Association of the inflammation-related proteome with dementia development at older age: results from a large, prospective, population-based cohort study

We conducted a case-cohort study based on the ESTHER study (Epidemiologische Studie zu Chancen der Verhütung, Früherkennung und optimierten Therapie chronischer Erkrankungen in der älteren Bevölkerung [German]). In this prospective cohort study implemented in Saarland, Germany, 9940 women and men aged 50 to 75 years at baseline were recruited during a general health checkup by their general practitioners between 2000 and 2002. Besides an age of 50–75 years, the inclusion criteria for the ESTHER study were physical and mental ability to participate in the study as well as knowledge of the German language. Participants were followed up concerning the incidence of major diseases and mortality 2, 5, 8, 11, 14, and 17 years after baseline. For details, see Löw et al. [17]. The sociodemographic baseline characteristics and common prevalent chronic diseases were similarly distributed in the respective age categories as in the German National Health Survey, a representative sample of the German population [17].

Information about a dementia diagnosis was collected during the 14- and 17-year follow-ups of the ESTHER study. The median follow-up time was 16.3 years (interquartile range: 13.5–17.0 years), and the maximum was 19.4 years due to the 2-year period of baseline recruitment. In brief, the collection of dementia diagnoses included sending standardized questionnaires to the study participants’ general practitioners (GPs). Participants who had dropped out during previous follow-ups due to ill health or had died were included in the dementia ascertainment through the GPs as well. If the GPs were aware of a dementia diagnosis for their patients, they were asked to provide a date of diagnosis and all available medical records documenting a dementia diagnosis. The latter included records from neurologists, psychiatrists, memory clinics, or other specialized providers. If the GP provided a mixed dementia diagnosis, available medical records were screened for an underlying Alzheimer’s disease or vascular dementia background and considered as Alzheimer’s disease, vascular dementia, or both. The Alzheimer’s disease diagnosis guideline used in Germany during the follow-up period of the ESTHER study was the one of the National Institute on Aging and the Alzheimer’s Association [18].

Inflammation-related, blood-based proteins were measured from serum samples collected during the health checkup at baseline (2000–2002). Blood samples were sent to the study centre and stored at −80°C until biomarker measurements took place in three waves in March 2018, December 2018, and September 2020 (referred to as time points t1, t2, and t3 in the following). At the time of the measurements, 10–25 μl of serum was extracted from different aliquots that had been thawed twice and sent with dry ice to the laboratories, which analysed the samples with the Olink Target 96 Inflammation panel, Olink Proteomics, Uppsala, Sweden. At t1 and t2, samples were analysed in the laboratory of Olink Proteomics, Uppsala Science Park, SE-75183 Uppsala, Sweden. At t3, the measurements were performed in the Research Unit Protein Science, German Research Center for Environmental Health, Helmholtz Center Munich, Heidemannstraße 1, 80939 München, Germany.

The Olink panels are based on a proximity extension assay technology (PEA) [19, 20]. Details on the reliability and stability of the technology are described elsewhere [21]. In brief, oligonucleotide-labelled antibody probe pairs are allowed to bind to their respective target proteins in the samples. Only if two antibodies are in close proximity, a polymerase chain reaction (PCR) reporter sequence is formed by DNA polymerization. This sequence is detected and quantified using high-throughput real-time quantitative PCR (qPCR) (Fluidigm® Biomark^TM HD system). The Olink Target 96 Inflammation panel allows the measurement of 92 biomarkers per sample. A list of all biomarkers of this panel is displayed in Supplemental Table 1.

At t1, t2, and t3, 22, 15, and 5 plates were used, respectively. To avoid batch effects, cases and controls were randomly distributed across plates and adjusted according to included interpolate controls. The average intra-assay coefficient of variance among all 92 measured biomarkers was 7%, 4%, and 3% at t1, t2, and t3, respectively. The average inter-assay coefficient of variance was 12%, 10%, and 10% at t1, t2, and t3, respectively. Furthermore, the quality of each serum sample was assessed by Olink technology [22]. All samples were measured successfully, and the number of quality control warnings was below 4% in all three timepoints. Of the 1435 randomly selected controls and 393 incident dementia cases, 46 serum samples of participants were excluded due to a quality control warning by Olink.

