Differential diagnosis of Alzheimer's disease using spectrochemical analysis of blood

Abstract

The progressive aging of the world's population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer's disease (AD; n = 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein ε4 genotype (APOE ε4) information, increased to 86% when individuals carried one or two alleles of ε4, and to 72% sensitivity and 77% specificity when individuals did not carry ε4 alleles. Early AD cases (n = 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB; n = 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD; n = 30), Parkinson's disease (PD; n = 32), and progressive supranuclear palsy (PSP; n = 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias.

Keywords: Alzheimer’s disease; apolipoprotein E; dementia with Lewy bodies; differential diagnosis; spectroscopy.

Fig. 1.

AD (n = 164) vs. HC (n = 202). (A and B) Preprocessed spectra (cut, rubber band baseline corrected, vector normalized) at the fingerprint and high region. (C and D) Preprocessed spectra (cut, second-order differentiated, vector normalized) along with the wavenumbers responsible for differentiation. At the fingerprint region: 1,744 cm⁻¹ (lipids), 1,624 cm⁻¹ (Amide I), 1,555 cm⁻¹ (Amide II), 1,512 cm⁻¹ (Amide II), 1,462 cm⁻¹ (lipids), and 1,396 cm⁻¹ [δ(CH₃) of proteins]. At the high region: 3,275 cm⁻¹ (–OH stretching), 3,132 cm⁻¹ [ν_s(N-H)], 3,005 cm⁻¹ [ν_as (=C-H), lipids and fatty acids], 2,970 cm⁻¹ [ν_as (CH₃), lipids and fatty acids], 2,924 cm⁻¹ [ν_as (CH₂), lipids], and 2,855 cm⁻¹ [ν_s(CH₂), lipids]. (E and F) The 1D scores plot (LD1) after cross-validated PCA-LDA. (G and H) Loading plots showing the wavenumbers responsible for discrimination. At the fingerprint region: 1,747 cm⁻¹ (lipids), 1,636 cm⁻¹ (Amide I), 1,570 cm⁻¹ (Amide II), 1,153 cm⁻¹ (carbohydrate), 1,076 cm⁻¹ [ν_s (PO₂⁻)], and 1,034 cm⁻¹ (glycogen). At the high region: 3,676 cm⁻¹ (–OH stretching), 3,622 cm⁻¹ (–OH stretching), 3,587 cm⁻¹ (–OH stretching), 3,325 cm⁻¹ (–NH stretching), 2,966 cm⁻¹ (–CH stretching of lipids), and 2,924 cm⁻¹ (–CH stretching of lipids). *P < 0.05; **P < 0.005; ***P < 0.0005; ****P < 0.00005.

Fig. S1.

AD (n = 164) vs. HC (n = 202). (A) Preprocessed spectra (cut, rubber band baseline corrected, vector normalized) at the fingerprint region 1,800–900 cm⁻¹. The spectral regions of interest are magnified along with the P values for the regions that were statistically significant: 1,750–1,735 cm⁻¹ (lipids; P = 0.006, 95% CI = 0.0006, 0.0035), 1,650–1,630 cm⁻¹ (Amide I, proteins; P = 0.095, 95% CI = −0.005, 0.0004), 1,590–1,580 cm⁻¹ (Amide II, proteins; P = 0.254, 95% CI = −0.0008, 0.003), 1,540–1,530 cm⁻¹ (Amide II, proteins; P = 0.003, 95% CI = −0.0031, −0.0007), 1,470–1,430 cm⁻¹ (lipids/proteins; P = 0.0002, 95% CI = 0.002, 0.006), 1,220–1,160 cm⁻¹ (carbohydrates, DNA/RNA; P = 0.0035, 95% CI = 0.0016, 0.0084), and 1,150–1,040 cm⁻¹ (carbohydrates, DNA/RNA; P = 0.0002, 95% CI = 0.0057, 0.0185). (B) Preprocessed spectra (cut, rubber band baseline corrected, vector normalized) at the high region 3,700–2,800 cm⁻¹. The spectral regions of interest are magnified along with the P values for the regions that were statistically significant: 2,950–2,850 cm⁻¹ (lipids; P = 0.0004, 95% CI = 0.021, 0.017) and 3,550–3,450 cm⁻¹ (–OH stretching; P = 0.0165, 95% CI = −0.048, −0.0047). (C and D) The 1D scores plots (cross-validated PCA-LDA) comparing individuals rather than spectra. Significant differences were seen in both spectral regions (fingerprint region: P = 0.0007, 95% CI = 0.004, 0.014; high region: P = 0.003, 95% CI = −0.035, −0.007). Data are expressed as the mean ± SD. **P < 0.005; ***P < 0.0005.

