Biomarkers as proxies for cognitive reserve: The role of high density lipoprotein cholesterol in first episode of psychosis

Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 Available online at www.sciencedirect.com Spanish Journal of Psychiatry and Mental Health journal homepage: http://http://www.elsevier.es/sjpmh Original article Biomarkers as proxies for cognitive reserve: The role of high density lipoprotein cholesterol in first episode of psychosis Biomarcadores como indicadores de la reserva cognitiva: rol del colesterol de lipoproteínas de alta densidad en el primer episodio de psicosis Rebeca Magdaleno Herreroa,b, Nancy Murillo-Garcíaa,b, Ángel Yorca-Ruiza,b, Karl Neergaarda, Benedicto Crespo-Facorroc,d,e, Rosa Ayesa-Arriolaa,e,∗, PAFIP Group aMarqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain bDoctoral School University of Cantabria (EDUC), Santander, Spain cHospital Universitario Virgen del Rocío, Department of Psychiatry, Universidad de Sevilla, Sevilla, Spain dInstituto de Investigación Sanitaria de Sevilla, IBiS, Spain eCentro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain article info Article history: Received 18 October 2022 Accepted 6 March 2023 Available online 02 June 2023 Keywords: Cognitive reserve First episodes of psychosis High-density lipoproteins Years of education Systolic blood pressure abstract Introduction:Theproxiesusedtocomposecognitivereserve (CR) for patientsofafirst episode of psychosis (FEP) have varied in the literature. The development of FEP is linked to peripheral pathways of the central nervous system, yet despite this knowledge, no research has considered the introduction of biomarkers as proxies for CR. Meanwhile, schizophrenia has been linked to the metabolic system, indicating that alterations in the levels of biological parameters, in particular high-density lipoproteins (HDL), cause worse global functioning and cognitive impairment. For these reasons, the present study aimed to create a quantifiable and objective CR index that adjusts for the multifactorial nature of FEP. Materialsand methods: We included668FEPpatients and 217healthycontrols.Participants were assessed for sociodemographic information, years of education, employment status, premorbid IQ and biological parameters: waist circumference, hypertension, and levels of HDL, triglycerides, and glucose. Results: The findings suggest that the years of education proxy showed correlational and higher relationship with HDL levels for both FEP patients (r=0.23, b= 0.185) and controls (r= 0.31, b=0.342). We found that the CR index composed of years of education and HDL levels showed a higher explanatory power for the phenomenon than the classical CR index composed of years of education, employment status and premorbid IQ. Conclusions: This article proposes an objective and quantifiable method to measure CR that is more the multifactorial nature of FEP. © 2023 SEP y SEPB. Published by Elsevier España, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Introduction Cognitive reserve (CR) is defined as a buffer against neuropathologies made possible through individual differences in neural networks that maintain efficient cognitive performance1 and thus actively compensate for the effects of a given disease.2 Previous research has shown that CR is a predictor of functionality and clinical and cognitive symptomatology.3–6 Thus, the higher ∗Corresponding author. E-mail address: [email protected] (R. Ayesa-Arriola). the CR, the better the patient’s performance in these areas, with CR functioning as a protective factor in patients with severe mental illness.7,8 CR is commonly estimated through the use of proxies and indices. One of the most common compositions of CR is composed of the proxies: years of education, premorbid IQ and employment status.6,9 Premorbid IQ proxy is related to patients’ functional outcomes10 and cognitive performance both at baseline and after disease onset,11 as well as to the levels of certain biomarkers12 Meanwhile, years of education and employment statusarerelatedtointellectualandenvironmentalfactorsatdifferent stagesoflife:yearsofeducationinearlylife,andemploymentstatus in middle and late life.13 https://doi.org/10.1016/j.rpsm.2023.03.002 1888-9891/© 2023 SEP y SEPB. Published by Elsevier España, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Please cite this article as: R. Magdaleno Herrero, N. Murillo-García, Á. Yorca-Ruiz et al., Biomarkers as proxies for cognitive reserve: The role of high density lipoprotein cholesterol in first episode of psychosis, Spanish Journal of Psychiatry and Mental Health, https://doi.org/10.1016/j.rpsm.2023.03.002 R.MagdalenoHerrero,N.Murillo-García,Á.Yorca-Ruizetal. Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 Research focused on CR in Alzheimer’s disease (AD) clarifies the importance of the proxy years of education, such that patients presenting with a higher level of education tended to have a lower incidence of developing the disease.14 Two explanations would justify this phenomenon. It is possible that highly educated patients have higher cognitive performance that would in turn require advanced pathology to be observed at clinical levels of symptomatology. It is possible as well that education stimulates neuroplasticity, providing resistance to adverse effects on brain function.15 Furthermore, previous heritability studies found that the genetic architecture of dementia was related to education,16,17 suggesting a causal effect on AD.18 However, neurological and psychiatric disorders are complex, polygenic and multifactorial. For this reason, endogenous and exogenous factors should be taken into account for the composition of CR. A recent