Altered Gene Expression Profiles in Peripheral Blood Mononuclear Cells in Obese Subjects

Jang, Kyungho; Tong, Teng; Lee, Jinhui; Park, Tae Sun; Lee, Howard

doi:10.1159/000507817

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…[ 35–37 ] Different gene expression studies performed in PBMC samples have assessed a differential expression profile in lean and people with obesity. [ 24,25 ] However, little is known about the differences in PBMC gene expression regulation in people with healthy obesity and in subjects with MONW. In the present study, we performed a global gene expression profile analysis in PBMC in healthy volunteers with NW, OW‐OB, or MONW, to assess their transcriptome profile to search for early metabolic risk biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Whole‐Genome Transcriptomics of PBMC to Identify Biomarkers of Early Metabolic Risk in Apparently Healthy People with Overweight‐Obesity and in Normal‐Weight Subjects

Costa

Stelt

Reynés

et al. 2023

Molecular Nutrition Food Res

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Scope Peripheral blood mononuclear cells (PBMC) provide a useful and minimally invasive source of biomarkers. Here to identify PBMC transcriptomic biomarkers predictive of metabolic impairment related to increased adiposity is aimed. Methods and results The study analyzed the global PBMC transcriptome in metabolically healthy (normoglycemic) volunteers with overweight‐obesity (OW‐OB, n = 12), and in subjects with metabolically obese normal‐weight (MONW, n = 5) phenotype, in comparison to normal‐weight (NW, n = 12) controls. The study identifies 1072 differentially expressed genes (DEGs) in OW‐OB versus NW and 992 in MONW versus NW. Hierarchical clustering of the top 100 DEGs clearly distinguishes OW‐OB and MONW from NW. Remarkably, the OW‐OB and MONW phenotypes share 257 DEGs regulated in the same direction. The top up‐regulated gene CXCL8, coding for interleukin 8, with a role in obesity‐related pathologies, is of special interest as a potential marker for predicting increased metabolic risk. CXCL8 expression is increased mainly in the MONW group and correlated directly with C‐reactive protein levels. Conclusions PBMC gene expression analysis of CXCL8 or a pool of DEGs may be used to identify early metabolic risk in an apparently healthy population regardless of their BMI, i.e., subjects with OW‐OB or MONW phenotype and to apply adequate and personalized nutritional preventive strategies.

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Section: Discussionmentioning

confidence: 99%

Whole‐Genome Transcriptomics of PBMC to Identify Biomarkers of Early Metabolic Risk in Apparently Healthy People with Overweight‐Obesity and in Normal‐Weight Subjects

Costa

Stelt

Reynés

et al. 2023

Molecular Nutrition Food Res

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“…[ 12 , 17 , 18 ] In this study, a VAT ≥ 100 cm 2 was defined as visceral obesity. Although the World Health Organization suggests that the BMI cutoff for obesity in Asian people should be 27.5 kg/m 2,[ 19 ] research on Asian NAFLD has used lower BMI thresholds for obesity (BMI ≥ 25 kg/m 2 ). [ 20 ] However, the definition of obesity by Chinese public health authorities has been consistent with a BMI ≥ 28 kg/m 2 for several years.…”

Section: Methodsmentioning

confidence: 99%

Metabolic status indicators and influencing factors in non-obese, non-centrally obese nonalcoholic fatty liver disease

Huang¹,

Wei²,

Yu³

et al. 2023

Medicine

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Non-obese nonalcoholic fatty liver disease (NAFLD) is characterized by metabolic disorders and related complications. This study aimed to provide an integrated description of clinical, metabolic, and influencing factors for a specific category of patients with non-obese NAFLD. A total of 36 participants with body mass index (BMI) < 28 kg/m 2 and visceral adipose tissue < 100 cm 2 were classified into 2 groups: the non-obese, non-centrally obese control group (n = 17) and non-obese, non-centrally obese NAFLD group (n = 19). Hypertriglyceridemia, impaired fasting glucose, low high-density lipoprotein cholesterol levels, and hypertension were used to determine whether participants were metabolically abnormal. Based on a logistic regression model, odds ratios for the factors influencing NAFLD with 95% confidence intervals were calculated. Insulin resistance (IR) and fasting plasma glucose (FPG) levels were higher in the NAFLD group than in the control group ( P < .05). The NAFLD group had a higher metabolic abnormality rate than the healthy control group (36.84% vs 5.88%, P = .044). Correlation analysis showed that IR was positively correlated with FPG and triglyceride ( P < .05). BMI was the main influencing factor of NAFLD (regression coefficient β = 0.631; odds ratio = 1.879; 95% confidence interval, 1.233–2.863). NAFLD patients with a BMI < 28 kg/m 2 and visceral adipose tissue < 100 cm 2 had more apparent IR, higher FPG, and a higher metabolic abnormality rate. IR may be affected by FPG and triglyceride. Even in non-obese and non-centrally obese individuals, BMI should be controlled to avoid NAFLD.

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“…Preliminary results have already shown sufficient differences in the gene expression between the obese and the non-obese subjects; the main objective is to identify the correlations between gene profiles and clinical obesity indices [ 103 ]. SIRT1 gene is considered an obesity biomarker, as its compensatory increase brings some additional metabolic dysfunctions in the body due to altered peptides in the endocrine system.…”

Section: Identification and Diagnosis Of Non-canonical Obesity Syndromesmentioning

confidence: 99%

Reviewed and updated Algorithm for Genetic Characterization of Syndromic Obesity Phenotypes

Rodríguez‐López

Gimeno-Ferrer

Santos

et al. 2022

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Background: Individuals with a phenotype of early-onset severe obesity associated with intellectual disability can have molecular diagnoses ranging from monogenic to complex genetic traits. Severe overweight is the major sign of a syndromic physical appearance and predicting the influence of a single gene and/or polygenic risk profile is extremely complicated among the majority of the cases. At present, considering rare monogenic bases as the principal etiology for the majority of obesity cases associated with intellectual disability is scientifically poor. The diversity of the molecular bases responsible for the two entities makes the appliance of the current routinely powerful genomics diagnostic tools essential. Objective: Clinical investigation of these difficult-to-diagnose patients requires pediatricians and neurologists to use optimized descriptions of signs and symptoms to improve genotype correlations. Methods: The use of modern integrated bioinformatics strategies which are conducted by experienced multidisciplinary clinical teams. Evaluation of the phenotype of the patient’s family is also of importance. Results: The next step involves discarding the monogenic canonical obesity syndromes and considering infrequent unique molecular cases, and/or then polygenic bases. Adequate management of the application of the new technique and its diagnostic phases is essential for achieving good cost/efficiency balances. Conclusion: With the current clinical management, it is necessary to consider the potential coincidence of risk mutations for obesity in patients with genetic alterations that induce intellectual disability. In this review, we describe an updated algorithm for the molecular characterization and diagnosis of patients with a syndromic obesity phenotype.

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Altered Gene Expression Profiles in Peripheral Blood Mononuclear Cells in Obese Subjects

Cited by 11 publications

References 36 publications

Whole‐Genome Transcriptomics of PBMC to Identify Biomarkers of Early Metabolic Risk in Apparently Healthy People with Overweight‐Obesity and in Normal‐Weight Subjects

Whole‐Genome Transcriptomics of PBMC to Identify Biomarkers of Early Metabolic Risk in Apparently Healthy People with Overweight‐Obesity and in Normal‐Weight Subjects

Metabolic status indicators and influencing factors in non-obese, non-centrally obese nonalcoholic fatty liver disease

Reviewed and updated Algorithm for Genetic Characterization of Syndromic Obesity Phenotypes

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