C-Reactive Protein and Specific Depression Symptoms Among Older Adults: An Exploratory Investigation of Multi-Plane Networks Using Cross-Sectional Data From NHANES (2017–2020)
Restricted accessResearch articleFirst published online January, 2023
C-Reactive Protein and Specific Depression Symptoms Among Older Adults: An Exploratory Investigation of Multi-Plane Networks Using Cross-Sectional Data From NHANES (2017–2020)
Studies investigating the association between C-reactive protein (CRP) and depression among older adults have yielded inconsistent results. We suspect that this may be due to varying associations between CRP and particular depression symptom criteria, and we addressed this challenge using network analysis.
Methods
We used cross-sectional data from prepandemic National Health and Nutrition Examination Survey questionnaires (2017–2020) and included a sample of 1698 adults aged 65 years or older. Depression symptoms were assessed using the Patient Health Questionnaire-9. Unregularized Mixed Graphical Models were estimated using the R package mgm before and after adjusting for relevant sociodemographic, clinical, and lifestyle covariates.
Results
In the model with no covariates, the only symptom criterion associated with CRP was “appetite problems.” This association remained robust after controlling for all covariates. Although not associated with CRP, other criteria such as “fatigue” and “concentration difficulty” showed associations with important covariates for older adults such as white blood cell count or hemoglobin, respectively.
Discussion
The CRP-related variability in the depression symptom network that we have demonstrated may help explain the reported inconsistencies. The present study stands as exploratory, and future research should focus on applying longitudinal designs and including several other inflammatory proteins and covariates that were not measured in the current network model.
AartsS.Van den AkkerM.TanF. E. S.VerheyF. R. J.MetsemakersJ. F. M.Van BoxtelM. P. J. (2011). Influence of multimorbidity on cognition in a normal aging population: A 12‐year follow‐up in the maastricht aging study. International Journal of Geriatric Psychiatry, 26(10), 1046–1053. https://doi.org/10.1002/gps.2642
2.
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders. American Psychiatric Publishing.
3.
AntyR.BekriS.LucianiN.Saint-PaulM. C.DahmanM.IannelliA.AmorI. B.Staccini-MyxA.HuetP. M.GugenheimJ.SadoulJ. L.Le Marchand-BrustelY.TranA.GualP. (2006). The inflammatory C-reactive protein is increased in both liver and adipose tissue in severely obese patients independently from metabolic syndrome, Type 2 diabetes, and NASH. Official Journal of the American College of Gastroenterology, 101(8), 1824–1833. https://doi.org/10.1111/j.1572-0241.2006.00724.x
4.
ApostolakisS.VogiatziK.KrambovitisE.SpandidosD. A. (2008). IL-1 cytokines in cardiovascular disease: Diagnostic, prognostic and therapeutic implications. Cardiovascular and Hematological Agents in Medicinal Chemistry (Formerly Current Medicinal Chemistry-Cardiovascular & Hematological Agents), 6(2), 150–158. https://doi.org/10.2174/187152508783955006
5.
AuB.SmithK. J.SmithK. J.GariépyG.SchmitzN. (2015). The longitudinal associations between C‐reactive protein and depressive symptoms: Evidence from the English longitudinal study of ageing (ELSA). International Journal of Geriatric Psychiatry, 30(9), 976–984. https://doi.org/10.1002/gps.4250
6.
BellJ. A.KivimäkiM.BullmoreE. T.SteptoeA.CarvalhoL. A. (2017). Repeated exposure to systemic inflammation and risk of new depressive symptoms among older adults. Translational Psychiatry, 7(8), e1208. https://doi.org/10.1038/tp.2017.155
7.
BondyE.NortonS. A.VossM.MarksR. B.BoudreauxM. J.TreadwayM. T.OltmannsT. F.BogdanR. (2021). Inflammation is associated with future depressive symptoms among older adults. Brain, Behavior, and Immunity-Health, 13(1), 100226. https://doi.org/10.1016/j.bbih.2021.100226
8.
BorsboomD.CramerA. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
Centers for Disease Control and Prevention (2021). 2017-2020 data documentation, codebook, and frequencies high-sensitivity C-reactive protein (hs-CRP) (HSCRP_I). National health and nutrition examination survey. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/P_HSCRP.htm
11.
