Peer Reviewed Articles About Salmon as Brain Food

Front end Aging Neurosci. 2020; 12: 76.

Fish Intake May Affect Brain Structure and Ameliorate Cerebral Ability in Good for you People

Keisuke Kokubun

^oneOpen Innovation Institute, Kyoto Academy, Kyoto, Nippon

Kiyotaka Nemoto

ⁱⁱPartition of Clinical Medicine, Department of Neuropsychiatry, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan

Yoshinori Yamakawa

^oneOpen Innovation Constitute, Kyoto Academy, Kyoto, Japan

³ImPACT Programme of Council for Scientific discipline, Technology and Innovation (Cabinet Office, Government of Japan), Chiyoda, Tokyo, Japan

⁴Institute of Innovative Inquiry, Tokyo Institute of Technology, Meguro, Tokyo, Japan

⁵Office for Academic and Industrial Innovation, Kobe University, Kobe, Japan

⁶NTT Data Institute of Management Consulting, Inc., Chiyoda, Tokyo, Japan

Received 2019 May 23; Accepted 2020 Mar 2.

Abstract

Equally the population ages worldwide, the prevalence of cognitive disorders including mild cognitive impairment (MCI) is increasing. MCI appears in 10–20% of adults aged 65 years and older and is generally referred to equally an intermediate stage betwixt normal cerebral aging and dementia. To develop timely prevention and early on treatment strategies by identifying biological factors, nosotros investigated the relationship between dietary consumption of fish, brain structure, and MCI in cognitively normal subjects. The encephalon structure was assessed using neuroimaging-derived measures including the "greyness-matter encephalon healthcare caliber (GM-BHQ)" and "fractional-anisotropy brain healthcare caliber (FA-BHQ)," which are approved as the international standard (H.861.one) by the International Telecommunication Union Telecommunication Standardization Sector. Dietary consumption of fish was calculated using the cursory cocky-administered diet history questionnaire (BDHQ), and MCI was assessed using the Retentivity Performance Index (MPI) of MCI screening method (MCI Screen). This study showed that fish intake was positively associated with both FA-BHQ and MPI, and FA-BHQ was more strongly associated with MPI than fish intake. Our findings are in line with those in previous studies, just our study farther indicates that the condition of the whole encephalon integrity measured by the FA-BHQ may mediate the relationship betwixt fish intake and MCI prevention in healthy people. In other words, FA-BHQ may be used to identify people at high take chances of MCI to provide the appropriate intervention.

Keywords: fractional-anisotropy brain healthcare caliber, gray-thing brain healthcare quotient, magnetic resonance imaging data, MCI Screen, cursory self-administered diet history questionnaire, fish intake

Introduction

As the population ages worldwide, the prevalence of cognitive disorders, including mild cognitive impairment (MCI), is growing (Alzheimer'south Disease International, 2015). MCI appears in 10–twenty% of adults aged 65 years and older (Langa and Levine, 2014) and is more often than not referred to equally an intermediate stage between normal cognitive aging and dementia (Vega and Newhouse, 2014). Individuals with MCI are vi.vii times more than likely to accept Alzheimer'due south disease (Advertizement) than cognitively normal individuals (Boyle et al., 2006). However, no medications have proven effective for MCI currently (Langa and Levine, 2014). Therefore, identification of biological factors that may exist implicated in MCI is essential for timely prevention and early treatment.

Fish is an important source of omega-iii fat acids (n-3 FAs) that are nowadays in the membranes of the brain tissue (Weiser et al., 2016); therefore, a fish-oriented dietary intake has been increasingly focused on to investigate the role of diet in the prevention of cerebral disorders. Indeed, it was found that a higher intake of n-three FAs or total polyunsaturated fat acids was associated with a lower risk of MCI (Devore et al., 2009; Zhang et al., 2015), probably considering eicosapentaenoic acid and docosahexaenoic acid (DHA) are protective factors in the nervous system in humans (Calon and Cole, 2007; Kou et al., 2008). In addition, fishery products are recommended as dietary sources considering fish consumption has been institute to be associated with a reduced risk of dementia (Huang et al., 2005) or Advertisement (Zhang et al., 2015).

