Background Low levels of high density lipoprotein (HDL) cholesterol and disturbed postprandial lipemia are associated with coronary heart disease. In the present study, we evaluated the variation of triglyceride (TG) postprandially in respect to serum HDL cholesterol levels.
Background
Low levels of high density lipoprotein (HDL) cholesterol and disturbed postprandial lipemia are associated with coronary heart disease. In the present study, we evaluated the variation of triglyceride (TG) postprandially in respect to serum HDL cholesterol levels.Results Fifty two Greek men were divided into 2 main groups: a) the low HDL group (HDL < 40 mg/dl), and b) the control group. Both groups were further matched according to fasting TG (matched-low HDL, and matched-control groups). The fasting TG concentrations were higher in the low HDL group compared to controls ( p  = 0.002). The low HDL group had significantly higher TG at 4, 6 and 8 h postprandially compared to the controls ( p  = 0.006,  p  = 0.002, and  p  < 0.001, respectively). The matched-low HDL group revealed higher TG only at 8 h postprandially ( p  = 0.017) compared to the matched-control group. ROC analysis showed that fasting TG ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response (auc = 0.962,  p  < 0.001).
Results
Fifty two Greek men were divided into 2 main groups: a) the low HDL group (HDL < 40 mg/dl), and b) the control group. Both groups were further matched according to fasting TG (matched-low HDL, and matched-control groups). The fasting TG concentrations were higher in the low HDL group compared to controls ( p  = 0.002). The low HDL group had significantly higher TG at 4, 6 and 8 h postprandially compared to the controls ( p  = 0.006,  p  = 0.002, and  p  < 0.001, respectively). The matched-low HDL group revealed higher TG only at 8 h postprandially ( p  = 0.017) compared to the matched-control group. ROC analysis showed that fasting TG ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response (auc = 0.962,  p  < 0.001).ppppppConclusions The delayed TG clearance postprandially seems to result in low HDL cholesterol even in subjects with low fasting TG. The fasting TG > 121 mg/dl are predictable for abnormal response to fatty meal.
Conclusions
The delayed TG clearance postprandially seems to result in low HDL cholesterol even in subjects with low fasting TG. The fasting TG > 121 mg/dl are predictable for abnormal response to fatty meal.

Background The hypothesis that low levels of high density lipoprotein (HDL) cholesterol is associated with coronary heart disease (CHD), raised since the 1950s [ 1 ]. Fifty years later, it was well-established [ 2 , 3 ] as it has been excellently proved after a number of large prospective studies [ 4 , 5 ]. In the PROCAM Study [ 6 ] 45% of men and women who developed CHD had an HDL cholesterol lower than 35 mg/dl. In the Framingham Heart Study, total cholesterol levels did not provide a predictive value in identifying people at risk for CHD compared to cholesterol/HDL ratio [ 7 ]. Furthermore, a change in ratio is better predictor for successful CHD risk reduction than changes in total cholesterol levels. There is no longer any doubt that HDL cholesterol is a powerful independent inverse predictor of CHD. On the other hand, the long duration of the postprandial lipemia and repetition of meals during the daytime leads to important changes of lipoproteins postprandially. Studies have shown that disturbed postprandial lipemia is found in patients with CHD [ 8 , 9 ] and other conditions [ 10 - 12 ] related to an increased risk of cardiovascular disease. Many studies comparing patients with CHD and controls have shown that postprandial triglyceride (TG) levels were an independent predictor of CHD in multivariate analysis [ 8 , 13 ]. In the present study, we evaluated the variation of TG postprandially in respect to serum HDL cholesterol levels. The delayed TG clearance postprandially seems to result in low HDL cholesterol even in subjects with low fasting TG.
Background
The hypothesis that low levels of high density lipoprotein (HDL) cholesterol is associated with coronary heart disease (CHD), raised since the 1950s [ 1 ]. Fifty years later, it was well-established [ 2 , 3 ] as it has been excellently proved after a number of large prospective studies [ 4 , 5 ]. In the PROCAM Study [ 6 ] 45% of men and women who developed CHD had an HDL cholesterol lower than 35 mg/dl. In the Framingham Heart Study, total cholesterol levels did not provide a predictive value in identifying people at risk for CHD compared to cholesterol/HDL ratio [ 7 ]. Furthermore, a change in ratio is better predictor for successful CHD risk reduction than changes in total cholesterol levels. There is no longer any doubt that HDL cholesterol is a powerful independent inverse predictor of CHD. On the other hand, the long duration of the postprandial lipemia and repetition of meals during the daytime leads to important changes of lipoproteins postprandially. Studies have shown that disturbed postprandial lipemia is found in patients with CHD [ 8 , 9 ] and other conditions [ 10 - 12 ] related to an increased risk of cardiovascular disease. Many studies comparing patients with CHD and controls have shown that postprandial triglyceride (TG) levels were an independent predictor of CHD in multivariate analysis [ 8 , 13 ]. In the present study, we evaluated the variation of TG postprandially in respect to serum HDL cholesterol levels. The delayed TG clearance postprandially seems to result in low HDL cholesterol even in subjects with low fasting TG.Results All participants ingested their fatty meal and tolerated it well. Baseline characteristics (Table  1 ) Table 1 Clinical characteristics of the two main study groups (all low HDL patients and controls). All biochemical values were obtained in the fasting state and for TG 4,6,8 h postprandially. Characteristics Low HDL n = 29 Controls n = 23 P values Age (years) 45(13) 51(9) NS BMI (kg/m 2 ) 26(2) 26(3) NS CHD -/+ 19/10 23/0 0.002 Hypertension-/+ 27/2 23/0 NS Smokers-/+ 17/12 23/0 <0.001 Diabetes mellitus 28/1 23/0 NS TC (mg/dl) 198(45) 194(38) NS HDL (mg/dl) 31(7) 53(19) <0.001 LDL (mg/dl) 141(39) 124(38) NS Apo A (mg/dl) 123(28) 153(42) 0.007 Apo B (mg/dl) 127(56) 126(30) NS Lp (a) (mg/dl) 25(25) 30(29) NS Glucose (mg/dl) 94(13) 90(11) NS TG 0  (mg/dl) 128(54) 89(30) 0.002 TG 4  (mg/dl) 207(102) 140(36) 0.006 TG 6  (mg/dl) 210(112) 135(45) 0.002 TG 8  (mg/dl) 192(92) 103(37) <0.001 AUC (mg/dl/h) 1459(631) 1007(253) <0.005 All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 The clinical characteristics of the two main study groups (low HDL and controls) are shown in Table  1 . Renal and liver function was normal as determined by measuring plasma creatinine, urea, uric acid, alanine aminotransferase, aspartate aminotransferase and γ-glutamate transferase. Therefore, there was no biochemical evidence of a fatty liver. Baseline glucose levels were similar in both groups. None of the patients fulfilled the criteria for the metabolic syndrome according to the National Cholesterol Education Program- Adults Treatment Panel III (NCEP ATP III) guidelines [ 14 ]. The plasma HDL cholesterol and apolipoprotein A were lower in low HDL group compared to controls ( p  < 0.001, and  p  = 0.007, respectively) by definition. The fasting TG (TG 0 ) concentrations were higher in low HDL cholesterol group compared to controls ( p  = 0.002). Postprandial TG concentrations in the two main groups (low HDL subjects and controls) (Table  1 , Figure  1 ) Figure 1 Schematic representation of TG-AUC of all groups in relation to time. The highest AUC values were observed in low HDL and low HDL-A groups which are characterized by low fasting HDL levels and the highest fasting TG levels compared to the other groups. The lowest AUC values were found in matched-low HDL, matched-controls, low HDL-N and control groups. These are characterized by lower fasting TG levels but with variable fasting HDL levels, ranging from 30(9) to 55(20) mg/dl. This means that the primary determinant of the magnitude of TG postprandial response is the fasting TG concentration. TG-AUC: triglyceride-area under the curve Low HDL-A: low HDL-Abnormal group Low HDL-N: low HDL-Normal group TG levels 4 h after the fatty meal (TG 4 ) The low HDL subjects had a significantly higher ( p  < 0.006) TG level compared to the controls. TG levels 6 h after the fatty meal (TG 6 ) The low HDL subjects had a significantly higher ( p  < 0.002) TG level compared to controls. TG levels 8 h after the fatty meal (TG 8 ) The low HDL subjects had a significantly higher ( p  < 0.001) TG level compared to the controls. Glucose did not show any change postprandially. The above groups were subdivided in matched-low HDL and matched-control group (matched for low fasting TG levels) and in low HDL-Abnormal (low HDL-A) and low HDL-Normal (low HDL-N) groups (based on their TG postprandial response). Results of the matched low HDL subjects and matched controls (Table  2 , Figure  1 ) Table 2 Clinical characteristics and biochemical parameters of the low HDL subjects matched to controls for fasting TG levels, and their response to a fatty meal. Characteristics Matched-low HDL n = 20 Matched-controls n = 20 P values Age (years) 47(13) 51(10) NS BMI (kg/m 2 ) 26(2) 25(2) NS CHD -/+ 11/9 20/0 0.001 Hypertension-/+ 18/2 20/0 NS Smokers-/+ 13/7 20/0 0.004 TC (mg/dl) 187(50) 197(35) NS HDL (mg/dl 31(8) 55(20) <0.001 LDL (mg/dl) 136(43) 128(38) NS Apo A (mg/dl) 123(31) 158(43) 0.009 Apo B (mg/dl) 123(63) 132(27) NS Lp (a) (mg/dl) 29(27) 28(23) NS Glucose (mg/dl) 95(14) 90(11) NS TG 0  (mg/dl) 100(31) 95(27) NS TG 4  (mg/dl) 153(55) 140(36) NS TG 6  (mg/dl) 172(105) 138(45) NS TG 8  (mg/dl) 154(78) 105(38) 