• Open Access

Benefits of modest weight or waist circumference loss in a remote North Queensland Indigenous population

Authors


Correspondence to: Dr Katina D'Onise, University of South Australia – Sansom Institute, City East Campus, North Terrace, Adelaide, SA 5000; e-mail: Katina.d'onise@unisa.edu.au

Abstract

Objective : To quantify the potential benefit to individuals of differing magnitudes of weight or waist circumference loss in an Indigenous population.

Method : Data were from the Well Person's Health Check, a cohort study in 19 rural Indigenous communities in Far North Queensland. Baseline data were collected between 1998 and 2000 from 2,583 people aged 15 to 75, an estimated participation rate of 44.5%. Follow-up data were collected between 2004 and 2007 from 729 participants. Associations between change in weight and waist circumference for those who were overweight or obese (n=486) with changes in serum lipids, fasting glucose, blood pressure and Gamma-Glutamyltransferase (GGT) were estimated using linear regression.

Results : Weight or waist circumference loss was associated in a dose response fashion with blood pressure reduction (e.g. 10% or greater weight loss compared with no weight loss was associated with reduction of 11.3 mmHg systolic (95% confidence interval −17.8, −4.8). Those with greater waist circumference loss had a greater reduction in GGT (−8.3, 95% confidence interval −23.5, 6.8) but there was no apparent increase in GGT reduction with increasing weight loss, although these were measured with low precision. There was no apparent effect of either weight or waist circumference loss on serum lipids and fasting glucose in this population.

Conclusions : This study shows potentially large beneficial effects of weight or waist circumference loss over several years in a remote living Indigenous cohort. The associations were large enough to be of clinical benefit, despite weight loss being modest for most.

Australian and international guidelines suggest that a weight loss of 5% to 10% will confer positive health benefits, both for the general population1 and for those with a chronic disease diagnosis such as diabetes.2 The best evidence is for a reduction in blood pressure, with a magnitude of benefit around 1.05 mmHg systolic and 0.92 mmHg diastolic per kilogram of weight loss.3,4 There are possibly other cardio-metabolic benefits, with a recent meta-analysis suggesting a modest positive effect of weight loss on serum lipids,5 and further studies suggesting benefits on glycaemia2 and a reduced risk of diabetes onset.6 Further, there is evidence that larger weight loss confers greater cardio-metabolic benefit. The majority of these studies, however, had a relatively short follow-up period (less than two years), which may be important given the findings that serum lipids benefits at least may wane over time.5

Aboriginal and Torres Strait Islanders have a high prevalence of a range of cardio-metabolic risk factors, relative to the non-Indigenous population. These include higher rates of tobacco smoking, hypertension, high GGT, hypertrigylceridaemia, and glycaemia, all of which are associated with overweight or obesity.7,8 The National Aboriginal and Torres Strait Islander Health Survey and National Health Survey conducted in 2004–05 found 59% of the Indigenous population living in remote areas were overweight or obese. After adjusting for age, Indigenous Australians were twice as likely to be obese as non-Indigenous Australians.9

There is evidence that different weight and waist circumference parameters should be used to identify cardio-metabolic risk in Aboriginal and Torres Strait Islander people compared with non-Indigenous people.10 Body mass index is thought to be a less sensitive marker of cardio-metabolic risk, and the associated risk with waist circumference has a lower cut-off compared with the non-Indigenous population. Further, there is evidence that Aboriginal and Torres Strait Islanders have a cardiovascular risk profile in excess of that predicted by traditional Framingham risk factors. For example, albuminuria confers a high cardiovascular risk.11 As such, the findings from non-Indigenous population groups may not be generalisable to Aboriginal and Torres Strait Islander people. Given this altered cardiovascular risk profile, combined with a high prevalence of overweight and obesity, it is important to assess the effect of both weight and waist circumference loss in this population.

This study aimed to document weight change over an average 6.6 years using data from the Well Person's Health Check (WPHC),12 and then quantify the potential benefit to overweight or obese individuals of differing magnitudes of weight or waist circumference loss, in a predominately Aboriginal and Torres Strait Islander population in remote Far North Queensland.

Methods

Study population

Baseline data were collected from 2,583 people in 19 rural Indigenous communities in Far North Queensland, who participated in the Well Person's Health Check between 1998 and 2000 (methods for this cross-sectional study have been reported in detail elsewhere).12 Based on the local census data, the study achieved a participation rate of 44.5% with greater participation noted in smaller communities. The follow up data were collected between 2004 and 2007 from 729 participants over an average of 6.6 years (range 4.4 to 9.0 years). The sample for this study was taken from those who participated both in the baseline and follow up survey (n=729) and had both a weight and a waist circumference measured at baseline and at follow up (n=704).

