Sources of Dietary Fiber Are Differently Associated with Prevalence of Depression
Abstract
:1. Introduction
2. Materials and Methods
2.1. Design and Study Population
2.2. Assessment of Dietary Fiber Intake
2.3. Evaluation of Severity and Prevalence of Depression
2.4. Statistical Analysis
3. Results
3.1. General Characteristics of the Study Population
3.2. Association between Dietary Fiber Intake and PHQ-9 Depressive Symptoms
3.3. Association between Dietary Fiber Intake and Clinical Depression Diagnosed by a Physician
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Clinical Diagnosis of Depression | |||
---|---|---|---|
No | Yes | p-Value | |
Participants | 348 (64.74) | 198 (36.26) | |
Age, y | 45.95 (12.93) | 47.12 (12.03) | 0.2978 |
Sex | |||
Male | 61 (17.53) | 36 (18.18) | 0.9074 |
Female | 287 (82.47) | 162 (81.82) | |
BMI, kg/m2 | 23.39 (3.40) | 24.24 (3.91) | 0.0083 |
Educational level | |||
Elementary or less | 67 (19.25) | 47 (23.74) | 0.2216 |
Junior high school | 52 (14.94) | 34 (17.17) | |
High school | 135 (38.79) | 78 (39.39) | |
College or more | 94 (27.01) | 39 (19.70) | |
Household income | |||
Q1 (lowest) | 43 (12.50) | 63 (32.14) | <0.0001 |
Q2 | 99 (28.78) | 57 (29.08) | |
Q3 | 111 (32.27) | 37 (18.88) | |
Q4 (highest) | 91 (26.45) | 39 (19.90) | |
Smoking status | |||
No | 295 (84.77) | 156 (78.79) | 0.0794 |
Yes | 53 (15.23) | 42 (21.21) | |
Drinking status | |||
No | 211 (60.63) | 122 (61.62) | 0.8554 |
Yes | 137 (39.37) | 76 (38.38) | |
Physical activity | |||
No | 229 (65.99) | 134 (67.68) | 0.7066 |
Yes | 118 (34.01) | 64 (32.32) | |
Health status | |||
Excellent | 5 (1.44) | 1 (0.51) | <0.0001 |
Very good | 50 (14.37) | 14 (7.07) | |
Good | 204 (58.62) | 88 (44.44) | |
Fair | 81 (23.28) | 72 (36.36) | |
Poor | 81 (23.28) | 72 (36.36) | |
Energy intake, kcal/d | 1793.90 (638.50) | 1605.80 (635.30) | 0.0011 |
Crude fiber, g/d | 5.62 (2.23) | 5.03 (2.28) | 0.0030 |
Cereal fiber, g/d | 5.23 (2.24) | 4.71 (2.34) | 0.0103 |
Vegetable fiber, g/d | 5.15 (1.95) | 4.61 (2.02) | 0.0021 |
Fruit fiber, g/d | 2.53 (2.32) | 2.16 (2.10) | 0.0627 |
Seaweed fiber, g/d | 0.83 (0.64) | 0.70 (0.62) | 0.0290 |
Mushroom fiber, g/d | 0.08 (0.07) | 0.06 (0.06) | 0.0193 |
References
- World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates; World Health Organization: Geneva, Switzerland, 2017. [Google Scholar]
- Jeon, H.J.; Lee, J.-Y.; Lee, Y.M.; Hong, J.P.; Won, S.-H.; Cho, S.-J.; Kim, J.-Y.; Chang, S.M.; Lee, D.; Lee, H.W. Lifetime prevalence and correlates of suicidal ideation, plan, and single and multiple attempts in a Korean nationwide study. J. Nerv. Ment. Dis. 2010, 198, 643–646. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ionescu, D.F.; Rosenbaum, J.F.; Alpert, J.E. Pharmacological approaches to the challenge of treatment-resistant depression. Dialogues Clin. Neurosci. 2015, 17, 111. [Google Scholar]
- Casacalenda, N.; Perry, J.C.; Looper, K. Remission in major depressive disorder: A comparison of pharmacotherapy, psychotherapy, and control conditions. Am. J. Psychiatry 2002, 159, 1354–1360. [Google Scholar] [CrossRef] [Green Version]
- Entsuah, A.R.; Huang, H.; Thase, M.E. Response and remission rates in different subpopulations with major depressive disorder administered venlafaxine, selective serotonin reuptake inhibitors, or placebo. J. Clin. Psychiatry 2001, 62, 869–877. [Google Scholar] [CrossRef]
- Blackwell, B. Adverse effects of antidepressant drugs. Drugs 1981, 21, 201–219. [Google Scholar] [CrossRef]
- Jacka, F.N.; Pasco, J.A.; Mykletun, A.; Williams, L.J.; Hodge, A.M.; O’Reilly, S.L.; Nicholson, G.C.; Kotowicz, M.A.; Berk, M. Association of Western and traditional diets with depression and anxiety in women. Am. J. Psychiatry 2010, 167, 305–311. [Google Scholar] [CrossRef] [Green Version]