0.017 AUC (mg/dl/h) 1142(322) 1007(253) NS All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 Subjects with low HDL cholesterol had higher TG concentration at 8 h postprandially ( p  = 0.017). Results of the low HDL subjects divided into those with (low HDL-N group) and without (low HDL-A group) a normal response to a fatty meal (Table  3 , Figure  1 ) Table 3 Clinical characteristics and biochemical parameters of the low HDL subjects with an abnormal (low HDL-A) and normal response (low HDL-N) to a fatty meal. Characteristics low HDL-A n = 13 low HDL-N n = 16 P values Age (years) 42(14) 48(13) NS BMI (kg/m 2 ) 26(2) 27(3) NS CHD-/+ 12/1 7/9 0.006 Hypertension-/+ 11/2 16/0 NS Smokers-/+ 6/7 11/5 NS TC (mg/dl) 221(16) 179(53) 0.007 HDL (mg/dl) 32(5) 30(9) NS LDL (mg/dl) 154(22) 130(46) NS Apo A (mg/dl) 131(18) 118(31) NS Apo B (mg/dl) 135(17) 122(68) NS Lp (a) (mg/dl) 10(7) 32(28) 0.012 Glucose (mg/dl) 92(11) 95(14) NS TG 0  (mg/dl) 172(43) 92(29) <0.001 TG 4  (mg/dl) 298(82) 135(35) <0.001 TG 6  (mg/dl) 321(74) 127(35) <0.001 TG 8  (mg/dl) 270(78) 129(37) < 0.001 AUC (mg/dl/h) 2074(381) 986(252) <0.012 All values are presented as means (standard deviation) BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 Thirteen (45%) of the low HDL subjects (low HDL-A group) had an abnormal TG response to the fatty meal test compared to the controls. The other sixteen low HDL subjects (low HDL-N group) had a TG response to the fatty meal that was similar to that in the control subjects. The low HDL subjects with an abnormal fatty meal response (low HDL-A group) had higher plasma total cholesterol and lower lipoprotein (a) concentrations ( p  = 0.007, and  p  < 0.012, respectively). As expected, postprandial TG levels were higher in those classified as having an abnormal response. TG levels of all groups (low HDL, controls, matched low HDL, matched controls, low HDL-A, low HDL-N) in relation to time are schematically represented in Figure  1 . Predictors of abnormal TG response In multivariate linear regression analysis, where the independent variables were age, body mass index (BMI), total cholesterol, HDL cholesterol, TG, lipoprotein (a), and areas under the curve (AUC) was the dependent variable, the TG 0  was the only predictor of high AUC values (Coefficience B = 7.15,  p  = 0.016). ROC analysis showed that TG 0  levels ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response (auc=0.962,  p  < 0.001). After we divided the low HDL men in low- or high- groups by using the ROC curve cut-off values of TG 0 , total cholesterol and lipoprotein (a), i.e. low/high TG 0 , low/high total cholesterol and low/high lipoprotein (a) groups, the only distinction that predicted an abnormal TG response was the high-TG 0  group ( p  < 0.001)
Results
All participants ingested their fatty meal and tolerated it well.Baseline characteristics (Table  1 ) Table 1 Clinical characteristics of the two main study groups (all low HDL patients and controls). All biochemical values were obtained in the fasting state and for TG 4,6,8 h postprandially. Characteristics Low HDL n = 29 Controls n = 23 P values Age (years) 45(13) 51(9) NS BMI (kg/m 2 ) 26(2) 26(3) NS CHD -/+ 19/10 23/0 0.002 Hypertension-/+ 27/2 23/0 NS Smokers-/+ 17/12 23/0 <0.001 Diabetes mellitus 28/1 23/0 NS TC (mg/dl) 198(45) 194(38) NS HDL (mg/dl) 31(7) 53(19) <0.001 LDL (mg/dl) 141(39) 124(38) NS Apo A (mg/dl) 123(28) 153(42) 0.007 Apo B (mg/dl) 127(56) 126(30) NS Lp (a) (mg/dl) 25(25) 30(29) NS Glucose (mg/dl) 94(13) 90(11) NS TG 0  (mg/dl) 128(54) 89(30) 0.002 TG 4  (mg/dl) 207(102) 140(36) 0.006 TG 6  (mg/dl) 210(112) 135(45) 0.002 TG 8  (mg/dl) 192(92) 103(37) <0.001 AUC (mg/dl/h) 1459(631) 1007(253) <0.005 All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 The clinical characteristics of the two main study groups (low HDL and controls) are shown in Table  1 . Renal and liver function was normal as determined by measuring plasma creatinine, urea, uric acid, alanine aminotransferase, aspartate aminotransferase and γ-glutamate transferase. Therefore, there was no biochemical evidence of a fatty liver. Baseline glucose levels were similar in both groups. None of the patients fulfilled the criteria for the metabolic syndrome according to the National Cholesterol Education Program- Adults Treatment Panel III (NCEP ATP III) guidelines [ 14 ]. The plasma HDL cholesterol and apolipoprotein A were lower in low HDL group compared to controls ( p  < 0.001, and  p  = 0.007, respectively) by definition. The fasting TG (TG 0 ) concentrations were higher in low HDL cholesterol group compared to controls ( p  = 0.002).
Baseline characteristics (Table  1 )
Clinical characteristics of the two main study groups (all low HDL patients and controls). All biochemical values were obtained in the fasting state and for TG 4,6,8 h postprandially.All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6The clinical characteristics of the two main study groups (low HDL and controls) are shown in Table  1 . Renal and liver function was normal as determined by measuring plasma creatinine, urea, uric acid, alanine aminotransferase, aspartate aminotransferase and γ-glutamate transferase. Therefore, there was no biochemical evidence of a fatty liver. Baseline glucose levels were similar in both groups. None of the patients fulfilled the criteria for the metabolic syndrome according to the National Cholesterol Education Program- Adults Treatment Panel III (NCEP ATP III) guidelines [ 14 ].The plasma HDL cholesterol and apolipoprotein A were lower in low HDL group compared to controls ( p  < 0.001, and  p  = 0.007, respectively) by definition. The fasting TG (TG 0 ) concentrations were higher in low HDL cholesterol group compared to controls ( p  = 0.002).pppPostprandial TG concentrations in the two main groups (low HDL subjects and controls) (Table  1 , Figure  1 ) Figure 1 Schematic representation of TG-AUC of all groups in relation to time. The highest AUC values were observed in low HDL and low HDL-A groups which are characterized by low fasting HDL levels and the highest fasting TG levels compared to the other groups. The lowest AUC values were found in matched-low HDL, matched-controls, low HDL-N and control groups. These are characterized by lower fasting TG levels but with variable fasting HDL levels, ranging from 30(9) to 55(20) mg/dl. This means that the primary determinant of the magnitude of TG postprandial response is the fasting TG concentration. TG-AUC: triglyceride-area under the curve Low HDL-A: low HDL-Abnormal group Low HDL-N: low HDL-Normal group TG levels 4 h after the fatty meal (TG 4 ) The low HDL subjects had a significantly higher ( p  < 0.006) TG level compared to the controls. TG levels 6 h after the fatty meal (TG 6 ) The low HDL subjects had a significantly higher ( p  < 0.002) TG level compared to controls. TG levels 8 h after the fatty meal (TG 8 ) The low HDL subjects had a significantly higher ( p  < 0.001) TG level compared to the controls. Glucose did not show any change postprandially. The above groups were subdivided in matched-low HDL and matched-control group (matched for low fasting TG levels) and in low HDL-Abnormal (low HDL-A) and low HDL-Normal (low HDL-N) groups (based on their TG postprandial response).
Postprandial TG concentrations in the two main groups (low HDL subjects and controls) (Table  1 , Figure  1 )
TG levels 4 h after the fatty meal (TG 4 ) The low HDL subjects had a significantly higher ( p  < 0.006) TG level compared to the controls.
TG levels 4 h after the fatty meal (TG 4 )
The low HDL subjects had a significantly higher ( p  < 0.006) TG level compared to the controls.pTG levels 6 h after the fatty meal (TG 6 ) The low HDL subjects had a significantly higher ( p  < 0.002) TG level compared to controls.
TG levels 6 h after the fatty meal (TG 6 )
The low HDL subjects had a significantly higher ( p  < 0.002) TG level compared to controls.pTG levels 8 h after the fatty meal (TG 8 ) The low HDL subjects had a significantly higher ( p  < 0.001) TG level compared to the controls. Glucose did not show any change postprandially. The above groups were subdivided in matched-low HDL and matched-control group (matched for low fasting TG levels) and in low HDL-Abnormal (low HDL-A) and low HDL-Normal (low HDL-N) groups (based on their TG postprandial response).
TG levels 8 h after the fatty meal (TG 8 )
The low HDL subjects had a significantly higher ( p  < 0.001) TG level compared to the controls.pGlucose did not show any change postprandially.The above groups were subdivided in matched-low HDL and matched-control group (matched for low fasting TG levels) and in low HDL-Abnormal (low HDL-A) and low HDL-Normal (low HDL-N) groups (based on their TG postprandial response).Results of the matched low HDL subjects and matched controls (Table  2 , Figure  1 ) Table 2 Clinical characteristics and biochemical parameters of the low HDL subjects matched to controls for fasting TG levels, and their response to a fatty meal. Characteristics Matched-low HDL n = 20 Matched-controls n = 20 P values Age (years) 47(13) 51(10) NS BMI (kg/m 2 ) 26(2) 25(2) NS CHD -/+ 11/9 20/0 0.001 Hypertension-/+ 18/2 20/0 NS Smokers-/+ 13/7 20/0 0.004 TC (mg/dl) 187(50) 197(35) NS HDL (mg/dl 31(8) 55(20) <0.001 LDL (mg/dl) 136(43) 128(38) NS Apo A (mg/dl) 123(31) 158(43) 0.009 Apo B (mg/dl) 123(63) 132(27) NS Lp (a) (mg/dl) 29(27) 28(23) NS Glucose (mg/dl) 95(14) 90(11) NS TG 0  (mg/dl) 100(31) 95(27) NS TG 4  (mg/dl) 153(55) 140(36) NS TG 6  (mg/dl) 172(105) 138(45) NS TG 8  (mg/dl) 154(78) 105(38) 0.017 AUC (mg/dl/h) 1142(322) 1007(253) NS All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 Subjects with low HDL cholesterol had higher TG concentration at 8 h postprandially ( p  = 0.017).
