Background: Late-life depression patients are at a high risk of developing Alzheimer’s disease, and diminished olfactory identification is an indicator in early screening for Alzheimer’s disease in the elderly. However, whether diminished olfactory identification is associated with risk of developing Alzheimer’s disease in late-life depression patients remains unclear. Methods: One hundred and twenty-five late-life depression patients, 50 Alzheimer’s disease patients, and 60 normal controls were continuously recruited. The participants underwent a clinical evaluation, olfactory test, neuropsychological assessment, and neuroimaging assessment. Results: The olfactory identification impairment in late-life depression patients was milder than that in Alzheimer’s disease patients. Diminished olfactory identification was significantly correlated with worse cognitive performance (global function, memory language, executive function, and attention) and reduced grey matter volume (olfactory bulb and hippocampus) in the late-life depression patients. According to a multiple linear regression analysis, olfactory identification was significantly associated with the memory scores in late-life depression group (B = 1.623, P < .001). The late-life depression with olfactory identification impairment group had worse cognitive performance (global, memory, language, and executive function) and more structural abnormalities in Alzheimer’s disease-related regions than the late-life depression without olfactory identification impairment group, and global cognitive function and logical memory in the late-life depression without olfactory identification impairment group was intact. Reduced volume observed in many areas (hippocampus, precuneus, etc.) in the Alzheimer’s disease group was also observed in late-life depression with olfactory identification impairment group but not in the late-life depression without olfactory identification impairment group. Conclusion: The patterns of cognitive impairment and structural abnormalities in late-life depression with olfactory identification impairment patients were similar to those in Alzheimer’s disease; olfactory identification may help identify late-life depression patients who are at a high risk of developing Alzheimer’s disease. Keywords: late-life depression, olfactory, Alzheimer’s disease, neuropsychology, neuroimaging Received: October 20, 2017; Revised: January 30, 2018; Accepted: March 3, 2018 © The Author(s) 2018. Published by Oxford University Press on behalf of CINP. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, 640 provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 Ben et al. | 641 Significance Statement Individuals with late-life depression (LLD) are at a high risk of developing Alzheimer’s disease (AD), and diminished olfactory identification (OI) serves as an effective biomarker in predicting AD in older adults. However, whether diminished OI is associated with the risk of developing AD in LLD patients remains unclear. We found that diminished OI is correlated with poor cognitiv -e per formance (particularly memory) and brain atrophy (olfactory bulb and hippocampus) in LLD patients. The LLD with OI impairment group had worse cognitive performance and more structural abnormalities in AD-related regions than the LLD without OI impair - ment group, and the patterns of structural abnormalities in the LLD with OI impairment patients were similar to those observed in the AD patients. The strong associations among diminished OI, cognitive impairment, and atrophy in AD-related regions in the LLD patients and the similarity in cognitive impairment and structural abnormalities between the LLD with OI impairment patients and AD patients suggest that OI may serve as an indicator for identifying LLD patients at a high risk of developing AD. Introduction Late-life depression (LLD) is among the most common disabling mild cognitive impairment (aMCI) patients and elderly controls mental illnesses in older people and affects 3.5 to 7.5% of the (Roberts et al., 2016), particularly in combination with neuro- geriatric population (Weyerer et al., 2005). LLD patients are con- psychological assessments and neuroimaging evaluations sidered at a high risk of developing dementia (Kaup et al., 2016; (Devanand et al., 2008; Lojkowska et al., 2011). Mirza et al., 2016), and LLD may share common mechanisms However, whether diminished OI could contribute to iden- with Alzheimer’s disease (AD), such as alterations in glucocortic- tifying LLD patients at a high risk of developing AD remains oid steroids, hippocampal atrophy, vascular disease, deficits in unclear, because studies specifically focusing on OI in LLD brain-derived neurotrophic factors, inflammatory changes, and patients are lacking, and previous studies investigating olfac- increased deposition of β-amyloid plaques (Butters et al., 2008; tory function in depression patients did not exclusively focus on Byers and Yaffe, 2011; Wang and Dan, 2014). Whether LLD is an older adults. It has been repeatedly reported that it was olfactory early manifestation or a risk factor of dementia remains contro- threshold (OT) rather than OI that was significantly impaired in versial; however, the early identification of LLD patients who are patients with depression (Naudin and Atanasova, 2014Khil et ; most likely to develop AD could be advantageous for timely inter - al., 2016; Croy and Hummel, 2017). In addition, OI impairment vention. Comprehensive assessments of cognitive function may is only pronounced in depression patients with first-time high contribute to this early screening process, and LLD patients with symptom severity and severe disease courses, and the preva- poor cognitive performance (particularly memory deficits) exhibit lence of OI impairment in depression patients is similar to that more structural abnormalities in AD-related regions, more func- in normal controls (approximately 15%) (Khil et al., 2016). The tional and white matter network abnormalities, a high amyloid patterns of olfactory impairment differ between AD and depres- load