Fig. 2.

Classification of AD before the incorporation of APOE ε4 genotype and age information. (A and B) The best classification techniques for the fingerprint region (GA-LDA) and high region (PCA-QDA). GA-LDA used 23 wavenumbers (900, 937, 960, 1,014, 1,072, 1,122, 1,234, 1,296, 1,308, 1,312, 1,342, 1,369, 1,396, 1,420, 1,508, 1,531, 1,535, 1,601, 1,651, 1,690, 1,705, 1,763, and 1,786 cm⁻¹) for classification; PCA-QDA loadings plot depicts the most discriminant wavenumbers. (C and D) Scores plots illustrating classification by GA-LDA and PCA-QDA at the two regions. (E) Table showing the sensitivities and specificities achieved by all of the chemometric techniques used for both spectral regions. The ones shown in bold provided the best classification results. DF, discriminant function.

Fig. S2.

AD vs. HC using PCA-LDA/QDA at the fingerprint region (1,800–900 cm⁻¹). (A and B) Loadings plots identifying the major discriminant wavenumbers after PCA-LDA and PCA-QDA, respectively. (C and D) The number of PCs used was optimized before PCA-LDA, using the Pareto function within the IRootLab toolbox, to prevent noise introduction. Ten PCs were used, representing >95% of the variance. (E and F) Scores plots illustrating classification by PCA-LDA and PCA-QDA, respectively. Sensitivity and specificity values are 62 and 62%, respectively, after PCA-LDA and 57 and 57%, respectively, after PCA-QDA (Fig. 2E). DF, discriminant function.

Fig. S3.

AD vs. HC using PCA-LDA/QDA at the high region (3,700–2,800 cm⁻¹). (A and B) Loadings plots identifying the major discriminant wavenumbers after PCA-LDA and PCA-QDA, respectively. (C and D) The number of PCs used was optimized before PCA-LDA, using the Pareto function within the IRootLab toolbox, to prevent noise introduction. Ten PCs were used, representing >90% of the variance. (E and F) Scores plots illustrating classification by PCA-LDA and PCA-QDA, respectively. Sensitivity and specificity are 64 and 64%, respectively, after PCA-LDA and 68 and 68%, respectively, after PCA-QDA. DF, discriminant function.

Fig. S4.

AD vs. HC using SPA-LDA/QDA at the fingerprint region (1,800–900 cm⁻¹). (A and B) Wavenumber selection for SPA-LDA and SPA-QDA; the selected variables were 1,435 and 1,628 cm⁻¹. (C and D) Cost/function plot identifying the optimal number of wavenumbers to be used for the SPA algorithm. (E and F) Scores plots illustrating classification by SPA-LDA and SPA-QDA, respectively. Sensitivity and specificity values are 55 and 55%, respectively, after SPA-LDA and 59 and 59%, respectively, after SPA-QDA (Fig. 2E). DF, discriminant function.

Fig. S5.

AD vs. HC using SPA-LDA/QDA at the high region (3,700–2,800 cm⁻¹). (A and B) Wavenumber selection for SPA-LDA and SPA-QDA; the selected variables were 3,152 and 3,661 cm⁻¹. (C and D) Cost/function plot identifying the optimal number of wavenumbers to be used for the SPA algorithm. (E and F) Scores plots illustrating classification by SPA-LDA and SPA-QDA, respectively. Sensitivity and specificity are 45 and 45%, respectively, after SPA-LDA and 50 and 50%, respectively, after SPA-QDA. DF, discriminant function.

Fig. S6.

