The NME7 gene is involved in the kinetics of glucose regulation

doi:10.21203/rs.3.rs-4976430/v1

Download PDF

Article

The NME7 gene is involved in the kinetics of glucose regulation

https://doi.org/10.21203/rs.3.rs-4976430/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The shape of the glycemic curve during the oral glucose tolerance test (OGTT) can predict type 2 diabetes mellitus (T2DM). Given that T2DM is common in several ciliopathies, the NME7 gene (non-metastatic cells 7), encoding a recognized member of the ciliome, was included in our panel of 19 candidate genes for glucose metabolism disturbances. The aim was to find out whether the variability of any of the genes is associated with the shape of the 3-hour glycemic curve. The study included 1,262 OGTT curves categorized into four groups according to their shape: monophasic, biphasic, triphasic and more complex multiphasic. Among all tested genes, only NME7 variants showed significant results. In the group with a biphasic trajectory, which is characterized by certain health benefits, we saw higher frequencies of wild-type homozygotes of the three linked NME7polymorphisms rs10732287 (p<0.01), rs4264046 (p=0.01) and rs10800438 (p=0.03). In contrast, two other variants of this block, rs4656659 (p=0.01) and rs2157597 (p=0.05), showed lower proportion of wild-type homozygotes among biphasic trajectories. In conclusion, a cluster of five linked NME7 polymorphisms showed strong association with a biphasic glycemic curve. Given the compelling health benefits associated with a biphasic curve, variability in the NME7 gene represents another piece of the complex mosaic influencing healthy energy processing.

Biological sciences/Genetics

Biological sciences/Physiology

Health sciences/Endocrinology

Health sciences/Medical research

glucose metabolism

type 2 diabetes mellitus

glycemic curve shape

glucose tolerance test

NME7 gene

ciliopathies

1.1. Factors affecting glucose homeostasis

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by hyperglycemia in the context of insulin resistance (IR) and impaired insulin secretion. The manifestation of the disease is usually preceded by a long-lasting period of impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT). T2DM is associated with serious microvascular and macrovascular complications including diabetic retinopathy, nephropathy, or vascular diseases threatening with heart attack, stroke, or impaired blood circulation in the lower limbs, which may even result in the need for amputation. T2DM reduces life expectancy by approximately 10 years [1]. In the Czech Republic, the prevalence reaches almost 10% with an alarming rise in younger age groups [2], probably in connection with the decrease in regular physical activity together with inadequate caloric intake resulting in increased prevalence of obesity. Excessive weight may also be a consequence of chronic stress or result of long-term exposure to endocrine disruptors, which destroy the hormonal balance in the body. Disrupted factors of the internal environment, which are complexly genetically coded and epigenetically regulated, can thus cause impaired energy homeostasis and excess of adipose tissue, chronic inflammation [3], gastrointestinal hormonal dysfunction, altered gut microbiome [4], and various combinations of all the above-mentioned imbalances. Prenatal developmental conditions may also play a role [5–8]. Diagnosis of overt T2DM thus reflects the interaction between a wide range of developmental and external factors with a genetic and epigenetic background [9–13].

Concerning genetic predisposition to the development of T2DM, genome-wide association studies and subsequent large-scale meta-analyses led to identification of more than 600 genetic loci that modulate the risk of developing T2DM or are involved in closely related regulatory mechanisms, such as variations in fasting plasma glucose [14]. However, the effect sizes of individual loci on either T2DM risk or fasting plasma glucose were modest, and these SNPs explained less than 10% of T2DM heritability and less than 5% of plasma glucose variance [15, 16]. Currently, the clinical applicability of the genetic information obtained is questionable [17]. Nevertheless, multi-omics analyses enhance the role of genotype data in developing new treatment approaches based on targeted therapy, advancing towards precision medicine tailored to the individual [18].

1.2. Oral glucose tolerance test course

The oral glucose tolerance test (OGTT) and the derived indices of insulin sensitivity and insulin secretion are being used as the most suitable approach for assessing glucose metabolism in epidemiological studies [19].

The shape of the glycemic curve reflects the dynamics of the beta-cell response to a glucose stimulus. The fasting blood glucose level before the start of the test and at the 120th min after drinking a solution containing 75 g of glucose serve as diagnostic criteria for metabolic disorders like IGT or diabetes. In recent years, many scientific teams have been engaged in the analysis of the shape of the glycemic curve aiming to capture and interpret its physiologically relevant information [20–23]. The 2-hour OGTT predominate, but studies evaluating the course of the 3-hour OGTT are no longer rare. Blood glucose is usually measured at half-hourly intervals and the curve shape is defined by the pattern of rising and subsequent falling glucose concentrations. Four main types of curves are usually distinguished. The monophasic curve is characterized by glycemia increasing to a single peak before gradually falling. The biphasic curve is rising to an initial peak, gradually falling to a nadir, and then rising again to a second peak. Triphasic curves with two complete peaks or more complex multiphasic curves are also described in studies based on the 3-hour or longer OGTT. The monophasic curve has been associated with impaired glucose regulation [20, 21], whereas biphasic and multiphasic curves appear to be more favorable for glucose homeostasis and other metabolic aspects [24].

1.3. Aims

Our aim was to evaluate a possible connection between glucose processing during the 3-hour OGTT and the genetic variability of candidate genes studied in connection with glucose metabolism disorders. For this purpose, we comprehensively characterized anthropometric and biochemical profiles of a large cohort of people showing a wide range of glucose tolerance states, including healthy individuals, people with IFG, IGT and also type 2 diabetics newly diagnosed by our examination. The biochemical component of our study focused on the detailed typology of glycemic curves, which was made possible by the extended 3-hour glucose test. Regarding the genetic background, we genotyped 37 single nucleotide polymorphisms (SNPs) listed in the methodological section 4.4. “Molecular Genetic Analysis“. These SNPs have been studied in 19 genes involved in glucoregulation, energy balance or inflammation: NME7, ATP1B1, BLZF1, GCK, KCNJ11, LRP5, PPARGC1A, PPARG, SLC30A8, TCF7L2, FTO, ZBTB16, THADA, PICALM, BIN1, CLU, CR1, MTNR1B and PNPLA3.