Notwithstanding, some studies accept shown that fish consumption does not have a protective event on MCI. A meta-analysis by Zeng et al. (2017) institute no statistically significant clan between fish intake and the risk of MCI. The inconsistency in the association between fish intake and MCI may be due to indirectness. In other words, the brain, the mediator between fish consumption and cognitive ability, could be influenced by many unpredictable biological and sociological factors other than fish intake; therefore, a direct and significant human relationship betwixt fish intake and MCI might be difficult to be observed. Therefore, agreement the effect of fish consumption on brain structure, rather than on MCI, is critical for the decision of modifiable factors that tin can decrease the risk of cognitive deficits and dementia by intervention before in life (Lopez et al., 2013). Notably, a previous report found that daily fish oil supplementation increased the gray matter (GM) book and decreased the white matter lesions (WMLs) in cognitively normal people (Witte et al., 2013); another study demonstrated that lower DHA levels in red blood cells were associated with lower total brain book and college WML volumes (Bowman et al., 2012; Tan et al., 2012). Information technology has been demonstrated that a member of the major facilitator superfamily transporters, i.east., major Facilitator Superfamily Domain Containing 2A (Mfsd2a), previously an orphan transporter, is involved in the DHA uptake into the brain (Nguyen et al., 2014). In addition, other previous studies also signal the link between brain structure and MCI. For instance, 1 previous study constitute significant differences in the white thing integrity between MCI patients with and without cerebral amyloidopathy, indicating that alteration in white matter integrity can serve as a potential biomarker of MCI (Lee et al., 2017). In the same vein, several studies bespeak that WML is associated with cognitive decline and incident dementia (Bokura et al., 2006; Buyck et al., 2009; Debette et al., 2010; Debette and Markus, 2010). Moreover, WML and cerebral deficits have been reported in various brute models contributing to the machinery description (Hainsworth et al., 2012; Prins and Scheltens, 2015). For instance, in a mouse model of vascular dementia, astroglial nuclear factor-kB contributed to white matter damage and cerebral impairment (Saggu et al., 2016).

Therefore, this study aimed to investigate the relationships between dietary consumption of fish and brain construction as well as between brain construction and MCI in cognitively normal subjects. We examined the encephalon construction using the neuroimaging-derived measures, including the grey-affair brain healthcare quotient (GM-BHQ) and the partial-anisotropy brain healthcare quotient (FA-BHQ), which are canonical as the international standard (H.861.1) by International Telecommunication Spousal relationship Telecommunication Standardization Sector (ITU-T). The fish intake volume was calculated using the brief cocky-administered diet history questionnaire (BDHQ; Sasaki et al., 2000), and MCI was assessed using the Retentiveness Performance Index (MPI) of MCI screening method (MCI Screen; the Medical Care Corporation). The tested hypotheses are presented as follows: (1) the frequency of fish consumption correlates with higher FA-BHQ (and/or GM-BHQ) scores; (2) The fish consumption frequency and the FA-BHQ (and/or GM-BHQ) scores independently correlate with MPI, and the FA-BHQ (and/or GM-BHQ) scores have a stronger association with MPI than the fish consumption frequency.

Materials and Methods

Subjects

Eighty-four good for you participants (47 females and 37 males) were recruited in Kyoto, Japan, in May to June 2017, with support of a personnel service company. Potential participants who had a history of neurological, psychiatric, or medical weather that could affect the cardinal nervous system were excluded from the written report. Nosotros administered the three items of "Depression," a subscale of the NEO-5 Factor Inventory (Costa and McCrae, 1992), to screen for depression. Eight participants whose Depression scores were higher than ix points were excluded from this report because they might be suffering from depression. Finally, the study included 76 participants (45 females and 31 males), aged 31–59 years [hateful (M) ± standard departure (SD): 47.0 ± 7.i years]. The boilerplate age of participants was lower than other prior experiments regarding MCI (c.f., Langa and Levine, 2014) because the purpose of the current written report is to clarify the association between brain structure and "cognitive power in healthy people." This study was approved by the Ethics Committees of Kyoto University (approval number 27-P-thirteen) and was performed in accordance with the guidelines and regulations of the institute. All participants provided written informed consent earlier participation, and participant anonymity has been preserved.