- Skarupski, K.A.; Tangney, C.; Li, H.; Evans, D.; Morris, M. Mediterranean diet and depressive symptoms among older adults over time. J. Nutr. Health Aging 2013, 17, 441–445. [Google Scholar] [CrossRef] [Green Version]
- Cherian, L.; Wang, Y.; Holland, T.; Agarwal, P.; Aggarwal, N.; Morris, M.C. DASH and Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) Diets Are Associated With Fewer Depressive Symptoms Over Time. J. Gerontol. Ser. A 2020. [Google Scholar] [CrossRef]
- Opie, R.; Itsiopoulos, C.; Parletta, N.; Sanchez-Villegas, A.; Akbaraly, T.N.; Ruusunen, A.; Jacka, F. Dietary recommendations for the prevention of depression. Nutr. Neurosci. 2017, 20, 161–171. [Google Scholar] [CrossRef]
- Shabbir, F.; Patel, A.; Mattison, C.; Bose, S.; Krishnamohan, R.; Sweeney, E.; Sandhu, S.; Nel, W.; Rais, A.; Sandhu, R. Effect of diet on serotonergic neurotransmission in depression. Neurochem. Int. 2013, 62, 324–329. [Google Scholar] [CrossRef]
- Markus, C.R. Dietary amino acids and brain serotonin function; implications for stress-related affective changes. Neuromol. Med. 2008, 10, 247. [Google Scholar] [CrossRef] [PubMed]
- DeVries, J.W. On defining dietary fibre. Proc. Nutr. Soc. 2003, 62, 37–43. [Google Scholar] [CrossRef] [Green Version]
- Sonnenburg, E.D.; Sonnenburg, J.L. Starving our microbial self: The deleterious consequences of a diet deficient in microbiota-accessible carbohydrates. Cell Metab. 2014, 20, 779–786. [Google Scholar] [CrossRef] [Green Version]
- Ndeh, D.; Rogowski, A.; Cartmell, A.; Luis, A.S.; Baslé, A.; Gray, J.; Venditto, I.; Briggs, J.; Zhang, X.; Labourel, A. Complex pectin metabolism by gut bacteria reveals novel catalytic functions. Nature 2017, 544, 65. [Google Scholar] [CrossRef]
- Trompette, A.; Gollwitzer, E.S.; Yadava, K.; Sichelstiel, A.K.; Sprenger, N.; Ngom-Bru, C.; Blanchard, C.; Junt, T.; Nicod, L.P.; Harris, N.L. Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis. Nat. Med. 2014, 20, 159. [Google Scholar] [CrossRef]
- Walker, A.W.; Ince, J.; Duncan, S.H.; Webster, L.M.; Holtrop, G.; Ze, X.; Brown, D.; Stares, M.D.; Scott, P.; Bergerat, A. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 2011, 5, 220. [Google Scholar] [CrossRef]
- Mayer, E.A. Gut feelings: The emerging biology of gut–brain communication. Nat. Rev. Neurosci. 2011, 12, 453. [Google Scholar] [CrossRef]
- Kim, C.-S.; Cha, L.; Sim, M.; Jung, S.; Chun, W.Y.; Baik, H.W.; Shin, D.-M. Probiotic supplementation improves cognitive function and mood with changes in gut microbiota in community-dwelling elderly: A randomized, double-blind, placebo-controlled, multicenter trial. J. Gerontol. Ser. A 2020. [Google Scholar] [CrossRef]
- Xu, H.; Li, S.; Song, X.; Li, Z.; Zhang, D. Exploration of the association between dietary fiber intake and depressive symptoms in adults. Nutrition 2018, 54, 48–53. [Google Scholar] [CrossRef]
- Gangwisch, J.E.; Hale, L.; Garcia, L.; Malaspina, D.; Opler, M.G.; Payne, M.E.; Rossom, R.C.; Lane, D. High glycemic index diet as a risk factor for depression: Analyses from the Women’s Health Initiative. Am. J. Clin. Nutr. 2015, 102, 454–463. [Google Scholar] [CrossRef] [Green Version]
- Gopinath, B.; Flood, V.M.; Burlutksy, G.; Louie, J.C.; Mitchell, P. Association between carbohydrate nutrition and prevalence of depressive symptoms in older adults. Br. J. Nutr. 2016, 116, 2109–2114. [Google Scholar] [CrossRef] [Green Version]
- Miki, T.; Eguchi, M.; Kurotani, K.; Kochi, T.; Kuwahara, K.; Ito, R.; Kimura, Y.; Tsuruoka, H.; Akter, S.; Kashino, I. Dietary fiber intake and depressive symptoms in Japanese employees: The Furukawa Nutrition and Health Study. Nutrition 2016, 32, 584–589. [Google Scholar] [CrossRef] [Green Version]
- Dietary Reference Intakes for Koreans 2015; Ministry of Health and Welfare and The Korean Nutrition Society: Sejong, Korea, 2015; pp. 186–187.