2.1. Metabolic profile of the cohort

A complete characterization was conducted on 1,262 individuals, comprising 1,035 women (mean age 34.7 ± 10.23 years; mean BMI 24.7 ± 5.30 kg/m²) and 227 men (mean age 36.5 ± 13.13 years; mean BMI 25.6 ± 4.55 kg/m²). Among women, 454 participants had a positive history of gestational diabetes mellitus (GDM) and 189 were diagnosed with polycystic ovary syndrome (PCOS) according to the European Society of Human Reproduction and Embryology consensus [25]. Based on our examination, 17 participants (12 women, 1.2% and 5 men, 2.2%; p = 0.21) were newly diagnosed with T2DM. Impaired fasting glucose was present in 100 individuals (76 women, 7.4% and 24 men, 10.8%; p = 0.10), impaired glucose tolerance based on 120th min of the OGTT was detected in 83 participants (69 women, 6.7% and 14 men, 6.3%; p = 1.00). Impaired glucose metabolism (IGM), encompasing either IFG, IGT, both disorders concurrently or overt T2DM, was present in 167 participants (131 women, 12.6% and 36 men, 15.9%; p = 0.19). According to the NCEP_ATPIII criteria [26], 139 participants (102 women, 10.0% and 37 men, 16.4%; p = 0.01) suffered from metabolic syndrome. Table 1 reports detailed anthropometric data and biochemical characteristics of the examined subjects. In the study, special attention was paid to glucose metabolism. Therefore, indices of insulin sensitivity were calculated for fasting as well as for dynamic conditions following the glucose load. Pancreatic beta cell function was also characterized for fasting state and for dynamic conditions. Details on the calculation of the indices listed in Table 1 are reported in methodological section 4.2. “Anthropometric and metabolic characterization of the subjects“.

2.2. Representation of individual types of curves

Among the 1,262 glycemic curves evaluated, 633 (50%) were monophasic, 221 (17.5%) biphasic, 351 (28%) triphasic, whereas 57 (4.5%) showed more complex patterns.

People pertaining to the monophasic and triphasic groups were on average 3 years older than people in the biphasic group. BMI was on average 1.3 kg/m² higher in people within the monophasic group compared to the other groups. Data presenting age and BMI as the basic characteristics of the given groups, based on the glycemic trajectories, are shown in Table 2.

Table 1

Anthropometric and metabolic characterization of the subjects.
parameter (units)	median [95% LCL; 95% UCL]
n	1,262
age (years)	33.3 [32.6; 34.0]
systolic blood pressure (mmHg)	115.0 [114.0; 115.0]
diastolic blood pressure (mmHg)	72.0 [71.0; 73.0]
BMI (kg/m²)	23.9 [23.5; 24.3]
WHR women	0.764 [0.759; 0.768]
WHR men	0.864 [0.849; 0.877]
basal glycemia (mmol/l)	4.8 [4.7; 4.8]
AUC_Glycemia(mmol*min/l)	1,046 [1,031; 1,059]
basal insulinemia (mIU/l)	6.16 [5.90; 6.30]
AUC_Insulin (pmol*min/l)	33,885 [32,598; 35,091]
basal C-peptide (nmol/l)	0.59 [0.58; 0.60]
AUC_C−peptide (pmol*min/l)	3.610⁵ [3.510⁵;3.7*10⁵]
hepatic insulin extraction (%)	68.1 [67.5; 68.8]
insulin sensitivity/resistance
HOMA IR	1.30 [1.26; 1.36]
OGIS 120 min (ml/min/m²)	457.7 [453.5; 462.1]
ISI_COMP ([(mg/dl)²(µU/ml)²]^−1/2)	8.48 [8.18; 8.81]
MCRest (ml/min/kg)	9.87 [9.75; 9.99]
Si_(oral) ((ml/min/kg)/(µU/ml))	0.15 [0.15; 0.16]
PREDIM (mg/min/kg)	6.97 [6.83; 7.17]
beta cell function
HOMA B (mIU/mmol)	103.5 [98.8; 106.2]
IGI (pmol/mmol)	88.5 [84.2; 95.1]
Ins₀/Glc₀ (pmol/mmol)	7.68 [7.40; 8.00]
AUC_Insulin/AUC_Glc (pmol/mmol)	32.7 [31.9; 33.9]
IGI*ISI_COMP	264.9 [257.9; 270.2]
lipid spectrum
cholesterol (mmol/l)	4.59 [4.54; 4.65]
HDL cholesterol women (mmol/l)	1.57 [1.54; 1.60]
HDL cholesterol men (mmol/l)	1.26 [1.21; 1.32]
LDL cholesterol (mmol/l)	2.56 [2.52; 2.63]
TAG (mmol/l)	0.86 [0.82; 0.89]
liver enzymes
ALT (ukat/l)	0.30 [0.30; 0.31]
AST (ukat/l)	0.35 [0.34; 0.36]
GGT (ukat/l)	0.23 [0.22; 0.24]
thyroid hormones
TSH (mIU/l)	2.25 [2.17; 2.33]
fT3 (pmol/l)	4.90 [4.85; 4.97]
fT4 (pmol/l)	15.2 [15.0; 15.3]

BMI—body mass index; WHR—waist-hip ratio; AUC_Glycemia—area under the glycemic curve; AUC_Insulin—area under the insulinemic curve; AUC_C−peptide—area under the C-peptide curve; HOMA IR—Homeostatic Model Assessment of Insulin Resistance; OGIS—Oral Glucose Insulin Sensitivity; ISI_COMP—Insulin Sensitivity Composite index; PREDIM—Peripheral Insulin Sensitivity Index; HOMA B—Homeostatic Model Assessment of Beta-cell function; IGI—Insulinogenic index; IGI*ISI_COMP—product of the IGI and the ISI_COMP index; HDL—high-density lipoprotein; LDL—low-density lipoprotein; TAG—triacylglycerols; ALT—alanine aminotransferase; AST—aspartate aminotransferase; GGT—γ-glutamyl transferase; TSH—thyrotropin; fT3—free triiodothyronine; fT4—free thyroxine.