Fish Intake Scale

We employed the following two of the four fish dietary addiction questions, which are included in the validated BDHQ (Sasaki et al., 2000), to appraise the frequency of fish intake: "how often take you eaten grilled fish during the preceding month?" and "how often have you eaten tempura or fried fish during the preceding month?" Participants were requested to respond to the following items on a 7-indicate scale: (one) 2 times and more than daily, (2) 1 fourth dimension daily, (iii) 4–6 times weakly, (4) 2–iii times weakly, (5) 1 fourth dimension weakly, (6) less than 1 time weakly, and (7) did not eat. The amount of fish consumed (gram) was calculated from the combined figure of these items by a estimator algorithm using the Standard Tables of Food Composition in Japan (Science and Engineering science Agency, 2010). Future, we call this figure "Fish Intake." Nosotros did non include other two questions of the four fish dietary habit questions, which are related to "sashimi and sushi" and "boiled fish, stew, soup, and miso soup," considering nosotros thought these 2 questions are ambiguous and might be differently perceived by different individuals.

Assessment of MCI

MCI was assessed using the Japanese version of the MCI Screen. MCI Screen is a x min staff-administered test to assess memory, executive role, and language (Shankle et al., 2005); it was developed past the Medical Care Corporation (Irvine, CA, U.s.) based on the protocol of the Consortium to Found a Registry for Alzheimer'due south Disease x-word recall exam. The score of MPI is computed past the results of sequential tasks, including three immediate recall tasks, a triadic comparing task, a judgment task, a delayed costless think chore, a cued-recall task, and a rehearsed think job. MPI ranges from 0 to 100 and larger values indicate better performance. The score can be used to discriminate amnestic or mixed cognitive MCI from normal crumbling with a 97% accurateness rate (Shankle et al., 2005).

MRI Data Conquering

All magnetic resonance imaging (MRI) information were collected using a 3-T Siemens scanner (Verio, Siemens Medical Solutions, Erlangen, Frg or MAGNETOM Prisma, Siemens, Munich, Germany) with a 32-aqueduct caput array coil. A high-resolution structural image was caused using a iii-dimensional (3D) T1-weighted magnetization-prepared rapid-conquering gradient echo (MP-RAGE) pulse sequence. The parameters were as follows: repetition time (TR), 1900 ms; repeat fourth dimension (TE), 2.52 ms; inversion fourth dimension (TI), 900 ms; flip angle, 9°; matrix size, 256 × 256; field of view (FOV), 256 mm; and slice thickness, one mm. DTI data were collected with spin-echo echo-planar imaging (SE-EPI) with GRAPPA (generalized autocalibrating partially parallel acquisitions). The paradigm slices were parallel to the orbitomeatal (OM) line. The parameters were as follows: TR, 14,100 ms; TE, 81 ms, flip angle, 90°; matrix size, 114 × 114; FOV, 224 mm; slice thickness, 2 mm. A baseline image (b = 0 s/mmⁱⁱ) and thirty different diffusion orientations were acquired with a b value of grand s/mm^two.

GM-BMQ and FA-BHQ

T1-weighted images were preprocessed and analyzed using Statistical Parametric Mapping 12 (SPM12; Wellcome Trust Centre for Neuroimaging, London, U.k.) running on MATLAB R2015b (Mathworks Inc., Sherborn, MA, United States). Each MPRAGE image was segmented into GM, white thing (WM), and cerebrospinal fluid (CSF) images. The segmented GM images were spatially normalized using the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) algorithm (Ashburner, 2007). A modulation footstep was besides incorporated into the preprocessing model to reflect regional volume and preserve the total GM volume from before the warp. As a concluding preprocessing step, all normalized, segmented, modulated images were smoothed with an 8 mm full width at one-half-maximum (FWHM) Gaussian kernel. Intracranial volume (ICV) was also calculated past summing the GM, WM, and CSF images for each subject. Proportional GM images were generated by dividing smoothed GM images by ICV to control for differences in whole-encephalon volume across participants. Using these proportional GM images, mean and standard deviation (SD) images were generated from all participants. Side by side, nosotros calculated the GM encephalon healthcare quotient (BHQ), which is similar to the intelligence quotient (IQ). The hateful value was defined as BHQ 100 and SD was defined as 15 BHQ points. By this definition, approximately 68% of the population is between BHQ 85 and BHQ 115, and 95% of the population is betwixt BHQ lxx and BHQ 130. Private GM quotient images were calculated using the following formula: 100 + fifteen × (individual proportional GM–mean)/SD. Regional GM quotients were and then extracted using an automated anatomical labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002) and averaged beyond regions to produce participant-specific GM-BHQs.