Protein levels are reported as Normalized Protein eXpression (NPX) values, a relative quantification unit logarithmically related to protein concentration. The number of samples with values below the lower limit of detection (LOD) varied strongly by biomarker and is shown in Supplemental Table 1. In total, 20 biomarkers with > 25% of the values below LOD were excluded from all analyses (grey-shaded biomarkers in Supplemental Table 1). Thereby, 72 out of the 92 biomarkers were considered evaluable markers. Biomarker values below the LOD were replaced by LOD/√2. The normalization of raw data was conducted with the R (R Core Team, 2020, version 3.6.3) package “OlinkAnalyze”, developed and maintained by the Olink Proteomics Data Science Team [23]. To normalize data from three different measurement points (t1, t2, and t3), reference sample normalization was used based on 17 and 16 bridging samples between each two measurement points (t1–t3 and t2–t3, pair-wise bridging). For details of the procedure, we refer to the white paper of OLINK Proteomics [22].

Data on sex, age, education, body mass index (BMI), physical activity, and life-time history of depression were collected during baseline assessment through a standardized self-administered questionnaire. The history of coronary heart disease (CHD) and diabetes mellitus were obtained from physician diagnoses. Furthermore, anti-diabetic drugs reported by the GP were used to complement diabetes mellitus diagnoses. Participants were considered to have cardiovascular disease (CVD) based upon CHD diagnoses from GPs or self-reported history of myocardial infarction, stroke, pulmonary embolism, or revascularization of coronary arteries. TaqMan single-nucleotide polymorphism (SNP) genotyping assays were used to determine apolipoprotein E (APOE) genotypes. More precisely, genotypes were analysed in an endpoint allelic discrimination using a PRISM 7000 Sequence detection system (Applied Biosystems) [24].

The selection of study participants from the ESTHER cohort for this case-cohort analysis is shown in Fig. 1. ESTHER participants were eligible for selection as cases or random controls. Participants were excluded if they withdrew consent to contact the GP (n=248), if the GP withdrew consent to be contacted (n=304), or if the GP could not be reached anymore (n=1035). Thereupon, dementia diagnosis information was requested for 8353 participants and received for 6940 participants (response rate: 83.1%). Moreover, participants were excluded if dementia incidence status could not be ascertained by GP questionnaires (either diagnosis was not available (n=412) or not confirmed (n=171)) or if blood samples were not available (n=73). Thus, information from 6284 participants was available for analyses. Except for their age, participants with available dementia information and blood samples and those who could not be used for analyses had comparable baseline characteristics (Supplemental Table 2). Fewer included than excluded participants were in the oldest age group of 70 to 75 years (13.8% compared to 19.0%) reflecting the challenges to obtain dementia information about deceased study participants from their GPs.

Olink inflammation panel measurements were performed in a case-cohort design among 1435 randomly selected study participants and all incident dementia cases of the rest of the cohort (n=393). To check if the random selection was successful, we compared the baseline characteristics of selected and non-selected controls and observed no substantial differences between the groups (Supplemental Table 3). After excluding participants with quality control warning, 389 incident dementia cases and 1393 randomly selected participants were available. As the randomly selected participants included 115 incident dementia cases, the study population comprised 504 participants with incident dementia and 1278 randomly selected controls.

First, to describe factors associated with dementia risk, categorized baseline characteristics of all-cause dementia cases and controls were compared using the χ²-test. Second, odds ratios (ORs) for all-cause dementia were estimated with a multivariate logistic regression model, including all baseline characteristics shown in Table 1.