Table 2

Age and BMI of the groups based on the shape of glycemic curves.
n = 1,262	monophasic n = 633	biphasic n = 221	triphasic n = 351	multiphasic n = 57	P-level
age (years)	34.1[33.2; 35.1]	31.4[28.3; 32.9]	34.0[32.4; 34.7]	28.7[26.6; 30.6]	< 0.01
BMI (kg/m²)	24.6[24.1; 25.1]	23.3[22.6; 24.1]	23.2[22.7; 23.8]	23.5[22.2; 24.2]	< 0.01

Data are given as median [95% LCL; 95% UCL], P-level according to Kruskal Wallis Z-value test

The representation of men and women within individual types of curves was significantly different with the most noticeable difference in distribution of the bi- and triphasic curves. Statistical comparison showed that a higher percentage of men had a biphasic curve (33% vs. 14% of women) and a higher percentage of women had a triphasic curve (30% vs. 19% of men), Chi² = 46; p < 0.0001, power of the test > 0.99.

The monophasic shape of the curve was predominant among newly diagnosed diabetics (15 out of 17; i.e. 88%), but also 70% of subjects with IFG and 78% with IGT in 120th min of the test. Furthermore, 75% of subjects meeting the criteria for metabolic syndrome also fell into the group with a monophasic curve. Furthermore, monophasic group included 53% of women with a history of GDM and 46% of women diagnosed with PCOS. These findings suggest that monophasic group shows worse metabolic health compared to individuals with biphasic, triphasic or more complex curves and is associated with the presence of many components of the metabolic syndrome. In contrast, the biphasic trajectory (and the rarer multiphasic pattern) appears most beneficial in terms of metabolic health as it is associated with the best glycemic and lipid profile. We have previously published a detailed analysis of the anthropometric and metabolic differences between curve-derived groups, as well as gender-specific variations based on glycemic trajectory shape [24]. In this study, we will focus on the genetic background possibly underlying these observations.

2.3. Genetic determination of glycemic curve shapes

Of the 37 SNPs listed in methodological section 4.4. that were tested in candidate genes associated with glucose metabolism (NME7, ATP1B1, BLZF1, GCK, KCNJ11, LRP5, PPARGC1A, PPARG, SLC30A8, TCF7L2, FTO, ZBTB16, THADA, PICALM, BIN1, CLU, CR1, MTNR1B, PNPLA3), only five variants in the NME7 gene were associated with the shape of the glycemic curve during the 3-hour OGTT. Four polymorphisms in this gene were significantly associated with the shape of the curves, while a fifth polymorphism showed borderline significance. Table 3 presents the proportions of individual NME7 genotypes for each glycemic trajectory. In Supplementary Material 1, we report detailed Chi-squared statistical outputs with exact numbers of these five genetic NME7 variants and the result of the analyses carried out separately for men and women (Supplementary Tables S1-S5).

Table 3

Genotype and allele frequencies of *NME7* gene polymorphisms in individual groups of glycemic trajectories.
GENOTYPES of NME7_rs4656659 (%)				ALLELES (%)
	TT	CT	CC	T	C	STAT_{Genotypic distribution}:
monophasic	45	43	12	67	33	Chi² = 16.43
biphasic	34	54	12	61	39	Power = 0.88
triphasic	39	53	8	66	34	P-level = 0.012
multiphasic	48	40	12	68	32

GENOTYPES of NME7_rs2157597 (%)				ALLELES (%)
	CC	CT	TT	C	T	STAT_{Genotypic distribution}:
monophasic	52	40	8	71	29	Chi² = 12.64
biphasic	42	48	10	66	34	Power = 0.76
triphasic	48	47	5	72	28	P-level = 0.049
multiphasic	52	41	7	73	27

GENOTYPES of NME7_rs10732287 (%)				ALLELES (%)
	CC	CT	TT	C	T	STAT_{Genotypic distribution}:
monophasic	46	43	11	67	33	Chi² = 19.39
biphasic	59	33	8	76	24	Power = 0.93
triphasic	43	48	9	67	33	P-level = 0.004
multiphasic	37	48	15	61	39

GENOTYPES of NME7_rs4264046 (%)				ALLELES (%)
	CC	CT	TT	C	T	STAT_{Genotypic distribution}:
monophasic	29	49	22	53	47	Chi² = 16.42
biphasic	39	44	17	61	39	Power = 0.88
triphasic	28	56	16	56	44	P-level = 0.012
multiphasic	25	53	22	52	48

GENOTYPES of NME7_rs10800438 (%)				ALLELES (%)
	GG	GT	TT	G	T	STAT_{Genotypic distribution}:
monophasic	31	49	20	56	44	Chi² = 14.21
biphasic	42	43	15	64	36	Power = 0.82
triphasic	32	53	15	59	41	P-level = 0.027
multiphasic	24	57	19	53	47

Data are given as percentages, P-level according to Chi-square test

Proceeding from the arrangement of individual NME7 SNPs on the chromosome 1q24.2 (Fig. 1), we found an association of the biphasic type of curves with five tightly linked polymorphisms. More precisely, as shown in Table 3, the wild-type alleles as well as wild-type homozygotes of the rs4656659 and rs2157597 SNPs were less frequent in the biphasic group of curves (p = 0.01 and 0.05, respectively). Conversely, the wild-type alleles and wild-type homozygotes of three other linked NME7 SNPs, rs10732287, rs4264046 and rs10800438, were more frequent in the biphasic group (p < 0.01, p = 0.01, and p = 0.03, respectively).

As demonstrated by the results of linkage analysis in Fig. 2, all these five NME7 variants are in very strong linkage disequilibrium (LD) and form one LD block marked as Block 1. The sixth tested polymorphism of the NME7 gene, rs4656671, already falls into a different LD block (marked as Block 2) and does not show a significant association with the shape of the glycemic curve.

2.4. Haplotype analysis

Carefully performed haplotype analysis showed that the two most abundant haplotypes, which are inherited as intact blocks with very rare recombination events, are precisely the ones indicated by association analysis. The first haplotype carries the wild-type variants of SNPs rs4656659 and rs2157597 in combination with the minor variants of SNPs rs10732287, rs4264046 and rs10800438, forming the TCTTT haplotype. It was calculated to be, either in the heterozygous or homozygous constellation, highly likely present in 52.2%. Presence of this haplotype was statistically significantly lower in individuals with biphasic course of the curve (40%) in comparison with other types of curves (55%), Chi² = 15.41, p < 0.01. The second haplotype, essentially the inverse of the first, includes the minor variants of SNPs rs4656659 and rs2157597 in combination with wild-type variants of SNPs rs10732287, rs4264046 and rs10800438, forming the CTCCG combination. Its presence - again either in heterozygous or homozygous constellation - was calculated as most probable in 49.7% individuals in our cohort. Presence of this haplotype was significantly higher in individuals with biphasic course of the curve (57%) compared to those with other curve types (48%), Chi² = 5.05, p = 0.02. The estimated frequency of other haplotypes with different SNP combinations was already significantly lower, as demonstrated in Table 4, which is a consequence of a very tight genetic link between the polymorphisms in a given haplotype constellation. Three individuals had to be excluded from the haplotype analysis due to insufficient genotyping data, as indicated in Table 4.

