Table_1_Impaired fasting glucose levels among perinatally HIV-infected adolescents and youths in Dar es Salaam, Tanzania.docx

ObjectiveThis study assessed impaired fasting glucose and associated factors among perinatally HIV-infected adolescents and youths in Dar es salaam Tanzania.BackgroundImpaired fasting glucose is a marker of heightened risk for developing type 2 diabetes among perinatally HIV-infected individuals. Therefore, identifying individuals at this stage is crucial to enable early intervention. Therefore, we assessed impaired fasting glucose (IFG) and associated factors among perinatally HIV-infected population in Dar es salaam Tanzania. MethodsA cross-sectional study was conducted among 152 adolescents and youth attending HIV clinic at Muhimbili National Hospital and Infectious Disease Centre from July to August 2020. Fasting blood glucose (>8 hours) was measured using one-touch selects LifeScan, CA, USA. We also examined C-Reactive Protein and interleukin-6 inflammatory biomarkers in relation to impaired fasting glucose (IFG). Associations between categorical variables were explored using Chi-square, and poison regression with robust variance was used to calculate the prevalence ratios.ResultsOf the 152 participants, the majority were male (n=83[54.6%]), and the median age was 15(14-18) years. Overweight or obesity was prevalent in 16.4%, while more than one in ten (13.2%) had high blood pressure (≥149/90mmHg). All participants were on antiretroviral therapy (ART); 46% had used medication for over ten years, and about one in three had poor medication adherence. Among the recruited participants, 29% had impaired fasting glucose. The odds of IFG were two times higher in males compared to females (PR, 2.07, 95% CI 1.19 -3.59 p=0.001). Moreover, we found with every increase of Interleukin 6 biomarker there was a 1.01 probability increase of impaired fasting glucose (PR, 1.01, 95% CI 1.00 – 1.02 p=0.003).ConclusionAbout one in three perinatally HIV-infected youths had impaired fasting glucose in Dar es Salaam, Tanzania, with males bearing the biggest brunt. Moreover, with every increase of 1.101 of the probability of having IFG increased. This calls for urgent measures to interrupt the progression to diabetes disease and prevent the dual burden of disease for this uniquely challenged population.</p

List of DENV-1 Tanzania sequences names, 2017–2019.

List of DENV-1 Tanzania sequences names, 2017–2019.</p

List of DENV-3 Tanzania sequences names, 2017–2019.

List of DENV-3 Tanzania sequences names, 2017–2019.</p

Distribution of dengue fever cases in Tanzania from 2017 to 2019.

The map shows 26 Tanzania mainland administrative regions. The five color-coded regions show dengue fever cases distribution between 2017–2019. Map created with QGIS 3.24.1 All shape files are openly available sources (https://www.nbs.go.tz/index.php/en/census-surveys/gis/385-2012-phc-shapefiles-level-one-and-two). The shapefiles were made based on the 2012 population and housing census, but in this study, the shapefile has been modified to capture all the regions and district information.</p

Genotyping of DENV-1 circulating in Tanzania in 2019.

Maximum likelihood phylogenetic tree reconstructed with the 341 sequences generated by this study and 69 additional sequences from GenBank to provide genotype reference and geographic-temporal context. The tree was rooted at midpoint. Genotype I was only detected from one sample in 2019, while genotype V was found widely circulated in the 2019 epidemic. The Tanzanian sequences (OM920075—OM920415) in red. Genotypes are presented with colored highlighted branches; Genotypes IV, III, V, II, and I are highlighted in pink, blue, green, gold, and purple, respectively. Contextual sequences are labeled with GenBank accession number, country of origin, and year of isolation.</p

Genotyping of DENV-3 circulating in Tanzania 2017–2018.

Maximum likelihood phylogenetic tree reconstructed with the 32 DENV-3 sequence generated by this study and 40 additional sequences from GenBank to provide genotype reference and geographic-temporal context. The tree was rooted at midpoint. pink, blue, green, gold and purple represents genotypes V, II, III, I, and IV, respectively. Tanzanian sequences (OM920035—OM920066) are in red. Contextual sequences are labeled with GenBank accession number, country of origin, and year of isolation.</p

Socio-demographic characteristics of patients with dengue infection in Tanzania from 2017 to 2019.

Socio-demographic characteristics of patients with dengue infection in Tanzania from 2017 to 2019.</p

Distribution of dengue samples per region from 2017–2019.

Distribution of dengue samples per region from 2017–2019.</p