with hypermetabolism, increased cognitive decline, and sion patients, and OI deficits may be significant in AD patients higher rates of conversion to AD (Lee et al., 2012Br ; endel et al., but not in depression patients (Naudin and Atanasova, 2014); 2015; Li et al., 2015; Mai et al., 2017), suggesting that LLD patients thus, previous studies involving small sample sizes have suc- with cognitive impairment may have a higher risk of developing cessfully differentiated AD patients from depression patients AD. However, suboptimal effort may be common in LLD patients using OI tests. At a cut-off score of 10/11 on the Sniffin’ Sticks and lead to bias in neuropsychological assessments (Benitez identification test, the specificity and sensitivity of OI impair - et al., 2011); thus, early screening requiring less time, cost and ment in distinguishing AD patients from depression patients subjective efforts may be more desirable for differentiating indi- is 95% and 100%, respectively (Solomon et al., 1998), and these viduals at a high risk of developing AD from LLD patients. rates are similar using the Pocket Smell Test (sensitivity = 80% Recently, diminished olfactory identification (OI) has been and specificity = 100%) (Pentzek et al., 2007). used as a supplemental assessment for the early detection of Because LLD patients with cognitive impairment are more AD and an effective biomarker of AD pathology due to its advan- likely to convert to AD, and OI impairment is associated with tages of being simple, cost-effective, and noninvasive (Laske cognitive impairment (particularly memory deficit) and the risk et al., 2015; Roberts et al., 2016; Lafaillemagnan et al., 2017). OI, of developing AD, diminished OI may contribute to identifying which is the ability to identify and denominate specific odors, prodromal AD in LLD patients, and LLD patients with OI impair - depends on several cognitive processes, such as semantic mem- ment (LLD-OII) may be at a high risk of developing AD. Therefore, ory access, denomination capacities, and comprehension of in the present study, we aimed to determine whether (1) LLD instructions (Rahayel et al., 2012 Roberts et ; al., 2016). Therefore, patients suffer from significant OI impairment, (2) diminished OI impairment is considered to reflect the extent of cognitive OI is associated with the cognitive impairment and reduced impairment and brain malfunction in older individuals. In grey matter volume in LLD patients, (3) LLD-OII patients have cross-sectional studies, individuals with diminished OI exhib- worse cognitive impairment and more structural abnormalities ited worse cognitive performance (memory, execution func- than LLD patients without OI impairment (LLD-NOII), and (4) the tion, and language) (Roberts et al., 2016), reduced hippocampal neuropsychological and neuroimaging characteristics of LLD-OII volume (HV) and olfactory bulb volume (OBV) (Thomann et al., patients are similar to those of AD patients. 2009), a thinner entorhinal cortex, increased cortical amyloid burden (Growdon et al., 2015), reduced blood flow in the fronto- METHODS temporal lobe (Wang et al., 2010), lower ratios of CSF t-tau and P -tau to Aβ (Lafaillemagnan et al., 2017), and a high propor - 181 1–42 Participants tion of these patients were APOE ε4 carriers (Dhilla et al., 2016). In a longitudinal study, OI impairment predicted a faster cog- One hundred and twenty-five LLD patients and 50 AD patients nitive decline and higher rate of conversion to AD in amnestic were continuously recruited from the Affiliated Brain Hospital of Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 642 | International Journal of Neuropsychopharmacology, 2018 Guangzhou Medical University (Guangzhou Huiai Hospital), and Neuroimaging Assessment 60 normal controls (NCs) were recruited from the community in Forty-five LLD patients, 20 AD patients, and 25 NC subjects vol- Guangzhou between May 2016 and July 2017. All subjects in our unteered to undergo MRI after the neuropsychological assess- study were from the Chinese Han population. All participants or ments and olfactory tests. All participants were ethnically their legal guardians provided written informed consent to par - Chinese Han and right-handed. ticipate in the study. This study was approved by the ethics com- mittees of The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital). MRI Data Acquisition The inclusion criteria for the LLD group were as follows: (1) The Philips 3.0 T MR system at the Affiliated Brain Hospital of patients >55 years of age; (2) patients who met the criteria for Guangzhou Medical University was used to acquire the imaging major depression disorder according to the DSM-IV after the age data. For each participant, an anatomical image was obtained of 55; and (3) patients whose clinical stage and diagnosis were using a sagittal 3D gradient-echo T1-weighted sequence confirmed by a trained physician at our hospital. The following (TR = 8.2 ms, TED = 3.8 ms, TI = 1100 ms, flip angle = 8°, 188 slices, exclusion criteria were applied: (1) a history of another major psy- slice thickness = 1 mm, Gap = 0 mm, matrix = 256 x 256, and inver - chiatric disorder, such as bipolar disorder or schizophrenia, (2) a sion time = 0). family history of schizophrenia or bipolar disorder, (3) physical illness that may induce emotion abnormalities, such as anaemia or hypothyroidism, (4) neurological disease, such as brain tumor Image Processing or stroke, and (5) current or previous psychotic symptoms. AD The MR data were preprocessed using the toolboxes in Statistical was diagnosed according to the NINCDS-ADRDA criteria by Parametric Mapping 12 (SPM 12, University College London) 2 trained neurologists (Mckhann et al., 1984). The LLD and AD (Tzourio-Mazoyer et al., 2002). Briefly, each T1 image was seg- diagnoses were the primary diagnoses. A system interview was mented into the cerebrospinal fluid, white matter, and grey mat- performed to collect participants’ demographic data and infor - ter and then normalized to the Montreal Neurological Institute mation regarding