Browsing by Author "Chikowore, Tinashe"

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Relationship of diet and physical activity with genetic susceptibility to obesity: a longitudinal analysis in adults in South Africa
(University of the Witwatersrand, Johannesburg, 2024) Muti, Monica; Chikowore, Tinashe; Ware, Lisa
Background Obesity-related disease conditions are a major public health concern in South Africa, exerting a healthcare cost of between ZAR 30 million and ZAR 36 million, the bulk of which is due to hypertension and type 2 diabetes. Moreover, evidence reveals that women in South Africa have higher BMI compared to men, yet men exhibit less insulin sensitivity and reduced beta cell function as well as stronger associations of adiposity with type 2 diabetes compared to women. The mechanisms underlying these sex differences are not known. BMI is highly polygenic in nature; however, genetic prediction of BMI has mostly been conducted using data from European ancestry populations that have poor predictive capacity in African ancestry populations. Moreover, the relationship of polygenic risks and proteomic profiles with regards to susceptibility to obesity and related cardiometabolic traits is yet to be explored in African populations. It has also been reported that using variants associated with the statistical variance of quantitative traits (vQTLs) like BMI aids in the depiction of components of BMI genetic susceptibility, which interacts with environmental factors such as diet and exercise. However, such studies are limited in continental Africans. Aim This thesis, sought to determine the interplay of diet and physical activity with BMI genetic susceptibility. The specific objectives were: 1. To determine the association of physical activity with BMI in middle-aged black South African men and women. 2. To develop a highly predictive genetic risk score for BMI and test its longitudinal predictive ability in middle-aged black South African men and women. 3. To determine gene x lifestyle (GXE) interactions that influence BMI in Black South African adult men and women. Methods Data from 11853 adult men and women in the African-Wits-INDEPTH partnership for Genomic studies (AWI-Gen) Cohort was used to fulfil objective 1. To fulfil objectives 2 and 3, data from 5921 AWI-Gen cohort participants in the three South African (SA) sites and a sub-study of AWI-Gen focusing on the factors influencing the risk of type 2 diabetes mellitus among middle-aged black South African men and women (GSK) was used. For objective 1, a sex-stratified meta-analysis of cross-sectional data from the study participants was used to assess the association of physical activity with BMI. The PRS-CSx method was used to develop a multi-ancestry PRS for BMI and evaluate its longitudinal prediction of severe obesity to meet objective 2. For objective 3, the Levene’s test, implemented in the OCSA Package, was used to determine candidate gene-interacting variants that exhibited trait variance heterogeneity in the study population. Detailed methods are in the relevant sections for each objective. Results Meeting the recommended weekly physical activity levels of at least 150 minutes was associated with a BMI that was 0.80kg/m2 lower in men (95% CI = -1.14; -0.47) and 0.68kg/m2 lower in women (95% Ci = -1.03; -0.33). Sex and site-specific differences were also observed in domains of physical activity with an inverse relationship between transport-related physical activity and BMI being observed among men in Agincourt (beta = -1.15 kg/m2, 95% CI = -2.26; -0.04) and Nanoro (beta = -0.79 kg/m2, 95%CI = -1.25; -0.33). Work related physical activity was associated with lower BMI in Navrongo men (beta = -0.76 kg/m2, 95% CI=-1.25; -0.27) and Nanoro women (beta = -0.90 kg/m2, 95%CI = -1.44; -0.36). The multi-ancestry PRS demonstrated superior predictive ability, explaining approximately 1.9% of variance in BMI compared to 0.7% and 1.2% explained by two scores developed using single ancestry methods. In addition, over a period of ten years, the multi-ancestry PRS was associated with repeated measures of BMI (β = 1.51 p = < 0.001) and there was significant longitudinal PRS * sex interaction (Pinteraction = 0.029), prompting subsequent sex-stratified analysis. In the combined analysis of men and women, being in the top 20% of the PRS distribution (top 20) was associated with three times greater hazard of severe obesity (hazard ratio = 2.98, 95% CI = 1.75 - 5.07, p = 5.33e-05) compared to being in the bottom 20% of the PRS distribution (bottom 20). This observation was shown to be driven by women, where being a woman in the top 20 was associated with 3.5 greater hazard of severe obesity (hazard ratio 3.48, 95% CI = 1.96 – 6.16, p = 1.94e-05) compared to being in the bottom 20 while the associations were not significant in men (hazard ratio = 1.13, 95% CI = 0.24 – 5.37, p = 0.878). Comparison of the associations of dysglycaemia with PRS, BMI and the proteomic score revealed no apparent sex differences in the association between BMI PRS and dysglycaemia for most of the glycaemic markers except for Matsuda Index though men exhibited lower insulin sensitivity compared to women. The proteomic score predicted higher insulin resistance in women than in men. Gene x lifestyle interaction analysis revealed novel interactions between three genetic variants with diet and lifestyle factors. The effect of the rs557505940 variant on BMI was accentuated by higher fruit intake (betainteraction = 0.03, Pinteraction = 0.04) in the combined analysis of men and women while higher SES, carbohydrate intake and self- reported physical activity attenuated the effect of rs527747185 (betainteraction = -0.349, Pinteraction = 0.037), rs3016751 (betainteraction = -0.056, Pinteraction = 0.035) and rs188275749 (betainteraction = -0.048, Pinteraction = 0.0001) respectively on BMI in men. Conclusions Sex and geographical differences exist in associations between domains of physical activity and BMI. In addition, genetic risk better predicts incident severe obesity in women than in men while proteomic profiles have a weak correlation with PRS and show heterogenous associations with dysglycaemia, fat distribution, nutrient patterns and physical activity between men and women. Novel GXE interactions were also observed. These results underscore the need for further inquiry into the sex differences in genetic risk and environmental factors associated with BMI. Furthermore, a precision approach to obesity prevention and control, paying attention to the sex differences and contextual factors may be more efficient.
