Inflammation and lipid metabolism as potential biomarkers of memory impairment across type 2 diabetes mellitus and severe mental disorders

Progress in Neuropsychopharmacology & Biological Psychiatry 127 (2023) 110817 Available online 15 June 2023 0278-5846/© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Inflammation and lipid metabolism as potential biomarkers of memory impairment across type 2 diabetes mellitus and severe mental disorders Joan Vicent S´ anchez-Ortí a , c , d , 1 , Patricia Correa-Ghisays a , b , c , d , e , 1 , * , Vicent Balanz´ a-Martínez a , b , c , e , f , * , Gabriel Selva-Vera a , b , c , e , Joan Vila-Franc´ es g , Rafael Magdalena-Benedito g , Constanza San-Martin b , c , h , Víctor M. Victor i , j , k , Irene Escribano-Lopez i , Antonio Hernandez-Mijares i , Juliana Vivas-Lalinde l , Benedicto Crespo-Facorro b , m , Rafael Tabar´ es-Seisdedos a , b , c , e a INCLIVA - Health Research Institute, Valencia, Spain b Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain c TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain d Faculty of Psychology, University of Valencia, Valencia, Spain e Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain f Mental Health Unit of Catarroja, Valencia, Spain g IDAL - Intelligent Data Analysis Laboratory, University of Valencia, Valencia, Spain h Department of Physiotherapy, University of Valencia, Valencia, Spain i Service of Endocrinology and Nutrition, University Hospital Dr. Peset, Spain j Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia, Spain k Department of Physiology, University of Valencia, Valencia, Spain l Department of Psychiatry, Mental Health Service, Manises, Valencia, Spain m Department of Psychiatry, Faculty of Medicine, University of Sevilla, HU Virgen del Rocío IBIS, Spain ARTICLE INFO Keywords: Neurocognition Inflammation Oxidative stress Lipid metabolism Type 2 diabetes mellitus Severe mental illnesses ABSTRACT Introduction: Neurocognitive impairment is a transdiagnostic feature across several psychiatric and cardiometabolic conditions. The relationship between inflammatory and lipid metabolism biomarkers and memory performance is not fully understood. This study aimed to identify peripheral biomarkers suitable to signal memory decline from a transdiagnostic and longitudinal perspective. Methods: Peripheral blood biomarkers of inflammation, oxidative stress and lipid metabolism were assessed twice over a 1-year period in 165 individuals, including 30 with schizophrenia (SZ), 42 with bipolar disorder (BD), 35 with major depressive disorder (MDD), 30 with type 2 diabetes mellitus (T2DM), and 28 healthy controls (HCs). Participants were stratified by memory performance quartiles, taking as a reference their global memory score (GMS) at baseline, into categories of high memory (H; n =40), medium to high memory (MH; n =43), medium to low memory (ML; n =38) and low memory (L; n =44). Exploratory and confirmatory factorial analysis, mixed one-way analysis of covariance and discriminatory analyses were performed. Results: L group was significantly associated with higher levels of tumor necrosis factor-alpha (TNF- α ) and lower levels of apolipoprotein A1 (Apo-A1) compared to those from the MH and H groups (p <0.05; η 2 p =0.06–0.09), with small to moderate effect sizes. Moreover, the combination of interleukin-6 (IL-6), TNF- α , c-reactive protein (CRP), Apo-A1 and Apo-B compounded the transdiagnostic model that best discriminated between groups with different degrees of memory impairment ( χ 2 =11.9–49.3, p <0.05–0.0001). Conclusions: Inflammation and lipid metabolism seem to be associated with memory across T2DM and severe mental illnesses (SMI). A panel of biomarkers may be a useful approach to identify individuals at greater risk of neurocognitive impairment. These findings may have a potential translational utility for early intervention and advance precision medicine in these disorders. * Corresponding authors at: Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Blasco Ib´ a˜ nez 15, 46010 Valencia, Spain Phone: +34 963983349 E-mail addresses: [email protected] (P. Correa-Ghisays), [email protected] (V. Balanz´ a-Martínez). 1 Contributed equally to this work Contents lists available at ScienceDirect Progress in Neuropsychopharmacology & Biological Psychiatry journal homepage: www.elsevier.com/locate/pnp https://doi.org/10.1016/j.pnpbp.2023.110817 Received 10 March 2023; Received in revised form 20 May 2023; Accepted 12 June 2023 Progress in Neuropsychopharmacology & Biological Psychiatry 127 (2023) 110817 2 1. Introduction Memory is a core construct for human neurocognition. The ability to encode, manipulate, consolidate and retrieve information is a critical determinant of daily performance and quality of life across different clinical populations (Millan et al., 2012). Important differences in memory performance exist that are strongly related to health and disease states variability between individuals (McWhirter et al., 2020). Likewise, memory impairments have been observed in a significant proportion of individuals with severe mental illness (SMI) and certain cardio-metabolic disorders, such as type 2 diabetes mellitus (T2DM) (Abramovitch et al., 