comorbidities, personal history, family his- template. A Gaussian kernel filter of 8 x 8 x 8 mm was used to tory, medication history, surgical history, and allergy history, and smooth the modulated image. physical and neurological examinations were performed. VBM Analysis Neuropsychological Assessments A general linear model was performed to investigate the After the participants underwent standard clinical assess- smoothed modulated grey matter images using SPM 12. An ments, they were interviewed by neuropsychologists to assess ANCOVA was performed to analyze the voxel-wise grey matter their global cognitive functioning using the Mini-mental State volume (Ashburner and Friston, 2000) differences among the Examination (MMSE) and depressive state using the 17-item groups, and the Least Significant Difference (LSD) posthoc test Hamilton Depression Rating Scale (HAMD-17). In addition, the was used to perform the multiple comparisons; the covariates participants in the NC and LLD groups underwent comprehen- entered in the model included the total brain volume (= total sive neuropsychological tests to assess their performance in the white matter volume + total grey matter volume), age, sex, and following 5 cognitive domains: memory (the Logical Memory years of education. This correction was confined in a grey mat- Test [LMT]), auditory (the Verbal Learning Test [AVLT]), lan- ter mask using SPM 12 and adjusted using false discovery rate guage (the Boston Naming Test [BNT], and the Verbal Fluency corrections (P < .05). The mean left and right hippocampal grey Test [VFT]), executive function (the Stroop Colour and Word Test matter volumes (HVs) were then extracted from the regions of [Stroop] and the Trail-Making Test [TMT]), attention (the Symbol- interest using the anatomical automatic labelling brain atlas Digit Modality Test [SDMT] and the Digit Span Test [DST]), and (Tzourio-Mazoyer et al., 2002). visuospatial skills (the Clock Drawing Test 4 [CDT4] and the Rey– Osterrieth complex figure [ROCF] test). The raw tests scores were adjusted using normative data, summed, and scaled to compute OBV the domain z scores (Ivnik et al., 1992; Roberts et al., 2016). The OBV was calculated by planimetric manual contouring, and all surfaces were added and multiplied by 1 (1-mm slice thick- Olfactory Assessments ness) to obtain the volume in mm. Details regarding the OBV The olfactory function, including OI and OT, was assessed using assessment are provided by Yousem et al. (Yousem et al., 1998), the Sniffin’ Sticks Screen 16 test (Hummel et al., 1997). The sub- and this procedure has been used in many studies investigating jects completed a questionnaire surveying factors that may the OBV (Negoias et al., 2010, 2016; Croy et al., 2013; Hummel influence olfactory function (i.e., history of nasal trauma and et al., 2013). The OBV assessments were performed by the same surgery, history of radiation or chemotherapy, difficulty breath- experimenter who was blind to the participants’ conditions. ing through one side of the nose, etc). Subjects were excluded if they had an active upper respiratory/sinus infection or respira- Statistic tory distress at the time of testing, congenital or traumatic anosmia, known nasal polyps or tumor, current or recent (prior The Statistical Package for the Social Sciences version 22.0 6 months) smoking status, and alcohol or substance depend- (IBM SPSS 22.0) was used to perform the statistical analyses. ence (Hummel et al., 1997). All olfactory assessments were per - Chlorpromazine equivalent doses were used to unify the doses formed in a quiet, odorless, ventilated room at the Affiliated of different medicines (Woods, 2003), and fluoxetine equivalent Brain Hospital of Guangzhou Medical University. All participants doses were used to unify the doses of different antidepressants underwent the OT and OI tests following neuropsychological (Yu et al., 2015). The differences in the demographic and clin- assessments. ical variables among the groups were evaluated by performing Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 Ben et al. | 643 a χ analysis, 1-way ANOVA, and Kruskal-Wallis nonparametric B = 1.623, P < .001, 95% CI was 1.056 to 2.190); however, no signifi- ANOVA as needed, and LSD posthoc tests were used to per - cant correlation was found between the OT scores and any cog- form the multiple comparisons. Partial correlation analyses nitive z scores (P> .05). were performed to analyze the associations between the olfac- In the LLD group, the OI scores were positively correlated tory function and cognitive scores in the LLD patients; the con- with the left OBV (r = 0.348, P = .028), right OBV (r = 0.371,P = .018), trol variables included age, sex, years of education, and HAMD mean OBV (r = 0.396, P = .011), left HV (r = 0.316,P = .047), and right scores. The associations between olfactory function and cogni- HV (r = 0.324, P = .041). The OT scores were positively correlated tive z scores in the LLD patients were analyzed by performing a with the mean OBV (r = 0.328, P = .039). The MMSE scores were multiple linear regression analysis. Partial correlation analyses positively correlated with the left OBV (r = 0.399, P = .011), right were performed to analyze the associations among the behav- OBV (r = 0.381, P = .015), mean OBV (r = 0.429, P = .006), and left HV ioral indexes (OI and MMSE) and neuroimaging indexes (bilat- (r = 0.398, P = .011) (see Figure 1). eral OBV and HV); the control variables included age, sex, and years of education. A score <10 indicated the presence of OI Comparison of Normal Controls, LLD Patients with impairment (Hummel et al., 2001), and the LLD patients were or without OI Impairment, and AD Patients divided into the following 2 groups: LLD-OII and LLD-NOII. An ANCOVA was performed to compare the cognitive scores among The prevalence of OI impairment was 11.7% (n = 7) in the NC group and 42.4% (n = 53) in the LLD group (χ = 17.474, P < .001). The demo- the NC without olfactory identification impairment (NC-NOII), LLD-OII, LLD-NOII, and AD; the control variables included age, graphic