Results of the matched low HDL subjects and matched controls (Table  2 , Figure  1 )
Clinical characteristics and biochemical parameters of the low HDL subjects matched to controls for fasting TG levels, and their response to a fatty meal.All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6Subjects with low HDL cholesterol had higher TG concentration at 8 h postprandially ( p  = 0.017).pResults of the low HDL subjects divided into those with (low HDL-N group) and without (low HDL-A group) a normal response to a fatty meal (Table  3 , Figure  1 ) Table 3 Clinical characteristics and biochemical parameters of the low HDL subjects with an abnormal (low HDL-A) and normal response (low HDL-N) to a fatty meal. Characteristics low HDL-A n = 13 low HDL-N n = 16 P values Age (years) 42(14) 48(13) NS BMI (kg/m 2 ) 26(2) 27(3) NS CHD-/+ 12/1 7/9 0.006 Hypertension-/+ 11/2 16/0 NS Smokers-/+ 6/7 11/5 NS TC (mg/dl) 221(16) 179(53) 0.007 HDL (mg/dl) 32(5) 30(9) NS LDL (mg/dl) 154(22) 130(46) NS Apo A (mg/dl) 131(18) 118(31) NS Apo B (mg/dl) 135(17) 122(68) NS Lp (a) (mg/dl) 10(7) 32(28) 0.012 Glucose (mg/dl) 92(11) 95(14) NS TG 0  (mg/dl) 172(43) 92(29) <0.001 TG 4  (mg/dl) 298(82) 135(35) <0.001 TG 6  (mg/dl) 321(74) 127(35) <0.001 TG 8  (mg/dl) 270(78) 129(37) < 0.001 AUC (mg/dl/h) 2074(381) 986(252) <0.012 All values are presented as means (standard deviation) BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 Thirteen (45%) of the low HDL subjects (low HDL-A group) had an abnormal TG response to the fatty meal test compared to the controls. The other sixteen low HDL subjects (low HDL-N group) had a TG response to the fatty meal that was similar to that in the control subjects. The low HDL subjects with an abnormal fatty meal response (low HDL-A group) had higher plasma total cholesterol and lower lipoprotein (a) concentrations ( p  = 0.007, and  p  < 0.012, respectively). As expected, postprandial TG levels were higher in those classified as having an abnormal response. TG levels of all groups (low HDL, controls, matched low HDL, matched controls, low HDL-A, low HDL-N) in relation to time are schematically represented in Figure  1 .
Results of the low HDL subjects divided into those with (low HDL-N group) and without (low HDL-A group) a normal response to a fatty meal (Table  3 , Figure  1 )
Clinical characteristics and biochemical parameters of the low HDL subjects with an abnormal (low HDL-A) and normal response (low HDL-N) to a fatty meal.All values are presented as means (standard deviation) BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG 0 : fasting plasma triglyceride concentration, TG 4,  TG 6,  TG 8 : plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6Thirteen (45%) of the low HDL subjects (low HDL-A group) had an abnormal TG response to the fatty meal test compared to the controls. The other sixteen low HDL subjects (low HDL-N group) had a TG response to the fatty meal that was similar to that in the control subjects. The low HDL subjects with an abnormal fatty meal response (low HDL-A group) had higher plasma total cholesterol and lower lipoprotein (a) concentrations ( p  = 0.007, and  p  < 0.012, respectively). As expected, postprandial TG levels were higher in those classified as having an abnormal response.ppTG levels of all groups (low HDL, controls, matched low HDL, matched controls, low HDL-A, low HDL-N) in relation to time are schematically represented in Figure  1 .Predictors of abnormal TG response In multivariate linear regression analysis, where the independent variables were age, body mass index (BMI), total cholesterol, HDL cholesterol, TG, lipoprotein (a), and areas under the curve (AUC) was the dependent variable, the TG 0  was the only predictor of high AUC values (Coefficience B = 7.15,  p  = 0.016). ROC analysis showed that TG 0  levels ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response (auc=0.962,  p  < 0.001). After we divided the low HDL men in low- or high- groups by using the ROC curve cut-off values of TG 0 , total cholesterol and lipoprotein (a), i.e. low/high TG 0 , low/high total cholesterol and low/high lipoprotein (a) groups, the only distinction that predicted an abnormal TG response was the high-TG 0  group ( p  < 0.001)
Predictors of abnormal TG response
In multivariate linear regression analysis, where the independent variables were age, body mass index (BMI), total cholesterol, HDL cholesterol, TG, lipoprotein (a), and areas under the curve (AUC) was the dependent variable, the TG 0  was the only predictor of high AUC values (Coefficience B = 7.15,  p  = 0.016). ROC analysis showed that TG 0  levels ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response (auc=0.962,  p  < 0.001). After we divided the low HDL men in low- or high- groups by using the ROC curve cut-off values of TG 0 , total cholesterol and lipoprotein (a), i.e. low/high TG 0 , low/high total cholesterol and low/high lipoprotein (a) groups, the only distinction that predicted an abnormal TG response was the high-TG 0  group ( p  < 0.001)pppDiscussion In the present study, we found that a slower clearance of TG-rich lipoproteins from circulation postprandially results in low fasting levels of HDL cholesterol. Additionally, fasting plasma TG concentration is the primary determinant of the magnitude of postprandial lipemia. Subjects with a low fasting HDL and low TG levels showed a delayed TG clearance at 8 h compared to those with a normal fasting HDL value. The increase in TGs (from 2 to 4 h) after meal consumption mainly reflects dietary TG absorption, whereas the return to fasting levels (from 6 to 9 h) is presumably a function of TG clearance [ 15 ]. In our study, the delayed response of TGs to the fatty meal in matched group for low fasting TG levels may be indicative for an HDL involvement. It has been proposed that elevated plasma TG concentrations promote the cholesterol ester exchange reactions mediated by cholesteryl ester transfer protein [ 16 ]. It is possible that in this transient hypertriglyceridemia, the HDL particles are TG-enriched via cholesteryl ester transfer protein mediated exchange with TG-rich lipoproteins. Such HDL-TG enriched particles are cleared more rapidly from the circulation [ 17 ] leading to low serum HDL cholesterol levels [ 18 , 19 ]. In subjects with initially low HDL concentrations and low fasting TG levels, it is possible that this reaction does not happen at all or happens in a less degree and the slower removal of TGs from the circulation could be explained by insufficient amount of HDL particles which are responsible for their clearance. The high TG and low HDL cholesterol phenotype has been frequently reported in "abdominal" obesity [ 20 , 21 ]. Obesity is associated with a range of metabolic abnormalities including fasting and postprandial dyslipidemia. This was clearly shown in the study of Couillard et al., whose study subjects were characterised by a BMI of 32.3 ± 4.5 kg/m 2  [ 22 ]. Our results showed that even in slightly overweight subjects with a BMI of 26 ± 2 kg/m 2 , this phenotype may provoke similar metabolic abnormalities. The HDL formation is closely associated with TG catabolism [ 23 ] as mentioned above. The deleterious effect of the high TG and low HDL phenotype on the rate of postprandial TG clearance has been also shown in children (mean age 14 years old) [ 24 ]. Patsch et al., [ 25 ] have proposed that low HDL cholesterol levels could result from an impaired TG lipolysis, a condition that would favour an exaggerated postprandial lipemia, in subject with hypoalphalipoproteinemia. In our study, no difference was observed in the early postprandial TG response (absorption phase) between normolipidemic controls and men with low fasting HDL cholesterol and low fasting plasma TG concentration. In contrast, the group with low HDL cholesterol (31 ± 7 mg/dl) and variable values of fasting TG (53–248 mg/dl, mean value 128 ± 54 mg/dl) revealed a higher TG curve postprandially compared to healthy subjects with moderate HDL cholesterol (53 ± 19 mg/dl) and low fasting TG levels (89 ± 30 mg/dl). When both groups were matched for fasting TG levels, this curve-difference was diminished, although HDL cholesterol levels remained significantly different between the two groups. When subjects with low HDL cholesterol were subdivided based on their response (normal or abnormal) to fat loading, fasting TG concentration seemed to be the critical determinant of such response. Since there are no official guidelines to determine normal postprandial TG ranges, the characterization of a TG response as "normal" or "abnormal" after fat loading was based on this report and on previous reports of ours and others. The peak TG mean value in control subjects was 139 ± 41 mg/dl (at 6 h) [ 11 ], 176 ± 17 mg/dl (at 3.5 h) [ 26 ], 177 mg/dl [ 27 ] and 186 mg/dl [ 28 ]. Higher TG concentrations at 4 h were reported by others, approximately 213 mg/dl and 248 mg/dl [ 29 - 32 ]. Therefore, we defined an abnormal postprandial TG response to the fatty meal as any postprandial TG concentration (at 4, 6 or 8 h) higher than 219 mg/dl which was the highest TG concentration in any hour in any control individual. Subjects with abnormal postprandial lipemia had fasting TG concentration twice higher than those with normal postprandial lipemia and the TG concentration remained much higher through all tests. Additionally, the ROC analysis showed that TG 0  level ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response. Couillard et al., [ 22 ] reported a significant association between the magnitude of the postprandial TG response and fasting plasma HDL cholesterol concentrations. However, the subjects included in their study showed a wider range of fasting TG concentrations (44–390 mg/dl) compared to ours (53–248 mg/dl). In the subgroup of isolated low fasting HDL cholesterol, fasting hypertriglyceridemia was a prerequisite in order to have an exaggerated postprandial TG response [ 22 ]. This finding was similar to ours; an abnormal response to the fatty meal was only dependent on fasting TG concentration. Cohen and co-workers [ 33 ] measured plasma TG and retinyl palmitate responses to different fat meals in endurance-trained men with a wide range of plasma HDL cholesterol concentrations (36–105 mg/dl). Their data indicated that the magnitude of postprandial lipemia is not primarily affected by the HDL cholesterol concentrations, which agrees to our results. However, they failed to show any correlation between HDL cholesterol levels and chylomicron remnant metabolism which is contradictory to our findings, although we did not directly measured chylomicron remnant particles. Postprandial hypertriglyceridemia is not a uniform abnormality. It pathophysiologic cause is not yet known. It is possible that the response to a fatty meal is gene dependent. It has been reported that a number of gene loci, such as these of apolipoprotein E, lipoprotein lipase, apolipoprotein CIII, apolipoprotein A1, apolipoprotein A4, cholesterol ester transfer protein are related to fat load response [ 34 , 35 ]. However, this gene polymorphism-dependence remains controversial, and it is more likely that the postprandial lipemia is a polygenic phenomenon, although the phenotype of the postprandial lipemia is probably one. This concept allowed us to study only the phenotypic manifestation of the postprandial lipemia, which would have immediate clinical implications. Our aim was to evaluate the postprandial response in men with isolated low fasting HDL cholesterol. Although it has been also described elsewhere that baseline TG levels impact on the postprandial response and that HDL cholesterol levels are predictors of this response, the novelty of this study lies on the fact that delayed postprandially TG clearance was observed in low HDL group and that emphasis should be given on the decrease of TG levels < 121 mg/dl, lower that those indicated by the NCEP ATP III guidelines.
