{"id":35536,"date":"2020-12-30T11:34:23","date_gmt":"2020-12-30T11:34:23","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=35536"},"modified":"2021-03-19T05:59:52","modified_gmt":"2021-03-19T05:59:52","slug":"the-diurnal-variation-of-thyroid-hormones-in-individuals-attending-tertiary-care-hospital-kanchipuram-district","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol13no4\/the-diurnal-variation-of-thyroid-hormones-in-individuals-attending-tertiary-care-hospital-kanchipuram-district\/","title":{"rendered":"The Diurnal Variation of Thyroid Hormones in Individuals Attending Tertiary Care Hospital, Kanchipuram District"},"content":{"rendered":"<p><strong>Introduction<\/strong><\/p>\n<p>The Thyroid hormones,\u00a0 thyroxine T4\u00a0 and triiodo thyronine\u00a0 T3 are produced by the thyroid gland and are pivotal for regulation of metabolism. Thyroxine T4 \u00a0and triiodo thyronine\u00a0 T3 are derived from tyrosine. Free forms of thyroid hormones, free thyroxine FT4 and free triiodo thyronine Ft3 are also important for the regulation of metabolism. The thyroid hormones are essential for proper development and differentiation of all cells of the human body (1). Large amounts\u00a0 of the thyroid hormones which are circulating in the blood are bound to transport protein. The free (unbound) thyroid\u00a0 hormones constitute a very small fraction and are set to be biologically\u00a0 active, which makes estimation of free thyroid hormones of greater diagnostic value. Thyroxine-binding globulin (TBG) is a high affinity binding protein in low concentration that binds approximately 80% of T3 and 75% of T4 (2).<\/p>\n<p>The anterior pituitary gland secrets thyroid stimulating hormones (TSH). TSH is a glycoprotein with a dimeric structure (\u03b1 and \u03b2). TSH is said to\u00a0 show a diurnal variation with a peak\u00a0 after midnight and nadir in the late afternoon. The variation in TSH is such that the peak value can be double the value at the nadir (3). The values of TSH can vary to 20 % in between measurements without any changes in the thyroid status (4).<\/p>\n<p>It is observed that all the\u00a0 circulating T4 is secreted by the thyroid gland while only 20% of T3 is derived from it. The rest 80% is said to be derived from the peripheral conversion of T4 to T3. There is a difference in the kinetics of T3 and T4\u00a0 metabolism due to the lower affinity of T3 for thyroid binding globulin (5). The difference of affinity is said to be 10-15 fold lower than T4(5).<\/p>\n<p>Thyroid illness is very common\u00a0 worldwide,\u00a0 it is a significant burden to the health care system\u00a0 of India. Recent studies which are\u00a0 based on thyroid illness have estimated 4.2 crore people to have thyroid illness in India(6). Subclinical hypothyroidism is said to be linked with many pathological abnormalities from infertility, complications during pregnancy, psychiatric illness, neuromuscular symptoms, cardiac dysfunction and mortality (7, 8).<\/p>\n<p>Pituitary disease, certain drugs can cause Non- Thyroidal Illness (NTI) and produce disturbance in TSH and thyroid hormone levels. Glucocorticoids, dopamine and octreotide are known to suppress TSH secretion. Drugs like amiodarone are said to affect thyroid hormone level by inhibiting their peripheral metabolism. Hence\u00a0 estimation of free thyroid hormones are important in those who are on\u00a0 amiodarone treatment (9).<\/p>\n<p>Recent evidences have documented the levels of free thyroid hormones in the blood are accurate when compared to the analysis of total thyroid hormones, thus free thyroxine FT4 hormone\u00a0 evaluation is practised as an routine procedure to know the thyroid status of the patients.<\/p>\n<p>Recent hypothesis on free thyroid hormones is that the bound thyroid hormones are not available for use on demand and only the free thyroid hormones are available for cellular uptake (10). As studies have proven that TT4 has a very poor affinity for the thyroid hormone nuclear receptor. While T3 is avidly bound to the latter and responsible for the clinical effects, indicating that T4 is the pre hormone and needs to be converted to the active hormone T3 (11,12).