Imagine your brain as a complex machine with many moving parts. In order to keep it running smoothly, it needs the right fuel. Just like a car needs gasoline, your brain relies on a variety of metabolites to function properly. But what happens when there’s a glitch in the system? Scientists have discovered that changes in the plasma metabolic profile can be an early sign of mild cognitive impairment (MCI), a condition that affects memory and thinking abilities. By analyzing the metabolites in the blood, researchers can identify unique biomarkers that signal the presence of MCI. But they didn’t stop there! They also found that combining these metabolites with physical performance tests, like hand grip strength and walking speed, can provide an even more accurate diagnosis. This breakthrough research has the potential to revolutionize how we detect and treat MCI, allowing for earlier interventions and improved outcomes. If you’re intrigued by the inner workings of your brain and want to learn more about this fascinating study, dive into the full research article!

ObjectiveUnbiased metabolic profiling has been initiated to identify novel metabolites. However, it remains a challenge to define reliable biomarkers for rapid and accurate diagnosis of mild cognitive impairment (MCI). Our study aimed to evaluate the association of serum metabolites with MCI, attempting to find new biomarkers and combination models that are distinct for MCI.MethodsA total of 380 participants were recruited (mean age: 72.5 ± 5.19 years). We performed an untargeted metabolomics analysis on older adults who underwent the Mini-Mental State Examination (MMSE), the Instrumental Activities of Daily Living (IADL), and physical performance tests such as hand grip, Timed Up and Go Test (TUGT), and walking speed. Orthogonal partial least squares discriminant analysis (OPLS-DA) and heat map were utilized to distinguish the metabolites that differ between groups.ResultsAmong all the subjects, 47 subjects were diagnosed with MCI, and methods based on the propensity score are used to match the MCI group with the normal control (NC) group (n = 47). The final analytic sample comprised 94 participants (mean age: 75.2 years). The data process from the metabolic profiles identified 1,008 metabolites. A cluster and pathway enrichment analysis showed that sphingolipid metabolism is involved in the development of MCI. Combination of metabolite panel and physical performance were significantly increased discriminating abilities on MCI than a single physical performance test [model 1: the area under the curve (AUC) = 0.863; model 2: AUC = 0.886; and model 3: AUC = 0.870, P < 0.001].ConclusionIn our study, untargeted metabolomics was used to detect the disturbance of metabolism that occurs in MCI. Physical performance tests combined with phosphatidylcholines (PCs) showed good utility in discriminating between NC and MCI, which is meaningful for the early diagnosis of MCI.

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