Discussion
In the present study, we found that a slower clearance of TG-rich lipoproteins from circulation postprandially results in low fasting levels of HDL cholesterol. Additionally, fasting plasma TG concentration is the primary determinant of the magnitude of postprandial lipemia.Subjects with a low fasting HDL and low TG levels showed a delayed TG clearance at 8 h compared to those with a normal fasting HDL value. The increase in TGs (from 2 to 4 h) after meal consumption mainly reflects dietary TG absorption, whereas the return to fasting levels (from 6 to 9 h) is presumably a function of TG clearance [ 15 ]. In our study, the delayed response of TGs to the fatty meal in matched group for low fasting TG levels may be indicative for an HDL involvement. It has been proposed that elevated plasma TG concentrations promote the cholesterol ester exchange reactions mediated by cholesteryl ester transfer protein [ 16 ]. It is possible that in this transient hypertriglyceridemia, the HDL particles are TG-enriched via cholesteryl ester transfer protein mediated exchange with TG-rich lipoproteins. Such HDL-TG enriched particles are cleared more rapidly from the circulation [ 17 ] leading to low serum HDL cholesterol levels [ 18 , 19 ]. In subjects with initially low HDL concentrations and low fasting TG levels, it is possible that this reaction does not happen at all or happens in a less degree and the slower removal of TGs from the circulation could be explained by insufficient amount of HDL particles which are responsible for their clearance.The high TG and low HDL cholesterol phenotype has been frequently reported in "abdominal" obesity [ 20 , 21 ]. Obesity is associated with a range of metabolic abnormalities including fasting and postprandial dyslipidemia. This was clearly shown in the study of Couillard et al., whose study subjects were characterised by a BMI of 32.3 ± 4.5 kg/m 2  [ 22 ]. Our results showed that even in slightly overweight subjects with a BMI of 26 ± 2 kg/m 2 , this phenotype may provoke similar metabolic abnormalities. The HDL formation is closely associated with TG catabolism [ 23 ] as mentioned above. The deleterious effect of the high TG and low HDL phenotype on the rate of postprandial TG clearance has been also shown in children (mean age 14 years old) [ 24 ]. Patsch et al., [ 25 ] have proposed that low HDL cholesterol levels could result from an impaired TG lipolysis, a condition that would favour an exaggerated postprandial lipemia, in subject with hypoalphalipoproteinemia.In our study, no difference was observed in the early postprandial TG response (absorption phase) between normolipidemic controls and men with low fasting HDL cholesterol and low fasting plasma TG concentration. In contrast, the group with low HDL cholesterol (31 ± 7 mg/dl) and variable values of fasting TG (53–248 mg/dl, mean value 128 ± 54 mg/dl) revealed a higher TG curve postprandially compared to healthy subjects with moderate HDL cholesterol (53 ± 19 mg/dl) and low fasting TG levels (89 ± 30 mg/dl). When both groups were matched for fasting TG levels, this curve-difference was diminished, although HDL cholesterol levels remained significantly different between the two groups. When subjects with low HDL cholesterol were subdivided based on their response (normal or abnormal) to fat loading, fasting TG concentration seemed to be the critical determinant of such response. Since there are no official guidelines to determine normal postprandial TG ranges, the characterization of a TG response as "normal" or "abnormal" after fat loading was based on this report and on previous reports of ours and others. The peak TG mean value in control subjects was 139 ± 41 mg/dl (at 6 h) [ 11 ], 176 ± 17 mg/dl (at 3.5 h) [ 26 ], 177 mg/dl [ 27 ] and 186 mg/dl [ 28 ]. Higher TG concentrations at 4 h were reported by others, approximately 213 mg/dl and 248 mg/dl [ 29 - 32 ]. Therefore, we defined an abnormal postprandial TG response to the fatty meal as any postprandial TG concentration (at 4, 6 or 8 h) higher than 219 mg/dl which was the highest TG concentration in any hour in any control individual. Subjects with abnormal postprandial lipemia had fasting TG concentration twice higher than those with normal postprandial lipemia and the TG concentration remained much higher through all tests. Additionally, the ROC analysis showed that TG 0  level ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response.Couillard et al., [ 22 ] reported a significant association between the magnitude of the postprandial TG response and fasting plasma HDL cholesterol concentrations. However, the subjects included in their study showed a wider range of fasting TG concentrations (44–390 mg/dl) compared to ours (53–248 mg/dl). In the subgroup of isolated low fasting HDL cholesterol, fasting hypertriglyceridemia was a prerequisite in order to have an exaggerated postprandial TG response [ 22 ]. This finding was similar to ours; an abnormal response to the fatty meal was only dependent on fasting TG concentration. Cohen and co-workers [ 33 ] measured plasma TG and retinyl palmitate responses to different fat meals in endurance-trained men with a wide range of plasma HDL cholesterol concentrations (36–105 mg/dl). Their data indicated that the magnitude of postprandial lipemia is not primarily affected by the HDL cholesterol concentrations, which agrees to our results. However, they failed to show any correlation between HDL cholesterol levels and chylomicron remnant metabolism which is contradictory to our findings, although we did not directly measured chylomicron remnant particles.Postprandial hypertriglyceridemia is not a uniform abnormality. It pathophysiologic cause is not yet known. It is possible that the response to a fatty meal is gene dependent. It has been reported that a number of gene loci, such as these of apolipoprotein E, lipoprotein lipase, apolipoprotein CIII, apolipoprotein A1, apolipoprotein A4, cholesterol ester transfer protein are related to fat load response [ 34 , 35 ]. However, this gene polymorphism-dependence remains controversial, and it is more likely that the postprandial lipemia is a polygenic phenomenon, although the phenotype of the postprandial lipemia is probably one. This concept allowed us to study only the phenotypic manifestation of the postprandial lipemia, which would have immediate clinical implications. Our aim was to evaluate the postprandial response in men with isolated low fasting HDL cholesterol. Although it has been also described elsewhere that baseline TG levels impact on the postprandial response and that HDL cholesterol levels are predictors of this response, the novelty of this study lies on the fact that delayed postprandially TG clearance was observed in low HDL group and that emphasis should be given on the decrease of TG levels < 121 mg/dl, lower that those indicated by the NCEP ATP III guidelines.Conclusions The delayed TG clearance postprandially seems to result in low HDL cholesterol levels even in subjects with low fasting TG concentration. Moreover, fasting plasma TG levels appear to be the primary determinant of the magnitude of postprandial lipemia. The fasting TG levels higher than 121 mg/dl are predictable for abnormal response to a fatty meal. This should be taken into account for the management of patients with fasting low HDL cholesterol levels.
Conclusions
The delayed TG clearance postprandially seems to result in low HDL cholesterol levels even in subjects with low fasting TG concentration. Moreover, fasting plasma TG levels appear to be the primary determinant of the magnitude of postprandial lipemia. The fasting TG levels higher than 121 mg/dl are predictable for abnormal response to a fatty meal. This should be taken into account for the management of patients with fasting low HDL cholesterol levels.Methods Subjects The study population consisted of 52 Greek men recruited from the Lipid Clinic of Onassis Cardiac Surgery Center, Athens, Greece. Heavy drinking, liver and renal disease, diabetes mellitus, metabolic syndrome, according to the NCEP ATP III guidelines [ 14 ], hypothyroidism and professional sport activity were exclusion criteria. No subject took lipid lowering drugs before entering the study. Only patients with CHD (defined by angiography) were taking soluble aspirin (100 mg) and isosorbide mononitrate (40 mg). No other patient or control subject was on medication. The study population was divided into 2 main categories: A: The low HDL group and the control group. 1. The low HDL group consisted of 29 men, mean age 45(13) years with low HDL cholesterol (< 40 mg/dl) according to NCEP ATP III guidelines [ 14 ]. 2.  The control group consisted of 23 healthy men, mean age 51(9) years with no family history of premature atherosclerosis, diabetes mellitus, arterial hypertension or dyslipidemia. Their fasting TG levels were < 150 mg/dl, total cholesterol < 240 mg/dl and HDL cholesterol = 40 mg/dl. All subjects were never smokers. B: Matched groups according to fasting TG level. 1. The matched-low HDL group consisted of 20 men, mean age 47(13) years with low HDL cholesterol and low fasting TG levels [100(31) mg/dl]. 2. The matched controls consisted of 20 men, mean age 51(10) years with lasting TG levels 95(27) mg/dl. Fat-rich meal protocol and blood sample All patients were studied in the outpatient clinic between 8.00–9.00 am after 12 hours (h) overnight fast. The fatty meal was consumed within 20 min and plasma TG concentrations were measured before and 4, 6 and 8 h after the fat load. During this 8 h period, the participants did not eat; they could only drink water and they did not smoke. Blood samples were drawn at 8:00 am (before the meal), at 12:30 pm (4 h after the meal), at 2:30 pm (6 h after the meal), and at 4:30 pm (8 h after the meal). In all samples total cholesterol, TG, HDL, apolipoprotein A and B, and lipoprotein (a) were measured. BMI was calculated as weight divided by height (expressed in kg/m 2 ). Fatty meal The fatty meal has been previously described [ 11 ]. Briefly, the fatty meal was a slight modification of that described by Patsch et al [ 8 ], consisting of 83.5% fat, 14.0 % carbohydrates and 2.5 % proteins. This was administered in a dose based on the patient's body surface area (350 g to 2 m 2 ). Definition of abnormal postprandial response We defined postprandial hypertri-glyceridemia as any postprandial TG concentration (at 4, 6 or 8 h) higher than the highest TG concentration in any hour in any control individual; this TG value was 219 mg/dl. Determination of blood lipids and glucose Plasma total cholesterol, TG and HDL cholesterol were measured using enzymatic colorimetric methods on a Roche Integra Biochemical analyzer with commercially available kits (Roche). The serum low-density lipoprotein cholesterol levels were calculated using the Friedewald formula [ 36 ]. Apolipoprotein A, B and Lipoprotein (a) were measured by nephelometery (Nephelometer:BN-100, Behring, Germany). Blood glucose was measured by the hexokinase method with a Dade Behring reagent on a Dimension (Dade Behring) instrument. All samples were analyzed within 24 h. All participants gave their informed consent before the study. The ethical committee of the Onassis Cardiac Surgery Center approved the study. Statistical analysis Values of numerical characteristics were tested for normality and are presented as mean value with one standard deviation, when normally distributed, or as median and range, when they deviated from normality. A t-test for independent samples or a Mann Whitney U test was used for the comparison of numerical values between two groups and ANOVA or Kruskal-Wallis test for three groups, with Bonferoni correction for post hoc analysis. The comparison of clinical categorical variables was performed with the use of chi-square test. Multivariate linear regression analysis was performed to determine the predictors of elevated AUC levels, where age, BMI, total cholesterol, HDL cholesterol, fasting TG (TG 0 ), and lipoprotein (a) were the independent variables tested. ROC curve statistics was used to estimate the cutoff value of baseline triglyceride, cholesterol and lipoprotein (a) levels (which they were significant univariate predictors of elevated TG levels postprandially) above which the low HDL men develop an abnormal TG response to fat load. Using those cut-off values, we created dichotomous variables for each one of the univariate predictors to test their significance in discriminating patients with abnormal TG response to fat load. The level of significance was set at  p  < 0.05. AUC for serial measurements of TG levels at baseline and after the fatty meal were calculated using the trapezoid rule.