ChandrashekaraS. (2014). C-reactive protein: An inflammatory marker with specific role in physiology, pathology, and diagnosis. Internet Journal of Rheumatology and Clinical Immunology, 2(S1), SR3. https://doi.org/10.15305/ijrci/v2iS1/117
12.
DantzerR.O’connorJ. C.FreundG. G.JohnsonR. W.KelleyK. W. (2008). From inflammation to sickness and depression: When the immune system subjugates the brain. Nature Reviews Neuroscience, 9(1), 46–56. https://www.nature.com/articles/nrn2297.pdf
13.
ElovainioM.AaltoA. M.KivimäkiM.PirkolaS.SundvallJ.LönnqvistJ.ReunanenA. (2009). Depression and C-reactive protein: Population-based health 2000 study. Psychosomatic Medicine, 71(4), 423–430. https://doi.org/10.1097/PSY.0b013e31819e333a
14.
FabbriE.AnY.ZoliM.TanakaT.SimonsickE. M.Kitner‐TrioloM. H.StudenskiS. A.ResnickS. M.FerrucciL. (2016). Association between accelerated multimorbidity and age‐related cognitive decline in older baltimore longitudinal study of aging participants without dementia. Journal of the American Geriatrics Society, 64(5), 965–972. https://doi.org/10.1111/jgs.14092
15.
FelgerJ. C.LiZ.HaroonE.WoolwineB. J.JungM. Y.HuX.MillerA. H. (2016). Inflammation is associated with decreased functional connectivity within corticostriatal reward circuitry in depression. Molecular Psychiatry, 21(10), 1358–1365. https://www.nature.com/articles/mp2015168.pdf
FriedE. I.CramerA. O. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12(6), 999–1020. https://doi.org/10.1177/1745691617705892
18.
FriedE. I.NesseR. M. (2015). Depression sum-scores don’t add up: Why analyzing specific depression symptoms is essential. BMC Medicine, 13(1), 1–11. https://doi.org/10.1186/s12916-015-0325-4
19.
FriedE. I.NesseR. M.ZivinK.GuilleC.SenS. (2014). Depression is more than the sum score of its parts: Individual DSM symptoms have different risk factors. Psychological Medicine, 44(10), 2067–2076. https://doi.org/10.1017/S0033291713002900
20.
FriedE. I.Von StockertS.HaslbeckJ. M. B.LamersF.SchoeversR. A.PenninxB. W. J. H. (2020). Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates. Psychological Medicine, 50(16), 2682–2690. https://doi.org/10.1017/S0033291719002770
21.
GuloksuzS.PriesL. K.Van OsJ. (2017). Application of network methods for understanding mental disorders: Pitfalls and promise. Psychological Medicine, 47(16), 2743–2752. https://doi.org/10.1017/S0033291717001350
22.
HaslbeckJ. M.WaldorpL. J. (2015). mgm: Structure estimation for time-varying mixed graphical models in high-dimensional data. arXiv preprint arXiv:1510.06871https://doi.org/10.48550/arXiv.1510.06871
23.
HeffnerK. L.WaringM. E.RobertsM. B.EatonC. B.GramlingR. (2011). Social isolation, C-reactive protein, and coronary heart disease mortality among community-dwelling adults. Social Science & Medicine, 72(9), 1482–1488. https://doi.org/10.1016/j.socscimed.2011.03.016
24.
HowrenM. B.LamkinD. M.SulsJ. (2009). Associations of depression with C-reactive protein, IL-1, and IL-6: A meta-analysis. Psychosomatic Medicine, 71(2), 171–186. https://doi.org/10.1097/PSY.0b013e3181907c1b
KappelmannN.CzamaraD.RostN.MoserS.SchmollV.TrastullaL.StochlJ.LucaeS.BinderE. B.KhandakerG. M.ArlothJ. (2021). Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study. Brain, Behavior, and Immunity, 95, 256–268. https://doi.org/10.1016/j.bbi.2021.03.024
27.
KopW. J.GottdienerJ. S.TangenC. M.FriedL. P.McBurnieM. A.WalstonJ.NewmanA.HirschC.TracyR. P. (2002). Inflammation and coagulation factors in persons> 65 years of age with symptoms of depression but without evidence of myocardial ischemia. The American Journal of Cardiology, 89(4), 419–424. https://doi.org/10.1016/S0002-9149(01)02264-0
28.