line of research in the area of AD has recently developed to address this complexity. For instance, studies have integrated extra proxies into the measure of CR, such as the tau protein,13 the A␤burden13 and the P300 latency.19 A previous study has described the influence of peripheral pathways in schizophrenia.20 This influence causes a link between schizophrenia and the metabolism. One reason for this link is that the probability of neuronal recruitment in the brain is related to the bioenergetic efficiency of neurons and astrocytes. There is thus a link between somatic metabolism and neurological deterioration, a relationship that is affected by external factors such as exercise anddiet.21 Initialresearchreportedthatbiomarkersofhigh-density lipoprotein (HDL), triglycerides, and fasting plasma glucose (FPG) were predictive of global cognitive impairment in schizophrenia.22 Subsequently, it was clarified that cognition was predicted by total cholesterol and glucose together with the proxy years of education.23 Recently, a study has been published reporting that the prediction model for social functioning in schizophrenia was formed by HDL, triglycerides and FPG, while the biomarker systolic blood pressure (SBP) needed to be added to the model to predict cognitiveimpairment.24 Todate,manystudieshavehighlightedthe importance of HDL levels in schizophrenia, indicating a relationship between low HDL levels and poorer global functioning, global cognition,25–27 poor social relationships,28 as well as increased suicidal behaviour.29 On the other hand, although it has been reported that HDL levels tends to remain stable during the first three years of the disease,30 any changes in these lipids could produce changes in brain structure and inhibition of synaptogenesis, and consequently neurodegenerative changes.31 This is because cholesterol plays an important role in the brain due to its involvement in myelination, synaptic growth and regeneration,31 and it has been shown that high levels of HDL may act as a protective factor against hippocampal atrophy.32 Tothe best ofour knowledge, nopreviousstudy hasassessedthe relationship between biological parameters and CR proxies in firstepisodepsychosis(FEP).Thislackofknowledgejustifies thepresent study, which focused on the search for neurobiological proxies of CR. Based on the scientific evidence, we hypothesised that HDL levels are related to CR proxies and specifically, that HDL levels could be an extra proxy for CR in patients with FEP. The aim of this study was to construct an index of CR that is objective, quantifiable and multifactorial. Materials and methods Participants The sample of the present study were individuals with a FEP of non-affective psychosis treated in a longitudinal and epidemiological programme called Programa de Atención a Fases Iniciales de Psicosis (PAFIP), carried out at the Marqués de Valdecilla University Hospital (Santander, Spain). Further details of the description of PAFIP have been described in previous papers.33,34 The patient group consisted of 668 participants with a first episode of non-affective psychosis recruited from February 2001 to January 2017. Participation in PAFIP was voluntary, and they were free to withdraw from the programme at any time. Participants were informed about the study and signed the informed consent form, which complied with international standards for research ethics and was approved by the local review board institution. The inclusion criteria for all participants were as follows: (1) were between the ages of 15 and 50 at the time of inclusion in the PAFIP programme; (2) maintained habitual residence in Cantabria, the area where the programme is carried out; (3) presented a first episode of non-affective psychosis, defined as those subjects with non-affective psychotic symptomatology, regardless of the time of evolution of their psychotic symptoms, who contact for the first time a specialised mental health team; (4) had no prior treatment with antipsychotic medication or, if previously treated,a totallifetime ofantipsychotic treatmentof <6weeks. That is, only individuals are included drug-naïve; and (5) followed the criteria set out in DSM-IV for schizophrenia spectrum disorders: schizophrenia, schizophreniform disorder, schizoaffective disorder, brief psychotic disorder, and other psychotic disorders not otherwise specified, excluding these types of psychotic disorder.35 Participants were excluded for any of the following reasons: (1) metthecriteriafordrugdependenceofDSM-IV; (2) met the criteria for mental retardation (IQ below 70) of DSM-IV; or (3) had a history of neurological disease or head injury. The Structured Clinical Interview for DSM-IV (SCID-I)36 was used to confirm the diagnoses, realised by an experienced psychiatrist within six months from the baseline visit. The control group consisted of 217 healthy volunteers recruited from the community through advertisements. Of the control group the inclusion criteria were as follows: (1) over 15 years of age, (2) good domain of the Spanish language, and (3) ability to give informed consent in writing. Exclusion criteria included an absence of history of psychiatric diagnoses related to psychotic illness spectrum, absence of organic brain pathology, and an absence of intellectual disability or substance use disorders according to DSM- V criteria. Sociodemographic variables To collect the sociodemographic information, we interviewed the patients and their relatives. The measures collected were sex, age, age at psychosis onset (defined as the age when the emergence of the first continuous psychotic symptom occurred), area of residence(“urban” vs. “rural”), relationship