Methods
Subjects The study population consisted of 52 Greek men recruited from the Lipid Clinic of Onassis Cardiac Surgery Center, Athens, Greece. Heavy drinking, liver and renal disease, diabetes mellitus, metabolic syndrome, according to the NCEP ATP III guidelines [ 14 ], hypothyroidism and professional sport activity were exclusion criteria. No subject took lipid lowering drugs before entering the study. Only patients with CHD (defined by angiography) were taking soluble aspirin (100 mg) and isosorbide mononitrate (40 mg). No other patient or control subject was on medication. The study population was divided into 2 main categories: A: The low HDL group and the control group. 1. The low HDL group consisted of 29 men, mean age 45(13) years with low HDL cholesterol (< 40 mg/dl) according to NCEP ATP III guidelines [ 14 ]. 2.  The control group consisted of 23 healthy men, mean age 51(9) years with no family history of premature atherosclerosis, diabetes mellitus, arterial hypertension or dyslipidemia. Their fasting TG levels were < 150 mg/dl, total cholesterol < 240 mg/dl and HDL cholesterol = 40 mg/dl. All subjects were never smokers. B: Matched groups according to fasting TG level. 1. The matched-low HDL group consisted of 20 men, mean age 47(13) years with low HDL cholesterol and low fasting TG levels [100(31) mg/dl]. 2. The matched controls consisted of 20 men, mean age 51(10) years with lasting TG levels 95(27) mg/dl.
Subjects
The study population consisted of 52 Greek men recruited from the Lipid Clinic of Onassis Cardiac Surgery Center, Athens, Greece. Heavy drinking, liver and renal disease, diabetes mellitus, metabolic syndrome, according to the NCEP ATP III guidelines [ 14 ], hypothyroidism and professional sport activity were exclusion criteria. No subject took lipid lowering drugs before entering the study. Only patients with CHD (defined by angiography) were taking soluble aspirin (100 mg) and isosorbide mononitrate (40 mg). No other patient or control subject was on medication.The study population was divided into 2 main categories:A: The low HDL group and the control group.1. The low HDL group consisted of 29 men, mean age 45(13) years with low HDL cholesterol (< 40 mg/dl) according to NCEP ATP III guidelines [ 14 ].2.  The control group consisted of 23 healthy men, mean age 51(9) years with no family history of premature atherosclerosis, diabetes mellitus, arterial hypertension or dyslipidemia. Their fasting TG levels were < 150 mg/dl, total cholesterol < 240 mg/dl and HDL cholesterol = 40 mg/dl. All subjects were never smokers.2.B: Matched groups according to fasting TG level.1. The matched-low HDL group consisted of 20 men, mean age 47(13) years with low HDL cholesterol and low fasting TG levels [100(31) mg/dl].2. The matched controls consisted of 20 men, mean age 51(10) years with lasting TG levels 95(27) mg/dl.Fat-rich meal protocol and blood sample All patients were studied in the outpatient clinic between 8.00–9.00 am after 12 hours (h) overnight fast. The fatty meal was consumed within 20 min and plasma TG concentrations were measured before and 4, 6 and 8 h after the fat load. During this 8 h period, the participants did not eat; they could only drink water and they did not smoke. Blood samples were drawn at 8:00 am (before the meal), at 12:30 pm (4 h after the meal), at 2:30 pm (6 h after the meal), and at 4:30 pm (8 h after the meal). In all samples total cholesterol, TG, HDL, apolipoprotein A and B, and lipoprotein (a) were measured. BMI was calculated as weight divided by height (expressed in kg/m 2 ). Fatty meal The fatty meal has been previously described [ 11 ]. Briefly, the fatty meal was a slight modification of that described by Patsch et al [ 8 ], consisting of 83.5% fat, 14.0 % carbohydrates and 2.5 % proteins. This was administered in a dose based on the patient's body surface area (350 g to 2 m 2 ). Definition of abnormal postprandial response We defined postprandial hypertri-glyceridemia as any postprandial TG concentration (at 4, 6 or 8 h) higher than the highest TG concentration in any hour in any control individual; this TG value was 219 mg/dl. Determination of blood lipids and glucose Plasma total cholesterol, TG and HDL cholesterol were measured using enzymatic colorimetric methods on a Roche Integra Biochemical analyzer with commercially available kits (Roche). The serum low-density lipoprotein cholesterol levels were calculated using the Friedewald formula [ 36 ]. Apolipoprotein A, B and Lipoprotein (a) were measured by nephelometery (Nephelometer:BN-100, Behring, Germany). Blood glucose was measured by the hexokinase method with a Dade Behring reagent on a Dimension (Dade Behring) instrument. All samples were analyzed within 24 h. All participants gave their informed consent before the study. The ethical committee of the Onassis Cardiac Surgery Center approved the study.
Fat-rich meal protocol and blood sample
All patients were studied in the outpatient clinic between 8.00–9.00 am after 12 hours (h) overnight fast. The fatty meal was consumed within 20 min and plasma TG concentrations were measured before and 4, 6 and 8 h after the fat load. During this 8 h period, the participants did not eat; they could only drink water and they did not smoke. Blood samples were drawn at 8:00 am (before the meal), at 12:30 pm (4 h after the meal), at 2:30 pm (6 h after the meal), and at 4:30 pm (8 h after the meal). In all samples total cholesterol, TG, HDL, apolipoprotein A and B, and lipoprotein (a) were measured. BMI was calculated as weight divided by height (expressed in kg/m 2 ).Fatty meal The fatty meal has been previously described [ 11 ]. Briefly, the fatty meal was a slight modification of that described by Patsch et al [ 8 ], consisting of 83.5% fat, 14.0 % carbohydrates and 2.5 % proteins. This was administered in a dose based on the patient's body surface area (350 g to 2 m 2 ).
Fatty meal
The fatty meal has been previously described [ 11 ]. Briefly, the fatty meal was a slight modification of that described by Patsch et al [ 8 ], consisting of 83.5% fat, 14.0 % carbohydrates and 2.5 % proteins. This was administered in a dose based on the patient's body surface area (350 g to 2 m 2 ).Definition of abnormal postprandial response We defined postprandial hypertri-glyceridemia as any postprandial TG concentration (at 4, 6 or 8 h) higher than the highest TG concentration in any hour in any control individual; this TG value was 219 mg/dl.
Definition of abnormal postprandial response
We defined postprandial hypertri-glyceridemia as any postprandial TG concentration (at 4, 6 or 8 h) higher than the highest TG concentration in any hour in any control individual; this TG value was 219 mg/dl.Determination of blood lipids and glucose Plasma total cholesterol, TG and HDL cholesterol were measured using enzymatic colorimetric methods on a Roche Integra Biochemical analyzer with commercially available kits (Roche). The serum low-density lipoprotein cholesterol levels were calculated using the Friedewald formula [ 36 ]. Apolipoprotein A, B and Lipoprotein (a) were measured by nephelometery (Nephelometer:BN-100, Behring, Germany). Blood glucose was measured by the hexokinase method with a Dade Behring reagent on a Dimension (Dade Behring) instrument. All samples were analyzed within 24 h. All participants gave their informed consent before the study. The ethical committee of the Onassis Cardiac Surgery Center approved the study.
Determination of blood lipids and glucose
Plasma total cholesterol, TG and HDL cholesterol were measured using enzymatic colorimetric methods on a Roche Integra Biochemical analyzer with commercially available kits (Roche). The serum low-density lipoprotein cholesterol levels were calculated using the Friedewald formula [ 36 ]. Apolipoprotein A, B and Lipoprotein (a) were measured by nephelometery (Nephelometer:BN-100, Behring, Germany). Blood glucose was measured by the hexokinase method with a Dade Behring reagent on a Dimension (Dade Behring) instrument. All samples were analyzed within 24 h.All participants gave their informed consent before the study. The ethical committee of the Onassis Cardiac Surgery Center approved the study.Statistical analysis Values of numerical characteristics were tested for normality and are presented as mean value with one standard deviation, when normally distributed, or as median and range, when they deviated from normality. A t-test for independent samples or a Mann Whitney U test was used for the comparison of numerical values between two groups and ANOVA or Kruskal-Wallis test for three groups, with Bonferoni correction for post hoc analysis. The comparison of clinical categorical variables was performed with the use of chi-square test. Multivariate linear regression analysis was performed to determine the predictors of elevated AUC levels, where age, BMI, total cholesterol, HDL cholesterol, fasting TG (TG 0 ), and lipoprotein (a) were the independent variables tested. ROC curve statistics was used to estimate the cutoff value of baseline triglyceride, cholesterol and lipoprotein (a) levels (which they were significant univariate predictors of elevated TG levels postprandially) above which the low HDL men develop an abnormal TG response to fat load. Using those cut-off values, we created dichotomous variables for each one of the univariate predictors to test their significance in discriminating patients with abnormal TG response to fat load. The level of significance was set at  p  < 0.05. AUC for serial measurements of TG levels at baseline and after the fatty meal were calculated using the trapezoid rule.