Table 4

Block 1 haplotypes with a higher than rare (2.5%) representation sorted by frequency.
haplotype	number of carriers women (n = 1,033)	number of carriers men (n = 226)	number of carriers all (n = 1,259)
1. TCTTT	550 (53.2%)	107 (47.3%)	657 (52.2%)
2. CTCCG	509 (49.3%)	117 (51.8%)	626 (49.7%)
3. TCCCG	377 (36.5%)	91 (40.3%)	468 (37.2%)
4. TCCTT	209 (20.2%)	47 (20.8%)	256 (20.3%)
5. CCCCG	110 (10.6%)	29 (12.8%)	139 (11.0%)
6. TCCTG	54 (5.2%)	11 (4.9%)	65 (5.2%)

Individuals who carry at least one copy of a given haplotype are listed, some may carry two identical copies of the same haplotype.

2.5. Haplotypes and biochemical parameters

Finding that the biphasic course of the glycemic curve is indicative of considerable health benefits compared to the other curves, especially the monophasic one, led us to consider whether a better biochemical profile can also be found in individuals who were assigned to the CTCCG haplotype by haplotype analysis, as this haplotype was shown to be linked with biphasic course of glycemic curve. We compared carriers of this haplotype (either in heterozygous or homozygous constellation) with non-carriers, and we focused on glucose metabolism and components of the metabolic syndrome. In connection with the data presented, it is important that the representation of the CTCCG haplotype was the same in both sexes (49.3% in women and 51.8% in men; p = 0.51), therefore the cohort was evaluated as a whole except for parameters with different reference ranges for women and men (WHR, HDL cholesterol). Table 5 demonstrates anthropometric and metabolic characterization of the subjects depending on the presence of the CTCCG haplotype as estimated by haplotype analysis.

Although no significant differences between carriers and noncarriers of a given haplotype were observed in terms of anthropometry or blood pressure, glucose metabolism and calculated indices of insulin sensitivity clearly indicate that carrying of the CTCCG haplotype is systematically reflected in biochemical profile and confirms our hypothesis that those carrying the haplotype have a metabolically healthier profile. This is evident not only when it comes to fasting levels of glucose, insulin and C-peptide, but it is also reflected in dynamic parameters as AUC_Glycemia, AUC_Insulin, AUC_C−peptide and, which is remarkable, all presented indices of insulin sensitivity.

Table 5

Anthropometric and metabolic characterization of the subjects according to the presence of the CTCCG haplotype as estimated by haplotype analysis.
parameter (units)	CTCCG haplotype +	CTCCG haplotype -	P-level
n	626	633	n/a
age (years)	33.2 [32.3; 34.3]	33.5 [32.4; 34.1]	0.58
systolic blood pressure (mmHg)	114 [113; 115]	115 [114; 116]	0.65
diastolic blood pressure (mmHg)	72 [71; 73]	72 [71; 74]	0.78
BMI (kg/m²)	23.7 [23.2; 24.1]	24.2 [23.6; 24.5]	0.10
WHR women	0.760 [0.751; 0.765]	0.769 [0.760; 0.777]	0.11
WHR men	0.862 [0.845; 0.877]	0.867 [0.848; 0.888]	0.49
basal glycemia (mmol/l)	4.7 [4.7; 4.8]	4.8 [4.8; 4.8]	0.01
AUC_Glycemia(mmol*min/l)	1,029 [1,008; 1,052]	1,059 [1,041; 1,079]	< 0.01
basal insulinemia (mIU/l)	5.9 [5.5; 6.2]	6.3 [6.0; 6.7]	0.04
AUC_Insulin (pmol*min/l)	31,757 [30,510; 33,309]	35,793 [34,209; 38,421]	< 0.01
basal C-peptide (nmol/l)	0.57 [0.56; 0.59]	0.61 [0.59; 0.63]	< 0.01
AUC_C−peptide (pmol*min/l)	3.510⁵ [3.410⁵;3.6*10⁵]	3.710⁵ [3.610⁵;3.8*10⁵]	< 0.01
hepatic insulin extraction (%)	68.5 [67.7; 69.4]	67.7 [66.8; 68.7]	0.14
insulin sensitivity/resistance
HOMA IR	1.24 [1.17; 1.32]	1.37 [1.29; 1.46]	0.02
OGIS 120 min (ml/min/m²)	462.5 [455.8; 468.6]	453.0 [448.0; 459.3]	< 0.01
ISI_COMP ([(mg/dl)²(µU/ml)²]^−1/2)	8.91 [8.56; 9.24]	7.98 [7.52; 8.38]	< 0.01
MCRest (ml/min/kg)	9.99 [9.81; 10.17]	9.76 [9.54; 9.93]	< 0.01
Si_(oral) ((ml/min/kg)/(µU/ml))	0.16 [0.15; 0.17]	0.15 [0.14; 0.16]	< 0.01
PREDIM (mg/min/kg)	7.18 [6.90; 7.38]	6.83 [6.64; 7.00]	0.03
beta cell function
HOMA B (mIU/mmol)	103.7 [95.7; 108.3]	103.2 [97.8; 107.3]	0.78
IGI (pmol/mmol)	89.1 [83.1; 95.8]	88.3 [83.2; 98.3]	0.59
Ins₀/Glc₀ (pmol/mmol)	7.40 [7.00; 7.85]	7.92 [7.56; 8.37]	0.11
AUC_Insulin/AUC_Glc (pmol/mmol)	31.3 [29.9; 32.7]	34.5 [32.7; 35.8]	< 0.01
IGI*ISI_COMP	267.9 [257.7; 275.5]	260.6 [253.3; 269.9]	0.21
lipid spectrum
cholesterol (mmol/l)	4.61 [4.55; 4.70]	4.58 [4.47; 4.65]	0.54
HDL chol. women (mmol/l)	1.58 [1.56; 1.64]	1.54 [1.50; 1.59]	0.03
HDL chol. men (mmol/l)	1.30 [1.23; 1.35]	1.21 [1.15; 1.31]	0.28
LDL cholesterol (mmol/l)	2.57 [2.47; 2.64]	2.56 [2.52; 2.64]	0.35
TAG (mmol/l)	0.84 [0.79; 0.88]	0.89 [0.83; 0.93]	0.09
liver enzymes
ALT (ukat/l)	0.30 [0.29; 0.31]	0.30 [0.29; 0.31]	0.59
AST (ukat/l)	0.36 [0.35; 0.37]	0.34 [0.34; 0.35]	0.07
GGT (ukat/l)	0.23 [0.22; 0.25]	0.23 [0.22; 0.24]	0.65
thyroid hormones
TSH (mIU/l)	2.19 [2.11; 2.33]	2.26 [2.19; 2.38]	0.99
fT3 (pmol/l)	4.89 [4.80; 4.99]	4.91 [4.84; 5.00]	0.71
fT4 (pmol/l)	15.0 [14.8; 15.2]	15.4 [15.1; 15.6]	0.13