status(“single”vs. “other”), socioeconomic status (SES) derived from their parent’s occupation (“low qualification worker” vs. “other”), first-degree family history of psychosis, cohabitation with parents or family (“yes” or “no”) and the mean chlorpromazine equivalent dose. Proxies of cognitive reserve Proxies for CR vary throughout the literature. In this research, we used the proxies premorbid IQ, education and occupation, as in previous research by González-Ortega et al.6and our own group.9 As in previous studies,2–6,9,35 premorbid IQ was evaluated using thevocabulary subtest ofthe WAIS-III37 as ameasureof crystallised intelligence. The proxy education was collected through the participants’ years of education classified according to the Spanish education system. Meanwhile, the proxy occupation, in this case a dichoto- 147 R.MagdalenoHerrero,N.Murillo-García,Á.Yorca-Ruizetal. Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 mous measure (unemployment: “yes” or “no”), was obtained through information on the employment situation of the participants. Assessment of biological disturbances The biological parameters selected for this research are those used for the pre-established criteria for the diagnosis of metabolic syndrome: abdominal obesity, cholesterol, triglycerides, hypertension, and hyperglycemia.38 We selected these biological parameters based on two recent meta-analyses that reported how the presence of these parameters was related to greater global cognitive impairment in patients with schizophrenia.39,40 The physical examination included: abdominal obesity (waist circumference: >102cm in men or >88cm in women) and hypertension (systolic blood pressure (SBP) ≥130mmHg). While laboratory investigations included: hyperglycemia (fasting plasma glucose (FPG) ≥110mg/dl), triglycerides levels (≥150mg/dl) and bloodHDL cholesterol(<40mg/dl inmen and<50mg/dl inwomen). Theseevaluations were carried out accordingtothe following NCEP criteria.41 Data analyses Statistical analyses were performed with the Statistical Package for Social Science version 23.0.42 The first step was to compare patients and HC in terms of sociodemographic data, CR proxies and biological parameters. Qualitative dichotomous variables were performed using chi-square and quantitative variables using univariate analysis of variance (ANOVA). We then performed Pearson correlations to explore whether there was a relationship between CR proxies and biological parameters. Subsequently, through multiple regression analysis, we searched for the causes of the relationships between CR and biological parameters at baseline. The dependent variables included in the model were waist circumference, levels of HDL, triglycerides, fasting plasma glucose, and systolic blood pressure. Backward stepwise model selection was used to determine the final model. This entailed the first model containing all predictors, while subsequent models were constructed through elimination of individual predictors based on the F-statistic criterion. Finally, in line with the methodology used in recent years in CR research, principal component analysis (PCA) and exploratory factor analysis (EFA) were carried out to create a “composite CR score”4for each participant, referred to as biological CR. In both, HC and patients, the reliability, validity and consistency of the creation of the new CR were explored.43–45 Bartlett’s test of specificity was performed to test the correlations between the proxies. Secondly, the Kaiser-Meyer-Olkin measure (KMO) was performed as a test of sampling adequacy and Cronbach’s alpha test was used to measure internal consistency. Finally, the final percentages of variance was used to verify that our biological CR explained more than 50% of the phenomenon and that, in addition, the components of biological CR contributed more than 0.30 to the composite CR score. All statistical tests, which were two-tailed with significance at p=0.05, were performed with independent Z-scores between patients and HC. Results Sociodemographic characteristics of the sample The total sample consisted of 668 FEP patients and 217 HC. There were no differences between patients and HC in terms of sex, area of residence, socioeconomic status of their parents and if they were living with their parents. However, as shown in Table 1, we found significant differences in relationship status (p<0.001), age (p=0.015), and cohabitation with family (couple and/or parents) (p=0.001), such that patients were more frequently single, older, and more likely to cohabit with their family. Differences between FEP patients and healthy controls Table 1 provides the comparisons between participants in terms of proxies of CR and biological parameters. Proxies of CR All CR proxies showed significant differences between patients and HC. The patients presented higher unemployment rates (p=<0.001), lower premorbid IQ (p= 0.005) and fewer years of education (p=0.007) than the HC. Biological parameters The results showed significant differences in all but hyperglycemia. On the one hand, we found that patients had more dysregulated levels of HDL (p=0.003) and SBP (p<0.001) than HC. On the other hand, HC had higher waist circumference (p=0.001) and triglycerides levels (p=0.022) than the patients. Relationships between CR proxies and HDL and SBP levels We explored the correlations between CR and the biological parameters in both groups, as you can see in Fig. 1. As shown in the figures, the patient group only showed significant relationships in HDL and SBP levels. The first figure shows the correlations between the classical CR (proxies:premorbidIQ,yearsofeducationandunemployment)and HDL and SBP levels, both separately and together. For