Diabetes mellitus and associated risk factors among HIV infected patients on HAART.

BACKGROUND: Understanding the impact of disease associations is becoming a priority in Kenya and other countries bearing the load of infectious diseases. With the increased incidences of non-communicable diseases and the endemicity of infectious diseases in Sub-Saharan Africa, their co-existence poses significant challenges to patients, health workers and an overwhelmed health sector. Classical risk factors for diabetes such as physical inactivity and unhealthy diet may not solely explain the current trends, suggesting the role of novel risk factors including infections/inflammation. HIV and its treatment have been identified as potential contributors especially to patients with family history of confirmed diabetes cases. Co-infections frequently observed during HIV infection also significantly influence both the epidemiological and pathophysiological of the link between HIV and diabetes. Understanding the correlates of HIV and diabetes is crucial to inform management and prevention strategies of the twin infections. We therefore aimed to determine the prevalence of diabetes mellitus and risk factors in a population of HIV infected patients on HAART. This study determined the association of diabetes/impaired glucose regulation in the context of HIV-1. A cross-sectional study was conducted at a comprehensive care clinic in Nairobi (Kenya). Participants were screened for diabetes and impaired glucose regulation using random blood glucose and glycated haemoglobin (HbA1c) This paper describes the prevalence of diabetes mellitus in Human Immunodeficiency Virus positive individuals and the associated risk factors. We have demonstrated that family history is a risk factor for diabetes. While age and BMI are known risk factors, they were not associated with diabetes in this study.

Hepatitis C virus genotypes in Kenya.

Hepatitis C virus is a great public-health concern worldwide. Phylogenetic analysis of the HCV genome has identified six different genotypes that have generally been divided into several subtypes. There is very little information on HCV seroprevalence and genotypes in Kenya. To determine the genotypes of HCV circulating in Kenya, blood donor samples were serologically tested and confirmed by polymerase chain reaction (PCR). Positive samples were cloned and sequenced, and phylogenetic analysis conducted to determine the HCV genotypes. One hundred Murex-seropositive samples were re-tested using a passive hemagglutination test, and 16 of these were identified as seropositive. Further testing of all of the samples by PCR identified only 10 of the 16 samples as positive. Thus, only 10 % (10/100) of the samples were viremic. Six were from females (60 %), and four were from males (40 %). The mean age of the positive donors was considerably low, at 25 +/- 9 years. Genotypic testing indicated the presence of genotype 1a (10 %) and genotype 2b (90 %). This study reports on HCV genotypes in a blood donor population in Kenya where little had been done to provide information on HCV genotypes.

HIV-1 drug resistance mutations among infants born to HIV-positive mothers in Busia, Kenya.

To determine HIV-1 subtypes and transmitted HIV-1 drug-resistant mutations among HIV-1-positive children born to HIV-positive mothers in Busia County, blood samples were collected from 53 children aged between 6 weeks and 5 years in 2011. Their mothers were HIV-1 positive and on antiretroviral therapy at the time the children were born. The samples were analyzed for HIV-1 drug resistance and subtypes through sequencing of portions of the HIV-1 pol gene. The generated sequences were analyzed for subtype diversity using the REGA and BLAST subtyping tools. HIV-1 drug resistance was determined using the Stanford University HIV database. Of the 53 samples that were successfully amplified and sequenced, 69.8% (37/53) were determined to be HIV-1 subtype A, 22.6% (12/53) were subtype D, 5.6% (3/53) were subtype C, and 1.8% (1/53) were subtype A1C. The prevalence of HIV-1 drug resistance mutations of any kind was 22.6% (12/53).