DTI data were preprocessed using FMRIB Software Library (FSL) 5.0.11 (Jenkinson et al., 2012). First, all diffusion images were aligned with the initial b0 image, and move correction and boil current baloney correction was performed using eddy_correct. Post-obit these corrections, FA images were calculated using dtifit. FA images were then spatially normalized into the standard Montreal Neurological Institute (MNI) space using FLIRT and FNIRT. Subsequently spatial normalization nosotros smoothed the data with an 8-mm FWHM. Mean and SD images were generated from all the FA images, and individual FA quotient images were calculated using the post-obit formula: 100 + 15 × (private FA–mean)/SD. Regional FA quotients were extracted using Johns Hopkins University (JHU) DTI-based white-matter atlases (Mori et al., 2008) and averaged beyond regions to produce participant-specific FA-BHQs. For more details, please see Nemoto et al. (2017).

Statistical Analysis

The hierarchical regression analysis was employed to assess the correlation betwixt MPI, FA-BHQ, and Fish Intake. In the model with FA-BHQ scores as the dependent variables, we entered the control variables (including GM-BHQ scores) in Pace 1 and Fish Intake in Step 2. In the model with MPI as the dependent variable, we entered the control variables in Step 1 and the main effects of FA-BHQ or Fish Intake in Step ii. In Footstep 3, we entered all the variables simultaneously. We added these variables to the models based on the following hypotheses: Fish Intake is closely related to FA-BHQ; FA-BHQ or Fish Intake is closely related to MPI; and FA-BHQ is more closely related to MPI than Fish Intake afterwards adjusting for demographic data. Trunk mass index (BMI) was included in the model considering obesity has been plant to be associated with an approximately 50–70% increased chance of MCI (Wang et al., 2017). In addition, length of didactics was as well included in the model because a seven-year period longitudinal study has revealed that the didactics level modulates the furnishings of WML on the hazard of MCI (Mortamais et al., 2014), possibly because of stronger myelination and more richly connected fiber tracts in the white thing in highly educated people (Teipel et al., 2009). Annual income, occupation with the longest tenure in her/his life, and the occupation tenure were too included in the regression analysis because income and occupation were found to correlate significantly with MCI in a previous study (Kengsakul et al., 2015). The significance level was determined at p < 0.05. All statistical analyses were performed using IBM SPSS Statistics Version xx (IBM Corp., Armonk, NY, United States). Data used for the analysis is provided in Supplementary Tabular array S1.

Results

Equally the population ages worldwide, the prevalence of cognitive disorders including MCI is increasing. To develop timely prevention and early on handling strategies past identifying biological factors, we investigated the human relationship between dietary consumption of fish, brain construction, and MCI in cognitively normal subjects. The brain structure was assessed using neuroimaging-derived measures including GM-BHQ and FA-BHQ. Dietary consumption of fish was calculated using BDHQ, and MCI was assessed using the MPI of MCI Screen. MPI ranges from 0 to 100 and larger values indicates amend performance. Descriptive statistics of subjects and correlation coefficients between scales are shown in Table 1. FA-BHQ scores correlated with Fish Intake (r = 0.304, p < 0.01) and MPI (r = 0.370, p < 0.01). In improver, GM-BHQ scores correlated with age (r = -0.411, p < 0.001), sexual practice (r = 0.273, p < 0.05), BMI (r = -0.259, p < 0.05), and annual income (r = -0.340, p < 0.01), merely not with Fish Intake (r = -0.014, p > 0.05) or MPI (r = 0.163, p > 0.05). Therefore, we decided to include the GM-BHQ score in the model every bit a control variable, just not as a master variable, in the following analyses.