patients, the highest correlation was between classical CR and the composite of HDL and SBP levels (r=0.23; p<0.001), while for HC the strongest correlation was between CR and HDL (r=0.31; p<0.001). In the next figure we observed that the strongest correlations in both groups were between the proxies premorbid IQ and years of education. In terms of the relationships involving HDL and SBP levels, the strongest correlation for patients was found with the composite of premorbid IQ and years of education (r=0.22; p<0.001), whereas the strongest correlation for the HC group was with years of education (r=0.33; p<0.001). The last figure shows that the strongest correlation for patients was between HDL and the composite of years of education and unemployment (r=0.21; p<0.001). Meanwhile for HC, the strongest correlation was between years of education with HDL (r=0.34; p<0.001). Predictive value of CR proxies on biological parameters Following the line of the previous results, we performed multiple regression analyses to explore the predictive capacity of CR proxies with HDL and SBP levels as dependent variables among patients (see Table 2) and HC (see Table 3). FEP patients As shown in Table 2, the three resulting models for SBP levels were significant (p<0.05), as were the two models for HDL levels (p<0.001). Of the five resulting models, we observed that there was greater predictive value for HDL levels, with Model 2 being the most significant (F=11.80; p<0.001). Model 2, formed by the proxies years of education and unemployment, had a higher predictive power, explaining 5.4% of HDL levels. Meanwhile, the two proxies were statistically significant (years of education: p<0.001; 148 R.MagdalenoHerrero,N.Murillo-García,Á.Yorca-Ruizetal. Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 Table 1 Comparisons between FEP patients and healthy controls on sociodemographic data, CR proxies and levels of biological parameters. FEP patients Healthy controls Value pPost hoc NMean (SD) NMean (SD) Sex (male): N (%) 668 374 (56.00) 217 132 (60.80) x2=1.57 0.210 Age 668 30.85 (9.96) 208 29.03 (7.54) F= 5.89 0.015 HC<FEP Urban area: N (%) 667 482 (72.30) 87 62 (71.30) x2=0.38 0.845 Single: N (%) 661 480 (72.60) 161 66 (41.00) x2=58.05 <0.001 HC<FEP SES of parents (low): N (%) 651 358 (55.00) 201 123 (61.20) x2=2.40 0.121 Living with parents: N (%) 659 344 (52.20) 86 43 (50.00) x2=0.15 0.701 Living with family: N (%) 659 482 (73.10) 86 77 (89.50) x2=10.91 0.001 HC<FEP Age at psychosis onset 653 29.70 (9.67) NA NA DUP 653 13.66 (32.93) NA NA Family psychiatric history: N (%) 663 165 (24.90) NA NA Chlorpromazine equivalent 665 206.10 (79.88) NA NA Cognitive reserve Education years 661 10.11 (3.28) 216 10.77 (2.72) F= 7.31 0.007 HC>FEP Premorbid IQ 449 95.98 (13.56) 201 99.03 (10.78) F= 7.92 0.005 HC>FEP Unemployed: N(%) 658 283 (43.00) 161 37 (23.00) x2=21.80 <0.001 HC<FEP Biological parameters Waist circumference 397 83.24 (11.63) 158 86.99 (12.79) F= 11.08 0.001 HC>FEP HDL 575 52.33 (14.84) 153 56.42 (15.57) F= 8.97 0.003 HC>FEP Triglycerides 580 83.89 (41.90) 153 93.10 (52.43) F= 5.23 0.022 HC>FEP SBP 526 118.46 (15.16) 152 107.05 (12.21) F=72.57 <0.001 HC<FEP FPG 656 86.04 (14.89) 153 84.45 (8.97) F= 1.61 0.205 Abbreviations: NA: not applicable; HC: healthy controls; FEP: FEP patients; DUP: duration of untreated psychosis; IQ: intelligence quotients; HDL: high density lipoprotein; SBP: systolic blood pressure; FPG: fasting plasma glucose. Table 2 Linear regression models pertaining to FEP patients with SBP and HDL levels as dependent variables. b(95% CI) t(df) ˇpr R 2adjusted Fp SBP (N=350) Model 1 0.169 0.020 3.39 0.018 Years of education −0.13 (−0.24–−0.01) −2.07 (0.06) −0.124 0.039 Premorbid IQ −0.08 (−0.20 – 0.04) −1.26 (0.06) −0.075 0.210 Unemployed 0.04 (−0.07 – 0.153) 0.79 (0.06) 0.042 0.430 Model 2 0.164 0.021 4.78 0.009 Years of education −0.12 (−0.24 – −0.00) −2.01 (0.06) −0.119 0.046 Premorbid IQ −0.07 (−0.19 – 0.05) −1.20 (0.06) −0.071 0.232 Model 3 0.151 0.020 8.13 0.005 Years of education −0.15 (−0.26 – −0.05) −2.85 (0.05) −0.151 0.005 HDL (N=378) Model 1 0.243 0.052 7.85 <0.001 Years of education 0.20 (0.08–0.30) 3.33 (0.06) 0.187 0.001 Unemployed 0.14 (0.03–0.24) 2.52 (0.05) 0.129 0.012 Premorbid IQ −0.01 (−0.12 – 0.11) −0.08 (0.06) −0.005 0.935 Model 2 0.243 0.054 11.80 <0.001 Years of education 0.19 (0.09–0.30) 3.64 (0.05) 0.185 <0.001 Unemployed 0.14 (0.03–0.24) 2.52 (0.05) 0.128 0.012 Abbreviations: IQ: intelligence quotients; HDL: high density lipoprotein; SBP: systolic blood pressure. unemployment: p=0.012), with years of education having a higher relationship (ˇ=0.185) than unemployment (ˇ=0.128). Healthy controls Asyoucan seeinTable3,three modelsweresignificant(p<0.05) with SBP as the dependent variable, while all three HDL models were significant (p<0.001). As with the patient group, of the five significant models in the HCs, Model 3 on the dependent variable HDL was the most prominent. Model 3 showed the highest predictive power with 11.6% of explanation of HDL levels. We see that this model was composed of years of education proxy with a relationship of ˇ=0.342. The results on waist circumference and levels of triglycerides and hyperglycemia in the HC group can be found in supplementary Table S1. Biological cognitive reserve The next step in the analyses was to carry out PCA and EFA with the most significant variables obtained in the regression models. After several attempts combining all possible components,exploratoryanalyses showedthat themost optimalvariables for the composite CR score were years of education and HDL levels. The two CR models in both groups obtained adequate levels of KMO (KMO>0.05) and significance in Bartlett’s test of specificity (p<0.001) (see online supplementary Table S2). The results reported that neither the classical CR nor our biological CR reached the established Cronbach’s alpha levels for either patients or HC (p<0.70) (see online supplementary Table S2). Asshown in Table 4,resultsof total variance explained indicated that biological CR would have a greater explanatory power for 149 R.MagdalenoHerrero,N.Murillo-García,Á.Yorca-Ruizetal. Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 Figure 1. Correlations between classical CR or its proxies and HDL and SBP levels separately or together. Post hoc Bonferroni group differences with significance: *p<0.05; **p<0.005; ***p<0.001. the phenomenon than classical CR for both groups of participants. Among the group of patients, classical CR explained 51.79% of the variance, while biological CR explained 59.06%. This percentage increase also occurred in the control group, where classical CR explained 51.04%, while biological CR increased to 67.12%. The results show that for both groups of participants the unemployment proxy in the classical CR had a degree of extraction lower than 0.30. These low values mean that this proxy did not provide sufficient information to be part of the classical CR. In contrast, the years of education proxy provided the largest information to the classical CR (>0.70), while providing a balanced contribution to the biological CR with its counterpart HDL, for both patients and HC (see online supplementary Table S3). Discussion The main objective of the present study was to explore an objective and quantifiable CR, taking into account endogenous and exogenous factors. Following this line of work, it is interesting to note that, for both groups of participants, the results found that the proposed biological CR has a greater explanatory value of the phenomenon than the classical CR commonly found in the literature. Therefore, we can affirm that introducing HDL levels, as a biological proxy, together with the years of education proxy, is a way of obtaining a CR adjusted to the multifactorial nature of psychosis. Furthermore, it was found that in the classical CR the proxy unemployment did not provide the necessary information load to be taken into account in the total index. Our research has found that years of education is the variable that best predicts HDL levels. These results are supported by previous studies suggesting that years of education in combination with HDL are necessary to form predictive models of cognitive impairment in schizophrenia.22–24 The fact that years of education is an indispensable proxy may be due to the fact that it often has an early onset, which makes it more important to take into account lived experiences and cognitive enrichment at an early age.46 Another reason why the need for this proxy might be justified is that education stimulates neuroplasticity before the development of the disease, which would promote cognitive protection against the adverse effects of psychosis.15 Furthermore, the need to include HDL levels is supported by previous arguments reporting its relationship with cognitive 150 R.MagdalenoHerrero,N.Murillo-García,Á.Yorca-Ruizetal. Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 Table 3 Linear regression models pertaining to HC with SBP and HDL levels as the dependent variables. b(95% CI) t(df) ˇpr R 2adjusted Fp SBP (N=148) Model 1 0.213 0.025 2.27 0.083 Years of education −0.22 (−0.40–−0.04) −2.45 (0.09) −0.222 0.015 Premorbid IQ 0.13 (−0.06 – 0.32) 1.34 (0.10) 0.118 0.183 Unemployed −0.02 (−0.19 – 0.16) −0.17 (0.09) −0.015 0.862 Model 2 0.212 0.032 3.41 0.036 Years of education −0.23 (−0.40 – −0.05) −2.58 (0.09) −0.226 0.011 Premorbid IQ 0.13 (−0.06 – 0.32) 1.35 (0.10) 0.118 0.179 Model 3 0.182 0.026 4.98 0.027 Years of education −0.18 (−0.34 – −0.02) −2.23 (0.08) −0.182 0.027 HDL (N=149) Model 1 0.343 0.100 6.46 <0.001 Years of education 0.33 (0.15–0.50) 3.74 (0.09) 0.331 <0.001 Unemployed 0.04 (−0.13 – 0.20) 0.46 (0.08) 0.037 0.645 Premorbid IQ 0.00 (−0.18 – 0.19) 0.03 (0.09) 0.002 0.978 Model 2 0.343 0.106 9.76 <0.001 Years of education 0.33 (0.17–0.49) 4.13 (0.08) 0.332 <0.001 Unemployed 0.04 (−0.13 – 0.20) 0.46 (0.08) 0.037 0.644 Model 3 0.342 0.111 19.41 <0.001 Years of education 0.34 (0.19–0.49) 4.41 (0.08) 0.342 <0.001 Abbreviations: IQ: intelligence quotients; HDL: high density lipoprotein; SBP: systolic blood pressure. Table 4 Total variance explained by comparing classical and biological CR in patients and controls. Initial eigenvalues Extraction sums of squared loadings Total % of variance Cumulative % Total % of variance Cumulative % Patients Components of classical CR 1 1.56 51.79 51.79 1.56 51.79 51.79 2 0.91 30.22 82.02 3 0.54 17.98 100.00 Components of the biological CR 1 1.17 59.06 59.06 1.17 59.06 59.06 2 0.81 40.94 100.00 Controls Components of classical CR 1 1.45 51.04 51.04 1.45 51.04 51.04 2 0.86 30.15 81.19 3 0.53 18.81 100.00 Components of the biological CR 1 1.37 67.12 67.12 1.37 67.12 67.12 2 0.67 32.88 100.00 performance and patient functionality,25,28 independently of the effect of practice, lifestyle, improvements in clinical symptomatology or levels of other biological parameters.32 The introduction of HDL levels in CR implies taking into account biochemical processes that have not been considered until now, since, at the brain level, HDL is located in the hippocampus and is related to the performance of cognitive domains.32,47–49 If we know that an imbalance in HDL levels causes hippocampal atrophy32 with consequent cognitive impairment and increased neurodegenerative speed,31 it is conceivable that it also affects the alternative neural networks referred to in