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Background: Eating two kiwifruit before breakfast by equi-carbohydrate partial exchange of cereal has been associated with lower postprandial glucose and insulin, but it increases the intake of fruit sugar. We assessed the effects of kiwifruit ingestion at breakfast over 7 weeks on metabolic and physiologic factors. Method: Forty-three healthy Asian participants were randomised to ingest 500 mL of carbonated water (control) or 500 mL of carbonated water plus two kiwifruit (intervention), before breakfast. Three-day weighed diet records were taken before and at week 4 during the intervention. Overnight fasting blood samples were taken at baseline and week 7. Forty-two participants completed the study (n = 22 control, n = 20 intervention). Results: The kiwifruit group consumed more fructose, vitamin C, vitamin E, and carbohydrates as a percentage of energy compared with the control group (p < 0.01). There was no evidence of between-group changes in metabolic outcomes at the end of the intervention, with the following mean (95% confidence interval) differences in fasting blood samples: glucose 0.09 (−0.06, 0.24) mmol/L; insulin −1.6 (−3.5, 0.3) µU/mL; uric acid −13 (−30, 4) µmol/L; triglycerides −0.10 (−0.22, 0.03) mmol/L; and total cholesterol −0.05 (−0.24, 0.14) mmol/L. There was a −2.7 (−5.5, 0.0) mmHg difference in systolic blood pressure for the intervention group compared with the control group. Conclusion: Eating two kiwifruit as part of breakfast increased fruit consumption and intake of antioxidant nutrients without a change in fasting insulin. There was a difference in systolic blood pressure and no adverse fructose-associated increases in uric acid, triglycerides, or total cholesterol. This simple intervention may provide health benefits to other demographic groups.

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BACKGROUND/OBJECTIVE: The relationship between postprandial glycaemic responses and cognitive performance, mood and satiety are inconsistent. The objective of this study is to compare the effects of different glycaemic responses, induced by beverages with different glycaemic index (GI) (sucrose and isomaltulose), and a non-glycaemic control (sucralose), on cognition, mood and satiety. SUBJECTS/METHODS: In this double-blinded, randomised crossover trial, healthy adults (n = 55) received sucrose (GI 65), isomaltulose (GI 32) and sucralose (non-caloric negative control) drinks on separate occasions. The Complex Figure test, the Word Recall test, Trail Making Test Part B and the Stroop test were administered 60 min after beverages ingestion. Mood and satiety were tested along with cognitive performance. RESULTS: Comparing between isomaltulose and sucrose, there were no significant differences in the mean (95% CI) for the following: Complex Figure: immediate recall -0.6 (-1.7, 0.5), delayed recall -0.8 (-1.9, 0.3); Word recall: immediate recall 0.2 (-0.7, 1.1), delayed recall 0.5 (-0.4, 1.4); Trail Making: completing time -2.4 (-7.5, 2.7) s; Stroop: time used for correct congruent responses -9 (-31, 14) ms and correct incongruent responses -18 (-42, 6) ms. No differences among beverages were found in the mood and satiety scores with exception that participants felt more energetic 60 min after isomaltulose ingestion (p = 0.028 for difference with sucrose) and hungrier 30 min after isomaltulose ingestion (p = 0.036 for difference with sucrose; p = 0.022 for difference with sucralose). CONCLUSION: Under these study conditions there is no convincing evidence for an effect of glycaemic response on cognitive performance, mood or satiety.

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Fat, protein and carbohydrate are essential macronutrients. Various organisations have made recommendations as to the energy contribution that each of these components makes to our overall diet. The extent of food refining and the ability of food systems to support future populations may also impact on how macronutrients contribute to our diet. In this Special Issue, we are calling for manuscripts from all disciplines to provide a broad-ranging discussion on macronutrients and health from personal, public and planetary perspectives.