Table 1

Descriptive statistics of subjects and correlations coefficients betwixt scales.

Variable		Mean	SD	1	2	iii	four	5	6	vii	8	nine	10	11	12	xiii	xiv	15	sixteen	17	xviii
1	Age	47.026	7.129
2	Sex (male = 1, female = two)	one.592	0.495	0.067
3	BMI	22.697	3.388	–0.100	-0.533***
4	Length of pedagogy	xiv.763	1.688	−0.463***	−0.229*	0.205
five	Almanac income	six.355	3.595	0.130	−0.622***	0.313**	0.058
vi	Tenure	5.039	1.076	0.165	–0.170	0.051	–0.156	0.255*
seven	Management	0.053	0.225	0.099	−0.284*	0.341**	0.068	0.307**	0.102
8	Special/Technical	0.224	0.419	0.047	–0.068	–0.038	–0.094	0.212	0.098	–0.127
9	Role worker	0.355	0.482	0.180	0.225	−0.286*	–0.092	–0.143	0.127	–0.175	-0.398***
10	Sales	0.132	0.340	–0.100	0.006	0.028	–0.015	0.059	–0.160	–0.092	–0.209	−0.289*
11	Service	0.145	0.354	–0.107	0.113	–0.014	–0.031	–0.188	–0.085	–0.097	–0.221	−0.305**	–0.160
12	Security	0.013	0.115	–0.017	–0.139	0.009	0.085	0.118	0.104	–0.027	–0.062	–0.086	–0.045	–0.048
xiii	Production	0.026	0.161	–0.093	–0.031	0.129	0.121	–0.039	–0.160	–0.039	–0.088	–0.122	–0.064	–0.068	–0.019
fourteen	Transportation	0.013	0.115	–0.163	–0.139	0.046	0.223	0.021	–0.112	–0.027	–0.062	–0.086	–0.045	–0.048	–0.013	–0.019
15	Unemployed	0.039	0.196	–0.106	–0.107	0.232*	0.150	−0.266*	–0.071	–0.048	–0.109	–0.150	–0.079	–0.083	–0.023	–0.033	–0.023
16	GM-BHQ	99.792	5.578	−0.411***	0.273*	−0.259*	0.046	−0.340**	–0.109	–0.007	–0.093	0.061	–0.211	0.175	–0.094	0.126	0.063	0.022
17	FA-BHQ	96.477	3.429	–0.114	0.149	–0.071	0.081	0.029	–0.066	0.068	0.045	–0.040	0.149	–0.144	0.056	0.055	–0.077	–0.108	0.153
eighteen	Fish Intake	41.921	26.027	0.025	–0.156	0.162	0.091	0.220	–0.062	–0.010	–0.021	0.022	0.125	–0.066	0.043	–0.012	–0.107	–0.047	–0.014	0.304**
xix	MPI	73.510	7.434	−0.546***	0.068	0.089	0.412***	–0.189	–0.078	0.032	0.145	–0.192	–0.003	0.063	0.013	–0.114	0.042	0.077	0.163	0.370**	0.125

Table 2 shows the results of regression analyses. In Step 1, none of the control variables significantly correlated with FA-BHQ scores. In addition, in the model with MPI equally the dependent variable, age (R = 0.695, b = -0.483, p < 0.001), length of instruction (R = 0.695, b = 0.275, p = 0.018), and special/technical occupation (R = 0.695, b = 0.259, p = 0.021) significantly correlated with MPI, indicating that MPI is higher in those with younger ages, high educational backgrounds, and special/technical occupation than in those with older ages, low educational backgrounds, and other occupations. In Step 2, Fish Intake (R = 0.493, b = 0.309, p = 0.016) was significantly associated with higher FA-BHQ scores, and FA-BHQ scores (R = 0.760, b = 0.336, p < 0.001), Fish Intake (R = 0.720, b = 0.206, p = 0.042), and annual income (R = 0.760/720, b = -0.304/-0.299, p = 0.025/0.039) were significantly associated with higher MPI. In Step three, FA-BHQ scores (R = 0.766, b = 0.301, p = 0.003) were significantly associated with MPI, simply Fish Intake (R = 0.766, b = 0.113, p = 0.251) was not.