There were a number of variables with missing information on either the baseline or the follow up sample. This included missing data for fasting glucose (missing n=130), GGT (n=119), triglycerides (n=113), total cholesterol (n=111), HDL cholesterol (n=112) and LDL cholesterol (n=236). Change in each of the cardio-metabolic risk factors was calculated subtracting the value at follow up from that at baseline. A further nine values for GGT change were dropped from analyses as the changes were considered too extreme for metabolic causes (more than 150 U/L); and two for triglyceride change (change of greater than 15 mmol/L).

Outcome variables

Participants were asked to remove footwear and heavy clothing and were weight recorded to the nearest 0.1 kg. Waist circumference was measured by trained staff at the level of the umbilicus, to the nearest centimetre. Both weight and waist circumference were categorised for the main analysis into the proportion of weight change from baseline. Categories were made for weight gain to no change (≤0%), 0–5% loss, 5–10% loss, and more than 10% loss. These categories were chosen to reflect increasingly clinically relevant weight or waist loss goals.

Blood pressure was recorded as the average of three measurements, which were taken with the participant seated after a ten-minute rest and over the course of the health check.

Blood tests taken after an eight-hour fast were collected by trained health staff. Specimens for fasting blood glucose, biochemistry and serum lipids were collected in an eight point millilitre clotted (SST) vacuum tube which was spun for 10 minutes in a portable centrifuge within one hour of collection. Blood tubes were sealed, packed in refrigerated portable coolers, and transported by air to the laboratory. γ-Glutamyltransferase (GGT) was measured by a kinetic photometric procedure with Cobas Integra 800 (Roche Diagnostic, USA). Blood glucose and blood lipids were measured using photometric enzyme endpoint assay with Cobas Integra 700/400 (Roche Diagnostic).

Analysis

An analysis of demographic and risk factors of the study sample compared with those who participated in the baseline sample but were not followed up was conducted, with comparisons using t-test or chi-square test.

Those with a body mass index of at least 25 were selected for further analysis (n=486). The mean change in the various risk factors was categorised by level of weight or waist circumference loss, and a non-parametric test for trend was conducted (based on the Kruksal Wallis test). This analysis was repeated using a linear regression model and controlling for baseline body mass index (for the weight outcomes) and waist circumference (for the waist circumference outcomes), baseline level of the risk factor examined, age, gender and ethnicity. Models examining the effect of weight or waist circumference loss on GGT were also run including a variable for risky alcohol drinking at baseline, as categorised by the 2001 National Health and Medical Research Council guidelines.13 Addition of alcohol to the models did not result in a substantive change, and so alcohol was not included in the final model.

A second analysis was conducted to determine any possible effect of missing data on the study findings. All laboratory test results were imputed using multivariate normal regression in Stata v12.0 which uses an iterative Markov chain Monte Carlo method.14

The study was approved by the Cairns and Hinterland Health Service District Ethics Committee, with support from the relevant peak Indigenous health councils. All analyses were conducted using Stata statistical software v 12.0.15

Results

The study sample had an average age of 39.5 years at baseline, which was slightly older than those who did not participate in the follow-up study (35.7 years). There was an equal proportion of males and females (Table 1). There were more Torres Strait Islanders and fewer Aboriginal people in those followed up compared with those who only participated in the baseline study. The study sample had a greater average body mass index and waist circumference than those who participated only in the baseline sample.

Table 1. Comparison of study sample and those lost to follow up, Well Person's Health Check 1998–2007.
BaselineStudy sample n=704Participated in baseline but not followed up n=1851
 % unless otherwise indicated (95% CI)% unless otherwise indicated (95% CI)
  1. a p≤0.05

Female52.0 (48.4, 55.6)50.0 (47.6, 52.1)
Age (mean, years)39.5 (38.5, 40.6)35.7 (35.0, 36.4)a
Aboriginal37.6 (34.1, 41.1)48.9 (46.6, 51.2)a
Torres Strait Islander50.2 (46.6, 53.8)28.2 (26.1, 30.3)a
Aboriginal and Torres Strait Islander11.1 (8.8, 13.4)4.6 (3.7, 5.6)a
Non-Indigenous11.0 (0.3, 1.9)18.3 (16.5, 20.0)a
Weight, men (mean, kg)85.0 (82.8, 87.2)79.1 (77.8, 80.3)a
Weight, women (mean, kg)78.3 (76.2, 80.4)72.2 (70.9, 73.6)a
Waist circumference, men (mean, cm)98.9 (97.2, 100.7)92.9 (91.9, 93.9)a
Waist circumference, women (mean, cm)100.2 (98.4, 102.0)93.2 (92.1, 94.4)a
Body mass index (mean)29.1 (28.6, 29.6)26.9 (26.6, 27.3)a