the definition of CR.1Ultimately, these problems could explain the patients’ inabilities to cope with the onset of their first episode of psychosis and subsequent cognitive declines. This factsupports the claimthat CR isa protective factorfor the development of severe mental illness, as suggested by previous research.7,8 The combination of years of education and HDL levels seems to have an important implication for CR. On the one hand, if a patient has low cognitive enrichment due to a low level of education, this influences the patient’s understanding, learning and reasoning about their health and achieving problem-solving skills to prevent alterations in their biological parameters and future chronic diseases.50 Moreover, if a patient has low HDL levels, it will start an inhibition of synaptogenesis and lead to neurodegenerative changes31 that in turn lead to worse cognitive functioning, making it difficult for the patient to increase their years of education. Therefore, as we can see, the relationship between these two proxies appears to be reciprocal. Another notable finding of our research is that the proxies premorbid IQ and employment status (unemployment) were discarded. One possible explanation is that years of education and HDL levels can be considered secondary. The results indicated a strong relationship between premorbid IQ and years of education, which leads us to believe that, if a patient has a low educational level, his or her cognitive enrichment will be poor and therefore will not reach a high premorbid IQ. Thus, we come to the possible conclusion that years of education is a predictor of the patient’s 151 R.MagdalenoHerrero,N.Murillo-García,Á.Yorca-Ruizetal. Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 crystallisedintelligence,makingthepremorbidIQproxyredundant inthe CRcompositescore. Onthe other hand,having low HDLlevels is related to poorer performance in the domains of verbal learning and memory.47–49 These domains represent the receiving, processing and storing of long-term knowledge, suggesting that low HDL affects the development of crystallised intelligence and, as a result, premorbid IQ. Secondly, if functionality is impaired, as predicted by low HDL levels, it will be more difficult for the patient to find a job, which helps to explain the relationship between HDL and employment. This paper cannot end without mentioning some limitations and strengths. One of the main limitations lies in not being able to explore the relationship with CR in other inflammatory or neuroimmune biomarkers such as interleukin receptor SHC signalling, hematopoietic cell lineage, interleukin-1/2 family signalling51 and activation of HERV-W.20 We also did not take into account the hormone levels of the participants and especially of the patients. Previous research has shown how antipsychotic medication influences hormone production and how hormones mediate HDL production.52 Another possible limitation lies in the collection of blood tests, as they were not performed on all participants. We also consider it a limitation that due to the lack of uniform information for occupation, it was used as a dichotomous variable (unemployed vs. active). As a final limitation, we can mention that we have not taken into account other factors that could potentially interfere with the results, such as habitual physical exercise, diet, substance use and type of antipsychotic. However, our study has several strengths consisting in the size and homogeneity of the samples, which allows us to consider our results for future CR research. To our knowledge, this is the first study to explore the relationships and predictions between CR proxies with biomarkers, as well as to explore their use as an additional proxy. The gap in the literature justified the present study’s focus on the search for neurobiological CR proxies, as patients with FEP tend to have unhealthy lifestyle habits that alter their metabolisms53,54 and thus their CR. Future researchisneeded to expand the use of biological CR basedonyears of education and HDL levels. In conclusion, this study proposes a new composition of CR with exogenous and endogenous factors, consisting of the proxies years of education and HDL levels. We showed that, in patients andHC,the strongest correlations were between years of education and HDL levels. In fact, years of education outperformed the other variables in terms of predicting HDL levels. A notable observation was that our proposed biological CR had a higher percentage of explanation of the phenomenon with only two proxies compared to the classical CR consisting of three proxies. In addition, its two proxies contributed information in a balanced way to the CR composite score. These findings allowed us to develop a quantifiable, objective and multifactorial CR. The possible impact of the biological CR is that is provides a more precise composition taking into account biomarkers through a question and a blood test, or, desirably, through non-invasive measures yet to be developed. These insights will allow for the selection and establishment of the most appropriate treatments for each FEP patient. Authors’ contributions All the authors have participated and have made substantial contributions to this paper. RMH: conception, design, statistical analysis, interpretations of data and drafting the article. NMG, AYR, BCF: interpretations of data and revising the article. KN: editing. RAA:conception, design,interpretations ofdataand revisingthe article. Funding The PAFIP project carried out on Marqués de Valdecilla Research Institute was supported by the Carlos III Health Institute (PI14/00639 and PI14/00918). In addition, this work was financed by a Miguel Servet from the Carlos III Health Institute contract (Dra. Rosa Ayesa-Arriola) (CP18/00003), and a predoctoral contract (Rebeca Magdaleno Herrero) (PREVAL22/03) from the Valdecilla Biomedical Research Institute and the University of Cantabria. No pharmaceutical company has financially supported the study. Conflict of interest The authors have no conflict of interest to declare. Acknowledgements The authors wish to thank all “Programa de Atención a Fases Iniciales de Psicosis” (PAFIP) research team, and especially to all patients and family members who participated in the study. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.rpsm.2023.03.002. References 1. Stern Y. Cognitive reserve. Neuropsychologia. 2009;47:2015–2028. 2. Amoretti S, Rosa AR, Mezquida G, Cabrera B, Ribeiro M, Molina M, et al. The impact of cognitive reserve, cognition and clinical symptoms on psychosocial functioning in first-episode psychoses. Psychol Med. 2020;52:526–537. 3. Amoretti S, Bernardo M, Bonnin CM, Bioque M, Cabrera B, Mezquida G, et al. The impact of cognitive reserve in the outcome of first-episode psychoses: 2-year follow-up study. Eur Neuropsychopharmacol. 2016;26:1638–1648. 4. Amoretti S, Cabrera B, Torrent C, Mezquida G, Lobo A, González-Pinto A, et al. Cognitive reserve as an outcome predictor: first-episode affective versus nonaffective psychosis. Acta Psychiatr Scand. 2018;138:441–455. 5. de la Serna E, Andrés-Perpi˜ ná S, Puig O, Baeza I, Bombin I, Bartrés-Faz D, et al. Cognitive reserve as a predictor of two year neuropsychological performance in early onset first-episode schizophrenia. Schizophr Res. 2013;143:125–131. 6. González-Ortega I, González-Pinto A, Alberich S, Echeburúa E, Bernardo M, Cabrera B, et al. Influence of social cognition as a mediator between cognitive reserve and psychosocial functioning in patients with first episode psychosis. Psychol Med. 2019;50:2702–2710. 7. Forcada I, Mur M, Mora E, Vieta E, Bartrés-Faz D, Portella MJ. The influence of cognitive reserve on psychosocial and neuropsychological functioning in bipolar disorder. Eur Neuropsychopharmacol. 2015;25:214–222. 8. Grande I, Sanchez-Moreno J, Sole B, Jimenez E, Torrent C, Bonnin CM, et al. High cognitive reserve in bipolar disorders as a moderator of neurocognitive impairment. J Affect Disord. 2017;208:621–627. 9. Magdaleno Herrero R, Ortiz-García de la Foz V, Murillo-García N, Vázquez- Bourgon J, Setién-Suero E, Crespo-Facorro B, et al. Sex differences in cognitive reserve among first episode of psychosis patients. Revista de Psiquiatría y Salud Mental. 2021. S1888989121001300. 10. Leeson VC, Barnes TRE, Hutton SB, Ron MA, Joyce EM. IQ as a predictor of functional outcome in schizophrenia: a longitudinal, four-year study of firstepisode psychosis. Schizophr Res. 2009;107:55–60. 11. Leeson VC, Sharma P, Harrison M, Ron MA, Barnes TRE, Joyce EM. IQ trajectory, cognitive reserve, and clinical outcome following a first episode of psychosis: a 3-year longitudinal study. Schizophr Bull. 2011;37:768–777. 12. Rentz DM, Mormino EC, Papp KV, Betensky RA, Sperling RA, Johnson KA. Cognitive resilience in clinical and preclinical Alzheimer’s disease: the association of amyloid and tau burden on cognitive performance. Brain Imaging Behav. 2017;11:383–390. 13. Yasuno F, Minami H, Hattori H. Interaction effect of Alzheimer’s disease pathology and education, occupation, and socioeconomic status as a proxy for cognitive reserve on cognitive performance: in vivo positron emission tomography study. Psychogeriatrics. 2020;20:585–593. 14. Dumurgier J, Paquet C, Benisty S, Kiffel C, Lidy C, Mouton-Liger F, et al. Inverse association between CSF A␤42 levels and years of education in mild form of Alzheimer’s disease: the cognitive reserve theory. Neurobiol Dis. 2010;40:456–459. 15. Vance DE, Roberson AJ, McGuinness TM, Fazeli PL. How neuroplasticity and cognitive reserve protect cognitive functioning. J Psychosoc Nurs Ment Health Serv. 2010;48:23–30. 152 R.MagdalenoHerrero,N.Murillo-García,Á.Yorca-Ruizetal. Spanish Journal of Psychiatry and Mental Health 17 (2024) 146–153 16. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–542. 17. Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science. 2013;340:1467–1471. 18. Larsson SC, Traylor M, Malik R, Dichgans M, Burgess S, Markus HS. Modifiable pathways in Alzheimer’s disease: Mendelian randomisation analysis. BMJ. 2017;359:j5375. 19. Khachatryan E, Wittevrongel B, Perovnik M, Tournoy J, Schoenmakers B, Van Hulle MM. Electrophysiological proxy of cognitive reserve index. Front Hum Neurosci. 2021;15:690856. 20. Leboyer M, Oliveira J, Tamouza R, Groc L. Is it time for immunopsychiatry in psychotic disorders? Psychopharmacology. 2016;233:1651–1660. 21. Stranahan AM, Mattson MP. Metabolic reserve as a determinant of cognitive aging. JAD. 2012;30:S5–S13. 22. Wysoki´ nski A, Dzienniak M, Kłoszewska I. Effect of metabolic abnormalities on cognitive performance and clinical symptoms in schizophrenia. Arch Psych Psych. 2013;15:13–25. 23. Nandeesha H, Keshri N, Rajappa M, Menon V. Association of hyperglycaemia and hyperlipidaemia with cognitive dysfunction in schizophrenia spectrum disorder. Arch Physiol Biochem. 2020:1–8. 24. Sánchez-Ortí JV, Balanzá-Martínez V, Correa-Ghisays P, Selva-Vera G, Vila- Francés J, Magdalena-Benedito R, et al. Specific metabolic syndrome components predict cognition and social functioning in people with type 2 diabetes mellitus and severe mental disorders. Acta Psychiatr Scand. 2022, acps.13433. 25. Adamowicz K, Kucharska-Mazur J. Dietary behaviors and metabolic syndrome in schizophrenia patients. JCM. 2020;9:537. 