The protective effect of rs373863828 on type 2 diabetes does not operate through a body composition pathway in adult Samoans

Cellular Senescence and Extracellular Vesicles in the Pathogenesis and Treatment of Obesity-A Narrative Review

This narrative review explores the pathophysiology of obesity, cellular senescence, and exosome release. When exposed to excessive nutrients, adipocytes develop mitochondrial dysfunction and generate reactive oxygen species with DNA damage. This triggers adipocyte hypertrophy and hypoxia, inhibition of adiponectin secretion and adipogenesis, increased endoplasmic reticulum stress and maladaptive unfolded protein response, metaflammation, and polarization of macrophages. Such feed-forward cycles are not resolved by antioxidant systems, heat shock response pathways, or DNA repair mechanisms, resulting in transmissible cellular senescence via autocrine, paracrine, and endocrine signaling. Senescence can thus affect preadipocytes, mature adipocytes, tissue macrophages and lymphocytes, hepatocytes, vascular endothelium, pancreatic β cells, myocytes, hypothalamic nuclei, and renal podocytes. The senescence-associated secretory phenotype is closely related to visceral adipose tissue expansion and metaflammation; inhibition of SIRT-1, adiponectin, and autophagy; and increased release of exosomes, exosomal micro-RNAs, pro-inflammatory adipokines, and saturated free fatty acids. The resulting hypernefemia, insulin resistance, and diminished fatty acid β-oxidation lead to lipotoxicity and progressive obesity, metabolic syndrome, and physical and cognitive functional decline. Weight cycling is related to continuing immunosenescence and exposure to palmitate. Cellular senescence, exosome release, and the transmissible senescence-associated secretory phenotype contribute to obesity and metabolic syndrome. Targeted therapies have interrelated and synergistic effects on cellular senescence, obesity, and premature aging.

Type 2 Diabetes Prevalence, Control and Management within Fiji,Kiribati, Samoa, the Solomon Islands, Tonga, and Vanuatu: A ScopingReview with a Systematic Approach

BACKGROUND

Type 2 diabetes (T2D) causes significant morbidity and is disproportionately prevalent in Pacific Island Countries (PICs). The socio-political demographics of PICs are rapidly changing, and health services must adapt to match the needs of their population.

OBJECTIVES

The objective of this study was to review the literature published within the last 15 years relating to T2D prevalence, control, and management, with a specific focus on targetable areas for future funding and research projects.

METHODS

This review was conducted using the PRISMA guidelines. Inclusion criteria were: discussion on T2D in the six PICs. Results were limited to those published between 1st January, 2006, and 27th July, 2023.

RESULTS

A total of 6,640 publications were retrieved, and 110 met the inclusion criteria. Nineteen additional studies were identified through hand-searching. T2D prevalence differed between countries but was predicted to increase in the coming decades, with projections of up to 31.2% by 2030 in Tonga. Factors associated with T2D varied between countries, including Indian-Fijian ethnicity in Fiji and tuberculosis in Kiribati. Control was generally poor, with high rates of undiagnosed diabetes and microvascular complications. Epidemiological data was limited in some cases, as was information describing the structure and function of diabetes services.

CONCLUSION

The prevalence, control, and management of T2D varied between Fiji, Kiribati, Samoa, the Solomon Islands, Tonga, and Vanuatu. Significant gaps remain in the data describing these domains; however, there are clearly targetable areas for future research and diabetes management programs.

Multivariate analysis of a missense variant in CREBRF reveals associations with measures of adiposity in people of Polynesian ancestries

The minor allele of rs373863828, a missense variant in CREB3 Regulatory Factor, is associated with several cardiometabolic phenotypes in Polynesian peoples. To better understand the variant, we tested the association of rs373863828 with a panel of correlated phenotypes (body mass index [BMI], weight, height, HDL cholesterol, triglycerides, and total cholesterol) using multivariate Bayesian association and network analyses in a Samoa cohort (n = 1632), Aotearoa New Zealand cohort (n = 1419), and combined cohort (n = 2976). An expanded set of phenotypes (adding estimated fat and fat-free mass, abdominal circumference, hip circumference, and abdominal-hip ratio) was tested in the Samoa cohort (n = 1496). In the Samoa cohort, we observed significant associations (log10 Bayes Factor [BF] ≥ 5.0) between rs373863828 and the overall phenotype panel (8.81), weight (8.30), and BMI (6.42). In the Aotearoa New Zealand cohort, we observed suggestive associations (1.5 < log10 BF < 5) between rs373863828 and the overall phenotype panel (4.60), weight (3.27), and BMI (1.80). In the combined cohort, we observed concordant signals with larger log10 BFs. In the Samoa-specific expanded phenotype analyses, we also observed significant associations between rs373863828 and fat mass (5.65), abdominal circumference (5.34), and hip circumference (5.09). Bayesian networks provided evidence for a direct association of rs373863828 with weight and indirect associations with height and BMI.