score, HADS and MFI

Moran, Teede and Vincent, 2014

Cross-Sectional

Australia

Female, 18-45 years

73

Vitamin D serum level

HADS ≥ 8

Age, BMI


      4 cross sectional and 1 case control study were analysed that reported correlative data for depression and vitamin D serum levels in participants (Fig 6). The total β-coefficient in the analysis showed that levels of vitamin D serum did not significantly correlate with rates of depression (β = -0.15, 95% CI -0.37 – 0.06, P = 0.17). A random effects model was used for this analysis as heterogeneity was assumed (Tau2 = 0.03 Chi2 = 148.16, df = 4, (P = < 0.00001), I2 = 97%).

Fig 6. A forest plot displaying the change in natural logarithm of the odds ratio ln(OR) of serum 25-hydroxyvitamin D with depression in respect to its correlation coefficient. The blue squares to the left of the mid line indicate that there is a negative correlation between levels of serum 25-hydroxyvitamin D and rates of depression. The blue squares to the right of the mid line indicate that there is a positive correlation between levels of serum 25-hydroxyvitamin D and rates of depression. The red diamond and dotted line indicate the total ln(OR) for the entire dataset and the horizontal lines represent the 95% confidence intervals for the associated ln(OR).

A random effects model was used in the forest plot as heterogeneity was assumed (appendix A).

 

 

Table 6. Summary of results from all meta-analysis of the relationship between vitamin D and depression

Analysis

Number of Studies

Participants

Vitamin D Category Description

Pooled OR, HR or β

P value

Fig 3 (Total)

18

97379

Cut off threshold

OR = 1.68

< 0.00001

 

Fig 3: < 50 nmol/L Subtotal

11

81930

Cut off threshold

OR = 1.68

< 0.00001

 

Fig 3: < 37.5 nmol/L Subtotal

2

6059

Cut off threshold

OR = 2.87

0.24

 

Fig 3: < 25 nmol/L Subtotal

5

9390

Cut off threshold

OR = 1.82

0.006

Fig 4 (Total)

11

55925

Highest v. Lowest quartile

OR = 0.75

< 0.00001

Fig 5 (Total)

4

13650

Cut off threshold

HR = 1.65

< 0.00001

 

Fig 5: < 50 nmol/L Subtotal

3

6292

Cut off threshold

HR = 1.58

< 0.0001

 

Fig 5: < 37.5 nmol/L Subtotal

1

7358

Cut off threshold

HR = 2.70

0.005

Fig 6

5

682

Serum level

β = -0.15

0.17


Discussion

      This systematic review analysed 40 observational type studies that looked at vitamin D deficiency and rates of depression, 7 out of 9 group or subgroup meta analyses found a significant association that confirms the relationship between vitamin D deficiency and depression (Table 6.). Its important to note that this does not imply there is a causal relationship between being deficient in vitamin D and depression as there are many other factors to consider that can potentially affect depression irrespective of vitamin D deficiency. All but 3 studies controlled for varying confounding factors but studies that controlled for confounders related to depression (socioeconomic status, health, smoking, alcohol, BMI, gender, stress and disease) held the highest validity over studies that didn’t control for such factors (Schaakxs et al., 2017). Due to grouping/subgrouping of data based off the exposure threshold and method of analysis, fair comparisons between studies was possible. Further subgrouping could have been made for outcome measures but due to the lack of studies testing for depression in the same way within already established subgroups, this was not possible, but may have improved comparisons. Some of the studies in the comparisons were looking at vitamin D deficiency and depression rates within specific populations that had a disease or certain health issue, whilst this does not cause a problem when the data is isolated by itself, this could inflate the association in-between studies, skewing the total effect from the pooled data. The season of testing and climate the participants lived could have a major effect on both the outcome and exposure measures, the climate of the country of testing was ascertained from all studies but the season of testing was not always recorded, this is one factor that could have been controlled for to further provide better comparisons as seasonal affective disorder may have had a huge influence on the results that go beyond UV-B exposure increasing vitamin D levels (Neumeister et al., 2001; Lambert et al., 2002; Magnusson and Boivin, 2003). From all the studies reviewed there is a clear association between vitamin D deficiency and depression, but due to the nature of study type reviewed, this cannot provide evidence to suggest that either one causes the other. It could also be the case that populations that have higher rates of depression are more prone to being deficient in vitamin D through agoraphobic behaviour and fatigue that are both associated with depression (Breier, 1984; Fava et al., 2013). This could increase the risk of vitamin D deficiency through lack of sun exposure from extended periods of time spent indoors. Obesity is also associated with depression (Luppino et al., 2010) as well as influencing vitamin D levels by reducing its bioavailability (Wortsman et al., 2000; Carrelli et al., 2016). Poor diet and eating disorders - which also influences vitamin D deficiency, are strongly related to depression (Araujo, Santos and Nardi, 2010; Mischoulon et al., 2011).

      In conclusion, a link between vitamin D deficiency and depression may be a result of inherent behaviours that are associated with depression that worsen one’s vitamin D status, studies have shown that there may also be a neurobiological link between vitamin D deficiency and mediators of depression. Therefore, a cyclical, causal relationship may be at play that would better explain the associations that have been seen between vitamin D and depression. More interventional studies would need to be carried out with stringent controls to provide evidence for a causal relationship. Despite this, supplementation of vitamin D3 to ensure vitamin D status is adequate would be a good preventative measure to ensure not only mental health but health in general was maintained.

 

References

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Appendices

Appendix - A

Statistical Analysis

Statistical analysis was carried out using (Review Manager (RevMan) [Computer program]. Version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014), search results were managed and complied using (Cite This For Me, 2019). Heterogeneity was measured using Cochran’s Q test and heterogeneity between studies was assumed when probability value was < 0.1 and the I2 statistic was > 50%. All heterogenous datasets used a random effects model for the analysis and all homogenous datasets used a fixed effects model for the analysis.

 

Appendix - B

Risk of bias and quality assessment

The black progress bar within each cell represents the percentage of stars that were rewarded for each category of bias and quality assessment within the scale used, compared to the max stars that can be awarded for each category within the scale. The more filled the cell is the higher the quality and less risk of bias in the corresponding category. 

Cross-sectional studies

The Newcastle Ottawa Scale for assessing quality and risk of bias was used to assess all cross-sectional studies reviewed (Stang, 2010).

Response rate was determined satisfactory within the selection bias category when it was > 80% (Fincham, 2008)

Newcastle Ottawa Scale (adapted for cross-sectional studies)

(Journals.plos.org, 2019)

Cohort Studies

The Newcastle Ottawa Scale for assessing quality and risk of bias was used to assess all cohort studies reviewed (Stang, 2010).

Response rate was determined satisfactory within the selection bias category when it was > 80% (Fincham, 2008)

Newcastle Ottawa Scale

(Ohri.ca, 2019)

 

Case Control Studies

The Newcastle Ottawa Scale for assessing quality and risk of bias was used to assess all case control studies reviewed (Stang, 2010).

Response rate was determined satisfactory within the selection bias category when it was > 80% (Fincham, 2008)

Newcastle Ottawa Scale

(Ohri.ca, 2019)