Figures 1 and 2 show the distribution of the per cent weight or waist circumference change from baseline to follow up in the entire study sample. The majority of people did not lose waist circumference (72.3%), with 15.3% of participants losing up to 5% of waist circumference, 8.4% losing 5% to less than 10% and 4.0% losing more than 10% of waist circumference. Similarly, for weight: 65.7% of participants did not lose weight (58% overall gained at least 2 kg), 18.7% lost less than 5% of weight, 10.2% lost 5% to less than 10% of their weight, and 5.4% of participants lost more than 10% of body weight.

Figure 1.

Distribution of the percent waist circumference change from baseline to follow up, Well Person's Health Check.

Figure 2.

Distribution of the percent weight change from baseline to follow up, Well Person's Health Check.

For people who were overweight or obese, there was evidence of a trend for both systolic (p<0.001) and diastolic blood pressure (p<0.001) reducing with increasing weight loss, with at least 5% of weight loss and 10% of waist circumference loss associated with lower blood pressure (Table 2). GGT reduced with lowering of waist circumference, but there was no similar effect on triglycerides. There was some evidence for weight loss leading to a greater reduction in triglycerides and GGT. There was a small reduction in total cholesterol with increasing waist circumference or weight loss, but no effect on fasting glucose, HDL cholesterol and LDL cholesterol.

Table 2. Mean change in cardiometabolic risk factors by category of interval weight or waist loss.
 Weight loss % of baseline (95% confidence interval)Test for trendWaist loss % of baseline (95% confidence interval)Test for trend
 ≤00-<5%5-<10%>10% ≤00-<5%5-<10%>10% 
  1. GGT–ɣ-Glutamyltransferase, HDL – High density lipoprotein, LDL – low density lipoprotein

Systolic blood pressure−1.59(−3.54, 0.36)−3.39(−7.24, 0.47)−9.51(−15.69, −3.33)−15.0(−24.09, −5.91) p<0.001−2.38(−4.36, −0.39)−5.25(−9.18, −1.31)−4.57(−11.31, 2.18)−18.0(−29.24, −6.76) p=0.003
Diastolic blood pressure4.31(2.76, 5.86)0.85(−1.45, 3.15)−2.11(−6.23, 2.02)−0.72(−5.13, 3.69) p<0.0013.61(2.20, 5.02)1.09(−1.63, 3.82)−0.39(−4.22, 3.44)−3.50(−12.72, 5.72) p=0.009
Fasting glucose0.69(0.37, 1.01)0.60(−0.13, 1.32)1.02(−0.06, 2.10)0.63(−0.97, 2.24) p=0.330.66(0.29, 1.02)1.0(0.41, 1.59)0.42(−0.58, 1.43)0.81(−0.72, 2.34) p=0.88
GGT1.53(−5.72, 8.79)−2.80(−9.58, 3.98)−1.71(−9.81, 6.39)−1.04(−15.05, 12.97) p=0.0021.23(−5.26, 7.72)−3.47(−10.18, 3.24)1.66(−7.80, 11.11)−8.44(−23.93, 7.05) p=0.05
Triglyceride0.02(−0.15, 0.19)0.20(−0.12, 0.52)−0.56(−1.29, 0.16)−0.33(−0.75, 0.09) p=0.0060.03(−0.14, 0.20)−0.13(−0.42, 0.16)−0.29 (−0.99, 0.40)0.01 (−1.60, 1.61) p=0.37
Total cholesterol−0.05(−0.20, 0.09)−0.04(−0.28, 0.21)−0.38(−0.81, 0.05)−0.29(−0.91, 0.33) p=0.18−0.02(−0.17, 0.12)−0.29(−0.51, −0.07)−0.15(−0.57, 0.28)−0.55(−1.53, 0.43) p=0.11
HDL0.04 (0.01, 0.07)0.06(0.02, 0.10)0.03(−0.04, 0.09)0.07(−0.06, 0.20) p=0.630.05(0.02, 0.08)0.03(−0.02, 0.09)0.04(−0.04, 0.12)−0.02(−0.16, 0.12) p=0.29
LDL−0.09(−0.22, 0.05)−0.15(−0.37, 0.07)−0.21(−0.53, 0.10)0.12(−0.69, 0.92) p=0.64−0.10(−0.24, 0.03)−0.21(−0.41, −0.01)0.03(−0.39, 0.44)0.10(−0.93, 1.13) p=0.96