- Kroenke, K.; Spitzer, R.L.; Williams, J.B. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 2001, 16, 606–613. [Google Scholar] [CrossRef]
- Gilbody, S.; Richards, D.; Brealey, S.; Hewitt, C. Screening for depression in medical settings with the Patient Health Questionnaire (PHQ): A diagnostic meta-analysis. J. Gen. Intern. Med. 2007, 22, 1596–1602. [Google Scholar] [CrossRef] [Green Version]
- Kroenke, K.; Spitzer, R.L.; Williams, J.B.; Löwe, B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. Gen. Hosp. Psychiatry 2010, 32, 345–359. [Google Scholar] [CrossRef]
- Wittkampf, K.A.; Naeije, L.; Schene, A.H.; Huyser, J.; van Weert, H.C. Diagnostic accuracy of the mood module of the Patient Health Questionnaire: A systematic review. Gen. Hosp. Psychiatry 2007, 29, 388–395. [Google Scholar] [CrossRef]
- Cryan, J.F.; Dinan, T.G. Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 2012, 13, 701. [Google Scholar] [CrossRef]
- Bravo, J.A.; Forsythe, P.; Chew, M.V.; Escaravage, E.; Savignac, H.M.; Dinan, T.G.; Bienenstock, J.; Cryan, J.F. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc. Natl. Acad. Sci. USA 2011, 108, 16050–16055. [Google Scholar] [CrossRef] [Green Version]
- Desbonnet, L.; Garrett, L.; Clarke, G.; Bienenstock, J.; Dinan, T.G. The probiotic Bifidobacteria infantis: An assessment of potential antidepressant properties in the rat. J. Psychiatr. Res. 2008, 43, 164–174. [Google Scholar] [CrossRef]
- Ruddick, J.P.; Evans, A.K.; Nutt, D.J.; Lightman, S.L.; Rook, G.A.; Lowry, C.A. Tryptophan metabolism in the central nervous system: Medical implications. Expert Rev. Mol. Med. 2006, 8, 1–27. [Google Scholar] [CrossRef]
- Furusawa, Y.; Obata, Y.; Fukuda, S.; Endo, T.A.; Nakato, G.; Takahashi, D.; Nakanishi, Y.; Uetake, C.; Kato, K.; Kato, T. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 2013, 504, 446. [Google Scholar] [CrossRef]
- Smith, P.M.; Howitt, M.R.; Panikov, N.; Michaud, M.; Gallini, C.A.; Bohlooly-Y, M.; Glickman, J.N.; Garrett, W.S. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 2013, 341, 569–573. [Google Scholar] [CrossRef] [Green Version]
- Howren, M.B.; Lamkin, D.M.; Suls, J. Associations of depression with C-reactive protein, IL-1, and IL-6: A meta-analysis. Psychosom. Med. 2009, 71, 171–186. [Google Scholar] [CrossRef] [Green Version]
- Dowlati, Y.; Herrmann, N.; Swardfager, W.; Liu, H.; Sham, L.; Reim, E.K.; Lanctôt, K.L. A meta-analysis of cytokines in major depression. Biol. Psychiatry 2010, 67, 446–457. [Google Scholar] [CrossRef]
- Kim, C.-S.; Shin, D.-M. Probiotic food consumption is associated with lower severity and prevalence of depression: A nationwide cross-sectional study. Nutrition 2019, 63, 169–174. [Google Scholar] [CrossRef]
- Li, F.; Liu, X.; Zhang, D. Fish consumption and risk of depression: A meta-analysis. J. Epidemiol. Community Health 2016, 70, 299–304. [Google Scholar] [CrossRef]
- Murakami, K.; Sasaki, S. Dietary intake and depressive symptoms: A systematic review of observational studies. Mol. Nutr. Food Res. 2010, 54, 471–488. [Google Scholar] [CrossRef]