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INTRODUCTION Prescribing physical activity is an inexpensive method to promote patients' long-term health, but determinants of adherence with physical activity prescriptions are seldom considered. AIM To identify facilitators and barriers experienced by adults with type 2 diabetes when prescribed regular walking. METHODS Participants were prescribed a regular walking routine that met current physical activity guidelines for type 2 diabetes management for a period of 3 months. Pre- and post-intervention questions considered participants' self-rated health and physical activity amount. Thematic analysis of recorded interviews held after the 3-month prescription identified barriers and facilitators to adherence for participants. RESULTS Twenty-eight adults (aged 60±9 years, body mass index 32.3±4.0kg/m2, HbA1c 59±16mmol/mol) participated in the 3-month intervention, providing 7 years of lived experience. Self-rated health (14%; 95% confidence interval (CI) 7-22%) and time spent walking (+11 min/day; 95% CI 4-18 min/day) increased following the prescription. Major themes motivating participants were: establishing a walking routine; the support of their family members; observing health benefits; and being monitored by a health professional. The greatest barriers were associated with walking in the evening and included feelings of insecurity in the dark or a preference for sedentary behaviour. DISCUSSION A prescription to walk increased time spent in physical activity and self-rated health in adults with type 2 diabetes. Health-care professionals can support walking prescriptions by promoting facilitators and reducing barriers to prescription adherence. Practical solutions to barriers include identifying alternative physical activity opportunities within the house or advice to develop support networks to provide company while walking.

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Generating feelings of satiety may be important in maintaining weight control. It has been hypothesised that the circulating concentration of glucose is a major determinant of satiety, yet the relationship between postprandial glycaemia and satiety is inconclusive. Our aim was to assess satiety following ingestion of beverages differing in glycaemic index (GI) containing either 50 g of sucrose (GI 65) or isomaltulose (PalatinoseTM) (GI 32). The beverages were matched for sweetness using a triangle sensory test. Seventy-seven participants were randomised to the order in which they received each beverage, 2 weeks apart. A standard lunch was given at 12.00 hours. Satiety was measured using 100-mm visual analogue scales (VAS) administered at 14.00 hours (baseline) and at 30, 60, 90, 120, 150 and 180 min after ingesting the beverage. Weighed diet records were kept from 17.00 to 24.00 hours. Mean differences for isomaltulose compared with sucrose AUC VAS were 'How hungry do you feel?' 109 (95 % CI -443, 661) mm × min; 'How satisfied do you feel?' 29 (95 % CI -569, 627) mm × min; 'How full do you feel?' -91 (95 % CI -725, 544) mm × min and 'How much do you think you can eat?' 300 (95 % CI -318, 919) mm × min. There was no between-treatment difference in satiety question responses or in dietary energy intake -291 (95 % CI -845, 267) kJ over the remainder of the day. In this experiment, feelings of satiety were independent of the GI of the test beverages. Any differences in satiety found between foods chosen on the basis of GI could be attributable to food properties other than the glycaemic-inducing potential of the food.

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Slow eating may be beneficial in reducing energy intake although there is limited research quantifying eating rate. Perceived speed of eating was self-reported by 78 adults using a standard question "On a scale of 1-5 (very slow-very fast), how fast do you believe you eat?" Timing the completion of meals on three occasions was used to assess objective eating rate. The mean (SD) speeds of eating by self-reported categories were 49 (13.7), 42 (12.2), and 35 (10.5) g/min for fast, medium, and slow eaters, respectively. Within each self-reported category, the range of timed speed of eating resulted in considerable overlap between self-identified 'fast', 'medium' and 'slow' eaters. There was 47.4% agreement (fair) between self-reported speed of eating and the objective measure of eating rate ( = 0.219). Self-reported speed of eating was sufficient at a group level to detect a significant difference (10.9 g/min (95% CI: 2.7, 19.2 g/min, p = 0.009)) between fast and slow; and fast and medium eaters (6.0 g/min (0.5, 11.6 g/min p = 0.033)). The mean difference (95% CI) between slow and medium eaters was 4.9 (-3.4, 12.2) g/min (p = 0.250). At an individual level, self-report had poor sensitivity. Compared to objectively measured speed of eating, self-reported speed of eating was found to be an unreliable means of assessing an individual's eating rate. There are no standard protocols for assessing speed of eating or eating rate. Establishing such protocols would enable the development of population reference ranges across various demographic groups that may be applicable for public health messages and in clinical management.