In a univariate, descriptive analysis, the median and interquartile range (IQR) of all inflammation-related protein levels of all-cause dementia, Alzheimer’s disease, and vascular dementia cases were separately compared with those of controls, using the Wilcoxon rank sum test. Additionally, in a multivariate approach, the ORs per one standard deviation (SD) increase of each inflammation-related protein were assessed separately with each outcome (all-cause dementia, Alzheimer’s disease, and vascular dementia incidence) in logistic regression models adjusted for potential confounders. In models for Alzheimer’s disease incidence, study participants with other (e.g. vascular dementia) or unknown dementia forms were excluded. The same was applied for the outcome vascular dementia incidence by excluding Alzheimer’s disease and other non-vascular dementia cases. The models were adjusted for age, sex, education, physical activity, BMI, CVD, diabetes, depression, and APOE genotype. All variables were used as categorical variables, as described in Table 1, except age, which was modelled continuously. The covariates were selected because they were statistically significantly associated with all-cause dementia, Alzheimer’s disease, or vascular dementia in a previous analysis with the ESTHER study participants [25]. Statistical test results were corrected for multiple testing by the Benjamini and Hochberg method for all tests carried out for one outcome [26]. A false discovery rate (FDR) < 0.05 was applied as the threshold for statistical significance. In a sensitivity analysis, the multivariate logistic regression model was repeated using weighted Cox regression, according to Barlow et al. [27].

We further aimed to identify those inflammation-related proteins whose association with a dementia outcome was independent of other inflammatory biomarkers. Therefore, all biomarkers, which were significantly associated with a dementia endpoint after FDR correction, were tested for the independence of the association by forward elimination. Only biomarkers having the strongest, independent, positive association with the outcome entered the regression model with the threshold for statistical significance of p<0.05 in the following logistic regression analysis. Moreover, biomarker clusters were built and named based on the identified independent biomarkers. All other biomarkers of the Olink inflammation panel, which were highly correlated (Spearman’s correlation coefficient r > 0.5) [28] with an independent biomarker, were put in its cluster. One biomarker might be in more than one cluster. We favoured this statistical approach over a principal component analysis because it has a higher transparency and is easier to reproduce by others, its results are easier to interpret, and the associations of the biomarkers with the dementia outcomes are being acknowledged in the decision about the number of clusters.

The associations of the independent biomarkers with dementia endpoints were further analysed in subgroup analyses based on age, sex, obesity, diabetes, history of CVD, and APOE ε4 polymorphism. These factors were selected a priori because they are important dementia risk factors and determinants of inflammation [29]. Apart from this, interaction terms were tested. In addition, the dose-response relationships between the independent biomarkers and dementia endpoints were assessed with restricted cubic spline curves [30].

Several sensitivity analyses were performed. To check for potential reverse causality, the associations between the independent biomarkers and dementia endpoints were analysed stratified by time of diagnosis (in the first 10 years of follow-up vs later years). Competing risk of death was examined by excluding subjects without dementia diagnosis who died before their 80th birthday, the average life expectancy of the cohort’s population. Fractional polynomials with first-order terms were used to determine each biomarker’s best fitting function with the outcomes [31]. The linear function was the best fitting one for almost all biomarkers (67 of 72). Since the low number of biomarkers with deviations in the best fitting function from linearity (6.9%) could have resulted from multiple testing, all were modelled linearly. In a sensitivity analysis, the multivariate logistic regression analysis was repeated with the five non-linear biomarkers modelled with their best fitting function. Finally, to examine the impact of persons with a potential acute infection on the overall results, subjects with C-reactive protein (CRP) levels > 20 mg/L were excluded.

To our knowledge, missing values of covariates were missing at random. The highest proportion of missing values was found for APOE polymorphism (7.5%). Thus, multiple imputation was used to impute missing values. Variables shown in Table 1 were used for the imputation model. Twenty data sets were imputed with the Markov Chain Monte Carlo (MCMC) method separately by sex with the SAS procedure PROC MI. All analyses were performed based on those 20 datasets with the SAS procedure PROC MIANALYZE.

Statistical tests were two-sided using an alpha level of 0.05. All statistical analyses were conducted with the Statistical Analysis System (SAS, version 9.4, Cary, North Carolina, USA).