- Liu, X.; Yan, Y.; Li, F.; Zhang, D. Fruit and vegetable consumption and the risk of depression: A meta-analysis. Nutrition 2016, 32, 296–302. [Google Scholar] [CrossRef]
- Adjibade, M.; Julia, C.; Allès, B.; Touvier, M.; Lemogne, C.; Srour, B.; Hercberg, S.; Galan, P.; Assmann, K.E.; Kesse-Guyot, E. Prospective association between ultra-processed food consumption and incident depressive symptoms in the French NutriNet-Santé cohort. BMC Med. 2019, 17, 78. [Google Scholar] [CrossRef] [Green Version]
- Gómez-Donoso, C.; Sánchez-Villegas, A.; Martínez-González, M.A.; Gea, A.; de Deus Mendonça, R.; Lahortiga-Ramos, F.; Bes-Rastrollo, M. Ultra-processed food consumption and the incidence of depression in a Mediterranean cohort: The SUN Project. Eur. J. Nutr. 2020, 59, 1093–1103. [Google Scholar] [CrossRef]
- Knüppel, A.; Shipley, M.J.; Llewellyn, C.H.; Brunner, E.J. Sugar intake from sweet food and beverages, common mental disorder and depression: Prospective findings from the Whitehall II study. Sci. Rep. 2017, 7, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Saghafian, F.; Malmir, H.; Saneei, P.; Milajerdi, A.; Larijani, B.; Esmaillzadeh, A. Fruit and vegetable consumption and risk of depression: Accumulative evidence from an updated systematic review and meta-analysis of epidemiological studies. Br. J. Nutr. 2018, 119, 1087–1101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Payne, M.E.; Steck, S.E.; George, R.R.; Steffens, D.C. Fruit, vegetable, and antioxidant intakes are lower in older adults with depression. J. Acad. Nutr. Diet. 2012, 112, 2022–2027. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pangestuti, R.; Kim, S.-K. Neuroprotective effects of marine algae. Mar. Drugs 2011, 9, 803–818. [Google Scholar] [CrossRef]
- Liu, F.; Zhao, W.; Zhao, F.; Dong, Q.; Wang, Y.; Wei, W.; Jia, L.; Li, L.; Lu, F. Dual Effect of the Acidic Polysaccharose Ulvan on the Inhibition of Aβ Fibrillation and Disintegration of Mature Fibrils. ACS Appl. Mater. Interfaces 2020. [Google Scholar] [CrossRef]
- Hehemann, J.-H.; Correc, G.; Barbeyron, T.; Helbert, W.; Czjzek, M.; Michel, G. Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota. Nature 2010, 464, 908. [Google Scholar] [CrossRef]
- Hehemann, J.-H.; Kelly, A.G.; Pudlo, N.A.; Martens, E.C.; Boraston, A.B. Bacteria of the human gut microbiome catabolize red seaweed glycans with carbohydrate-active enzyme updates from extrinsic microbes. Proc. Natl. Acad. Sci. USA 2012, 109, 19786–19791. [Google Scholar] [CrossRef] [Green Version]
- Kashyap, P.C.; Marcobal, A.; Ursell, L.K.; Smits, S.A.; Sonnenburg, E.D.; Costello, E.K.; Higginbottom, S.K.; Domino, S.E.; Holmes, S.P.; Relman, D.A. Genetically dictated change in host mucus carbohydrate landscape exerts a diet-dependent effect on the gut microbiota. Proc. Natl. Acad. Sci. USA 2013, 110, 17059–17064. [Google Scholar] [CrossRef] [Green Version]
- Bode, L. Human milk oligosaccharides: Every baby needs a sugar mama. Glycobiology 2012, 22, 1147–1162. [Google Scholar] [CrossRef] [Green Version]
- Marcobal, A.; Barboza, M.; Froehlich, J.W.; Block, D.E.; German, J.B.; Lebrilla, C.B.; Mills, D.A. Consumption of human milk oligosaccharides by gut-related microbes. J. Agric. Food Chem. 2010, 58, 5334–5340. [Google Scholar] [CrossRef] [Green Version]