<\/p>\n<p>In many physiologic and disease states, there is an alteration in serum binding protein which can\u00a0 affect the Immunoassays of thyroid hormone (13). The assay of thyrotropin is useful in detecting thyroid related disorders (14). Recent evidences have proven that the excess variation in the free thyroid hormone values is observed due to the different methods of immunoassays that are performed\u00a0 (15-19). Enzyme linked immunosorbent assay has been proved to be a useful test for the detection of FT3, FT4 and TSH status of an individual (3).<\/p>\n<p><strong>Materials \u00a0and \u00a0Methodology<\/strong><\/p>\n<p>The study was conducted from March 2019 to September 2019. The study\u00a0 was a cross sectional study. Patients aged above 18 years to 60 years were selected. Convenient sampling method was followed. The\u00a0 study included 60 samples\u00a0 collected from\u00a0 30 patients from attending the outpatient, Department of General medicine, Karpaga Vinayaga Institute of Medical Sciences and Research Centre. The samples were collected from 6AM-8AM in the morning\u00a0 and the second sample was collected in the night from 8.00PM to 9.00PM\u00a0 and\u00a0 informed consent was obtained from all the participants. The institutional ethical committee had approved the study. FreeT3 (FT3),FreeT4(FT4) and\u00a0 Thyroid Stimulating Hormone (TSH) were measured by Enzyme linked immunosorbent assay, using Avantor kit (9).<\/p>\n<p><strong>Inclusion criteria<\/strong><\/p>\n<p>Patients above 18 years- 60years of age were selected for the study.<\/p>\n<p><strong>Exclusion criteria<\/strong><\/p>\n<p>People with known history of thyroid disorder (with or without treatment), Diabetes mellitus, Hypertension, Chronic renal failure.<\/p>\n<p>Patients not willing to take part in the study.<\/p>\n<p>Patients who are fulfilling inclusion &amp; exclusion criteria, after obtaining written informed consent will be subjected to complete history taking, physical examination, serum FT3, FT4 &amp; TSH level estimation at two time intervals.<\/p>\n<p>Serum FreeT3 (FT3),FreeT4(FT4), Thyroid Stimulating Hormone(TSH) levels were measured using Enzyme linked immunosorbent assay (ELISA). Appropriate statistical methods were used to analyse the study.<\/p>\n<p><strong>Biological Reference Interval (20)<\/strong><\/p>\n<p>FreeT3 (FT3): 1.4-4.2 pg\/mL<\/p>\n<p>FreeT4 (FT4): 0.8-2.0 ng\/dL<\/p>\n<p>TSH: 0.39-6.16 \u00b5IU\/mL<\/p>\n<p><strong>Statistics<\/strong><\/p>\n<p>Descriptive analysis was carried out by mean and standard deviation for quantitative variables, frequency and proportion for categorical variables. Non normally distributed quantitative variables was summarized by median and interquartile range (IQR). Data was also represented using appropriate diagrams like bar diagram, pie diagram and box plots.<\/p>\n<p>All Quantitative variables were checked for normal distribution within each category of explanatory variable by using visual inspection of histograms and normality Q-Q plots. Shapiro- wilk test was also conducted to assess normal distribution.\u00a0 Shapiro wilk test p value of &gt;0.05 was considered as normal distribution.<\/p>\n<p>For normally distributed Quantitative parameters the mean values were compared between study groups using Independent sample t-test (2 groups). Correlation between the manual and FT3, FT4 and TSH (day and night) were assessed by Pearson correlation coefficient. Data was presented in scatter plots.<\/p>\n<p>Bland-Altmann plots were computed to assess the level of agreement between various manual methods and FT3, FT4 and TSH to assess at different time periods (day and night)<\/p>\n<p>Pearson\u2019s\u00a0 value (p) &lt; 0.05 was considered statistically significant. IBM SPSS version 22 was used for statistical analysis (21).<\/p>\n<p><strong>Results<\/strong><\/p>\n<p>A total of 30 subjects were included in the analysis.