Data are given as median [95% LCL; 95% UCL], P-level according to Mann-Whitney test. BMI—body mass index; WHR—waist-hip ratio; AUC_Glycemia—area under the glycemic curve; AUC_Insulin—area under the insulinemic curve; AUC_C−peptide—area under the C-peptide curve; HOMA IR—Homeostatic Model Assessment of Insulin Resistance; OGIS—Oral Glucose Insulin Sensitivity; ISI_COMP—Insulin Sensitivity Composite index; PREDIM—Peripheral Insulin Sensitivity Index; HOMA B—Homeostatic Model Assessment of Beta-cell function; IGI—Insulinogenic index; IGI*ISI_COMP—product of the IGI and the ISI_COMP index; HDL—high-density lipoprotein; LDL—low-density lipoprotein; TAG—triacylglycerols; ALT—alanine aminotransferase; AST—aspartate aminotransferase; GGT—γ-glutamyl transferase; TSH—thyrotropin; fT3—free triiodothyronine; fT4—free thyroxine.

In the present study, we evaluated the association between the course of the glycemic curve during the 3-hour OGTT and genetic variability of candidate genes studied in connection with insulin secretion and action, energy balance regulation, or inflammation, as the proinflammatory state is closely related to obesity and diabetes [27–28]. Of the 37 SNPs tested in 19 genes, only variants in NME7 were associated with the shape of the glycemic curve during the OGTT, which reflects the dynamics of the glucose processing and thus metabolic health.

The product of the gene is the NME7 protein (nucleoside diphosphate kinase 7, non-metastatic cells 7), an acknowledged member of ciliome involved in the biogenesis and function of cilia. Variability in human motile cilia-related genes may represent a connection between cilia motility, islet beta-cell function and disturbances in glucose homeostasis, even T2DM. This functional interdependence may provide a new potential target for a personalized approach in diabetes treatment. Cilia have diverse roles in energy homeostasis, central signaling, and peripheral regulation of energy metabolism in adipose tissue and the pancreas. Mutations in genes encoding ciliary proteins may result in signaling defects and lead to disorders such as obesity and T2DM [29]. Multiple human genetic diseases, such as polycystic kidney disease, Meckel-Gruber syndrome, Joubert syndrome or Bardet-Biedl and Alström syndrome, the last two characterized also by disorders of energy metabolism, have been linked to primary cilia dysfunction [30–31]. A study using cell lines and mouse models demonstrated that the MC4R protein, a member of the melanocortin receptor family and a well-known regulator of energy homeostasis and food intake, is localized in cilia [32]. Functional primary cilia play a requisite role in pancreatic endocrine cell differentiation, morphology and function [33]. Pancreatic beta-cell cilia dysfunction impairs glucose and insulin cooperation [34]. Primary cilia are sensors of the islet environment. Therefore, they are very sensitive to small changes in the local islet microenvironment. Dysfunction in this sensory mechanism may reduce the ability of the beta cells to adapt to these changes. The primary cilia can detect both acute changes, which could provide feedback to dial up or down hormone secretion, and more sustained changes, which could be used for transcriptional reprogramming and long-term adaptation [35]. The cilia can not only detect signals, but also transmit information. They mediate trafficking of insulin receptor to the cell surface [36]. Two studies have reported that human pancreatic islet cells express motile cilia genes and proteins and that these motile components mediate active movement of primary cilia via ATP hydrolysis, a key energy-generating reaction [37–38]. Beta-cell cilia movement occurs in response to extracellular glucose and is coupled to glucose-stimulated Ca²⁺ influx into beta-cells and insulin secretion [37]. According to study of Walker et al., disruptions in these motile cilia gene regulatory modules are associated with early-stage beta cell-intrinsic defects in T2DM. Our recent study in rats showed that knock-out Nme7-/- animals lacking completely the Nme7 gene displayed a variety of symptoms consistent with the presentation of primary ciliary dyskinesia [39]. Heterozygous Nme7+/- rats had elevated insulin levels along with decreased glucose tolerance. In addition, these animals showed fibrosis of pancreatic islets [40]. Regarding human studies, the first results showing the possible involvement of NME7 genetic variability in glucose metabolism come from research conducted on French-Canadian population from the Saguenay-Lac-St-Jean (SLSJ) region of Quebec (Canada), demonstrating link of the region on chromosome 1 overlaping NME7 and BLZF1 genes to insulin resistence [41]. Based on these indications and considering the importance of cilia motility in beta-cell function and the association of Nme7 gene defects in mice with primary ciliary dyskinesia coupled with glucose dysregulation, we have investigated the NME7 genetic variability in relation to glucose processing in a large sample of adults differing in their degree of insulin sensitivity.

Novel results presented here confirm the involvement of the NME7 gene in human glucose metabolism. First, there is a strong association of the NME7 polymorphisms with the shape of the glycemic curve, therefore, the gene is related to the way in which the human organism processes administered glucose. This connection was not evident in any of the other 18 candidate genes evaluated in our study. Given the significant relationship between glycemic curve type and health condition both in current and previously published studies [20–24], it is apparent what potential the projection of NME7 genetic variability may have on human health. Second, we find particularly fascinating the observation that strong linkage disequilibrium between NME7 variants was clearly detectable already on the basis of OGTT, i.e. on the analysis of glycemic curves. The initial detection of an association of glycemic curves with NME7 variability, or more precisely the way in which the five variants were associated with the biphasic curve, was subsequently confirmed by performed haplotype analysis. Third, thanks to haplotype analysis and the assignment of individual haplotypes to our subjects, the CTCCG haplotype linked with biphasic course of glycemic curve could have been directly tested for the relationship with biochemical parameters. As expected, a connection with glucose metabolism and especially insulin sensitivity was shown. The assessment of detailed clinical impact of NME7 gene variation is beyond the scope of the current study and remains to be elucidated. However, it is an important signal regarding dispositions for the further development of metabolic health during aging, which is essential aspect given the relatively low average age of our participants.