2021; Arvanitakis et al., 2020; Martinez-Aran and Vieta, 2015). Memory deficits are prevalent across these disorders, and contribute significantly to patients’ social functioning, quality of life and occupational outcomes (Cambridge et al., 2018; Green et al., 2019; S´ anchez-Moreno et al., 2018). In this regard, growing evidence supports the idea that neurobiological factors subserving disrupted memory processes transcend diagnostic categories (Bourgognon and Cavanagh, 2020). At the molecular level, it is now generally accepted that complex memory processes involve dynamic interactions with immuneinflammation, oxidative stress and metabolic pathways. There are consistent findings supporting that pro- and anti-inflammatory cytokines, such as interleukin (IL)-1, IL-4, IL-6, IL-10 and tumor necrosis factor-alpha (TNF- α ) play a key role in information processing in the human brain (Levin et al., 2021). Importantly, immune-inflammatory biomarkers are found at higher concentrations in individuals with memory deficits (Süβ et al., 2021). Thus, it has been suggested that an increased pro-inflammatory response makes difficult for individuals to use effective cognitive strategies for encoding, manipulating and retrieving information (Tetlow et al., 2019). Likewise, oxidative stress and metabolic changes may have a direct influence on memory impairment and cognitive rehabilitation (Franzoni et al., 2021; Hua et al., 2021a). Recent findings support that abnormal blood lipid profiles are risk factors for memory impairment among healthy individuals (Sanjana et al., 2021). Overall, increasing evidence suggests that, regardless of clinical diagnoses, similar immune-inflammation, oxidative stress, and metabolic pathways could subserve disrupted memory processes, suggesting common pathways for memory impairment. To date, significant overlap in neurobiological factors, neural substrates, and genetic underpinnings promote an integrative rather than a reductionist approach to neurocognitive performance. The National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) project provides an integrative framework for translational research that is focused on functional dimensions of behavior and cognitive/affective processes, and transcends traditional DSM/ICD categories of psychiatric disorders (Insel, 2014). These dimensions are studied along a span of functioning from normal to abnormal, with the understanding that each one is situated in, and affected by, environmental and neurodevelopmental contexts. In this regard, RDoC suggest that memory processes should be one of core domains in the human brain functioning and that memory deficits are a core feature across somatic and psychiatric disorders. Accordingly, the identification of neurobiological changes underlying these deficits seems relevant (Cuthbert, 2020). To our knowledge, no previous studies have identified valid biomarkers for memory dysfunction in a sample of individuals with T2DM and SMI. This research gap hampers better understanding whether immuneinflammatory, oxidative stress and metabolic biomarkers can be used to identify individuals with memory impairment. This study aimed to identify peripheral biomarkers valid for memory dysfunction from a combined, transdiagnostic sample of individuals with T2DM and SMI. 2. Materials and methods 2.1. Study design and ethical considerations This study is part of a project aimed at identifying and validating peripheral biomarkers for neurocognitive deficits in major depressive disorder (MDD), bipolar disorder (BD), schizophrenia (SZ) and T2DM. This prospective and comparative cohort project was conducted between April 2015 and January 2018, and it investigated the association and evolution of certain peripheral blood biomarkers and neurocognitive impairments in a unique longitudinal cohort of individuals with somatic and psychiatric disorders. Demographic, clinical, neurocognitive and functional data, and biomarkers of peripheral blood were collected at baseline (T1) and after one year (T2). Individuals with SMI were recruited from mental health units (MHUs) in several towns in the province of Valencia, Spain (Foios, Catarroja, Paterna, and Sagunto); the psychiatry outpatient clinic and endocrinology department of the University Hospital Dr. Peset; and the Miguel Servet MHU in Valencia City. Healthy controls (HC) were extracted from the general population residing/living in the same areas as the individuals with SMI. Participants were demographically matched. All participants provided informed consent after the study procedures were explained. The ethic committees or institutional review boards at each participating center approved the study protocol, and the study was conducted in accordance with the ethical principles of the Declaration of Helsinki. For this article, only those variables related to the present study aims were included in the analyses. 