Capturing abnormal biomarker trajectories in unhealthy aging

We evaluated Aβ misfolded proteins, glucose metabolism, cerebral blood flow, functional activity and/or structural tissue brain patterns in a cohort of 1,171 subjects from the ADNI database (Methods section, Study participants; Supplementary Table 1). These five biological factors were mapped in vivo using corresponding neuroimaging techniques (Fig. 1a; Methods section, Data Description and Processing): Florbetapir positron emission tomography (PET; for Aβ deposition), Fluorodeoxyglucose PET (for glucose metabolism), Arterial Spin Labeling (ASL, for cerebral blood flow), resting functional magnetic resonance imaging (MRI; for neuronal activity at rest) and structural MRI (for structural tissular properties). Each participant was previously diagnosed at each visit as healthy control (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) or probable Alzheimer’s disease patient (LOAD). In addition, participants were cognitively and genetically characterized (for example, according to the Mini Mental State Examination (MMSE) or to the number of apoeɛ4 allele copies, respectively). See Supplementary Table 1 for a detailed sample description across all data modalities. For each mentioned biological factor, representative regional values were calculated for 78 regions covering all the grey matter15. See Methods section (image processing subsections) for a description of evaluated multimodality imaging measurements.

Figure 1: Representation of the multifactorial data-driven generative approach. (a) Brain multimodality images and plasma/CSF biomarkers. (b) Regional patterns for Aβ deposition across the entire sample. (c) Reconstructed regional Aβ characteristic trajectories for HC to LOAD (left) and HC to HC (right) clinical transitions, over a 30-year aging period. (d) Regional (left) and total (right) Aβ abnormality trajectories during the age-mediated clinical transitions. Full size image

We proceeded to reconstruct the characteristic trajectory of each biological factor at each brain region during healthy or unhealthy aging. For this, different aging-mediated disease trajectories were generated using a generative spatiotemporal model (Fig. 1b–d; MFDDA, Methods section and Supplementary Note 1), covering all possible LOAD-associated clinical state transitions during a 30-year period of aging (from 40 to 70 years of age). Clinical transitions considered were: HC to HC, HC to EMCI, HC to LMCI and HC to LOAD state (Fig. 1c). Next, spatiotemporal abnormality trajectories for each specific biomarker were obtained comparing the mean characteristic curve for each diseased clinical transition with the corresponding curve for the healthy aging transition (Fig. 2a–e; MFDDA, Methods section). In addition, these trajectories were used to calculate a total LOAD–abnormality index for each biological factor and brain region, that is, the normalized area under the obtained abnormality curve (MFDDA, Methods section).

Figure 2: Spatiotemporal abnormalities for LOAD progression (HC to AD clinical transitions) over a 30-year aging period. Regional abnormality trajectories and LOAD–abnormality indices for Aβ deposition (a), metabolic dysfunction (b), vascular dysregulation (c), functional impairment (d) and grey matter atrophy (e). Full size image

This generative procedure was repeated 500 times via a bootstrapping technique, which improved the robustness of the estimations and allowed to control the stability of the results. Finally, each factor-regional trajectory and associated abnormality scores were calculated as the mean of all the bootstrap outcomes. Similarly, aging/disease characteristic trajectories and corresponding abnormality trajectories/scores were generated for MMSE, included as a measure of symptoms severity13, and for 146 plasma and 87 CSF potential biomarkers (Supplementary Tables 2 and 3). For further details see Fig. 1, Methods section (MFDDA subsection) and Supplementary Note 1.

Multifactorial biomarker ordering in LOAD progression

Identification of the sequence of pathological events underlying LOAD progression is still a major challenge (Fig. 2). In the last decade, different models have been proposed9,10,11,12,13,14,16. These studies have contributed to the understanding of the ordering in biomarker abnormalities associated with LOAD, using different observational9,10 or data-driven perspectives11,12,13,14. In addition to being less sensitive to subjective criteria, data-driven models present the advantage of being directly applicable to different diseases. For example, in refs 11, 12 a probabilistic event-based model is applied to Alzheimer’s and Huntington’s cohorts, providing disease-specific pathologic event orderings and individual disease states, without the assumption of a priori event ordering or requiring an initial grouping of the patients into clinical stages.