<\/p>\n<p><strong>Table 1: Descriptive analysis of age in study population (N=30)<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"16%\"><strong>Parameter<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"19%\"><strong>Mean \u00b1 SD<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"12%\"><strong>Median<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"15%\"><strong>Minimum<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"15%\"><strong>Maximum<\/strong><\/td>\n<td style=\"text-align: center;\" colspan=\"2\" width=\"21%\"><strong>95% C.I<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"10%\"><strong>Lower<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"10%\"><strong>Upper<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"16%\">Age<\/td>\n<td style=\"text-align: center;\" width=\"19%\">35.03 \u00b1 13.01<\/td>\n<td style=\"text-align: center;\" width=\"12%\">37.00<\/td>\n<td style=\"text-align: center;\" width=\"15%\">18.00<\/td>\n<td style=\"text-align: center;\" width=\"15%\">60.00<\/td>\n<td style=\"text-align: center;\" width=\"10%\">30.18<\/td>\n<td style=\"text-align: center;\" width=\"10%\">39.89<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In the study population, the mean of age was 35.03 \u00b1 13.01 with the minimum age was 18 years and maximum age of 60 years. ( Table 1).<\/p>\n<p><strong>Table 2: Gender analysis of the study population (N=30) &#8211; descriptive<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"33%\"><strong>Gender<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"33%\"><strong>Frequency<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"33%\"><strong>Percentages<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"33%\">Male<\/td>\n<td style=\"text-align: center;\" width=\"33%\">11<\/td>\n<td style=\"text-align: center;\" width=\"33%\">36.67%<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"33%\">Female<\/td>\n<td style=\"text-align: center;\" width=\"33%\">19<\/td>\n<td style=\"text-align: center;\" width=\"33%\">63.33%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Eleven (36.67%) participants were males and the remaining 19 (63.33%) participants were female. (Table 2 &amp; Figure 1)<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-35540\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig1-150x150.jpg\" alt=\"Figure 1: Gender analysis- Pie chart (N=30)\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig1-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig1-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig1.jpg 541w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 1: Gender analysis- Pie chart (N=30)<\/strong><\/p>\n<p><a href=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig1.jpg\" target=\"_blank\">Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Table 3: Descriptive analysis of thyroid profile day and night (N=30)<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"23%\"><strong>Parameter<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"16%\"><strong>Mean \u00b1 SD<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"11%\"><strong>Median<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"12%\"><strong>Minimum<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"12%\"><strong>Maximum<\/strong><\/td>\n<td style=\"text-align: center;\" colspan=\"2\" width=\"24%\"><strong>95% C.I<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"10%\"><strong>Lower<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"13%\"><strong>Upper<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"23%\">Day FT3 (pg\/mL)<\/td>\n<td style=\"text-align: center;\" width=\"16%\">2.43 \u00b1 0.56<\/td>\n<td style=\"text-align: center;\" width=\"11%\">2.36<\/td>\n<td style=\"text-align: center;\" width=\"12%\">1.24<\/td>\n<td style=\"text-align: center;\" width=\"12%\">3.95<\/td>\n<td style=\"text-align: center;\" width=\"10%\">2.22<\/td>\n<td style=\"text-align: center;\" width=\"13%\">2.64<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"23%\">Day FT4 (ng\/dL)<\/td>\n<td style=\"text-align: center;\" width=\"16%\">1.14 \u00b1 0.44<\/td>\n<td style=\"text-align: center;\" width=\"11%\">1.01<\/td>\n<td style=\"text-align: center;\" width=\"12%\">0.46<\/td>\n<td style=\"text-align: center;\" width=\"12%\">2.14<\/td>\n<td style=\"text-align: center;\" width=\"10%\">0.98<\/td>\n<td style=\"text-align: center;\" width=\"13%\">1.31<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"23%\">Day TSH (\u00b5IU\/mL)<\/td>\n<td style=\"text-align: center;\" width=\"16%\">2.74 \u00b1 