Statistical analysis
Values of numerical characteristics were tested for normality and are presented as mean value with one standard deviation, when normally distributed, or as median and range, when they deviated from normality. A t-test for independent samples or a Mann Whitney U test was used for the comparison of numerical values between two groups and ANOVA or Kruskal-Wallis test for three groups, with Bonferoni correction for post hoc analysis. The comparison of clinical categorical variables was performed with the use of chi-square test. Multivariate linear regression analysis was performed to determine the predictors of elevated AUC levels, where age, BMI, total cholesterol, HDL cholesterol, fasting TG (TG 0 ), and lipoprotein (a) were the independent variables tested. ROC curve statistics was used to estimate the cutoff value of baseline triglyceride, cholesterol and lipoprotein (a) levels (which they were significant univariate predictors of elevated TG levels postprandially) above which the low HDL men develop an abnormal TG response to fat load. Using those cut-off values, we created dichotomous variables for each one of the univariate predictors to test their significance in discriminating patients with abnormal TG response to fat load. The level of significance was set at  p  < 0.05. AUC for serial measurements of TG levels at baseline and after the fatty meal were calculated using the trapezoid rule.pAuthors' contributions GK conceived the study and participated in the development of the hypothesis, the study design and drafting of the manuscript. KA is a research associate who participated in the development of the hypothesis, study design and drafting of the manuscript. NP participated in data analysis and in the interpretation of the findings. NK is a physician who participated in the study design and recruitment of subjects, clinical evaluation and collection of blood samples. KS is a senior nurse who participated in the study design, in the recruitment of subjects and in blood sample collection. EP is a biochemist who participated in the laboratory part of the study. DC participated in the design of the study and its coordination.
Authors' contributions
GK conceived the study and participated in the development of the hypothesis, the study design and drafting of the manuscript.KA is a research associate who participated in the development of the hypothesis, study design and drafting of the manuscript.NP participated in data analysis and in the interpretation of the findings.NK is a physician who participated in the study design and recruitment of subjects, clinical evaluation and collection of blood samples.KS is a senior nurse who participated in the study design, in the recruitment of subjects and in blood sample collection.EP is a biochemist who participated in the laboratory part of the study.DC participated in the design of the study and its coordination.


Standard approaches to communicating risk to patients do not appear to be very effective, argues Alaszewski. We need a new approach that takes patients' own perceptions into account

Doctors and other health professionals play a key role in communicating risk information. They are advisers to patients, especially when patients have to make fateful decisions that can irrevocably change their lives. There is a developing body of literature on the ways in which risk information can be effectively communicated [ 1 , 2 ]. However, much of this literature focuses on the nature of risk information and ways in which the transfer of this information can be improved. It does not fully take into account the complexity of the real world of clinical practice, nor the importance of considering patients as active partners in communication.The Rational Model of Risk Communication Much of the discussion of risk communication is grounded in the rational model of risk communication [ 3 , 4 ]. This model emphasises the role and position of experts such as doctors who have the ability to identify relevant risk knowledge. In the context of medical decision-making this is knowledge about the probable consequences of different courses of action based on scientific research. The role of the doctor is to make such knowledge available so that the patient can then use it to make an informed decision. With the rational model, when there is evidence that patients have  not  used risk knowledge effectively, then the response of the professional is to consider ways in which risk communication can be improved, such as by improving its presentation or mode of communication. When patients appear to be making irrational or harmful decisions, for example, continuing to smoke, choosing not to vaccinate a child against measles, mumps, and rubella, or not complying with medication, the professional's response is to work harder to convey the risks.
 Patients actively seek information on risks from many different sources (Illustration: Margaret Shear, Public Library of Science) But the rational model contains two key flaws. One relates to the nature of risk knowledge and the second to the nature of communication [ 5 ]. Within the rational model, risk knowledge is treated as a relatively simple and straightforward matter—in other words, there is a single uncontested source of knowledge that is relatively easy to access. In reality, risk knowledge is often a complex matter. While such knowledge may be produced by scientific research, it can and often is contested. There may be a scientific consensus, for example, that eating beef or having your child vaccinated against measles, mumps, and rubella is relatively safe, but there are often alternative scientific views, sometimes represented by high-profile media “mavericks” who emphasise the potential hazards [ 6 ]. Risk knowledge cannot actually be used directly by patients to inform their decision-making. Scientific research such as in epidemiology generates knowledge about the probability of harmful events occurring within  populations . Individual patients need information on their own  personal  risks. Expert assessments of risk tend to focus on the knowable and measurable components of risk, that is, the extent to which future events are the same as and predictable by the knowledge of past events. Such assessments by definition exclude uncertainty—those aspects that cannot be assessed and measured. Given the speed of social and technological change, it is not clear that the past is an effective guide to the future. As such, there is an increasing awareness of the uncertainty of risk assessment, for example, in relationship to new diseases such as HIV/AIDS or new technologies such as mobile phones or genetically modified foods.
The Rational Model of Risk Communication
Much of the discussion of risk communication is grounded in the rational model of risk communication [ 3 , 4 ]. This model emphasises the role and position of experts such as doctors who have the ability to identify relevant risk knowledge. In the context of medical decision-making this is knowledge about the probable consequences of different courses of action based on scientific research. The role of the doctor is to make such knowledge available so that the patient can then use it to make an informed decision.With the rational model, when there is evidence that patients have  not  used risk knowledge effectively, then the response of the professional is to consider ways in which risk communication can be improved, such as by improving its presentation or mode of communication. When patients appear to be making irrational or harmful decisions, for example, continuing to smoke, choosing not to vaccinate a child against measles, mumps, and rubella, or not complying with medication, the professional's response is to work harder to convey the risks.notBut the rational model contains two key flaws. One relates to the nature of risk knowledge and the second to the nature of communication [ 5 ]. Within the rational model, risk knowledge is treated as a relatively simple and straightforward matter—in other words, there is a single uncontested source of knowledge that is relatively easy to access. In reality, risk knowledge is often a complex matter. While such knowledge may be produced by scientific research, it can and often is contested. There may be a scientific consensus, for example, that eating beef or having your child vaccinated against measles, mumps, and rubella is relatively safe, but there are often alternative scientific views, sometimes represented by high-profile media “mavericks” who emphasise the potential hazards [ 6 ].Risk knowledge cannot actually be used directly by patients to inform their decision-making. Scientific research such as in epidemiology generates knowledge about the probability of harmful events occurring within  populations . Individual patients need information on their own  personal  risks. Expert assessments of risk tend to focus on the knowable and measurable components of risk, that is, the extent to which future events are the same as and predictable by the knowledge of past events. Such assessments by definition exclude uncertainty—those aspects that cannot be assessed and measured. Given the speed of social and technological change, it is not clear that the past is an effective guide to the future. As such, there is an increasing awareness of the uncertainty of risk assessment, for example, in relationship to new diseases such as HIV/AIDS or new technologies such as mobile phones or genetically modified foods.populationspersonalThe Need for a Person-Centred Approach Within the rational model of risk communication, the emphasis is on the flow of knowledge from the knowledgeable doctor to the uninformed patient. However, communication is a two-way process, and increasingly there is awareness of the active role of patients and the public [ 7 ]. Patients actively seek information, especially when they are aware that they are facing a crucial decision. While they can use traditional sources such as friends and relatives, if they have the skills and resources they can, through media such as the Internet, access highly sophisticated risk knowledge. For example, via the Cochrane Collaboration Web site ( www.cochrane.org ) they can find the latest evidence-based assessments of medical treatments and technologies, or via the Dr. Foster Web site ( www.drfoster.co.uk ) they can find the risks associated with different treatment facilities in the United Kingdom. Many patients access a variety of different sources, so they can clearly compare and evaluate the information provided by each. Patients do give particular credibility to sources that they know, which may include family and friends but also medical advisers with whom they have developed a relationship. They are particularly concerned about the trustworthiness of particular sources. While individuals can use their personal experience to evaluate the trustworthiness of personal sources, such as a particular relative or doctor, they often use contextual information to judge the trustworthiness of impersonal sources [ 8 ]. For example, information provided by a source that has an identifiable commercial interest, such as a company marketing a food product, will be considered as less trustworthy than a source without such an interest, for example, an expert committee of scientists. Patients will actively interpret risk information. If the information is timely and relevant it will tend to be accepted. Patients tend to define relevance in terms of the way they view or frame a situation, and there may be considerable differences between the ways that experts and patients view the same situation. As Zinn notes, the ways in which individuals frame and perceive risk will be highly influenced by their social situation, especially their personal biography [ 9 ]. Individuals may identify and respond to the same risks in very different ways. For example, Ziegeler has shown how the background and social context of individuals who have been diagnosed as having multiple sclerosis influence the ways in which they identify and manage their risks and opportunities [ 9 ].