LuccaU.TettamantiM.MosconiP.ApoloneG.GandiniF.NobiliA.TalloneM. V.DetomaP.GiacominA.ClericoM.TempiaP.GualaA.FasoloG.RivaE. (2008). Association of mild anemia with cognitive, functional, mood and quality of life outcomes in the elderly: The “health and anemia” study. PLoS ONE, 3(4), e1920. https://doi.org/10.1371/journal.pone.0001920
29.
Mac GiollabhuiN.EllmanL. M.CoeC. L.ByrneM. L.AbramsonL. Y.AlloyL. B. (2020). To exclude or not to exclude: Considerations and recommendations for C-reactive protein values higher than 10 mg/L. Brain, Behavior, and Immunity, 87, 898–900. https://doi.org/10.1016/j.bbi.2020.01.023
30.
MajdM.SaundersE. F.EngelandC. G. (2020). Inflammation and the dimensions of depression: A review. Frontiers in Neuroendocrinology, 56, 100800. https://doi.org/10.1016/j.yfrne.2019.100800
31.
MoriarityD. P.AlloyL. B. (2021). Back to basics: The importance of measurement properties in biological psychiatry. Neuroscience and Biobehavioral Reviews, 123, 72–82. https://doi.org/10.1016/j.neubiorev.2021.01.008
32.
MoriarityD. P.HornS. R.KautzM. M.HaslbeckJ. M.AlloyL. B. (2021a). How handling extreme C-reactive protein (CRP) values and regularization influences CRP and depression criteria associations in network analyses. Brain, Behavior, and Immunity, 91, 393–403. https://doi.org/10.1016/j.bbi.2020.10.020
33.
MoriarityD. P.van BorkuloC.AlloyL. B. (2021b). Inflammatory phenotype of depression symptom structure: A network perspective. Brain, Behavior, and Immunity, 93, 35–42. https://doi.org/10.1016/j.bbi.2020.12.005
34.
NgA.TamW. W.ZhangM. W.HoC. S.HusainS. F.McIntyreR. S.HoR. C. (2018). IL-1β, IL-6, TNF-α and CRP in elderly patients with depression or alzheimer’s disease: Systematic review and meta-analysis. Scientific Reports, 8(1), 1–12. https://doi.org/10.1038/s41598-018-30487-6
35.
O’ConnorM. F.BowerJ. E.ChoH. J.CreswellJ. D.DimitrovS.HambyM. E.HoytM. A.MartinJ. L.RoblesT. F.SloanE. K.ThomasK. S.IrwinM. R. (2009). To assess, to control, to exclude: Effects of biobehavioral factors on circulating inflammatory markers. Brain, Behavior, and Immunity, 23(7), 887–897. https://doi.org/10.1016/j.bbi.2009.04.005
36.
PascoJ. A.NicholsonG. C.WilliamsL. J.JackaF. N.HenryM. J.KotowiczM. A.SchneiderH. G.LeonardB. E.BerkM. (2010). Association of high-sensitivity C-reactive protein with de novo major depression. The British Journal of Psychiatry, 197(5), 372–377. https://doi.org/10.1192/bjp.bp.109.076430
37.
PascoeM. C.CrewtherS. G.CareyL. M.CrewtherD. P. (2011). Inflammation and depression: Why poststroke depression may be the norm and not the exception. International Journal of Stroke, 6(2), 128–135. https://doi.org/10.1111/j.1747-4949.2010.00565.x
38.
PrasadS.SungB.AggarwalB. B. (2012). Age-associated chronic diseases require age-old medicine: Role of chronic inflammation. Preventive Medicine, 54(Suppl), S29–S37. https://doi.org/10.1016/j.ypmed.2011.11.011
39.
ReubenD. B.ChehA. I.HarrisT. B.FerrucciL.RoweJ. W.TracyR. P.SeemanT. E. (2002). Peripheral blood markers of inflammation predict mortality and functional decline in high‐functioning community‐dwelling older persons. Journal of the American Geriatrics Society, 50(4), 638–644. https://doi.org/10.1046/j.1532-5415.2002.50157.x
40.
