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EBioMedicine Jun 2024Birth weight (BW) is associated with risk of cardiometabolic disease (CMD) in adulthood, which may depend on the state of obesity, in particular if developed at a young...
BACKGROUND
Birth weight (BW) is associated with risk of cardiometabolic disease (CMD) in adulthood, which may depend on the state of obesity, in particular if developed at a young age. We hypothesised that BW and a polygenic score (PGS) for BW were associated with cardiometabolic risk and related plasma protein levels in children and adolescents. We aimed to determine the modifying effect of childhood obesity on these associations.
METHODS
We used data from The cross-sectional HOLBAEK Study with 4263 participants (median [IQR] age, 11.7 [9.2, 14.3] years; 57.1% girls and 42.9% boys; 48.6% from an obesity clinic and 51.4% from a population-based group). We gathered information on BW and gestational age, anthropometrics, cardiometabolic risk factors, calculated a PGS for BW, and measured plasma proteins using Olink Inflammation and Cardiovascular II panels. We employed multiple linear regression to examine the associations with BW as a continuous variable and performed interaction analyses to assess the effect of childhood obesity on cardiometabolic risk and plasma protein levels.
FINDINGS
BW and a PGS for BW associated with cardiometabolic risk and plasma protein levels in childhood and adolescence. Childhood obesity modified the associations between BW and measures of insulin resistance, including HOMA-IR (βadj [95% CI per SD] for obesity: -0.12 [-0.15, -0.08]; normal weight: -0.04 [-0.08, 0.00]; Pinteraction = 0.004), c-peptide (obesity: -0.11 [-0.14, -0.08]; normal weight: -0.02 [-0.06, 0.02]; Pinteraction = 5.05E-04), and SBP SDS (obesity: -0.12 [-0.16, -0.08]; normal weight: -0.06 [-0.11, -0.01]; Pinteraction = 0.0479). Childhood obesity also modified the associations between BW and plasma levels of 14 proteins (e.g., IL15RA, MCP1, and XCL1; Pinteraction < 0.05).
INTERPRETATION
We identified associations between lower BW and adverse metabolic phenotypes, particularly insulin resistance, blood pressure, and altered plasma protein levels, which were more pronounced in children with obesity. Developing effective prevention and treatment strategies for this group is needed to reduce the risk of future CMD.
FUNDING
Novo Nordisk Foundation (NNF15OC0016544, NNF0064142 to T.H., NNF15OC0016692 to T.H. and A.K., NNF18CC0033668 to S.E.S, NNF18SA0034956 to C.E.F., NNF20SA0067242 to DCA, NNF18CC0034900 to NNF CBMR), The Innovation Fund Denmark (0603-00484B to T.H.), The Danish Cardiovascular Academy (DCA) and the Danish Heart Foundation (HF) (PhD2021007-DCA to P.K.R, 18-R125-A8447-22088 (HF) and 21-R149-A10071-22193 (HF) to M.A.V.L., PhD2023009-HF to L.A.H), EU Horizon (668031, 847989, 825694, 964590 to A.K.), Innovative Health Initiative (101132901 for A.K.), A.P. Møller Foundation (19-L-0366 to T.H.), The Danish National Research Foundation, Steno Diabetes Center Sjælland, and The Region Zealand and Southern Denmark Health Scientific Research Foundation.
PubMed: 38918147
DOI: 10.1016/j.ebiom.2024.105205 -
Med (New York, N.Y.) Jun 2024Obesity rates have nearly tripled in the past 50 years, and by 2030 more than 1 billion individuals worldwide are projected to be obese. This creates a significant...
BACKGROUND
Obesity rates have nearly tripled in the past 50 years, and by 2030 more than 1 billion individuals worldwide are projected to be obese. This creates a significant economic strain due to the associated non-communicable diseases. The root cause is an energy expenditure imbalance, owing to an interplay of lifestyle, environmental, and genetic factors. Obesity has a polygenic genetic architecture; however, single genetic variants with large effect size are etiological in a minority of cases. These variants allowed the discovery of novel genes and biology relevant to weight regulation and ultimately led to the development of novel specific treatments.
METHODS
We used a case-control approach to determine metabolic differences between individuals homozygous for a loss-of-function genetic variant in the small integral membrane protein 1 (SMIM1) and the general population, leveraging data from five cohorts. Metabolic characterization of SMIM1 individuals was performed using plasma biochemistry, calorimetric chamber, and DXA scan.