3.16<\/td>\n<td style=\"text-align: center;\" width=\"11%\">1.76<\/td>\n<td style=\"text-align: center;\" width=\"12%\">0.98<\/td>\n<td style=\"text-align: center;\" width=\"12%\">8.57<\/td>\n<td style=\"text-align: center;\" width=\"10%\">1.55<\/td>\n<td style=\"text-align: center;\" width=\"13%\">3.92<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"23%\">Night FT3 (pg\/mL)<\/td>\n<td style=\"text-align: center;\" width=\"16%\">2.66 \u00b1 0.55<\/td>\n<td style=\"text-align: center;\" width=\"11%\">2.57<\/td>\n<td style=\"text-align: center;\" width=\"12%\">1.62<\/td>\n<td style=\"text-align: center;\" width=\"12%\">4.01<\/td>\n<td style=\"text-align: center;\" width=\"10%\">2.46<\/td>\n<td style=\"text-align: center;\" width=\"13%\">2.87<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"23%\">Night FT4 (ng\/dL)<\/td>\n<td style=\"text-align: center;\" width=\"16%\">1.3 \u00b1 0.61<\/td>\n<td style=\"text-align: center;\" width=\"11%\">1.06<\/td>\n<td style=\"text-align: center;\" width=\"12%\">0.63<\/td>\n<td style=\"text-align: center;\" width=\"12%\">2.80<\/td>\n<td style=\"text-align: center;\" width=\"10%\">1.08<\/td>\n<td style=\"text-align: center;\" width=\"13%\">1.53<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"23%\">Night TSH (\u00b5IU\/mL)<\/td>\n<td style=\"text-align: center;\" width=\"16%\">3.21 \u00b1 3.18<\/td>\n<td style=\"text-align: center;\" width=\"11%\">2.30<\/td>\n<td style=\"text-align: center;\" width=\"12%\">1.02<\/td>\n<td style=\"text-align: center;\" width=\"12%\">8.58<\/td>\n<td style=\"text-align: center;\" width=\"10%\">2.03<\/td>\n<td style=\"text-align: center;\" width=\"13%\">4.40<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify; text-justify: inter-ideograph; line-height: 150%; margin: 0in 0in 8.0pt 0in;\">The mean of Day FT3 was 2.43 \u00b1 0.56 pg\/mL, minimum was 1.24 and maximum was 3.95 (95% CI 2.22 to 2.64). The mean of Day FT4 was 1.14 \u00b1 0.44 ng\/dL, minimum was 0.46 and maximum was 2.14 (95% CI 0.98 to 1.31). The mean of Day TSH was 2.74 \u00b1 3.16 \u00b5IU\/mL, minimum was 0.98 and maximum was 8.57 (95% CI 1.55 to 3.92). The mean of Night FT3 was 2.66 \u00b1 0.55 pg\/mL, minimum was 1.62 and maximum was 4.01 (95% CI 2.46 to 2.87). The mean of Night FT4 was 1.3 \u00b1 0.61 ng\/dL, minimum was 0.63 and maximum was 2.8 (95% CI 1.08 to 1.53). The mean of Night TSH was 3.21 \u00b1 3.18 \u00b5IU\/mL, minimum was 1.02 and maximum was 8.58 (95% CI 2.03 to 4.4) (Table 3).<\/p>\n<p><strong>Table 4: Agreement between day and night values of TFT parameters<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"120\"><strong>TFT parameters<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"120\"><strong>Intra class correlation<\/strong><\/td>\n<td style=\"text-align: center;\" colspan=\"2\" width=\"240\"><strong>95% CI<\/strong><\/td>\n<td style=\"text-align: center;\" rowspan=\"2\" width=\"120\"><strong>P value<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"120\"><strong>Lower<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"120\"><strong>Upper<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"120\"><strong>FT3<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"120\">0.863<\/td>\n<td style=\"text-align: center;\" width=\"120\">0.732<\/td>\n<td style=\"text-align: center;\" width=\"120\">0.932<\/td>\n<td style=\"text-align: center;\" width=\"120\">&lt;0.001<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"120\"><strong>FT4<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"120\">0.858<\/td>\n<td style=\"text-align: center;\" width=\"120\">0.723<\/td>\n<td style=\"text-align: center;\" width=\"120\">0.930<\/td>\n<td style=\"text-align: center;\" width=\"120\">&lt;0.001<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"120\"><strong>TSH<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"120\">0.971<\/td>\n<td style=\"text-align: center;\" width=\"120\">0.939<\/td>\n<td style=\"text-align: center;\" width=\"120\">0.986<\/td>\n<td style=\"text-align: center;\" width=\"120\">&lt;0.001<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>There is a positive correlation between day and night TFT values (p&lt;0.001) (Table 4).