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The effect on cognitive test scores of generating differences in postprandial glycaemia using test foods or beverages has been inconsistent. Methodological issues may account for some of the variable results requiring further investigation using strong study designs into the relationship between glycaemia and cognitive functioning. The objective of this study was to determine the effects of postprandial glycaemia on cognitive function by examining cognition after consumption of foods that differ only by the rate of digestion of available carbohydrate in a population of young adults. In a double-blind, randomised, crossover trial, sixty-five participants received trifle sweetened either with a higher-glycaemic index (GI) sugar (sucrose; GI 65) or a lower-GI sugar (isomaltulose; GI 34). Cognitive tests were completed prior to trifle consumption, and 60 and 120 min after. There was no between-trifle difference at 60 min in performance on free word recall (0·0 (95 % CI -0·6, 0·5)), short delay word recall (0·0 (95 % CI -0·5, 0·5)), long delay word recall (0·0 (95 % CI -0·6, 0·6)), letter-number sequence recall (0·3 (95 % CI - 0·2, 0·7)) and visuo-spatial recall (-0·2 (95 % CI -0·6, 0·2)) tests. At 120 min, no difference was detected in any of these tests. The participants performed 7·7 (95 % CI 0·5,14·9) s faster in Reitan's trail-making test B 60 min after the higher-GI trifle than the lower-GI trifle (P = 0·037). Our findings of a null effect on memory are generally consistent with other works in which blinding and robust control for confounding have been used.

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We aimed to investigate the isolated effect of glycaemia on cognitive test performance by using beverages sweetened with two different glucose-fructose disaccharides, sucrose and isomaltulose. In a randomised crossover design, 70 healthy adults received a low-glycaemic-index (GI) isomaltulose and sucralose beverage (GI 32) and a high-GI sucrose beverage (GI 65) on two occasions that were separated by two weeks. Following beverage ingestion, declarative memory and immediate word recall were examined at 30, 80 and 130 min. At 140 min, executive function was tested. To confirm that the glycaemic response of the test beverages matched published GI estimates, a subsample (n = 12) of the cognitive testing population (n = 70) underwent glycaemic response testing on different test days. A significantly lower value of mean (95% CI) blood glucose concentration incremental area under the curve (iAUC) was found for isomaltulose, in comparison to the blood glucose concentration iAUC value for sucrose, the difference corresponding to -44 mmol/L∙min (-70, -18), p = 0.003. The mean (95% CI) difference in numbers of correct answers or words recalled between beverages at 30, 80 and 130 min were 0.1 (-0.2, 0.5), -0.3 (-0.8, 0.2) and 0.0 (-0.5, 0.5) for declarative memory, and -0.5 (-1.4, 0.3), 0.4 (-0.4, 1.3) and -0.4 (-1.1, 0.4) for immediate free word recall. At 140 min, the mean difference in the trail-making test between beverages was -0.3 sec (-6.9, 6.3). None of these differences were statistically or clinically significant. In summary, cognitive performance was unaffected by different glycaemic responses to beverages during the postprandial period of 140 min.

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Nutritional education for pre- and type 2 diabetes empowers individuals to make positive dietary and lifestyle choices. As the world migrates to digital devices, opportunities arise for education resources to reach a broad spectrum of society. This study aimed to develop and test the effectiveness of an electronic nutritional education resource for people with pre- and type 2 diabetes within the multi-ethnic New Zealand population. A needs assessment was conducted via ethnic-specific discussion groups (n = 29), followed by a population-based online survey (n = 448). An educational resource, including an educational video and pre- and post-questionnaires, was developed and tested online among 156 participants (17 with pre- and type 2 diabetes, 118 interested lay public and 21 health professionals). There was a strong desire to learn nutrition through simple, visual, practical, and culturally appropriate online educational resources. After interacting with the educational resource, the accuracy of identifying foods that increase blood glucose concentration improved by 17.4% (p = 0.013) in people with pre- and type 2 diabetes, 12.8% (p = 0.003) in health professionals, and 16.3% (p < 0.001) in interested lay public. There was an improvement among ethnic minority participants of 14.1% (p = 0.003). Most participants expressed intentions to make positive dietary and lifestyle choices. The electronic nutrition education resource was found to be an effective means for delivering education. It has potential to bridge the gap between the limited supply of healthcare resources and the increasing demand for diabetes nutrition education.

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The satiating capacity of carbohydrate staples eaten alone is dependent upon the energy density of the food but relative satiety when starchy staples are incorporated into mixed meals is uncertain. Our aim was to assess the satiating effects of three carbohydrate staples; jasmine rice, penne pasta, and Agria potato, each consumed within a standard mixed meal. Cooked portions of each staple containing 45 g carbohydrate were combined with 200 g of meat sauce and 200 g of mixed vegetables in three mixed meals. The quantities of staple providing 45 g carbohydrate were: Rice, 142 g; pasta, 138 g and potato 337 g. Participants (n = 14) consumed each of the mixed meals in random order on separate days. Satiety was assessed with using visual analogue scales at baseline and for 3 h post meal. In an area-under-the-curve comparison, participants felt less hungry (mean (SD)) following potato 263 (230) than following rice 374 (237) or pasta 444 (254) mm∙min, and felt fuller, more satisfied, and wanted to eat less following the potato compared with the rice and pasta meals (p for all <0.01). The superior satiating effect of potato compared with rice and pasta in a mixed meal was consistent with its lower energy density.