It should also be noted that although men had a higher representation of biphasic curves compared to women, as mentioned in the Chap. 2.2. “Representation of individual types of curves“, the presentation of the CTCCG haplotype associated with a biphasic curve was the same in both sexes. Therefore, the higher accumulation of men in the group with a biphasic course of the glycemic curve is most likely not mediated by the genetic variability of the NME7 gene.

In conclusion, our study presents the relationship between the glycemic curve shape and human NME7 gene variability. Building on previous research linking the NME7 gene to glucose processing in rat models, we are now demonstrating in humans an association of NME7 haplotypes with the way in which the organism processes the administered glucose. This knowledge is significant especially in light of the fact that the shape of the glycemic curve during the OGTT is a reflection of different susceptibility to metabolic disorders. Along with proving the link between the shape of the glucose curve and specific haplotypes of the NME7 gene, we also demonstrate a direct association of a particular haplotype with metabolic health. The fundamental question and the subject of further investigation remains the explanation of the mechanism of how specific variants of the NME7 gene are reflected in a cascade of biochemical processes affecting the dynamics of glucose homeostasis and related metabolic pathways.

4.1. Study subjects

In the years 2001–2023, adult Czech individuals with varying degrees of glucose tolerance were continuously examined at the Institute of Endocrinology in Prague. Examinations were based on genetic, anthropometric and biochemical characterization including the 3-hour OGTT. Exclusion criteria included serious diseases where passing a glucose test would pose a health risk, pregnancy or age below 18 and above 75 years. The study protocol was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Institute of Endocrinology. All participants signed an informed consent concerning the course of the examination and had the opportunity to ask questions before participating in the study.

4.2. Anthropometric and metabolic characterization of the subjects

Venous blood samples were taken at 8 a.m. after an overnight fasting. Glucose metabolism was characterized by blood glucose (enzymatic reference method with hexokinase, Roche, Cobas 6000, Roche Diagnostic, Mannheim), C-peptide (ECLIA, Roche, Cobas 6000, Roche Diagnostic, Mannheim), and insulin (ECLIA, Roche, Cobas 6000, Roche Diagnostic, Mannheim). During the 3-hour OGTT (75 g of glucose in 250 ml of water), trajectories of blood glucose, insulin and C-peptide were analyzed with sampling using cannula every 30 min of the test. Areas under the glycemic (AUC_Glycemia), C-peptide (AUC_C−peptide) and insulin curves (AUC_Insulin) were calculated. Indices of insulin sensitivity were calculated for fasting condition (homeostasis model HOMA IR) and for dynamic conditions following the glucose load (ISI_comp also known as Matsuda´s index, OGIS calculated for 120 min, MCRest, Si(oral), and PREDIM). Pancreatic beta cell function was characterized by HOMA B for fasting state and by IGI for dynamic conditions. In addition, oral disposition index of beta cell function related to insulin sensitivity IGI*ISI_comp was assessed. Also, hepatic extraction was computed by exploiting insulin and C-peptide data through appropriate formula [42]. Details regarding the calculation methodology of the indices listed in Table 1 have been previously published [23, 43–46].

Lipid profile was assessed by total cholesterol (enzymatic colorimetric test, Roche, Cobas 6000, Roche Diagnostic, Mannheim), high-density lipoprotein cholesterol (homogeneous enzymatic colorimetric test, Roche, Cobas 6000, Roche Diagnostic, Mannheim), low-density lipoprotein cholesterol (homogeneous enzymatic colorimetric test, Roche, Cobas 6000, Roche Diagnostic, Mannheim), and by triglyceride concentrations (enzymatic colorimetric test, Roche, Cobas 6000, Roche Diagnostic, Mannheim).

Thyroid hormones TSH, fT3, fT4 (ECLIA; Cobas 6000, Roche Diagnostics, Mannheim, Germany) and liver enzymes ALT, AST, and GGT (IFCC method with pyridoxalphosphate; Cobas 6000, Roche Diagnostics, Mannheim, Germany) were evaluated.

Systolic and diastolic blood pressure were measured in a rest state.

Body height and weight were determined to calculate body mass index (BMI), waist and hip circumferences were measured in order to evaluate body fat distribution and to calculate waist to hip ratio (WHR). Metabolic syndrome was diagnosed according to NCEP_ATPIII criteria [26]. Polycystic ovary syndrome was diagnosed based on the European Society of Human Reproduction and Embryology consensus (ESHRE criteria) [25].

4.3. Classification of the OGTT curves

The shape of the glucose curve was classified as monophasic if glycemia simply increased and then gradually decreased (one peak, one inflection point). The shape was biphasic if the blood glucose showed a further increase after a previous decrease (two inflection points). The three-phase shape was characterized by two complete peaks and three inflecion points. In the 3-hour OGTT, much more complex and heterogeneous curve shapes were observed. In some people, there were also three- and four-phase curves with 4 and 5 complete peaks, resp. These were evaluated together as multiphasic type of glycemic curves.

Variations in shape were considered significant if the difference was at least 2% (this criterion was necessary to avoid the detection of false minima and maxima in the glucose curve) according to the literature focused on this issue [47].