FINDINGS
We found that individuals homozygous for a loss-of-function genetic variant in SMIM1 gene, underlying the blood group Vel, display excess body weight, dyslipidemia, altered leptin to adiponectin ratio, increased liver enzymes, and lower thyroid hormone levels. This was accompanied by a reduction in resting energy expenditure.
CONCLUSION
This research identified a novel genetic predisposition to being overweight or obese. It highlights the need to investigate the genetic causes of obesity to select the most appropriate treatment given the large cost disparity between them.
FUNDING
This work was funded by the National Institute of Health Research, British Heart Foundation, and NHS Blood and Transplant.
PubMed: 38906141
DOI: 10.1016/j.medj.2024.05.015 -
Cureus Jun 2024Type 2 diabetes mellitus (T2DM) is a consequence of insulin resistance, insulin deficiency, or both. It is usually seen in adults and is a consequence of genetic...
Metformin Monotherapy With and Without Lifestyle Changes Affects Anthropometric Parameters, Blood Pressure, Blood Glucose, and Lipid Profile in Indian Patients With Newly Diagnosed Type 2 Diabetes.
INTRODUCTION
Type 2 diabetes mellitus (T2DM) is a consequence of insulin resistance, insulin deficiency, or both. It is usually seen in adults and is a consequence of genetic (polygenic inheritance), endogenous (obesity and or hormonal factors), and environmental factors (e.g., obesogenic environment, endocrine disrupting chemicals, stress, and medicines). The prevalence of T2DM has increased over the past few decades. South Asians, including Indians, are more prone to central adiposity and develop lifestyle diseases like T2DM at body mass index values lower than those considered normal for the Western population. Generally, the first line of treatment is metformin monotherapy with lifestyle changes in patients with T2DM. Most of the research conducted on this drug is on Western subjects. Since the Indian population has genetic differences in the site of deposition of adipose and is more prone to develop lifestyle diseases, the effect of metformin may be different in Indians.
METHODS
Seventy-one (34 female, non-pregnant, non-lactating) adults with newly diagnosed T2DM were recruited in this short-duration pilot study after obtaining written informed consent. Patients regularly taking any drug were excluded, as were patients with chronic comorbidities. Treatment was initiated with metformin 500 mg OD. Lifestyle changes were recommended according to the age and physical condition of the patients. Anthropometric parameters (age, weight, height, BMI, waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)), blood pressure, glycemic status (fasting and 2 h PP glucose and HbA1c), and lipid profile of the subjects were recorded before initiating and six months after initiating metformin monotherapy with lifestyle changes.
RESULTS
Small but statistically significant improvements were observed in the WHR,WHtR, blood pressure, blood glucose, and glycated hemoglobin. Although improvement was also observed in weight and lipid profile, these changes were not statistically significant.
CONCLUSION
This study shows that metformin monotherapy with lifestyle changes is suitable for patients of Indian origin and results in improvement in the WHR, WHtR, blood pressure, plasma glucose, and glycated hemoglobin.
PubMed: 38868550
DOI: 10.7759/cureus.62131 -
Research Square May 2024Satiation is the physiologic process that regulates meal size and termination, and it is quantified by the calories consumed to reach satiation. Given its role in energy...
Satiation is the physiologic process that regulates meal size and termination, and it is quantified by the calories consumed to reach satiation. Given its role in energy intake, changes in satiation contribute to obesity's pathogenesis. Our study employed a protocolized approach to study the components of food intake regulation including a standardized breakfast, a gastric emptying study, appetite sensation testing, and a satiation measurement by an test. These studies revealed that satiation is highly variable among individuals, and while baseline characteristics, anthropometrics, body composition and hormones, contribute to this variability, these factors do not fully account for it. To address this gap, we explored the role of a germline polygenic risk score, which demonstrated a robust association with satiation. Furthermore, we developed a machine-learning-assisted gene risk score to predict satiation and leveraged this prediction to anticipate responses to anti-obesity medications. Our findings underscore the significance of satiation, its inherent variability, and the potential of a genetic risk score to forecast it, ultimately allowing us to predict responses to different anti-obesity interventions.
PubMed: 38826309
DOI: 10.21203/rs.3.rs-4402499/v1 -
Journal of Gastrointestinal Surgery :... May 2024Bariatric surgery (BS) is currently the most effective long-term treatment of severe obesity. However, the interindividual variability observed in surgical outcomes...
BACKGROUND
Bariatric surgery (BS) is currently the most effective long-term treatment of severe obesity. However, the interindividual variability observed in surgical outcomes suggests a moderating effect of several factors, including individual genetic background. This study aimed to investigate the contribution of the genetic architecture of body mass index (BMI) to the variability in weight loss outcomes after BS.