data were as follows: NC-NOII group, male 19, female 34, age 65.3 ± 7.3 years, and years of education 9.2 ± 3.4; LLD-NOII sex, years of education, and HAMD scores. An ANCOVA was per - formed to compare the OBV among the 4 groups, and the control group, male 14, female 58, age 66.4 ± 6.0 years, years of education 8.4 ± 3.9, and HAMD 7 (3, 15); and LLD-OII group, male 15, female variables included age, sex, years of education, and total brain volume. LSD posthoc tests were used to perform the multiple 38, age 67.1 ± 6.6 years, years of education 7.5± 4.2, and HAMD 8 (4, 15). Both LLD groups exhibited overall cognitive impairment, comparisons. but the LLD-OII group demonstrated worse cognitive performance (global, memory, language, executive function, and attention) than Results the LLD-NOII group (P < .05). A reduced OBV was found in the LLD- NOII, LLD-OII, and AD groups. Additional details are provided in Demographic Data Table 3. The demographic data of the different groups are listed in VBM Analysis Table 1. The NC group had significantly higher scores (MMSE, OI, and OT), and the AD group had significant lower scores (MMSE, A significant difference was observed in many areas among OI, and OT) than the LLD group (P < .05). No significant difference the subjects in the NC-NOII, LLD-NOII, LLD-OII, and AD groups was observed in the OT (t = 1.001, P = .318) and olfactory identi- (see Table 4 and Figure 2A). Compared with the NC-NOII group, fication (t = 1.029, P = .305) between the males and females. No the AD group displayed significantly reduced grey matter vol- significant correlation was found between the olfactory function ume in the bilateral hippocampus, bilateral parahippocampal and chlorpromazine equivalent doses and between the olfac- gyrus, bilateral superior temporal gyrus, bilateral middle tem- tory function and fluoxetine equivalent doses in all participants poral gyrus, bilateral inferior temporal gyrus, bilateral amyg- (P > .05). dala, bilateral entorhinal cortex, right inferior occipital gyrus, left middle frontal gyrus, and left inferior frontal gyrus (see Figure 2B). Compared with the NC-NOII group, the LLD-OII Correlation Analysis group displayed diminished grey matter volume in the bilateral OI was positively correlated with the MMSE, AVLT (immediate insula, bilateral hippocampus, bilateral parahippocampal gyrus, recall, short-term delayed recall, long-term delayed recall, and right superior temporal gyrus, right middle temporal gyrus, recognition), LMT (immediate recall and delayed recall), BNT, right inferior frontal gyrus, and right middle frontal gyrus (see VFT, and digital span and negatively correlated with Stroop A in Figure 2C). Additionally, compared with the LLD-NOII group, the the LLD patients (P < .05) (see Table 2). No correlation was found AD group displayed significant grey matter volume reductions between the OT and any cognitive scores (P > .05). According to in the bilateral hippocampus, bilateral parahippocampal gyrus, the multiple linear regression analysis, OI was significantly cor - bilateral insula, bilateral precuneus, left middle temporal gyrus, related with the memory z scores in the LLD group ( = R 0.242, left superior temporal gyrus, left inferior temporal gyrus, left Table 1. Demographic and Clinical Data of All Participants NC (n = 60) LLD (n = 125) AD (n = 50) F/χ2/Z P Posthoc Age 65.4 ± 7.3 66.7 ± 6.2 71.9 ± 9.9 11.702 <.001 - Male/female 24/36 29/96 22/28 9.535 .009 - Years of education 9.1 ± 3.5 8.0 ± 4.0 6.8 ± 4.1 4.700 .010 - HAMD 1 (0, 3) 7 (3, 15) 5 (2, 9) 69.010 <.001 - MMSE 26.8 ± 1.9 22.7 ± 5.3 12.4 ± 5.1 133.389 <.001 A>B>C OI 11.8 ± 1.7 9.9 ± 2.7 5.8 ± 1.8 88.795 <.001 A>B>C OT 7.6 ± 2.5 6.0 ± 2.7 4.6 ± 1.8 17.417 <.001 A>B>C Abbreviations: AD, Alzheimer’s disease; HAMD, Hamilton Depression Rating Scale; LLD, late life depression; MMSE, Mini-mental State Examination; NC, normal con- trol, OI, olfactory identification; OT, olfactory threshold. In posthoc multiple comparisons, A means normal control group, B means LLD group, and C means AD group. Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 644 | International Journal of Neuropsychopharmacology, 2018 rectus gyrus, right middle temporal gyrus, right inferior tem- No significant differences were observed between the NC-NOII poral gyrus, right fusiform gyrus, right inferior occipital gyrus, and LLD-NOII groups, LLD-NOII and LLD-OII groups, and LLD-OII right middle occipital gyrus, and right angular (see Figure 2D). and AD groups. Discussion Table 2. Correlations between OI and Neuropsychological Test This study is the first to focus on OI in LLD patients, and the AD Scores in LLD Patients risk in LLD patients was analyzed from the perspective of olfac- Cognition Neuropsychological Test r P tory function for the first time. We primarily found 4 results. First, the LLD patients suffered from significant OI and OT impair - Global MMSE 0.422 < .001 ment, and the AD patients had worse OI and OT than the LLD Memory AVLT immediate recall 0.401 < .001 patients. Second, the diminished OI in the LLD patients was cor - short-term delayed Recall 0.439 < .001 related with poor cognitive performance (particularly memory) long-term delayed Recall 0.312 .002 and reduced grey matter volume (bilateral OBV and HV); the OT recognition 0.332 .012 was positively correlated with the mean OBV; however, no corre- LMT immediate recall 0.382 < .001 lations were observed between the OT and any cognitive scores delayed Recall 0.384 < .001 in the LLD patients. Third, the LLD-OII group showed worse cog- Language BNT 0.314 .002 VFT 0.322 .001 nitive performance and more structural abnormalities than the Executive TMT B -0.128 .211 LLD-NOII group, and no significant reduction in the grey matter Stroop A -0.223 .028 volume was found in the LLD-NOII group. Finally, the reduced Stroop SIE -0.047 .647 volume in many areas (hippocampus, precuneus, etc.) in the AD Visual-space ROCF 0.122 .234 group was also observed in the LLD with OI impairment group CDT4 -0.010 .923 but not in the LLD without OI impairment group. Attention SMDT 0.135 .165 In previous studies, the OT, but not OI, was significantly Digital span 0.220 .030 impaired (Naudin and Atanasova, 2014; Croy and Hummel, 2017; Khil et al., 2016) in depression patients, and the prevalence of Abbreviations: AVLT, auditory verbal learning; BNT, Boston Naming Test; CDT, OI impairment in depression patients (15.0%) was similar to Clock Drawing Test; DST, Digit Span Test; LMT, Logical Memory Test; ROCF, Rey- that in the normal controls (15.3%) (Khil et al., 2016), suggesting Osterrieth complex figure; SDMT, Symbol-Digit Modality Test; TMT, Trail-Making that olfactory deficits in depression patients are likely caused Test; VFT, Verbal Fluency Test. Figure 1. Partial correlation analysis of olfactory function and grey matter volume in patients with late life depression (LLD). Control variables included age, gender, and years of education. Blue represents olfactory identification (OI) and red represents olfactory threshold (OT). (A) OT (r = 0.019, P = .906), OI (r = 0.316, P = .047). (B) OT (r = 0.017, P = .919), OI (r = 0.324, P = .041); (C) OT (r = 0.309, P = .052), OI (r = 0.348, P = .028). (D) OT (r = 0.288, P = .072), OI (r = 0.371, P = .018). (E) OT (r = 0.328, P = .039), OI (r = 0.429, P = .006). Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 Ben et al. | 645 Table 3. Comparison among normal control, LLD with or without OI, and AD NC-NOII (n = 53) LLD-NOII (n = 72) LLD-OII (n = 53) AD (n = 50) χ2/Z/F P Posthoca OI 12.2 ± 1.4 11.8 ± 1.4 7.3 ± 1.8 5.8 ± 1.8 171.827 ＜.001 A, B＞C>D OT 7.9 ± 2.4 6.3 ± 2.4 5.3 ± 2.6 4.6 ± 1.8 8.774 ＜.001 A>B, C, D HAMD - 7（3, 15） 8（4, 15） - -0.689 .491 - MMSE 26.8 ± 1.9 24.6 ± 3.6 20.4 ± 6.3 12.4 ± 5.1 80.800 ＜.001 A, B＞C>D AVLT immediate recall 21.3 ± 4.1 18.1 ± 5.4 13.9 ± 5.6 - 14.180 ＜.001 A＞B＞C Short-term delayed 7.7 ± 1.9 6.1 ± 2.5 3.6 ± 3.0 - 21.010 ＜.001 A＞B＞C recall Long-term delayed 7.0 ± 2.3 5.2 ± 2.8 3.2 ± 3.2 - 12.505 .012 A＞B＞C recall Recognition 21.9 ± 1.8 21.1 ± 2.2 19.2 ± 3.8 - 6.750 .002 A,B＞C LMT immediate recall 5.7 ± 2.6 4.6 ± 2.8 3.0 ± 2.4 - 6.738 .002 A,B＞C Delayed recall 4.3 ± 2.5 3.1 ± 2.4 1.6 ± 1.8 - 10.517 < .001 A,B＞C BNT 22.7 ± 2.6 20.5 ± 3.9 18.2 ± 4.9 - 10.493 ＜.001 A＞B＞C VFT 15.0 ± 4.1 13.7 ± 3.6 11.4 ± 4.1 - 6.757 .002 A,B＞C TMTB 60.8 ± 28.6 80.8 ± 33.7 88.2 ± 36.8 - 4.831 .009 A＜B,C Stroop A 27.7 ± 5.8 33.0 ± 8.1 37.4 ± 10.6 9.734 ＜.001 A < B < C Stroop SIE 51.3 ± 26.7 59.5 ± 33.0 60.4 ± 29.9 - 0.276 .759 - ROCF 27.9 ± 3.1 23.9 ± 6.2 22.9 ± 8.4 - 4.179 .017 A＞B,C CDT4 3.6 ± 0.7 3.5 ± 0.7 3.4 ± 0.9 - 0.391 .677 - SMDT 37.6 ± 11.0 26.6 ± 9.7 25.0 ± 11.8 - 7.236 .001 A＞B,C Digital span 17.1 ± 3.7 14.6 ± 3.8 13.6 ± 3.7 - 5.569 .005 A＞B,C Left OBV 37.40 ± 4.01 33.12 ± 4.91 29.98 ± 5.04 27.27 ± 4.10 13.386 ＜.001 A>B,C,D B>D Right OBV 37.03 ± 4.67 34.84 ± 5.37 30.91 ± 4.64 27.51 ± 2.97 13.370 ＜.001 A,B>C>D Mean OBV 37.35 ± 4.04 33.98 ± 4.61 30.44 ± 4.43 27.39 ± 3.22 16.022 ＜.001 A>B>C>D Abbreviations: AD, Alzheimer’s disease; AVLT, Auditory Verbal Learning; BNT, Boston Naming Test; CDT, Clock Drawing Test; DST, Digit Span Test; HAMD, Hamilton Depression Rating Scale; LLD-OII, late life depression with olfactory identification; LLD-NOII, late life depression without olfactory identification; LMT Logical Memory Test; MMSE, Mini-mental State Examination; NC-NOII, normal control without olfactory identification; OBV, olfactory bulb volume; OI, olfactory identification; OT, olfactory threshold; ROCF, Rey-Osterrieth complex figure, SDMT, Symbol-Digit Modality Test; TMT, Trail-Making Test; VFT Verbal Fluency Test. In posthoc multiple comparisons, A means normal control without OI impairment group, B means LLD without OI impairment group, C means LLD with OI impair - ment group, D means AD group. Comparison of OBV included 25 NC-NOII subjects, 25 LLD-NOII patients, 20 LLD-OII patients, and 20 AD patients. the prevalence of OI impairment in the NC subjects in this study Table 4. Comparison of Grey Matter Volume among Normal Control without OI Impairment, LLD with/without OI Impairment, and AD was similar to that reported in a previous study, ageing may have only a limited contribution to the increased prevalence observed MNI among the LLD patients. Therefore, the olfactory deficit in LLD patients may result from malfunctions in perceptual processes Brain Area x y z Cluster F in primary and high-order olfactory cortices. Left hippocampus -32 -12 -16 5013 20.452 The olfactory pathway is intertwined with the cognitive Left middle temporal gyrus 66 -34 0 1601 12.814 pathway, and many brain areas, such as the OB, hippocam- Right angular 54 -62 44 278 10.858 pus, amygdala, and orbitofrontal cortex, that are involved in Left lateral prefrontal cortex -6 38 -28 223 9.924 OI processing are also damaged in AD patients (Naudin and Left inferior temporal gyrus 48 -34 -22 180 11.668 Atanasova, 2014). The OB is considered the first central relay Left middle temporal gyrus -67 -44 8 126 8.969 station of the olfactory system, and the importance of the OB is Left inferior frontal gyrus -50 38 6 116 9.724 not limited to olfactory processing. Reduced OBV may not only Right precuneus 12 -64 64 113 9.224 reduce odor input and lead to diminished OT but could also pos- Left lateral inferior occipital corte-62 x -64 -2 102 8.386 sibly affect neurotransmitter concentrations and alter function- ing in limbic and reward related areas, resulting in significant Abbreviation: MNI, Montreal Neurological Institute coordinate. OI impairment and cognitive deficits (Naudin and Atanasova, F test covariates included age, gender, years of education, and total brain volume. 