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Introduction
Multi-modality medical imaging study, especially brain MRI, greatly facilitates the research on subclinical brain disease. However, there is still a lack of such studies with a wider age span of participants. The Multi-modality MEdical imaging sTudy bAsed on KaiLuan Study (META-KLS) was designed to address this issue with a large sampl...
Citations
... Since December 2020, participants have been voluntarily recruited in the Multi-modality MEdical imaging sTudy bAsed on KLS (META-KLS), a subset of the KLS. Additional File 1 [22,[26][27][28][29][30] and Additional File 2: Fig. S1 provide a brief illustration of the META-KLS, and the detailed descriptions for this prospective cohort have been published recently [31]. Specifically, participants in the META-KLS voluntarily performed multi-modality brain magnetic resonance imaging (MRI) examinations to facilitate the assessment of brain health. ...
... Neuroimaging data were acquired using a 3.0-Tesla MRI scanner (General Electric 750W, Milwaukee, WI, USA). According to the META-KLS protocol, standardized sequences included three-dimensional (3D) brain volume (BRAVO) for brain macrostructural volume analysis based on high-resolution T1-weighted imaging (T1WI), diffusion tensor imaging (DTI) for brain microstructural integrity analysis, 3D fluid-attenuated inversion recovery (FLAIR) for WMH analysis, T2-weighted imaging and susceptibility-weighted angiography for cerebral small vessel disease (CSVD) evaluation, and diffusion-weighted imaging for the determination of ischemic stroke [31]. The parameters were listed in Additional File 3: Table S1. ...
Background
The relationship between variation in serum uric acid (SUA) levels and brain health is largely unknown. This study aimed to examine the associations of long-term variability in SUA levels with neuroimaging metrics and cognitive function.
Methods
This study recruited 1111 participants aged 25–83 years from a multicenter, community-based cohort study. The SUA concentrations were measured every two years from 2006 to 2018. We measured the intraindividual SUA variability, including the direction and magnitude of change by calculating the slope value. The associations of SUA variability with neuroimaging markers (brain macrostructural volume, microstructural integrity, white matter hyperintensity, and the presence of cerebral small vessel disease) and cognitive function were examined using generalized linear models. Mediation analyses were performed to assess whether neuroimaging markers mediate the relationship between SUA variation and cognitive function.
Results
Compared with the stable group, subjects with increased or decreased SUA levels were all featured by smaller brain white matter volume (beta = − 0.25, 95% confidence interval [CI] − 0.39 to − 0.11 and beta = − 0.15, 95% CI − 0.29 to − 0.02). Participants with progressively increased SUA exhibited widespread disrupted microstructural integrity, featured by lower global fractional anisotropy (beta = − 0.24, 95% CI − 0.38 to − 0.10), higher mean diffusivity (beta = 0.16, 95% CI 0.04 to 0.28) and radial diffusivity (beta = 0.19, 95% CI 0.06 to 0.31). Elevated SUA was also associated with cognitive decline (beta = − 0.18, 95% CI − 0.32 to − 0.04). White matter atrophy and impaired brain microstructural integrity mediated the impact of SUA increase on cognitive decline.
Conclusions
It is the magnitude of SUA variation rather than the direction that plays a critical negative role in brain health, especially for participants with hyperuricemia. Smaller brain white matter volume and impaired microstructural integrity mediate the relationship between increased SUA level and cognitive function decline. Long-term stability of SUA level is recommended for maintaining brain health and preventing cognitive decline.
... Since 2020, participants have been enrolled in a subset of the KLS, called the Multimodality Medical Imaging Study Based on Kailuan Study (META-KLS), which specifically focused on the analysis of neuroimaging. A detailed description of the META-KLS has been published [11]. These participants were recruited using the same method as in the KLS. ...
... Demographic questionnaires, clinical information, and laboratory examinations were prospectively collected every 2 years from 2006 to 2018, according to standardized protocols from 11 local hospitals [9,10]. Measurements of clinical features are described in the Supplementary Materials and the previously published protocol [11]. ...
... A standardized and validated neuroimaging data processing workflow was developed to enable an accurate, objective, highefficiency, and reproducible analysis. Detailed descriptions of the neuroimaging data processing can be found in the published protocol [11]. Neuroimaging features, including relative volume of brain macrostructure, relative volume of WMH, and brain microstructural integrity, were defined as the outcome of this study. ...