METHODS
A total of 106 patients with severe obesity who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy were followed up for 5 years. Changes in BMI (BMIchange) and percentage of total weight loss (%TWL) were evaluated during the postoperative period. Polygenic risk scores (PRSs), including 50 genetic variants, were calculated for each participant to determine their genetic risk of high BMI based on a previous genome-wide association study. Generalized estimating equation models were used to study the role of the individual's polygenic score and other factors on BMIchange and %TWL in the long term after surgery.
RESULTS
This study found an effect of the polygenic score on %TWL and BMIchange, in which patients with lower scores had better outcomes after surgery than those with higher scores. Furthermore, when analyzing only patients who underwent RYGB, the results were replicated, showing greater weight loss after surgery for patients with lower polygenic scores.
DISCUSSION
Our results indicate that genetic background assessed with PRSs, along with other individual factors, such as biological sex, age, and preoperative BMI, has an effect on BS outcomes and could represent a useful tool for estimating surgical outcomes in advance.
PubMed: 38821212
DOI: 10.1016/j.gassur.2024.05.029 -
Genes Apr 2024Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous... (Review)
Review
Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous environments (e.g., sex, disease-affection status, or presence/absence of an exposure) and the other in response to continuous environments (e.g., physical activity, nutritional measurements, or continuous socioeconomic measures). Here we develop a novel model to jointly account for dichotomous and continuous environments. We develop the model in terms of a joint genotype-by-sex (for the dichotomous environment) and genotype-by-social determinants of health (SDoH; for the continuous environment). Using this model, we show how a depression variable, as measured by the Beck Depression Inventory-II survey instrument, is not only underlain by genetic effects (as has been reported elsewhere) but is also significantly determined by joint G×Sex and G×SDoH interaction effects. This model has numerous applications leading to potentially transformative research on the genetic and environmental determinants underlying complex diseases.
Topics: Gene-Environment Interaction; Humans; Models, Genetic; Genotype; Depression; Models, Statistical; Male
PubMed: 38790175
DOI: 10.3390/genes15050547 -
Obesity Pillars Sep 2024Obesity is a multifactorial neurohormonal disease that results from dysfunction within energy regulation pathways and is associated with increased morbidity, mortality,... (Review)
Review
BACKGROUND
Obesity is a multifactorial neurohormonal disease that results from dysfunction within energy regulation pathways and is associated with increased morbidity, mortality, and reduced quality of life. The most common form is polygenic obesity, which results from interactions between multiple gene variants and environmental factors. Highly penetrant monogenic and syndromic obesities result from rare genetic variants with minimal environmental influence and can be differentiated from polygenic obesity depending on key symptoms, including hyperphagia; early-onset, severe obesity; and suboptimal responses to nontargeted therapies. Timely diagnosis of monogenic or syndromic obesity is critical to inform management strategies and reduce disease burden. We outline the physiology of weight regulation, role of genetics in obesity, and differentiating characteristics between polygenic and rare genetic obesity to facilitate diagnosis and transition toward targeted therapies.
METHODS
In this narrative review, we focused on case reports, case studies, and natural history studies of patients with monogenic and syndromic obesities and clinical trials examining the efficacy, safety, and quality of life impact of nontargeted and targeted therapies in these populations. We also provide comprehensive algorithms for diagnosis of patients with suspected rare genetic causes of obesity.
RESULTS
Patients with monogenic and syndromic obesities commonly present with hyperphagia (ie, pathologic, insatiable hunger) and early-onset, severe obesity, and the presence of hallmark characteristics can inform genetic testing and diagnostic approach. Following diagnosis, specialized care teams can address complex symptoms, and hyperphagia is managed behaviorally. Various pharmacotherapies show promise in these patient populations, including setmelanotide and glucagon-like peptide-1 receptor agonists.
CONCLUSION
Understanding the pathophysiology and differentiating characteristics of monogenic and syndromic obesities can facilitate diagnosis and management and has led to development of targeted pharmacotherapies with demonstrated efficacy for reducing body weight and hunger in the affected populations.
PubMed: 38766314
DOI: 10.1016/j.obpill.2024.100110 -
Lipids in Health and Disease May 2024Kidney cancer has become known as a metabolic disease. However, there is limited evidence linking metabolic syndrome (MetS) with kidney cancer risk. This study aimed to...
BACKGROUND
Kidney cancer has become known as a metabolic disease. However, there is limited evidence linking metabolic syndrome (MetS) with kidney cancer risk. This study aimed to investigate the association between MetS and its components and the risk of kidney cancer.