However, in general, previous models of LOAD progression considered an insufficient number of interrelated neuropathological factors and/or brain areas. For example, it is common to find multimodal analyses of grey matter atrophy, Aβ deposition and/or functional impairment, in which vascular dysregulation is not included (see refs 1, 9, 10, 16). In other studies14,17, conclusions have been based only on the observation of specific brain areas, those presumably more affected in advanced LOAD states (for example, hippocampal, ventricles and entorhinal regions). In refs 11, 12, the analyses were limited to structural, cognitive, Aβ classification in negative and positive subjects, and/or a few peripheral protein biomarkers (CSF Aβ 1–42 , tau and phosphorylated tau (ptau)); brain vascular, functional and metabolic components, as well as other relevant peripheral protein biomarkers, were not considered. See the Discussion section for further description of previous models. Motivated by this lack of integrative LOAD models, here we aimed to identify a comprehensive multifactorial biomarker ordering in LOAD progression based on the spatiotemporal abnormality levels obtained previously for the whole HC to LOAD clinical transition.

First, we did a clinical pairwise comparison between all imaging biomarkers, based on their reconstructed spatiotemporal abnormalities. For each pair of factors (imaging modalities), and for each brain region and time point, a value of 1 was assigned to the factor with the higher abnormality value. This comparison was repeated across all brain regions and time points, and the results were summarized in a 5 × 5 hierarchical matrix (Fig. 3a). Each element i,j (i,j=1..5) of this hierarchical matrix reflects the percentage of regions and time points at which the imaging modality j exceeded in abnormality magnitude the modality i during the HC to LOAD clinical transition. The columns of the matrix were reordered keeping from left to right the factors predominating in effect levels. We observed a remarkable predominance of the vascular dysregulation component over the other pathologic biomarkers (Fig. 3a). In total, the vascular factor was ∼80% more abnormal across all brain regions and time points than were the other factors considered. It was followed in spatiotemporal abnormality levels by Aβ deposition, metabolic dysfunction, functional impairment and grey matter atrophy.

Figure 3: Data-driven spatiotemporal ordering in LOAD progression. (a) Hierarchical matrix reflecting pairwise comparisons in factor abnormality levels. Element i,j represents the total percentage of regions and time points at which the biological factor j is more abnormal than is the factor i. (b) Multifactorial temporal ordering in disease progression, based on the factor-specific abnormality trajectories (temporal abnormalities averaged across all brain regions), memory deficit and three CSF biomarkers (Aβ 1−42 , tau and ptau). All of the results were calculated for the HC to LOAD clinical transition. Dotted lines indicate 95 % confidence intervals, reflecting the uncertainties associated to the estimated mean trajectories, and obtained with 500 bootstrapping resamples. Inset figure provides detail of the trajectories obtained for early states of the disease (HC to EMCI transition). Note how in the initial states the vascular component is separating from the other components, while Aβ, metabolism and functional dysregulation remain close, with a notable overlap among their confidence intervals, until more advanced pathological states. See Supplementary Fig. 2 for equivalent results obtained evaluating the model assuming a sigmoid (instead of linear) relationship between age and disease state, respectively. Full size image