- Marcobal, A.; Barboza, M.; Sonnenburg, E.D.; Pudlo, N.; Martens, E.C.; Desai, P.; Lebrilla, C.B.; Weimer, B.C.; Mills, D.A.; German, J.B. Bacteroides in the infant gut consume milk oligosaccharides via mucus-utilization pathways. Cell Host Microbe 2011, 10, 507–514. [Google Scholar] [CrossRef] [Green Version]
- Wong, J.M.; De Souza, R.; Kendall, C.W.; Emam, A.; Jenkins, D.J. Colonic health: Fermentation and short chain fatty acids. J. Clin. Gastroenterol. 2006, 40, 235–243. [Google Scholar] [CrossRef] [PubMed]
- Davie, J.R. Inhibition of histone deacetylase activity by butyrate. J. Nutr. 2003, 133, 2485S–2493S. [Google Scholar] [CrossRef] [PubMed]
- Brown, A.J.; Goldsworthy, S.M.; Barnes, A.A.; Eilert, M.M.; Tcheang, L.; Daniels, D.; Muir, A.I.; Wigglesworth, M.J.; Kinghorn, I.; Fraser, N.J. The Orphan G protein-coupled receptors GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids. J. Biol. Chem. 2003, 278, 11312–11319. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Arpaia, N.; Campbell, C.; Fan, X.; Dikiy, S.; van der Veeken, J.; Deroos, P.; Liu, H.; Cross, J.R.; Pfeffer, K.; Coffer, P.J. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 2013, 504, 451. [Google Scholar] [CrossRef]
- Bajury, D.M.; Rawi, M.H.; Sazali, I.H.; Abdullah, A.; Sarbini, S.R. Prebiotic evaluation of red seaweed (Kappaphycus alvarezii) using in vitro colon model. Int. J. Food Sci. Nutr. 2017, 68, 821–828. [Google Scholar] [CrossRef]
- Xu, S.-Y.; Aweya, J.J.; Li, N.; Deng, R.-Y.; Chen, W.-Y.; Tang, J.; Cheong, K.-L. Microbial catabolism of Porphyra haitanensis polysaccharides by human gut microbiota. Food Chem. 2019, 289, 177–186. [Google Scholar] [CrossRef]
- Singdevsachan, S.K.; Auroshree, P.; Mishra, J.; Baliyarsingh, B.; Tayung, K.; Thatoi, H. Mushroom polysaccharides as potential prebiotics with their antitumor and immunomodulating properties: A review. Bioact. Carbohydr. Diet. Fibre 2016, 7, 1–14. [Google Scholar] [CrossRef]
- Varshney, J.; Ooi, J.H.; Jayarao, B.M.; Albert, I.; Fisher, J.; Smith, R.L.; Patterson, A.D.; Cantorna, M.T. White button mushrooms increase microbial diversity and accelerate the resolution of Citrobacter rodentium infection in mice. J. Nutr. 2013, 143, 526–532. [Google Scholar] [CrossRef] [Green Version]
- Sonnenburg, J.L.; Xu, J.; Leip, D.D.; Chen, C.-H.; Westover, B.P.; Weatherford, J.; Buhler, J.D.; Gordon, J.I. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science 2005, 307, 1955–1959. [Google Scholar] [CrossRef] [Green Version]
- Van der Sluis, M.; De Koning, B.A.; De Bruijn, A.C.; Velcich, A.; Meijerink, J.P.; Van Goudoever, J.B.; Büller, H.A.; Dekker, J.; Van Seuningen, I.; Renes, I.B. Muc2-deficient mice spontaneously develop colitis, indicating that MUC2 is critical for colonic protection. Gastroenterology 2006, 131, 117–129. [Google Scholar] [CrossRef] [PubMed]