4.4. Molecular genetic analysis

We optimized genotyping protocol and genotypic data of candidate genes or possibly their immediate surroundings: NME7 (rs4656659, rs2157597, rs10732287, rs4264046, rs10800438, rs4656671), ATP1B1 (rs2051145), BLZF1 (rs7539415), GCK (rs1799884), KCNJ11 (rs5219), LRP5 (rs3736228), PPARGC1A (rs8192678), PPARG (rs1801282), SLC30A8 (rs13266634), TCF7L2 (rs12255372, rs7903146, rs7901695), FTO (rs1121980, rs17817449, rs1421085, rs9939609), ZBTB16 (rs11214863, rs2846027, rs567057, rs593731, rs661223, rs675044, rs681200, rs686989, rs763857), THADA (rs7578597), PICALM (rs3851179), BIN1 (rs744373), CLU (rs11136000), CR1 (rs3818361), MTNR1B (rs10830963) and PNPLA3 (rs738409). DNA for genetic analyses was isolated from leukocytes of peripheral blood (QuickGene 610L). Genotyping was carried out through allelic discrimination based on the principle of the Real-Time PCR method using TaqMan probes (Applied Biosystems) on the Light Cycler 480 (Roche) device.

Linkage analysis was performed using Haploview software (Haploview 4.1, Broad Institute of MIT and Harvard, Cambridge, MA, USA).

Haplotype analysis was then performed using Phase version 2.1.1 [48–50], which generated the most likely form of haplotypes for each individual. Haplotype analysis was first processed for all 8 SNPs shown in Figs. 1 and 2, including 2 variants in the vicinity of the NME7 gene, rs7539415 upstream and rs2051145 downstream from the gene. This approach led to the generation of 41 distinct haplotype variants, many of which, however, were extremely rare. Therefore, only variants found in 2.5% or more individuals were kept for statistical evaluation. These are presented in Table 4.

4.5. Calculations and statistical evaluation

Appropriate experimental calculations and data analysis methods for assessing both IS and beta-cell function were used [23, 42–46]. To evaluate the frequency distribution of individual alleles in the selected genetic polymorphisms, frequency tables were prepared. In the first step, Fisher's exact test was applied. In addition, log-linear models (frequency analysis) were used, particularly when analyzing the structure of relationships among more than two categorical variables. To assess deviation from the Hardy-Weinberg equilibrium, the Chi-square test was applied. Differences in biochemical and anthropometric data were tested by non-parametric Mann-Whitney test. Kruskal-Wallis Z-value Test with Bonferroni correction was used for multiple comparisons. Two-sided p-value of less than 0.05 was assumed as statistically significant. The power analysis was conducted using NCSS/PASS2020 software (NCSS, LLC. Kaysville, UT, USA).

Competing interests:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contribution

DV: conceptualization, project administration, original draft preparation and writing. JV: conceptualization, project administration, genetic analyzes. MV: statistical analyzes. PL: graphic processing. MS, TG and HK: medical supervision during glucose tests and medical data interpretation. AT: data curation, review and editing. LŠ and OŠ: initiators of the project proposal, language correction. DCh: data completion and management. KK: haplotype analyzes. BB: final approval. All authors have read and agreed to the published version of the manuscript.

Acknowledgement

This work was supported by grant projects AZV NU20-01-00308 and GA17-13491S. Many thanks to Giovanni Pacini, an excellent research scientist with extensive experience in glucose regulation, for reading the manuscript and providing expert comments.

Data Availability

Original data presented in the study are available from the corresponding author upon reasonable request. These data are not publicly available due to privacy policy.

