The participants’ demographic data are presented in Table 1. As previously described, 155 participants provided the complete information required for this study. Among them, 36 patients were diagnosed with MCI, with 19 of them attributed to MCI due to AD and 17 to MCI due to non-AD. Additionally, 64 patients were diagnosed with dementia (31 patients with AD and 33 with dementia due to non-AD). Eight were diagnosed with psychiatric disorders (Table 1). The sex ratio (female/male), age, duration of education, CDR-Sum of Boxes, MMSE scores, Alzheimer’s Disease Assessment Scale (Japanese version) cognitive subscale (ADAS-Jcog), Functional Activities Questionnaire (FAQ), and ApoE4 positivity are shown in Table 1. The groups displayed significant differences in terms of age between CN and AD (P = 0.0265) and between CN and non-AD dementia (P = 0.0022). Furthermore, sex differences were observed between CN and psychiatric disorders (P = 0.0035). Significant differences were evident across all cognitive and functional assessments, including the MMSE, CDR-Sum of Boxes, FAQ, and ADAS-Jcog, when comparing CN with MCI due to AD, MCI due to non-AD, AD, and non-AD dementia (P < 0.0001), except for FAQ in MCI due to non-AD (P = 0.0177). ApoE4 positivity rates were significantly different between the CN and AD groups (P = 0.0044).

Firstly, the association between HTS and Neucop-Q results and cognitive status (CN, MCI, and dementia) was evaluated. As shown in Additional file 2, HTS and partially “N” and “Cimp/Pimp/Nimp” in Neucop-Q, demonstrated significant differences in cognitive status in all participants, confirming the validity of these simple screening tests [5]. HTS and “Cimp/Pimp/Nimp” in Neucop-Q lost significance when AD dementia and MCI due to AD were excluded, suggesting that these simple screening tests might be specific to cognitive impairment due to AD. (Additional file 3, right) Among participants excluded from non-AD dementias and MCI due to non-AD, thus focusing on AD pathology, “P” did not show significance, suggesting that Pimp is not related to cognitive status related to AD. (Additional file 3, left) Moreover, the likelihood ratio test was conducted on HTS and NeuroQ subscores with cognitive status (cognitively unimpaired, MCI, dementia) and amyloid PET (positive versus negative) as factors. “N” and “Cimp/Pimp/Nimp” were associated with cognitive status, and HTS and “Cimp/Pimp/Nimp” with amyloid PET results, indicating that HTS is specific to Alzheimer’s pathology. (Additional file 4)

Table 2 shows the sensitivity, specificity, PPV, and NPV of HTSpos, Cimp, Pimp, and Nimp for predicting amyloid FBB PET and tau PET positivity using [18F]PI-2620 or [18F]florzolotau. These assessments are crucial for the diagnosis of non-AD tauopathies [9, 12, 15,16,17,18,19].

For participants with amyloid or tau PET positivity, HTSpos exhibited the highest specificity and PPV (amyloid PET: 0.930 and 0.870, tau PET: 0.944 and 0.957, respectively). Cimp and Nimp showed a high NPV for predicting amyloid PET result (negativity) (0.750 and 0.725, respectively). Similar results were observed in patients with cognitive impairment due to AD (AD-dementia or MCI due to AD), which is crucial for determining indications for AD disease-modifying treatments (Additional file 5). For predicting tau PET positivity among all participants, HTS, followed by Nimp, showed a high PPV (0.957 and 0.811, respectively) due to an increase in the number of PET-positive subjects. Notably, Pimp showed high specificity for predicting non-AD tauopathy among non-AD participants with amyloid PET negativity (0.854). Collectively, HTSpos, Cimp, and Nimp have diagnostic utility in predicting amyloid pathology, and Pimp has diagnostic value in non-AD tauopathy.

Finally, we assessed the sensitivity, specificity, PPV, and NPV of various combinations of “Cnor/imp,” “Pnor/imp,” and “Nnor/imp” to detect AD or MCI due to AD (Table 3). Among all the combinations (Cimp/Pimp/Nimp, Cnor/Pimp/Nimp, Cimp/Pnor/Nimp, Cimp/Pimp/Nnor, Cnor/Pnor/Nimp, Cnor/Pimp/Nnor, Cimp/Pnor/Nnor, and Cnor/Pnor/Nnor), Cimp/Pnor/Nimp exhibited the highest specificity, PPV, and NPV (0.972, 0.833, and 0.752, respectively). Meanwhile, Cimp/Pimp/Nnor displayed the highest specificity and PPV among non-AD participants without amyloid PET + (0.979 and 0.800, respectively); however, caution should be exercised in interpreting these results as the number of cases (n = 5) was limited.