The findings were similar following adjustment for baseline body mass index (for the weight outcome) and waist (for the waist outcome), baseline of the cardio-metabolic risk factor examined, age, gender and ethnicity (Table 3). A reduction in both systolic and diastolic blood pressure was strongly associated with higher weight or waist circumference loss. Those with greater waist circumference loss had a greater reduction in GGT but there was no apparent increase in GGT reduction with increasing weight loss, although both of these model estimates were measured with low precision (wide confidence intervals). There was no strong effect seen after controlling for potential confounders of either weight or waist circumference loss on serum lipids and fasting glucose.

Table 3. Multivariable analysis of the change in weight or waist circumference from baseline to follow up.a
 % of loss from baselineWeight loss, mean (95% CIb)Waist circumference loss, mean (95% CI)
  1. a Controlling for age, gender, ethnicity, baseline weight and baseline of the risk factor, reference is no weight/waist circumference loss or weight/waist circumference gain

  2. b 95% confidence interval

Systolic blood pressure<5
5-<10
10+
−1.6 (−5.5, 2.4)
−6.0 (−11.1, −0.9)
−11.3 (−17.8, −4.8)
−3.2 (−7.4, 1.0)
−3.0 (−8.4, 2.5)
−12.8 (−20.3, −5.2)
Diastolic blood pressure<5
5-<10
10+
−1.1 (−3.4, 1.3)
−1.0 (−4.1, 2.0)
−3.7 (−7.6, 0.1)
−1.9 (−4.4, 0.5)
−3.6 (−6.8, −0.4)
−6.1 (−10.5, −1.7)
Fasting glucose<5
5-<10
10+
0.1 (−0.6, 0.8)
0.8 (−0.1, 1.8)
−0.2 (−1.4, 1.1)
0.4 (−0.3, 1.2)
−0.6 (−1.6, 0.5)
−0.4 (−1.8, 1.1)
GGT<5
5-<10
10+
−2.8 (−10.1, 4.5)
1.6 (−7.8, 11.0)
−0.6 (−13.2, 12.1)
0.2 (−7.6, 7.9)
−0.2 (−11.0, 10.5)
−8.3 (−23.5, 6.8)
Triglyceride<5
5-<10
10+
0.2 (−0.1, 0.5)
−0.2 (−0.6, 0.2)
−0.3 (−0.9, 0.3)
−0.1 (−0.5, 0.2)
−0.3 (−0.8, 0.1)
0.3 (−0.3, 0.9)
Total cholesterol<5
5-<10
10+
0.1 (−0.1, 0.4)
−0.02 (−0.3, 0.3)
−0.04 (−0.5, 0.4)
−0.2 (−0.5, 0.1)
−0.1 (−0.4, 0.3)
−0.2 (−0.7, 0.3)
HDL<5
5-<10
10+
0.01 (−0.04, 0.06)
−0.04 (−0.1, 0.03)
0.01 (−0.08, 0.1)
−0.01 (−0.1, 0.04)
−0.01 (−0.1, 0.1)
−0.1 (−0.2, 0.04)
LDL<5
5-<10
10+
0.1 (−0.1, 0.3)
0.2 (−0.1, 0.5)
0.3 (−0.1, 0.8)
−0.05 (−0.3, 0.2)
0.2 (−0.2, 0.6)
0.2 (−0.4, 0.8)

A repeat multivariable analysis of the effect of weight or waist circumference loss on cardio-metabolic risk factors was conducted using imputed values for serum lipids, GGT and fasting glucose to assess the effect of missing values on the results. (Results are available from the authors on request). Imputation had no real effect on the serum lipid and fasting glucose findings. There was, however, an increase of the reduction in GGT for each weight and waist loss category all measured with more narrow confidence intervals. Results for GGT were essentially unchanged when also controlling for risky alcohol intake in the imputed analysis (weight loss of 10% or more GGT was −4.5 (95% CI −26.2, 17.1), and for waist circumference loss of 10% or more GGT was −12.8 (95% CI −40.6, 15.0) – data not shown.