The World Obesity Federation website. Available online: (2024). https://www.worldobesity.org/
Institute of Health Information and Statistics of the Czech Republic. Available online: (2024). https://www.uzis.cz/
Hameed, I. et al. Type 2 diabetes mellitus: From a metabolic disorder to an inflammatory condition. World J. Diabetes. 6, 598–612 (2015).
Hartstra, A. V., Bouter, K. E., Bäckhed, F. & Nieuwdorp, M. Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care. 38, 159–165 (2015).
Palatianou, M. E., Simos, Y. V., Andronikou, S. K. & Kiortsis, D. N. Long-term metabolic effects of high birth weight: a critical review of the literature. Horm. Metab. Res. 46, 911–920 (2014).
Kajantie, E. et al. Insulin sensitivity and secretory response in adults born preterm: the Helsinki Study of Very Low Birth Weight Adults. J. Clin. Endocrinol. Metab. 100, 244–250 (2015).
Harder, T., Rodekamp, E., Schellong, K., Dudenhausen, J. W. & Plageman, A. Birth weight and subsequent risk of type 2 diabetes: a meta-analysis. Am. J. Epidemiol. 165, 849–857 (2007).
Vaag, A. et al. Genetic, nongenetic and epigenetic risk determinants in developmental programming of type 2 diabetes. Acta Obstet. Gynecol. Scand. 93, 1099–1108 (2014).
Stein, S. A., Maloney, K. L. & Pollin, T. I. Genetic Counseling for Diabetes Mellitus. Curr. Genet. Med. Rep. 2, 56–67 (2014).
Brunetti, A., Chiefari, E. & Foti, D. Recent advances in the molecular genetics of type 2 diabetes mellitus. World J. Diabetes. 5, 128–140 (2014).
Bouret, S., Levin, B. E. & Ozanne, S. E. Gene-environment interactions controlling energy and glucose homeostasis and the developmental origins of obesity. Physiol. Rev. 95, 47–82 (2015).
Franks, P. W., Mesa, J. L., Harding, A. H. & Wareham, N. J. Gene-lifestyle interaction on risk of type 2 diabetes. Nutr. Metab. Cardiovasc. Dis. 17, 104–124 (2007).
O´Rahilly, S. Human genetics illuminates the paths to metabolic disease. Nature. 462, 307–314 (2009).
Shojima, N. & Yamauchi, T. Progress in genetics of type 2 diabetes and diabetic complications. J. Diabetes Investig. 14, 503–515 (2023).
Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).
Scott, R. A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).
Vassy, J. L. & Meigs, J. B. Is genetic testing useful to predict type 2 diabetes? Best Pract. Res. Clin. Endocrinol. Metab. 26, 189–201 (2012).
Hu, C. & Jia, W. Multi-omics profiling: the way towards precision medicine in metabolic diseases. J. Mol. Cell. Biol. 13, 576–593 (2021).
Stumvoll, M. et al. Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity. Diabetes Care. 23, 295–301 (2000).
Kim, J. Y. et al. The Shape of the Glucose Response Curve During an Oral Glucose Tolerance Test Heralds Biomarkers of Type 2 Diabetes Risk in Obese Youth. Diabetes Care. 39, 1431–1439 (2016).
Bervoets, L., Mewis, A. & Massa, G. The shape of the plasma glucose curve during an oral glucose tolerance test as an indicator of Beta cell function and insulin sensitivity in end-pubertal obese girls. Horm. Metab. Res. 47, 445–451 (2015).
Chung, S. T. et al. Time to glucose peak during an oral glucose tolerance test identifies prediabetes risk. Clin. Endocrinol. (Oxf). 87, 484–491 (2017).
Tura, A. et al. Shape of glucose, insulin, C-peptide curves during a 3-h oral glucose tolerance test: any relationship with the degree of glucose tolerance? Am. J. Physiol. Regul. Integr. Comp. Physiol. 300, R941–948 (2011).
Vejrazkova, D. et al. The Glycemic Curve during the Oral Glucose Tolerance Test: Is It Only Indicative of Glycoregulation? Biomedicines 11, 1278 (2023).
Rotterdam, E. S. H. R. E. & ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertil. Steril. 81, 19–25 (2004).
Expert Panel on Detection. Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 285, 2486–2497 (2001).
Rehman, K. & Akash, M. S. Mechanisms of inflammatory responses and development of insulin resistance: how are they interlinked? J. Biomed. Sci. 23, 87 (2016).
Akash, M. S., Rehman, K. & Chen, S. Role of inflammatory mechanisms in pathogenesis of type 2 diabetes mellitus. J. Cell. Biochem. 114, 525–531 (2013).
Engle, S. E., Bansal, R., Antonellis, P. J. & Berbari, N. F. Semin. Cilia signaling and obesity. Cell. Dev. Biol. 110, 43–50 (2021).
Mujahid, S. et al. The Endocrine and Metabolic Characteristics of a Large Bardet-Biedl Syndrome Clinic Population. J. Clin. Endocrinol. Metab. 103, 1834–1841 (2018).
Collin, G. B. et al. Mutations in ALMS1 cause obesity, type 2 diabetes and neurosensory degeneration in Alström syndrome. Nat. Genet. 31, 74–78 (2002).
Siljee, J. E. et al. Subcellular localization of MC4R with ADCY3 at neuronal primary cilia underlies a common pathway for genetic predisposition to obesity. Nat. Genet. 50, 180–185 (2018).
Lee, E. Y. & Hughes, J. W. Rediscovering Primary Cilia in Pancreatic Islets. Diabetes Metab. J. 47, 454–469 (2023).
Volta, F. et al. Glucose homeostasis is regulated by pancreatic beta-cell cilia via endosomal EphA-processing. Nat. Commun. 10, 5686 (2019).
Idevall-Hagren, O., Nilsson, C. I. & Sanchez, G. Keeping pace: the primary cilium as the conducting baton of the islet. Diabetologia. 67, 773–782 (2024).
Starks, R. D. et al. Regulation of Insulin Receptor Trafficking by Bardet Biedl Syndrome Proteins. PLoS Genet. 11, e1005311 (2015).
Cho, J. H. et al. Islet primary cilia motility controls insulin secretion. Sci. Adv. 8, eabq8486 (2022).
Walker, J. T. et al. RFX6-mediated dysregulation defines human β cell dysfunction in early type 2 diabetes. Preprint at https://www.biorxiv.org/content/ (2021). 10.1101/2021.12.16.466282v1
Šedová, L. et al. Semi-Lethal Primary Ciliary Dyskinesia in Rats Lacking the Nme7 Gene. Int. J. Mol. Sci. 22, 3810 (2021).
Šedová, L. et al. Heterozygous Nme7 Mutation Affects Glucose Tolerance in Male Rats. Genes (Basel). 12, 1087 (2021).
Vcelak, J. et al. PS4 Genome-wide association scans and bioinformatic tools for identifying candidate pathways. Abstracts of the 43rd European Association for the Study of Diabetes (EASD) annual meeting, 18–21 September 2007, Amsterdam, The Netherlands. Diabetologia 50, S128 (2007).
Piccinini, F., Man, D., Vella, C. & Cobelli, A. A Model for the Estimation of Hepatic Insulin Extraction After a Meal. IEEE Trans. Biomed. Eng. 63, 1925–1932 (2016).
Pacini, G. & Mari, A. Methods for clinical assessment of insulin sensitivity and beta-cell function. Best Pract. Res. Clin. Endocrinol. Metab. 17, 305–322 (2003).
Tura, A. et al. Prediction of clamp-derived insulin sensitivity from the oral glucose insulin sensitivity index. Diabetologia. 61, 1135–1141 (2018).
Retnakaran, R., Qi, Y., Goran, M. I. & Hamilton, J. K. Evaluation of proposed oral disposition index measures in relation to the actual disposition index. Diabet. Med. 26, 1198–1203 (2009).
Tura, A., Kautzky-Willer, A. & Pacini, G. Insulinogenic indices from insulin and C-peptide: comparison of beta-cell function from OGTT and IVGTT. Diabetes Res. Clin. Pract. 72, 298–301 (2006).
Tura, A., Ludvik, B., Nolan, J. J., Pacini, G. & Thomaseth, K. Insulin and C-peptide secretion and kinetics in humans: direct and model-based measurements during OGTT. Am. J. Physiol. Endocrinol. Metab. 281, E966–974 (2001).
Stephens, M., Smith, N. J. & Donnelly, P. A new statistical method for haplotype reconstruction from population data. Am. J. Hum. Genet. 68, 978–989 (2001).
Stephens, M. & Donnelly, P. A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am. J. Hum. Genet. 73, 1162–1169 (2003).
Stephens, M. & Scheet, P. Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am. J. Hum. Genet. 76, 449–462 (2005).

No competing interests reported.

Supplementaryinformation1.pdf

Download PDF

Editor invited by journal
06 Sep, 2024
Submission checks completed at journal
06 Sep, 2024
First submitted to journal
26 Aug, 2024

You are reading this latest preprint version

The NME7 gene is involved in the kinetics of glucose regulation

Status:

Version 1

Abstract

Figures

1. Introduction

1.1. Factors affecting glucose homeostasis

1.2. Oral glucose tolerance test course

1.3. Aims

2. Results

2.1. Metabolic profile of the cohort

2.2. Representation of individual types of curves

2.3. Genetic determination of glycemic curve shapes

2.4. Haplotype analysis

2.5. Haplotypes and biochemical parameters

3. Discussion

4. Methods

4.1. Study subjects

4.2. Anthropometric and metabolic characterization of the subjects

4.3. Classification of the OGTT curves

4.4. Molecular genetic analysis

4.5. Calculations and statistical evaluation

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1