TABLE 2

Multiple regression analysis of FA-BHQ, Fish Intake, and MPI.

	FA-BHQ				MPI
	Step ane		Stride 2		Step ane		Step 2				Step 3
	β^a,b	p-value	β^a,b	p-value	β^a,b	p-value	β^a,b	p-value	β^a,b	p-value	β^a,b	p-value
Demographic variables
Historic period	–0.039	0.811	–0.096	0.542	–0.483	< 0.001***	–0.470	< 0.001***	–0.521	< 0.001***	–0.492	< 0.001***
Sex (male = i, female person = ii)	0.277	0.132	0.291	0.101	0.142	0.324	0.049	0.713	0.151	0.281	0.064	0.634
BMI	0.021	0.894	–0.050	0.748	0.098	0.433	0.091	0.425	0.051	0.681	0.066	0.569
Length of education	0.111	0.445	0.067	0.633	0.275	0.018*	0.238	0.026*	0.246	0.031*	0.226	0.035*
Annual income	0.161	0.380	0.087	0.627	–0.249	0.088	–0.304	0.025*	–0.299	0.039*	–0.325	0.017*
Tenure	–0.035	0.789	0.006	0.960	0.042	0.684	0.054	0.568	0.069	0.493	0.067	0.475
Management	0.105	0.460	0.171	0.222	0.168	0.137	0.133	0.199	0.212	0.060	0.160	0.131
Special/Technical	0.085	0.542	0.111	0.409	0.259	0.021*	0.230	0.024*	0.276	0.012*	0.243	0.018
Sales	0.176	0.215	0.150	0.271	0.012	0.914	–0.047	0.645	–0.005	0.961	–0.051	0.621
Service	–0.126	0.353	–0.098	0.453	0.038	0.720	0.081	0.411	0.057	0.585	0.086	0.378
Security	0.095	0.442	0.090	0.448	0.035	0.721	0.003	0.975	0.031	0.740	0.004	0.962
Production	0.033	0.795	0.067	0.591	–0.161	0.116	–0.172	0.066	–0.139	0.165	–0.159	0.091
Transportation	–0.079	0.534	–0.024	0.843	–0.048	0.628	–0.022	0.811	–0.012	0.903	–0.005	0.959
Unemployed	–0.049	0.733	–0.022	0.871	–0.049	0.660	–0.033	0.747	–0.032	0.772	–0.025	0.806
GM-BHQ	0.191	0.226	0.115	0.457	–0.094	0.450	–0.158	0.169	–0.145	0.241	–0.179	0.123
Chief variables
FA-BHQ							0.336	< 0.001***			0.301	0.003**
Fish Intake			0.309	0.016*					0.206	0.042*	0.113	0.251
R	0.404	0.693	0.493	0.308	0.695	< 0.001***	0.760	< 0.001***	0.720	< 0.001***	0.766	< 0.001***
R ²	0.163		0.243		0.482		0.577		0.518		0.586

In summary, Fish Intake had a positive correlation with FA-BHQ scores afterwards adjusting for demographic information. Fish Intake or FA-BHQ scores had a positive correlation with MPI after adjusting for demographic information. In add-on, FA-BHQ scores had a higher positive correlation with MPI than Fish Intake did.

We next examined the advisable frequency of fish intake in Tables 3, 4. Effigy i is drawn using the information appeared in Table 3. Multiple comparing of the adapted-mean FA-BHQ scores with analysis of covariance (ANCOVA) revealed significant differences betwixt "less than one fourth dimension per week" and "ii times per week" (p = 0.005) and between "less than 1 fourth dimension per calendar week" and "more than ii times per week" (p = 0.034), consequent with the issue of regression model Step 2 (with FA-BHQ every bit the dependent variable) in Table 2. In add-on, multiple comparisons using adjusted-mean MPI scores with ANCOVA institute significant differences between "less than 1 time per calendar week" and "more than 2 times per week" (p = 0.037), consistent with the result of regression model Step two (with MPI as the dependent variable) in Table 2. Withal, no meaning differences were found if FA-BHQ scores were used as control variables, consistent with the result of regression model Footstep 3 (with MPI equally the dependent variable) in Table two. Further, multiple comparison of the not-adjusted mean FA-BHQ scores using one-style analysis of variance (ANOVA) with the Scheffe's post hoc test revealed pregnant differences between "less than one time per week" and "2 times per week" (p = 0.017), merely not betwixt "less than 1 time per calendar week" and "more two times per week" (p = 0.152). However, multiple comparisons of the non-adjusted hateful scores of MPI using ANOVA with the Scheffe'southward postal service hoc test showed no meaning differences.