<\/p>\n<p><strong>Table 5: One sample t test to assess the significance\u00a0<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"19%\"><strong>Parameter<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"10%\"><strong>Number<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"10%\"><strong>Mean<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"14%\"><strong>Std. Deviation<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"14%\"><strong>t<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"14%\"><strong>Df<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"14%\"><strong>Sig. (2-tailed)<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"19%\">Difference of FT3<\/td>\n<td style=\"text-align: center;\" width=\"10%\">30<\/td>\n<td style=\"text-align: center;\" width=\"10%\">0.2370<\/td>\n<td style=\"text-align: center;\" width=\"14%\">.28917<\/td>\n<td style=\"text-align: center;\" width=\"14%\">4.489<\/td>\n<td style=\"text-align: center;\" width=\"14%\">29<\/td>\n<td style=\"text-align: center;\" width=\"14%\">.000<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"19%\">Difference of FT4<\/td>\n<td style=\"text-align: center;\" width=\"10%\">30<\/td>\n<td style=\"text-align: center;\" width=\"10%\">0.1603<\/td>\n<td style=\"text-align: center;\" width=\"14%\">.28403<\/td>\n<td style=\"text-align: center;\" width=\"14%\">3.092<\/td>\n<td style=\"text-align: center;\" width=\"14%\">29<\/td>\n<td style=\"text-align: center;\" width=\"14%\">.004<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"19%\">Difference of TSH<\/td>\n<td style=\"text-align: center;\" width=\"10%\">30<\/td>\n<td style=\"text-align: center;\" width=\"10%\">0.4770<\/td>\n<td style=\"text-align: center;\" width=\"14%\">.76972<\/td>\n<td style=\"text-align: center;\" width=\"14%\">3.394<\/td>\n<td style=\"text-align: center;\" width=\"14%\">29<\/td>\n<td style=\"text-align: center;\" width=\"14%\">.002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-35542\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig2-150x150.jpg\" alt=\"Figure 2: Bland-Altmann plot depicting the level of agreement between FT3 day and night\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig2-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig2-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig2.jpg 600w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 2:\u00a0<\/strong><strong>Bland-Altmann plot depicting the level of agreement between FT3 day and night<\/strong><\/p>\n<p><a href=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig2.jpg\" target=\"_blank\">Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Bland-Altman analysis showed the average difference between night, day values of FT3 was 0.23 pg\/mL with a 95% confidence interval of -0.329 to 0.903. There was also a high risk of proportion bias (P value&lt;0.001). (Figure 2)<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-35543\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig3-150x150.jpg\" alt=\"Figure 3: Bland-Altmann plot depicting the level of agreement between FT4 day and night\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig3-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig3-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig3.jpg 599w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 3:\u00a0<\/strong><strong>Bland-Altmann plot depicting the level of agreement between FT4 day and night<\/strong><\/p>\n<p><a href=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig3.jpg\" target=\"_blank\">Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Bland-Altman analysis showed the average difference between night day values of FT4 was 0.16 ng\/dL with a 95% confidence interval of -0.396 to 0.716. There was also a high risk of proportion bias (P value&lt;0.001). (Figure 3)<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-35544\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig4-150x150.jpg\" alt=\"Figure 4: Bland-Altmann plot depicting the level of agreement between TSH day and night\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig4-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig4-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig4.jpg 490w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 