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There is scant information on how a time lag between the cessation of eating and commencement of physical activity affects postprandial glycaemia. Starting at baseline (t = 0), participants ingested white bread containing 50 g of available carbohydrates within 10 min. Using two crossover conditions, we tested the effect over 2 h on postprandial glycaemia of participants undertaking light activity at 15 or 45 min following baseline and compared it with a sedentary control condition. The activity involved cycling on a stationary ergometer for 10 min at 40 revolutions per min with zero resistance. Seventy-eight healthy adults were randomized to the 15 or 45 min activity arm and then randomised to the order in which they undertook the active and sedentary conditions. Cycling 45 min after baseline changed the course of the blood glucose response (likelihood ratio chi square = 31.47, p < 0.01) and reduced mean blood glucose by 0.44 mmol/L (95% confidence interval 0.14 to 0.74) at 60 min when compared with the sedentary control. No differences in postprandial blood glucose response were observed when cycling started 15 min after baseline compared with the sedentary control. Undertaking activity after waiting for 30 min following eating might be optimal in modifying the glycaemic response.

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The effect that blood glucose concentration has on feelings of satiety is unclear. Our aims were to assess satiety and subsequent energy intake following the ingestion of trifle sweetened with sucrose or isomaltulose whilst measuring plasma glucose concentration to confirm glycemic differences between trifles. Seventy-seven healthy adults participated in a double-blind crossover trial where trifle sweetened with sucrose or isomaltulose was consumed on separate days with a two-week washout. Blood was sampled at the baseline, 1 and 2 h postprandially, and satiety assessed using visual analogue scales (VAS). Weighed diet records were taken on test days. A statistically significant difference in blood glucose concentration between trifles was found at 60 min following consumption, with the isomaltulose trifle having a 0.69 mmol/L (95% confidence interval: -1.07, -0.31) lower concentration when compared with the sucrose trifle. Mean satiety response by area-under-the-curve (AUC) was not significantly different between trifles. Mean (SD) appetite scores for the sucrose and isomaltulose trifles were 4493 (2393) and 4527 (2590) mm·min, respectively, with a between trifle difference of -9 (95% CI: -589, 572) mm·min. Mean (SD) energy intake for the remainder of the day following trifle consumption was 3894 kJ (1950 kJ) and 3530 kJ (1926 kJ) after the sucrose and isomaltulose trifles, respectively, and was not significantly different (p = 0.133). The differing glycemic response to trifle was not related to satiety or to subsequent energy intake.

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Despite availability of diabetes and nutrition information for people with pre- and type 2 diabetes, the uptake and understanding of these resources may differ among ethnic groups. Our objective was to explore dietary knowledge and diabetes experiences amongst Maori, European, Pacific Island, Indian and East Asian people living in New Zealand with a focus on carbohydrate-containing foods. A registered diabetes dietitian led ethnic-specific discussions in groups involving 29 people with pre- or type 2 diabetes. Discussions were audio-recorded, fully transcribed and coded independently by two investigators. Themes were developed using deductive and inductive techniques. Five themes emerged: knowledge, concerns, achievements, simplicity and self-determination. Nutritional knowledge was lacking and a greater awareness of trustworthy dietary resources was needed. There were concerns about diabetes complications and appropriate carbohydrate-containing foods and portions. Contrary to this, people felt proud when achieving dietary goals and grateful for support from health care providers and family. Participants were willing to engage in self-care if advice from health professionals was given in plain language, and in a culturally appropriate manner. Given the desire to take an active role in diabetes self-management and willingness to use electronic devices, an ethnic-specific nutrition education resource could be a valuable tool.