TABLE iii

Fish intake frequency and FA-BHQ.

	Fish intake frequency	Number of subjects	Non-adapted hateful^c	Adjusted mean^d	p-value of multiple comparisons
0	Less than i time per calendar week	17	94.336 ± 3.591	94.369
ane	1 time per week	24	96.451 ± 3.010	96.552	0.070^0–1
2	2 times per week	21	97.842 ± 3.251	97.641	0.005^0–2**, 0.313^1–2
three	More than ii times per week	14	97.074 ± 3.208	97.163	0.034^0–three*, 0.613^1–three, 0.693^2–iii

TABLE iv

Fish intake frequency and MPI.

	Fish intake frequency	Number of subjects	Non-adjusted mean^c	Adapted hateful^d	p-value of multiple comparisons
0	Less than ane time per week	17	72.764 ± 5.883	70.875
1	1 time per calendar week	24	73.387 ± vii.238	74.722	0.068^0–1
two	2 times per calendar week	21	73.516 ± eight.845	72.810	0.329^0–2, 0.312^i–two
3	More than 2 times per week	14	74.617 ± vii.814	75.681	0.037^0–iii*, 0.650^1–3, 0.179^2–3

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Fish intake frequency and FA-BHQ. The graph shows hateful and standard error.

Figures 2, 3 show images of MRI for subjects with the highest and the lowest scores of FA-BHQ, respectively.

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MRI image of the highest FA-BHQ score.

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MRI paradigm of the everyman FA-BHQ score.

Discussion

As the population ages worldwide, the prevalence of cognitive disorders, including MCI, is growing (Alzheimer's Disease International, 2015). MCI appears in ten–twenty% of adults aged 65 years and older (Langa and Levine, 2014) and is generally referred to as an intermediate stage between normal cerebral aging and dementia (Vega and Newhouse, 2014). To develop timely prevention and early treatment strategies, nosotros investigated the relationship between dietary consumption of fish and the encephalon structure also as betwixt the encephalon structure and MCI in cognitively normal subjects. The brain structure was assessed using neuroimaging-derived measures including GM-BHQ and FA-BHQ, which are approved as the international standard (H.861.1) by ITU-T. Dietary consumption of fish was calculated using BDHQ (Sasaki et al., 2000), and MCI was assessed using the MPI of MCI Screen (the Medical Care Corporation). MPI ranges from 0 to 100 and larger values point better performance. The present written report indicates that fish intake is positively associated with both FA-BHQ and MPI, and FA-BHQ scores are more strongly associated with MPI than fish intake.

One previous study plant meaning differences in the white thing integrity betwixt MCI patients with and without cerebral amyloidopathy, indicating that amending in white matter integrity can serve every bit a potential biomarker of MCI (Lee et al., 2017). In addition, several studies indicate that WML is associated with cerebral decline and incident dementia (Bokura et al., 2006; Buyck et al., 2009; Debette et al., 2010. Debette and Markus, 2010). Further, longitudinal studies reveals that WM DTI indices, including FA, tin can be used to predict cognitive reject and medial temporal lobe cloudburst in subjective cognitive impairment (SCI) and MCI patients, indicating that DTI-derived information tin can be used as the predictor of conversion from MCI to AD (Selnes et al., 2013). Moreover, lower DHA levels in red blood cells are associated with greater WML volumes (Bowman et al., 2012; Tan et al., 2012), and daily fish oil supplementation has been found to subtract WMLs in cognitively normal people (Witte et al., 2013). In addition, it has been demonstrated that a member of the major facilitator superfamily, Mfsd2a (previously an orphan transporter), is the major transporter for DHA uptake into the encephalon (Nguyen et al., 2014).