SHBG, free testosterone, and Type 2 Diabetes risk in middle-aged African men: a longitudinal study
(Oxford University Press, 2024) Norris, Shane; Seipone, Ikanyeng D.; Mendham, Amy E.; Storbeck, Karl-Heinz; Oestlund, Imken; Kufe, Clement N.; Chikowore, Tinashe; Masemola, Maphoko; Crowther, Nigel J.; Kengne, Andre Pascal; Olsson, Tommy; Brown, Todd; Micklesfield, Lisa K.; Goedecke, Julia H.
Objectives: To investigate longitudinal changes in SHBG and free testosterone (free T) levels among Black middle-aged African men, with and without coexistent HIV, and explore associations with incident dysglycaemia and measures of glucose metabolism. Design: This longitudinal study enrolled 407 Black South African middle-aged men, comprising primarily 322 men living without HIV (MLWOH) and 85 men living with HIV (MLWH), with normal fasting glucose at enrollment. Follow-up assessments were conducted after 3.1 ± 1.5 years. Methods: At baseline and follow-up, SHBG, albumin, and total testosterone were measured and free T was calculated. An oral glucose tolerance test at follow-up determined dysglycaemia (impaired fasting glucose, impaired glucose tolerance, type 2 diabetes) and glucose metabolism parameters including insulin sensitivity (Matsuda index), insulin resistance (homeostasis model assessment of insulin resistance), and beta(β)- cell function (disposition index). The primary analysis focussed on MLWOH, with a subanalysis on MLWH to explore whether associations in MLWOH differed from MLWH. Results: The prevalence of dysglycaemia at follow-up was 17% (n = 55) in MLWOH. Higher baseline SHBG was associated with a lower risk of incident dysglycaemia (odds ratio 0.966; 95% confidence interval 0.945-0.987) and positively associated with insulin sensitivity (β = 0.124, P < .001) and β-cell function (β = 0.194, P = .001) at follow-up. Free T did not predict dysglycaemia. In MLWH, dysglycaemia prevalence at follow-up was 12% (n = 10). Neither baseline SHBG nor free T were associated with incident dysglycaemia and glucose metabolism parameters in MLWH. Conclusion: SHBG levels predict the development of dysglycaemia in middle-aged African men but do not exhibit the same predictive value in MLWH.
Variability of polygenic prediction for body mass index in Africa
(BioMed Central (BMC), 2024) Norris, Shane A.; Chikowore, Tinashe; Läll, Kristi; Micklesfield, Lisa K.; Lombard, Zane; Goedecke, Julia H.; Fatumo, Segun; Magi, Reedik; Ramsay, Michele; Franks, Paul W.; Pare, Guillaume; Morris, Andrew P.
Background: Polygenic prediction studies in continental Africans are scarce. Africa’s genetic and environmental diversity pose a challenge that limits the generalizability of polygenic risk scores (PRS) for body mass index (BMI) within the continent. Studies to understand the factors that affect PRS variability within Africa are required. Methods: Using the first multi-ancestry genome-wide association study (GWAS) meta-analysis for BMI involving continental Africans, we derived a multi-ancestry PRS and compared its performance to a European ancestry-specific PRS in continental Africans (AWI-Gen study) and a European cohort (Estonian Biobank). We then evaluated the factors affecting the performance of the PRS in Africans which included fine-mapping resolution, allele frequencies, linkage disequilibrium patterns, and PRS-environment interactions. Results: Polygenic prediction of BMI in continental Africans is poor compared to that in European ancestry individuals. However, we show that the multi-ancestry PRS is more predictive than the European ancestry-specific PRS due to its improved fine-mapping resolution. We noted regional variation in polygenic prediction across Africa’s East, South, and West regions, which was driven by a complex interplay of the PRS with environmental factors, such as physical activity, smoking, alcohol intake, and socioeconomic status. Conclusions: Our findings highlight the role of gene-environment interactions in PRS prediction variability in Africa. PRS methods that correct for these interactions, coupled with the increased representation of Africans in GWAS, may improve PRS prediction in Africa.