- Dantzer, R.; O’Connor, J.C.; Freund, G.G.; Johnson, R.W.; Kelley, K.W. From inflammation to sickness and depression: When the immune system subjugates the brain. Nat. Rev. Neurosci. 2008, 9, 46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- He, B.; Xu, W.; Santini, P.A.; Polydorides, A.D.; Chiu, A.; Estrella, J.; Shan, M.; Chadburn, A.; Villanacci, V.; Plebani, A. Intestinal bacteria trigger T cell-independent immunoglobulin A2 class switching by inducing epithelial-cell secretion of the cytokine APRIL. Immunity 2007, 26, 812–826. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Check, E. Bitter Pills; Nature Publishing Group: London, UK, 2004. [Google Scholar]
- O’Mahony, S.M.; Clarke, G.; Borre, Y.; Dinan, T.; Cryan, J. Serotonin, tryptophan metabolism and the brain-gut-microbiome axis. Behav. Brain Res. 2015, 277, 32–48. [Google Scholar] [CrossRef] [PubMed]
PHQ-9 Depressive Symptoms | ||||
---|---|---|---|---|
Normal | Mild | Moderate to Severe | p-Value | |
Participants | 1930 (79.95) | 360 (14.91) | 124 (5.14) | |
Age, y | 42.82 (12.16) | 40.57 (12.17) | 42.1 (13.64) | 0.0057 |
Sex | ||||
Male | 821 (42.54) | 106 (29.44) | 36 (29.03) | <0.0001 |
Female | 1109 (57.46) | 254 (70.56) | 88 (70.97) | |
BMI, kg/m2 | 23.78 (3.64) | 23.48 (3.49) | 23.97 (4.23) | 0.2728 |
Educational level | ||||
Elementary or less | 128 (6.63) | 31 (8.61) | 27 (21.77) | 0.2216 |
Junior high school | 148 (7.67) | 23 (6.39) | 9 (7.26) | |
High school | 705 (36.53) | 135 (37.50) | 47 (37.90) | |
College or more | 949 (49.17) | 171 (47.50) | 41 (33.06) | |
Household income | ||||
Q1 (lowest) | 134 (6.95) | 39 (10.83) | 31 (25) | <0.0001 |
Q2 | 454 (23.55) | 90 (25) | 37 (29.84) | |
Q3 | 637 (33.04) | 102 (28.33) | 33 (26.61) | |
Q4 (highest) | 703 (36.46) | 129 (35.83) | 23 (18.55) | |
Smoking status | ||||
No | 1591 (82.44) | 282 (78.33) | 79 (63.71) | <0.0001 |
Yes | 339 (17.56) | 78 (21.67) | 45 (36.29) | |
Drinking status | ||||
No | 943 (48.86) | 165 (45.83) | 60 (48.39) | 0.5732 |
Yes | 987 (51.14) | 195 (54.17) | 64 (51.61) | |
Physical activity | ||||
No | 1172 (60.73) | 240 (66.67) | 86 (69.35) | 0.0234 |
Yes | 758 (39.27) | 120 (33.33) | 38 (30.65) | |
Health status | ||||
Excellent | 111 (5.75) | 5 (1.39) | 0 (0) | <0.0001 |
Very good | 607 (31.45) | 54 (15) | 7 (5.65) | |
Good | 1015 (52.59) | 186 (51.67) | 42 (33.87) | |
Fair | 180 (9.33) | 105 (29.17) | 49 (39.52) | |
Poor | 17 (0.88) | 10 (2.78) | 26 (20.97) | |
Energy intake, kcal/d | 1818.04 (614.15) | 1840.10 (674.25) | 1727.19 (706.29) | 0.2207 |
Crude fiber, g/d | 5.49 (2.23) | 5.26 (2.35) | 4.16 (2.08) | 0.0013 |
Cereal fiber, g/d | 4.83 (1.96) | 4.77 (2.03) | 4.16 (2.08) | 0.0015 |
Vegetable fiber, g/d | 5.58 (2.65) | 5.20 (2.52) | 4.61 (2.78) | <0.0001 |
Fruit fiber, g/d | 2.3 (1.81) | 2.14 (1.76) | 1.77 (1.81) | 0.0027 |
Seaweed fiber, g/d | 0.76 (0.57) | 0.77 (0.57) | 0.6 (0.57) | 0.0082 |
Mushroom fiber, g/d | 0.09 (0.07) | 0.10 (0.08) | 0.06 (0.06) | <0.0001 |
Crude | Model 1 | Model 2 | |||||||
---|---|---|---|---|---|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | |||||||
Depressive Symptom | Normal | Mild | Moderate to Severe | Normal | Mild | Moderate to Severe | Normal | Mild | Moderate to Severe |
Crude fiber (g/d) | |||||||||
Q1 (<3.72) | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