Next, to create and compare factor-specific abnormality curves during LOAD development, for each biological factor we calculated the average abnormality curve across all brain regions and, after normalizing by the maximum abnormality value, depicted together the final average curves (Fig. 3b). In the averaging calculation, each region’s multifactorial abnormality curves were weighted according to the region’s relevance during the pathological progression. For this, we assumed the sum of each region’s abnormality levels across all biological factors to be a local multifactorial measure of vulnerability to the disease (Fig. 4). With the purpose of also analysing symptoms severity and peripheral protein alterations as a function of disease progression, we included in our multifactorial analysis the abnormality trajectory obtained for MMSE and for three commonly referenced CSF proteins (Aβ 1–42 , tau and ptau10). Again, we observed (Fig. 3b) a higher abnormality magnitude for the vascular component, which exceeded the alterations in the other factors. Consistent with the previous hierarchical results (Fig. 3a), the vascular dysregulation was followed in abnormality magnitude by Aβ deposition, metabolic dysfunction, functional impairment and grey matter atrophy. We noticed similar abnormality levels for Aβ deposition, glucose metabolism and neuronal function at the early stages of the disease. However, these three factors diverged in abnormality levels with disease progression, explaining the global differences observed in the hierarchical matrix (Fig. 3a), and coinciding also with a ‘slower’ but consistent increase in structural atrophy. Around the last phase of the LMCI period, the structural atrophy becomes more abnormal (in terms of biomarker distance to the healthy state) than the functional impairment. In addition, we observed symptoms of memory impairment from very early disease stages. Contrary to what previous observational models proposed9,10, alterations in memory preceded the abnormalities observed for different molecular biomarkers (for example, CSF Aβ 1–42 , tau and ptau proteins). This suggests that cognitive decline associated with LOAD is not a final output of large brain changes, but a continuous consequence of subtle pathological alterations in primary disease factors (for example, vascular dysregulation and Aβ effects).

Figure 4: Regional total abnormality levels associated with LOAD progression. Brain regions were sorted from maximum to minimum total effect values, to illustrate their multifactorial damage. Note the across-brain consistent change in the vascular component, which is considerably less prominent for other factors (for example, functional and structural alterations). Full size image

See Discussion section for further biological interpretation of these results.

Peripheral vascular and inflammatory alterations

Proteins execute central functions in living organisms and their peripheral concentrations/interactions are strongly associated with individual health conditions. This makes the analysis of peripheral protein dynamics a crucial step towards understanding the biological mechanisms underlying aging and associated neurodegenerative diseases. Particularly, peripheral plasma and CSF protein measurements have been suggested as promising biomarkers of pre-symptomatic pathological processes underlying LOAD18,19. Here similar to the imaging biomarkers, we aimed to explore possible abnormalities in plasma and CSF proteins associated with LOAD progression. For this, 146 plasma and 87 CSF protein biomarkers were analysed and sorted according to their obtained LOAD–abnormality indices (Fig. 5; Supplementary Tables 2 and 3).

Figure 5: Total CSF and plasma biomarkers abnormality levels associated with LOAD progression. Total CSF (a) and plasma (b) biomarkers abnormality levels associated with LOAD progression. For detailed lists of biospecimens and the obtained abnormality values for intermediate disease states, see Supplementary Tables 2 and 3. Full size image

Heart-type fatty acid-binding protein (hFABP) was identified as the most abnormal CSF biospecimen (Fig. 5a,b, Supplementary Table 3). CSF hFABP levels are known to be significantly altered in LOAD patients20, having a high predictive power of the progression from MCI to LOAD states20,21. The CSF hFABP level is significantly associated with longitudinal atrophy of the entorhinal cortex and other LOAD-vulnerable neuroanatomical regions22, and is also considered a sensitive biomarker of specific cardiovascular disorders23. Cortisol and Apolipoprotein A (Apo A) were identified as the other most abnormal CSF biospecimens (Fig. 5a). Cortisol is a relevant risk factor for stress, glucose and cardiovascular dysregulation24, which has been strongly linked to early phases of LOAD progression and to the hyperactivity of the hypothalamic-pituitary-adrenal axis25. Apo A is a high-density lipoprotein with a central role in lipid metabolism. Peripheral Apo A concentration is strongly associated with the integrity of the vascular system and the risk of developing cardiovascular disorders26. In addition, we observed high abnormality levels for other CSF measurements previously associated with LOAD progression, for example, tau, ptau, ferritin and Aβ 1–42 (scored in the total LOAD–abnormality positions 4, 6, 10 and 13, respectively, out of all of the 87 considered CSF biomarkers). However, and contrary to what has been suggested by previous LOAD models9,10 (Fig. 6a), alterations observed in Aβ 1-42 proteins were considerably lower than those observed for other CSF proteins (for example, hFBAP, cortisol and Apo A, with approximately a twofold higher average abnormality level for these latest descriptors). This effect was consistent from early to advanced disease states, which might suggest that hFBAP, cortisol and Apo A protein levels in the CSF could be earlier LOAD biomarkers than Aβ 1–42 concentration.