Recently, considerable progress has been made in the field of blood biomarkers for neurodegenerative dementias [11]. Biomarkers specific to AD pathology include the Aβ42/40 ratio [11, 14, 20, 21]. pTau181 [22,23,24] exhibits a strong correlation with Aβ toxicity. Other neurodegenerative biomarkers include GFAP [25, 26], which serves as a marker for glial activation and neuroinflammation, and NFL [27], which is indicative of axonal damage. As shown in Additional file 6 and 7, all well-established biomarkers showed a significant difference in cognitive status (CN, MCI, and dementia), consistent with previous publications [11, 28], and also showed significant correlation between biomarkers, except between NFL vs. Aβ42/40 and Centiloid (Additional file 8).

Next, we investigated the correlation between plasma AD biomarkers and results obtained from the HTS and simple questions in Neucop-Q (Figs. 1, 2, 3 and 4). Wilcoxon tests revealed that the plasma Aβ42/40 ratio was significantly lower and pTau181 were higher in patients with HTSpos than in patients with HTSneg [neg vs. pos, Aβ42/40: 0.098 ± 0.013 vs. 0.086 ± 0.009 pg/mL (P < 0.0001); pTau181: 2.69 ± 1.57 vs. 3.86 ± 1.09 pg/mL (P < 0.0001)] (Fig. 1). Similarly, differences were observed between patients with Cnor and Cimp [nor vs. imp, Aβ42/40: 0.102 ± 0.013 vs. 0.095 ± 0.013 pg/mL (P = 0.0022); pTau181: 2.35 ± 1.43 vs. 2.84 ± 1.40 pg/mL (P = 0.0095); GFAP: 287.9 ± 200.2 vs. 401.1 ± 370.7 pg/mL (P = 0.0088)] (Fig. 2A). Conversely, patients with Pimp exhibited significant differences in GFAP [nor vs. imp, GFAP: 310.8 ± 239.3 vs. 489.8 ± 466.7 pg/mL (P = 0.0061)] but not in the Aβ42/40 ratio or pTau181 (Fig. 2B). In the case of Nimp, all plasma biomarkers showed significantly positive values for pathology [nor vs. imp, Aβ42/40: 0.102 ± 0.014 vs. 0.093 ± 0.012 pg/mL (P = 0.0010); pTau181: 2.34 ± 1.29 vs. 3.09 ± 1.56 pg/mL (P = 0.0010); NFL: 22.9 ± 13.1 vs. 31.2 ± 19.2 pg/mL (P = 0.0015); GFAP: 291.2 ± 234.4 vs. 444.1 ± 383.7 pg/mL (P = 0.0002)] (Fig. 2C). Overall, HTSpos, Cimp, and Nimp were strongly associated with biomarkers of Aβ pathology, whereas Pimp was associated with biomarkers of neuroinflammation but not Aβ pathology.

In combinations of Neucop-Q, Cimp/Pnor/Nimp was strongly associated with biomarkers of Aβ pathology, such as plasma Aβ42/40 ratio and pTau181, supporting the abovementioned findings [other vs. Cimp/Pnor/Nimp: Aβ42/40 ratio, 0.100 ± 0.013 vs. 0.088 ± 0.011 pg/mL (P = 0.0006), pTau181, 2.50 ± 1.46 vs. 3.38 ± 0.89 pg/mL (P = 0.0006)] (Fig. 3).

Next, we analyzed the association between HTS/Neucop-Q and the CL scale of amyloid PET. As shown in (Fig. 4), HTSpos, Nimp, and “Cimp/Pnor/Nimp” showed significantly higher CL values, consistent with the findings related to plasma biomarkers [HTSneg vs. HTSpos: 32.3 ± 46.9 vs. 93.0 ± 49.3 (P < 0.0001); Nimp vs. Nnor: 56.7 ± 57.0 vs. 24.0 ± 43.0 (P = 0.0006); other vs. Cimp/Pnor/Nimp: 28.4 ± 46.0 vs. 87.0 ± 54.9 (P < 0.0001)].

Finally, we have also performed Logistic regression analysis assessing factors associated between blood biomarker status and head-turning sign or Neucop-Q. (Additional file 9). There were significant interactions between pTau181 and Cimp. On the other hand, the lack of independent effect for a single biomarker for other signs could be interpreted as a general effect of Alzheimer’s pathology.