Q2 (3.72 to <5.16) | Reference | 0.80 (0.58–1.08) | 0.37 (0.22–0.62) | Reference | 0.83 (0.60–1.13) | 0.38 (0.222–0.634) | Reference | 0.72 (0.49–1.08) | 0.47 (0.22–0.97) |
Q3 (5.16 to <6.85) | Reference | 0.73 (0.54–1.01) | 0.45 (0.28–0.74) | Reference | 0.80 (0.59–1.11) | 0.48 (0.29–0.78) | Reference | 0.63 (0.36–1.10) | 0.77 (0.29–2.07) |
Q4 (≥6.85) | Reference | 0.70 (0.51–0.96) | 0.43 (0.26–0.71) | Reference | 0.78 (0.56–1.07) | 0.45 (0.27–0.74) | Reference | 0.52 (0.21–1.29) | 0.74 (0.15–3.65) |
Cereal fiber (g/d) | |||||||||
Q1 (<3.31) | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
Q2 (3.31 to <4.59) | Reference | 0.93 (0.68–1.28) | 0.63 (0.38–1.03) | Reference | 0.94 (0.68–1.29) | 0.64 (0.39–1.05) | Reference | 0.80 (0.56–1.14) | 0.63 (0.35–1.15) |
Q3 (4.59 to <6.08) | Reference | 0.99 (0.72–1.35) | 0.73 (0.45–1.17) | Reference | 1.04 (0.76–1.43) | 0.77 (0.48–1.24) | Reference | 0.85 (0.58–1.24) | 0.80 (0.42–1.51) |
Q4 (≥6.08) | Reference | 0.84 (0.61–1.16) | 0.46 (0.27–0.78) * | Reference | 0.86 (0.62–1.19) | 0.48 (0.28–0.82) * | Reference | 0.63 (0.39–0.99) | 0.51 (0.23–1.15) |
Vegetable fiber (g/d) | |||||||||
Q1 (<3.48) | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
Q2 (3.48 to <5.05) | Reference | 0.86 (0.63–1.18) | 0.46 (0.28–0.75) | Reference | 0.89 (0.65–1.22) | 0.47 (0.284–0.767) | Reference | 0.81 (0.57–1.14) | 0.50 (0.28–0.91) |
Q3 (5.05 to <7.03) | Reference | 0.90 (0.66–1.23) | 0.42 (0.25–0.70) | Reference | 1.00 (0.72–1.35) | 0.45 (0.27–0.75) | Reference | 0.47 (0.59–1.24) | 0.63 (0.24–0.90) |
Q4 (≥7.03) | Reference | 0.66 (0.48–0.92) * | 0.44 (0.27–0.72) | Reference | 0.74 (0.53–1.03) | 0.47 (0.29–0.78) | Reference | 0.63 (0.40–1.01) | 0.62 (0.28–1.36) |
Fruit fiber (g/d) | |||||||||
Q1 (<0.80) | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
Q2 (0.80 to <1.84) | Reference | 0.90 (0.65–1.23) | 0.44 (0.27–0.72) | Reference | 0.85 (0.62–1.16) | 0.41 (0.25–0.68) | Reference | 0.97 (0.69–1.37) | 0.73 (0.41–1.30) |
Q3 (1.84 to <3.31) | Reference | 0.96 (0.70–1.31) | 0.30 (0.17–0.52) * | Reference | 0.90 (0.65–1.23) | 0.27 (0.15–0.47) * | Reference | 1.18 (0.81–1.71) | 0.63 (0.32–1.26) |
Q4 (≥3.31) | Reference | 0.78 (0.56–1.07) | 0.51 (0.32–0.81) | Reference | 0.72 (0.51–1.00) | 0.44 (0.20–0.71) | Reference | 1.09 (0.70–1.70) | 1.52 (0.74–3.13) |
Seaweed fiber (g/d) | |||||||||
Q1 (<0.31) | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
Q2 (0.31 to <0.61) | Reference | 1.29 (0.93–1.77) | 0.41 (0.24–0.69) | Reference | 1.23 (0.93–1.77) | 0.39 (0.23–0.67) | Reference | 1.36 (0.96–1.91) | 0.48 (0.26–0.87) |
Q3 (0.61 to <1.02) | Reference | 0.97 (0.69–1.35) | 0.64 (0.41–1.01) | Reference | 0.99 (0.72–1.39) | 0.63 (0.40–0.99) | Reference | 1.04 (0.72–1.51) | 0.80 (0.46–1.39) |
Q4 (≥1.02) | Reference | 1.22 (0.89–1.69) | 0.34 (0.20–0.60) * | Reference | 1.24 (0.89–1.71) | 0.33 (0.19–0.58) * | Reference | 1.22 (0.84–1.76) | 0.38 (0.20–0.72) * |
Mushroom fiber (g/d) | |||||||||
Q1 (<0.03) | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
Q2 (0.03 to <0.08) | Reference | 0.90 (0.64–1.25) | 0.47 (0.30–0.74) | Reference | 0.83 (0.59–1.16) | 0.44 (0.276–0.690) | Reference | 0.77 (0.54–1.10) | 0.50 (0.26–0.76) |