AD vs. HC using GA-LDA/QDA at the fingerprint region (1,800–900 cm⁻¹). (A and B) Wavenumber selection for GA-LDA (900, 937, 960, 1,014, 1,072, 1,122, 1,234, 1,296, 1,308, 1,312, 1,342, 1,369, 1,396, 1,420, 1,508, 1,531, 1,535, 1,601, 1,651, 1,690, 1,705, 1,763, and 1,786 cm⁻¹) and GA-QDA (903, 922, 1,003, 1,030, 1,057, 1,080, 1,096, 1,165, 1,188, 1,196, 1,215, 1,331, 1,342, 1,346, 1,350, 1,373, 1,462, 1,501, 1,520, 1,524, 1,555, 1,562, 1,605, 1,609, 1,624, 1,632, and 1,670 cm⁻¹). (C and D) Cost/function plot identifying the optimal number of wavenumbers to be used for the GA algorithm. (E and F) Scores plots illustrating classification by GA-LDA and GA-QDA, respectively. Sensitivity and specificity are 70 and 70%, respectively, after GA-LDA and 61 and 65%, respectively, after GA-QDA. DF, discriminant function.

Fig. S7.

AD vs. HC using GA-LDA/QDA at the high region (3,700–2,800 cm⁻¹). (A and B) Wavenumber selection for GA-LDA (2,893, 2,951, 3,074, 3,136, 3,140, 3,294, 3,368, 3,580, and 3,626 cm⁻¹) and GA-QDA (2,889, 2,928, 2,947, 2,978, 3,005, 3,059, 3,132, 3,256, 3,352, and 3,553 cm⁻¹). (C and D) Cost/function plot identifying the optimal number of wavenumbers to be used for the GA algorithm. (E and F) Scores plots illustrating classification by GA-LDA and GA-QDA, respectively. Sensitivity and specificity are 67 and 66%, respectively, after GA-LDA and 44 and 36%, respectively, after GA-QDA. DF, discriminant function.

Fig. 3.

Classification of AD after the incorporation of APOE ε4 genotype and age information. (A and B) Scores plot after GA-LDA, which provided the highest sensitivity (72%) and specificity (77%) for individuals with no APOE ε4 alleles. The different sensitivities and specificities achieved by all of the classification methods are also shown. (C and D) Scores plot after GA-QDA, which provided sensitivity and specificity of 86%, for individuals with one or two APOE ε4 alleles. (E and F) Scores plot after GA-LDA for the comparison of AD with HC (<65 y); sensitivity and specificity were 68 and 65%, respectively. (G and H) Scores plot after GA-LDA for the comparison of AD with HC (≥65 y old); sensitivity and specificity were 66 and 67%, respectively. DF, discriminant function.

Fig. S8.

Classification techniques for AD vs. HC after the incorporation of APOE ε4 genotype information. (A and B) Wavenumber selection using GA-LDA for individuals with no APOE ε4 alleles [AD (n = 71), HC (n = 142)]: 903, 914, 926, 960, 972, 1,296, 1,339, 1,358, 1,385, 1,670, 1,678, and 1,786 cm⁻¹. Wavenumber selection using GA-QDA for individuals with one or two APOE ε4 alleles [AD (n = 88), HC (n = 49)]: 984, 1,065, 1,138, 1,211, 1,454, and 1,767 cm⁻¹. (C and D) Cost/function plot identifying the optimal number of wavenumbers to be used for the GA algorithm. (E and F) Scores plots illustrating classification by GA-LDA and GA-QDA, respectively. The optimal sensitivities and specificities were 72 and 77%, respectively, for individuals with no APOE ε4 alleles and 86 and 86%, respectively, for individuals with one or two APOE ε4 alleles (Fig. 3 B and D). DF, discriminant function.

Fig. 4.

Early AD (n = 14) vs. HC (n = 202). (A and B) Scores plot, illustrating classification by GA-LDA, and loadings plot, identifying the major discriminant variables by GA-LDA, which achieved the highest sensitivity and specificity of 80 and 74%, respectively. (C) Sensitivities and specificities achieved by all chemometric techniques. DF, discriminant function.

Fig. 5.

Duration of AD. The first group (0.5–1 y) includes 32 AD patients, the second group (1.5–5 y) includes 111 patients, and the third group (6–18 y) includes 17 patients. The most discriminant wavenumbers were 1,547 cm⁻¹ (Amide II), 1,504 cm⁻¹ (Amide II, phenyl rings), 1,369 cm⁻¹ (cytosine, guanine), 1,219 cm⁻¹ (ν_as of PO₂⁻ of DNA/RNA), 1,080 cm⁻¹ (ν_s of PO₂⁻ of DNA/RNA), and 1,034 cm⁻¹ (collagen). Data are expressed as mean (±SD). *P < 0.05; **P < 0.005.

Fig. 6.