Background: The cumulative effect of body mass index (BMI) on brain health remains ill-defined. The effects of overweight on brain health across different age groups need clarification. We analyzed the effect of cumulative BMI on neuroimaging features of brain health in adults of different ages. Methods: This study was based on a multicenter, community-based cohort study. We modeled the trajectories of BMI over 16 years to evaluate cumulative exposure. Multimodality neuroimaging data were collected once for volumetric measurements of the brain macrostructure, white matter hyperintensity (WMH), and brain microstructure. We used a generalized linear model to evaluate the association between cumulative BMI and neuroimaging features. Two-sample Mendelian randomization analysis was performed using summary level of BMI genetic data from 681,275 individuals and neuroimaging genetic data from 33,224 individuals to analyze the causal relationships. Results: Clinical and neuroimaging data were obtained from 1,074 adults (25 to 83 years). For adults aged under 45 years, brain volume differences in participants with a cumulative BMI of >26.2 kg/m² corresponded to 12.0 years [95% confidence interval (CI), 3.0 to 20.0] of brain aging. Differences in WMH were statistically substantial for participants aged over 60 years, with a 6.0-ml (95% CI, 1.5 to 10.5) larger volume. Genetic analysis indicated causal relationships between high BMI and smaller gray matter and higher fractional anisotropy in projection fibers. Conclusion: High cumulative BMI is associated with smaller brain volume, larger volume of white matter lesions, and abnormal microstructural integrity. Adults younger than 45 years are suggested to maintain their BMI below 26.2 kg/m² for better brain health. Trial Registration: This study was registered on clinicaltrials.gov (Clinical Indicators and Brain Image Data: A Cohort Study Based on Kailuan Cohort; No. NCT05453877; https://clinicaltrials.gov/ct2/show/NCT05453877).
... Detailed descriptions of the rationale, design, and database building of META-KLS have been published previously. 19 The primary goal of this cohort was to investigate subclinical brain morphological and functional alterations related to various risk factors and provide high-quality evidence for the prevention and early intervention of neurological diseases and cerebrovascular diseases. As of September 2022, 1195 participants had performed brain MRI examination for once. ...
... The anthropometric parameters of participants were measured by well-trained medical staff following standard instruments and protocols. 19,20 While the participants stood barefoot in light clothing, body weight was measured to the precision of 0.1 kg using a calibrated platform scale. Standing height was accurate to the precision of 0.1 cm using a platform scale altimeter. ...
... The neuroimaging data collection has been described in detail elsewhere. 19 All brain MRI examinations were performed using a 3.0 T MRI scanner (GE Healthcare 750 W, Milwaukee, Wisconsin, USA). Briefly, the sequences used in this cohort study were three-dimensional (3D) brain volume for brain macrostructural volume evaluation based on high-resolution T1-weighted imaging (T1WI), diffusion tensor imaging (DTI) for brain microstructural integrity assessment, 3D fluid-attenuated inversion recovery (FLAIR) for the evaluation of WMH, susceptibilityweighted angiography (SWAN) for cerebral microbleeds (CMB), T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI) (with FLAIR together) for the detection of enlarged perivascular spaces (EPVS) and lacune. ...
Background
The relationship between the fluctuation in body size and brain health is poorly understood. This study aimed to examine the associations of long-term variability in body mass index (BMI) and waist-to-hip ratio (WHR) with neuroimaging metrics that approximate brain health.
Methods
This cohort study recruited 1114 participants aged 25–83 years from a multicenter, community-based cohort study in China. We modeled the BMI and WHR trajectories of participants during 2006–2018 and assessed the BMI and WHR variability (direction and speed of change) by calculating the slope. Generalized linear models were applied to investigate the associations of BMI and WHR variability with MRI markers of brain tissue volume, white matter microstructural integrity, white matter hyperintensity (WMH), and cerebral small vessel disease (CSVD).
