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How biomarker patterns can be utilized to identify individuals with a high disease burden: a bioinformatics approach towards predictive, preventive, and personalized (3P) medicine.

ABSTRACT: Prevalences of non-communicable diseases such as depression and a range of somatic diseases are continuously increasing requiring simple and inexpensive ways to identify high-risk individuals to target with predictive and preventive approaches. Using k-mean cluster analytics, in study 1, we identified biochemical clusters (based on C-reactive protein, interleukin-6, fibrinogen, cortisol, and creatinine) and examined their link to diseases. Analyses were conducted in a US American sample (from the Midlife in the US study, N = 1234) and validated in a Japanese sample (from the Midlife in Japan study, N = 378). In study 2, we investigated the link of the biochemical clusters from study 1 to childhood maltreatment (CM). The three identified biochemical clusters included one cluster (with high inflammatory signaling and low cortisol and creatinine concentrations) indicating the highest disease burden. This high-risk cluster also reported the highest CM exposure. The current study demonstrates how biomarkers can be utilized to identify individuals with a high disease burden and thus, may help to target these high-risk individuals with tailored prevention/intervention, towards personalized medicine. Furthermore, our findings raise the question whether the found biochemical clusters have predictive character, as a tool to identify high-risk individuals enabling targeted prevention. The finding that CM was mostly prevalent in the high-risk cluster provides first hints that the clusters could indeed have predictive character and highlight CM as a central disease susceptibility factor and possibly as a leverage point for disease prevention/intervention.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-021-00255-0.

SUBMITTER: Bertele N

PROVIDER: S-EPMC8648886 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundQuantification of motor performance has a promising role in personalized medicine by diagnosing and monitoring, e.g. neurodegenerative diseases or health problems related to aging. New motion assessment technologies can evolve into patient-centered eHealth applications on a global scale to support personalized healthcare as well as treatment of disease. However, uncertainty remains on the limits of generalizability of such data, which is relevant specifically for preventive or predictive applications, using normative datasets to screen for incipient disease manifestations or indicators of individual risks.ObjectiveThis study explored differences between healthy German and Japanese adults in the performance of a short set of six motor tests.MethodsSix motor tasks related to gait and balance were recorded with a validated 3D camera system. Twenty-five healthy adults from Chiba, Japan, participated in this study and were matched for age, sex, and BMI to a sample of 25 healthy adults from Berlin, Germany. Recordings used the same technical setup and standard instructions and were supervised by the same experienced operator. Differences in motor performance were analyzed using multiple linear regressions models, adjusted for differences in body stature.ResultsFrom 23 presented parameters, five showed group-related differences after adjustment for height and weight (R 2 between .19 and .46, p<.05). Japanese adults transitioned faster between sitting and standing and used a smaller range of hand motion. In stepping-in-place, cadence was similar in both groups, but Japanese adults showed higher knee movement amplitudes. Body height was identified as relevant confounder (standardized beta >.5) for performance of short comfortable and maximum speed walks. For results of posturography, regression models did not reveal effects of group or body stature.ConclusionsOur results support the existence of a population-specific bias in motor function patterns in young healthy adults. This needs to be considered when motor function is assessed and used for clinical decisions, especially for personalized predictive and preventive medical purposes. The bias affected only the performance of specific items and parameters and is not fully explained by population-specific ethnic differences in body stature. It may be partially explained as cultural bias related to motor habits. Observed effects were small but are expected to be larger in a non-controlled cross-cultural application of motion assessment technologies with relevance for related algorithms that are being developed and used for data processing. In sum, the interpretation of individual data should be related to appropriate population-specific or even better personalized normative values to yield its full potential and avoid misinterpretation.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-021-00236-3.

Project description:The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS).We applied clinical informatic approaches and developed a questionnaire-suboptimal health status questionnaire-25 (SHSQ-25) for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross-sectional study of 3,405 participants in China.We found a correlation between SHS and systolic blood pressure, diastolic blood pressure, plasma glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol among men, and a correlation between SHS and systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides and HDL cholesterol among women.The SHSQ-25 is a self-rated questionnaire of perceived health complaints, which can be used as a new instrument for PPPM. An ongoing longitudinal SHS cohort survey (China Sub-optimal Health Cohort Study, COACS) consisting of 50,000 participants will provide a powerful health trial to use SHSQ-25 for its application to PPPM through patient stratification and therapy monitoring using innovative technologies of predictive diagnostics and prognosis: an effort of paradigm shift from reactive to predictive medicine.

Project description:BackgroundSeveral ocular factors have been identified for primary angle-closure glaucoma (PACG), such as a small cornea, elevated intraocular pressure (IOP), shallow anterior chamber, and short axial length (AL). However, the relationship between the severity of PACG and various ocular parameters [IOP, anterior chamber depth, AL, central corneal thickness] is not fully understood.MethodsA 7-year cross-sectional study. A total of 2254 eyes of 1312 PACG patients (females = 856 [1479 eyes] and males = 456 [775 eyes]) were included. A detailed eye examination was performed. The participants were categorized into gender subgroups followed by subdivision into three different severity groups according to their mean deviation (MD) of the visual fields results as follows: mild (MD ≤ 6 dB), moderate (MD 6-12 dB), and severe (MD > 12 dB) PACG. The associations of ocular biometry with severity of PACG were analyzed using paired Student's t test, multivariate linear regression, and logistic regression analysis.ResultsThere was a significant positive correlation between the MD and AL in the female subgroup (B = 0.663, p = 0.001, 95%CI = 1.070 to 1.255) but not in the male subgroup. Increased AL levels (mild [OR = 1], moderate [OR = 1.047, p = 0.062, 95%CI = 0.947 to 2.462], and severe [OR = 1.274, p < 0.001, 95%CI = 1.114 to 1.457]) were only associated with the severity of PACG in females. Paired Student's t tests showed that the long AL female eyes have a higher MD value than in the short AL female eyes (mean difference = 3.09, t = 6.846, p < 0.001) in the same subjects, but not in the male subgroup (p = 0.648).ConclusionsThe AL was positively and significantly related to the severity of PACG in female but not male subjects. This finding refers to the PACG pathogenesis and suggests the use of AL assessment in glaucoma monitoring, diagnosis, and progression. This may contribute to further development of personalized strategies in preventive medicine.

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How biomarker patterns can be utilized to identify individuals with a high disease burden: a bioinformatics approach towards predictive, preventive, and personalized (3P) medicine.

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