SmithK. J.AuB.OllisL.SchmitzN. (2018). The association between C-reactive protein, interleukin-6 and depression among older adults in the community: A systematic review and meta-analysis. Experimental Gerontology, 102, 109–132. https://doi.org/10.1016/j.exger.2017.12.005
41.
SongB. M.LeeJ. M.ChoiW.YoumY.ChuS. H.ParkY. R.KimH. C. (2015). Association between C reactive protein level and depressive symptoms in an elderly Korean population: Korean social life, health and aging project. BMJ Open, 5(2), e006429. http://dx.doi.org/10.1136/bmjopen-2014-006429
42.
SoysalP.VeroneseN.ThompsonT.KahlK. G.FernandesB. S.PrinaA. M.SolmiM.SchofieldP.KoyanagiA.TsengP. T.LinP. Y.ChuC. S.CoscoT. D.CesariM.CarvalhoA. F.StubbsB. (2017). Relationship between depression and frailty in older adults: A systematic review and meta-analysis. Ageing Research Reviews, 36, 78–87. https://doi.org/10.1016/j.arr.2017.03.005
43.
SparkmanN. L.JohnsonR. W. (2008). Neuroinflammation associated with aging sensitizes the brain to the effects of infection or stress. Neuroimmunomodulation, 15(4–6), 323–330. https://doi.org/10.1159/000156474
44.
TerekeciH. M.KucukardaliY.OnemY.ErikciA. A.KucukardaliB.SahanB.SayanO.CelikS.GulecM.SanisogluY. S.NalbantS.TopC.OktenliC. (2010). Relationship between anaemia and cognitive functions in elderly people. European Journal of Internal Medicine, 21(2), 87–90. https://doi.org/10.1016/j.ejim.2009.12.005
45.
TrevisanC.VeroneseN.BolzettaF.De RuiM.CorrellC. U.ZambonS.MusacchioE.SartoriL.PerissinottoE.CrepaldiG.SolmiM.ManzatoE.SergiG. (2016). Low hemoglobin levels and risk of developing depression in the elderly: Results from the prospective PRO. VA study. The Journal of Clinical Psychiatry, 77(12), e1549–e1556. https://doi.org/10.4088/JCP.15m10270
46.
ValentineR. J.VieiraV. J.WoodsJ. A.EvansE. M. (2009). Stronger relationship between central adiposity and C-reactive protein in older women than men. Menopause, 16(1), 84–89. https://doi.org/10.1097/gme.0b013e31817fcb8f
47.
ValentineR. J.WoodsJ. A.McAuleyE.DantzerR.EvansE. M. (2011). The associations of adiposity, physical activity and inflammation with fatigue in older adults. Brain, Behavior, and Immunity, 25(7), 1482–1490. https://doi.org/10.1016/j.bbi.2011.06.002
48.
ValkanovaV.EbmeierK. P.AllanC. L. (2013). CRP, IL-6 and depression: A systematic review and meta-analysis of longitudinal studies. Journal of Affective Disorders, 150(3), 736–744. https://doi.org/10.1016/j.jad.2013.06.004
49.
WhiteJ.KivimäkiM.JokelaM.BattyG. D. (2017). Association of inflammation with specific symptoms of depression in a general population of older people: The English longitudinal study of ageing. Brain, Behavior, and Immunity, 61, 27–30. https://doi.org/10.1016/j.bbi.2016.08.012
50.
WilliamsD. R.RastP. (2020). Back to the basics: Rethinking partial correlation network methodology. British Journal of Mathematical and Statistical Psychology, 73(2), 187–212. https://doi.org/10.1111/bmsp.12173
51.
WoloshinS.SchwartzL. M. (2005). Distribution of C-reactive protein values in the United States. New England Journal of Medicine, 352(15), 1611–1613. https://doi.org/10.1056/NEJM200504143521525
52.
ZhuS.PatelK. V.BandinelliS.FerrucciL.GuralnikJ. M. (2009). Predictors of interleukin‐6 elevation in older adults. Journal of the American Geriatrics Society, 57(9), 1672–1677. https://doi.org/10.1111/j.1532-5415.2009.02426.x
53.
ZimmermanM. (2019). Using the 9-item patient health questionnaire to screen for and monitor depression. JAMA, 322(21), 2125–2126. https://doi.org/10.1001/jama.2019.15883
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