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Despite initial enthusiasm, the relationship between glycemic index (GI) and glycemic response (GR) and disease prevention remains unclear. This review examines evidence from randomized, controlled trials and observational studies in humans for short-term (e.g., satiety) and long-term (e.g., weight, cardiovascular disease, and type 2 diabetes) health effects associated with different types of GI diets. A systematic PubMed search was conducted of studies published between 2006 and 2018 with key words glycemic index, glycemic load, diabetes, cardiovascular disease, body weight, satiety, and obesity. Criteria for inclusion for observational studies and randomized intervention studies were set. The search yielded 445 articles, of which 73 met inclusion criteria. Results suggest an equivocal relationship between GI/GR and disease outcome. The strongest intervention studies typically find little relationship among GI/GR and physiological measures of disease risk. Even for observational studies, the relationship between GI/GR and disease outcomes is limited. Thus, it is unlikely that the GI of a food or diet is linked to disease risk or health outcomes. Other measures of dietary quality, such as fiber or whole grains may be more likely to predict health outcomes. Interest in food patterns as predictors of health benefits may be more fruitful for research to inform dietary guidance.

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Controlling postprandial glycaemia helps to prevent and manage non-communicable diseases. One strategy in controlling glycaemia may be to consume meals in two parts; a preload, followed by the remainder of the meal. Our aim was to test preloading a rice meal given for breakfast and lunch on different days, either by splitting the meal (rice preload followed by rice meal) or by using kiwifruit as a preload compared with consuming the rice meal in one sitting. Primary outcomes were glycaemic and insulinaemic responses with secondary outcomes of other hormonal responses, subjective satiety, and subsequent energy intake. Following breakfast, postprandial glycaemic peak concentration was 0.9 (95% CI: 0.2, 1.6) mmol/L lower for the kiwifruit preload compared with the rice meal eaten in one sitting. Following lunch, glycaemic peak concentrations were 1.0 (0.7, 1.4) and 1.1 (0.5, 1.7) mmol/L lower for the rice-split and kiwifruit preload compared with the rice meal alone, respectively. Postprandial insulinaemia area-under-the-curve was 1385 (87, 2684) mU/L·min less for the kiwifruit preload compared with the rice-split. There were no differences among treatments for subsequent energy intake. Meal splitting is useful for lowering postprandial glycaemia, and replacing part of a meal with kiwifruit may help with insulin efficiency without detriment to subsequent energy intake.

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BACKGROUND/OBJECTIVES: The European Food Safety Authority approved a health claim (ID558) relating to lowered postprandial glycaemia when fructose replaces 30% of sucrose in foods and beverages. We assessed the effects of partial replacement of sucrose with fructose on serum glucose, uric acid and blood pressure. SUBJECTS/METHODS: A randomised, crossover, double blind trial of 12 normoglycaemic participants consuming beverages containing 50 g blends of fructose and sucrose in proportions; 67% sucrose/33% fructose (67%S:33%F); 50% each (50%S:50%F) and 33%S:67%F; a 100% sucrose reference beverage was tested twice. Serum glucose and uric acid concentrations were measured at 0, 15, 30, 45, 60, 90 and 120 min and incremental area-under-the-curve (iAUC) calculated. RESULTS: The geometric mean (95% CI) glycaemic iAUC following the 100% sucrose, 67%S:33%F, 50%S:50%F and 33%S:67%F blended beverages were 96 (63,145), 71 (46,109), 60 (39, 93) and 39 (12, 86) mmol/L min, respectively. At 33% fructose replacement, the proportionally lower iAUC of -28.5% (95% CI: -62.1, 5.2) mmol/L min was not different to sucrose alone. The response was lowered by fructose replacement of 50 and 67% and overall there was an inverse association (p < 0.001). The mean uricaemic iAUC to the respective beverages were 1320 (393, 2248), 3062 (1553, 4570), 3646 (2446, 4847), 3623 (2020, 5226) µmol/L min. Uric acid concentration was raised by all fructose-containing beverages with 33% fructose replacement causing an increase of 1741 (95% CI: 655, 2829) µmol/L min compared with sucrose alone. Blood pressure was not different among beverages. CONCLUSIONS: Reduced postprandial glycaemia was achieved by the substitution of sucrose with fructose although elevated uricaemic responses should be cautioned.