The Need for a Person-Centred Approach
Within the rational model of risk communication, the emphasis is on the flow of knowledge from the knowledgeable doctor to the uninformed patient. However, communication is a two-way process, and increasingly there is awareness of the active role of patients and the public [ 7 ]. Patients actively seek information, especially when they are aware that they are facing a crucial decision. While they can use traditional sources such as friends and relatives, if they have the skills and resources they can, through media such as the Internet, access highly sophisticated risk knowledge. For example, via the Cochrane Collaboration Web site ( www.cochrane.org ) they can find the latest evidence-based assessments of medical treatments and technologies, or via the Dr. Foster Web site ( www.drfoster.co.uk ) they can find the risks associated with different treatment facilities in the United Kingdom. Many patients access a variety of different sources, so they can clearly compare and evaluate the information provided by each.Patients do give particular credibility to sources that they know, which may include family and friends but also medical advisers with whom they have developed a relationship. They are particularly concerned about the trustworthiness of particular sources. While individuals can use their personal experience to evaluate the trustworthiness of personal sources, such as a particular relative or doctor, they often use contextual information to judge the trustworthiness of impersonal sources [ 8 ]. For example, information provided by a source that has an identifiable commercial interest, such as a company marketing a food product, will be considered as less trustworthy than a source without such an interest, for example, an expert committee of scientists.Patients will actively interpret risk information. If the information is timely and relevant it will tend to be accepted. Patients tend to define relevance in terms of the way they view or frame a situation, and there may be considerable differences between the ways that experts and patients view the same situation. As Zinn notes, the ways in which individuals frame and perceive risk will be highly influenced by their social situation, especially their personal biography [ 9 ]. Individuals may identify and respond to the same risks in very different ways. For example, Ziegeler has shown how the background and social context of individuals who have been diagnosed as having multiple sclerosis influence the ways in which they identify and manage their risks and opportunities [ 9 ].Features of a Person-Centred Approach Standard approaches to risk communication, whether targeted at groups or individuals, do not appear to be very effective. For example Ruston and Clayton have shown the ways in which women disregard information and conceptually distance themselves from the risk of coronary heart disease—this applies even to those admitted to hospital with the disease [ 10 ]. Coleman has documented the failure of strategies that focus on providing information about the risks of teenage pregnancy to have any marked effect [ 11 ]. If doctors want to communicate effectively, then they need to develop a person-centred approach to risk communication, one that recognises that communication forms part of a relationship and builds upon it. Communication should be a dialogue that develops as the relationship develops, and those involved should have complementary and linked roles. Thus, the initial stage of communication could involve identifying the key issues, that is, those that cause concern for the patient. In this phase the emphasis might be on the patient talking and the doctor listening. If there is a major difference in the ways in which patient and doctor are framing the risk issues, then there might need to be an exchange or negotiation in which both parties adjust their mutual expectations and seek a mutually acceptable definition of what the problem is. If such an exchange does not take place, and if the patient's definitions are disregarded and not acknowledged, there is the danger that the patient will passively acquiesce but treat much of the information provided by the doctor as irrelevant and disregard it. If and when there is agreement, then there is the possibility of discussing the future and the likely consequences of taking different actions (risk communication in its traditional form). During this part of the exchange the emphasis might shift towards the doctor talking more and the patient listening more. There is a transfer of information, but it is a two-way process. The doctor should learn something about the patient's situation, including the risks that the patient is concerned about and the patient's beliefs about the nature of such risks. The patient should learn about the doctor's views of the nature of the risks that the patient is facing and the options for managing such risks. Underpinning the development of an effective relationship is the development of mutual trust. While trust usually takes time to develop, it is possible even during a short but positive exchange, in which mutual respect is shown, for a form of “swift” trust to be developed [ 12 ]. While I have focussed on communication in face-to-face relationship, the same issues and processes can be identified in more impersonal communication, such as the provision of risk information in health promotion campaigns. In such campaigns special mechanisms need to be created for dialogue. For example, Jones has described a project that engages young drug users in Hong Kong by helping them make videos about drug use, and has shown how such techniques can be used to evaluate and improve current health promotion adverts [ 13 ].
Features of a Person-Centred Approach
Standard approaches to risk communication, whether targeted at groups or individuals, do not appear to be very effective. For example Ruston and Clayton have shown the ways in which women disregard information and conceptually distance themselves from the risk of coronary heart disease—this applies even to those admitted to hospital with the disease [ 10 ]. Coleman has documented the failure of strategies that focus on providing information about the risks of teenage pregnancy to have any marked effect [ 11 ]. If doctors want to communicate effectively, then they need to develop a person-centred approach to risk communication, one that recognises that communication forms part of a relationship and builds upon it. Communication should be a dialogue that develops as the relationship develops, and those involved should have complementary and linked roles.Thus, the initial stage of communication could involve identifying the key issues, that is, those that cause concern for the patient. In this phase the emphasis might be on the patient talking and the doctor listening. If there is a major difference in the ways in which patient and doctor are framing the risk issues, then there might need to be an exchange or negotiation in which both parties adjust their mutual expectations and seek a mutually acceptable definition of what the problem is. If such an exchange does not take place, and if the patient's definitions are disregarded and not acknowledged, there is the danger that the patient will passively acquiesce but treat much of the information provided by the doctor as irrelevant and disregard it.If and when there is agreement, then there is the possibility of discussing the future and the likely consequences of taking different actions (risk communication in its traditional form). During this part of the exchange the emphasis might shift towards the doctor talking more and the patient listening more. There is a transfer of information, but it is a two-way process. The doctor should learn something about the patient's situation, including the risks that the patient is concerned about and the patient's beliefs about the nature of such risks. The patient should learn about the doctor's views of the nature of the risks that the patient is facing and the options for managing such risks.Underpinning the development of an effective relationship is the development of mutual trust. While trust usually takes time to develop, it is possible even during a short but positive exchange, in which mutual respect is shown, for a form of “swift” trust to be developed [ 12 ].While I have focussed on communication in face-to-face relationship, the same issues and processes can be identified in more impersonal communication, such as the provision of risk information in health promotion campaigns. In such campaigns special mechanisms need to be created for dialogue. For example, Jones has described a project that engages young drug users in Hong Kong by helping them make videos about drug use, and has shown how such techniques can be used to evaluate and improve current health promotion adverts [ 13 ].Conclusion There are no quick technical fixes for communicating risk information. If health professionals are serious about communicating risk information so that patients and others can make informed choices, they need to recognise that communication is a two-way process, and they need to take time to access patients' accounts and perceptions. Such investment should pay off both in an improved relationship and also in improved concordance with treatments.
Conclusion
There are no quick technical fixes for communicating risk information. If health professionals are serious about communicating risk information so that patients and others can make informed choices, they need to recognise that communication is a two-way process, and they need to take time to access patients' accounts and perceptions. Such investment should pay off both in an improved relationship and also in improved concordance with treatments.


Of the 300 or so viruses that cause disease in humans, HIV may have the greatest adaptive advantage. Like most persistent viruses—including the herpesviruses Epstein–Barr and cytomegalovirus (CMV)—HIV employs various strategies to counteract its host's response to infection. But HIV possesses a unique ability to sustain a progressive attack on the immune system—infecting the very cells that coordinate the immune response—leaving the body susceptible even to normally harmless microorganisms. It is these so-called opportunistic infections, rather than the human immunodeficiency virus itself, that makes HIV so deadly. The specific mechanisms that engineer this ongoing systemic attack have been the subject of intense research.HIV targets white blood cells with protein surface receptors called CD4. These CD4, or helper, T-cells normally orchestrate the body's immune response by signaling killer T-cells (which are also called CD8 T-cells, after their CD8 surface receptors) and other immune cells to multiply and differentiate—that is, become specially equipped to recognize a particular pathogen, or antigen. At the onset of infection, the immune system appears to respond normally, with a strong attack led by HIV-specific CD8 T-cells that initially contain the virus. But as the infection progresses, CD4 counts drop and the body's ability to renew T-cells decreases while its proportion of “antigen-experienced” CD8 T-cells increases. While the biological effect of this hyperactivity is unclear, it is apparent that patients with elevated immune activity face a poor prognosis. Investigating the interaction among immune activation, CD8 T-cell differentiation, and HIV prognosis, Victor Appay and colleagues report that a close connection between elevated immune activation and elevated levels of highly differentiated T-cells may bring further insights into how HIV exhausts the immune system.To examine the effect of elevated immune activation on T-cells, the researchers analyzed T-cells from a group of HIV-infected individuals collected at two distinct points in time: at the onset of acute infection—which is characterized by vigorous HIV replication—and after treatment, when viral replication is suppressed. To explore the connection between T-cell differentiation and clinical status, the researchers analyzed the T-cells from a group of untreated infected individuals divided into three subsets based on stage of infection: acute infection, chronic infection without progression, and chronic infection with signs of progression.During acute HIV infection, the vast majority (80%–90%) of the CD8 T-cell population was activated—not just the HIV-specific CD8 T-cells. Surprisingly, CD8 T-cells specific to the Epstein–Barr and CMV viruses showed significant activation levels during acute infection, suggesting that HIV may indirectly promote the replication of these viruses. When the researchers investigated the effects of this activation on T-cell differentiation, they found a correlation between increasing antigen concentrations and increasing CD8 T-cell activation and proliferation. And when Laura Papagno et al. analyzed the differentiation state of CD8 T-cells in individuals at different stages of infection, they found a progression in the proportion of highly differentiated CD8 T-cells associated with HIV disease progression.These results, the researchers conclude, show that chronic overactivation of the immune system during HIV infection produces the large pool of highly differentiated T-cells observed in HIV infection. T-cells go through various stages toward late differentiation, and it may be that the early-differentiated CD8 T-cells, which maintain the ability to proliferate, offer protective immunity. But highly differentiated cells, they propose, exhibit characteristics associated with “replicative senescence”—they are in effect old, worn-out cells that can no longer proliferate. Though replicative senescence is a natural process for most cells, in the context of HIV—in which infected individuals also lose the ability to replenish T-cells—it creates an aging population of T-cells that are less effective at fighting infection.