2.2. Participants SZ, BD, and MDD, were diagnosed according to Diagnostic and Statistical Manual of Mental Disorders - DSM-5 (APA, 2014) criteria. T2DM was diagnosed based on the Standards of Care criteria of the American Diabetes Association (ADA, 2015). Participants with MDD and BD should meet the remission criteria (Tohen et al., 2009) of an acute affective episode, defined as Young Mania Rating Scale (YMRS) score ≤6 and Hamilton Rating Scale for Depression (HRSD) score ≤8, and individuals with SZ had to be clinically stable, defined as Positive and Negative Syndrome Scale (PANSS) score ≤36 (Andreasen et al., 2005). Individuals with T2DM had to be free of severe diabetic neuropathy and kidney disease (serum creatinine <1.5 mg/dl). For recruitment as HC, the absence of physical illness, pharmacological treatments, and family history of SMI in first-degree relatives were required. Ability to understand study procedures and willingness to give written consent was required for participation. General exclusion criteria for all groups included: current hospitalization, documented cognitive impairment not secondary to psychiatric disorder (intellectual disability or major neurocognitive disorder, i.e. dementia), disability or inability that prevented understanding of the protocol, current substance abuse disorder (except for nicotine), pregnancy, intake of steroids, corticosteroids, antioxidants, antibiotics, and immunologic therapies, fever over 38 ◦C, and history of vaccination within 4 weeks of the evaluation. The same inclusion and exclusion criteria were used at T1 and T2. 2.3. Clinical and neuropsychological assessments The assessments were conducted by the same experienced psychologists and psychiatrists of the research group. Sociodemographic and clinical data, including sex, age, years of education, body mass index (calculated as kg/m 2 ), number of prescribed psychopharmacological medications, laterality (defined as manual, ocular and crural dominance) and comorbidity measured from Kaplan-Feinstein Scale (KFS) (see supplementary material) (Rosas-Carrasco et al., 2011), were collected. Neurocognitive performance was evaluated using a comprehensive battery of neuropsychological tests and subtests previously used by our J.V. S´ anchez-Ortí et al. Progress in Neuropsychopharmacology & Biological Psychiatry 127 (2023) 110817 3 group (Ali˜ no-Dies et al., 2020; Correa-Ghisays et al., 2017, 2019, 2022; Gar´ es-Caballer et al., 2022; Luperdi et al., 2021; Salazar-Fraile et al., 2009; San Martín-Valenzuela et al., 2020; S´ anchez-Ortí et al., 2022; Selva-Vera et al., 2010; Tabar´ es-Seisdedos et al., 2008). Memory performance was assessed with the following neuropsychological tests: (i) Verbal memory and learning were assessed by California Verbal Learning Test (CVLT) (Delis et al., 1987) in its Spanish version Complutense Verbal Learning Test (TAVEC) immediate recall of the first learning trial, total immediate recall, immediate recall of the interference list, short-term free recall, long-term free recall variables (Benedet and Alejandre, 2014); (ii) Visual memory was assessed by Rey- Osterrieth Complex Figure Test (ROCFT) two minutes after the copy (fRey2) and 20 min after the copy (fRey20) (Rey, 1999); (iii) Working memory was assessed by Wechsler Adult Intelligence Scale III edition (WAIS-III) digit span forward and backwards (Weschler, 1999); (iv) Working and immediate memory were assessed by Trail Making Test (TMT) Part B (Reitan and Wolfson, 1985). A global memory score (GMS) was calculated by averaging performance scores on each test, resulting from prior factorial analysis. There are no consensus-based clinical thresholds for the categorization of memory performance so that the participants were stratified by memory performance quartiles, taking as a reference their GMS at baseline. The distribution by quartile was: ≤ − 22.90 (quartile-4 group: low GMS magnitude), −22.91 to 28.25 (quartile-3 group: low-medium GMS magnitude), 28.26 to 68.10 (quartile-2 group: high-medium GMS magnitude), ≥68.10 (quartile-1 group: high GMS magnitude). The groups created at T1 were maintained at T2, with prior verification of stability in GMS. 2.4. Determination of biomarkers in peripheral blood Venous blood extraction was performed, and the serum and plasma samples were kept in a freezer at −80 ◦C. (i) Cytokines: serum cytokine concentration was determined using Luminex® X-MAP technology (Luminex Corp., Austin, TX, USA) based on flow cytometry. The following cytokines were analyzed: IL-6, interleukin 10 (IL-10) and TNF- α . Sample processing and data analysis were performed according to the manufacturer’s instructions. (ii) Oxidative stress markers and mitochondrial metabolism: oxidative stress in leukocytes was evaluated using fluorimetry techniques in a fluoroscan (Synergy MX). Plated in 96- well plates, 100,000 cells in each, were incubated 30 min at 37 ◦C with the corresponding fluorochrome: dichlorofluorescein diacetate indicated reactive oxygen species (ROS) production (485 nm excitation, 535 nm emission), MitoSOX measured mitochondrial ROS (mROS) (510 nm excitation, 580 nm emission), tetramethylrodamin methyl ester (552 nm excitation, 574 nm emission) assessed membrane potential, nonylacridin orange mitochondrial mass (495 nm excitation, 519 nm emission), and 5-chloromethylfluorescein diacetate measured intracellular glutathione (492 nm excitation, 517 nm emission). We used the monocyte cell line U-937 as an internal control to avoid the possible fluctuation of fluorescence associated with time. Serum lipid peroxidation levels were measured using a commercial thiobarbituric acid reactive substances kit, and performed according to the manufacturer’s instructions. CRP levels were determined by an immunonephelometric assay (Behring Nephelometer II, Dade Behring, Inc., Newark, DE, USA). (iii) Lipid markers. Venous blood was extracted into Vacutainer® tubes and centrifuged at 1500g for 10 min at 4 ◦C, after which biochemical parameters were evaluated. A Beckman LX-20 autoanalyzer (Beckman Coulter, La Brea, CA, US) was employed to assessed lipid metabolism, apolipoprotein A1 (Apo-A1) and apolipoprotein B (Apo-B), content was estimated with Friedewald’s formula (Behring Nephelometer II, Dade Behring, Inc., Newark, DE, USA). 