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Background: The consumption of large amounts of fructose from added sugars results in the hepatic production and export of uric acid into the circulation. Objective: Our aim was to test whether fructose present in fruit is of sufficient quantity or in a form that will increase uric acid concentration. Design: Seventy-three participants were randomly assigned to 1 of 3 groups to ingest small (205 g) and large (410 g) servings of apple segments, small (170 mL) and large (340 mL) servings of apple juice, or a glucose and a fructose control beverage. Within each group, participants ingested both treatments in a crossover design. The fructose control and the large servings of apple and juice contained 26.7 g fructose. Test foods were ingested within 10 min. Blood samples were taken at baseline and at 30 and 60 min after intake. Results: Plasma uric acid concentrations increased after the intake of all fructose-containing treatments and decreased after the glucose beverage. The mean (95% CI) increase in uric acid at 30 min was 15 µmol/L (10, 21 µmol/L) for the fructose control and 19 µmol/L (8, 30 µmol/L) and 17 µmol/L (9, 24 µmol/L) for the large servings of apple and apple juice, respectively. There was no difference in change in uric acid between baseline and 30 min when comparing the apple (3 µmol/L; 95% CI: -8, 14 µmol/L) and apple juice (-7 µmol/L; 95% CI: -18, 5 µmol/L) with the fructose control. Blood pressure taken 70 min after ingestion was unaffected by any treatment (P > 0.05). There was no difference in change in satiety scores between the fructose and glucose control beverages (P > 0.05). Participants felt more satiated 30 min after ingesting whole apple than after apple juice. The glycemic response reflected the amount of glucose in each treatment. Conclusions: The body acutely responds to fructose regardless of source. Longer-term studies are required to assess how small and transient increases in plasma uric acid contribute to health. This trial was registered with the Australian New Zealand Clinical Trials Registry at https://www.anzctr.org.au/trial/registration/trialreview.aspx?id=367974 as ACTRN12615000215527.

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Vitamin B12 deficiency leads to serious health problems, whilst sub-optimal status is associated with raised biochemical markers of disease risk. Identifying at-risk groups could benefit both individuals and public health. Dietary data were sourced from the New Zealand Adult Nutrition Survey 2008/2009, involving a nationally representative sample of 4721 participants. Ethnic groupings were by regional origin: Maori and Pacific Islands, New Zealand European, East and South-East Asian, and South Asian. Diets were assessed using 24-h recalls and from responses to a questionnaire. Blood samples were obtained from a subset (n = 3348). The mean (95% CI) vitamin B12 intake of the Maori and Pacific Islands group was 5.1 (4.7, 5.5) µg/day, New Zealand Europeans 4.1 (3.8, 4.3) µg/day, East and South-East Asians 4.5 (3.7, 5.3) µg/day, and South Asians 3.0 (2.5, 3.6) µg/day. Overall, 20.1% of the sample had vitamin B12 inadequacy (<221 pmol/L). South Asians had the lowest vitamin B12 concentration at 282 (251, 312) pmol/L, whilst Maori/Pacific and East/South-East Asians had the highest, at 426 (386, 466) and 425 (412, 437) pmol/L, respectively. The main dietary determinant of serum vitamin B12 concentration was whether or not people ate red meat, with a regression coefficient of 27.0 (95% CI: 6.6, 47.5). It would be helpful for health agencies to be aware of the potential for compromised vitamin B12 status in South Asian communities.

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PURPOSE: Iodine deficiency affects 30% of populations worldwide. The amount of thyroglobulin (Tg) in blood increases in iodine deficiency and also in iodine excess. Tg is considered as a sensitive index of iodine status in groups of children and adults, but its usefulness for individuals is unknown. The aim of this study was to determine the diagnostic performance of Tg as an index of iodine status in individual adults. METHODS: Adults aged 18-40 years (n = 151) provided five spot urine samples for the measurement of urinary iodine concentration expressed as µg/L (UIC), µg/g of creatinine (I:Cre), and µg/day (estimated UIE); the mean of the five samples was used as the reference standard. Participants also provided a blood sample for the determination of Tg, thyroid-stimulating hormone (TSH), and free thyroxine (FT4). RESULTS: The median of UIC, I:Cre, estimated UIE, and Tg was 72 (range 16-350) µg/L, 90 (range 33-371) µg/g, 129 (range 41-646) µg/day, and 16.4 (range 0.8-178.9) µg/L, respectively. Using Tg cut-offs of >10, >11, >13, and >15 µg/L, the sensitivity and specificity for UIC, I:Cre, and estimated UIE ranged from 52 to 79% and 20-48%, respectively, below the acceptable value of ≥80%. Furthermore, receiver-operating characteristic (ROC) curves for Tg using the three measurements of urinary iodine were situated close to the chance line and the area under the curve ranged from 0.49 to 0.52. CONCLUSIONS: The results from this cross-sectional study indicate that Tg has low sensitivity and specificity to repeated measures of urinary iodine excretion. Further studies are still needed to investigate the usefulness of Tg as a biomarker of individual iodine status.

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