A few previous studies, mostly with a cross-sectional study design, investigated the association between single inflammatory biomarkers and all-cause dementia or Alzheimer’s disease [6‐9]. To our knowledge, only two previous studies examined a whole panel of inflammatory biomarkers for dementia as the outcome in a cross-sectional design. In the BioFINDER study, Whelan and colleagues [32] measured 270 proteins with the Olink immunoassay in cerebrospinal fluid (CSF) and plasma of 161 Alzheimer’s disease patients, 75 amyloid beta positive (Aβ+) patients with mild cognitive impairment (MCI+), and 415 amyloid beta negative (Aβ−) cognitively normal individuals (MCI−). Interestingly, approximately half of the CSF proteins correlated at least modestly with their analogues in plasma, indicating that findings in plasma samples partially reflected the situation in CSF. Compared to Aβ−/MCI− individuals, CSF levels of 32 proteins and plasma levels of 33 proteins were statistically significantly associated with Alzheimer’s disease (false discovery corrected p-value < 0.05). The comparison of Aβ+/MCI+ patients with Aβ−/MCI− individuals was replicated in an independent cohort. Thereby, 10 CSF and six plasma markers could be replicated. However, a replication analysis in an independent sample was not performed for Alzheimer’s disease. Six of the identified 33 proteins for Alzheimer’s disease in plasma samples corresponded with our findings in serum samples and can now be considered replicated by our study (Casp-8, CXCL5, CXCL6, ST1A1, TRAIL, uPA). Gaetani and colleagues measured biomarker levels of the Olink inflammation panel in CSF samples of 34 AD-MCI cases and 25 controls having other neurological diseases (OND) [33]. In univariate analyses, 11 of 46 analysed biomarkers were found to have the highest discriminatory ability between AD-MCI and OND. Four of those biomarkers (SIRT2, HGF, MMP-10, CXCL5) were also selected as discriminatory factors during penalized logistic regression (LASSO regression).

However, the studies of Whelan and Gaetani were cross-sectional, and evidence from longitudinal studies on this field is still sparse [10]. The recently published longitudinal study of Walker et al. [34] reported statistically significant associations between inflammatory biomarkers measured in midlife (C-reactive protein and a composite score of fibrinogen, white blood cell count, von Willebrand factor, and factor VIII) and cognitive decline over 20 years in a population-based cohort study with 12,336 participants. Our longitudinal results with a broad panel of inflammatory proteins and the endpoints all-cause dementia, Alzheimer’s disease, and vascular dementia complement and expand these findings.

Interestingly, the frequently discussed inflammatory biomarker IL-6 was not significantly associated with any dementia outcome in our study but highly correlated with EN-RAGE and VEGF-A [6‐8]. However, apart from the inflammatory biomarkers discussed above, many others were statistically significantly associated with dementia outcomes as well but highly correlated with the highlighted proteins. IL-10, for example, is currently discussed by others as a risk factor for Alzheimer’s disease [9]. In our study, IL-10 was also statistically significantly associated with all-cause dementia and vascular dementia even after correction for multiple testing. In addition, a subunit of the IL-10 receptor (IL-10RB) was significantly associated with all-cause dementia and Alzheimer’s disease. Both IL-10 and IL-10RB were highly correlated with VEGF-A and IL-10RB, additionally with LAP TGF-beta-1 and CX3CL1. Due to the high correlation of these biomarkers, it is not possible to decide with our study design which of the biomarkers are the most clinically relevant ones and are causally associated with the outcome. Basic research is needed to elucidate this open question and the role of the identified biomarkers in the aetiology of dementia. The underlying mechanisms are likely to be complex because it is known that the multifactorial process of inflammation comes along with increases in the levels of many inflammatory proteins. Therefore, it might be necessary to look into inflammatory protein networks/clusters rather than focusing on single proteins in future studies. For example, for Alzheimer’s disease, we identified two such protein clusters. The EN-RAGE and the LAP TGF-beta-1 cluster consist of respectively nine inflammatory proteins significantly associated with Alzheimer’s disease (Supplemental Tables 14 and 15). The overlap of the two clusters is only three proteins (HGF, CD244, and uPA).