4:\u00a0<\/strong><strong>Bland-Altmann plot depicting the level of agreement between TSH day and night<\/strong><\/p>\n<p><a href=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2020\/11\/Vol_13_No_4_Diu_San_Fig4.jpg\" target=\"_blank\">Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Bland-Altman analysis showed the average difference between night day values of TSH was 0.47 mIU\/mL with a 95% confidence interval of -1.030 to 1.984. There was also a high risk of proportion bias (P value&lt;0.001). (Figure 4)<\/p>\n<p><strong>Discussion<\/strong><\/p>\n<p>The diurnal rhythmicity of TSH is demonstrated with peaks from 8 PM to 2 AM and a nadir from 7 AM to 2 PM. Superimposed on the diurnal rhythm, multiple short lived fluctuations are observed (22).<\/p>\n<p>All these studies point out the importance of establishing age specific reference interval for thyroid (23). The age group of participants in our study was 35.03 \u00b1 13.01 years, the minimum age was 18 years and maximum was 60 years. There were\u00a0 36.67% males and 63.3% female participants.<\/p>\n<p>The mean FT3 values in day are 2.43\u00b10.56 pg\/mL with minimum 1.24 and maximum 3.95 with 95% CI. The mean FT4 values in day are 1.14\u00b10.44 ng\/dL with minimum 0.46 and maximum 2.14 with 95% CI. The mean TSH in day was 2.74\u00b13.16 \u00b5IU\/mL, minimum was 0.98 and maximum 8.57. The mean night FT3 is 2.66\u00b10.55 pg\/mL with minimum 1.62 and maximum 4.01. The mean FT4 in night was 1.3\u00b10.61 ng\/dL, minimum 0.63 and maximum 2.8. The mean night TSH was 3.21\u00b13.18 \u00b5IU\/mL, minimum 1.62 and maximum 8.58. The interclass correlation of FT3 is 0.0863, FT4 is 0.858 and TSH is 0.971 with\u00a0 p value &lt; 0.001.<\/p>\n<p>Liyanage YSH<sup>\u00a0\u00a0 <\/sup>in his study has observed that paired t test between Log TSH am &#8211; log TSH pm is 2.616 with p value 0.013. paired t test between log FT4 am &#8211; log FT4 pm is 4.118 with p value 0.000 (24). In our study the one sample t test showed a difference of FT3 with t 4.489\u00a0 (p value 0.000), the difference of FT4 with t value 3.092 (p value 0.004). The difference of TSH with t value 3.394 (p value 0.002).<\/p>\n<p>Liyanage YSH in his study has observed a statistically significant difference between morning and afternoon samples for TSH, p=0.013 and FT4 p&lt;0.001 (24). In our study the p value for morning and afternoon samples of FT3, FT4, TSH were 0.000, 0.004 and 0.002 respectively.<\/p>\n<p>Sviridonova A.M et al in his study stated that the morning median TSH value in patients with subclinical hypothyroidism was 5.83 \u00b5IU\/mL, in afternoon it was 3.79 \u00b5IU\/mL (25). In our study the day TSH median value is 1.76 \u00b5IU\/mL, in night it was 3.30 \u00b5IU\/mL.<\/p>\n<p>Thus in our study, the difference between day and night samples of FT3, FT4 and TSH were 0.23 pg\/mL,0.16 ng\/dL and 0.47 \u00b5IU\/mL with p value &lt; 0.001. This shows statistical significance.<\/p>\n<p><strong>Conclusion<\/strong><\/p>\n<p>In our study 36.67% (n=11) participants were males. 63.3% (n=19) participants were females. The mean FT3 in day was 2.43\u00b10.56 pg\/mL, FT4 in day was 1.14\u00b10.44 ng\/dL and TSH in day was 2.74\u00b13.16 \u00b5IU\/mL. The mean night FT3 was 2.66\u00b10.55 pg\/mL, night FT4 was 1.3\u00b10.61 ng\/dL and night TSH was 3.21\u00b13.18 \u00b5IU\/mL. The one sample t test shows difference of FT3 with t 4.489 and p value 0.000, FT4 with t value 3.092 and p value 0.004 and TSH with t value 3.394 and p value 0.002. Interpretation of TSH levels does also rely on the time of sampling. However fluctuations in diurnal variations in thyroid hormone levels are less. In our study although statistically significant, all the values are within the normal biological reference interval implying that diurnal variation in Thyroid function test has no relevance in routine testing. Further studies in larger cohort are essential to desire important conclusions.<\/p>\n<p><strong>Conflict of Interest<\/strong><\/p>\n<p>There is no conflict of interest.<\/p>\n<p><strong>References<\/strong><\/p>\n<ol>\n<li>M, Alauddin .M, Islam.M.S, Mannan.A, Bhuiya.R.H, Nasrin.F. 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