Figure 6: Hypothetical and data-driven models of LOAD progression. Hypothetical (a) and data-driven (b) models of LOAD progression. (a) Adapted from Jack et al.10, (with permission from Elsevier). (b) On the basis of our statistical analysis (Results section, Figs 2, 3, 4, 5, Supplementary Tables 2 and 3). Confidence intervals were omitted for visual clarity. Crucial inter-model differences are: (1) the absence of a vascular component in a and the subsequent assumption that Aβ measurements are the earliest biomarkers, whereas in b the vascular dysfunction is the earliest/stronger altered event, followed by Aβ deposition; (2) CSF Aβ 42 and tau are proposed in a as the two major proteinopathies underlying LOAD, with higher sensitivity to disease progression than the metabolic/structural and memory biomarkers, however our results suggest that these proteins were not the strongest altered CSF proteins during disease progression (for example, plasma IP-10, PAPP-A and total proinsulin, and CSF hFABP, cortisol and Apo A, showed higher sensitivity) while imaging and memory biomarkers appeared consistently as earlier biomarkers (see Results section, and Supplementary Tables 2 and 3); (3) in a, abnormalities in cognitive decline are only detectable at advanced abnormality levels for the considered biological biomarkers. In contrast, in b, alterations in cognition are observable in parallel with changes in the primary disease factors (for example, vascular/metabolic dysfunction and Aβ deposition) and exceed in magnitude alterations observed for CSF Aβ 1−42 , tau and ptau. Full size image

Among all of the studied plasma biospecimens, interferon-γ-induced protein 10 (IP-10) presented the highest abnormality levels (Figs 5a and 6b, Supplementary Table 2). Alterations in plasma IP-10 reflect peripheral inflammation processes, which are a characteristic feature in aging and associated neurodegenerative disorders27. Among other functions, IP-10 is a strong modulator of angiogenesis28, which has a key role in poor vascularization and abnormal vasculature disorders29. IP-10 was followed in total abnormality levels by pregnancy-associated plasma protein A (PAPP-A), a predictor of adverse vascular events, including high risk of heart infarction30. Total and intact proinsulin followed IP-10 and PAPP-A in plasma abnormality levels. Proinsulin is the main precursor of insulin (scored at position 10 out of all the 146 plasma biomarkers). The consistent alterations of insulin and associated proteins in LOAD, and the presence of common cellular responses and pathogenesis, have motivated the classification of this disorder as a form of type III diabetes31. Peripheral insulin is suggested to enter the brain via a saturation mechanism involving the blood–brain barrier (BBB)32. Alterations in BBB permeability, which recently have been observed at early stages of LOAD33, might be associated with alterations in brain insulin resistance34. Moreover, peripheral and brain insulin alterations may alter the BBB transport of amino acids and drugs32, as well as induce changes in brain glucose, Aβ and ptau regulations35. Glutathione S-transferase alpha and plasma matrix metalloproteinase 1 (MMP1) proteins were also identified with high abnormality levels. Glutathione S-transferase alpha alterations are strongly associated with oxidative stress36, which is caused by the age-dependent imbalance between the generation and detoxification of reactive oxygen and nitrogen species37. Among other relevant pathogenic functions, oxidative stress constitutes a regular pathway for different brain mechanisms leading to BBB dysfunction38. Brain MMP1 concentrations have been found to be significantly elevated in LOAD subjects39. Matrix metalloproteinase alterations are thought to be linked to neuroinflammatory processes40,41 and BBB dysfunction39,41. In summary, changes of these plasma biomarkers suggest an early alteration of the peripheral vascular system during LOAD progression, as well as allude to other relevant pathologic mechanisms (for example, inflammatory hyperactivation).