The legacy of Francis Crick is explored by two scientists who were influenced by his work

‘You’, your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells…” — Crick (1994, p. 3)‘You’, your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells…” — Crick (1994, p. 3)‘You’, your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells…” — Crick (1994, p. 3)Francis Crick was an evangelical atheist. He believed that scientific understanding removed the need for religious explanations of natural phenomena. From James Watson's and his early work, the structure of DNA explained the α, the origins of life. This was a starting point; from the elucidation of the structure of DNA, there was an explosion, a massive diversity of science that in part removed the need to postulate a creator or a creation myth. Francis still felt that life was no less astonishing just because it was biological and natural in origin. He had a consistent and completely rational world view without a need to invoke vitalism, or any non-material force ( M. Crick 2004 ). And in the last decades of his life, he applied this philosophy to the Ω, consciousness.Once the structure of DNA was known, the physicist George Gamow formed the RNA Tie Club, with Francis and eighteen others including his close friends Leslie Orgel and Sydney  Brenner (2001) ; it was an ingathering that sowed seeds for future molecular biologists ( Judson 1996 ). DNA had become the “α,” the beginning ( Bronowski 1978 ), not just of Francis's career, but of a whole new culture of scientific life and understanding ( Crick 1966 ).Ten years later, the secrets of DNA transcription and translation unmasked, Francis turned to consciousness. He admitted he knew little at first, only that the structure of consciousness was as tough a problem as DNA's structure. DNA was certainly not played out, but the Ferrier Lectures in the Proceedings of the Royal Society of London by David Hubel and Torsten Wiesel were just available, tempting Francis with an almost physicist's view of neurons in action. Hubel and Wiesel wrote of functional architectures, embedded in beautiful, almost crystalline structure. The comprehension of mind invoked by a biological mechanism appeared ripe for the sort of thoughtful, theoretical science he had applied to DNA. Francis was now sixty years old and moved from Cambridge to the Salk Institute in La Jolla, California. Francis began with the brightest young minds he could find.David Marr was a young mathematician and physiologist whose doctoral thesis on a theory of mammalian brain function at Cambridge had brought him into some contact with Brenner and Francis. A professor at the Massachusetts Institute of Technology, he began working with Tomasio Poggio of the Max Plank Institute in Tübingen on a computational theory of neuroscience. Following an invitation from Francis, Poggio and Marr spent the month of April, 1979 extending their intense examination of the core problems of visual perception. They spent hours sitting at the most western end of the Salk Institute, at the cafeteria or in Francis's office, gazing into the Pacific Ocean with all its daily changes, discussing not only architecture of visual cortex and visual perception, but the ramifications of a good theory of brain function. We know of these conversations, as the probing of Marr by Francis is captured in the final chapter of Marr's now classic book “Vision” ( Marr 1982 ). (Although Marr speaks of a three-way conversation, judging from our own experiences as Francis's younger colleagues, the interlocutor simply seems to be Francis.)Marr had been diagnosed with acute leukemia in the winter of 1978 ( Marr and Vaina 1991 ). The one-month visit to the Salk Institute was an intellectual gift, for eighteen months later, Marr died. Francis had simultaneously lost a young friend and colleague who had brought an “incisive mind and creative energy” ( Crick 1994 , p. 77) and his best new ideas of a theoretical neurology to the brain ( Marr 1969 ,  1970 ). And he saw the tragedy of Marr being cut off from solving the big problems for which he was so clearly destined.During those early years, Francis must have thought that consciousness was tractable—if only the right way of thinking was brought to bear on it. Francis's brain was capable of collecting and filing away many disparate data, which he could then combine uniquely and imaginatively, leading to that “dramatic moment of sudden enlightenment that floods the minds when the right idea clicks into place” ( Crick 1990 , p. 141). Whatever his initial thoughts about the nature of the problem, Francis soon came to realize that the problem of consciousness was even tougher than he imagined, that the “click” was not happening with consciousness. In 1988, he wrote, “I have yet to produce any theory that is both novel and also explains many disconnected facts in a convincing way” ( Crick 1990 , p. 162).Over the quarter century he was at the Salk Institute, Francis did propose solutions to some smaller problems in neuroscience ( Sejnowski 2004 ) and brought consciousness into the scientific fold ( Rich and Stevens 2004 ). But something else was going on quietly and behind the scenes. Francis was building an army to help him take on consciousness. This was not empire building with Francis as the head of a group of directed scientists in the Cambridge or German model. Francis continually encouraged and assisted young scientists to approach the hardest problems of the brain. Marr and Poggio were just the first recruits he helped embolden. He started his long-time collaboration with Christof Koch, once a post-doctoral trainee with Poggio, on “The Problem of Consciousness” ( Crick and Koch 1990 ,  1992 ). His door was always open to graduate students, postdoctoral trainees, faculty who wanted to discuss those problems as many others and we can attest. Francis could be found daily at tea time, an ingathering of the Salk Institute computational and vision laboratories of Simon LeVay, Terry Sejnowski and Thomas Albright, surrounded by graduate students and post-doctoral trainees, with conversation ranging across science—Francis listening to their stories of their explorations and encouraging them to reach beyond their horizons. Francis had a “love of the truth and helped others to move to the truth” ( Watson 2004 ).When Francis worked on the structure of DNA, he had some simple facts, such as Chargaff's Laws, and means to make point mutations from which it could be determined how function followed structure. But not a single neuroanatomist knew how many neurons actually converged in their input to a particular single cell. No one knew how to eliminate a specific cell type from a circuit— to make a point mutation, so to speak, in the structure of consciousness. His 1979 article in Scientific American, “Thinking about the Brain,” did not have much impact at the time, even when it explicitly described three needed methods: first, a method by which all the connections to a single neuron could be stained; second, a method by which “all neurons of just one type could be inactivated, leaving the others more or less unaltered”; and third, a means to differentially stain each cortical area, “…so that we could see exactly how many there are, how big each one is and exactly how it is connected to other areas.” By the mid-1980s, Francis had realized that these massive holes in our understanding of the most simple brain facts were not being filled. Something needed to be done.Over the twenty years since the RNA Tie Club, molecular biology had matured. Francis actively began encouraging the inclusion of the critical tools of molecular biology in the study of neural circuits and perception; in his thinking, molecular biology was critical to understand how the brain worked because it provided tools. He would encourage junior scientists, postdoctoral trainees, and faculty—all those who had visited him over the years—to think about using these tools. He would give short homilies about the plethora of sub-types of neurons in the retina; would not the cortex be at least as rich in possibilities? Molecular tools could unravel this knot.As we reminisced after Francis's death, we discovered that Francis had spoken with each of us on these molecular methods, across a twenty-year interval. In the mid-1980s, Francis spoke with Ralph, pressing him to consider how he might do highly specific lesions of single neuron types in motion cortex using molecular identifiers. At the time, the only tools imaginable were some sort of killer antibody approach. Twenty years later, Ed recalls Francis continuing to encourage this cross-disciplinary molecular and systems approach. It was absolutely imperative to Francis's vision of the maturation of neuroscience that there would be a conjoining of molecular biology and systems neuroscience. We are sure we were not unique in hearing this call; with how many others had he shared his vision?The science of the mind is a thinker's game. It is chess against the grandest masters, biological evolution and natural selection—and we are just learning to move the pieces. Our viewpoint is often myopic, with our noses pressed against the back row of the chessboard. It is hard to see the pieces, let alone their arrangement or the strategies they are forming. Francis may not have had the overview needed to reveal evolution's gambit, but he knew the moves needed to clear the “tangle of difficulties” ( Crick 1994 , p. 77) that prevented an unfogged view of his opponent's pieces.Francis hoped for simplicity. He wrote, “Curiously enough, in biology it is sometimes those basic problems that look impossibly difficult to solve which yield most easily…. The biological problems that are really difficult to unscramble are those where there is almost infinity of plausible answers and one has to painstakingly attempt to distinguish between them.” ( Crick 1990 , p. 157–158). Watson and Crick had picked the right pieces of information to construct their model. Francis early on had had the same hopes to open the doors of consciousness (to paraphrase  Huxley 1963 ). Watson and Crick used their intuition to fill in the gaps. But Francis found that there were just too many possibilities, and the gaps in knowledge were still just too big for consciousness.In 1999, Francis felt that gentle and informal direction was not enough. Thus, he convened a meeting of molecular biologists and neuroscientists at the Salk Institute to encourage them to work together. He brought scientists including Tom Albright, Ursula Bellugi, Ed Callaway, Rusty Gage, Steve Heinemann, Terry Sejnowski, Chuck Stevens, and Inder Verma into one room and said it was time to get serious. He reminded them of the advantages of genetic methods for targeting specific cell types within complex neural circuits, and he reiterated the need for methods that could be used to identify, manipulate, and observe neural circuits in action. Not only were methods to be used in transgenic mice, but also methods based on viral vectors were needed to study the visual system of monkeys. From this, a number of initiatives moved forward, with studies ranging from the molecular biology of Williams syndrome to basic molecular tool building ( Naldini et al. 1996 ;  Blomer et al. 1997 ;  Bellugi et al. 1999 ;  Pfeifer et al. 2001 ;  Zhao et al. 2001 ;  Kaspar et al. 2002a ,  2002b ,  2003 ;  Lechner et al. 2002 ;  Lein et al. 2004 ).Today the tools are emerging at an ever faster pace, at least in part due to Francis's maneuvers behind the scenes and his encouragement of junior scientists. Time is curing Francis's bout of scientific prematurity ( Stent 1972 ). Individual cell types will soon be reversibly inactivated ( Johns et al. 1999 ;  Lechner et al. 2002 ;  Slimko et al. 2002 ;  Ibanez-Tallon et al. 2004 ); viral methods of tracing connections will start to fill in the gaps; new sensor methods for simultaneously recording from hundreds and thousands of identified neurons are coming ( Guerrero and Isacoff 2001 ;  Zemelman and Miesenbock 2001 ;  Tsien 2003 ). There is a new field Francis termed “molecular psychology” or “molecular biology of systems neuroscience”; Albright simply calls it Neuroscience.In 2001, Francis was diagnosed with colon cancer. He realized that the problem of the neural correlates of consciousness might outlast him. Francis was walking with a cane, still not waiting for anyone, nor allowing anyone to wait for him. He continued to find time for new faces in the field and continued to work on consciousness. While he had made many strides forward, he saw the race for him was winding down. He had had his hope for understanding the structure of consciousness. He had laid the groundwork. He decided to encapsulate his ideas in a “Framework” paper with Koch ( Crick and Koch 2003 ). For many of us it was clear that he was laying out where he would go, had he enough time.Each of the points of the Framework could form a major research initiative. Perhaps they should. But the central point is the  approach  to understanding consciousness; it is both structural and functional, peering forward into the future into what the shape might be. It was clear to his friends and colleagues that Francis was leaving a last testament.approachAs the cancer finally caught up with Francis, he focused on the role of the rarely studied claustrum ( Sherk 1986 ). He wrote internal memos, brought friends and colleagues to working lunches at home with Odile, his wife of fifty-five years. Why do this? Why all this focus on another part of the brain, when only months remained? (Indeed it turned out to be weeks.) Was it his way of saying goodbye, of bringing his extended family close again? We think not. Francis wanted to make sure his plan went forward. He stressed to his visitors queries about the origins of the claustrum, its molecular biology, its role in consciousness. He was using his framework, pointing out the route to understanding the Ω of his career.Francis was doing what he truly loved to his last moments. He needed to be doing science, perhaps more than ever, to take him away from the physical pain that he surely felt. He had built his army. Perhaps none of us even knew we had enlisted, but we had. And he was setting us off on the long march forward into a time that soon would not be for him. Francis died on the cusp of a new age of molecular systems neuroscience. Soon, we will have the tools and the data, but we will not have Francis. Francis had existed between the α of DNA and the Ω of consciousness. And for a man who never believed in the afterlife, he had indeed achieved immortality.