Findings
Progressive weight gain during follow-up was associated with lower global fractional anisotropy (beta = −0.18, 95% confidence interval [CI] −0.34 to −0.02), higher mean diffusivity (beta = 0.15, 95% CI 0.01–0.30) and radial diffusivity (beta = 0.17, 95% CI 0.02–0.32). Weight loss was also associated with a lower burden of periventricular WMH (beta = −0.26, 95% CI −0.48 to −0.03) and a lower risk of moderate-to-severe basal ganglia enlarged perivascular spaces (BG-EPVS, odds ratio [OR] = 0.41, 95% CI 0.20–0.83). Among overweight populations, weight loss was linked with smaller volumes of WMH (beta = −0.47, 95% CI −0.79 to −0.15), periventricular WMH (beta = −0.57, 95% CI −0.88 to −0.26), and deep WMH (beta = −0.36, 95% CI −0.69 to −0.03), as well as lower risk of CSVD (OR = 0.22, 95% CI 0.08–0.62), lacune (OR = 0.12, 95% CI 0.01–0.91) and moderate-to-severe BG-EPVS (OR = 0.24, 95% CI 0.09–0.61). In adults with central obesity, WHR loss was positively associated with larger gray matter volume (beta = 0.50, 95% CI 0.11–0.89), hippocampus volume (beta = 0.62, 95% CI 0.15–1.09), and parahippocampal gyrus volume (beta = 0.85, 95% CI 0.34–1.37). The sex-stratification and age-stratification analyses revealed similar findings with the main results, with the pattern of associations significantly presented in the individuals at mid-life and late-life.
Interpretation
Long-term stability of BMI level is essential for maintaining brain health. Progressive weight gain is associated with impaired white matter microstructural integrity. Weight and WHR losses are associated with improved general brain health. Our results contribute to a better understanding of the integrated associations between variations in obesity measures and brain health.
Funding
This study was supported by grants No. 62171297 (Han Lv) and 61931013 (Zhenchang Wang) from the 10.13039/501100001809National Natural Science Foundation of China, No. 7242267 from the Beijing Natural Science Foundation (Han Lv), and No. [2015] 160 from the Beijing Scholars Program (Zhenchang Wang).
... The KaiLuan study is an ongoing population-based study executing in the Kailuan community of Tangshan in Northern China (14). From December 2020 to October 2021, a total of 910 patients were included. ...
... The MRI consisted of at least five sequences that followed predetermined standardized protocols: T1weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and susceptibility weighted imaging (SWI). In our previous study, we recorded the exact parameter settings for the five sequences (14). Based on the Neuroimaging Standards for Research into Small Vessel Disease (STRIVE) recommendation there are four main kinds of imaging markers: WMHs, lacunar, CMBs, and EPVS (16). ...
Background
As the retinal microvasculature shares similarities with the cerebral microvasculature, numerous studies have shown that retinal vascular is associated with cognitive decline. In addition, several population-based studies have confirmed the association between retinal vascular and cerebral small vessel disease (CSVD) burden. However, the association of retinal vascular with CSVD burden as well as cognitive function has not been explored simultaneously. This study investigated the relations of retinal microvascular parameters (RMPs) with CSVD burden and cognitive function.
Methods
We conducted a cross-sectional study of participants in the KaiLuan study. Data were collected from subjects aged ≥18 years old who could complete retinal photography and brain magnetic resonance imaging (MRI) between December 2020 to October 2021 in the Kailuan community of Tangshan. RMPs were evaluated using a deep learning system. The cognitive function was measured using the Montreal Cognitive Assessment (MoCA). We conducted logistic regression models, and mediation analysis to evaluate the associations of RMPs with CSVD burden and cognitive decline.
Results
Of the 905 subjects (mean age: 55.42±12.02 years, 54.5% female), 488 (53.9%) were classified with cognitive decline. The fractal dimension (FD) [odds ratio (OR), 0.098, 95% confidence interval (CI): 0.015–0.639, P=0.015] and global vein width (OR: 1.010, 95% CI: 1.005–1.015, P<0.001) were independent risk factors for cognitive decline after adjustment for potential confounding factors. The global artery width was significantly associated with severe CSVD burden (OR: 0.985, 95% CI: 0.974–0.997, P=0.013). The global vein width was sightly associated with severe CSVD burden (OR: 1.005, 95% CI: 1.000–1.010, P=0.050) after adjusting for potential confounders. The multivariable-adjusted odds ratios (95% CI) in highest tertile versus lowest tertile of global vein width were 1.290 (0.901–1.847) for cognitive decline and 1.546 (1.004–2.290) for severe CSVD burden, respectively. Moreover, CSVD burden played a partial mediating role in the association between global vein width and cognitive function (mediating effect 6.59%).
Conclusions
RMPs are associated with cognitive decline and the development of CSVD. A proportion of the association between global vein width and cognitive decline may be attributed to the presence of CSVD burden.