26. Grover S, Padmavati R, Sahoo RP, Gopal S, Nehra S, Ganesh RA, et al. Relationship of metabolic syndrome and neurocognitive deficits in patients with schizophrenia. Psychiatry Res. 2019;278:56–64. 27. Lindenmayer JP, Khan A, Kaushik S, Thanju A, Praveen R, Hoffman L, et al. Relationship between metabolic syndrome and cognition in patients with schizophrenia. Schizophr Res. 2012;142:171–176. 28. Storch Jakobsen A, Speyer H, Nørgaard HCB, Hjorthøj C, Krogh J, Mors O, et al. Associations between clinical and psychosocial factors and metabolic and cardiovascular risk factors in overweight patients with schizophrenia spectrum disorders – baseline and two-years findings from the CHANGE trial. Schizophr Res. 2018;199:96–102. 29. Ayesa-Arriola R, Canal Rivero M, Delgado-Alvarado M, Setién-Suero E, González-Gómez J, Labad J, et al. Low-density lipoprotein cholesterol and suicidal behaviour in a large sample of first-episode psychosis patients. World J Biol Psychiatry. 2018;19:S158–S161. 30. Vázquez-Bourgon J, Ibᘠnez Alario M, Mayoral-van Son J, Gómez Revuelta M, AyesaArriolaR,JuncalRuizM,etal.A3-year prospective study on the metabolic effect of aripiprazole, quetiapine and ziprasidone: a pragmatic clinical trial in first episode psychosis patients. Eur Neuropsychopharmacol. 2020;39:46–55. 31. OrthM,Bellosta S. Cholesterol:itsregulation and roleincentral nervous system disorders. Cholesterol. 2012;2012:e292598. 32. Gjerde PB, Simonsen CE, Lagerberg TV, Steen NE, Ueland T, Andreassen OA, et al. Improvementin verbal learning over the first year of antipsychotic treatment is associated with serum HDL levels in a cohort of first episode psychosis patients. Eur Arch Psychiatry Clin Neurosci. 2020;270:49–58. 33. Crespo-Facorro B, Pérez-Iglesias R, Ramirez-Bonilla M, Martínez-García O, Llorca J, Luis Vázquez-Barquero J. A practical clinical trial comparing haloperidol, risperidone, and olanzapine for the acute treatment of first-episode nonaffective psychosis. J Clin Psychiatry. 2006;67:1511–1521. 34. Pelayo-Terán JM, Pérez-Iglesias R, Ramírez-Bonilla M, González-Blanch C, Martínez-García O, Pardo-García G, et al. Epidemiological factors associated with treated incidence of first-episode non-affective psychosis in Cantabria: insights from the Clinical Programme on Early Phases of Psychosis. Early Interv Psychiatry. 2008;2:178–187. 35. Ayesa-Arriola R, Ortíz-García de la Foz V, Murillo-García N, Vázquez-Bourgon J, Juncal-Ruiz M, Gómez-Revuelta M, et al. Cognitive reserve as a moderator of outcomes in five clusters of first episode psychosis patients: a 10-year followup study of the PAFIP cohort. Psychol Med. 2021:1–15. 36. First M, Spitzer R, Gibbon M, Williams J. User’s guide for the structured clinical interview for DSM-IV axis I disorders SCID-I: clinical version. New York State Psychiatric Institute, Biometrics Department; 2001. 37. Wechsler D. Wechsler Adult Intelligence Scale-III. The Psychological Corporation; 1997. 38. Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365:1415–1428. 39. Hagi K, Nosaka T, Dickinson D, Lindenmayer JP, Lee J, Friedman J, et al. Association between cardiovascular risk factors and cognitive impairment in people with schizophrenia: a systematic review and meta-analysis. JAMA Psychiatry. 2021;78:510. 40. Zheng W, Jiang W-L, Zhang X, Cai D-B, Sun J-W, Yin F, et al. Use of the RBANS to evaluate cognition in patients with schizophrenia and metabolic syndrome: a meta-analysis of case–control studies. Psychiatr Q. 2021. 41. Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285:2486–2497. 42. IBM Corp. IBM SPSS Statistic for Windows, version 23.0. Armonk, NY: IBM Corp.; 2015. 43. Cortés S, Burgos S, Adaros H, Lucero B, Quirós-Alcalá L. Environmental health risk perception: adaptation of a population-based questionnaire from Latin America. IJERPH. 2021;18:8600. 44. Kagaigai A, Anaeli A, Mori AT, Grepperud S. Do household perceptions influence enrolment decisions into community-based health insurance schemes in Tanzania? BMC Health Serv Res. 2021;21:162. 45. Melkamu Asaye M, Gelaye KA, Matebe YH, Lindgren H, Erlandsson K. Valid and reliable neonatal near-miss assessment scale in Ethiopia: a psychometric validation. Global Health Action. 2022;15:2029334. 46. Ko H, Kim S, Kim K, Jung S-H, Shim I, Cha S, et al. Genome-wide association study of occupational attainment as a proxy for cognitive reserve. Brain. 2022;145:1436–1448. 47. Antoniades M, Schoeler T, Radua J, Valli I, Allen P, Kempton MJ, et al. Verbal learning and hippocampal dysfunction in schizophrenia: a meta-analysis. Neurosci Biobehav Rev. 2018;86:166–175. 48. Lancon C, Dassa D, Fernandez J, Richieri R, Padovani R, Boyer L. Are cardiovascular risk factors associated with verbal learning and memory impairment in patients with schizophrenia? A cross-sectional study. Cardiovasc Psychiatry Neurol. 2012;2012:204043. 49. Tamminga CA, Stan AD, Wagner AD. The hippocampal formation in schizophrenia. Am J Psychiatry. 2010;167:1178–1193. 50. Gottfredson L, Deary IJ. Intelligence predicts health and longevity, but why? Curr Dir Psychol Sci. 2004. 51. Fernandes BS, Dai Y, Jia P, Zhao Z. Charting the proteome landscape in major psychiatric disorders: from biomarkers to biological pathways towards drug discovery. Eur Neuropsychopharmacol. 2022;61:43–59. 52. Giménez S. Colesterol y mujer. Farmacia Profesional. 2005;19:54–59. 53. Arango C, Bobes J, Kirkpatrick B, Garcia-Garcia M, Rejas J. Psychopathology, coronary heart disease and metabolic syndrome in schizophrenia spectrum patientswithdeficitversus non-deficit schizophrenia: findingsfromtheCLAM- ORS study. Eur Neuropsychopharmacol. 2011;21:867–875. 54. Van Gaal LF. Long-term health considerations in schizophrenia: metabolic effects and the role of abdominal adiposity. Eur Neuropsychopharmacol. 2006;16:S142–S148. 153