2014; Croy and Hummel, 2017). Moreover, OB atrophy occurs FDR correction, P < .05. early in the disease process of AD and is correlated with global cognitive impairment, suggesting that the OBV may be advan- by dysfunction in perceptual processes or primary olfactory tageous for the early recognition of AD (Thomann et al., 2009). cortex. In the present study, compared with the NC group, the The hippocampus is an important part of the secondary olfac- LLD patients suffered from significant OT and OI impairment, tory cortex and is among the first brain areas to be damaged in and the prevalence of OI impairment in the LLD patients (41.6%) AD; the degree of hippocampal atrophy may be associated with was significant higher than that in the NC subjects (10.2%). disease severity (Poulin et al., 2011). Therefore, the presence of The increased prevalence of OI impairment in the depression AD pathology and dysfunction in the hippocampus may lead patients in our study may be partially due to ageing, because all to an inability to store and retrieve memories of smell and cor - participants in our study were older, and OI deterioration may rectly identify odorants (Growdon et al., 2015). Consistent with begin at the age of 50 (Zhang and Wang, 2017). However, because previous studies, we found that the reductions in the OBV and Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 646 | International Journal of Neuropsychopharmacology, 2018 HV were correlated with diminished OI and poor cognitive per - formance in the LLD patients. Additionally, OI impairment was associated with worse cognitive performance (global cognitive function, language, executive function, and attention), particu- larly severe memory deficits, in the LLD patients. This find- ing is consistent with previous observations in normal elderly individuals and aMCI patients (Roberts et al., 2016), suggesting that diminished OI may play a similar role in LLD patients as in normal elderly individuals and aMCI patients. Moreover, the OT was positively correlated with the OBV, but not the HV, and no correlation was found between the OT and any cognitive scores because the OT supposedly reflects malfunctions at the primary processing level. Diminished OI has been repeatedly shown to contribute to identifying individuals at a high risk of developing AD among normal older adults and aMCI patients (Devanand et al., 2008; Lojkowska et al., 2011). Because certain LLD patients may be pro- dromal AD patients, and diminished OI is strongly associated with cognitive impairment and high-order brain area dysfunc- tion in LLD patients, diminished OI may play a similar role in LLD patients, which could be advantageous in identifying indi- viduals at a high risk of developing AD. We hypothesize that LLD-OII patients, but not LLD-NOII patients, may be at a high risk of developing AD, and the following 3 observations support our hypothesis. First, the LLD-OII patients exhibited worse cognitive per - formance than the LLD-NOII patients. Both the LLD-OII and LLD-NOII patients suffered from overall cognitive impairment (memory, language, executive function, attention, and visuo- spatial skills), which is consistent with the results of previous studies (Wang and Blazer, 2015). However, global cognitive func- tion, logical memory, verbal fluency, and verbal recognition in the LLD-NOII group was intact, and the LLD-OII group exhibited worse performance in the MMSE scores, all memory scores, all language scores, and Stroop A scores than the LLD-NOII group. Memory deficits are typical symptoms of AD (Mckhann et al., 1984), and the MMSE, AVLT, and LMT have long been used in early AD screening (Ivnik et al., 1990; Tombaugh and Mcintyre, 1992; Dumont et al., 1999;). In addition, LLD patients with cognitive impairment (memory deficit) have been shown to be at a high risk of developing AD (Lee et al., 2012). Therefore, the similar pat- terns of cognitive impairment between the LLD-OII patients and AD patients indicates that LLD-OII patients may be more sus- ceptible to developing dementia than LLD-NOII patients. Second, the LLD-OII patients had more structural abnor - malities than the LLD-NOII patients. A significantly decreased grey matter volume was found in the LLD-OII group compared with that in the NC-NOII group, and the decreases in the AD group were greater than those in the LLD-NOII group; however, no significant difference was found between the LLD-NOII and NC-NOII groups, LLD-OII and AD groups, and LLD-OII and LLD- NOII groups. Thus, more structural abnormalities were observed in the LLD-OII patients than in the LLD-NOII patients, suggest- ing that LLD-OII patients may be at a higher risk of developing Figure 2. Comparison of grey matter volume among normal control without olfactory identification (OI) impairment, and late life depression (LLD) with or dementia. without OI impairment and Alzheimer’s disease. ANCOVA, posthoc LSD test Third, the pattern of structural abnormalities in the LLD- for multiple comparisons, covariates including total brain volume, age, gender, OII patients was similar to that in the AD patients. Many dam- and years of education. This correction was confined within a grey matter mask aged areas in the LLD-OII patients, such as the hippocampus, and determined using false discovery rate (FDR) correction (P < .05). The color 10 OB, and precuneus, were not only altered in the AD patients scale bar shows the logarithmic scale of P values (-lo ).g The closer to the yel- in the present study but have also been repeatedly reported low, the more significant is the difference between groups. Abbreviations: AD, Alzheimer’s disease; LLD-OII, late life depression with olfactory identification; in previous studies involving patients who were preferentially LLD-NOII, late life depression without olfactory identification; NC-NOII, normal affected by AD (Yang et al., 2012). Additionally, many structural control without olfactory identification. abnormalities are neurostructural predictors of AD; for instance, Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 Ben et al. | 647 hippocampal atrophy is the most typical neuroimaging charac- Brendel M, Pogarell O, Xiong G, Delker A, Bartenstein P, teristic of AD and has been listed as a diagnostic criterion for AD Rominger A, Alzheimer’s Disease Neuroimaging Initiative (Mckhann et al., 1984), and a reduced OBV may contribute to the (2015) Depressive symptoms accelerate cognitive decline in early diagnosis of AD (Thomann et al., 2009). Considering that amyloid-positive MCI patients. Eur J Nucl Med Mol Imaging the pattern of structural abnormalities