METHODS
UK Biobank data was used in this study. MetS was defined as having three or more metabolic abnormalities, while pre-MetS was defined as the presence of one or two metabolic abnormalities. Hazard ratios (HRs) and 95% confidence intervals (CIs) for kidney cancer risk by MetS category were calculated using multivariable Cox proportional hazards models. Subgroup analyses were conducted for age, sex, BMI, smoking status and drinking status. The joint effects of MetS and genetic factors on kidney cancer risk were also analyzed.
RESULTS
This study included 355,678 participants without cancer at recruitment. During a median follow-up of 11 years, 1203 participants developed kidney cancer. Compared to the metabolically healthy group, participants with pre-MetS (HR= 1.36, 95% CI: 1.06-1.74) or MetS (HR= 1. 70, 95% CI: 1.30-2.23) had a significantly greater risk of kidney cancer. This risk increased with the increasing number of MetS components (P for trend < 0.001). The combination of hypertension, dyslipidemia and central obesity contributed to the highest risk of kidney cancer (HR= 3.03, 95% CI: 1.91-4.80). Compared with participants with non-MetS and low genetic risk, those with MetS and high genetic risk had the highest risk of kidney cancer (HR= 1. 74, 95% CI: 1.41-2.14).
CONCLUSIONS
Both pre-MetS and MetS status were positively associated with kidney cancer risk. The risk associated with kidney cancer varied by combinations of MetS components. These findings may offer novel perspectives on the aetiology of kidney cancer and assist in designing primary prevention strategies.
Topics: Humans; Metabolic Syndrome; Kidney Neoplasms; Female; Male; Middle Aged; Risk Factors; Prospective Studies; Proportional Hazards Models; Adult; Aged; Hypertension; Obesity, Abdominal; Dyslipidemias
PubMed: 38760801
DOI: 10.1186/s12944-024-02138-5 -
American Journal of Human Genetics Jun 2024Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of...
Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.
Topics: Humans; Genome-Wide Association Study; Obesity; Body Mass Index; Genetic Predisposition to Disease; Polymorphism, Single Nucleotide; Multifactorial Inheritance; Genetic Loci; Mendelian Randomization Analysis
PubMed: 38754426
DOI: 10.1016/j.ajhg.2024.04.016 -
MedRxiv : the Preprint Server For... May 2024The prevalence of co-occurring heavy alcohol consumption and obesity is increasing in the United States. Despite neurobiological overlap in the regulation of alcohol...
BACKGROUND
The prevalence of co-occurring heavy alcohol consumption and obesity is increasing in the United States. Despite neurobiological overlap in the regulation of alcohol consumption and eating behavior, alcohol- and body mass index (BMI)-related phenotypes show no or minimal genetic correlation. We hypothesized that the lack of genetic correlation is due to mixed effect directions of variants shared by AUD and BMI.
METHODS
We applied MiXeR, to investigate shared genetic architecture between AUD and BMI in individuals of European ancestry. We used conjunctional false discovery rate (conjFDR) analysis to detect loci associated with both phenotypes and their directional effect, Functional Mapping and Annotation (FUMA) to identify lead single nucleotide polymorphisms (SNPs), Genotype-Tissue Expression (GTEx) samples to examine gene expression enrichment across tissue types, and BrainXcan to evaluate the shared associations of AUD and BMI with brain image-derived phenotypes.
RESULTS
MiXeR analysis indicated polygenic overlap of 80.9% between AUD and BMI, despite a genetic correlation (r ) of -.03. ConjFDR analysis yielded 56 lead SNPs with the same effect direction and 76 with the opposite direction. Of the 132 shared lead SNPs, 53 were novel for both AUD and BMI. GTEx analyses identified significant overexpression in the frontal cortex (BA9), hypothalamus, cortex, anterior cingulate cortex (BA24), hippocampus, and amygdala. Amygdala and caudate nucleus gray matter volumes were significantly associated with both AUD and BMI in BrainXcan analyses.
CONCLUSIONS
More than half of variants significantly associated with AUD and BMI had opposite directions of effect for the traits, supporting our hypothesis that this is the basis for their lack of genetic correlation. Follow-up analyses identified brain regions implicated in executive functioning, reward, homeostasis, and food intake regulation. Together, these findings clarify the extensive polygenic overlap between AUD and BMI and elucidate several overlapping neurobiological mechanisms.
PubMed: 38746260
DOI: 10.1101/2024.05.03.24306773