The nowadays results were derived from healthier and younger participants than those in almost other studies. Our findings are in line with those in previous studies, but our written report further plant that the whole brain integrity measured by the FA-BHQ might be involved in the interaction between fish intake and MCI in good for you people. In other words, FA-BHQ may be used to place people at loftier risk of MCI to provide the proper intervention. The findings in our study suggest that people with a good whole brain health status, measured by FA-BHQ, tend to accept lower risks of MCI. Although some previous studies constitute a direct consequence of fish intake on prevention of MCI, the findings were limited and inconsistent (Zeng et al., 2017). Notably, the present written report indicates that broader approaches can be used to foreclose MCI by focusing on non merely dietary factors but also FA-BHQ scores.

Additionally, multiple comparisons showed that FA-BHQ scores were significantly different between "less than 1 time per week" and "2 times per week" as well as between "less than 1 time per calendar week" and "more than 2 times per calendar week" fish intakes. These results indicate that fish diet should exist taken at least 2 times a week to maintain high FA-BHQ scores. As shown in Figure 1, FA-BHQ scores of "more than 2 times per calendar week" were marginally lower than those of "ii times per calendar week" considering intake of fish dishes is more than likely to be accompanied with salad oil consumption, thus negatively affecting appropriate balance betwixt omega-6 and omega-3 fatty acids and increasing the risk for overweight/obesity and coronary heart disease (Simopoulos, 2008).

Our analysis showed no clan betwixt fish intake and GM-BHQ. The event contradicts with the finding in the study by Raji et al. (2014), which indicates that consuming fish dishes at least weekly is related to larger GM volumes in the brain areas responsible for memory and cognition in cognitively normal elderly individuals. The discrepancy may upshot from the fact that many factors, including not only biological but besides sociological factors, bear on GM-BHQ, every bit shown in our previous studies (Nemoto et al., 2017; Kokubun et al., 2018; Kokubun and Yamakawa, 2019). In this sense, our findings are in line with another finding that although the brain volume was significantly related to fish consumption, information technology was not significantly associated with intake of plasma omega-3 fatty acids, suggesting that lifestyle factors accompanied by dietary intake of fish can also touch on GM-BHQ (Raji et al., 2014).

Our study showed no clan between GM-BHQ and MPI. This result is in line with that in a previous written report, which indicates that WM tract degeneration is prominent in SCI and MCI patients and is at to the lowest degree in part contained of overlying gray matter atrophy (Selnes et al., 2012). In improver, Agosta et al. (2014) found that compared with healthy controls and cognitively unimpaired Parkinson's illness patients, patients with Parkinson's disease and MCI showed WM abnormalities in the inductive superior corona radiata, genu, and corpus callosum and the inductive inferior fronto-occipital, uncinate, and superior longitudinal fasciculi bilaterally, although no GM atrophy was found (Agosta et al., 2014).

This report has two limitations. First, the clan of brain health with actual diseases was not examined in this study, and the analogy of the association of brain health with actual diseases may have improved the validity of the results. Second, the sample size was small-scale in this study, and studies with a larger sample size may have increased the generalizability of the results. Therefore, time to come studies are needed to explore the human relationship between FA-BHQ and bodily diseases using larger sample sizes to discern the mechanisms.

Data Availability Statement

All datasets generated for this study are included in the article/Supplementary Fabric.

Ethics Statement

The studies involving human being participants were reviewed and approved by the Ethics Committees of Kyoto Academy. The patients/participants provided their written informed consent to participate in this written report.

Author Contributions

KK wrote the principal manuscript text and prepared the figures and tables. KN did MRI data analysis including GM- and FA-BHQ adding. YY was responsible for the conceptualization, data curation, funding acquisition, and projection administration. All authors reviewed and edited the manuscript.

Disharmonize of Interest

YY was employed past NTT Data Institute of Management Consulting, Inc. The remaining authors declare that the research was conducted in the absence of whatever commercial or financial relationships that could be construed as a potential disharmonize of interest.

Footnotes

Funding. This work was funded by the ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Nihon).

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