in the LLD-OII patients 42:716–724. was similar to that in the AD patients, LLD-OII patients, but not Butters MA, Young JB, Lopez O, Aizenstein HJ, Mulsant BH, LLD-NOII patients, may suffer from early AD, the prodrome of Reynolds CF 3rd, DeKosky ST, Becker JT (2008) Pathways link- which is depression. ing late-life depression to persistent cognitive impairment The present study has a few limitations. First, our results and dementia. Dialogues Clin Neurosci 10:345–357. relied on a cross-sectional analysis. Longitudinal studies are Byers AL, Yaffe K (2011) Depression and risk of developing needed to track the progression of cognitive decline and con- dementia. Nat Rev Neurol 7:323–331. version rate of AD in patients with LLD and verify whether OI Croy I, Hummel T (2017) Olfaction as a marker for depression. impairment could serve as a biomarker for differentiating early J Neurol 264:631–638. AD from patients with LLD. Second, OI deficits, depression, and Croy I, Negoias S, Symmank A, Schellong J, Joraschky P, Hummel cognitive impairment may also be predictors of Parkinson’s T (2013) Reduced olfactory bulb volume in adults with a his- Disease, Dementia with Lewy Bodies, and other neurodegen- tory of childhood maltreatment. Chem Senses 38:679–684. erative diseases (Doty, 2008). Future studies combining CSF Devanand DP, Liu X, Tabert MH, Pradhaban G, Cuasay K, Bell K, biomarkers, positron emission tomography, and gene poly- de Leon MJ, Doty RL, Stern Y, Pelton GH (2008) Combining morphisms will be advantageous for addressing potential con- early markers strongly predicts conversion from mild cog- founds. Third, although we excluded participants with a current nitive impairment to alzheimer’s disease. Biol Psychiatry smoking status, acute respiratory tract infection, history of 64:871–879. nasal surgery, and other conditions that could affect olfaction, Dhilla Albers A, Asafu-Adjei J, Delaney MK, Kelly KE, Gomez-Isla we were unable to exclude the influence of a history of smoking, T, Blacker D, Johnson KA, Sperling RA, Hyman BT, Betensky allergies, medicine, or nasal diseases because these aspects are RA, Hastings L, Albers MW (2016) Episodic memory of odors common in most participants. Finally, we analyzed only the grey stratifies alzheimer biomarkers in normal elderly. Ann Neurol matter alterations in the participants, and their neuroimaging 80:846–857. characteristics may not have been fully elucidated; further stud- Doty RL (2008) The olfactory vector hypothesis of neurodegen- ies using multi-model inference and network-based analyses erative disease: is it viable? Ann Neurol 63:7–15. are currently in progress. Dumont R, Willis JO, Veizel K, Zibulsky J (1999) Wechsler In summary, the LLD patients suffered from significant OI Abbreviated Scale of Intelligence. Rehabil Couns Bull impairment, and diminished OI was associated with cognitive 3346–3346. impairment (particularly memory deficit) and reduced grey Growdon ME, Schultz AP, Dagley AS, Amariglio RE, Hedden T, matter volume (OB and hippocampus). The LLD-OII patients had Rentz DM, Johnson KA, Sperling RA, Albers MW, Marshall GA worse cognitive performance and more structural abnormalities (2015) Odor identification and alzheimer disease biomarkers than the LLD-NOII patients, and the neuropsychological and in clinically normal elderly. Neurology 84:2153–2160. neuroimaging characteristics of the LLD-OII patients were simi- Hayasaka Y, Purgato M, Magni LR, Ogawa Y, Takeshima N, Cipriani lar to those of the AD patients, suggesting that LLD-OII patients A, Barbui C, Leucht S, Furukawa TA (2015) Dose equivalents may be at a high risk of developing AD. Longitudinal studies are of antidepressants: evidence-based recommendations from currently ongoing to determine whether LLD-OII patients have a randomized controlled trials. J Affect Disord 180:179–184. faster cognitive decline and higher rate of conversion to AD and Hummel T, Sekinger B, Wolf SR, Pauli E, Kobal G (1997) ‘Sniffin’ whether a combination of OI and other predictors improves the sticks’: olfactory performance assessed by the combined sensitivity and specificity of predicting AD. testing of odor identification, odor discrimination and olfac- tory threshold. Chem Senses 22:39–52. Hummel T, Konnerth CG, Rosenheim K, Kobal G (2001) Screening Acknowledgments of olfactory function with a four-minute odor identification test: reliability, normative data, and investigations in patients This work was supported by the National Natural Science Foundation of China (nos. 81571333 and 85711333), Guangzhou with olfactory loss. Ann Otol Rhinol Laryngol 110:976–981. Hummel T, Henkel S, Negoias S, Galván JR, Bogdanov V, Hopp Municipal Pharmaceutical Health Science and Technology Project (nos. 20151A031003 and 20141A011044), Guangzhou P, Hallmeyer-Elgner S, Gerber J, Reuner U, Haehner A (2013) Olfactory bulb volume in patients with temporal lobe epi- Municipal Science and Technology Program (no. 1563000496), and Science and Technology Department of Guangdong Province lepsy. J Neurol 260:1004–1008. Ivnik RJ, Malec JF, Tangalos EG, Petersen RC, Kokmen E, Kurland major science and technology (no. 2016B010108003). LT (1990) The Auditory-Verbal Learning Test (AVLT): norms for ages 55 years and older. Psychol Assess 2:304–312. Statement of Interest Ivnik RJ, Malec JF, Smith GE, Tangalos EG, Petersen RC, Kokmen E, Kurland LT (1992) Mayo’s older americans normative studies: None. WAIS—R norms for ages 56 to 97. Clin Neuropsychol 6:83–104. Kaup AR, Byers AL, Falvey C, Simonsick EM, Satterfield S, References Ayonayon HN, Smagula SF, Rubin SM, Yaffe K (2016) Trajectories of depressive symptoms in older adults and risk Ashburner J, Friston KJ (2000) Voxel-based morphometry–the of dementia. JAMA Psychiatry 73:525–531. methods. Neuroimage 11:805–821. Khil L, Rahe C, Wellmann J, Baune BT, Wersching H, Berger K Benitez A, Horner MD, Bachman D (2011) Intact cognition in (2016) Association between major depressive disorder and depressed elderly veterans providing adequate effort. Arch odor identification impairment. J Affect Disord 203:332–338. Clin Neuropsychol 26:184–193. Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018 648 | International Journal of Neuropsychopharmacology, 2018 Lafaille-Magnan ME, Poirier J, Etienne P, Tremblay-Mercier J, atrophy is prominent in early alzheimer’s disease and relates Frenette J, Rosa-Neto P, Breitner JCS, PREVENT-AD Research to symptom severity. Psychiatry Res 194:7–13. Group (2017) Odor identification as a biomarker of preclinical Rahayel S, Frasnelli J, Joubert S (2012) The effect of Alzheimer’s AD in older adults at risk. Neurology 89:327–335. disease and Parkinson’s disease on olfaction: a meta-analysis. Laske C, Sohrabi HR, Frost SM, López-de-Ipiña K, Garrard P, Behav Brain Res 231:60–74. Buscema M, Dauwels J, Soekadar SR, Mueller S, Linnemann Roberts RO, Christianson TJ, Kremers WK, Mielke MM, Machulda C, Bridenbaugh SA, Kanagasingam Y, Martins RN, O’Bryant SE MM, Vassilaki M, Alhurani RE, Geda YE, Knopman DS, Petersen (2015) Innovative diagnostic tools for early detection of alz- RC (2016) Association between olfactory dysfunction and heimer’s disease. Alzheimers Dement 11:561–578. amnestic mild cognitive impairment and alzheimer disease Lee GJ, Lu PH, Hua X, Lee S, Wu S, Nguyen K, Teng E, Leow AD, dementia. JAMA Neurol 73:93–101. Jack CR Jr, Toga AW, Weiner MW, Bartzokis G, Thompson Solomon GS, Petrie WM, Hart JR, Brackin HB Jr (1998) Olfactory PM, Alzheimer’s Disease Neuroimaging Initiative (2012) dysfunction discriminates alzheimer’s dementia from major Depressive symptoms in mild cognitive impairment predict depression. J Neuropsychiatry Clin Neurosci 10:64–67. greater atrophy in alzheimer’s disease-related regions. Biol Thomann PA, Dos Santos V, Toro P, Schönknecht P, Essig M, Psychiatry 71:814–821. Schröder J (2009) Reduced olfactory bulb and tract volume Li W, Ward BD, Liu X, Chen G, Jones JL, Antuono PG, Li SJ, Goveas in early Alzheimer’s disease–a MRI study. Neurobiol Aging JS (2015) Disrupted small world topology and modular organ- 30:838–841. ization of functional networks in late life depression with Tombaugh TN, McIntyre NJ (1992) The mini-mental state exami- and without amnestic mild cognitive impairment. J Neurol nation: a comprehensive review. J Am Geriatr Soc 40:922–935. Neurosurg Psychiatry 86:1097. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Lojkowska W, Sawicka B, Gugala M, Sienkiewicz-Jarosz H, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated Bochynska A, Scinska A, Korkosz A, Lojek E, Ryglewicz D anatomical labeling of activations in SPM using a macro- (2011) Follow-up study of olfactory deficits, cognitive func- scopic anatomical parcellation of the MNI MRI single-subject tions, and volume loss of medial temporal lobe structures in brain. Neuroimage 15:273–289. patients with mild cognitive impairment. Curr Alzheimer Res Wang J, Eslinger PJ, Doty RL, Zimmerman EK, Grunfeld R, Sun 8:689–698. X, Meadowcroft MD, Connor JR, Price JL, Smith MB, Yang QX Mai N, Zhong X, Chen B, Peng Q, Wu Z, Zhang W, Ouyang C, (2010) Olfactory deficit detected by fmri in early Alzheimer’s Ning Y (2017) Weight rich-club analysis in the white matter disease. Brain Res 1357:184–194. network of late-life depression with memory deficits. Front Wang S, Blazer DG (2015) Depression and cognition in the elderly. Aging Neurosci 9:279. Annu Rev Clin Psychol 11:331–360. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Wang S, Dan GB (2014) Depression and cognition in the elderly. Stadlan EM (1984) Clinical diagnosis of alzheimer’s disease: Annu Rev Clin Psychol 11:331–60. report of the NINCDS-ADRDA work group under the auspices Weyerer S, Eifflaender-Gorfer S, Köhler L, Jessen F, Maier W, Fuchs of department of health and human services task force on A, Pentzek M, Kaduszkiewicz H, Bachmann C, Angermeyer alzheimer’s disease. Neurology 34:939–944. MC (2005) Prevalence and risk factors for depression in non- Mirza SS, Wolters FJ, Swanson SA, Koudstaal PJ, Hofman A, demented primary care attenders aged 75 years and older . Tiemeier H, Ikram MA (2016) Ten-year trajectories of depres- J Affect Disord 111:153–163. sive symptoms and risk of dementia: a population-based Woods SW (2003) Chlorpromazine equivalent doses for the study. Lancet Psychiatry 3:628–635. newer atypical antipsychotics. J Clin Psychiatry 64:663–667. Naudin M, Atanasova B (2014) Olfactory markers of depression Yang J, Pan P, Song W, Huang R, Li J, Chen K, Gong Q, Zhong J, Shi H, and Alzheimer’s disease. Neurosci Biobehav Rev 45:262–270. Shang H (2012) Voxelwise meta-analysis of gray matter anom- Negoias S, Croy I, Gerber J, Puschmann S, Petrowski K, Joraschky alies in alzheimer’s disease and mild cognitive impairment P, Hummel T (2010) Reduced olfactory bulb volume and olfac- using anatomic likelihood estimation. J Neurol Sci 316:21–29. tory sensitivity in patients with acute major depression. Yousem DM, Geckle RJ, Bilker WB, Doty RL (1998) Olfactory bulb Neuroscience 169:415–421. and tract and temporal lobe volumes. Normative data across Negoias S, Hummel T, Symmank A, Schellong J, Joraschky P, Croy I decades. Ann N Y Acad Sci 855:546–555. (2016) Olfactory bulb volume predicts therapeutic outcome in Yu H, Purgato M, Magni LR, Ogawa Y, Takeshima N, Cipriani A, major depression disorder. Brain Imaging Behav 10:367–372. Barbui C, Leucht S, Furukawa TA (2015) Dose equivalents of Pentzek M, Grass-Kapanke B, Ihl R (2007) Odor identification antidepressants: evidence-based recommendations from in alzheimer’s disease and depression. Aging Clin Exp Res randomized controlled trials. J Affect Disord 180:179. 19:255–258. Zhang C, Wang X (2017) Initiation of the age-related decline of Poulin SP, Dautoff R, Morris JC, Barrett LF, Dickerson BC, odor identification in humans: a meta-analysis. Ageing Res Alzheimer’s Disease Neuroimaging Initiative (2011) Amygdala Rev 40:45–50. Downloaded from https://academic.oup.com/ijnp/article-abstract/21/7/640/4938805 by Ed 'DeepDyve' Gillespie user on 03 July 2018
International Journal of Neuropsychopharmacology – Oxford University Press
Published: Mar 15, 2018
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