Lipid-to-protein, phosphate-to-carbohydrate, and RNA-to-DNA ratios related to age. (A) Scores plot after cross-validated PCA-LDA showing differences and similarities of AD patients before and after the age of 65 y old; no significant differences were found after statistical analysis (P = 0.6440, 95% CI = −0.00391, 0.0070). (B–D) Intensity ratios of important spectral regions: lipid-to-protein (1,450/1,539 cm⁻¹), phosphate-to-carbohydrate (1,045/1,545 cm⁻¹), and RNA-to-DNA (1,060/1,230 cm⁻¹), respectively. Data are expressed as mean (±SD). The two tables shown next to these graphs show the different subgroups and their mean age (±SD). *P < 0.05; **P < 0.005.

Fig. 7.

Other types of neurodegenerative diseases. (A and B) The 1D scores plots after cross-validated PCA-LDA representing all of the different groups of neurodegenerative diseases [AD (n = 164), DLB (n = 34), PD (n = 32), HC (n = 202), other (n = 117)] using LD1 and LD2, respectively. (C and D) The 1D scores plots after cross-validated PCA-LDA after selection of FTD (n = 30) and PSP (n = 31) for additional analysis using LD1 and LD2, respectively.

Fig. S9.

Diagnosis of other neurodegenerative diseases. (A) Scores and loading plots after cross-validated PCA-LDA for the diagnosis of DLB along with the six most discriminatory wavenumbers: 1,709 cm⁻¹ (lipid), 1,666 cm⁻¹ (Amide I), 1,555 cm⁻¹ (Amide II), 1,501 cm⁻¹ (Amide II), 1,076 cm⁻¹ (symmetric stretching of PO₂⁻ of DNA/RNA), and 1,034 cm⁻¹ (glycogen; P = 0.052, 95% CI = −0.0353, 0.0002). (B) Scores and loading plots after cross-validated PCA-LDA for the diagnosis of PD along with the six most discriminatory wavenumbers: 1,744 cm⁻¹ (lipid), 1,705 cm⁻¹ (lipid), 1,666 cm⁻¹ (Amide I), 1,632 cm⁻¹ (Amide I), 1,555 cm⁻¹ (Amide II), and 1,408 cm⁻¹ (–COO symmetric stretching of fatty/amino acids; P = 0.331, 95% CI = −0.0282, 0.0089). (C) Scores and loading plots after cross-validated PCA-LDA for the diagnosis of FTD along with the six most discriminatory wavenumbers: 1,732 cm⁻¹ (lipid), 1,674 cm⁻¹ (Amide I), 1,624 cm⁻¹ (Amide I), 1,528 cm⁻¹ (Amide II), 1,466 cm⁻¹ (bending vibration of proteins/lipids), and 1,381 (–COO symmetric stretching of fatty/amino acids; P = 0.0041, 95% CI = −0.0264, −0.0049). (D) Scores and loading plots after cross-validated PCA-LDA for the diagnosis of PSP along with the six most discriminatory wavenumbers: 1,732 cm⁻¹ (lipid), 1,636 cm⁻¹ (Amide I), 1,531 cm⁻¹ (Amide II), 1,497 cm⁻¹ (Amide II), 1,396 cm⁻¹ (–COO symmetric stretching of fatty/amino acids), and 1,076 cm⁻¹ (symmetric stretching of PO₂⁻ of DNA/RNA); P < 0.0001, 95% CI = −0.0439, −0.0201). **P < 0.005; ***P < 0.0005.

Fig. 8.

AD (n = 164) vs. DLB (n = 34). (A and B) Scores plot, illustrating classification, and loadings plot, identifying the major discriminant wavenumbers, after PCA-QDA. (C) Sensitivity and specificity achieved for the segregation of AD from DLB; PCA-QDA achieved the best results, with 90% sensitivity and 90% specificity. DF, discriminant function.

Fig. S10.

AD vs. other types of dementia. (A) Scores and loading plots illustrating AD vs. DLB after cross-validated PCA-LDA along with the discriminatory wavenumbers and a table with their tentative assignments (P = 0.178, 95% CI = 0.0012, 0.0157). (B) Scores and loading plots illustrating AD vs. FTD after cross-validated PCA-LDA along with the discriminatory wavenumbers and a table with their tentative assignments (P = 0.0040, 95% CI = 0.0041, 0.0216). *P < 0.05; **P < 0.005.