2.5. Statistical analyses Data were analyzed using Statistical Package for Social Sciences (SPSS) version 26.0 for Windows (IBM Corp, 2019). 2.5.1. Preliminary analysis Exploratory factorial analysis (EFA) was carried out with the variables that make up the memory variables at T1 and confirmatory factorial analysis (CFA) with the same variables at T2. Both analyses were performed with the main axis extraction method. Cronbach’s Alpha ( α ) statistic was calculated in both occasions to assess the internal consistency of the variables included in the analysis (Cortina, 1993; Cronbach, 1951). For all analyses, α >0.70 was considered appropriate. To confirm if this analysis is correct, the Kaiser-Meyer-Olkin (KMO) tests and the Bartlett sphericity test were performed. Following previous literature, the agglomerated neuropsychological tests quantifies different types of memory composing a valid indicator of memory function (Donders, 2020; S´ anchez-Cubillo et al., 2009). 2.5.2. Primary analysis Descriptive analyses were conducted using a one-way analysis of variance (ANOVA) for continuous variables and the chi-squared test for categorical variables. The differences between groups for biomarkers at T1 and T2 and their evolution over time were assessed using a mixed one-way analysis of covariance (ANCOVA), with diagnosis (psychiatric disorders and T2DM), sex, age, years of education, BMI and psychopharmacological medications as co-variables. Adjusting for co-variables was used because the distribution of those factors significantly differed across the groups and was a confounding variable. The stability of the GMS scores over time was assessed using intra-ANCOVA. Normality was assumed for all continuous variables because the sample was statistically verified using Kolmogorov-Smirnov test. In turn, this guaranteed that the variables for T1 and T2 could be assessed using an ANOVA/ ANCOVA. A post hoc analysis with a Bonferroni corrected pairwise t-test and Mann-Whitney U were performed to examine the differences between groups. 2.5.3. Model analysis To test the ability of peripheral biomarkers to discriminate between groups with different degrees of memory performance, a discriminant analysis was performed, comparing by pairs of groups, in which used all biomarkers. This procedure was performed at both times separately in order to ensure the consistency of the biomarkers tested. Subsequently, a single model using only significant biomarkers was tested in each group stratified by GMS in order to inform how well the model discriminates between different degrees of memory performance. For all analyses, p < 0.05 was considered statistically significant. 3. Results 3.1. Sample description At T1, the sample consisted of 165 participants, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 HCs. Participants were stratified by memory performance quartiles, taking as a reference their GMS at baseline, into categories: 40 in the high memory (H) group (SZ = 1, BD =8, MDD =11, T2DM =4 and HC =16), 43 in the medium-high memory (MH) group (SZ =7, BD =5, MDD =10, T2DM =10 and HC = 11), 38 in the medium-low memory (ML) group (SZ =8, BD =12, MDD =6, T2DM =11 and HC =1), and 44 in the low memory (L) group (SZ = 14, BD =17, MDD =8, T2DM =5 and HC =0). Forty participants were lost to follow-up at T2 (retention rate: 75.7%). At T2, the sample consisted of 125 individuals: 34 in the H group (SZ =1, BD =8, MDD =9, T2DM =4 and HC =12), 30 in the MH group (SZ =7, BD =4, MDD =5, T2DM =7 and HC =7), 27 in the ML group (SZ =6, BD =8, MDD =6, T2DM =8 and HC =0), and 34 in the L group (SZ =13, BD =9, MDD =7, T2DM =5 and HC =0). A summary of the sociodemographic and clinical characteristics of the participants is presented in Table 1. Females represented half of the J.V. S´ anchez-Ortí et al. Progress in Neuropsychopharmacology & Biological Psychiatry 127 (2023) 110817 4 total sample (48%). The mean age of the whole sample was 46.2 (SD: 12.3) years. The H group was characterized by a significantly higher years of education and significantly lower BMI than the other groups. The comorbidity and laterality were similar in all groups. Moreover, the L group was prescribed a significantly higher number of psychiatric medications than the other groups. These outcomes were maintained over time. GMS scores remained stable at one-year follow-up (F =0.54; p >0.05). 