APOE ε4-negative subjects had stronger associations between inflammation biomarkers and dementia outcomes than APOE ε4-positive individuals. Interestingly, the interaction of APOE ε4 and EN-RAGE for all-cause dementia was statistically significant. One explanation could be that the absolute Alzheimer’s disease risk of APOE ε4 carriers is so pronounced that the additional presence or absence of a weaker risk factor, such as inflammation, may not have much impact. In contrast, the potential impact of inflammation on the total dementia risk of APOE ε4 non-carriers is relatively high compared to other dementia risk factors.

With a 41% increased dementia risk by 1 SD increase of either CX3CL1 or EN-RAGE levels, the associations of these two inflammatory biomarkers with dementia were stronger than the associations of male sex, education, CVD, and diabetes with all-cause dementia in our cohort. The strengths of the associations were comparable to the one of physical inactivity. Only age, APOE ε4 genotype, and a depression treated with anti-depressants (likely as an indicator of a major depression) were stronger risk factors for all-cause dementia in our study. This highlights the relevance of chronic inflammation as a strong, independent factor associated with dementia.

The identified inflammatory biomarkers should be further studied in basic science to further elucidate mechanisms and how they contribute to dementia pathogenesis. After this has been done, they may be used as drug targets, early diagnostic markers, and components of dementia prediction scores. Our results also add to current discussions about the potential of the anti-inflammatory drug low-dose aspirin in dementia prevention. A recent analysis of two cohort studies showed that the use of low-dose aspirin for 10 years and more is associated with a lower risk for all-cause dementia, Alzheimer’s disease, and vascular dementia in patients with pre-existing coronary heart disease [55].

The strengths of this study comprise a large sample size, the representative sample of an older adult population, a long follow-up period (17 years), and the prospective cohort design limiting the risk of reverse causality. Moreover, the diversity of inflammatory biomarkers (72 biomarkers analysed) and the high sensitivity and specificity of Olink’s proximity extension assays [19, 20] used for the biomarker measurements can be assigned to the study’s strengths.

The observational study design is one of the limitations of this study. Although analyses were controlled for confounders, residual confounding cannot be entirely excluded. Apart from this, the latency between the onset and the clinical diagnosis of dementia can be longer than the follow-up time of 17 years [56]. However, results were still statistically significant after excluding events in the first 10 years of follow-up. Thus, we can assume that there is no strong indication of reverse causality in our study results.

In the ESTHER study, dementia information is collected via GPs. After a referral to various neurologists, psychiatrists, memory clinics, or other specialized providers in the study region, diagnoses were obtained from the medical records of specialists. Although this process reflects the community-based clinical setting in Germany and enhances the generalizability of the study, it implicates a possible occurrence of underdiagnosed dementia. In addition, dementia diagnostics were performed heterogeneously in routine practice (not following any study protocol) and dementia subtypes were often not differentially assessed. This may be one reason why the ratio of Alzheimer’s disease to vascular dementia diagnoses is not as high in our study as in other studies with homogenous subtype diagnostics based on biomarkers of Alzheimer’s disease pathology among all study participants.

Due to cost issues, biomarker measurements were performed in a case-cohort design and not in the total cohort and only in baseline samples and not additionally in follow-up samples. The latter point might have led to an underestimation of the results because inflammation status could change during the follow-up. Furthermore, cost issues did not allow replication of the results in another independent study yet and this should be aimed for by future research. Such an external validation is crucial in studies with omics data to increase the generalizability of the results.

The biomarkers TNF and IFN-gamma had to be excluded since the proportion of values below LOD was >25% in the total study sample. After Olink improved the inflammation panel in 2019, better results could be achieved for these biomarkers. However, the improved panel was only used in a fraction of our study sample (n = 388). When analysing the data only in this sub-sample, TNF and IFN-gamma showed weak and not statistically significant associations with all three dementia outcomes (Supplemental Table 23).

Lastly, it has to be stated that our study results refer to an almost exclusively Caucasian population with blood samples taken between the ages of 50 and 75 years and may not be generalized to other types of populations.