Estimation of mean population salt intakes using spot urine samples and associations with body mass index, hypertension, raised blood sugar and hypercholesterolemia: Findings from STEPS Survey 2019, Nepal

Abstract

Background High dietary salt intake is recognized as a risk factor for several non-communicable diseases (NCDs), in particular cardiovascular diseases (CVDs), including heart attack and stroke. Accurate measurement of population level salt intake is essential for setting targeted goals and plans for salt reduction strategies. We used a spot urine sample to estimate the mean population salt intake in Nepal and evaluated the association of salt intake with excess weight, hypertension, raised blood sugar and hypercholesterolemia, and a number of socio-demographic characteristics. Methods A population-based cross-sectional study was carried out from February to May 2019 using a WHO STEPwise approach to surveillance. Spot urine was collected from 4361 participants aged 15–69 years for the analysis of salt intake. We then used the INTERSALT equation to calculate population salt intake. Student’s ‘t’ test, one-way ANOVA and multivariable linear regression were used to assess the association between salt intake and a number of factors. Statistical significance was accepted at P < .05. Results The average (±SD) age of participants was 40 (14.1) years. Mean salt intake, derived from spot urine samples, was estimated to be 9.1g/d. A total of 70.8% of the population consumed more than the WHO’s recommended amount of 5g salt per day, with almost one third of the population (29%) consuming more than 10g of salt per day. Higher salt intake was significantly associated with male gender (β for male = 0.98g; 95%CI:0.87,1.1) and younger age groups (β25–39 years = 0.08; 95%CI:-0.08,0.23) and higher BMI (β = 0.19; 95%CI:0.18,0.21). Participants who were hypertensive and had raised blood cholesterol consumed less salt than people who had normal blood pressure and cholesterol levels (P<0.001). Conclusions Salt consumption in Nepal is high, with a total of 70.8% of the population having a mean salt intake >5g/d, well above the World Health Organization recommendation. High salt intake was found to be associated with sex, age group, education, province, BMI, and raised cholesterol level of participants These findings build a strong case for action to reduce salt consumption in Nepal in order to achieve the global target of 30% reduction in population salt intake by 2025.

Estimating mean population salt intake using spot urine samples in Nepal: a cross-sectional study

“…Consistent to our results, several previous studies reported the presence of proportional biases of spot urine-based equations in the Bland–Altman plots indicating positive or negative correlation of individual difference with the average mean intake [28,31,33,39–41]. Salt intake estimated by the INTERSALT without potassium (8.51 ± 1.73 g/day) in the present study was closely consistent with the findings of recent WHO STEPwise approach to noncommunicable disease risk factor surveillance (STEPS survey) of Nepal using the same equation (9.1 ± 1.8 g/day) [42]. This indicates that the INTERSALT equations showed substantial underestimation of 24-h estimates in both studies, why application of these equations to estimate 24-h salt intake should be cautious in Nepal.…”

Association between Eating Habits and Sodium Intake among Chinese University Students

“…Meanwhile, the dietary preferences brought about by gender make females prefer processed foods and snack foods compared to males [ 46 , 47 ], which can lead to higher sodium intake. While inconsistent with other studies, a relationship between BMI and sodium intake was not discovered, probably because of information bias in self-reported BMI by students as the primary reason [ 48 , 49 ].…”

Estimation of sodium consumption in hypertensive patients based on urine sodium excretion: A Preliminary study

“…Participants disposed of their first-morning urine at 5:00 a.m. on the first day, and all subsequent samples were collected. The method was accurate for gauging 24-hour urine completion in population-based biomarker investigations [9,19,20].…”