3.2. Memory as a neurocognitive domain The results of the EFA and CFA analyses are shown on Table 2. Cronbach’s α value indicated an appropriate internal consistency at both times ( α =0.84–0.85, respectively). This high stability over time supports of quality assurance regarding the memory construct. Once the reliability of the neurocognitive domain was verified, the dimensionality of tests set was analyzed. For that purpose, we carried out an EFA. Since KMO showed a value close to 1, the sample was considered to be sufficiently representative of the population. Moreover, Bartlett’s sphericity test showed a significance level <0.0001, which evidences a significant relationship between the variables included. Kaiser’s rule provided a factor structure with one factor that explained 54.11% of the total variance. Thus, the existence of a single factor was considered, which was termed GMS. In order to confirm that the adjustment of the set of tests into a single factor was adequate, a CFA was performed. The neuropsychological battery was administered again at T2, to 125 participants; repeating the previous process and guiding the analysis to the confirmation of a single factor. 3.3. Between-group comparisons of peripheral biomarkers Peripheral serum markers at T1 and T2 for the four groups are shown in Table 3. Overall, individuals with low memory obtained significantly higher scores for TNF- α biomarker. Significant differences existed between L group compared to MH and H groups for TNF- α at T1 (p <0.01; η 2 p =0.07) and at T2 only with H group (p <0.05; η 2 p =0.06). However, members of the L group showed significantly lower concentrations of apolipoprotein A1 (Apo-A1) compared to MH and H groups at both assessments (p <0.01; η 2 p =0.07–0.09). In all cases, the effect size was small to moderate. Differences between the time points within each group were not significant. 3.4. Discriminatory ability of peripheral biomarkers for memory performance The results regarding the discriminatory ability of serum peripheral markers showed that the combination of IL-6, TNF- α , CRP, Apo-A1 and Apo-B was the transdiagnostic model that best discriminated between groups with different memory performance degree ( χ 2 =11.9–49.3, p < 0.05–0.0001) (Fig. 1). According to the model, elevated proinflammatory activity and impaired lipid metabolism allows for the differentiation of individuals with impaired memory performance. 4. Discussion To our knowledge this is the first study aimed to identify peripheral biomarkers valid for memory decline from a transdiagnostic sample of HC and individuals with T2DM and psychiatric disorders. The present results show, on the one hand, that memory performance was significantly associated at both assessments with changes in two of the biomarkers analyzed in this study. Specifically, compared to individuals with better memory performance, those with highest memory impairment had higher levels of TNF- α and lower concentrations of Apo- A1, with small to moderate effect sizes. On the other hand, the discriminant analysis showed that a model combining all three proinflammatory biomarkers (IL-6, TNF- α , and CRP) and the two biomarkers of impaired lipid metabolism (Apo-A1 and Apo-B) was able to distinguish individuals with different degrees of memory performance. However, biomarkers of oxidative stress and the anti-inflammatory cytokine IL-10 were not associated with memory function in this combined transdiagnostic sample. Regarding the nature of the two biomarkers associated with the highest memory impairment, TNF- α is one of the most studied biomarkers in relation to cognitive functions. More specifically, its relationship with memory impairment has been reported in the disorders included in our study. For instance, memory decline has been found to Table 1 Peripheral serum markers at T1 and T2. H MH ML L Statistical analyses Variables a T1 (n =40) T2 (n =34) T1 (n =43) T2 (n =30) T1 (n =38) T2 (n =27) T1 (n =44) T2 (n =34) T1 (p) e Post hoc test g T2 (p) e Post hoc test g Sex b, f, h 27(67%) 25(73%) 19(44%) 13(43%) 12(32%) 9(35%) 21(48%) 16(47%) 10.4** ML,MH < H 11.3** ML,MH < H Age 39.0 (12.2) 40.6 (12.3) 44.7 (14.5) 46.0 (13.6) 51.2 (10.5) 52.3 (11.1) 52.4 (10.2) 51.9 (10.8) 10.9**** H <ML,L MH <L 6.6**** H <ML,L Years of education 16.1 (4.1) 16.8 (3.8) 13.6 (4.3) 14.0 (3.8) 11.4 (3.7) 12.5 (5.1) 8.7(2.9) 9.1(2.9) 28.5**** L,ML,MH <H L <ML, MH 22.1**** L,ML,MH <H L <ML, MH BMI 25.5 (4.9) 26.5 (4.9) 29.9 (5.4) 31.5 (6.3) 30.5 (5.2) 30.9 (5.2) 30.9 (5.9) 30.1 (5.1) 8.3**** H <MH, ML,L 5.3** H <MH, ML,L KFS 0.7(1.3) 1.0(1.4) 1.5(2.1) 1.8(2.3) 1.5(1.5) 1.4(1.7) 1.5(1.8) 1.2(1.7) 2.3 p=0.08 2.4 p=0.07 Psychiatric medicines c 1.2(1.5) 1.0(1.4) 1.5(1.7) 1.6(1.8) 2.0(1.8) 2.3(1.9) 3.7(2.4) 3.5(2.2) 14.5**** H,MH,ML <L 10.2**** H,MH <L Laterality d, f, h 35(88%) 29(85%) 39(90%) 26(87%) 35(92%) 25(93%) 41(93%) 31(91%) 2.6 p=0.84 6.3 p=0.38 Abbreviations: T1 =time 1, T2 =time 2, H =high, MH =medium-high, ML =medium-low, L =low, BMI =body mass index, KFS =Kaplan-Feinstein Scale. ANOVA = Analysis of variance. (Significant level: *p ≤0.05; **p ≤0.01; ***p ≤0.001; ****p ≤0.0001). a Expressed as mean(standard deviation) except when indicated. b female n(%). c number. d right-handers n (%). e ANOVA. f Chi-squared test. g Bonferroni test. h Mann-Whitney U test. J.V. S´ anchez-Ortí et al. Progress in Neuropsychopharmacology & Biological Psychiatry 127 (2023) 110817 5 be negatively associated with TNF- α and its receptors in BD (Chakrabarty et al., 2019; Hua et al., 2021b) and SMI (Hoseth et al., 2016) and, in middle-aged adults with T2DM, serum TNF- α level were negatively associated with working memory performance (Dyer et al., 2020). Although few studies exist on lipid markers, levels of apolipoproteins seem to be related to memory impairment. Moreover, Apo-A1 seems to be implicated in the early phases of cognitive decline. Indeed, a decrease in the blood levels of Apo-A1 has been related to the transition from mild cognitive impairment to dementia (Slot et al., 2017). In this line, the lower levels of Apo-A1 found in the group with the highest memory impairment could be interpreted as a correlate of a similar cognitive decline in individuals without diagnosed dementia in our study. Conversely, it could be speculated that levels of Apo-A1 within the normal range could protect from cognitive impairment in that population. Nevertheless, the analyses in our study do not allow to test that hypothesis. Likewise, TNF-alpha and Apo-A1 may be more sensitive (true positives) to phenotypic changes in memory, which is why they are higher in the group with worse cognitive performance; while the remaining significant markers (IL-6, CRP, and Apo-B) are more specific (true negatives) for such changes. These hypotheses remain to be tested in future studies. As mentioned, memory dysfunction in individuals with T2DM and SMIs has been shown to be associated with several peripheral biomarkers signalling abnormalities in systemic inflammation and lipid metabolism. However, previous research has mostly assessed this relationship for each disorder separately, by using several memory variables, and/or by means of correlational analysis, instead of stratifying performance on a global memory score, as made in the present study. In a combined transdiagnostic sample, individuals at one extreme of overall memory performance, e.g., highest impairment, had changes in two biomarkers only. Given the absence of similar studies, the present findings should be considered preliminary and await confirmation. Regarding the results of the discriminant analysis, we found that a biomarker-based model was able to distinguish individuals with different degrees of memory performance. The model included all three pro-inflammatory biomarkers (TNF-a, IL-6 and CRP) and the two biomarkers of lipid metabolism (Apo-A1 and Apo-B). These results concur with previous evidence supporting these relationships in the disorders analyzed. There is meta-analytic evidence that plasma levels of proinflammatory biomarkers are negatively associated with cognitive impairment, and specifically memory decline, in individuals with SMI (Misiak et al., 2018; Bora, 2019; Morrens et al., 2022; Patlola et al., 2023). Indeed, higher CRP levels were significantly, although weakly associated with verbal, visual and working memory impairment across MDD, BD and SZ (Morrens et al., 2022). Similarly, IL-6 correlated with worse verbal memory, whereas TNF- α showed a trend for working memory (Morrens et al., 2022). In individuals with SZ, proinflammatory biomarkers (higher IL-6, IL-1β, TNF- α and CRP plasma levels) are significantly related with worse performance in five cognitive domains – attention-processing speed, executive function, working memory, verbal and visual learning and memory (Patlola et al., 2023). In individuals with T2DM, meta-analytic evidence also supports this relationship (Anita et al., 2022). For instance, elevated plasma IL-6 and TNF- α were related with worse cognition, including memory decline (Marioni et al., 2010). Far less studies have been conducted on the relationship between apolipoproteins and cognition in SMI. The present results suggest that apolipoproteins are also involved in memory decline and converge with those of recent studies (Hui et al., 2017; Zhang et al., 2022; Rao et al., 2021). Serum Apo-B levels independently predicted memory performance in MDD (Hui et al., 2017), whereas Apo-B and CRP levels were negatively associated with global cognition in a mixed sample of MDD and BD (Zhang et al., 2022). Moreover, both the Apo-A1 and Apo-B levels predicted memory decline in a large study of chronic SZ (Rao et al., 2021). Interestingly, biomarkers of systemic inflammation have been associated with decreased levels of Apo-A1 across SZ, BD, and MDD (Morris et al., 2021). We are not aware of similar studies conducted in T2DM. The overlapping etiopathological pathways subserving cardiometabolic and psychiatric disorders has fueled the search for biomarkers related to neurocognitive decline. Previous studies have examined whether a panel of peripheral biomarkers can predict cognitive dysfunction in SMI. For instance, Apo-B and CRP levels were negatively associated with global cognition in a mixed sample of MDD and BD (Zhang et al., 2022). Concise panels of biomarkers have been associated with neurocognitive outcomes also in non-psychiatric Table 2 Exploratory and confirmatory factorial analysis. FA a Time Factor Value KMO χ 2 (p) b Test Saturation levels Total percent