Q3 (0.08 to <0.14) | Reference | 1.13 (0.83–1.56) | 0.28 (0.16–0.48) | Reference | 1.05 (0.76–1.45) | 0.26 (0.15–0.45) * | Reference | 1.02 (0.71–1.46) | 0.25 (0.13–0.48) |
Q4 (≥0.14) | Reference | 1.04 (0.75–1.43) | 0.21 (0.12–0.39) * | Reference | 0.95 (0.69–1.32) | 0.20 (0.11–0.36) * | Reference | 0.90 (0.61–1.32) | 0.18 (0.01–0.37) * |
Crude | Model 1 | Model 2 | |
---|---|---|---|
Clinical Depression | OR (95% CI) | OR (95% CI) | OR (95% CI) |
Crude fiber (g/d) | |||
Q1 (<3.94) | Reference | Reference | Reference |
Q2 (3.94 to <5.05) | 0.52 (0.29–0.92) | 0.50 (0.28–0.88) | 0.49 (0.22–1.07) |
Q3 (5.05 to <6.61) | 0.67 (0.37–1.22) | 0.66 (0.36–1.19) | 0.76 (0.27–2.14) |
Q4 (≥6.61) | 0.56 (0.31–1.02) | 0.55 (0.30–1.00) | 0.54 (0.11–2.63) |
Cereal fiber (g/d) | |||
Q1 (<3.20) | Reference | Reference | Reference |
Q2 (3.20 to <4.82) | 1.01 (0.57–1.79) | 0.99 (0.56–1.75) | 1.33 (0.66–2.67) |
Q3 (4.82 to <6.60) | 0.74 (0.41–1.36) | 0.72 (0.39–1.34) | 0.88 (0.42–1.83) |
Q4 (≥6.60) | 0.68 (0.37–1.26) | 0.71 (0.39–1.30) | 1.11 (0.47–2.61) |
Vegetable fiber (g/d) | |||
Q1 (<3.48) | Reference | Reference | Reference |
Q2 (3.48 to <4.86) | 0.75 (0.42–1.33) | 0.73 (0.41–1.30) | 0.77 (0.38–1.56) |
Q3 (4.86 to <6.45) | 0.70 (0.39–1.26) | 0.68 (0.38–1.24) | 0.87 (0.39–1.92) |
Q4 (≥6.45) | 0.63 (0.35–1.15) | 0.59 (0.32–1.09) | 0.71 (0.29–1.75) |
Fruit fiber (g/d) | |||
Q1 (<0.82) | Reference | Reference | Reference |
Q2 (0.82 to <1.77) | 0.85 (0.46–1.56) | 0.88 (0.48–1.60) | 1.33 (0.69–2.56) |
Q3 (1.77 to <3.37) | 0.63 (0.34–1.16) | 0.64 (0.35–1.18) | 0.97 (0.47–2.01) |
Q4 (≥3.37) | 0.75 (0.41–1.36) | 0.77 (0.42–1.39) | 1.29 (0.57–2.90) |
Seaweed fiber (g/d) | |||
Q1 (<0.30) | Reference | Reference | Reference |
Q2 (0.30 to <0.60) | 0.65 (0.36–1.17) | 0.64 (0.35–1.17) | 0.74 (0.34–1.41) |
Q3 (0.60 to <1.05) | 0.56 (0.31–1.00) | 0.58 (0.32–1.05) | 0.76 (0.39–1.48) |
Q4 (≥1.05) | 0.39 (0.21–0.73) * | 0.38 (0.21–0.71) * | 0.45 (0.23–0.88) * |
Mushroom fiber (g/d) | |||
Q1 (<0.02) | Reference | Reference | Reference |
Q2 (0.02 to <0.05) | 0.64 (0.35–1.15) | 0.67 (0.37–1.22) | 0.89 (0.45–1.74) |
Q3 (0.05 to <0.10) | 0.56 (0.31–0.99) | 0.60 (0.34–1.09) | 0.89 (0.47–1.73) |
Q4 (≥0.10) | 0.62 (0.34–1.13) | 0.66 (0.36–1.22) | 0.87 (0.44–1.72) |
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Kim, C.-S.; Byeon, S.; Shin, D.-M. Sources of Dietary Fiber Are Differently Associated with Prevalence of Depression. Nutrients 2020, 12, 2813. https://doi.org/10.3390/nu12092813
Kim C-S, Byeon S, Shin D-M. Sources of Dietary Fiber Are Differently Associated with Prevalence of Depression. Nutrients. 2020; 12(9):2813. https://doi.org/10.3390/nu12092813
Chicago/Turabian StyleKim, Chong-Su, Seohyeon Byeon, and Dong-Mi Shin. 2020. "Sources of Dietary Fiber Are Differently Associated with Prevalence of Depression" Nutrients 12, no. 9: 2813. https://doi.org/10.3390/nu12092813
APA StyleKim, C.-S., Byeon, S., & Shin, D.-M. (2020). Sources of Dietary Fiber Are Differently Associated with Prevalence of Depression. Nutrients, 12(9), 2813. https://doi.org/10.3390/nu12092813