Discussion

While the majority of the cohort gained weight over the course of the study, just over 20% had stable weight or lost weight over an average 6.6 year period. Modest weight or waist circumference loss was associated with a large reduction in systolic and diastolic pressure and possibly also GGT. These findings are strengthened by an apparent dose-response effect with a greater benefit for greater weight or waist circumference loss. There was, however, no clear apparent effect on fasting glucose or serum lipids in this cohort. Given the combined significant burden of disease as a result of obesity and hypertension in the Indigenous population, (estimated at 16% of the total burden of disease),16 these findings suggest a high degree of benefit from either weight or waist circumference loss over the course of several years.

The effect sizes on systolic but not diastolic blood pressure were similar to those reported elsewhere and are likely to be clinically relevant.17 A meta-analysis of the effect of weight loss on hypertension found an average reduction of 5.1 kg was associated with a 4.44 mm Hg (95% CI, −5.93 to −2.95) reduction in systolic and 3.57 mm Hg (95% CI, −4.88 to −2.25) reduction in diastolic blood pressure.3 In this study, the benefits on systolic blood pressure are comparable. It may be, however, that other factors are involved in the blood pressure reduction seen that were not accounted for in this study, such as anti-hypertensive medication use. Over the average 6.6 years of follow up, there was no real difference in serum lipids for those who did or did not lose weight, which is consistent with many studies that had a follow-up period of longer than one year, but not with those with a shorter period of follow up where benefits on total cholesterol have been reported. There was, however, an indication that HDL levels changed in a desirable direction (increased) with waist circumference loss, although the effects were small and measured with low precision. Given there is evidence that Indigenous people have generally low HDL levels in general community surveys,18,19) this finding warrants further exploration in different Indigenous cohorts in Australia.

There was some evidence for an association between weight loss and a reduced GGT which is an important marker of metabolic risk. Although non-alcoholic fatty liver disease may have a greater association with waist circumference than BMI, this study found no real difference between the association of weight or waist circumference loss with a reduction in GGT (although effects were in a positive direction for waist circumference loss). This did not, however, correspond to an association between weight or waist circumference loss and triglycerides in this population when examined in a multi-variable model controlling for potential confounding. Likewise, there was no apparent benefit on fasting glucose in this general population sample, which is contrary to findings reported elsewhere,2 although those studies were often from diabetic samples. Importantly for both lipids and glycaemia, there was no clear dose response effect of weight or waist circumference loss, suggesting that for this population there was no real effect.

Although the proportion who participated in the baseline study was relatively high compared with similar community-based studies, the proportion followed up was low (28.2%). This was further compounded by missing data. The combined effect is a risk of selection bias being introduced, although not necessarily so.20 There was a greater proportion of Torres Strait Islanders in the follow-up group compared with Aboriginal and non-Indigenous people, which suggests that the findings of this study may be more generalisable to Torres Strait Islanders rather than all Australian Indigenous people. The follow-up group was also more likely to have a higher BMI with a larger waist circumference. While it is possible for those who are more overweight to be able to lose weight faster, and to possibly have a different effect on the risk factors examined here – thereby introducing selection bias – both per cent change in weight or waist circumference were examined along with controls for baseline levels of these outcomes. This is likely to have limited any possible effect of selection bias resulting from differing weight or waist circumference between the baseline only and follow-up sample. Further, the sensitivity analysis with imputed data showed only an increased apparent beneficial effect of weight or waist circumference loss on GGT and no real difference for all other variables, suggesting that missing data did not have a substantive biasing effect on the findings presented here.

A further potential limitation is that as a cohort study, rather than a randomised controlled trial, there is a risk that at least a proportion of the weight or waist circumference loss was due to increasing ill health with increasing age. In this study, however, the associated benefits with blood pressure reduction provide evidence against this possibility for at least the majority of participants. The results are similar to those of a randomised controlled trial of a physical and activity intervention in urban living Aboriginal and Torres Strait Islanders, where a loss of 2.5 kg was associated with a reduction in systolic blood pressure of 4 mmHg and diastolic 2 mmHg,21 lending some weight to the validity of the current study findings. It is also not possible from the study design to determine the method in which the weight was lost (e.g. via dietary restriction, increased physical activity, pharmaceutical) and so while potential benefits of weight loss have been reported here, the mechanism of the weight loss is unknown.

This study has demonstrated potentially large beneficial effects of weight or waist circumference loss over several years on cardio-metabolic risk profile in a remote living Indigenous cohort, although the findings are limited by the use of a cohort study design. The associations were large enough to be of clinical benefit despite weight loss being modest for most (up to 5% of baseline weight).

Ancillary