of variance explained Cronbach’s Alpha ( α ) E 1 1 0.84 1478.8**** Tav-IRF 0.76 54.11 0.84 Tav-TIR 0.91 Tav-IRI 0.59 Tav-ST 0.80 Tav-LT 0.80 ROCFT-fRey2 0.79 ROCFT-fRey20 0.77 WAIS-DSF 0.52 WAIS-DSB 0.60 TMT-B 0.70 C 2 1 0.85 1237.4**** Tav-IRF 0.77 57.58 0.85 Tav-TIR 0.92 Tav-IRI 0.68 Tav-ST 0.84 Tav-LT 0.85 ROCFT-fRey2 0.79 ROCFT-fRey20 0.80 WAIS-DSF 0.54 WAIS-DSB 0.58 TMT-B 0.73 Abbreviations: FA =Factorial analysis, E =Exploratory, C =Confirmatory, KMO =Kaiser-Meyer-Olkin sample adequacy measure, TAVEC =Complutense Verbal Learning Test, Tav-IRF =immediate recall of the first learning trial, Tav-TIR =total immediate recall, Tav-IRI =immediate recall of the interference list, Tav-ST = short-term free recall, Tav-LT =long-term free recall variables, ROCFT =Rey-Osterrieth Complex Figure Test, fRey2 =figure two minutes after the copy, fRey20 =20 min after the copy, WAIS-III =Wechsler Adult Intelligence Scale III edition, DSF =digit span forward, DSB =digit span backwards, TMT =Trial Making Test, B =Part B. KMO: 1–0.9 excellent; 0.8–0.9 good; 0.7–0.8 moderate; 0.6–0.7 poor; 0.5–0.6 low; <0.5 very low. NS =Not Significant. (NS =p >0.05; *p ≤0.05; **p ≤0.01; ***p ≤0.001; ****p ≤0.0001). Saturation levels >0.40. Cronbach’s Alpha ( α ) >0.70. a Oblique rotation: Oblimin with Kaiser, Extraction method: Main axis. b Chi-squared test (Bartlett sphericity test). J.V. S´ anchez-Ortí et al. Progress in Neuropsychopharmacology & Biological Psychiatry 127 (2023) 110817 6 populations, such as individuals living with HIV (Marcotte et al., 2013) and those with ischemic stroke (Peng et al., 2021). However, fewer studies have assessed a larger set of biomarkers, such that used in our study. Moreover, the biomarker-based model found in this study could be framed into the “allostatic load” model. Allostatic load (AL) represents the cumulative ‘wear and tear’ on body systems that results from chronic stress and/or inefficient management of the systems that promote adaptation (McEwen and Stellar, 1993). Indeed, the primary mediators of AL include biomarkers such as pro- and anti-inflammatory cytokines, as well as stress hormones and their antagonists (McEwen, 2003), and make part of the set of biomarkers composing the AL index (McCrory et al., 2023). Interestingly, increased AL has been linked to cognitive decline among older adults (Juster et al., 2010). Moreover, an increase in AL has been inversely related to cognitive flexibility among overweight adults, and to inhibitory control and working memory among normal-weight adults (Ottino-Gonz´ alez et al., 2019). In individuals with psychosis, a higher AL index was associated with worse scores on overall cognitive performance (Piotrowski et al., 2019). However, oxidative stress biomarkers were not significantly associated with memory impairment in the present study. These results converge with those of a recent review, which show not significant relationships between oxidative damage and cognitive performance (Nantachai et al., 2022). Nevertheless, oxidative damage has been associated with neurocognitive impairment in individuals with SZ (Yang et al., 2022) and diabetes (Li et al., 2023). The present findings support that a transdiagnostic biomarker-based model is related to cognitive impairment, with potential implications for clinical practice and research. For example, a biomarker-based model indicative of the development of altered lipid metabolism has been recently validated, which in turn can be useful to prevent the neurocognitive decline associated with cardiovascular complications in individuals with T2DM and SZ (Veitch et al., 2022). Transdiagnostic Table 3 Peripheral serum markers at T1 and T2. H MH ML L Statistical analyses Variables a T1 (n = 40) T2 (n =34) T1 (n =43) T2 (n =30) T1 (n =38) T2 (n =27) T1 (n = 44) T2 (n =34) T1 (p) b Post hoc test c η 2 p d T2 (p) b Post hoc test c η 2 p d Inflammatory markers IL-6 2.3 (1.3) 2.8(1.4) 2.8(1.5) 3.0(1.7) 3.5(3.7) 3.1(2.6) 2.7 (2.1) 3.0(1.8) 2.4 p=0.06 0.2 p=0.86 IL-10 29.7 (27.4) 15.6 (15.7) 28.6 (26.4) 13.2 (8.2) 27.0 (32.5) 14.0 (17.9) 35.0 (34.8) 16.2 (23.9) 0.4 p=0.70 0.5 p=0.67 TNF- α 8.6 (2.0) 9.2(2.2) 8.6(2.5) 10.9 (2.7) 10.0 (2.8) 11.1 (2.8) 10.7 (3.8) 12.9 (6.7) 3.9** H,MH <L 0.07 2.6* H <L 0.06 CRP 2.3 (2.5) 2.5(2.4) 4.6(6.6) 3.7(4.6) 3.5(3.1) 3.7(3.9) 4.2 (4.2) 3.8(5.8) 1.8 p=0.13 .05 p=0.98 Oxidative stress markers GSH 208.0 (95.9) 191.1 (89.6) 202.2 (116.3) 190.4 (92.4) 206.0 (100.4) 179.0 (84.7) 193.4 (94.7) 188.2 (105.6) 0.3 p=0.79 0.5 p=0.68 ROS 157.6 (86.7) 306.1 (133.2) 137.5 (97.4) 319.1 (181.9) 141.6 (82.5) 270.4 (186.9) 122.2 (41.3) 323.9 (204.4) 2.2 p=0.09 0.8 p=0.46 mROS 177.0 (66.6) 203.9 (96.5) 168.6 (71.9) 211.1 (95.6) 171.3 (70.8) 215.7 (96.3) 163.6 (60.8) 205.4 (129.8) .04 p=0.98 0.1 p=0.95 SOD 108.9 (47.3) 97.5 (37.1) 105.6 (50.1) 101.9 (34.5) 106.0 (50.0) 102.7 (25.2) 101.1 (19.4) 97.7 (18.0) .06 p=0.97 0.6 p=0.60 ΔΨm 48.7 (30.3) 42.2 (28.5) 41.4 (21.2) 46.8 (39.9) 46.3 (33.7) 42.2 (32.3) 49.1 (55.3) 40.1 (30.5) 0.2 p=0.83 0.5 p=0.67 Lipid metabolism markers Apo-A1 161.0 (27.3) 170.6 (32.5) 155.6 (29.8) 157.4 (31.1) 145.7 (17.2) 147.6 (22.2) 139.1 (24.6) 129.6 (27.3) 4.0** L < MH,H 0.07 3.9** L < MH,H 0.09 Apo-B 92.7 (20.2) 98.0 (22.7) 93.7 (25.3) 91.5 (25.5) 98.7 (26.2) 102.5 (24.0) 96.8 (24.7) 101.5 (21.0) 0.3 p=0.76 1.0 p=0.39 Abbreviations: T1 =Time 1, T2 =Time 2, H =high, MH =medium-high, ML =medium-low, L =low, IL-6 =Interleukin-6, IL-10 =Interleukin-10, TNF- α =Tumor Necrosis Factor alpha, CRP =C-reactive protein, GSH =Glutathione, ROS =Reactive Oxygen Species, mROS =Mitochondrial Reactive Oxygen Species, SOD = Superoxide Dismutase, ΔΨm =Mitochondrial membrane potential, Apo-A1 =Apolipoprotein A1, Apo-B =Apolipoprotein B. (Significant level: *p ≤0.05; **p ≤0.01; ***p ≤0.001; ****p ≤0.0001). Effect size ( η 2 p: small ≈0.02; moderate ≈0.15; large ≈0.35). a Expressed as mean(standard deviation). b ANCOVA. c Bonferroni test. d Partial Eta-Squared ( η 2 p). Fig. 1. Discriminatory capacity of the biomarkers-based model to differentiate between groups. J.V. S´ anchez-Ortí et al. Progress in Neuropsychopharmacology & Biological Psychiatry 127 (2023) 110817 7 models of cognition in high comorbidity disorders represent a shift towards a more dimensional and mechanism-based understanding of psychopathology. Growing evidence supports that variability of cardiometabolic and immune-inflammatory activity play a key role in the neurocognitive decline and that stabilization of these parameters may serve as a therapeutic target for improving neurocognitive performance, especially in the comorbidities population (Arnoriaga-Rodríguez and Fern´ andez-Real, 2019; Zhou et al., 2021). Furthermore, these biomarkers could be useful for early detecting neurocognitive impairment across a range of disorders, thereby preventing clinical progression (Liu et al., 2021; Menculini et al., 2021; Kupka et al., 2021). The RDoC approach has the potential to inform the development of more effective treatments that could be applicable to broader range of disorders. For example, inflammatory biomarkers, specifically elevated CRP level, can be relevant for diagnosis and monitoring for individuals with psychosis (Ullah et al., 2021). Furthermore, given the close relationship between the effects of psychotropic drugs and inflammation, it could be useful in predicting the adherence of individuals to future treatments. This targeted approach can contribute to expedite the efficiency and improve the specificity of clinical trials and psychopharmacological treatment (Song et al., 2020; Zhang et al., 2022). Thus, RDoC perspective generates an opportunity to investigate common and differential patterns of neurocognitive performance across different disorders that could improve treatment in those individuals where traditional therapies have not worked well due to the inflammation. Several limitations of this study should be considered. First, the inflammatory processes among participants could fluctuate depending on the type and clinical status of the disorder and pharmacological treatments (Tatay-Manteiga et al., 2017). Second, several relevant variables were not collected at baseline, such as current smoker status. Third, after a year of follow-up, high sample attrition was observed. Fourth, the relatively small sample size and single follow-up assessment may reduce the generalizability of the present results. However, the multicenter design of the study may increase the external validity of the results. In conclusion, this study emphasizes the potential relationship of immune-inflammatory and lipid metabolism activity in the memory processes across several conditions. Specifically, the biomarker-based model presented in this study is able to discern the memory impairment in individuals with T2DM and psychiatric disorders. If confirmed by future studies, this model could facilitate a more accurate and early detection and treatment of memory deficits. Moreover, our findings further support that keeping healthy immunological system and metabolism may be essential for information processing and memory consolidation. Therefore, whether these assumptions eventually fulfill the aim of transcending the limitations of current psychiatric nosologies, will enable us to move forward in our understanding of physiological and molecular bases from transdiagnostic perspective. Funding This work was supported by: Carlos III Health Institute (ISCIII) grants [grant number PI19/0838]. European Regional Development Fund (ERDF ‘‘A way to build Europe’’) and Ministry of Education of the Valencian Regional Government [grant number PROMETEO/2019/ 027]. FISABIO [grant number UGP-14-184 Project]. Ministry of Science and Innovation [grant number PID2021-129099OB-I00]. Declaration of Competing Interest None. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.pnpbp.2023.110817. References Abramovitch, A., Short, T., Schweiger, A., 2021. The C factor: cognitive dysfunction as a transdiagnostic dimension in psychopathology. Clin. Psychol. Rev. 86, 102–127. Ali˜ no-Dies, M., S´ anchez-Ortí, J.V., Correa-Ghisays, P., Balanz´ a-Martínez, V., Vila- Franc´ es, J., Selva-Vera, G., et al., 2020. Grip strength, neurocognition, and social functioning in people with Type-2 diabetes mellitus, major depressive disorder, bipolar disorder, and schizophrenia. Front. Psychol. 11, 525–231. American Diabetes Association, 2015. 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