In sub-Saharan Africa (SSA), epidemiological data for chronic kidney disease (CKD) are scarce. We conducted a prospective cross-sectional study including 952 patients in an outpatient clinic in Tanzania to explore CKD prevalence estimates and the association with cardiovascular and infectious disorders. According to KDIGO, we measured albumin-to-creatinine ratio and calculated eGFR using CKD-EPI formula. Factors associated with CKD were calculated by logistic regression. Venn diagrams were modelled to visualize interaction between associated factors and CKD. Overall, the estimated CKD prevalence was 13.6% (95% CI 11–16%). Ninety-eight patients (11.2%) (95% CI 9–14%) were categorized as moderate, 12 (1.4%) (95% CI 0–4%) as high, and 9 (1%) (95% CI 0–3%) as very high risk according to KDIGO. History of tuberculosis (OR 3.75, 95% CI 1.66–8.18; p = 0.001) and schistosomiasis (OR 2.49, 95% CI 1.13–5.18; p = 0.02) were associated with CKD. A trend was seen for increasing systolic blood pressure (OR 1.02 per 1 mmHg, 95% CI 1.00–1.03; p = 0.01). Increasing BMI (OR 0.92 per 1kg/m2, 95% CI 0.88–0.96; p = <0.001) and haemoglobin (OR 0.82 per 1g/dL, 95% CI 0.72–0.94; p = 0.004) were associated with risk reduction. Diabetes was associated with albuminuria (OR 2.81, 95% CI 1.26–6.00; p = 0.009). In 85% of all CKD cases at least one of the four most common factors (hypertension, diabetes, anaemia, and history of tuberculosis or schistosomiasis) was associated with CKD. A singular associated factor was found in 61%, two in 14%, and ≥3 in 10% of all CKD cases. We observed a high prevalence estimate for CKD and found that both classical cardiovascular and neglected infectious diseases might be associated with CKD in a semi-rural population of SSA. Our finding provides further evidence for the hypothesis that the “double burden” of non-communicable and endemic infectious diseases might affect kidney health in SSA.

IntroductionEpidemiological data about diabetes mellitus (DM) for sub-Saharan Africa (SSA) are scarce and the utility of glycated hemoglobin (HbA1c) to diagnose DM is uncertain in African populations with a high proportion of anemia.Research design and methodsIn a cross-sectional study, age-adjusted prevalence rates and predictors for DM and pre-DM were prospectively assessed by HbA1c in a semirural walk-in population of Tanzania (n=992). Predictors for DM were calculated by logistic regression. Correlations between HbA1c, hemoglobin, and blood glucose levels were done by Pearson’s correlation.ResultsOverall, DM and pre-DM prevalence rates were 6.8% (95% CI 5.3 to 8.5) and 25% (95% CI 22.8 to 28.3), respectively. There was an increase in DM prevalence in patients 50–59 (14.9%; 95% CI 9.1 to 22.5), ≥60 years old (18.5%; 95% CI 12.2 to 26.2) and in patients with overweight (9.3%; 95% CI 5.9 to 13.7), obesity (10.9%; 95% CI 6.9 to 16) compared with patients 18–29 years old (2.2%; 95% CI 0.9 to 4.4) (p<0.001) and to normal-weight patients (3.6%; 95% CI 2.1 to 5.6) (p<0.01), respectively. Age (OR 1.08, 95% CI 1.05 to 1.12; p<0.001), body mass index (BMI) (OR 1.10, 95% CI 1.04 to 1.16; p<0.001), and acute infection (OR 3.46, 95% CI 1.02 to 10.8; p=0.038) were predictors for DM. Comparing patients with a BMI of 20 kg/m2 and a BMI of 35 kg/m2, the relative risk for DM increases in average by 2.12-fold (range 1.91–2.24) across the age groups. Comparing patients 20 years old with patients 70 years old, the relative risk for DM increases in average 9.7-fold (range 8.9–10.4) across the BMI groups. Overall, 333 patients (36%) suffered from anemia. Pearson’s correlation coefficients (r) between HbA1c and hemoglobin was −0.009 (p=0.779), and between HbA1c and fasting blood glucose and random blood glucose, it was 0.775 and 0.622, respectively (p<0.001).ConclusionWe observed a high prevalence of DM and pre-DM, mainly triggered by increasing age and BMI, and provide evidence that HbA1c is suitable to assess DM also in populations of SSA with high proportions of anemia.Trial registration numberNCT03458338.

Background: Semiquantitative dipstick tests are utilized for albuminuria screening. Methods: In a prospective cross-sectional survey, we analyzed the diagnostic test validity of the semiquantitative colorimetric indicator-dye-based Combur9-Test® and the albumin-specific immunochromatographic assay Micral-Test® for the detection of albuminuria, the distribution of the semiquantitative measurements within the albuminuria stages according to KDIGO, and the utility for albuminuria screening compared with an albumin-to-creatinine ratio (ACR) in a walk-in population. Results: In 970 subjects, albuminuria (≥30 mg/g) was detected in 12.7% (95% CI 85.6–96.3%) with the ACR. Sensitivity was 82.9% (95% CI 75.1–89.1%) and 91.9% (95% CI 88.7–96.9%) and specificity 71.5% (95% CI 68.4–74.6%) and 17.5% (95% CI 15.0–20.2%) for the Combur9-Test® and Micral-Test®, respectively. Correct classification to KDIGO albuminuria stages A2/A3 with the Combur9-Test® was 15.4%, 51.4%, and 87.9% at cut-offs of 30, 100, and ≥300 mg/dL, and with the Micral-Test® it was 1.8%, 10.5%, and 53.6% at cut-offs of 2, 5, and 10 mg/dL, respectively. Overall, disagreement to KDIGO albuminuria was seen in 27% and 73% with the Combur9-Test® and Micral-Test®, respectively. From the total population, 62.5% and 15.3% were correctly ruled out and 2.2% and 1% were missed as false-negatives by the Combur9-Test® and Micral-Test®, respectively. Conclusion: Compared to the Combur9-Test®, the utility of the Micral-Test® is limited, because the fraction of correctly ruled out patients is small and a large proportion with a positive Micral-Test® require a subsequent ACR conformation test.